Identification of potential molecular targets for thyroid cancer using multiple-omics analysis and machine learning

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Thyroid cancer incidence is rising globally.
• Novel molecular markers are essential for early detection and personalized therapy.

Purpose of the Study:

• To identify novel molecular markers for thyroid cancer.
• To investigate the role of identified genes in tumor progression and immune response.

Main Methods:

• Differential gene expression analysis of thyroid cancer microarray data.
• Machine learning and Weighted Gene Co-expression Network Analysis (WGCNA) for signature gene identification.
• Validation using immunohistochemistry and RT-PCR.

Main Results:

• Identified 348 differentially expressed genes (DEGs), with 18 selected as feature genes.
• WGCNA highlighted 12 key genes; FN1 and CRABP1 showed significant links to clinical features, survival, and immune infiltration.
• CRABP1 was downregulated and FN1 was upregulated in thyroid cancer tissues.

Conclusions:

• FN1 and CRABP1 are potential molecular targets for thyroid cancer.
• These genes influence tumor progression, immune response, and prognosis.
• Findings provide insights for early diagnosis and personalized treatment strategies.