Prognosis, immune microenvironment, and personalized treatment prediction in Rho GTPase-activating protein 4-mutant cervical cancer: Computer strategies for precision oncology

Abstract

AimsCervical cancer with different mutations is associated with specific genomic differences.We developed a new mutation prediction model of the ARHGAP4 gene for cervical cancer. Main methodsWe conducted a panoramic analysis of CESC mutations based on The Cancer Genome Atlas-Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) database. We made copy number variation analysis and correlation analysis of somatic mutations and tumor mutation load fraction. Then we established a prediction model of ARHGAP4 mutation, screened related genes based on the risk scores, calculated the correlation between the risk score and immune microenvironment, and analyzed drug sensitivity. Key findingsThe prediction model of ARHGAP4 mutation based on mRNA expression is closely related to the survival rate of cervical cancer patients and to the effect of immunotherapy. The prediction model is also related to the infiltration of immune cells and human leukocyte antigen family expression in the immune microenvironment. After computational analysis, three drugs (cytarabine, docetaxel, imatinib) were identified as potential agents for the ARHGAP4 mutation high-risk group, and two drugs (erlotinib, methotrexate) were shown to have therapeutic significance for patients in the low-risk group. The expression of ARHGAP4 was higher in cervical cancer tissues. The proliferation ability of HeLa and SiHa cells decreased after ARHGAP4 knockdown. SignificanceThis study provides not only a new approach for the prediction of the response of the cervical cancer patients to targeted drug therapy but also a new strategy for combining risk stratification with precision treatment.