Wenzhong Zheng1, Shiqiang Zhang2, Huan Guo3, Xiaobao Chen1, Zhangcheng Huang1, Shaoqin Jiang1, Mengqiang Li4. 1. Department of Urology, Fujian Province, Fujian Medical University Union Hospital, Gulou District, 29 Xinquan Road, Fuzhou, 200001, People's Republic of China. 2. Department of Urology, Kidney and Urology Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, Guangdong, People's Republic of China. 3. Department of Urology, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy Center, Shenzhen University, Shenzhen, Guangdong, People's Republic of China. 4. Department of Urology, Fujian Province, Fujian Medical University Union Hospital, Gulou District, 29 Xinquan Road, Fuzhou, 200001, People's Republic of China. limengqiang1976@163.com.
Abstract
BACKGROUND: Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. METHOD: Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. RESULTS: The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. CONCLUSION: Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.
BACKGROUND: Tumor angiogenesis, an essential process for cancer proliferation and metastasis, has a critical role in prognostic of kidney renal clear cell carcinoma (KIRC), as well as a target in guiding treatment with antiangiogenic agents. However, tumor angiogenesis subtypes and potential epigenetic regulation mechanisms in KIRC patient remains poorly characterized. System evaluation of angiogenesis subtypes in KIRC patient might help to reveal the mechanisms of KIRC and develop more target treatments for patients. METHOD: Ten independent tumor angiogenesis signatures were obtained from molecular signatures database (MSigDB) and gene set variation analysis was performed to calculate the angiogenesis score in silico using the Cancer Genome Atlas (TCGA) KIRC dataset. Tumor angiogenesis subtypes in 539 TCGA-KIRC patients were identified using consensus clustering analysis. The potential regulation mechanisms was studied using gene mutation, copy number variation, and differential methylation analysis (DMA). The master transcription factors (MTF) that cause the difference in tumor angiogenesis signals were completed by transcription factor enrichment analysis. RESULTS: The angiogenesis score of a prognosis related angiogenesis signature including 189 genes was significantly correlated with immune score, stroma score, hypoxia score, and vascular endothelial growth factor (VEGF) signal score in 539 TCGA KIRC patients. MMRN2, CLEC14A, ACVRL1, EFNB2, and TEK in candidate gene set showed highest correlation coefficient with angiogenesis score in TCGA-KIRC patients. In addition, all of them were associated with overall survival in both TCGA-KIRC and E-MTAB-1980 KIRC data. Clustering analysis based on 183 genes in angiogenesis signature identified two prognosis related angiogenesis subtypes in TCGA KIRC patients. Two clusters also showed different angiogenesis score, immune score, stroma score, hypoxia score, VEGF signal score, and microenvironment score. DMA identified 59,654 differential methylation sites between two clusters and part of these sites were correlated with tumor angiogenesis genes including CDH13, COL4A3, and RHOB. In addition, RFX2, SOX13, and THRA were identified as top three MTF in regulating angiogenesis signature in KIRC patients. CONCLUSION: Our study indicate that evaluation the angiogenesis subtypes of KIRC based on angiogenesis signature with 183 genes and potential epigenetic mechanisms may help to develop more target treatments for KIRC patients. Video Abstract.
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