Publications
2024 |
Lopez-Fernandez, A.; Gallejones-Eskubi, J.; Saz-Navarro, Dulcenombre M.; Gómez-Vela, F. Breast Cancer Biomarker Analysis Using Gene Co-expression Networks Conference IWBBIO 2024: International Work-Conference on Bioinformatics and Biomedical Engineering , Springer Nature Switzerland, 2024, ISBN: 978-3-031-64636-2. Abstract | Links | BibTeX | Tags: Bioinformatics, Biomarkers, Gene co-expression network @conference{Lopez-Fernandez2024c,Gene co-expression networks have emerged as a robust tool for conducting comprehensive analyses of gene expression patterns. These networks, constructed through inference algorithms, facilitate the exploration of various biological processes and enable the identification of novel biomarkers from which to explore new lines of disease research. This work found that breast cancer stromal cells are strongly dysregulated in genes related to modifications in cellular structures that hold stromal tissue cells together, inflammatory responses, and molecules implicated in immune system regulation. Finally, ANAPC11, LRFN5, COL8A2, TEX11, DOCK9, CPLX1, LONP2, and LAT2 biomarkers were suggested in the context of stromal breast tumors. |
Figueroa-Martinez, J.; Saz-Navarro, Dulcenombre M.; Lopez-Fernandez, A.; Rodríguez-Baena, D.; Gómez-Vela, F. Computational Ensemble Gene Co-Expression Networks for the Analysis of Cancer Biomarkers Journal Article In: Informatics, vol. 11, no. 2, pp. 14, 2024, ISSN: 2227-9709. Abstract | Links | BibTeX | Tags: Bioinformatics, Biomarkers, Breast cancer, Gene co-expression network, Prostate cancer, Stromal tissue @article{Figueroa-Martinez2024,Gene networks have become a powerful tool for the comprehensive examination of gene expression patterns. Thanks to these networks generated by means of inference algorithms, it is possible to study different biological processes and even identify new biomarkers for such diseases. These biomarkers are essential for the discovery of new treatments for genetic diseases such as cancer. In this work, we introduce an algorithm for genetic network inference based on an ensemble method that improves the robustness of the results by combining two main steps: first, the evaluation of the relationship between pairs of genes using three different co-expression measures, and, subsequently, a voting strategy. The utility of this approach was demonstrated by applying it to a human dataset encompassing breast and prostate cancer-associated stromal cells. Two gene networks were computed using microarray data, one for breast cancer and one for prostate cancer. The results obtained revealed, on the one hand, distinct stromal cell behaviors in breast and prostate cancer and, on the other hand, a list of potential biomarkers for both diseases. In the case of breast tumor, ST6GAL2, RIPOR3, COL5A1, and DEPDC7 were found, and in the case of prostate tumor, the genes were GATA6-AS1, ARFGEF3, PRR15L, and APBA2. These results demonstrate the usefulness of the ensemble method in the field of biomarker discovery. |