International Journal of General Medicine (Nov 2021)

Identification of an Innate Immune-Related Prognostic Signature in Early-Stage Lung Squamous Cell Carcinoma

  • Li L,
  • Yu X,
  • Ma G,
  • Ji Z,
  • Bao S,
  • He X,
  • Song L,
  • Yu Y,
  • Shi M,
  • Liu X

Journal volume & issue
Vol. Volume 14
pp. 9007 – 9022

Abstract

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Liang Li,1 Xue Yu,2 Guanqiang Ma,1 Zhiqi Ji,1 Shihao Bao,1 Xiaopeng He,1,3 Liang Song,1,3 Yang Yu,1,3 Mo Shi,1,3 Xiangyan Liu1,3 1Department of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People’s Republic of China; 2Department of Pediatrics, Wuhan Children’s Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, 420100, People’s Republic of China; 3Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, People’s Republic of ChinaCorrespondence: Mo Shi; Xiangyan LiuDepartment of Thoracic Surgery, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250021, People’s Republic of ChinaEmail [email protected]; [email protected]: Early-stage lung squamous cell carcinoma (LUSC) progression is accompanied by changes in immune microenvironments and the expression of immune-related genes (IRGs). Identifying innate IRGs associated with prognosis may improve treatment and reveal new immunotherapeutic targets.Methods: Gene expression profiles and clinical data of early-stage LUSC patients were obtained from the Gene Expression Omnibus and The Cancer Genome Atlas databases and IRGs from the InnateDB database. Univariate and multivariate Cox regression and LASSO regression analyses were performed to identify an innate IRG signature model prognostic in patients with early-stage LUSC. The predictive ability of this model was assessed by time-dependent receiver operator characteristic curve analysis, with the independence of the model-determined risk score assessed by univariate and multivariate Cox regression analyses. Overall survival (OS) in early-stage LUSC patients was assessed using a nomogram and decision curve analysis (DCA). Functional and biological pathways were determined by gene set enrichment analysis, and differences in biological functions and immune microenvironments between the high- and low-risk groups were assessed by ESTIMATE and the CIBERSORT algorithm.Results: A signature involving six IRGs (SREBF2, GP2, BMX, NR1H4, DDX41, and GOPC) was prognostic of OS. Samples were divided into high- and low-risk groups based on median risk scores. OS was significantly shorter in the high-risk than in the low-risk group in the training (P < 0.001), GEO validation (P = 0.00021) and TCGA validation (P = 0.034) cohorts. Multivariate Cox regression analysis showed that risk score was an independent risk factor for OS, with the combination of risk score and T stage being optimally predictive of clinical benefit. GSEA, ESTIMATE, and the CIBERSORT algorithm showed that immune cell infiltration was higher and immune-related pathways were more strongly expressed in the low-risk group.Conclusion: A signature that includes these six innate IRGs may predict prognosis in patients with early-stage LUSC.Keywords: early-stage lung squamous cell carcinoma, prognosis, risk score, gene signature, innate immune-related genes, immune cell infiltration

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