Ovarian cancer is the deadliest gynecological malignancy, with around 310,000 women worldwide being diagnosed with the cancer in 2020. Resistance to chemotherapeutics is inevitable in most ovarian cancer patients, but little is known about the precise mechanisms of chemoresistance in ovarian cancer. This study aimed to uncover novel chemoresistance mechanisms using the bioinformatics tools GEO2R and DAVID by compiling a list of differentially expressed genes and then clustering them into specific expression patterns. Differential gene expression data of in vitro studies comparing paclitaxel- and cisplatin-resistant subtypes to wild-type counterparts were obtained from GSE73935 and GSE26465 and analyzed in GEO2R. A common list of 8,697 differentially expressed genes between both datasets was extracted using the dplyr package in R. The list of common genes was input into the DAVID functional annotation clustering tool. Results were considered statistically significant at the threshold of (p<0.05) and a false discovery rate under .05. The protein–protein interactions network was used in order to ascertain the molecular mechanisms of specific differentially expressed proteins in relation to the clusters they relate to. Genes that were differentially expressed fell into established patterns of chemoresistance well established in ovarian cancer, such as epithelial–mesenchymal transition and autophagy. However, expression profiles consistent with chemoresistance pathways in other cancers, but not well established in ovarian cancer, were also identified, namely pathways of transcription regulation, ubiquitin-binding, protein transport, the TGF-beta signaling pathway, cell projection, and cell polarity. These findings suggest novel chemoresistance pathways that could be targeted to resensitize chemoresistant cells to chemotherapeutics or for early prediction of chemoresistance in patients.