A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer.
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ABSTRACT: Purpose
Radiotherapy resistance is now recognized as the major obstacle to the effective therapeutic management of non-small-cell lung cancer (NSCLC). As a single biomarker has limited effect in stratifying NSCLC patients, this research aimed to identify long non-coding RNAs (lncRNAs) correlated with radiotherapy response to ameliorate forecast of NSCLC prognosis.Methods
In a cohort of NSCLC patients with radiotherapy history (n = 96) from TCGA, genetic data of lncRNA expression profiling were performed. To identify radioresponse-related lncRNA sets which dysregulated significantly between radiosensitive (RS) and radioresistant (RR) groups, differential expression analysis was carried out. Cox relative regression was implemented to set up a radioresponse-related risk model. Moreover, we adopted survival analysis to measure the predictive potentiality of the prognosis model.Results
Four radioresponse-related lncRNAs (CASC19, LINC01977, LINC02471, and MAGI2-AS3) were screened to create a prognostic signature. Then, we described a lncRNA signature-based regulatory network and explored the correlation of the immune microenvironment and the signature. Additionally, in vitro assays uncovered inhibition of LINC01977 weakened radioresistance of NSCLC cells.Conclusion
We provided a novel radioresponse-related lncRNAs signature with excellent clinical potency for an effective prognostic forecast of patients.
SUBMITTER: Song J
PROVIDER: S-EPMC8478572 | biostudies-literature |
REPOSITORIES: biostudies-literature
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| S-EPMC9287891 | biostudies-literature
Project description:Chromatin regulators drive cancer epigenetic changes, and lncRNA can play an important role in epigenetic changes as chromatin regulators. We used univariate Cox, LASSO, and multivariate Cox regression analysis to select epigenetic-associated lncRNA signatures. Twenty-five epigenetic-associated lncRNA signatures (CELncSig) were identified to establish the immune prognostic model. According to Kaplan-Meier analysis, the overall survival of the high-risk group was significantly lower than the low-risk group. Receiver operating characteristic (ROC) curves, C-index, survival curve, nomogram, and principal component analysis (PCA) were performed to validate the risk model. In GO/KEGG analysis, differentially expressed lncRNAs were correlated with the PI3K-Akt pathway, suggesting that they were highly associated with the metastasis of LUAD. Interestingly, in the immune escape analysis, the TIDE score was lower, and the possibility of immune dysfunction is also slighter in the high-risk group, which means they still have the potential to receive immunotherapy. And CELncsig is highly correlated with immune pathways T_cell_co-inhibition and Check-point. Also, the IMvigor210 cohort analysis indicated that our risk-scoring model has significant potential clinical application value in lung cancer immunotherapy. And we also screened out ten potential chemotherapy agents using the 'pRRophetic' package.
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Project description:Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.
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Project description:Lung adenocarcinoma (LUAD) has been the major cause of tumor-associated mortality in recent years and has a poor prognosis. Pyroptosis is regulated via the activation of inflammasomes and participates in tumorigenesis. However, the effects of pyroptosis-related lncRNAs (PRlncRNAs) on LUAD have not yet been completely elucidated. Therefore, we attempted to systematically explore patterns of cell pyroptosis to establish a novel signature for predicting LUAD survival. Based on TCGA database, we set up a prognostic model by incorporating PRlncRNAs with differential expression using Cox regression and LASSO regression. Kaplan-Meier analysis was conducted to compare the survival of LUAD patients. We further simplified the risk model and created a nomogram to enhance the prediction of LUAD prognosis. Altogether, 84 PRlncRNAs with differential expression were discovered. Subsequently, a new risk model was constructed based on five PRlncRNAs, GSEC, FAM83A-AS1, AL606489.1, AL034397.3 and AC010980.2. The proposed signature exhibited good performance in prognostic prediction and was related to immunocyte infiltration. The nomogram exactly forecasted the overall survival of patients and had excellent clinical utility. In the present study, the five-lncRNA prognostic risk signature and nomogram are trustworthy and effective indicators for predicting the prognosis of LUAD.
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