Harnessing machine learning technique to authenticate differentially expressed genes in oral squamous cell carcinoma

Amisha Patel; Saswati Mahapatra; Ashok Kumar Bishoyi; Abhishek Sharma; Abhijit Makwana; Tripti Swarnkar; Anubha Gupta; Prasan Kumar Sahoo; Sejal Shah

doi:10.1016/j.oooo.2024.10.075

Harnessing machine learning technique to authenticate differentially expressed genes in oral squamous cell carcinoma

Oral Surg Oral Med Oral Pathol Oral Radiol. 2024 Oct 17:S2212-4403(24)00590-X. doi: 10.1016/j.oooo.2024.10.075. Online ahead of print.

Authors

Amisha Patel¹, Saswati Mahapatra², Ashok Kumar Bishoyi³, Abhishek Sharma⁴, Abhijit Makwana⁵, Tripti Swarnkar⁶, Anubha Gupta⁷, Prasan Kumar Sahoo⁸, Sejal Shah⁹

Affiliations

¹ Department of Microbiology, School of Science, RK University, Rajkot, Gujarat 360020, India.
² Department of Computer Application, Faculty of Technology, Siksha 'O' Anusandhan University, Bhubaneshwar, Odisha 751030, India.
³ Department of Microbiology, Faculty of Science, Marwadi University, Rajkot, Gujarat 360003, India.
⁴ Department of Biotechnology and Bioengineering, Institute of Advanced Research, Gandhinagar, Gujarat 382426, India.
⁵ Shree Nathalal Parekh Cancer Institute, Rajkot Cancer Society, Rajkot, Gujarat 360003, India.
⁶ Department of Computer Application, National Institute of Technology, Raipur CG (NITRR), Chhattisgarh 492010, India.
⁷ SBILab, Department of ECE, and Centre of Excellence in Healthcare, IIIT, Delhi, 110020, India.
⁸ Department of Computer Science and Information Engineering, Chang Gung University, Taoyuan City, 33302, Taiwan.
⁹ Department of Bioinformatics, Faculty of Engineering and Technology, Marwadi University, Rajkot, Gujarat, 360003, India. Electronic address: [email protected].

PMID: 39505585
DOI: 10.1016/j.oooo.2024.10.075

Abstract

Objective: Advancements in early detection of the disease, prognosis and the development of therapeutic strategies necessitate tumor-specific biomarkers. Despite continuous efforts, no molecular marker has been proven to be an effective therapeutic tool for the early detection of cancer. The study aims to determine diagnostic and prognostic signature genes that may be involved in cancer pathology and hence, may serve as molecular markers.

Study design: Eight candidate genes were selected based on our prior study of transcriptomic sequencing and validated in 100 matched pair samples of oral squamous cell carcinoma (OSCC). We further utilized machine learning approaches and examined the diagnostic presentation and predictive ability of the OSCC genes retrieved from publicly available The Cancer Genome Atlas (TCGA) database and compared with our results.

Results: We conducted qPCR analysis to validate the expression of each gene and observed that each gene was present in the majority of OSCC samples. The predictive ability of selected genes was stable (with an average accuracy of 84%) across different classifiers. However, on validation with our dataset, it showed 75% accuracy, which might be because of the demographic variation of the samples.

Conclusions: The present research outlines cancer-associated molecular biomarkers that might eventually contribute to an enhanced prognosis of cancer patient by identifying novel therapeutic targets.