Revealing underlying regulatory mechanisms of LINC00313 in Osimertinib-resistant LUAD cells by ceRNA network analysis

BACKGROUND

Osimertinib, a third-generation epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI), is the preferred treatment for EGFR-mutated lung cancer. However, acquired resistance inevitably develops. While non-coding RNAs have been implicated in lung cancer through various functions, the molecular mechanisms responsible for osimertinib resistance remain incompletely elucidated.

METHODS

RNA-sequencing technology was employed to determine differentially expressed lncRNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) between H1975 and H1975OR cell lines. Starbase 2.0 was utilized to predict DE-lncRNA and DE-mRNA interactions, constructing ceRNA networks. Subsequently, functional and pathway enrichment analysis were performed on target DE-mRNAs to identify pathways associated with osimertinib resistance. Key target DE-mRNAs were then selected as potential risk signatures for lung adenocarcinoma (LUAD) prognostic modeling using multivariate Cox regression analyses. The Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) and immunohistochemistry staining were used for result validation.

RESULTS

Functional analysis revealed that the identified DE-mRNAs primarily enriched in EGFR-TKI resistance pathways, especially in the PI3K/Akt signaling pathway, where their concerted actions may lead to osimertinib resistance. Specifically, upregulation of LINC00313 enhanced COL1A1 expression by acting as a miR-218-5p sponge, triggering an upstream response that activates the PI3K/Akt pathway, potentially contributing to osimertinib resistance. Furthermore, the expressions of LINC00313 and COL1A1 were validated by qRT-PCR, and the activation of the PI3K/Akt pathway was confirmed by immunohistochemistry staining.

CONCLUSIONS

Our results suggest that the LINC00313/miR-218-5p/COL1A1 axis potentially contributes to osimertinib resistance through the PI3K/Akt signaling pathway, providing novel insights into the molecular mechanisms underlying acquired osimertinib resistance in LUAD. Additionally, our study may aid in the identification of potential therapeutic targets for overcoming resistance to osimertinib.

Integrated analysis of purine metabolism assists in predicting prognosis and treatment decisions for patients with lung adenocarcinoma

The incidence of lung cancer, especially lung adenocarcinoma (LUAD), has recently increased. Targeted therapy and immunotherapy combined with conventional treatment have shown surprising benefits in enhancing the LUAD patient's prognosis. For the purpose of guiding treatment planning and the prognosis of LUAD, more research is required. The particular aim of this work was to establish a purine metabolism scoring (PMS) model for the purpose of individually forecasting treatment outcomes and overall survival for patients who have LUAD. Clinical and whole genome data were obtained from the TCGA-LUAD cohort via "UCSC". The 25 driver purine metabolism-related prognostic genes were determined founded on univariate Cox regression. Then PMS was developed through stepwise LASSO Cox regression. Survival analysis indicated that patients who have PMS experienced worse outcomes. We validated the PGM2 effect on lung adenocarcinoma malignancy in in vitro experiments. Univariate as well as multivariate Cox regression suggested that PMS was an independent prognostic indicator for LUAD patients, which was confirmed in subgroup analysis. Functional assay demonstrated that immune response as well as cytotoxicity pathways have a connection with lower PMS, and patients who have low PMS possess an active immune microenvironment. Moreover, the LUAD patients who have low PMS showed greater sensitivity to immunotherapy, targeted therapy, as well as chemotherapy. Knockdown of PGM2 was discovered to decrease the proliferation, invasion, as well as migration of lung adenocarcinoma cells in an in vitro assay. Pertaining to this particular research, we created a PMS model and conducted a thorough analysis of purine metabolism in LUAD in order to determine prognosis and offer recommendations for treatment. This finding offered a fresh concept for the clinical management of LUAD and novel therapy protocols.

Construction and validation of a prognostic model for stemness-related genes in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer with poor overall prognosis. Early identification of high-risk patients and individualized treatment can help extend the survival time of patients. This study aimed to construct and validate a prognostic prediction least absolute shrinkage and selection operator (LASSO) model for stemness-related genes in LUAD.

Methods

Firstly, LUAD RNA-sequencing data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor stemness index based on mRNA expression (mRNAsi) was calculated, and the relationship between mRNAsi and the survival prognosis as well as clinical features of LUAD patients was analyzed. Then, the weighted gene co-expression network analysis (WGCNA) method was used to screen for gene modules highly correlated with mRNAsi, and functional annotation [Gene Ontology (GO) analysis] and pathway enrichment analysis [Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis] were performed for the selected stemness-related gene module. Furthermore, prognosis-associated genes were determined from the stemness-related genes through univariate Cox analysis, and a prognostic model was constructed using LASSO analysis. Finally, a series of validations including survival curve analysis, receiver operating characteristic (ROC) curve analysis, and risk analysis were conducted for the prognostic model, and nomogram based on the risk model and various clinicopathological features were constructed.

Results

LUAD patients with high mRNAsi had a higher mortality rate than those with low mRNAsi. GO analysis showed that stemness-related genes were mainly involved in mRNA processing and extracellular matrix organization, while KEGG analysis revealed their involvement in cell cycle and PI3K-Akt signaling pathways. A prognostic model based on 12 stemness-related genes was constructed using LASSO regression. Validation of the prognostic model demonstrated its good accuracy in predicting the prognosis of LUAD patients.

Conclusions

mRNAsi plays an important role in the occurrence and development of LUAD. This study successfully constructed a prognostic prediction LASSO model for stemness-related genes in LUAD, which can serve as a novel prognostic indicator for LUAD and may be an effective complement to the current Tumor Node Metastasis (TNM) clinical staging of LUAD.

Identifying the tumor-associated macrophage of lung adenocarcinoma reveals immune landscape through omics data integration

The tumor-associated macrophages (TAM) play a crucial role in lung adenocarcinoma (LUAD), which can cause the proliferation, migration and invasion of tumor cells. In particular, TAMs mainly regulate changes in the tumor microenvironment thereby contributing to tumorigenesis and progression. Recently, an increasing number of studies are using single-cell RNA (Sc-RNA) sequencing to investigate changes in the composition and transcriptomics of the tumor microenvironment. We obtained Sc-RNA sequencing data of LUAD from GEO database and transcriptome data with clinical information of LUAD patients from TCGA database. A group of important genes in the state transition of TAMs was identified by analyzing TAMs at the single-cell level, while 5 TAM-related prognostic genes were obtained by omics data integration, and a prognostic model was constructed. GOBP analysis revealed that TAM-related genes were mainly enriched in tumor-promoting and immunosuppression-related pathways. After ROC analysis, it was found that the AUC of the prognosis model reached 0.751, with well predictive effectiveness. The 5 unique genes, HLA-DMB, HMGN3, ID3, PEBP1, and TUBA1B, was finally identified through synthesized analysis. The transcriptional characteristics of 5 genes were determined through GEPIA2 database and RT-qPCR. The increased expression of TUBA1B in advanced LUAD may serve as a prognostic indicator, while low expression of PEBP1 in LUAD may have the potential to become a therapeutic target.

Bioinformatics analysis of the prognostic role of alternative splicing data in lung adenocarcinoma

Background

As a post-transcriptional regulatory mechanism, alternative splicing (AS) is engaged in a variety of pathophysiological processes, and it has been widely reported in connection with the occurrence, progression, metastasis, and drug resistance of cancer. However, the research on AS in lung adenocarcinoma (LUAD) is very limited. In addition, the prognostic effect of AS event (ASE) on LUAD and its related mechanism are not clear. This study aimed to explore the role and potential prognostic value of ASE in LUAD.

Methods

Relevant data and ASE datasets of the sample were acquired from The Cancer Genome Atlas (TCGA) and TCGASpliceSeq databases. We constructed a new prognostic criterion based on ASEs. Then, Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis were used to construct the model. Based on this model, the risk score of each ASE was calculated, and the reliability of this model was evaluated by Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses. Finally, these results were verified on different network platforms.

Results

We identified seven types of ASEs related to survival. The prognostic risk model for ASEs was established. The Kaplan-Meier curve showed that compared to the low-risk group, the overall survival (OS) rate of LUAD patients in the high-risk group was lower. ROC curve analysis showed that the prognostic risk model of LUAD patients was well predicted, and the area under the curve (AUC) also confirmed this.

Conclusions

This study screened the ASE related to the prognosis of LUAD patients, and provided a theoretical basis for further study of the correlation between ASE and the prognosis of LUAD patients. It has provided new ideas for developing new biomarkers and therapeutic targets for LUAD patients.

Exploring diverse programmed cell-death patterns to develop a novel gene signature for predicting the prognosis of lung adenocarcinoma patients

Background

Programmed cell death (PCD) plays a critical role in tumor progression and malignancy, and exploring its relationship with lung adenocarcinoma (LUAD)'s survival outcomes is important for personalized diagnosis and treatment. This study aimed to identify survival-related genes and construct an effective prognostic indicator for LUAD based on 12 forms of PCD.

Methods

A total of 1,933 candidate genes related to PCD were collected from published studies and public data center. A prognostic gene signature, called the cell death index (CDI), was established based on RNA-Seq and immunohistochemistry (IHC). IHC staining on tissue microarray was applied for the validation of protein level. Moreover, GSE42127, GSE72094 were used as validation datasets.

Results

The CDI based on expression level of nine genes (CCNB2, HMGA1, CACNA2D2, BUB1B, BTG2, KIF14, PTGDS, SERPINB5, BRCA1) was highly predictive for overall survival (OS) of LUAD in our cohort [36-month area under the curve (AUC): 0.750, 60-month AUC: 0.809]. The CDI was further validated in independent cohorts (GSE72094, 36-month AUC: 0.717, 60-month AUC: 0.737; GSE42127, 12-month AUC: 0.829, 60-month AUC: 0.663). And the CDI was found to be an independent prognostic factor after adjusting for other clinical characteristics. Furthermore, the high-CDI group was associated with upregulated tumor immune infiltration compared to the low-CDI group.

Conclusions

This study identified a 9-gene signature (CDI) based on PCD-related genes that accurately predicted the prognosis of LUAD patients. The CDI could serve as a valuable prognostic indicator and guide personalized therapeutic strategies for LUAD.

Keratin gene signature expression drives epithelial-mesenchymal transition through enhanced TGF-β signaling pathway activation and correlates with adverse prognosis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) stands as the foremost histological subtype of non-small-cell lung cancer, accounting for approximately 40% of all lung cancer diagnoses. However, there remains a critical unmet need to enhance the prediction of clinical outcomes and therapy responses in LUAD patients. Keratins (KRTs), serving as the structural components of the intermediate filament cytoskeleton in epithelial cells, play a crucial role in the advancement of tumor progression. This study investigated the prognostic significance of the KRT family gene and developed a KRT gene signature (KGS) for prognostic assessment and treatment guidance in LUAD.

Methods

Transcriptome profiles and associated clinical details of LUAD patients were meticulously gathered from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The KGS score was developed based on the expression of five prognostic KRT genes (KRT7, KRT8, KRT17, KRT18, and KRT80), and the upper quartile of the KGS score was chosen as the cutoff. The Kaplan-Meier method was evaluated to compare survival outcomes between KGS-high and KGS-low groups. The underlying mechanism was further investigated by GSEA, GSVA, and other bioinformatic algorithms.

Results

High expression of the KGS signature exhibited a robust association with poorer overall survival (OS) in the TCGA-LUAD dataset (HR: 1.81; 95% CI: 1.35-2.42, P = 0.00011). The association was further corroborated in three external GEO cohorts, including GSE31210 (HR: 3.31; 95% CI: 1.7-6.47, P = 0.00017), GSE72094 (HR: 1.95; 95% CI: 1.34-2.85, P = 0.00057) and GSE26939 (HR: 3.19; 95% CI: 1.74-5.84, P < 0.0001). Interestingly, KGS-high tumors revealed enrichments in TGF-β and WNT-β catenin signaling pathways, exhibited heightened activation of the epithelial-mesenchymal transition (EMT) pathway and proved intensified tumor stemness compared to their KGS-low counterparts. Additionally, KGS-high tumor cells exhibited increased sensitivity to several targeted agents, including gefitinib, erlotinib, lapatinib, and trametinib, in comparison to KGS-low cells.

Conclusion

This study developed a KGS score that independently predicts the prognosis in LUAD. High expression of KGS score, accompanied by upregulation of TGF-β and WNT-β catenin signaling pathways, confers more aggressive EMT and tumor progression.

Analysis of prognostic factors and establishment of prediction model of lung adenocarcinoma based on SEER database

Background

Few models have been developed to predict survival outcomes for lung adenocarcinoma (LUAD). In this study, we aimed to establish a nomogram for the prediction of cancer-specific survival (CSS) in LUAD patients which can be further developed as a convenient web-based calculator.

Methods

We performed a retrospective analysis of 50,007 LUAD patients selected from the Surveillance, Epidemiology, and End Result (SEER) 18 registry database. To enhance the reliability of the analysis, the patients' data were further randomly divided into the training cohort (70%) and validation cohort (30%). The optimal age cut-off points were determined using X-tile software, and patients were divided into three age groups: 10-72, 73-79, and 80-99 years. We selected independent prognostic factors from 17 variables by Cox regression, and plotted a visual nomogram to predict the 1-, 3-, and 5-year CSS. The predictive performance of the nomogram was evaluated through the concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve. To facilitate CSS forecast, a web-based calculator has subsequently been developed.

Results

We selected sex, age, race, marital status, N stage, tumor size, surgery, radiotherapy, chemotherapy, and metastasis (bone, brain, liver, and lung) as independent prognostic factors. The C-index was 0.779 [95% confidence interval (CI): 0.775-0.783] in the training set prediction model, and 0.782 (95% CI: 0.778-0.786) in the validation set. ROC analysis showed that area under the curve (AUC) values were 0.700, 0.733 and 0.669 for the 1-, 3- and 5-year CSS in the training set and 0.700, 0.744 and 0.669 in the validation set, respectively. In the nomogram calibration curve, there was strong correlation between the observed and predictive values. A web-based calculator can be accessed at: https://hjhlovelfb.shinyapps.io/DynNomapp/.

Conclusions

This nomogram model has good predictive power and can help clinicians identify LUAD patients at high risk of cancer-related death. This nomogram is expected to be a precise and personalized tool for predicting the prognosis of patients with LUAD.

Transcription factor-target gene regulatory network analysis in human lung adenocarcinoma

Background

Transcription factors (TFs) play a crucial role in the occurrence and progression of lung adenocarcinoma (LUAD), and targeting TFs is an important direction for treating LUAD. However, targeting a single TF often fails to achieve satisfactory therapeutic outcomes. Furthermore, the regulatory TF-target gene networks involved in the development of LUAD is complex and not yet fully understood.

Methods

In this study, we performed RNA sequencing (RNA-seq) to analyze the transcriptome profile of human LUAD tissues and matched adjacent nontumor tissues. We selected the differentially expressed TFs, performed enrichment analysis and survival curve analysis, and predicted the regulatory networks of the top differential TFs with their target genes. Finally, alternative splicing analyses were also performed.

Results

We found that TFs GRHL3, SIX1, SIX2, SPDEF, and ETV4 were upregulated, while TAL1, EPAS1, SOX17, NR4A1, and EGR3 were significantly downregulated in LUAD tissues compared to normal tissues. We propose a potential GRHL3-CDH15-Wnt-β-catenin pro-oncogenic signaling axis and a potential TAL1-ADAMTS1-vascular antioncogenic signaling axis. In addition, we found that alternative splicing of intron retention (IR), approximate IR (XIR), multi-IR (MIR), approximate MIR (XMIR), and approximate alternative exon ends (XAE) showed abnormally increased frequencies in LUAD tissues.

Conclusions

These findings revealed a novel TF-target gene regulatory axis related to tumorigenesis and provided potential therapeutic targets and mechanisms for LUAD.

Identification of Crucial Genes and Signaling Pathways in Alectinib-Resistant Lung Adenocarcinoma Using Bioinformatic Analysis

Alectinib, a second-generation anaplastic lymphoma kinase (ALK) inhibitor, has been shown to be effective for patients with ALK-positive non-small cell lung cancer (NSCLC). However, alectinib resistance is a serious problem worldwide. To the best of our knowledge, little information is available on its molecular mechanisms using the Gene Expression Omnibus (GEO) database. In this study, the differentially expressed genes (DEGs) were selected from the gene expression profile GSE73167 between parental and alectinib-resistant human lung adenocarcinoma (LUAD) cell samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) annotation enrichment analyses were conducted using Database for Annotation, Visualization and Integrated Discovery (DAVID). The construction of protein-protein interaction (PPI) network was performed to visualize DEGs. The hub genes were extracted based on the analysis of the PPI network using plug-in cytoHubba of Cytoscape software. The functional roles of the key genes were investigated using Gene Expression Profiling Interactive Analysis (GEPIA), University of Alabama at Birmingham Cancer (UALCAN), Gene Set Enrichment Analysis (GSEA), and Tumor Immune Estimation Resource (TIMER) analysis. The networks of kinase, miRNA, and transcription-factor targets of SFTPD were explored using LinkedOmics. The drug sensitivity analysis of SFTPD was analyzed using the RNAactDrug database. Results showed a total of 144 DEGs were identified. Five hub genes were extracted, including mucin 5B (MUC5B), surfactant protein D (SFTPD), deleted in malignant brain tumors 1 (DMBT1), surfactant protein A2 (SFTPA2), and trefoil factor 3 (TFF3). The survival analysis using GEPIA displayed that low expression of SFTPD had a significantly negative effect on the prognosis of patients with LUAD. GSEA revealed that low expression of SFTPD was positively correlated with the pathways associated with drug resistance, such as DNA replication, cell cycle, drug metabolism, and DNA damage repair, including mismatch repair (MMR), base excision repair (BER), homologous recombination (HR), and nucleotide excision repair (NER). The SFTPD expression was negatively correlated with the drug sensitivity of alectinib according to RNAactDrug database. The expression of SFTPD was further validated in parental H3122 cells and alectinib-resistant H3122 cells by quantitative reverse transcription PCR (RT-qPCR). In conclusion, our study found that the five hub genes, especially low expression of SFTPD, are closely related to alectinib resistance in patients with LUAD.

CircSFMBT2 Plays an Oncogenic Role in Lung Adenocarcinoma Depending on the miR-1305/SALL4 Axis

Circular RNAs (circRNAs) exhibit significant functions in diverse malignant tumors, including lung adenocarcinoma (LUAD). In this study, we aimed to elucidate the role of circRNA scm like with four mbt domains 2 (circSFMBT2) in LUAD. Quantitative real-time polymerase chain reaction (qRT-PCR), western blot assay or immunohistochemistry (IHC) assay was performed for quantification of circSFMBT2, microRNA-1305 (miR-1305), spalt like transcription factor 4 (SALL4), proliferating Cell Nuclear Antigen (PCNA) or Ki-67. 5-ethynyl-2'-deoxyuridine (EdU) assay, transwell assay and flow cytometry analysis were applied to analyze cell proliferation, metastasis and apoptosis, respectively. Mouse xenograft model was established to explore the function of circSFMBT2. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were used to estimate the relationship between miR-1305 and circSFMBT2 or SALL4. CircSFMBT2 was upregulated in LUAD and related to advanced TNM stage and poor prognosis. CircSFMBT2 knockdown suppressed cell proliferation, metastasis, glycolysis and induced apoptosis in LUAD cells in vitro as well as tumor formation in vivo. CircSFMBT2 directly targeted miR-1305, and miR-1305 inhibition reversed circSFMBT2 knockdown-mediated inhibitory effects on LUAD malignant behaviors. SALL4 was the target gene of miR-1305. MiR-1305 overexpression repressed the malignant phenotypes of LUAD cells, while SALL4 enhancement abated the effects. CircSFMBT2 aggravated the progression of LUAD by the miR-1305/SALL4 axis, which might provide a diagnostic and prognostic marker for LUAD.

Combining single-cell RNA sequencing data and transcriptomic data to unravel potential mechanisms and signature genes of the progression of idiopathic pulmonary fibrosis to lung adenocarcinoma and predict therapeutic agents

Patients with idiopathic pulmonary fibrosis (IPF) have a significantly higher prevalence of lung adenocarcinoma (LUAD) than normal subjects, although the underlying association is unclear. The raw data involved were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis and weighted gene co-expression network analysis were used to screen for differentially expressed genes (DEGs) and modular signature genes (MSGs). Genes intersecting DEGs and MSGs were considered hub genes for IPF and LUAD. Machine learning algorithms were applied to capture epithelial cell-derived signature genes (EDSGs) shared. External cohort data were exploited to validate the robustness of EDSGs. Immunohistochemical staining and K-M plots were used to denote the prognostic value of EDSGs in LUAD. Based on EDSGs, we constructed a TF-gene-miRNA regulatory network. Molecular docking can validate the strength of action between candidate drugs and EDSGs. Epithelial cells, 650 DEGs, and 1773 MSGs were shared by IPF and LUAD. As for 379 hub genes, we performed pathway and functional enrichment analysis. By analyzing sc-RNA seq data, we identified 1234 marker genes of IPF epithelial cell-derived and 1481 of LUAD. And these genes shared 8 items with 379 hub genes. Through the machine learning algorithms, we further fished TRIM2, S100A14, CYP4B1, LMO7, and SFN. The ROC curves emphasized the significance of EDSGs in predicting the onset of LUAD and IPF. The TF-gene-miRNA network revealed regulatory relationships behind EDSGs. Finally, we predicted appropriate therapeutic agents. Our study preliminarily identified potential mechanisms between IPF and LUAD, which will inform subsequent studies.

Bioinformatics and Experimental Study revealed LINC00982/ miR-183-5p/ABCA8 Axis Suppresses LUAD Progression

BACKGROUND

Lung adenocarcinoma (LUAD) is a major health challenge worldwide with an undesirable prognosis. LINC00982 has been implicated as a tumor suppressor in diverse human cancers; however, its role in LUAD has not been fully characterized.

METHODS

Expression level and prognostic value of LINC00982 were investigated in pan-cancer and lung cancer from The Cancer Genome Atlas (TCGA) project. Differential expression analysis based on the LINC00982 expression level was performed in LUAD followed by gene set enrichment analysis (GSEA) and functional enrichment analyses. The association between LINC00982 expression and tumor immune microenvironment characteristics was evaluated. A potential ceRNA regulatory axis was identified and experimentally validated.

RESULTS

We found that LINC00982 expression was downregulated and correlated with poor prognosis in LUAD. Enrichment analyses revealed that LINC00982 could inhibit DNA damage repair and cell proliferation, but enhance tumor metabolic reprogramming. We identified a competing endogenous RNA network involving LINC00982, miR-183-5p, and ATP-binding cassette subfamily A member 8 (ABCA8). Luciferase assays confirmed that miR-183-5p can interact with LINC00982 and ABCA8. Forced miR-183-5p expression reduced LINC00982 transcript levels and suppressed ABCA8 expression.

CONCLUSIONS

Our findings revealed the LINC00982/miR-183-5p/ABCA8 axis as a potential therapeutic target in LUAD.

Comparison of gene mutation profile in different lung adenocarcinoma subtypes by targeted next-generation sequencing

BACKGROUND

Disease prognosis after resection of lung cancer could be affected by pathological subtypes. In this study, we investigated the difference of gene variation and significantly altered pathways between adenocarcinoma in situ (AIS)/microinvasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC) subtypes to reveal the molecular mechanism of prognosis differences.

METHODS

Sixty one tumor tissues were subjected to DNA extraction and customized 136 gene targeted next-generation sequencing. Comparisons between groups were performed with two-sided Fisher's exact test for categorical variables and two-tailed unpaired t test for numerical variables.

RESULTS

A total of 402 somatic mutations involved in 70 genes were detected in all these samples, and 74.29% of these genes were mutated in at least two samples. PMS2, ARID1A, EGFR, and POLE were the most frequently mutated genes. ALK_EML4 fusion was observed in one IAC patient and RET_ KIF5B fusion in one AIS patient. A significant higher proportion of patients with TP53 gene mutation was observed in the IAC group (P = 0.0057). The average onset age in IAC group is 62.48 years, which is greater than other subtypes (P = 0.0166). It revealed that mutations in genes involved in the mTOR signaling pathway (56.52% vs 26.32%, P = 0.0288) and Hippo signaling pathway (34.78% vs 10.53%, P = 0.0427) were significantly enriched in IAC subtypes, suggesting the key involvement of mTOR and Hippo signaling pathways in lung tumor development and malignant progression.

CONCLUSIONS

This study revealed the heterogeneity of gene mutations and significantly altered pathways between different lung cancer subtypes, suggesting the potential mechanism of different prognosis.

Recurrence prediction of lung adenocarcinoma using an immune gene expression and clinical data trained and validated support vector machine classifier

Background

Immune microenvironment plays a critical role in cancer from onset to relapse. Machine learning (ML) algorithm can facilitate the analysis of lab and clinical data to predict lung cancer recurrence. Prompt detection and intervention are crucial for long-term survival in lung cancer relapse. Our study aimed to evaluate the clinical and genomic prognosticators for lung cancer recurrence by comparing the predictive accuracy of four ML models.

Methods

A total of 41 early-stage lung cancer patients who underwent surgery between June 2007 and October 2014 at New York University Langone Medical Center were included (with recurrence, n=16; without recurrence, n=25). All patients had tumor tissue and buffy coat collected at the time of resection. The CIBERSORT algorithm quantified tumor-infiltrating immune cells (TIICs). Protein-protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to unearth potential molecular drivers of tumor progression. The data was split into training (75%) and validation sets (25%). Ensemble linear kernel support vector machine (SVM) ML models were developed using optimized clinical and genomic features to predict tumor recurrence.

Results

Activated natural killer (NK) cells, M0 macrophages, and M1 macrophages showed a positive correlation with progression. Conversely, T CD4+ memory resting cells were negatively correlated. In the PPI network, TNF and IL6 emerged as prominent hub genes. Prediction models integrating clinicopathological prognostic factors, tumor gene expression (45 genes), and buffy coat gene expression (47 genes) yielded varying receiver operating characteristic (ROC)-area under the curves (AUCs): 62.7%, 65.4%, and 59.7% in the training set, 58.3%, 83.3%, and 75.0% in the validation set, respectively. Notably, merging gene expression with clinical data in a linear SVM model led to a significant accuracy boost, with an AUC of 92.0% in training and 91.7% in validation.

Conclusions

Using ML algorithm, immune gene expression data from tumor tissue and buffy coat may enhance the precision of lung cancer recurrence prediction.

Dual inhibition of KIF11 and BCL2L1 induces apoptosis in lung adenocarcinoma cells

EGFR-mutant lung adenocarcinoma (LUAD) mostly depends on EGFR for survival and consequently responds well to EGFR inhibitors. However, resistance to the drugs develops almost universally during treatment. We previously demonstrated that EGFR-mutant LUAD cell lines, HCC827 and H1975, have subpopulations of cells, which we termed HCC827 GR2 and H1975 WR7 cells, that can thrive independently of EGFR signaling. These EGFR-independent EGFR-mutant cancer cells are difficult to treat because they lack sensitivity to EGFR inhibitors. Therefore, the development of novel strategies to target EGFR-independent EGFR-mutant LUAD is particularly important. We found that high expression of kinesin family member 11 (KIF11) correlated with poor survival in patients with LUAD. We also observed that KIF11 silencing caused cell cycle arrest at G2/M in HCC827 GR2 and H1975 WR7 cells. Furthermore, dual silencing of KIF11 plus BCL2L1, an anti-apoptotic BCL2 family member, in these two EGFR-independent sublines resulted in marked apoptosis levels. Dual inhibition of KIF11 plus BCL2L1 also induced apoptosis in HCC827 and H1975 parental cells and a KRAS-mutant LUAD cell line, H441. These findings collectively suggest that dual inhibition of KIF11 plus BCL2L1 may be a new approach for the treatment of LUAD.

Effects of TRAF3 on the proliferation and migration of lung adenocarcinoma depend partly on pyroptosis

BACKGROUND

Tumor necrosis factor receptor-associated factor 3 (TRAF3) has specific regulatory effects on a wide range of diseases, including tumors. However, the effect and mechanism of TRAF3 on lung adenocarcinoma (LUAD) are still unknown. The aim of the present study was to make clear the role and potential mechanism of TRAF3 in LUAD.

METHODS

TIMER2.0 database and western blot were applied to detect the expression of TRAF3 in lung adenocarcinoma tissue. Kaplan-Meier Plotter database was utilized to explore the effect of TRAF3 on the clinical prognosis of lung adenocarcinoma patients. Specific siRNA was used to inhibit the expression of TRAF3 in LUAD cells (A549 and H1299). CCK-8 and EdU assays were performed for assessing LUAD cells proliferation. Wound healing assay and transwell assay were performed for determining cells migration. CCK-8 assay was used to assess the response of the LUAD cells to paclitaxel. TIMER2.0 bioinformatics and western blot were employed to detect the effects of TRAF3 on pyroptosis in LUAD.

RESULTS

TRAF3 was highly expressed in lung adenocarcinoma tissues and cell lines. Patients with TRAF3 hyperexpression had a good prognosis compared to those with lower expression. TRAF3 inhibition notably induced proliferation and migration of LUAD cells. Inhibition of TRAF3 also weakened the sensitivity of LUAD cells to paclitaxel. Moreover, bioinformatics results showed that TRAF3 was positively correlated with the expression of pyroptosis-related genes in LUAD. Western blot assays showed that TRAF3 inhibition visibly decreased the expression of apoptosis-associated speck-like protein (ASC), cleaved caspase-1 and matured- IL-1β.

CONCLUSIONS

Inhibition of TRAF3 promotes the proliferation and migration of LUAD cells, and reduces the sensitivity of LUAD cells to paclitaxel. The effects of TRAF3 on LUAD cells were mediated in part by caspase-1-dependent pyroptosis.

Significance of CD80 as a Prognostic and Immunotherapeutic Biomarker in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the primary cause of death among pulmonary cancer patients. Upregulation of CD80 may interact with cytotoxic T lymphocyte antigen 4 (CTLA4) to promote tumor progression and provide a potential target for biological antitumor therapy. However, the role of CD80 in LUAD is still unclear. To investigate the function of CD80 in LUAD, we collected transcriptomic data from 594 lung samples from The Cancer Genome Atlas of America (TCGA) database, along with the corresponding clinical information. We systematically explored the role of CD80 in LUAD using bioinformatics methods, including GO enrichment analysis, KEGG pathway analysis, Gene Set Enrichment Analysis (GSEA), co-expression analysis, and the CIBERSORT algorithm. Finally, we investigated the differences between the two subgroups of CD80 expression in terms of some drug sensitivity, using the pRRophetic package to screen small molecular drugs for therapeutic use. A predictive model based on CD80 for LUAD patients was successfully constructed. In addition, we discovered that the CD80-based prediction model was an independent prognostic factor. Co-expression analysis revealed 10 CD80-related genes, including oncogenes and immune-related genes. Functional analysis showed that the differentially expressed genes in patients with high CD80 expression were mainly located in immune-related signaling pathways. CD80 expression was also associated with immune cell infiltration and immune checkpoints. Highly expressing patients were more sensitive to several drugs, such as rapamycin, paclitaxel, crizotinib, and bortezomib. Finally, we found evidence that 15 different small molecular drugs may benefit the treatment of LUAD patients. This study found that elevated CD80 pairs could improve the prognosis of LUAD patients. CD80 is likely to be a potential as a prognostic and therapeutic target. The future use of small molecular drugs in combination with immune checkpoint blockade to enhance antitumor therapy and improve prognosis for LUAD patients is promising.

Cullin7 induces docetaxel resistance by regulating the protein level of the antiapoptotic protein Survivin in lung adenocarcinoma cells

Background

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer (NSCLC). Chemotherapy resistance is the main cause of chemotherapy failure. Cullin7 (Cul7) is highly expressed in LUAD and is associated with poor prognosis. Moreover, Cul7 is abnormally overexpressed in docetaxel-resistant LUAD cells. Therefore, further exploration of the role and molecular mechanism of Cul7 in LUAD docetaxel resistance is necessary.

Methods

We established docetaxel-resistant cell lines (A549DTX and H358DTX cell lines) by exposing cells to gradually increasing concentrations of docetaxel. Cell (A549, A549DTX, H358, and H358DTX cell lines) sensitivity to docetaxel was determined via a 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymmethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium, inner salt (MTS) assay. And then quantitative polymerase chain reaction (qPCR) and Western blotting were performed to measure the expression of Cul7 and Survivin in A549, A549DTX, H358, and H358DTX cell lines. Subsequently, we knocked down Cul7 in docetaxel-resistant cells and overexpressed Cul7 in parental cells via lentiviral transduction to further validate the correlation between Cul7 and docetaxel resistance, while exploring the molecular mechanism of docetaxel resistance it caused. Immunofluorescence and immunohistochemical (IHC) staining were also used to evaluate the expression and cellular localization of Cul7. To confirm the effect of Cul7 expression on cell apoptosis, we used flow cytometry to detect the apoptosis rate of A549 and A549DTX cells with the same drug concentration.

Results

Cul7 was highly expressed in A549DTX and H358DTX cells. However, when Cul7 expression was knocked down in A549DTX and H358DTX cells, cell sensitivity to docetaxel was significantly increased. In addition, we found that Cul7 was coexpressed with Survivin. Silencing Survivin reversed the docetaxel insensitivity caused by Cul7 overexpression. High expression of Cul7 and Survivin in docetaxel-resistant LUAD cells inhibited the intrinsic apoptosis pathway and promoted cell proliferation. Therefore, the Cul7/Survivin axis may play a role in inducing LUAD docetaxel chemoresistance.

Conclusions

Cul7 and Survivin were both highly expressed in docetaxel-resistant LUAD cells. Our results suggest that Cul7 may inhibit apoptosis and promote the proliferation of LUAD cells by increasing the Survivin protein level, which in turn contributes to docetaxel chemoresistance in LUAD.

Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is a prevalent subtype of lung cancer associated with high mortality rates. We aimed to utilize single-cell multiomics analysis to identify the key molecules involved in ubiquitination modification, which plays a role in LUAD development and progression.

METHODS

We use a systematic approach to analyze LUAD-related single-cell and bulk transcriptome datasets from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Single-cell RNA sequencing (scRNA-seq) data were normalized, clustered, and annotated with the Seurat package in R. InferCNV was used to distinguish malignant from epithelial cells, and AUCell evaluated the area under the curve (AUC) score of ubiquitination-related enzymes. Survival and differential analyses identified significant molecular markers associated with ubiquitination. PSMD14 expression was confirmed using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and Western blot assays, and its knockdown cell lines were assessed for effects on cellular processes and tumor formation in mice. PSMD14's interacting proteins were predicted, and its impact on AGR2 protein half-life and ubiquitination was evaluated. Rescue experiments involving PSMD14 overexpression and AGR2 silencing assessed their impact on malignant behaviors.

RESULTS

By means of single-cell sequencing analysis, we probed the ubiquitination modification landscape in the LUAD microenvironment. Malignant cells had elevated scores for enzymes and ubiquitin-binding domains compared to normal epithelial cells, with 53 ubiquitination-related molecules showing prognostic disparities. FGR, PSMD14, and ZBTB16 were identified as genes with prognostic significance, with PSMD14 showing higher expression in epithelial and malignant cells. Two missense mutation sites were identified in PSMD14, which had a high copy number amplification ratio and positive correlation with messenger RNA (mRNA) expression. PSMD14 expression and tumor stage were found to be independent prognostic factors, and interfering with PSMD14 expression reduced the malignant behavior of LUAD cells. PSMD14 was found to bind to AGR2 protein and reduce its ubiquitination, leading to increased AGR2 stability. Knockdown of AGR2 inhibited the enhancement of cell viability, invasion, and migration resulting from PSMD14 overexpression.

CONCLUSIONS

This study examined ubiquitination modifications in LUAD using sequencing data, identifying PSMD14's critical role in malignancy regulation and its potential as a prognostic and therapeutic biomarker. These insights enhance understanding of LUAD mechanisms and treatment.

Identification of lysosomal genes associated with prognosis in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, representing 40% of all cases of this tumor. Despite immense improvements in understanding the molecular basis, diagnosis, and treatment of LUAD, its recurrence rate is still high.

Methods

RNA-seq data from The Cancer Genome Atlas (TCGA) LUAD cohort were download from Genomic Data Commons Portal. The GSE13213 dataset from Gene Expression Omnibus (GEO) was used for external validation. Differential prognostic lysosome-related genes (LRGs) were identified by overlapping survival-related genes obtained via univariate Cox regression analysis with differentially expressed genes (DEGs). The prognostic model was built using Kaplan-Meier curves and least absolute shrinkage and selection operator (LASSO) analyses. In addition, univariate and multivariate Cox analyses were employed to identify independent prognostic factors. The responses of patients to immune checkpoint inhibitors (ICIs) were further predicted. The pRRophetic package and rank-sum test were used to compute the half maximal inhibitory concentrations (IC50) of 56 chemotherapeutic drugs and their differential effects in the low- and high-risk groups. Moreover, quantitative real-time polymerase chain reaction, Western blot, and human protein atlas (HPA) database were used to verify the expression of the four prognostic biomarkers in LUAD.

Results

Of the nine candidate differential prognostic LRGs, GATA2, TFAP2A, LMBRD1, and KRT8 were selected as prognostic biomarkers. The prediction of the risk model was validated to be reliable. Cox independent prognostic analysis revealed that risk score and stage were independent prognostic factors in LUAD. Furthermore, the nomogram and calibration curves of the independent prognostic factors performed well. Differential analysis of ICIs revealed CD276, ICOS, PDCD1LG2, CD27, TNFRSF18, TNFSF9, ENTPD1, and NT5E to be expressed differently in the low- and high-risk groups. The IC50 values of 12 chemotherapeutic drugs, including epothilone.B, JNK.inhibitor.VIII, and AKT.inhibitor.VIII, significantly differed between the two risk groups. KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in LUAD cell lines. In addition, KRT8 and TFAP2A were highly expressed, while GATA2 and LMBRD1 were poorly expressed in tumor tissues.

Conclusions

Four key prognostic biomarkers-GATA2, TFAP2A, LMBRD1, and KRT8-were used to construct a significant prognostic model for LUAD patients.

Construction of subtype classifiers and validation of a prognostic risk model based on hypoxia-associated lncRNAs for lung adenocarcinoma

BACKGROUND

Studies have shown that long non-coding RNAs (lncRNAs) are found to be hypoxia-regulated lncRNAs in cancer. Lung adenocarcinoma (LUAD) is the leading cause of cancer death worldwide, and despite early surgical removal, has a poor prognosis and a high recurrence rate. Thus, we aimed to identify subtype classifiers and construct a prognostic risk model using hypoxia-associated long noncoding RNAs (hypolncRNAs) for LUAD.

METHODS

Clinical data of LUAD samples with prognosis information obtained from the Gene Expression Omnibus (GEO), acted as validation dataset, and The Cancer Genome Atlas (TCGA) databases, served as training dataset, were used to screen hypolncRNAs in each dataset by univariate Cox regression analysis; the intersection set was used for subsequent analyses. Unsupervised clustering analysis was performed based on the expression of hypolncRNAs using the 'ConsensuClusterPlus' package. The tumor microenvironment (TME) was compared between LUAD subgroups by analyzing the expression of immune cell infiltration, immune components, stromal components, immune checkpoints, and chemokine secretion. To identify robust prognostically associated hypolncRNAs and construct a risk score model, multivariate Cox regression analysis was performed.

RESULTS

A total of 14 hypolncRNAs were identified. Based on the expression of these hypolncRNAs, patients with LUAD were classified into three hypolncRNA-regulated subtypes. The three subtypes differed significantly in immune cell infiltration, stromal score, specific immune checkpoints, and secretion of chemokines and their receptors. The Tumor Immune Dysfunction and Exclusion (TIDE) scores and myeloid-derived suppressor cell (MDSC) scores were also found to differ significantly among the three hypolncRNA-regulated subtypes. Four of the 14 hypolncRNAs were used to construct a signature to distinguish the overall survival (OS) in TCGA dataset (P<0.0001) and GEO dataset (P=0.0032) and sensitivity to targeted drugs in patients at different risks of LUAD.

CONCLUSIONS

We characterized three regulatory subtypes of hypolncRNAs with different TMEs. We developed a signature based on hypolncRNAs, contributing to the development of personalized therapy and representing a new potential therapeutic target for LUAD.

Relationship between PD-L1 expression and molecular aberrances in lung adenocarcinoma with solid components

Background

Previous studies have evaluated the expression of programmed cell death ligand 1 (PD-L1) in terms of genetic mutation in lung adenocarcinoma (LUAD). However, there are no corresponding large-sample studies in Chinese patients with LUAD with solid components (LUAD-SC). Furthermore, it remains unknown whether the relationship that exists between PD-L1 expression levels and clinicopathological and molecular profiles in small biopsy specimens is consistent with that in surgically-resected specimens. The present study explored the clinicopathological features and genetic correlation of PD-L1 expression in LUAD-SC.

Methods

We collected 1,186 LUAD-SC specimens from Fudan University, Zhongshan Hospital. The tumors were divided into PD-L1 negative, low, and high groups according to the tumor proportion score (TPS)-assessed expression of PD-L1. The mutational information of all specimens was assessed. Each group's clinicopathological features were also assessed. The relationship between PD-L1 expression levels and clinicopathological features, the overlap with driver genes and the prognostic value were analyzed.

Results

In 1,090 resected specimens, a high PD-L1 expression level was more prevalent in the group with predominant SCs, which was remarkably correlated with lymphovascular invasion and a more advanced clinical stage. In addition, the PD-L1 expression level was significantly related to EGFR, KRAS, and BRAF mutations and ROS1 fusions. Meanwhile, in 96 biopsy specimens, the solid-dominant type and EGFR showed a significant difference in PD-L1 expression. Furthermore, compared with their resected counterparts, the biopsy specimens were significantly associated with solid predominant, advanced tumor-node-metastasis (TNM) stage, and high PD-L1 expression. Finally, high PD-L1 expression can be considered a poor prognostic factor for overall survival (OS).

Conclusions

LUAD-SC with high PD-L1 expression levels is linked to unique clinicopathologic characteristics as well as driver mutations. It is important to evaluate the percentage of solid components in both punctured and excised specimens, which may help identify cases of high PD-L1 expression.

Target genes of N6-methyladenosine regulatory protein ALKBH5 are associated with prognosis of patients with lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) exhibits high fatality rates, and effective treatments are lacking. The expression of the N6-methyladenosine (m6A) regulatory protein ALKBH5 is associated with lung cancer. To identify new therapeutic targets for LUAD, we screened target genes of ALKBH5 and analyzed their potential mechanisms of action.

Methods

LUAD samples from The Cancer Genome Atlas (TCGA) were used to analyze the expression of ALKBH5 and screen for genes with correlated expression. Intersection of the genes upregulated in cells with ALKBH5 silencing with the genes significantly associated with ALKBH5 were defined as ALKBH5 target genes. STRING was used to evaluate interactions between the target genes, and the relationship between ALKBH5 target gene expression and LUAD patient prognosis was analyzed using the R package Survminer. Target genes were evaluated by functional enrichment analyses.

Results

ALKBH5 was highly expressed in LUAD tissues and was significantly associated with a poor prognosis. Fifteen ALKBH5 target genes were identified, primarily enriched in protein processing in the endoplasmic reticulum, transcriptional coregulator activity, and cell activation involved in the immune response. Upregulation of ZNF777, TCOF1, CPLX2, and ABL1 was associated with a poor prognosis, whereas upregulation of ZER1, VPS53, and RRBP1 was associated with a good prognosis.

Conclusions

This study provides potential therapeutic targets for LUAD and a basis for further studies on the mechanism underlying the effects of ALKBH5.

Genomic alterations dissection revealed MUC4 mutation as a potential driver in lung adenocarcinoma local recurrence

Background

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, of which genomic alterations play a major role in tumorigenesis. The prognosis of LUAD has been improved these years but nearly half of the patients still develop recurrence even after radical resection. The underlying mechanism driving LUAD recurrence especially genomic alterations is complicated and worth exploring.

Methods

Forty-one primary tumors and 43 recurrent tumors were collected from 41 LUAD patients who received surgery resection after recurrence. Whole exon sequencing (WES) was performed to make genomic landscapes. WES data were aligned to genome and further analyzed for somatic mutation, copy number variation and structure variation. MutsigCV was used to identify significantly mutated genes and recurrence specific genes.

Results

Significantly mutated genes including EGFR, MUC4 and TP53 were identified in primary and recurrent tumors. Some were found to be more specifically mutated in recurrent tumors, such as the MUC17, KRAS and ZNF families. In recurrent tumors, ErbB signaling pathway, MAPK pathway and cell cycle pathway were highly activated, which maybe the mechanism driving recurrence. The adjuvant therapy would affect tumor evolution and molecular features during recurrence. MUC4 was highly mutated in this study cohort, and it was a potential driver gene in LUAD recurrence by activating ErbB signaling pathway as a ligand of ERBB2.

Conclusions

Genomic alteration landscape was changing during LUAD recurrence to construct a more suitable environment for the survival of tumor cells. Several potential driver mutations and targets during LUAD recurrence were identified, such as MUC4, and more investigation was needed to verify the specific functions and roles.

Identification of a polycomb group-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Background

Several studies have reported the role of polycomb group (PcG) genes in human cancers; however, their role in lung adenocarcinoma (LUAD) is unknown.

Methods

Firstly, consensus clustering analysis was used to identify PcG patterns among the 633 LUAD samples in the training dataset. The PcG patterns were then compared in terms of the overall survival (OS), signaling pathway activation, and immune cell infiltration. The PcG-related gene score (PcGScore) was developed using Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) algorithm to estimate the prognostic value and treatment sensitivity of LUAD. Finally, the prognostic ability of the model was validated using a validation dataset.

Results

Two PcG patterns were obtained by consensus clustering analysis, and the two patterns showed significant differences in prognosis, immune cell infiltration, and signaling pathways. Both the univariate and multivariate Cox regression analyses confirmed that the PcGScore was a reliable and independent predictor of LUAD (P<0.001). The high- and low-PCGScore groups showed significant differences in the prognosis, clinical outcomes, genetic variation, immune cell infiltration, and immunotherapeutic and chemotherapeutic effects. Lastly, the PcGScore demonstrated exceptional accuracy in predicting the OS of the LUAD patients in a validation dataset (P<0.001).

Conclusions

The study indicated that the PcGScore could serve as a novel biomarker to predict prognosis, clinical outcomes, and treatment sensitivity for LUAD patients.

Identification of the key miRNA-mRNA regulatory network in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, has a high incidence rate and is a serious threat to human health. However, the pathogenesis of LUAD is still unclear. Further research on the pathogenesis of LUAD may provide targets for the early diagnosis and treatment of LUAD.

Methods

First, a transcriptome analysis was conducted to sequence the messenger RNA (mRNA) and micro RNA (miRNA) of the LUAD and adjacent control tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were then performed for the functional annotation. Next, a differential miRNA-differential mRNA regulatory network was then constructed, and the function of the mRNAs in the network was analyzed and the key regulatory molecules (the hub molecules) were identified. Cytohubba was then used to analyze the top 20 hub molecules in the total miRNA-mRNA network, and the miRNAs regulating the top 20 hub genes (of which 2 were upregulated and 18 were downregulated). Finally, the key molecules were identified.

Results

By analyzing the function of the mRNA molecules in the regulatory network, we found that the immune response was inhibited, as were the movement and adhesion of immune-related cells; however, cell tumorigenesis, body death, and tumor cell proliferation were activated. The functions of the 20 hub molecules were mainly related to cytotoxicity, cell exosmosis, and cell adhesion mediated by immune cells. Further, we found that miR-5698, miR224-5p, and miR4709-3P regulate multiple key genes (e.g., PECAM1, CX3CR1, KLRD1, and CXCL12), and may be the key miRNAs regulating LUAD.

Conclusions

Immune response, cell tumorigenesis, and tumor cell proliferation play central roles in the overall regulatory network. miR-5698, miR224-5p, and miR4709-3p may be important biomarkers for the occurrence and development of LUAD and have great potential in the prognosis of LUAD patients and the development of new therapeutic targets.

Bioinformatics analysis based on DNA methylation data identified in lung adenocarcinoma subgroups with different immune characteristics and clinical outcomes

Background

DNA methylation can be used to predict clinical outcomes and improve the classification of tumors. The present study aimed to develop a new lung adenocarcinoma (LUAD) classification system according to the immune cell gene-related methylation sites and to reveal the survival outcomes, clinical characteristics, immune cell infiltration, stem cell characteristics, and genomic variations of each molecular subgroup.

Methods

The DNA methylation sites of LUAD samples collected from The Cancer Genome Atlas (TCGA) database were analyzed, and the prognosis-related differential methylation sites (DMS) were screened. Consistent clustering of the samples was conducted using ConsensusClusterPlus, and the classification results were verified by principal component analysis (PCA). The survival and clinical results, immune cell infiltration, stemness, DNA mutation, and copy number variation (CNV) of each molecular subgroup were analyzed.

Results

A total of 40 DMS were obtained by difference and univariate COX analyses, and the TCGA LUAD samples were divided into three subgroups: cluster 1 (C1), cluster 2 (C2), and cluster 3 (C3). Among these subgroups, the overall survival (OS) of C3 was significantly higher than that of C1 and C2. Compared with C1 and C3, C2 had the lowest innate immune cell and adaptive immune cell infiltration scores; the lowest stromal score, immune score, and iconic immune checkpoint expression; and the highest expression of messenger RNA (mRNA) expression-based stemness indices (mRNAsi), DNA methylation-based stemness index (mDNAsi), and tumor mutational burden (TMB).

Conclusions

In this study, we proposed a LUAD typing system based on DMS, which was closely related to the survival, clinical features, immune characteristics, and genomic variations of LUAD, and may contribute to the development of personalized therapy for new specific subtypes.

Long non-coding RNA DINO promotes cisplatin sensitivity in lung adenocarcinoma via the p53-Bax axis

Background

The damage-induced non-coding (DINO) RNA is a newly identified long non-coding RNA (lncRNA) found in human cells with DNA damage. The treatment of tumors with cisplatin can induce DNA damage; however, whether the lncRNA DINO is involved in the treatment of non-small cell lung cancer (NSCLC) has not yet been elucidated.

Methods

The expression of the lncRNA DINO in lung adenocarcinoma cells was detected using quantitative real-time polymerase chain reaction (qRT-PCR). The lung adenocarcinoma cell line, A549, and derived cisplatin-resistant cell line, A549R, were selected to construct cell models with lncRNA DINO overexpression or interference via lentiviral transfection. After cisplatin treatment, changes in the apoptosis rate were measured. Changes in the p53-Bax axis were detected by qRT-PCR and Western blot. Cycloheximide (CHX) interference demonstrated the stability of p53 with new protein production induced by the lncRNA DINO. The in vivo experiments involved intraperitoneal injection of nude mice with cisplatin after subcutaneous tumor formation, and the tumor diameters and weights were recorded. Immunohistochemistry and hematoxylin and eosin (H&E) staining were performed following tumor removal.

Results

We found that the lncRNA DINO was significantly down-regulated in NSCLC. DINO overexpression enhanced the sensitivity of NSCLC cells to cisplatin, while DINO down-regulation decreased the sensitivity of NSCLC cells to cisplatin. Mechanistic investigation indicated that DINO enhanced the stability of p53 and mediated the activation of the p53-Bax signaling axis. Our results also demonstrated that the lncRNA DINO could partially reverse cisplatin resistance induced by silencing the p53-Bax axis, and could inhibit subcutaneous tumorigenesis in nude mice after cisplatin treatment in vivo.

Conclusions

The lncRNA DINO regulates the sensitivity of lung adenocarcinoma to cisplatin by stabilizing p53 and activating the p53-Bax axis, and thus, may be a novel therapeutic target to overcome cisplatin resistance.

Development of a cuproptosis-related signature for prognosis prediction in lung adenocarcinoma based on WGCNA

Background

Cuproptosis is a novel mitochondrial respiration-dependent cell death mechanism induced by copper that can kill cancer cells via copper carriers in cancer therapy. However, the clinical significance and prognostic value of cuproptosis in lung adenocarcinoma (LUAD) remains unclear.

Methods

We performed a comprehensive bioinformatics analysis of the cuproptosis gene set, including copy number aberration, single-nucleotide variation, clinical characteristics, survival analysis, etc. Cuproptosis-related gene set enrichment scores (cuproptosis Z-scores) were calculated in The Cancer Genome Atlas (TCGA)-LUAD cohort using single-sample gene set enrichment analysis (ssGSEA). Modules significantly associated with cuproptosis Z-scores were screened by weighted gene co-expression network analysis (WGCNA). The hub genes of the module were then further screened by survival analysis and least absolute shrinkage and selection operator (LASSO) analysis, in which TCGA-LUAD (497 samples) and GSE72094 (442 samples) were used as the training and validation cohorts, respectively. Finally, we analyzed the tumor characteristics, immune cell infiltration levels, and potential therapeutic agents.

Results

Missense mutation and copy number variant (CNV) events were general in the cuproptosis gene set. We identified 32 modules, of which the MEpurple (107 genes) and MEpink (131 genes) modules significantly positively and negatively correlated with cuproptosis Z-scores, respectively. We identified 35 hub genes significantly related to overall survival and constructed a prognostic model consisting of 7 cuproptosis-related genes in patients with LUAD. Compared with the low-risk group, patients in the high-risk group had a worse overall survival and gene mutation frequency, as well as significantly higher tumor purity. In addition, infiltration of immune cells was also significantly different between the 2 groups. Furthermore, the correlation between the risk scores and half-maximum inhibitory concentration (IC50) of antitumor drugs in the Genomics of Drug Sensitivity in Cancer (GDSC) v. 2 database was explored, revealing differences in drug sensitivity across the 2 risk groups.

Conclusions

Our study provided a valid prognostic risk model for LUAD and improved understanding of its heterogeneity, which may aid in the development of personalized treatment strategies.

Acetylcholine promotes chronic stress-induced lung adenocarcinoma progression via α5-nAChR/FHIT pathway

Chronic stress significantly elevates the expression levels of various neurotransmitters in the tumour microenvironment, thereby promoting the cell growth and metastasis of lung adenocarcinoma (LUAD). However, the role of chronic stress in the progression of LUAD remains unclear. In this study, we found that chronic restraint stress increases the levels of the neurotransmitter acetylcholine (ACh), and the α5-nicotinic acetylcholine receptor (α5-nAChR) and decreased fragile histidine triad (FHIT) expression in vivo. Crucially, the increased ACh levels promoted LUAD cell migration and invasion via modulation of the α5-nAChR/DNA methyltransferase 1 (DNMT1)/FHIT axis. In a chronic unpredictable stress (CUMS) mouse model, chronic stress promotes tumour development, accompanied by changes in α5-nAChR, DNMT1, FHIT, and vimentin. Together, these findings reveal a novel chronic stress-mediated LUAD signalling pathway: chronic stress enforces lung adenocarcinoma cell invasion and migration via the ACh/α5-nAChR/FHIT axis, which could be a potential therapeutic target for chronic stress-related LUAD.

The relationship between tumor infiltrating immune cells and the prognosis of patients with lung adenocarcinoma

Background

To depict the immune infiltration characteristics of tumor cells in patients with lung adenocarcinoma (LUAD) and evaluate the predictive value and significance of tumor immune cells on the prognosis of LUAD patients.

Methods

The clinical characteristics and transcriptome of LUAD patients were obtained from The Cancer Genome Atlas (TCGA), and the immune cell abundance in LUAD tissue was evaluated using the CIBERSORT algorithm. We created a simplified immune cell-based Cox regression model according to the survival status of patients and clarified the correlation between the survival status of patients and seven types of immune cells. An immune cell-based risk prediction model was created by Cox proportional hazards regression. Subsequently, the gene expression profile of LUAD patients was obtained from the Gene Expression Omnibus (GEO) database to validate the tumor immune infiltration and patient prognosis prediction model attained using the CIBERSORT algorithm.

Results

The abundance of 22 tumor-infiltrating immune cells in these patients was detected using the CIBERSORT algorithm. According to Pearson correlation analysis, the immune cells appeared to be closely related to each other. The immune cell composition was remarkably different between the LUAD tumor tissue and paracancerous tissue. The simplified COX model showed that seven kinds of immune cells have predictive value for the prognosis and survival status of LUAD. The receiver operating characteristic curve (ROC) curve confirmed that the prediction model performed well for 1-, 3-, and 5-year survival status. The calibration curve suggested that the prediction model was consistent with the clinical results. Correlation analysis revealed that the clinical features were significantly related to immune cell infiltration. A total of 246 LUAD specimens were from the GEO database, and the risk score model suggested that high risk scores were indicative of a poor prognosis. Finally, enzyme-linked immunosorbent assay (ELISA) revealed that the expressions of tumor necrosis factor-α (TNF-α), interleukin 8 (IL-8), IL-6, and interferon-γ (IFN-γ) in tumor tissues were remarkably higher compared with those in adjacent tissues.

Conclusions

There is a close correlation between the tumor-infiltrating immune cells and the prognosis and clinical characteristics of LUAD patients. The risk score model based on TCGA and GEO designed in this study can be applied in clinical practice.

A five-gene expression signature of centromeric proteins with prognostic value in lung adenocarcinoma

Background

Centromere proteins (CENPs) form a large protein family. Sixteen proteins in this family are positioned at the centromere throughout the cell cycle. The overexpression of CENPs is common in many cancers and predicts a poor prognosis. However, a comprehensive analysis of CENPs expression has not been conducted, and their clinical significance in lung adenocarcinoma (LUAD) is unclear.

Methods

We investigated the expression differences of the CENP family in LUAD using The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) cohorts. Kaplan-Meier curve survival analysis was performed to assess their independent prognostic values. We then tested 5 clinical LUAD specimens by quantitative real time polymerase chain reaction (qRT-PCR). The risk model was constructed with least absolute shrinkage and selection operator (LASSO). Cox regression analyses were carried out to determine independent prognostic indicators. Weighted gene coexpression network analysis (WGCNA) was employed to define the coexpression networks.

Results

The messenger RNA (mRNA) expression of 15 differential CENP proteins was higher in LUAD than in normal lung tissues. Among them, 10 CENP proteins had significant prognostic value. The risk model comprising CENPF, CENPU, CENPM, CENPH, and CENPW showed a significant correlation [hazard ratio (HR) 1.75, 95% confidence interval (CI): 1.3-2.35; P=2e-04]. However, the prognostic accuracy was not strong [1-year survival: area under curve (AUC) 0.63; 3-year survival: AUC 0.62; 5-year survival: AUC 0.6]. The qRT-PCR results showed that the 5 CENPs were upregulated in LUAD tissues compared to in normal lung tissues. A total of 441 hub genes coexpressed with the 5 CENPs were identified.

Conclusions

CENPF, CENPU, CENPM, CENPH, and CENPW have prognostic values and may be potential targets for LUAD treatment.

Identification of four metabolic subtypes and key prognostic markers in lung adenocarcinoma based on glycolytic and glutaminolytic pathways

BACKGROUND

Glucose and glutamine are the main energy sources for tumor cells. Whether glycolysis and glutaminolysis play a critical role in driving the molecular subtypes of lung adenocarcinoma (LUAD) is unknown. This study attempts to identify LUAD metabolic subtypes with different characteristics and key genes based on gene transcription profiling data related to glycolysis and glutaminolysis, and to construct prognostic models to facilitate patient outcome prediction.

METHODS

LUAD related data were obtained from the Cancer Genome Atlas and Gene Expression Omnibus, including TCGA-LUAD, GSE42127, GSE68465, GSE72094, GSE29013, GSE31210, GSE30219, GSE37745, GSE50081. Unsupervised consensus clustering was used for the identification of LUAD subtypes. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and CytoNCA App in Cytoscape 3.9.0 were used for the screening of key genes. The Cox proportional hazards model was used for the construction of the prognostic risk model. Finally, qPCR analysis, immunohistochemistry and immunofluorescence colocalization were used to validate the core genes of the model.

RESULT

This study identified four distinct characterized LUAD metabolic subtypes, glycolytic, glutaminolytic, mixed and quiescent types. The glycolytic type had a worse prognosis than the glutaminolytic type. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) were identified as hub genes driving the glycolytic/glutaminolytic LUAD. In addition, the risk assessment model constructed based on three genes (SPP1, SLC2A1 and AGER) had good predictive performance and could be validated in multiple independent external LUAD cohorts. These three genes were differentially expressed in LUAD and lung normal tissues, and might be potential prognostic markers for LUAD.

CONCLUSION

LUAD can be classified into four different characteristic metabolic subtypes based on the glycolysis- and glutaminolysis-related genes. Nine genes (CXCL8, CNR1, AGER, ALB, S100A7, SLC2A1, TH, SPP1, LEP) may play an important role in the subtype-intrinsic drive. This metabolic subtype classification, provides new biological insights into the previously established LUAD subtypes.

Secreted proteins MDK, WFDC2, and CXCL14 as candidate biomarkers for early diagnosis of lung adenocarcinoma

BACKGROUND

Early diagnosis of lung adenocarcinoma (LUAD), one of the most common types of lung cancer, is very important to improve the prognosis of patients. The current methods can't meet the requirements of early diagnosis. There is a pressing need to identify novel diagnostic biomarkers. Secretory proteins are the richest source for biomarker research. This study aimed to identify candidate secretory protein biomarkers for early diagnosis of LUAD by integrated bioinformatics analysis and clinical validation.

METHODS

Differentially expressed genes (DEGs) of GSE31210, gene expression data of early stage of LUAD, were analyzed by GEO2R. Upregulated DEGs predicted to encode secreted proteins were obtained by taking the intersection of the DEGs list with the list of genes encoding secreted proteins predicted by the majority decision-based method (MDSEC). The expressions of the identified secreted proteins in the lung tissues of early-stage LUAD patients were further compared with the healthy control group in mRNA and protein levels by using the UALCAN database (TCGA and CPTAC). The selected proteins expressed in plasma were further validated by using Luminex technology. The diagnostic value of the screened proteins was evaluated by receiver operating characteristic (ROC) analysis. Cell counting kit-8 assay was carried out to investigate the proliferative effects of these screened proteins.

RESULTS

A total of 2183 DEGs, including 1240 downregulated genes and 943 upregulated genes, were identified in the GSE31210. Of the upregulated genes, 199 genes were predicted to encode secreted proteins. After analysis using the UALCAN database, 16 molecules were selected for further clinical validation. Plasma concentrations of three proteins, Midkine (MDK), WAP four-disulfide core domain 2 (WFDC2), and C-X-C motif chemokine ligand 14 (CXCL14), were significantly higher in LUAD patients than in healthy donors. The area under the curve values was 0.944, 0.881, and 0.809 for MDK, WFDC2, and CXCL14, 0.962 when combined them. Overexpression of the three proteins enhanced the proliferation activity of A549 cells.

CONCLUSIONS

MDK, WFDC2, and CXCL14 were identified as candidate diagnostic biomarkers for early-stage LUAD and might also play vital roles in tumorigenesis.

KIAA1429 promotes tumorigenesis and gefitinib resistance in lung adenocarcinoma by activating the JNK/ MAPK pathway in an m(6)A-dependent manner

Non-small cell lung cancer is the leading cause of cancer related mortality worldwide, and lung adenocarcinoma (LUAD) is one of the most common subtypes. The role of N6-methyladenosine (m⁶A) modification in tumorigenesis and drug resistance in LUAD remains unclear. In this study, we evaluated the effects of vir-like m⁶A methyltransferase-associated protein (KIAA1429) depletion on proliferation, migration, invasion, and drug resistance of LUAD cells, and identified m⁶A-dependent downstream genes influenced by KIAA1429. We found that KIAA1429 activated Jun N-terminal kinase (JNK) mitogen-activated protein kinase (MAPK) pathway as a novel signaling event, which is responsible for tumorigenesis and resistance to gefitinib in LUAD cells. KIAA1429 and MAP3K2 showed high expression in LUAD patients' tissues. Knockdown of KIAA1429 inhibited MAP3K2 expression in an m⁶A methylation-dependent manner, restraining the progression of LUAD cells and inhibiting growth of gefitinib-resistant HCC827 cells. KIAA1429 positively regulated MAP3K2 expression, activated JNK/ MAPK pathway, and promoted drug resistance in gefitinib-resistant HCC827 cells. We reproduced the in vitro results in nude mouse xenografted with KIAA1429 knockdown cells. Our study showed that the mechanism of m⁶A KIAA1429-mediated gefitinib resistance in LUAD cells occurs by activating JNK/ MAPK signaling pathway. These findings provide potential targets for molecular therapy and clinical treatment in LUAD patients with gefitinib resistance.

A novel glycosylation-related gene signature predicts survival in patients with lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common malignant tumor that seriously affects human health. Previous studies have indicated that abnormal levels of glycosylation promote progression and poor prognosis of lung cancer. Thus, the present study aimed to explore the prognostic signature related to glycosyltransferases (GTs) for LUAD.

METHODS

The gene expression profiles were obtained from The Cancer Genome Atlas (TCGA) database, and GTs were obtained from the GlycomeDB database. Differentially expressed GTs-related genes (DGTs) were identified using edge package and Venn diagram. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and ingenuity pathway analysis (IPA) methods were used to investigate the biological processes of DGTs. Subsequently, Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct a prognostic model for LUAD. Kaplan-Meier (K-M) analysis was adopted to explore the overall survival (OS) of LUAD patients. The accuracy and specificity of the prognostic model were evaluated by receiver operating characteristic analysis (ROC). In addition, single-sample gene set enrichment analysis (ssGSEA) algorithm was used to analyze the infiltrating immune cells in the tumor environment.

RESULTS

A total of 48 DGTs were mainly enriched in the processes of glycosylation, glycoprotein biosynthetic process, glycosphingolipid biosynthesis-lacto and neolacto series, and cell-mediated immune response. Furthermore, B3GNT3, MFNG, GYLTL1B, ALG3, and GALNT13 were screened as prognostic genes to construct a risk model for LUAD, and the LUAD patients were divided into high- and low-risk groups. K-M curve suggested that patients with a high-risk score had shorter OS than those with a low-risk score. The ROC analysis demonstrated that the risk model efficiently diagnoses LUAD. Additionally, the proportion of infiltrating aDCs (p < 0.05) and Tgds (p < 0.01) was higher in the high-risk group than in the low-risk group. Spearman's correlation analysis manifested that the prognostic genes (MFNG and ALG3) were significantly correlated with infiltrating immune cells.

CONCLUSION

In summary, this study established a novel GTs-related risk model for the prognosis of LUAD patients, providing new therapeutic targets for LUAD. However, the biological role of glycosylation-related genes in LUAD needs to be explored further.

Low-dose anti-VEGFR2 therapy promotes anti-tumor immunity in lung adenocarcinoma by down-regulating the expression of layilin on tumor-infiltrating CD8(+)T cells

PURPOSE

Our study intended to explore how low-dose anti-angiogenic drugs affected anti-tumor immunity of tumor-infiltrating exhausted CD8⁺T cells and achieved better clinical response when combined with immunotherapy. We set out to find potential targets or predictive biomarker on CD8⁺T cells for immunotherapy.

METHODS

We tested different doses of anti-VEGFR2 antibody combined with anti-PD1 antibody to treat LUAD in vivo and analyzed tumor-infiltrating CD8⁺T cells by flow cytometry. CD8⁺T cells overexpressing LAYN were co-cultured with LA795 cell lines to identify the function of LAYN in CD8⁺T cells. We also analyzed clinical samples from advanced LUAD patients treated with anti-angiogenesis therapy combined with immunotherapy.

RESULTS

Low-dose anti-VEGFR2 antibody combined with anti-PD1 antibody treatment delayed tumor growth and prolonged the survival time of tumor-bearing mice. The number of tumor-infiltrating CD8⁺T cells was reduced and the expression of LAYN was down-regulated in tumor-infiltrating CD8⁺T cells in the low-dose anti-VEGFR2 combination group. It was found that LAYN inhibited the killing function of CD8⁺T cells. In patients with advanced LUAD who received anti-angiogenesis therapy combined with immunotherapy, the LAYN⁺CD8⁺T cell subpopulation in good responders was significantly higher than that in poor responders. Furthermore, we demonstrated the expression of LAYN was regulated by upstream transcription factor NR4A1.

CONCLUSION

Low-dose anti-VEGFR2 antibody combined with anti-PD1 antibody therapy promoted anti-tumor immunity and the downregulation of LAYN in tumor-infiltrating CD8⁺T cells played an important role in this process. These findings had implications for improving the efficacy of immune checkpoint blockade therapy and further optimized clinical treatment guidelines in advanced LUAD.

MiR-202-3p inhibits the proliferation and metastasis of lung adenocarcinoma cells by targeting RRM2

Background

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, and its pathogenesis is still unclear. The present study aimed to investigate the role of miR-202-3p and its downstream target gene, ribonucleotide reductase regulatory subunit M2 (RRM2), in the occurrence and development of LUAD and elucidate the correlation between RRM2 and the clinicopathological stage and prognosis of LUAD.

Methods

The expression of miR-202-3p was analyzed using the CancerMIRNome database and quantitative polymerase chain reaction (qPCR). The effects of miR-202-3p and RRM2 on the proliferation, migration, and invasion of A549 cells were analyzed. A dual luciferase reporter assay was used to verify the targeting of miR-202-3p and RRM2. Additionally, the correlation between RRM2 expression and clinicopathology was analyzed.

Results

(I) MiR-202-3p was lowly expressed in LUAD and the LUAD cell lines. qPCR confirmed that microRNA (miRNA) transfection was effective and sufficient for subsequent experiments. (II) MiR-202-3p inhibited the proliferation, invasion, and migration of LUAD cells. (III) There was a targeting relationship between miR-202-3p and RRM2, and miR-202-3p affected the expression of the RRM2 protein. RRM2 was highly expressed in lung cancer tissue. (IV) RRM2 was associated with the clinicopathological staging of lung cancer. The prognosis of patients with low RRM2 expression was better, and the prognostic sensitivity of RRM2 to lung cancer was high. RRM2 may exert its effects via the Notch pathway. (V) Si-RRM2 inhibited the expression of the RRM2 protein. RRM2 promoted the proliferation, migration, and invasion of LUAD cells. A miR-202-3p inhibitor restored the inhibitory effect of si-RRM2 on LUAD cells.

Conclusions

MiR-202-3p was lowly expressed in lung cancer tissue. MiR-202-3p overexpression inhibited the proliferation and metastasis of lung cancer cells. RRM2 was highly expressed in lung cancer tissue and promoted the proliferation and metastasis of lung cancer cells. MiR-202-3p targeted and inhibited RRM2, thereby reducing the proliferation and metastasis of LUAD cells. LUAD patients with low RRM2 expression had a better prognosis, and the expression level of RRM2 was correlated with the clinical characteristics of lung cancer patients.

Survival benefit of resection surgery for lung adenocarcinoma with bone metastases and a post-operative prognosis nomogram establishment and validation

Background

Surgical resection is not usually recommended for lung adenocarcinoma (LUAD) patients with bone metastases. However, the criteria for surgery are constantly being adjusted and there is a need to focus on the prognostic role of cancer-directed surgery (CDS) for bone metastatic LUAD patients investigate the factors influencing survival of CDS. We determined the survival benefit of CDS for LUAD patients with bone metastases and to develop a prognostic nomogram to predict overall survival (OS) for patients after surgery.

Methods

LUAD patients with bone metastases from the Surveillance, Epidemiology, and End Results (SEER) database between 2010-2015 were included and divided into CDS and non-CDS groups. The propensity score matching (PSM) was used to balance baseline characteristics. We used Kaplan-Meier curves and log-rank tests to compare cancer-specific survival (CSS) and OS between the two groups. Patients underwent CDS were randomly divided into training and validation cohorts to develop and validate a nomogram model to predict postoperative prognosis outcome-OS.

Results

Patients who underwent CDS had a better OS and CSS than those who did not underwent CDS (e.g., 1-year OS rate: 56.9% vs. 30.1%). Independent prognostic factors were selected by Cox regression analysis for CDS patients including age, sex, race, histological grade, N stage, and chemotherapy, and a nomogram was constructed to predict 1-, 2-, and 3-year OS after surgery according to the prognostic factors. The calibration curve and receiver operating characteristic (ROC) curve for the nomogram showed the model had a high predictive accuracy. [Area under the curve (AUC) at 1, 2, and 3 years in the training cohort were 0.735, 0.756 and 0.782, and in the validation cohort were 0.703, 0.758 and 0.836, respectively]. In addition, patients were divided into high-risk and low-risk groups based on prognostic scores, and Kaplan-Meier curves showed significant differences in prognosis between the two groups.

Conclusions

These results indicated that patients with bone metastasis of LUAD received survival benefit from CDS. The prognostic nomograms could assist clinicians in specifying individualized assessments, but further research is needed.

Molecular typing and prognostic model of lung adenocarcinoma based on cuprotosis-related lncRNAs

Background

Previous research has shown the heterogeneity of lung adenocarcinoma (LUAD) accounts for the different effects and prognoses of the same treatment. Cuprotosis is a newly discovered form of programmed cell death involved in the development of tumors. Therefore, it is important to study the long non-coding RNAs (lncRNAs) that regulate cuprotosis to identify molecular subtypes and predict survival of LUAD.

Methods

The expression profile, clinical, and mutation data of LUAD were downloaded from The Cancer Genome Atlas (TCGA), and the "ConsensusClusterPlus" package was used to cluster LUADs based on cuprotosis-related lncRNAs (CR-lncRNAs). The least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were used to construct a prognostic model. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were used for assessing immune cells infiltration and immune function. The tumor microenvironment (TME) score was calculated by ESTIMATE, and the tumor mutational burden (TMB) and Tumor Immune Dysfunction and Exclusion (TIDE) were used to evaluate the efficacy of immunotherapy.

Results

Firstly, 501 CR-lncRNAs were identified based on the co-expression relationship of 19 cuprotosis genes. And univariate Cox further obtained 34 prognosis-related CR-lncRNAs. The unsupervised consensus clustering divided LUAD samples into cluster A and cluster B, and showed cluster A had better prognosis, more immune cells infiltration, stronger immune function, and a higher TME score. Subsequently, we used Lasso Cox regression to construct a prognostic model, and univariate and multivariate Cox analyses showed the risk score could be an independent prognostic indicator. Immune cells infiltration, immune function, and TME score were increased markedly in the low-risk group, while TMB and TIDE suggested the efficacy of immunotherapy might be increased in high-risk group.

Conclusions

Our research identified two new molecular subtypes and constructed a novel prognostic model of LUAD which could provide new direction for its diagnosis, treatment, and prognosis.

Development and validation of a combined metabolism and immune prognostic model in lung adenocarcinoma

Background

Tumor metabolism and immune response can affect the biological behavior of tumor cells. There is an obvious relationship between glycolysis and immune response. However, the association between metabolism and immune response and prognosis in lung adenocarcinoma (LUAD) has not yet been examined in a comprehensive and detailed manner. The establishment of reliable models for predicting the prognosis of LUAD based on glycolysis ability and immune status is still highly anticipated.

Methods

The expression of genes were obtained from online databases, and the differentially expressed genes in LUAD tissues and adjacent tissues were identified. We used LUAD samples in The Cancer Genome Atlas (TCGA) database as training set and the Gene Expression Omnibus (GEO) databases as validation sets. The best predictive model was constructed using least absolute selection and shrinkage operator (LASSO) regression and Cox regression. The receiver operator characteristic (ROC) curve is used to verify the accuracy of the model. The expression status of the Glycolysis-related genes (GRGs) and the status of the immune cells in LUCD patients were further analyzed. The protein levels of the 3 identified genes were then tested in LUAD patients.

Results

We identified 3 GRGs and immune-related genes (i.e., fibroblast growth factor 2, hyaluronan-mediated motor receptor, and nuclear receptor 0B2) and constructed a stable comprehensive index of glycolysis and immunity (CIGI) prediction model. The validation results for this CIGI model were quite stable across different datasets and patient subgroups and the CIGI score can be included as an independent prognostic factor for LUAD patients. The area under the curve (AUC) values of 1-, 3- and 5-year of the finally established nomogram model are 0.767, 0.735 and 0.769. Further analysis showed that LUAD patients in the low-risk group had lower levels of glycolytic gene expression than those in the high-risk group and exhibited an immunosuppressed state. Finally, hyaluronan-mediated motor receptor may play a role in inhibiting cancer, while fibroblast growth factor 2 and nuclear receptor 0B2 may play roles in promoting cancer.

Conclusions

In this study, we established a new prognostic prediction model for LUAD patients that combines glycolysis ability and immune status.

A cuproptosis-related long non-coding RNA signature to predict the prognosis and immune microenvironment characterization for lung adenocarcinoma

BACKGROUND

Cuproptosis or copper-dependent cell death is a newly identified non-apoptotic cell death pathway which plays a critical role in the development of multiple cancers. Long non-coding RNAs (lncRNAs) are increasingly recognized as crucial regulators of programmed cell death and lung adenocarcinoma (LUAD) development, and a comprehensive understanding of cuproptosis-related lncRNAs may improve prognosis prediction of LUAD. However, few studies have explored the association of cuproptosis-related lncRNAs with the prognosis of LUAD.

METHODS

The RNA sequencing data and corresponding clinical information of patients were extracted from The Cancer Genome Atlas (TCGA) database. Five hundred LUAD patients were randomly divided into a training (n=250) and a testing cohort (n=250). Pearson correlations were performed to identify cuproptosis-related lncRNAs, and univariate Cox regression was performed to screen prognostic lncRNAs. A cuproptosis-related lncRNAs prognostic signature (CLPS) was constructed by the least absolute shrinkage and selection operator Cox regression. Kaplan-Meier analysis, receiver operating characteristic curves, and multivariate Cox regression were performed to verify the prognostic performance of CLPS. Additionally, immune cell infiltration was estimated using the single-sample gene-set enrichment analysis. pRRophetic algorithm and Tumor Immune Dysfunction and Exclusion algorithm were used to assess the immunotherapy and chemotherapy response, respectively.

RESULTS

CLPS was established based on 61 cuproptosis-related prognostic lncRNAs and exhibited a satisfactory performance predicting LUAD patients' survival (area under the curve at 1, 3, 5 years was 0.784, 0.749, 0.775, respectively). multivariate Cox analysis confirmed the independent prognostic effect of CLPS (hazard ratio: 1.128; 95% confidence interval: 1.071-1.189; P<0.001), and a nomogram containing it exhibited robust validity in prognostic prediction. We further demonstrated a higher CLPS-risk score was associated with lower levels of signatures including immune cell infiltration, immune activation, and immune checkpoints.

CONCLUSIONS

The CLPS serves as an effective predictor for the prognosis and therapeutic responses of LUAD patients. Our findings provide promising novel biomarkers and therapeutic targets for LUAD.

The prognostic value and multiomic features of m6A-related risk signature in lung adenocarcinoma

OBJECTIVES

N⁶-methyladenosine (m6A), a predominant RNA modification, has been recently linked to messenger RNA splicing, stability and expression, and its dysregulation may be important in the initiation as well as development of human cancers. The current study was proposed to investigate the clinico-pathological value and multiomic characteristics of m6A-linked genes in the diagnosis and prognosis of lung adenocarcinoma (LUAD).

METHODS

The expression levels and mutation types of 21 previously identified m6A regulators were analyzed using the TCGA (The Cancer Genome Atlas) database. The patients were categorized into two groups, a training group (n=392) and a testing group (n=98). Next, the prognostic score of m6A regulators was determined by the Cox survival analysis and a regression model of LASSO to develop a risk profile for patients with LUAD. Moreover, features of risk signature, including chemosensitivity, tumor immune microenvironment and genetic mutation, were also explored.

RESULTS

In total, 18 of 21 m6A regulators showed significantly differential expression in LUAD (P<0.05). Among them, 6 genes were observed to be associated with the Overall Survival (OS) of patients with LUAD. Three genes (IGF2BP1 and 2, and HNRNPC) were further evaluated as a prognostic signature in LUAD. Patients, grouped as high risk based on the median of risk score, had poorer OS in comparison with those in low-risk group (P<0.05). The accuracy of our prognostic signatures was high: the AUC were 0.67, 0.59, 0.64 (training set), and 0.65, 0.69, 0.64 (testing set) at survival of 1- , 3- and 5-year, respectively. The prognostic performance of IGF2BP1, IGF2BP2 and HNRNPC was successfully validated in two independent external cohorts. High-risk score was an indicator of chemoresistance, TP53 mutation and increased infiltration of immune cells, and in vitro assessment of the cellular function of HNRNPC confirmed that the gene is involved in cell proliferation and invasion.

CONCLUSION

The prognostic signature based on m6A regulators might provide novel insights into prognostic assessment and individualized treatment for patients with LUAD.

High expression of PTGES3 is an independent predictive poor prognostic biomarker and correlates with immune infiltrates in lung adenocarcinoma

BACKGROUND

Immune-infiltration was positively relationship with overall survival in lung adenocarcinoma (LUAD). Nevertheless, the potential clinical value of PTGES3, especially in terms of prognosis and tumor immune-infiltration in LUAD had not been fully elucidated.

METHODS

Original data available from TCGA and GEO databases and integrated via R3.6.3. Kaplan-Meier and Cox regression methods were used to examine the effect of PTGES3 expression in overall survival, and nomogram was performed to illustrate the correlation between the PTGES3 expression and the risk of LUAD. The associate between PTGES3 and cancer immune characteristics were analyzed via the TISIDB databases. Western blot and RT-qPCR were used to analyze PTGES3 expression in the clinical lung adenocarcinoma tissue samples or non-small cell lung cancer cell lines.

RESULTS

PTGES3 mRNA and protein expression were significantly elevated in LUAD compared with normal lung tissues. Up-regulated PTGES3 was significantly associated with pathologic stage and TM stage. Kaplan-Meier survival analysis and subgroup analysis showed that up-regulated PTGES3 was associated with a worse overall survival of LUAD (HR = 1.71 (1.27-2.31), p < 0.001). Multivariate Cox analysis showed that high PTGES3 expression was an independent factor affecting overall survival (HR = 1.64 (1.14-2.37), p < 0.001). GO and KEGG analysis revealed that the cell cycle, regulation of DNA replication, and regulation of innate immune response were enriched. A positive correlation between PTGES3 expression and immune infiltrating levels of Th2 cells was found.

CONCLUSION

PTGES3 may play an important role in the cell cycle and as an independent predictive prognostic biomarker correlates with immune infiltrates in lung adenocarcinoma.

Comprehensive analysis of CXCR family members in lung adenocarcinoma with prognostic values

Background

The expression profiles and molecular mechanisms of CXC chemokine receptors (CXCRs) in Lung adenocarcinoma (LUAD) have been extensively explored. However, the comprehensive prognostic values of CXCR members in LUAD have not yet been clearly identified.

Methods

Multiple available datasets, including Oncomine datasets, the cancer genome atlas (TCGA), HPA platform, GeneMANIA platform, DAVID platform and the tumor immune estimation resource (TIMER) were used to detect the expression of CXCRs in LUAD, as well as elucidate the significance and value of novel CXCRs-associated genes and signaling pathways in LUAD.

Results

The mRNA and/or protein expression of CXCR1, CXCR2, CXCR3, CXCR4, CXCR5 and CXCR6 displayed predominantly decreased in LUAD tissues as compared to normal tissues. On the contrary, compared with the normal tissues, the expression of CXCR7 was significantly increased in LUAD tissues. Subsequently, we constructed a network including CXCR family members and their 20 related genes, and the related GO functions assay showed that CXCRs connected with these genes participated in the process of LUAD through several signal pathways including Chemokine signaling pathway, Cytokine-cytokine receptor interaction and Neuroactive ligand-receptor interaction. TCGA and Timer platform revealed that the mRNA expression of CXCR family members was significantly related to individual cancer stages, cancer subtypes, patient’s gender and the immune infiltration level. Finally, survival analysis showed that low mRNA expression levels of CXCR2 (HR = 0.661, and Log-rank P = 1.90e−02), CXCR3 (HR = 0.674, and Log-rank P = 1.00e−02), CXCR4 (HR = 0.65, and Log-rank P = 5.01e−03), CXCR5 (HR = 0.608, and Log-rank P = 4.80e−03) and CXCR6 (HR = 0.622, and Log-rank P = 1.85e−03) were significantly associated with shorter overall survival (OS), whereas high CXCR7 mRNA expression (HR = 1.604, and Log-rank P = 4.27e−03) was extremely related with shorter OS in patients.

Conclusion

Our findings from public databases provided a unique insight into expression characteristics and prognostic values of CXCR members in LUAD, which would be benefit for the understanding of pathogenesis, diagnosis, prognosis prediction and targeted treatment in LUAD.

Effect of PPP1R14D gene high expression in lung adenocarcinoma knocked out on proliferation and apoptosis of DMS53 cell

PURPOSE

Globally, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer-related mortality. Lung adenocarcinoma (LUAD) is a common subtype of lung cancer and carries a poor prognosis. Treatment outcomes biomarkers in LUAD are critical, and there is currently a paucity of data; therefore, there is a need for novel biomarkers and newer therapeutic targets.

METHODS

Bayesian analysis was used to obtain the whole-genome t value of LUAD. Gene set enrichment analysis (GSEA) was conducted to obtain the normalized enrichment scores (NES) of the whole genome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed using the Gene Set Analysis Toolkit. Herein, we investigated the PPP1R14D expression level at the protein level in LUAD and the impact of PPP1R14D knockdown on the proliferation and apoptosis of LUAD cells in vitro.

RESULTS

A total of 483 LUAD samples and 59 normal control samples were included, and 904 differentially expressed genes (DEGs) and 504 LUAD-related genes reported in the literature were obtained. The DEGs showed that PPP1R14D was the most significantly up-regulated gene. Western blot of 30 cases of LUAD tissue and adjacent normal tissue also found that PPP1R14D was significantly highly expressed in cancer tissues. Lentivirus-mediated shRNA strategy effectively inhibited PPP1R14D expression in human LUAD cells DMS53, while PPP1R14D knockdown induced apoptosis and cell proliferation in DMS53 cells.

CONCLUSION

Abnormally up-regulated PPP1R14D promotes the survival and proliferation of tumor cells in human LUAD and may serve as a therapeutic and diagnostic target for LUAD.

LncRNA FAM83A-AS1 facilitates tumor proliferation and the migration via the HIF-1α/ glycolysis axis in lung adenocarcinoma

Background: Lung adenocarcinoma (LUAD), the major subtype of lung cancer, is among the leading cause of cancer-related death worldwide. Energy-related metabolic reprogramming metabolism is a hallmark of cancer shared by numerous cancer types, including LUAD. Nevertheless, the functional pathways and molecular mechanism by which FAM83A-AS1 acts in metabolic reprogramming in lung adenocarcinoma have not been fully elucidated.

Methods: We used transwell, wound-healing scratch assay, and metabolic assays to explore the effect of FAM83A-AS1 in LUAD cell lines. Western blotting, Co-IP assays, and ubiquitination assays were used to detect the effects of FAM83A-AS1 on HIF-1α expression, degradation, and its binding to VHL. Moreover, an in vivo subcutaneous tumor formation assay was used to detect the effect of FAM83A-AS1 on LUAD.

Results: Herein, we identified FAM83A-AS1 as a metabolism-related lncRNA, which was highly correlated with glycolysis, hypoxia, and OXPHOS pathways in LUAD patients using bioinformatics analysis. In addition, we uncovered that FAM83A-AS1 could promote the migration and invasion of LUAD cells, as well as influence the stemness of LUAD cells in vivo and vitro. Moreover, FAM83A-AS1 was shown to promote glycolysis in LUAD cell lines in vitro and in vivo, and was found to influence the expression of genes related to glucose metabolism. Besides, we revealed that FAM83A-AS1 could affect glycolysis by regulating HIF-1α degradation. Finally, we found that FAM83A-AS1 knockdown could inhibit tumor growth and suppress the expression of HIF-1α and glycolysis-related genes in vivo.

Conclusion: Our study demonstrates that FAM83A-AS1 contributes to LUAD proliferation and stemness via the HIF-1α/glycolysis axis, making it a potential biomarker and therapeutic target in LUAD patients.

Network Pharmacology and Bioinformatics Analysis Identifies Potential Therapeutic Targets of Paxlovid Against LUAD/COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a pandemic in many countries around the world. The virus is highly contagious and has a high fatality rate. Lung adenocarcinoma (LUAD) patients may have higher susceptibility and mortality to COVID-19. While Paxlovid is the first oral drug approved by the U.S. Food and Drug Administration (FDA) for COVID-19, its specific drug mechanism for lung cancer patients infected with COVID-19 remains to be further studied.

METHODS

COVID-19 related genes were obtained from NCBI, GeneCards, and KEGG, and then the transcriptome data for LUAD was downloaded from TCGA. The drug targets of Paxlovid were revealed through BATMAN-TCM, DrugBank, SwissTargetPrediction, and TargetNet. The genes related to susceptibility to COVID-19 in LUAD patients were obtained through differential analysis. The interaction of LUAD/COVID-19 related genes was evaluated and displayed by STRING, and a COX risk regression model was established to screen and evaluate the correlation between genes and clinical characteristics. The Venn diagram was drawn to select the candidate targets of Paxlovid against LUAD/COVID-19, and the functional analysis of the target genes was performed using KEGG and GO enrichment analysis. Finally, Cytoscape was used to screen and visualize the Hub Gene, and Autodock was used for molecular docking between the drug and the target.

RESULT

Bioinformatics analysis was performed by combining COVID-19-related genes with the gene expression and clinical data of LUAD, including analysis of prognosis-related genes, survival rate, and hub genes screened out by the prognosis model. The key targets of Paxlovid against LUAD/COVID-19 were obtained through network pharmacology, the most important targets include IL6, IL12B, LBP. Furthermore, pathway analysis showed that Paxlovid modulates the IL-17 signaling pathway, the cytokine-cytokine receptor interaction, during LUAD/COVID-19 treatment.

CONCLUSIONS

Based on bioinformatics and network pharmacology, the prognostic signature of LUAD/COVID-19 patients was screened. And identified the potential therapeutic targets and molecular pathways of Paxlovid Paxlovid in the treatment of LUAD/COVID. As promising features, prognostic signatures and therapeutic targets shed light on improving the personalized management of patients with LUAD.

Comprehensive analysis of PD-L1 expression, tumor-infiltrating lymphocytes, and tumor microenvironment in LUAD: differences between Asians and Caucasians

Backgrounds

The characteristics of programmed cell death protein-1 (PD-L1) expression, tumor-infiltrating lymphocytes (TILs), and tumor microenvironment (TME) in lung adenocarcinoma (LUAD) patients are closely related to immunotherapy, and there are differences between Asians and Caucasians.

Methods

Acquire the transcriptome data of the Cancer Genome Atlas and Chinese LUAD patients. R software was used to analyze the differential expression of genes, prognosis, and gene function. Use CIBERSORT for TIL-related analysis and ESTIMATE for TME-related analysis.

Results

The expression of PD-L1 in tumor tissues of Caucasian LUAD patients was lower than that in normal tissues, while there was no significant difference in Asians. There was no statistical difference between PD-L1 expression and prognosis. The composition of TILs between Caucasian and Asian LUAD patients was quite different. There was no correlation between TILs and prognosis in Caucasians. However, the higher content of resting mast cells indicated a better prognosis in Asians. The Caucasian patients with higher immune and estimate scores had a better prognosis (p = 0.021, p = 0.025). However, the Asian patients with a higher estimate score had a worse prognosis (p = 0.024). The high expression of COL5A2 (p = 0.046, p = 0.027) and NOX4 (p = 0.020, p = 0.019) were both associated with the poor prognosis in Caucasians and Asians.

Conclusion

There are many differences in the characteristics of PD-L1 expression, TILs, and TME between Caucasian and Asian LUAD patients. This provides a certain hint for the selection of specific immunotherapy strategies separately for Caucasian and Asian LUAD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01221-3.