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Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

ABSTRACT: Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we systematically explored small RNAs in 14 EAC patients with different subtypes using small RNA sequencing. MiRNAs and tRNA-derived RNAs (tDRs) accounted for the majority of mapped reads and the total number of miRNAs and tDRs maintained a relative balance. To explore the correlations between small RNAs expression and EAC with different clinical characteristics, we performed the weighted gene co-expression network analysis (WGCNA) and screened for hub small RNAs. From the key modules, we identified 9 small RNAs that were significantly related to clinical characteristics in EAC patients. Gene ontology and pathway analyses revealed that these molecules were involved in the pathogenesis of EAC. Our work provided new insights into EAC pathogenesis and successfully identified several small RNAs as candidate biomarkers for diagnosis and prognosis of EAC.

SUBMITTER: Shi Z

PROVIDER: S-EPMC8507262 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Shi Zhiwen Z Qu Xinyu X Guo Chenyan C Zhang Lihong L Peng Chuyue C Xie Zhu Z Hua Keqin K Qiu Junjun J

Aging 20210920 18

Endocervical adenocarcinoma (EAC) is an aggressive type of endocervical cancer. At present, molecular research on EAC mainly focuses on the genome and mRNA transcriptome, the investigation of small RNAs in EAC has not been fully described. Here, we systematically explored small RNAs in 14 EAC patients with different subtypes using small RNA sequencing. MiRNAs and tRNA-derived RNAs (tDRs) accounted for the majority of mapped reads and the total number of miRNAs and tDRs maintained a relative bala ...[more]

PMID: 34542422

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Project description:RelevanceThe pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer.PurposeLong non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs.MethodsThis study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA-RNA binding protein-mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells.ResultsAn lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients' overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA networks in OCs.ConclusionThese new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.

Dataset Information

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

Publications

Identification of clinical trait-related small RNA biomarkers with weighted gene co-expression network analysis for personalized medicine in endocervical adenocarcinoma.

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