Nanyang Zhang1, Wenbing Xu2, Shijie Wang2, Yan Qiao2, Xiaoxiao Zhang2. 1. The Affiliated Hospital of Qingdao University, Qingdao, China. Electronic address: nanyang.zhang@outlook.com. 2. The Affiliated Hospital of Qingdao University, Qingdao, China.
Abstract
PURPOSE: Chemotherapy-induced alopecia (CIA) is a common and often stressful adverse effect associated with chemotherapy. CIA can cause more psychosocial pressure in patients, including effects on sexuality, self-esteem, and social relationships. We analyzed publicly available data to identify drugs formulated for topical use targeting the relevant CIA molecular pathways by using computational tools. METHODS: The genes associated with CIA were determined by text mining, and the gene ontology of the gene set was studied using the Functional Enrichment analysis tool. Protein-protein interaction network analysis was performed using the String database. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in CIA. FINDINGS: Our analysis identified 427 genes common to CIA text-mining concepts. Gene enrichment analysis and protein-protein interaction analysis yielded 19 genes potentially targetable by a total of 29 drugs that could possibly be formulated for topical application. IMPLICATIONS: The findings from the present analysis would give a new thought to help discover more effective agents, and present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
PURPOSE: Chemotherapy-induced alopecia (CIA) is a common and often stressful adverse effect associated with chemotherapy. CIA can cause more psychosocial pressure in patients, including effects on sexuality, self-esteem, and social relationships. We analyzed publicly available data to identify drugs formulated for topical use targeting the relevant CIA molecular pathways by using computational tools. METHODS: The genes associated with CIA were determined by text mining, and the gene ontology of the gene set was studied using the Functional Enrichment analysis tool. Protein-protein interaction network analysis was performed using the String database. Enriched gene sets belonging to the identified pathways were queried against the Drug-Gene Interaction database to find drug candidates for topical use in CIA. FINDINGS: Our analysis identified 427 genes common to CIA text-mining concepts. Gene enrichment analysis and protein-protein interaction analysis yielded 19 genes potentially targetable by a total of 29 drugs that could possibly be formulated for topical application. IMPLICATIONS: The findings from the present analysis would give a new thought to help discover more effective agents, and present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
Authors: Yongqian Zhang; Hongmin Wang; Feifei Wang; Wenhua Ma; Na Li; Changwen Bo; YingChun Zhao; Li He; Ming Liu Journal: Comput Math Methods Med Date: 2022-09-27 Impact factor: 2.809