Comprehensive Analysis of a Nine-Gene Signature Related to Tumor Microenvironment in Lung Adenocarcinoma

TM6SF1 suppresses the progression of lung adenocarcinoma and M2 macrophage polarization by inactivating the PI3K/AKT/mtor pathway

Transmembrane 6 superfamily 1 (TM6SF1) is lowly expressed in lung adenocarcinoma (LUAD), but the function and mechanisms of TM6SF1 remain unclear. Thus, we attempt to explore the function of TM6SF1 and its underlying mechanisms in LUAD. qRT-PCR was used for detecting TM6SF1 mRNA expression. Immunohistochemistry staining was used for detecting the expression of MMP-2, TM6SF1, Ki67, MMP-9, and CD163 proteins. E-cadherin, p-PI3K, Vimentin, AKT, N-cadherin, PI3K, p-AKT, mTOR, p-mTOR, and marker proteins of M2 macrophages were evaluated using Western blot. CD206 protein expression was examined via immunofluorescence. The IL-10 concentration was measured via enzyme-linked immunosorbent assay (ELISA). Using CCK-8, colony formation and transwell assays, cell proliferation, migration, and invasion were assessed. A549 cells were injected into the mice's flank for establishing a mouse tumor model and into the tail vein for establishing the lung metastasis model. HE staining was performed to detect pathological changes in lung tissues. Decreased TM6SF1 expression was found in LUAD tissues and cells. TM6SF1 overexpression inhibited cell viability, proliferation, invasion, migration, EMT, and polarization of M2 macrophages in LUAD cells, along with tumor growth and metastasis in xenograft mice. Bioinformatics analysis demonstrated that TM6SF1 was correlated with the tumor microenvironment. TM6SF1 overexpression reduced expression levels of p-mTOR, p-PI3K, p-AKT, mTOR, and AKT. TM6SF1-caused inhibition of proliferation, migration, invasion and EMT, as M2 macrophage polarization was reversed by the PI3K activator in LUAD cells. TM6SF1 inactivated the PI3K/AKT/mTOR pathway to suppress LUAD malignancy and polarization of M2 macrophages, providing insight for developing new LUAD treatments.

Unraveling the tumor immune microenvironment of lung adenocarcinoma using single-cell RNA sequencing

Tumor immune microenvironment (TIME) and its indications for lung cancer patient prognosis and therapeutic response have become new hotspots in cancer research in recent years. Tumor cells, immune cells, various regulatory factors, and their interactions in the TIME have been suggested to commonly influence lung cancer development and therapeutic outcome. The heterogeneity of TIME is composed of dynamic immune-related components, including various cancer cells, immune cells, cytokine/chemokine environments, cytotoxic activity, or immunosuppressive factors. The specific composition of cell subtypes may facilitate or hamper the response to immunotherapy and influence patient prognosis. Various markers have been found to stratify the patient prognosis or predict the therapeutic outcome. In this article, we systematically reviewed the recent advancement of TIME studies in lung adenocarcinoma (LUAD) using single-cell RNA sequencing (scRNA-seq) techniques, with specific focuses on the roles of TIME in LUAD development, TIME heterogeneity, indications of TIME in patient prognosis and therapeutic response during immunotherapy and drug resistance. The main findings in TIME heterogeneity and relevant markers or models for prognosis stratification and response prediction have been summarized. We hope that this review provides an overview of TIME status in LUAD and an inspiration for future development of strategies and biomarkers in LUAD treatment.

Single-cell and bulk RNA sequencing analysis of B cell marker genes in TNBC TME landscape and immunotherapy

Objective

This study amied to investigate the prognostic characteristics of triple negative breast cancer (TNBC) patients by analyzing B cell marker genes based on single-cell and bulk RNA sequencing.

Methods

Utilizing single-cell sequencing data from TNBC patients, we examined tumor-associated B cell marker genes. Transcriptomic data from The Cancer Genome Atlas (TCGA) database were used as the foundation for predictive modeling. Independent validation set was conducted using the GSE58812 dataset. Immune cell infiltration into the tumor was assessed through various, including XCELL, TIMER, QUANTISEQ, CIBERSORT, CIBERSORT-ABS, and ssGSEA. The TIDE score was utilized to predict immunotherapy outcomes. Additional investigations were conducted on the immune checkpoint blockade gene, tumor mutational load, and the GSEA enrichment analysis.

Results

Our analysis encompassed 22,106 cells and 20,556 genes in cancerous tissue samples from four TNBC patients, resulting in the identification of 116 B cell marker genes. A B cell marker gene score (BCMG score) involving nine B cell marker genes (ZBP1, SEL1L3, CCND2, TNFRSF13C, HSPA6, PLPP5, CXCR4, GZMB, and CCDC50) was developed using TCGA transcriptomic data, revealing statistically significant differences in survival analysis (P<0.05). Functional analysis demonstrated that marker genes were predominantly associated with immune-related pathways. Notably, substantial differences between the higher and lower- BCMG score groups were observed in terms of immune cell infiltration, immune cell activity, tumor mutational burden, TIDE score, and the expression of immune checkpoint blockade genes.

Conclusion

This study has established a robust model based on B-cell marker genes in TNBC, which holds significant potential for predicting prognosis and response to immunotherapy in TNBC patients.

SEL1L3 as a link molecular between renal cell carcinoma and atherosclerosis based on bioinformatics analysis and experimental verification

BACKGROUND

Renal cancer, the most common type of kidney cancer, develops in the renal tubular epithelium. Atherosclerosis of the aorta is the primary cause of atherosclerosis. However, the underlying mechanisms remain unclear.

METHODS

The renal clear cell carcinoma RNA sequence profile was obtained from The Cancer Genome Atlas (TCGA) database, and the atherosclerosis datasets GSE28829 and GSE43292 based on GPL570 and GPL6244 was obtained from the Gene Expression Omnibus (GEO) database. The difference and hub genes were identified by the Limma protein-protein interaction (PPI) network in R software. Functional enrichment, survival, and immunoinfiltration analyses were performed. The role of SEL1L3 in the ErbB/PI3K/mTOR signaling pathway, apoptosis, invasion, cell cycle, and inflammation was analyzed using western blotting.

RESULTS

764 DEGs were identified from TCGA Kidney Renal Clear Cell Carcinoma (KIRC) dataset. A total of 344 and 117 DEGs were screened from the GSE14762 and GSE53757 datasets, respectively. Functional enrichment analysis results primarily indicated enrichment in the transporter complex, DNA-binding transcription activator activity, morphogenesis of the embryonic epithelium, stem cell proliferation, adrenal overactivity and so on. Fifteen common DEGs overlapped among the three datasets. The PPI network revealed that SEL1L3 was the core gene. Survival analysis showed that lower SEL1L3 expression levels led to a worse prognosis. Immune cell infiltration analysis showed that SEL1L3 expression was significantly correlated with antibody-drug conjugates (aDC), B cells, eosinophils, interstitial dendritic cells (iDC), macrophages, and more.

CONCLUSIONS

SEL1L3 plays an important role in renal clear cell carcinoma and atherosclerosis and may be a potential link between them.

Phosphorylated MAPK11 promotes the progression of clear cell renal cell carcinoma by maintaining RUNX2 protein abundance

Previous studies have demonstrated that mitogen-activated protein kinase 11 (MAPK11) functions as an important point of integration in signalling transduction pathways and controlling endocellular processes, including viability of cells, differentiation, proliferation and apoptosis, through the sequence phosphorylation of the substrate protein Ser/Thr kinase protein cascade. Though MAPK 11 plays an important role in various tumours, especially in the invasive and metastatic processes, its expression and molecular mechanism in clear cell renal cell carcinoma (ccRCC) remain unclear. Runt-associated transcription factor 2 (RUNX2), a main transcription factor for osteoblast differentiation and chondrocyte maturation, has high expression in a number of tumours. In this study, the mRNA and protein levels of targeted genes in ccRCC tissues and adjacent tissues are analysed using the Cancer Genome Atlas (TCGA) database and western blotting. The ccRCC cell proliferation was measured with colony formation and EdU assay, and cell migration was examined through transwell assay. The interactive behaviour between proteins was detected with immunoprecipitation. Half-life period of RUNX2 protein was measured with cycloheximide chase assay. The results of the study indicated overexpression of MAPK11 and RUNX2 in ccRCC tissues and cell lines. MAPK11 and RUNX2 promoted the ccRCC cell proliferation and migration. Additionally, physical interaction took place between RUNX2 and P-MAPK11, which functioned to sustain the stability of RUNX2 protein. The high expression of RUNX2 could neutralize the functional degradation in MAPK11. And the outcomes of the study suggest that the P-MAPK11/RUNX2 axis may be used as a potential therapeutic target of ccRCC.

Circ_0001715 accelerated lung adenocarcinoma process by the miR-1322/CANT1 axis

Lung adenocarcinoma (LUAD) is a type of lung cancer, which belongs to non-small cell lung cancer and has seriously endangered the physical and mental health of people. The study of circRNAs (circRNAs) has been increasingly hot in recent years, in which circRNAs also play an important regulatory role in cancer. The aim of this study was to investigate the biological molecular mechanisms of circ_0001715 in the progression of LUAD. The expression of circ_0001715, miR-1322 and calcium-activated nucleotidase 1 (CANT1) in LUAD tissues and cell lines was assessed by quantitative reverse transcription PCR (RT-qPCR) and western bot assay. Clone formation assay, 5-Ethynyl-2'-Deoxyuridine (EDU) assay and wound healing assay were used to verify the proliferation ability of cells. Dual-luciferase reporter assay and RNA pull-down assay were performed to characterize the interactions between the three factors. Finally, a mouse tumor model was constructed to assess the tumorigenicity of circ_0001715. RT-qPCR assay results showed that circ_0001715 expression was significantly increased in LUAD tissues and cell lines. Finally, knockdown of circ_0001715 could inhibit tumor growth in vivo. Circ_0001715 regulated the progression of LUAD through the miR-1322/CANT1 axis. The results of this study provided ideas for understanding the molecular mechanisms of circ_0001715 in LUAD.

Regulation and Functions of α6-Integrin (CD49f) in Cancer Biology

Over the past decades, our knowledge of integrins has evolved from being understood as simple cell surface adhesion molecules to receptors that have a complex range of intracellular and extracellular functions, such as delivering chemical and mechanical signals to cells. Consequently, they actively control cellular proliferation, differentiation, and apoptosis. Dysregulation of integrin signaling is a major factor in the development and progression of many tumors. Many reviews have covered the broader integrin family in molecular and cellular studies and its roles in diseases. Nevertheless, further understanding of the mechanisms specific to an individual subunit of different heterodimers is more useful. Thus, we describe the current understanding of and exploratory investigations on the α6-integrin subunit (CD49f, VLA6; encoded by the gene itga6) in normal and cancer cells. The roles of ITGA6 in cell adhesion, stemness, metastasis, angiogenesis, and drug resistance, and as a diagnosis biomarker, are discussed. The role of ITGA6 differs based on several features, such as cell background, cancer type, and post-transcriptional alterations. In addition, exosomal ITGA6 also implies metastatic organotropism. The importance of ITGA6 in the progression of a number of cancers, including hematological malignancies, suggests its potential usage as a novel prognostic or diagnostic marker and useful therapeutic target for better clinical outcomes.

Development and validation of a 13-gene signature associated with immune function for the detection of Alzheimer's disease

Current knowledge of Alzheimer's disease (AD) etiology and effective therapy remains limited. Thus, the identification of biomarkers is crucial to improve the detection and treatment of patients with AD. Using robust rank aggregation method to analyze the microarray data from Gene Expression Omnibus database, we identified 1138 differentially expressed genes in AD. We then explored 13 hub genes by weighted gene co-expression network analysis, least absolute shrinkage, and selection operator, and logistic regression in the training dataset. The detection model, which composed of CD163, CDC42SE1, CECR6, CSF1R, CYP27A1, EIF4E3, H2AFJ, IFIT2, IL10RA, KIAA1324, PSTPIP1, SLA, and TBC1D2 genes, along with APOE gene, showed that the area under the curve for detecting AD was 0.821 (95% confidence interval [CI] = 0.782-0.861) and the model was validated in ADNI dataset (area under the curve = 0.776; 95%CI = 0.686-0.865). Notably, the 13 genes in the model were highly enriched in immune function. These findings have implications for the detection and therapeutic target of AD.

Poor Prognosis of Oral Squamous Cell Carcinoma Correlates With ITGA6

OBJECTIVES

Oral cancer is the ninth most common cancer worldwide and a leading cause of cancer-related death. Oral squamous cell carcinoma (OSCC) accounts for 90% of all oral cancers. Autophagy is a conserved essential catabolic process related to OSCC. The aim of this study was to elucidate diagnostic and prognostic autophagy-related biomarkers in OSCC.

METHODS

The OSCC gene expression data set was obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between the OSCC samples and adjacent healthy tissues were identified by R software. The Human Autophagy Database was screened, which revealed 222 autophagy-related genes. The autophagy-related DEGs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied. Protein-protein interaction network analysis was performed in the STRING database. cytoHubba in the Cytoscape software was applied to determine the top 10 hub genes. The data set of patients with OSCC from The Cancer Genome Atlas (TCGA) was used to evaluate the prognostic value of the 10 hub genes. The association between prognosis-related hub genes and immune infiltrates was explored.

RESULTS

Twenty-seven autophagy-related DEGs were identified. The top 10 hub genes were CCL2, CDKN2A, CTSB, CTSD, CXCR4, ITGA6, MAP1LC3A, MAPK3, PARP1, and RAB11A. ITGA6 was identified as the most efficient biomarker. Receiver operating characteristic curve analysis indicated that ITGA6 had the highest diagnostic accuracy for OSCC (area under the curve = 0.925). ITGA6 expression was significantly related to immune infiltrates.

CONCLUSIONS

The autophagy-related gene ITGA6 might be an efficient diagnostic and prognostic biomarker in OSCC.

The specific phagocytosis regulators could predict recurrence and therapeutic effect in thyroid cancer: A study based on bioinformatics analysis

BACKGROUND

Thyroid cancer (TC) is one of the growing cancers and is prone to recurrence. Meanwhile, in immunotherapy, antibody-dependent cellular phagocytosis (ADCP) phagocytosis related regulators (PRs) play an important role. This study aims to investigate the prognostic value of specific PRs in TC.

METHODS

The purpose of this study was to identify specific PRs in TC patients by retrieving RNA-seq and Clustered Regularly Interspaced Short Palindromic Repeats-cas9 data and an algorithm based on LASSO was used to construct the PRs-signature. Subsequently, prognosis value of PRs-signature for recurrence-free survival (RFS) was explored through various statistical analysis, including Cox regression analysis, Kaplan-Meier analysis, and receiver operating characteristic curve. Additionally, an analysis of immune cell content by risk group was conducted using CIBERSORT, single sample gene set enrichment analysis and MCP-counter algorithms, with a particular focus on the correlation between macrophages and specific PRs.

RESULTS

We identified 36 specific PRs, and a PRs-signature was constructed using 5-prognostic PRs (CAPN6, MUC21, PRDM1, SEL1L3, and CPQ). Receiver operating characteristic analysis showed that predictive power of PRs-signature was decent, and the PRs risk score as an independent prognostic factor was found to be correlated with RFS showed by multivariate cox regression analysis. Meanwhile, a lower RFS was observed in the high-risk group than in the low-risk group. The results of the 3 algorithms suggested that our PRs-signature may have certain significance for macrophage content and ADCP. Interestingly, the low-risk group had higher levels of mRNA expression than the high-risk group at PDCD1, CTLA4, and pro-inflammatory factors from macrophage.

CONCLUSION

For the purpose of prognostic management, this study developed a prediction model. And the cross-talk between certain PRs and TC patients was revealed in this study. Besides, the PRs-signature can predict the immunotherapy response, macrophage content, and ADCP status. TC patients will benefit from these developments by gaining insight into novel therapeutic strategies.

Core genes in lung adenocarcinoma identified by integrated bioinformatic analysis

This study aims to identify potential core genes of lung adenocarcinoma (LUAD). Three datasets (GSE32863, GSE43458, and GSE116959) were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between LUAD and normal tissues were filtrated by GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed via Metascape database. The protein-protein interaction (PPI) network was constructed and core genes were identified using STRING and Cytoscape. Core genes expressions and their relevant clinical characteristics were performed via Oncomine and UALCAN databases respectively. The correlation between core genes and immune infiltrates was investigated by TIMER database. Kaplan-Meier plotter was performed for survival analysis. The signal pathway network of core genes was mapped by KEGG Mapper analysis tool. In this study, ten core genes were significantly related to overall survival (OS) of LUAD patients, which can provide clues for prognosis of LUAD.

Identification of critical prognosis signature associated with lymph node metastasis of stomach adenocarcinomas

Lymph node metastasis (LNM) is an important factor affecting the prognosis of patients with gastric adenocarcinoma (STAD), which is the most common malignancy of the human digestive system. Current detection techniques have limited sensitivity and specificity, and there is a lack of effective biomarkers to screen for LNM. Therefore, it is critical to screen for biomarkers that predict LNM in STAD. Gene expression differential analysis (false discovery rate < 0.05, |log₂Fold change| ≥1.5) was performed on 102 LNM samples, 224 non-LNM samples, and 29 normal gastric tissue samples from The Cancer Genome Atlas (TCGA) STAD dataset, and 269 LNM-specific genes (DEGs) were obtained. Enrichment analysis showed that LNM-specific genes functioned mainly in cytokine-cytokine receptor interactions, calcium signaling, and other pathways. Ten DEGs significantly associated with overall survival in STAD patients were screened by multivariate Cox regression, and an LNM-based 10-mRNA prognostic signature was established (Logrank P < 0.0001). This 10-mRNA signature was well predicted in both the TCGA training set and the Gene Expression Omnibus validation dataset (GSE84437) and was associated with survival in patients with LNM or advanced-stage STAD. Using Kaplan-Meier survival, receiver operating characteristic curve, C-index analysis, and decision curve analysis, the 10-mRNA signature was found to be a more effective predictor of prognosis in STAD patients than the other two reported models (P < 0.0005). Protein-protein interaction network and gene set enrichment analysis of the 10-mRNA signature revealed that the signature may affect the expression of multiple biological pathways and related genes. Finally, the expression levels of prognostic genes in STAD tissues and cell lines were verified using qRT-PCR, Western blot, and the Human Protein Atlas database. Taken together, the prognostic signature constructed in this study may become an indicator for clinical prognostic assessment of LNM-STAD and provide a new strategy for future targeted therapy.

Investigation of M2 macrophage-related gene affecting patients prognosis and drug sensitivity in non-small cell lung cancer: Evidence from bioinformatic and experiments

Background

The progression process of lung cancer can be accelerated by M2 macrophages. However, genes that affect M2 macrophage polarization remain unidentified.

Methods

The Cancer Genome Atlas, Gene Expression Omnibus, and Arrayexpress databases were used to obtain open-access data. The analysis of public data was mostly performed with R studio. The RNA levels of specific genes were detected using quantitative real-time PCR. The proliferation ability of the cells was assessed by CCK8, colony formation, and EdU assays.

Results

Based on the multiple datasets, we noticed a poor prognosis in patients with high M2 macrophage infiltration. There were 114 genes differentially expressed between high and low M2 macrophages infiltrated samples, regarded as M2 macrophage-related genes. Subsequently, a prognosis prediction signature consisting of ABHD5, HS3ST2, TM6SF1, CAPZA2, LEPROT, HNMT, and MRO was identified and presented a satisfactory performance. The pathway enrichment results revealed a positive correlation between riskscore and enrichment scores for most immunotherapy-related positive terms. Also, there might be an increase in genomic instability among patients at high risk. Interestingly, low risk patients are most likely to benefit from PD-1 therapy, while high risk patients may benefit from CTLA-4 therapy. Meanwhile, the estimated IC50 of seven drugs differs significantly between two risk groups, including Cisplatin, Docetaxel, Doxorubicin, Gefitinib, Paclitaxel, Sunitinib and Vinorelbine. Moreover, further experiments indicated that HNMT was overexpressed and can enhance the proliferation ability in lung cancer cells.

Conclusions

In summary, our study identified the molecules significantly affecting M2 macrophage infiltration and identified a prognosis signature that robustly indicated patients prognosis. Moreover, we validated the cancer-promoting effect of HNMT using in vitro experiments.

Tissue Proteogenomic Landscape Reveals the Role of Uncharacterized SEL1L3 in Progression and Immunotherapy Response in Lung Adenocarcinoma

The fundamental pursuit to complete the human proteome atlas and the unmet clinical needs in lung adenocarcinoma have prompted us to study the functional role of uncharacterized proteins and explore their implications in cancer biology. In this study, we characterized SEL1L3, a previously uncharacterized protein encoded from chromosome 4 as a dysregulated protein in lung adenocarcinoma from the large-scale tissue proteogenomics data set established using the cohort of Taiwan Cancer Moonshot. SEL1L3 was expressed in abundance in the tumor parts compared with paired adjacent normal tissues in 90% of the lung adenocarcinoma patients in our cohorts. Moreover, survival analysis revealed the association of SEL1L3 with better clinical outcomes. Intriguingly, silencing of SEL1L3 imposed a reduction in cell viability and activation of ER stress response pathways, indicating a role of SEL1L3 in the regulation of cell stress. Furthermore, the immune profiles of patients with higher SEL1L3 expression were corroborated with its active role in immunophenotype and favorable clinical outcomes in lung adenocarcinoma. Taken together, our study revealed that SEL1L3 might play a vital role in the regulation of cell stress, interaction with cancer cells and the immune microenvironment. Our research findings provide promising insights for further investigation of its molecular signaling network and also suggest SEL1L3 as a potential emerging adjuvant for immunotherapy in lung adenocarcinoma.

Comprehensive Analysis of a Novel Immune-Related Gene Signature in Lung Adenocarcinoma

Lung cancer is the major cause of cancer-related deaths around the world. Lung adenocarcinoma (LUAD), the most common subtype of lung cancer, contributed to the majority of mortalities and showed different clinical outcomes in prognosis. Tumor-infiltrated immune cells at the tumor site are associated with better survival and immunotherapy response. Thus, it is essential to further investigate the molecular mechanisms and new prognostic biomarkers of lung adenocarcinoma development and progression. In this study, a six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was established to predict the prognosis of LUAD patients, as well as predictive value. The prognostic risk model was also significantly associated with the infiltration of immune cells in LUAD microenvironments. To sum up, a novel immune-related six-gene signature (CR2, FGF5, INSL4, RAET1L, AGER, and TNFRSF13C) was identified that could predict LUAD survival and is highly related to B cells and dendritic cells, which may provide a theoretical basis of personalized treatment for targeted immunotherapy.

A more novel and powerful prognostic gene signature of lung adenocarcinoma determined from the immune cell infiltration landscape

Background

Lung adenocarcinoma (LUAD) is the leading histological subtype of lung cancer worldwide, causing high mortality each year. The tumor immune cell infiltration (ICI) is closely associated with clinical outcome with LUAD patients. The present study was designed to construct a gene signature based on the ICI of LUAD to predict prognosis.

Methods

Downloaded the raw data of three cohorts of the TCGA-LUAD, GSE72094, and GSE68465 and treat them as training cohort, validation cohort one, and validation cohort two for this research. Unsupervised clustering detailed grouped LUAD cases of the training cohort based on the ICI profile. The univariate Cox regression and Kaplan-Meier was adopted to identify potential prognostic genes from the differentially expressed genes recognized from the ICI clusters. A risk score-based prognostic signature was subsequently developed using LASSO-penalized Cox regression analysis. The Kaplan-Meier analysis, Cox analysis, ROC, IAUC, and IBS were constructed to assess the ability to predict the prognosis and effects of clinical variables in another two independent validation cohorts. More innovatively, we searched similar papers in the most recent year and made comprehensive comparisons with ours. GSEA was used to discover the related signaling pathway. The immune relevant signature correlation identification and immune infiltrating analysis were used to evaluate the potential role of the signature for immunotherapy and recognize the critical immune cell that can influence the signature's prognosis capability.

Results

A signature composed of thirteen gene including ABCC2, CCR2, CERS4, CMAHP, DENND1C, ECT2, FKBP4, GJB3, GNG7, KRT6A, PCDH7, PLK1, and VEGFC, was identified as significantly associated with the prognosis in LUAD patients. The thirteen-gene signature exhibited independence in evaluating the prognosis of LUAD patients in our training and validation cohorts. Compared to our predecessors, our model has an advantage in predictive power. Nine well know immunotherapy targets, including TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, and PDCD1 were recognized correlating with our signature. The mast cells were found to play vital parts in backing on the thirteen-gene signature's outcome predictive capacity.

Conclusions

Collectively, the current study indicated a robust thirteen-gene signature that can accurately predict LUAD prognosis, which is superior to our predecessors in predictive ability. The immune relevant signatures, TBX2, TNF, CTLA4, HAVCR2, GZMB, CD8A, PRF1, GZMA, PDCD1, and mast cells infiltrating were found closely correlate with the thirteen-gene signature's power.

Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data

The occurrence of lung adenocarcinoma (LUAD) is a complicated process, involving the genetic and epigenetic changes of proto-oncogenes and oncogenes. The objective of this study was to establish new predictive signatures of lung adenocarcinoma based on copy number variations (CNVs) and gene expression data. Next-generation sequencing was implemented to obtain gene expression and CNV information. According to univariate, multivariate survival Cox regression analysis, and LASSO analysis, the expression profiles of lung adenocarcinoma patients were screened and a risk score formula was established and experimentally validated in a local cohort. The model was evaluated by three independent cohorts (TCGA-LUAD, GSE31210, and GSE30219), and then validated by clinical samples from LUAD patients. A total of 844 CNV-related differentially expressed genes (CNV-related DEGs) were identified. These genes are significantly associated with the imbalance of various oxidative stress pathways. A CNV-associated-six gene signature was dramatically linked to overall survival in lung adenocarcinoma samples from both training and validation groups. Functional enrichment analysis further revealed involvement of genes in p53 signaling pathway and cell cycle as well as the mismatch repair pathway. Risk score is an independent marker considering clinical parameters and had better prediction in clinical subpopulation. The same signature also classified tumor tissues of clinical patients with CNV detected from their corresponding nontumorous tissues with an accuracy of 0.92. In conclusion, we identified a new class of 6 CNV-related gene markers that may act as efficient prognostic predictors of lung adenocarcinoma, thus contributing to individualized treatment decisions in patients.

Clustering analysis and prognostic signature of lung adenocarcinoma based on the tumor microenvironment

Because of immunotherapy failure in lung adenocarcinoma, we have tried to find new potential biomarkers for differentiating different tumor subtypes and predicting prognosis. We identified two subtypes based on tumor microenvironment-related genes in this study. We used seven methods to analyze the immune cell infiltration between subgroups. Further analysis of tumor mutation load and immune checkpoint expression among different subgroups was performed. The least absolute shrinkage and selection operator Cox regression was applied for further selection. The selected genes were used to construct a prognostic 14-gene signature for LUAD. Next, a survival analysis and time-dependent receiver operating characteristics were performed to verify and evaluate the model. Gene set enrichment analyses and immune analysis in risk groups was also performed. According to the expression of genes related to the tumor microenvironment, lung adenocarcinoma can be divided into cold tumors and hot tumors. The signature we built was able to predict prognosis more accurately than previously known models. The signature-based tumor microenvironment provides further insight into the prediction of lung adenocarcinoma prognosis and may guide individualized treatment.

Identification and Validation of Immune-Related Long Non-Coding RNA Signature for Predicting Immunotherapeutic Response and Prognosis in NSCLC Patients Treated With Immunotherapy

Background

Numerous studies have reported that long non-coding RNAs (lncRNAs) play important roles in immune-related pathways in cancer. However, immune-related lncRNAs and their roles in predicting immunotherapeutic response and prognosis of non-small cell lung cancer (NSCLC) patients treated with immunotherapy remain largely unexplored.

Methods

Transcriptomic data from NSCLC patients were used to identify novel lncRNAs by a custom pipeline. ImmuCellAI was utilized to calculate the infiltration score of immune cells. The marker genes of immunotherapeutic response-related (ITR)-immune cells were used to identify immune-related (IR)-lncRNAs. A co-expression network was constructed to determine their functions. LASSO and multivariate Cox analyses were performed on the training set to construct an immunotherapeutic response and immune-related (ITIR)-lncRNA signature for predicting the immunotherapeutic response and prognosis of NSCLC. Four independent datasets involving NSCLC and melanoma patients were used to validate the ITIR-lncRNA signature.

Results

In total, 7,693 novel lncRNAs were identified for NSCLC. By comparing responders with non-responders, 154 ITR-lncRNAs were identified. Based on the correlation between the marker genes of ITR-immune cells and lncRNAs, 39 ITIR-lncRNAs were identified. A co-expression network was constructed and the potential functions of 38 ITIR-lncRNAs were annotated, most of which were related to immune/inflammatory-related pathways. Single-cell RNA-seq analysis was performed to confirm the functional prediction results of an ITIR-lncRNA, LINC01272. Four-ITIR-lncRNA signature was identified and verified for predicting the immunotherapeutic response and prognosis of NSCLC. Compared with non-responders, responders had a lower risk score in both NSCLC datasets (P<0.05). NSCLC patients in the high-risk group had significantly shorter PFS/OS time than those in the low-risk group in the training and testing sets (P<0.05). The AUC value was 1 of responsiveness in the training set. In melanoma validation datasets, patients in the high-risk group also had significantly shorter OS/PFS time than those in the low-risk group (P<0.05). The ITIR-lncRNA signature was an independent prognostic factor (P<0.001).

Conclusion

Thousands of novel lncRNAs in NSCLC were identified and characterized. In total, 39 ITIR-lncRNAs were identified, 38 of which were functionally annotated. Four ITIR-lncRNAs were identified as a novel ITIR-lncRNA signature for predicting the immunotherapeutic response and prognosis in NSCLC patients treated with immunotherapy.

Cellular and Molecular Profiling of Tumor Microenvironment and Early-Stage Lung Cancer

Lung cancers are broadly divided into two categories: non-small-cell lung carcinoma (NSCLC), which accounts for 80–85% of all cancer cases, and small-cell lung carcinoma (SCLC), which covers the remaining 10–15%. Recent advances in cancer biology and genomics research have allowed an in-depth characterization of lung cancers that have revealed new therapy targets (EGFR, ALK, ROS, and KRAS mutations) and have the potential of revealing even more biomarkers for diagnostic, prognostic, and targeted therapies. A new source of biomarkers is represented by non-coding RNAs, especially microRNAs (miRNAs). MiRNAs are short non-coding RNA sequences that have essential regulatory roles in multiple cancers. Therefore, we aim to investigate the tumor microenvironment (TME) and miRNA tumor profile in a subset of 51 early-stage lung cancer samples (T1 and T2) to better understand early tumor and TME organization and molecular dysregulation. We analyzed the immunohistochemistry expression of CD4 and CD8 as markers of the main TME immune populations, E-cadherin to evaluate early-stage epithelial-to-mesenchymal transition (EMT), and p53, the main altered tumor suppressor gene in lung cancer. Starting from these 4 markers, we identified and validated 4 miRNAs that target TP53 and regulate EMT that can be further investigated as potential early-stage lung cancer biomarkers.

Integrated Analysis of Genes Associated With Immune Microenvironment and Distant Metastasis in Uveal Melanoma

Inflammatory infiltration plays an essential role in the progression of tumor malignancy. The aim of this study was to identify genes associated with inflammatory microenvironment and clinical traits for survival prediction of uveal melanoma (UVM) patients. The datasets and clinical characteristics of UVM were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We divided the UVM patients into low and high immune cell infiltration groups, identified differentially expressed genes (DEGs), constructed weighted gene co-expression network, and established prognostic prediction model and nomogram for UVM. Our analysis showed that DEGs were enriched in cytokine signaling in immune system, positive regulation of immune response and adaptive immune system. A total of fifteen candidate genes were extracted from DEGs and genes that were positively associated with tumor metastasis. Subsequently, five prognostic genes were selected to construct the final prognostic prediction model, including two up-regulated genes LHFPL3 antisense RNA 1 (LHFPL3-AS1) and LYN proto-oncogene (LYN), and three down-regulated genes SLCO4A1 antisense RNA 1 (SLCO4A1-AS1), Zinc-α2-glycoprotein 1 (AZGP1) and Deleted in Liver Cancer-1 (DLC1) in the high risk group. The model showed an Area Under Curve (AUC) value of 0.877. Our analysis highlighted the importance of immune-related genes in the progression of UVM and also provided potential targets for the immunotherapy of UVM.

B3GNT3 as a prognostic biomarker and correlation with immune cell infiltration in lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most common malignant cancer in humans and because of low long-term survival rates, exploration of the molecular mechanisms underlying its progression, as well as novel prognostic predictors, is urgently needed. B3GNT3, a type II transmembrane protein located in the Golgi apparatus, is essential for forming extended core 1 oligosaccharides and is reportedly involved in malignant transformation.

Methods

The Cancer Genome Atlas (TCGA) and GSE68465 were used to analyze the expression of B3GNT3 in LUAD and normal tissues and overall survival. Real time quantitative polymerase chain reaction (qPCR) and western blot were conducted to measure the mRNA and protein levels of B3GNT3, respectively. Functional enrichment of differentially expressed genes was explored using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. We performed univariate and multivariate Cox regression analyses and a meta-analysis to reveal an independent factor for LUAD. We evaluated the correlation between immune infiltration levels and cumulative survival in the TIMER database. The correlation between B3GNT3 and immune cell infiltration was assessed via Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT). The association of DNA methylation of B3GNT3 and prognosis was determined. A nomogram that incorporated expression and clinical features was additionally built for prognostic prediction. Cell proliferation, cloning, and invasion were conducted to validate the roles of B3GNT3 in LUAD.

Results

B3GNT3 was more highly expressed in LUAD tissues than in normal lung tissues, consistent with the mRNA and protein levels in LUAD cells. B3GNT3 was an independent factor for LUAD. Moreover, the levels of B3GNT3 were related to immune cell infiltration in LUAD microenvironments. DNA methylation of B3GNT3 correlated with the mRNA of B3GNT and overall survival of LUAD patients. The expression of B3GNT3 was highly valuable for the prediction of diagnosis. Knockdown of B3GNT3 inhibited LUAD cell viability and cloning ability, and hindered invasion.

Conclusions

B3GNT3 was highly associated with immune cell infiltration, acting as an important biomarker for the prognosis and diagnosis of LUAD.

Identification of four hub genes in venous thromboembolism via weighted gene coexpression network analysis

Background

The pathogenic mechanisms of venous thromboembolism (VT) remain to be defined. This study aimed to identify differentially expressed genes (DEGs) that could serve as potential therapeutic targets for VT.

Methods

Two human datasets (GSE19151 and GSE48000) were analyzed by the robust rank aggregation method. Gene ontology and Kyoto encyclopedia of genes and genomes pathway enrichment analyses were conducted for the DEGs. To explore potential correlations between gene sets and clinical features and to identify hub genes, we utilized weighted gene coexpression network analysis (WGCNA) to build gene coexpression networks incorporating the DEGs. Then, the levels of the hub genes were analyzed in the GSE datasets. Based on the expression of the hub genes, the possible pathways were explored by gene set enrichment analysis and gene set variation analysis. Finally, the diagnostic value of the hub genes was assessed by receiver operating characteristic (ROC) analysis in the GEO database.

Results

In this study, we identified 54 upregulated and 10 downregulated genes that overlapped between normal and VT samples. After performing WGCNA, the magenta module was the module with the strongest negative correlation with the clinical characteristics. From the key module, FECH, GYPA, RPIA and XK were chosen for further validation. We found that these genes were upregulated in VT samples, and high expression levels were related to recurrent VT. Additionally, the four hub genes might be highly correlated with ribosomal and metabolic pathways. The ROC curves suggested a diagnostic value of the four genes for VT.

Conclusions

These results indicated that FECH, GYPA, RPIA and XK could be used as promising biomarkers for the prognosis and prediction of VT.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12872-021-02409-4.