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ARID3B increases ovarian tumor burden and is associated with a cancer stem cell gene signature.

ABSTRACT: Ovarian cancer is the most deadly gynecological malignancy since most patients have metastatic disease at the time of diagnosis. Therefore, identification of critical pathways that contribute to ovarian cancer progression is necessary to yield novel therapeutic targets. Recently we reported that the DNA binding protein ARID3B is overexpressed in human ovarian tumors. To determine if ARID3B has oncogenic functions in vivo, ovarian cancer cell lines stably expressing ARID3B were injected intraperitoneally into nude mice. Overexpression of ARID3B increased tumor burden and decreased survival. To assess how ARID3B contributes to the increased tumor growth in vivo, we identified ARID3B induced genes in tumor ascites cells. ARID3B induced expression of genes associated with metastasis and cancer stem cells (CD44, LGR5, PROM1 (CD133), and Notch2). Moreover, ARID3B increased the number of CD133+ (a cancer stem cell marker) cells compared to control cells. The increase in CD133+ cells resulting from ARID3B expression was accompanied by enhanced paclitaxel resistance. Our data demonstrate that ARID3B boosts production CD133+ cells and increases ovarian cancer progression in vivo.

SUBMITTER: Roy L

PROVIDER: S-EPMC4226688 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature

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ARID3B increases ovarian tumor burden and is associated with a cancer stem cell gene signature.

Roy Lynn L Samyesudhas Serene J SJ Carrasco Martin M Li Jun J Joseph Stancy S Dahl Richard R Cowden Dahl Karen D KD

Oncotarget 20140901 18

Ovarian cancer is the most deadly gynecological malignancy since most patients have metastatic disease at the time of diagnosis. Therefore, identification of critical pathways that contribute to ovarian cancer progression is necessary to yield novel therapeutic targets. Recently we reported that the DNA binding protein ARID3B is overexpressed in human ovarian tumors. To determine if ARID3B has oncogenic functions in vivo, ovarian cancer cell lines stably expressing ARID3B were injected intraperi ...[more]

PMID: 25327563

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Project description:ObjectiveTumors are highly heterogeneous, and within their parenchyma, a small population of tumor-stem cells possessing differentiation potential, high oncogenicity, and self-renewal capabilities exists. These cells are pivotal in mediating tumor development, chemotherapy resistance, and recurrence. Ovarian cancer shares characteristics with tumor stem cells, making it imperative to investigate molecular markers associated with these cells.MethodsStem cell-related genes were collected, and molecular subtypes were established based on gene expression profiles from The Cancer Genome Atlas using the R package tool "ConsensusClusterPlus." Multi-gene prognostic markers were identified using LASSO regression analysis. Gene set enrichment analysis was employed to gain insights into the potential molecular mechanisms of these identified markers. The robustness of these prognostic markers was analyzed across different cohorts, and their clinical independence was determined through multivariate Cox analysis. A nomogram was constructed to assess the model's clinical applicability. Immunohistochemistry was performed to validate the expression of hub genes.ResultsUtilizing 49 tumor stem cell-related genes associated with prognosis, 362 ovarian cancer samples were divided into two distinct clusters, revealing significant prognostic disparities. A seven-gene signature (GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9), identified through LASSO regression, exhibited stability and robustness across various platforms. Multivariate Cox regression analysis confirmed the signature's independence in predicting survival in patients with ovarian cancer. Furthermore, a nomogram combining the gene signature demonstrated strong predictive abilities. Immunohistochemistry results indicated significantly elevated GALP, CACNA1C, COL16A1, PENK, C4BPA, PSMA2, and CXCL9 expression in cancer tissues.ConclusionThe seven-gene signature holds promise as a valuable tool for decision-making and prognosis prediction in patients with ovarian cancer.

Project description:Lung squamous cell carcinoma (LUSC) is a common type of lung cancer with high incidence and mortality rate. Tumor mutational burden (TMB) is an emerging biomarker for selecting patients with non-small cell lung cancer (NSCLC) for immunotherapy. This study aimed to reveal TMB involved in the mechanisms of LUSC and develop a model to predict the overall survival of LUSC patients. The information of patients with LUSC were obtained from the cancer genome atlas database (TCGA). Differentially expressed genes (DEGs) between low- and the high-TMB groups were identified and taken as nodes for the protein-protein interaction (PPI) network construction. Gene oncology (GO) enrichment analysis and gene set enrichment analysis (GSEA) were used to investigate the potential molecular mechanism. Then, we identified the factors affecting the prognosis of LUSC through cox analysis, and developed a risk score signature. Kaplan-Meier method was conducted to analyze the difference in survival between the high- and low-risk groups. We constructed a nomogram based on the risk score model and clinical characteristics to predict the overall survival of patients with LUSC. Finally, the signature and nomogram were further validated by using the gene expression data downloaded from the Gene Expression Omnibus (GEO) database. 30 DEGs between high- and low-TMB groups were identified. PPI analysis identified CD22, TLR10, PIGR and SELE as the hub genes. Cox analysis indicated that FAM107A, IGLL1, SELE and T stage were independent prognostic factors of LUSC. Low-risk scores group lived longer than that of patients with high-risk scores in LUSC. Finally, we built a nomogram that integrated the clinical characteristics (TMN stage, age, gender) with the three-gene signature to predict the survival probability of LUSC patients. Further verification in the GEO dataset. TMB might contribute to the pathogenesis of LUSC. TMB-associated genes can be used to develope a model to predict the OS of lung squamous cell carcinoma patients.

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ARID3B increases ovarian tumor burden and is associated with a cancer stem cell gene signature.

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ARID3B increases ovarian tumor burden and is associated with a cancer stem cell gene signature.

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