Genomic analysis of the endosomal sorting required for transport complex III pathway genes as therapeutic and prognostic biomarkers for endometrial carcinoma.

Background: The genes involved in the endosomal sorting required for transport complex (ESCRT)-III pathway is a protective mechanism that delays cell death by repairing damaged plasma membranes. We aimed to evaluate if targeting ESCRT-III genes may be used as biomarkers for predicting the clinical outcomes of endometrial carcinoma (EC).
Methods: Transcriptome RNA sequence (RNA-seq) data and genomic information of EC samples were obtained from The Cancer Genome Atlas (TCGA). The expression level, pathological relationship, pathway alterations, mutation, functional enrichment, associations with tumor infiltrating lymphocytes (TILs), and survival information of ESCRT-III genes including charged multivesicular body protein 2A (CHMP2A), CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP5, and CHMP7 in EC and normal tissues were explored through multiple datasets analysis.
Results: Our study demonstrated that CHMP2B, CHMP3, CHMP4B, CHMP5, CHMP5, and CHMP7 were significantly lower, whereas CHMP2A and CHMP4C were significantly higher in EC tissue than in normal tissue. All ESCRT pathway genes were significantly differentially expressed between tumor grades 2 and 3 and were positively correlated with each other. Except for CHMP5, the other seven ESCRT pathway genes were the most frequently mutated genes in the EC samples among all cancer types. Moreover, CHMP2A and CHMP7 had better prognostic potential in EC. CHMP2A, CHMP4B, and CHMP7 were significantly correlated with all four molecular subtypes in TCGA. Increased expression of CHMP2A and CHMP7 and decreased expression of CHMP4B were observed in EC samples than in serous carcinoma type samples. Furthermore, they were associated with tumor stages 1 and 2 and good survival outcomes for EC. Functional analysis revealed that the ESCRT-III genes were involved in the biological process (BP) of the membrane budding and multivesicular body (MVB) pathway; CHMP2A and CHMP7 participated in the ESCRT and ESCRT III complex disassembly, while CHMP5 was involved in ESCRT and ESCRT III complex assembly. Conclusions: Mutations in CHMP2A and CHMP7 correspond to a better prognostic potential in EC. Upregulation of CHMP2A and CHMP7 and downregulation of CHMP4B are good prognostic indicators of the histological type, early tumor grade, and promising survival markers, thus becoming potential biomarkers and therapeutic targets for EC. 2022 Translational Cancer Research. All rights reserved.

Introduction

Endometrial carcinoma (EC) is one of the most common gynecological cancers worldwide, with an estimated new cases and deaths of 65,950 and 12,550, respectively (1). EC has two subgroups, namely, types I and II. Type I is characterized by an upregulation of hormonal receptors. It consists of 60–70% of EC and histological grades 1 and 2. Type II is characterized by low levels of estrogen and progesterone receptor expression. Moreover, it is associated with high-grade EC and serous or clear cell carcinoma with poor prognosis (2). After surgical resection, chemotherapy and radiotherapy for EC, the 5-year disease-free survival (DFS) of patients diagnosed in the early stages, stages III or IV, and recurrent or metastatic disease decreased progressively to 74.2–90.8%, 57.3–66.2%, 20.1–25.5%, and 16%, respectively (3). Targeted therapies for tyrosine kinases and immunotherapy using immune-checkpoint inhibitors (4) are clinical strategies used in cancer treatment. These modalities aim to kill tumor cells and protect normal cells.

The endosomal sorting required for transport complex III (ESCRT-III) acts as a protective mechanism that delays cell death by repairing damaged plasma membranes due to necroptosis (5), pyroptosis (6), and ferroptosis (7). The ESCRT-III complex incorporates charged multivesicular body protein 2A (CHMP2A), CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP5, and CHMP7, which belong to the chromatin-modifying protein/charged multivesicular body protein (CHMP) family (8) (Figure S1). These are involved in the MVB sorting pathway and are important in recycling and degradation of membrane proteins. ESCRT-I, ESCRT-II, and ESCRT-III, as well as VPS4A and ALIX were triggered by Ca2+ influx, recruited to damaged lysosomes and mediated lysosomal membrane repair (8). Detailed information on the location and synonyms of these eight ESCRT-III genes is listed in Tables S1,S2. Since regulated cell death usually occurs in drug resistant tissues during EC therapy (9), we aimed to determine whether ESCRT-III genes might function as potential targets in the treatment and prognosis of EC.

Searching for tumor genomic biomarker using microarray RNA sequencing (RNA-Seq) data was recently applied to determine cancer prognosis. In this study, we performed bioinformatics technology with a high-throughput screening data to identify the ESCRT-III pathway gene signature in EC using the UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery, TISIDB, Gene Expression Profiling Interactive Analysis (GEPIA), and NIH National Cancer Institute CDC Data Portal dataset on the uterine corpus endometrial carcinoma (UCEC) dataset of the Cancer Genome Atlas (TCGA) database. Data on gene expression profiles, pathological relationship, somatic mutation status, function enrichment pathway, correlation with tumor infiltrating lymphocytes (TILs), and survival analysis were generated to assess if ESCRT-III pathway genes could be regarded as potential therapeutic targets and candidate prognostic biomarkers for EC. We present the following article in accordance with the STREGA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-22-660/rc).

Methods

Acquisition of data on ESCRT-III pathway genes

This study conformed to the provisions of the Declaration of Helsinki (as revised in 2013). Data on the transcriptional levels of over- and under-expressed ESCRT pathway genes as determined by Transcriptome RNA sequence (RNA-Seq) analysis were collected using the UALCAN analysis (http://ualcan.path.uab.edu) (EC: 546; normal: 35) (10) from the UCEC dataset of the TCGA database (https://portal.gdc.cancer.gov/). We also conducted GEPIA (http://gepia.cancer-pku.cn/) (11) for data mining and visualization of the RNA sequencing expression data to validate the expression profiles of the ESCRT pathway genes in UCEC primary tumors (n=174) from TCGA-UCEC and GTEx-Uterus projects (12). The matched TCGA normal and GTEx data were combined into the normal group (n=91). The default parameters were |Log2FC| Cutoff of 1, P value cutoff of 0.01, and log2 (transcripts per million, TPM +1) for log-scale were used. A jitter size of 0.4 was generated. Z-values representing standard deviations from the median across EC and normal endometrial tissue samples were identified using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset (http://ualcan.path.uab.edu/analysis-prot.html) (13) (EC: 100; normal: 31) and normalized by Log2 spectral count ratio values both in each sample profile and across samples.

Clinical pathological relationship and pathway analyses of the ESCRT genes

The relationship between ESCRT genes and basic clinical pathological information, including subtype, tumor grade, histology, individual cancer stages, and methylation status, were further explored using the UALCAN and CPTAC datasets. We systematically assessed the ESCRT genes involved in key pathways across EC samples based on the CPTAC dataset, which were represented by Z-values. Associations of the ESCRT-III pathway gene expression and immune/molecular subtypes across EC analyzed using the Kruskal-Wallis Test -log10(P value) were identified on the TISIDB (http://cis.hku.hk/TISIDB/index.php) (14) dataset.

Function enrichment and pathway analyses of the ESCRT pathway genes

To obtain the enriched biological functions and pathways of the ESCRT pathway genes, we conducted the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on TISIDB. Statistical significance was set at P<0.05. Connections among the ESCRT pathway gene expression were analyzed via the Pearson’s correlation on the GEPIA dataset.

Validation of ESCRT gene mutations in the EC samples

We obtained the mutation data on the ESCRT pathway genes in the TCGA-UCEC cohort from the NIH National Cancer Institute GDC Data Portal database (https://portal.gdc.cancer.gov). We calculated the number of cases of ESCRT genes affected by simple somatic mutation (SSM) across the TCGA project (n=530). Cases affected by copy number variation (CNV) gains and losses in the TCGA database (n=510) were also analyzed computationally. Disease type in the UCEC project included adenomas, adenocarcinomas, cystic, mucinous, serous, and epithelial neoplasms. Statistical calculations of mutation types, including somatic mutations, CNV losses, CNV gains, SSM affected, variant effect predictor (VEP) impact mutation, Sorting Intolerant From Tolerant (SIFT) impact mutation, Polyphen Impact Mutation, Consequence Type Mutation, Type Mutation and Variant Caller Mutation were obtained and listed in Table S3. The frequency and pathogenicity of the variants in the exons were determined using bioinformatic prediction tools. We also gathered the clinical survival time and status of the EC patients.

Association between gene expression and TILs

We utilized the online tools, TISIDB and GEPIA2021 (http://gepia2021.cancer-pku.cn/sub-expression.html), based on the TCGA database using Spearman correlations to investigate the correlations between genes and their immune microenvironment (15). The relative abundance of 28 TIL types was determined using gene set variation analysis (GSVA) based on gene expression profiles (16).

Survival analysis

To screen the relationship between the expression profiles of the ESCRT pathway genes and the overall survival (OS) between high- and low-expression groups from the TCGA-UCEC dataset, we inferred the Kaplan-Meier survival curves on UALCAN [N (high vs. low/medium) =137 vs. 408] and TISDB analysis. Genes with a statistical significance, as determine by conducting the log-rank test (P<0.05), were considered prognostic genes of EC. We also carried out a univariate Cox proportional hazards regression analysis in GEPIA with EC samples that had high and low expression levels in 86 cases from the TCGA to evaluate their OS and DFS. The Cox proportional hazard ratio (HR) was based on the Cox PH model, while the 95% confidence interval (CI) was included in the survival plot as a dotted line. The median expression threshold for splitting the high- and low-expression cohorts as group cutoff was established. Patients who exhibited an upregulation showed expression values > third quartile. They were then divided into high- or low-expression groups with a median group cutoff of 50%. ESCRT gene mutations associated with survival rates were collected.

Statistical analysis

RNA-seq expression of the TPM of ESCRT pathway genes in the TCGA-UCEC and GTEx-Uterus data, clinicopathological relationships, and pathways were analyzed via ANOVA. The ESCRT pathway genes associated with immune and molecular subtypes were analyzed using the Kruskal-Wallis test -log10(P value). The Chi-square and the Fisher’s exact tests were used to compare the mutations. The Spearman’s correlation test was used to explore the correlation between gene expression levels and the immune cell types. The Kaplan-Meier survival curves were used to analyze the OS, and univariate Cox proportional hazards regression analysis was used to evaluate the OS and DFS. The statistical significance was set at P<0.05.

Online database

The online databases were as follows: TCGA (https://portal.gdc.cancer.gov/); UALCAN (http://ualcan.path.uab.edu) (10); TISIDB (http://cis.hku.hk/TISIDB/index.php (14); GEPIA: Gene Expression Profiling Interactive Analysis (http://gepia.cancer-pku.cn/) (11); GEPIA2021 (http://gepia2021.cancer-pku.cn/sub-expression.html; CPTAC Confirmatory/Discovery dataset (http://ualcan.path.uab.edu/analysis-prot.html) (13); and NIH National cancer institute CDC Data Portal database (https://portal.gdc.cancer.gov).

Results

Validation of the ESCRT-III pathway genes in EC samples

We investigated the transcriptional RNA expression levels of the key genes in the ESCRT-III pathway in EC and normal endometrial tissues. CHMP2B, CHMP5, CHMP5, and CHMP7 were significantly lower, while CHMP2A and CHMP4C were significantly higher in EC samples (n=546) than in normal endometrial samples (n=35) in the UALCAN analysis of the TCGA-UCEC database (Figure S2A, Table S4). We also verified the expression values of the ESCRT genes by conducting the GEPIA analysis, which indicated a significantly higher expression level of CHMP4C in the EC samples (n=174) than in the normal samples (n=91) on the TCGA-UCEC and GTEx-Uterus data (n=13). Genes, such as CHMP2A, CHMP4B, and CHMP5, have higher levels of expression (Figure S2B, Table S4). On the other hand, CHMP2B, CHMP3, CHMP5 and CHMP7 had lower levels of expression in the EC samples. In addition, differential analysis through CPTAC characterized by Z-values revealed that CHMP2A, CHMP2B, CHMP3, and CHMP4B were markedly lower in the EC samples (n=100) and normal endometrial tissues (n=31), whereas CHMP4C and CHMP5 showed significantly higher differences in EC samples (Figure S2C, Table S4). CHMP2A had a higher level of expression on the UALCAN dataset than on the CPTAC. CHMP5 had a significantly lower expression on the UALCAN dataset than on the CPTAC. This may possibly be due to different sample sizes and comparison methods with transcriptional RNA expression levels on UALACN, GEPIA, and the Z-value of standard deviations on CPTAC.

Analysis of the clinical pathological characteristics of the ESCRT-III pathway genes in EC samples

We further compared the expression profiles of ESCRT pathway genes in different tumor stages, grades, subtypes, histological/immune/molecular subtypes, TP53 mutants, methylation, and pathways altered on the TCGA-UCEC and GTEx-Uterus datasets in the UALCAN, CPTAC, and TISDIB analyses, respectively. In the UALCAN analysis, CHMP2A, CHMP2B, CHMP4C, CHMP5, and CHMP7 were significantly differentially expressed among the International Federation of Gynecology and Obstetrics (FIGO) stages 1 to 3 and the normal sample. Different analyses revealed a significantly increased expression of CHMP2A, CHMP4C, and CHMP7, as well as decreased expression of CHMP2B and CHMP5 in endometrioid and serous carcinoma samples compared with normal samples. In addition, CHMP2A, CHMP4B, CHMP5, and CHMP7 were significantly different between the endometrioid and serous carcinoma samples. An increase in the expression of CHMP2A and CHMP7 and a decrease in the expression of CHMP4B were observed in the EC samples compared with the serous carcinoma samples. CHMP2A, CHMP4B, CHMP4C, CHMP5, and CHMP7 were significantly associated with TP53 mutation status in EC samples. Moreover, we found that the methylation status of CHMP2A, CHMP2B, CHMP4C, and CHMP5 was strongly correlated with their expression levels (, Table S4). In summary, we found that CHMP2B, CHMP3, CHMP4B, CHMP5, and CHMP7 were significantly downregulated, while CHMP2A, CHMP4C were significantly upregulated in the EC samples.

Figure 1

Correlation analysis of the ESCRT pathway genes in clinical pathological characteristics of type, FIGO stage, histological grade, histological subtypes, TP53 mutant, and methylation, which are visualized using the cluster heat maps in (A) UALCAN and (B) CPTAC dataset, and (C) pathway altered. ESCRT, Endosomal Sorting Complex Required for Transport; FIGO, International Federation of Gynecology and Obstetrics; CPTAC, Clinical Proteomic Tumor Analysis Consortium; N, normal; O, others; G, grade, defined as G1, well-differentiated; G2, moderately differentiated; G3, poorly differentiated or undifferentiated; S, stage; EC, endometrioid carcinoma; SC, serous carcinoma; MCA, mixed-cell adenocarcinoma; TP53-M, TP53 mutant; TP53-NM, TP53 non-mutant.

In the CPTAC analysis, all ESCRT pathway genes were significantly differentially expressed between histologic grades 2 and 3. CHMP2A, CHMP2B, CHMP3, CHMP4B, and CHMP4C were significantly differentially expressed between grade 1, grade 2, and normal samples. To investigate the correlations between the pathway alterations, CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, and CHMP5 were differentially expressed between endometrial cancer tissues (n=100) and normal endometrial tissues (n=31) in HIPPO, WNT, mTOR, NRF2, RTK, P53/Rb, SWI-SNF, MYC/MYCN pathway, and chromatin modifier status. This was determined by conducting the Pearson correlation analysis (, Table S4).

TISDIB analysis showed that CHMP2A, CHMP4B, and CHMP5 were significantly associated with tumor stage, while CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, and CHMP7 were significantly differentially expressed among different tumor grades. Except for CHMP7, all other ESCRT genes were significantly associated with immune subtypes, including wound healing, IFN-gamma dominant, inflammatory, lymphocyte depleted, immunologically quiet, and TGF-b dominant. This was determined by performing the Kruskal-Wallis test (Figure S3). CHMP2A, CHMP4B, CHMP5, and CHMP7 were significantly associated with a molecular subtype, including POLE (DNA polymerase epsilon), low copy number (CN-LOW), high copy number (CN-HIGH), and microsatellite instability (MSI) (Table S4).

Functional enrichment analysis

GO and KEGG enrichment analyses were performed to determine the biological significance of the genes involved in the ESCRT pathway. Endocytosis (I04144) was the most significantly enriched KEGG pathway analysis, which showed that ESCRT genes were significantly enriched in the endosomal sorting complex required for transport (ESCRT) (hsa917729), membrane trafficking (R-HAS-199991), vesicle-mediated transport (R-HAS-5653656), and macroautophagy (R-HAS-1632852). The main cellular components (CC) were the ESCRT complex (GO:0036452), ESCRT III complex (GO:0000815), endosome membrane (GO:0010008), late endosome (GO:0005770), late endosome membrane (GO:0031902), and endosomal part (GO:0044440).

The GO analysis demonstrated that all the ESCRT pathway genes (CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP5, and CHMP7) were mainly involved in the biological process (BP) of membrane budding (GO:0006900), endosome organization (GO:0007032), endosomal transport (GO:0016197), multivesicular body organization (GO:0036257), multivesicular body assembly (GO:0036258), multi-organism membrane organization (GO:0044803), and multi-organism membrane budding (GO:1902592), which were related to the main ESCRT function. In addition, CHMP5 participated in the ESCRT complex assembly (GO:1904895), ESCRT III complex assembly (GO:1904902), CHMP2A, CHMP5, and CHMP7, which participated in the ESCRT complex disassembly (GO:1904896) and ESCRT III complex disassembly (GO:1904903). In addition, CHMP3 was involved in the regulation of early endosome to late endosome transport (GO:2000641). On the other hand, CHMP2B, CHMP3, and CHMP5 were involved in the endosome-to-lysosome transport (GO:0008333). CHMP3 and CHMP5 were involved in the multivesicular body sorting pathway (GO:0071985). CHMP4B was involved in the protein localization to the membrane (GO:0072657), membrane fission (GO:0090148), and the establishment of protein localization to the membrane (GO:0090150) (). Detailed information on the above mentioned involvements is shown in .

Table 1

ESCRT pathway genes mutation across TCGA project on NIH national cancer institute CDC Data Portal database

	Symbol
	CHMP2A	CHMP2B	CHMP3	CHMP4B	CHMP4C	CHMP5	CHMP6	CHMP7
Project
SSM (N=530)
SSM affected cases	21	26	15	22	14	19	9	36
SSM affected cases in TCGA %	0.0396	0.0491	0.0283	0.0415	0.0264	0.0358	0.017	0.0679
CNV (N=510)
CNV gains	26	11	8	22	14	21	53	3
CNV gains in TCGA %	0.0510	0.0216	0.0157	0.0431	0.0275	0.0412	0.1039	0.0059
CNV losses	23	23	9	11	5	5	7	32
CNV losses in TCGA %	0.0451	0.0451	0.0176	0.0216	0.0098	0.0098	0.0137	0.0627
All SSM affected cases in UCEC	70	59	32	55	31	45	28	68
Somatic mutation	22	35	16	24	19	22	9	43
Somatic mutation in all SSM affected cases in UCEC %	0.3143	0.5932	0.5	0.4364	0.6129	0.4888	0.3214	0.6324
Disease type
Adenomas and adenocarcinomas	32	31	18	32	21	26	28	40
Cystic, mucinous and serous neoplasms	38	28	13	23	10	19	0	28
Epithelial neoplasms			1
Survival
Dead	13	10	5	10	8	14	16	9
Alive	57	49	27	45	23	31	51	59
Survival rate	0.82	0.72	0.81	0.76	0.68	0.65	0.25	0.84
Interval of last follow-up (Year)	5.095	5.142	5.136	5.106	5.106	5.095	5.008	5.106

ESCRT, endosomal sorting complex required for transport; TCGA, The Cancer Genome Atlas; CNV, copy-number variant; SSM, simple somatic mutation; SNV, single nucleotide variation; UCEC, uterine corpus endometrial carcinoma.

The Pearson correlation coefficient analysis results demonstrated that all of the concerned genes enriched in the ESCRIhsa917729) KEGG pathway in the GEPIA dataset were connected with each other significantly by transcripts per million (TPM) expression values (Figure S4).

ESCRT gene mutations in the EC samples

We observed seven ESCRT pathway genes that were most frequently mutated in the EC samples obtained from the simple somatic mutation (SSM) TCGA cohort (n=530), which included various cancer types, including CHMP2A (3.96%), CHMP2B (4.91%), CHMP3 (2.83%), CHMP4B (4.15%), CHMP4C (2.64%), CHMP5 (3.58%), and CHMP7 (6.97%). CHMP5 (1.70%) was ranked third on the GDC Data Portal (Figure S5). Different mutation categories of the ESCRT genes in the TCGA-UCEC project showed that moderate somatic mutations and missense mutations were the most common types (), CHMP7 ranked most of the various mutations type. Also, CHMP5 firstly showed CNV gains (). Moreover, the ESCRT pathway genes frequently mutated in the modifier of VEP impact mutation (), deleterious and tolerated of SIFT impact mutation (), probably_damaging and benign of polyphen impact mutation (), missense_variant, downstream_gene_variant, 3_prime_UTR_variant of consequence type mutation (), single base substitution of type mutation () and somaticsniper mutation () (, Table S3).

Figure 2

The ESCRT pathway gene mutation types in EC based on the different categories on the TCGA-UCEC database. The tumor mutation burden of each sample is displayed using various colors in the box plots. (A) Somatic mutation; (B) SSM and CNV; (C) VEP impact mutation; (D) SIFT impact mutation; (E) Polyphen impact mutation; (F) consequence type mutation; (G) type mutation; (H) Variant caller mutation. ESCRT, Endosomal Sorting Complex Required for Transport; EC, endometrial carcinoma; TCGA-UCEC, The Cancer Genome Atlas uterine corpus endometrial carcinoma; SSM, simple somatic mutation; CNV, copy-number variant; SNV, single nucleotide variation; VEP, variant effect predictor; SIFT, Sorting Intolerant From Tolerant.

Table 2

The whole results of GO and KEGG enrichment analysis of the ESCRT genes in EC

Ontology ID	Description	Genes
BP
GO:0000045	Autophagosome assembly	CHMP4B
GO:0000070	Mitotic sister chromatid segregation	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0000226	Microtubule cytoskeleton organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0000281	Mitotic cytokinesis	CHMP4B,
GO:0000819	Sister chromatid segregation	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0000910	Cytokinesis	CHMP4B, CHMP4C,
GO:0000920	Cell separation after cytokinesis	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0001881	Receptor recycling	CHMP5
GO:0001894	Tissue homeostasis	CHMP4B
GO:0001919	Regulation of receptor recycling	CHMP5
GO:0006612	Protein targeting to membrane	CHMP4B
GO:0006620	Posttranslational protein targeting to membrane	CHMP4B
GO:0006887	Exocytosis	CHMP2A,
GO:0006900	Membrane budding	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0006914	Autophagy	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6
GO:0006997	Nucleus organization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0006998	Nuclear envelope organization	CHMP2A, CHMP4B, CHMP7
GO:0007032	Endosome organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007033	Vacuole organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007034	Vacuolar transport	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007040	Lysosome organization	CHMP5
GO:0007041	Lysosomal transport	CHMP2B, CHMP5
GO:0007051	Spindle organization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0007059	Chromosome segregation	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007067	Mitotic nuclear division	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007080	Mitotic metaphase plate congression	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0007088	Regulation of mitotic nuclear division	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0007098	Centrosome cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0007099	Centriole replication	CHMP2A
GO:0007346	Regulation of mitotic cell cycle	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0008333	Endosome to lysosome transport	CHMP2B, CHMP3, CHMP5
GO:0009838	Abscission	CHMP4C
GO:0010324	Membrane invagination	CHMP2A
GO:0010458	Exit from mitosis	CHMP2A, CHMP4B, CHMP7
GO:0010506	Regulation of autophagy	CHMP4B
GO:0010507	Negative regulation of autophagy	CHMP4B
GO:0010639	Negative regulation of organelle organization	CHMP2A, CHMP4B
GO:0010824	Regulation of centrosome duplication	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0010826	Negative regulation of centrosome duplication	CHMP2A
GO:0010948	Negative regulation of cell cycle process	CHMP2A, CHMP4C
GO:0016050	Vesicle organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0016197	Endosomal transport	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0016236	Macroautophagy	CHMP3, CHMP4B
GO:0016241	Regulation of macroautophagy	CHMP4B
GO:0016242	Negative regulation of macroautophagy	CHMP4B
GO:0016482	Cytosolic transport	CHMP3
GO:0017157	Regulation of exocytosis	CHMP2A
GO:0019058	Viral life cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0019068	Virion assembly	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0019076	Viral release from host cell	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0031023	Microtubule organizing center organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0031468	Nuclear envelope reassembly	CHMP4B, CHMP7
GO:0032465	Regulation of cytokinesis	CHMP4C
GO:0032466	Negative regulation of cytokinesis	CHMP4C
GO:0032886	Regulation of microtubule-based process	CHMP4B, CHMP5
GO:0032386	Regulation of intracellular transport	CHMP3
GO:0032509	Endosome transport via multivesicular body sorting pathway	CHMP2B, CHMP3
GO:0032510	Endosome to lysosome transport via multivesicular body sorting pathway	CHMP2B, CHMP3
GO:0032886	Regulation of microtubule-based process	CHMP2A, CHMP2B, CHMP3, CHMP4B
GO:0032984	Macromolecular complex disassembly	CHMP2A, CHMP2B, CHMP5, CHMP7
GO:0036257	Multivesicular body organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0036258	Multivesicular body assembly	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0039702	Viral budding via host ESCRT complex	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6, CHMP7
GO:0043112	Receptor metabolic process	CHMP5
GO:0043241	Protein complex disassembly	CHMP2A, CHMP2B, CHMP5, CHMP7
GO:0043900	Regulation of multi-organism process	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0043901	Negative regulation of multi-organism process	CHMP3
GO:0043902	Positive regulation of multi-organism process	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0043903	Regulation of symbiosis, encompassing mutualism through parasitism	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0044770	Cell cycle phase transition	CHMP2A, CHMP4B, CHMP7
GO:0044772	Mitotic cell cycle phase transition	CHMP2A, CHMP4B, CHMP7
GO:0044801	Single-organism membrane fusion	CHMP2B, CHMP3
GO:0044803	Multi-organism membrane organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0045022	Early endosome to late endosome transport	CHMP3
GO:0045047	Protein targeting to ER	CHMP4B
GO:0045324	Late endosome to vacuole transport	CHMP7
GO:0045786	Negative regulation of cell cycle	CHMP2A, CHMP4C
GO:0045921	Positive regulation of exocytosis	CHMP2A
GO:0046599	Regulation of centriole replication	CHMP2A
GO:0046600	Negative regulation of centriole replication	CHMP2A
GO:0046605	Regulation of centrosome cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0046606	Negative regulation of centrosome cycle	CHMP2A
GO:0046755	Viral budding	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0048284	Organelle fusion	CHMP2B, CHMP3, CHMP4C
GO:0048524	Positive regulation of viral process	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0048871	Multicellular organismal homeostasis	CHMP4B
GO:0050000	Chromosome localization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0048525	Negative regulation of viral process	CHMP3
GO:0050792	Regulation of viral process	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:0050890	Cognition	CHMP2B
GO:0051047	Positive regulation of secretion	CHMP2A
GO:0051225	Spindle assembly	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0051258	Protein polymerization	CHMP2A, CHMP3
GO:0051259	Protein oligomerization	CHMP2A, CHMP3, CHMP4B
GO:0051260	Protein homooligomerization	CHMP2A, CHMP4B
GO:0051291	Protein heterooligomerization	CHMP2A, CHMP3
GO:0051297	Centrosome organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0051298	Centrosome duplication	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0051302	Regulation of cell division	CHMP4C
GO:0051303	Establishment of chromosome localization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0051310	Metaphase plate congression	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0051493	Regulation of cytoskeleton organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0051494	Negative regulation of cytoskeleton organization	CHMP2A
GO:0060627	Regulation of vesicle-mediated transport	CHMP3
GO:0051640	Organelle localization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0051656	Establishment of organelle localization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0051782	Negative regulation of cell division	CHMP4C
GO:0051783	Regulation of nuclear division	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0060249	Anatomical structure homeostasis	CHMP2B, CHMP4B
GO:0060627	Regulation of vesicle-mediated transport	CHMP2A
GO:0061025	Membrane fusion	CHMP2B, CHMP3
GO:0061511	Centriole elongation	CHMP2A
GO:0061763	Multivesicular body-lysosome fusion	CHMP2B, CHMP3
GO:0070050	Neuron cellular homeostasis	CHMP2B
GO:0070507	Regulation of microtubule cytoskeleton organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
GO:0070972	Protein localization to endoplasmic reticulum	CHMP4B
GO:0070997	Neuron death	CHMP4B
GO:0071985	Multivesicular body sorting pathway	CHMP3, CHMP5
GO:0072599	Establishment of protein localization to endoplasmic reticulum	CHMP4B
GO:0072657	Protein localization to membrane	CHMP4B
GO:0080171	Lytic vacuole organization	CHMP5
GO:0090148	Membrane fission	CHMP4B
GO:0090150	Establishment of protein localization to membrane	CHMP4B
GO:0090169	Regulation of spindle assembly	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0090174	Organelle membrane fusion	CHMP2B, CHMP3
GO:0090224	Regulation of spindle organization	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0090307	Mitotic spindle assembly	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:0090611	Ubiquitin-independent protein catabolic process via the multivesicular body sorting pathway	CHMP4B, CHMP4C
GO:0097352	Autophagosome maturation	CHMP3
GO:0097576	Vacuole fusion	CHMP2B, CHMP3
GO:0098534	Centriole assembly	CHMP2A
GO:0098813	Nuclear chromosome segregation	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0098927	Vesicle-mediated transport between endosomal compartments	CHMP3
GO:1901673	Regulation of mitotic spindle assembly	CHMP2A, CHMP2B, CHMP4C, CHMP5
GO:1901214	Regulation of neuron death	CHMP4B
GO:1902115	Regulation of organelle assembly	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:1902116	Negative regulation of organelle assembly	CHMP2A, CHMP4B
GO:1902186	Regulation of viral release from host cell	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:1902187	Negative regulation of viral release from host cell	CHMP3
GO:1902188	Positive regulation of viral release from host cell	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:1902590	Multi-organism organelle organization	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:1902592	Multi-organism membrane budding	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
GO:1903335	Regulation of vacuolar transport	CHMP3
GO:1903649	Regulation of cytoplasmic transport	CHMP3
GO:1902850	Microtubule cytoskeleton organization involved in mitosis	CHMP2A, CHMP2B, CHMP4B, CHMP4C, CHMP5
GO:1903532	Positive regulation of secretion by cell	CHMP2A
GO:1903541	Regulation of exosomal secretion	CHMP2A
GO:1903543	Positive regulation of exosomal secretion	CHMP2A
GO:1903722	Regulation of centriole elongation	CHMP2A
GO:1903723	Negative regulation of centriole elongation	CHMP2A
GO:1903900	Regulation of viral life cycle	CHMP2A, CHMP2B, CHMP3, CHMP4C
GO:1903901	Negative regulation of viral life cycle	CHMP3
GO:1903902	Positive regulation of viral life cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C
GO:1904895	ESCRT complex assembly	CHMP6
GO:1904896	ESCRT complex disassembly	CHMP2A, CHMP5, CHMP7
GO:1904902	ESCRT III complex assembly	CHMP6
GO:1904903	ESCRT III complex disassembly	CHMP2A, CHMP5, CHMP7
GO:1905037	Autophagosome organization	CHMP4B
GO:1990182	Exosomal secretion	CHMP2A
GO:2000641	Regulation of early endosome to late endosome transport	CHMP3
GO:2000785	Regulation of autophagosome assembly	CHMP4B
MF
GO:0002020	Protease binding	CHMP3
GO:0031210	Phosphatidylcholine binding	CHMP2A, CHMP3
GO:0045296	Cadherin binding	CHMP2B, CHMP4B, CHMP5
GO:0047485	Protein N-terminus binding	CHMP6
GO:0050839	Cell adhesion molecule binding	CHMP2B, CHMP4B, CHMP5
GO:0008565	Protein transporter activity	CHMP7
GO:0098631	Protein binding involved in cell adhesion	CHMP2B, CHMP4B, CHMP5
GO:0098632	Protein binding involved in cell-cell adhesion	CHMP2B, CHMP4B, CHMP5
GO:0098641	Cadherin binding involved in cell-cell adhesion	CHMP2B, CHMP4B, CHMP5
GO:0070405	Ammonium ion binding	CHMP2A, CHMP3
GO:1990381	Ubiquitin-specific protease binding	CHMP3
CC
GO:000815	ESCRT III complex	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6, CHMP7
GO:0005635	Nuclear envelope	CHMP2A, CHMP4B, CHMP7
GO:0005770	Late endosome	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6
GO:0005913	Cell-cell adherens junction	CHMP5
GO:0010008	Endosome membrane	CHMP2A, CHMP2B, CHMP3, CHMP4C, CHMP5, CHMP6, CHMP7
GO:0030117	Membrane coat	CHMP2A
GO:0030496	Midbody	CHMP4C
GO:0031902	Late endosome membrane	CHMP2A, CHMP2B, CHMP3, CHMP4C, CHMP6
GO:0036452	ESCRT complex	CHMP2A, CHMP2B, CHMP3, CHMP4C, CHMP6, CHMP7
GO:0044440	Endosomal part	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP7
GO:0048475	Coated membrane	CHMP2A, CHMP4B
GO:0090543	Flemming body	CHMP4C
GO:0098552	Side of membrane	CHMP4B
GO:0098562	Cytoplasmic side of membrane	CHMP4B
KEGG pathway
Hsa04144	Endocytosis	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5
Reactome pathway
R-HAS-162588	Budding and maturation of HIV virion	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-2262752	Cellular responses to stress	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6, CHMP7
R-HAS-421837	Clathrin derived vesicle budding	CHMP2A
R-HAS-1643685	Disease	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-917729	Endosomal Sorting Complex Required For Transport (ESCRT)	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-162906	HIV Infection	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-162587	HIV Life Cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-5663205	Infectious disease	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-162599	Late Phase of HIV Life Cycle	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-432720	Lysosome Vesicle Biogenesis	CHMP2A
R-HAS-1632852	Macroautophagy	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP6, CHMP7
R-HAS-199991	Membrane Trafficking	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-5653656	Vesicle-mediated transport	CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP6, CHMP7
R-HAS-199992	Trans-Golgi Network Vesicle Budding	CHMP2A

BP, biological process; CC, cellular component; ESCRT, MF, molecular function; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes; ER,endoplasmic reticulum; HIV, human immunodeficiency virus.

ESCRT genes associated with TILs

We explored the correlation between the expression of ESCRT genes and TILs, including 28 distinct immune cell types in the EC samples (n=546) in the TCGA-UCEC database with Spearman correlations using the online tools, TISIDB and GEPIA2021. We found that CHMP2A, CHMP3, CHMP4B, CHMP5, CHMP5, and CHMP7 were positively correlated with the infiltration of CD8. CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, and CHMP7 were positively correlated with the infiltration of CD4. CHMP4B, and CHMP5 were positively correlated with the infiltration of Tregs. CHMP3, CHMP4B, and CHMP5 were positively correlated with the infiltration of NK cells. On the other hand, CHMP2A and CHMP5 were negatively correlated with the infiltration of CD4. CHMP4C was negatively correlated with the infiltration of NK and Treg. CHMP2B and CHMP4C were negatively correlated with the infiltration of CD8, B cells, and macrophages. CHMP2A, CHMP3, CHMP5, and CHMP7 were negatively correlated with B cells (, Table S5). We also detected CHMP2A (P=0.0081), CHMP4B (P=0.0005), CHMP4C (P=0.0002), CHMP5 (P=0.0231) and CHMP5 (P=0.0061) were significantly related with TILs (rho value) by multiple liner regression (). Significant differences between the ESCRT genes and TILs were observed in the TCGA-UCEC tumor, normal, and GTEx-uterus datasets in the GEPIA analysis, in which the parameters were grouped by tissue (P<0.05) (, Table S5). These results indicate that the ESCRT-III pathway genes may be effective markers in EC immunotherapy.

Figure 3

Relationship between the abundance of TILs and gene expression. This was determined via conducting the Spearman correlation in the EC sample on the TCGA-UCEC database in the TISDB analysis. The stacked bar graph shows the infiltration of 28 immune cells in each sample. Each color represents a rho (A) or P value (B) in a type of immune cell. The X axis represents the association between the ESCRT pathway gene expression and immune subtypes across UCEC. The Y axis represents the TIL abundance Kruskal-Wallis Test (−log 10pv). (A) Rho in Lymphocyte; (B) P value in Lymphocyte; (C) the difference between the ESCRT genes: CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, CHMP5, CHMP5, CHMP7 and TILs on TCGA-UCEC tumor, normal and GTEx-Uterus dataset. The Y axis represents log (TPM+1) in GEPIA analysis. UCEC, uterine corpus endometrial carcinoma; TPM, transcripts per million; TILs, tumor-infiltrating immune cells; Act CD8, activated CD8 T cell; Tcm CD8, central memory CD8 T cell; Tem CD8, effector memory CD8 T cell; Act CD4, Activated CD4 T cell; Tcm CD4, central memory CD4 T cell; Tem CD4, Effector memory CD4 T cell; Tfh, T follicular helper cell; Tgd, gamma delta T cell; Th1, type 1 T helper cell; Th17, type 17 T helper cell; Th2, type 2 T helper cell; Treg, regulatory T cell; Act B, activated B cell; Imm B, immature B cell; Mem B, memory B cell; NK, natural killer cell; CD56bright, CD56bright natural killer cell; CD56dim, CD56dim natural killed cell; MDSC, myeloid derived suppressor cell; NKT, natural killer T cell; Act DC, activated dendritic cell; pDC, plasmacytoid dendritic cell; iDC, immature dendritic cell.

Table 3

Relationship between TILs (rho value) and ESCRT genes by multiple liner regression

Model	CHMP2A	CHMP2B	CHMP3	CHMP4B	CHMP4C	CHMP5	CHMP6	CHMP7
Parameter estimates	0.1175	−0.002861	0.01904	0.1020	−0.09654	0.05768	0.09136	0.01939
Standard error	0.04108	0.03143	0.02620	0.02584	0.02275	0.02394	0.03072	0.02259
95% CI	0.03321 to 0.2018	−0.06735 to 0.06163	−0.03473 to 0.07280	0.04903 to 0.1550	−0.1432 to −0.04985	0.008554 to 0.1068	0.02832 to 0.1544	−0.02697 to 0.06575
[t]	2.860	0.09102	0.7264	3.950	4.243	2.409	2.974	0.8583
P value	0.0081	0.9281	0.4738	0.0005	0.0002	0.0231	0.0061	0.3983

TILs, tumor-infiltrating immune cells; ESCRT, endosomal sorting complex required for transport; CI, confidence interval.

Survival analysis and efficacy evaluation of the ESCRT genes

We performed the Kaplan-Meier survival analysis in UALCAN (Figure S6A). The patients were assigned to high (n=136) or low/medium (n=407) expression groups. The univariate Cox regression analysis in TISIDB (Figure S6B) from the TCGA-UCEC database was used to predict the prognostic values of the ESCRT genes on the OS of patients with EC. The Univariate Cox proportional hazards regression analysis in the GEPIA from TCGA also evaluated OS and DFS in patients with EC. We found that a low expression of CHMP4B (log-rank P=0.037) in UALCAN and a high expression of CHMP2A in TISDIB (log-rank P=0.0203) had a favorable OS for EC, while high expression of CHMP2A [(log-rank) P=0.016, hazard ratio (HR) (high) =0.44, P(HR) =]0.018) and CHMP7 [(log-rank) P=0.027, HR (high) =0.47, P(HR) =0.03] in GEPIA were positively correlated with a better prognosis of EC (Figure S6C).

In addition, we also performed a survival analysis using the Kaplan-Meier method on the ESCRT mutation genes; the 5-year OS rates/intervals of the last follow-up were as follows: CHMP2A (82%, 5.095), CHMP2B (72%, 5.142), CHMP3 (81%, 5.136), CHMP4B (76%, 5.106), CHMP4C (68%, 5.106), CHMP5 (65%, 5.095), CHMP5 (25%, 5.008), and CHMP7 (84%, 5.106) (Figure S7). The CHMP5 mutations positively correlated with a spoor prognosis. CHMP2A, CHMP3, and CHMP7 mutations demonstrated a good potential for being a prognostic tool in patients with EC (Figure S6, Table S6).

Discussion

Type I EC is associated with microsatellite instability hypermutated (MSI-H) and a copy-number low (CNL) integrated genomic categories in TCGA and frequent mutations in the mTOR pathway (2). Type II EC is characterized by a copy-number high (CN-H) and frequent mutation of TP53 (17). We found that CHMP2B, CHMP3, CHMP4B, CHMP5, CHMP5, and CHMP7 were significantly lower, whereas CHMP2A and CHMP4C were significantly higher in EC samples than in normal tissues. Since histological grade and clinical stage greatly affected the prognosis prediction of EC (1), we explored it found that all of the ESCRT pathway genes were significantly differentially expressed between grades 2 and 3. Increased expression of CHMP2A and CHMP7 and decreased expression of CHMP4B were related to endometrioid carcinoma samples compared with serous carcinoma samples. Furthermore, the results of the survival analysis revealed that upregulation of CHMP2A and CHMP7 and downregulation of CHMP4B corresponded to a good prognosis in patients with EC. All three genes were significantly associated with all four molecular subtypes in TCGA. Therefore, we indicated that high levels of CHMP2A and CHMP7 and low levels of CHMP4B were associated with good prognostic tumor type and early grade and were effective indicators of good prognosis in EC. CHMP2A, CHMP2B, CHMP3, CHMP4B, CHMP4C, and CHMP5 were significantly correlated with HIPPO, WNT, mTOR, NRF2, RTK, P53/Rb, SWI-SNF, MYC/MYCN pathway, and chromatin modifier status in EC tissues. As such, temsirolimus and everolimus, which are mTOR inhibitors, are effective in the treatment of recurrent chemotherapy (18,19). Moreover, ESCRT-III genes are effective markers for targeted therapy.

We found that most of the ESCRT pathway genes were frequently mutated in EC samples compared with the other cancer types obtained from the TCGA cohort. Additionally, missense mutations were the most common type of mutation. CHMP7 was the first modifier of VEP impact mutation, which was deleterious and tolerated by the SIFT Impact Mutation. Mutations in CHMP2A and CHMP7 had a good prognostic potential in EC. Dysfunction of ESCRT was associated with cancer, myopathy, and neurodegeneration. Missense mutations in CHMP4B were associated with posterior subcapsular or polar cataracts (20-22). Other studies showed that CHMP2B mutations that were linked to defects in the process of dissociation from ESCRT disrupted endosome-to-lysosome trafficking, degradation, and recycling. Thus, this resulted in defective mitochondria, imbalanced iron homeostasis, reactive oxygen species, neuronal damage (23,24), endocytic and autophagic defects (25-27) in autosomal dominant presenile dementia (28), amyotrophic lateral sclerosis, cortical basal degeneration (CBD) or familial frontotemporal lobar degeneration (FTLD) and frontotemporal dementia (FTD) (29).

The results of functional analysis revealed that the endocytosis and ESCRT was the most significantly enriched KEGG and Reactome pathways associated with the ESCRT genes in ECs. All concerned ESCRT pathway genes were mainly involved in the BPs of membrane budding, multivesicular body organization, and assembly. The main related CC were the ESCRT and ESCRT III complex. The latter was involved in transmembrane protein delivery and in the promotion of nuclear envelope sealing and mitotic spindle disassembly during the late endosome anaphase (30). Protein sorting from the endosome to the vacuole/lysosome transportation and degradation in eukaryotic cells required the formation of MVBs, which, in turn, required the sequential functioning of ESCRT, -I, -II, and -III complexes in four steps: cargo recognition and sorting, which was initiated by the binding of ESCRT-0 to the Phosphatidyl inositol 3-phosphate (PI3P) on endosomes; cargo sequestration, in which ESCRT-0 recruited ESCRT-I and interacted with ESCRT-II and -III; MVB vesicle formation, in which ESCRT-III proteins dissociated and assembled; and the ESCRT disassembly, in which the final step of the MVB pathway was formed (31-33). CHMP5 served as an acceptor for the ESCRT-II complex on the endosomal membranes in the ESCRT-III complex, thus participating in ESCRT and ESCRT III complex assembly in BP, while CHMP2A, CHMP5, and CHMP7 participated in the BP of ESCRT and ESCRT III complex disassembly. In the late stages of cytokinesis, CHMP3 was responsible for endosomal sorting/trafficking of the EGF receptor. CHMP4C was a key component of the cytokinesis checkpoint that delayed abscission and prevented both premature chromosome bridges of the cells and the accumulation of DNA damages (34,35). CHMP2B, CHMP3, and CHMP5 were involved in the BP of the multivesicular body sorting pathways and the regulation of early to late endosome transport. All of the key genes were significantly and positively correlated with each other, revealing that the ESCRT pathway might also be involved in the mechanism of the MVB pathway in EC.

On the other hand, tumor-infiltrating immune cells (TICs) modulated cancer cell functions in the tumor microenvironment (36). This could be a basis for immunotherapy, in which the endogenous immune response against tumor cells would be stimulated. Furthermore, this could be associated with the prognosis of patients with EC. ECs stimulate immune checkpoints and activate negative feedback mechanisms in a locally immunosuppressed environment to evade the immune system (37). Memory CD4+ T cells, activated NK cells, and dendritic cells all decreased with an increase in EC grade and stage, which was related to the occurrence of EC. However, high levels of Tregs indicate a good prognosis (38). Our preliminary results show strong correlations between all ESCRT genes and infiltration levels of multiple immune cell types in EC samples. CHMP2A and CHMP7 were positively correlated with the infiltration of CD4 and negatively correlated with B cells, while CHMP4B was associated with the infiltration of CD4, CD8, Treg, and NK cells. These three genes may potentially be immune-related indicators for the prognosis of EC and may become the basis for EC immunotherapies.

It has been found that EC cells represent the highest activated programmed death receptor-1 (PD1)/PD-L1expression in endometrioid endometrial adenocarcinoma (EEC), serous endometrial adenocarcinoma (ESC), and clear cell subtypes for 40–80%, 10–68%, and 23–69%, respectively. On the other hand, inactivate tumor-infiltrating CD4 and CD8 T cells are found in the tumor microenvironment (39). Targeting the PD1 pathway may be a great strategy in enhancing the antitumor immune response for EC treatment. Anti-PD-L1 antibodies, such as pembrolizumab, nivolumab, and atezolizumab, have been proven clinically effective in tumors with mismatch-repair deficiency in EC patients in clinical trials, with a 13–48% ORR, 19.0% PFS, and 68.8% OS rates (40-44). Pembrolizumab is an immune checkpoint blockade anti-PD1 antibody that has been used in patients with POLE-mutated (43) and MMR-deficient endometrial cancer (45). We observed that, except for CHMP7, all other ESCRT genes were significantly associated with the immune subtype. CHMP2A, CHMP4B, CHMP5, and CHMP7 were significantly associated with the POLE-mutated molecular subtype in TCGA, which was identified in 10% of endometrioid subtypes. Therefore, elucidation of CHMP2A, CHMP4B, CHMP7, and immune cell interplay would assist in the prediction of immunotherapy responses and development of novel immunotherapy targets.

Conclusions

Our present study confirmed that RNA expression data on multiple datasets showed the relationship between ESCRT-III genes and EC. ESCRT-III genes may be used as clinical biomarkers for the prognostic prediction and immunotherapy strategies in patients with EC. Further research should be conducted to determine the mechanisms of ESCRT genes as biomarkers and therapeutic targets for the treatment of EC.

The article’s supplementary files as