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Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine

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Abstract

Background

The N7-methylguanosine modification (m7G) of the 5′ cap structure in the mRNA plays a crucial role in gene expression. However, the relation between m7G and tumor immune remains unclear. Hence, we intended to perform a pan-cancer analysis of m7G which can help explore the underlying mechanism and contribute to predictive, preventive, and personalized medicine (PPPM / 3PM).

Methods

The gene expression, genetic variation, clinical information, methylation, and digital pathological section from 33 cancer types were downloaded from the TCGA database. Immunohistochemistry (IHC) was used to validate the expression of the m7G regulator genes (m7RGs) hub-gene. The m7G score was calculated by single-sample gene-set enrichment analysis. The association of m7RGs with copy number variation, clinical features, immune-related genes, TMB, MSI, and tumor immune dysfunction and exclusion (TIDE) was comprehensively assessed. CellProfiler was used to extract pathological section characteristics. XGBoost and random forest were used to construct the m7G score prediction model. Single-cell transcriptome sequencing (scRNA-seq) was used to assess the activation state of the m7G in the tumor microenvironment.

Results

The m7RGs were highly expressed in tumors and most of the m7RGs are risk factors for prognosis. Moreover, the cellular pathway enrichment analysis suggested that m7G score was closely associated with invasion, cell cycle, DNA damage, and repair. In several cancers, m7G score was significantly negatively correlated with MSI and TMB and positively correlated with TIDE, suggesting an ICB marker potential. XGBoost-based pathomics model accurately predicts m7G scores with an area under the ROC curve (AUC) of 0.97. Analysis of scRNA-seq suggests that m7G differs significantly among cells of the tumor microenvironment. IHC confirmed high expression of EIF4E in breast cancer. The m7G prognostic model can accurately assess the prognosis of tumor patients with an AUC of 0.81, which was publicly hosted at https://pan-cancer-m7g.shinyapps.io/Panca-m7g/.

Conclusion

The current study explored for the first time the m7G in pan-cancer and identified m7G as an innovative marker in predicting clinical outcomes and immunotherapeutic efficacy, with the potential for deeper integration with PPPM. Combining m7G within the framework of PPPM will provide a unique opportunity for clinical intelligence and new approaches.

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Data availability

The data supporting the findings of this study are deposited in TCGA and GEO databases. The single cell sequencing datasets can be found in online repositories of GEO (GSE152938).

Code availability

The analyses methods and used packages are illustrated in the “Materials and methods” section. All other R and Python code and analyses are available from the corresponding author upon request.

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Funding

This work received financial support from Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project (S2021016); Guangxi Natural Science Foundation (2021JJA140081); and Basic Research Skills Enhancement Project for Young and Middle-aged Teachers in Universities in Guangxi (2021KY0087).

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Authors and Affiliations

  1. Division of Colorectal & Anal Surgery, Department of Gastrointestinal Surgery, Guangxi Medical University Cancer Hospital, Nanning, The People’s Republic of China

    Xiaoliang Huang, Zuyuan Chen, Xiaoyun Xiang, Yanling Liu, Xingqing Long, Kezhen Li, Mingjian Qin, Chenyan Long, Xianwei Mo, Weizhong Tang & Jungang Liu

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  1. Xiaoliang Huang
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Contributions

Xiaoliang Huang, Zuyuan Chen, Xiaoyun Xiang, Yanling Liu, Xingqing Long, Xianwei Mo, Weizhong Tang Jungang Liu, Kezhen Li, and Mingjian Qin: conceived and designed the experiments; Xiaoliang Huang, Zuyuan Chen, Xiaoyun Xiang, Yanling Liu, Xingqing Long, Xianwei Mo, Chenyan Long, Weizhong Tang: analyzed the data; Xiaoliang Huang, Zuyuan Chen, Xiaoyun Xiang, Yanling Liu, Xingqing Long, Xianwei Mo, Weizhong Tang, Jungang Liu, Kezhen Li and Mingjian Qin: helped with reagents/materials/analysis tools; Xiaoliang Huang, Zuyuan Chen, Xiaoyun Xiang, Yanling Liu, Xingqing Long, Xianwei Mo, Chenyan Long, Weizhong Tang, Jungang Liu, Kezhen Li and Mingjian Qin: contributed to the writing of the manuscript. All authors reviewed the manuscript.

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Correspondence to Xianwei Mo, Weizhong Tang or Jungang Liu.

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Huang, X., Chen, Z., Xiang, X. et al. Comprehensive multi-omics analysis of the m7G in pan-cancer from the perspective of predictive, preventive, and personalized medicine. EPMA Journal 13, 671–697 (2022). https://doi.org/10.1007/s13167-022-00305-1

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