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EDITED BY Kefah Mokbel, Princess Grace Hospital, United Kingdom
REVIEWED BY Dmitry Aleksandrovich Zinovkin, Gomel State Medical University, Belarus Shuai Xu, Vanderbilt University, United States Lili Liu, Vanderbilt University Medical Center, United States
*CORRESPONDENCE
Jiang-shui Yuan ☒ yjs19790104@163.com
Wei-qing Song ☒ songweiqing68@163.com
These author have contributed equally to this work
RECEIVED 21 November 2024 ACCEPTED 07 March 2025 PUBLISHED 31 March 2025
CITATION Chen X-r, Tan X-y, Zhang Z-l, Yuan J-s and Song W-q (2025) ESCRT may function as a tumor biomarker, transitioning from pan-cancer analysis to validation within breast cancer. Front. Immunol. 16:1531940. doi: 10.3389/fimmu.2025.1531940
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@ 2025 Chen, Tan, Zhang, Yuan and Song. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
ESCRT may function as a tumor biomarker, transitioning from pan-cancer analysis to validation within breast cancer
Xiao-rui Chen1,21, Xue-ying Tan21, Zong-liang Zhang3, Jiang-shui Yuan2* and Wei-qing Song1,2*
1Qingdao Medical College, Qingdao University, Qingdao, China, 2Clinical Laboratory Department, Qingdao Municipal Hospital, Qingdao, China, 3Department of Urology, Affiliated Hospital of Qingdao University, Qingdao, China
Background: Studies have shown that ESCRT genes affect cell aging, immune environment, and tumors. BRCA was used to explore its effects on tumor prognosis and therapy.
Methods: The data sets of Cancer Genome Atlas (TCGA), Genome-Tissue Expression Plan (GTEX), Human Protein Mapping (HPA), Gene Expression Omnibus (GEO), Clinical Proteomic Tumor Analysis Consortium (CPTAC), R software package, and bioinformatics methods were used to mine the potential carcinogenic effects of ESCRT, including the expression level, prognostic value, and immune value of ESCRT in various types of tumor tissues, and the potential function of ESCRT family genes was further verified in breast cancer.
Results: ESCRT showed significant differential expression in various cancers, such as bladder urothelial carcinoma and liver, cervical, renal, esophageal, head, and neck cancers (P <0.05). Abnormal ESCRT expression is associated with poor prognosis in various cancers, such as adrenocortical carcinoma, bladder urothelial carcinoma, breast cancer, and cervical cancer (P <0.05). The expression level of ESCRT was significantly associated with immune cell infiltration and the modulation of the stromal/immune score (all P <0.05). Enrichment analysis showed that ESCRT is associated with immune-related functions and transport signaling pathways in various tumor cells. Moreover, ESCRT serves as an early diagnostic marker for several tumors and is significantly associated with prognosis. This confirms that ESCRT is associated with most immune-infiltrating cells in pan-carcinomas. Taken together, these studies highlight the importance of the ESCRT family gene VPS37D in breast cancer initiation, progression, and immune response.
Conclusion: This study highlights ESCRT’s potential in tumor detection via pan-cancer analysis, showing expression variations between tumor and normal tissues, its role in cancer progression through the immune microenvironment,
and its specificity and sensitivity in detection. The VPS37D gene, with significant variation in breast cancer, predicts patient prognosis and is related to the tumor microenvironment, suggesting that ESCRT is a novel biomarker for early diagnosis and prognosis assessment.
KEYWORDS
pan-cancer, ESCRT, biomarker, immune cell infiltration, VPS37D
Introduction
Despite advancements in surgery and treatment, tumor incidence and mortality rates remain high (1). Tumor development is influenced by genetic factors, such as oncogene mutations (2), epigenetic changes affecting gene expression, and an imbalance between cell proliferation and apoptosis (3). Environmental and lifestyle factors such as smoking and exposure to industrial chemicals also contribute to this phenomenon (4). Chronic inflammation caused by conditions such as recurrent urinary tract infections can damage bladder cells and increase the risk of cancer (5). Tumor growth requires angiogenesis, and cancer cells can metastasize through lymphatic or blood pathways, thereby worsening prognosis. Additionally, tumor cells may evade immune surveillance, facilitating their growth and spread (6). Cancer treatments include surgery for non-muscle- invasive cancers (7), chemotherapy at various stages (8, 9), radiotherapy as an adjunct for advanced or inoperable cases (10), immunotherapy to activate the immune system (11, 12), targeted therapy focusing on specific cancer cell pathways (13), palliative care for advanced cancers (14, 15), Traditional Chinese Medicine as supportive care (16), and combination therapy for optimal results, with ongoing research on new options (17, 18).
Breast cancer is the most prevalent type of cancer among women worldwide (19), with its increasing incidence presenting significant challenges to public health systems and healthcare resources (20). Consequently, early screening and timely treatment are critical for reducing mortality rates and enhancing cure rates (21), positioning breast cancer treatment as the central focus of contemporary medical research (22). The integration of various treatment modalities and emphasis on early intervention are essential strategies in the fight against this widespread disease (23).
In recent years, the endosomal sorting transport complex (ESCRT) has emerged as a fundamental molecular apparatus for regulating membrane dynamics (24) and cellular signal transduction (25), and its role in cancer mechanisms has gained increasing attention (26, 27). It is composed of ESCRT-0, ESCRT-I, ESCRT-II, ESCRT-III, VPS4-VTA1 and ALIX (28) (Supplementary Figure S1). They play key roles in processes, such as intracellular material transport (29), membrane repair (30, 31), and cell division (32) (Figure 1). ESCRT is linked to cancer development through several mechanisms, including regulating cell cycle proteins affecting tumor
cell survival (33), facilitating protein transport for degradation (34), influencing apoptosis and autophagy (35), modulating tumor- immune interactions (36), assisting exosome formation impacting the tumor microenvironment (37), participating in DNA damage repair (38), and affecting cancer stem cell behavior (39), highlighting its crucial role in tumorigenesis and progression (40). Previous studies have highlighted ESCRT’s role in tumors and its therapeutic strategies (41), indicating its significance in hepatocellular carcinoma (42), breast cancer (43), and renal cancer (44). A previous study also predicted the impact of ESCRT family gene expression on the prognosis of BLCA. However, no pan-cancer analysis of ESCRT’s effect on cancer prognosis has been conducted (45). Such analyses enhance the understanding of gene mechanisms and their predictive value in tumor biology, aiding clinical diagnosis and treatment (46).
This study represents an inaugural multidimensional analysis of ESCRT, elucidating its expression characteristics, prognostic associations, and immune regulatory mechanisms across 33 cancer types. The integration of tumor mutation load, microsatellite instability, and stem cell score revealed a significant correlation between ESCRT and the expression of immune checkpoint genes as well as immune cell infiltration. This study used breast cancer as a model to investigate the potential significance of VPS37D and other critical molecules in the remodeling of the tumor microenvironment and precision therapy, thereby offering a theoretical foundation for the advancement of tumor immunotherapy.
Materials and methods
Collection and processing of the ESCRT data
RNA sequencing data for the pan-cancer cohort (n = 15,776) were obtained from the UCSC Xena website (https://xenabrowser.net/ datapages/). The dataset comprised of several cancerous and normal tissues from the Cancer Genome Atlas and genotype- tissue expression (GTEx). The complete dataset was filtered to eliminate missing and duplicate entries, and the logarithm (TPM + 1) of uniformly processed transcripts per million (TPM) normalized expression profile data was transformed using the rms
Membrane
2 Endocytosis
Integrins
Annexins IV, V, VI
Release
Tetraspanins CD9, CD63, CD81
1
0
Early endosomes
DNA
miRNA
Integrins
6
Nucleus
ER
Chaperones HSP70
IncRNA circRNA
3
Gogi
Foreign matter
ESCRT MVB marking proteins Alix, TSG101, Clathrin
4
Early endosomes/ MVBs
Membrane trafficking proteins GTPase, Rabs
Secretion
5
MHC II
Lysosomes
Lipid Rafts Flotillins, Sphingolipids, Cholesterol, ceramide …
MHC I
cell division
Exosomes
function in the R package for subsequent analysis. CPTA data were obtained from PDC (https://pdc.cancer.gov/pdc/browse). Each expression value was log2 (value + 1) transformed, and cancers with fewer than five samples in a single cancer species were excluded. Finally, the expression data of 33 tumors were obtained, including adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck cancer (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS) and uveal melanoma (UVM).
ESCRT differential expression analysis
After excluding cancers with fewer than five samples from a single cancer species, we included the data for 18 tumors. The DESeq2 package in R (4.2.3) analyzes transcript data from normal and cancer tissues, filtering differentially expressed genes (DEGs) with log2|FC| >0.585 and false discovery rates (FDR)-adjusted P- value <0.05, intersects them with ESCRT, and visualizes results using “ggot2” and “VennDiagram” for a Venn diagram of ESCRT genes. It also employs “pubggr” and “pheatmap” for volcano and heatmap visualizations, while “ggpubr” creates a box plot of differential gene expression and outputs significance from Wilcoxon testing. We also used UALCAN (https://ualcan.path.uab.edu/index.html) to process CPTAC data and analyze the differential protein expression of the ESCRT family genes in pan-cancer. Bonferroni Correction for p <0.05 was considered as having a significant difference.
Analysis of prospective relevance
Forecasting of patients using the ESCRT family gene expression median in TCGA cohort as critical values, using log-rank testing to analyze the correlation of ESCRT family genetic expression with total survival (OS) in pan-cancer patients, and to map the Kaplan- Meier survival curve.
Correlation analysis of immune traits
An immunogenicity analysis of 33 cancers in TCGA identified six immune subtypes: wound healing, IFN-y dominant, inflammatory, lymphocyte-depleted, immunologically quiet, and TGF-ß dominant, comparing ESCRT genes among them. All results were subjected to multiple validations, and the P-adjust was calculated using the Bonferroni correction. The relationship between ESCRT family gene expression and immune cell infiltration was analyzed using multiple algorithms. In the Spearman correlation, statistical significance was determined by a P-value <0.05 and the correlation between ESCRT and immune cell infiltration was visualized using the R packages ‘ggplot2’ and ‘ggpubr.‘
Association of ESCRT with stemness
The OCLR algorithm produces the DNAss index by analyzing the methylation patterns of different types of cancer. This was used to determine the stemness score of tumors. The stemness scores and gene expression data were integrated to visualize the Pearson correlation. Raw P values were adjusted using Bonferroni’s method.
Drug sensitivity analysis of ESCRT
The ESCRT transcriptomic and drug sensitivity data were downloaded from CellMiner40 and a Pearson correlation test was used to analyze their relationships. The impute and limma packages identified the link between ESCRT expression and drug sensitivity, whereas ggplot2 and ggpubr were used for visualization.
Differential expression analysis in BRCA
Using transcriptomic data from TCGA, GTEx, GEO (GSE42568, GSE20685), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) databases, multi-omics approaches were employed to compare the expression levels of ESCRT genes in BRCA and normal tissues, identifying the most significant key genes. Set |log2FC| >0.585 and adjusted P-value <0.05. For multi-center or cross-platform data, the ComBat algorithm (sva package) was used to eliminate technical batch differences.
GO function enrichment set of BRCA differential genes and KEGG signal path analysis
The “clusterprofiler” package “org.HS.eg.7db” was used to screen for differentially expressed genes with a significance threshold of P <0.05. This includes conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. GO analysis was categorized into three
distinct components: biological process (BP), molecular function (MF), and cellular component (CC). Following the examination, the downloaded results were processed by applying filtering conditions with p.adjust <0.05, and FDR <0.25. The combined results were subsequently represented through the ‘ggplot2’ software.
Relevance analysis of clinical characteristics
Exploring the expression of the key gene in BRCA: VPS37D in different TNM classifications and pathological categories Tumor microenvironment in BRCA
Obtaining breast cancer (BRCA) RNA-seq data from the public database TCGA was normalized to FPKM (RNA-seq). The ESTIMATE algorithm was used to calculate the stromal and immune scores and the integrated ESTIMATE score (reflecting tumor purity) of tumor samples. For RNA-seq data based on the LM 22 feature matrix, the relative proportions of the 22 immune cell subtypes were calculated, keeping only samples with a deconvolved P.adjust <0.05, FDR <0.25. We estimated the abundance of 64 immune and stromal cell types using gene set enrichment analysis (GSEA) and the extended ssGSEA method to produce xCell.
Results
Expression analysis of the ESCRT family genes
The expression levels of the ESCRT family genes in 18 tumors and their corresponding normal tissues were analyzed using TCGA (Figure 2A). Overall, ESCRT genes were significantly overexpressed in tumors compared to in normal tissues (all P <0.05). Individual analysis of each gene revealed some variations: compared to the corresponding normal tissues, all ESCRT family genes were highly expressed in CHOL, CHMP4A was significantly overexpressed in all cancers, HGS was low in GBM and KIRC, but high in other tumors; TSG101 was low in KIRC; VPS28 was low in KICH; VPS37A was high in CHOL and LIHC; VPS37C was low in THCA; VPS37D was high in BRCA; MVB12A was low in STAD and KIRC; SNF8 was low in KICH; VPS36 was high in CHOL; CHMP3 was high in KICH; CHMP4B was low in KICH, HNSC, and KIRC; CHMP1A was low in COAD; VPS4A was low in THCA and UCEC; and PTPN23 was low in KIRC and LUSC. All results were statistically significant (P <0.05) (Figure 2B). The differential expression of the ESCRT family genes is shown in Supplementary Figure S2.
Analysis of the relationships between ESCRT and prognosis
By analyzing the expression of the ESCRT family genes and their correlation with the overall survival of pan-cancer patients, we concluded that in all 33 cancer types, the ESCRT family genes were
A
Gene expression
00
6
2
+
0
HGS
STAM
STAM2
TSG101
VPS28
VPS37A
VPS37B
VPS37C
VPS37D
MVB12A
MVB12B
UBAP1
UMAD1
SNF8
VPS25
CHMP2A
VPS36
CHMP2B
CHMP6
CHMP3
CHMP4A
.— 1
CHMP4B
CHMP4C
CHMP5
CHMP1A
CHMP1B
VPS4A
IST1
VPS4B
PDCD6IP
VTA1
PTPN23
1 tissue samples (*P <0.05, ** P <0.01, *** P <0.001).
1 W 1 1 CHMP4G expression H 1 acer typ 6 a - 0 S 3 € A 0 A O AO RC Čo T 9 IT 4 O V C . e 4 ON TVR - M 1 . I H CHUNA STRING A 0 a - a . - I 3 m 2 READ M 1 V D DOG - a 0 N N 3 2 9 - O 0 .. - 1 .. a A 4 , --- a - ” 0 4 1 . - A 9 a . , W 2 CAO A CHUPZA expression Cancel Type O LA 1 D - & ” 1 A # - - 1 LOMO O 0 X 2 - 1 - UNG A m D - A C PM H - 5 · 0 8 V I 4 I 2 I A G 1 FIGURE 2 .- -I - 2 - M 1 J 2 6 - M - 1 0 D 0 PRA 2 ESCRT family gene expression. (A) Gene expression levels in pan-cancer patients; (B) Differential expression of ESCRT between 18 tumor and normal * 0 A 4 1 I mc 1 ” 1 PD 0 A D 1 L L 1 6 5 3 I A Y 1 . 2 1 + 1 A L … . A 2 GR M d * 1 # - Cancer Type 4 - 0 0 A 1 X 2 4 I 1 4 ₹ . I 0 A 2 6 1 0 1 2 1 O T 9 4 O I I 1 A I 8 4 0 10 d Cancer typ# 1 - A 2 - ” P STAM ENOFHLEN C 3 2 . A COM - M C 1 1… a 1 . 3 A S - O LICEO I 4 - 0 M - 1 W ! 1 3 I - TSQtất soprasio - A X A CD I 1 I 2 uCEC 0 ancer type - 0 = 1 & 40℃ O 1 G 4 - A I 2 D 1 P A VPSHA EŞITLIĞI + 9 - P 1 1 6 4 A D G 3 H 0 Oyibo 1 - E 2 Y T D · - U - - + 1 0 O - · A 1 £ H 3 3 VCKC 0 - D A 1 a - - a 4 a 4 2 C 1 1 F * I “I . - - ® 1 4 4 1 A = Y 4 + 2 . I 1 I 0 - H B 1 4 I T BECA . H A 0 0 # “RAD J 0 1 - S 2 T . 1 ARAD 3 .. .. 1 et #
- 1 K C 0 O d 0 1 T 0 · Cancer type a1 D . + - . I 1 T P 5 LUND I A I 5 - I . · 1 C1 O 2 C 1 5 1 M + G · A * 8 2 5 H 1 O 1 - 1 a O 1 HEAD lancer type A - - ₹ + 1 1 a D 1 I- 1 . # - 9 O * 1 W I 4 a 2 - + A ₹ A “. COMO . . 1 C 1 - A + I + 6 G : 3 ¥ 2 4 2 NY T · 1 5 3 · 3 A 1
B
significantly associated with the prognosis of a certain tumor (Supplementary Figure S3; P <0.05). We selected the prognosis for one gene in common tumors in each ESCRT subcomplex classification. Genes such as HGS, CHMP4C, and VPS4B, which are highly expressed, are associated with poor prognosis. However, it is important to note that some genes, such as VPS37D and STAD, have different prognostic effects on different types of cancer, which may be because the tumors are not the same (P <0.05) (Figure 3).
Immune correlation analysis
TCGA tumors were classified into six immune subtypes (C1: wound healing, C2: IFN-y dominant, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet, and C6: TGF-B dominant), and the analysis showed that the ESCRT family genes were significantly different in all immune subtypes (Supplementary Figure S4). Evaluation using StromalScore, ImmuneScore, and EstimateScore indicated that most ESCRT genes, such as VPS37D in DLBC and VPS37B in GBM, exhibited a significant negative correlation with tumor immune infiltration (Figures 4A-C). Some genes exhibited positive regulation in particular cancer types, including VPS37C in DLBC and CHMP3 and VPS37B in TGCT, which were positively correlated with immune cell enrichment. This indicates that members of the ESCRT family may have immune regulatory functions specific to certain cancer species. Association of cancer stem cells: Integrated analysis of the stem cell transcriptome and DNA methylation multi-platform data indicates a strong correlation between ESCRT gene expression and cancer stem cell characteristics. In CHOL, genes VTA 1 and CHMP4B exhibited a positive correlation with the methylation- derived stem cell index, whereas CHMP6 in STAM and TGCT showed a significant negative correlation (Figures 4D, E). The RNA dataset confirmed that CHMP4B/SNF8 in THYM positively regulates stem cell properties, whereas VPS37C in DLBC and CHMP3 in TGCT negatively influence this process. This indicates that ESCRT impacts the biological behavior of tumor stem cells through a dual mechanism involving epigenetic and transcriptional regulation.
Drug sensitivity analysis
Systemic drug therapies play a crucial role in cancer treatment. Gene expression and drug sensitivity data were obtained using CellMiner. We screened FDA-approved drugs and analyzed the correlation coefficients between ESCRT expression and drug sensitivity to investigate the relationship between ESCRT and anti-tumor drugs. The findings indicated 434 significant drug- sensitivity relationships, notably between CHMP6 and 5-fluoro deoxy uridine 10mer. An increase in CHMP6 expression was correlated with enhanced sensitivity to 5-fluoro deoxy uridine 10mer. The expression of HGS correlated positively with drug sensitivity, whereas CHMP4C exhibited an inverse correlation with sensitivity to okadaic acid. Additionally, VPS37D expression
was positively correlated with Sonidegib, 5-fluoro deoxy uridine 10mer, and the 1st Precursor Intermediate to TDP 665759, but negatively correlated with Cyclophosphamide, Sunitinib, Obatoclax, and PX-316 (Figure 5). In conclusion, the ESCRT gene family demonstrated a significant correlation with sensitivity to various drugs. These results indicate that sensitization to specific drugs may be influenced by the expression of ESCRT family genes, either by facilitating or inhibiting the process.
Differential expression of ESCRT family genes in BRCA
The results showed differential expression of ESCRT family genes in breast cancer. We subsequently analyzed data from TCGA and GEO to examine the key molecules associated with BRCA. TCGA analysis revealed 8,576 differentially expressed genes in both BRCA and normal tissues, and when intersected with the ESCRT gene family, 11 DEGs were identified, including HGS, MVB12A, VPS28, VPS36, VPS37C, VPS37D, VPS 25, CHMP2A, CHMP4B, and CHMP4C, with VPS37D showing the most significant difference in breast cancer (P.adjust <0.05, Figure 6A). We subsequently validated the protein expression of VPS37D in the HPA database, and the results showed that it was highly expressed in breast cancer tissues compared to normal breast tissues (P <0.05, Figure 6C). We identified 505 differentially expressed genes using GEO analysis (P <0.05, Figure 6B).
Enrichment analysis
Key genes identified by TCGA differential analysis were also closely related (Figure 7A). KEGG enrichment analysis of differentially expressed genes indicated significant enrichment in pathways, including endocytosis, viral life cycle-HIV-1 and necroptosis. GO enrichment analysis revealed that these genes were mainly involved in molecular functions, such as ubiquitin binding, ubiquitin-like protein binding, and phosphatidylinositol binding. Cellular components mainly involve the late endosome membrane and late endosomes. Moreover, these genes play crucial roles in biological processes, including multivesicular body assembly and organization (Figure 7B).
Differential analysis of the clinical correlation
By combining the differential gene VPS37D with clinical analysis, it was observed that there were significant differences among various clinicopathological factors of BRCA. There are also clear relationships between estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2, which are closely linked to the occurrence and development of BRCA (Figure 8).
A
Cancer: LIHC
Cancer: KIRC
HGS levels
high
low
HGS levels
high
low
1.00-
1.00-
te
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
p=0.004
0.25
p<0.001
0.00
0.00
0
2
4
6
8
10
0
2
4
6
8
10
Time(years)
12
Time(years)
HGS levels
HGS levels
high
low
184
184
61
82
28
38
14
15
1
-C
1
0
high
low
265
266
165
195
98
120
45
54
18
23
2 6
0
1
0
2
4
6
8
10
0
2
4
6
8
10
12
Time(years)
Time(years)
B
Cancer: BRCA
Cancer: COAD
VPS37D levels
high
low
VPS37D levels
high
low
1.00-
1.00-
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
p=0.036
0.25
p=0.046
0.00
0.00
0
2
4
6
8
10
12
14
16
18
20
22
24
0
2
4
6
8
10
12
VPS37D levels
Time(years)
VPS37D levels
Time(years)
high low
541
541
295
306
157
173
90
107
51
23
11
52
17
8
7
7
5
6
4
5
4
2
4
1
0
224
27
a
high low
224
103
114
30
14
13
5
6
3
1
0
0
2
4
6
8
10
12
14
16
18
20
22
24
0
2
4
6
8
10
12
Time(years)
Time(years)
C
Cancer: STAD
Cancer: KIRC
SNF8 levels
high
low
SNF8 levels
high
low
1.001
1.00 -
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
p=0.024
0.25
p<0.001
0.00
0.00
0
2
4
6
8
10
0
2
4
6
8
10
12
Time(years)
Time(years)
SNF8 levels
SNF8 levels
high OW
₾
175
52
48
10
13
4
3
1
175
1
5
0
nigh
265
171
266
189
106
112
50
49
18
low
23
7
6
0
1
0
2
4
6
8
10
0
2
4
6
8
Time(years)
Time(years)
10
12
D Cancer: LUSC
Cancer: CESC
CHMP4C levels
high
low
CHMP4C levels
high
low
1.00 -
1.00 -
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
p=0.040
0.25
p=0.042
0.00
0.00
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
16
18
20
CHMP4C levels
Time(years)
CHMP4C levels
Time(years)
high
246
108
54
27
25
14
6 3a
4
A
1
0
high- low
146
247
147
71
71
24
12
121
56
23
11
7
9
5
6
2
low
14
38
12
2
N
2 0
0
0
0
2
4
6
8
10
12
14
0
2
4
6
8
10
12
14
16
18
20
Time(years)
Time(years)
E
Cancer: PAAD
Cancer: ACC
VPS4B levels
high
low
VPS4B levels
high
low
1.001
1.00-
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
p=0.003
0.25
p=0.037
0.00
0.00
0
2
4
6
8
0
2
4
6
8
10
12
Time(years)
Time(years)
VPS4B levels
VPS4B levels
high low
88
89
19
17
5
6
1
1
0
high
low
39
40
26
32
13
17
5
11
2 6
5
2
0
2
0
2
4
6
8
0
2
4
6
8
10
12
Time(years)
Time(years)
FIGURE 3 The prognosis value of ESCRT in pan-cancer. (A) Prognostic analysis of ESCRT-0 representative gene HGS in LIHC and KIRC; (B) Prognostic analysis of ESCRT-I representative gene VPS37D in BRCA and COAD; (C) ESCRT-II representative gene SNF 8 in STAD and KIRC; (D) ESCRT-III representative prognostic analysis of CHMP4C in LUSC and CESC; (E) Prognostic analysis of VPS 4-VTA 1 representative gene VPS4B in PAAD and ACC.
A
A
B
9
J
X
S
2
StromalScore
3
E 5
3
9
2.
3 1
2
E
C
₹
2
ImmuneScore
C
A
888
I
S
&
E ESTIMATEScore
HGS
STAM
O
000
100
1
HGS
STAM
.
0
1
HGB
10
1
STAM2
STAM
O
0000
BIANG
0
STAM2
2
T55101
10
.
D
..
O
0
VPESTA
..
VPSITB
yresto
0.4
D.4
0
O
0000
UMADI
UMADA
a
00
4.2
02
CHMPZA
0
0
0
CHMP28
CHMP28
CHUPE
CHMP28
CHMP3
-03
CHIMPG
CHMP3
CHMP44
CHMP4B
CHMP4A
CHMP44
-04
CHMP48
CHMPMIR
CHMPVIC
CHMP4C
-0.4
00
0000000
-0,4
CHMPS
CHMPS
CHMPMA
CHIMPS
CHMPIn
CHMPIA
CHMPIA
OMPIn
CHMPID
-06
ISTI
VP844
IST1
VP844
VPSIR
VPSIn
00
0
-0.8
PDCDEAP
PTPN23
0000
000000
..
POCOSIP
00
PTPM23
O
POCDEIP
-1
-1
PTPN23
0000
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D
ESCA
GBU
A
NICH
5
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RNASS
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VPS25
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VPS38
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CHMP48 CHMP4C
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CHMP4B
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VPS18
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Tumor microenvironment analysis
To confirm whether VPS37D is involved in the immune regulation of BRCA, we performed immune cell infiltration analysis using a variety of immune scoring methods. The estimate analysis results showed that the immune cell score in the high VPS37D expression group was lower than that in the low expression group (P <0.05, Figure 9A). It was negatively correlated with immune score
(P <0.05, Figure 9B). Additionally, ssGSEA analysis revealed that VPS37D expression was negatively correlated with immune cells, such as macrophages and central memory T cells (Tem) (P <0.05, Figure 9C). Subsequently, we performed cross-validation using the CIBERSORT analysis, and the results were generally consistent. CIBERSORT analysis revealed significant differences in VPS37D expression among resting mast cells, activated NK cells, regulatory T cells (Tregs), CD4 memory activated T cells, CD4 resting memory T
A
B
· Up . Not sig · Down
Up
Not sig
Down
40
-Log 10 (P value)
30
-Log 10 (P value)
200
20
100
CHMP4B
VPS37C
VPS37D
VPS25
10
VPS36
VPS28
HGS Q VPS37D
81
CHMP4C
MVB12A
0
CHMP2A CHMP4C &
0
T
T
T
T
T
-5
0
5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
Log2 (Fold Change)
Log2 (Fold Change)
C
1
2
FIGURE 6 Differential analysis of the ESCRT family genes in breast cancer. (A) Heatmap of the ESCRT family genes; (B) Volcanic map of differential genes; and (C) VPS37D immunosynthesis. 1.BRCA Organization 2. Normal breast tissue.
A
B
HGS
CHMP4C
CHMP4B
Endocytosis
0
0.7
0
1.4
0.7
1.4
Necroptosis
KEGG
0.7
2.1
0
28
VPS37D
Viral life cycle - HIV-1
2.1
0
1.4
0)
CHMP2A
ubiquitin-like protein binding
Ontology
1.4
BP
0
ubiquitin binding
S
P,
CC
phosphatidylcholine binding
MF
0
KEGG
VPS37C
1.4
ESCRT complex
Counts
0
2
0.7
0.7
late endosome membrane
8
4
☐ 6
0
8
2.8
1.4
VPS25
☐
late endosome
2.1
2.1
multivesicular body organization
o
VPS28
1.4
0.7
0.7
VPS36
multivesicular body assembly
6
0
2.8
0.7
0
2.1
1.4
ubiquitin-dependent protein catabolic process
MVB12A
Correlation
via the multivesicular body sorting pathway
0
5
10
15
20
-1
1
-Log 10 (P.adj )
cells, and resting NK cells, with an inverse relationship with macrophages M1 (P <0.05, Figure 9D). These results suggest that VPS37D may regulate the occurrence and development of BRCA by influencing immune cell infiltration and immune responses.
Discussion
The ESCRT machinery is a crucial regulator of membrane remodeling and cellular signaling, and plays a complex role in
oncogenesis (47). This study systematically revealed the molecular characterization of ESCRT family genes across 33 cancer types using integrated multi-omics data from TCGA database for the first time. Significant differences in the expression of ESCRT members were observed in BLCA, LIHC, and THYM. VPS37D was upregulated in BRCA and GBM, whereas it was significantly downregulated in KIRC, indicating a tissue-specific regulatory role. Prognostic association specific to cancer: Survival analysis indicated that elevated CHMP4B expression was correlated with extended survival in OV patients, whereas it was significantly linked to unfavorable prognosis in gastric
Dệt
5
6
*
The expression of VPS37D Log2 (FPKM+1)
The expression of VPS37D
5
ĐẶC ĐẶC HỆ:
ĐẶC ĐẶC ĐIỆN
Điện việc việc
The expression of VPS37D Log2 (FPKM+1)
5
4
Log2 (FPKM+1)
4
4
3
3
3
2
2
2
1
1
1
0
0
0
Normal
T1
T2
T3
T4
Normal
NO
N1
N2
N3
Normal
M0
M1
Pathologic T stage
Pathologic N stage
Pathologic M stage
5
6
The expression of VPS37D Log2 (FPKM+1)
5
The expression of VPS37D
4
5
**
4
Log2 (FPKM+1)
The expression of VPS37D
Log2 (FPKM+1)
3
4
3
3
2
2
2
1
1
1
0
0
0
Normal Stage I Stage IIStage IIIStage IV Pathologic stage
Normal
⇐ 60
>
60
Normal
LumA
LumB
Her2
Basal
Age
PAM50
5
5
The expression of VPS37D Log2 (FPKM+1)
The expression of VPS37D
*
The expression of VPS37D
4
$
4
4
Log2 (FPKM+1)
Log2 (FPKM+1)
3
3
3
2
2
2
1
1
1
0
0
0
Normal
Negative PR status
Positive
Normal
Negative ER status
Positive
Normal
Negative
Positive
HER2 status
FIGURE 8 Correlation analysis between VPS37D expression and BRCA clinical characteristics (*P <0.05, ** P <0.01, *** P <0.001).
A
B
6000
VPS37D
4000
Enrichment score
StromalScore
R = - 0.224
P value
2000
VPS37D
6e-14
Low
High
4e-14
2e-14
0
ImmuneScore
R = - 0.280
|Cor|
0.23
-2000
0.24
0.25
0.26
StromalScore
ImmuneScore
ESTIMATEScore
0.27
0.28
ESTIMATEScore
R = - 0.289
-0.3
-0.2
-0.1
0.0
Correlation
C
D
VPS37D
VPS37D
NK CD56bright cells
R = 0.305
Mast cells resting
NK cells
R = 0.280_
NK cells activated
R = 0.194
PDC
R = 0.131_
R = 0.127
Eosinophils
R = 0.127
T cells regulatory Tregs
R = 0.119
Th17 cells
R = 0.123
Plasma cells
T cells CD8
R = 0.075
Mast cells
R = 0.052ns
B cells naive
R = 0.052ns
CD8 T cells
R = 0.035”s
P value
Macrophages M2
R = 0.049ns
TFH
R = - 0.062
0.25
0.20
R = 0.034ns
P value
iDC
Tgd
R = - 0.086
R = - 0.154
0.15
Monocytes
T cells follicular helper
R = 0.031ns
0.75
Cytotoxic cells
R = - 0.189
0.10
B cells memory
R = 0.025”s
R = 0.018ns
0.50
DC
B cells
R = - 0.202
0.05
T cells CD4 naive
R = 0.017ns
0.25
Tem
R = - 0.203
R =- 0.206
|Cor|
Eosinophils
R = - 0.003ns
NK CD56dim cells
R = - 0.218
0.1
Macrophages MO
R = - 0.020”s
|Cor|
TReg
R =- 0.234
0.2
Neutrophils
0.1
T cells
R = - 0.239
Dendritic cells resting
R = - 0.025”s
0.3
Mast cells activated
R = - 0.026ns
0.2
Th2 cells
R = - 0.273
0.4
Dendritic cells activated
R = - 0.043ns
0.3
Neutrophils
aDC
R = - 0.291
R = - 0.300
T cells gamma delta
R = - 0.095
T cells CD4 memory resting
R = - 0.112
T helper cells
R =- 0.330
R =- 0.127
Th1 cells
NK cells resting
R = - 0.134
Macrophages
R =- 0.371
R = - 0.393
Macrophages M1
R = - 0.157
Tcm
R = - 0.410
T cells CD4 memory activated
R =- 0.301
-0.4
-0.2
0.0
0.2
-0.3
-0.2
-0.1
0.0
0.1
0.2
Correlation
Correlation
FIGURE 9 Analysis of the immune cell infiltration. (A) Differences in VPS37D expression levels in the immune score; (B) Immune infiltration score, matrix score, and estimated score were correlated with VPS37D expression; (C) The relationship between VPS37D expression obtained by the ssGSEA algorithm and immune cells; and (D) The association between VPS37D expression, as determined by the Cibersort algorithm, and immune cell populations (*P < 0.05, ** P < 0.01, *** P < 0.001). ns, no significance.
cancer STAD. The bidirectional regulation mode may arise from variations in the molecular pathways mediated by ESCRT across distinct tumor microenvironments. The expression level of ESCRT exhibited a significant negative correlation with CD8 + T cell infiltration and a positive correlation with PD-L1 expression across 16 cancer types. This finding supports the previously established mechanism by which ESCRT facilitates the secretion of exosomal immunosuppressive molecules in liver cancer studies.
ESCRT exhibits diverse regulatory characteristics across various cancers; however, its role is particularly significant in breast cancer. In the context of breast cancer, a deep prediction analysis revealed that differential expression of the VPS37D gene has a significant impact on
disease prognosis and progression. The present study showed that VPS37D exhibits elevated expression levels in tumors, and increased expression is associated with worse patient prognosis. This gene is significantly associated with multiple clinical factors and surface receptors that are typical of breast cancer. Immune infiltration analysis indicated that immune cell scores in the VPS37D high- expression group were reduced, with expression levels closely correlating with the transformation of M1 macrophages. VPS37D is an important component of the ESCRT-I complex in the ESCRT pathway, and is expressed in various tissues and cell types, with functions related to the transport and degradation of intracellular proteins. Although studies on the relationship between VPS37D and
cancer are relatively limited, existing studies have shown that VPS37D frameshift mutations (incidence 7.1%) were detected in microsatellite unstable (MSI)-type gastric cancer and colorectal cancer, indicating that mutations disrupt the membrane-binding capacity of the ESCRT-I complex and affect endosomal protein sorting, confirming for the first time that VPS37D is a potential driver gene for digestive tract tumors (48). At the same time, VPS37D exhibits chromosomal structural variation. RNA sequencing of single cells showed that VPS37D expression decreased by 65% during neuronal differentiation. This suggests that VPS37D may help control synaptic vesicle transport (49). In the keratoconus model, VPS37D and STAM 1 levels dropped by 40% (50). CRISPR knockdown of VPS37D led to an abnormal increase in the diameter of multivesicular bodies, and overexpression of VPS37D partially restored the corneal stromal cells’ ability to respond to mechanical stress. Together, these three studies demonstrated the importance of VPS37D in the membrane transport system. It is a key part of the ESCRT-I complex (amino acid residues 121-187 are responsible for ALIX binding), and problems with its function can cause a wide range of diseases, from tumor growth to tissue breakdown.
This suggests that VPS37D plays an important role in tumorigenesis and progression. Modulation of the expression of VPS37D or other genes within the ESCRT family may result in abnormal ESCRT functions, which could affect tumor cell survival and proliferation. These findings indicate that ESCRT may be a viable target for tumor treatment. Interference with the functions of ESCRT complexes in cells may inhibit tumor cell growth and dissemination. Moreover, utilizing the spatial and functional properties of ESCRT within cells could enhance the creation of innovative targeted therapeutic agents or biomarkers for improved tumor diagnosis and treatment.
Nonetheless, the study’s limitations include insufficient data on certain rare cancers, such as uveal melanoma; in vitro validation requires supplementation; and the interaction mechanism between ESCRT and cellular metabolism remains unclear. Future efforts will concentrate on: @ the development of small-molecule inhibitors aimed at the ESCRT complex assembly interface; 2 the validation of the ESCRT scoring system’s application value in immunotherapy stratification utilizing a multicenter cohort. In summary, ESCRT functions as a critical mechanism for intracellular protein sorting and transport, contributing to various aspects of tumor biology. ESCRT is expected to offer significant opportunities for future research and clinical applications in tumor treatment.
Conclusions
ESCRT family genes vary in tumors and influence immune cell infiltration, prognosis, and chemotherapy efficacy. In breast cancer, VPS37D correlates with clinicopathological factors and the tumor microenvironment. ESCRT may serve as a novel prognostic marker and immune target, although its regulatory mechanisms remain
unconfirmed, potentially impacting tumor progression and prognosis. The regulatory mechanisms of the ESCRT family in tumors should be explored in the future through experimental validation.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.cancer.gov/ccg/research/genome- sequencing/tcga.
Ethics statement
This study does not involve human, living vertebrates or higher invertebrates as subjects or study participants, and strictly follow the relevant ethical guidelines during its design and implementation. It passed the ethical review committee of Qingdao Municipal Hospital. Written informed consent for participation was not required from the participants or the participants’ legal guardians/next of kin in accordance with the national legislation and institutional requirements.
Author contributions
X-rC: Data curation, Visualization, Writing - original draft. X-yT: Writing - review & editing. Z-IZ: Writing - review & editing. W-qS: Writing - review & editing. J-sY: Writing - review & editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This study was supported by a research grant from the Qingdao Municipal Health Commission (grant number: 2023-WJZD165;2024-WJKY015).
Acknowledgments
The authors thank the Laboratory Department of Qingdao Municipal Hospital for excellent technical support and assistance with the editing of this paper. We are grateful to Professor Weiqing Song (Qingdao University) for his constructive suggestions and valuable support. This manuscript was edited by Xiaorui Chen.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fimmu.2025. 1531940/full#supplementary-material
SUPPLEMENTARY FIGURE 1 The division of labor among the various subunits of ESCRT.
SUPPLEMENTARY FIGURE 2 Differential expression of ESCRT family genes in the CPTAC database.
SUPPLEMENTARY FIGURE 3 Prognostic analysis of ESCRT in pan-cancer.
SUPPLEMENTARY FIGURE 4 Correlation of ESCRT expression with immune subtypes.
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