BIOINFORMATICS APPROACHES FOR MULTI-OMICS ANALYSIS OF THE TUMOR MICROENVIRONMENT: INTEGRATING PROTEOMICS, EXOSOMES AND IMMUNE SIGNALING
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Cancer stem cell (CSC) plasticity is increasingly recognized as a central driver of tumor initiation, therapeutic resistance, metastatic dissemination, and disease recurrence. Yet, the dynamic transitions between stem-like and differentiated malignant states remain poorly resolved due to the intrinsic heterogeneity of tumors and the limitations of bulk profiling methods. Recent advances in single-cell sequencing technologies have created an unprecedented opportunity to dissect CSC states at high resolution; however, extracting mechanistic insights from these data requires sophisticated computational methods.
In this work, we present an integrative bioinformatic framework for characterizing CSC plasticity across diverse cancer types using single-cell transcriptomic, epigenomic, and proteogenomic datasets. Our approach synthesizes multiple layers of information—including transcriptional variability, regulatory network inference, cell trajectory reconstruction, chromatin accessibility, ligand–receptor communication, and lineage mapping—to identify key determinants that regulate transitions between stem-like, progenitor-like, and differentiated malignant phenotypes. Through integrative clustering, pseudotime analysis, and machine-learning–based state prediction, we uncover conserved molecular programs and dynamic regulatory modules that define CSC behavior.
Furthermore, we implement cell–cell communication modeling to map how CSC niche interactions influence plasticity, focusing on immune–CSC crosstalk, stromal signaling circuits, and exosome-mediated molecular transport. The combined analysis highlights novel candidate biomarkers and therapeutic vulnerabilities associated with reversible stemness states.
Together, this study demonstrates that integrated single-cell bioinformatic analysis provides a powerful framework to decode cancer stem cell plasticity, enabling a deeper mechanistic understanding of tumor evolution and identifying new avenues for precision oncology and targeted therapeutic interventions.
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