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. 2021 May 6:11:607224.
doi: 10.3389/fonc.2021.607224. eCollection 2021.

YTHDF1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy

Jian Hu  1 , Dongxu Qiu  1 , Anze Yu  1 , Jiao Hu  1 , Hao Deng  1 , Huihuang Li  1 , Zhenglin Yi  1 , Jinbo Chen  1 , Xiongbing Zu  1
Affiliations

YTHDF1 Is a Potential Pan-Cancer Biomarker for Prognosis and Immunotherapy

Jian Hu et al. Front Oncol. .

Abstract

Background: YTH N6-methyladenosine RNA binding protein 1 (YTHDF1) has been indicated proven to participate in the cross-presentation of tumor antigens in dendritic cells and the cross-priming of CD8+ T cells. However, the role of YTHDF1 in prognosis and immunology in human cancers remains largely unknown.

Methods: All original data were downloaded from TCGA and GEO databases and integrated via R 3.2.2. YTHDF1 expression was explored with the Oncomine, TIMER, GEPIA, and BioGPS databases. The effect of YTHDF1 on prognosis was analyzed via GEPIA, Kaplan-Meier plotter, and the PrognoScan database. The TISIDB database was used to determine YTHDF1 expression in different immune and molecular subtypes of human cancers. The correlations between YTHDF1 expression and immune checkpoints (ICP), tumor mutational burden (TMB), microsatellite instability (MSI), and neoantigens in human cancers were analyzed via the SangerBox database. The relationships between YTHDF1 expression and tumor-infiltrated immune cells were analyzed via the TIMER and GEPIA databases. The relationships between YTHDF1 and marker genes of tumor-infiltrated immune cells in urogenital cancers were analyzed for confirmation. The genomic alterations of YTHDF1 were investigated with the c-BioPortal database. The differential expression of YTHDF1 in urogenital cancers with different clinical characteristics was analyzed with the UALCAN database. YTHDF1 coexpression networks were studied by the LinkedOmics database.

Results: In general, YTHDF1 expression was higher in tumors than in paired normal tissue in human cancers. YTHDF1 expression had strong relationships with prognosis, ICP, TMB, MSI, and neoantigens. YTHDF1 plays an essential role in the tumor microenvironment (TME) and participates in immune regulation. Furthermore, significant strong correlations between YTHDF1 expression and tumor immune-infiltrated cells (TILs) existed in human cancers, and marker genes of TILs were significantly related to YTHDF expression in urogenital cancers. TYHDF1 coexpression networks mostly participated in the regulation of immune response and antigen processing and presentation.

Conclusion: YTHDF1 may serve as a potential prognostic and immunological pan-cancer biomarker. Moreover, YTHDF1 could be a novel target for tumor immunotherapy.

Keywords: YTHDF1; human cancer; immune infiltration; immunotherapy; prognosis; tumor microenvironment.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
YTHDF1 expression levels in human cancers. (A) YTHDF1 expression in different cancers and paired normal tissue in the Oncomine database. (B) YTHDF1 expression levels in different cancer types from the TCGA database analyzed by the TIMER database. (*P < 0.05, **P < 0.01, ***P < 0.001). (C) YTHDF1 expression in several cancers and paired normal tissue in the GEPIA database. (D) the expression of YTHDF1 in different cancer cell lines analyzed by the BioGPS database. (E) the expression of YTHDF1 in normal tissue analyzed by the BioGPS database.
Figure 2
Figure 2
Kaplan-Meier survival curve of human cancers with high and low YTHDF1 expression analyzed by the GEPAI database (A–J) and the Kaplan-Meier plotter database (K, L). (A–D) High YTHDF1 expression was related to worse OS and DFS in LIHC (n = 364) and UVM cohorts (n = 78). (E, F) High YTHDF1 expression was related to worse OS in MESO (n = 82) and UCS cohorts (n = 740). (G, H) High YTHDF1 expression was related to worse DFS in BLCA (n = 400) and ACC cohorts (n = 76). (I, J) High YTHDF1 expression was related to better OS and DFS in KIRC cohorts (n = 316). (K, L) higher YTHDF1 expression was related to poorer RFS in TGCT (n = 105) and KIRP (n = 183). OS, overall survival; DFS, disease free survival; RFS, relapse-free survival.
Figure 3
Figure 3
The relationship between YTHDF1 expression and pan-cancer immune subtypes. (A) in BRCA, (B) in COAD, (C) in HNSC, (D) in KIRC, (E) in LGG, (F) in LUAD, (G) in LUSC, (H) in OV, (I) in PRAD, (J) in SKCM, (K) in STAD, (L) in UCEC.
Figure 4
Figure 4
The relationship between YTHDF1 expression and pan-cancer molecular subtypes. (A) in BRCA, (B) in COAD, (C) in ESCA, (D) in HNSC, (E) in KIRP, (F) in LGG, (G) in LUSC, (H) in OV, (I) in PCPG, (J) in READ, (K) in STAD, (L) in UCEC.
Figure 5
Figure 5
The relationship between YTHDF1 expression and pan-cancer immune checkpoint genes. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 6
Figure 6
The relationship between YTHDF1 expression and TMB (A), MSI (B), neoantigen (C) and ESTIMATE score (D) in human cancers. TMB, tumor mutational burden; MSI, microsatellite instability; MMR genes, mismatch repair genes; ESTIMATE, Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data.
Figure 7
Figure 7
The relationship between YTHDF1 expression and infiltrating immune cells of human cancers and urogenital cancers. (A) the relationship between YTHDF1 expression level and infiltrating levels of B cells, CD4+ T cells, CB8+ T cells, macrophages, neutrophils, dendritic cell in human cancers. (B) the relationship between YTHDF1 expression level and infiltrating levels of B cell lineages, CB8+ T cells, cytotoxic lymphocytes, endothelial cells, fibroblasts, monocytic cell lineages, myeloid dendritic cells, neutrophils, natural killer cells, T cells in six urogenital cancers. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 8
Figure 8
YTHDF1 genomic alterations in six urogenital cancers analyzed by the cBioPortal database (A–C) and YTHDF1 differential expression in bladder cancer with different clinical subgroups (D–I) analyzed by the UALCAN database. (A) OncoPrint of YTHDF1 gene alterations in cancer cohort. (Different colors means different types of genetic alterations and amplification accounts for the largest proportion). (B) main type of YTHDF1 gene alterations in cancer groups. (C) Details of YTHDF1 gene alteration types in cancer cohort; (D–I) YTHDF1 differential expression in bladder cancer with individual cancer stages (n = 419) (D), histological subtypes (n = 422) (E), patient sex (n = 421) (F), molecular subtypes (n = 427) (G), nodal metastasis status (n = 385) (H), TP53 mutation status (n = 429) (I). (*P < 0.05, **P < 0.01, ***P < 0.001).
Figure 9
Figure 9
YTHDF1 coexpression genes in BLCA analyzed by the LinkedOmics database. (A) Highly correlated genes of YTHDF1 tested by Pearson test in BLCA cohort. (B, C) Top 50 positive coexpression genes (B) and negative coexpression genes (C) of YTHDF1 in heat map in BLCA; (D) Directed acyclic graph of YTHDF1 GO analysis (biological process) in BLCA cohort. (E) Volcano plot of YTHDF1 KEGG pathways in BLCA cohort.
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