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. 2022 Nov 5;12(1):18813.
doi: 10.1038/s41598-022-22879-6.

Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

Junde Zhao #  1 , Jiani Zou #  1 , Wenjian Jiao  1 , Lidong Lin  1 , Jiuling Wang  2 , Zhiheng Lin  3
Affiliations

Construction of N-7 methylguanine-related mRNA prognostic model in uterine corpus endometrial carcinoma based on multi-omics data and immune-related analysis

Junde Zhao et al. Sci Rep. .

Abstract

N-7 methylguanine (m7G) is one of the most common RNA base modifications in post-transcriptional regulation, which participates in multiple processes such as transcription, mRNA splicing and translation during the mRNA life cycle. However, its expression and prognostic value in uterine corpus endometrial carcinoma (UCEC) have not been systematically studied. In this paper, the data such as gene expression profiles, clinical data of UCEC patients, somatic mutations and copy number variants (CNVs) are obtained from the cancer genome atlas (TCGA) and UCSC Xena. By analyzing the expression differences of m7G-related mRNA in UCEC and plotting the correlation network maps, a risk score model composed of four m7G-related mRNAs (NSUN2, NUDT3, LARP1 and NCBP3) is constructed using least absolute shrinkage and selection operator (LASSO), univariate and multivariate Cox regression in order to identify prognosis and immune response. The correlation of clinical prognosis is analyzed between the m7G-related mRNA and UCEC via Kaplan-Meier method, receiver operating characteristic (ROC) curve, principal component analysis (PCA), t-SNE, decision curve analysis (DCA) curve and nomogram etc. It is concluded that the high risk is significantly correlated with (P < 0.001) the poorer overall survival (OS) in patients with UCEC. It is one of the independent risk factors affecting the OS. Differentially expressed genes are identified by R software in the high and low risk groups. The functional analysis and pathway enrichment analysis have been performed. Single sample gene set enrichment analysis (ssGSEA), immune checkpoints, m6A-related genes, tumor mutation burden (TMB), stem cell correlation, tumor immune dysfunction and rejection (TIDE) scores and drug sensitivity are also used to study the risk model. In addition, we have obtained 3 genotypes based on consensus clustering, which are significantly related to (P < 0.001) the OS and progression-free survival (PFS). The deconvolution algorithm (CIBERSORT) is applied to calculate the proportion of 22 tumor infiltrating immune cells (TIC) in UCEC patients and the estimation algorithm (ESTIMATE) is applied to work out the number of immune and matrix components. In summary, m7G-related mRNA may become a potential biomarker for UCEC prognosis, which may promote UCEC occurrence and development by regulating cell cycles and immune cell infiltration. It is expected to become a potential therapeutic target of UECE.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Transcriptomics analysis of m7G high and low expressions in UCEC tissues. (A) The heat map shows 20 DEGs. (B) Correlation network diagram of DEGs. (C) GO enrichment analysis results. (D) KEGG enrichment analysis results.
Figure 2
Figure 2
m7G-related gene mutations and CNV information in UCEC. (A) One hundred and fifty-two (28.73%) out of 529 patients show different genetic alterations. Of the missense mutation is the most common type of mutations. (B) 28 m7G-related gene copy number variants. (C) CNV change in location of intracellular m7G-related genes.
Figure 3
Figure 3
Construction of m7G-related mRNA prognostic model. (A) Screening prognostic mRNAs using univariate COX regression analysis. (B) LASSO regression analysis. (C) Multivariate COX proportional risk regression analysis of four prognostic mRNAs for establishment of a prognostic model. (D) Heatmap of prognostic-related m7G-related mRNAs. Green represents low expression of m7G-related mRNA, red represents high expression of m7G-related mRNA.
Figure 4
Figure 4
Survival analysis and comparison between the high and low risk groups. (A,B) Median distribution of risk scores and survival status of UCEC patients. (C) Kaplan–Meier survival curves of patients in the high and low risk groups in the TCGA cohort. (D–G) Survival status of high and low expression groups with NSUN2, NUDT3, LARP1 and NCBP3 genes. (H) ROC curves and AUC values of the model in the TCGA cohort. (I) PCA analysis results. (J) t-SNE analysis results.
Figure 5
Figure 5
Independent prognostic analysis of risk score model. (A,B) Univariate and multivariate COX regression analysis of OS in the TCGA cohort. (C) DCA curve of scores and score-clinical variable integration mode. (D) Survival prognosis nomogram of UCEC patients. (E) Calibration charts for predicting 1, 3 and 5-year survival of UCEC patients.
Figure 6
Figure 6
Functional enrichment analysis and immune infiltration levels of differential genes in the two risk subgroups. (A) GO analysis of differentially expressed genes. (B) Infiltration levels of immune cells. (C) Infiltration scores of immune pathways. Differential expression of 18 common immune checkpoints (D) and 12 M6A-related genes (E) between the two risk subgroups.
Figure 7
Figure 7
GSEA of M7G high expression (A–H) and low expression (I–L) samples.
Figure 8
Figure 8
Survival, TME and immune cell infiltration analysis of consensus clustering recognition and typing. (A) Consensus clustering CDFs of k = 2–9. (B) Relative change in area under CDF curve of k = 2–9. (C) Tracking plot of k = 2–9. (D) Consensus clustering matrix of k = 3. (E) Differential distribution of 4 prognostic risk genes in 3 types among the three groups with respect to OS (F) and PFS (G) Kaplan–Meier survival curve. (H) ESTIMATEScore. (I) ImmuneScore; (J) StromalScore. (K) TumorPurity; Principal component analysis plot of immune cell infiltration matrix of 3 types (L) and variance analysis plot (M).
Figure 9
Figure 9
Correlation analysis of TMB levels between the two risk subgroups. (A,B) Distribution of the first 20 mutant genes in the high and low risk groups. (C) Difference analysis of TMB in the two risk subgroups. (D) Correlation between TMB and risk score. (E,F) Analysis of OS in UCEC patients in combination with TMB levels and risk scores.
Figure 10
Figure 10
The risk score model is closely related to stem cells, TIDE scores and drug sensitivity. Correlation between the risk scores with RNAss (A) and DNAss (B) stem cells. (C) Differential expression of TIDE scores between the low and the high risk groups. (D–I) Drug sensitivity analysis of two risk subgroups.
Figure 11
Figure 11
The result of RT-PCR.
See this image and copyright information in PMC

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