A TP53-associated gene signature for prediction of prognosis and therapeutic responses in lung squamous cell carcinoma

Proteomic insights into the molecular mechanism of anlotinib inhibition in TP53-mutated non-small cell lung cancer

OBJECTIVE

Tumor protein 53 (TP53) is the commonly mutated gene in non-small cell lung cancer (NSCLC) that is associated with poor prognosis, and anlotinib exerts inhibitory effects on TP53-mutated NSCLC. The aim of this study was to investigate the inhibitory effect of anlotinib on TP53-mutated NSCLC and its possible mechanism.

METHODS

The growth ability of TP53-mutated NSCLC cells were tested by Cell counting kit-8 assay. Proteins in TP53-mutated NSCLC cells treated with anlotinib were analyzed using label-free liquid chromatography-mass spectrometry. Differentially represented proteins were analyzed by KEGG, GO, and PPIs. TP53 pathway related proteins were verified using western blotting.

RESULTS

The cell viability was significantly reduced in TP53-mutated NSCLC cell as opposed to TP53 wild cell by anlotinib treatment. 126 differentially represented proteins (37 upregulated and 89 downregulated) were found between the anlotinib and control groups in TP53-mutated NSCLC cell. Bioinformatics analyses revealed that the differentially represented proteins were primarily involved in catalytic activity, cellular processes, and metabolite interconversion. PANTHER Classification System found that anlotinib mainly impacted the p53 signaling pathway, De novo purine biosynthesis and Integrin signaling. KEGG enrichment and PPI networks of the differentially represented proteins revealed cyclin-dependent kinase 1 (CDK1) and mitogen-activated protein kinase kinase 3 (MAP2K3) as the core protein, which are related to the p53 signaling pathway. Western blotting also revealed that anlotinib significantly suppressed the expression of CDK1 and MAP2K3 in TP53-mutated NSCLC cells, that indicated the possible mechanism may involve the MAP2K3/p53/CDK1 pathway.

CONCLUSIONS

Our findings showed that anlotinib selectively inhibited the growth of TP53-mutated NSCLC cells and downregulated the expression levels of CDK1 and MAP2K3. The MAP2K3/p53/CDK1 pathway may be the molecular mechanism underlying anlotinib's efficacy in TP53-mutated NSCLC.

STATEMENT OF SIGNIFICANCE

Tumor protein 53 (TP53) is the commonly mutated gene in non-small cell lung cancer (NSCLC) that is associated with poor prognosis, and anlotinib exerts inhibitory effects on TP53-mutated NSCLC. However, the action mechanism of anlotinib in the treatment of TP53-mutated NSCLC remains unclear. In this study, we used label-free quantitative proteomics to reveal the molecular mechanism of anlotinib inhibition in TP53-mutated NSCLC. We found that anlotinib significantly inhibited the growth of TP53-mutated NSCLC cells and downregulated the expression levels of CDK1 and MAP2K3. The MAP2K3/p53/CDK1 pathway may be the molecular mechanism underlying anlotinib's efficacy in TP53-mutated NSCLC. Our study promotes the use of anti-angiogenic drugs in TP53-mutated NSCLC. It provides new ideas for the treatment of TP53-mutated NSCLC.

Harnessing TAGAP to improve immunotherapy for lung squamous carcinoma treatment by targeting c-Rel in CD4+ T cells

Revealing the immunosenescence, particularly in CD4+ T cell function in lung squamous carcinoma (LUSC) assists in devising individual treatment strategies. This study identifies differentially expressed genes (DEGs) between ROS1 mutated (ROS1MUT) and wild-type (ROS1WT) LUSC samples from the TCGA database. Using WGCNA, immune-related DEGs (IRGs) were screened. Prognostic signatures derived from IRGs were used to compare immune infiltration, chemotherapy sensitivity, and immune-phenotyping score (IPS) between high- and low-risk subgroups. Hub gene abundance in different cell clusters was analyzed via Sc-seq. TAGAP overexpression or silencing was employed to assess its impact on cytokines production and differentiation of CD4+ T cells, downstream c-Rel expression, and tumor progression. High-risk subgroups exhibited decreased infiltration of natural killer, follicular helper T, and CD8+ T cells, but increased plasma, CD4+ memory resting T, and macrophage M2 cells. These subgroups were more sensitive to Sunitinib and CTLA4 blockade. TAGAP expression was significantly reduced in LUSC. Overexpressing TAGAP enhanced CD4+ T cells to produce cytokines, promoted differentiation into Th1/Th17 cells, inhibited Treg conversion, and suppressed LUSC cell phenotype in vitro. TAGAP overexpression in CD4+ T cells also inhibited LUSC tumor growth and boosted immune infiltration in vivo. TAGAP's effects on CD4+ T cells were partly reversed by c-Rel overexpression, highlighting TAGAP's role in rejuvenating CD4+ T cells and exerting anticancer effects by inhibiting c-Rel. This study elucidates the novel therapeutic potential of targeting TAGAP to modulate CD4+ T cell activity in immunotherapy for LUSC.

Genomic Profiling Reveals Immune-Related Gene Differences in Lung Cancer Patients Stratified by PD1/PDL1 Expression: Implications for Immunotherapy Efficacy

Lung cancer remains a leading cause of global cancer-related mortality, and the exploration of innovative therapeutic approaches, such as PD1/PDL1 immunotherapy, is critical. This study leverages comprehensive data from the Cancer Genome Atlas (TCGA) to investigate the differential expression of PD1/PDL1 in lung cancer patients and explores its implications. Clinical data, RNA expression, somatic mutations, and copy number variations of 1017 lung cancer patients were obtained from TCGA. Patients were categorized into high (HE) and low (LE) PD1/PDL1 expression groups based on mRNA levels. Analyses included differential gene expression, functional enrichment, protein-protein interaction networks, and mutational landscape exploration. The study identified 391 differentially expressed genes, with CD4 and PTPRC among the upregulated genes in the HE group. Although overall survival did not significantly differ between HE and LE groups, enrichment analysis revealed a strong association with immunoregulatory signaling pathways, emphasizing the relevance of PD1/PDL1 in immune response modulation. Notably, TP53 mutations were significantly correlated with high PD1/PDL1 expression. This study provides a comprehensive analysis of PD1/PDL1 expression in lung cancer, uncovering potential biomarkers and highlighting the intricate interplay between PD1/PDL1 and the immune response. The identified upregulated genes, including CD4 and PTPRC, warrant further investigation for their roles in the context of lung cancer and immunotherapy. The study underscores the importance of considering molecular heterogeneity in shaping personalized treatment strategies for lung cancer patients. Limitations, such as the retrospective nature of TCGA data, should be acknowledged.

Breathing new insights into the role of mutant p53 in lung cancer

The tumour suppressor gene p53 is one of the most frequently mutated genes in lung cancer and these defects are associated with poor prognosis, albeit some debate exists in the lung cancer field. Despite extensive research, the exact mechanisms by which mutant p53 proteins promote the development and sustained expansion of cancer remain unclear. This review will discuss the cellular responses controlled by p53 that contribute to tumour suppression, p53 mutant lung cancer mouse models and characterisation of p53 mutant lung cancer. Furthermore, we discuss potential approaches of targeting mutant p53 for the treatment of lung cancer.

Analysis and exploration of regulatory mechanisms and potential prognostic biomarkers in squamous cell carcinoma of the lung by expression profiling

Background

Lung cancer is the most common malignant tumor in China. In 2016, more than 800,000 new cases of lung cancer were diagnosed in China. Squamous cell carcinoma of the lung, a type of non-small cell lung cancer (NSCLC), accounts for 25-30% of all lung cancer cases, and has an overall 5-year survival rate of about 32.53%, lower than adenocarcinoma for which there have been far more therapeutic advances in the last few decades. The purpose of this study was to explore the mechanisms of the disease and to identify potential prognostic biomarkers.

Methods

This study analyzed lung squamous cell carcinoma of the lung tissues and paraneoplastic tissues to identify differentially expressed genes (DEGs). We conducted a Gene Set Enrichment Analysis and prognostic analysis by constructing competing endogenous RNA (ceRNA) networks; we performed a correlation analysis of the target genes and verified the targeting relationship of the ceRNA by cellular assays. We assessed the effects of the target genes on tumor cell proliferation, invasion and apoptosis by Cell Counting Kit-8 (CCK-8) assays, invasion assays, and caspase 3/7 assays, respectively.

Results

We identified 4,039 downregulated genes and 1,924 upregulated genes. The p53 pathway, cell-cycle pathway and mismatch-repair (MMR) pathway were activated, while the mitogen-activated protein kinase pathway was inhibited. Two ceRNA networks centered on the long non-coding RNAs (lncRNAs) MAGI2-AS3 and LINC01089 were constructed. MAGI2-AS3 was found to regulate five messenger RNAs (mRNAs) (i.e., MBNL2, ATP5L, FAM103A1, MDH1, and STXBP1) through three microRNAs (miRNAs), whereas LINC01089 was found to regulate six mRNAs (i.e., ZFP36L2, APBB2, PDLIM3, MYADM, PHF5A, and SLC26A9) through two miRNAs. The expression of these lncRNAs and mRNAs was significantly associated with prognosis (P<0.05). A significant correlation was also found between the expression of MAGI2-AS3 and MBNL2 (R=0.51), and both signatures were also significantly associated with prognosis. We also found that MAGI2-AS3 and MBNL2 had a regulatory relationship at the cellular level, for example, high expression of MBNL2 was noted to inhibit cancer cell proliferation and migration yet promote apoptosis.

Conclusions

MAGI-AS3 and MBNL2 are both differentially expressed in squamous cell carcinoma of the lung and are potential prognostic markers. A significant association was also found between MAGI2-AS3 and the expression of MBNL2 (R=0.51). High expression of MBNL2 inhibits cancer cell proliferation and migration, yet promotes cancer cell apoptosis.

Utilizing TP53 hotspot mutations as effective predictors of gemcitabine treatment outcome in non-small-cell lung cancer

TP53 mutations are recognized to correlate with a worse prognosis in individuals with non-small cell lung cancer (NSCLC). There exists an immediate necessity to pinpoint selective treatment for patients carrying TP53 mutations. Potential drugs were identified by comparing drug sensitivity differences, represented by the half-maximal inhibitory concentration (IC50), between TP53 mutant and wild-type NSCLC cell lines using database analysis. In addition, clinical data from NSCLC patients were collected to evaluate both their TP53 status and their response to gemcitabine, thereby facilitating further validation. Subsequently, NSCLC cell lines with different TP53 status (A549 and H1299) were subjected to gemcitabine treatment to investigate the association between TP53 mutations and gemcitabine response. According to the dataset, NSCLC cell lines carrying TP53 mutations displayed heightened sensitivity to gemcitabine. From a clinical standpoint, patients exhibiting TP53 hotspot mutations demonstrated prolonged overall survival upon gemcitabine treatment. In vitro, overexpressing various hotspot TP53 mutations significantly sensitized H1299 cells to gemcitabine. Moreover, the knockdown of TP53 in A549 cells notably augmented sensitivity to gemcitabine treatment, as evidenced by cell viability and reproductive cell death assays. Conversely, the overexpression of wild-type TP53 in H1299 cells led to an increased resistance against gemcitabine. Gemcitabine is a treatment option for patients with non-small cell lung cancer (NSCLC) who carry TP53 hotspot mutations. This potential effectiveness might arise from its ability to disrupt DNA damage repair processes, leading to G2/M phase cell cycle arrest or an augmentation of mitotic abnormalities, eventually cause cell death. As a result, when planning treatment strategies for NSCLC patients possessing TP53 hotspot mutations, gemcitabine should be considered to incorporate into the indication.

Prognostic Indicators for Precision Treatment of Non-Small Cell Lung Carcinoma

Non-small cell lung cancer (NSCLC) has established predictive biomarkers that enable decisions on treatment regimens for many patients. However, resistance to therapy is widespread. It is therefore essential to have a panel of molecular biomarkers that may help overcome therapy resistance and prevent adverse effects of treatment. We performed in silico analysis of NSCLC prognostic indicators, separately for adenocarcinomas and squamous carcinomas, by using The Cancer Genome Atlas (TCGA) and non-TCGA data sources in cBioPortal as well as UALCAN. This review describes lung cancer biology, elaborating on the key genetic alterations and specific genes responsible for resistance to conventional treatments. Importantly, we examined the mechanisms associated with resistance to immune checkpoint inhibitors. Our analysis indicated that a robust prognostic biomarker was lacking for NSCLC, especially for squamous cell carcinomas. In this work, our screening uncovered previously unidentified prognostic gene expression indicators, namely, MYO1E, FAM83 homologs, and DKK1 for adenocarcinoma, and FGA and TRIB1 for squamous cell carcinoma. It was further observed that overexpression of these genes was associated with poor prognosis. Additionally, FAM83 homolog and TRIB1 unexpectedly harbored copy number amplifications. In conclusion, this study elucidated novel prognostic indicators for NSCLC that may serve as targets to overcome therapy resistance toward improved patient outcomes.

Clinicopathological Characteristics and Prognosis Analysis of Lung Carcinoma With p40/TTF1 Coexpression and Lung Adenosquamous Carcinoma: Lung Carcinoma With p40/TTF1 Coexpression Is a Rare Tumor With High Metastatic Potential

Background. Lung carcinoma with p40/TTF1 coexpression (LC-PTC) is a very rare tumor with poor prognosis, and few cases have been reported to date. Objectives. To better understand biological behavior and prognosis of LC-PTC. Methods. We collected 9 examples of LC-PTC and compared them with 36 lung adenosquamous carcinomas during the same period in clinicopathologic characteristics, biologic behaviour, and prognosis. Results. Lung carcinoma with p40/TTF1 coexpression mainly occurred in middle-aged and elderly men; 8 tumors belonged to the peripheral type, and 1 belonged to the central type. The rates of lymph node and distant metastasis were 88% (7/8) and 50% (4/8), respectively; 2 patients died during follow-up. Histologically, the LC-PTC showed nest-like growth pattern without glandular growth pattern; the surface of 2 tumors was covered with ciliated columnar epithelium and tumor cells grew under the columnar epithelium. In all patients, tumor cells diffusely coexpressed p40 and TTF1. Although there was no significant difference in the maximum diameter of tumor with lymph node metastasis or with distant metastasis between LC-PTC and lung adenosquamous carcinoma, LC-PTC had a higher rate of lymph node metastasis and distant metastasis. There was no significant difference in overall survival of patients between LC-PTC and lung adenosquamous carcinoma. Additional histologic evaluation of normal pulmonary structures revealed that p40/TTF1 coexpression cells existed in bronchial mucosa and the number of cells coexpressing p40/TTF1 increased gradually from proximal bronchus to distal bronchus. Conclusions. Lung carcinoma with p40/TTF1 coexpression is a rare tumor with high metastatic potential and may originate from p40/TTF1 coexpression cells in distal bronchial mucosa.

Immune killer cells treatment for previously treated stage IV NSCLC patients

The 5-year survival is poor for stage IV non-small cell lung cancer (NSCLC). Recently, cell immunotherapy has emerged as a new treatment strategy. This study aimed to evaluate the efficacy and safety of Immune killer cells (IKC) in patients with stage IV NSCLC after the failure of prior chemotherapy. This study enrolled 26 patients with stage IV NSCLC who failed at least two lines of chemotherapy with or without targeted therapy. The IKC was given alone weekly for 24 weeks. The primary endpoint was progression-free survival (PFS). Secondary outcomes included overall survival (OS), pain intensity, quality of life (QOL), and safety. The median PFS for the intent-to-treat (ITT) population (i.e., all enrolled patients) was 3.8 month. In the per-protocol (PP) population (i.e., patients receiving > 12 IKC infusions), the median PFS was 5.6 months. Moreover, the ITT population showed a 1-year survival rate of 60.0%, while that for the PP population was 85.7%. Only 7 out of 200 AEs (3.5%) were related to the IKC infusion, and they were all rated as grade 1 in severity. The IKC infusion was well tolerated. This novel immunotherapy prolonged the PFS and improved the survival compared with historical data. It might be a potential treatment strategy for stage IV NSCLC patient who failed prior chemotherapy.ClinicalTrials.gov identifier: NCT03499834.

FOXA2 Activates RND1 to Regulate Arachidonic Acid Metabolism Pathway and Suppress Cisplatin Resistance in Lung Squamous Cell Carcinoma

BACKGROUND

The primary cause of cancer-related fatalities globally is lung cancer. Although the chemotherapy drug cisplatin (DDP) has brought certain benefits to patients, the rapid development of drug resistance has greatly hindered treatment success.

METHODS

We used the lung squamous cell carcinoma (LUSC) mRNA data set to explore the differentially expressed gene (RND1) in LUSC and detected RND1 expression in LUSC cells and DDP-resistant cells by qRT-PCR. Meanwhile, we performed abnormal expression treatment on RND1 and conducted CCK8, colony formation, and flow cytometry to evaluate the impact of RND1 expression on cell proliferation, apoptosis, and DDP resistance. In addition, we analyzed metabolism pathways involving RND1 using GSEA. We also used online tools such as hTFtarget and JASPAR to screen for the upstream transcription factor FOXA2 of RND1 and verified their relationship through CHIP and dual luciferase experiments. Finally, we validated the role of FOXA2-RND1 in DDP resistance in LUSC through the above experiments.

RESULTS

RND1 was downregulated in LUSC, and overexpression of RND1 repressed proliferation and DDP resistance of LUSC cells and facilitated cell apoptosis. RND1 modulated the arachidonic acid (AA) metabolism pathway, and FOXA2 positively manipulated RND1 expression. By activating FOXA2, stabilizing RND1, and regulating AA levels, the sensitivity of LUSC cells to DDP could be enhanced.

CONCLUSION

Our study suggested that FOXA2 positively modulated the RND1-AA pathway, which repressed the resistance of LUSC cells to DDP.

p53 Genetics and Biology in Lung Carcinomas: Insights, Implications and Clinical Applications

The TP53 gene is renowned as a tumor suppressor, playing a pivotal role in overseeing the cell cycle, apoptosis, and maintaining genomic stability. Dysregulation of p53 often contributes to the initiation and progression of various cancers, including lung cancer (LC) subtypes. The review explores the intricate relationship between p53 and its role in the development and progression of LC. p53, a crucial tumor suppressor protein, exists in various isoforms, and understanding their distinct functions in LC is essential for advancing our knowledge of this deadly disease. This review aims to provide a comprehensive literature overview of p53, its relevance to LC, and potential clinical applications.

Cuproptosis-Related lncRNA Predict Prognosis and Immune Response of LUAD

Background

Lung cancer is the leading cause of cancer deaths worldwide, primarily due to lung adenocarcinoma (LUAD). However, the heterogeneity of programmed cell death results in varied prognostic and predictive outcomes. This study aimed to develop an LUAD evaluation marker based on cuproptosis-related lncRNAs.

Methods

First, transcriptome data and clinical data related to LUAD were downloaded from the Cancer Genome Atlas (TCGA), and cuproptosis-related genes were analyzed to identify cuproptosis-related lncRNAs. Univariate, LASSO, and multivariate Cox regression analyses were conducted to construct cuproptosis-associated lncRNA models. LUAD patients were categorized into high-risk and low-risk groups using prognostic risk values. Kaplan-Meier analysis, PCA, GSEA, and nomograms were employed to evaluate and validate the results.

Results

7 cuproptosis-related lncRNAs were identified, and a risk model was created. High-risk tumors exhibited cuproptosis-related gene alterations in 95.54% of cases, while low-risk tumors showed alterations in 85.65% of cases, mainly involving TP53. The risk value outperformed other clinical variables and tumor mutation burden as a predictor of 1-, 3-, and 5-year overall survival. The cuproptosis-related lncRNA-based risk model demonstrated high validity for LUAD evaluation, potentially influencing individualized treatment approaches. Expression analysis of four candidate cuproptosis-related lncRNAs (AL606834.1, AL161431.1, AC007613.1, and LINC02835) in LUAD tissues and adjacent normal tissues revealed significantly higher expression levels of AL606834.1 and AL161431.1 in LUAD tissues, positively correlating with tumor stage, lymph node metastasis, and histopathological grade. Conversely, AC007613.1 and LINC02835 exhibited lower expression levels, negatively correlating with these factors. High expression of AL606834.1 and AL161431.1 indicated poor prognosis, while low expression of AC007613.1 and LINC02835 was associated with unfavorable outcomes. Univariate and multivariate analyses confirmed these lncRNAs as independent risk factors for LUAD prognosis.

Conclusion

The 4 cuproptosis-related (lncRNAsAL606834.1, AL161431.1, AC007613.1, and LINC02835) can accurately predict the prognosis of patients with LUAD and may provide new insights into clinical applications and immunotherapy.

Unlocking the potential: A novel prognostic index signature for acute myeloid leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy characterized by challenges in treatment, including drug resistance and frequent relapse. Recent research highlights the crucial roles of tumor microenvironment (TME) in assisting tumor cell immune escape and promoting tumor aggressiveness. This study delves into the interplay between AML and TME. Through the exploration of potential driver genes, we constructed an AML prognostic index (AMLPI). Cross-platform data and multi-dimensional internal and external validations confirmed that the AMLPI outperforms existing models in terms of areas under the receiver operating characteristic curves, concordance index values, and net benefits. High AMLPIs in AML patients were indicative of unfavorable prognostic outcomes. Immune analyses revealed that the high-AMLPI samples exhibit higher expression of HLA-family genes and immune checkpoint genes (including PD1 and CTLA4), along with lower T cell infiltration and higher macrophage infiltration. Genetic variation analyses revealed that the high-AMLPI samples associate with adverse variation events, including TP53 mutations, secondary NPM1 co-mutations, and copy number deletions. Biological interpretation indicated that ALDH2 and SPATS2L contribute significantly to AML patient survival, and their abnormal expression correlates with DNA methylation at cg12142865 and cg11912272. Drug response analyses revealed that different AMLPI samples tend to have different clinical selections, with low-AMLPI samples being more likely to benefit from immunotherapy. Finally, to facilitate broader access to our findings, a user-friendly and publicly accessible webserver was established and available at http://bioinfor.imu.edu.cn/amlpi. This server provides tools including TME-related AML driver genes mining, AMLPI construction, multi-dimensional validations, AML patients risk assessment, and figures drawing.

Advancements in the diagnosis and treatment of sub‑centimeter lung cancer in the era of precision medicine (Review)

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

Identification of Genetic Variants in Exon 4 of TP53 in Lung Carcinoma and in Silico Prediction of Their Significance

Lung cancer is a severe and the leading cause of cancer related deaths in men and women all over the world. Tumor suppressor protein (TP53) encoded by the TP53 gene which plays a pivotal role in various cellular tumor suppression processes viz cell cycle arrest and apoptosis. Henceforth, the present study was aimed to TP53 exon4 variants from lung carcinoma. Histopathologic and clinically proven 20 patients of lung cancer were enrolled in this study the average age of patients was 45 ± 8 years which categorized as early onset of lung cancer. Genomic DNA was isolated from the blood specimen of patients. Extracted DNA was subjected to PCR amplification for exon 4 of TP53 using appropriate primers and subsequently amplified products were applied to nucleotide alterations via using the DNA sanger sequencing. The genetic analysis documented five variants in exon4 of TP53 which include viz. 4 substitutions [c.215 > C at codon 72, C. 358-359AA > GG at codon 120] were highly prevalent, occurring in 63% and 25% frequency in patients. Other two variants viz. C. 358 A > C at codon 120, C. 365T > G at codon 122 were present at frequency of 15% whilst one deletion variant [152 del C] was found with 5% frequency. Furthermore, alterations on codon 72, 120,122 and 51 were characterized as possibly damaging by Poly Phen-2 and decreased stability using stability bioinformatic tool. Taken together all these findings infer that TP53 gene involved in modulation and susceptibility to lung cancer.

Exploration of Ginkgo biloba leaves on non-small cell lung cancer based on network pharmacology and molecular docking

BACKGROUND

Pharmacological studies have found Ginkgo biloba leaves have the effect of inhibiting neoplasms, it is clinically used in treating various neoplasms. However, the mechanism of Ginkgo biloba leaves in treating non-small cell lung cancer (NSCLC) remains unclear.

METHODS

The active components and corresponding targets of Ginkgo biloba leaves were obtained from the traditional Chinese medicine systems pharmacology database and analysis platform (TCMSP) database, and the targets of NSCLC were obtained from the GeneCards, OMIM, TTD, and DrugBank databases. The common targets of NSCLC and Ginkgo biloba leaves were obtained from VENNY 2.1.0. The STRING database was utilized to construct protein-protein intersections, by using the Cytoscape 3.7.1 software, the protein-protein intersection was optimized and the drug-disease network diagram was constructed. The DAVID database was utilized to perform GO and KEGG analysis. Finally, The Autodock Vina software was used to perform molecular docking of core components and targets.

RESULTS

The key components of Ginkgo biloba leaves in treating NSCLC include quercetin, luteolin, and kaempferol, which may act on Tp53, AKT1, and TNF. Bioinformatic annotation analysis results suggest that Ginkgo biloba leaves may implicated in PI3K-AKT and MAPK signaling pathways. The molecular docking results show the firm affinity between key ingredients and targets.

CONCLUSION

The potential mechanism of Ginkgo biloba leaves in treating NSCLC has been discussed in this study, which provides a theoretical basis for the clinical treatment of NSCLC and further experimental validation.

GLIPR2: a potential biomarker and therapeutic target unveiled - Insights from extensive pan-cancer analyses, with a spotlight on lung adenocarcinoma

Background

Glioma pathogenesis related-2 (GLIPR2), an emerging Golgi membrane protein implicated in autophagy, has received limited attention in current scholarly discourse.

Methods

Leveraging extensive datasets, including The Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Human Protein Atlas (HPA), and Clinical Proteomic Tumor Analysis Consortium (CPTAC), we conducted a comprehensive investigation into GLIPR2 expression across diverse human malignancies. Utilizing UALCAN, OncoDB, MEXPRESS and cBioPortal databases, we scrutinized GLIPR2 mutation patterns and methylation landscapes. The integration of bulk and single-cell RNA sequencing facilitated elucidation of relationships among cellular heterogeneity, immune infiltration, and GLIPR2 levels in pan-cancer. Employing ROC and KM analyses, we unveiled the diagnostic and prognostic potential of GLIPR2 across diverse cancers. Immunohistochemistry provided insights into GLIPR2 expression patterns in a multicenter cohort spanning various cancer types. In vitro functional experiments, including transwell assays, wound healing analyses, and drug sensitivity testing, were employed to delineate the tumor suppressive role of GLIPR2.

Results

GLIPR2 expression was significantly reduced in neoplastic tissues compared to its prevalence in healthy tissues. Copy number variations (CNV) and alterations in methylation patterns exhibited discernible correlations with GLIPR2 expression within tumor tissues. Moreover, GLIPR2 demonstrated diagnostic and prognostic implications, showing pronounced associations with the expression profiles of numerous immune checkpoint genes and the relative abundance of immune cells in the neoplastic microenvironment. This multifaceted influence was evident across various cancer types, with lung adenocarcinoma (LUAD) being particularly prominent. Notably, patients with LUAD exhibited a significant decrease in GLIPR2 expression within practical clinical settings. Elevated GLIPR2 expression correlated with improved prognostic outcomes specifically in LUAD. Following radiotherapy, LUAD cases displayed an increased presence of GLIPR2+ infiltrating cellular constituents, indicating a notable correlation with heightened sensitivity to radiation-induced therapeutic modalities. A battery of experiments validated the functional role of GLIPR2 in suppressing the malignant phenotype and enhancing treatment sensitivity.

Conclusion

In pan-cancer, particularly in LUAD, GLIPR2 emerges as a promising novel biomarker and tumor suppressor. Its involvement in immune cell infiltration suggests potential as an immunotherapeutic target.

The endoplasmic reticulum stress-related genes and molecular typing predicts prognosis and reveals characterization of tumor immune microenvironment in lung squamous cell carcinoma

BACKGROUND

Endoplasmic reticulum stress (ERS) acts critical roles on cell growth, proliferation, and metastasis in various cancers. However, the relationship between ERs and lung squamous cell carcinoma (LUSC) prognoses still remains unclear.

METHODS

The consensus clustering analysis of ERS-related genes and the differential expression analysis between clusters were investigated in LUSC based on TCGA database. Furthermore, ERS-related prognostic risk models were constructed by LASSO regression and Cox regression analyses. Then, the predictive effect of the risk model was evaluated by Kaplan-Meier, Cox regression, and ROC Curve analyses, as well as validated in the GEO cohort. According to the optimal threshold, patients with LUSC were divided into high- and low- risk groups, and somatic mutations, immune cell infiltration, chemotherapy response and immunotherapy effect were systematically analyzed.

RESULTS

Two ERS-related clusters were identified in patients with LUSC that had distinct patterns of immune cell infiltration. A 5-genes ERS-related prognostic risk model and nomogram were constructed and validated. Kaplan-Meier curves and Cox regression analysis showed that ERS risk score was an independent prognostic factor (p < 0.001, HR = 1.317, 95% CI = 1.159-1.496). Patients with low-risk scores presented significantly lower TIDE scores and significantly lower IC50 values for common chemotherapy drugs such as cisplatin and gemcitabine.

CONCLUSION

ERS-related risk signature has certain prognostic value and may be a potential therapeutic target and prognostic biomarker for LUSC patients.

Exploration of kiwi root on non-small cell lung cancer based on network pharmacology and molecular docking

BACKGROUND

Kiwi root is a Chinese herb clinically used in the treatment of lung neoplasm; however, the multi-target mechanism of kiwi root in the treatment of non-small cell lung cancer (NSCLC) remains to be elucidated. Thus, this study aimed to investigate the molecular mechanisms of kiwi root in the treatment of NSCLC through network pharmacology and molecular docking techniques.

METHODS

The active components and targets of kiwi root were obtained from the TCMSP database, and NSCLC-related targets were obtained from the GeneCards, OMIM, and DrugBank databases. The intersection targets of NSCLC and kiwi root were obtained from VENNY 2.1.0. Then, the common targets were imported into the STRING database, and by using the Cytoscape 3.7.1 software, drug-disease network diagrams were created. Afterwards, the DAVID database was utilized to perform bioinformatic annotation. Finally, molecular docking of key components and key targets was performed by Autodock Tools.

RESULTS

A total of 4083 NSCLC-related disease genes were collected from the GeneCards, OMIM,and DrugBank databases, and 177 non-duplicated drug targets were acquired from the TCMSP database. A total of 138 intersection target genes were obtained, in which TP53, AKT1, and TNF were the key targets.

CONCLUSION

Through network pharmacology techniques, the mechanism of kiwi root in the treatment of NSCLC has been uncovered and provides a theoretical basis for the clinical treatment of NSCLC with kiwi root, which requires further experimental validation.

MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study

OBJECTIVES

To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS).

METHODS

We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA).

RESULTS

Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977).

CONCLUSION

The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

Co-mutation of TP53 and TTN is Correlated with the Efficacy of Immunotherapy in Lung Squamous Cell Carcinoma

BACKGROUND

Immunotherapy has been a promising treatment in advanced lung cancer. However, only a few patients could benefit from it. Herein, we aimed to explore mutationrelated predictive biomarkers in lung squamous cell carcinoma (LUSC), which could help develop clinical immunotherapy strategies and screen beneficial populations.

METHODS

Co-occurrence and mutually exclusive analysis was conducted on the TCGA-LUSC cohort. Correlations between the gene mutation status and tumor mutation burden (TMB) levels, and neo-antigen levels were analyzed by Wilcoxon test. Kaplan-Meier method was employed to analyze the progression-free survival (PFS) of lung cancer patients with immunotherapy. Gene set enrichment analysis (GSEA) was used to investigate the functional changes affected by TP53mut/TTNmut. The immune cell infiltration landscape in co-mutation subgroups was analyzed using CIBERSORT.

RESULTS

1) TP53, TTN, CSMD3, MUC16, RYR2, LRP1B, USH2A, SYNE1, ZFHX4, FAM135B, KMT2D, and NAV3 were frequently mutated in LUSC patients. 2) TMB levels in highly mutated groups were higher than that in wild type groups. 3) There were higher neoantigen levels in mutation group compared to the wild-type group, and LUSC patients in mutation group had longer PFS. 4) TP53mut/TTNmut co-mutation group exhibited higher TMB levels and better response to immunotherapy. 5) A host of immune-related signaling pathways was inhibited in TP53mut/TTNmut subgroup. 6) There were more T follicular helper cells and NK cells were in TP53mut/TTNmut subgroup than in the WT subgroup.

CONCLUSION

The LUSC patients with TP53 and TTN co-mutation had higher TMB levels and better response to immunotherapy. The TP53 and TTN co-mutation is a promising novel biomarker to assist LUSC immunotherapy evaluation.

The identification of a PTEN-associated gene signature for the prediction of prognosis and planning of therapeutic strategy in endometrial cancer

Background

Endometrial cancer (EC) is one of the most common malignancies among women. To improve the prognosis and treatment of EC, finding out a phosphatase and tensin homolog deleted on chromosome 10 (PTEN)-associated prognostic signature would be beneficial.

Methods

EC clinical data, genetic mutation data, and transcriptome data were downloaded from The Cancer Genome Atlas (TCGA) database. To clarify the specific PTEN-associated signature, cox regression analyses were performed. The clinical value of the selected signature on the overall survival (OS) and the secretoglobin family 2A member 1 (SCGB2A1)-independent analysis, immune and functional analysis were investigated respectively.

Results

Five hundred and fourteen EC samples were screened and PTEN mutation occupied 57%. Enrichment analysis indicated that mutant-type PTEN was enriched for pathways related to the upregulated human T-cell leukemia virus-1 (HTLV-1) infection and estrogen signaling pathway. SCGB2A1 was identified by cox regression analysis. Immune analysis exhibited significant immune infiltration with higher expression of T cells, B cells, and macrophage groups. Immune-checkpoint transcripts CD274 molecule (CD274), and cytotoxic T-lymphocyte associated protein 4 (CTLA4), hepatitis A virus cellular receptor 2 (HAVCR2), lymphocyte activation gene 3 (LAG3), programmed cell death 1 (PDCD1), PDCD1 ligand 2 (PDCD1LG2), T cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibition motif domains (TIGIT), and sialic acid binding immunoglobulin like lectin 15 (SIGLEC15) were discovered statistically different. In addition, the low-SCGB2A1 group had worse OS than the high-SCGB2A1 group. SCGB2A1 showed significant area under the curve (AUC) values in a time-dependent receiver operating characteristic (ROC) analysis. Prevalence of microsatellite instability (MSI) was detected and SCGB2A1 showed a negative correlation with EC. Immune checkpoint blockade (ICB) response indicated a worse immune response in the low-SCGB2A1 group. The distribution of one-class linear regression (OCLR) scores reflected the negative correlation between messenger RNA expression-based stemness index (mRNAsi) and prognostic gene expression. Furthermore, several SCGB2A1-related signaling pathways in EC were identified.

Conclusions

SCGB2A1 is a prognostic immunometabolic signature for patients with EC, which may help improve the prognosis and therapeutic effect.

The value of CDC42 effector protein 2 as a novel prognostic biomarker in liver hepatocellular carcinoma: a comprehensive data analysis

BACKGROUND

The prognostic significance of CDC42 effector protein 2 (CDC42EP2) and its association with tumor-infiltrating immune cells (TIICs) have not been explored in liver hepatocellular carcinoma (LIHC). This study aims to assess the potential prognostic value of CDC42EP2 by conducting a comprehensive analysis of online databases pertaining to LIHC.

METHODS

We evaluated the potential of CDC42EP2 as a prognostic biomarker by utilizing online databases such as TIMER, GEPIA2, KM, OSlihc, HPA, and LinkedOmics.

RESULTS

In LIHC, we observed that the mRNA and protein expression of CDC42EP2 were upregulated compared to normal tissues. Upregulated CDC42EP2 expression was associated with a worse prognosis based on the clinicopathological characteristics of patients with LIHC. Furthermore, CDC42EP2 was positively associated with TIICs. In the co-expression and functional enrichment analyses of CDC42EP2, 11,416 genes showed positive associations with CDC42EP2 while 8,008 genes showed negative associations. CDC42EP2-related co-expression genes were involved in protein localization to the endoplasmic reticulum, translational initiation, and RNA catabolic processes in gene set enrichment analysis-Gene Ontology (GSEAGO), and regulated the ribosome, spliceosome, and primary immune deficiency in the GSEAKyoto Encyclopedia of Genes and Genomes (KEGG) pathway. In a survival map, 23 and 17 genes that exhibited positive associations with CDC42EP2 showed a significant hazard ratio (HR) for overall survival and disease-free survival, respectively.

CONCLUSION

Our findings demonstrated that CDC42EP2 is a novel prognostic biomarker and a potential tumor immune therapeutic target in patients with LIHC.

The Evaluation of Prognostic Value and Immune Characteristics of Ferroptosis-Related Genes in Lung Squamous Cell Carcinoma

Background  The purpose of our study was to construct a prognostic model based on ferroptosis-related gene signature to improve the prognosis prediction of lung squamous carcinoma (LUSC). Methods  The mRNA expression profiles and clinical data of LUSC patients were downloaded. LUSC-related essential differentially expressed genes were integrated for further analysis. Prognostic gene signatures were identified through random forest regression and univariate Cox regression analyses for constructing a prognostic model. Finally, in a preliminary experiment, we used the reverse transcription-quantitative polymerase chain reaction assay to verify the relationship between the expression of three prognostic gene features and ferroptosis. Results  Fifty-six ferroptosis-related essential genes were identified by using integrated analysis. Among these, three prognostic gene signatures (HELLS, POLR2H, and POLE2) were identified, which were positively affected by LUSC prognosis but negatively affected by immune cell infiltration. Significant overexpression of immune checkpoint genes occurred in the high-risk group. In preliminary experiments, we confirmed that the occurrence of ferroptosis can reduce three prognostic gene signature expression. Conclusions  The three ferroptosis-related genes could predict the LUSC prognostic risk of antitumor immunity.

Comprehensive analysis of m6A methylomes in idiopathic pulmonary arterial hypertension

Idiopathic pulmonary arterial hypertension (IPAH) is a serious and fatal disease. Recently, m6A has been reported to play an important role in the lungs of IPAH patients and experimental pulmonary hypertension models. However, the meaning of m6A mRNAs in the peripheral blood of IPAH patients remains largely unexplored. We aimed to construct a transcriptome-wide map of m6A mRNAs in the peripheral blood of IPAH patients. M6A RNA Methylation Quantification Kit was utilized to measure the total m6A levels in the peripheral blood of IPAH patients. A combination of MeRIP-seq, RNA-seq and bioinformatics analysis was utilized to select m6A-modified hub genes of IPAH. MeRIP-qPCR and RT-qPCR were used to measure the m6A levels and mRNA levels of TP53, RPS27A, SMAD3 and FoxO3 in IPAH patients. Western blot was performed to assess the protein levels of m6A related regulators and m6A related genes in experimental PH animal models, hypoxia-treated and PDGF-BB induced PASMCs. We found that the total m6A levels were increased in peripheral blood of IPAH patients and verified that m6A levels of RPS27A and SMAD3 were significantly elevated and m6A levels of TP53 and FoxO3 were significantly reduced. The mRNA or protein levels of RPS27A, SMAD3, TP53 and FoxO3 were changed in human blood samples, experimental PH animal models and PDGF-BB induced PASMCs. Moreover, METTL3 and YTHDF1 were increased in the hypoxia induced pulmonary hypertension rat model, hypoxia-treated and PDGF-BB induced PASMCs. These finding suggested that m6A may play an important role in IPAH.

PROS1, a clinical prognostic biomarker and tumor suppressor, is associated with immune cell infiltration in breast cancer: A bioinformatics analysis combined with experimental verification

BACKGROUND

PROS1 is an encoding gene that can generate protein S. This protein is a glycoprotein found in plasma that conducts physiological functions with vitamin K. However, the impact of its expression remains absent in the progression and prognosis of breast cancer (BC).

METHODS

In this study, we comprehensively explored the expression of PROS1 in BC and its relationship with BC patient survival, prognosis, and other clinicopathological features. We investigated how PROS1 influenced the malignant biological behavior of BC cells. A series of enrichment analyses were conducted, and the immune landscape was explored in BC affected by PROS1. We also determined correlations between PROS1 and common drug sensitivities used for BC treatments.

RESULTS

PROS1 had low expression in BC, which tended to result in poor survival of BC patients. Overexpressed PROS1 inhibited the migration and invasion of BC cells as well as the epithelial-mesenchymal transition process by downregulating SNAIL. Functional enrichment analyses revealed that PROS1 was more active in extracellular matrix (ECM) organization and structural constituent, ECM-receptor interaction, and other pathways with its related genes. PROS1 was also found to affect immune activity, including various immune cells infiltrating BC. BC patients with high PROS1 expression tended to have lower IC50 values of three common medications and obtained better efficacy.

CONCLUSIONS

PROS1 can become a promising prognostic factor and a possible therapeutic target in BC patients and suppress BC cell metastatic potential. In addition, PROS1 is a crucial factor in immune infiltration in BC.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

HER2-positive breast cancer progresses rapidly after pyrotinib resistance: acquired RET gene fusion and TP53 gene mutation are potential reasons

Approximately 15-20% of the patients with breast cancer overexpress human epidermal growth factor receptor 2 ( HER2 ). HER2 -positive breast cancer is highly aggressive and has a high relapse rate, suggesting that it is prone to and progresses rapidly after drug resistance. Pyrotinib resistance and changes in patients' conditions after drug resistance are challenging clinical issues and require medical attention. Recently, there are few clinical reports on changes in patients' conditions after pyrotinib resistance. We report a case of a 46-year-old patient with HER2 -positive breast cancer who developed resistance to pyrotinib and rapidly progressed to uncontrolled liver failure in less than a week. To elucidate the cause of the rapid progression, we collected samples of the patient's ascites and performed next-generation sequencing (NGS). On the basis of the NGS results, we speculated that the rapid progression after pyrotinib resistance might be due to RET gene fusion and TP53 gene mutations. Therefore, this case report aims to alert oncologists that patients with HER2 -positive breast cancer, who are resistant to pyrotinib or other targeted drugs, could experience rapid or even flare-up progression and that RET gene fusion and TP53 gene mutations might be potential causes.

Establishment of a prognostic signature for patients with advanced lung squamous cell carcinoma based on tumor-infiltrating immune cells

Background

With the advancements in the fields of science, technology, and medical therapy, there is an increasing awareness among the general public regarding tumor-infiltrating immune cells. These immune cells have a close association with the prognosis of clinical patients with lung cancer.

Methods

The research used a comprehensive analysis and assessed tumor-infiltrating immune cells in advanced lung squamous cell carcinoma (LUSC) using The Cancer Genome Atlas (TCGA) database and the CIBERSORT algorithm. The research examined 22 types of tumor-infiltrating immune cells and observed notable differences in the infiltration patterns of immune cells between normal tissue and advanced LUSC.

Results

Univariate Cox regression analyses revealed a positive correlation between macrophages M2 and patient prognoses, as well as potential influences on patient prognosis by natural killer (NK) cells resting, monocytes, and activated mast cells. Multivariate Cox regression models were developed, incorporating three types of immune cells. The efficacy of the model was evaluated using a receiver operating characteristic (ROC) curve. Furthermore, the research constructed a nomogram model to individually predict the mortality risk in patients with advanced LUSC. This prediction model serves as a valuable tool for clinicians, enabling them to provide effective guidance based on tumor-infiltrating immune cells for advanced LUSC patients.

Conclusions

The research comprehensively analyzed and evaluated 22 types of tumor-infiltrating immune cells from advanced LUSC, revealing the correlation between immune cell infiltration and overall survival (OS) in clinical patients. Based on the nomogram of NK cells resting, monocytes, and macrophages M2, it can make specific prognostic predictions for advanced LUSC patients.

Comprehensive analysis of the role of a four-gene signature based on EMT in the prognosis, immunity, and treatment of lung squamous cell carcinoma

Epithelial-mesenchymal transition (EMT), a biological process through which epithelial cells transform into mesenchymal cells, contributes to tumor progression and metastasis. However, a comprehensive analysis of the role of EMT-related genes in Lung squamous cell carcinoma (LUSC) is still lacking. In this study, data were downloaded from available databases, including The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The association between differentially expressed EMT-related genes (EMT-RDGs) and LUSC prognosis, drug sensitivity, mutation, and immunity was analyzed using bioinformatics methods. In the results, Lasso and univariate Cox regression analyses identified four EMT-RDGs that were differentially expressed, and used to establish a prognostic model capable of distinguishing between high- and low-risk groups. Then, prognostic factors were identified by multivariate Cox regression analysis and used to construct a nomogram. The high-risk group had a significantly poorer prognosis than the low-risk group. The tumor immune environment was significantly different between the two groups, with the low-risk group exhibiting a better response to immunotherapy. In addition, the half-maximal inhibitory concentration prediction indicating that the constructed model could effectively predict sensitivity to chemotherapy. This study provides new reference for further exploration of new clinical therapeutic strategies for LUSC.

Hallmark guided identification and characterization of a novel immune-relevant signature for prognostication of recurrence in stage I-III lung adenocarcinoma

The high risk of postoperative mortality in lung adenocarcinoma (LUAD) patients is principally driven by cancer recurrence and low response rates to adjuvant treatment. Here, A combined cohort containing 1,026 stage I-III patients was divided into the learning (n = 678) and validation datasets (n = 348). The former was used to establish a 16-mRNA risk signature for recurrence prediction with multiple statistical algorithms, which was verified in the validation set. Univariate and multivariate analyses confirmed it as an independent indicator for both recurrence-free survival (RFS) and overall survival (OS). Distinct molecular characteristics between the two groups including genomic alterations, and hallmark pathways were comprehensively analyzed. Remarkably, the classifier was tightly linked to immune infiltrations, highlighting the critical role of immune surveillance in prolonging survival for LUAD. Moreover, the classifier was a valuable predictor for therapeutic responses in patients, and the low-risk group was more likely to yield clinical benefits from immunotherapy. A transcription factor regulatory protein-protein interaction network (TF-PPI-network) was constructed via weighted gene co-expression network analysis (WGCNA) concerning the hub genes of the signature. The constructed multidimensional nomogram dramatically increased the predictive accuracy. Therefore, our signature provides a forceful basis for individualized LUAD management with promising potential implications.

Intratumoral Microbiota-Host Interactions Shape the Variability of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma in Recurrence and Metastasis

Differences in tissue microbiota-host interaction between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) about recurrence and metastasis have not been well studied. In this study, we performed bioinformatics analyses to identify the genes and tissue microbes significantly associated with recurrence or metastasis. All lung cancer patients were divided into the recurrence or metastasis (RM) group and the nonrecurrence and nonmetastasis (non-RM) group according to whether or not they had recurred or metastasized within 3 years after the initial surgery. Results showed that there were significant differences between LUAD and LUSC in gene expression and microbial abundance associated with recurrence and metastasis. Compared with non-RM, the bacterial community of RM had a lower richness in LUSC. In LUSC, host genes significantly correlated with tissue microbe, whereas host-tissue microbe interaction in LUAD was rare. Then, we established a novel multimodal machine learning model based on genes and microbes to predict the recurrence and metastasis risk of a LUSC patient, which achieves an area under the curve (AUC) of 0.81. In addition, the predicted risk score was significantly associated with the patient's survival. IMPORTANCE Our study elucidates significant differences in RM-associated host-microbe interactions between LUAD and LUSC. Besides, the microbes in tumor tissue could be used to predict the RM risk of LUSC, and the predicted risk score is associated with patients' survival.

The role of oncogenes and tumor suppressor genes in determining survival rates of lung cancer patients in the population of North Sumatra, Indonesia

Background: Gaining a better understanding of molecular alterations in the pathogenesis of lung cancer reveals a significant change in approach to the management and prognosis of lung cancer. Several oncogenes and tumor suppressor genes have been identified and have different roles related to survival rates in lung cancer patients. This study aims to determine the role of KRAS, EGFR, and TP53 mutations in the survival rate of lung cancer patients in the population of North Sumatra. Methods: This is a retrospective cohort study involving 108 subjects diagnosed with lung cancer from histopathology specimens. DNA extractions were performed using FFPE followed by PCR examinations for assessing the expressions of EGFR, RAS, and TP53 protein. Sequencing analysis was carried out to determine the mutations of EGFR exon 19 and 21, RAS protein exon 2, and TP53 exon 5-6 and 8-9. Data input and analysis were conducted using statistical analysis software for Windows. The survival rate analysis was presented with Kaplan Meier. Results: 52 subjects completed all procedures in this study. Most of the subjects are male (75%), above 60 years old (53.8%), heavy smokers (75%), and suffer from adenocarcinoma type of lung cancer (69.2%). No subjects showed KRAS exon 2 mutations. Overall survival rates increased in patients with EGFR mutations (15 months compared to 8 months; p=0.001) and decreased in patients with TP53 mutations (7 months compared to 9 months; p=0.148). Also, there was increasing Progression-Free Survival in patients with EGFR mutations (6 months compared to 3 months) ( p=0.19) and decreasing PFS in patients with TP53 mutations (3 months compared to 6 months) ( p=0.07). Conclusions: There were no KRAS mutations in this study. EGFR mutations showed a higher survival rate, while TP53 mutations showed a lower survival rate in overall survival and progression-free survival.

Network pharmacology and bioinformatics analysis identifies potential therapeutic targets of Naringenin against COVID-19/LUSC

Background

Coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease that has posed a serious threat to people's daily lives and caused an unprecedented challenge to public health and people's health worldwide. Lung squamous cell carcinoma (LUSC) is a common type of lung malignancy with a highly aggressive nature and poor prognosis. Patients with LUSC could be at risk for COVID-19, We conducted this study to examine the potential for naringenin to develop into an ideal medicine and investigate the underlying action mechanisms of naringenin in COVID-19 and LUSC due to the anti-viral, anti-tumor, and anti-inflammatory activities of naringenin.

Methods

LUSC related genes were obtained from TCGA, PharmGKB, TTD,GeneCards and NCBI, and then the transcriptome data for COVID-19 was downloaded from GEO, DisGeNET, CTD, DrugBank, PubChem, TTD, NCBI Gene, OMIM. The drug targets of Naringenin were revealed through CTD, BATMAN, TCMIP, SymMap, Chemical Association Networks, SwissTargetPrediction, PharmMapper, ECTM, and DGIdb. The genes related to susceptibility to COVID-19 in LUSC patients were obtained through differential analysis. The interaction of COVID-19/LUSC related genes was evaluated and demonstrated using STRING to develop a a COX risk regression model to screen and evaluate the association of genes with clinical characteristics. To investigate the related functional and pathway analysis of the common targets of COVID-19/LUSC and Naringenin, KEGG and GO enrichment analysis were employed to perform the functional analysis of the target genes. Finally, The Hub Gene was screened and visualized using Cytoscape, and molecular docking between the drug and the target was performed using Autodock.

Results

We discovered numerous COVID-19/LUSC target genes and examined their prognostic value in LUSC patients utilizing a variety of bioinformatics and network pharmacology methods. Furthermore, a risk score model with strong predictive performance was developed based on these target genes to assess the prognosis of LUSC patients with COVID-19. We intersected the therapeutic target genes of naringenin with the LUSC, COVID-19-related targets, and identified 354 common targets, which could be used as potential target genes for naringenin to treat COVID-19/LUSC. The treatment of COVID-19/LUSC with naringenin may involve oxidative stress, anti-inflammatory, antiviral, antiviral, apoptosis, immunological, and multiple pathways containing PI3K-Akt, HIF-1, and VEGF, according to the results of the GO and KEGG enrichment analysis of these 354 common targets. By constructing a PPI network, we ascertained AKT1, TP53, SRC, MAPK1, MAPK3, and HSP90AA1 as possible hub targets of naringenin for the treatment of COVID-19/LUSC. Last but not least, molecular docking investigations showed that naringenin has strong binding activity in COVID-19/LUSC.

Conclusion

We revealed for the first time the pharmacological targets and potential molecular processes of naringenin for the treatment of COVID-19/LUSC. However, these results need to be confirmed by additional research and validation in real LUSC patients with COVID-19.

Bayesian network enables interpretable and state-of-the-art prediction of immunotherapy responses in cancer patients

Immune checkpoint inhibitors, especially PD-1/PD-L1 blockade, have revolutionized cancer treatment and brought tremendous benefits to patients who otherwise would have had a limited prognosis. Nonetheless, only a small fraction of patients respond to immunotherapy, and the costs and side effects of immune checkpoint inhibitors cannot be ignored. With the advent of machine and deep learning, clinical and genetic data have been used to stratify patient responses to immunotherapy. Unfortunately, these approaches have typically been "black-box" methods that are unable to explain their predictions, thereby hindering their responsible clinical application. Herein, we developed a "white-box" Bayesian network model that achieves accurate and interpretable predictions of immunotherapy responses against nonsmall cell lung cancer (NSCLC). This tree-augmented naïve Bayes (TAN) model accurately predicted durable clinical benefits and distinguished two clinically significant subgroups with distinct prognoses. Furthermore, our state-of-the-art white-box TAN approach achieved greater accuracy than previous methods. We hope that our model will guide clinicians in selecting NSCLC patients who truly require immunotherapy and expect our approach to be easily applied to other types of cancer.

Determining the mechanism of action of the Qishan formula against lung adenocarcinoma by integration of network pharmacology, molecular docking, and proteomics

BACKGROUND

Lung adenocarcinoma (LUAD) is the main pathological type of lung cancer. Qishan formula (QSF) is reportedly efficacious against LUAD. However, its mechanisms of action currently remain elusive. Therefore, network pharmacology, molecular docking techniques and proteomics were used to verify the potential pharmacological effects of QSF in the treatment of LUAD.

METHODS

The active ingredients and potential targets of QSF were obtained from the TCMSP, chemical source network and construct a drug-component-target networks using Cytoscape v3.7.2. Data for disease targets were obtained from 5 databases: TCGA, OMIM, DrugBank, DisGeNET, and GeneCards. Drug disease cross targets were used to construct protein-protein interaction networks for selecting the core targets using the STRING database and enrichment pathway networks using the DAVID database. Finally, TMT quantitative proteomics was used to identify the possible core targets and action pathways. Molecular docking to verify the affinity between components and targets.

RESULTS

Network pharmacology identified core components of QSF against LUAD included baicalein, methylophiopogonone B, quercetin, kaempferol, isorhamnetin, and luteolin, which can act on 10 key targets (SRC, TP53, PIK3R1, MAPK3, STAT3, MAKP1, HSP90AA1, PIK3CA, HRAS, and AKT1). QSF might play a therapeutic role in LUAD by regulating biological processes such as signal transduction, protein phosphorylation, cell proliferation, and apoptosis, as well as the PI3K/AKT, MAPK, FoxO, and other signaling pathways. Proteomics identified 207 differentially expressed proteins, and by integrating with network pharmacology and molecular docking results we found that 6 core components of QSF may target TP53 against LUAD through the PI3K/AKT signaling pathway.

CONCLUSION

QSF is a multitarget recipe potentially exerting pleiotropic effects in LUAD.

A narrative review of genetic biomarkers in non-small cell lung cancer: an update and future perspectives

Background and Objective

Lung cancer has long been the leading cause of cancer deaths in the United States. Lung cancer has a poor prognosis, and our understanding of who will maximally benefit from different therapies is incomplete. This article discusses genetic biomarkers that may help in this regard.

Methods

From origin until February 25, 2022, PubMed database was searched for terms "non-small cell lung cancer", "genomics" and "biomarker", with special attention paid to literature published within the past 10 years. Search was language restricted to English. Additional literature was identified through hand searches of the references of retrieved literature.

Key Content and Findings

The most robustly described biomarkers for non-small cell lung cancer (NSCLC) are assessment of specific gene mutations. These are currently used in clinical practice for both prediction and prognostication. Abnormal mutation status of STK11/LKB1 and KEAP1-NFE2L2 are associated with poor response to radiotherapy (RT), and STK11/LKB1 is further associated with resistance to PD-L1 immunotherapy. Abnormal TP53 is associated with decreased benefit from cisplatin in squamous cell carcinoma (SCC). In terms of prognostication, RB1 mutations are associated with decreased overall survival (OS) in NSCLC and KEAP1-NFE2L2 mutations are associated with increased local recurrence (LR).Additional work has focused on gene expression levels, as well as analysis of genetic factors and signaling molecules affecting the tumor microenvironment (TME). High levels of Rad51c and NFE2L2 are associated with resistance to chemotherapy, and high Rad51c levels are further associated with resistance to RT. High nuclear expression of β-catenin has additionally been associated with poor RT response. Further, there is increasing evidence that some long non-coding RNAs (lncRNAs) may play a crucial role in regulation of tumor radiosensitivity. Much of this work has had promising early results but will require further validation before routine clinical use. Finally, there is evidence that quantification of some signaling molecules and microRNAs (miRNAs) may have clinical utility in predicting adverse outcomes in RT.

Conclusions

An improved understanding of tumor genetics in NSCLC has led to the development of targeted therapies and improved prognostication. As more work is done in this field, more and more genetic biomarkers will become candidates for clinical use. Much work will be required to validate these findings in the clinical setting.

[Survival efficacy of MDS/AML patients with TP53 abnormal received allogeneic hematopoietic stem cell transplantation]

Objective: TP53-abnormal MDS/acute myeloid leukemia (AML) patients' allogeneic hematopoietic stem cell transplantation (allo-HSCT) treatment's effectiveness and influencing factors should be studied. Methods: 42 patients with TP53 gene status change MDS/AML who underwent allo-HSCT from 2014.8.1 to 2021.7.31 at the Hematology Hospital of the Chinese Academy of Medical Sciences were the subject of a retrospective analysis. The 42 patients were divided into three groups: the TP53 deletion group (group A) , TP53 mono-alle mutation group (group B) , and TP53 multi-hit group (group C) . The differences in clinical features and prognostic factors after transplantation were analyzed. Results: There were 42 MDS/AML patients, including 21 patients with MDS, and 21 patients with AML. The median follow-up period was 34.0 (7.5-75.0) months and the median patient age at the time of transplantation was 41.5 (18-63) years old. The total OS was 66.3% (95% CI 53.4%-82.4%) in 3 years after transplantation, and EFS was 61.0% (95% CI 47.7%-78.0%) in 3 years. For 3 years after receiving hematopoietic stem cell transplantation, there were no statistically significant differences in 3-year OS and EFS in groups A, B, and C (P≥0.05) . The 3 years OS was 82.5% (95% CI 63.1%-100.0%) in group A, 60.6% (95% CI 43.5%-84.4%) in group B, and 57.1% (95% CI 30.1%-100.0%) in group C. Univariate analysis revealed that the number of co-mutant genes, pre-HSCT treatment, and disease type did not affect prognosis, while age, karyotype, co-mutation, positive blast cell before transplantation, and positive blast cell after transplantation were common prognostic factors for OS and EFS (P<0.1) . MRD levels before transplantation were found to be independent risk factors for OS (P=0.037, HR=33.40, 95% CI 1.24-901.17) in a multivariate analysis. Conclusion: Patients with MDS/AML who have TP53 mutations can benefit from allo-HSCT, but patients with complex karyotypes have a worse prognosis. Meanwhile, the final flow cytometry (FCM) monitoring blast cell test before HSCT has a certain guiding significance for prognostic assessment.

Therapeutic strategies for non-small cell lung cancer: Experimental models and emerging biomarkers to monitor drug efficacies

While new targeted therapies have considerably changed the treatment and prognosis of non-small cell lung cancer (NSCLC), they are frequently unsuccessful due to primary or acquired resistances. Chemoresistance is a complex process that combines cancer cell intrinsic mechanisms including molecular and genetic abnormalities, aberrant interactions within the tumor microenvironment, and the pharmacokinetic characteristics of each molecule. From a pharmacological point of view, two levers could improve the response to treatment: (i) developing tools to predict the response to chemo- and targeted therapies and (ii) gaining a better understanding of the influence of the tumor microenvironment. Both personalized medicine approaches require the identification of relevant experimental models and biomarkers to understand and fight against chemoresistance mechanisms. After describing the main therapies in NSCLC, the scope of this review will be to identify and to discuss relevant in vitro and ex vivo experimental models that are able to mimic tumors. In addition, the interests of these models in the predictive responses to proposed therapies will be discussed. Finally, this review will evaluate the involvement of novel secreted biomarkers such as tumor DNA or micro RNA in predicting responses to anti-tumor therapies.

A ferroptosis-related prognostic model with excellent clinical performance based on the exploration of the mechanism of oral squamous cell carcinoma progression

As a hot topic today, ferroptosis is closely involved in the progression and treatment of cancer. Accordingly, we built a prognostic model around ferroptosis to predict the overall survival of OSCC patients. We used up to 6 datasets from 3 different databases to ensure the credibility of the model. Then, through differentially expressed, Univariate Cox, and Lasso regression analyses, a model composed of nine prognostic-related differently expressed ferroptosis-related genes (CISD2, DDIT4, CA9, ALOX15, ATG5, BECN1, BNIP3, PRDX5 and MAP1LC3A) were constructed. Moreover, Kaplan-Meier curves, Receiver Operating Characteristic curves and principal component analysis used to verify the model's predictive ability showed the model's superiority. To deeply understand the mechanism of ferroptosis affecting the occurrence, development and prognosis of OSCC, we performed enrichment analysis in different risk groups identified by the model. The results showed that numerous TP53-related, immune-related and ferroptosis-related functions and pathways were enriched. Further immune microenvironment analysis and mutation analysis have once again revealed the correlation between risk score and immunity and TP53 mutation. Finally, the correlation between risk score and OSCC clinical treatment, as well as Nomogram show the brilliant clinical application prospects of the prognostic model.

Significance of logistic regression scoring model based on natural killer cell-mediated cytotoxic pathway in the diagnosis of colon cancer

Background

The poor clinical accuracy to predict the survival of colon cancer patients is associated with a high incidence rate and a poor 3-year survival rate. This study aimed to identify the poor prognostic biomarkers of colon cancer from natural killer cell-mediated cytotoxic pathway (NKCP), and establish a logistical regression scoring model to predict its prognosis.

Methods

Based on the expressions and methylations of NKCP-related genes (NRGs) and the clinical information, dimensionality reduction screening was performed to establish a logistic regression scoring model to predict survival and prognosis. Risk score, clinical stage, and ULBP2 were used to establish a logistic regression scoring model to classify the 3-year survival period and compare with each other. Comparison of survival, tumor mutation burden (TMB), estimation of immune invasion, and prediction of chemotherapeutic drug IC50 were performed between low- and high-risk score groups.

Results

This study found that ULBP2 was significantly overexpressed in colon cancer tissues and colon cancer cell lines. The logistic regression scoring model was established to include six statistically significant features: S = 1.70 × stage - 9.32 × cg06543087 + 6.19 × cg25848557 + 1.29 × IFNA1 + 0.048 × age + 4.37 × cg21370856 - 8.93, which was used to calculate risk score of each sample. The risk scores, clinical stage, and ULBP2 were classified into three-year survival, the 3-year prediction accuracy based on 10-fold cross-validation was 80.17%, 67.24, and 59.48%, respectively. The survival time of low-risk score group was better than that of the high-risk score group. Moreover, compared to high-risk score group, low-risk score group had lower TMB [2.20/MB (log10) vs. 2.34/MB (log10)], higher infiltration score of M0 macrophages (0.17 vs. 0.14), and lower mean IC50 value of oxaliplatin (3.65 vs 3.78) (p < 0.05).

Conclusions

The significantly upregulated ULBP2 was a poor prognostic biomarker of colon cancer. The risk score based on the six-feature logistic regression model can effectively predict the 3-year survival time. High-risk score group demonstrated a poorer prognosis, higher TMB, lower M0 macrophage infiltration score, and higher IC50 value of oxaliplatin. The six-feature logistic scoring model has certain clinical significance in colon cancer.

Unique profile on the progress free survival and overall survival in patients with advanced non-small cell lung cancer in the Qujing area, Southwest China

Background

China's southwestern region, Qujing, harbors a high incidence of non-small cell lung cancer (NSCLC) and related mortality. This study was designed to reveal the impact of an immune-related prognostic signature (IRPS) on advanced NSCLC in the Qujing.

Methods

Tissue specimens from an independent cohort of 37 patients with advanced NSCLC were retrospectively evaluated to determine the relationship between the IRPS estimated by next-generation sequencing (NGS) and clinical outcome. To compare the IRPS in tissue and the clinical outcomes between Qujing and non-Qujing populations, we analyzed datasets of 23 patients with advanced NSCLC from The Cancer Genome Atlas (TCGA) database. In addition, an independent cohort (n=111) of blood specimens was retrospectively analyzed to determine the relationship between the IRPS and clinical outcome. Finally, we evaluated the utility of the blood IRPS in classifying 24 patients with advanced NSCLC who might benefit from immunotherapy.

Results

In cohort 1, the Qujing population with tTMB-H (≥ 10 mutations/Mb) or KRAS mutations had shorter progression-free survival (PFS) (hazard ratio [HR] 0.37, 0.14 to 0.97, P = 0.04; HR 0.23, 0.08 to 0.66, P < 0.01) and overall survival (OS) (HR 0.05, 0.01 to 0.35, P < 0.01; HR 0.22, 0.07 to 0.66, P < 0.01). In cohort 2 of the Qujing population, bTMB-H (≥ 6 mutations per Mb) and KRAS mutations were related to PFS (HR 0.59, 0.36 to 0.99, P = 0.04; HR 0.50, 0.26 to 0.98, P = 0.04) and OS (HR 0.58, 0.35 to 0.96, P = 0.03; HR 0.48, 0.25 to 0.93, P = 0.03). Notably, the Qujing population with bTMB-H had superior PFS (HR 0.32, 0.09 to 1.09, P = 0.01), OS (HR 0.33, 0.10 to 1.13, P < 0.01) and objective response rates (ORRs) (83.3% vs. 14.3% vs. 20.0%, P <0.01) to immunotherapy than other populations.

Conclusions

These findings show that tTMB, bTMB and KRAS mutations appear to be independent validated IRPSs that predict the clinical outcomes of Qujing populations with advanced NSCLC and that bTMB may be used as a reliable IRPS to predict the clinical benefit from anti-PD-1 therapies among populations from Qujing with advanced NSCLC.

Gene Screening in High-Throughput Right-Censored Lung Cancer Data

Background

Advances in sequencing technologies have allowed collection of massive genome-wide information that substantially advances lung cancer diagnosis and prognosis. Identifying influential markers for clinical endpoints of interest has been an indispensable and critical component of the statistical analysis pipeline. However, classical variable selection methods are not feasible or reliable for high-throughput genetic data. Our objective is to propose a model-free gene screening procedure for high-throughput right-censored data, and to develop a predictive gene signature for lung squamous cell carcinoma (LUSC) with the proposed procedure.

Methods

A gene screening procedure was developed based on a recently proposed independence measure. The Cancer Genome Atlas (TCGA) data on LUSC was then studied. The screening procedure was conducted to narrow down the set of influential genes to 378 candidates. A penalized Cox model was then fitted to the reduced set, which further identified a 6-gene signature for LUSC prognosis. The 6-gene signature was validated on datasets from the Gene Expression Omnibus.

Results

Both model-fitting and validation results reveal that our method selected influential genes that lead to biologically sensible findings as well as better predictive performance, compared to existing alternatives. According to our multivariable Cox regression analysis, the 6-gene signature was indeed a significant prognostic factor (p-value < 0.001) while controlling for clinical covariates.

Conclusions

Gene screening as a fast dimension reduction technique plays an important role in analyzing high-throughput data. The main contribution of this paper is to introduce a fundamental yet pragmatic model-free gene screening approach that aids statistical analysis of right-censored cancer data, and provide a lateral comparison with other available methods in the context of LUSC.

Construction and Validation of Prognosis Nomogram for Metastatic Lung Squamous Cell Carcinoma: A Population-Based Study

Purpose: This study aimed to establish a nomogram to predict overall survival in lung squamous cell carcinoma patients with metastasis for clinical decision-making. Methods: We investigated lung squamous cell carcinoma patients diagnosed with stage M1 in the Surveillance, Epidemiology, and Final Results database between 2010 and 2015. They were divided into training cohort and validation cohort. In the training cohort, statistically significant prognostic factors were identified using univariate and multivariate Cox regression analysis, and an individualized nomogram model was developed. The model was evaluated by C-index, area under the curve, calibration plot, decision curve analysis, and risk group stratification. Results: In total, 9910 patients were included in our study, including 6937 in the training cohort and 2937 in the validation cohort. Factors containing age, T stage, N stage, bone metastasis, brain metastasis, liver metastasis, surgery, chemotherapy, and radiotherapy were independent prognostic factors for overall survival and were used in the construction of the nomogram. The C-index in the training cohort and validation cohort were 0.711 (95% confidenc interval: 0.705-0.717) and 0.707 (95% confidenc interval: 0.697-0.717), respectively. The time-dependent area under the curve of both groups was higher than 0.7 within 5 years. Calibration plots indicated that the nomogram-predicted survival was consistent with the recorded 6-month, 1-year, and 2-year prognoses. Furthermore, decision curve analysis revealed that the nomogram was clinically useful and had a better discriminative ability to recognize patients at high risk than the TNM criteria-based tumor staging. And then we developed an overall survival risk classification system based on the nomogram total points for each patient, which divided all patients into a high-risk group and a low-risk group. Finally, we implemented this nomogram in a free online tool. Conclusion: We constructed a nomogram and a corresponding risk classification system predicting the overall survival of lung squamous cell carcinoma patients with metastasis. These tools can assist in patients’ counseling and guide treatment decision-making.

Identification of N6-methylandenosine related lncRNA signatures for predicting the prognosis and therapy response in colorectal cancer patients

Despite recent advances in surgical and multimodal therapies, the overall survival (OS) of advanced colorectal cancer (CRC) patients remains low. Thus, discerning sensitive prognostic biomarkers to give the optimistic treatment for CRC patients is extremely critical. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play an important role in CRC progression. Nonetheless, few studies have focused on the impact of m6A-related lncRNAs on the prognosis, tumor microenvironment (TME) and treatment of CRC. In this study, 1707 m6A-related lncRNAs were identified through Pearson correlation analysis and Weighted co-expression network analysis (WGCNA) using The Cancer Genome Atlas (TCGA) cohort. Then, 28 m6A-related prognostic lncRNAs were screened by univariate Cox regression analysis, followed by identifying two clusters by consensus clustering analysis. A prognostic model consisted of 8 lncRNA signatures was constructed by the least absolute shrinkage and selection operator (LASSO). Kaplan–Meier curve analysis and a nomogram were performed to investigate the prognostic ability of this model. The risk score of prognostic model act as an independent risk factor for OS rate. Functional enrichment analysis indicated that lncRNA signatures related tumor immunity. The low-risk group characterized by increased microsatellite instability-high (MSI-H), mutation burden, and immunity activation, indicated favorable odds of OS. Moreover, the lncRNA signatures were significantly associated with the cancer stem cell (CSC) index and drug sensitivity. In addition, 3 common immune genes shared by the lncRNA signatures were screened out. We found that these immune genes were widely distributed in 2 cell types of TME. Finally, a ceRNA network was constructed to identify ZEB1-AS1 regulatory axis in CRC. We found that ZEB1-AS1 was significantly overexpressed in tumor tissues, and was related to the metastasis of EMT and the chemoresistance of 5-Fu in CRC. Therefore, our study demonstrated the important role of m6A-related lncRNAs in TME remodeling. Moreover, these results illustrated the levels of ZEB1-AS1 might be valuable for predicting the progression and prognosis of CRC, and further provided a new target for the diagnosis and treatment of CRC patients.

N6-Methyladenosine (m6A)-Related lncRNAs Are Potential Signatures for Predicting Prognosis and Immune Response in Lung Squamous Cell Carcinoma

Background

Despite increasing understanding of m⁶A-related lncRNAs in lung cancer, the role of m⁶A-related lncRNAs in the prognosis and treatment of lung squamous cell carcinoma is poorly understood to date. Thus, the current study aims to elucidate its role and build a model to predict the prognosis of LUSC patients.

Materials and Methods

The data of the current study were accessed from the TCGA database. Pearson correlation analysis was performed to identify lncRNAs correlated to m⁶A. Next, an m⁶A-related lncRNAs risk model was built using a single factor, least absolute association, selection operator, and multivariate Cox regression analysis.

Results

The relevance between 23 m⁶A genes and 14,056 lncRNAs is shown by Pearson correlation analysis by Sankey diagram. Multivariate Cox regression analysis determined that 11 m⁶A-lncRNAs show predictive potential in prognosis, which is confirmed by the consistency index, Kaplan–Meier analysis, principal component analysis, and ROC curve. Additionally, the immune analysis showed that the enrichment of immune cells, major histocompatibility complex molecules, and immune checkpoints in the high and low-risk subgroups were markedly disparate, with the high-risk group showing a stronger immune escape ability and a worse response to immunotherapy.

Conclusion

In conclusion, the risk model based on m⁶A-related lncRNAs showed great promise in predicting the prognosis and the efficacy of immunotherapy.

Expression of Mucin Family Proteins in Non-Small-Cell Lung Cancer and its Role in Evaluation of Prognosis

Lung cancer is still the major contributor to cancer-related mortality. Over 85% of patients suffer from non-small-cell lung cancer (NSCLC). Mucins (MUCs) are large glycoproteins secreted or membrane-bound produced by epithelial cells in normal and malignant tissues. They are the major components of the mucous gel that covers the surface of the respiratory epithelium. Certain MUCs have been used or proposed to act as biomarkers for lung cancer. Nevertheless, the expression, messenger ribonucleic acid (mRNA) levels, and the prognostic value of MUCs in NSCLC are yet to be investigated systematically. In this research, the biological information of MUC proteins in patients with NSCLC was examined using a series of databases. The results based on gene expression profiling interactive analysis (GEPIA) illustrated that the expression of MUC3A, MUC4, MUC5B, MUC13, MUC16, and MUC21 mRNAs was remarkably upmodulated in lung adenocarcinoma (LUAD) patients, whereas the MUC1 expression was downregulated in lung squamous cell carcinoma (LUSC) patients. Kaplan–Meier plotter (KM Plotter) analysis revealed that elevated mRNA expression levels of MUC3A and MUC16 were linked to unfavourable overall survival (OS) in NSCLC, while increased mRNA expression of MUC1 and MUC15 was linked to good OS, especially in LUAD patients. In addition, differential expression of MUC1, MUC3A/3B, MUC8, MUC12, MUC15, and MUC16 mRNA was linked to the prognoses of NSCLC patients with varied clinical-pathological subtypes. Genetic alterations of MUCs in NSCLC primarily involved mutations, fusion, amplification, deep deletion, and multiple alterations according to cancer genomics (cBioPortal). Therefore, we propose that combinations of MUC proteins can act as prognostic biomarkers and demonstrate the therapeutic potential for NSCLC-related therapy.

DNA damage repair gene signature model for predicting prognosis and chemotherapy outcomes in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is prone to metastasis and likely to develop resistance to chemotherapeutic drugs. DNA repair has been reported to be involved in the progression and chemoresistance of LUSC. However, the relationship between LUSC patient prognosis and DNA damage repair genes is still unclear.

METHODS

The clinical information of LUSC patients and tumour gene expression level data were downloaded from the TCGA database. Unsupervised clustering and Cox regression were performed to obtain molecular subtypes and prognosis-related significant genes based on a list including 150 DNA damage repair genes downloaded from the GSEA database. The coefficients determined by the multivariate Cox regression analysis and the expression level of prognosis-related DNA damage repair genes were employed to calculate the risk score, which divided LUSC patients into two groups: the high-risk group and the low-risk group. Immune viability, overall survival, and anticarcinogen sensitivity analyses of the two groups of LUSC patients were performed by Kaplan-Meier analysis with the log rank test, ssGSEA and the pRRophetic package in R software. A time-dependent ROC curve was applied to compare the survival prediction ability of the risk score, which was used to construct a survival prediction model by multivariate Cox regression. The prediction model was used to build a nomogram, the discriminative ability of which was confirmed by C-index assessment, and its calibration was validated by calibration curve analysis. Differentially expressed DNA damage repair genes in LUSC patient tissues were retrieved by the Wilcoxon test and validated by qRT-PCR and IHC.

RESULT

LUSC patients were separated into two clusters based on molecular subtypes, of which Cluster 2 was associated with worse overall survival. A prognostic prediction model for LUSC patients was constructed and validated, and a risk score calculated based on the expression levels of ten DNA damage repair genes was employed. The clinical utility was evaluated by drug sensitivity and immune filtration analyses. Thirteen-one genes were upregulated in LUSC patient samples, and we selected the top four genes that were validated by RT-PCR and IHC.

CONCLUSION

We established a novel prognostic model based on DNA damage repair gene expression that can be used to predict therapeutic efficacy in LUSC patients.