Esra Gov

A 19-Gene Signature of Serous Ovarian Cancer Identified by Machine Learning and Systems Biology: Prospects for Diagnostics and Personalized Medicine

Ovarian cancer is a major cause of cancer deaths among women. Early diagnosis and precision/personalized medicine are essential to reduce mortality and morbidity of ovarian cancer, as with new molecular targets to accelerate drug discovery. We report here an integrated systems biology and machine learning (ML) approach based on the differential coexpression analysis to identify candidate systems biomarkers (i.e., gene modules) for serous ovarian cancer. Accordingly, four independent transcriptome datasets were statistically analyzed independently and common differentially expressed genes (DEGs) were identified. Using these DEGs, coexpressed gene pairs were unraveled. Subsequently, differential coexpression networks between the coexpressed gene pairs were reconstructed so as to identify the differentially coexpressed gene modules. Based on the established criteria, “SOV-module” was identified as being significant, consisting of 19 genes. Using independent datasets, the diagnostic capacity of the SOV-module was evaluated using principal component analysis (PCA) and ML techniques. PCA showed a sensitivity and specificity of 96.7% and 100%, respectively, and ML analysis showed an accuracy of up to 100% in distinguishing phenotypes in the present study sample. The prognostic capacity of the SOV-module was evaluated using survival and ML analyses. We found that the SOV-module's performance for prognostics was significant ( p -value = 1.36 × 10 –4 ) with an accuracy of 63% in discriminating between survival and death using ML techniques. In summary, the reported genomic systems biomarker candidate offers promise for personalized medicine in diagnosis and prognosis of serous ovarian cancer and warrants further experimental and translational clinical studies.

Co-expressed functional module-related genes in ovarian cancer stem cells represent novel prognostic biomarkers in ovarian cancer

Ovarian cancer is the leading cause of death from gynecologic malignancies. Cancer stem cells (CSC) seem to play a crucial role in tumor metastasis, recurrence, and chemoresistance. Therefore, CSCs offer significant potential for developing therapeutic targets and to understand tumor recurrence and chemoresistance mechanisms. In the present study, our aim was the identification of the gene group in ovarian CSCs (O-CSCs) and the potential of the resultant gene group in ovarian cancer prognosis. Two different microarray data sets were analyzed by comparing gene expression levels between O-CSCs and cancer samples. The O-CSC co-expression network was reconstructed and its modules were identified. According to the analysis results, 74 mutual DEGs were identified. The O-CSC-specific co-expression network included 32 nodes and 95 edges (network density: 19%), while the co-expression network in cancer samples was reconstructed with 74 nodes and 1066 edges (network density: 39%). Understanding of the molecular mechanism and signatures of O-CSCs should provide valuable insight into chemotherapy resistance and recurrence of ovarian tumors. A highly connected 12 gene module in O-CSC samples of BAMB1, NFKB12, EZR, TNFAIP3, C1orf86, PMAIP1, GEM, KHDRBS3, FILIP1, FGFR2, TGFBR3 and PEG10, (network density: 67%) was identified. Prognostic performance of these genes was evaluated independently using six ovarian cancer datasets (n = 1933 patient samples) via survival analysis. These co-expressed genes were determined as prognostic targets in ovarian cancer. Through literature search validation, five genes (C1orf86, PMAIP1, FILIP1, NFKB12 and PEG10) suggested as novel molecular targets in ovarian cancer. The presented prognostic biomarkers here provide a resource for the understanding of tumor recurrence and chemoresistance and may facilitate critical research directions and development of new prognostic and therapeutic strategies for ovarian cancer. CSCs: cancer stem cells; O-CSCs: ovarian CSCs; FACS: fluorescence-activated cell sorting; SP: side population; MP: main population; TFs: transcription factors.