Dataset Information

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma.

ABSTRACT: Epigenetic factors play crucial roles in carcinogenesis by modifying chromatin architecture. Here, we established an epigenetic biosignature-based model for examining survival in patients with lung adenocarcinoma (LUAD). We retrieved gene-expression profiles and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus and clustered the data into training (n = 490) and Validation (n = 226) datasets, respectively. To establish an epigenetic model, we identified prognostic epigenetic regulation-related genes by LASSO and Cox regression analyses, and established a novel 11-gene signature, including EPC1, GADD45A, HCFC2, RCOR1, SMARCAL1, TLE2, TRIM28, and ZNF516, for predicting LUAD overall survival (OS). The biosignature performed optimally in both the training and validation sets according to receiver operating characteristic and calibration plots. Moreover, the biosignature classified patients into high- and low-risk clusters with distinct survival times, with Cox regression analysis revealing the biosignature as an independent LUAD prognostic index. Furthermore, the generated nomogram integrating the prognostic gene biosignature and clinical indices predicted LUAD OS with high efficiency and outperformed tumor-node-metastasis staging in LUAD survival prediction. These results demonstrated the efficacy of the epigenetic signature prognostic nomogram for reliably predicting LUAD OS and its potential application for informing clinical decision making and individualized treatment.

SUBMITTER: Wang J

PROVIDER: S-EPMC7746339 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma.

Wang Jiao J He Li L Tang Yunliang Y Li Dan D Yang Yuting Y Zeng Zhenguo Z

Aging 20201118 22

Epigenetic factors play crucial roles in carcinogenesis by modifying chromatin architecture. Here, we established an epigenetic biosignature-based model for examining survival in patients with lung adenocarcinoma (LUAD). We retrieved gene-expression profiles and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus and clustered the data into training (<i>n</i> = 490) and Validation (<i>n</i> = 226) datasets, respectively. To establish an epigenetic model, we identified prognost ...[more]

PMID: 33221751

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Project description:BackgroundDNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.MethodThe DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.ResultWe identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.ConclusionsThe present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.

Project description:BackgroundThe high case-fatality rate of patients with lung adenocarcinoma (LUAD) emphasizes the importance of identifying a robust and reliable prognostic signature for LUAD patients. Endoplasmic reticulum (ER) stress results from protein misfolding imbalance and has been shown to participate in the development of cancer. We aimed to develop and validation a reliable and robust ER stress-related prognostic signature to accurately predict prognosis for patients with LUAD.MethodsThe mRNA expressions data and the clinical information were downloaded from The Cancer Genome Atlas (TCGA) as training set. The data of external validation sets were downloaded from GEO database with the accession number GSE 30219, GSE 31210, GSE 50081 and GSE 37745. Univariate Cox regression analyses was performed to identify mRNAs associated with overall survival (OS) in LUAD. ER-associated genes were retrieved using GeneCards database. Next, we construct the best risk score model by the least absolute shrinkage and selection operator (LASSO) regression with tenfold cross-validation. Subsequently, predictive models and risk scores were developed in the TCGA training dataset. Cox proportional hazards regression models were used for univariate and multivariate analysis of risk score and clinicopathologic characteristics. As a validation set GSE30219, GSE31210 and (GSE50081+GSE37745) were used to validate the predictive performance of the model in TCGA. Finally, functional enrichment analysis, including the gene ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathways and gene set enrichment analysis (GSEA) were performed to further explore function and mechanisms.ResultsA prognostic prediction model based on eight genes was developed in the TCGA training dataset. As expected, in validation sets, patients with higher risk scores were found to have worse prognosis. Time-dependent ROC curve analyses demonstrated that the risk score model was reliable. The nomograms showed excellent predictive ability. Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for LUAD. Additionally, functional enrichment analysis showed that the relevant biomarkers were enriched in cell cycle and glycolysis related signaling pathways.ConclusionsThe 8-gene signature may enable improved the prediction of clinical events and decisions about management of LUAD.

Project description:BackgroundAutophagy, a highly conserved self-digesting process, has been deeply involved in the development and progression of oral squamous cell carcinoma (OSCC). However, the prognostic value of autophagy-related genes (ARGs) for OSCC still remains unclear. Our study set out to develop a multigene expression signature based on ARGs for individualized prognosis assessment in OSCC patients.MethodsBased on The Cancer Genome Atlas (TCGA) database, we identified prognosis-related ARGs through univariate COX regression analysis. Then we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to identify an optimal autophagy-related multigene signature with the subsequent validation in testing set, GSE41613 and GSE42743 datasets.ResultsWe identified 36 prognosis-related ARGs for OSCC. Subsequently, the multigene signature based on 13 prognostic ARGs was constructed and successfully divided OSCC patients into low and high-risk groups with significantly different overall survival in TCGA training set (p < 0.0001). The autophagy signature remained as an independent prognostic factor for OSCC in univariate and multivariate Cox regression analyses. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves for 1, 3, and 5-year survival were 0.758, 0.810, 0.798, respectively. Then the gene signature was validated in TCGA testing set, GSE41613 and GSE42743 datasets. Moreover, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and single-sample gene set enrichment analysis (ssGSEA) revealed the underlying biological characteristics and signaling pathways associated with this signature in OSCC. Finally, we constructed a nomogram by combining the gene signature with multiple clinical parameters (age, gender, TNM-stage, tobacco, and alcohol history). The concordance index (C-index) and calibration plots demonstrated favorable predictive performance of our nomogram.ConclusionIn summary, we identified and verified a 13-ARGs prognostic signature and nomogram, which provide individualized prognosis evaluation and show insight for potential therapeutic targets for OSCC.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature.MethodsIn this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients.ResultsWe identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P < 0.05 and |Fold change| > 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels.ConclusionOur identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Dataset Information

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma.

Publications

Development and validation of a nomogram with an epigenetic signature for predicting survival in patients with lung adenocarcinoma.

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