Computational Prediction of Biological Signatures for Candidate Driver Genes Associated with Ovarian Cancer

Ovarian cancer is a prominent cause of cancer deaths among women, accounting for the most deaths of any cancer in the female reproductive system. The functions of a gene in ovarian tissue are affected by its expression level. Oncogenes and tumor suppressor genes are often differentially expressed between ovarian cancer and normal tissues. We selected 50 genes with the highest expression values based on TCGA ovarian cancer RNA-seq data as the input genes of this study. We then conducted protein-protein interaction and functional annotation analysis. The 43 interacted genes were enriched with 11 GO and 13 KEGG Pathway terms. The 22 out of 43 genes that are not highly expressed in the normal ovarian tissue, as well as 12 other genes from the list of input genes with ovarian cancer association reported from published literature, were selected for further investigation. The Multiple Sequence Comparison by Log-Expectation (MUSCLE) tool was run on those 34 genes, and all genes were associated with conserved domains. Finally, survival analysis was performed on 34 candidate genes using the Kaplan-Meier Plotter to evaluate the impact they have on ovarian cancer progression. We reported 21 genes (C3, CLU, COLIAI, COLlA2, COL3A1, CTCFL, FN1, HSPAS, IGF2, PSAP, SPARC, TUBB, ALDOA, GAPDH, HSP90AB1, PKM, RPLP0, RPLPI, RPS4, RPS6, UBC) that had domain conservation and significant p-values for survival analysis.