Objective: AT-rich interactive domain 1A (ARID1A) plays an important role as a tumor suppressor gene in ovarian clear cell carcinoma (OCCC), but the clinical application of ARID1A remains unclear. The aim of this study was to analyze clinicopathological parameters, molecular interactions and immune-infiltration in patients with low ARID1A expression and to provide candidate target drugs. Methods: We investigated the clinicopathologic parameters, specific gene sets/genes, and immunological relevance according to ARID1A expression in 998 OCCC patients from 12 eligible studies (using meta-analyses); 30 OCCC patients from the Hanyang University Guri Hospital (HYGH) cohort; and 52 OCCC patients from gene set enrichment (GSE) 65986 (25 patients), 63885 (9 patients), and 54809 (6 patients and 12 healthy people) of the Gene Expression Omnibus (GEO). We analyzed network-based pathways based on gene set enrichment analysis (GSEA) and performed in vitro drug screening. Results: Low ARID1A expression was associated with poor survival in OCCC from the metaanalysis, HYGH cohort and GEO data. In GSEA, low ARID1A expression was related to the tumor invasion process as well as a low immune-infiltration. In silico cytometry showed that CD8 T cells were decreased with low ARID1A expression. In pathway analysis, ARID1A was associated with angiogenic endothelial cell signaling. In vitro drug screening revealed that cabozantinib and bicalutamide effectively inhibited specific hub genes, such as vascular endothelial growth factor-A and androgen receptor, in OCCC cells with low ARID1A expression. Conclusions: Therapeutic strategies making use of low ARID1A could contribute to better clinical management/research for patients with OCCC.
BMI1 is known to play a key role in the regulation of stem cell self-renewal in both endogenous and cancer stem cells. High BMI1 expression has been associated with poor prognosis in a variety of human tumors. The aim of this study was to reveal the correlations of BMI1 with survival rates, genetic alterations, and immune activities, and to validate the results using machine learning. We investigated the survival rates according to BMI1 expression in 389 and 789 breast cancer patients from Kangbuk Samsung Medical Center (KBSMC) and The Cancer Genome Atlas, respectively. We performed gene set enrichment analysis (GSEA) with pathway-based network analysis, investigated the immune response, and performed in vitro drug screening assays. The survival prediction model was evaluated through a gradient boosting machine (GBM) approach incorporating BMI1. High BMI1 expression was correlated with poor survival in patients with breast cancer. In GSEA and in in silico flow cytometry, high BMI1 expression was associated with factors indicating a weak immune response, such as decreased CD8+ T cell and CD4+ T cell counts. In pathway-based network analysis, BMI1 was directly linked to transcriptional regulation and indirectly linked to inflammatory response pathways, etc. The GBM model incorporating BMI1 showed improved prognostic performance compared with the model without BMI1. We identified telomerase inhibitor IX, a drug with potent activity against breast cancer cell lines with high BMI1 expression. We suggest that high BMI1 expression could be a therapeutic target in breast cancer. These results could contribute to the design of future experimental research and drug development programs for breast cancer.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.