Prognostic validity of the gene signature

Differentially expressed genes (DEGs) involved in the circadian clock were analyzed in the tumor and tumor-adjacent normal tissues of LUAD patients from the TCGA cohort using the “limma” package in R (false discovery rate (FDR) <0.05). Univariate Cox regression analysis was used to identify circadian genes related to overall survival (OS). A gene signature for the prediction of OS was constructed with the DEGs for the circadian clock using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis and the “glmnet” package in R. DEGs served as independent variables, and OS as the response variable.

A risk score based on the expression of identified candidate genes was calculated for each patient according to the following formula: score = sum (normalized gene expression level × regression coefficient). Patients were classified as either high- or low-risk using the median score as the cut-off value. The survival analysis of different risk groups was determined with the “survminer” R package. In order to validate the performance of the signature, we used the principal components analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) to analyze dimensionality reduction. The “prcomp” function in the R “stats” package was used to carry out the PCA. The data distribution for high-risk and low-risk patients was also mapped using t-SNE and the “Rtsne” package in R. The predictive value of the gene signature was evaluated with time-dependent Receiver operating characteristics (ROC) curve analysis using the “timeROC” package in R. The associations between the risk score, clinical characteristics (gender, age, smoking history, and stage), and OS were assessed with univariate and multivariate Cox regression analyses.

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