High expression of HECW1 is associated with the poor prognosis and cancer progression of gastric cancer

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However, the prognostic role of HECW1 expression in gastric cancer (GC) remains unclear. Methods The Tumor Immunoassay Resource (TIMER2.0) system evaluated the association of HECW1 with tumor-infiltrating lymphocytes in carcinomas. The UALCAN assessed HECW1 mRNA expression levels in GC tissues and examined their associations with clinicopathological characteristics. The Kaplan Meier-plotter analyzed the effect of HECW1 on the survival of GC patients. The cBioPortal retrieved information about genetic variants in HECW1 gene. Protein‒protein interaction (PPI) networks associated with HECW1 were explored using the STRING database. The functional effects of HECW1 on GC cells were evaluated through proliferation (Cell Counting Kit-8), apoptosis (Flow cytometry), and migration (Transwell and wound healing assays). The RNA-Seq was applied to explore the underlying mechanisms. Results HECW1 demonstrated significant overexpression in GC tumor tissues, correlating with adverse clinical outcomes. Clinically, elevated HECW1 expression exhibited an inverse association with tumor-infiltrating CD8 + T lymphocytes while demonstrating a positive correlation with macrophages, DCs, and neutrophils infiltration, suggesting its potential involvement in tumor immune evasion mechanisms. Functional validation revealed that HECW1 knockdown markedly suppressed GC cell proliferation and migratory capacity, concurrently promoting apoptotic cell death. Mechanistic investigations identified that HECW1 exerts its oncogenic effects through dysregulation of the Hippo signaling pathway, with its silencing effectively attenuating tumor progression via pathway modulation. Conclusions HECW1 upregulation is significantly associated with poor prognosis and immune infiltration in GC patients, emphasizing its potential as a prognostic biomarker. HECW1 gastric cancer immune infiltration Hippo signaling pathway Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Gastric cancer (GC) is highly aggressive and metastatic and ranks among the leading causes of cancer-related mortality worldwide[ 1 , 2 ]. Despite advances in surgical techniques and targeted therapies, progress has been made in the diagnosis and treatment of gastric cancer, but the prognosis of patients with advanced gastric cancer is still poor. Therefore, a deep understanding of the biological mechanisms underlying the development of GC is essential for the designing novel therapeutic interventions. Ubiquitination, an enzymatic process mediating covalent attachment of ubiquitin moieties to substrate proteins[ 3 ], serves as a critical post-translational modification regulating diverse pathological processes including oncogenesis, metabolic disorders, and neurodegenerative diseases[ 4 ]. The E3 ubiquitin ligase, which has the function of determining substrate specificity, plays a decisive role in the ubiquitination process[ 5 ]. E3 ubiquitin ligases mainly catalyze the formation of covalent bonds between ubiquitin molecules and specific amino acid residues on target proteins to achieve ubiquitination modifications, with lysine being the most common target for ubiquitination[ 6 ]. The HECT-domain ligase HECW1, initially implicated in familial amyotrophic lateral sclerosis pathogenesis[ 7 ], demonstrates multifaceted tumor-modulatory functions. Mechanistic studies reveal its catalytic activity-independent regulation of p53-mediated apoptosis[ 8 ] and NCOA4-driven ferroptosis in gliomas[ 9 ]. Emerging evidence further identifies HECW1-mediated suppression of Wnt/β-catenin signaling as a growth inhibitory pathway in cervical carcinogenesis[ 10 ]. Nevertheless, the pathophysiological role and molecular targets of HECW1 in gastric cancer remain unexplored, warranting systematic investigation. In this study, the expression, prognostic value, and function of HECW1 were systematically analyzed using bioinformatics databases. Moreover, our results demonstrated that HECW1 knockdown inhibited GC cell proliferation and migration by regulating the Hippo signaling pathway. Our findings comprehensively elucidate the significance of HECW1 in GC progression and identify HECW1 as a novel prognostic factor. These results suggest HECW1 may serve as a potential therapeutic target for GC. 2. Materials and methods 2.1 Cell culture and management Human GC cell lines (AGS, MKN-45, MKN-28, MKN-27) and healthy gastric mucosal epithelial cells (GES-1) were purchased from Shanghai Fuheng Biotechnology Corporation. Cell culture was performed in RPMI-1640 medium (Hyclone, USA, sh30809.1) containing 10% fetal bovine serum (MeisenCTCC, USA, CTCC-002-071-50) and 1% penicillin-streptomycin (Gibco, USA, 15140122) at 37°C with a CO 2 concentration of 5%. 2.2 Cell Transfection HECW1 siRNA and YAP overexpression plasmid was obtained from Azenta, with the following sequences: Human Negative control sense: UUCUCCGAACGUGUCACGU; Human Negative control antisense: ACGUGACACGUUCGGAGAA Human HECW1 si-1 sense: CAGCUGCAAUUCCGAUUUGTT; Human HECW1 si-1 antisense: CAAAUCGGAAUUGCAGCUGTT; Human HECW1 si-2 sense: GAUGAGGUCUUGUCCGAAATT; Human HECW1 si-2 antisense: UUUCGGACAAGACCUCAUCTT. Cells in the logarithmic growth phase were seeded onto plates to achieve 40–60% confluence by the next day. Transfection with siRNA was then performed using jetPRIME® (Polyplus, France; 101000046). The culture medium was replaced with fresh medium within 24 hours post-transfection. Transfection efficiency was evaluated at 48 hours post-transfection via quantitative real-time PCR (qRT-PCR) and Western blot analysis. 2.3 RNA extraction and qRT-PCR We used FastPure Cell/Tissue Total RNA Isolation Kit V2 (vazyme, China, RC112-01) to extract RNA from GC tissues and GC cells. Reverse transcription was performed using HiScript ® III all in one RT SuperMix for qPCR (vazyme, China, R333-01). qRT-PCR was performed using the ChamQ universal SYBR qPCR Master Mix kit (vazyme, China, Q711-02). The specific primers used are listed below. Human GAPDH forward primer: GAAGGTGAAGGTCGGAGTC; Human GAPDH reverse primer: GAAGATGGTGATGGGATTTC; Human HECW1 forward primer: GCAGTTTGTCACGGGAACAT.; Human HECW1 reverse primer: GGAAGATCCAGTCGGTTGAA. 2.4 CCK-8 assays Cell counting kit-8 (CCK8) (beyotime, China, C0037) was used to detect the proliferation of GC cells. 3*10 3 cells were inoculated into 96-well plates, and 10µl of CCK8 and 90ul of serum-free medium mix were added to each well and injected every 24 hours. After incubation for 2h, absorbance values were recorded at 450nm using an enzyme-labeled instrument (Thermo Scientific, MA, USA). 2.5 Transwell assay 24 hours after transfection of the cells, the cells were diluted to 1*10 6 cells/mL with serum-free 1640 medium and 200 µL was taken and added to the transwell chambers. Then 500µL of 1640 medium containing 10% FBS was added to the transwell lower chamber. After incubation for 24h, the cells were fixed with methanol and stained with crystal violet. Finally, the photographs were observed with an inverted microscope (Olympus, Tokyo, Japan). 2.6 Western blot assay Total proteins from transfected GC cells were extracted with RIPA lysate (Beyotime, China, P0013B) containing 1% PMSF (Solarbio, China, P0100). protein concentration was detected by BCA kit (Beyotime, China, P0012). Protein samples were separated by electrophoresis using 10% Precast Protein Plus Gel (Yeasen, China, 36276ES10) and then transferred to PVDF membrane (Millipore, USA, ISEQ00010). The membrane was closed with rapid closure solution (Ncmbio, China, P30500) for 30 min, and then incubated with β-actin (CST, USA, 4967S), HECW1(Proteintech, China, 24695-1-AP), YAP antibody (CST, USA, 14074T) and p-YAP (Affinity, China, AF3328), which were diluted at 1:1000, at 4°C overnight. The next day, the membrane was washed four times (5 min each) with TBST and then incubated with HRP-conjugate Mouse Anti-Rabbit IgG Antibody (CST, USA, 7076P2) which was diluted at 1:5000, at room temperature for 2 h. After incubation, the washing was repeated. Bands were then measured by a chemiluminescence imaging device (Bio-Rad, California, USA) using an ECL detection kit (Beyotime, China, P0018S). β-actin was used as an internal reference. 2.7 Immune infiltration analysis TIMER 2.0[ 11 ] is an online database that can be used to analyze immune infiltration in different cancer types[ 12 ], including cancer-associated fibroblasts (CAFs), B cells, CD4 + T cells, CD8 + T cells, neutrophils, macrophages and dendritic cells (DCs). We analyzed the expression of HECW1 in different cancers and the correlation with immune infiltration. 2.8 Kaplan-Meier plotter The Kaplan Meier[ 13 ] is capable of assessing the correlation between the expression of all genes and survival in 21 tumor types. Statistical tools applied include Cox proportional hazards regression and misdiagnosis rate calculations. Setting to split patients by median with a follow-up threshold of 60 months. 2.9 Gene co-expression analysis Gene Expression Profiling Interactive Analysis (GEPIA2) is an online database of 60,498 genes and 198,619 isoforms of type[ 14 ]. We analyzed the association between HECW1 expression and marker genes of tumor-infiltrating immune cells (mainly T cells, TAM, macrophages, NK cells, DC cells, T helper cells and neutrophils) using data from the TCGA and GTEx projects in conjunction with RNA sequencing expression analysis. Correlation coefficients were determined using the Spearman method. 2.10 Genetic variation We retrieved HECW1 gene alterations through cBioPortal[ 15 ] and studied them in 33 cancers. The Mutation Module provides HECW1 mutation sites to improve the understanding of its potential role in GC development. 2.11 Flow cytometry Cells (1×10⁶ – 3×10⁶) were collected, centrifuged, and washed twice with pre-chilled PBS. The pellet was resuspended in 500 µl Apoptosis Positive Control Solution (Liankebio, China; Cat# AT107) and incubated on ice for 30 minutes. After centrifugation and PBS washing, cells were resuspended in 500 µl 1× Binding Buffer. Annexin V-APC (5 µl) and PI (10 µl) were added to each tube, followed by gentle vortexing and a 5-minute incubation at room temperature in the dark. Apoptosis analysis was performed immediately using a flow cytometer (Beckman Coulter, USA), with data processed by FlowJo 10 software. 2.12 Co-culture of cancer cells and T cells The peripheral blood from health donors were provided by Surgery Department of Wuxi No.2 People’s Hospital. The peripheral blood mononuclear cells (PBMC) was isolated from the peripheral blood with Lymphoprep (STEMCELL, Canada, 19654) and activated with ImmunoCult Human CD3/CD28 T Cell Activator (STEMCELL, Canada, 100–0784) in ImmunoCult-XF T Cell Expansion Medium (STEMCELL, Canada, 10981) supplemented with IL2. Pre-activated CD8⁺ T cells were washed with PBS, resuspended in fresh complete medium, and co-cultured with GC cells transfected with specified plasmids in 48-well plates at an optimized effector-to-target ratio. The co-culture system was maintained for 48 hours under standard conditions (37°C, 5% CO₂). Following co-culture, T cells were harvested and treated with protein transport inhibitor (BD) for 6 hours at 37°C to accumulate intracellular cytokines. Cells were subsequently fixed and permeabilized using the BD Cytofix/Cytoperm™ Kit (554714) according to the manufacturer's protocol. Intracellular staining was performed with fluorochrome-conjugated antibodies targeting IFN-γ (Miltenyi Biotec, 130-113-497) and TNF-α (Miltenyi Biotec, 130-118-974). Data acquisition was conducted using a Beckman CytoFlex flow cytometer, and quantitative analysis was performed with FlowJo software. 2.13 RNA-Seq analysis Total RNAs were extracted from both si-HECW1 and si-Ctrl samples. We employed RNA-Seq technology (Novogene, China) for the identification of DEGs (Differentially Expressed Genes)[ 16 ]. Briefly, the raw fastq data generated from RNA-seq was first trimmed using Trimmomatic (V0.35). The trimmed reads were then compared to the human reference genome (NCBI GRCh38) by utilizing TopHat (version 2.0.12) and default parameter settings. The resulting aligned bam files were then processed using Cufflinks (Version 2.2.1) for gene quantification. Genes meeting the threshold of FPKM ≥ 1 (Fragments Per Kilobase of transcript per Million mapped reads) across all samples were included in the subsequent analysis to identify DEGs. The R Bioconductor package DESeq2 was utilized to screen out the differentially expressed genes (DEGs). The P value for correction 2 or < 0.5 were set as the cut-off criteria for identifying DEGs. 2.14 Statistics Statistical differences were analyzed using GraphPad Prism 9 software. The data is displayed as the mean ± standard error of the mean (SEM). For enrichment analysis, R 4.4.0 were utilized. To examine the variations between the two groups, the student's t-test was employed. p < 0.05 was recognized statistically significant. 3. Results 3.1 HECW1 was over-regulated and associated with survival in gastric cancer The pan-cancer expression profile of HECW1 was initially retrieved through the TIMER 2.0 database, demonstrating significantly elevated expression levels in stomach adenocarcinoma (STAD) compared with paired adjacent normal tissues (Fig. 1 A). Subsequent validation using the UALCAN platform [ 17 ] confirmed HECW1 mRNA overexpression in GC clinical specimens versus normal controls (Fig. 1 B). Comparative analysis revealed upregulated HECW1 expression in GC cell lines (HGC-27, MKN-28, MKN-45, AGS) relative to normal gastric epithelial cells (GES-1), with transcriptional activation confirmed by qRT-PCR (Fig. 1 C) and protein overexpression validated by Western blot (Fig. 1 D). Prognostic evaluation via Kaplan-Meier Plotter[ 13 ] identified HECW1 overexpression as a negative predictor for both overall survival (OS; HR = 1.48, log-rank P = 0.018) and relapse-free survival (RFS; HR = 2.12, log-rank P = 0.023) in STAD patients (Fig. 1 E-F). Multivariate Cox regression analysis of TCGA-STAD data established HECW1 expression (HR = 1.649, 95% CI = 1.045–2.603, P = 0.032) and advanced age (> 65 years) (HR = 1.701, 95% CI = 1.068–2.710, P = 0.025) as independent prognostic factors for GC (Table 1). 3.2 Association of HECW1 expression with clinicopathologic characteristics The relationship between HECW1 mRNA expression and clinical parameters in STAD was assessed by UALCAN, which is a comprehensive and interactive web resource for analyzing TCGA transcriptome and clinical patient data based on TPM normalization. The results showed that HECW1 was differentially expressed in patients of individual cancer stages, race, gender, age, tumor grade, H. pylori infection status, histological subtypes, nodal metastasis status, and TP53 mutation status (Fig. 2 A-I). 3.3 HECW1 gene alterations in GC Comprehensive genomic profiling of HECW1 alterations was performed through cBioPortal analysis platform using 32 cancer cohorts from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas (n = 10,967 tumor samples). The investigation identified 529 somatic alterations spanning the entire HECW1 coding sequence (amino acid positions 1-1606), comprising 444 missense mutations, 58 truncating mutations, 21 splice site variants, 5 gene fusions, and 1 in-frame mutation. A recurrent hotspot mutation R1555W (Arg1555Trp) was identified as the most prevalent alteration (Fig. 3 B). Notably, STAD exhibited distinct mutation patterns characterized by predominant missense substitutions and gene amplifications (Fig. 3 A). 3.4 Immunological infiltration of the HECW1 gene is present in gastric cancer The TIMER 2.0 database was employed to analyze the correlations between HECW1 expression level and immune infiltration in STAD. Multi-algorithm deconvolution (EPIC, MCP-COUNTER, XCELL, TIDE) revealed a positive correlation between HECW1 expression and cancer-associated fibroblast (CAF) infiltration (Fig. 4 A) alongside reduced tumor purity in STAD. In GC, elevated HECW1 levels inversely correlated with CD8⁺ T cell infiltration and promoted macrophages, DCs and neutrophils recruitment (Fig. 4 B). These findings suggest that HECW1 may function as a potential immunomodulator driving immunosuppressive niche formation through dysregulation of cytotoxic T cell trafficking and macrophage polarization. Functional validation through PBMC coculture assays demonstrated that HECW1-knockdown GC cells increased CD3⁺CD8⁺ T cell proportions (Fig. 4 C) and enhanced IFN-γ/TNF-α secretion (Fig. 4 D-E), suggesting its role in modulating T cell activity. GEPIA2 analysis further showed HECW1 co-expression with immune markers (NK cells, DCs, TAMs, etc.) in GC versus normal tissues (Table 2). 3.5 Effect of knockdown of HECW1 on the biological behavior of tumors Following database findings, we validated HECW1's functional role in gastric cancer through in vitro experiments. Two independent siRNAs were used to knock down HECW1 in AGS and MKN-45 cells, with knockdown efficiency confirmed by RT-qPCR and Western blot (Fig. 5 A-B). Subsequently, we found that the proliferation of GC cells in the low HECW1-expressing group was significantly lower than that of the control group with CCK8 (Fig. 5 C). Depletion of HECW1 significantly induced apoptosis in both AGS and MKN-45 via Annexin V/PI assay (Fig. 5 D). Additionally, Transwell and Wound Healing assay revealed that migration of GC cells with low HECW1 expression was impaired (Fig. 5 E-F). 3.6 Gene enrichment analysis of the HECW1 gene in tumors Protein-protein interaction (PPI) network analysis using the STRING database and Cytoscape identified HECW1-interacting proteins (Fig. 6 A). Functional enrichment analysis (GO/KEGG) of these interactors revealed significant associations with ubiquitin-proteasome system regulation and Hippo signaling pathway (Fig. 6 B-C). siRNA-mediated HECW1 knockdown in GC cells increased phosphorylated YAP (p-YAP) levels while reducing total YAP expression (Fig. 6 D). 3.7 Identification of differentially expressed genes and functional enrichment analysis To further investigate the role of HECW1 in GC and its impact on tumor suppression, RNA-Seq was performed based on si-HECW1 and si-Ctrl group in MKN-45 cells. As the volcano plots illustrated, after data integration, gene expression profiles from RNA Sequencing identified 952 differentially expressed genes. Among all the DEGs, 398 genes were upregulated and 554 were downregulated in si-HECW1 compared with the si-Ctrl in MKN-45 (Fig. 7 A), grounded on the cut-off criteria (|logFC|> 2, P adj < 0.01). DEGs were selected for integrated analysis. To explore the biology pathways of the DEGs, we performed KEGG analysis for up and down DEGs (Fig. 7 B-C). Enriched KEGG pathways of the DEGs were shown in Fig. 7 C, including Hippo signaling pathway and some other pathways, which was consistent with the results analyzed above in the public database. 3.8 Hippo pathway activation blocks the anti-tumor effects induced by HECW1 knockdown To validate the HECW1-YAP regulatory axis, we co-transfected HECW1 siRNA with a YAP overexpression plasmid in GC cells. Western blot analysis showed that HECW1 knockdown increased phosphorylated YAP (p-YAP) levels, while YAP overexpression elevated total YAP expression (Fig. 8 A). Functional assays demonstrated that HECW1 silencing suppressed proliferation (CCK-8 assay), which was rescued by YAP overexpression (Fig. 8 B). Similarly, HECW1 depletion induced apoptosis (Annexin V/PI staining), whereas YAP ectopic expression reversed this phenotype (Fig. 8 C). Transwell and wound healing assays confirmed that HECW1 knockdown impaired migration, an effect counteracted by YAP overexpression (Fig. 8 D-E). 4. Discussion Ubiquitination, a pivotal post-translational modification mechanism, critically regulates proteostasis through mediating proteasomal degradation, modulating protein-protein interactions, facilitating DNA repair, and controlling transcriptional activity[ 18 , 19 ]. This enzymatic cascade is principally governed by E3 ubiquitin ligases, which confer substrate specificity by catalyzing the covalent attachment of ubiquitin molecules to target proteins[ 20 ]. HECW1, a NEDD4-family E3 ligase, has emerged as a multifunctional regulator orchestrating diverse cellular processes including mitotic control, intercellular communication, and inflammatory responses[ 21 ]. HECW1 promoted metastasis in non-small cell lung cancer by mediating ubiquitination of Smad4[ 22 ]; induced NCOA4-regulated ferroptosis in glioma by ubiquitination and degradation of ZNF350[ 9 ]; and inhibited growth of cervical cancer cells by promoting ubiquitination of DVL1[ 10 ].Our systematic analysis extended these findings to GC, demonstrating significant correlations between HECW1 overexpression and reduced patient survival (log-rank P = 0.018, HR = 1.48). Functional interrogation through HECW1 knockdown in GC cell lines demonstrated significant suppression of cellular proliferation, enhanced apoptotic activity, and impaired migratory capacity. These findings collectively indicate that HECW1 critically promotes the malignant progression of gastric carcinoma. Furthermore, we revealed that HECW1 regulated immune processes. This implies that further exploration of the prognostic value and immunomodulatory function of HECW1 in GC is warranted. Immunotherapy has made a significant impact in the treatment of tumors[ 23 ]. The abundance and distribution of tumor-infiltrating lymphocytes (TILs) serve as significant biomarkers in the tumor immune microenvironment (TIME), demonstrating close associations with prognosis and responsiveness to immunotherapy[ 24 ]. In primary cancers of the gastrointestinal tract, the presence of anti-tumor immune infiltration is often associated with a better prognosis [ 25 ].Our study identified HECW1 as a key modulator of tumor immune microenvironment dynamics, critically regulating both the abundance and phenotypic diversity of tumor-infiltrating immune cells. High HECW1 expression inversely correlated with CD8 + T lymphocyte infiltration while showing a positive association with macrophages, DCs, and neutrophils infiltration.CD8 + T cells were the most important anti-tumor effector cells during immunotherapy and their number and functional status largely determine their anti-tumor effects[ 26 ]. Macrophages regulate the immune response to pathogens and maintain tissue homeostasis[ 27 ]. DCs were specialized antigen-presenting cells with the unique ability to induce naïve T cell activation and effector differentiation[ 28 ]. The role of neutrophils in tumors was paradoxical: neutrophils were able to mediate a wide range of anti-tumor and pro-tumor activities, ranging from direct tumor cell killing to tumor cell proliferation, angiogenesis, metastasis and coordination of other immune responses[ 29 ]. Most malignant tumors reprogram macrophages into tumor-associated macrophages (TAMs) that drive tumor progression by enhancing proliferation, invasion, and metastatic dissemination[ 30 ]. Furthermore, TAMs contribute to therapeutic resistance against both conventional chemotherapy and immune checkpoint inhibitors[ 31 ]. Functional validation through PBMC coculture assays demonstrated that HECW1-knockdown GC cells increased CD3⁺CD8⁺ T cell proportions and enhanced IFN-γ/TNF-α secretion, suggesting its role in modulating T cell activity. The Hippo signaling pathway mediates diverse cellular functions including immunomodulation, exerting tumor-promoting effects and immunosuppressive functions through multiple mechanisms[ 32 ]. The Hippo-YAP axis and its downstream effectors YAP and transcriptional co-activator PDZ-binding motif (TAZ) were essential regulators of stem cell dynamics and tumorigenesis[ 33 ]. Aberrant activation of the YAP/TAZ signaling cascade commonly occurred in various malignancies. In renal cell carcinoma, YAP promoted VEGFA expression and stimulates tumor angiogenesis through Gli2-mediated mechanisms[ 34 ]. The circular RNA circPPP1R12A enhanced rectal cancer progression and metastasis via Hippo-YAP pathway activation[ 35 ]. Emerging evidence indicated bidirectional regulation of this pathway, where multiple proteins modulated its activity. CD248 activation promoted non-small cell lung cancer metastasis through Hippo signaling stimulation[ 36 ], while RARγ knockdown inhibits Hippo-YAP signaling and suppresses colorectal cancer development[ 37 ]. To investigate the functional role of HECW1 in gastric carcinogenesis, we systematically generated a PPI network using established bioinformatics platforms and subsequently conducted comprehensive GO and KEGG pathway enrichment analyses. Integrated analysis of these network-associated genes with transcriptomic profiling data from GC cells revealed a significant functional association between HECW1 and key components of the Hippo signaling pathway. To investigate YAP's regulatory function in gastric cancer, we performed siRNA-mediated HECW1 knockdown, revealing its inhibitory effect on Hippo-YAP signaling. Rescue experiments demonstrated that YAP overexpression reversed the tumor-suppressive phenotypes caused by HECW1 depletion. Transcriptomic profiling integrated with in vitro validation confirmed HECW1-mediated Hippo-YAP pathway regulation. While current findings are limited by the absence of in vivo validation, future studies will clarify this mechanism using animal models. 5. Conclusion Elevated HECW1 expression is associated with poor prognosis and increased immune cell infiltration in GC. Mechanistically, HECW1 knockdown suppresses GC progression by inhibiting cell proliferation, apoptosis, and migration through Hippo pathway activation. These findings highlight HECW1 as both a prognostic biomarker and a potential therapeutic target for precision oncology strategies in GC. Declarations Acknowledgements Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Key Project of Scientific Research from Jiangsu Commission of Health (ZDB2020026); Wuxi Taihu Lake Talent Plan, Team in Medical and Health Profession; Wuxi Medical Key Discipline Construction Project, Medical Development Discipline; Postgraduate Research & Practice Innovation Program of Jiangsu Province (SJCX23_0690). Author Contribution Z Yang: Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. P Zhou: Software, Methodology, Investigation. L Wang: Visualization, Methodology, Investigation. X Yang: Visualization, Methodology, Investigation. M Yang: Software, Formal analysis. J Xia: Writing – review & editing, Writing – original draft, Resources, Project administration, Investigation, Funding acquisition, Conceptualization Acknowledgement We are grateful for the open access to the following databases: the Tumor Immunoassay Resource (TIMER2.0) system, the Gene Expression Profiling Interaction Analysis (GEPIA2), the Kaplan-Meier-plotter, the cBioPortal, and the STRING database. Their valuable data have been crucial in supporting our research. Data Availability Data is provided within the manuscript or supplementary information files. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229–63. Dong D, Yu X, Xu J, Yu N, Liu Z, Sun Y. Cellular and molecular mechanisms of gastrointestinal cancer liver metastases and drug resistance. Drug Resist Updat. 2024;77:101125. 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Mackey JBG, Coffelt SB, Carlin LM. Neutrophil Maturity in Cancer. Front Immunol 2019, 10:1912. Ngambenjawong C, Gustafson HH, Pun SH. Progress in tumor-associated macrophage (TAM)-targeted therapeutics. Adv Drug Deliv Rev. 2017;114:206–21. Pu Y, Ji Q. Tumor-Associated Macrophages Regulate PD-1/PD-L1 Immunosuppression. Front Immunol. 2022;13:874589. Wang Z, Wang F, Ding XY, Li TE, Wang HY, Gao YH, Wang WJ, Liu YF, Chen XS, Shen KW. Hippo/YAP signaling choreographs the tumor immune microenvironment to promote triple negative breast cancer progression via TAZ/IL-34 axis. Cancer Lett. 2022;527:174–90. Wu H, Che YN, Lan Q, He YX, Liu P, Chen MT, Dong L, Liu MN. The Multifaceted Roles of Hippo-YAP in Cardiovascular Diseases. Cardiovasc Toxicol 2024. Xu S, Zhang H, Chong Y, Guan B, Guo P. YAP Promotes VEGFA Expression and Tumor Angiogenesis Though Gli2 in Human Renal Cell Carcinoma. Arch Med Res. 2019;50:225–33. Zheng X, Chen L, Zhou Y, Wang Q, Zheng Z, Xu B, Wu C, Zhou Q, Hu W, Wu C, Jiang J. A novel protein encoded by a circular RNA circPPP1R12A promotes tumor pathogenesis and metastasis of colon cancer via Hippo-YAP signaling. Mol Cancer. 2019;18:47. Wu J, Zhang Q, Yang Z, Xu Y, Liu X, Wang X, Peng J, Xiao J, Wang Y, Shang Z, et al. CD248-expressing cancer-associated fibroblasts induce non-small cell lung cancer metastasis via Hippo pathway-mediated extracellular matrix stiffness. J Cell Mol Med. 2024;28:e70025. Guo PD, Lu XX, Gan WJ, Li XM, He XS, Zhang S, Ji QH, Zhou F, Cao Y, Wang JR, et al. RARγ Downregulation Contributes to Colorectal Tumorigenesis and Metastasis by Derepressing the Hippo-Yap Pathway. Cancer Res. 2016;76:3813–25. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx GelsandBlotsimages.docx Cite Share Download PDF Status: Published Journal Publication published 29 May, 2025 Read the published version in World Journal of Surgical Oncology → Version 1 posted Editorial decision: Revision requested 13 Apr, 2025 Reviews received at journal 11 Apr, 2025 Reviews received at journal 10 Apr, 2025 Reviews received at journal 06 Apr, 2025 Reviewers agreed at journal 06 Apr, 2025 Reviews received at journal 05 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviews received at journal 02 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviews received at journal 30 Mar, 2025 Reviewers agreed at journal 30 Mar, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers invited by journal 29 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 25 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5974654","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436105743,"identity":"2120313d-cc76-4c0a-8528-1dbc17247cc7","order_by":0,"name":"Zhihui Yang","email":"","orcid":"","institution":"Jiangnan University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Zhihui","middleName":"","lastName":"Yang","suffix":""},{"id":436105744,"identity":"64118a2d-6d38-474c-aa64-073b5db9237b","order_by":1,"name":"Peng Zhou","email":"","orcid":"","institution":"The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Zhou","suffix":""},{"id":436105745,"identity":"711357d2-086a-4418-be22-c89795be5ed5","order_by":2,"name":"Liping Wang","email":"","orcid":"","institution":"Jiangnan University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Wang","suffix":""},{"id":436105746,"identity":"14fd2fc6-9e42-41fa-a594-a193bf7d37f2","order_by":3,"name":"Xiao Yang","email":"","orcid":"","institution":"Jiangnan University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Yang","suffix":""},{"id":436105747,"identity":"4dbe8d03-c819-41e5-a21c-430b8990f36a","order_by":4,"name":"Mengqi Yang","email":"","orcid":"","institution":"The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Yang","suffix":""},{"id":436105748,"identity":"75ced0a8-2d9b-4d52-939c-7f32748184dc","order_by":5,"name":"Jiazeng Xia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie3Qv0vEMBTA8VcC1yWSNfVX/4VA4Tio4L/SItwt1vFwEHkiZO16f4pjQqBdqq4Vl+t/4OggYlrvxCGlq2C+Q0sf/fCaAvh8fzI1XCkDgvbO9xMyTSIM9sRMkV1CDeTn7XEidL0lB9IcJ7WWhy8PC4jvnisO12mO4aNykQhVZsmSzptcHhUNt+vMjEOzypFeZS7CQClSyDM6Vz2RloAlgTQ5cipcZAYaLeE0KbtvEmNPPscJs/9n2CL4bgv0HxbgOImwAvLxtKS87e7Tfp09S7LIqlUi6aWTiLbeBpu1OWflhX4t5O1pvNFd+3aTnpRh4yTAVfj++5naCUA2HHMkhtMTn8/n++d9AZ/SWvjK8nDIAAAAAElFTkSuQmCC","orcid":"","institution":"Jiangnan University Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Jiazeng","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2025-02-06 15:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5974654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5974654/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12957-025-03866-3","type":"published","date":"2025-05-29T15:57:01+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79813585,"identity":"cad16671-c8eb-442e-9d1a-1e243c71b836","added_by":"auto","created_at":"2025-04-03 07:10:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97140,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDysregulated expression pattern of HECW1 in gastric cancer. (A) \u003c/strong\u003ePan-cancer analysis using TIMER2.0 database revealed significantly aberrant HECW1 expression in GC tissues compared with normal controls.\u003cstrong\u003e (B) \u003c/strong\u003eValidation through UALCAN platform confirmed HECW1 upregulation in GC clinical specimens.\u003cstrong\u003e (C) \u003c/strong\u003eComparative analysis of HECW1 mRNA levels between normal gastric epithelial cells (GES-1) and four GC cell lines (HGC-27, MKN-28, MKN-45, AGS) by qRT-PCR.\u003cstrong\u003e (D) \u003c/strong\u003eWestern blot analysis demonstrating elevated HECW1 protein expression in GC cell lines compared with GES-1.\u003cstrong\u003e (E-F) \u003c/strong\u003ePrognostic significance of HECW1 expression in STAD cohort from Kaplan-Meier Plotter database:\u003cstrong\u003e (E) \u003c/strong\u003eOverall survival (OS, n = 375 patients) and\u003cstrong\u003e (F) \u003c/strong\u003erelapse-free survival (RFS, n = 375 patients). *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/2369beb1bfc1715c287c5a12.png"},{"id":79813967,"identity":"fe4df0da-259d-4428-a47b-ee7f2737a297","added_by":"auto","created_at":"2025-04-03 07:18:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of HECW1 expression and clinical characteristics of STAD.\u003c/strong\u003e(A) Expression of HECW1 in STAD based on individual cancer stages. (B) Expression of HECW1 in STAD based on patient’s race. (C) Expression of HECW1 in STAD based on patient’s gender. (D) Expression of HECW1 in STAD based on patient’s age. (E) Expression of HECW1 in STAD based on tumor grade. (F) Expression of HECW1 in STAD based on\u003cem\u003e H.pylori\u003c/em\u003e infection status. (G) Expression of HECW1 in STAD based on histological subtypes. (H) Expression of HECW1 in STAD based on nodal metastasis status. (I) Expression of HECW1 in STAD based on TP53 muation status. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01,***\u003cem\u003eP\u003c/em\u003e＜0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/267518903673ef9ce0b778ec.png"},{"id":79813575,"identity":"7217e07b-9120-48f4-affa-f52ba04841e5","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenomic landscape of HECW1 alterations across human cancers. (A)\u003c/strong\u003eHistograms of HECW1 mutations in 32 kinds of cancer. \u003cstrong\u003e(B) \u003c/strong\u003eHECW1 mutation map across protein domains.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/87b756f844084ca5a8017ee2.png"},{"id":79813573,"identity":"9c3d19b4-cc0b-40db-a46d-413824c535a8","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":95622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunological infiltration of the HECW1 gene is present in GC. (A)\u003c/strong\u003e The expression level of HECW1 in STAD showed a positive correlation with the infiltration of cancer-associated fibroblasts. \u003cstrong\u003e(B)\u003c/strong\u003e Analysis of the correlation between HECW1 gene expression levels and immune cell infiltration. \u003cstrong\u003e(C)\u003c/strong\u003e Statistical quantitation of CD3\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells. \u003cstrong\u003e(D)\u003c/strong\u003e Statistical quantitation of TNF-α\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells. \u003cstrong\u003e(E)\u003c/strong\u003e Statistical quantitation of IFN-γ\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003eT cells. *\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026nbsp;\u0026lt; 0.01,***\u003cem\u003eP\u003c/em\u003e＜0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/036e9f789c4e17a1f31b9228.png"},{"id":79813569,"identity":"169b8dc6-da72-4768-9f86-07a7b8bc5ae9","added_by":"auto","created_at":"2025-04-03 07:10:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":301997,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHigh HECW1 Expression in GC Cells. (A) \u003c/strong\u003eThe expression of HECW1 was detected by qRT-PCR after HECW1 knockdown.\u003cstrong\u003e (B) \u003c/strong\u003eThe protein of HECW1 was detected by western blot assays. \u003cstrong\u003e(C)\u003c/strong\u003eThe effect of HECW1 on proliferation was verified by Cell Counting Kit-8 after the knockdown of HECW1 in the AGS and MKN-45 cells.\u003cstrong\u003e (D)\u003c/strong\u003e Annexin V/PI dual staining revealed AGS and MKN-45 increase in apoptotic populations following HECW1 knockdown compared to scramble controls.\u003cstrong\u003e (E) \u003c/strong\u003eTranswell assays of AGS and MKN-45 cells treated as indicated. \u003cstrong\u003e(F)\u003c/strong\u003eWound healing assays were conducted to detect the invasion of these transfected GC cells. Transwell assays were stained by crystal violet solution. Scale bar, \u003cstrong\u003e(E) \u003c/strong\u003e100μm,\u003cstrong\u003e (F) \u003c/strong\u003e100μm. \u003cem\u003e*p\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/4ebc1b983f857e1184c249ec.png"},{"id":79813574,"identity":"b9e2d005-76aa-409b-bad0-f6424063c296","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":70362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene enrichment analysis of the HECW1 gene in tumors. (A)\u003c/strong\u003eExtraction of STRING database proteins interacting with HECW1 and mapping of PPI networks using Sytoscape processing. \u003cstrong\u003e(B)\u003c/strong\u003eEnrichment analysis of HECW1 by KEGG. \u003cstrong\u003e(C)\u003c/strong\u003eEnrichment analysis of HECW1 by GO. \u003cstrong\u003e(D)\u003c/strong\u003e The protein level of p-YAP and YAP after silencing HECW1 or not (si-NC).\u003cem\u003e *p\u003c/em\u003e \u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/4e67900332f582bce277b398.png"},{"id":79813580,"identity":"5803fea3-232b-4360-89d7-83bf51ffb407","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferentially expressed genes (DEGs) and pathways analyzed by bioinformatics.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003eIdentification of differentially expressed genes. Volcano plot of gene expression profiles. Red/blue symbols classify the upregulated/downregulated genes according to the criteria: |logFC|\u0026gt; 2 and adjusted p-value. \u003cstrong\u003e(B)\u003c/strong\u003eBubble Diagram exhibiting the most enriched KEGG pathways of up-regulated DEGs. \u003cstrong\u003e(C)\u003c/strong\u003eThe same for \u003cstrong\u003e(B)\u003c/strong\u003e but for the down-regulated DEGs.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/239d5f3c97bc1b7eec3daaf4.png"},{"id":79813578,"identity":"fee39c4c-8bfe-4b24-85fe-7566764321c4","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":243533,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHippo pathway activation blocks the anti-tumor effects induced by HECW1 knockdown. (A) \u003c/strong\u003eThe protein level of YAP after silencing of HECW1 or silencing of HECW1 followed by addition of YAP overexpression plasmid and not (si-NC). \u003cstrong\u003e(B) \u003c/strong\u003eCCK-8 assays were performed to detect the proliferation of these transfected GC cells.\u003cstrong\u003e (C) \u003c/strong\u003eFunctional rescue of apoptosis through Annexin V-FITC/PI dual-labeling assay. (\u003cstrong\u003eD\u003c/strong\u003e)Transwell assays of AGS and MKN-45 cells treated as indicated. \u003cstrong\u003e(E)\u003c/strong\u003eWound healing assays were conducted to detect the invasion of these transfected GC cells. Transwell assays were stained by crystal violet solution. Scale bar, \u003cstrong\u003e(D) \u003c/strong\u003e100μm,\u003cstrong\u003e (E) \u003c/strong\u003e100μm.\u003cem\u003e *p\u003c/em\u003e\u0026lt; 0.05, \u003cem\u003e**p\u003c/em\u003e \u0026lt; 0.01, \u003cem\u003e***p\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/0fc5b3c8cbe79d49ac88dad4.png"},{"id":83782792,"identity":"fbdff627-1253-404b-be16-7f0079db73f1","added_by":"auto","created_at":"2025-06-02 16:05:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2530214,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/c1158ac4-c0f5-4a68-be7a-7a8369933f7e.pdf"},{"id":79813562,"identity":"ad3cbbd1-8ebb-4871-8f73-c6d742edef7f","added_by":"auto","created_at":"2025-04-03 07:10:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25805,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/2c93fb53fa614d58a5cbcf7a.docx"},{"id":79813563,"identity":"8a928bdb-dee7-4491-a493-f68aae4fbb85","added_by":"auto","created_at":"2025-04-03 07:10:04","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28381,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/930cd1c587516010fda040cb.docx"},{"id":79813572,"identity":"7fefcb1b-222e-4f8e-86d4-15e479468eed","added_by":"auto","created_at":"2025-04-03 07:10:05","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":703153,"visible":true,"origin":"","legend":"","description":"","filename":"GelsandBlotsimages.docx","url":"https://assets-eu.researchsquare.com/files/rs-5974654/v1/a95222b422dd323a6c87468e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"High expression of HECW1 is associated with the poor prognosis and cancer progression of gastric cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is highly aggressive and metastatic and ranks among the leading causes of cancer-related mortality worldwide[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite advances in surgical techniques and targeted therapies, progress has been made in the diagnosis and treatment of gastric cancer, but the prognosis of patients with advanced gastric cancer is still poor. Therefore, a deep understanding of the biological mechanisms underlying the development of GC is essential for the designing novel therapeutic interventions.\u003c/p\u003e \u003cp\u003eUbiquitination, an enzymatic process mediating covalent attachment of ubiquitin moieties to substrate proteins[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], serves as a critical post-translational modification regulating diverse pathological processes including oncogenesis, metabolic disorders, and neurodegenerative diseases[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The E3 ubiquitin ligase, which has the function of determining substrate specificity, plays a decisive role in the ubiquitination process[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. E3 ubiquitin ligases mainly catalyze the formation of covalent bonds between ubiquitin molecules and specific amino acid residues on target proteins to achieve ubiquitination modifications, with lysine being the most common target for ubiquitination[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The HECT-domain ligase HECW1, initially implicated in familial amyotrophic lateral sclerosis pathogenesis[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], demonstrates multifaceted tumor-modulatory functions. Mechanistic studies reveal its catalytic activity-independent regulation of p53-mediated apoptosis[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] and NCOA4-driven ferroptosis in gliomas[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Emerging evidence further identifies HECW1-mediated suppression of Wnt/β-catenin signaling as a growth inhibitory pathway in cervical carcinogenesis[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nevertheless, the pathophysiological role and molecular targets of HECW1 in gastric cancer remain unexplored, warranting systematic investigation.\u003c/p\u003e \u003cp\u003eIn this study, the expression, prognostic value, and function of HECW1 were systematically analyzed using bioinformatics databases. Moreover, our results demonstrated that HECW1 knockdown inhibited GC cell proliferation and migration by regulating the Hippo signaling pathway. Our findings comprehensively elucidate the significance of HECW1 in GC progression and identify HECW1 as a novel prognostic factor. These results suggest HECW1 may serve as a potential therapeutic target for GC.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Cell culture and management\u003c/h2\u003e \u003cp\u003eHuman GC cell lines (AGS, MKN-45, MKN-28, MKN-27) and healthy gastric mucosal epithelial cells (GES-1) were purchased from Shanghai Fuheng Biotechnology Corporation. Cell culture was performed in RPMI-1640 medium (Hyclone, USA, sh30809.1) containing 10% fetal bovine serum (MeisenCTCC, USA, CTCC-002-071-50) and 1% penicillin-streptomycin (Gibco, USA, 15140122) at 37\u0026deg;C with a CO\u003csub\u003e2\u003c/sub\u003e concentration of 5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Cell Transfection\u003c/h2\u003e \u003cp\u003eHECW1 siRNA and YAP overexpression plasmid was obtained from Azenta, with the following sequences:\u003c/p\u003e \u003cp\u003eHuman Negative control sense: UUCUCCGAACGUGUCACGU;\u003c/p\u003e \u003cp\u003eHuman Negative control antisense: ACGUGACACGUUCGGAGAA\u003c/p\u003e \u003cp\u003eHuman HECW1 si-1 sense: CAGCUGCAAUUCCGAUUUGTT;\u003c/p\u003e \u003cp\u003eHuman HECW1 si-1 antisense: CAAAUCGGAAUUGCAGCUGTT;\u003c/p\u003e \u003cp\u003eHuman HECW1 si-2 sense: GAUGAGGUCUUGUCCGAAATT;\u003c/p\u003e \u003cp\u003eHuman HECW1 si-2 antisense: UUUCGGACAAGACCUCAUCTT.\u003c/p\u003e \u003cp\u003eCells in the logarithmic growth phase were seeded onto plates to achieve 40\u0026ndash;60% confluence by the next day. Transfection with siRNA was then performed using jetPRIME\u0026reg; (Polyplus, France; 101000046). The culture medium was replaced with fresh medium within 24 hours post-transfection. Transfection efficiency was evaluated at 48 hours post-transfection via quantitative real-time PCR (qRT-PCR) and Western blot analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 RNA extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eWe used FastPure Cell/Tissue Total RNA Isolation Kit V2 (vazyme, China, RC112-01) to extract RNA from GC tissues and GC cells. Reverse transcription was performed using HiScript \u0026reg; III all in one RT SuperMix for qPCR (vazyme, China, R333-01). qRT-PCR was performed using the ChamQ universal SYBR qPCR Master Mix kit (vazyme, China, Q711-02). The specific primers used are listed below.\u003c/p\u003e \u003cp\u003eHuman GAPDH forward primer: GAAGGTGAAGGTCGGAGTC;\u003c/p\u003e \u003cp\u003eHuman GAPDH reverse primer: GAAGATGGTGATGGGATTTC;\u003c/p\u003e \u003cp\u003eHuman HECW1 forward primer: GCAGTTTGTCACGGGAACAT.;\u003c/p\u003e \u003cp\u003eHuman HECW1 reverse primer: GGAAGATCCAGTCGGTTGAA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 CCK-8 assays\u003c/h2\u003e \u003cp\u003eCell counting kit-8 (CCK8) (beyotime, China, C0037) was used to detect the proliferation of GC cells. 3*10\u003csup\u003e3\u003c/sup\u003e cells were inoculated into 96-well plates, and 10\u0026micro;l of CCK8 and 90ul of serum-free medium mix were added to each well and injected every 24 hours. After incubation for 2h, absorbance values were recorded at 450nm using an enzyme-labeled instrument (Thermo Scientific, MA, USA).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transwell assay\u003c/h2\u003e \u003cp\u003e24 hours after transfection of the cells, the cells were diluted to 1*10\u003csup\u003e6\u003c/sup\u003e cells/mL with serum-free 1640 medium and 200 \u0026micro;L was taken and added to the transwell chambers. Then 500\u0026micro;L of 1640 medium containing 10% FBS was added to the transwell lower chamber. After incubation for 24h, the cells were fixed with methanol and stained with crystal violet. Finally, the photographs were observed with an inverted microscope (Olympus, Tokyo, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Western blot assay\u003c/h2\u003e \u003cp\u003eTotal proteins from transfected GC cells were extracted with RIPA lysate (Beyotime, China, P0013B) containing 1% PMSF (Solarbio, China, P0100). protein concentration was detected by BCA kit (Beyotime, China, P0012). Protein samples were separated by electrophoresis using 10% Precast Protein Plus Gel (Yeasen, China, 36276ES10) and then transferred to PVDF membrane (Millipore, USA, ISEQ00010). The membrane was closed with rapid closure solution (Ncmbio, China, P30500) for 30 min, and then incubated with β-actin (CST, USA, 4967S), HECW1(Proteintech, China, 24695-1-AP), YAP antibody (CST, USA, 14074T) and p-YAP (Affinity, China, AF3328), which were diluted at 1:1000, at 4\u0026deg;C overnight. The next day, the membrane was washed four times (5 min each) with TBST and then incubated with HRP-conjugate Mouse Anti-Rabbit IgG Antibody (CST, USA, 7076P2) which was diluted at 1:5000, at room temperature for 2 h. After incubation, the washing was repeated. Bands were then measured by a chemiluminescence imaging device (Bio-Rad, California, USA) using an ECL detection kit (Beyotime, China, P0018S). β-actin was used as an internal reference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Immune infiltration analysis\u003c/h2\u003e \u003cp\u003eTIMER 2.0[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] is an online database that can be used to analyze immune infiltration in different cancer types[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], including cancer-associated fibroblasts (CAFs), B cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, CD8\u003csup\u003e+\u003c/sup\u003e T cells, neutrophils, macrophages and dendritic cells (DCs). We analyzed the expression of HECW1 in different cancers and the correlation with immune infiltration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Kaplan-Meier plotter\u003c/h2\u003e \u003cp\u003eThe Kaplan Meier[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] is capable of assessing the correlation between the expression of all genes and survival in 21 tumor types. Statistical tools applied include Cox proportional hazards regression and misdiagnosis rate calculations. Setting to split patients by median with a follow-up threshold of 60 months.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Gene co-expression analysis\u003c/h2\u003e \u003cp\u003eGene Expression Profiling Interactive Analysis (GEPIA2) is an online database of 60,498 genes and 198,619 isoforms of type[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. We analyzed the association between HECW1 expression and marker genes of tumor-infiltrating immune cells (mainly T cells, TAM, macrophages, NK cells, DC cells, T helper cells and neutrophils) using data from the TCGA and GTEx projects in conjunction with RNA sequencing expression analysis. Correlation coefficients were determined using the Spearman method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Genetic variation\u003c/h2\u003e \u003cp\u003eWe retrieved HECW1 gene alterations through cBioPortal[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and studied them in 33 cancers. The Mutation Module provides HECW1 mutation sites to improve the understanding of its potential role in GC development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Flow cytometry\u003c/h2\u003e \u003cp\u003eCells (1\u0026times;10⁶ \u0026ndash; 3\u0026times;10⁶) were collected, centrifuged, and washed twice with pre-chilled PBS. The pellet was resuspended in 500 \u0026micro;l Apoptosis Positive Control Solution (Liankebio, China; Cat# AT107) and incubated on ice for 30 minutes. After centrifugation and PBS washing, cells were resuspended in 500 \u0026micro;l 1\u0026times; Binding Buffer. Annexin V-APC (5 \u0026micro;l) and PI (10 \u0026micro;l) were added to each tube, followed by gentle vortexing and a 5-minute incubation at room temperature in the dark. Apoptosis analysis was performed immediately using a flow cytometer (Beckman Coulter, USA), with data processed by FlowJo 10 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Co-culture of cancer cells and T cells\u003c/h2\u003e \u003cp\u003eThe peripheral blood from health donors were provided by Surgery Department of Wuxi No.2 People\u0026rsquo;s Hospital. The peripheral blood mononuclear cells (PBMC) was isolated from the peripheral blood with Lymphoprep (STEMCELL, Canada, 19654) and activated with ImmunoCult Human CD3/CD28 T Cell Activator (STEMCELL, Canada, 100\u0026ndash;0784) in ImmunoCult-XF T Cell Expansion Medium (STEMCELL, Canada, 10981) supplemented with IL2. Pre-activated CD8⁺ T cells were washed with PBS, resuspended in fresh complete medium, and co-cultured with GC cells transfected with specified plasmids in 48-well plates at an optimized effector-to-target ratio. The co-culture system was maintained for 48 hours under standard conditions (37\u0026deg;C, 5% CO₂). Following co-culture, T cells were harvested and treated with protein transport inhibitor (BD) for 6 hours at 37\u0026deg;C to accumulate intracellular cytokines. Cells were subsequently fixed and permeabilized using the BD Cytofix/Cytoperm\u0026trade; Kit (554714) according to the manufacturer's protocol. Intracellular staining was performed with fluorochrome-conjugated antibodies targeting IFN-γ (Miltenyi Biotec, 130-113-497) and TNF-α (Miltenyi Biotec, 130-118-974). Data acquisition was conducted using a Beckman CytoFlex flow cytometer, and quantitative analysis was performed with FlowJo software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 RNA-Seq analysis\u003c/h2\u003e \u003cp\u003eTotal RNAs were extracted from both si-HECW1 and si-Ctrl samples. We employed RNA-Seq technology (Novogene, China) for the identification of DEGs (Differentially Expressed Genes)[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Briefly, the raw fastq data generated from RNA-seq was first trimmed using Trimmomatic (V0.35). The trimmed reads were then compared to the human reference genome (NCBI GRCh38) by utilizing TopHat (version 2.0.12) and default parameter settings. The resulting aligned bam files were then processed using Cufflinks (Version 2.2.1) for gene quantification. Genes meeting the threshold of FPKM\u0026thinsp;\u0026ge;\u0026thinsp;1 (Fragments Per Kilobase of transcript per Million mapped reads) across all samples were included in the subsequent analysis to identify DEGs. The R Bioconductor package DESeq2 was utilized to screen out the differentially expressed genes (DEGs). The \u003cem\u003eP\u003c/em\u003e value for correction\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and fold change\u0026thinsp;\u0026gt;\u0026thinsp;2 or \u0026lt;\u0026thinsp;0.5 were set as the cut-off criteria for identifying DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Statistics\u003c/h2\u003e \u003cp\u003eStatistical differences were analyzed using GraphPad Prism 9 software. The data is displayed as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error of the mean (SEM). For enrichment analysis, R 4.4.0 were utilized. To examine the variations between the two groups, the student's t-test was employed. \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was recognized statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.1 HECW1 was over-regulated and associated with survival in gastric cancer\u003c/h2\u003e \u003cp\u003eThe pan-cancer expression profile of HECW1 was initially retrieved through the TIMER 2.0 database, demonstrating significantly elevated expression levels in stomach adenocarcinoma (STAD) compared with paired adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequent validation using the UALCAN platform [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] confirmed HECW1 mRNA overexpression in GC clinical specimens versus normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Comparative analysis revealed upregulated HECW1 expression in GC cell lines (HGC-27, MKN-28, MKN-45, AGS) relative to normal gastric epithelial cells (GES-1), with transcriptional activation confirmed by qRT-PCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) and protein overexpression validated by Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Prognostic evaluation via Kaplan-Meier Plotter[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] identified HECW1 overexpression as a negative predictor for both overall survival (OS; HR\u0026thinsp;=\u0026thinsp;1.48, log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018) and relapse-free survival (RFS; HR\u0026thinsp;=\u0026thinsp;2.12, log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) in STAD patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-F). Multivariate Cox regression analysis of TCGA-STAD data established HECW1 expression (HR\u0026thinsp;=\u0026thinsp;1.649, 95% CI\u0026thinsp;=\u0026thinsp;1.045\u0026ndash;2.603, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032) and advanced age (\u0026gt;\u0026thinsp;65 years) (HR\u0026thinsp;=\u0026thinsp;1.701, 95% CI\u0026thinsp;=\u0026thinsp;1.068\u0026ndash;2.710, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025) as independent prognostic factors for GC (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association of HECW1 expression with clinicopathologic characteristics\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe relationship between HECW1 mRNA expression and clinical parameters in STAD was assessed by UALCAN, which is a comprehensive and interactive web resource for analyzing TCGA transcriptome and clinical patient data based on TPM normalization. The results showed that HECW1 was differentially expressed in patients of individual cancer stages, race, gender, age, tumor grade, \u003cem\u003eH. pylori\u003c/em\u003e infection status, histological subtypes, nodal metastasis status, and TP53 mutation status (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.3 HECW1 gene alterations in GC\u003c/h2\u003e \u003cp\u003eComprehensive genomic profiling of HECW1 alterations was performed through cBioPortal analysis platform using 32 cancer cohorts from The Cancer Genome Atlas (TCGA) Pan-Cancer Atlas (n\u0026thinsp;=\u0026thinsp;10,967 tumor samples). The investigation identified 529 somatic alterations spanning the entire HECW1 coding sequence (amino acid positions 1-1606), comprising 444 missense mutations, 58 truncating mutations, 21 splice site variants, 5 gene fusions, and 1 in-frame mutation. A recurrent hotspot mutation R1555W (Arg1555Trp) was identified as the most prevalent alteration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Notably, STAD exhibited distinct mutation patterns characterized by predominant missense substitutions and gene amplifications (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Immunological infiltration of the HECW1 gene is present in gastric cancer\u003c/h2\u003e \u003cp\u003eThe TIMER 2.0 database was employed to analyze the correlations between HECW1 expression level and immune infiltration in STAD. Multi-algorithm deconvolution (EPIC, MCP-COUNTER, XCELL, TIDE) revealed a positive correlation between HECW1 expression and cancer-associated fibroblast (CAF) infiltration (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) alongside reduced tumor purity in STAD. In GC, elevated HECW1 levels inversely correlated with CD8⁺ T cell infiltration and promoted macrophages, DCs and neutrophils recruitment (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). These findings suggest that HECW1 may function as a potential immunomodulator driving immunosuppressive niche formation through dysregulation of cytotoxic T cell trafficking and macrophage polarization. Functional validation through PBMC coculture assays demonstrated that HECW1-knockdown GC cells increased CD3⁺CD8⁺ T cell proportions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC) and enhanced IFN-γ/TNF-α secretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E), suggesting its role in modulating T cell activity. GEPIA2 analysis further showed HECW1 co-expression with immune markers (NK cells, DCs, TAMs, etc.) in GC versus normal tissues (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Effect of knockdown of HECW1 on the biological behavior of tumors\u003c/h2\u003e \u003cp\u003eFollowing database findings, we validated HECW1's functional role in gastric cancer through in vitro experiments. Two independent siRNAs were used to knock down HECW1 in AGS and MKN-45 cells, with knockdown efficiency confirmed by RT-qPCR and Western blot (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Subsequently, we found that the proliferation of GC cells in the low HECW1-expressing group was significantly lower than that of the control group with CCK8 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Depletion of HECW1 significantly induced apoptosis in both AGS and MKN-45 via Annexin V/PI assay (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Additionally, Transwell and Wound Healing assay revealed that migration of GC cells with low HECW1 expression was impaired (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-F).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Gene enrichment analysis of the HECW1 gene in tumors\u003c/h2\u003e \u003cp\u003eProtein-protein interaction (PPI) network analysis using the STRING database and Cytoscape identified HECW1-interacting proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Functional enrichment analysis (GO/KEGG) of these interactors revealed significant associations with ubiquitin-proteasome system regulation and Hippo signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB-C). siRNA-mediated HECW1 knockdown in GC cells increased phosphorylated YAP (p-YAP) levels while reducing total YAP expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Identification of differentially expressed genes and functional enrichment analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the role of HECW1 in GC and its impact on tumor suppression, RNA-Seq was performed based on si-HECW1 and si-Ctrl group in MKN-45 cells. As the volcano plots illustrated, after data integration, gene expression profiles from RNA Sequencing identified 952 differentially expressed genes. Among all the DEGs, 398 genes were upregulated and 554 were downregulated in si-HECW1 compared with the si-Ctrl in MKN-45 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA), grounded on the cut-off criteria (|logFC|\u0026gt; 2, \u003cem\u003eP\u003c/em\u003eadj\u0026thinsp;\u0026lt;\u0026thinsp;0.01). DEGs were selected for integrated analysis. To explore the biology pathways of the DEGs, we performed KEGG analysis for up and down DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-C). Enriched KEGG pathways of the DEGs were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC, including Hippo signaling pathway and some other pathways, which was consistent with the results analyzed above in the public database.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Hippo pathway activation blocks the anti-tumor effects induced by HECW1 knockdown\u003c/h2\u003e \u003cp\u003eTo validate the HECW1-YAP regulatory axis, we co-transfected HECW1 siRNA with a YAP overexpression plasmid in GC cells. Western blot analysis showed that HECW1 knockdown increased phosphorylated YAP (p-YAP) levels, while YAP overexpression elevated total YAP expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Functional assays demonstrated that HECW1 silencing suppressed proliferation (CCK-8 assay), which was rescued by YAP overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Similarly, HECW1 depletion induced apoptosis (Annexin V/PI staining), whereas YAP ectopic expression reversed this phenotype (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). Transwell and wound healing assays confirmed that HECW1 knockdown impaired migration, an effect counteracted by YAP overexpression (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eD-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUbiquitination, a pivotal post-translational modification mechanism, critically regulates proteostasis through mediating proteasomal degradation, modulating protein-protein interactions, facilitating DNA repair, and controlling transcriptional activity[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This enzymatic cascade is principally governed by E3 ubiquitin ligases, which confer substrate specificity by catalyzing the covalent attachment of ubiquitin molecules to target proteins[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. HECW1, a NEDD4-family E3 ligase, has emerged as a multifunctional regulator orchestrating diverse cellular processes including mitotic control, intercellular communication, and inflammatory responses[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. HECW1 promoted metastasis in non-small cell lung cancer by mediating ubiquitination of Smad4[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]; induced NCOA4-regulated ferroptosis in glioma by ubiquitination and degradation of ZNF350[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; and inhibited growth of cervical cancer cells by promoting ubiquitination of DVL1[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Our systematic analysis extended these findings to GC, demonstrating significant correlations between HECW1 overexpression and reduced patient survival (log-rank \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018, HR\u0026thinsp;=\u0026thinsp;1.48). Functional interrogation through HECW1 knockdown in GC cell lines demonstrated significant suppression of cellular proliferation, enhanced apoptotic activity, and impaired migratory capacity. These findings collectively indicate that HECW1 critically promotes the malignant progression of gastric carcinoma.\u003c/p\u003e \u003cp\u003eFurthermore, we revealed that HECW1 regulated immune processes. This implies that further exploration of the prognostic value and immunomodulatory function of HECW1 in GC is warranted. Immunotherapy has made a significant impact in the treatment of tumors[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The abundance and distribution of tumor-infiltrating lymphocytes (TILs) serve as significant biomarkers in the tumor immune microenvironment (TIME), demonstrating close associations with prognosis and responsiveness to immunotherapy[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In primary cancers of the gastrointestinal tract, the presence of anti-tumor immune infiltration is often associated with a better prognosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].Our study identified HECW1 as a key modulator of tumor immune microenvironment dynamics, critically regulating both the abundance and phenotypic diversity of tumor-infiltrating immune cells. High HECW1 expression inversely correlated with CD8\u003csup\u003e+\u003c/sup\u003e T lymphocyte infiltration while showing a positive association with macrophages, DCs, and neutrophils infiltration.CD8\u003csup\u003e+\u003c/sup\u003e T cells were the most important anti-tumor effector cells during immunotherapy and their number and functional status largely determine their anti-tumor effects[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Macrophages regulate the immune response to pathogens and maintain tissue homeostasis[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. DCs were specialized antigen-presenting cells with the unique ability to induce na\u0026iuml;ve T cell activation and effector differentiation[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The role of neutrophils in tumors was paradoxical: neutrophils were able to mediate a wide range of anti-tumor and pro-tumor activities, ranging from direct tumor cell killing to tumor cell proliferation, angiogenesis, metastasis and coordination of other immune responses[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Most malignant tumors reprogram macrophages into tumor-associated macrophages (TAMs) that drive tumor progression by enhancing proliferation, invasion, and metastatic dissemination[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Furthermore, TAMs contribute to therapeutic resistance against both conventional chemotherapy and immune checkpoint inhibitors[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Functional validation through PBMC coculture assays demonstrated that HECW1-knockdown GC cells increased CD3⁺CD8⁺ T cell proportions and enhanced IFN-γ/TNF-α secretion, suggesting its role in modulating T cell activity.\u003c/p\u003e \u003cp\u003eThe Hippo signaling pathway mediates diverse cellular functions including immunomodulation, exerting tumor-promoting effects and immunosuppressive functions through multiple mechanisms[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The Hippo-YAP axis and its downstream effectors YAP and transcriptional co-activator PDZ-binding motif (TAZ) were essential regulators of stem cell dynamics and tumorigenesis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Aberrant activation of the YAP/TAZ signaling cascade commonly occurred in various malignancies. In renal cell carcinoma, YAP promoted VEGFA expression and stimulates tumor angiogenesis through Gli2-mediated mechanisms[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The circular RNA circPPP1R12A enhanced rectal cancer progression and metastasis via Hippo-YAP pathway activation[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Emerging evidence indicated bidirectional regulation of this pathway, where multiple proteins modulated its activity. CD248 activation promoted non-small cell lung cancer metastasis through Hippo signaling stimulation[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], while RARγ knockdown inhibits Hippo-YAP signaling and suppresses colorectal cancer development[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. To investigate the functional role of HECW1 in gastric carcinogenesis, we systematically generated a PPI network using established bioinformatics platforms and subsequently conducted comprehensive GO and KEGG pathway enrichment analyses. Integrated analysis of these network-associated genes with transcriptomic profiling data from GC cells revealed a significant functional association between HECW1 and key components of the Hippo signaling pathway. To investigate YAP's regulatory function in gastric cancer, we performed siRNA-mediated HECW1 knockdown, revealing its inhibitory effect on Hippo-YAP signaling. Rescue experiments demonstrated that YAP overexpression reversed the tumor-suppressive phenotypes caused by HECW1 depletion. Transcriptomic profiling integrated with in vitro validation confirmed HECW1-mediated Hippo-YAP pathway regulation. While current findings are limited by the absence of \u003cem\u003ein vivo\u003c/em\u003e validation, future studies will clarify this mechanism using animal models.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eElevated HECW1 expression is associated with poor prognosis and increased immune cell infiltration in GC. Mechanistically, HECW1 knockdown suppresses GC progression by inhibiting cell proliferation, apoptosis, and migration through Hippo pathway activation. These findings highlight HECW1 as both a prognostic biomarker and a potential therapeutic target for precision oncology strategies in GC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Key Project of Scientific Research from Jiangsu Commission of Health (ZDB2020026); Wuxi Taihu Lake Talent Plan, Team in Medical and Health Profession; Wuxi Medical Key Discipline Construction Project, Medical Development Discipline; Postgraduate Research \u0026amp; Practice Innovation Program of Jiangsu Province (SJCX23_0690).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eZ Yang: Writing \u0026ndash; original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. P Zhou: Software, Methodology, Investigation. L Wang: Visualization, Methodology, Investigation. X Yang: Visualization, Methodology, Investigation. M Yang: Software, Formal analysis. J Xia: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Resources, Project administration, Investigation, Funding acquisition, Conceptualization\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe are grateful for the open access to the following databases: the Tumor Immunoassay Resource (TIMER2.0) system, the Gene Expression Profiling Interaction Analysis (GEPIA2), the Kaplan-Meier-plotter, the cBioPortal, and the STRING database. Their valuable data have been crucial in supporting our research.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74:229\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong D, Yu X, Xu J, Yu N, Liu Z, Sun Y. Cellular and molecular mechanisms of gastrointestinal cancer liver metastases and drug resistance. Drug Resist Updat. 2024;77:101125.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan S, Wang R, Zhang Y, Li X, Gan Y, Gao F, Rong P, Wang W, Li W. The role of ubiquitination and deubiquitination in tumor invasion and metastasis. 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Tumor-Associated Macrophages Regulate PD-1/PD-L1 Immunosuppression. Front Immunol. 2022;13:874589.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Wang F, Ding XY, Li TE, Wang HY, Gao YH, Wang WJ, Liu YF, Chen XS, Shen KW. Hippo/YAP signaling choreographs the tumor immune microenvironment to promote triple negative breast cancer progression via TAZ/IL-34 axis. Cancer Lett. 2022;527:174\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu H, Che YN, Lan Q, He YX, Liu P, Chen MT, Dong L, Liu MN. The Multifaceted Roles of Hippo-YAP in Cardiovascular Diseases. Cardiovasc Toxicol 2024.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu S, Zhang H, Chong Y, Guan B, Guo P. YAP Promotes VEGFA Expression and Tumor Angiogenesis Though Gli2 in Human Renal Cell Carcinoma. Arch Med Res. 2019;50:225\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X, Chen L, Zhou Y, Wang Q, Zheng Z, Xu B, Wu C, Zhou Q, Hu W, Wu C, Jiang J. A novel protein encoded by a circular RNA circPPP1R12A promotes tumor pathogenesis and metastasis of colon cancer via Hippo-YAP signaling. Mol Cancer. 2019;18:47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Zhang Q, Yang Z, Xu Y, Liu X, Wang X, Peng J, Xiao J, Wang Y, Shang Z, et al. CD248-expressing cancer-associated fibroblasts induce non-small cell lung cancer metastasis via Hippo pathway-mediated extracellular matrix stiffness. J Cell Mol Med. 2024;28:e70025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo PD, Lu XX, Gan WJ, Li XM, He XS, Zhang S, Ji QH, Zhou F, Cao Y, Wang JR, et al. RARγ Downregulation Contributes to Colorectal Tumorigenesis and Metastasis by Derepressing the Hippo-Yap Pathway. Cancer Res. 2016;76:3813\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"HECW1, gastric cancer, immune infiltration, Hippo signaling pathway","lastPublishedDoi":"10.21203/rs.3.rs-5974654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5974654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe E3 ubiquitin ligase HECW1 was found to be involved in ubiquitination modifications during malignant progression of multiple tumors. However, the prognostic role of HECW1 expression in gastric cancer (GC) remains unclear.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe Tumor Immunoassay Resource (TIMER2.0) system evaluated the association of HECW1 with tumor-infiltrating lymphocytes in carcinomas. The UALCAN assessed HECW1 mRNA expression levels in GC tissues and examined their associations with clinicopathological characteristics. The Kaplan Meier-plotter analyzed the effect of HECW1 on the survival of GC patients. The cBioPortal retrieved information about genetic variants in HECW1 gene. Protein‒protein interaction (PPI) networks associated with HECW1 were explored using the STRING database. The functional effects of HECW1 on GC cells were evaluated through proliferation (Cell Counting Kit-8), apoptosis (Flow cytometry), and migration (Transwell and wound healing assays). The RNA-Seq was applied to explore the underlying mechanisms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHECW1 demonstrated significant overexpression in GC tumor tissues, correlating with adverse clinical outcomes. Clinically, elevated HECW1 expression exhibited an inverse association with tumor-infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T lymphocytes while demonstrating a positive correlation with macrophages, DCs, and neutrophils infiltration, suggesting its potential involvement in tumor immune evasion mechanisms. Functional validation revealed that HECW1 knockdown markedly suppressed GC cell proliferation and migratory capacity, concurrently promoting apoptotic cell death. Mechanistic investigations identified that HECW1 exerts its oncogenic effects through dysregulation of the Hippo signaling pathway, with its silencing effectively attenuating tumor progression via pathway modulation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHECW1 upregulation is significantly associated with poor prognosis and immune infiltration in GC patients, emphasizing its potential as a prognostic biomarker.\u003c/p\u003e","manuscriptTitle":"High expression of HECW1 is associated with the poor prognosis and cancer progression of gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 07:09:59","doi":"10.21203/rs.3.rs-5974654/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-13T15:12:32+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-11T10:37:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-10T11:19:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-06T16:17:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282937358306506305481231711071942788686","date":"2025-04-06T16:02:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-05T08:29:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"333266264239623694375157024462094067773","date":"2025-04-03T07:04:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-02T08:05:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"251386349542355774612285888880522246135","date":"2025-04-01T23:53:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84431727435036339438134164398592015793","date":"2025-03-31T05:06:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-30T08:58:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315005287149916953945468467920915481078","date":"2025-03-30T08:48:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119492740115828656145413901792581108154","date":"2025-03-29T12:32:22+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-29T05:02:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-26T00:20:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"World Journal of Surgical Oncology","date":"2025-03-25T15:57:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"world-journal-of-surgical-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"wjso","sideBox":"Learn more about [World Journal of Surgical Oncology](http://wjso.biomedcentral.com)","snPcode":"12957","submissionUrl":"https://submission.nature.com/new-submission/12957/3","title":"World Journal of Surgical Oncology","twitterHandle":"@OncoBioMed","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6818ec37-08ed-4dd4-9ce2-928b46407e1a","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T15:59:04+00:00","versionOfRecord":{"articleIdentity":"rs-5974654","link":"https://doi.org/10.1186/s12957-025-03866-3","journal":{"identity":"world-journal-of-surgical-oncology","isVorOnly":false,"title":"World Journal of Surgical Oncology"},"publishedOn":"2025-05-29 15:57:01","publishedOnDateReadable":"May 29th, 2025"},"versionCreatedAt":"2025-04-03 07:09:59","video":"","vorDoi":"10.1186/s12957-025-03866-3","vorDoiUrl":"https://doi.org/10.1186/s12957-025-03866-3","workflowStages":[]},"version":"v1","identity":"rs-5974654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5974654","identity":"rs-5974654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}