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Dissecting Crucial Gene Markers Involved in HPV-Associated Oropharyngeal Squamous Cell Carcinoma from RNA-Sequencing Data through Explainable Artificial Intelligence.
Sekaran, Karthik; Varghese, Rinku Polachirakkal; Krishnan, Sasikumar; Zayed, Hatem; El Allali, Achraf; Doss, George Priya C.
Afiliação
  • Sekaran K; School of Biosciences and Technology, Vellore Institute of Technology, 632014 Vellore, India.
  • Varghese RP; School of Biosciences and Technology, Vellore Institute of Technology, 632014 Vellore, India.
  • Krishnan S; Department of Sensor and Biomedical Technology, School of Electronics Engineering, Vellore Institute of Technology, 632014 Vellore, India.
  • Zayed H; Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, 2713 Doha, Qatar.
  • El Allali A; Bioinformatics Laboratory, College of Computing, Mohammed VI Polytechnic University, 43150 Ben Guerir, Morocco.
  • Doss GPC; School of Biosciences and Technology, Vellore Institute of Technology, 632014 Vellore, India.
Front Biosci (Landmark Ed) ; 29(6): 220, 2024 Jun 18.
Article em En | MEDLINE | ID: mdl-38940026
ABSTRACT

BACKGROUND:

The incidence rate of oropharyngeal squamous cell carcinoma (OPSCC) worldwide is alarming. In the clinical community, there is a pressing necessity to comprehend the etiology of the OPSCC to facilitate the administration of effective treatments.

METHODS:

This study confers an integrative genomics approach for identifying key oncogenic drivers involved in the OPSCC pathogenesis. The dataset contains RNA-Sequencing (RNA-Seq) samples of 46 Human papillomavirus-positive head and neck squamous cell carcinoma and 25 normal Uvulopalatopharyngoplasty cases. The differential marker selection is performed between the groups with a log2FoldChange (FC) score of 2, adjusted p-value < 0.01, and screened 714 genes. The Particle Swarm Optimization (PSO) algorithm selects the candidate gene subset, reducing the size to 73. The state-of-the-art machine learning algorithms are trained with the differentially expressed genes and candidate subsets of PSO.

RESULTS:

The analysis of predictive models using Shapley Additive exPlanations revealed that seven genes significantly contribute to the model's performance. These include ECT2, LAMC2, and DSG2, which predominantly influence differentiating between sample groups. They were followed in importance by FAT1, PLOD2, COL1A1, and PLAU. The Random Forest and Bayes Net algorithms also achieved perfect validation scores when using PSO features. Furthermore, gene set enrichment analysis, protein-protein interactions, and disease ontology mining revealed a significant association between these genes and the target condition. As indicated by Shapley Additive exPlanations (SHAPs), the survival analysis of three key genes unveiled strong over-expression in the samples from "The Cancer Genome Atlas".

CONCLUSIONS:

Our findings elucidate critical oncogenic drivers in OPSCC, offering vital insights for developing targeted therapies and enhancing understanding its pathogenesis.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Orofaríngeas / Biomarcadores Tumorais Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Orofaríngeas / Biomarcadores Tumorais Limite: Humans Idioma: En Ano de publicação: 2024 Tipo de documento: Article