Yutao Wang1, Kexin Yan2, Linhui Wang1, Jianbin Bi3. 1. Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China. 2. Department of Dermatology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China. 3. Department of Urology, China Medical University, The First Hospital of China Medical University, Shenyang, Liaoning, China. jianbinbi@cmu.edu.cn.
Abstract
BACKGROUND: There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear. METHOD: TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. "GSVA" package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. "Survival" package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model. RESULTS: In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model's genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway. CONCLUSION SUBSECTIONS: These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.
BACKGROUND: There is evidence that long non-coding RNA (lncRNA) is related to genetic stability. However, the complex biological functions of these lncRNAs are unclear. METHOD: TCGA - KIRC lncRNAs expression matrix and somatic mutation information data were obtained from TCGA database. "GSVA" package was applied to evaluate the genomic related pathway in each samples. GO and KEGG analysis were performed to show the biological function of lncRNAs-mRNAs. "Survival" package was applied to determine the prognostic significance of lncRNAs. Multivariate Cox proportional hazard regression analysis was applied to conduct lncRNA prognosis model. RESULTS: In the present study, we applied computational biology to identify genome-related long noncoding RNA and identified 26 novel genomic instability-associated lncRNAs in clear cell renal cell carcinoma. We identified a genome instability-derived six lncRNA-based gene signature that significantly divided clear renal cell samples into high- and low-risk groups. We validated it in test cohorts. To further elucidate the role of the six lncRNAs in the model's genome stability, we performed a gene set variation analysis (GSVA) on the matrix. We performed Pearson correlation analysis between the GSVA scores of genomic stability-related pathways and lncRNA. It was determined that LINC00460 and LINC01234 could be used as critical factors in this study. They may influence the genome stability of clear cell carcinoma by participating in mediating critical targets in the base excision repair pathway, the DNA replication pathway, homologous recombination, mismatch repair pathway, and the P53 signaling pathway. CONCLUSION SUBSECTIONS: These data suggest that LINC00460 and LINC01234 are crucial for the stability of the clear cell renal cell carcinoma genome.
Entities:
Keywords:
Computational biology; Gene set variation analysis; Genome instability; Long non-coding RNA; Risk signature
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