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Published on: December 4, 2016
Integrating multi-omics data and machine learning to identify endocrine disrupting chemicals targeting key
Mengzhuo Zheng1, Debiao Wang2, Bo Guan3
1College of Informatics, Huazhong Agricultural University, Wuhan, 430070, China.
Abstract:
Clear cell renal cell carcinoma (ccRCC) is a highly aggressive and metastatic malignancy that poses a serious threat to patient health. While its development involves both genetic and environmental influences, the interactions between genetic susceptibility and endocrine-disrupting chemicals (EDCs) remain poorly understood. In this study, we integrated multi-omics datasets and applied machine learning approaches to identify EDCs and their associated target genes implicated in ccRCC pathogenesis. We analyzed bulk and single-cell RNA sequencing data and evaluated 101 machine learning algorithms to construct a robust prognostic model. This analysis identified 8 EDCs potentially involved in ccRCC: Diethylnitrosamine, Diethylstilbestrol, Resveratrol, 4,4'-diaminodiphenylmethane, Trichloroethylene, Arsenic, Lead, and 2,3,5-(triglutathion-S-yl)hydroquinone. 5 EDC-associated prognostic genes-BIRC5, CCND1, RRM2, CDH1, and TRIB3-were also identified. Pathway enrichment and immune infiltration analyses revealed intricate interactions between these genes and the tumor microenvironment. Furthermore, cell-cell communication analysis revealed distinct signaling patterns among EDC-associated subpopulations, offering new insights into how EDCs may modulate gene expression and contribute to ccRCC progression. This study provides a foundation for future investigations into EDC-driven tumor biology and may guide the development of targeted therapies and preventative strategies against ccRCC.
Insights
This study identifies 8 endocrine-disrupting chemicals (EDCs) and 5 key genes linked to clear cell renal cell carcinoma (ccRCC) progression. Findings reveal EDC-gene interactions influencing the tumor microenvironment, aiding ccRCC treatment strategies.
Area of Science:
- Oncology
- Environmental Health
- Genomics
Background:
- Clear cell renal cell carcinoma (ccRCC) is an aggressive cancer with poorly understood genetic and environmental risk factors.
- The specific roles of endocrine-disrupting chemicals (EDCs) in ccRCC development and progression are not well-defined.
Purpose of the Study:
- To identify EDCs and their associated genes involved in ccRCC pathogenesis using multi-omics and machine learning.
- To elucidate the mechanisms by which EDCs influence ccRCC through gene expression and tumor microenvironment interactions.
Main Methods:
- Integration of multi-omics datasets, including bulk and single-cell RNA sequencing.
- Application and evaluation of 101 machine learning algorithms to build a prognostic model for ccRCC.
- Pathway enrichment, immune infiltration, and cell-cell communication analyses.
Main Results:
- Identification of 8 potential EDCs (e.g., Diethylnitrosamine, Arsenic, Lead) implicated in ccRCC.
- Discovery of 5 prognostic genes (BIRC5, CCND1, RRM2, CDH1, TRIB3) associated with EDC exposure.
- Characterization of gene-environment interactions within the tumor microenvironment and distinct cellular signaling patterns.
Conclusions:
- This research highlights specific EDCs and genes as critical players in ccRCC pathogenesis.
- The findings offer novel insights into EDC-driven tumor biology and ccRCC progression.
- This study lays the groundwork for developing targeted therapies and preventative strategies for ccRCC.
