Abstract
Identification of novel clinical biomarker in clear cell renal carcinoma (ccRCC) is warranted. Integrating transcriptome (n=1669), DNA methylation (n=577) and copy number data (n=832), we developed a method to identify driver biomarkers by analyzing the omics-level dynamics of Epithelial-Mesenchymal Transition (EMT)-related genes in ccRCC. We first identified 504 expression dynamic changed genes involved in ccRCC-associated key pathways such as EMT, cell cycle, EGFR and PI3K/AKT signaling. Further analysis identified 229 (90 gene promoters) aberrant expression quantitative trait methylation (eQTM) and 256 genes with expression quantitative trait copy number (eQTCN) alterations. Among them, FOXM1 was affected by both eQTM and eQTCN. FOXM1 copy number amplification (115/500, 23% of patients), occurred in an amplified peak in chromosome 12q13.3, was enriched in late-stage ccRCC samples and was associated with worse survival. FOXM1-overexpressed pT3 patients with distant metastasis showed ~25% shorter overall survival in both training (log-rank P=0.006) and validation (log-rank P=0.018) cohorts. The eQTM-gene hybrid signature (cg00044170 and FOXM1), superior to either gene expression or DNA methylation alone, showed great potential in diagnosing localized ccRCC in training (area under curve = 0.958) and validation datasets. FOXM1 could be a novel prognostic biomarker and shed light for early diagnosis at molecular level in ccRCC.
Highlights
In 2018, approximately 403,000 new cases of kidney cancer were diagnosed worldwide, with >43% patients succumbing to the disease [1]
From the perspective of multi-omics, we identified driver genes in clear cell Renal cell carcinoma (RCC) (ccRCC) by investigation of the information underlies the dynamic changes of EMTrelated genes (Figure 1)
Given that the abnormal methylation of expression quantitative trait methylation (eQTM) is associated with the expression change of expression dynamic changed gene (EDCG), we investigated their DNA methylation patterns in ccRCC based on eleven grouping methods
Summary
In 2018, approximately 403,000 new cases of kidney cancer were diagnosed worldwide, with >43% patients succumbing to the disease [1]. Patients achieved 5-year survival >90% only if they were diagnosed with early and localized kidney cancer, which is defined as patients with pT1/pT2 disease but without regional lymph node metastasis nor distant metastasis (stage I/II, American Joint Committee on Cancer 8th edition). 5-year survival rate drops to 12% for patients with distant metastasis [5]. Only about 65% of patients were diagnosed with localized disease [5]. Improving early diagnostic rate is beneficial for patient survival. The advanced ccRCC is usually characterized by highly invasiveness, regional and distant metastasis, and postsurgical relapse [6, 7]. Systematic identification of the driving regulators in progression of ccRCC is crucial and valuable
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