Gene signatures and cancer-immune phenotypes based on expression of WTAP and YTHDF1 in breast cancer patients

doi:10.21203/rs.3.rs-1734801/v1

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Gene signatures and cancer-immune phenotypes based on expression of WTAP and YTHDF1 in breast cancer patients

https://doi.org/10.21203/rs.3.rs-1734801/v1

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Background: N6-methyladenosine (m6A) modification, which serves as a critical regulator of transcript expression, plays an important role in regulating immune response in cancer patients. Wilms’ tumor 1-associated protein (WTAP) and YTH N6-Methyladenosine RNA Binding Protein 1 (YTHDF1) are respectively considered as encoder and reader in m6A regulation. The aim of this study was to investigate the relationship between WTAP and YTHDF1 gene and immunological features of breast cancer.

Methods: We investigated expression of WTAP and YTHDF1 in 1171 breast cancer samples in the Cancer Genome Atlas (TCGA) database. Patients were divided into two groups with a consensus clustering method based on WTAP and YTHDF1 expression. And then, we compared the clinical information of the patients and performed gene set enrichment analysis (GSEA) to determine the differences in functions between two groups. Immune infiltration analysis was performed using Estimate, CIBERSORT and single sample gene set enrichment analysis (ssGSEA). In addition, the expression of immune checkpoint inhibitor (ICIS) targets and tumor mutational burden were assessed in the tumor samples.

Results: We determined two distinct groups of breast cancer. Group 1 had higher expression of WTAP and lower expression of YTHDF1, and group 2 expressed reversely. Group 2 had higher N stage and pathological stage compared to group 1. Group1 had stronger immune infiltration and higher expression of ICIS targets. In addition, WGCNA identified 15 genes, that were associated with WTAP and YTHDF1 expression and immune score.

Conclusions: WTAP and YTHDF1 affect the immune environment of breast cancer and may have the potential to provide new targets for immunotherapy of patients.

breast cancer

WTAP

YTHDF1

immune contexture

m6A modification

bioinformatics

It showed that the incidence of breast cancer is increasing with age ¹. Breast cancer has surpassed lung cancer as the most prevalent cancer in the world and ranks first in female malignancy mortality ². Although chemotherapy and endocrine therapy for breast cancer have achieved good results in this field in recent years, clinical challenges remain, including treatment of drug resistance, recurrence and metastasis ³. Therefore, further development of new therapeutic tools is necessary. In recent years, immunotherapy has been shown to be effective in cancer, and an increasing number of trials have demonstrated the effectiveness of immunotherapy ⁴. However, immunotherapy may have limitations in some patients⁵. Therefore, identifying new immunotherapeutic markers and revealing the underlying mechanisms of immune checkpoints are important for the precise treatment of patients.

m6A is an important RNA modification that plays an important role in stem cell differentiation, carcinogenesis and immunoregulation⁶. m6A modification is a dynamic and reversible process mediated by three different types of regulatory proteins: methyltransferases(writers), demethylases (erasers), and m6A-binding proteins(readers)⁷. WTAP is a key component of the methyltransferase complex(writers)⁸, which is intimately involved in the initiation and progression of various types of human cancers. WTAP has been shown to affect T-cell infiltration and to be associated with gastric cancer prognosis ⁹. YTHDF1 is a m6A-binding protein(reader), has been shown to be significantly associated with tumor immunity ¹⁰ and may serve as an immune-related biomarker and a new target for anticancer immunotherapy¹¹. A study shown that YTHDF1 was correlation with worse clinicopathological characteristics and overall survival and resulted in lower immune cell infiltration in breast cancer patients¹².

To further understand the role of WTAP and YTHDF1 in breast cancer tumor immunity, we analyzed transcriptomic data from TCGA-BRCA database. We divided patients into two groups based on WTAP and YTHDF1 expression and compared the differences in clinical characteristics, biological pathways, immune infiltration, immune checkpoint expression and mutation profiles between the two groups using bioinformatics methods.

Data sources and processing

All patient gene expression data (HTseq-Counts and HTseq-FPKM) and clinical data were downloaded from UCSC Xena browser (https://xenabrowser.netanddatapages). HTseq-Counts data from 1171（tumor=1072，normal=99）breast specimens were further analyzed by Log2(counts+1) transformation and analyzed by HTseq-Counts counts were used for differential analysis.

The simple nucleotide variation data (Mutect2) of BRCA patients were downloaded from the TCGA database and processed with the R-package MAFTOOLS. And incorporated the analysis of the landscape involved in the mutation. A waterfall chart shows the patient's genetic mutation. Tumor mutational burden (TMB) is calculated based on simple nucleotide variations, defined as the number of mutations per megabyte.

Our datasets used in the study were consistent with the publication guidelines provided by the online database. The ethics approval and informed consent were not required. All methods were carried out in accordance with relevant guidelines and regulations.

Immuno-infiltration analysis

Tumor cells contain cells with multiple components and the different cells constitute the tumor microenvironment. ESTIMATE is used to determine the proportion of immune cells in the tumor microenvironment by the expression of genes in tumor patients. Thus, we can use this algorithm to estimate the tumor microenvironment (TME), mesenchymal score (mesenchymal content), immune score (degree of immune cell infiltration), assessment score (combined mesenchymal and immune score), and tumor purity in tumor samples using the R package¹³.

CIBERROST was used to calculate the abundance of immune cell infiltration in the tumor samples. The immune cell expression data was downloaded via (https://cibersort.stanford.edu), resulting in the proportion of different immune cells per breast cancer patient¹⁴.

The extent of immune cell infiltration in tumor patients is assessed by ssGSEA.

Consensus clustering analysis based on WTAP and YTHDF1

The expression data of WTAP and YTHDF1 in breast tumor patients were extracted and the patients were subjected to Consensus clustering analysis, so that all patients were divided into two groups.

Gene Set Enrichment Analysis (GSEA) functional and pathway analysis

GSEA was analyzed by R package (ClusterProfiler), by differentially expressed gene analysis between the two groups of patients, which in turn demonstrated significant functions and pathways for both groups, with adjusted p-values <0.05, normalized concentration scores (|NES|) >1, and significant differences.

Differential gene expression

Expression profile data (HTseq-counts) were compared using the R package DESeq2 to identify differentially expressed genes in the two groups. The threshold was |log2FoldChange|>1, adjusted for a P value <0.05.

Functional enrichment analysis

We performed Gene Ontology (GO) enrichment analysis of the grouped differentially expressed genes using the R package ClusterProfiler to understand the pathways enriched by the differential genes. And a protein-protein interaction (PPI) network was constructed from the string database¹⁵. Next, we differentially used the genes with immune infiltration after analysis.

Statistical analysis

All data were statistically analyzed by R (4.1.2) and SPSS software (version 25.0), and illustration panels were assembled by Adobe Illustrator stitching (CC 2021). Wilcoxon rank sum test was used for analysis. Spearman's and person coefficients were used for correlation analysis. All hypothesis tests were two-sided and p-values < 0.05 were considered significant.

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).

WTAP is a key component of the methyltransferase complex(writers), which appeared to both promote tumor growth and inhibit lymph node metastasis. YTHDF1, a m6A-binding protein(reader), has been shown to be higher expression in breast cancers than normal breast tissues and high expression of YTHDF1 was correlation with worse clinicopathological characteristics and overall survival and resulted in lower immune cell infiltration in patients¹². In our analysis of breast cancer samples from TCGA data, WTAP expression was reduced but YTHDF1 expression was increase in breast cancer samples compared with normal tissue. Thus, we divided 1072 breast cancer patients in TCGA into two groups by Consensus clustering: group 1 (WTAP: high expression; YTHDF1: low expression) and group 2 (YTHDF1: high expression; WTAP: low expression). This allowed further exploration of differences in clinical characteristics, prognosis, and function between the two groups of patients. We found that group 2 had a higher lymph node stage than group 1 in terms of clinical characteristics, but there was no survival difference between the two groups in terms of survival, which may be related to treatment differences between patients. In terms of biological function, GSEA analysis showed that many immune-related pathways were enriched in group 1, such as stimulatory responses to organisms, responses to bacteria, immune system processes, regulatory and immune effector processes, and defense responses. This analysis suggests that group 1 plays a more important role in the immune response.

Next, we used ESTIMATE, CIBERSORT and ssGSEA to compare the differences between the two groups in terms of immune cell proportions and immune infiltration and immune infiltration. Estimate analysis illustrated that group 1 had a higher mesenchymal score, immune score and estimate score than ancestor 2, indicating that group 1 had more active anti-tumor immunity. CIBERSORT analysis showed that group 1 had a significantly higher proportion of CD8 T cells and CD4 memory T cells than patients in group 2, and the higher proportion of CD8 T cells in the immune cells indicated a better prognosis for breast cancer patients¹⁷. ssGSEA analysis illustrated that the 24 immune cell subtypes differed significantly between the two groups and included multiple types of T cells, such as CD8 T cells, CD4 T cells, natural killer cells, dendritic cells, helper T cells, memory T cells, etc. Tumor-infiltrating immune cells have a significant impact on patient control and response to immunotherapy¹⁸. Dendritic cells have been shown to present tumor antigens to T and maintain cytotoxic T-cell activity ¹⁹,thereby exerting anti-tumor effects. Natural killer cells recognize what kind of cells are normal, inhibit tumor cell proliferation and exert precise anti-tumor effects^20,21. Based on the above tabulation of immune cell studies, group 1 showed more significant immune infiltration than group 2, suggesting that group 1 patients had better immune activity in the tumor environment and may have a better response to immunotherapy.

Antibodies block co-inhibitory receptors expressed by anti-tumor T cells and thereby disrupt immune tolerance to tumor cells²² .Many immunotherapies targeting immune checkpoints have been widely used in breast cancer, such as PD1 and PD-L1 immune check inhibitors (Pembrolizumab) in advanced breast cancer with promising results ²³ and clinical trials of neoadjuvant chemotherapy in early-stage breast cancer ²⁴. CTLA4 has also been shown to be an immunotherapy target for advanced breast cancer²⁵. In addition, we also analyzed some other immune targets lymphocyte activation gene-3 (LAG3), T-cell immunoglobulin-3 (TIM-3), T-cell immunoglobulin ITIM domain (TIGIT) and some have been used Immune checkpoints in clinical trials (TLR, OX40, 4-1BB, ICOS, CD40, IL-12, IDO), of which TLR, 4-1BB, ICOS, CD40, IL-12, IDO, TIM-3 and TIGIT are in the group 1 was significantly expressed, indicating that patients in group 1 had the best response to immunotherapy

Then, we analyzed the mutational data of the two groups of patients and found that the mutational landscape of the two groups was slightly different, but the TMB of the two groups was significantly different, and the higher the TMB, the better the effect of immunotherapy.

We selected the brown modules with significantly different immune scores among the six color modules obtained by WGCNA and screened 15 genes based on MM > 0.6 and GS > 0.15, which were found to be involved in pathways closely related to immunity such as B cell proliferation and activation, lymphocyte proliferation, macrophage activation and regulation of immune effects by GO enrichment analysis. All of the above immunoassays suggest that group 1 may respond better to immunotherapy, and this result may be due to WTAP and YTHDF1-mediated m6a modifications. Although we bioinformatics analyzed WTAP and YTHDF1 as a biomarker of response to immunotherapy in breast cancer, we are not conducting relevant experimental validation, and WTAP and YTHDF1 may be expressed differently in different subtypes of breast cancer; May limit its predictive power. However, it provides a possible biomarker for predicting the efficacy of immunotherapy for breast cancer and may become a target for immunotherapy.

Based on WTAP and YTHDF1 expression in breast cancer TCGA, We found that group 1 (WTAP: high expression; YTHDF1: low expression) had lower lymph node staging and significantly higher immune cell infiltration, ICI targets, and TMB than group 2 (WTAP: low expression; YTHDF1: high expression). Therefore, group 1 may have better response to immunotherapy. Our findings suggest that WTAP and YTHDF1 may provide new targets and predictors for immunotherapy in breast cancer patients.

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The original contributions presented in the study are included in the article and Supplementary Material. Further inquiries can be directed to the corresponding authors.

Competing interest

The authors declare no competing interests

Funding

This work was supported by the National Natural Science Foundation of China (81260329).

Author Contributions

Zhengkui Sun conceived and designed the study, Huoping Peng analyzed the data and drafted the manuscript. All authors have read and approved this manuscript.

Acknowledgments

The authors thank the contributions from the TCGA network.

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Table 1 Clinical features of two groups
	group 1	group 2	p value
Number	432	460
Age (years, mean ±SD)	59±13	57±13	0.003
T stage [n (%)]			0.267
T1	121 (28.0)	108 (23.5)
T2	252 (58.3)	278 (60.4)
T3	47 (10.9)	53 (11.5)
T4	12 (2.8)	21 (4.6)
N stage [n (%)]			<0.001
N0	250 (57.9)	193 (42.0)
N1	115 (26.6)	177 (38.5)
N2	41 (9.5)	61 (13.3)
N3	26 (6.0)	29 (6.3)
M stage [n (%)]			0.274
M0	426 (98.6)	449 (97.6)
M1	6 (1.4)	11 (2.4)
Pathological stage[n(%)]			0.062
stage I	87 (20.1)	67 (14.6)
stage II	253 (58.6)	269 (58.5)
stage III	86 (19.9)	113 (24.6)
stage IV	6 (1.4)	11 (2.4)

Table 2 Multivariable logistic regression for clustering (group 2 vs. group 1)
Variables	Multivariable OR (95% CI)	p value
age	0.987 (0.977-0.997)	0.014
T stage
T2 vs. T1	1.104 (0.802-1.520)	0.543
T3 vs. T1	1.048 (0.639-1.716)	0.854
T4 vs. T1	1.617 (0.730-3.584)	0.236
N stage
N1 vs. N0	1.871 (1.377-2.541)	<0.001
N2 vs. N0	1.811 (1.159-2.830)	0.009
N3 vs. N0	1.328 (0.735-2.400)	0.347
M stage	1.290 (0.449-3.704)	0.637

No competing interests reported.

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Gene signatures and cancer-immune phenotypes based on expression of WTAP and YTHDF1 in breast cancer patients

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