Genomic Insights Into Cancer Susceptibility: A Multivariate Approach

Background: This study addresses rising concerns in breast cancer, prostate cancer, colon cancer, renal cancer, and lung cancer. The focus is on early detection through genomic analysis to reduce fatality rates. Aim: Integrate exploratory data analysis, dimensionality reduction, clustering, and machine learning to analyze a dataset of 802 samples. Identify genetic markers crucial for classifying different cancer types. Design: Merge datasets and use hierarchically clustered heatmaps for exploratory analysis. Apply PCA and LDA for dimensionality reduction. Employ clustering techniques for co-expressed genes and sample clusters associated with each cancer type. Research Limitations: Reliance on available datasets, potential biases, and biological system complexity are limitations. Interpretability of selected genes and model generalization to diverse populations needs further investigation. Research Novelty: Novel approach integrating exploratory data analysis, dimensionality reduction, and clustering to unveil molecular intricacies. Use multiple clustering methods on genes and samples for enhanced understanding. Use boosting models to get an accurate classification. Result: Identified gene clusters with similar expression patterns and those specific to each cancer type. Application of multiclass SVM, Random Forest, boosting models like CatBoost, LightGBM, XGBoost, and Deep Neural Network models showcased accurate cancer type classification. Conclusion: Comprehensive genomic analysis offers a promising avenue for early cancer detection. Integration of methodologies provides valuable insights into molecular bases, paving the way for improved diagnostics and targeted therapies. Further validation and refinement are crucial for impactful clinical applications.