Rho GTPase–related prognostic signature for esophageal squamous cell carcinoma identifies and validates CTTN as an independent adverse prognostic factor

Rho GTPase–related prognostic signature for esophageal squamous cell carcinoma identifies and validates CTTN as an independent adverse prognostic factor | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Rho GTPase–related prognostic signature for esophageal squamous cell carcinoma identifies and validates CTTN as an independent adverse prognostic factor Hua Chen, Jun-er Xu, Qiancheng Lin, Pingping Dong, Wangkai Xie, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8143466/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Rho GTPases orchestrate actin dynamics and tumor cell motility, but their prognostic relevance in esophageal squamous cell carcinoma (ESCC) remains unclear. We aimed to identify Rho GTPase–related differentially expressed genes (DEGs), build a prognostic model, evaluate important gene expression against clinicopathological features, and examine CTTN function in vitro. Methods Transcriptomic data from GSE53625 and TCGA-ESCC were used. Rho GTPase–linked DEGs from tumor–normal comparisons entered univariate Cox, LASSO, and multivariable Cox analyses to construct a risk-score model; immune infiltration and a nomogram were also evaluated. For CTTN, 60 paired ESCC tissues were examined by Western blot and immunohistochemistry, and 18 additional pairs by qPCR, with correlations to clinicopathological variables and survival. ESCC cell lines were used to test the effects of CTTN expression on migration, invasion, and proliferation. Results We identified 107 Rho GTPase–related DEGs enriched in actin cytoskeleton–remodeling pathways. Five genes (RHOV, RHOA, PIK3R1, CTTN, ARHGEF37) formed a strong prognostic signature that separated patients into high- and low-risk groups with clearly different outcomes and maintained independent prognostic value after adjustment for stage and nodal status. High-risk tumors showed a distinct immune landscape with altered B-cell, NK-cell, mast-cell, and neutrophil abundance. CTTN was consistently upregulated versus normal tissue, especially in advanced stage and nodal-metastatic disease, and enhanced ESCC cell migration and invasion. Conclusions We establish a Rho GTPase–based prognostic model for ESCC and validate CTTN as an independent adverse prognostic marker that drives migratory and invasive phenotypes, highlighting CTTN as a potential therapeutic target. Esophageal squamous cell carcinoma Prognostic model Rho GTPase CTTN(Cortactin) Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 INTRODUCTION Esophageal carcinoma (ESCA) is one of the world’s deadliest and most malignant cancers, ranking seventh by incidence and sixth by mortality [ 1 , 2 ] . By 2040, the global ESCA burden is estimated to rise to approximately 957,000 cases, with deaths exceeding 880,000 [ 3 ] . Particularly, in East Asia—especially in Japan—esophageal cancers are predominantly squamous. Nationwide registry data indicate that esophageal squamous cell carcinoma (ESCC) accounts for approximately 86–90% of esophageal cancers in Japan, with similarly high proportions reported in China. [ 1 , 4 , 5 ] . Most patients with ESCC are not diagnosed at the very earliest stage, contributing to poor outcomes [ 6 ] . In Japan’s nationwide registry, only 39.2% are stage I at diagnosis (IA 33.4%, IB 5.8%), indicating approximately 60.8% are diagnosed beyond early stage [ 4 ] . And in developing countries, where most of the world’s ESCC occurs, the 5-year survival rates of patients are less than 5% [ 7 , 8 ] . Early cancer screening and endoscopic treatment could improve the 5-year survival rates of ESCC patients [ 9 ] . Molecular targeted therapy has been recognized as the future therapeutic strategy for cancer treatments [ 10 ] . In ESCC, interrogation of the tumor microenvironment is ongoing; although anti–PD-1/PD-L1 monoclonal antibodies elicit clinically meaningful responses in a subset of patients, the overall clinical benefit remains very limited [ 11 ] . Therefore, it is very importance to explore more effective and accurate biomarkers, and it is equally vital to improve immunotherapy through perceiving the tumor microenvironment. Rho GTPases are small GTPases belonging to the Ras superfamily [ 12 ] . Highly expressed Rho GTPases members, like RhoA, Rac1, and CDC42, regulates the actin cytoskeleton, cell migration, and cell differentiation [ 13 ] . And other less-expressed Rho GTPases are prospected to be involved in supporting other important processes such as cytoskeletal regulation and cell migration [ 14 , 15 ] . And there are also specific members of Rho GTPases which could regulate various cellular functions such as filopodia formation, vesicular transport, and cytoplasmic division, by switching between inactive GDP-bound and active GTP-bound states [ 16 – 19 ] . In tumor immunity, Rho GTPases signaling are important for immune cells. Tumor dissemination and immune evasion are the two major processes that could be control by Rho GTPases signaling [ 20 , 25 ] . However, previous studies indicate that Rho GTPases have mutations and expression alterations determined by the type of the cancer, typically among gastric cancer (GC), colorectal cancer (CRC) and hepatocellular carcinoma (HCC) [ 20 – 24 ] . Among all types of cancer, the expression of Rho GTPases and their related proteins in ESCC remains totally unclear, and their potential immunotherapeutic targets can be further analyzed. In this research, we started by collecting ESCC patients’ data from the Cancer Genome Atlas (TCGA) database and the GSE53625 dataset from the Gene Expression Omnibus (GEO) database. Through univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis, to establish a prognostic model for ESCC patients based on Rho GTPases related genes (RGRGs). ESCC patients were divided into high-risk and low-risk groups based on their respective risk scores. Overall survival (OS) of ESCC patients in the low-risk group was significantly higher than that in the high-risk group, both in training and in external validation sets. In addition, the tumor immune microenvironment of two groups was also investigated. Finally, we focus on CTTN, the prognostic gene with high relevance, and performed subsequent cell functional experiments on this protein. Methods Datasets and acquisition Fragments per kilobase million (FPKM) normalized expression profile data and corresponding clinical information of 83 ESCC samples were derived from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/ ). Data were extracted from TCGA-ESCA and samples of Squamous Cell Carcinoma types were retained in clinical information. Additionally, GSE53625 which contained the microarray-based of ESCC patients and corresponding clinical information were downloaded from gene expression omnibus (GEO) database. Patients with no follow-up duration and recorded date of death were excluded, and all data were converted by log2 for the following analysis. Moreover, a total of 289 Rho GTPases related genes (RGRGs) were collected from the Genecards database ( https://www.genecards.org/ ). Identification of RGRGs between ESCC and normal tissues Intersected with 289 RGRGs, 107 RGRGs were identified as DEGs by R package “limma” with a threshold of |log2FC| >1 and false discovery rate (FDR) < 0.05. The upregulating or downregulating situation of 107 RGRGs was shown in heatmap by R package “pheatmap”. Development of a RGRGs related prognostic model Univariate Cox regression analysis was constructed to evaluate the correlation between 107 RGRGs and survival status in the TCGA cohort. Subsequently, 6 RGRGs were clarified for further analysis, with P < 0.05. Based on 6 RGRGs expression levels, Kaplan-Meier survival analysis was performed with P < 0.05 as the threshold. Then, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox regression analysis were performed with R package “glmnet” to screen candidate RGRGs and establish prognostic model related to RGRGs. The penalty parameter (λ) was determined according to the minimum criterion, and 5 RGRGs and their coefficients were finally obtained. The GEO data set GSE53625 was selected as the training set and the TCGA cohort was classified as the validation set. Youden’s index of 5-year receiver operating characteristics (ROC) curve was used as the risk cut-point for the GSE53625 of ESCC patients, and the risk score was the largest. According to the cut-point, the patients in the GSE53625 were divided into low-risk group and high-risk group. In addition, Kaplan-Meier survival analysis and time-dependent ROC analysis were employed utilizing R packages “survival”, “survminer”, and “timeROC”. Kaplan-Meier curves and 1-year, 3-year, and 5-year overall survival (OS) ROC curves were drawn respectively. Then, the risk score of TCGA cohort was calculated according to the same formula as the TCGA cohort. The cut-point in ESCC from TCGA cohort was defined as the Youden’s index of ROC curve for 5-year survival and risk score. Patients were also divided into two groups based on cut-point and Kaplan-Meier survival curves, as well as 1-year, 3-year, and 5-year time-dependent ROC curves were plotted. Establishment of a predictive prognostic nomogram based On RGRGs After ensuring that the risk scores of patients in GEO dataset GSE53625 and other clinical characteristics (including age, gender, grade, stage, T stage, and N stage) were complete, 179 ESCC patients continued to be included in the further analysis. Univariate and multivariate Cox regression analyses were performed to verify whether these factors were associated with the prognosis of ESCC patients. On the basis of independent prognostic factors, the 1-year, 3-year and 5-year survival probabilities were represented by R packages “rms” and “survival”, and the predicted prognostic nomogram was plotted. Calibration curves were used to evaluate the differentiation, calibration and clinical application value of the nomogram. Immune infiltration analysis based on RGRGs To analyze variations of the immune microenvironment in ESCC patients, we performed an immune infiltration analysis of 50 patients in GEO database utilizing R package “CIBERSORT”. To delineate the immune landscape associated with the risk groups, we applied the CIBERSORT algorithm with the LM22 signature matrix to infer the relative proportions of 22 tumor-infiltrating immune cell subsets from bulk gene-expression profiles. The calculation outcomes for seven immune infiltration evaluation algorithms were downloaded from the TIMER2.0 database and applied to all individuals in the TCGA database. In addition, clinical information was extracted from individuals with ESCC. Then, the differences in immune cell infiltration between the two risk groups were investigated, and the heat map was drawn to show immune cells at different levels of infiltration. GO and KEGG enrichment analysis We used the "clusterProfiler" package in R to analyze the biological pathways and functions associated with RhoGTPase-related genes. First, we assessed the biological pathways these genes are involved in using the KEGG database. Next, we conducted a GO enrichment analysis to explore their impact on biological processes, molecular functions, and cellular components in detail. Quantitative real − time polymerase chain reaction (qRT-PCR) qRT-PCR was conducted to assess the CTTN expression levels in tumors and adjacent tissues. The 18-paired ESCC tissues were collected from patients who underwent surgical resection for ESCC at the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China). The total RNAs was extracted using TRIzol Reagent and was reverse-transcribed with ReverTra Ace®qPCR RT Master Mix with gDNA Remover (TOYOBO, Japan). The qPCR reactions were conducted using Hieff® Qpcr SYBR Green Master Mix (Yeasen Biotechnology (Shanghai)) in a 20µl reaction volume. Each reaction contained 10µl of 2×SYBR Green RT-PCR Master Mix, 0.4µl of each 10 µM forward and reverse primer, 1µl of cDNA sample, and nuclease-free water to make up the final volume to 20 µl. The amplification process consisted of an initial denaturation step at 95°C for 5 minutes, followed by 40 cycles of denaturation at 95°C for 10 seconds and annealing at 60°C for 30 seconds. The relative expression of the gene was determined using the 2^-DCt method. The primers, the sequences of which are given in Supplementary Table, were provided by Sangon Biotech Co.,Ltd (Shanghai, China). All data were presented as the means ± SEM of three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001. Microarray immunohistochemistry of ESCC CTTN is detected in tissue microarrays (TMAs), including tumor and normal tissues. The expressions of CTTN in tissues were detected by immunohistochemistry. Initially, TMAs were incubated in xylene and then soaked in distilled water for dewaxing, followed by treatment with citrate buffer (Beijing Zhongshan Jinqiao Biotechnology, China) for antigen recovery. The endogenous peroxidase activity was inhibited by 0.5% hydrogen peroxide solution. Subsequently, they were washed with 0.01M phosphate buffer (PBS, pH 7.4) and then closed with 5% goat serum for 30min. The tissue sections were then incubated in a humidification chamber at 25℃ for 2 hours with the following antibodies: CTTN antibody (sc-25272; Santa Cruz Biotechnology, Dallas, Texas; 1:50 dilution). After washing with PBS, staining is performed using DAB (Dakota, Carpinteria, CA, USA) according to the provided guidelines. The sections were then stained with hematoxylin, hydrated with gradient alcohol, and sealed with a neutral glue. Finally, slide images were obtained for calculating the CTTN scores using the following scoring formula: $$\:{\text{log}}_{2}\left(\frac{IOD}{Arear}\times\:{10}^{4}+1\right)=score$$ Cell lines Two ESCC cell lines, Kyse-150 and TE-1, were purchased from the Chinese Academy of Sciences (Shanghai) Cell Bank. The cells were preserved in Dulbecco Modified Eagle medium (Gibco, Grand Island, NY) supplemented with 10% heat inactivated fetal bovine serum (FBS; Gibco). Construction of plasmid expressing CTTN The CTTN open reading frame DNA fragment with HA tag was inserted into the BamHI and XhoI cleavage sites of pcDNA3.1(+) vector. The constructed plasmids were confirmed by sequencing. The plasmids were then transfected into Kyse-150 and TE-1 ESCC cells using Lipofectamine 2000 according to the manufacturer's protocol. The expression of CTTN in these cells was confirmed by HA-tag and CTTN-specific antibodies. CTTN knockdown by small interfering RNA (siRNA) Cell viability Cell viability was assessed using the cell counting kit-8 (CCK-8) (Dojindo, Kumamoto, Japan). The Kyse-150 cell lines were inoculated into a 96-well plate (5 × 103 cells/well) and transfected with vectors (pcDNA3.1, pcDNA3.1‐CTTN‐HA). After 48 hours, they were treated with CCK-8 reagent at 37°C for 2 hours. The absorbance at 450 nm was measured by enzyme-labeled instrument. All the experiments were conducted in triples. Immunofluorescence Kyse150 cells grown to log phase were enzymatically harvested, counted, and plated onto 12-well plate glass coverslips, then transfected post-adhesion. After 48 hours, media were changed, cells were PBS-washed, and fixed in 4% paraformaldehyde at 37°C for 15 minutes. Three PBS washes, 0.1% Triton X-100 permeabilization, and another trio of washes ensued. A 30-minute 5% goat serum block at 37°C followed. Post-block, primary antibody in 1% goat serum-PBS incubated overnight at 4°C. Next, after three PBS washes, fluorophore-tagged secondary antibody, similarly diluted, incubated for an hour at 37°C in dim light. Further washes led to DAPI staining for 3 minutes at 37°C. Lastly, coverslips were mounted, air-dried, and examined microscopically for immunofluorescence. Transwell migration/invasion assays and Wound healing assays The polycarbonate membranes in Transwell chambers were coated with Matrigel (Corning, NY, USA). We transferred 1 × 10^5 esophageal squamous cell carcinoma (ESCC) cells in serum-free medium to the top chamber and added serum-containing medium to the bottom chamber, incubating at 37°C for 36 hours (KYSE-150) or24 hours (TE-1). After incubation, non-invading cells on the top of the membrane were removed by scrubbing. The migrating or invading cells on the bottom side were fixed with 4% paraformaldehyde (PFA), stained with 0.5% crystal violet, and counted under a microscope (Leica, London, UK) from five random fields per well. For wound healing assays, ESCC cells (5 × 10^5 cells per well) were seeded into 6-well plates. After transfection, a scratch wound was created with a sterile 10 µL pipette tip in the monolayer of ESCC cells. Images of the wound were taken at 0, 12, and 24 hours using a microscope at the same location. Wound healing rates were measured by calculating the mean distance between the edges of the wound. Statistical analysis Statistical analysis and mapping were conducted using R (R&R Studio), GraphPad Prism 7 (GraphPad Software, CA, USA), and SPSS Statistics (version 23.0; IBM SPSS, Chicago, IL). Differences between groups were assessed using analysis of variance and independent-sample t-tests. TCGA, GEO, and immunohistochemical expression data were categorized into high and low groups using X-tile software. The prognostic significance of CTTN was evaluated with Kaplan–Meier plots, and a Cox regression model was employed to analyze independent survival risk factors. Statistical significance was defined as P < 0.05. Results Identification of differentially expressed RGRGs between ESCC and normal tissues To investigate the differentially expressed genes (DEGs) between esophagus squamous cell carcinoma (ESCC) and normal tissues, we compared the gene expression between 14 normal tissues and 83 ESCC from GSE53625 with thresholds of |log2FC| >1 and false discovery rate (FDR) < 0.05. A total of 6,269 DEGs were identified (Fig. 1 A). After intersecting with 289 Rho GTPases related genes (RGRGs) from the GeneCards database, 107 RGRGs among DEGs were remained (Fig. 1 B). And the expression level of these differentially expressed RGRGs are shown in Fig. 1 C. A RGRGs related prognostic model for ESCC To figure out whether 107 RGRGs are concerned with the prognosis of ESCC patients, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox regression analysis were performed. Univariate Cox regression analysis was utilized for preliminary screening of survival related genes. According to P < 0.05, 6 RGRGs (RHOA, MAPK14, PIK3R1, CTTN, RHOV, and ARHGEF37) were selected for further analysis, among which 4 RGRGs were correlated with increased risk (HR 1) (Fig. 2 A). Kaplan-Meier curves of 6 RGRGs were shown in Fig. 2 C, respectively. The results indicated that the patients with high PI3KR1 (P = 0.00018) and CTTN (P < 0.0001) expression had a significantly worse prognosis. Next, LASSO Cox regression analysis was utilized based on the results of univariate Cox regression analysis (Fig. 2 C). According to the optimal λ value, GSE53625 was used as the training set, and a prognostic gene model was established based on 5 RGRGs (RHOV, RHOA, PIK3R1, CTTN, and ARHGEF37, Fig. 2 B). Youden index of 5-year receiver operating characteristics (ROC) curve was used as the risk cut-point for ESCC patients from GSE53625, delimited by the highest risk score. According to the cut-point = 8.07, 179 ESCC patients were divided into high-risk group and low-risk group (high risk: 89 cases, low risk: 90 cases). Patients in the high-risk group had a higher mortality rate than those in the low-risk group (Fig. 2 C). In addition, the Kaplan-Meier survival analysis showed that there were significant differences in survival probability between the high- and low- risk groups, and the survival probability of the high-risk group was significantly lower than that of the low-risk group (P < 0.001, Fig. 3 D). To assess the sensitivity of the prognostic gene model, the time-dependent ROC analysis was adopted. The area under curve (AUC) in ROC was 0.677 for 1-year, 0.695 for 3-year, 0.695 for 5-year (Fig. 2 C). The validity of the prognostic gene model was further verified in the TCGA cohort (Fig. 2 D). Like the GSE53625, these patients in the TCGA cohort were divided into two groups based on cut-point value for risk score (Fig. 2 D). The results of Kaplan-Meier survival analysis were the same as those of the training set, and the survival probability of the high- and low- risk group was significantly different (P = 0.029, Fig. 2 D). The time-dependent ROC analysis results illustrated that the model had a good prognostic effect, with 1-year, 3-year and 5-year AUC of 0.638, 0.682 and 0.770, respectively (Fig. 2 D). A predictive prognostic nomogram based on RGRGs To explore whether risk scores derived from our Rho-family of little G protein related prognostic gene model and other relevant clinical characteristics could be considered as independent prognostic factors, we performed Cox regression analysis. Univariate analysis was constructed to identify factors which might influence the survival probability of ESCC patients from GSE53625. Then, multivariate analysis was performed to control the potential confounders. Multivariate analysis indicated that the risk score was an independent risk factor for survival of ESCC patients (HR = 1.603, 95% CI, 1.294–1.985, P < 0.001, Fig. 3 A-C). The results of clinical characteristic analysis showed that there were significant differences in status (P = 1.8e-05) and N stages (P = 0.04) in ESCC high- and low- risk groups (Fig. 3 C). Based on the relevant prognostic factors, a prognostic nomogram was established to effectively predict the prognosis of ESCC patients (Fig. 4 A). To evaluate the nomogram, the calibration curves were adopted. The calibration curves of the nomogram in 1-year, 3-year, and 5-year indicated a strong consistency between the observed and predicted values (Fig. 4 B-C). Immune infiltration analysis based on RGRGs To explore the immune microenvironment in ESCC patients, we performed a series of immune infiltration analyses. The distribution of specific immune cells in 50 ESCC patients from GEO database was demonstrated in Fig. 5 A. The results indicated that most ESCC patients have T cells regulatory (T-regs), T cells CD4 memory resting, T cells CD8, monocytes, macrophages M2, macrophages M1, and macrophages M0. Besides, the differential box diagram of 22 immune cells in high-risk and low-risk groups from GEO database showed that the abundance of B cells memory, NK cells activated, and Mast cells resting in the high-risk group were higher than that in the low-risk group, while the abundance of NK cells resting and Mast cells activated in the high-risk group were lower than that in the low-risk group (Fig. 5 B). The differences in immune infiltration levels between the two risk groups were further analyzed through seven algorithms to provide insights into immunotherapy in ESCC patients. The results revealed that B cells and NK cells were more abundant in the high-risk group, while neutrophils were more abundant in the low-risk group (Fig. 5 C). Expression association and survival analysis of CTTN The differential expression of CTTN in tumor samples of TCGA-ESCC and GSE53625 was analyzed compared with healthy tissues, CTTN expression levels were found to be significantly increased in tumor samples (p < 0.001, p = 2.3e-12,Fig. 6 A-C). Survival analysis emphasizes that the survival probability was significantly reduced in the group with high expression of CTTN compared with the group with low CTTN expression (Fig. 6 D, p < 0.0001), indicating that CTTN is a risk factor. Validation of CTTN at gene and protein levels To verify the consistency of CTTN gene and protein expression levels in ESCC, we designed a series of experiments. Firstly, we verified the specificity of the CTTN antibody(Fig. 7 ). And then, we detected CTTN expression in ESCC by Quantitative real-time polymerase chain reaction (qRT-PCR) and Immunohistochemistry (IHC). qRT-PCR was performed on 18 pairs of tumors and adjacent tissues, indicating that the mRNA expression of CTTN was significantly increased in tumors (p < 0.001,Fig. 8 A).Through the Human Protein Atlas (HPA) database, we found that the staining intensity of CTTN in ESCC was greater than that in normal tissues, indicating that the expression of CTTN in ESCC was higher than that in normal tissues at the protein level. In addition, IHC was performed on the tissue microarray (TMA) cohort of 60 pairs of ESCC patients obtained (Fig. 8 B). Statistically, the score was 1 in most of the normal tissues and 4 in most of the ESCC samples, also suggesting that the expression of CTTN in ESCC was significantly higher than that in normal tissues (Fig. 8 C). CTTN tissue microarray analysis In a cohort of 60 ESCC patients with TMAs, we classified patients with a score of 4 as a group with high CTTN expression, and those with a score of 1–3 as a group with low CTTN expression. According to the clinical information of the patients, Kaplan-Meier analysis was conducted, and it was found that the overall survival (OS) and disease-free survival (DFS) of the patients with high CTTN expression were significantly lower than that in low CTTN expression groups (p = 0.00016, p = 3e-04, Fig. 9 A-B). In clinical characteristic correlation analysis, the expression levels of CTTN in Stage3 patients were significantly higher than that in Stage1 and Stage2 patients (p = 0.038, p = 0.02). Based on N stage, CTTN expression levels in N1 and N4 patients were significantly higher than those in N0 patients (p = 0.014, p = 0.026). After that, patients with Stage1 and Stage2-4 were divided into two groups to analyze CTTN expressions. The results showed that patients with advanced ESCC with stage2-4 had higher CTTN expressions. Meanwhile, patients with N0 and N1-3 were also divided into two different groups. The results indicated that the CTTN expression levels were significantly increased in patients with ESCC after lymph node metastasis (p = 0.00056, 9C-F). These results suggested that CTTN might be related to the metastasis of ESCC cells. Modulation of CTTN expression alters cell migration and invasion To investigate how CTTN affects the function of ESCC cells in vitro, we overexpressed CTTN in Kyse-150 and TE-1 ESCC cells by transfecting of pcDNA3.1-CTTN, respectively (Fig. 10 A-D). Meanwhile, we performed immunofluorescence analysis on esophageal squamous cell carcinoma cells with CTTN overexpression and control group cells, using phalloidin to label the F-actin cytoskeleton. The study revealed that cells overexpressing CTTN showed significant morphological changes in the cytoskeleton (Fig. 10 E-F). This finding suggests that CTTN overexpression may stimulate cell migration and invasion by reshaping the cytoskeleton. The results from Transwell assays showed that overexpression of CTTN significantly improved the migration and invasion ability of cells (Fig. 11 A-B). In contrast, CTTN was depleted from Kyse-150 cells by siRNA, suggesting that CTTN knockdown significantly decreased cell migration and invasion (Fig. 11 C). In addition, similar results of stable overexpression of CTTN induced by DOX in Kyse-150-CTTN cells were also observed (Fig. 11 D). Cell proliferation and apoptosis analysis indicated that CTTN overexpression did not affect the viability of Kyse-150 ESCC cells (Fig. 11 E-F). Discussion Esophageal cancer (ESCA) is one of the most common malignant tumors, and esophageal squamous cell carcinoma (ESCC) is the main subtype of ESCA [ 1 ] . Different cancer subtypes have specific prognostic characteristics and patterns, with significant differences in tumor progression, tumor microenvironment, and clinical treatment [ 26 , 27 ] . Therefore, when we concentrated on ESCC to obtain a more accurate understanding to aid clinical treatment, we first collected gene expression data and clinical information for ESCC patients from TCGA and GEO databases. And after a total of 107 DEGs were identified, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were conducted, we conducted a high-accuracy prognostic prediction model including 5 Rho GTPases related genes (RGRGs). Clinical characteristics and risk factors were synthesized by the nomogram, and the accuracy of the prognosis nomogram was confirmed by calibration curves. And by the meantime, these results also strongly revealed the important role of RGRGs in ESCC. Before our study, there were few focuses on the construction of cancer prognosis prediction model based on Rho GTPases. It has been reported that the expression of Rho family GTPase 1 (RND1) was down-regulated in glioblastoma (GBM), and it was found that the over expression of RND1 promoted the activity of p53-SLC7A11 signaling pathway, thereby inducing lipid peroxidation and ferroptosis in GBM [ 28 ] . The role of Rho GTPases in ESCA has not been elucidated, only NF-ĸB has been investigated to regulate the invasion process of ESCC cells, and RHOA and ROCK have been identified as upstream regulators of NF-ĸB in the process [ 29 , 30 ] . Our study explored the specific role of RGRGs in ESCC for the first time, built a prognostic prediction model based on RGRGs. And furthermore, we explored the tumor immune microenvironment and performed cell experiments on key genes in the prognostic prediction model to prove their specific biological functions. To deepen the differentiation between the high-risk and low-risk groups of ESCC patients in the GEO cohort and explore the variations in the immune microenvironment, we conducted a series of prognostic and immune infiltration analyses. And the results showed that the survival probability of the low-risk group was much higher than that of the high-risk group, suggesting that RGRGs in the prognostic prediction model may become prognostic risk factors. In addition, there were significant differences in the degree of immune cell infiltration in the high-risk and low- risk groups in the GSE53625 dataset. Interestingly, we found that the abundance of NK cells activated, and Mast cells resting in the high-risk group were higher than that in the low-risk group, while the abundance of NK cells resting and Mast cells activated in the high-risk group were lower than that in the low-risk group. Studies have indicated that high infiltration of Mast cells was associated with the progression of ESCC and reduced postoperative survival, and that high concentrations of Tregs in ESCC could lead to immune escape and promote tumor progression [ 31 , 32 ] . This was consistent with our findings. Furthermore, investigated the immune microenvironment of ESCC patients through single-cell analysis and validated the expression levels of prognostic predictive model genes. The results were consistent with the a forementioned immune infiltration analysis, in which T cell types illustrated that PD-1/PD-L1 might play a role in ESCC, and some research also showed that Rho GTPases were regulatory factors in the regulation of PD-1/PD-L1 expression in breast cancer, thereby controlling tumor development [ 33 – 36 ] . TGFβ was closely associated with Rho GTPases and was also an important regulator of NK cells, macrophages and T cells, which might be related to the high expression and potential function of these immune cells [ 37 – 41 ] . To further determine the biological role of key genes in the prognostic prediction model, we conducted cell functional experiments. Based on the results of Kaplan-Meier survival and single-cell analyses, CTTN was selected as a possible key gene in the model, for subsequent experiments. Actin-binding and nuclear factor-associated protein cortactin (CTTN) is encoded by the CTTN gene (synonym: EMS1) on chromosome 11q13 [ 42 ] . It was originally identified in 1991 as a substrate for the carcinogenic tyrosine protein kinase v-src [ 43 ] . Since CTTN is the direct binding protein of the actin related protein complex 2/3 (Arp2/3), it can initiate the aggregation of actin nuclei and further regulate the actin cytoskeleton [ 44 – 46 ] . Therefore, it plays a role in the formation of foot process and lamellar pseudopodia, pathogen invasion of cells, and tumor invasion and metastasis [ 47 – 49 ] . CTTN has been studied in many tumors, and in head and neck squamous cell carcinomas (HNSCC), it has been reported that overexpression of CTTN significantly increased local recurrence rates and reduced 5-year survival [ 50 , 51 ] . Similarly, overexpression of CTTN in breast cancer has been shown to lead to increased invasion and migration of tumor cells [ 52 ] . The possible mechanisms of CTTN in the process of tumor development include intracellular phosphorylation, cytoskeletal protein changes, the role of adhesion, and the formation of invadopodium [ 42 , 43 ] . Our study is the first to attempt to clarify that CTTN might be a prognostic biomarker for ESCC and explore its mechanism of action on tumorigenesis and metastasis. The results from tissue microarrays (TMAs) showed that CTTN could affect tumor cells metastasis in patients with ESCC, so we conducted follow-up cell experiments. Overexpression of CTTN has been shown to promote the migration and invasion of ESCC cells. Our research further demonstrated that CTTN expression was positively correlated with the IC50 values of oxaliplatin in ESCC cells, suggesting that CTTN may be associated with reduced chemosensitivity. Declarations Fundings No funding Declaration of Interest Statement ： We declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability statement ： Public data (GEO GSE53625, TCGA-ESCA/squamous) were used; study datasets and code are available from the corresponding author on reasonable request. 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13:47:42","extension":"html","order_by":61,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":159735,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/2a20c6ae6c367d8e2b9c4660.html"},{"id":97367370,"identity":"919e49bc-0899-43a2-921b-18e44fdafd4e","added_by":"auto","created_at":"2025-12-03 16:18:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":504310,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVolcano plot of differentially expressed genes in esophageal squamous cell carcinoma and heatmap of Rho GTPases-related genes\u003c/strong\u003e\u003cbr\u003e\n(A-B) Intersection of differentially expressed genes in esophageal squamous cell carcinoma and Rho GTPases-related genes\u003cbr\u003e\n(C) Heatmap showing the top 50 significantly differentially expressed Rho GTPases-related genes in esophageal squamous cell carcinoma.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/cd44e0a865ab51091eb76513.png"},{"id":97258618,"identity":"8c6899a6-48a7-404c-9e3e-f9bca0373183","added_by":"auto","created_at":"2025-12-02 13:47:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":224758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and validation of the RGRGs model\u003c/strong\u003e\u003cbr\u003e\n(A) Univariate COX analysis\u003cbr\u003e\n(B) Lasso regression analysis\u003cbr\u003e\n(C) Risk score distribution and prognostic analysis in the GSE53625 training set\u003cbr\u003e\n(D) Risk score distribution and prognostic analysis in the TCGA-ESCC validation set\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/0f4b2bff1b280b2d666a0117.png"},{"id":97258619,"identity":"fa81b290-dc06-487d-80be-2339c1abe364","added_by":"auto","created_at":"2025-12-02 13:47:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":182196,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical correlation of risk scores\u003c/strong\u003e\u003cbr\u003e\n(A-B) Forest plots of univariate and multivariate COX regression analyses based on clinical information and risk scores\u003cbr\u003e\n(C) Proportional chart of high- and low-risk groups across clinical stages\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/9d762e143f02345639b34cec.png"},{"id":97258681,"identity":"86086098-71e8-4a6a-b3d2-e77dbfa6332d","added_by":"auto","created_at":"2025-12-02 13:47:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":179539,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredictive nomogram analysis related to the RGRGs prognostic model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) A nomogram was constructed combining the RGRGs prognostic model and clinicopathological data to predict 1-year, 3-year, and 5-year overall survival for esophageal squamous cell carcinoma patients\u003c/p\u003e\n\u003cp\u003e(C) To assess the predictive accuracy of this model and other factors (e.g., age, gender,and stage), a time-dependent decision curve analysis (DCA) was performed\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/4da6a832b4b704af70aee546.png"},{"id":97368095,"identity":"fe0d0274-142e-43df-b335-76ae91cae904","added_by":"auto","created_at":"2025-12-03 16:21:33","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":183222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune infiltration analysis of risk scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Immune infiltration analysis using the CIBERSORT algorithm\u003c/p\u003e\n\u003cp\u003e(B) Immune infiltration analysis using the MCP algorithm\u003c/p\u003e\n\u003cp\u003e(C) Immune infiltration analysis using the quantiseq algorithm.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/60d1582886d988b5cd124d87.png"},{"id":97258625,"identity":"31f867a8-3384-492d-b7ae-0083d70b79f7","added_by":"auto","created_at":"2025-12-02 13:47:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":161407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpression level and prognostic analysis of CTTN\u003c/strong\u003e\u003cbr\u003e\n(A-B) In the GSE53625 esophageal squamous cell carcinoma patient dataset, CTTN is highly expressed in esophageal squamous cell carcinoma patients, and patients with higher CTTN expression have a poorer prognosis\u003c/p\u003e\n\u003cp\u003e(C-D) In the TCGA-ESCC esophageal squamous cell carcinoma patient dataset, CTTN is highly expressed in esophageal squamous cell carcinoma patients, and patients with higher CTTN expression have a poorer prognosis\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/da3e058b220331a7a536fe35.png"},{"id":97367165,"identity":"0144baca-1d52-497a-99f6-f62d8560b923","added_by":"auto","created_at":"2025-12-03 16:17:18","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":151071,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of CTTN Antibody Specificity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) After CTTN overexpression in esophageal squamous cell carcinoma cells Kyse150, WB experiments were conducted using Santa Cruz antibody. CTTN showed normal expression in the control group, while the band in the pcDNA3.1-CTTN-HA transfected group became significantly darker.\u003c/p\u003e\n\u003cp\u003e(B) Similarly, after overexpressing CTTN in TE-1 cells, WB experiments with Santa Cruz antibody showed normal CTTN expression in the control group, with a noticeably darker band in the pcDNA3.1-CTTN-HA transfected group.\u003c/p\u003e\n\u003cp\u003e(C) Immunohistochemical staining was performed with 1:300 diluted antibody.\u003c/p\u003e\n\u003cp\u003e(D) In Kyse150 cells transfected with the CTTN overexpression plasmid, immunofluorescence results indicated co-localization of overexpressed CTTN and HA proteins, further proving the excellent specificity of the CTTN antibody.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/a5151215c33b3cccec2b1ae9.png"},{"id":97366894,"identity":"ba016165-7593-4971-bdb4-880404e907af","added_by":"auto","created_at":"2025-12-03 16:12:29","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":231339,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eValidation of CTTN at transcriptional and protein levels\u003c/strong\u003e\u003cbr\u003e\n(A) Expression of CTTN at the transcriptional level in 18 pairs of cancer and adjacent tissues from esophageal squamous cell carcinoma patients\u003cbr\u003e\n(B-C) Scoring and statistical analysis of CTTN expression levels in tissue microarrays from 60 pairs of esophageal squamous cell carcinoma patients\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/b70a2b4354b052fa09808240.png"},{"id":97258685,"identity":"9f08fc43-f236-4c27-8e08-ef6f996f13e7","added_by":"auto","created_at":"2025-12-02 13:47:42","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":123388,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between CTTN expression and clinical staging\u003c/strong\u003e\u003cbr\u003e\n(A) Prognostic analysis of CTTN expression levels and overall survival\u003cbr\u003e\n(B) Prognostic analysis of CTTN expression levels and disease-free survival\u003cbr\u003e\n(C-F) Correlation between CTTN expression levels and Stage as well as N stages.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/b5b87f9daca0b1d3bb4f261c.png"},{"id":97366943,"identity":"0b2b3c6b-2a36-49a9-ab5b-3af045c11b4a","added_by":"auto","created_at":"2025-12-03 16:14:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":281237,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression and knockdown of CTTN in Kyse-150 and TE-1 cells\u003c/strong\u003e\u003cbr\u003e\n(A) Overexpression of CTTN in Kyse-150 cells by transfecting pcDNA3.1-CTTN-HA\u003cbr\u003e\n(B) Overexpression of CTTN in TE-1 cells by transfecting pcDNA3.1-CTTN-HA\u003cbr\u003e\n(C) DOX-induced expression in the constructed Kyse150-Lv-CTTN-HA cells\u003cbr\u003e\n(D) Knockdown of CD2AP expression in Kyse-150 cells using two siRNAs\u003c/p\u003e\n\u003cp\u003e(E)CTTN overexpression alters the cytoskeletal morphology of Kyse150 cells\u003c/p\u003e\n\u003cp\u003e(F) CTTN overexpression alters the cytoskeletal morphology of TE-1 cells\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/4591ba0b800d8890eba9bcec.png"},{"id":97258633,"identity":"2a0f44a8-eba7-4739-b0a9-9a0270f8556e","added_by":"auto","created_at":"2025-12-02 13:47:40","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":352558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverexpression of CTTN promotes the migration and invasion of ESCC cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Overexpression of CTTN by plasmid transfection in Kyse150 cells significantly promoted cell migration and invasion\u003c/p\u003e\n\u003cp\u003e(B) Overexpression of CTTN by plasmid transfection in TE-1 cells significantly promoted cell migration and invasion\u003c/p\u003e\n\u003cp\u003e(C) DOX-induced expression in Kyse150-Lv-CTTN-HA cells significantly promoted cell migration and invasion\u003c/p\u003e\n\u003cp\u003e(D) Knockdown of CTTN expression by siRNA transfection in Kyse150 cells significantly inhibited cell migration and invasion\u003c/p\u003e\n\u003cp\u003e(E) Overexpression of CTTN in Kyse-150 cells; cell proliferation was assessed by CCK8 assay, showing that altering CTTN expression does not affect cell proliferation\u003cbr\u003e\n(F) Overexpression of CTTN in TE-1 cells; cell proliferation was assessed by CCK8 assay, showing that altering CTTN expression does not affect cell proliferation\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/cca06c122fe637533f36fac3.png"},{"id":99321026,"identity":"cf9a7786-219f-4d89-a48d-cae63482f4f8","added_by":"auto","created_at":"2025-12-31 16:39:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3762029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8143466/v1/a670f7ca-566e-40af-8275-30877c68af7d.pdf"}],"financialInterests":"","formattedTitle":"Rho GTPase–related prognostic signature for esophageal squamous cell carcinoma identifies and validates CTTN as an independent adverse prognostic factor","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eEsophageal carcinoma (ESCA) is one of the world\u0026rsquo;s deadliest and most malignant cancers, ranking seventh by incidence and sixth by mortality \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. By 2040, the global ESCA burden is estimated to rise to approximately 957,000 cases, with deaths exceeding 880,000 \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Particularly, in East Asia\u0026mdash;especially in Japan\u0026mdash;esophageal cancers are predominantly squamous. Nationwide registry data indicate that esophageal squamous cell carcinoma (ESCC) accounts for approximately 86\u0026ndash;90% of esophageal cancers in Japan, with similarly high proportions reported in China. \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Most patients with ESCC are not diagnosed at the very earliest stage, contributing to poor outcomes \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In Japan\u0026rsquo;s nationwide registry, only 39.2% are stage I at diagnosis (IA 33.4%, IB 5.8%), indicating approximately 60.8% are diagnosed beyond early stage \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. And in developing countries, where most of the world\u0026rsquo;s ESCC occurs, the 5-year survival rates of patients are less than 5% \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Early cancer screening and endoscopic treatment could improve the 5-year survival rates of ESCC patients \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eMolecular targeted therapy has been recognized as the future therapeutic strategy for cancer treatments \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. In ESCC, interrogation of the tumor microenvironment is ongoing; although anti\u0026ndash;PD-1/PD-L1 monoclonal antibodies elicit clinically meaningful responses in a subset of patients, the overall clinical benefit remains very limited \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Therefore, it is very importance to explore more effective and accurate biomarkers, and it is equally vital to improve immunotherapy through perceiving the tumor microenvironment.\u003c/p\u003e\u003cp\u003eRho GTPases are small GTPases belonging to the Ras superfamily \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Highly expressed Rho GTPases members, like RhoA, Rac1, and CDC42, regulates the actin cytoskeleton, cell migration, and cell differentiation \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. And other less-expressed Rho GTPases are prospected to be involved in supporting other important processes such as cytoskeletal regulation and cell migration \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. And there are also specific members of Rho GTPases which could regulate various cellular functions such as filopodia formation, vesicular transport, and cytoplasmic division, by switching between inactive GDP-bound and active GTP-bound states \u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn tumor immunity, Rho GTPases signaling are important for immune cells. Tumor dissemination and immune evasion are the two major processes that could be control by Rho GTPases signaling \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. However, previous studies indicate that Rho GTPases have mutations and expression alterations determined by the type of the cancer, typically among gastric cancer (GC), colorectal cancer (CRC) and hepatocellular carcinoma (HCC) \u003csup\u003e[\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Among all types of cancer, the expression of Rho GTPases and their related proteins in ESCC remains totally unclear, and their potential immunotherapeutic targets can be further analyzed.\u003c/p\u003e\u003cp\u003eIn this research, we started by collecting ESCC patients\u0026rsquo; data from the Cancer Genome Atlas (TCGA) database and the GSE53625 dataset from the Gene Expression Omnibus (GEO) database. Through univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis, to establish a prognostic model for ESCC patients based on Rho GTPases related genes (RGRGs). ESCC patients were divided into high-risk and low-risk groups based on their respective risk scores. Overall survival (OS) of ESCC patients in the low-risk group was significantly higher than that in the high-risk group, both in training and in external validation sets. In addition, the tumor immune microenvironment of two groups was also investigated. Finally, we focus on CTTN, the prognostic gene with high relevance, and performed subsequent cell functional experiments on this protein.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eDatasets and acquisition\u003c/h2\u003e\u003cp\u003eFragments per kilobase million (FPKM) normalized expression profile data and corresponding clinical information of 83 ESCC samples were derived from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data were extracted from TCGA-ESCA and samples of Squamous Cell Carcinoma types were retained in clinical information. Additionally, GSE53625 which contained the microarray-based of ESCC patients and corresponding clinical information were downloaded from gene expression omnibus (GEO) database. Patients with no follow-up duration and recorded date of death were excluded, and all data were converted by log2 for the following analysis. Moreover, a total of 289 Rho GTPases related genes (RGRGs) were collected from the Genecards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003cspan address=\"https://www.genecards.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eIdentification of RGRGs between ESCC and normal tissues\u003c/h3\u003e\n\u003cp\u003eIntersected with 289 RGRGs, 107 RGRGs were identified as DEGs by R package \u0026ldquo;limma\u0026rdquo; with a threshold of |log2FC| \u0026gt;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The upregulating or downregulating situation of 107 RGRGs was shown in heatmap by R package \u0026ldquo;pheatmap\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eDevelopment of a RGRGs related prognostic model\u003c/h3\u003e\n\u003cp\u003eUnivariate Cox regression analysis was constructed to evaluate the correlation between 107 RGRGs and survival status in the TCGA cohort. Subsequently, 6 RGRGs were clarified for further analysis, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Based on 6 RGRGs expression levels, Kaplan-Meier survival analysis was performed with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the threshold. Then, least absolute shrinkage and selection operator (LASSO) Cox regression analysis and multivariate Cox regression analysis were performed with R package \u0026ldquo;glmnet\u0026rdquo; to screen candidate RGRGs and establish prognostic model related to RGRGs. The penalty parameter (λ) was determined according to the minimum criterion, and 5 RGRGs and their coefficients were finally obtained. The GEO data set GSE53625 was selected as the training set and the TCGA cohort was classified as the validation set. Youden\u0026rsquo;s index of 5-year receiver operating characteristics (ROC) curve was used as the risk cut-point for the GSE53625 of ESCC patients, and the risk score was the largest. According to the cut-point, the patients in the GSE53625 were divided into low-risk group and high-risk group. In addition, Kaplan-Meier survival analysis and time-dependent ROC analysis were employed utilizing R packages \u0026ldquo;survival\u0026rdquo;, \u0026ldquo;survminer\u0026rdquo;, and \u0026ldquo;timeROC\u0026rdquo;. Kaplan-Meier curves and 1-year, 3-year, and 5-year overall survival (OS) ROC curves were drawn respectively. Then, the risk score of TCGA cohort was calculated according to the same formula as the TCGA cohort. The cut-point in ESCC from TCGA cohort was defined as the Youden\u0026rsquo;s index of ROC curve for 5-year survival and risk score. Patients were also divided into two groups based on cut-point and Kaplan-Meier survival curves, as well as 1-year, 3-year, and 5-year time-dependent ROC curves were plotted.\u003c/p\u003e\n\u003ch3\u003eEstablishment of a predictive prognostic nomogram based On RGRGs\u003c/h3\u003e\n\u003cp\u003eAfter ensuring that the risk scores of patients in GEO dataset GSE53625 and other clinical characteristics (including age, gender, grade, stage, T stage, and N stage) were complete, 179 ESCC patients continued to be included in the further analysis. Univariate and multivariate Cox regression analyses were performed to verify whether these factors were associated with the prognosis of ESCC patients. On the basis of independent prognostic factors, the 1-year, 3-year and 5-year survival probabilities were represented by R packages \u0026ldquo;rms\u0026rdquo; and \u0026ldquo;survival\u0026rdquo;, and the predicted prognostic nomogram was plotted. Calibration curves were used to evaluate the differentiation, calibration and clinical application value of the nomogram.\u003c/p\u003e\n\u003ch3\u003eImmune infiltration analysis based on RGRGs\u003c/h3\u003e\n\u003cp\u003eTo analyze variations of the immune microenvironment in ESCC patients, we performed an immune infiltration analysis of 50 patients in GEO database utilizing R package \u0026ldquo;CIBERSORT\u0026rdquo;. To delineate the immune landscape associated with the risk groups, we applied the CIBERSORT algorithm with the LM22 signature matrix to infer the relative proportions of 22 tumor-infiltrating immune cell subsets from bulk gene-expression profiles. The calculation outcomes for seven immune infiltration evaluation algorithms were downloaded from the TIMER2.0 database and applied to all individuals in the TCGA database. In addition, clinical information was extracted from individuals with ESCC. Then, the differences in immune cell infiltration between the two risk groups were investigated, and the heat map was drawn to show immune cells at different levels of infiltration.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eGO and KEGG enrichment analysis\u003c/h2\u003e\u003cp\u003eWe used the \"clusterProfiler\" package in R to analyze the biological pathways and functions associated with RhoGTPase-related genes. First, we assessed the biological pathways these genes are involved in using the KEGG database. Next, we conducted a GO enrichment analysis to explore their impact on biological processes, molecular functions, and cellular components in detail.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eQuantitative real − time polymerase chain reaction (qRT-PCR)\u003c/h3\u003e\n\u003cp\u003eqRT-PCR was conducted to assess the CTTN expression levels in tumors and adjacent tissues. The 18-paired ESCC tissues were collected from patients who underwent surgical resection for ESCC at the First Affiliated Hospital of Wenzhou Medical University (Wenzhou, China). The total RNAs was extracted using TRIzol Reagent and was reverse-transcribed with ReverTra Ace\u0026reg;qPCR RT Master Mix with gDNA Remover (TOYOBO, Japan). The qPCR reactions were conducted using Hieff\u0026reg; Qpcr SYBR Green Master Mix (Yeasen Biotechnology (Shanghai)) in a 20\u0026micro;l reaction volume. Each reaction contained 10\u0026micro;l of 2\u0026times;SYBR Green RT-PCR Master Mix, 0.4\u0026micro;l of each 10 \u0026micro;M forward and reverse primer, 1\u0026micro;l of cDNA sample, and nuclease-free water to make up the final volume to 20 \u0026micro;l. The amplification process consisted of an initial denaturation step at 95\u0026deg;C for 5 minutes, followed by 40 cycles of denaturation at 95\u0026deg;C for 10 seconds and annealing at 60\u0026deg;C for 30 seconds. The relative expression of the gene was determined using the 2^-DCt method. The primers, the sequences of which are given in Supplementary Table, were provided by Sangon Biotech Co.,Ltd (Shanghai, China). All data were presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM of three independent experiments. *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\n\u003ch3\u003eMicroarray immunohistochemistry of ESCC\u003c/h3\u003e\n\u003cp\u003eCTTN is detected in tissue microarrays (TMAs), including tumor and normal tissues. The expressions of CTTN in tissues were detected by immunohistochemistry. Initially, TMAs were incubated in xylene and then soaked in distilled water for dewaxing, followed by treatment with citrate buffer (Beijing Zhongshan Jinqiao Biotechnology, China) for antigen recovery. The endogenous peroxidase activity was inhibited by 0.5% hydrogen peroxide solution. Subsequently, they were washed with 0.01M phosphate buffer (PBS, pH 7.4) and then closed with 5% goat serum for 30min. The tissue sections were then incubated in a humidification chamber at 25℃ for 2 hours with the following antibodies: CTTN antibody (sc-25272; Santa Cruz Biotechnology, Dallas, Texas; 1:50 dilution). After washing with PBS, staining is performed using DAB (Dakota, Carpinteria, CA, USA) according to the provided guidelines. The sections were then stained with hematoxylin, hydrated with gradient alcohol, and sealed with a neutral glue. Finally, slide images were obtained for calculating the CTTN scores using the following scoring formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\text{log}}_{2}\\left(\\frac{IOD}{Arear}\\times\\:{10}^{4}+1\\right)=score$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCell lines\u003c/h2\u003e\u003cp\u003eTwo ESCC cell lines, Kyse-150 and TE-1, were purchased from the Chinese Academy of Sciences (Shanghai) Cell Bank. The cells were preserved in Dulbecco Modified Eagle medium (Gibco, Grand Island, NY) supplemented with 10% heat inactivated fetal bovine serum (FBS; Gibco).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eConstruction of plasmid expressing CTTN\u003c/h2\u003e\u003cp\u003eThe CTTN open reading frame DNA fragment with HA tag was inserted into the BamHI and XhoI cleavage sites of pcDNA3.1(+) vector. The constructed plasmids were confirmed by sequencing. The plasmids were then transfected into Kyse-150 and TE-1 ESCC cells using Lipofectamine 2000 according to the manufacturer's protocol. The expression of CTTN in these cells was confirmed by HA-tag and CTTN-specific antibodies. CTTN knockdown by small interfering RNA (siRNA)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eCell viability\u003c/h2\u003e\u003cp\u003eCell viability was assessed using the cell counting kit-8 (CCK-8) (Dojindo, Kumamoto, Japan). The Kyse-150 cell lines were inoculated into a 96-well plate (5 \u0026times; 103 cells/well) and transfected with vectors (pcDNA3.1, pcDNA3.1‐CTTN‐HA). After 48 hours, they were treated with CCK-8 reagent at 37\u0026deg;C for 2 hours. The absorbance at 450 nm was measured by enzyme-labeled instrument. All the experiments were conducted in triples.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eImmunofluorescence\u003c/h2\u003e\u003cp\u003eKyse150 cells grown to log phase were enzymatically harvested, counted, and plated onto 12-well plate glass coverslips, then transfected post-adhesion. After 48 hours, media were changed, cells were PBS-washed, and fixed in 4% paraformaldehyde at 37\u0026deg;C for 15 minutes. Three PBS washes, 0.1% Triton X-100 permeabilization, and another trio of washes ensued. A 30-minute 5% goat serum block at 37\u0026deg;C followed. Post-block, primary antibody in 1% goat serum-PBS incubated overnight at 4\u0026deg;C. Next, after three PBS washes, fluorophore-tagged secondary antibody, similarly diluted, incubated for an hour at 37\u0026deg;C in dim light. Further washes led to DAPI staining for 3 minutes at 37\u0026deg;C. Lastly, coverslips were mounted, air-dried, and examined microscopically for immunofluorescence.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eTranswell migration/invasion assays and Wound healing assays\u003c/h2\u003e\u003cp\u003eThe polycarbonate membranes in Transwell chambers were coated with Matrigel (Corning, NY, USA). We transferred 1 \u0026times; 10^5 esophageal squamous cell carcinoma (ESCC) cells in serum-free medium to the top chamber and added serum-containing medium to the bottom chamber, incubating at 37\u0026deg;C for 36 hours (KYSE-150) or24 hours (TE-1). After incubation, non-invading cells on the top of the membrane were removed by scrubbing. The migrating or invading cells on the bottom side were fixed with 4% paraformaldehyde (PFA), stained with 0.5% crystal violet, and counted under a microscope (Leica, London, UK) from five random fields per well. For wound healing assays, ESCC cells (5 \u0026times; 10^5 cells per well) were seeded into 6-well plates. After transfection, a scratch wound was created with a sterile 10 \u0026micro;L pipette tip in the monolayer of ESCC cells. Images of the wound were taken at 0, 12, and 24 hours using a microscope at the same location. Wound healing rates were measured by calculating the mean distance between the edges of the wound.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis and mapping were conducted using R (R\u0026amp;R Studio), GraphPad Prism 7 (GraphPad Software, CA, USA), and SPSS Statistics (version 23.0; IBM SPSS, Chicago, IL). Differences between groups were assessed using analysis of variance and independent-sample t-tests. TCGA, GEO, and immunohistochemical expression data were categorized into high and low groups using X-tile software. The prognostic significance of CTTN was evaluated with Kaplan\u0026ndash;Meier plots, and a Cox regression model was employed to analyze independent survival risk factors. Statistical significance was defined as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eIdentification of differentially expressed RGRGs between ESCC and normal tissues\u003c/h2\u003e\u003cp\u003eTo investigate the differentially expressed genes (DEGs) between esophagus squamous cell carcinoma (ESCC) and normal tissues, we compared the gene expression between 14 normal tissues and 83 ESCC from GSE53625 with thresholds of |log2FC| \u0026gt;1 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 6,269 DEGs were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). After intersecting with 289 Rho GTPases related genes (RGRGs) from the GeneCards database, 107 RGRGs among DEGs were remained (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). And the expression level of these differentially expressed RGRGs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eA RGRGs related prognostic model for ESCC\u003c/h2\u003e\u003cp\u003eTo figure out whether 107 RGRGs are concerned with the prognosis of ESCC patients, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) Cox regression analysis, and multivariate Cox regression analysis were performed. Univariate Cox regression analysis was utilized for preliminary screening of survival related genes. According to P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, 6 RGRGs (RHOA, MAPK14, PIK3R1, CTTN, RHOV, and ARHGEF37) were selected for further analysis, among which 4 RGRGs were correlated with increased risk (HR\u0026thinsp;\u0026lt;\u0026thinsp;1), 2 RGRGs were correlated with decreased risk (HR\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Kaplan-Meier curves of 6 RGRGs were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, respectively. The results indicated that the patients with high PI3KR1 (P\u0026thinsp;=\u0026thinsp;0.00018) and CTTN (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) expression had a significantly worse prognosis. Next, LASSO Cox regression analysis was utilized based on the results of univariate Cox regression analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). According to the optimal λ value, GSE53625 was used as the training set, and a prognostic gene model was established based on 5 RGRGs (RHOV, RHOA, PIK3R1, CTTN, and ARHGEF37, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Youden index of 5-year receiver operating characteristics (ROC) curve was used as the risk cut-point for ESCC patients from GSE53625, delimited by the highest risk score. According to the cut-point\u0026thinsp;=\u0026thinsp;8.07, 179 ESCC patients were divided into high-risk group and low-risk group (high risk: 89 cases, low risk: 90 cases). Patients in the high-risk group had a higher mortality rate than those in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). In addition, the Kaplan-Meier survival analysis showed that there were significant differences in survival probability between the high- and low- risk groups, and the survival probability of the high-risk group was significantly lower than that of the low-risk group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). To assess the sensitivity of the prognostic gene model, the time-dependent ROC analysis was adopted. The area under curve (AUC) in ROC was 0.677 for 1-year, 0.695 for 3-year, 0.695 for 5-year (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The validity of the prognostic gene model was further verified in the TCGA cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Like the GSE53625, these patients in the TCGA cohort were divided into two groups based on cut-point value for risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The results of Kaplan-Meier survival analysis were the same as those of the training set, and the survival probability of the high- and low- risk group was significantly different (P\u0026thinsp;=\u0026thinsp;0.029, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The time-dependent ROC analysis results illustrated that the model had a good prognostic effect, with 1-year, 3-year and 5-year AUC of 0.638, 0.682 and 0.770, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eA predictive prognostic nomogram based on RGRGs\u003c/h2\u003e\u003cp\u003eTo explore whether risk scores derived from our Rho-family of little G protein related prognostic gene model and other relevant clinical characteristics could be considered as independent prognostic factors, we performed Cox regression analysis. Univariate analysis was constructed to identify factors which might influence the survival probability of ESCC patients from GSE53625. Then, multivariate analysis was performed to control the potential confounders. Multivariate analysis indicated that the risk score was an independent risk factor for survival of ESCC patients (HR\u0026thinsp;=\u0026thinsp;1.603, 95% CI, 1.294\u0026ndash;1.985, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). The results of clinical characteristic analysis showed that there were significant differences in status (P\u0026thinsp;=\u0026thinsp;1.8e-05) and N stages (P\u0026thinsp;=\u0026thinsp;0.04) in ESCC high- and low- risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Based on the relevant prognostic factors, a prognostic nomogram was established to effectively predict the prognosis of ESCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). To evaluate the nomogram, the calibration curves were adopted. The calibration curves of the nomogram in 1-year, 3-year, and 5-year indicated a strong consistency between the observed and predicted values (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eImmune infiltration analysis based on RGRGs\u003c/h2\u003e\u003cp\u003eTo explore the immune microenvironment in ESCC patients, we performed a series of immune infiltration analyses. The distribution of specific immune cells in 50 ESCC patients from GEO database was demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA. The results indicated that most ESCC patients have T cells regulatory (T-regs), T cells CD4 memory resting, T cells CD8, monocytes, macrophages M2, macrophages M1, and macrophages M0. Besides, the differential box diagram of 22 immune cells in high-risk and low-risk groups from GEO database showed that the abundance of B cells memory, NK cells activated, and Mast cells resting in the high-risk group were higher than that in the low-risk group, while the abundance of NK cells resting and Mast cells activated in the high-risk group were lower than that in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). The differences in immune infiltration levels between the two risk groups were further analyzed through seven algorithms to provide insights into immunotherapy in ESCC patients. The results revealed that B cells and NK cells were more abundant in the high-risk group, while neutrophils were more abundant in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eExpression association and survival analysis of CTTN\u003c/h2\u003e\u003cp\u003eThe differential expression of CTTN in tumor samples of TCGA-ESCC and GSE53625 was analyzed compared with healthy tissues, CTTN expression levels were found to be significantly increased in tumor samples (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, p\u0026thinsp;=\u0026thinsp;2.3e-12,Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-C). Survival analysis emphasizes that the survival probability was significantly reduced in the group with high expression of CTTN compared with the group with low CTTN expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), indicating that CTTN is a risk factor.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eValidation of CTTN at gene and protein levels\u003c/h2\u003e\u003cp\u003eTo verify the consistency of CTTN gene and protein expression levels in ESCC, we designed a series of experiments. Firstly, we verified the specificity of the CTTN antibody(Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). And then, we detected CTTN expression in ESCC by Quantitative real-time polymerase chain reaction (qRT-PCR) and Immunohistochemistry (IHC). qRT-PCR was performed on 18 pairs of tumors and adjacent tissues, indicating that the mRNA expression of CTTN was significantly increased in tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001,Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA).Through the Human Protein Atlas (HPA) database, we found that the staining intensity of CTTN in ESCC was greater than that in normal tissues, indicating that the expression of CTTN in ESCC was higher than that in normal tissues at the protein level. In addition, IHC was performed on the tissue microarray (TMA) cohort of 60 pairs of ESCC patients obtained (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). Statistically, the score was 1 in most of the normal tissues and 4 in most of the ESCC samples, also suggesting that the expression of CTTN in ESCC was significantly higher than that in normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eCTTN tissue microarray analysis\u003c/h2\u003e\u003cp\u003eIn a cohort of 60 ESCC patients with TMAs, we classified patients with a score of 4 as a group with high CTTN expression, and those with a score of 1\u0026ndash;3 as a group with low CTTN expression. According to the clinical information of the patients, Kaplan-Meier analysis was conducted, and it was found that the overall survival (OS) and disease-free survival (DFS) of the patients with high CTTN expression were significantly lower than that in low CTTN expression groups (p\u0026thinsp;=\u0026thinsp;0.00016, p\u0026thinsp;=\u0026thinsp;3e-04, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA-B). In clinical characteristic correlation analysis, the expression levels of CTTN in Stage3 patients were significantly higher than that in Stage1 and Stage2 patients (p\u0026thinsp;=\u0026thinsp;0.038, p\u0026thinsp;=\u0026thinsp;0.02). Based on N stage, CTTN expression levels in N1 and N4 patients were significantly higher than those in N0 patients (p\u0026thinsp;=\u0026thinsp;0.014, p\u0026thinsp;=\u0026thinsp;0.026). After that, patients with Stage1 and Stage2-4 were divided into two groups to analyze CTTN expressions. The results showed that patients with advanced ESCC with stage2-4 had higher CTTN expressions. Meanwhile, patients with N0 and N1-3 were also divided into two different groups. The results indicated that the CTTN expression levels were significantly increased in patients with ESCC after lymph node metastasis (p\u0026thinsp;=\u0026thinsp;0.00056, 9C-F). These results suggested that CTTN might be related to the metastasis of ESCC cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eModulation of CTTN expression alters cell migration and invasion\u003c/h2\u003e\u003cp\u003eTo investigate how CTTN affects the function of ESCC cells in vitro, we overexpressed CTTN in Kyse-150 and TE-1 ESCC cells by transfecting of pcDNA3.1-CTTN, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA-D). Meanwhile, we performed immunofluorescence analysis on esophageal squamous cell carcinoma cells with CTTN overexpression and control group cells, using phalloidin to label the F-actin cytoskeleton. The study revealed that cells overexpressing CTTN showed significant morphological changes in the cytoskeleton (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE-F). This finding suggests that CTTN overexpression may stimulate cell migration and invasion by reshaping the cytoskeleton. The results from Transwell assays showed that overexpression of CTTN significantly improved the migration and invasion ability of cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eA-B). In contrast, CTTN was depleted from Kyse-150 cells by siRNA, suggesting that CTTN knockdown significantly decreased cell migration and invasion (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eC). In addition, similar results of stable overexpression of CTTN induced by DOX in Kyse-150-CTTN cells were also observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). Cell proliferation and apoptosis analysis indicated that CTTN overexpression did not affect the viability of Kyse-150 ESCC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003eE-F).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eEsophageal cancer (ESCA) is one of the most common malignant tumors, and esophageal squamous cell carcinoma (ESCC) is the main subtype of ESCA \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Different cancer subtypes have specific prognostic characteristics and patterns, with significant differences in tumor progression, tumor microenvironment, and clinical treatment \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Therefore, when we concentrated on ESCC to obtain a more accurate understanding to aid clinical treatment, we first collected gene expression data and clinical information for ESCC patients from TCGA and GEO databases. And after a total of 107 DEGs were identified, and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were conducted, we conducted a high-accuracy prognostic prediction model including 5 Rho GTPases related genes (RGRGs). Clinical characteristics and risk factors were synthesized by the nomogram, and the accuracy of the prognosis nomogram was confirmed by calibration curves. And by the meantime, these results also strongly revealed the important role of RGRGs in ESCC.\u003c/p\u003e\u003cp\u003eBefore our study, there were few focuses on the construction of cancer prognosis prediction model based on Rho GTPases. It has been reported that the expression of Rho family GTPase 1 (RND1) was down-regulated in glioblastoma (GBM), and it was found that the over expression of RND1 promoted the activity of p53-SLC7A11 signaling pathway, thereby inducing lipid peroxidation and ferroptosis in GBM \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The role of Rho GTPases in ESCA has not been elucidated, only NF-ĸB has been investigated to regulate the invasion process of ESCC cells, and RHOA and ROCK have been identified as upstream regulators of NF-ĸB in the process \u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Our study explored the specific role of RGRGs in ESCC for the first time, built a prognostic prediction model based on RGRGs. And furthermore, we explored the tumor immune microenvironment and performed cell experiments on key genes in the prognostic prediction model to prove their specific biological functions.\u003c/p\u003e\u003cp\u003eTo deepen the differentiation between the high-risk and low-risk groups of ESCC patients in the GEO cohort and explore the variations in the immune microenvironment, we conducted a series of prognostic and immune infiltration analyses. And the results showed that the survival probability of the low-risk group was much higher than that of the high-risk group, suggesting that RGRGs in the prognostic prediction model may become prognostic risk factors. In addition, there were significant differences in the degree of immune cell infiltration in the high-risk and low- risk groups in the GSE53625 dataset. Interestingly, we found that the abundance of NK cells activated, and Mast cells resting in the high-risk group were higher than that in the low-risk group, while the abundance of NK cells resting and Mast cells activated in the high-risk group were lower than that in the low-risk group. Studies have indicated that high infiltration of Mast cells was associated with the progression of ESCC and reduced postoperative survival, and that high concentrations of Tregs in ESCC could lead to immune escape and promote tumor progression \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. This was consistent with our findings. Furthermore, investigated the immune microenvironment of ESCC patients through single-cell analysis and validated the expression levels of prognostic predictive model genes. The results were consistent with the a forementioned immune infiltration analysis, in which T cell types illustrated that PD-1/PD-L1 might play a role in ESCC, and some research also showed that Rho GTPases were regulatory factors in the regulation of PD-1/PD-L1 expression in breast cancer, thereby controlling tumor development \u003csup\u003e[\u003cspan additionalcitationids=\"CR34 CR35\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. TGFβ was closely associated with Rho GTPases and was also an important regulator of NK cells, macrophages and T cells, which might be related to the high expression and potential function of these immune cells \u003csup\u003e[\u003cspan additionalcitationids=\"CR38 CR39 CR40\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo further determine the biological role of key genes in the prognostic prediction model, we conducted cell functional experiments. Based on the results of Kaplan-Meier survival and single-cell analyses, CTTN was selected as a possible key gene in the model, for subsequent experiments.\u003c/p\u003e\u003cp\u003eActin-binding and nuclear factor-associated protein cortactin (CTTN) is encoded by the CTTN gene (synonym: EMS1) on chromosome 11q13\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. It was originally identified in 1991 as a substrate for the carcinogenic tyrosine protein kinase v-src\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Since CTTN is the direct binding protein of the actin related protein complex 2/3 (Arp2/3), it can initiate the aggregation of actin nuclei and further regulate the actin cytoskeleton \u003csup\u003e[\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Therefore, it plays a role in the formation of foot process and lamellar pseudopodia, pathogen invasion of cells, and tumor invasion and metastasis \u003csup\u003e[\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. CTTN has been studied in many tumors, and in head and neck squamous cell carcinomas (HNSCC), it has been reported that overexpression of CTTN significantly increased local recurrence rates and reduced 5-year survival \u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Similarly, overexpression of CTTN in breast cancer has been shown to lead to increased invasion and migration of tumor cells \u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. The possible mechanisms of CTTN in the process of tumor development include intracellular phosphorylation, cytoskeletal protein changes, the role of adhesion, and the formation of invadopodium \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Our study is the first to attempt to clarify that CTTN might be a prognostic biomarker for ESCC and explore its mechanism of action on tumorigenesis and metastasis.\u003c/p\u003e\u003cp\u003eThe results from tissue microarrays (TMAs) showed that CTTN could affect tumor cells metastasis in patients with ESCC, so we conducted follow-up cell experiments. Overexpression of CTTN has been shown to promote the migration and invasion of ESCC cells. Our research further demonstrated that CTTN expression was positively correlated with the IC50 values of oxaliplatin in ESCC cells, suggesting that CTTN may be associated with reduced chemosensitivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFundings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest Statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublic data (GEO GSE53625, TCGA-ESCA/squamous) were used; study datasets and code are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University. Written informed consent was obtained from all patients prior to tissue collection and use of clinical data. All procedures involving human participants complied with the Declaration of Helsinki.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. ;71(3):209\u0026ndash;249. doi: 10.3322/caac.21660. Epub 2021 Feb 4. PMID: 33538338\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQiu H, Cao S, Xu R (2021) Cancer incidence, mortality, and burden in China: a time-trend analysis and comparison with the United States and United Kingdom based on the global epidemiological data released in 2020. Cancer Commun (Lond) 41(10):1037\u0026ndash;1048 Epub 2021 Jul 20. 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PMID: 30850562; PMCID: PMC6459259\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Esophageal squamous cell carcinoma, Prognostic model, Rho GTPase, CTTN(Cortactin)","lastPublishedDoi":"10.21203/rs.3.rs-8143466/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8143466/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRho GTPases orchestrate actin dynamics and tumor cell motility, but their prognostic relevance in esophageal squamous cell carcinoma (ESCC) remains unclear. We aimed to identify Rho GTPase\u0026ndash;related differentially expressed genes (DEGs), build a prognostic model, evaluate important gene expression against clinicopathological features, and examine CTTN function in vitro.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eTranscriptomic data from GSE53625 and TCGA-ESCC were used. Rho GTPase\u0026ndash;linked DEGs from tumor\u0026ndash;normal comparisons entered univariate Cox, LASSO, and multivariable Cox analyses to construct a risk-score model; immune infiltration and a nomogram were also evaluated. For CTTN, 60 paired ESCC tissues were examined by Western blot and immunohistochemistry, and 18 additional pairs by qPCR, with correlations to clinicopathological variables and survival. ESCC cell lines were used to test the effects of CTTN expression on migration, invasion, and proliferation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eWe identified 107 Rho GTPase\u0026ndash;related DEGs enriched in actin cytoskeleton\u0026ndash;remodeling pathways. Five genes (RHOV, RHOA, PIK3R1, CTTN, ARHGEF37) formed a strong prognostic signature that separated patients into high- and low-risk groups with clearly different outcomes and maintained independent prognostic value after adjustment for stage and nodal status. High-risk tumors showed a distinct immune landscape with altered B-cell, NK-cell, mast-cell, and neutrophil abundance. CTTN was consistently upregulated versus normal tissue, especially in advanced stage and nodal-metastatic disease, and enhanced ESCC cell migration and invasion.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eWe establish a Rho GTPase\u0026ndash;based prognostic model for ESCC and validate CTTN as an independent adverse prognostic marker that drives migratory and invasive phenotypes, highlighting CTTN as a potential therapeutic target.\u003c/p\u003e","manuscriptTitle":"Rho GTPase–related prognostic signature for esophageal squamous cell carcinoma identifies and validates CTTN as an independent adverse prognostic factor","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-02 13:47:35","doi":"10.21203/rs.3.rs-8143466/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e55e5a49-baeb-4b82-9dc4-a0940e219927","owner":[],"postedDate":"December 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-31T12:49:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-02 13:47:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8143466","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8143466","identity":"rs-8143466","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}