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Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies
Published on: August 11, 2023
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Harnessing machine learning technique to authenticate differentially expressed genes in oral squamous cell carcinoma
Amisha Patel1, Saswati Mahapatra2, Ashok Kumar Bishoyi3
1Department of Microbiology, School of Science, RK University, Rajkot, Gujarat 360020, India.
Oral Surgery, Oral Medicine, Oral Pathology and Oral Radiology
|November 6, 2024
Summary
This study identifies novel oral squamous cell carcinoma (OSCC) signature genes for improved cancer diagnosis and prognosis. These potential molecular biomarkers could lead to better therapeutic strategies and patient outcomes.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Effective cancer biomarkers are crucial for early detection, prognosis, and therapeutic development.
- Current molecular markers lack therapeutic efficacy for early cancer detection.
Purpose of the Study:
- To identify diagnostic and prognostic signature genes in oral squamous cell carcinoma (OSCC).
- To evaluate the potential of these genes as molecular markers for cancer pathology.
Main Methods:
- Eight candidate genes were selected and validated in 100 matched OSCC samples using qPCR.
- Machine learning approaches and The Cancer Genome Atlas (TCGA) database were utilized for analysis.
Main Results:
- All selected genes were expressed in the majority of OSCC samples.
- Machine learning models showed a stable predictive accuracy of 84% for OSCC detection.
- Validation with the study's dataset yielded 75% accuracy, potentially due to demographic variations.
Conclusions:
- The research highlights cancer-associated molecular biomarkers for enhanced OSCC prognosis.
- Identified biomarkers may serve as novel therapeutic targets for improved patient outcomes.
