A large quantity of M2-polarized tumor-associated macrophages (TAMs) is present in the tissue, ascitic fluid and peritoneum of ovarian cancer patients. A thorough understanding of the roles of M2-TAM in the development of ovarian cancer may provide new insight into the treatment of this disease. The rapid advancement of omics techniques presents a great challenge to biologists to extract meaningful biological information from vast pools of data. In the present study, using microarray method, we identified 996 genes in SKOV3 ovarian carcinoma cells that underwent expression level changes under the influence of TAMs. Subsequently, based on the protein-protein interactions network and the differentially expressed genes, a network showing the influence of TAMs on SKOV3 cells was constructed. The resulting network was analyzed with CFinder software and four modules were found; these modules were further analyzed using David software to perform functional annotations. It was found that module I was mainly related to tumorigenesis and cell cycle. Hence, 31 genes in module I were analyzed with Cytoscape software to generate a gene-function network, which revealed that four gene proteins (E2F1, RB1, CDK2 and CDK4) were functional. Based on literature review, we postulated that CDK2 and CDK4 were key players in the network. In the subsequent molecular experiments, western blot analysis and kinase activity detection demonstrated that TAMs can significantly boost the expression levels and activities of CDK2 and CDK4 in SKOV3 cells. With 3H-TdR incorporation and flow cytometry assay, the proliferation and cell cycle distribution of SKOV3 cells were detected in the absence or presence of CDK2 and CDK4 inhibitors and the results confirmed that the two kinases played a key role in TAM cells enhancing SKOV3 cell proliferation by promoting G0/G1 to S transition. In the present study, we identified the specific changes in the gene expression profile of SKOV3 cells under the influence of TAMs and explored a method for analyzing the gene expression profile data. The results may aid in the design of subsequent molecular experiments.