Genomics Proteomics & BioinformaticsBioinformaticsPeer-reviewed journal article
Pan-cancer Analysis Reveals m6A Variation and Cell-specific Regulatory Network in Different Cancer Types
Yao LiniD, Jingyi LiiD, Shuaiyi LiangiD, Y CheniD, Yue-Qi LiiD, Yixian CuniD, Lei TianiD, Yuanli ZhouiD, Yitong CheniD, Jiemei ChuiD, Hubin CheniD, Qiang LuoiD, Ruili ZhengiD, Gang WangiD, Hao LiangiD, Ping CuiiD, Sanqi AniD
Abstract
As the most abundant mRNA modification in mRNA, N6-methyladenosine (m6A) plays a crucial role in RNA fate, impacting cellular and physiological processes in various tumor types. However, our understanding of the function and role of the m6A methylome in tumor heterogeneity remains limited. Herein, we collected and analyzed m6A methylomes across nine human tissues from 97 m6A-seq and RNA-seq samples. Our findings demonstrate that m6A exhibits different heterogeneity in most tumor tissues compared to normal tissues, which contributes to the diverse clinical outcomes in different cancer types. We also found that the cancer type-specific m6A level regulated the expression of different cancer-related genes in distinct cancer types. Utilizing a novel and reliable method called "m6A-express", we predicted m6A- regulated genes and revealed that cancer type-specific m6A-regulated genes contributed to the prognosis, tumor origin and infiltration level of immune cells in diverse patient populations. Furthermore, we identified cell-specific m6A regulators that regulate cancer-specific m6A and constructed a regulatory network. Experimental validation was performed, confirming that the cell-specific m6A regulator CAPRIN1 controls the m6A level of TP53. Overall, our work reveals the clinical relevance of m6A in various tumor tissues and explains how such heterogeneity is established. These results further suggest the potential of m6A for cancer precision medicine for patients with different cancer types.
- organism: human
- assay: multiome
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: protein structure prediction
- research focus: drug response / drug discovery
- approach: statistical model
- Citations
- 31
- FWCI
- 4.21
- Field percentile
- top 4%
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