Integrated Machine Learning and Bioinformatic Analyses Constructed a Sulfur Metabolism-related Breast Cancer Risk Model and Identified Heat-shock Protein A9 as a Potential Therapeutic Target for Human Breast Cancer

This is a cancer research which combines public breast cancer gene expression data and sulfur metabolism-related geneset. In this work, we identified breast cancer patients as two different subgroup according sulfur metabolism-related molecular pattern, and confirmed opposite clinical characters in the two breast cancer clusters. Additionally, HSPA9 has been proved as a tumor development-associated marker from sulfur metabolism-related genes. The RNA sequencing data of breast invasive carcinoma (BRCA) samples and normal samples was extracted from TCGA and GTEx databases via UCSC XENA website. Total 332 genes associated with sulfur metabolism were collected from molecular signatures database with gene set labels ( GO:0051176, GO:0042762, GO:0000101, and GO:0006790).