A Mutation-Related Long Noncoding RNA Signature of Genome Instability Predicts Immune Infiltration and Hepatocellular Carcinoma Prognosis - Wu, Jianhua; Ren, Xueting; Wang, Nan; Zhou, Ruina; Chen, Mengsha; Cai, Yifan; Lin, Shuai; Zhang, Hao; Xie, Xin; Dang, Chengxue; Zhang, Shuqun; Zhou, Zhangjian (Frontiers in Genetics, 2021)
Background Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance. Methods Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan–Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications. Results A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (P < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (P < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance (area under the curve [AUC] = 0.709). Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-year survival AUC = 0.710 and 5-year survival AUC = 0.707). Conclusion The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.