Differential expression of WTAP and YTHDF1 and Consensus clustering analysis based on the two genes
Wilcoxon rank sum test was performed on tumor tissues and normal tissues using RNA sequencing data from the TCGA database of breast cancer (BRCA), and found that the expression of WTAP was lower in tumor tissues than in normal tissues and YTHDF1 was higher in tumor tissues than in normal tissues (Figure. 1a, b). After that, we performed Consensus clustering of 1072 breast cancer samples based on the expression matrix of WTAP and YTHDF1 and divided the samples into two groups (Figure 1c and Supplementary Figure 1). The Consensus clustering classified high WTAP and low YTHDF1 expression into group 1 (n=527), and low WTAP and high YTHDF1 expression into group 2 (n=545). To further explore the clustering trend, we used spearman correlation analysis to explore the correlation between the two genes (Figure 1d). No significant correlation was found between the two genes (cor=0.0239, p=0.433)
Clinical Correlation in Breast Cancer
To further investigate whether there was a difference in prognosis and clinical characteristics of the patients after grouping, we first did a survival analysis and plotted survival curves for the two groups (Supplementary Figure2) and found no significant differences. We obtained clinical data for 892 patients after invalidating clinical data. We did this for group 1 (n=432) and group 2 (n=460) and we subsequently explored the association between the two subgroups and clinical characteristics and pathological staging. Analysis indicated that group 1 had lower N stage and higher age than group 2 (Table 1). Age and N stage were independent factors influencing clustering (Table 2). The results showed that group 1 patients had better prognosis.
Analysis of immune-related pathways by GSEA
Functional differences between the two groups after clustering by GSEA analysis, with differential genes in both group 2 and group 1 in GSEA. Our enrichment analysis by MSigDB Collection (c5.cp.v7.0.symbs.gmt) identified a number of genes enriched to important pathways associated with immunity (Figure 2a), which contain stimulatory responses to organisms, responses to bacteria, immune system processes, regulation and regulation of immune effector processes and defense responses.
Comparison of immune infiltration
Crosstalk between tumor cells and the surrounding stroma extensively affects tumor progression and patient prognosis 16. by ESTIMATE, CIBERSORT and ssGSEA, in the analysis, group 1 had a higher and statistically significant difference in immune score, interstitial score and composite score than adequate, while tumor purity was lower than in group 2 (Figure 2 b). In addition, we explored the proportion of immune cells in all tumor patients (Supplementary Figure3) and studied the difference of immune cells between the two groups of patients by CIBERSORT. The analysis showed that group 1 had higher CD8T, CD4 memory T cells, B cells and other immune cells. (Figure 2c). SSGSEA showed that 25 immune cell subtypes (e.g. activated B cells, activated CD4 T cells, activated CD8 T cells, activated dendritic cells, natural killer cells and natural killer T cells, effector memory CD4 T cells, effector memory CD 8 cells) were in group high expression in group 1 (Figure 2d). These analyses all strongly suggest that group 1 had stronger immune infiltration than group 2, especially CD 8 T cells, which play an important role in tumor immunity.
Immunotherapy sensitivity analysis
To assess the sensitivity of breast cancer patients to immunotherapy. We compared the detection of Programmed Cell Death 1 (PDCD1) (also known as PD1) and some other breast cancer-related immune checkpoints, such as CD274(also known as PDL1), Programmed Cell Death 1 Ligand 2(PDCD1LG2)(also known as PDL2), Cytotoxic T-Lymphocyte Associated Protein 4(CTLA4), CD80,CD86, Lymphocyte Activating3(LAG3), Hepatitis A Virus Cellular Receptor 2(HAVCR2) (also known as TIM3), T Cell Immunoreceptor With Ig And ITIM Domains(TIGIT), Toll Like Receptor (TLR), TNF Receptor Superfamily Member 4(TNFRSF4)(also known as OX40), TNF Receptor Superfamily Member 9(TNFRSF9)( also known as 4-1BB), Inducible T Cell Costimulator (ICOS), CD40, Interleukin 12 Receptor Subunit Beta (1IL12RB1)( also known as IL-12), IDO1( also known as IDO), between the two groups (Figure 3A-D). We can see that the expression of PD1, PDL1, PDL2, CTLA4, CD86, TIM3, TIGHT, TLR, 4-1BB, ICOS, CD40, IL-12, and IDO in patients in group 1 was significantly higher than that in group 2. It indicates that this part of patients may be more sensitive to immunotherapy.
Tumor Mutational Burden Comparison
TMB is a measure of the number of tumor mutations; the more mutations, the more neoantigens, which are immunogenic and stimulate T cells to exert antitumor effects. Numerous studies have confirmed the link between higher TMB and ICI efficacy, suggesting that TMB may be a good predictive biomarker. We analyzed the mutation status of the two groups of patients (Fig. 4a, b), and group 1 had higher TMB than group 2(Fig. 4c), which indicated that group 1 was more sensitive to immunotherapy than group 2.
Identification of WGCNA with M6A and immune-related Hub genes
By differential analysis, we obtained 385 DEGs (of which 155 were down-regulated and 230 up-regulated) in the differentially expressed genes of group 1 and group 2, as shown in (Figure 5a). We then used analysis software (Sangerbox 3.0) to perform WCGNA analysis on these differential genes (Figure 5b), which were clustered into 6 modules (Figure 5c-d), in order to explore which modules were associated with m6A and immunity. We analyzed the association between modules and immune traits (Figure5e). Here we selected the brown module for the next step of the hub gene screen because it was significantly associated with immunity and weakly correlated with m6a. Fifteen hub genes (Figure 5f) were obtained in the brown module based on MM>0.6 and GS> 0.15 (FCAMR, SPIB, CLEC17A, MS4A1, FCER2, VPREB3, CR2, FCRL1, TCL1A, CNR2, CXCR5, BLK, BEND4, CD1B and AC008878.3).
Functional enrichment of the Hub gene and its relationship to immune infiltration
To explore the biochemical functions of the 15 hub genes, on the one hand, we performed GO enrichment analysis on the 15 hubs (Figure 6a), where the most significantly enriched pathway was B-cell activation, and other immune-related pathways were enriched. On the other hand, we performed PPI protein interaction analysis and correlation between hub genes through the string database (Figure 6b-c), which showed significant correlation between them. hub gene-immune correlation analysis further confirmed that most of the hub genes were associated with immunity (Figure 6d).