Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.

Rakhshaninejad, M; Fathian, M; Shirkoohi, R; Barzinpour, F; Gandomi, AH

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.

Rakhshaninejad, M Fathian, M Shirkoohi, R Barzinpour, F Gandomi, AH

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Publisher:: BMC
Publication Type:: Journal Article
Citation:: BMC Bioinformatics, 2024, 25, (1), pp. 33
Issue Date:: 2024-01-22

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Field	Value	Language
dc.contributor.author	Rakhshaninejad, M
dc.contributor.author	Fathian, M
dc.contributor.author	Shirkoohi, R
dc.contributor.author	Barzinpour, F
dc.contributor.author	Gandomi, AH
dc.date.accessioned	2024-08-01T03:26:30Z
dc.date.available	2024-01-15
dc.date.available	2024-08-01T03:26:30Z
dc.date.issued	2024-01-22
dc.identifier.citation	BMC Bioinformatics, 2024, 25, (1), pp. 33
dc.identifier.issn	1471-2105
dc.identifier.issn	1471-2105
dc.identifier.uri	http://hdl.handle.net/10453/179893
dc.description.abstract	Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.
dc.format	Electronic
dc.language	eng
dc.publisher	BMC
dc.relation.ispartof	BMC Bioinformatics
dc.relation.isbasedon	10.1186/s12859-024-05657-1
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	01 Mathematical Sciences, 06 Biological Sciences, 08 Information and Computing Sciences
dc.subject.classification	Bioinformatics
dc.subject.classification	31 Biological sciences
dc.subject.classification	46 Information and computing sciences
dc.subject.classification	49 Mathematical sciences
dc.subject.mesh	Humans
dc.subject.mesh	Female
dc.subject.mesh	Biomarkers, Tumor
dc.subject.mesh	Precision Medicine
dc.subject.mesh	Algorithms
dc.subject.mesh	Drug Delivery Systems
dc.subject.mesh	Breast Neoplasms
dc.subject.mesh	Humans
dc.subject.mesh	Breast Neoplasms
dc.subject.mesh	Drug Delivery Systems
dc.subject.mesh	Algorithms
dc.subject.mesh	Female
dc.subject.mesh	Biomarkers, Tumor
dc.subject.mesh	Precision Medicine
dc.subject.mesh	Humans
dc.subject.mesh	Female
dc.subject.mesh	Biomarkers, Tumor
dc.subject.mesh	Precision Medicine
dc.subject.mesh	Algorithms
dc.subject.mesh	Drug Delivery Systems
dc.subject.mesh	Breast Neoplasms
dc.title	Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach.
dc.type	Journal Article
utslib.citation.volume	25
utslib.location.activity	England
utslib.for	01 Mathematical Sciences
utslib.for	06 Biological Sciences
utslib.for	08 Information and Computing Sciences
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-08-01T03:26:24Z
pubs.issue	1
pubs.publication-status	Published online
pubs.volume	25
utslib.citation.issue	1

Abstract:

Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

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http://hdl.handle.net/10453/179893