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. 2025 Oct 8;16:1832. doi: 10.1007/s12672-025-03382-6

Database analysis reveals endophilin A expression as a marker of metastasis and prognosis in breast cancer

Vikrant Mehta 1,2,#, Sohidul Islam 3,4,#, Harit Kasana 3, Harish Chander 3,4,✉
PMCID: PMC12508320  PMID: 41060548

Abstract

Background

Endophilin A, a family of Bin-Amphoterisin-Rvs (BAR) domain-conserved proteins, is found in several tissues and is associated with disease pathogenesis. In patients with breast cancer, endophilin A expression pertains to boosted tumour cell endocytosis, migration, and invadopodia formation, potentially linked to a poor prognosis.

Methods

This study aimed to determine the expression of endophilin A isoforms (SH3GL1, SH3GL2, and SH3GL3) in breast cancer using databases like UALCAN, GEPIA2, TIMER 2.0, geneMANIA, Enrichr, Km Plotter, and GENT2.

Results

We report the elevated expression of SH3GL1 in TNBC and HER2 subtypes of breast carcinoma, and that positively correlated with advancing stage as well as lymph node metastasis, compared to SH3GL2 and SH3GL3. Additionally, the study demonstrates that in contrast to SH3GL1 and SH3GL3, elevated SH3GL2 transcript levels were positively associated with improved Overall Survival (OS), Relapse Free Survival (RFS), and Distance Metastasis Free Survival (DMFS compared to low expression levels among the breast cancer patients. Further, we also show the associated immune filtration with each endophilin isoform and link the expression of endophilin A isoform with p53 expression.

Conclusions

Based on our findings, it is possible to conclude that endophilin A isoforms—long recognised for orchestrating endocytosis and metastatic behaviour—may represent a critical molecular fulcrum in breast cancer progression. In particular, elevated expression of endophilin A2 (SH3GL1) and SH3GL2 strongly correlates with poor prognosis and node-positive breast cancer, highlighting their potential as promising biomarkers for breast cancer assessment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03382-6.

Keywords: Endophilin A, Breast cancer, P53, Metastasis, Prognosis, Bioinformatics

Highlights

  • Distinctive expression of endophilin A isoforms: The mRNA levels of SH3GL1 were considerably higher in breast cancer tissues in contrast to normal tissues, but SH3GL2 and SH3GL3 levels were diminished.

  • Correlation with molecular subtypes: SH3GL1 was linked to advanced tumour stages and node metastases in all subtypes of breast cancer. Only the TNBC subtype exhibited enrichment in SH3GL2 and SH3GL3.

  • Interaction with mutant-p53: In contrast to SH3GL2 and SH3GL3, which were markedly hypermethylated in breast tumours, SH3GL1 expression decreased in both p53 mutant and non-mutant groups.

  • Prognostic value: Despite high SH3GL2 expression was found to correspond with improved survival consequences, high SH3GL1 expression was attributed to worse DMFS, OS, and RFS. SH3GL3 demonstrated complex trends of survival.

  • Immune infiltration correlation: While there were minimal associations between SH3GL2 and SH3GL3 expression and different types of immune cells, SH3GL1 expression was strongly linked with CD4+ T cells. A pan-cancer investigation revealed similar trends.

  • Protein–protein interaction and gene ontology: Genes involving MAPK signaling and cell adhesion were associated with SH3GL1, SH3GL2, and SH3GL3. They were implicated in biological processes such as EGFR signaling in cancer, clathrin vesicle coat, and cadherin interaction, according to a gene ontology assessment.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03382-6.

Introduction

Worldwide, breast cancer will likely be the most prevalent and second-leading cause of death in 2022, with 20 million new, and 9.7 million fatalities cases forecast by GLOBOCAN 2022 [1]. Of these patients, 50% have metastatic evolution, which presents considerable hurdles for clinical care, highlighting the need for early detection, as the disease shows marked inter- and intra-tumoral heterogeneity at both morphological and molecular levels [2]. Further, this molecular complexity not only facilitates diverse survival pathways in breast cancer cells but also underpins its classification into three major subtypes—Luminal, HER2-enriched, and Triple-Negative Breast Cancer (TNBC)—each with distinct prognostic and therapeutic implications [3]. This subtypes of Breast cancer may differ in prevalence, prognosis, therapy response, and metastasis patterns, with HER2-enriched and TNBC showing high aggressiveness, poor differentiation, and worse outcomes [4].

In trend, several studies have shown that lipid membrane proteins, that is, BIN/amphiphysin/Rvs (BAR) promote membrane to the cytoskeleton interaction along with various tiny GTPase (comprising IRSp53, Toca-1, Toca-2/FBP17, and Toca-3/CIP4) for driving migration, division, protrusions, membrane deformation, nucleation of actin fibers, and diverse signalling respectively [5–7]. Endophilins, a BAR-domain conserved proteins-classified into A and B subfamilies based on sequence analysis. For instances, endophilin A1 (SH3P4, SH3GL2, EEN-B1), A2 (SH3P8, SH3GL1), and A3 (SH3P13, SH3GL3)—while the B subfamily consists of endophilin B1 (SH3GLB1) and B2 (Bif1, SH3GLB2, EEN) [8]. The N-BAR domain of endophilin induces membrane curvature through its amphipathic helices and BAR region, potentially triggering a feedback loop that promotes invagination at threshold concentrations, though the specific contributions remain undefined [9]. For instances, endophilin A2 (SH3GL1), comprised of an N-terminal BAR and C-terminal SH3 domain for recognizing and initiating transmembrane curvature and control the localization of proteins linked to FEME and invadopodia formation respectively [10, 11]. Likewise, in a number of cancer types, including breast and lung cancer, the FEME priming proteins SHIP2, FBP17, CIP4, Lamellipodin, and SH3BP1 all encourage tumor metastasis [12–15]. Similarly, studies on SH3GL1 knockdown showed impaired EGFR internalization, cell motility, and receptor-mediated endocytosis [16] reducing 3D invasion, MT1-MMP uptake, and extracellular matrix degradation, thereby limiting TNBC tumor growth and pulmonary metastases in breast xenograft models [17]. Further investigation, however, reveals that endophilin-A3, but not A1 or A2 isoforms, are associated with the tumour biomarker CD166/ALCAM, serves to generate endocytic sites at which CD166 can be taken up in clathrin-independent cargo [18] triggered by exterior galectin-8 induce malignant cells to become more adhesive and migratory [19]. However, CD166 has been suggested as a target for immunotherapy and identified as a Colorectal cancer stem cell marker [20] and appears to be important for the disease advancement. Beyond membrane dynamics, endophilins regulate neuronal cargo trafficking—implicated in neurodegenerative diseases like Parkinson’s—and paradoxically promote colon cancer metastasis by enhancing cell motility through interaction with RacGEF TIAM1 [21, 22].

Despite substantial advances in molecular diagnostics and neoadjuvant chemotherapy, breast cancer (BC) remains a major global health burden, emphasizing the critical need for novel therapeutic targets and reliable biomarkers to improve patient stratification and enable personalized treatment. Endophilin A isoforms, whose dysregulation through altered transcriptional control or post-translational modifications has been implicated in diverse pathologies, remain understudied in BC. Building on prior biomarker studies such as CIAPIN1 and miRNA profiles [23, 24], we employed multiple bioinformatics platforms—including UALCAN, GEPIA2, GENT2, TIMER 2.0, GeneMANIA, Enrichr, and KM Plotter—to systematically delineate the expression patterns of endophilin A isoforms (SH3GL1, SH3GL2, and SH3GL3) in BC. Our integrative analysis uncovers their significant diagnostic, prognostic, and immune-related implications, offering promising avenues for precision oncology and targeted therapies in breast cancer.

Materials and methods

UALCAN Database (RRID:SCR_015827)

UALCAN is a dynamic web platform that allows scientists to easily examine cancer transcriptomic information gathered by The Cancer Genome Atlas (TCGA) using third-generation transcriptomic data.

By incorporating UALCAN into our investigation, we evaluated the corresponding transcriptional abundance of endophilin A isoforms (SH3GL1, SH3GL2 and SH3GL3) among both normal and malignant breast cancer tissues. Additionally, using the UALCAN database, we evaluated the degree of expression variations across several breast cancer molecular subtypes, cancer stages of development, metastasis of lymph nodes, p53 mutant status, promoter methylation, as well as pan-cancer analyses [25]. UALCAN can be accessed at http://ualcan.path.uab.edu.

GEPIA database

GEPIA2 is a recently built web service for examining data obtained from RNA sequencing derived from tumours and normal tissues from TCGA and GTEx programs. It may be accessed at http://gepia2.cancer-pku.cn. A log2FC value of 1 and log2 (TPM + 1) for the log scale was utilized, with a threshold p-value of 0.01 for gene expression [26]. To determine the relationship concerning endophilin A activity, prognostic significance and survival of patient’s data at the messenger RNA (mRNA) level, the GEPIA was utilised. The log-rank p-value and the Hazard Ratio (HR) were computed and made available publicly. Concurrently, GEPIA were used to Characterizing immune cells as well as uncovered the association of several endophilin A isoform and marker genes of tumor-infiltrating immune cells (TIICs) in breast cancer patients.

TIMER 2.0 (RRID:SCR_018737)

Over 10,000 samples spanning 32 distinct kinds of cancer are included within the Tumour Immune Estimation Resource database (TIMER- available at http://timer.cistrome.org) [27] used to uncover the differential expression of endophilin A isoforms and the quantity of each of the six major immunological infiltration as well a Pan-Cancer analysis in patients having breast cancer. To determine the statistical significance of data retrieved using this repository, the Wilcoxon analysis is employed. Besides, we utilized the TISIB repository (http://cis.hku.h.TISISB/index.php) to clarify the infiltration of distinct immune cell subsets expression concerning different isoforms of endophilin A expression in Pan-Cancer analysis.

Protein–protein interaction and functional enrichment analysis

GeneMANIA (RRID:SCR_005709, is available at http://genemania.org.), an easy-to-use web-based platform used to assess the roles of each of the three endophilin A isoforms, we built a PPI network using this database in our investigation [28] Relatively versatile web-based interface for thorough and methodical functional annotation and evaluation is offered by Metascape (RRID:SCR_016620), decipher the biological significance of an extensive array of transcripts. [29].

Survival analysis

The KM Plotter (publicly accessible at https://kmplot.com/analysis), were used to the prognostic relevance and the survival of patients concerning OS, DMFS, RFS, the degree of expression associated with an endophilin A isoforms has been ascertained. Using the server’s homepage, the log-rank P coefficients and hazard ratio (HR) were computed [30]

Gene ontology analysis

Employing the EnRichr (RRID:SCR_001575) program (https://maayanlab.cloud/Enrichr/), the gene ontology (GO) inclusion investigation of endophilin A isoforms was performed concerning biological process, molecular function, and cellular components [31]. Additionally, EnRichr utilized for gene inclusion network evaluation while the KEGG (RRID:SCR_012773) 2021 human, BioPlanet 2019 database was used to identify the signaling cascades [32]

GENT2

The GENT2 (Available at http://gent2.appex.kr/gent2/) database web platform obtains its transcriptome information through the NCBI GEO datasets (accessible at https://www. ncbi.nlm. nih.gov/geo/). We employed GENT2 for pan-cancer analysis of endophilin A isoforms [33]

Statistical analysis

The statistical analysis was done using the student’s t-test, log-rank test, and Spearman’s correlation assessment to evaluate mRNA levels, construct survival curves, and assess the correlation of endophilin A isoforms in breast cancer tissues respectively. Significance was defined as a p-value of less than 0.05.

Results

Differential transcription expression levels of endophilin A isoforms in normal and cancer tissue

UALCAN and GEPIA2 datasets were employed to examine the expression of endophilin A isoform transcript in breast carcinoma and normal control tissues to determine the precise expression patterns of these various subtypes in breast cancer patients. The data from UALCAN and GEPIA2 showed that compared to controls, tumor samples had greater expression levels of SH3GL1. The mRNA levels of SH3GL1 (Fig. 1A, D) were elevated in breast cancer tissues. On the other hand, the transcript levels of SH3GL2 (Fig. 1B, E) and SH3GL3 (Fig. 1C, F) were low in tumor samples. Moreover, we also performed a pan cancer analysis using various databases like UALCAN (Figure S1), TIMER2.0 (Figure S2), and GENT2 (Figure S3). The analysis indicated that all members of endophilin A subfamily showed significantly differential expression in various malignancies compared to normal tissues (Figure S1, S2, S3). In summary, we found that among three isoforms of endophilin A, SH3GL1 showed significantly elevated differential expression in breast cancer datasets in contrast to normal control group.

Fig. 1.

Fig. 1

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Transcript levels of endophilin A isoforms in normal and breast tumor tissues according to UALCAN (A–C), and GEPIA2 (D–F)

Endophilin A expression with regard to molecular subtypes of BC, advancing stages, and node dissemination

Five primary subtypes of breast carcinoma are known based on the presence or absence of biomarkers (ER, PR, and HER2) like Normal, Luminal A, Luminal B, HER2 enriched and TNBC. Since HER2 and TNBC are associated with worse prognosis therefore, we expanded our study to ascertain whether these subtypes had an elevated level of endophilin A isoforms.

Analysis UALCAN and GEPIA2 for endophilin A expression, we found that, in contrast to SH3GL2 and SH3GL3, SH3GL1 levels were elevated across all molecular subtypes compared to control group (Fig. 2A, D) whereas both the SH3GL2 and SH3GL3 were highly enriched in TNBC subtype only (Fig. 2B, C, E, F). We further looked at any potential correlations of positive node metastases, and the stage of the tumor with elevated expression of endophilin A isoforms. The results revealed an overall significant increase in SH3GL1 expression level with advancing stage and node metastasis of the breast tumor (Fig. 3A, D). However, the SH3GL2 were low in with increasing infiltration and advancing stage (Fig. 3B, E). SH3GL3 also showed a similar pattern as we observed a downregulation of its transcript levels with both advancing stage and node metastasis (Fig. 3C, F). Together, the analysis showed that the SH3GL1 isoform of endophilin A exhibits markedly elevated expression across all molecular subtypes of breast carcinoma and has a significant association with both the tumor’s stage and node metastasis.

Fig. 2.

Fig. 2

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Molecular subtypes of breast cancer and endophilin A isoforms expression according to UALCAN (A–C), and GEPIA2 (D–F)

Fig. 3.

Fig. 3

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Endophilin A isoforms expression and its association with the Status of node metastasis (A–C) and with the breast cancer stage according to UALCAN (D–F)

Endophilin A expression correlates with mutant-p53 expression and epigenetic modification within endophilin A promoter

It is well-known that mutant p53’s gain of function induces the transcription of multiple genes that either confer chemoresistance and invasion or metastasis [34, 35]. Therefore, we broadened the focus of our analysis to examine the expression of distinctive endophilin A isoforms in the UALCAN datasets between the"p53 mutant"and"p53 non-mutant"families. As expected, the evaluation revealed that the relative expression of SH3GL1 variants had increased in each of the p53 mutant and non-mutant groups (Figure S4A). Conversely, the transcript levels of other endophilin A isoforms, SH3GL2 and SH3GL3, were considerably reduced in each of the p53 mutant and non-mut groups in relation to the normal tissues (Figure S4B, C).

Further to see the effect of epigenetic methylation on endophilin A expression, we carried out promoter methylation assessment as current evidence suggests that methylation of the DNA promoter, a significant epigenetic modification, is being explored for carcinoma of the breast screening, prognosis, and cure, demonstrating promise as an effective prognostic or predictive biomarker in clinical evaluation [36]. In contrast to SH3GL1, which was slightly hypermethylated (non-significant; p = 0.945) (Figure S4D) in the breast tumor case compared to normal, our research revealed that the endophilin A isoforms of SH3GL2 and SH3GL3 were significantly hypermethylated (Figures S4E, F).

Prognostic values and clinicopathologic parameters of endophilin A isoforms mRNA expression in all breast cancer samples

In order to examine the value of endophilin A in terms of prognosis, we sought to investigate the correlation between endophilin A isoforms expression with the survival and prognosis in cancer patients. Consequently, Km Plotter application was utilized to examine the association of various endophilin A isoforms with breast cancer patient prognosis. As shown in Fig. 4, elevated mRNA transcript of SH3GL1 were associated with worse DMFS, OS, and RFS (A, B and C) whereas the high SH3GL2 expression associated with better DMFS, OS, and significantly associated with RFS (D, E and F) of breast cancer patients compared to low levels of expression. Interestingly, SH3GL3 elevated level of mRNA expression associated with decreased DMFS (HR = 1.15, logrank p = 0.068), however, we observed an opposite trend for the relapse-free survival (HR = 0.78, logrank p = 2.1e−06) as high levels were positively correlated with significantly better RFS (G, H and I) compared to low expression levels.

Fig. 4.

Fig. 4

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Prognostic significance of endophilin A isoforms in the survival of breast cancer patients. Kaplan–Meier survival plots were obtained from Km plots for SH3GL1 (A–C) in terms of DMFS, OS, and RFS. Km plots for SH3GL2 (D–F) in terms of DMFS, OS, and RFS. Km plots for SH3GL3 (G–I) in terms of DMFS, OS, and RFS. DMFS distant metastasis-free survival, OS overall survival, RFS relapse-free survival

These data suggest that, the patient survival data from Km plot analysis indicated that members of endophilin A subfamily may have a significant correlation with the DMFS, OS, and RFS of breast cancer patients suggesting their potential significance in predicting survival of patients with breast cancer.

Relationship among endophilin A isoforms expression and immune filtration in breast cancer

The occurrence of breast cancer is significantly correlated with infiltration of various types of immune cell [37]. To ascertain the degree of correlation across these alterations and immune infiltration magnitude, TIMER, and TISIDB databases were used to investigate the correlation between the immune infiltration and endophilin A isoforms (SH3GL1, SH3GL2, and SH3GL3) expression in breast cancer survivors. The result of TIMER database revealed that SH3GL2 variants expression had positive correlation with CD4+ T cells. Conversely, the SH3GL1 and SH3GL3 genes showed weakly negative or significantly low correlation with CD8+ T cells, B-cell, Macrophage, and CD4+ T cells respectively (Fig. 5A–C).

Fig. 5.

Fig. 5

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Correlation of endophilin A isoforms expression and immune filtration in BC. Analysis of correlation between immune cells and SH3GL1 (A) SH3GL2 (B), and SH3GL3 (C) from TIMER database. Spearman correlation between SH3GL1 (D) SH3GL2 (E), SH3GL3 (F) and B cells, CD4+ T cells, CD8+ T cells, macrophage, neutrophil, and dendritic cells from TISIDB database

Additionally, we used TISIDB database to further investigate the immune cell infiltration. A noteworthy positive association was evident for SH3GL1 and SH3GL3 isoforms with CD4+ T-cell (Spearman rho = 0.162, p = 6.28e−08), Dendritic cell (Spearman rho = 0.092, p = 0.00233), and B-cell (Spearman rho = 0.099, p = 0.00101), CD4 + (Spearman rho = 0.14, p = 3.48e−06), CD8+ T cell (Spearman rho = 0.242, p = 5.24e−16), Dendritic cell (Spearman rho = 0.103, p = 6e−04), respectively. On the contrary hand, a barely positive or insignificant association was found between B-cell (Spearman rho = 0.014, p = 0.647), Macrophage (Spearman rho = 0.072, p = 0.0165), and CD8+ (Spearman rho = −0.062, p = 0.0385) in SH3GL1, CD4+ (Spearman rho = −0.056, p = 0.0621), Macrophage (Spearman rho = −0.023, p = 0.454), and Dendritic cell (Spearman rho = −0.021, p = 0.49) with SH3GL2, and Macrophage (Spearman rho = −0.016, p = 0.000124) with SH3GL3, accordingly (Fig. 5D–F). Additionally, the expression of endophilin A subfamily with immune cell infiltration across pan cancer was examined (Figure S5, A–C).

Correlation/enrichment analysis of protein–protein interaction of endophilin A isoforms in breast cancer

Leveraging the TCGA dataset, the most significant ten genes that had the strongest positive correlation with SH3GL1, 2, and 3 were retrieved from UALCAN. Among all the genes, MAP2K2 (as determined by UALCAN) and GEPIA2 (Pearson correlation coefficient of 0.63; Fig. 6A, B) showed the most positive correlation. Similarly, the most closely correlated genes of SH3GL2 and SH3GL3 from UALCAN (Fig. 6C, E) and GEPIA2 (Fig. 6D, F) are shown. Additionally, bcGenExMiner v5.0 was used to generate the heat map displaying the correlation of all endophilin A isoforms with top 10 most closely correlated genes. (Fig S6, A–C). Certain breast cancer subtypes exhibit elevated MAP2K2 activity, which leads to sustained MAPK signalling activation, promoting tumor cell proliferation, survival, and angiogenesis, contributing to tumor growth and progression. Subsequently was discovered that breast cancer cells displayed the CRMP family, which is important in the transduction of the Sema3A/NP1/PlexA signal, even though its high expression decreases motility and intrusiveness [38, 39] Likewise, since LRMP restricts oncogenic signalling pathways such as p-STAT3, p-PI3K, p-AKT, p-MEK, and EMT pathways, as well as positively corresponding with cell adhesion pathways, tumour-infiltrating immune cells, and markers, it corresponds to a better prognosis in patients with LUAD [40].

Fig. 6.

Fig. 6

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Analysis of endophilin A isoforms and their most closely correlated genes. Correlation between SH3GL1 and MAP2K2 from UALCAN (A) and GEPIA2 (B). Correlation between SH3GL2 and CRMP1 from UALCAN (C) and GEPIA2 (D). Correlation between SH3GL3 and LRMP from UALCAN (E) and GEPIA2 (F)

Gene ontology studies and interaction network related to endophilin A isoforms expression

Using information retrieved from the GeneMANIA database, we developed a PPI network for endophilin A isoforms and its top 20 correlated genes (Fig. 7A–C). Among these genes were SH3GL1, ARHGAP8, IMPDH1, SH3GL2, SH3GL3, SH3D4A, SORBS2, SH3KP1, DPPA4, DNM1, SYNJ2, LRRK2, CALM1, HRH3, CHRM2, SYNJ, ADRA2B, FAU, PDCD6IP, NCF1, RAPH1 (Fig. 7A). Consequently, (Fig. 7B, C) illustrates an additional PPI network containing the top 20 linked genes of SH3GL2 and SH3GL3, respectively. Likewise, the Enrichr database used for the SH3GL1, SH3GL2, and SH3GL3 subtypes, GO enrichment analysis has been carried out, evaluating pathways and ontological features following the biological process (Figures S7A, S8A, S9A), molecular function (Figures S7B, S8B, S9B), cellular component (Figures S7C, S8C, S9C), KEGG 2021 (Figures S7D, S8D, S9D), and Bio-planet 2019 (Figures S7E, S8E, S9E). According to the findings of the cluster modelling, SH3GL1 has significant involvement in several cellular processes and cascades related to governing the practice of cadherin interaction and clathrin vesicle coat, EGFR and mTOR signalling in cancer, the progression of the mitotic cell cycle, and other related activities (Figure S7 A–E). Similarly, neuronal system, chemical synaptic transmission and nicotine addiction are associated with SH3GL2 and SH3GL3 endophilin A isoforms. (Figures S8 and S9).

Fig. 7.

Fig. 7

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Protein–protein interaction network study of endophilin A isoforms from GeneMANIA for SH3GL1 (A), SH3GL2 (B), and SH3GL3 (C)

Discussion

Endophilin A is involved in cellular pathways in a number of malignancies. Nevertheless, since tumors showed both elevated and lowered levels, its function seems to be complicated. The sequence homology of endophilin isoforms A1–A3 renders them redundant; nevertheless, phosphorylation reorients this conformational flip, enabling the BAR protein family to control membrane remodeling processes [9, 41, 42], governing in transmembrane curvature formation and detection, promotes nutrient and membrane protein uptake, with Clathrin-mediated endocytosis (CME) serving as the predominant route in resting cells [43]. Moreover, Clathrin-independent pathways (CIE), including Fast Endophilin-Mediated Endocytosis (FEME), handling selective subsets of cargos of endophilin, in particular SH3GL1, notably MT1-MMP (ADAM9, 15, and 19, but not ADAM10, 12, or TACE [44]), EGFR, HER2, and BCR [11, 19, 43, 45] that are significant regulators of metastasis and immune suppression.

In view of that, in the present study, we analysed the online cancer databases (Fig. 8) and profiled the transcript levels of different endophilin A isoforms such as SH3GL1, SH3GL2 and SH3GL3 expression in different breast cancer subtypes which revealed a significant elevated upregulation of SH3GL1 while SH3GL2 and SH3GL3 were downregulated in cancerous tissues compared to normal tissues. Moreover, we observed upregulated levels of SH3GL1, particularly in TNBC and HER2 variants of breast cancer. Additionally, the investigation associated elevated expression of SH3GL1 with lymph node metastases and advancing stage of BC. Likewise, evidence from recent research indicates that increased endophilin A2 protein levels positively correlate with invasion, migration, and a poorer prognosis in HER2+ breast tumors [46]. Endophilin depletion, on the other hand, inhibits cell migration [47] and may prevent tumors from spreading. Expanding our exploration to the realm of p53 mutations through considering the link between p53 mutation and cancer progression, our study explored endophilin A isoforms in p53 mutant and non-mutant groups. SH3GL1 showed increased expression in both groups, while SH3GL2 and SH3GL3 were significantly reduced in p53 mutant cases which could be due to disruption transcriptional regulation by altering upstream elements and epigenetic landscapes.

Fig. 8.

Fig. 8

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Bioinformatics workflow referring to the methodological framework—This schematic illustrates the bioinformatics workflow for analyzing breast cancer (BRCA) data using TCGA-BRCA and metabolic datasets. The analysis integrates multiple study characteristics, such as immune cell infiltration, p53 mutation and DNA methylation, differential expression, pan-cancer analysis, enrichment analysis, and gene ontology analysis, and compares normal and diseased tissue. Each study parameter is connected to particular databases, including UALCAN, GEPIA2, TIMER 2.0, TISIDB, GENT2, Km Plotter, GeneMANIA, and EnRichr, to provide biological insights and detailed data analysis

DNA methylation, a key epigenetic regulator, is strongly associated with cancer progression. Our analysis revealed that elevated endophilin A isoform expression in breast cancer corresponds to distinct promoter methylation patterns—SH3GL1 hypomethylation and SH3GL2/SH3GL3 hypermethylation—correlating with distinct survival pattern. Survival analysis further indicates that elevated SH3GL2 and SH3GL3 expression is associated with improved prognosis, whereas elevated SH3GL1 expression correlates with worse outcomes. These findings suggest that methylation-induced alterations in chromatin structure and DNA–protein interactions may modulate endophilin A gene regulation in breast cancer. Immune cell infiltration is a recognized predictor of ICI therapy response in solid tumors. Our analysis reveals a potential link between endophilin A isoform expression and immune infiltration in breast cancer. Notably, SH3GL1 expression showed a strong positive association with CD4⁺ T cells, suggesting an immunomodulatory role, while SH3GL2 and SH3GL3 exhibited minimal correlation with immune cell infiltration. A pan-cancer analysis revealed similar trends. Indeed, blocking FEME enhances EGFR accumulation, [11] potentially boosting anti-cancer therapies, as Dynamin—but not Clathrin—inhibition improves antibody-dependent cellular cytotoxicity (ADCC) against cetuximab, trastuzumab, and avelumab, [48] enabling NK cells to target opsonized cells via FcγRs and eliminate them using perforin and granzymes [49]. The associations with immune cell infiltration and protein–protein interactions may further contribute to our understanding of the complex molecular landscape in breast cancer.

Accounting all consideration, this study implies how important endophilin A isoforms in breast cancer and highlights their potential as promising prognostic biomarkers and therapeutic targets. However, these findings are derived exclusively from transcriptomic database analyses, functional validation will be critical to translating these insights into therapeutic strategies.

Limitation of the study

This study highlights endophilin A isoforms—particularly SH3GL2—as promising prognostic markers and therapeutic targets in breast cancer, however, these findings are solely from publicly available datasets and lack experimental validation. Further in vitro and in vivo validation is imperative to solidify these findings and harness their therapeutic potential, particularly into SH3GL2 in FEME, could uncover novel therapies by leveraging its regulated nature to precisely control cellular processes. Future work will focus on elucidating the underlying mechanisms through in vitro and in vivo experimental models.

Supplementary Information

Additional file 1. (19.7KB, docx)
12672_2025_3382_MOESM2_ESM.pptx (3.5MB, pptx)

Additional file 2: Figure S1. Pan-cancer analysis according to UALCAN for SH3GL1, SH3GL2, and SH3GL3expression. Figure S2. Pan-cancer analysis according to TIMER2.0 for SH3GL1, SH3GL2, and SH3GL3expression. Figure S3. Pan-cancer analysis according to GENT2 for SH3GL1, SH3GL2, and SH3GL3expression. Figure S4. Impact of p53 mutation and promoter methylation on Endophilin A Isoforms expression in breast cancer patients and normal samples as per analysis from UALCAN. Levels of SH3GL1, SH3GL2, and SH3GL3in normal vs mutant p53 vs non-mutant p53 patients. Promoter methylation and the expression of SH3GL1, SH3GL2, and SH3GL3in normal vs tumor samples from UALCAN database. Figure S5. Heat map depicting the correlation of SH3GL1, SH3GL2, and SH3GL3expression with 28 different immune cells. Red color indicates more positive while blue colour indicated more negative correlation. Figure S6. Heat map depicting the correlation of SH3GL1, SH3GL2, and SH3GL3expression with top 10 most closely correlated genes. Figure S7. Gene ontology study of SH3GL1. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019. Figure S8. Gene ontology study of SH3GL2. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019. Figure S9. Gene ontology study of SH3GL3. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019

Acknowledgements

The authors acknowledge the developers of various database and web servers that are used in this study and thankful to the National Institute of Biologicals (NIB) for providing the necessary infrastructure as well. S.I. acknowledges UGC-NFOBC, New Delhi for providing Junior Research Fellowship (JRF) (202122-BR02000346).

Abbreviations

BC

Breast cancer

SH3GL1

Endophilin A2

SH3GL2

Endophilin A1

SH3GL3

Endophilin A3

CME

Clathrin-mediated endocytosis

FEME

Fast endophilin mediated endocytosis

ALCAM

Activated leukocyte cell adhesion molecule

ADCC

Antibody-dependent cellular cytotoxicity

ER

Estrogen receptor

PR

Progesterone receptor

HER2

Human epidermal growth factor receptor 2

TNBC

Triple negative breast cancer

OS

Overall survival

DMFS

Distance metastasis free survival

RFS

Relapse free survival

MT1-MMP

Membrane type 1-matrix metalloproteinase

BCR

B-cell receptor

EGF

Epidermal growth factor

Author contributions

V.M: Software, Data curation, Writing—review & editing, Methodology, Conceptualization S. I: Investigation, Validation, Formal analysis, Writing—original draft, Writing—review & editing, Methodology, Software H.K: Resources H.C: Conceptualization, Investigation, Writing—review & editing, Supervision, Validation.

Funding

The authors declare that no funding was received from any agency for this study.

Data availability

The datasets used and/or analysed during the current study are freely available from the corresponding web-tools described in the Methodology section.

Declarations

Ethical approval and consent to participate

No ethical approval and consent were required for the study.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Vikrant Mehta and Sohidul Islam contributed equally.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Additional file 1. (19.7KB, docx)
12672_2025_3382_MOESM2_ESM.pptx (3.5MB, pptx)

Additional file 2: Figure S1. Pan-cancer analysis according to UALCAN for SH3GL1, SH3GL2, and SH3GL3expression. Figure S2. Pan-cancer analysis according to TIMER2.0 for SH3GL1, SH3GL2, and SH3GL3expression. Figure S3. Pan-cancer analysis according to GENT2 for SH3GL1, SH3GL2, and SH3GL3expression. Figure S4. Impact of p53 mutation and promoter methylation on Endophilin A Isoforms expression in breast cancer patients and normal samples as per analysis from UALCAN. Levels of SH3GL1, SH3GL2, and SH3GL3in normal vs mutant p53 vs non-mutant p53 patients. Promoter methylation and the expression of SH3GL1, SH3GL2, and SH3GL3in normal vs tumor samples from UALCAN database. Figure S5. Heat map depicting the correlation of SH3GL1, SH3GL2, and SH3GL3expression with 28 different immune cells. Red color indicates more positive while blue colour indicated more negative correlation. Figure S6. Heat map depicting the correlation of SH3GL1, SH3GL2, and SH3GL3expression with top 10 most closely correlated genes. Figure S7. Gene ontology study of SH3GL1. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019. Figure S8. Gene ontology study of SH3GL2. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019. Figure S9. Gene ontology study of SH3GL3. Gene ontologyfrom Enrichr database according to GO biological process 2021, GO molecular function 2021, GO cellular component 2021, KEGG 2021 Human, and Bioplanet 2019

Data Availability Statement

The datasets used and/or analysed during the current study are freely available from the corresponding web-tools described in the Methodology section.

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