AUTHOR=Tang Liansha , Li Wanjiang , Xu Hang , Zheng Xiaonan , Qiu Shi , He Wenbo , Wei Qiang , Ai Jianzhong , Yang Lu , Liu Jiyan 

TITLE=Mutator-Derived lncRNA Landscape: A Novel Insight Into the Genomic Instability of Prostate Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.876531

DOI=10.3389/fonc.2022.876531

ISSN=2234-943X

ABSTRACT=Background: Increasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). Genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.
Methods: Patients diagnosed with PCa were recruited from The Cancer Genome Atlas (TCGA) program. Genomic instability-associated lncRNAs were identified by a mutator hypothesis-originated calculative approach. A signature (GILncSig) was derived by genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of GILncSig was established by Cox regression and stratified analysis in the train set. Then its prognostic value and association with clinical features were verified by Kaplan-Meier (K-M) analysis, receiver operating characteristic (ROC) curve in the test and total patients set.
Results: A total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed a genomic instability-derived lncRNA signature (GILncSig) based on ten lncRNAs with independent prognostic value. GILncSig separated patients into high-risk (n=121) group and low-risk (n=121) group in the train set. Patients with high GILncSig score suffered from more frequent BCR than those with low GILncSig score. The results were further validated in the test, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). High GILncSig risk score was significantly associated with high mutation burden and the low critical gene expression (PTEN and CDK12) in PCa. The predictive performance of our BCR model based on GILncSig outperformed other existing BCR models of PCa based on lncRNAs. The GILncSig also showed a remarkable ability to predict BCR in the subgroup of patients with TP53 mutation or wild type.
Conclusion: In summary, we developed a prognostic signature of BCR for PCa based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.