Prognostic significance of immune landscape in tumour microenvironment of endometrial cancer

Abstract Tumour microenvironment (TME) is crucial to tumorigenesis. This study aimed to uncover the differences in immune phenotypes of TME in endometrial cancer (EC) using Uterine Corpus Endometrial Carcinoma (UCEC) cohort and explore the prognostic significance. We employed GVSA enrichment analysis to cluster The Cancer Genome Atlas (TCGA) EC samples into immune signature cluster modelling, evaluated immune cell profiling in UCEC cohort (n = 538) and defined four immune subtypes of EC. Next, we analysed the correlation between immune subtypes and clinical data including patient prognosis. Furthermore, we analysed the expression of immunomodulators and DNA methylation modification. The profiles of immune infiltration in TCGA UCEC cohort showed significant difference among four immune subtypes of EC. Among each immune subtype, natural killer T cells (NKT), dendritic cells (DCs) and CD8+T cells were significantly associated with EC patients survival. Each immune subtype exhibited specific molecular classification, immune cell characterization and immunomodulators expression. Moreover, the expression immunomodulators were significantly related to DNA methylation level. In conclusion, the identification of immune subtypes in EC tissues could reveal unique immune microenvironments in EC and predict the prognosis of EC patients.

(IMs) expression and epigenetic modification, and the repertoires of B cell receptor (BCR) and T cell receptor (TCR) provides a wealth of information on tumour development. 5 Consequently, immunotherapy has been developed as alternative or complementary treatment strategy for cancer to traditional radiotherapy and chemotherapy. In particular, CTLA-4, PD-1 and PD-L1 antibodies have shown good efficacy in cancer treatment. 6 Up-regulation of inhibitory immune checkpoints by tumour-infiltrating immune cells by cancer cells leads to cancer progression from host immunosurveillance. Changes in DNA methylation pattern and enrichment of methylated histone marks in the promoter regions could be major contributors to the up-regulation of immune checkpoints (ICs) in the tumour microenvironment. 7 Endometrial cancer (EC) is a common cancer in the woman, with an estimated 61 880 new cases and 12 160 deaths in the United States in 2019. 8 EC develops in about 142 000 women worldwide and estimated 42 000 women may die from EC each year. 9 Different treatment methods such as surgery, radiotherapy, brachytherapy and chemotherapy are currently applied to EC treatment based on the estimation of recurrence risk. However, current risk assessment system based on clinical, histological, imaging, biological prognostic factors is insufficient to account for evolutionary and prognostic heterogeneity of EC.
Therefore, in this study we performed extensive analysis based on TCGA UCEC cohort (Uterine Corpus Endometrial Carcinoma Cohort).
We applied GVSA analysis, xCell method and multivariate Cox proportional hazards regression analysis to develop immune-based prognostic signature of EC for better immunotherapy strategy.

| Immune signature clustering
All EC samples (n = 538) available in TCGA were scored for 83 identified gene expression signatures. Next, GVSA enrichment analysis of all samples was performed. 16

| IM gene expression correlation with dna methylation
To study the relationship between gene expression and DNA methylation of immunomodulators, we mapped DNA methylation probes to genes using bioconductor packages Illumina

| Statistical analysis
Comparison was analysed by one-way analysis of variance (ANOVA).   Figure 1C). In addition, each cancer immune subtype exhibited a subtype-specific immune molecule expression characterization ( Figure 1D). For example, the relative levels of the interferon (IFN)-inducible gene-IFIT3 and IFN were elevated in C1 but were significantly lower in C3.

| Composition of tumour immune infiltrates
The heatmap of immune cells for 4 different immune subtypes is shown in Figure 2A. We found that different immune cells showed significant changes among EC with 4 different immune subtypes.
In particular, the numbers of NKT and basophilic were significantly higher in C3 ( Figure 2B). The detailed comparisons of major immune cells distribution in different EC immune subtypes are shown in Figure 2C.

Estimation of STromal and Immune cells in MAlignant Tumours
using Expression data (ESTIMATE) is a concept based on gene expression signatures to evaluate stromal and immune cells in tumour samples. 24 Therefore, we first analysed the correlation of stromal scores and immune scores which indicated the levels of infiltrating stromal and immune cells, respectively, in EC immune subtypes ( Figure 2D and Figure S1). Next, we analysed the correlation between immune score's Spearman and the heatmap in each EC immune subtype. Macrophages M1, pDC, aDC, CD8 + Tcm, CD4 + memory T cells were more relevant to immune scoring.
( Figure 2E). Finally, we calculated stromal, immune and ESTIMATE scores to judge tumour purity in EC tissues. The results showed that the scores had no significant differences except stromal scores in C1 immune subtype ( Figure 2F).

| Prognostic significance of immune subtypes
Next, we wondered whether different immune subtypes could significantly distinguish overall survival of EC patients. We found that C3 had the best prognosis, whereas C2 and C4 had less favourable outcomes although they contained substantial immune component, and C1 had the least favourable outcome ( Figure 3A). Moreover, Kaplan-Meier curve analysis of immune subtypes showed that natural killer T cells (NKT), dendritic cells (DCs), CD8 + T cells, basophils were significantly associated with OS in EC patients ( Figure 3B). To confirm prognostic significance of different immune subtypes, we calculated concordance index (C-index). 25 The immune cell types were the most significant prognostic variables, with the highest C-index in CD4 + Tcm, cDC, CD4 + Tem ( Figure 3C).

| Key immune cells differed in different stages of EC
We also wondered whether different immune subtypes were as-  Figure 4A). The proportion of major immune cells was also different in different EC tumour stage ( Figure 4B). In particular, macrophages M1, macrophages M2, Th1, Th2, Tregs, CD8 + T naive cells, CD8 + Tcm, and CD8 + Tem cells showed significant changes among EC with different stages ( Figure 4C and Figure S2).

| Immunomodulators expression in EC samples
Since agonists and antagonists of immunomodulators (IMs) are increasingly used in cancer immunotherapy, 26 we retrieved immunomodulatory genes from TCGA and 78 IMs were listed in Table S1. Next, we conducted principal component analysis (PCA) to investigate the difference between four immune subtypes based on immune genes expression profiles. The results showed that the four groups were generally distributed in different directions ( Figure 5A). in different immune subtypes. The results showed that DNA methylation of many IM genes, for example CD28, LAG3, CTLA4, PDL2 and VTCN1, was inversely correlated with gene expression ( Figure 5D).
In addition, B cell receptor (BCR) and T cell receptor (TCR) repertoire analysis showed that BCR and TCR diversity measured by both species richness and Shannon entropy exhibited significant differences in four immune subtypes ( Figure 5E).

| D ISCUSS I ON
Although most EC patients can be diagnosed and treated at the early stage, 15% patients are diagnosed at the locally advanced or occult metastatic stage and suffer from tumour recurrence due to limited response to surgery and radiotherapy. 9 In addition, the traditional Furthermore, C3 subtype exhibited pronounced Th17 signature, consistent with recent data that Th17 signature is correlated with better survival of cancer patients. 36 In contrast, C2 subtype was IFN-ɣ dominant and indicated less favourable survival, although it exhibited CD8 T cell signature with high M1 Macrophage content, which would support strong anti-tumour immunity. 37 Human  38 As in endometrial cancer, recent research proved it that the tumour microenvironment reshapes NK cell phenotype and leads to promote tumour progression. 39 The balance between Th1 and Th2 is important for the immune system homeostasis. 40 TGF-β may affect Th1-Th2 balance within the tumour microenvironment. Collectively, our results suggested that immune cell profiles in TME of EC tissues could predict prognosis outcome of EC patients.
It is known that abnormal DNA methylation is implicated in tumorigenesis, since it inhibits the expression of key genes involved in regulating cancer cell differentiation, proliferation and invasion. 41 The extent  The caveat to the use of TCGA data should be noted. We also correlated mutations in almost 300 cancer driver genes with immune subtypes and found 30 significant associations ( Figure 6). analysis. Therefore, we could not evaluate complex immune cell phenotypes and in TME in EC tissues. Further work is needed to determine the functional aspects of these associations, which will be important to verify our conclusion.
In conclusion, we identified four immune subtypes in EC tissues.
The specific features of these immune subtypes were associated with gene expression signatures and epigenetic modulation of immunoregulatory genes and immune cells, which could shape unique immune microenvironments in EC and predict the prognosis of EC patients.

ACK N OWLED G EM ENTS
This study was supported by grant from National Natural Science

CO N FLI C T O F I NTE R E S T
The authors declared no competing interest.

AUTH O R CO NTR I B UTI O N
LBL performed the research, LBL analysed the data and wrote the paper, WXP designed the research study and wrote the paper.