- 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. - 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. - 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.. - The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. - To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.. - The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.. - Long non-coding RNA (lncRNA) predom- inate. - The samples with the number of somatic mutations in the top 25% were defined as the genomic unstable (GU)-like group. - The samples with the number of somatic mutations in the bottom 25% were defined as the genomically stable (GS)-like group. - We downloaded the GSVA score from the molecular signatures database (http://software.broadinstitute.org/gsea/msigdb) to con- struct the gene set. - We performed multivariate Cox proportional hazard regression analysis to evaluate the weighting co- efficient in the risk signature. - Differences in long non-coding RNAs. - We obtained two clustering results, and the number of somatic mutations in the two groups was significantly different (Fig. - Next, we compared the expression levels of the genomic instability driver ubiquilin4 (UBQLN4) in the GS-like and the GU-like groups (Fig.. - We found that the expression of UBQLN4 was significantly up-regulated in the genetically unstable Table 1 lncRNAs related to genetic instability. - ZNF582-AS E-10 1.32E-07. - LINC E-10 1.32E-07. - lncRNA Long non-coding RNAs. - We then analyzed the function of the mRNAs in the co-expression module to determine the associated biological processes. - Differnent expression long non-coding RNA. - 0.10 0.05 qvalue. - We constructed a multivariate Cox proportional hazard regression model for ccRCC in the training set based on 26 genomic stable state-related lncRNAs. - The coefficients of the risk fac- tors in the model are shown in Table 2. - Risk scores for each sample in the training and test sets were calculated using the GILncSig method. - patients in the higher risk group had a risk score >. - In TCGA-KIRC cohort, we found that patients in the low-risk group had bet- ter clinical outcomes (Fig. - Patients in the low-risk group in the training set (Fig. - 2 (A) Difference analysis of the group that Somatic cell mutations are in the top 25% between the group that Somatic cell mutations are in the last 25% in RCC. - differences in MSH2 and RFC1 expression patterns be- tween the samples in the high- and low-risk groups (Fig. - Expression levels of MSH2 in the low-risk group were significantly higher than those of the high-risk group (P <. - We found that subgroups of patients in the low-risk group achieve better outcomes (Fig. - In the biological process, the network is mainly enriched in the monovalent inorganic homeostasis. - In the cellular component, the network is mainly enriched in apical part of cell and apical plasma membrane. - In the molecular function, the network is mainly enriched in monovalent inorganic cation transmembrane transporter activity and receptor ligand activity. - Tumor mutation landscapes in high- and low-risk groups To compare mutations in the high- and low-risk groups, we drew a panorama of mutations in the two groups (Fig. - A total of 88.24% of the samples had mutations in the low-risk group. - In the all set, train set and test set, patients in the low-risk group had a better prognosis than those in the high-risk group ( P <. - 5 (A-C) The previously reported genetic instability related factor MSH2 showed significant differences in expression patterns between high- risk group and low-risk group in the all set ( P = 9.1e-05), train set ( P = 0.0059) and test set ( P = 0.0057). - (D-F) The previously reported genetic instability related factor RFC1 showed significant differences in expression patterns between high-risk group and low-risk group in the all set ( P = 6.8e-07), train set ( P = 0.0066) and test set ( P = 1.8E-05). - (A) In the low-risk group, the mutation rate was 88.24%. - (B) In the high-risk group, the mutation rate was 84.62%. - It is crucial to ensure that a set of effective mechanisms is formed in the cell. - We found that six lncRNAs in the model could be used as independent prognostic markers for renal cancer. - Accord- ing to our previous description, the six lncRNAs in the model should be closely related to these processes.. - After a careful literature search, we found that the bio- logical process of LINC00460 and LINC01234 in the GILncSig has not been reported to date. - The online version contains supplementary material available at https://doi.. - org/10.1186/s . - The datasets analysed during the current study are available in the TCGA repository, [https://portal.gdc.cancer.gov. - 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