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Gradient boosting reveals spatially diverse cholesterol gene signatures in colon cancer
Xiuxiu Yang1, Debolina Chatterjee1, Justin L Couetil2
1Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, United States.
Abstract:
Colon cancer (CC) is the second most common cause of cancer deaths and the fourth most prevalent cancer in the United States. Recently cholesterol metabolism has been identified as a potential therapeutic avenue due to its consistent association with tumor treatment effects and overall prognosis. We conducted differential gene analysis and KEGG pathway analysis on paired tumor and adjacent-normal samples from the TCGA Colon Adenocarcinoma project, identifying that bile secretion was the only significantly downregulated pathway. To evaluate the relationship between cholesterol metabolism and CC prognosis, we used the genes from this pathway in several statistical models like Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and the eXtreme Gradient Boosting (XGBoost) to identify the genes which contributed highly to the predictive ability of all models, ADCY5, and SLC2A1. We demonstrate that using cholesterol metabolism genes with XGBoost models improves stratification of CC patients into low and high-risk groups compared with traditional CPH, RF and LR models. Spatial transcriptomics (ST) revealed that SLC2A1 (glucose transporter 1, GLUT1) colocalized with small blood vessels. ADCY5 localized to stromal regions in both the ST and protein immunohistochemistry. Interestingly, both these significant genes are expressed in tissues other than the tumor itself, highlighting the complex interplay between the tumor and microenvironment, and that druggable targets may be found in the ability to modify how "normal" tissue interacts with tumors.
Insights
Cholesterol metabolism genes, particularly ADCY5 and SLC2A1, can improve colon cancer risk prediction. These genes highlight the tumor microenvironment
Area of Science:
- Oncology
- Molecular Biology
- Genetics
Background:
- Colon cancer (CC) is a leading cause of cancer mortality and morbidity.
- Cholesterol metabolism is increasingly recognized for its role in cancer prognosis and treatment response.
- Understanding the molecular underpinnings of CC progression is crucial for developing novel therapeutic strategies.
Purpose of the Study:
- To investigate the role of cholesterol metabolism pathways in colon cancer.
- To identify key genes within these pathways that predict CC patient prognosis.
- To evaluate the efficacy of advanced machine learning models in CC risk stratification.
Main Methods:
- Differential gene expression and KEGG pathway analysis on TCGA Colon Adenocarcinoma data.
- Prognostic modeling using Cox proportional Hazard (CPH), Random Forest (RF), Lasso Regression (LR), and eXtreme Gradient Boosting (XGBoost).
- Spatial transcriptomics (ST) and immunohistochemistry to localize gene expression.
Main Results:
- Bile secretion was identified as the only significantly downregulated KEGG pathway in CC.
- ADCY5 and SLC2A1 emerged as key predictive genes across multiple statistical models.
- XGBoost models incorporating cholesterol metabolism genes significantly improved CC patient risk stratification compared to traditional models.
- Spatial analysis revealed SLC2A1 colocalized with blood vessels and ADCY5 with stromal regions, indicating expression outside the tumor itself.
Conclusions:
- Cholesterol metabolism, specifically the bile secretion pathway, is altered in colon cancer.
- ADCY5 and SLC2A1 are significant prognostic biomarkers for colon cancer.
- Advanced machine learning models enhance risk prediction for CC patients.
- The tumor microenvironment and its interaction with normal tissues, involving genes like ADCY5 and SLC2A1, represent potential therapeutic targets.
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