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Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis.

ABSTRACT: The progression of non-small cell lung cancer (NSCLC) is linked to epithelial-mesenchymal transition (EMT), a biologic process that enables tumor cells to acquire a migratory phenotype and resistance to chemo- and immunotherapies. Discovery of novel biomarkers in NSCLC progression is essential for improved prognosis and pharmacological interventions. In the current study, we performed an integrated bioinformatics analysis on gene expression datasets of TGF-β-induced EMT in NSCLC cells to identify novel gene biomarkers and elucidate their regulation in NSCLC progression. The gene expression datasets were extracted from the NCBI Gene Expression Omnibus repository, and differentially expressed genes (DEGs) between TGF-β-treated and untreated NSCLC cells were retrieved. A protein-protein interaction network was constructed and hub genes were identified. Functional and pathway enrichment analyses were conducted on module DEGs, and a correlation between the expression levels of module genes and survival of NSCLC patients was evaluated. Prediction of interactions of the biomarker genes with transcription factors and miRNAs was also carried out. We described four protein clusters in which DEGs were associated with ubiquitination (Module 1), regulation of cell death and cell adhesions (Module 2), oxidation-reduction reactions of aerobic respiration (Module 3) and mitochondrial translation (Module 4). From the module genes, we identified ten prognostic gene biomarkers in NSCLC. Low expression levels of KCTD6, KBTBD7, LMO7, SPSB2, RNF19A, FOXA2, DHTKD1, CDH1 and PDHB and high expression level of KLHL25 were associated with reduced overall survival of NSCLC patients. Most of these biomarker genes were involved in protein ubiquitination. The regulatory network of the gene biomarkers revealed their interaction with tumor suppressor miRNAs and transcription factors involved in the mechanisms of cancer progression. This ten-gene prognostic signature can be useful to improve risk prediction and therapeutic strategies in NSCLC. Our analysis also highlights the importance of deregulation of ubiquitination in EMT-associated NSCLC progression.

SUBMITTER: Giannos P

PROVIDER: S-EPMC8615219 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80-85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein-protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.

Project description:Background: Non-small-cell lung cancer (NSCLC) remains the leading cause of cancer morbidity and mortality worldwide. In the present study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC. Methods: The microarray datasets of GSE18842, GSE30219, GSE31210, GSE32863 and GSE40791 from Gene Expression Omnibus database were downloaded. The differential expressed genes (DEGs) between NSCLC and normal samples were identified by limma package. The construction of protein-protein interaction (PPI) network, module analysis and enrichment analysis were performed using bioinformatics tools. The expression and prognostic values of hub genes were validated by GEPIA database and real-time quantitative PCR. Based on these DEGs, the candidate small molecules for NSCLC were identified by the CMap database. Results: A total of 408 overlapping DEGs including 109 up-regulated and 296 down-regulated genes were identified; 300 nodes and 1283 interactions were obtained from the PPI network. The most significant biological process and pathway enrichment of DEGs were response to wounding and cell adhesion molecules, respectively. Six DEGs (PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5) which significantly up-regulated in NSCLC tissues, were selected as hub genes according to the results of module analysis. The GEPIA database further confirmed that patients with higher expression levels of these hub genes experienced a shorter overall survival. Additionally, CMap predicted the 20 most significant small molecules as potential therapeutic drugs for NSCLC. DL-thiorphan was the most promising small molecule to reverse the NSCLC gene expression. Conclusions: Based on the gene expression profiles of 696 NSCLC samples and 237 normal samples, we first revealed that PTTG1, TYMS, ECT2, COL1A1, SPP1 and CDCA5 could act as the promising novel diagnostic and therapeutic targets for NSCLC. Our work will contribute to clarifying the molecular mechanisms of NSCLC initiation and progression.

Project description:BackgroundOne of the most frequent malignancies is lung carcinoma which poses heavy burden on the global health. The link among differentially expressed genes (DEGs) and lung cancer patients' clinical outcomes was still missing. In this study, we integrated transcriptome data with clinical data to investigate the relationship between them in lung carcinoma patients.MethodsTo begin, DEGs were identified using the Gene Expression Omnibus (GEO) gene expression pattern (GSE180347). Then, these DEGs are being searched in the TCGA database using the DEGs collected in the preceding phase. The Kaplan-Meier plotter was then used to assess the predictive value of these DEGs in patients with lung cancer.ResultsOur study revealed a total of 45 DEGs, 15 of which were up-regulated and 30 of which were down-regulated. These DEGs were mostly enriched in cytokine receptor binding and cytokine activity, according to GO enrichment analysis. These DEGs were mostly enriched in cytokine-cytokine receptor interaction, according to KEGG enrichment analysis. Based on the PPI network, which comprises of 12 DEGs, a major module was discovered. They are mostly interested in cytotoxicity mediated by natural killer cells. Among all 45 DEGs, the mutations of NCAM1 account for the most cases in TCGA database with a percentage above 15%. Among the 12 DEGs in the significant module, higher expression of FAS, GPR29, HAVCR2, and NCAM1 exhibits longer survival time with hazard ratio and 95% confident interval of 0.79 (0.69-0.89), 0.80 (0.70-0.90), 0.71 (0.60-0.84), and 0.73 (0.62-0.86), respectively. However, higher expression of FCGR3A and IFNG exhibits shorter survival time with hazard ratio and 95% confident interval of 1.50 (1.32-1.71) and 1.15 (1.02-1.31), respectively.ConclusionOur results demonstrate significant correlation between some DEGs and the survival outcome in lung adenocarcinomas patients, providing a comprehensive bioinformatics study in anticipation of future molecular mechanisms and biomarker studies.

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Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis.

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