Table_4_Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal .xlsx (21.04 kB)
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Table_4_Development and Interpretation of a Genomic Instability Derived lncRNAs Based Risk Signature as a Predictor of Prognosis for Clear Cell Renal Cell Carcinoma Patients.xlsx

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posted on 21.05.2021, 05:23 authored by Huiying Yang, Xiaoling Xiong, Hua Li
Background

Clear cell renal cell carcinoma (ccRCC) is a kind of frequently diagnosed cancer, leading to high death rate in patients. Genomic instability (GI) is regarded as playing indispensable roles in tumorigenesis and impacting the prognosis of patients. The aberrant regulation of long non-coding RNAs (lncRNAs) is a main cause of GI. We combined the somatic mutation profiles and expression profiles to identify GI derived lncRNAs (GID-lncRNAs) in ccRCC and developed a GID-lncRNAs based risk signature for prognosis prediction and medication guidance.

Methods

We decided cases with top 25% cumulative number of somatic mutations as genomically unstable (GU) group and last 25% as genomically stable (GS) group, and identified differentially expressed lncRNAs (GID-lncRNAs) between two groups. Then we developed the risk signature with all overall survival related GID-lncRNAs with least absolute shrinkage and selection operator (LASSO) Cox regression. The functions of the GID-lncRNAs were partly interpreted by enrichment analysis. We finally validated the effectiveness of the risk signature in prognosis prediction and medication guidance.

Results

We developed a seven-lncRNAs (LINC00460, AL139351.1, AC156455.1, AL035446.1, LINC02471, AC022509.2, and LINC01606) risk signature and divided all samples into high-risk and low-risk groups. Patients in high-risk group were in more severe clinicopathologic status (higher tumor grade, pathological stage, T stage, and more metastasis) and were deemed to have less survival time and lower survival rate. The efficacy of prognosis prediction was validated by receiver operating characteristic analysis. Enrichment analysis revealed that the lncRNAs in the risk signature mainly participate in regulation of cell cycle, DNA replication, material metabolism, and other vital biological processes in the tumorigenesis of ccRCC. Moreover, the risk signature could help assess the possibility of response to precise treatments.

Conclusion

Our study combined the somatic mutation profiles and the expression profiles of ccRCC for the first time and developed a GID-lncRNAs based risk signature for prognosis predicting and therapeutic scheme deciding. We validated the efficacy of the risk signature and partly interpreted the roles of the seven lncRNAs composing the risk signature in ccRCC. Our study provides novel insights into the roles of genomic instability derived lncRNAs in ccRCC.

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References