Identification of hub genes to determine drug-disease correlation in breast carcinomas.
Breast cancer
Drug response
Drug-disease correlation
Machine learning
Journal
Medical oncology (Northwood, London, England)
ISSN: 1559-131X
Titre abrégé: Med Oncol
Pays: United States
ID NLM: 9435512
Informations de publication
Date de publication:
28 Dec 2023
28 Dec 2023
Historique:
received:
26
08
2023
accepted:
11
11
2023
medline:
28
12
2023
pubmed:
28
12
2023
entrez:
28
12
2023
Statut:
epublish
Résumé
The exact molecular mechanism underlying the heterogeneous drug response against breast carcinoma remains to be fully understood. It is urgently required to identify key genes that are intricately associated with varied clinical response of standard anti-cancer drugs, clinically used to treat breast cancer patients. In the present study, the utility of transcriptomic data of breast cancer patients in discerning the clinical drug response using machine learning-based approaches were evaluated. Here, a computational framework has been developed which can be used to identify key genes that can be linked with clinical drug response and progression of cancer, offering an immense opportunity to predict potential prognostic biomarkers and therapeutic targets. The framework concerned utilizes DeSeq2, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape, and machine learning techniques to find these crucial genes. Total RNA extraction and qRT-PCR were performed to quantify relative expression of few hub genes selected from the networks. In our study, we have experimentally checked the expression of few key hub genes like APOA2, DLX5, APOC3, CAMK2B, and PAK6 that were predicted to play an immense role in breast cancer tumorigenesis and progression in response to anti-cancer drug Paclitaxel. However, further experimental validations will be required to get mechanistic insights of these genes in regulating the drug response and cancer progression which will likely to play pivotal role in cancer treatment and precision oncology.
Identifiants
pubmed: 38153604
doi: 10.1007/s12032-023-02246-9
pii: 10.1007/s12032-023-02246-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
36Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
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