Identification and validation of m5c-related lncRNA risk model for ovarian cancer

• Main Authors: Chong Wang (Author), Chunxiao Zhang (Author), Shimin Yang (Author), Jiangdong Xiang (Author), Dongmei Zhou (Author), Xiaowei Xi (Author)
• Format: Book
• Published: BMC, 2023-05-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s13048-023-01182-6; 1757-2215
• Keywords: Gynecology and obstetrics; RG1-991
• Source: Journal of Ovarian Research, Vol 16, Iss 1, Pp 1-16 (2023)

Abstract Ovarian cancer (OC) is one of the common malignant tumors that seriously threaten women's health, and there is a lack of clinical prognostic predictors, while m5c and lncRNA have been shown to be predictive of multiple cancers, including OC. Therefore, our goal was to construct a risk model for OC based on m5c-related lncRNA.340 m5c-related lncRNA were identified and a novel risk model of OC ground on nine m5C-related lncRNA was constructed using LASSO-COX regression analysis. Kaplan-Meier analysis showed there was a significant difference in prognosis between risk groups. We established a nomogram which was a good predictor of overall survival. In addition, GSEA was enriched in multiple pathways and immune function analysis suggested that immune infiltration varies depending on the risk group. In vitro experiments show that AC005562.1, a key lncRNA of the risk model, is highly expressed in OC cells and promotes OC cell proliferation. Finally, we further explored the potential biological markers of m5c-related lncRNA in OC with WGCNA analysis and established a ceRNA network. In conclusion,we have developed a reliable m5c-related prediction model and performed systematic validation and exploration of various aspects. These results can be used for the assessment of OC prognosis and the discovery of novel biomarkers.