The bioinformatics analysis of RIOX2 gene in lung adenocarcinoma and squamous cell carcinoma.
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ABSTRACT:
SUBMITTER: Sun B
PROVIDER: S-EPMC8638848 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundLung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration, and biological function of different exportins in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), as well as their relationship with the prognosis of patients with LUAD and LUSC have not been fully clarified.MethodsTo analyze the differential expression, prognostic value, genetic variation, biological function, and immune cell infiltration of exportins in patients with LUAD and LUSC, the ONCOMINE; UALCAN; Human Protein Atlas (HPA); Kaplan-Meier plotter; cBioPortal; Search Tool for the Retrieval of Interacting Genes/Proteins (STRING); Database for Annotation, Visualization, and Integrated Discovery (DAVID); Tumor Immune Estimation Resource (TIMER); and LinkedOmics databases were used in this study.ResultsThe transcriptional and protein expression levels of CSE1L and XPO1/5/6/7 were increased in patients with LUAD and LUSC, and the increased transcriptional levels of CSE1L and XPO5/6/7 were related to worse prognosis. An increased transcriptional level of XPO1 was associated with a better prognosis. These results indicated that CSE1L and XPO1/5/6/7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Moreover, the high mutation rate of exportins in non-small cell lung cancer was 50.48%, and the largest proportion of mutations included high messenger RNA expression. The expression of exportins was significantly correlated with the infiltration of various immune cells. Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of microRNAs and transcription factor E2F1.ConclusionsOur study provides novel insights into the selection of prognostic biomarkers of exportins in LUAD and LUSC.
| S-EPMC10183495 | biostudies-literature
Project description:Lung cancer is a highly prevalent type of cancer with a poor 5-year survival rate of about 4-17%. Eighty percent lung cancer belongs to non-small-cell lung cancer (NSCLC). For a long time, the treatment of NSCLC has been mostly guided by tumor stage, and there has been no significant difference between the therapy strategy of lung adenocarcinoma (LUAD) and squamous cell lung carcinoma (SCLC), the two major subtypes of NSCLC. In recent years, important molecular differences between LUAD and SCLC are increasingly identified, indicating that targeted therapy will be more and more histologically specific in the future. To investigate the LUAD and SCLC difference on multi-omics scale, we analyzed the methylation and gene expression data together. With the Boruta method to remove irrelevant features and the MCFS (Monte Carlo Feature Selection) method to identify the significantly important features, we identified 113 key methylation features and 23 key gene expression features. HNF1B and TP63 were found to be dysfunctional on both methylation and gene expression levels. The experimentally determined interaction network suggested that TP63 may play an important role in connecting methylation genes and expression genes. Many of the discovered signature genes have been supported by literature. Our results may provide directions of precision diagnosis and therapy of LUAD and SCLC.
| S-EPMC7019569 | biostudies-literature
Project description:Background: Lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD) are the two major subtypes of non-small cell lung cancer that pose a serious threat to human health. However, both subtypes currently lack effective indicators for early diagnosis. Methods: To identify tumor-specific indicators and predict cancer-related signaling pathways, LUSC and LUAD gene weighted co-expression networks were constructed. Combined with clinical data, core genes in LUSC and LUAD modules were then screened using protein-protein interaction networks and their functions and pathways were analyzed. Finally, the effect of core genes on survival of LUSC and LUAD patients was evaluated. Results: We identified 12 network modules in LUSC and LUAD, respectively. LUSC modules “purple” and “green” and LUAD modules “brown” and “pink” are significantly associated with overall survival and clinical traits of tumor node metastasis, respectively. Eleven genes from LUSC and eight genes from LUAD were identified as candidate core genes, respectively. Survival analysis showed that high expression of SLIT3, ABI3BP, MYOCD, PGM5, TNXB, and DNAH9 are associated with decreased survival in LUSC patients. Furthermore, high expression of BUB1, BUB1B, TTK, and UBE2C are associated with lower patient survival. Conclusions: We found biomarker genes and biological pathways for LUSC and LUAD. These network hub genes are associated with clinical characteristics and patient outcomes and they may play important roles in LUSC and LUAD.
| S-EPMC9704934 | biostudies-literature
Project description:Accumulating evidence indicates that immunotherapy helped to improve the survival and quality-of-life of patients with lung adenocarcinoma (LUAD) or lung squamous cell carcinoma (LUSC) besides chemotherapy and gene targeting treatment. This study aimed to develop immune-related gene signatures in LUAD and LUSC subtypes, respectively. LUAD and LUSC samples were divided into high- and low-abundance groups of immune cell infiltration (Immunity_H and Immunity_L) based on the abundance of immune cell infiltrations. The distribution of immune cells was significantly different between the high- and low-immunity subtypes in LUAD and LUSC samples. The differentially expressed genes (DEGs) between those two groups in LUAD and LUSC contain some key immune-related genes, such as PDL1, PD1, CTLA-4, and HLA families. The DEGs were enriched in multiple immune-related pathways. Furthermore, the seven-immune-related-gene-signature (CD1B, CHRNA6, CLEC12B, CLEC17A, CLNK, INHA, and SLC14A2) prognostic model-based high- and low-risk groups were significantly associated with LUAD overall survival and clinical characteristics. The eight-immune-related-gene-signature (C4BPB, FCAMR, GRAPL, MAP1LC3C, MGC2889, TRIM55, UGT1A1, and VIPR2) prognostic model-based high- and low-risk groups were significantly associated with LUSC overall survival and clinical characteristics. The prognostic models were tested as good ones by receiver operating characteristic, principal component analysis, univariate and multivariate analysis, and nomogram. The verifications of these two immune-related-gene-signature prognostic models showed consistency in the train and test cohorts of LUAD and LUSC. In addition, patients with LUAD in the low-risk group responded better to immunotherapy than those in the high-risk group. This study revealed two reliable immune-related-gene-signature models that were significantly associated with prognosis and tumor microenvironment cell infiltration in LUAD and LUSC, respectively. Evaluation of the integrated characterization of multiple immune-related genes and pathways could help to predict the response to immunotherapy and monitor immunotherapy strategies.
| S-EPMC8649721 | biostudies-literature
Project description:BackgroundTumor stem cells play important roles in the survival, proliferation, metastasis and recurrence of tumors. We aimed to identify new prognostic biomarkers for lung squamous cell carcinoma (LUSC) based on the cancer stem cell theory.MethodsRNA-seq data and relevant clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify significant modules and hub genes, and prognostic signatures were constructed with the prognostic hub genes.ResultsLUSC patients in the TCGA database have higher mRNA expression-based stemness index (mRNAsi) in tumor tissue than in adjacent normal tissue. In addition, some clinical features and outcomes were highly correlated with the mRNAsi. WGCNA revealed that the pink and yellow modules were the most significant modules related to the mRNAsi; the top 10 hub genes in the pink module were enriched mostly in epidermal development, the secretory granule membrane, receptor regulator activity and the cytokine-cytokine receptor interaction. The protein-protein interaction (PPI) network revealed that the top 10 hub genes were significantly correlated with each other at the transcriptional level. In addition, the top 10 hub genes were all highly expressed in LUSC, and some were differentially expressed in different TNM stages. Regarding the survival analysis, the nomogram of a prognostic signature with three hub genes showed high predictive value.ConclusionmRNAsi-related hub genes could be a potential biomarker of LUSC.
| S-EPMC7247832 | biostudies-literature
Project description:Lung squamous cell carcinoma (LUSC) is often diagnosed at the advanced stage with poor prognosis. The mechanisms of its pathogenesis and prognosis require urgent elucidation. This study was performed to screen potential biomarkers related to the occurrence, development and prognosis of LUSC to reveal unknown physiological and pathological processes. Using bioinformatics analysis, the lung squamous cell carcinoma microarray datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were analyzed to identify differentially expressed genes (DEGs). Furthermore, PPI and WGCNA network analysis were integrated to identify the key genes closely related to the process of LUSC development. In addition, survival analysis was performed to achieve a prognostic model that accomplished good prediction accuracy. Three hundred and thirty-seven up-regulated and 119 down-regulated genes were identified, in which four genes have been found to play vital roles in LUSC development, namely CCNA2, AURKA, AURKB, and FEN1. The prognostic model contained 5 genes, which were all detrimental to prognosis. The AUC of the established prognostic model for predicting the survival of patients at 1, 3, and 5 years was 0.692, 0.722, and 0.651 in the test data, respectively. In conclusion, this study identified several biomarkers of significant interest for additional investigation of the therapies and methods of prognosis of lung squamous cell carcinoma.
| S-EPMC7215920 | biostudies-literature
Project description:Long non-coding RNAs (lncRNAs) have been implicated in pathogenesis of various cancers, including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). We used cBioPortal to analyze lncRNA alteration frequencies and their ability to predict overall survival (OS) using 504 LUSC and 522 LUAD samples from The Cancer Genome Atlas (TCGA) database. In LUSC, 624 lncRNAs had alteration rates > 1% and 64 > 10%. In LUAD 625 lncRNAs had alteration rates > 1% and 36 > 10%. Among those, 620 lncRNAs had alteration frequencies > 1% in both LUSC and LUAD, while 22 were LUSC-specific and 23 were LUAD-specific. Twenty lncRNAs had alteration frequencies > 10% in both LUSC and LUAD, while 44 were LUSC-specific and 16 were LUAD specific. Genome ontology and pathway analyses produced similar results for LUSC and LUAD. Two lncRNAs (IGF2BP2-AS1 and DGCR5) correlated with better OS in LUSC, and three (MIR31HG, CDKN2A-AS1 and LINC01600) predicted poor OS in LUAD. Chip-seq and luciferase reporter assays identified potential IGF2BP2-AS1, DGCR5 and LINC01600 promoters and enhancers. This study presented lncRNA landscapes and revealed differentially expressed, highly altered lncRNAs in LUSC and LUAD. LncRNAs that act as oncogenes and lncRNA-regulating transcription factors provide novel targets for anti-lung cancer therapeutics.
| S-EPMC5421846 | biostudies-literature
Project description:The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.
| S-EPMC6781567 | biostudies-literature
Project description:BackgroundLung squamous cell carcinoma (LUSC) is one of the most common types of lung carcinoma and has specific clinicopathologic characteristics. In this study, we screened novel molecular biomarkers relevant to the prognosis of LUSC to explore new diagnostic and treatment approaches for this disease.MethodsWe downloaded GSE73402 from the Gene Expression Omnibus (GEO) database. GSE73402 contains 62 samples, which could be classified as four subtypes according to their pathology and stages. Via weighted gene coexpression network analysis (WGCNA), the main module was identified and was further analyzed using differentially expressed genes (DEGs) analysis. Then, by protein-protein interaction (PPI) network and Gene Expression Profiling Interactive Analysis (GEPIA), hub genes were screened for potential biomarkers of LUSC.ResultsVia WGCNA, the yellow module containing 349 genes was identified, and it is strongly related to the subtype of CIS (carcinoma in situ). DEGs analysis detected 180 genes that expressed differentially between the subtype of CIS and subtype of early-stage carcinoma (Stage I and Stage II). A PPI network of DEGs was constructed, and the top 20 genes with the highest correlations were selected for GEPIA database to explore their effect on LUSC survival prognosis. Finally, ITGA5, TUBB3, SCNN1B, and SERPINE1 were screened as hub genes in LUSC.ConclusionsITGA5, TUBB3, SCNN1B, and SERPINE1 may have great diagnostic and prognostic significance for LUSC and have great potential to be new treatment targets for LUSC.
| S-EPMC8519687 | biostudies-literature
Project description:PurposeThere is plenty of evidence showing that autophagy plays an important role in the biological process of cancer. The purpose of this study was to establish a novel autophagy-related prognostic marker for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).MethodsThe mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by using a univariate Cox proportional regression model to select candidate autophagy-related prognostic genes. Bioinformatics analysis of gene function using the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) platforms was performed. A multivariate Cox proportional regression model helped to develop a prognostic signature from the pool of candidate genes. On the basis of this prognostic signature, we could divide LUAD and LUSC patients into high-risk and low-risk groups. Further survival analysis demonstrated that high-risk patients had significantly shorter disease-free survival (DFS) than low-risk patients. The signature which contains six autophagy-related genes (EIF4EBP1, TP63, BNIP3, ATIC, ERO1A and FADD) showed good performance for predicting the survival of LUAD and LUSC patients by having a better Area Under Curves (AUC) than other clinical parameters. Its efficacy was also validated by data from the Gene Expression Omnibus (GEO) database.ConclusionCollectively, the prognostic signature we proposed is a promising biomarker for monitoring the outcomes of LUAD and LUSC.
| S-EPMC6953332 | biostudies-literature