Dataset Information

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

ABSTRACT: Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

SUBMITTER: Sheng M

PROVIDER: S-EPMC6431770 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

Sheng Mengying M Xie Xueying X Wang Jun J Gu Wanjun W

Evolutionary bioinformatics online 20190321

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 publ ...[more]

PMID: 30923439

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Project description:Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan-Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which (CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.

Project description:Previous studies have reported that the aberrant expression of circulating microRNAs (miRNAs/miRs) can be used as diagnostic and prognostic markers in non-small cell lung cancer (NSCLC). The present study aimed to assess the diagnostic and prognostic predictive values of four plasma miRNAs for NSCLC. A total of 12 candidate miRNAs were selected that have previously been reported to be aberrantly expressed in NSCLC, and their plasma levels in the training set were detected via reverse transcription-quantitative PCR analysis. The screened out miRNAs were further validated in the testing set. The area under the curve (AUC) of the receiver operating characteristic curve was constructed to evaluate diagnostic performance. Kaplan-Meier survival analysis was performed to assess the association between the plasma miRNA levels and disease-free survival (DFS) time. The results demonstrated that 4/12 plasma miRNAs (miR-210, miR-1290, miR-150 and miR-21-5p) were highly expressed in patients with NSCLC compared with their expression levels in patients with benign lung disease (BLD) and healthy controls in the training and testing sets, respectively. The AUC values of the four-miRNA panel were 0.96 and 0.93 in the training and testing sets, respectively, for distinguishing patients with NSCLC from healthy controls, which were similar to the AUC values for distinguishing patients with NSCLC from patients with BLD (0.96 and 0.94). The AUC values of the four-miRNA panel in patients with stage I NSCLC were comparable to that of patients with stage II-III NSCLC (0.942 and 0.965). Patients with high plasma levels of miR-210 and miR-150 had worse DFS than those with low plasma levels of these miRNAs. In addition, patients whose plasma levels of the four miRNAs decreased by >50% after surgery exhibited a good DFS. Taken together, the results of the present study suggest that these four miRNAs (miR-210, miR-1290, miR-150 and miR-21-5p) act as useful biomarkers for early diagnosis and prognosis of NSCLC.

Dataset Information

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

Publications

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

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