High-Throughput Analysis of Core Pathways in HeLa Cells: Single-Cell Sequencing and AI-driven Modeling of Multi-Pathway Interaction Networks

HeLa cells, the first successfully cultured human cancer cell line, are pivotal in cancer research, virology, and drug screening. However, their multi-omics heterogeneity and complex cancer-related cascades challenge traditional bulk sequencing, which fails to capture dynamic cell-cell interactions and resolve pathway crosstalk. This review systematically examines single-cell multi-omics technologies (transcriptomics, proteomics, and data integration) and AI-driven network modeling (graph neural networks, deep learning) for decoding HeLa cells' core pathways and metastasis mechanisms. It reveals single-cell-level lipid metabolic heterogeneity in cell cycle regulation, dynamic coupling of Ras/NF-B and PI3K/AKT pathways, and HPV protein-accelerated cell cycles. Glycolysis and oxidative phosphorylation synergize to meet energy demands during metabolic reprogramming, while lactate promotes invasion. Apoptosis resistance involves high anti-apoptotic gene expression and endoplasmic reticulum stress proteins. AI models address data sparsity, predicting metabolic pathways, drug responses, and cell communication networks. Future research should develop high-sensitivity spatial multi-omics, HeLa-specific AI models, and organoid platforms to advance precision medicine.