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. 2021 Jul 8:9:e11733.
doi: 10.7717/peerj.11733. eCollection 2021.

Identification of a circadian gene signature that predicts overall survival in lung adenocarcinoma

Xinliang Gao  1 , Mingbo Tang  1 , Suyan Tian  2 , Jialin Li  1 , Wei Liu  1
Affiliations

Identification of a circadian gene signature that predicts overall survival in lung adenocarcinoma

Xinliang Gao et al. PeerJ. .

Abstract

Background: Lung adenocarcinoma (LUAD) is one of the most common subtypes of lung cancer which is the leading cause of death in cancer patients. Circadian clock disruption has been listed as a likely carcinogen. However, whether the expression of circadian genes affects overall survival (OS) in LUAD patients remains unknown. In this article, we identified a circadian gene signature to predict overall survival in LUAD.

Methods: RNA sequencing (HTSeq-FPKM) data and clinical characteristics were obtained for a cohort of LUAD patients from The Cancer Genome Atlas (TCGA). A multigene signature based on differentially expressed circadian clock-related genes was generated for the prediction of OS using Least Absolute Shrinkage and Selection Operator (LASSO)-penalized Cox regression analysis, and externally validated using the GSE72094 dataset from the GEO database.

Results: Five differentially expressed genes (DEGs) were identified to be significantly associated with OS using univariate Cox proportional regression analysis (P < 0.05). Patients classified as high risk based on these five DEGs had significantly lower OS than those classified as low risk in both the TGCA cohort and GSE72094 dataset (P < 0.001). Multivariate Cox regression analysis revealed that the five-gene-signature based risk score was an independent predictor of OS (hazard ratio > 1, P < 0.001). Receiver operating characteristic (ROC) curves confirmed its prognostic value. Gene set enrichment analysis (GSEA) showed that Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to cell proliferation, gene damage repair, proteasomes, and immune and autoimmune diseases were significantly enriched.

Conclusion: A novel circadian gene signature for OS in LUAD was found to be predictive in both the derivation and validation cohorts. Targeting circadian genes is a potential therapeutic option in LUAD.

Keywords: Circadian clock; Gene signature; Lung adenocarcinoma; Overall survival; TCGA.

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Conflict of interest statement

The authors declare there are no competing interests.

Figures

Figure 1
Figure 1. Identification of the candidate genes involved in the circadian cycle in the TCGA cohort.
(A) Venn diagram of DEGs and prognostic genes that correlate with OS in tumor and tumor-adjacent normal tissue. (B) Forest plots of the five genes that overlap between DEGs and prognostic genes that relate to OS on univariate Cox regression analysis. (C) The mRNA heatmap of five candidate genes. (D–H) The expression of five candidate genes in tumor and normal tissue.
Figure 2
Figure 2. Analysis of the prognostic value of the five-gene-signature based risk score in the TCGA cohort.
(A) The distribution of the risk scores in the TCGA cohort. (B) The distributions of OS status, OS time and risk score in the TCGA cohort. (C) PCA plot of the TCGA cohort. (D) t-SNE analysis of the TCGA cohort. (E) Kaplan–Meier curves for the OS of patients in the high- respective low-risk group in the TCGA cohort. (F) AUC of time-dependent ROC curves that confirm the prognostic performance of the risk score in the TCGA cohort.
Figure 3
Figure 3. Validation of the five-gene-signature based risk score in the GSE72094 dataset.
(A) The distribution of the risk scores in the GSE72094 dataset. (B) The distributions of OS status, OS time and risk score in the GSE72094 dataset. (C) PCA plot of the GSE72094 dataset. (D) t-SNE analysis of the GSE72094 dataset. (E) Kaplan–Meier curves for the OS of patients in the high- respective low-risk group in the GSE72094 dataset. (F) AUC of time-dependent ROC curves that confirm the prognostic performance of the risk score in the GSE72094 dataset.
Figure 4
Figure 4. Multivariate Cox regression analyses of factors affecting OS in the TCGA LUAD cohort (A) and the GSE72094 dataset (B).
Figure 5
Figure 5. The KEGG enrichment plots of tumorigenesis pathways.
(A) Pyrimidine metabolism. (B) Cell cycle. (C) Proteasome. (D) Base excision repair. (E) Homologous recombination. (F) DNA replication.
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