The Expression of YTHDF2 in Various Cancer Types
We used the TIMER database to study the differential expression of YTHDF2 in tumor tissues and adjacent normal tissues. Figure 1A shows that YTHDF2 was overexpressed in bladder urothelial carcinoma, cholangiocarcinom, esophageal carcinoma, head and neck squamous cell carcinoma, lung adenocarcinoma, prostate adenocarcinoma, liver hepatocellular carcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma tissues, compared with that in the adjacent normal tissues. However, the expression of YTHDF2 was lower in kidney chromophobe, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, skin cutaneous melanoma, and thyroid carcinoma tissues, compared with that in the adjacent non-tumorous tissues. There was no expression difference of YTHDF2 between tumor tissues and adjacent normal tissues in breast invasive carcinoma, lung squamous cell carcinoma, and rectum adenocarcinoma. Unfortunately, we could not compare the expression of YTHDF2 between tumor and non-tumor tissues in adrenocortical carcinoma, lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), glioblastoma multiforme (GBM), acute myeloid leukemia (LAML), lower-grade glioma (LGG), mesothelioma, ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromo- cytoma and paraganglioma, sarcoma (SARC), skin cutaneous melanoma, testicular germ cell tumor, thymoma, uterine carcinosarcoma, and uveal melanoma, for the unavailable of adjacent normal tissues.
To provide a more comprehensive evaluation of YTHDF2 expression in various cancer types, we also used the online GEPIA database to compare the expression of YTHDF2 in 33 cancer types paired with normal tissues in TCGA and GTEx database. Figure1B shows that the expression of YTHDF2 was elevated in many cancer types, especially in DLBC, GBM, PAAD, and THYM. In addition, when only the tumor tissues was evaluated from the TCGA cohort, we found that the expression of YYTHDF2 was the highest in LUAD tumor tissues, while lowest in the PCPG tumor tissues (Figure 1C). Whereas studying the normal tissues from the GTEx database, we found that YTHDF2 expression was highest in Fallopian tissues and lowest in the heart (Figure 1D).
Prognostic Analysis of YTHDF2 in Cancers
To study the association between YTHDF2 expression and prognosis, we performed a series of survival-associated analysis, including OS, DSS, and PFI. Cox proportional hazards model analysis showed that the expression of YTHDF2 was associated with OS in LIHC (p =0.005), KIRC (p =0.023), KICH (p = 0.038), ACC (p = 0.028), LGG (p < 0.001), READ (p = 0.05) and SARC (p< 0.001) (Figure2A). Furthermore, YTHDF2 was a high-risk factor in LIHC, LGG, ACC, SARC, and KICH, while a low-risk gene in other cancer types, particularly in READ. Kaplan-Meier survival analysis also demonstrated a significant negative correlation between YTHDF2 expression and OS in patients with LIHC (p = 0.005), KICH (p = 0.043), ACC (p = 0.013), LGG ( p < 0.001), and SARC ( p = 0.029), whereas in patients with KIRC (P = 0.02) and READ ( p = 0.05) (Figure 2B), high YTHDF2 expression was associated with a longer survival time.
Moreover, analysis of PFI data (Figure 3A) revealed an association between high YTHDF2 expression and poor prognosis in patients with LIHC (p =0.016), KIRC (p =0.008), KICH (p = 0.034), ACC (p = 0.002), LGG (p < 0.001) and KIRP (p< 0.027). However, in patients with CHOL (p =0.017) and KIRC (p = 0.008), YTHDF2 expression did not follow such a relationship with prognosis. Kaplan-Meier survival analysis revealed a negative correlation between YTHDF2 expression and prognosis in patients with ACC (p = 0.001), KICH (p = 0.04), KIRP (p =0.03), LIHC ( p = 0.016) and LGG ( p < 0.001) (Figure 3B).
Regarding the association between YTHDF2 expression and DSS, forest plots showed a negative correlation between YTHDF2 expression and PFI in ACC (p =0.031), LGG (p < 0.001) and SARC (p =0.001), while a positive correlation in patients with KIRC (p = 0.007) (Figure 3C). KM analysis showed that individuals with in ACC (p = 0.014), LGG (p < 0.001) and SARC (p = 0.002) had a high YTHDF2 expression level, but a poor PFI. On the contrary, patients with high YTHDF2 expression had a longer survival time in KIRC (p = 0.006) (Figure 3D).
YTHDF2 Expression Is Related to Immune Checkpoint Genes in Human Cancers
ICP genes have been demonstrated to have significant influences on immune cells infiltration and the outcomes of immunotherapy . Hence, we explored the association between YTHDF2 expression and ICP genes in human cancers to explore the potential role of YTHDF2 in immunotherapy. Forty-seven ICP genes were verified closely related to expression in most cancer types (Figure 4A). The expression of YTHDF2 was positively correlated with immune checkpoint genes in COAD, KICH, KIRC, KIRP, LGG, LIHC, PAAD, PRAD, PCPG and UVM, especially in KICH, LGG, LIHC and UVM. These results suggested that the high expression of YTHDF2 might predict the efficiency of therapeutic effects of immunotherapies targeting ICP genes. In BLCA, BRCA, GBM, LUAD, LUSC, and THYM, YTHDF2 is inversely correlated with the ICP genes, suggesting that a poor immunotherapy outcome would get when YTHDF2 is highly expressed in patients.
Correlations Between YTHDF2 Expression and MMR, TMB, and MSI in Cancers
Microsatellites (MS) are simple repetitive sequences of nucleotide bases that are liable to make errors during DNA replication, which could be recognize and repair by MMR genes. Tumors with defective MMR systems are susceptible to microsatellite mutations, which lead to high levels of MSI, and in turn cause the accumulated mutations in cancer-related genes and the aggravated TMB. Therefore, we investigated the relationship between YTHDF2 expression and several MMR genes, including MLH1, MSH2, MSH6, PMS2 and EPCAM. YTHDF2 was positively correlated with MMR gene expression in all the cancer types, excluding CHOL and UCS (Figure 5A). As TMB is one of the biomarkers that can predict the therapeutic effects of immune checkpoint blockers (ICBs), we examined the association between YTHDF2 expression and TMB in cancers. The results showed that the expression of YTHDF2 and TMB were positively correlated in GBMLGG, COAD, COADREAD, STAD and LIHC, but negatively correlated in THCA (Figure 5B). We also studied the association between the expression of YTHDF2 and MSI, another immune checkpoint inhibitor (ICI) reaction, and found that they are positively correlated in GBM, CESC and STAD, while negatively correlated in BRCA, PRAD, HNSC, THCA and DLBC (Figure 5C). As the MMR, TMB, and MSI have all proven to be correlated with the sensitivity to ICP-related immunotherapy,the above results further confirmed our hypothesis that YTHDF2 might influence the anti-tumor immunity.
The influence of YTHDF2 on TME
TME plays a vital role in modulating malignant progression and influencing the response of immunotherapy. To assess the association between the expression of YTHDF2 expression and the composition of TME, we calculated the estimate core, stromal core, Immune score, and tumor purity in 33 cancers. As shown in this study, YTHDF2 expression was inversely correlated with the estimate score, stromal core and immune score in most cancers except LGG and PPAD, and a positively correlated with tumor purity in most cancers. (Figure 6A-D). These results demonstrated that the expression of YTHDF2 was closely related to the composition of TME in cancer.
Correlation Between YTHDF2 Expression and Immune Infiltrating Levels in Cancers
It has been proven that immune cells in the TME could affect the survival of cancer patients. Accompanied with the prognostic role of YTHDF2 identified in our pan-cancer study, it would be meaningful to explore the association between the expression of YTHDF2 and immune infiltration. We detected the correlation between YTHDF2 expression and immune infiltration levels in 39 different cancers by calculating their correlation coefficients in TIMER. The results indicated that YTHDF2 expression was significantly correlated with tumor purity in 12 cancer types. Furthermore, YTHDF2 expression was also significantly correlated with the infiltration levels of B cells, CD4+ T cells, CD8+ T cells, dendritic cells, macrophages, and neutrophils in 19, 18, 20, 22, 13, and 23 cancer types, respectively (Figure 7A). To better understand the relationship between YTHDF2 expression and differential infiltration of immune cells, we analyzed the correlations between the YTHDF2 and different immune cell markers in KICH, KIRP, LGG, LIHC, PPAD, THYM, UVM, KIRC, READ, ACC and SARC, using the TIMER database. We found that after tumor purity adjustment, YTHDF2 expression was positively correlated with most immunocell-marker genes in most tumors (Figure 7B). We also selected hepatocellular carcinoma (HCC) tissues with obvious immune cells infiltration, from which we confirmed a positive correlation between the protein expression of YTHDF2 and the infiltration of CD3+CD8+ T cells and CD68+ macrophages using immunohistochemistry (Figure 7C-D).
CD8+ T cells are the most significant and effective anti-tumor T cells. After infiltrating in tumor tissues, CD8+ T cells gradually turn into a dysfunctional exhaustion state under a long-term continuous stimulation of tumor-associated antigens. We call this process T-cell exhaustion, which is an important mechanism for the weakening of anti-tumor effect . Interestingly, we also found the expression of YTHDF2 was significantly correlated with T cell exhaustion markers, including PDCD1, CTLA4, TIGIT, LAG3 and HAVCR2 (Figure 7E). HAVCR2 is a vital gene that can mediate T cell exhaustion. In this study, we detected a positive correlation between YTHDF2 and HAVCR2, which indicated that YTHDF2 might play an important role in HAVCR2 mediated T cell exhaustion. Moreover, we also confirmed the positive correlation of YTHDF2 and other immunosuppressive molecules, suggesting the relevance between YTHDF2 and T cell exhaustion, as well as a vital role of YTHDF2 in the TME mediated immune escape.
We also conducted the biological significance of YTHDF2 expression in different cancers. The GO functional annotation and KEGG pathway analysis are shown in Figure 8A. Our data indicate that overexpression of YTHDF2 can positively regulate cell adhesion, cell cycle, and several immune-related functions. Cell cycle related pathways are also enriched in LIHC, KICH and ACC, while PD1, CTLA4, IL-10 and B cell related pathways are enriched in LGG, SRAC, READ and KIRC (Figure 8B).