Dataset Information

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

ABSTRACT:

Background

Recurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes.

Methods

The differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas (TCGA; training dataset) were identified. Weighted correlation network analysis was performed to identify co-methylation networks. Differentially methylated genes and lncRNAs with opposite expression-methylation levels were used for the screening of prognosis-associated RNAs. The prognostic model was constructed and its performance was validated in the GSE39279 dataset.

Results

A total of 664 DERs and 981 DMRs (including 972 genes) in recurrent LUSC tissues were identified. Three co-methylation modules, including 226 differentially methylated genes, were significantly associated with LUSC. Among prognosis-associated RNAs, 18 DERs/DMRs with opposite methylation-expression levels were included in the methylation prognostic risk model. LUSC patients with high risk scores had a poor prognosis compared with patients who had low risk scores (TCGA: HR = 3.856, 95% CI [2.297-6.471]; GSE39279: HR = 3.040, 95% CI [1.435-6.437]). This model had a high accuracy in predicting the prognosis (AUC = 0.903 and 0.800, respectively), equivalent to the nomogram model inclusive of clinical variables.

Conclusions

Referring to the methylation levels of the 16-RNAs might help to predict the survival outcomes in LUSC.

SUBMITTER: Wang W

PROVIDER: S-EPMC8958968 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

Wang Weiqing W Xiang Ming M Liu Hui H Chu Xiao X Sun Zhaoyun Z Feng Liang L

PeerJ 20220324

<h4>Background</h4>Recurrence is a risk factor for the prognosis of lung squamous carcinoma (LUSC). DNA methylation levels of RNAs are also associated with LUSC prognosis. This study aimed to construct a prognostic model with high performance in predicting LUSC prognosis using the methylation levels of lncRNAs and genes.<h4>Methods</h4>The differentially expressed RNAs (DERs) and differentially methylated RNAs (DMRs) between the recurrent and non-recurrent LUSC tissues in The Cancer Genome Atlas ...[more]

PMID: 35356464

Similar Datasets

Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Project description:Lung squamous cell carcinoma (LSCC) is one of the primary types of non‑small cell lung carcinoma, and patients with recurrent LSCC usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LSCC. Methylation data on LSCC (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The Cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. Differentially methylated protein‑coding genes (DMGs)/long non‑coding RNAs (DM‑lncRNAs) between recurrence‑associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of DMGs or DM‑lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (DEGs) between the high‑ and low‑risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 DMGs and DM‑lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine‑like 1, heat shock protein 90 alpha family class A member 1, isovaleryl‑CoA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan‑Meier analysis, the DNA methylation level‑based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 DEGs between the high‑ and low‑risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10‑DMG methylation level‑based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LSCC.

Project description:BackgroundNo biomarkers have been identified for the prognosis of lung squamous cell carcinoma (LUSC). Risk models based on m6A-lncRNAs help to predict survival in some cancers. However, very few studies have reported m6A-lncRNA risk models in LUSC. We aimed to construct a prognostic model based on m6A-lncRNAs in LUSC.MethodsThe clinical and RNA-sequencing information of 504 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Prognostic m6A-lncRNAs were identified by a Pearson correlation analysis and univariate Cox regression analysis. The ConsensusClusterPlus algorithm was used to cluster the prognostic m6A-lncRNAs. The overall survival (OS) and clinicopathological characteristics of the 2 clusters were compared. A gene set enrichment analysis (GSEA) analysis was performed to analyze the genes enriched in the 2 clusters. A least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct the risk-score model. Two hundred and forty eight patients were randomly chosen from TCGA-LUSC cohort for the training set. The receiver operating characteristic (ROC) curve analysis was used to assess the predictive ability of the model. The clinical characteristics and OS in the high- and low-risk groups were compared. The independent prognostic value of the model was tested by Cox regression analyses.ResultsThirteen m6A-lncRNAs were identified as prognostic lncRNAs and classified into cluster A and cluster B. The OS of patients in cluster A was better than that of patients in cluster B (P<0.001). Patients in cluster B had higher expressions of immune checkpoints. Immune score, stromal score, and ESTIMATE score were higher in cluster B (P<0.001). Seven of the 13 lncRNAs were used to construct the risk-score model. Patients in the high-risk group had a worse OS. ROC curves showed a under the curve (AUC) of 0.639 in the training set and 0.624 in the validation set. A high risk was associated with cluster B, a high immune score, and stage III-IV disease. Patients in the high-risk group had increased expressions of immune checkpoints. The Cox regression analyses showed that the risk-score model had independent prognostic value for OS. The risk-score model retained its prognostic value in different subgroups.ConclusionsThe m6A-lncRNA risk-score model is an independent prognostic factor for OS in LUSC patients. However, the risk-score model need to be further tested clinically.

Dataset Information

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

Background

Methods

Results

Conclusions

Publications

A prognostic risk model based on DNA methylation levels of genes and lncRNAs in lung squamous cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets