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A novel prognostic model based on three integrin subunit genes-related signature for bladder cancer.
Tu, Hongtao; Liu, Haolin; Zhang, Longfei; Tan, Zhiyong; Wang, Hai; Jiang, Yongming; Xia, Zhongyou; Guo, Liwei; Xia, Xiaodong; Gu, Peng; Liu, Xiaodong.
Afiliação
  • Tu H; Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Liu H; The First Affiliated Hospital of Kunming Medical University, Yunnan Province Clinical Research Center for Chronic Kidney Disease, Kunming, China.
  • Zhang L; Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China.
  • Tan Z; Department of Vascular Surgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.
  • Wang H; Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Jiang Y; Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Xia Z; The First Affiliated Hospital of Kunming Medical University, Yunnan Province Clinical Research Center for Chronic Kidney Disease, Kunming, China.
  • Guo L; Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Xia X; The First Affiliated Hospital of Kunming Medical University, Yunnan Province Clinical Research Center for Chronic Kidney Disease, Kunming, China.
  • Gu P; Department of Urology, The Second Affiliated Hospital of Kunming Medical University, Kunming, China.
  • Liu X; Department of Urology, The First Affiliated Hospital of Kunming Medical University, Kunming, China.
Front Oncol ; 12: 970576, 2022.
Article em En | MEDLINE | ID: mdl-36267977
Background: Presently, a comprehensive analysis of integrin subunit genes (ITGs) in bladder cancer (BLCA) is absent. This study endeavored to thoroughly analyze the utility of ITGs in BLCA through computer algorithm-based bioinformatics. Methods: BLCA-related materials were sourced from reputable databases, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). R software-based bioinformatics analyses included limma-differential expression analysis, survival-Cox analysis, glmnet-Least absolute shrinkage and selection operator (LASSO), clusterProfiler-functional annotation, and gsva-estimate-immune landscape analysis. The expression difference of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR). Results: Among the 11 ITGs that were abnormally expressed in BLCA, ITGA7, ITGA5, and ITGB6 were categorized as the optimal variables for structuring the risk model. The high-risk subcategories were typified by brief survival, abysmal prognosis, prominent immune and stromal markers, and depressed tumor purity. The risk model was also an isolated indicator of the impact of clinical outcomes in BLCA patients. Moreover, the risk model, specifically the high-risk subcategory with inferior prognosis, became heavily interlinked with the immune-inflammatory response and smooth muscle contraction and relaxation. Conclusion: This study determined three ITGs with prognostic values (ITGA7, ITGA5, and ITGB6), composed a novel (ITG-associated) prognostic gene signature, and preliminarily probed the latent molecular mechanisms of the model.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article