Αρχειοθήκη ιστολογίου

Αλέξανδρος Γ. Σφακιανάκης
ΩτοΡινοΛαρυγγολόγος
Αναπαύσεως 5
Άγιος Νικόλαος Κρήτη 72100
2841026182
6032607174

Πέμπτη 10 Ιουνίου 2021

Identification of a tumor microenvironment-associated prognostic gene signature in bladder cancer by integrated bioinformatic analysis

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Int J Clin Exp Pathol. 2021 May 15;14(5):551-566. eCollection 2021.

ABSTRACT

Bladder cancer is a common malignancy in the urinary system. Stromal and immune cells in tumor microenvironments, including those in the bladder cancer microenvironment, can serve as prognostic markers. However, the complex processes of bladder cancer necessitate large-scale evaluation to better understand the underlying mechanisms and identify biomarkers for diagnosis and treatment. We used the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data algorithm to assess the association between stromal and immune cell-related genes and overall survival of patients with bladder cancer. We also identified and evaluated differentially expressed genes between cancer and non-cancer tissues from The Cancer Genome Atlas. Patients were categorized into different prognosis groups according to their stromal/immune scores based on differential gen e expression. In addition, the prognostic value of the differentially expressed genes was assessed in a separate validation cohort using the Gene Expression Omnibus microarray dataset GSE13507, which identified nine genes (TNC, CALD1, PALLD, TAGLN, TGFB1I1, HSPB6, RASL12, CPXM2, and CYR61) associated with overall survival. Multivariate regression analysis showed that three genes (TNC, CALD1, and PALLD) were possible independent prognostic markers for patients with bladder cancer. Multiple gene set enrichment analysis of individual genes showed strong correlations with stromal and immune interactions, indicating that these nine genes may be related to carcinogenesis, invasion, and metastasis of bladder cancer. These findings provide useful insight into the molecular mechanisms of bladder cancer development, and suggest candidate biomarkers for prognosis and treatment.

PMID:34093942 | PMC:PMC8167492

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