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A Somatic Mutation-Derived LncRNA Signature of Genomic Instability Predicts Prognosis for Patients With Liver Cancer.

ABSTRACT: Background: Genomic instability is considered as one of the hallmarks of hepatocellular carcinoma (HCC) and poses a significant challenge to the clinical treatment. The emerging evidence has revealed the roles of long non-coding RNAs (lncRNAs) in the maintenance of genomic instability. This study is aimed to develop a genomic instability-related lncRNA signature for determining HCC prognosis and the suitability of patients for immunotherapy. Methods: In this study, data related to transcriptome profiling, clinical features, and the somatic mutations of patients with HCC were downloaded from The Cancer Genomic Atlas (TCGA). Bioinformatics analysis was performed to identify and construct a somatic mutation-derived genomic instability-associated lncRNA signature (GILncSig). Single-sample gene set enrichment analysis (ssGSEA) was applied to estimate the levels of immune cell infiltration. A nomogram was constructed, and calibration was performed to assess the effectiveness of the model. Results: In the study, seven genomic instability-related lncRNAs were identified and used to define a prognostic signature. Patients with HCC were stratified into high- and low-risk groups with significant differences in the survival (median survival time = 1.489, 1.748 year; p = 0.006) based on the optimal cutoff value (risk score = 1.010) of the risk score in the training group. In addition, GILncSig was demonstrated to be an independent risk factor for the patients with HCC when compared to the clinical parameters (p < 0.001). According to the receiver operating characteristic (ROC) curve, nomogram, and calibration plot, the signature could predict the survival rate for the patients with HCC in the 1st, 3rd, and 5th years. Furthermore, ssGSEA revealed the potential of the signature in guiding decisions for administering clinical treatment. Conclusions: In this study, we developed a novel prognostic model based on the somatic mutation-derived lncRNAs and validated it using an internal dataset. The independence of the GILncSig was estimated using univariate and follow-up multivariate analyses. Immunologic analysis was used to evaluate the complex factors involved in the HCC progression.

SUBMITTER: Guo C

PROVIDER: S-EPMC8421795 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Lung adenocarcinoma (LUAD) is a highly heterogeneous tumor with substantial somatic mutations and genome instability, which are emerging hallmarks of cancer. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in genomic instability. However, the identification of genome instability-related lncRNAs (GInLncRNAs) and their clinical significance has not been investigated in LUAD. Methods: We determined GInLncRNAs by combining somatic mutation and transcriptome data of 457 patients with LUAD and probed their potential function using co-expression network and Gene Ontology (GO) enrichment analyses. We then filtered GInLncRNAs by Cox regression and LASSO regression to construct a genome instability-related lncRNA signature (GInLncSig). We subsequently evaluated GInLncSig using correlation analyses with mutations, external validation, model comparisons, independent prognostic significance analyses, and clinical stratification analyses. Finally, we established a nomogram for prognosis prediction in patients with LUAD and validated it in the testing set and the entire TCGA dataset. Results: We identified 161 GInLncRNAs, of which seven were screened to develop a prognostic GInLncSig model (LINC01133, LINC01116, LINC01671, FAM83A-AS1, PLAC4, MIR223HG, and AL590226.1). GInLncSig independently predicted the overall survival of patients with LUAD and displayed an improved performance compared to other similar signatures. Furthermore, GInLncSig was related to somatic mutation patterns, suggesting its ability to reflect genome instability in LUAD. Finally, a nomogram comprising the GInLncSig and tumor stage exhibited improved robustness and clinical practicability for predicting patient prognosis. Conclusion: Our study identified a signature for prognostic prediction in LUAD comprising seven lncRNAs associated with genome instability, which may provide a useful indicator for clinical stratification management and treatment decisions for patients with LUAD.

Project description:BackgroundGenomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.MethodsIntegrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.ResultsA computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.ConclusionIn summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

Project description: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-years survival AUC = 0.710 and 5-years 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.

Project description:Numerous pan-genomic studies identified alterations in protein-coding genes and signaling pathways involved in bladder carcinogenesis, while non-coding somatic alterations remain weakly explored. The goal of this study was to identify clinical biomarkers in non-coding regions for bladder cancer patients. We have previously identified in bladder tumors two non-coding mutational hotspots occurring at high frequencies (≥30%). These mutations are located close to the GPR126 and PLEKHS1 genes, at the guanine or the cytosine of a TGAACA core motif flanked, on both sides, by a stretch of palindromic sequences. Here, we hypothesize that such a pattern of recurrent non-coding mutations could be a signature of somatic genomic instability specifically involved in bladder cancer. We analyzed 26 additional mutable non-coding sites with the same core motif in a cohort of 103 bladder cancers composed of 44 NMIBC cases and 59 MIBC cases using high-resolution melting (HRM) and Sanger sequencing. Five bladder cancers were additionally analyzed for protein-coding gene mutations using a targeted NGS panel composed of 571 genes. Expression levels of three members of the APOBEC3 family genes were assessed using real-time quantitative RT-PCR. Non-coding somatic mutations were observed for at least one TGAACA core motif locus in 62.1% (64/103) of bladder tumor samples. These non-coding mutations co-occurred in the bladder tumors but were absent in prostate tumor, HPV-positive Head and Neck Squamous Cell Carcinoma, and high microsatellite instability (MSI-H) colorectal tumor series. This signature of palindromic non-coding somatic mutations, specific to bladder tumors, was not associated with patients' outcome and was more frequent in females. Interestingly, this signature was associated with high tumor mutational burden (TMB) and high expression levels of APOBEC3B and interferon inducible genes. We identified a new type of somatic genomic instability targeting the TGAACA core motif loci flanked by palindromic sequences in bladder cancer. This mutational signature is a promising candidate clinical biomarker for the early detection of relapse and a major low-cost alternative to the TMB to monitor the response to immunotherapy for bladder cancer patients.

Project description:BackgroundGenomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.MethodsThe present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.ResultsResults of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.ConclusionsIn conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

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A Somatic Mutation-Derived LncRNA Signature of Genomic Instability Predicts Prognosis for Patients With Liver Cancer.

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