# Common Molecular Mechanisms and Biomarkers in Breast, Colon and Ovarian Cancer
## 5 Conclusions  
The analysis conducted on common proteins involved in breast, colon, and ovarian cancers has revealed significant findings that contribute to the understanding of the molecular similarities and shared biological mechanisms between these pathologies. Data mining identified 483 proteins for breast cancer, 521 for colon cancer, and 223 for ovarian cancer. Interaction network analysis revealed shared clusters, including pathways for cancer progression, DNA repair, and cell proliferation. The analysis found 27 proteins to be common across the three cancer types. These proteins participate in crucial biological processes like DNA damage response, cell proliferation, and apoptosis. Notably, they are involved in KEGG pathways linked to multiple cancers and infections. The study identified MSH2 and KIT as proteins that showed significant changes in expression across breast, colon, and ovarian cancer. The high expression of these proteins correlated with better survival outcomes, suggesting their potential as common biomarkers for all three cancer types. The opposing expression patterns of MSH2 and KIT across breast,colon, and ovarian cancer highlight potential implications for diagnosis and therapeutic strategies. Their distinct regulatory roles in genomic stability and proliferative signaling suggest that assessing their combined expression could provide valuable insights for patient stratification and treatment selection. Future studies are needed to explore their clinical applicability and validate their relevance as biomarkers in personalized oncology. While this study is based on a rigorous in-silico approach, we acknowledge that experimental validation is essential to confirm the clinical relevance of the identified biomarkers. Future research should include validation in independent patient cohorts and experimental models to strengthen the applicability of these findings. Expanding the dataset and performing longitudinal studies could provide deeper insights into the role of these biomarkers in disease progression and treatment response. The methodological framework presented here lays the foundation for further studies that will validate and refine these results in a clinical setting. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15137018/s1](https://www.mdpi.com/article/10.3390/app15137018/s1)]([https://www.mdpi.com/article/10.3390/app15137018/s1](https://www.mdpi.com/article/10.3390/app15137018/s1)), Table S1: Complete list of curated ontologies from the STRING Database for 15 breast cancer-related search terms; Table S2: Complete list of curated ontologies from the STRING Database for 24 colon cancer-related search terms; Table S3: Complete list of curated ontologies from the STRING Database for 15 ovarian cancer-related search terms; Table S4: List of unique proteins found for breast cancer (483), colon cancer (521), and ovarian cancer (223); Table S5: List of biological processes identified through the functional enrichment analysis of Gene Ontology for proteins shared among breast, colon, and ovarian cancer; Table S6: List of cellular components identified through the functional enrichment analysis of Gene Ontology for proteins shared among breast, colon, and ovarian cancer; Table S7: List of molecular functions identified through the functional enrichment analysis of Gene Ontology for proteins shared among breast, colon, and ovarian cancer; Table S8: List of KEGG pathways identified through the functional analysis of proteins shared among breast, colon, and ovarian cancer; Figure S1: Protein-protein interaction network of MCODE clusters: analysis of KEGG pathways; Figure S2: Expression levels (RNAseq) of additional proteins from the MCODE1, MCODE2, and MCODE3 clusters in tumor patient samples with breast invasive carcinoma (BRCA), colon adenocarcinoma (COAD), rectum adenocarcinoma (READ), and ovarian serous cystadenocarcinoma (OV).