» Articles » PMID: 39861138

Leveraging Single-Cell Multi-Omics to Decode Tumor Microenvironment Diversity and Therapeutic Resistance

Abstract

Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic resistance. These technologies are able to integrate data from molecular genomic, transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution scale that give rise to the full cellular and molecular complexity in the TME. Understanding the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune cells, and ECs has led to novel insights into their immense heterogeneity in functions, which can have important consequences on tumor behavior. In-depth studies have uncovered immune evasion mechanisms, including the exhaustion of T cells and metabolic reprogramming in response to hypoxia from cancer cells. Single-cell multi-omics also revealed resistance mechanisms, such as stromal cell-secreted factors and physical barriers in the extracellular matrix. Future studies examining specific metabolic pathways and targeting approaches to reduce the heterogeneity in the TME will likely lead to better outcomes with immunotherapies, drug delivery, etc., for cancer treatments. Future studies will incorporate multi-omics data, spatial relationships in tumor micro-environments, and their translation into personalized cancer therapies. This review emphasizes how single-cell multi-omics can provide insights into the cellular and molecular heterogeneity of the TME, revealing immune evasion mechanisms, metabolic reprogramming, and stromal cell influences. These insights aim to guide the development of personalized and targeted cancer therapies, highlighting the role of TME diversity in shaping tumor behavior and treatment outcomes.

Citing Articles

Effects of circulating RNAs on tumor metabolism in lung cancer (Review).

Zhao P, Zhu Z, Zheng X, Song Y, Chen C, Xu G Oncol Lett. 2025; 29(4):204.

PMID: 40070786 PMC: 11894507. DOI: 10.3892/ol.2025.14950.


Genetic and Epigenetic Intersections in COVID-19-Associated Cardiovascular Disease: Emerging Insights and Future Directions.

Sabit H, Arneth B, Altrawy A, Ghazy A, Abdelazeem R, Adel A Biomedicines. 2025; 13(2).

PMID: 40002898 PMC: 11852909. DOI: 10.3390/biomedicines13020485.

References
1.
Li X, Zhang Q, Chen G, Luo D . Multi-Omics Analysis Showed the Clinical Value of Gene Signatures of C1QC and SPP1 TAMs in Cervical Cancer. Front Immunol. 2021; 12:694801. PMC: 8290180. DOI: 10.3389/fimmu.2021.694801. View

2.
Zhou X, Ling Y, Cui J, Wang X, Long N, Teng W . Mitochondrial RNA modification-based signature to predict prognosis of lower grade glioma: a multi-omics exploration and verification study. Sci Rep. 2024; 14(1):12602. PMC: 11144219. DOI: 10.1038/s41598-024-63592-w. View

3.
Li Y, Cai H, Yang J, Xie X, Pei S, Wu Y . Decoding tumor heterogeneity in uveal melanoma: basement membrane genes as novel biomarkers and therapeutic targets revealed by multi-omics approaches for cancer immunotherapy. Front Pharmacol. 2023; 14:1264345. PMC: 10562578. DOI: 10.3389/fphar.2023.1264345. View

4.
Arima Y, Matsueda S, Saya H . Significance of Cancer-Associated Fibroblasts in the Interactions of Cancer Cells with the Tumor Microenvironment of Heterogeneous Tumor Tissue. Cancers (Basel). 2023; 15(9). PMC: 10177529. DOI: 10.3390/cancers15092536. View

5.
Tan Z, Li H, Huang Y, Fu S, Wang H, Wang J . Multi-omics landscape analysis reveals the pan-cancer association of arginine biosynthesis genes with tumor immune evasion and therapy resistance. Heliyon. 2024; 10(5):e26804. PMC: 10925990. DOI: 10.1016/j.heliyon.2024.e26804. View