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Seven protective miRNA signatures for prognosis of cervical cancer.

ABSTRACT: Cervical cancer is the second cause of cancer death in females in their 20s and 30s, but there were limited studies about its prognosis. This study aims to identify miRNA related to prognosis and study their functions. TCGA data of patients with cervical cancer were used to build univariate Cox's model with single clinical parameter or miRNA expression level. Multivariate Cox's model was built using both clinical information and miRNA expression levels. At last, STRING was used to enrich gene ontology or pathway for validated targets of significant miRNAs, and visualize the interactions among them. Using univariate Cox's model with clinical parameters, we found that two clinical parameters, tobacco use and clinical stage, and seven miRNAs were highly correlated with the survival status. Only using the expression level of miRNA signatures, the model could separate patients into high-risk and low-risk groups successfully. An optimal feature-selected model was proposed based on two clinical parameters and seven miRNAs. Functional analysis of these seven miRNAs showed they were associated to various pathways related to cancer, including MAPK, VEGF and P53 pathways. These results helped the research of identifying targets for targeted therapy which could potentially allow tailoring of treatment for cervical cancer patients.

SUBMITTER: Liu B

PROVIDER: S-EPMC5302945 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Cervical cancer (CC) is a major health threat to females, and distal metastasis is common in patients with advanced CC. Anoikis is necessary for the development of distal metastases. Understanding the mechanisms associated with anoikis in CC is essential to improve its survival rate. Methods: The expression matrix of long non-coding RNAs (lncRNAs) from cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) patients was extracted from The Cancer Genome Atlas (TCGA), and highly relevant anoikis-related lncRNAs (ARLs) were identified by the single sample gene set enrichment analysis (ssGSEA) method. ARLs-related molecular subtypes were discerned based on prognosis-related ARLs. ARLs-related prognostic risk score (APR_Score) was calculated and risk model was constructed using LASSO COX and COX models. In addition, we also assessed immune cell activity in the immune microenvironment (TME) for both subtypes and APR_Score groups. A nomogram was utilized for predicting improved clinical outcome. Finally, this study also discussed the potential of ARLs-related signatures in predicting response to immunotherapy and small molecular drugs. Results: Three ARLs-related subtypes were identified from TCGA-CESC (AC1, AC2, and AC3), with AC3 patients having the highest ARG scores, higher angiogenesis scores, and the worst prognosis. AC3 had lower immune cell scores in TME but higher immune checkpoint gene expression and higher potential for immune escape. Next, we constructed a prognostic risk model consisting of 7-ARLs. The APR_Score exhibited a greater robustness as an independent prognostic indicator in predicting prognosis, and the nomogram was a valuable tool for survival prediction. ARLs-related signatures emerged as a potential novel indicator for immunotherapy and small molecular drug selection. Conclusion: We firstly constructed novel ARLs-related signatures capable of predicting prognosis and offered novel ideas for therapy response in CC patients.

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Seven protective miRNA signatures for prognosis of cervical cancer.

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