2026-05-05T19:49:35Zhttps://uvadoc.uva.es/oai/request
oai:uvadoc.uva.es:10324/805172025-12-17T14:02:06Zcom_10324_23459com_10324_954com_10324_894col_10324_23460
UVaDOC author Martínez de Paz, Pedro José author Gómez Pilar, Javier author Martín Fernández, Marta author Ceballos, Francisco C. author Gómez Sánchez, Esther author Hornero Sánchez, Roberto author Tamayo Gómez, Eduardo 2025-12-11T14:27:03Z 2025-12-11T14:27:03Z 2023 IEEE/ACM Trans Comput Biol Bioinform. 2023 Nov-Dec;20(6):3660-3668. 1545-5963 https://uvadoc.uva.es/handle/10324/80517 10.1109/TCBB.2023.3309998 3660 6 3668 IEEE/ACM Transactions on Computational Biology and Bioinformatics 20 1557-9964 Sepsis is among the most common causes of death in intensive care units. Septic shock is a type of circulatory shock that shows signs and symptoms that are similar to non-septic shock. Despite the impact of shock on patients and the economic burden, knowledge of the pathophysiology of septic shock is scarce. In this context, weighted gene co-expression network analysis can help to elucidate the molecular mechanisms of this condition. The gene expression dataset used in this study was downloaded from the Gene Expression Omnibus, which contains 80 patients with septic shock, 33 patients with non-septic shock, and 15 healthy controls. Our novel analysis revealed five gene modules specific for patients with septic shock and three specific gene modules for patients with non-septic shock. Interestingly, genes related to septic shock were mainly involved in the immune system and endothelial cells, while genes related to non-septic shock were primarily associated with endothelial cells. Together, the results revealed the specificity of the genes related to the immune system in septic shock. The novel approach developed here showed its potential to identify critical pathways for the occurrence and progression of these conditions while offering new treatment strategies and effective therapies. spa Gene Co-Expression Networks Offer New Perspectives on Sepsis Pathophysiology info:eu-repo/semantics/article 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URL https://uvadoc.uva.es/bitstream/10324/80517/1/MartinezPaz_2023_IEEEACM.pdf File MD5 b43220fdf30394f4d24b7ccc1fa072cf 2214384 application/pdf MartinezPaz_2023_IEEEACM.pdf