RESUMO
Hepatocellular carcinoma is one of the most malignant tumors, and the therapeutic effects of traditional treatments are poor. It is urgent to explore and identify new biomarkers and therapeutic targets to develop novel treatments which are individualized and effective. Three hallmarks, including E2F targets, G2M checkpoint and DNA repair, were collected by GSEA analysis. The panel of E2F-related gene signature consisted of five genes: HN1, KIF4A, CDCA3, CDCA8 and SSRP1. They had various mutation rates ranging from 0.8 to 5% in hepatocellular carcinoma, and patients with gene mutation had poorer prognosis. Among these genes, HN1 has the greatest mutation rate, and SSRP1 has the greatest impact on the model with a B (COX) value of 0.8842. Patients with higher expression of these genes had poorer prognosis. Kaplan-Meier curves in stratified survival analysis confirmed that patients with high risk scores had poor prognosis (p < 0.05). The results of univariate and multivariate COX survival analysis showed that risk score was closely related to the overall survival of patients with hepatocellular carcinoma. For clinical validation, we found that all the genes in the model were upregulated in hepatocellular carcinoma tissues compared to normal liver tissues, which was consistent with the previous results we obtained. Our study demonstrated that a panel of E2F target genes signature including five genes could predict the prognosis of hepatocellular carcinoma. This panel gene signature can facilitate the development of individualized and effective treatment for hepatocellular carcinoma.
RESUMO
Background: Ovarian cancer (OV) is the leading cause of death in gynecological cancer. The dysregulation of N6-methyladenosine (m6A) modification is commonly found in cancers. However, there is a lack of research into m6A RNA methylation regulators in OV. Methods: The RNA-Seq of 379 OV tissues and 88 healthy ovarian tissues was downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, respectively. A Gene Ontology (GO) functional analysis was performed to verify the function of m6A RNA methylation regulators. Kaplan-Meier (K-M) curves and the log-rank (Mantel-Cox) test were used for the survival analysis. A Cox regression analysis was used to identify the genes related to overall survival (OS) and build the prediction model. Results: m6A RNA methylation regulators were dysregulated in OV tissues compared with normal tissues (P<0.05), and patients with a high expression of KIAA1429 and YTHDC2 had a poor prognosis (P<0.05). A prognostic model was constructed based on the m6A RNA methylation regulators. Based on the risk signature, the patients were classified into high- and low-risk groups. The low-risk group's OS rate was significantly better than that of the high-risk group. The validity and accuracy of the prognostic model were verified by using TCGA and Gene Expression Omnibus (GEO) datasets, and the risk score from the prognostic model acted as an independent prognostic indicator in predicting the survival of OV patients. Conclusions: m6A RNA methylation regulators were dysregulated in OV tissues. More importantly, the prognostic model comprising the five selected m6A RNA methylation regulators could be a valuable tool for predicting the prognosis of OV patients.
