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Item type:Publication, Pharmacogenomics, biomarker network, and allele frequencies in colorectal cancer(Springer Science and Business Media LLC, 2019-10-15) ;Andrés López-Cortés; ;Santiago Guerrero ;Gabriela Jaramillo-KoupermannÁngela León Cáceres - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis(MDPI AG, 2020-02-05) ;Alejandro Cabrera-Andrade ;Andrés López-Cortés ;Gabriela Jaramillo-Koupermann; Yunierkis Pérez-Castillo<jats:p>Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its molecular etiology has still not been determined with precision. Therefore, we applied a consensus strategy with the use of several bioinformatics tools to prioritize genes involved in its pathogenesis. Subsequently, we assessed the physical interactions of the previously selected genes and applied a communality analysis to this protein–protein interaction network. The consensus strategy prioritized a total list of 553 genes. Our enrichment analysis validates several studies that describe the signaling pathways PI3K/AKT and MAPK/ERK as pathogenic. The gene ontology described TP53 as a principal signal transducer that chiefly mediates processes associated with cell cycle and DNA damage response It is interesting to note that the communality analysis clusters several members involved in metastasis events, such as MMP2 and MMP9, and genes associated with DNA repair complexes, like ATM, ATR, CHEK1, and RAD51. In this study, we have identified well-known pathogenic genes for osteosarcoma and prioritized genes that need to be further explored.</jats:p> - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing(MDPI AG, 2020-11-22) ;Alejandro Cabrera-Andrade ;Andrés López-Cortés ;Gabriela Jaramillo-Koupermann ;Humberto González-DíazAlejandro Pazos<jats:p>Osteosarcoma is the most common type of primary malignant bone tumor. Although nowadays 5-year survival rates can reach up to 60–70%, acute complications and late effects of osteosarcoma therapy are two of the limiting factors in treatments. We developed a multi-objective algorithm for the repurposing of new anti-osteosarcoma drugs, based on the modeling of molecules with described activity for HOS, MG63, SAOS2, and U2OS cell lines in the ChEMBL database. Several predictive models were obtained for each cell line and those with accuracy greater than 0.8 were integrated into a desirability function for the final multi-objective model. An exhaustive exploration of model combinations was carried out to obtain the best multi-objective model in virtual screening. For the top 1% of the screened list, the final model showed a BEDROC = 0.562, EF = 27.6, and AUC = 0.653. The repositioning was performed on 2218 molecules described in DrugBank. Within the top-ranked drugs, we found: temsirolimus, paclitaxel, sirolimus, everolimus, and cabazitaxel, which are antineoplastic drugs described in clinical trials for cancer in general. Interestingly, we found several broad-spectrum antibiotics and antiretroviral agents. This powerful model predicts several drugs that should be studied in depth to find new chemotherapy regimens and to propose new strategies for osteosarcoma treatment.</jats:p>
