Precision Biomarker Identification in Gynecological Cancers Using Coexpression Networks and Attention-Based LSTM in...

Area of Science:

• Genomics
• Bioinformatics
• Oncology

Background:

• Cervical cancer (CC) and ovarian cancer (OC) are leading causes of cancer-related death in women.
• Effective early diagnosis and prognosis biomarkers are crucial for improving patient outcomes.
• Current diagnostic and prognostic tools require enhancement for gynecological malignancies.

Purpose of the Study:

• To identify and validate novel gene biomarkers for cervical and ovarian cancer using integrated bioinformatics and Healthcare 4.0 approaches.
• To explore the diagnostic and prognostic potential of identified biomarkers for targeted therapy.
• To leverage machine learning and network analysis for robust biomarker discovery.

Main Methods:

• Differential gene expression analysis of microarray datasets to identify candidate genes.
• Machine learning algorithms (mRMR, SVM-RFE, AttLSTM) for selecting significant differentially expressed genes (MDEGs).
• Weighted Gene Co-expression Network Analysis (WGCNA) and Protein-Protein Interaction (PPI) network analysis to identify coexpressed and hub genes.

Main Results:

• Four hub genes (MCM3, FOXM1, SH3BP5, PAPSS2) were identified as key players in CC and OC.
• FOXM1 and MCM3 showed significant upregulation, while SH3BP5 and PAPSS2 were downregulated in cancer tissues.
• These genes demonstrated high prognostic significance with strong discriminatory power in ROC analysis and significant binding affinity with FDA-approved drugs.

Conclusions:

• FOXM1, MCM3, SH3BP5, and PAPSS2 are potential biomarkers for early prognosis and diagnosis of CC and OC.
• These biomarkers may facilitate the development of targeted therapeutic strategies for gynecological cancers.
• The study highlights the utility of integrated bioinformatics and Healthcare 4.0 in biomarker discovery.