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

  • ️Sat Jan 01 2022

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

Xiaoliang Huang et al. EPMA J. 2022.

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-022-00305-1.

Keywords: Protein-protein interaction analysis; Immune regulation; Immunotherapy; Machine learning; Multi-omics; N7-methylguanosine modification; Pan-cancer analysis; Predictive preventive personalized medicine (PPPM / 3PM); Single-cell transcriptome sequencing; Tumor microenvironment.

© The Author(s), under exclusive licence to European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

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Conflict of interest statement

Conflict of interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1

Flow chart of this study. The sources of data and the main article structure are shown here

Fig. 2
Fig. 2

Differential expression of m7G regulator genes in pan-cancer and the impact on prognosis. A Differential expression of m7RGs. The blue dots indicate low gene expression in the tumor and the red dots represent high gene expression in tumors. B Heat map of m7RGs expression in different cancers. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 3
Fig. 3

CNV and SNP of m7G regulator genes. A SNV oncoplot. The cancer map shows the distribution of m7G mutations and the classification of SNV types (e.g., missense mutations, frame-shift loss, and nonsense mutations). Selected cancer samples are displayed together with a side and top bars showing the number of variations in each sample or gene. B The SNV frequency of genes in cancers. The darker the color, the higher the mutation frequency. Numbers represent the percentage of samples that have the corresponding mutated gene for a given cancer. 0 indicates that there was no mutation in the gene coding region, and no number indicates that there was no mutation in any region of the gene. C CNV pie charts in 33 cancers. The combined heterozygous or homozygous CNVs for each gene in different cancer types are shown in the CNV pie chart. D CNV correlation with mRNA expression. The association between paired mRNA expression and CNV percentage in samples was based on a Pearson product-moment correlation coefficient. The size of the point represents the statistical significance, the bigger the dot size, the higher the statistical significance

Fig. 4
Fig. 4

Methylation of m7G regulator genes and their correlation with expression. A Methylation difference of m7RGs in pan-cancer. Blue dots represent hypomethylation and red dots represent hypermethylation. The solid circles represent FDR < 0.05, namely p < 0.05, significant correlation. B Correlation between mRNA expression and methylation. Negative correlation is indicated by blue dots and positive correlation is indicated by red dots. The bluer the color, the stronger the negative correlation, and the redder the color, the stronger the positive correlation

Fig. 5
Fig. 5

Correlation between GDSC drug sensitivity and mRNA expression. Blue represents a negative correlation, suggesting higher the gene expression, the lower the drug amount required, and the higher the sensitivity

Fig. 6
Fig. 6

Differential expression of m7G score and the correlation with staging. A Correlation between m7RGs and m7G score. Red is positive, blue is negative, and the darker the color, the stronger the correlation. B m7G score was analyzed by combining GTEX and TCGA databases. The box line represents the average value. The box line of the red box block is higher than that of the blue box block, indicating a positive correlation and vice versa

Fig. 7
Fig. 7

Effect of m7G scores on prognosis. A OS forest map of m7G score. B DSS forest map of m7G score. C PFI forest map of m7G score

Fig. 8
Fig. 8

Single cell level functional analysis of m7G. The darker the color, the stronger the correlation. *p < 0.05, **p < 0.01, ***p < 0.001

Fig. 9
Fig. 9

Relationship between m7G score and tumor immune microenvironment. A Correlation analysis of m7G tumor-infiltrating immune cells. *p < 0.05, **p < 0.01, ***p < 0.001. B–D Immune score, microenvironment score, and stroma score of m7G. The triangle indicates no statistical significance, while the circle indicates statistical significance. The greater the absolute value of the score, the higher the correlation

Fig. 10
Fig. 10

Relationship between m7G score and immune regulator gene. A Correlation analysis of m7G scores with immune checkpoints gene expression. B Correlation analysis of m7G score and immune activation gene expression. C Correlation analysis of m7G scores and immunosuppressive gene expression. D Correlation analysis of m7G scores with chemokines. E Correlation analysis of m7G scores with chemokine receptors. The colors range from blue to red, indicating the degree of correlation between tumor and related factors. The number of * indicates the degree of correlation

Fig. 11
Fig. 11

Correlation between m7G score and markers of immunotherapy response. A, B Correlation between m7G score and TMB (A) and MSI (B). The more outward spread of the dots and lines, the higher the correlation score of related tumors. C Correlation between m7G score and TIDE score. The triangle indicates no statistical significance, while the circle indicates statistical significance. The greater the absolute value of the score, the higher the correlation

Fig. 12
Fig. 12

Correlation between m7G score and Tumor-specific total mRNA expression (TmS). A Comparison of TmS abundance in different tumors. B OS forest map of m7G score. C DSS forest map of m7G score. D PFI forest map of m7G score. E The relationship between m7G score and TmS

Fig. 13
Fig. 13

Single-cell transcriptomic atlas of KIRC. A tSEN plot representation of KIRC samples with 11 distinct cell types. B tSEN plot representation of KIRC from two different samples. C tSEN plot representation of m7G scores in different cell types. D Comparison of m7G scores in different KIRC tumor microenvironment cells. The blue horizontal line on the violin plot indicates the median m7G score. The letters at the top indicate a statistical difference between cells for two comparisons. Different letters indicate that the difference is statistically significant

Fig. 14
Fig. 14

Immunohistochemical staining results and statistical graphs of EIF4E in breast cancers. A Immunohistochemical image of breast cancer. B Statistical results of EIF4E expression in thirty-five pairs of breast cancer and normal samples

Fig. 15
Fig. 15

Pathomics-based machine learning model to predict m7G scores. A Representative images for image processing and feature extraction using CellProfiler. B Ranking the importance of variables for predicting m7G scores. C Confusion matrix for the random forest model. D Confusion matrix for the XGBoost model. E AUC of XGBoost model in predicting m7G score in pan-cancer. F AUC of XGBoost model in predicting m7G score in different tumors

Fig. 16
Fig. 16

Construction of m7G prognostic model. A Correlation of m7RGs with m7G scores, tumor type, tumor stage, gender, age, overall survival, TMB, MSI, immune microenvironment score, CNV and TIDE score. B Selection of tuning parameter (λ) in the LASSO regression using tenfold-cross-validation via minimum criteria. The C-index is plotted versus log (λ). At the optimal values log(λ), where features are selected, dotted vertical lines are set by using the minimum criteria and the 1 standard error of the minimum criteria. C LASSO coefficient profiles for features, each coefficient profile plot is produced versus log (λ) sequence. Dotted vertical line is set at the non-zero coefficients selected via tenfold cross-validation. D The relationship between risk scores and the survival status and survival time. E ROC curves for prognostic models predicting survival at 1, 3, and 5 years

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