Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality
| Autor: | Sheikh Muhammad Saiful Islam, Fazlul Huq, M. A. Hossain, Julian M.W. Quinn, Mohammad Ali Moni |
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| Rok vydání: | 2019 |
| Předmět: |
Multivariate analysis
Datasets as Topic Health Informatics Bioinformatics CDH1 Transcriptome Machine Learning 03 medical and health sciences 0302 clinical medicine Gene expression medicine Humans Computer Simulation 030212 general & internal medicine Gene Survival analysis 030304 developmental biology Ovarian Neoplasms 0303 health sciences Univariate analysis biology business.industry Computational Biology medicine.disease Survival Analysis Computer Science Applications Gene Expression Regulation Neoplastic biology.protein Disease Progression Female Ovarian cancer business |
| Zdroj: | Journal of biomedical informatics. 100 |
| ISSN: | 1532-0480 |
| Popis: | Ovarian cancer (OC) is a common cause of cancer death among women worldwide, so there is a pressing need to identify factors influencing OC mortality. Much OC patient clinical data is publicly accessible via the Broad Institute Cancer Genome Atlas (TCGA) datasets which include patient age, cancer site, stage and subtype and patient survival, as well as OC gene transcription profiles. These allow studies correlating OC patient survival (and other clinical variables) with gene expression to identify new OC biomarkers to predict patient mortality. We integrated clinical and tissue transcriptome data from patients available from the TCGA portal. We determined OC mRNA expression levels (compared to normal ovarian tissue) of 41 genes already implicated in OC progression, and assessed how their OC tissue expression levels predicts patient survival. We employed Cox Proportional Hazard regression models to analyse clinical factors and transcriptomic information to determine the relative effects on survival that is associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age and ovary tumour site significantly correlated with patient survival. The univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of TLR4, BSCL2, CDH1, ERBB2, and SCGB2A1 were evident, while multivariate analysis that considered the 41 genes simultaneously revealed a significant relationship of survival with TLR4, BSCL2, CDH1, ERBB2 and PTPRE genes. However, analyses that considered all 41 genes with clinical variables together identified genes TLR4, BSCL2, CDH1, ERBB2, BRCA2 and SCGB2A1 as independently related to survival in OC. These studies indicate that the latter genes influence OC patient survival, i.e., expression levels of these genes provide mechanistic and predictive information in addition to that of the clinical traits. Our study provides strong evidence that these genes are important prognostic indicators of patient survival that give clues to biological processes that underlie OC progression and mortality. |
| Databáze: | OpenAIRE |
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