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. 2023 Oct 30;9(11):e21285.
doi: 10.1016/j.heliyon.2023.e21285. eCollection 2023 Nov.

Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma

Ting Liu  1 , Yang Wang  2 , Zhizhao Li  3 , Lei Sun  1 , Kun Yang  1 , Jiamin Chen  1 , Xiaoyi Han  1 , Liming Qi  1 , Xingang Zhou  1 , Peng Wang  1
Affiliations

Establishment of a new molecular subtyping and prognostic signature with m6A/m5C/m1A/m7G regulatory genes for hepatocellular carcinoma

Ting Liu et al. Heliyon. .

Abstract

Background: RNA modification, including m6A, m5C, m1A, and m7G, participated in tumor progress. Therefore, the purpose of the present study was to explore the role of m6A/m5C/m1A/m7G regulatory genes in the prognosis and tumor microenvironment (TME) for hepatocellular carcinoma (HCC).

Methods: 71 m6A/m5C/m1A/m7G regulatory genes expression for HCC was detected, differentially expressed genes were screened, and molecular forms were classified by unsupervised consensus clustering. Cox regression and the Least Absolute Shrinkage and Selection Operator (LASSO) analysis were applied to establish a prognostic signature. Time-dependent receiver operating characteristic (ROC) curves were evaluated for clinical effectiveness and accuracy of the prognostic hazard model. In cluster subtypes and risk models, the differences in prognosis, immune cell infiltration, immune checkpoint, immunotherapy, and drug sensitivity between different subtypes were evaluated.

Results: HCC patients were classified into two clusters (cluster 1 and cluster 2) according to the expression of 71 m6A/m5C/m1A/m7G regulatory genes. Cluster 1 had a poor prognosis and different immune cell infiltration. Cluster 1 had higher immune checkpoint expression and TIDE score than cluster 2. Subsequently, we construct a five-gene prognostic model of m6A/m5C/m1A/m7G regulatory genes (YTHDF2, YTHDF1,YBX1, TRMT61A, TRMT10C). The Kaplan-Meier and ROC curve analysis showed that the prognostic signature exhibited good predictability. The risk score was considered an independent poor prognostic index. The high-risk group had higher immune checkpoint expression and higher TIDE scores. 5-Fluorouracil, docetaxel, doxorubicin, etoposide, gemcitabine, paclitaxel, sorafenib, and vinblastine were more suitable for high-risk patients. ECM receptor interaction, cell cycle, and Leishmania infection were enriched in the high-risk group.

Conclusion: The clustering subgroups and prognostic model of m6A/m5C/m1A/m7G regulatory genes were linked with bad prognosis and TME for HCC, and had the potential to be a novel tool to evaluate the outcomes of HCC patients.

Keywords: Hepatocellular carcinoma; Molecular typing; Prognostic genes; Tumor microenvironment; m6A/m5C/m1A/m7G regulatory genes.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Heapmap of m6A/m5C/m1A/m7G regulated genes in HCC patients and normal tissues (*P < 0.05, **P < 0.01, ***P < 0.001).
Fig. 2
Fig. 2
GO and KEGG enrichment analysis of m6A/m5C/m1A/m7G regulated genes.
Fig. 3
Fig. 3
The clinical values in m6A/m5C/m1A/m7G regulated gene subgroups in HCC patients based on consensus clustering. (A–C) The consensus clustering of m6A/m5C/m1A/m7G regulated genes. (D) Kaplan-Meier survival analysis in cluster1 and cluster2 groups. (E–G) The significant differences in the transcriptome in PCA, tSNE, and UMAP analysis. (H)The difference in expression of immune cell infiltration in both clusters. (I) The expression of immune checkpoints in both subgroups. (J) The difference of TIDE scores in both subgroups. (*P < 0.05, **P < 0.01,***P < 0.001).
Fig. 4
Fig. 4
Construction of prognostic model for m6A/m5C/m1A/m7G regulated genes. (A)Univariate Cox regression of differentially expressed m6A/m5C/m1A/m7G regulated genes. (B–C) The 5 m6A/m5C/m1A/m7G-regulated genes were identified using LASSO method.
Fig. 5
Fig. 5
Prognostic analysis of m6A/m5C/m1A/m7G regulated genes in the training cohort (whole TCGA). (A) The risk score was divided into high risk and low risk.(B)The low-risk groups had a better prognosis than high-risk groups. (C)Principal component analysis plot. (D)The AUC of 1-,3-, and 5-year was 0.767,0.69.0.693, respectively.
Fig. 6
Fig. 6
Univariate and multivariate Cox regression of risk score and clinical features.
Fig. 7
Fig. 7
Validation of the prognostic model of five-gene signature. (A, B)The low-risk groups had a better prognosis than high-risk groups in the internal testing cohort (randomly selected, n=186) and external validation cohort (ICGC). (C, D)Receiver operating characteristic (ROC) curves for the risk model in the internal and external testing cohort.
Fig. 8
Fig. 8
The Correlation between risk score and clinicpathological features. (***P < 0.001,**P < 0.01,*P < 0.05, ns: no significance).
Fig. 9
Fig. 9
The correlation between risk score and immunity and mutational analysis. (A)The correlation between risk score and immune cell infiltration. (B)The correlation between risk score and immune checkpoints. (C)The correlation between risk score and TIDE score. (D, E) The mutation alteration in high-risk group and low-risk group. (****P < 0.0001,***P < 0.001,**P < 0.01,*P < 0.05).
Fig. 10
Fig. 10
The expression of TRMT10C, TRMT61A, YBX1, YTHDF1, and YTHDF2. (A-E)The mRNA expression of TRMT10C, TRMT61A, YBX1, YTHDF1, and YTHDF2 in TCGA database. (F-J)The mRNA expression of TRMT10C, TRMT61A, YBX1, YTHDF1, and YTHDF2 in ICGC database. (K–O) The protein expression of TRMT10C, TRMT61A, YBX1, YTHDF1, and YTHDF2 in UALCAN database. (P–T) Kaplan-Meier survival analysis of TRMT10C, TRMT61A, YBX1, YTHDF1, and YTHDF2 in high-expression and low-expression groups. (***P < 0.001,**P < 0.01,*P < 0.05).
Fig. 11
Fig. 11
The drug sensitivity analysis of risk score in HCC.
Fig. 12
Fig. 12
The enrichment analysis in high-risk groups and low-risk groups. (A)High-risk groups. (B) Low-risk groups.
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