Low expression of ARHGAP40 in colorectal cancer facilitates tumor progression by activating the RhoA pathway

Low expression of ARHGAP40 in colorectal cancer facilitates tumor progression by activating the RhoA pathway | 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 Low expression of ARHGAP40 in colorectal cancer facilitates tumor progression by activating the RhoA pathway Bin Lian, Na You, Jingyu Wang, Cong Wang, Yunjie Wen, Jiandong Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7317386/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 ARHGAP40, downregulated in various tumors including basal cell carcinoma, has an unclear role in colorectal cancer (CRC). This study aimed to elucidate the function and clinical significance of ARHGAP40 in CRC. Methods Immunohistochemistry (IHC) was utilized to assess the quantity of ARHGAP40 protein in both tumor and normal tissues. RNA interference (RNAi) and lentiviral vectors were used to either overexpress ARHGAP40 or knockdown ARHGAP40 in CRC cells. The CCK-8 test was used to measure cell proliferation, and flow cytometry was used to measure cell apoptosis. Scratch and Transwell tests were used to explore how ARHGAP40 affected the migration and invasion of CRC cells. RNA-sequence, western blotting, RhoA pull-down, and coimmunoprecipitation (co-IP) methods were used to figure out how ARHGAP40 affects CRC cells. Results Researchers discovered that the expression of ARHGAP40 was significantly lower in human colorectal cancer tissues. This low level of ARHGAP40 expression was linked to tumor differentiation, invasion depth, lymph node metastasis, TNM stage, and a poor outcome in CRC patients. Overexpression of ARHGAP40 also greatly decreased cell proliferation, made it harder for cells to migrate and invade, and raised the apoptosis rates of CRC cells. Also, RhoA activity was enhanced after ARHGAP40 was knocked down. Conclusion ARHGAP40 levels downregulated in CRC, and it might be possible for it to act as a new tumor suppressor in CRC by controlling RhoA activity. Bin Lian and Na You contributed equally to this work (co–first authors). ARHGAP40 Colorectal cancer RhoA activity Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Highlights 1. ARHGAP40 expression is significantly downregulated in CRC tissues, correlating with poor tumor differentiation, deeper invasion, lymph node metastasis, advanced TNM stage, and unfavorable prognosis. 2. Overexpression of ARHGAP40 suppresses CRC cell proliferation, reduces migration/invasion, and increases apoptosis. 3. ARHGAP40 inhibits tumor progression by regulating RhoA GTPase activity. 4. Proposes ARHGAP40 as a novel tumor suppressor and prognostic biomarker in CRC, suggesting therapeutic targeting of RhoA signaling. Background A large number of people suffer from colorectal cancer (CRC), which influences the digestive system. Based on global cancer statistics from 2022, colorectal cancer has the third highest rate of new cases and the second highest rate of deaths [ 1 ]. Even though advancements in surgery, chemotherapy, and radiation therapy have reduced the rates of recurrence and death, multidrug resistance remains a significant problem for effective disease treatment, and the survival rates of CRC patients have not improved significantly [ 2 – 5 ]. To gain a deeper understanding of the progression of CRC, further research is necessary. This could lead to the identification of new therapeutic targets and diagnostic biomarkers. As Rho homologous GTPase-activating proteins, ARHGAPs are very important for controlling the Rho family of small GTPases and their effects on many biological processes, including cell growth, adhesion, migration, and invasion [ 6 , 7 ]. New studies show that the ARHGAP family may also play a role in the development of several cancers. One study showed that ARHGAP1 controls the epithelial-to-mesenchymal transition (EMT) in breast cancer cells by blocking RhoA signaling [ 8 ]. Genes in the ARHGAP family have also been linked to the development of colorectal cancer in several studies, but the exact role of these genes in colorectal cancer is still not clear. The generation of a distinctive RHOGAP variant with a unique functional domain by ARHGAP8 contributes to the development of CRC [ 9 ]. The expression change of p300 by ARHGAP30 leads to the apoptosis of colorectal cancer cells, which makes the tumor suppressor protein p53 more active and acetylated [ 10 ]. Our most recent study shows that methylation significantly lowers ARHGAP40 levels in basal cell carcinoma (BCC), which suggests that it could be used as a new biomarker to tell the difference between BCC and trichoblastoma [ 11 ]. We still need to acquire more knowledge about the expression of ARHGAP40 in colorectal cancer and its role in clinical scenarios. RhoA is a small GTPase belonging to the Rho family and stands for Ras homologue family member A. RhoA and other GTPases function as molecular switches, changing from an active state bound to GTP to an inactive state governed by GAPs [ 12 ]. The activation of RhoA is a highly important part of the reorganization of the actin cytoskeleton and exerts a considerable influence on the growth and invasion of colorectal cancer cells [ 13 , 14 ]. Further research needs to be conducted to figure out exactly how RhoA leads to the growth of CRC at the molecular level. This work reveals new details about the expression, role, and regulatory mechanism of ARHGAP40 in the progression of CRC. Our findings show that ARHGAP40 expression levels have significantly decreased in colorectal cancer tissue samples and cell lines. Its low level is linked to an advanced TNM stage and can be used as a standalone indicator of poor survival in CRC patients. Using gain- and loss-of-function studies, we also looked at how ARHGAP40 expression affects the function of CRC cells in vitro . our research indicates that reducing ARHGAP40 levels leads to the activation of RhoA in CRC cells. Accordingly, our discoveries could offer valuable perceptions of the pathogenesis of CRC and identify ARHGAP40 as a potential therapeutic target for CRC therapy. Materials and methods Patients and samples This study included 103 patients with CRC. All patients underwent surgical resection and had not received prior chemotherapy or radiotherapy. Clinical and pathological characteristics of the cohort are summarized in Table 1 . Formalin-fixed, paraffin-embedded (FFPE) tissue samples were obtained from the Department of Pathology, Jinling Hospital. The study design and use of archival tissues were approved by the Jinling Hospital Ethics Committee, which waived the requirement for informed consent due to the retrospective nature of the analysis. Table 1 Combined characteristics of all 103 patients in the present study. Characteristic Case (%) Gender Male 63 (61.17%) Female 40 (38.83%) Age, years ≤ 65 54 (52.43%) >65 49 (47.57%) T stage T1 6 (5.83%) T2 17 (16.5%) T3 24 (23.3%) T4 56 (54.37%) Differention Well/moderately differentiated 74 (71.84%) Poorly differentiated 29 (28.16%) Lymph node metastases Yes 47 (45.63%) No 56 (54.37%) TNM Stage I 17 (16.5%) II 30 (29.1%) III 51 (49.5%) IV 5 (4.9%) Immunohistochemical staining Immunohistochemical analysis was performed on slices of tumor tissue. To summarize, tumor samples were dissected, stored in formalin, embedded in paraffin, and sectioned into 3–4 µm slices. To remove the paraffin on the slides, xylene was applied to the sections. The samples were then rehydrated with ethanol added in decreasing concentrations. Subsequently, the antigen was fixed with a repair solution bath in water. After that, PBS (pH 7.4) was used to wash the pieces three times. The slides were first incubated with bovine serum albumin diluted in PBS for 15 minutes at 37°C. Then, the primary antibody (ARHGAP40, NBP1-94112; Novus Biologicals, USA; dilution 1:1000) was added and incubated at 4°C overnight. The slides were washed three times with PBS and then treated with horseradish peroxidase-conjugated secondary antibodies from Zhongshan Gold Bridge in Beijing, China, for an hour at room temperature. Three more washes in PBS were given to all the samples, and then they were treated with 3,3'-diaminobenzidine (DAB). Haematoxylin was used to color the nuclei. Images were taken with an Olympus BX61 microscope (magnification, 400 ×) and then analyzed with ImagePro Plus software. The sections were sealed with neutral balsam. A score of 2 to 3 meant that the level of expression was high, and a score of 0 to 1 meant that it was low. Cell lines and cell culture We get human colorectal cancer cell lines SW480, HCT116, and SW620 from ATCC (ATCC, USA). We also get normal colonic epithelial cells NCM-460 and the human embryonic kidney epithelial cell line HEK293T from ATCC (ATCC, USA). The cells were grown in high-glucose DMEM medium (Gibco, USA) with 10% FBS (Gibco, USA) added to it. The cells were grown at 37℃ with 5% CO2. Lentiviral vector infection We made the ARHGAP40 overexpression plasmid by adding the ARHGAP40 gene to the pLVX-IRES-puro lentiviral vector. These plasmids psPAX2 and pMD2.G, along with the pLVX-IRES-puro blank vector or the pLVX-IRES-puro-ARHGAP40 vector were put into HEK293T cells using Lipofectamine 2000 reagent (Invitrogen, USA). The viral supernatant was taken 48 hours after transfection and used to infect human CRC cell lines. After that, puromycin (1mM) was used to choose cell lines for stable overexpression. Then, RT-qPCR was used to confirm that ARHGAP40 was overexpressed. RNA interference (siRNA) Transient knockdown of ARHGAP40 in CRC cells was conducted using siRNA (targeting sequence: 5′-CUGAUUUGCUCAAAAGGUUCA-3′). The cells were transfected with small interfering RNAs (siRNAs) using Lipofectamine 2000 reagent (Invitrogen, USA) according to the manufacturer's guidelines. The effects of RNA silencing were then verified by RT-qPCR. Analysis of cell viability Colorectal cancer cells in the logarithmic growth phase were put in 96-well plates so that there were 2000 cells in each well. The cells were grown in an incubator for 0, 1, 2, 3, and 4 days. Then, 10 µL of CCK-8 solution (Sangon, Shanghai, China) was added to each well, and then incubated at 37℃ for 4 hours. After that, a microplate reader (Tecan, Switzerland) was used to measure the absorbance of each well at 450nm. Wound-healing assay CRC cells were grown in a 6 cm dish until they were fully covered. At that point, a 100 µl pipette tip was used to make a scratch. The cells were washed to get rid of dirt and then grown in serum-free medium. At 0 and 12 hours, pictures were taken with an Olympus CKX53 microscope (magnification, 200 ×), and ImageJ was used to measure the distance that cells moved. There were three analyses, and the findings shown are representative of all three. Transwell assay Transwell experiments were employed to investigate cell migration and invasion. For the migrition test, 5×10 5 cells were put in the upper Transwell chamber (Corning, NY, USA) and resuspended in serum-free culture medium. At the same time, 600 mL of DMEM with 10% FBS was added to the bottom chamber. Cotton swabs were used to get rid of the cells that hadn't moved to the bottom of the chamber after 48 hours of growth. To fix the cells, 4% paraformaldehyde was used, and then 0.1% crystal violet was used to color them. Before adding cells for the invasion test, Matrigel (Corning, NY, USA) was spread on the Transwell membrane. The rest of the steps were the same as for the migration test. Then, through the use of a phase contrast microscope (Olympus, Tokyo, Japan), photographs of the cells that had migrated were taken in three separate fields per membrane at a magnification of 200 ×. Detection of apoptosis by Annexin V/PI staining We used Annexin V/propidium iodide (PI) labelling (Sangon, Shanghai, China) to check for cell apoptosis in CRC. Cells were gathered, washed with PBS at 4°C, and then spun at 1300 rpm. The cells were mixed again with 200 µl 1× binding buffer. Then, the cell mixture was put into a spinning tube and 3 µl of Annexin V-FITC from Sango Biotech in Shanghai, China was added. Cells were put in 0.5 ml of cold binding water and 5 µl of PI were added. They were then allowed to remain in the dark for 15 minutes. The cells were then analyzed right away using a Beckman Coulter flow cytometer (Beckman, USA). After that, FlowJo software was employed to examine the data. RNA extraction and RT-qPCR To get total RNA, an RNA extraction kit from Sangon Biotech in Shanghai, China, was used. M-MLV reverse transcriptase from Sangon Biotech in Shanghai, China, was then used to reverse transcribe the RNA. We made use of Green-2-Go qPCR Mastermix (Sangon Biotech, Shanghai, China) and conformed to the manufacturer's guidelines to carry out real-time quantitative PCR (RT-qPCR). As a standard gene, GAPDH was used. For RT-qPCR, the primers had the following sequences: ARHGAP40: 5′- TTGGATGGAGGTGGAACAGAT-3′ (forward) 5′- GCACTGAGCGAGCATAGATG-3′ (reverse); GAPDH: 5′- GAAGGTGAAGGTCGGAGTCA-3′ (forward) 5′- TTGAGGTCAATGAAGGGGTC-3′ (reverse). An analysis of relative quantification was conducted using the 2 − ΔΔCt method. RNA sequencing High-throughput RNA sequencing was adopted to examine ARHGAP40's putative role in the progression of colorectal cancer. To sum up, TRIzol reagent (Sangon, Shanghai, China) was used to separate total RNA from SW620 cells that had been treated with either ARHGAP40 siRNA or a negative control siRNA. After that, these samples were sent to Novogene Biotech Co. Ltd. in Beijing, China, for further RNA-seq research. The NEBNext UltraTM RNA Library Prep Kit for Illumina was used to prepare sequencing libraries according to the directions given by NEB (NEB, USA). The quality of the libraries was checked with an Agilent Bioanalyzer 2100. Then, the Illumina HiSeq platform was used to find the gene expression levels of each group. Protein‒protein interaction (PPI) network analysis In order to analyze differentially expressed genes (DEGs), a protein–protein interaction (PPI) network analysis was performed using the STRING database ( http://string-db.org/ ). The PPI network data files were visualized using the Cytoscape software, and key genes were identified. Western blot and immunoprecipitation (IP) analyses To break down the cells, they were put on ice for 30 minutes and mixed with RIPA lysis buffer and a protease inhibitor mixture from Byotime in Shanghai, China. The supernatants were collected after the lysates were spun at 100,000 × g for 15 minutes. We used the BCA protein test to find out how much protein was in the samples overall. 100 µg of protein from each sample was boiled in sample buffer, then separated using 4–20% SDS-PAGE, and finally transferred onto polyvinylidene difluoride membranes. The membranes were blocked for an hour at room temperature with 5% nonfat milk. They were then washed with PBST and put in a freezer overnight to be exposed to the main antibodies (anti-GAPDH, Cell Signaling Technology, 5174, diluted 1:1000; anti-RhoA, Abcam, ab187027, diluted 1:1000). We washed the blots three times with PBST and then put them at 37°C for an hour with Byotime A0208 secondary antibodies that had been diluted 1:5000. The Super Enhanced Chemiluminescence Plus Detection Reagent (ACE, China) was then used to find the antibodies that were bound. We used the Tanon 5200 Chemiluminescent Imaging System in this study to record chemiluminescence signals from protein bands (Tanon, 5200). A co-IP experiment was done to look at how the proteins interact with each other. Once the liquid part was separated after centrifuging at 12,000 rpm for 15 minutes at 4°C, protein samples were obtained. A standard sample of about 20 microliters of cellular lysate was collected and used. After that, antibodies were added to magnetic beads that had been left at room temperature for one hour. We used an anti-FLAG (DYKDDDDK) antibody from ACE in China to do FLAG-IP. For the IgG-IP control experiment, mouse IgG-agarose (ACE, China) was used. Also, the samples were left to sit with protein A/G agarose beads at 4°C overnight. The immune complexes that had been precipitated were then analyzed using Western blotting. Detection of RhoA activity With a RhoA activation kit from NewEast Biosciences in Wuhan, China, and following the manufacturer's instructions, the test was done. In short, CRC cells were broken down and then mixed with a protease inhibitor cocktail in 1× lysis/assay water. The supernatant was recovered and kept cold until it could be used right away. It had been sonicated and centrifuged at 12,000 × g for 15 minutes at 4°C. After that, a 1M solution of MgCl 2 was added until the concentration reached 60 mM. The solution was kept cold the whole time. Proteins that didn't interact with each other specifically were taken out after centrifuging at 1000 × g for 10 minutes at 4°C. The supernatants were then collected. After that, an active anti-RhoA monoclonal antibody and 20 µl of resuspended Protein A/G agarose beads were added. After that, the cases with the samples were put in a 4°C area for an hour. The antibody-bead complexes were spun at 1000 × g and washed three times with 1× lysis/assay solution. They were then mixed with 60 µl of SDS sample solution that contained 2-ME reducing agent for western immunoblotting with an anti-RhoA polyclonal antibody. Survival analysis The publicly available bioinformatics datasets GSE17537 (CRC tumors: 177), GSE14385 (CRC tumors: 91), GSE12945 (CRC tumors: 62) GSE17536 (CRC tumors: 56) and GSE14333 (CRC tumors: 290) with survival information were obtained to uncover the associations between ARHGAP40 expression and overall survival (OS) or disease-free survival (DFS) in CRC patients. The bioinformatics analyses were performed with PrognoScan ( http://www.abren.net/PrognoScan ). In the case the survival curves crossed, the landmark analysis was used. Statistical analysis For this study, IBM SPSS Statistics (version 24.0; Chicago, IL, USA) was used to look at the data. To compare the two groups, non-parametric tests were used. The Chi-square test and Logistic regression analysis were adopted to study ARHGAP40 expression and its connection with diverse clinicopathological factors. The Kaplan-Meier method, along with the log-rank test and landmark analysis, was used to do the survival analysis. It was thought that there was statistical significance if the P-value was less than 0.05. Results ARHGAP40 is downregulated in CRC tissues and cells To investigate the role of ARHGAP40 in colorectal cancer (CRC), we first performed immunohistochemical (IHC) staining to assess ARHGAP40 expression in CRC tissues. As shown in Fig. 1 A and summarized in Table 2 , ARHGAP40 expression was significantly reduced in CRC tumor tissues compared to normal tissues (Fig. 1 A; Table 2 , P < 0.001). Furthermore, we evaluated ARHGAP40 mRNA levels in colorectal cancer cell lines and the normal colorectal epithelial cell line NCM-460. The results indicated that ARHGAP40 expression was markedly lower in colorectal cancer cells than in NCM-460 cells (Fig. 1 B). Collectively, these findings demonstrate that ARHGAP40 is expressed at low levels in both colorectal cancer cells and tumor tissues. Table 2 Expression of ARHGAP40 in CRC tumor and adjacent normal tissues. ARHGAP40 expression Tumor tissue Adjacent normal tissue P value Cases Percentage Cases Percentage Low 50 48.54% 27 26.21% < 0.001 *** High 53 51.46% 76 73.79% P < 0.05 was considered statistically significant χ2 test was performed Correlations between ARHGAP40 expression levels and clinicopathological parameters in CRC patients We further analyzed the association between ARHGAP40 expression and clinicopathological characteristics of CRC patients. Chi-square test results demonstrated that reduced ARHGAP40 expression was significantly correlated with increased lymph node metastasis, greater tumor invasion depth ( P = 0.004), poorer tumor differentiation ( P < 0.001), and higher TNM stage ( P < 0.001) (Table 3 ). Additionally, ARHGAP40 expression showed significant associations with patients' age ( P = 0.014) and sex ( P = 0.008) (Table 3 ). Subsequently, logistic regression analyses were performed to further clarify the relationship between ARHGAP40 expression and clinicopathological parameters. Prior to logistic regression, the Box-Tidwell transformation in SPSS was applied to assess linearity in the logit; linearity was confirmed for all variables except for differentiation (Supplementary Table S1 ). Univariate logistic regression analysis revealed that ARHGAP40 expression was associated with sex ( P = 0.009), age ( P = 0.015), T stage ( P = 0.006), tumor differentiation ( P < 0.001), lymph node metastasis ( P < 0.001), and TNM stage ( P < 0.001) (Table 4 ). Factors that reached statistical significance in the univariate analysis were further evaluated in the multivariate logistic regression. Given the non-linear relationship between lymph node metastasis and TNM stage, these factors were analyzed separately in the multiple regression model (Supplementary Table S2 ). As presented in Table 5 , multivariate analysis demonstrated that ARHGAP40 expression was significantly associated with differentiation grade ( P = 0.008) and lymph node involvement ( P < 0.001). Furthermore, when TNM stage was included in the multivariate model, ARHGAP40 expression was also found to be significantly related to the depth of tumor invasion ( P = 0.015) and TNM stage ( P < 0.001) (Table 5 ). Table 3 Association between ARHGAP40 expression with the clinicopathological features of CRC. Characteristics ARHGAP40 Expression χ2 P-value Low High Gender Male 24 39 7.090 0.008 ** Female 26 14 Age ≤ 65 20 34 6.017 0.014 * >65 30 19 Invasion Depth T1/T2 5 18 8.518 0.004 ** T3/T4 45 35 Differentiation High/ Moderate 26 48 18.971 < 0.001 *** Poorly 24 5 Lymph mode metastasis No 4 43 55.464 < 0.001 *** Yes 46 10 TNM Stage Ⅰ/Ⅱ 5 42 49.725 < 0.001 *** Ⅲ/Ⅳ 45 11 P < 0.05 was considered statistically significant χ2 test was performed Table 4 Univariate logistic regression analysis of the association between ARHGAP40 expression and clinicopathological characteristics in patients with CRC. Clinicopathological characteristics OR 95% CI P -value Gender (Male vs. Female) 3.018 1.322–6.887 0.009 ** Age (> 65 vs. ≤65) 0.373 0.168–0.827 0.015 * T stage (T3 & T4 vs. T1 & T2) 0.216 0.073–0.639 0.006 ** Differentiation (Poorly vs. Well & Moderate) Lymph mode metastasis(Yes vs. No) 0.113 0.020 0.039–0.331 0.006–0.069 < 0.001 *** < 0.001 *** TNM stage (Ⅲ & Ⅳ vs. Ⅰ / Ⅱ) 0.029 0.009–0.091 < 0.001 *** T, tumor; N, node; M, metastasis; OR, odds ratio; CI, confidence interval. Table 5 Multivariate logistic regression analysis of the association between ARHGAP40 expression and clinicopathological characteristics in patients with CRC. A Clinicopathological characteristics OR 95% CI P -value Gender (Male vs. Female) 1.771 0.488–6.420 0.385 Age (> 65 vs. ≤65) 0.644 0.190–2.186 0.480 T stage (T3 & T4 vs. T1 & T2) 0.077 0.010–0.613 0.015 * Differentiation (Poorly vs. Well & Moderate) 0.096 0.016–0.561 0.009 ** TNM stage (Ⅲ & Ⅳ vs. Ⅰ / Ⅱ) 0.049 0.013–0.180 65 vs. ≤65) 0.616 0.175–2.172 0.451 T stage (T3 & T4 vs. T1 & T2) 0.235 0.030–1.843 0.168 Differentiation (Poorly vs. Well & Moderate) 0.080 0.012–0.516 0.008 ** Lymph mode metastasis(Yes vs. No) 0.029 0.007–0.129 < 0.001 *** A: Multivariate analysis of clinical features (gender, age, T stage, tumor differentiation, TNM stage) and ARHGAP40 expression. B: Multivariate analysis of clinical features (gender, age, T stage, tumor differentiation, lymph node metastasis) and ARHGAP40 expression. T, tumor; N, node; M, metastasis; OR, odds ratio; CI, confidence interval. ARHGAP40 promotes apoptosis and inhibits proliferation in colorectal cancer cells Given that reduced ARHGAP40 expression is closely associated with disease progression in colorectal cancer, we further investigated the functional role of ARHGAP40 in CRC cells. We manipulated ARHGAP40 expression in SW480, SW620, and HCT116 cell lines by transfecting them with either an ARHGAP40 overexpression plasmid or ARHGAP40-targeted siRNA. RT-qPCR analysis confirmed efficient overexpression and knockdown of ARHGAP40 (Supplementary Fig. S1 A, S1B). CCK-8 assays demonstrated that upregulation of ARHGAP40 significantly decreased cell viability across all three CRC cell lines (Fig. 2 A), whereas silencing ARHGAP40 resulted in enhanced cell proliferation (Fig. 3 A). Additionally, flow cytometry analysis revealed that ARHGAP40 overexpression markedly increased apoptosis in CRC cells (Fig. 2 B), while ARHGAP40 knockdown substantially reduced cell apoptosis compared with the control group (Fig. 3 B). Collectively, these findings suggest that ARHGAP40 suppresses CRC cell proliferation and promotes apoptotic cell death. ARHGAP40 inhibits the migration and invasion of colorectal cancer cells. We employed scratch wound healing and Transwell assays to assess the impact of ARHGAP40 on the migratory and invasive abilities of CRC cells. The scratch wound healing assay demonstrated that cells transfected with the ARHGAP40 overexpression vector exhibited significantly slower wound closure compared to cells transfected with the control vector (Fig. 4 A, 4 C). Similarly, Transwell migration assays revealed a marked reduction in cell migration rates in ARHGAP40-overexpressing cells relative to negative controls (Fig. 4 B, 4 D). Consistent results were observed in Matrigel invasion assays, where ARHGAP40 overexpression significantly impaired the invasive capacity of CRC cells (Fig. 4 B, 4 D). Conversely, knockdown of ARHGAP40 led to enhanced migration and invasion of colorectal cancer cells, as demonstrated by both scratch wound healing and Transwell invasion assays (Fig. 5 A– 5 D). These findings suggest that ARHGAP40 may inhibit the migratory and invasive potentials of CRC cells. Low ARHGAP40 expression is associated with the prognosis of CRC Our previous findings demonstrated that ARHGAP40 expression is markedly reduced in tumor tissues from patients with colorectal cancer (CRC). To further explore the prognostic value of ARHGAP40, we analyzed data from the GEO database, examining its association with survival outcomes in CRC. Five independent datasets were utilized: GSE17537 (177 samples), GSE143985 (91 samples), GSE17536 (56 samples), GSE12945 (62 samples), and GSE14333 (290 samples), encompassing CRC cases at various clinical stages. Analysis revealed that lower ARHGAP40 expression was significantly associated with reduced overall survival (OS) in both the GSE17537 and GSE143985 cohorts (GSE17537, HR = 0.3034, 95% CI = 0.09597–0.9595, Cox P = 0.0423; GSE143985, HR = 0.2892, 95% CI = 0.1046–0.8000, Cox P = 0.0169; Fig. 6 A). Furthermore, in the GSE17536, GSE12945, and GSE14333 cohorts, decreased ARHGAP40 expression was strongly correlated with shorter disease-free survival (DFS) (GSE17536, HR = 0.4169, 95% CI = 0.2076–0.8375, Cox P = 0.0140; GSE14333, HR = 0.3239, 95% CI = 0.1342–0.7817, Cox P = 0.0121; Fig. 6 B). Collectively, these results indicate that the downregulation of ARHGAP40 in CRC is associated with poorer prognosis. ARHGAP40 functions by regulating the activity of RhoA in CRC cells ARHGAP40 exerts its effects in CRC cells primarily by regulating RhoA activity. To elucidate the underlying mechanisms and signaling pathways influenced by ARHGAP40, we performed RNA sequencing on ARHGAP40-knockdown and control CRC cells. This analysis identified 2,212 differentially expressed genes (|fold change| ≥ 1 and p < 0.05) upon ARHGAP40 depletion, with 1,130 genes significantly downregulated and 1,082 genes significantly upregulated (Fig. 7 A). KEGG enrichment analysis revealed that the upregulated genes were predominantly involved in the HIF-1 signaling pathway and glycolytic processes (Fig. 7 B). Utilizing the Reactome database, we found that ARHGAP40 was associated with multiple signaling cascades, including Rho GTPase signaling, Rho GTPase effectors, receptor tyrosine kinases, WNT signaling, and GPCR downstream signaling (Fig. 7 C). Further protein–protein interaction (PPI) analysis using the STRING database suggested that ARHGAP40 interacts with members of the Rho-GTPase family, notably RhoA and CDC42 (Fig. 7 D). Other ARHGAP family proteins, such as ARHGAP28, ARHGAP26, and ARHGAP18, have been reported to inactivate RhoA [ 6 , 15 , 16 ]. Notably, RhoA overexpression is associated with increased invasion, unfavorable prognosis, and chemotherapy resistance in CRC patients [ 15 – 17 ]. Based on these insights, we hypothesized that ARHGAP40 could directly bind to RhoA and modulate its function in CRC cells. To test this, we performed co-immunoprecipitation (co-IP) assays following transfection with Flag-ARHGAP40 or a Flag-empty vector. The results confirmed a direct interaction between ARHGAP40 and RhoA (Fig. 8 A). Next, we assessed RhoA activity in CRC cell lines treated with either ARHGAP40 siRNA or control siRNA. Pull-down assays using RhoA-binding domain beads captured the active, GTP-bound RhoA, which was then quantified by Western blotting. As shown in Figs. 8 B and 8 C, ARHGAP40 overexpression significantly inhibited RhoA activation, whereas ARHGAP40 knockdown markedly increased RhoA activation. These findings suggest that ARHGAP40 may inhibit CRC progression by binding to and modulating RhoA activity. Discussion Despite significant advances in the diagnosis and treatment of colorectal cancer, many patients continue to experience poor prognoses due to the risk of tumor recurrence or metastasis [ 18 ]. Further research is required to gain a deeper understanding of the mechanisms underlying cancer initiation and to identify reliable biomarkers for the diagnosis of colorectal cancer. The primary objective of this study is to elucidate the role of ARHGAP40 in the progression and metastasis of colorectal cancer. Through the use of clinical specimens and in vitro cellular experiments, we demonstrate that ARHGAP40 suppresses tumor growth and interacts with the RhoA signaling pathway. These findings not only enhance our understanding of the molecular mechanisms involved in colorectal cancer but also suggest that ARHGAP40 may serve as a potential therapeutic target for CRC. The ARHGAP gene family encodes at least 32 RhoGAP proteins, which participate in a wide range of biological processes, including neuronal development, cell division, migration, angiogenesis, and tumor suppression [ 8 , 19 , 20 ]. In recent years, increasing attention has been paid to the role of ARHGAP family genes in tumorigenesis and tumor progression. For instance, ARHGAP10/GPX4 has been shown to induce ferroptosis in ovarian cancer cells and inhibit tumor growth, findings that are consistent with clinical observations [ 21 ]. Similarly, ARHGAP24 has been implicated in the proliferation and metastasis of breast cancer cells, as well as in the pathogenesis of renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC) [ 22 , 23 ]. Recent studies have also reported associations between ARHGAP family genes and colorectal cancer (CRC). For example, the RhoGAP6 isoform 1 variant has been identified as a potential biomarker for CRC progression [ 24 ]. In addition, elevated expression of ARHGAP8 has been observed in colorectal tumor tissues, where it regulates CRC cell activity through the ARHGAP1/CDC42GAP/p50RHOGAP pathway [ 9 , 25 ]. Furthermore, ARHGAP4 expression is significantly increased in CRC patients and is associated with poorer prognosis [ 26 ]. To date, the role of ARHGAP40 in tumors, particularly in colorectal cancer (CRC), remains largely unexplored. This study is the first to demonstrate that ARHGAP40 possesses tumor-suppressive properties and may serve as a potential biomarker for CRC. Our findings revealed that ARHGAP40 expression is significantly reduced in both colorectal cancer cells and tissues compared to their normal counterparts. Moreover, ARHGAP40 mRNA levels were strongly associated with key clinicopathological features of CRC, including invasion depth, tumor differentiation, lymph node metastasis, and TNM stage. Mechanistic investigations further showed that ARHGAP40 suppression enhances CRC cell proliferation and inhibits apoptosis, whereas its activation exerts the opposite effects. Collectively, these results highlight the unique and important role of ARHGAP40 in regulating CRC growth and progression. Metastasis, characterized by its selective nature, is a leading cause of cancer-related mortality [ 27 ]. In colorectal cancer, the elevated death rates are largely attributed to the dissemination of malignant cells from the primary tumor to distant sites [ 28 ]. However, the underlying mechanisms driving CRC metastasis remain incompletely understood. Previous studies have demonstrated that the ARHGAP gene family can influence tumor metastasis by regulating RhoA or other members of the Rho family [ 6 ]. Consistent with findings for other ARHGAP proteins, our study revealed that downregulation of ARHGAP40 significantly enhances the migratory and invasive abilities of colorectal cancer cells, whereas overexpression of ARHGAP40 yields the opposite effects. These results indicate that ARHGAP40 may play a crucial role in suppressing CRC cell metastasis in vitro, potentially through modulation of Rho GTPase signaling pathways. Rho family proteins dynamically alternate between an active GTP-bound state and an inactive GDP-bound state. Members of the ARHGAP family regulate this transition by stimulating the intrinsic GTPase activity of Rho GTPases, thereby promoting their conversion to the inactive GDP-bound form [ 6 , 29 ]. RhoA, a key member of the Rho GTPase family, is frequently overexpressed in various human tumors and is implicated in cell proliferation, cell cycle regulation, and cytoskeletal reorganization [ 30 , 31 ]. The role of RhoA in colorectal cancer (CRC), however, appears to be complex and context-dependent. Some studies have reported that high RhoA expression is associated with significantly reduced five-year survival rates following surgical resection in CRC patients [ 15 ]. and that RhoA upregulation is linked to enhanced migration and invasion of CRC cells in vitro [ 13 ]. In contrast, research by Rodrigues et al. demonstrated that reduced RhoA expression in CRC is associated with increased lymph node metastasis, and that RhoA inactivation can augment the migratory and invasive capabilities of CRC cells in vitro [ 32 ]. Moreover, their in vivo mouse model experiments suggested that loss of RhoA function significantly contributes to CRC metastasis [ 32 ]. In our study, RNA-seq analysis of downstream genes revealed that Rho GTPase signaling is more active in cells lacking ARHGAP40. STRING-based protein interaction analysis further confirmed that ARHGAP40 physically interacts with RhoA. These findings suggest that ARHGAP40 may modulate colorectal cancer progression at least in part by regulating the RhoA signaling pathway. Rho GTPases play a pivotal role in the regulation of cellular signaling pathways. These molecules cycle between an active, GTP-bound state and an inactive, GDP-bound state [ 33 ]. Upon activation, Rho GTPases engage with a variety of downstream effectors, thereby influencing their activities and subcellular localization [ 34 ]. The dynamic regulation of Rho GTPase activity is orchestrated by the opposing actions of guanine nucleotide exchange factors (GEFs), which promote activation, and GTPase-activating proteins (GAPs), which facilitate inactivation [ 35 ]. Several members of the ARHGAP family, such as ARHGAP5, ARHGAP10, and ARHGAP28, have been reported to promote the intracellular accumulation of RhoA in cancer cells [ 36 – 38 ]. Therefore, in this study, we examined whether ARHGAP40 could function as an effective GAP for RhoA in colorectal cancer cells. Limitations This study has several limitations. First, some of the conclusions drawn require further validation through in vivo experiments. Second, additional studies are needed to confirm the relationship between ARHGAP40 and RhoA and to elucidate the underlying mechanisms. Finally, the potential interactions between ARHGAP40 and other members of the Rho family proteins warrant further investigation. Conclusions In conclusion, our research demonstrates that ARHGAP40, functioning as a tumor suppressor, is expressed at low levels in colorectal cancer and is involved in regulating cell proliferation, metastasis, and viability. Importantly, our findings indicate that ARHGAP40 exerts its tumor-suppressive effects in colorectal cancer by negatively regulating RhoA. Collectively, these results suggest that ARHGAP40 may serve as a novel and promising therapeutic target for the treatment of colorectal cancer. Declarations Acknowledgements We express our gratitude to Prof. Junhua Wu of the Medical School of Nanjing University for his immensely valuable guidance and assistance throughout the duration of this study. In addition, we extend our appreciation to colleagues from Guangzhou Huayin Medical Laboratory Center including Zhihui Quan, Mingming Huang, and Jiewei Mao for their technical support and engaging discussions. Author Contributions Bin Lian: Conception and design, Collection and assembly of data, Manuscript writing, Final approval of manuscript. Na You: Conception and design, Data analysis and interpretation, Manuscript writing, Final approval of manuscript. Jingyu Wang: Collection and assembly of data, Manuscript writing, Final approval of manuscript. Cong Wang: Collection and assembly of data, Manuscript writing, Final approval of manuscript. Yunjie Wen: Manuscript writing, Final approval of manuscript. Jiandong Wang: Provision of study materials or patients, Data analysis and interpretation, Manuscript writing, Final approval of manuscript. Funding This research was funded by the Guangzhou Postdoctoral Innovation Practice Base Project of Guangzhou Huayin Medical Laboratory Center Co, Ltd. Data availability The datasets produced and inspected in the present study can be retrieved from the corresponding author upon request. Conflict of interest The authors have disclosed that they have no conflicts of interest. Ethical Statement The study design and use of archival tissues were approved by the Jinling Hospital Ethics Committee, which waived the requirement for informed consent due to the retrospective nature of the analysis. References Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024. https://doi.org/10.3322/caac.21834 . Chen Z, He L, Zhao L, Zhang G, Wang Z, Zhu P, et al. circREEP3 drives colorectal cancer progression via activation of FKBP10 transcription and restriction of antitumor immunity. Adv Sci (Weinh). 2022;9:e2105160. https://doi.org/10.1002/advs.202105160 . Rauf A, Abu-Izneid T, Khalil AA, Imran M, Shah ZA, Emran TB et al. Berberine as a potential anticancer agent: A comprehensive review. Molecules 2021;26. https://doi.org/10.3390/molecules26237368 Emran TB, Shahriar A, Mahmud AR, Rahman T, Abir MH, Siddiquee MF, et al. Multidrug resistance in cancer: Understanding molecular mechanisms, immunoprevention and therapeutic approaches. Front Oncol. 2022;12:891652. https://doi.org/10.3389/fonc.2022.891652 . Islam MR, Islam F, Nafady MH, Akter M, Mitra S, Das R et al. Natural small molecules in breast cancer treatment: Understandings from a therapeutic viewpoint. Molecules 2022;27. https://doi.org/10.3390/molecules27072165 Komatsu M, Ichikawa H, Chiwaki F, Sakamoto H, Komatsuzaki R, Asaumi M, et al. ARHGAP-RhoA signaling provokes homotypic adhesion-triggered cell death of metastasized diffuse-type gastric cancer. Oncogene. 2022;41:4779–94. https://doi.org/10.1038/s41388-022-02469-6 . Liu W, Xia K, Zheng D, Huang X, Wei Z, Wei Z, et al. Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma. BMC Cancer. 2023;23:1179. https://doi.org/10.1186/s12885-023-11673-w . Géci I, Bober P, Filová E, Amler E, Sabo J. The role of ARHGAP1 in Rho GTPase inactivation during metastasizing of breast cancer cell line MCF-7 after treatment with doxorubicin. Int J Mol Sci. 2023;24. https://doi.org/10.3390/ijms241411352 . Johnstone CN, Castellví-Bel S, Chang LM, Bessa X, Nakagawa H, Harada H, et al. ARHGAP8 is a novel member of the RHOGAP family related to ARHGAP1/CDC42GAP/p50RHOGAP: Mutation and expression analyses in colorectal and breast cancers. Gene. 2004;336:59–71. https://doi.org/10.1016/j.gene.2004.01.025 . Wang J, Qian J, Hu Y, Kong X, Chen H, Shi Q, et al. ArhGAP30 promotes p53 acetylation and function in colorectal cancer. Nat Commun. 2014;5:4735. https://doi.org/10.1038/ncomms5735 . Yu N, Yuan B, Cai J, Liu J, Zhang W, Bao W, et al. Loss of ARHGAP40 expression in basal cell carcinoma via CpG island hypermethylation. Exp Dermatol. 2023;32:2094–101. https://doi.org/10.1111/exd.14950 . Huang H, Wang S, Guan Y, Ren J, Liu X. Molecular basis and current insights of atypical Rho small GTPase in cancer. Mol Biol Rep. 2024;51:141. https://doi.org/10.1007/s11033-023-09140-7 . Zhang GY, Yang WH, Chen Z. Upregulated STAT3 and RhoA signaling in colorectal cancer (CRC) regulate the invasion and migration of CRC cells. Eur Rev Med Pharmacol Sci. 2016;20:2028–37. Ding AX, Wang H, Zhang JM, Yang W, Kuang YT. lncRNA BANCR promotes the colorectal cancer metastasis through accelerating exosomes-mediated M2 macrophage polarization via regulating RhoA/ROCK signaling. Mol Cell Biochem. 2023. https://doi.org/10.1007/s11010-023-04709-z . Yeung CY, Taylor SH, Garva R, Holmes DF, Zeef LA, Soininen R, et al. Arhgap28 is a RhoGAP that inactivates RhoA and downregulates stress fibers. PLoS ONE. 2014;9:e107036. https://doi.org/10.1371/journal.pone.0107036 . Liang L, Gu W, Li M, Gao R, Zhang X, Guo C, et al. The long noncoding RNA HOTAIRM1 controlled by AML1 enhances glucocorticoid resistance by activating RHOA/ROCK1 pathway through suppressing ARHGAP18. Cell Death Dis. 2021;12:702. https://doi.org/10.1038/s41419-021-03982-4 . Jeong D, Park S, Kim H, Kim CJ, Ahn TS, Bae SB, et al. RhoA is associated with invasion and poor prognosis in colorectal cancer. Int J Oncol. 2016;48:714–22. https://doi.org/10.3892/ijo.2015.3281 . Ruihua H, Mengyi Z, Chong Z, Meng Q, Xin M, Qiulin T, et al. RhoA regulates resistance to irinotecan by regulating membrane transporter and apoptosis signaling in colorectal cancer. Oncotarget. 2016;7:87136–46. https://doi.org/10.18632/oncotarget.13548 . Lu L, Wu M, Lu Y, Zhao Z, Liu T, Fu W, et al. MicroRNA-424 regulates cisplatin resistance of gastric cancer by targeting SMURF1 based on GEO database and primary validation in human gastric cancer tissues. Onco Targets Ther. 2019;12:7623–36. https://doi.org/10.2147/ott.S208275 . Xin R, Tang XM, Jiang YJ, Yu F, Li S, Jia CY, et al. Construction of a novel MYC-associated ceRNA regulatory network to identify prognostic biomarkers in colon adenocarcinoma. J Oncol. 2022;2022:3216285. https://doi.org/10.1155/2022/3216285 . Yang C, Wu S, Mou Z, Zhou Q, Zhang Z, Chen Y, et al. Transcriptomic analysis identified ARHGAP family as a novel biomarker associated with tumor-promoting immune infiltration and nanomechanical characteristics in bladder cancer. Front Cell Dev Biol. 2021;9:657219. https://doi.org/10.3389/fcell.2021.657219 . Qi L, Sun B, Yang B, Lu S. circRNA RPPH1 facilitates the aggravation of breast cancer development by regulating miR-542-3p/ARHGAP1 pathway. Cancer Biother Radiopharm. 2022;37:708–19. https://doi.org/10.1089/cbr.2020.4381 . Ke H, Shao J, Hu J, Song X, Han H, Zhu Z, et al. ARHGAP10, transcriptionally regulated by sodium butyrate, promotes ferroptosis of ovarian cancer cells. Front Biosci (Landmark Ed). 2024;29:167. https://doi.org/10.31083/j.fbl2905167 . Meng B, Wang P, Zhao C, Yin G, Meng X, Li L, et al. miR-21-5p serves as a promoter in renal cell carcinoma progression through ARHGAP24 downregulation. Environ Sci Pollut Res Int. 2022;29:39985–93. https://doi.org/10.1007/s11356-021-18343-z . Yang W, Wang B, Yu Q, Liu T, Li T, Tian T, et al. ARHGAP24 represses β-catenin transactivation-induced invasiveness in hepatocellular carcinoma mainly by acting as a GTPase-independent scaffold. Theranostics. 2022;12:6189–206. https://doi.org/10.7150/thno.72134 . Guo F, Liu Y, Huang J, Li Y, Zhou G, Wang D, et al. Identification of Rho GTPase activating protein 6 isoform 1 variant as a new molecular marker in human colorectal tumors. Pathol Oncol Res. 2010;16:319–26. https://doi.org/10.1007/s12253-009-9226-1 . Hossain MJ, Chowdhury UN, Islam MB, Uddin S, Ahmed MB, Quinn JMW, et al. Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer. Comput Biol Med. 2021;135:104539. https://doi.org/10.1016/j.compbiomed.2021.104539 . Fu MS, Pan SX, Cai XQ, Hu YX, Zhang WJ, Pan QC. Analysis of ARHGAP4 expression with colorectal cancer clinical characteristics and prognosis. Front Oncol. 2022;12:899837. https://doi.org/10.3389/fonc.2022.899837 . Zhou X, Liu Z, He T, Zhang C, Jiang M, Jin Y, et al. DDX10 promotes the proliferation and metastasis of colorectal cancer cells via splicing RPL35. Cancer Cell Int. 2022;22:58. https://doi.org/10.1186/s12935-022-02478-1 . Townsend AR, Chong LC, Karapetis C, Price TJ. Selective internal radiation therapy for liver metastases from colorectal cancer. Cancer Treat Rev. 2016;50. https://doi.org/10.1016/j.ctrv.2016.09.007 . :148 – 54. Streets AJ, Prosseda PP, Ong AC. Polycystin-1 regulates ARHGAP35-dependent centrosomal RhoA activation and ROCK signaling. JCI Insight 2020;5. https://doi.org/10.1172/jci.insight.135385 Zhang F, Sahu V, Peng K, Wang Y, Li T, Bala P, et al. Recurrent RhoGAP gene fusion CLDN18-ARHGAP26 promotes RHOA activation and focal adhesion kinase and YAP-TEAD signalling in diffuse gastric cancer. Gut. 2024. https://doi.org/10.1136/gutjnl-2023-329686 . Liu X, Yu Z, Li Y, Huang J. CX3CL1 and its receptor CX3CR1 interact with RhoA signaling to induce paclitaxel resistance in gastric cancer. Heliyon. 2024;10:e29100. https://doi.org/10.1016/j.heliyon.2024.e29100 . Rodrigues P, Macaya I, Bazzocco S, Mazzolini R, Andretta E, Dopeso H, et al. RHOA inactivation enhances Wnt signalling and promotes colorectal cancer. Nat Commun. 2014;5:5458. https://doi.org/10.1038/ncomms6458 . Jaffe AB, Hall A. Rho GTPases: biochemistry and biology. Annu Rev Cell Dev Biol. 2005;21:247–69. https://doi.org/10.1146/annurev.cellbio.21.020604.150721 . Moissoglu K, Kiessling V, Wan C, Hoffman BD, Norambuena A, Tamm LK, et al. Regulation of Rac1 translocation and activation by membrane domains and their boundaries. J Cell Sci. 2014;127:2565–76. https://doi.org/10.1242/jcs.149088 . Spindler V, Schlegel N, Waschke J. Role of GTPases in control of microvascular permeability. Cardiovasc Res. 2010;87:243–53. https://doi.org/10.1093/cvr/cvq086 . Tian T, Chen ZH, Zheng Z, Liu Y, Zhao Q, Liu Y, et al. Investigation of the role and mechanism of ARHGAP5-mediated colorectal cancer metastasis. Theranostics. 2020;10:5998–6010. https://doi.org/10.7150/thno.43427 . Sekiguchi M, Sobue A, Kushima I, Wang C, Arioka Y, Kato H, et al. ARHGAP10, which encodes Rho GTPase-activating protein 10, is a novel gene for schizophrenia risk. Transl Psychiatry. 2020;10:247. https://doi.org/10.1038/s41398-020-00917-z . Kasuya K, Nagakawa Y, Hosokawa Y, Sahara Y, Takishita C, Nakajima T, et al. RhoA activity increases due to hypermethylation of ARHGAP28 in a highly liver-metastatic colon cancer cell line. Biomed Rep. 2016;4:335–39. https://doi.org/10.3892/br.2016.582 . Additional Declarations No competing interests reported. Supplementary Files SupplymentaryFigureS1.tif Supplementary Fig. S1. Quantitative RT-PCR measurement of ARHGAP40 overexpression (A) and knockdown efficiency (B) in the CRC cell lines. SupplymentaryFigureS2.jpg Supplementary Fig. S2. Original Western Blot images of the Western-Blots for Fig. 8A-8C showing the bands with molecular weight markers. SupplementaryTableS1.docx SupplementaryTableS2.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:43:07","extension":"html","order_by":37,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":180330,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/adc7141e0e5565452007dcfa.html"},{"id":91839216,"identity":"1be6c99d-81c3-4010-83cb-e5f645187c88","added_by":"auto","created_at":"2025-09-22 09:51:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":681197,"visible":true,"origin":"","legend":"\u003cp\u003eLow expression of ARHGAP40 was observed in CRC tissue samples and CRC cell lines.\u003c/p\u003e\n\u003cp\u003e(A) Representative immunohistochemical staining results of ARHGAP40 in colorectal cancer (CRC) tissues of different differentiation degrees and adjacent non-tumor tissues (magnification, 400 ×). (a) Normal human colon tissues; (b) poorly differentiated CRC; (c) moderately differentiated CRC; (d) well-differentiated CRC.\u003c/p\u003e\n\u003cp\u003e(B) Expression profile of the ARHGAP40 gene in normal intestinal epithelial cell line and colorectal cancer cell lines measured using qRT-PCR.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/8c52acf5a3358b904bd540c9.png"},{"id":91839217,"identity":"307b9bca-a91c-4326-a4b8-1ecf06a1d7a7","added_by":"auto","created_at":"2025-09-22 09:51:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":298815,"visible":true,"origin":"","legend":"\u003cp\u003eARHGAP40 overexpression inhibited cell proliferation and promoted cell apoptosis of CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Cell viability analysis via CCK8.\u003c/p\u003e\n\u003cp\u003e(B) Percentages of apoptotic ARHGQP40‐overexpressing cells were determined using flow cytometry.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/0573828ce140535e96c342b0.png"},{"id":91838295,"identity":"077f9278-2251-4119-ba10-b18738729483","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":246395,"visible":true,"origin":"","legend":"\u003cp\u003eARHGAP40 knockdown promoted cell proliferation and inhibited apoptosis of CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Cell viability analysis via CCK8.\u003c/p\u003e\n\u003cp\u003e(B) Percentages of apoptotic ARHGAP40-knockdown cells were determined using flow cytometry.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/2bcda0d5fcc4261fc6cda5bf.png"},{"id":91839218,"identity":"e2372b03-6081-4d3b-b951-15e6283f7a64","added_by":"auto","created_at":"2025-09-22 09:51:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1184460,"visible":true,"origin":"","legend":"\u003cp\u003eARHGAP40 overexpression suppressed migration and invasion of CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Representative images of scratch wound assay in ARHGAP40 overexpression and control CRC cells (magnification, 200 ×).\u003c/p\u003e\n\u003cp\u003e(B) Representative figure for cell migration and invasion by Transwell assay (magnification, 200 ×).\u003c/p\u003e\n\u003cp\u003e(C) Quantitative analysis of the distance of the scratch wound healing experiment.\u003c/p\u003e\n\u003cp\u003e(D) Results of Transwell assay quantification.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/ec8dba6b8e918063749bff6d.png"},{"id":91839221,"identity":"293dc8e9-2584-4b71-9987-63a404868ee4","added_by":"auto","created_at":"2025-09-22 09:51:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1177674,"visible":true,"origin":"","legend":"\u003cp\u003eKnockdown of ARHGAP40 facilitated cell migration and invasion of CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Representative images of scratch wound assay in ARHGAP40 knockdown and control CRC cells (magnification, 200 ×).\u003c/p\u003e\n\u003cp\u003e(B) Representative figure for cell migration and invasion by Transwell assay (magnification, 200 ×).\u003c/p\u003e\n\u003cp\u003e(C) Quantitative analysis of the distance of the scratch wound healing experiment.\u003c/p\u003e\n\u003cp\u003e(D) Results of Transwell assay quantification.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/c8c79082a422c178b8eb52c8.png"},{"id":91842210,"identity":"8a3f0c1a-22f2-4aa3-81bf-e1e4db2f9e08","added_by":"auto","created_at":"2025-09-22 09:59:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":637863,"visible":true,"origin":"","legend":"\u003cp\u003eSurvival analysis of ARHGAP40 mRNA expression according to the GEO databases.\u003c/p\u003e\n\u003cp\u003e(A) Kaplan–Meier survival analysis of ARHGAP40 levels with OS in CRC patients in GEO database.\u003c/p\u003e\n\u003cp\u003e(B) Kaplan–Meier survival analysis of ARHGAP40 levels with DFS in CRC patients in GEO database.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/7f2b7f9b6fa305dc9250cc39.png"},{"id":91838312,"identity":"47a04d4a-b053-4013-86bd-171de6e2484c","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":254705,"visible":true,"origin":"","legend":"\u003cp\u003ePathway enrichment analysis of RNA-seq expression profiling in ARHGAP40 knockdown CRC cells.\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot displaying differential gene expression by RNA-seq in siNC versus siARHGAP40 CRC cells.\u003c/p\u003e\n\u003cp\u003e(B) Enrichment results of KEGG signaling pathway in DEGs revealed by RNA-seq.\u003c/p\u003e\n\u003cp\u003e(C) The results of Reactome pathway enrichment analysis after RNA sequencing (RNA-seq).\u003c/p\u003e\n\u003cp\u003e(D) The correlation of ARHGAP40 and other proteins is analyzed in String database.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/c2a099ddfbcfbcfdc8fdcad9.png"},{"id":91838307,"identity":"c4823053-7915-4e7e-892e-6afc666e431a","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":223434,"visible":true,"origin":"","legend":"\u003cp\u003eInteractions between ARHGAP40 and RhoA and the effect of ARHGAP40 on RhoA activity.\u003c/p\u003e\n\u003cp\u003e(A) Co-immunoprecipitation (co-IP) assays for the interaction between ARHGAP40 and RhoA.\u003c/p\u003e\n\u003cp\u003e(B) RhoA activation assay showing decreased RhoA activity in ARHGAP40-overexpression CRC cells.\u003c/p\u003e\n\u003cp\u003e(C) RhoA activation assay showing enhanced RhoA activity in ARHGAP40 knockdown CRC cells.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/593fa8932c5521989aca059a.png"},{"id":104397597,"identity":"398786b1-b166-4918-bc6d-346e3f497e03","added_by":"auto","created_at":"2026-03-11 11:52:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5701846,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/f7c29b83-e3d0-4e37-9f33-f70d57d66841.pdf"},{"id":91838325,"identity":"b7232b67-63dc-48cc-9ee2-542767e18e5b","added_by":"auto","created_at":"2025-09-22 09:43:07","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":6830232,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S1. \u003c/strong\u003eQuantitative RT-PCR measurement of ARHGAP40 overexpression (A) and knockdown efficiency (B) in the CRC cell lines.\u003c/p\u003e","description":"","filename":"SupplymentaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/51dbdd371c8da40e2cbb9313.tif"},{"id":91838302,"identity":"c87295cc-a340-4f0d-8384-f51b79a9b74d","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":198370,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Fig. S2.\u003c/strong\u003e Original Western Blot images of the Western-Blots for Fig. 8A-8C showing the bands with molecular weight markers.\u003c/p\u003e","description":"","filename":"SupplymentaryFigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/eec5c58ee15947d6af102912.jpg"},{"id":91838299,"identity":"a4728f4d-46ff-4c4f-9afb-78bd98a25e4f","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":17055,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/a5535843d6dc04616caace0a.docx"},{"id":91838301,"identity":"b049612a-47d6-40ba-8e5c-a68dff265e91","added_by":"auto","created_at":"2025-09-22 09:43:06","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":17247,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTableS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-7317386/v1/746f1bdf88c8b83cd946ded3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Low expression of ARHGAP40 in colorectal cancer facilitates tumor progression by activating the RhoA pathway","fulltext":[{"header":"Highlights","content":"\u003cp\u003e1. ARHGAP40 expression is significantly downregulated in CRC tissues, correlating with poor tumor differentiation, deeper invasion, lymph node metastasis, advanced TNM stage, and unfavorable prognosis.\u003c/p\u003e\u003cp\u003e2. Overexpression of ARHGAP40 suppresses CRC cell proliferation, reduces migration/invasion, and increases apoptosis.\u003c/p\u003e\u003cp\u003e3. ARHGAP40 inhibits tumor progression by regulating RhoA GTPase activity.\u003c/p\u003e\u003cp\u003e4. Proposes ARHGAP40 as a novel tumor suppressor and prognostic biomarker in CRC, suggesting therapeutic targeting of RhoA signaling.\u003c/p\u003e"},{"header":"Background","content":"\u003cp\u003eA large number of people suffer from colorectal cancer (CRC), which influences the digestive system. Based on global cancer statistics from 2022, colorectal cancer has the third highest rate of new cases and the second highest rate of deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Even though advancements in surgery, chemotherapy, and radiation therapy have reduced the rates of recurrence and death, multidrug resistance remains a significant problem for effective disease treatment, and the survival rates of CRC patients have not improved significantly [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. To gain a deeper understanding of the progression of CRC, further research is necessary. This could lead to the identification of new therapeutic targets and diagnostic biomarkers.\u003c/p\u003e\u003cp\u003eAs Rho homologous GTPase-activating proteins, ARHGAPs are very important for controlling the Rho family of small GTPases and their effects on many biological processes, including cell growth, adhesion, migration, and invasion [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. New studies show that the ARHGAP family may also play a role in the development of several cancers. One study showed that ARHGAP1 controls the epithelial-to-mesenchymal transition (EMT) in breast cancer cells by blocking RhoA signaling [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Genes in the ARHGAP family have also been linked to the development of colorectal cancer in several studies, but the exact role of these genes in colorectal cancer is still not clear. The generation of a distinctive RHOGAP variant with a unique functional domain by ARHGAP8 contributes to the development of CRC [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The expression change of p300 by ARHGAP30 leads to the apoptosis of colorectal cancer cells, which makes the tumor suppressor protein p53 more active and acetylated [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Our most recent study shows that methylation significantly lowers ARHGAP40 levels in basal cell carcinoma (BCC), which suggests that it could be used as a new biomarker to tell the difference between BCC and trichoblastoma [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. We still need to acquire more knowledge about the expression of ARHGAP40 in colorectal cancer and its role in clinical scenarios.\u003c/p\u003e\u003cp\u003eRhoA is a small GTPase belonging to the Rho family and stands for Ras homologue family member A. RhoA and other GTPases function as molecular switches, changing from an active state bound to GTP to an inactive state governed by GAPs [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The activation of RhoA is a highly important part of the reorganization of the actin cytoskeleton and exerts a considerable influence on the growth and invasion of colorectal cancer cells [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Further research needs to be conducted to figure out exactly how RhoA leads to the growth of CRC at the molecular level.\u003c/p\u003e\u003cp\u003eThis work reveals new details about the expression, role, and regulatory mechanism of ARHGAP40 in the progression of CRC. Our findings show that ARHGAP40 expression levels have significantly decreased in colorectal cancer tissue samples and cell lines. Its low level is linked to an advanced TNM stage and can be used as a standalone indicator of poor survival in CRC patients. Using gain- and loss-of-function studies, we also looked at how ARHGAP40 expression affects the function of CRC cells \u003cem\u003ein vitro\u003c/em\u003e. our research indicates that reducing ARHGAP40 levels leads to the activation of RhoA in CRC cells.\u003c/p\u003e\u003cp\u003eAccordingly, our discoveries could offer valuable perceptions of the pathogenesis of CRC and identify ARHGAP40 as a potential therapeutic target for CRC therapy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients and samples\u003c/h2\u003e\u003cp\u003eThis study included 103 patients with CRC. All patients underwent surgical resection and had not received prior chemotherapy or radiotherapy. Clinical and pathological characteristics of the cohort are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Formalin-fixed, paraffin-embedded (FFPE) tissue samples were obtained from the Department of Pathology, Jinling Hospital. The study design and use of archival tissues were approved by the Jinling Hospital Ethics Committee, which waived the requirement for informed consent due to the retrospective nature of the analysis.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCombined characteristics of all 103 patients in the present study.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCase (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e63 (61.17%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (38.83%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (52.43%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49 (47.57%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (5.83%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (54.37%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWell/moderately differentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74 (71.84%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorly differentiated\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29 (28.16%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastases\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47 (45.63%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e56 (54.37%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (16.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (29.1%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (49.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eImmunohistochemical staining\u003c/h3\u003e\n\u003cp\u003eImmunohistochemical analysis was performed on slices of tumor tissue. To summarize, tumor samples were dissected, stored in formalin, embedded in paraffin, and sectioned into 3\u0026ndash;4 \u0026micro;m slices. To remove the paraffin on the slides, xylene was applied to the sections. The samples were then rehydrated with ethanol added in decreasing concentrations. Subsequently, the antigen was fixed with a repair solution bath in water. After that, PBS (pH 7.4) was used to wash the pieces three times. The slides were first incubated with bovine serum albumin diluted in PBS for 15 minutes at 37\u0026deg;C. Then, the primary antibody (ARHGAP40, NBP1-94112; Novus Biologicals, USA; dilution 1:1000) was added and incubated at 4\u0026deg;C overnight. The slides were washed three times with PBS and then treated with horseradish peroxidase-conjugated secondary antibodies from Zhongshan Gold Bridge in Beijing, China, for an hour at room temperature. Three more washes in PBS were given to all the samples, and then they were treated with 3,3'-diaminobenzidine (DAB). Haematoxylin was used to color the nuclei. Images were taken with an Olympus BX61 microscope (magnification, 400 \u0026times;) and then analyzed with ImagePro Plus software. The sections were sealed with neutral balsam. A score of 2 to 3 meant that the level of expression was high, and a score of 0 to 1 meant that it was low.\u003c/p\u003e\n\u003ch3\u003eCell lines and cell culture\u003c/h3\u003e\n\u003cp\u003eWe get human colorectal cancer cell lines SW480, HCT116, and SW620 from ATCC (ATCC, USA). We also get normal colonic epithelial cells NCM-460 and the human embryonic kidney epithelial cell line HEK293T from ATCC (ATCC, USA). The cells were grown in high-glucose DMEM medium (Gibco, USA) with 10% FBS (Gibco, USA) added to it. The cells were grown at 37℃ with 5% CO2.\u003c/p\u003e\n\u003ch3\u003eLentiviral vector infection\u003c/h3\u003e\n\u003cp\u003eWe made the ARHGAP40 overexpression plasmid by adding the ARHGAP40 gene to the pLVX-IRES-puro lentiviral vector. These plasmids psPAX2 and pMD2.G, along with the pLVX-IRES-puro blank vector or the pLVX-IRES-puro-ARHGAP40 vector were put into HEK293T cells using Lipofectamine 2000 reagent (Invitrogen, USA). The viral supernatant was taken 48 hours after transfection and used to infect human CRC cell lines. After that, puromycin (1mM) was used to choose cell lines for stable overexpression. Then, RT-qPCR was used to confirm that ARHGAP40 was overexpressed.\u003c/p\u003e\n\u003ch3\u003eRNA interference (siRNA)\u003c/h3\u003e\n\u003cp\u003eTransient knockdown of ARHGAP40 in CRC cells was conducted using siRNA (targeting sequence: 5\u0026prime;-CUGAUUUGCUCAAAAGGUUCA-3\u0026prime;). The cells were transfected with small interfering RNAs (siRNAs) using Lipofectamine 2000 reagent (Invitrogen, USA) according to the manufacturer's guidelines. The effects of RNA silencing were then verified by RT-qPCR.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eAnalysis of cell viability\u003c/h2\u003e\u003cp\u003eColorectal cancer cells in the logarithmic growth phase were put in 96-well plates so that there were 2000 cells in each well. The cells were grown in an incubator for 0, 1, 2, 3, and 4 days. Then, 10 \u0026micro;L of CCK-8 solution (Sangon, Shanghai, China) was added to each well, and then incubated at 37℃ for 4 hours. After that, a microplate reader (Tecan, Switzerland) was used to measure the absorbance of each well at 450nm.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eWound-healing assay\u003c/h3\u003e\n\u003cp\u003eCRC cells were grown in a 6 cm dish until they were fully covered. At that point, a 100 \u0026micro;l pipette tip was used to make a scratch. The cells were washed to get rid of dirt and then grown in serum-free medium. At 0 and 12 hours, pictures were taken with an Olympus CKX53 microscope (magnification, 200 \u0026times;), and ImageJ was used to measure the distance that cells moved. There were three analyses, and the findings shown are representative of all three.\u003c/p\u003e\n\u003ch3\u003eTranswell assay\u003c/h3\u003e\n\u003cp\u003eTranswell experiments were employed to investigate cell migration and invasion. For the migrition test, 5\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were put in the upper Transwell chamber (Corning, NY, USA) and resuspended in serum-free culture medium. At the same time, 600 mL of DMEM with 10% FBS was added to the bottom chamber. Cotton swabs were used to get rid of the cells that hadn't moved to the bottom of the chamber after 48 hours of growth. To fix the cells, 4% paraformaldehyde was used, and then 0.1% crystal violet was used to color them. Before adding cells for the invasion test, Matrigel (Corning, NY, USA) was spread on the Transwell membrane. The rest of the steps were the same as for the migration test. Then, through the use of a phase contrast microscope (Olympus, Tokyo, Japan), photographs of the cells that had migrated were taken in three separate fields per membrane at a magnification of 200 \u0026times;.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDetection of apoptosis by Annexin V/PI staining\u003c/h2\u003e\u003cp\u003eWe used Annexin V/propidium iodide (PI) labelling (Sangon, Shanghai, China) to check for cell apoptosis in CRC. Cells were gathered, washed with PBS at 4\u0026deg;C, and then spun at 1300 rpm. The cells were mixed again with 200 \u0026micro;l 1\u0026times; binding buffer. Then, the cell mixture was put into a spinning tube and 3 \u0026micro;l of Annexin V-FITC from Sango Biotech in Shanghai, China was added. Cells were put in 0.5 ml of cold binding water and 5 \u0026micro;l of PI were added. They were then allowed to remain in the dark for 15 minutes. The cells were then analyzed right away using a Beckman Coulter flow cytometer (Beckman, USA). After that, FlowJo software was employed to examine the data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eRNA extraction and RT-qPCR\u003c/h2\u003e\u003cp\u003eTo get total RNA, an RNA extraction kit from Sangon Biotech in Shanghai, China, was used. M-MLV reverse transcriptase from Sangon Biotech in Shanghai, China, was then used to reverse transcribe the RNA. We made use of Green-2-Go qPCR Mastermix (Sangon Biotech, Shanghai, China) and conformed to the manufacturer's guidelines to carry out real-time quantitative PCR (RT-qPCR). As a standard gene, GAPDH was used. For RT-qPCR, the primers had the following sequences:\u003c/p\u003e\u003cp\u003eARHGAP40: 5\u0026prime;- TTGGATGGAGGTGGAACAGAT-3\u0026prime; (forward)\u003c/p\u003e\u003cp\u003e5\u0026prime;- GCACTGAGCGAGCATAGATG-3\u0026prime; (reverse);\u003c/p\u003e\u003cp\u003eGAPDH: 5\u0026prime;- GAAGGTGAAGGTCGGAGTCA-3\u0026prime; (forward)\u003c/p\u003e\u003cp\u003e5\u0026prime;- TTGAGGTCAATGAAGGGGTC-3\u0026prime; (reverse).\u003c/p\u003e\u003cp\u003eAn analysis of relative quantification was conducted using the 2\u0026thinsp;\u0026minus;\u0026thinsp;ΔΔCt method.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRNA sequencing\u003c/h2\u003e\u003cp\u003eHigh-throughput RNA sequencing was adopted to examine ARHGAP40's putative role in the progression of colorectal cancer. To sum up, TRIzol reagent (Sangon, Shanghai, China) was used to separate total RNA from SW620 cells that had been treated with either ARHGAP40 siRNA or a negative control siRNA. After that, these samples were sent to Novogene Biotech Co. Ltd. in Beijing, China, for further RNA-seq research. The NEBNext UltraTM RNA Library Prep Kit for Illumina was used to prepare sequencing libraries according to the directions given by NEB (NEB, USA). The quality of the libraries was checked with an Agilent Bioanalyzer 2100. Then, the Illumina HiSeq platform was used to find the gene expression levels of each group. \u003cb\u003eProtein‒protein interaction (PPI) network analysis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn order to analyze differentially expressed genes (DEGs), a protein\u0026ndash;protein interaction (PPI) network analysis was performed using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The PPI network data files were visualized using the Cytoscape software, and key genes were identified.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eWestern blot and immunoprecipitation (IP) analyses\u003c/h2\u003e\u003cp\u003eTo break down the cells, they were put on ice for 30 minutes and mixed with RIPA lysis buffer and a protease inhibitor mixture from Byotime in Shanghai, China. The supernatants were collected after the lysates were spun at 100,000 \u0026times; g for 15 minutes. We used the BCA protein test to find out how much protein was in the samples overall. 100 \u0026micro;g of protein from each sample was boiled in sample buffer, then separated using 4\u0026ndash;20% SDS-PAGE, and finally transferred onto polyvinylidene difluoride membranes. The membranes were blocked for an hour at room temperature with 5% nonfat milk. They were then washed with PBST and put in a freezer overnight to be exposed to the main antibodies (anti-GAPDH, Cell Signaling Technology, 5174, diluted 1:1000; anti-RhoA, Abcam, ab187027, diluted 1:1000). We washed the blots three times with PBST and then put them at 37\u0026deg;C for an hour with Byotime A0208 secondary antibodies that had been diluted 1:5000. The Super Enhanced Chemiluminescence Plus Detection Reagent (ACE, China) was then used to find the antibodies that were bound. We used the Tanon 5200 Chemiluminescent Imaging System in this study to record chemiluminescence signals from protein bands (Tanon, 5200).\u003c/p\u003e\u003cp\u003eA co-IP experiment was done to look at how the proteins interact with each other. Once the liquid part was separated after centrifuging at 12,000 rpm for 15 minutes at 4\u0026deg;C, protein samples were obtained. A standard sample of about 20 microliters of cellular lysate was collected and used. After that, antibodies were added to magnetic beads that had been left at room temperature for one hour. We used an anti-FLAG (DYKDDDDK) antibody from ACE in China to do FLAG-IP. For the IgG-IP control experiment, mouse IgG-agarose (ACE, China) was used. Also, the samples were left to sit with protein A/G agarose beads at 4\u0026deg;C overnight. The immune complexes that had been precipitated were then analyzed using Western blotting.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eDetection of RhoA activity\u003c/h2\u003e\u003cp\u003eWith a RhoA activation kit from NewEast Biosciences in Wuhan, China, and following the manufacturer's instructions, the test was done. In short, CRC cells were broken down and then mixed with a protease inhibitor cocktail in 1\u0026times; lysis/assay water. The supernatant was recovered and kept cold until it could be used right away. It had been sonicated and centrifuged at 12,000 \u0026times; g for 15 minutes at 4\u0026deg;C. After that, a 1M solution of MgCl\u003csub\u003e2\u003c/sub\u003e was added until the concentration reached 60 mM. The solution was kept cold the whole time. Proteins that didn't interact with each other specifically were taken out after centrifuging at 1000 \u0026times; g for 10 minutes at 4\u0026deg;C. The supernatants were then collected. After that, an active anti-RhoA monoclonal antibody and 20 \u0026micro;l of resuspended Protein A/G agarose beads were added. After that, the cases with the samples were put in a 4\u0026deg;C area for an hour. The antibody-bead complexes were spun at 1000 \u0026times; g and washed three times with 1\u0026times; lysis/assay solution. They were then mixed with 60 \u0026micro;l of SDS sample solution that contained 2-ME reducing agent for western immunoblotting with an anti-RhoA polyclonal antibody.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSurvival analysis\u003c/h2\u003e\u003cp\u003e The publicly available bioinformatics datasets GSE17537 (CRC tumors: 177), GSE14385 (CRC tumors: 91), GSE12945 (CRC tumors: 62) GSE17536 (CRC tumors: 56) and GSE14333 (CRC tumors: 290) with survival information were obtained to uncover the associations between ARHGAP40 expression and overall survival (OS) or disease-free survival (DFS) in CRC patients. The bioinformatics analyses were performed with PrognoScan (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.abren.net/PrognoScan\u003c/span\u003e\u003cspan address=\"http://www.abren.net/PrognoScan\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In the case the survival curves crossed, the landmark analysis was used.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eFor this study, IBM SPSS Statistics (version 24.0; Chicago, IL, USA) was used to look at the data. To compare the two groups, non-parametric tests were used. The Chi-square test and Logistic regression analysis were adopted to study ARHGAP40 expression and its connection with diverse clinicopathological factors. The Kaplan-Meier method, along with the log-rank test and landmark analysis, was used to do the survival analysis. It was thought that there was statistical significance if the \u003cem\u003eP-value\u003c/em\u003e was less than 0.05.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eARHGAP40 is downregulated in CRC tissues and cells\u003c/h2\u003e\u003cp\u003eTo investigate the role of ARHGAP40 in colorectal cancer (CRC), we first performed immunohistochemical (IHC) staining to assess ARHGAP40 expression in CRC tissues. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, ARHGAP40 expression was significantly reduced in CRC tumor tissues compared to normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, we evaluated ARHGAP40 mRNA levels in colorectal cancer cell lines and the normal colorectal epithelial cell line NCM-460. The results indicated that ARHGAP40 expression was markedly lower in colorectal cancer cells than in NCM-460 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Collectively, these findings demonstrate that ARHGAP40 is expressed at low levels in both colorectal cancer cells and tumor tissues.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eExpression of ARHGAP40 in CRC tumor and adjacent normal tissues.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARHGAP40 expression\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eTumor tissue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eAdjacent normal tissue\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c7\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCases\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e73.79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eχ2 test was performed\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eCorrelations between ARHGAP40 expression levels and clinicopathological parameters in CRC patients\u003c/h2\u003e\u003cp\u003eWe further analyzed the association between ARHGAP40 expression and clinicopathological characteristics of CRC patients. Chi-square test results demonstrated that reduced ARHGAP40 expression was significantly correlated with increased lymph node metastasis, greater tumor invasion depth (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), poorer tumor differentiation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and higher TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, ARHGAP40 expression showed significant associations with patients' age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014) and sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Subsequently, logistic regression analyses were performed to further clarify the relationship between ARHGAP40 expression and clinicopathological parameters. Prior to logistic regression, the Box-Tidwell transformation in SPSS was applied to assess linearity in the logit; linearity was confirmed for all variables except for differentiation (Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Univariate logistic regression analysis revealed that ARHGAP40 expression was associated with sex (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), age (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), T stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), tumor differentiation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lymph node metastasis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Factors that reached statistical significance in the univariate analysis were further evaluated in the multivariate logistic regression. Given the non-linear relationship between lymph node metastasis and TNM stage, these factors were analyzed separately in the multiple regression model (Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). As presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, multivariate analysis demonstrated that ARHGAP40 expression was significantly associated with differentiation grade (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008) and lymph node involvement (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, when TNM stage was included in the multivariate model, ARHGAP40 expression was also found to be significantly related to the depth of tumor invasion (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) and TNM stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between ARHGAP40 expression with the clinicopathological features of CRC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eARHGAP40 Expression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eχ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e7.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e6.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.014\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eInvasion Depth\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1/T2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e8.518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT3/T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eDifferentiation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh/ Moderate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e18.971\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoorly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eLymph mode metastasis\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e55.464\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eTNM Stage\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅠ/Ⅱ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e49.725\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅢ/Ⅳ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eχ2 test was performed\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate logistic regression analysis of the association between ARHGAP40 expression and clinicopathological characteristics in patients with CRC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClinicopathological characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender (Male vs. Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.322\u0026ndash;6.887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (\u0026gt;\u0026thinsp;65 vs. \u0026le;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.373\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.168\u0026ndash;0.827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage (T3 \u0026amp; T4 vs. T1 \u0026amp; T2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.216\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.073\u0026ndash;0.639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation (Poorly vs. Well \u0026amp; Moderate)\u003c/p\u003e\u003cp\u003eLymph mode metastasis(Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.113\u003c/p\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.039\u0026ndash;0.331\u003c/p\u003e\u003cp\u003e0.006\u0026ndash;0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage (Ⅲ \u0026amp; Ⅳ vs. Ⅰ / Ⅱ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.009\u0026ndash;0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eT, tumor; N, node; M, metastasis; OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of the association between ARHGAP40 expression and clinicopathological characteristics in patients with CRC.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003eA\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClinicopathological characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (Male vs. Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.771\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.488\u0026ndash;6.420\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (\u0026gt;\u0026thinsp;65 vs. \u0026le;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.190\u0026ndash;2.186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.480\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT stage (T3 \u0026amp; T4 vs. T1 \u0026amp; T2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.010\u0026ndash;0.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDifferentiation (Poorly vs. Well \u0026amp; Moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.016\u0026ndash;0.561\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTNM stage (Ⅲ \u0026amp; Ⅳ vs. Ⅰ / Ⅱ)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.013\u0026ndash;0.180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003csup\u003e\u003cb\u003eB\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eClinicopathological characteristics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eOR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e95% CI\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eP\u003c/b\u003e\u003cb\u003e-value\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGender (Male vs. Female)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.977\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.259\u0026ndash;3.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAge (\u0026gt;\u0026thinsp;65 vs. \u0026le;65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.175\u0026ndash;2.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.451\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT stage (T3 \u0026amp; T4 vs. T1 \u0026amp; T2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.030\u0026ndash;1.843\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.168\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDifferentiation (Poorly vs. Well \u0026amp; Moderate)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.012\u0026ndash;0.516\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLymph mode metastasis(Yes vs. No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.007\u0026ndash;0.129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eA: Multivariate analysis of clinical features (gender, age, T stage, tumor differentiation, TNM stage) and ARHGAP40 expression.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eB: Multivariate analysis of clinical features (gender, age, T stage, tumor differentiation, lymph node metastasis) and ARHGAP40 expression.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eT, tumor; N, node; M, metastasis; OR, odds ratio; CI, confidence interval.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eARHGAP40 promotes apoptosis and inhibits proliferation in colorectal cancer cells\u003c/h2\u003e\u003cp\u003eGiven that reduced ARHGAP40 expression is closely associated with disease progression in colorectal cancer, we further investigated the functional role of ARHGAP40 in CRC cells. We manipulated ARHGAP40 expression in SW480, SW620, and HCT116 cell lines by transfecting them with either an ARHGAP40 overexpression plasmid or ARHGAP40-targeted siRNA. RT-qPCR analysis confirmed efficient overexpression and knockdown of ARHGAP40 (Supplementary Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA, S1B). CCK-8 assays demonstrated that upregulation of ARHGAP40 significantly decreased cell viability across all three CRC cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA), whereas silencing ARHGAP40 resulted in enhanced cell proliferation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, flow cytometry analysis revealed that ARHGAP40 overexpression markedly increased apoptosis in CRC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), while ARHGAP40 knockdown substantially reduced cell apoptosis compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Collectively, these findings suggest that ARHGAP40 suppresses CRC cell proliferation and promotes apoptotic cell death.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eARHGAP40 inhibits the migration and invasion of colorectal cancer cells.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe employed scratch wound healing and Transwell assays to assess the impact of ARHGAP40 on the migratory and invasive abilities of CRC cells. The scratch wound healing assay demonstrated that cells transfected with the ARHGAP40 overexpression vector exhibited significantly slower wound closure compared to cells transfected with the control vector (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Similarly, Transwell migration assays revealed a marked reduction in cell migration rates in ARHGAP40-overexpressing cells relative to negative controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Consistent results were observed in Matrigel invasion assays, where ARHGAP40 overexpression significantly impaired the invasive capacity of CRC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eConversely, knockdown of ARHGAP40 led to enhanced migration and invasion of colorectal cancer cells, as demonstrated by both scratch wound healing and Transwell invasion assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA\u0026ndash;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). These findings suggest that ARHGAP40 may inhibit the migratory and invasive potentials of CRC cells.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003eLow ARHGAP40 expression is associated with the prognosis of CRC\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eOur previous findings demonstrated that ARHGAP40 expression is markedly reduced in tumor tissues from patients with colorectal cancer (CRC). To further explore the prognostic value of ARHGAP40, we analyzed data from the GEO database, examining its association with survival outcomes in CRC. Five independent datasets were utilized: GSE17537 (177 samples), GSE143985 (91 samples), GSE17536 (56 samples), GSE12945 (62 samples), and GSE14333 (290 samples), encompassing CRC cases at various clinical stages.\u003c/p\u003e\u003cp\u003eAnalysis revealed that lower ARHGAP40 expression was significantly associated with reduced overall survival (OS) in both the GSE17537 and GSE143985 cohorts (GSE17537, HR\u0026thinsp;=\u0026thinsp;0.3034, 95% CI\u0026thinsp;=\u0026thinsp;0.09597\u0026ndash;0.9595, Cox P\u0026thinsp;=\u0026thinsp;0.0423; GSE143985, HR\u0026thinsp;=\u0026thinsp;0.2892, 95% CI\u0026thinsp;=\u0026thinsp;0.1046\u0026ndash;0.8000, Cox P\u0026thinsp;=\u0026thinsp;0.0169; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Furthermore, in the GSE17536, GSE12945, and GSE14333 cohorts, decreased ARHGAP40 expression was strongly correlated with shorter disease-free survival (DFS) (GSE17536, HR\u0026thinsp;=\u0026thinsp;0.4169, 95% CI\u0026thinsp;=\u0026thinsp;0.2076\u0026ndash;0.8375, Cox P\u0026thinsp;=\u0026thinsp;0.0140; GSE14333, HR\u0026thinsp;=\u0026thinsp;0.3239, 95% CI\u0026thinsp;=\u0026thinsp;0.1342\u0026ndash;0.7817, Cox P\u0026thinsp;=\u0026thinsp;0.0121; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Collectively, these results indicate that the downregulation of ARHGAP40 in CRC is associated with poorer prognosis.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eARHGAP40 functions by regulating the activity of RhoA in CRC cells\u003c/h2\u003e\u003cp\u003eARHGAP40 exerts its effects in CRC cells primarily by regulating RhoA activity. To elucidate the underlying mechanisms and signaling pathways influenced by ARHGAP40, we performed RNA sequencing on ARHGAP40-knockdown and control CRC cells. This analysis identified 2,212 differentially expressed genes (|fold change| \u0026ge; 1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) upon ARHGAP40 depletion, with 1,130 genes significantly downregulated and 1,082 genes significantly upregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). KEGG enrichment analysis revealed that the upregulated genes were predominantly involved in the HIF-1 signaling pathway and glycolytic processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). Utilizing the Reactome database, we found that ARHGAP40 was associated with multiple signaling cascades, including Rho GTPase signaling, Rho GTPase effectors, receptor tyrosine kinases, WNT signaling, and GPCR downstream signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther protein\u0026ndash;protein interaction (PPI) analysis using the STRING database suggested that ARHGAP40 interacts with members of the Rho-GTPase family, notably RhoA and CDC42 (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Other ARHGAP family proteins, such as ARHGAP28, ARHGAP26, and ARHGAP18, have been reported to inactivate RhoA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Notably, RhoA overexpression is associated with increased invasion, unfavorable prognosis, and chemotherapy resistance in CRC patients [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBased on these insights, we hypothesized that ARHGAP40 could directly bind to RhoA and modulate its function in CRC cells. To test this, we performed co-immunoprecipitation (co-IP) assays following transfection with Flag-ARHGAP40 or a Flag-empty vector. The results confirmed a direct interaction between ARHGAP40 and RhoA (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). Next, we assessed RhoA activity in CRC cell lines treated with either ARHGAP40 siRNA or control siRNA. Pull-down assays using RhoA-binding domain beads captured the active, GTP-bound RhoA, which was then quantified by Western blotting. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC, ARHGAP40 overexpression significantly inhibited RhoA activation, whereas ARHGAP40 knockdown markedly increased RhoA activation. These findings suggest that ARHGAP40 may inhibit CRC progression by binding to and modulating RhoA activity.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eDespite significant advances in the diagnosis and treatment of colorectal cancer, many patients continue to experience poor prognoses due to the risk of tumor recurrence or metastasis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Further research is required to gain a deeper understanding of the mechanisms underlying cancer initiation and to identify reliable biomarkers for the diagnosis of colorectal cancer. The primary objective of this study is to elucidate the role of ARHGAP40 in the progression and metastasis of colorectal cancer. Through the use of clinical specimens and in vitro cellular experiments, we demonstrate that ARHGAP40 suppresses tumor growth and interacts with the RhoA signaling pathway. These findings not only enhance our understanding of the molecular mechanisms involved in colorectal cancer but also suggest that ARHGAP40 may serve as a potential therapeutic target for CRC.\u003c/p\u003e\u003cp\u003eThe ARHGAP gene family encodes at least 32 RhoGAP proteins, which participate in a wide range of biological processes, including neuronal development, cell division, migration, angiogenesis, and tumor suppression [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In recent years, increasing attention has been paid to the role of ARHGAP family genes in tumorigenesis and tumor progression. For instance, ARHGAP10/GPX4 has been shown to induce ferroptosis in ovarian cancer cells and inhibit tumor growth, findings that are consistent with clinical observations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, ARHGAP24 has been implicated in the proliferation and metastasis of breast cancer cells, as well as in the pathogenesis of renal cell carcinoma (RCC) and hepatocellular carcinoma (HCC) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent studies have also reported associations between ARHGAP family genes and colorectal cancer (CRC). For example, the RhoGAP6 isoform 1 variant has been identified as a potential biomarker for CRC progression [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, elevated expression of ARHGAP8 has been observed in colorectal tumor tissues, where it regulates CRC cell activity through the ARHGAP1/CDC42GAP/p50RHOGAP pathway [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, ARHGAP4 expression is significantly increased in CRC patients and is associated with poorer prognosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, the role of ARHGAP40 in tumors, particularly in colorectal cancer (CRC), remains largely unexplored. This study is the first to demonstrate that ARHGAP40 possesses tumor-suppressive properties and may serve as a potential biomarker for CRC. Our findings revealed that ARHGAP40 expression is significantly reduced in both colorectal cancer cells and tissues compared to their normal counterparts. Moreover, ARHGAP40 mRNA levels were strongly associated with key clinicopathological features of CRC, including invasion depth, tumor differentiation, lymph node metastasis, and TNM stage. Mechanistic investigations further showed that ARHGAP40 suppression enhances CRC cell proliferation and inhibits apoptosis, whereas its activation exerts the opposite effects. Collectively, these results highlight the unique and important role of ARHGAP40 in regulating CRC growth and progression.\u003c/p\u003e\u003cp\u003eMetastasis, characterized by its selective nature, is a leading cause of cancer-related mortality [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In colorectal cancer, the elevated death rates are largely attributed to the dissemination of malignant cells from the primary tumor to distant sites [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, the underlying mechanisms driving CRC metastasis remain incompletely understood. Previous studies have demonstrated that the ARHGAP gene family can influence tumor metastasis by regulating RhoA or other members of the Rho family [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Consistent with findings for other ARHGAP proteins, our study revealed that downregulation of ARHGAP40 significantly enhances the migratory and invasive abilities of colorectal cancer cells, whereas overexpression of ARHGAP40 yields the opposite effects. These results indicate that ARHGAP40 may play a crucial role in suppressing CRC cell metastasis in vitro, potentially through modulation of Rho GTPase signaling pathways.\u003c/p\u003e\u003cp\u003eRho family proteins dynamically alternate between an active GTP-bound state and an inactive GDP-bound state. Members of the ARHGAP family regulate this transition by stimulating the intrinsic GTPase activity of Rho GTPases, thereby promoting their conversion to the inactive GDP-bound form [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. RhoA, a key member of the Rho GTPase family, is frequently overexpressed in various human tumors and is implicated in cell proliferation, cell cycle regulation, and cytoskeletal reorganization [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The role of RhoA in colorectal cancer (CRC), however, appears to be complex and context-dependent. Some studies have reported that high RhoA expression is associated with significantly reduced five-year survival rates following surgical resection in CRC patients [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. and that RhoA upregulation is linked to enhanced migration and invasion of CRC cells in vitro [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In contrast, research by Rodrigues et al. demonstrated that reduced RhoA expression in CRC is associated with increased lymph node metastasis, and that RhoA inactivation can augment the migratory and invasive capabilities of CRC cells in vitro [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Moreover, their in vivo mouse model experiments suggested that loss of RhoA function significantly contributes to CRC metastasis [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In our study, RNA-seq analysis of downstream genes revealed that Rho GTPase signaling is more active in cells lacking ARHGAP40. STRING-based protein interaction analysis further confirmed that ARHGAP40 physically interacts with RhoA. These findings suggest that ARHGAP40 may modulate colorectal cancer progression at least in part by regulating the RhoA signaling pathway.\u003c/p\u003e\u003cp\u003eRho GTPases play a pivotal role in the regulation of cellular signaling pathways. These molecules cycle between an active, GTP-bound state and an inactive, GDP-bound state [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Upon activation, Rho GTPases engage with a variety of downstream effectors, thereby influencing their activities and subcellular localization [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The dynamic regulation of Rho GTPase activity is orchestrated by the opposing actions of guanine nucleotide exchange factors (GEFs), which promote activation, and GTPase-activating proteins (GAPs), which facilitate inactivation [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Several members of the ARHGAP family, such as ARHGAP5, ARHGAP10, and ARHGAP28, have been reported to promote the intracellular accumulation of RhoA in cancer cells [\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, in this study, we examined whether ARHGAP40 could function as an effective GAP for RhoA in colorectal cancer cells.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. First, some of the conclusions drawn require further validation through in vivo experiments. Second, additional studies are needed to confirm the relationship between ARHGAP40 and RhoA and to elucidate the underlying mechanisms. Finally, the potential interactions between ARHGAP40 and other members of the Rho family proteins warrant further investigation.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our research demonstrates that ARHGAP40, functioning as a tumor suppressor, is expressed at low levels in colorectal cancer and is involved in regulating cell proliferation, metastasis, and viability. Importantly, our findings indicate that ARHGAP40 exerts its tumor-suppressive effects in colorectal cancer by negatively regulating RhoA. Collectively, these results suggest that ARHGAP40 may serve as a novel and promising therapeutic target for the treatment of colorectal cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to Prof. Junhua Wu of the Medical School of Nanjing University for his immensely valuable guidance and assistance throughout the duration of this study. In addition, we extend our appreciation to colleagues from Guangzhou Huayin Medical Laboratory Center including Zhihui Quan, Mingming Huang, and Jiewei Mao for their technical support and engaging discussions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBin Lian: Conception and design, Collection and assembly of data, Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003eNa You: Conception and design, Data analysis and interpretation, Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003eJingyu Wang: Collection and assembly of data, Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003eCong Wang: Collection and assembly of data, Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003eYunjie Wen: Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003eJiandong Wang: Provision of study materials or patients, Data analysis and interpretation, Manuscript writing, Final approval of manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by the Guangzhou Postdoctoral Innovation Practice Base Project of Guangzhou Huayin Medical Laboratory Center Co, Ltd.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets produced and inspected in the present study can be retrieved from the corresponding author upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have disclosed that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design and use of archival tissues were approved by the Jinling Hospital Ethics Committee, which waived the requirement for informed consent due to the retrospective nature of the analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3322/caac.21834\u003c/span\u003e\u003cspan address=\"10.3322/caac.21834\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChen Z, He L, Zhao L, Zhang G, Wang Z, Zhu P, et al. circREEP3 drives colorectal cancer progression via activation of FKBP10 transcription and restriction of antitumor immunity. Adv Sci (Weinh). 2022;9:e2105160. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/advs.202105160\u003c/span\u003e\u003cspan address=\"10.1002/advs.202105160\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRauf A, Abu-Izneid T, Khalil AA, Imran M, Shah ZA, Emran TB et al. Berberine as a potential anticancer agent: A comprehensive review. Molecules 2021;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules26237368\u003c/span\u003e\u003cspan address=\"10.3390/molecules26237368\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEmran TB, Shahriar A, Mahmud AR, Rahman T, Abir MH, Siddiquee MF, et al. Multidrug resistance in cancer: Understanding molecular mechanisms, immunoprevention and therapeutic approaches. Front Oncol. 2022;12:891652. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.891652\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.891652\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eIslam MR, Islam F, Nafady MH, Akter M, Mitra S, Das R et al. Natural small molecules in breast cancer treatment: Understandings from a therapeutic viewpoint. Molecules 2022;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules27072165\u003c/span\u003e\u003cspan address=\"10.3390/molecules27072165\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKomatsu M, Ichikawa H, Chiwaki F, Sakamoto H, Komatsuzaki R, Asaumi M, et al. ARHGAP-RhoA signaling provokes homotypic adhesion-triggered cell death of metastasized diffuse-type gastric cancer. Oncogene. 2022;41:4779\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41388-022-02469-6\u003c/span\u003e\u003cspan address=\"10.1038/s41388-022-02469-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu W, Xia K, Zheng D, Huang X, Wei Z, Wei Z, et al. Construction of a prognostic risk score model based on the ARHGAP family to predict the survival of osteosarcoma. BMC Cancer. 2023;23:1179. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12885-023-11673-w\u003c/span\u003e\u003cspan address=\"10.1186/s12885-023-11673-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eG\u0026eacute;ci I, Bober P, Filov\u0026aacute; E, Amler E, Sabo J. The role of ARHGAP1 in Rho GTPase inactivation during metastasizing of breast cancer cell line MCF-7 after treatment with doxorubicin. Int J Mol Sci. 2023;24. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijms241411352\u003c/span\u003e\u003cspan address=\"10.3390/ijms241411352\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJohnstone CN, Castellv\u0026iacute;-Bel S, Chang LM, Bessa X, Nakagawa H, Harada H, et al. ARHGAP8 is a novel member of the RHOGAP family related to ARHGAP1/CDC42GAP/p50RHOGAP: Mutation and expression analyses in colorectal and breast cancers. Gene. 2004;336:59\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.gene.2004.01.025\u003c/span\u003e\u003cspan address=\"10.1016/j.gene.2004.01.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang J, Qian J, Hu Y, Kong X, Chen H, Shi Q, et al. ArhGAP30 promotes p53 acetylation and function in colorectal cancer. Nat Commun. 2014;5:4735. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms5735\u003c/span\u003e\u003cspan address=\"10.1038/ncomms5735\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYu N, Yuan B, Cai J, Liu J, Zhang W, Bao W, et al. Loss of ARHGAP40 expression in basal cell carcinoma via CpG island hypermethylation. Exp Dermatol. 2023;32:2094\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/exd.14950\u003c/span\u003e\u003cspan address=\"10.1111/exd.14950\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHuang H, Wang S, Guan Y, Ren J, Liu X. Molecular basis and current insights of atypical Rho small GTPase in cancer. Mol Biol Rep. 2024;51:141. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11033-023-09140-7\u003c/span\u003e\u003cspan address=\"10.1007/s11033-023-09140-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang GY, Yang WH, Chen Z. Upregulated STAT3 and RhoA signaling in colorectal cancer (CRC) regulate the invasion and migration of CRC cells. Eur Rev Med Pharmacol Sci. 2016;20:2028\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDing AX, Wang H, Zhang JM, Yang W, Kuang YT. lncRNA BANCR promotes the colorectal cancer metastasis through accelerating exosomes-mediated M2 macrophage polarization via regulating RhoA/ROCK signaling. Mol Cell Biochem. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11010-023-04709-z\u003c/span\u003e\u003cspan address=\"10.1007/s11010-023-04709-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYeung CY, Taylor SH, Garva R, Holmes DF, Zeef LA, Soininen R, et al. Arhgap28 is a RhoGAP that inactivates RhoA and downregulates stress fibers. PLoS ONE. 2014;9:e107036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0107036\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0107036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiang L, Gu W, Li M, Gao R, Zhang X, Guo C, et al. The long noncoding RNA HOTAIRM1 controlled by AML1 enhances glucocorticoid resistance by activating RHOA/ROCK1 pathway through suppressing ARHGAP18. Cell Death Dis. 2021;12:702. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41419-021-03982-4\u003c/span\u003e\u003cspan address=\"10.1038/s41419-021-03982-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeong D, Park S, Kim H, Kim CJ, Ahn TS, Bae SB, et al. RhoA is associated with invasion and poor prognosis in colorectal cancer. Int J Oncol. 2016;48:714\u0026ndash;22. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/ijo.2015.3281\u003c/span\u003e\u003cspan address=\"10.3892/ijo.2015.3281\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuihua H, Mengyi Z, Chong Z, Meng Q, Xin M, Qiulin T, et al. RhoA regulates resistance to irinotecan by regulating membrane transporter and apoptosis signaling in colorectal cancer. Oncotarget. 2016;7:87136\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18632/oncotarget.13548\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.13548\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLu L, Wu M, Lu Y, Zhao Z, Liu T, Fu W, et al. MicroRNA-424 regulates cisplatin resistance of gastric cancer by targeting SMURF1 based on GEO database and primary validation in human gastric cancer tissues. Onco Targets Ther. 2019;12:7623\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2147/ott.S208275\u003c/span\u003e\u003cspan address=\"10.2147/ott.S208275\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eXin R, Tang XM, Jiang YJ, Yu F, Li S, Jia CY, et al. Construction of a novel MYC-associated ceRNA regulatory network to identify prognostic biomarkers in colon adenocarcinoma. J Oncol. 2022;2022:3216285. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2022/3216285\u003c/span\u003e\u003cspan address=\"10.1155/2022/3216285\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang C, Wu S, Mou Z, Zhou Q, Zhang Z, Chen Y, et al. Transcriptomic analysis identified ARHGAP family as a novel biomarker associated with tumor-promoting immune infiltration and nanomechanical characteristics in bladder cancer. Front Cell Dev Biol. 2021;9:657219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fcell.2021.657219\u003c/span\u003e\u003cspan address=\"10.3389/fcell.2021.657219\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eQi L, Sun B, Yang B, Lu S. circRNA RPPH1 facilitates the aggravation of breast cancer development by regulating miR-542-3p/ARHGAP1 pathway. Cancer Biother Radiopharm. 2022;37:708\u0026ndash;19. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1089/cbr.2020.4381\u003c/span\u003e\u003cspan address=\"10.1089/cbr.2020.4381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKe H, Shao J, Hu J, Song X, Han H, Zhu Z, et al. ARHGAP10, transcriptionally regulated by sodium butyrate, promotes ferroptosis of ovarian cancer cells. Front Biosci (Landmark Ed). 2024;29:167. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31083/j.fbl2905167\u003c/span\u003e\u003cspan address=\"10.31083/j.fbl2905167\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMeng B, Wang P, Zhao C, Yin G, Meng X, Li L, et al. miR-21-5p serves as a promoter in renal cell carcinoma progression through ARHGAP24 downregulation. Environ Sci Pollut Res Int. 2022;29:39985\u0026ndash;93. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11356-021-18343-z\u003c/span\u003e\u003cspan address=\"10.1007/s11356-021-18343-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang W, Wang B, Yu Q, Liu T, Li T, Tian T, et al. ARHGAP24 represses β-catenin transactivation-induced invasiveness in hepatocellular carcinoma mainly by acting as a GTPase-independent scaffold. Theranostics. 2022;12:6189\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.72134\u003c/span\u003e\u003cspan address=\"10.7150/thno.72134\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGuo F, Liu Y, Huang J, Li Y, Zhou G, Wang D, et al. Identification of Rho GTPase activating protein 6 isoform 1 variant as a new molecular marker in human colorectal tumors. Pathol Oncol Res. 2010;16:319\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12253-009-9226-1\u003c/span\u003e\u003cspan address=\"10.1007/s12253-009-9226-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHossain MJ, Chowdhury UN, Islam MB, Uddin S, Ahmed MB, Quinn JMW, et al. Machine learning and network-based models to identify genetic risk factors to the progression and survival of colorectal cancer. Comput Biol Med. 2021;135:104539. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.compbiomed.2021.104539\u003c/span\u003e\u003cspan address=\"10.1016/j.compbiomed.2021.104539\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFu MS, Pan SX, Cai XQ, Hu YX, Zhang WJ, Pan QC. Analysis of ARHGAP4 expression with colorectal cancer clinical characteristics and prognosis. Front Oncol. 2022;12:899837. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fonc.2022.899837\u003c/span\u003e\u003cspan address=\"10.3389/fonc.2022.899837\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhou X, Liu Z, He T, Zhang C, Jiang M, Jin Y, et al. DDX10 promotes the proliferation and metastasis of colorectal cancer cells via splicing RPL35. Cancer Cell Int. 2022;22:58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12935-022-02478-1\u003c/span\u003e\u003cspan address=\"10.1186/s12935-022-02478-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTownsend AR, Chong LC, Karapetis C, Price TJ. Selective internal radiation therapy for liver metastases from colorectal cancer. Cancer Treat Rev. 2016;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ctrv.2016.09.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ctrv.2016.09.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. :148\u0026thinsp;\u0026ndash;\u0026thinsp;54.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eStreets AJ, Prosseda PP, Ong AC. Polycystin-1 regulates ARHGAP35-dependent centrosomal RhoA activation and ROCK signaling. JCI Insight 2020;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1172/jci.insight.135385\u003c/span\u003e\u003cspan address=\"10.1172/jci.insight.135385\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang F, Sahu V, Peng K, Wang Y, Li T, Bala P, et al. Recurrent RhoGAP gene fusion CLDN18-ARHGAP26 promotes RHOA activation and focal adhesion kinase and YAP-TEAD signalling in diffuse gastric cancer. Gut. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/gutjnl-2023-329686\u003c/span\u003e\u003cspan address=\"10.1136/gutjnl-2023-329686\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLiu X, Yu Z, Li Y, Huang J. CX3CL1 and its receptor CX3CR1 interact with RhoA signaling to induce paclitaxel resistance in gastric cancer. Heliyon. 2024;10:e29100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e29100\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e29100\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRodrigues P, Macaya I, Bazzocco S, Mazzolini R, Andretta E, Dopeso H, et al. RHOA inactivation enhances Wnt signalling and promotes colorectal cancer. Nat Commun. 2014;5:5458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/ncomms6458\u003c/span\u003e\u003cspan address=\"10.1038/ncomms6458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJaffe AB, Hall A. Rho GTPases: biochemistry and biology. Annu Rev Cell Dev Biol. 2005;21:247\u0026ndash;69. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev.cellbio.21.020604.150721\u003c/span\u003e\u003cspan address=\"10.1146/annurev.cellbio.21.020604.150721\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMoissoglu K, Kiessling V, Wan C, Hoffman BD, Norambuena A, Tamm LK, et al. Regulation of Rac1 translocation and activation by membrane domains and their boundaries. J Cell Sci. 2014;127:2565\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1242/jcs.149088\u003c/span\u003e\u003cspan address=\"10.1242/jcs.149088\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSpindler V, Schlegel N, Waschke J. Role of GTPases in control of microvascular permeability. Cardiovasc Res. 2010;87:243\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/cvr/cvq086\u003c/span\u003e\u003cspan address=\"10.1093/cvr/cvq086\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTian T, Chen ZH, Zheng Z, Liu Y, Zhao Q, Liu Y, et al. Investigation of the role and mechanism of ARHGAP5-mediated colorectal cancer metastasis. Theranostics. 2020;10:5998\u0026ndash;6010. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7150/thno.43427\u003c/span\u003e\u003cspan address=\"10.7150/thno.43427\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSekiguchi M, Sobue A, Kushima I, Wang C, Arioka Y, Kato H, et al. ARHGAP10, which encodes Rho GTPase-activating protein 10, is a novel gene for schizophrenia risk. Transl Psychiatry. 2020;10:247. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-020-00917-z\u003c/span\u003e\u003cspan address=\"10.1038/s41398-020-00917-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKasuya K, Nagakawa Y, Hosokawa Y, Sahara Y, Takishita C, Nakajima T, et al. RhoA activity increases due to hypermethylation of ARHGAP28 in a highly liver-metastatic colon cancer cell line. Biomed Rep. 2016;4:335\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3892/br.2016.582\u003c/span\u003e\u003cspan address=\"10.3892/br.2016.582\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\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":"ARHGAP40, Colorectal cancer, RhoA activity","lastPublishedDoi":"10.21203/rs.3.rs-7317386/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7317386/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eARHGAP40, downregulated in various tumors including basal cell carcinoma, has an unclear role in colorectal cancer (CRC). This study aimed to elucidate the function and clinical significance of ARHGAP40 in CRC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eImmunohistochemistry (IHC) was utilized to assess the quantity of ARHGAP40 protein in both tumor and normal tissues. RNA interference (RNAi) and lentiviral vectors were used to either overexpress ARHGAP40 or knockdown ARHGAP40 in CRC cells. The CCK-8 test was used to measure cell proliferation, and flow cytometry was used to measure cell apoptosis. Scratch and Transwell tests were used to explore how ARHGAP40 affected the migration and invasion of CRC cells. RNA-sequence, western blotting, RhoA pull-down, and coimmunoprecipitation (co-IP) methods were used to figure out how ARHGAP40 affects CRC cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearchers discovered that the expression of ARHGAP40 was significantly lower in human colorectal cancer tissues. This low level of ARHGAP40 expression was linked to tumor differentiation, invasion depth, lymph node metastasis, TNM stage, and a poor outcome in CRC patients. Overexpression of ARHGAP40 also greatly decreased cell proliferation, made it harder for cells to migrate and invade, and raised the apoptosis rates of CRC cells. Also, RhoA activity was enhanced after ARHGAP40 was knocked down.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eARHGAP40 levels downregulated in CRC, and it might be possible for it to act as a new tumor suppressor in CRC by controlling RhoA activity.\u003c/p\u003e\n\u003cp\u003eBin Lian and Na You contributed equally to this work (co–first authors).\u003c/p\u003e","manuscriptTitle":"Low expression of ARHGAP40 in colorectal cancer facilitates tumor progression by activating the RhoA pathway","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 09:43:01","doi":"10.21203/rs.3.rs-7317386/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":"395fb792-ef30-426d-bd61-2f283ac8523f","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T11:41:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-22 09:43:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7317386","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7317386","identity":"rs-7317386","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}