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Integrative Analysis of Bioinformatics and Machine Learning Algorithms Identifies a Novel Diagnostic Model Based on Costimulatory Molecule for Predicting Immune Microenvironment Status in Lung Adenocarcinoma

  • Wen-Yu Zhai
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Fang-Fang Duan
    Affiliations
    Department of Medical Oncology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
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  • Yi-Zhi Wang
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Jun-Ye Wang
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Ze-Rui Zhao
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Yao-Bin Lin
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Bing-Yu Rao
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Si Chen
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
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  • Lie Zheng
    Correspondence
    Address correspondence to Hao Long, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, and Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, 651 Dongfeng Rd. E., Guangzhou 510060, Guangdong, People’s Republic of China; or Lie Zheng, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, and Medical Imaging Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-Sen University Cancer Center, 651 Dongfeng Rd. E., Guangzhou 510060, Guangdong, People’s Republic of China.
    Affiliations
    Medical Imaging Division, Department of Medical Imaging and Interventional Radiology, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
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  • Hao Long
    Correspondence
    Address correspondence to Hao Long, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, and Department of Thoracic Surgery, Sun Yat-Sen University Cancer Center, 651 Dongfeng Rd. E., Guangzhou 510060, Guangdong, People’s Republic of China; or Lie Zheng, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, and Medical Imaging Division, Department of Medical Imaging and Interventional Radiology, Sun Yat-Sen University Cancer Center, 651 Dongfeng Rd. E., Guangzhou 510060, Guangdong, People’s Republic of China.
    Affiliations
    Department of Thoracic Surgery, State Key Laboratory of Oncology in Southern China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China

    Lung Cancer Research Center, Sun Yat-Sen University, Guangzhou, China
    Search for articles by this author
      Costimulatory molecules are an indispensable signal for activating immune cells. However, the features of many costimulatory molecule genes (CMGs) in lung adenocarcinoma (LUAD) are poorly understood. We systematically explored expression patterns of CMGs in the tumor immune microenvironment (TIME) status of LUAD patients. Their expression profiles were downloaded from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Two robust TIME subtypes (“hot” and “cold”) were classified by K-means clustering and estimation of stromal and immune cells in malignant tumor tissues using expression data. The “hot” one presented higher infiltration in activated immune cells and enrichments in the immune cell receptor signaling pathway and adaptive immune response. Three CMGs (CD80, LTB, and TNFSF8) were screened as final diagnostic markers by means of Least Absolute Shrinkage Selection Operator and Support Vector Machine–Recursive Feature Elimination algorithms. Accordingly, the diagnostic nomogram for predicting individualized TIME status showed satisfactory diagnostic accuracy in The Cancer Genome Atlas training cohort as well as GSE31210 and GSE180347 validation cohorts. Immunohistochemistry staining of 16 specimens was conducted, and it revealed an apparently positive correlation between the expression of CMG biomarkers and pathologic response to immunotherapy. Therefore, our diagnostic nomogram provided individualized predictions in TIME status of LUAD patients with good predictive accuracy, which could serve as a potential tool for identifying ideal candidates for immunotherapy.
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