# Common Molecular Mechanisms and Biomarkers in Breast, Colon and Ovarian Cancer
## 4 Discussion
Until such validation is available, these observations should be considered as preliminary hypotheses for further exploration. The fact that breast, colon, and ovarian cancers share a significant set of proteins involved in similar biological processes opens the door to identifying common biomarkers. While clinical sample validation is crucial, the findings suggest that MSH2 and KIT could be used as biomarkers for a wide range of cancers, not only for diagnosis but also for prognosis and therapy customization. This approach could not only improve diagnostic accuracy, but also enable the implementation of more specific therapies for these cancer types. The approach of identifying shared proteins between different cancer types holds enormous potential in the field of personalized medicine. By identifying key proteins involved in various stages of carcinogenesis, it is possible to develop more precise and targeted therapies that consider both the shared mechanisms among cancer types and the unique characteristics of each tumor. This approach could also facilitate the treatment of cancers that are currently difficult to treat due to their molecular heterogeneity. Our study does not aim to identify biomarkers that are superior to those described in previous research focused on a single tumor type, but rather to provide a rigorous analysis that reveals the robust markers present in multiple cancer types. The identification of shared proteins among breast, colon, and ovarian cancer not only highlights common molecular mechanisms, but also presents new perspectives on the biological interconnection between these tumors. Specifically, the differential expression of MSH2 and KIT across the three cancer types suggests their potential role in patient stratification and the design of therapeutic strategies. Furthermore, the presence of common biomarkers in multiple tumors could be key in assessing a patient's risk of developing another cancer with similar biological characteristics. While these findings are promising, additional validation in preclinical and clinical models is essential to confirm their applicability. Additionally, expanding the analysis to other cancer types would be valuable in confirming whether the proteins identified in this study play a common role in a broader range of pathologies. Longitudinal studies are also recommended to assess how alterations in the expressions of these proteins affect disease progression and treatment response. Combining these strategies will provide a deeper understanding of the molecular foundations of cancer and contribute to the advancement of more effective and personalized treatments. The advantages of this work lie in its comprehensive approach using advanced bioinformatics tools, enabling the identification of common proteins among three different cancer types efficiently. The use of artificial intelligence applied to the search for molecular markers through the analysis of proteomic data represents a very promising advancement [19]. Proteomic research currently requires advanced technologies due to its complexity [18],The protein-protein interaction network analysis, the GO/KEGG enrichment analyses, and the identification of MCODE clusters provided an in-depth understanding of shared molecular mechanisms, representing an advantage over more traditional studies that may focus on a single cancer type. The methodology employed in this study integrates multiple bioinformatics tools to ensure a comprehensive and reliable analysis. By combining STRING, GeneCodis4, GEPIA2, and Metascape, the approach allows for robust data filtering and interpretation, minimizing the inclusion of irrelevant information. While these tools rely on publicly available databases, their curation, consistent updates and validation processes maintain high reliability in biomedical research. Furthermore, the sequential nature of the analysis strengthens the identification of meaningful biomarkers, providing a solid foundation for future experimental validation.