A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

Abstract

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.

Identification and validation of a novel redox-related lncRNA prognostic signature in lung adenocarcinoma

“…Based on the expression profiling of the TCGA dataset, the Pearson correlation coefficients between redox-related protein-coding genes and all lncRNAs were calculated by the built-in function ‘cor.test’ in R [ 20 ]. Subsequently, lncRNAs with |correlation coefficients| >0.4 and p values <0.001 were considered as redox-related lncRNAs for further analysis [ 21 ].…”

An Immune Gene-Related Five-lncRNA Signature for to Predict Glioma Prognosis

“…We used cor.test function to detect the relationships between lncRNAs (Zhang et al, 2020a). Then, we evaluated the accuracy of the risk model using univariate, multivariate, and receiver operating characteristic (ROC) curves.…”

A Robust Panel Based on Mitochondrial Localized Proteins for Prognostic Prediction of Lung Adenocarcinoma

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