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Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma.

ABSTRACT: 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.

SUBMITTER: Geng W

PROVIDER: S-EPMC8039462 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description: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.

Project description:Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan–Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan–Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

Project description:A major goal in translational cancer research is to identify biological signatures driving cancer progression and metastasis. A common technique applied in genomics research is to cluster patients using gene expression data from a candidate prognostic gene set, and if the resulting clusters show statistically significant outcome stratification, to associate the gene set with prognosis, suggesting its biological and clinical importance. Recent work has questioned the validity of this approach by showing in several breast cancer data sets that "random" gene sets tend to cluster patients into prognostically variable subgroups. This work suggests that new rigorous statistical methods are needed to identify biologically informative prognostic gene sets. To address this problem, we developed Significance Analysis of Prognostic Signatures (SAPS) which integrates standard prognostic tests with a new prognostic significance test based on stratifying patients into prognostic subtypes with random gene sets. SAPS ensures that a significant gene set is not only able to stratify patients into prognostically variable groups, but is also enriched for genes showing strong univariate associations with patient prognosis, and performs significantly better than random gene sets. We use SAPS to perform a large meta-analysis (the largest completed to date) of prognostic pathways in breast and ovarian cancer and their molecular subtypes. Our analyses show that only a small subset of the gene sets found statistically significant using standard measures achieve significance by SAPS. We identify new prognostic signatures in breast and ovarian cancer and their corresponding molecular subtypes, and we show that prognostic signatures in ER negative breast cancer are more similar to prognostic signatures in ovarian cancer than to prognostic signatures in ER positive breast cancer. SAPS is a powerful new method for deriving robust prognostic biological signatures from clinically annotated genomic datasets.

Project description:BackgroundIncreasing evidence has emerged to reveal the correlation between genomic instability and long non-coding RNAs (lncRNAs). The genomic instability-derived lncRNA landscape of prostate cancer (PCa) and its critical clinical implications remain to be understood.MethodsPatients 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 from genomic instability-associated lncRNAs to classify PCa patients into high-risk and low-risk groups. The biochemical recurrence (BCR) model of a genomic instability-derived lncRNA signature (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 and receiver operating characteristic (ROC) curve in the test set and the total patient set. The regulatory network of transcription factors (TFs) and lncRNAs was established to evaluate TF-lncRNA interactions.ResultsA total of 95 genomic instability-associated lncRNAs of PCa were identified. We constructed the GILncSig based on 10 lncRNAs with independent prognostic value. GILncSig separated patients into the high-risk (n = 121) group and the 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 set, the whole TCGA cohort, and different subgroups stratified by age and Gleason score (GS). A high GILncSig risk score was significantly associated with a high mutation burden and a 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. Transcription factors, such as FOXA1, JUND, and SRF, were found to participate in the regulation of lncRNAs with prognostic value.ConclusionIn summary, we developed a prognostic signature of BCR based on genomic instability-associated lncRNAs for PCa, which may provide new insights into the epigenetic mechanism of BCR.

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Identification of the Prognostic Significance of Somatic Mutation-Derived LncRNA Signatures of Genomic Instability in Lung Adenocarcinoma.

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