Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality
Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality
Ovarian cancer (OC) is a common cause of death from cancer among women worldwide, so there is a pressing need to identify factors influencing mortality. Much OC patient clinical data is now publically accessible (including patient age, cancer site stage and subtype), as are large datasets of OC gene transcription profiles. These have enabled studies correlating OC patient survival with clinical variables and with gene expression but it is not well understood how these two aspects interact to influence mortality. To study this we integrated clinical and tissue transcriptome data from the same patients available from the Broad Institute Cancer Genome Atlas (TCGA) portal. We investigated OC mRNA expression levels (relative to normal patient tissue) of 26 genes already strongly implicated in OC, assessed how their expression in OC tissue predicts patient survival then employed Cox Proportional Hazard regression models to analyse both clinical factors and transcriptomic information to determine relative risk of death associated with each factor. Multivariate analysis of combined data (clinical and gene mRNA expression) found age, ovary tumour site and cancer stage IB significantly correlated with patient survival. Univariate analysis also confirmed significant differences in patient survival time when altered transcription levels of KLK6, CD36, MEF2C and SCGB2A1 were evident, while multivariate analysis that considered the 26 genes simultaneously revealed a significant relationship of mortality with KLK6, CD36 and E2F1 genes. However, analysis that considered all 26 genes with clinical variables together identified WFDC2, E2F1, BRCA1, KLK6, SCGB2A1 and SLPI genes as independently related to mortality in OC. This indicated that the latter genes affect OC patient survival, i.e., provided mechanistic and predictive information in addition to that of the clinical traits and provide strong evidence that these genes are critical markers of processes that underlie OC progression and mortality.
Quinn Julian、Huq Fazlul、Islam Sheikh Muhammad Saiful、Hossain Md. Ali、Moni Mohammad Ali
Bone biologydivisions, Garvan Institute of Medical ResearchThe Universityof Sydney,Sydney Medical School, School of Medical Science, Discipline of Biomedical SciencesDept. of Pharmacy, Manarat International UniversityDept of CSE, Manarat International University||Dept ofCSE,Jahangirnagar UniversityBone biologydivisions, Garvan Institute of Medical Research||The Universityof Sydney,Sydney Medical School, School of Medical Science, Discipline of Biomedical Sciences
肿瘤学基础医学医学研究方法
Ovarian cancerClinical factorsGene expressionSurvival analysisRNA seqmolecular pathways
Quinn Julian,Huq Fazlul,Islam Sheikh Muhammad Saiful,Hossain Md. Ali,Moni Mohammad Ali.Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality[EB/OL].(2025-03-28)[2025-06-05].https://www.biorxiv.org/content/10.1101/473165.点此复制
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