CUI Yanqi 1 , ZHAO Hu 2 , ZHANG Yawei 2 , NI Lin 2 , LIAN Duohuang 3 , YANG Jingrong 3 , YE Shixin 3 , XU Fengfeng 3 , ZHANG Jincan 3 , ZENG Zhiyong 1,3
  • 1. Fuzong Clinical College of Fujian Medical University, Fuzhou, 350025, P. R. China;
  • 2. Department of General Surgery, The 900th Hospital of Joint Logistics Support Force, Fuzong Clinical Medical College Affiliated Fujian Medical University, Fuzhou, 350025, P. R. China;
  • 3. Department of Cardiothoracic Surgery, The 900th Hospital of Joint Logistics Support Force, Fuzong Clinical Medical College Affiliated Fujian Medical University, Fuzhou, 350025, P. R. China;
ZENG Zhiyong, Email: 13295916182@163.com
Circadian rhythm; lung adenocarcinoma; machine learning; immune microenvironment; single-cell analysis; nomogram
10.7507/1007-4848.202401052
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Objective To explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). Methods The Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. GO, KEGG, and GSEA enrichment analyses were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox analysis, LASSO regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients' 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Finally, single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. Results Differentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poored prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. Through scRNA-seq analysis, RORA and KLF10 were mainly expressed in natural killer cells. Conclusion The prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.