Subtype Classification, Immune Infiltration, and Prognosis Analysis of Lung Adenocarcinoma Based on Pyroptosis-Related Genes.

The effect of pyroptosis-related genes (PRGs) on the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Thus, this study is aimed at evaluating the prognostic value of PRGs in patients with LUAD and to elucidate their role in the TME and their effect on immunotherapy. Transc...

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Publicado en:BioMed Research International pp. 1 - 17
Autores principales: Liu, Qi, Fang, Liguang, Gao, Chundi, Liu, Cun, Yu, Haiyang, Wu, Jibiao, Hou, Lin, Sun, Changgang
Formato: research tables/charts Journal Article
Publicado: Wiley-Blackwell 10/12/2022
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 10/12/2022
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      pub: Wiley-Blackwell
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        atl: Subtype Classification, Immune Infiltration, and Prognosis Analysis of Lung Adenocarcinoma Based on Pyroptosis-Related Genes.
      aug:
        au:
          Liu, Qi
          Fang, Liguang
          Gao, Chundi
          Liu, Cun
          Yu, Haiyang
          Wu, Jibiao
          Hou, Lin
          Sun, Changgang
        affil: College of First Clinical Medicine, Shandong University of Traditional Chinese Medicine, Jinan, 250014 Shandong, China
      sug:
        subj:
          Adenocarcinoma of Lung
          Apoptosis
          Genes
          Cell Physiology
          Immunotherapy
          Human
          Adenocarcinoma of Lung Classification
          Adenocarcinoma of Lung Immunology
          Adenocarcinoma of Lung Prognosis
          Adenocarcinoma of Lung Drug Therapy
          Gene Expression
          Algorithms
          Regression
          Univariate Statistics
          Progression-Free Survival
      ab: The effect of pyroptosis-related genes (PRGs) on the tumor microenvironment (TME) in lung adenocarcinoma (LUAD) remains unclear. Thus, this study is aimed at evaluating the prognostic value of PRGs in patients with LUAD and to elucidate their role in the TME and their effect on immunotherapy. Transcriptomic and clinical data were obtained from the Cancer Genome Atlas and the Gene Expression Omnibus databases (GSE3141, GSE31210). Patients with LUAD were classified using consistent clustering, and the differences in the TME for each type were determined using the ESTIMATE and CIBERSORT algorithms. PRGs were screened using univariate regression analysis, and a prognostic risk model was constructed using LASSO regression analysis. The tumor mutational burden and the tumor immune dysfunction and exclusion algorithms were used to predict therapeutic sensitivity in LUAD patients. Then, we evaluated the potential therapeutic interventions using the GDSC database. LUAD patients in cluster 2 had significantly shorter overall survival and progression-free survival rates, lower immune scores, and higher infiltration of T follicular helper cells than those in cluster 1. We used five PRGs to classify patients with LUAD into different risks groups and found that the high-risk group is sensitive to immunotherapy; however, its immune-related pathways were inhibited, which may be related to tumor metabolic reprogramming. Lastly, we identified several potential therapeutic drugs for application in low-risk patients who were less sensitive to immunotherapy. Overall, our findings showed that PRGs can be used to predict prognosis and may aid in the development of personalized therapeutic strategies in LUAD patients.
      pubtype: Academic Journal
      doctype:
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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