An Investigation into the Influence of ACTG2 on Gastric Cancer Prognosis and Cellular Biological Behaviors

An Investigation into the Influence of ACTG2 on Gastric Cancer Prognosis and Cellular Biological Behaviors | 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 Article An Investigation into the Influence of ACTG2 on Gastric Cancer Prognosis and Cellular Biological Behaviors Yang Shuai, Zhao Wenwen, Xiao Jingwen, Feng Qingqing, Zhao Wenfei, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6582968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background This study investigates the impact of ACTG2 expression on poor prognosis in gastric cancer (GC) patients, as well as the biological behavior of GC cells. Methods Differential gene expression analysis was conducted on GC samples with distant metastasis and those without, utilizing the TCGA database. We constructed a protein interaction network for the differentially expressed genes and identified the target gene ACTG2. Subsequently, we explored the correlation between ACTG2 gene expression levels and prognosis, as well as clinical features. A GC cell line exhibiting significantly low ACTG2 expression was selected, and ACTG2 expression was upregulated using a human ACTG2 overexpression plasmid. The effects of ACTG2 on cancer cell proliferation, apoptosis, invasion, and migration were verified through CCK-8, flow cytometry, Transwell, and cell scratch assays. Results A total of 55 differentially expressed genes associated with distant metastasis of cancer were identified from the TCGA database. The expression of ACTG2 was significantly reduced in cancer tissues and showed a significant correlation with overall survival, progression-free survival, and post-progression survival of cancer patients. Further analysis confirmed that ACTG2 expression is positively correlated with the infiltration levels of CD8 + T cells, CD4 + T cells, neutrophils, monocytes, macrophages, and dendritic cells, demonstrating the potential role of ACTG2 in immune regulation. Notably, the ACTG2 gene is significantly downregulated in the cancer cell lines HGC-27 and AGS. After upregulating ACTG2 expression, the proliferation, invasion, and migration abilities of HGC-27 and AGS cells were significantly reduced, while their apoptosis ability was significantly enhanced. Conclusion The reduced expression of ACTG2 in gastric cancer (GC) may correlate with a poor prognosis, highlighting its potential significance as a prognostic biomarker. Furthermore, the overexpression of ACTG2 has been shown to inhibit the proliferation, migration, and invasion of GC cells, thereby underscoring its potential role in the pathogenesis and progression of GC. Biological sciences/Cancer Biological sciences/Cancer/Cancer microenvironment Biological sciences/Cancer/Gastrointestinal cancer Biological sciences/Cancer/Metastases Biological sciences/Cancer/Tumour biomarkers ACTG2 GC Prognosis Cellular Biological Behaviors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Gastric cancer (GC) is one of the most prevalent cancers worldwide. According to GLOBOCAN 2022 statistics, nearly 1 million new cases and 660,000 deaths are attributed to GC annually, making it the fifth most common cancer and the fifth leading cause of cancer-related deaths globally 1 . China, as a high-incidence region, ranks fifth in incidence and third in mortality, with 359,000 new cases and 260,000 deaths each year 1 . Despite declining trends in both incidence and mortality rates, GC remains a significant global health concern due to its complex heterogeneity and multiple risk factors 1 . Early-stage GC often presents with no apparent symptoms, and the limited prevalence of early screening programs, coupled with inadequate public awareness and a lack of preventive measures in China, results in low detection rates for early-stage GC. While advancements in medical philosophy and technology have contributed to improved survival rates among GC patients, the propensity for metastasis, recurrence, and drug resistance underscores the critical need to understand the underlying molecular mechanisms of GC metastasis in order to develop more effective treatment strategies and improve outcomes for this deadly disease. Studies have indicated that the progression and recurrence of cancer are influenced not only by the underlying genetic alterations within tumors but also by the tumor microenvironment (TME), where tumor-infiltrating immune cells play a pivotal role 2 . The continuous interplay between the TME and tumor cells significantly impacts tumor initiation, progression, metastasis, and treatment response. Cancer progression markers are, to a certain extent, dependent on the functions of the TME, which facilitates cancer cell invasion, metastasis, angiogenesis, and immune evasion 3 . The intricate interactions between tumor cells and tumor-infiltrating immune cells are crucial to cancer development. Therefore, a comprehensive understanding of the molecular mechanisms by which the tumor immune microenvironment influences cancer progression provides new hope for combating this devastating disease. ACTG2 is a gene that encodes actin gamma-2, a critical structural protein composed of seven exons and six introns, spanning a total length of 14.4 kb and located on human chromosome 2q35. This gene is adjacent to several other genes associated with smooth muscle 4 . The protein it encodes consists of 377 amino acids and is responsible for forming myofibrils within cells. Actin, as a fundamental and ubiquitous structural protein present in all human cells, plays pivotal roles in various cellular functions, including cell division, migration, chromatin remodeling, and vesicle trafficking 5 , 6 . Despite its importance, the role of ACTG2 in digestive tract tumors has been infrequently studied, with limited research reported in hepatocellular carcinoma (HCC) 7 , 8 , small intestinal neuroendocrine tumors (SI-NETs) 9 , and colorectal cancer (CRC) 10 . This study employs a combined approach of bioinformatics and fundamental experimentation to conduct a comprehensive investigation into the expression levels, prognostic value, and biological functions of the ACTG2 gene in gastric cancer (GC). Notably, the perspectives discussed herein have not been previously reported in the literature. Our research aims to elucidate the role of ACTG2 in assessing prognosis and understanding the underlying mechanisms of GC progression. This work provides a theoretical foundation for predicting patient outcomes, identifying novel therapeutic targets, and developing effective treatment strategies for GC. 2 MATERIALS AND METHODS 2.1 Data Source Relevant transcriptomic and clinical data from gastric cancer (GC) patients, including 412 GC tumor tissues and 36 normal tissues, were retrieved from the TCGA database ( https://portal.gdc.cancer.gov/ ). These data were consolidated and curated using R and Perl, with incomplete entries excluded to ensure data integrity. 2.2 Screening, Identification, and Analysis of Key Genes 2.2.1 Differential Expression Analysis (DEGs) The GC sample data from the TCGA database were categorized into two groups based on the presence or absence of distant metastasis: the non-distant metastasis group (M0) and the distant metastasis group (M1). Differential expression analysis of genes was performed between these two groups. Utilizing R software, specifically the BiocManager, pheatmap, and ggplot2 packages, a matrix was constructed to facilitate the analysis. The criteria for screening differentially expressed genes (DEGs) were established as follows: |log2FC| > 1 for the fold change in expression and p < 0.05 for statistical significance. A volcano plot was generated to visually represent these DEGs. 2.2.2 Construction of PPI Network and Screening of Core Genes in the Network Differentially expressed genes were uploaded to the STRING database ( https://string-db.org/ ) to construct a protein-protein interaction (PPI) network specific to distant metastasis in gastric cancer (GC). Utilizing 11 topological analysis methods available through the CytoHubba plugin in Cytoscape software—including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), BottleNeck, Eccentricity, Closeness, Radiality, Betweenness, and Stress—scores were calculated for each gene. Genes exhibiting downregulated expression were selected, and their scores from these 11 analytical approaches were consolidated and ranked using Excel. The top five genes from this comprehensive ranking were subsequently scrutinized for in-depth analysis. 2.2.3 Identification, Expression Analysis, and Validation of Key Genes Following a search for the five selected genes in PubMed and the exclusion of those with clear experimental data, the target gene was identified as ACTG2. The expression of ACTG2 across various cancer types was analyzed using the TIMER 2.0 database, with a specific emphasis on its expression in gastric cancer. The expression of ACTG2 in gastric cancer was further validated through in vitro cell experiments and immunohistochemical staining in the HpA database. 2.2.4 Survival Analysis Utilizing the Kaplan-Meier Plotter database, we examined the relationship between ACTG2 gene expression levels and overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS) in gastric cancer patients, subsequently generating Kaplan-Meier (K-M) curves. Additionally, we leveraged the UALCAN database to investigate the association between ACTG2 expression and clinical characteristics, including gastric cancer grade, stage, and lymph node metastasis stage. Both univariate and multivariate Cox analyses were conducted to evaluate the prognostic significance of ACTG2. 2.2.5 Immune Infiltration Analysis Employing the Immune Association module in the TIMER 2.0 database, we assessed the correlation between the ACTG2 gene and the infiltration levels of various immune cell types in gastric cancer. Furthermore, we utilized SangerBox ( http://sangerbox.com/ ) to analyze the relationships between immune checkpoint genes associated with ACTG2 in gastric cancer. We calculated the stromal score, immune score, and ESTIMATE score for each gastric cancer patient, and subsequently analyzed the correlations between these scores and ACTG2 expression. 2.3 In Vitro Cellular Experiments 2.3.1 Materials and Reagents The human gastric cancer cell lines SGC-7901, HGC-27, and AGS were acquired from the Cell Bank of Shanghai, China, while the human normal gastric mucosal cell line GES-1 was obtained from the Beijing Cancer Institute. The culture medium utilized was DMEM high glucose medium (GIBCO, catalog number 11965092), supplemented with fetal bovine serum (FBS) (GIBCO, catalog number A5669701) and 0.25% trypsin digestive solution (containing EDTA), which were sourced from Dalian Meilun Biotechnology Co., Ltd. Additionally, penicillin-streptomycin (10,000 U/mL) was obtained from GIBCO (catalog number 15140148). The cells were cultured in an incubator maintained at 37°C with 5% CO 2 . Lipofectamine™ 3000 was procured from Life Technologies (Thermo Fisher). The human ACTG2 overexpression plasmid was generated by cloning human ACTG2 cDNA into the eukaryotic expression vector pcDNA3.1, which was self-constructed. Opti-MEM reduced serum medium was also purchased from Life Technologies (Thermo Fisher). 2.3.2 RNA Extraction and qRT-PCR Total RNA was extracted from the cells using Trizol, and its quality and concentration were assessed prior to reverse transcription into cDNA. The primer sequences were as follows: for ACTG2, the forward primer sequence was 5'-GCGTGTAGCACCTGAAGAG-3', and the reverse primer sequence was 5'-GAATGGCGACGTACATGGCA-3'; for Tubulin, the forward primer sequence was 5'-CGGACCAATACGACCAAATCCG-3', and the reverse primer sequence was 5'-AGCCACATCGCTCAGACACC-3'. The reaction system was established according to the kit instructions. The reaction conditions were as follows: 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds, 55–60°C for 20 seconds, and 72°C for 20 seconds. Tubulin served as an internal reference, and the relative expression level of the target gene was calculated using the 2 −ΔΔCt method. The experiment was conducted in triplicate. 2.3.3 Western Blotting Cell lysates were prepared using RIPA lysis buffer, followed by protein separation through SDS-PAGE and transfer to PVDF membranes. After blocking with 5% non-fat milk, the blots were incubated overnight at 4℃ with primary antibodies. Following three washes, secondary antibodies were added, and the blots were incubated for one hour at room temperature. After an additional three washes, the blots were visualized using enhanced chemiluminescence reagents (ECL). The results were captured with a gel imaging system, with Tubulin serving as the internal reference. The grayscale values of each protein were analyzed using Image J software, allowing for the calculation of relative expression levels of different proteins. 2.3.4 Experimental Grouping Gastric cancer cells HGC-27 and AGS, which were in optimal growth conditions, were selected. Approximately 1 ml of 0.25% trypsin was added, followed by 3 ml of complete medium to neutralize the trypsin. The cells were then pipetted repeatedly to create a single-cell suspension. Subsequently, the cells were centrifuged, sedimented, plated, and cultured in a cell incubator at 37℃ with 5% CO 2 . Lipofectamine 3000 was utilized for the transfection of pcDNA3.1 and pcDNA3.1-ACTG2 (human ACTG2 overexpression plasmid). The cells were categorized into different groups based on the transfection: Control group (blank control), NC group (transfected with pcDNA3.1), and ACTG2 group (transfected with pcDNA3.1-ACTG2). 2.3.5 CCK-8 The experiment was conducted with three groups: Control, NC, and ACTG2. Well-cultured human gastric cancer (GC) cells, specifically HGC-27 and AGS, were seeded into 96-well plates at a density of 1 × 10 3 cells per well. At designated time points (0, 6, 12, 24, 36, and 48 hours), 10 µL of CCK-8 solution was added to each well. The cells were then incubated for 3 hours in an incubator, after which the absorbance at 450 nm was measured using a microplate reader. This experiment was repeated three times. 2.3.6 Flow Cytometry for Apoptosis Detection Human GC cells (HGC-27 and AGS) collected from each group were washed twice with phosphate-buffered saline (PBS). Following centrifugation, the cells were resuspended in 500 µL of PBS and incubated with 5 µL of FITC-labeled Annexin V antibody and 5 µL of propidium iodide (PI) staining solution for 30 minutes at room temperature (RT). After centrifugation, the supernatant was discarded, and the cells were washed three times with PBS. Subsequently, the cells were analyzed using a BD FACSAria III flow cytometer, and the flow cytometry data were analyzed using FlowJo software. This experiment was also repeated three times. 2.3.7 Transwell HGC-27 and AGS cells were digested and resuspended in serum-free medium, adjusting the cell density to 1.5 × 10 5 cells/ml. A total of 300 µl of the cell suspension was added to each Transwell insert, while 600 µl of medium containing 10% FBS was added to the lower chamber. The cells were incubated at 37°C for 24 hours. For the invasion assay, the upper chamber of the Transwell insert was pre-coated with matrix gel, whereas no gel was used for the migration assay. Following incubation, the medium in the lower chamber was removed, and the cells were washed twice with PBS buffer. The cells were then fixed with 4% paraformaldehyde for 10 minutes and washed twice with PBS buffer again. Subsequently, the cells were stained with crystal violet for 10 minutes, washed twice with PBS buffer, and the cells in the upper chamber were wiped off using a cotton swab. The cells in the lower chamber were examined under a microscope, and three random fields were selected for photography, counting, and analysis. This experiment was repeated three times. 2.3.8 Wound Healing Assay HGC-27 and AGS cells were plated onto 6-cm dishes at a density of approximately 5 × 10 5 cells per dish. The cells were cultured overnight until they reached confluence. A uniform straight line was then scratched across each well of the 6-cm dishes using the tip of a pipette. After scratching, the cells were washed three times with PBS to remove any floating cells, and serum-free medium was subsequently added. Photographs were taken at 0, 12, and 24 hours using a microscope, with PBS washes performed prior to each photography session to eliminate floating cells. The distance between the scratch lines and the number of migrating cells were measured and recorded. This experiment was repeated three times. 2.4 Statistical Analysis All experiments were independently repeated at least three times, and the resulting data were statistically analyzed using SPSS 26.0. Graphs and charts were generated using GraphPad Prism 9.5 software. For the quantitative data confirmed to follow a normal distribution, results were expressed as mean ± standard deviation (x̄ ± s). Comparisons among multiple groups were performed using one-way and two-way ANOVA, while comparisons between two groups were analyzed using paired sample t-tests. The significance level was set at α = 0.05. 3 Results 3.1 Screening and Identification of Key Genes Related to Distant Metastasis in GC Based on the TCGA Database 3.1.1 Downloading and Organization of TCGA Database Data, and Analysis of DEGs A total of 448 gastric cancer (GC) samples with complete RNA sequencing and metastasis information were downloaded from the TCGA database, consisting of 36 cases with distant metastasis and 412 cases without distant metastasis. These samples were categorized into two groups based on the presence or absence of distant metastasis: the distant metastasis group (M1) and the non-distant metastasis group (M0). Differential gene expression analysis was conducted on the samples from these two groups. The criteria for selecting differentially expressed genes (DEGs) were established as follows: |logFC| (logarithm of fold change) > 1 and p < 0.05. A total of 55 DEGs were identified, comprising 19 upregulated genes and 36 downregulated genes (Fig. 1 A). 3.1.2 PPI Network Construction and Identification of Key Gene Using Cytoscape software, we employed 11 topological algorithms to calculate the scores for each gene within the PPI network (Fig. 1 B). Subsequently, the 36 downregulated genes were comprehensively ranked based on their scores using Excel software. Table 1 presents the top five genes from this ranking. Through a search of the PubMed literature, we preliminarily identified ACTG2 as the target gene of interest. 3.1.3 ACTG2 is Lowly Expressed in GC The ACTG2 gene has been significantly downregulated in various tumor types, including breast cancer, colon cancer, lung cancer, and rectal cancer, while it is notably upregulated in cholangiocarcinoma and hepatocellular carcinoma (Fig. 1 C), which aligns with findings reported in the literature we reviewed. The figure highlights the expression of ACTG2 in gastric cancer (GC), where we observe significant downregulation compared to adjacent normal tissues (p < 0.001). To validate the differential expression of ACTG2 between human normal gastric mucosal cells and GC cells, we conducted preliminary cellular experiments using a human normal gastric mucosal cell line (GES-1) and three GC cell lines (SGC-7901, HGC-27, and AGS). Compared to GES-1, ACTG2 was found to be lowly expressed in GC cells, with statistically significant differences observed in HGC-27 and AGS cells (p < 0.05) (Fig. 1 D). Furthermore, we utilized the HPA database to download immunohistochemical staining images, which revealed that ACTG2 expression levels were lower in GC tissues compared to adjacent normal tissues (Fig. 1 E, F). These validations are consistent with the analytical results obtained from the database. Table 1 Top 5 Genes Ranked Comprehensively by Excel Based on 11 Topological Algorithms in Protein-Protein Interaction Network Node name MCC DMNC MNC Degree EPC BottleNeck EcCentricity Closeness Radiality Betweenness Stress SMYD1 57.00 0.44 7.00 8.00 13.22 4.00 0.19 19.15 5.38 210.88 464.00 ACTG2 58.00 0.48 7.00 7.00 12.82 1.00 0.16 17.08 4.93 24.41 102.00 MYBPC1 34.00 0.37 7.00 7.00 12.85 1.00 0.16 17.55 5.06 89.33 260.00 COL22A1 13.00 0.29 6.00 7.00 12.51 2.00 0.16 17.62 5.03 114.40 324.00 PAX4 12.00 0.32 5.00 7.00 11.32 14.00 0.24 18.67 5.46 260.73 492.00 3.1.4 Low Expression of ACTG2 is Associated With Poor Prognosis in Gastric Cancer Survival analysis was conducted using the Kaplan-Meier Plotter database, where ACTG2 expression values were dichotomized into high and low groups based on the median value. The survival curves were generated using the log-rank test. The results indicated that, for overall survival (OS) analysis, a total of 592 gastric cancer (GC) patients were included. The survival duration in the low ACTG2 expression group (191 patients) was significantly shorter than that in the high expression group (401 patients), with a statistically significant difference observed (HR = 0.75, 95% CI = 0.61–0.92, p = 0.0055) (Fig. 2 A). For progression-free survival (PFS), 358 GC patients were analyzed, revealing that the PFS for patients in the low ACTG2 expression group (141 patients) was shorter than that of the high expression group (217 patients), with a statistically significant difference (HR = 0.62, 95% CI = 0.49–0.8, p = 0.00018) (Fig. 2 B). In terms of post-progression survival (PPS), 221 GC patients were evaluated, demonstrating that the survival time of patients in the low ACTG2 expression group (158 patients) was shorter than that of the high expression group (63 patients), with this difference also being statistically significant (HR = 0.63, 95% CI = 0.46–0.87, p = 0.0048) (Fig. 2 C). Collectively, these findings suggest that low expression of ACTG2 is associated with a poor prognosis in gastric cancer. 3.1.5 The Relationship Between ACTG2 Expression and Clinical Characteristics Analysis utilizing the UALCAN database revealed that ACTG2 expression was significantly lower in tumor tissues compared to adjacent normal tissues across all tumor grades (1–3) (p < 0.001). Specifically, ACTG2 expression was markedly reduced in patients with grade 3 tumors compared to those with grade 1 tumors (p < 0.0001). Similarly, grade 2 tumors exhibited lower ACTG2 expression compared to grade 3 tumors (p < 0.0001) (Fig. 2 D). Furthermore, ACTG2 expression was significantly downregulated in all tumor stages (1–4) relative to adjacent normal tissues (p < 0.01). Notably, within tumor stages, ACTG2 expression was significantly lower in stage 1 tumors compared to stages 2 and 3 (p < 0.001) (Fig. 2 E). Additionally, a decreasing trend in ACTG2 expression was observed with increasing lymph node metastasis stage (p < 0.01) (Fig. 2 F). Independent prognostic analysis underscored the statistical significance of age, stage (p < 0.001), and risk score (p < 0.01) in both multivariate and univariate Cox regression models (Figs. 2 G and 2 H). Collectively, these findings suggest that ACTG2 may serve as a predictive biomarker for prognosis in gastric cancer (GC) patients. 3.1.6 The Relationship Between ACTG2 Expression and Immune Infiltration Analysis of the TIMER 2.0 database's gene module revealed a significant positive correlation between ACTG2 expression and the infiltration levels of CD8 + T cells, CD4 + T cells, neutrophils, monocytes, macrophages, and dendritic cells (p < 0.001) (Table 2 and Fig. 3 A). Utilizing the Sangerbox database to investigate immune checkpoints closely associated with ACTG2, we identified notable correlations (p < 0.01) between ACTG2 expression and 21 out of 60 immune checkpoint pathway genes (Inhibitory = 24, Stimulatory = 36) (Fig. 3 B). Furthermore, we calculated the stromal score, immune score, and estimate score for each gastric cancer (GC) patient based on ACTG2 expression. The results indicated a positive correlation between ACTG2 expression and all three scores: stromal score (r = 0.70, p = 3.5E-59), immune score (r = 0.25, p = 4.4E-7), and estimate score (r = 0.52, p = 3.8E-28) (Fig. 3 C). Table 2 Correlation Between ACTG2 Expression and Immune Cells Types of Immune Cells Rho P purity -0.149 3.57e-03 CD8 + T cells 0.173 7.00e-04 CD4 + T cells 0.373 5.8e-14 neutrophils 0.182 3.81e-04 monocytes 0.23 6.04e-06 macrophages 0.429 2.10e-18 dendritic cells 0.228 7.16e-06 3.2 In Vitro Cellular Experiments 3.2.1 ACTG2 is Lowly Expressed in GC Cells Using qRT-PCR and Western blotting, we consistently observed low expression levels of ACTG2 across various gastric cancer (GC) cell lines (Fig. 4 A, 4 B, and 4 C). Based on these findings, we selected two GC cell lines, HGC-27 and AGS, characterized by significantly lower ACTG2 expression, for subsequent experiments targeting the ACTG2 gene. 3.2.2 Overexpression Vector Upregulates ACTG2 Expression and Verification We employed a human ACTG2 overexpression plasmid to upregulate ACTG2 expression in the GC cell lines HGC-27 and AGS. Subsequent quantification of the upregulated ACTG2 expression levels through qRT-PCR and Western blotting confirmed successful overexpression. Specifically, qRT-PCR analysis revealed a significant increase in RNA expression levels, while Western blotting exhibited markedly thicker bands and a notable elevation in ACTG2 protein levels following transfection with the human ACTG2 overexpression plasmid in both HGC-27 and AGS cells (Fig. 4 D- 4 I), thereby validating the successful overexpression. 3.2.3 Overexpression of ACTG2 Inhibits Proliferation of GC Cells The CCK-8 cell proliferation assay was employed to investigate the impact of ACTG2 overexpression on the proliferation of GC cells. The results demonstrated that the proliferation ability of GC cells HGC-27 and AGS was significantly reduced compared to the negative control cell lines after ACTG2 overexpression (p < 0.0001) (Fig. 5 A, 5 B). 3.2.4 Overexpression of ACTG2 Promotes Apoptosis in GC Cells To further elucidate the effect of ACTG2 overexpression on apoptosis in GC cells, we utilized Annexin-V/PI double staining and flow cytometry. The results indicated that compared to the Control and NC groups, the ACTG2 group exhibited distinct peaks of early and late apoptosis, with statistically significant differences (p < 0.0001). This validates the potential of ACTG2 to induce apoptosis (Fig. 5 C- 5 F). 3.2.5 Overexpression of ACTG2 Inhibits Migration and Invasion of GC Cells To evaluate the invasive capabilities of gastric cancer (GC) cells, Transwell chambers coated with Matrigel were utilized in the upper chamber. GC cells (HGC-27 and AGS) were allowed to invade through the Matrigel-coated membrane to reach the lower chamber, where the number of invasive cells was quantified using crystal violet staining. The ACTG2 group demonstrated significantly reduced invasive capabilities compared to both the Control and NC groups (p < 0.001) (Fig. 6 A, 6 B, 6 C). Similarly, in uncoated Transwell chambers, GC cells (HGC-27 and AGS) migrated directly from the upper to the lower chamber, and the number of migrated cells was quantified by crystal violet staining. In line with the invasion results, the migration capacity of GC cells in the ACTG2 group was significantly lower than that of the Control and NC groups (p < 0.001) (Fig. 6 D, 6 E, 6 F). Furthermore, the wound healing assay further confirmed that the migration abilities of GC cells (HGC-27 and AGS) in the ACTG2 group were inferior to those in the Control and NC groups, with statistically significant differences observed (p < 0.05) (Fig. 6 G, 6 H, 6 I, 6 J). 4 DISCUSSION Gastric cancer (GC) represents a prevalent and perilous tumor type globally, necessitating urgent and comprehensive investigations into its pathogenesis and therapeutic strategies. Early-stage GC often presents with minimal symptoms, with radical surgical resection being the primary treatment option. However, due to insufficient early screening programs in China, the majority of GC patients are diagnosed at advanced stages, characterized by late tumor staging and substantial tumor burden. Systemic chemotherapy, which includes fluoropyrimidines, platinum agents, and taxanes, serves as the cornerstone treatment for advanced GC. Unfortunately, both monotherapy and combination therapies have failed to extend median survival beyond 12 months, leading to a poor prognosis, with a 5-year survival rate below 10% 11 . The high degree of heterogeneity in GC predisposes patients to metastasis, local recurrence, and drug resistance, further contributing to an unfavorable prognosis 12 . Therefore, it is essential to actively investigate the molecular mechanisms underlying GC progression. Tumor metastasis, the primary determinant of cancer-related mortality, presents a formidable challenge in gastric cancer (GC) management. Despite therapeutic advancements, metastatic GC remains associated with dismal 5-year survival rates, underscoring the urgent need to decipher the molecular drivers of this process and highlighting the significance of ACTG2's potential role in GC progression. As a critical component of the actin cytoskeleton, ACTG2 exhibits functional versatility through its involvement in cellular motility, contractility, and structural maintenance. Based on its biological characteristics and existing literature, we hypothesize its direct involvement in tumor cell migration - a hypothesis particularly relevant to gastrointestinal malignancies where emerging preclinical evidence supports this mechanistic link. Edfeldt et al. discovered significantly lower ACTG2 mRNA expression in metastatic small intestinal neuroendocrine tumors (SI-NETs) compared to primary tumors. Treatment with the epigenetic modulator DZNep or miR-145 overexpression in CNDT2.5 cells induced ACTG2 expression, with DZNep concurrently upregulating miR-145 - suggesting a potential regulatory axis. Consistent with breast cancer findings, miR-145 overexpression enhanced ACTG2 expression in SI-NETs, with functional implications for tumor biology 14 – 17 . Tang et al. identified reduced ACTG2 expression in colorectal cancer (CRC) tissues/cells, demonstrating post-transcriptional regulation by miR-3918 through RNA pull-down and luciferase assays. They established a competing endogenous RNA (ceRNA) network where MIR497HG sequesters miR-3918, thereby increasing ACTG2 expression and inhibiting CRC progression - effects reversed by ACTG2 knockdown 10 . In hepatocellular carcinoma (HCC) 7 , 8 , Wu et al. showed that ACTG2 overexpression promotes migration/invasion in vitro and in vivo, while shRNA-mediated knockdown reduces metastatic potential. Mechanistically, ACTG2 silencing decreased Notch1 expression in SMMC-7721 cells, and Notch1 pathway activation rescued ACTG2-mediated migration defects, establishing a functional link between ACTG2 and EMT-related metastasis. These findings underscore the crucial role of ACTG2 in cancer metastasis, with no reports in gastric cancer (GC). Utilizing data from The Cancer Genome Atlas (TCGA), which includes 448 GC samples (36 of which exhibit distant metastasis),and pinpointed low-expressed genes, ultimately selecting ACTG2 as the key gene. Analysis using TIMER 2.0 confirmed the significant downregulation of ACTG2 across various cancers, including GC. Preliminary experiments and immunohistochemical staining from the Human Protein Atlas (HPA) database further validated the downregulation of ACTG2 in GC. Patients with metastatic GC experience poor prognosis and reduced survival, highlighting the critical need for novel prognostic biomarkers and therapeutic targets for this malignancy. Consequently, we performed a survival prognosis analysis focusing on ACTG2. Using Kaplan-Meier Plotter, we identified low ACTG2 expression as significantly associated with reduced overall survival, progression-free survival, and post-progression survival in gastric cancer, with UALCAN analysis further correlating low ACTG2 with advanced tumor grade, stage, and lymph node metastasis—findings validated by Cox regression demonstrating its independent prognostic value beyond age, stage, and risk score. Collectively, these findings position ACTG2 as a promising prognostic gene for GC. Previous studies have implicated tumor-infiltrating immune cells as fundamental contributors to immune tolerance and evasion in gastric cancer (GC) 18 . This raises the question: Can ACTG2 influence GC progression by modulating immune cells within the tumor microenvironment (TME)? To address this, we investigated the immunological implications of ACTG2. Utilizing the Immune Association module of the TIMER 2.0 database, we discovered a strong correlation between ACTG2 expression and the infiltration levels of various immune cells, including CD8 + T cells, CD4 + T cells, neutrophils, monocytes, macrophages, and dendritic cells. This suggests that ACTG2, within the context of GC, modulates these immune cells through immunological signals, thereby altering their functionality and impacting the overall TME. Furthermore, we identified 60 immune checkpoint pathway genes (24 inhibitory and 36 stimulatory) in GC patients that significantly correlated with ACTG2 expression. Calculations of stromal, immune, and estimate scores based on ACTG2 expression revealed positive correlations with all three scores, indicating ACTG2's regulatory role in immune cell infiltration and checkpoint genes within the GC TME. These findings underscore ACTG2's involvement in immune evasion and suppression, providing a theoretical foundation for GC immunotherapy and suggesting its potential as a therapeutic target 19 – 21 . Nonetheless, the correlation between ACTG2 and the immune microenvironment warrants further validation through immunohistochemistry and TME analysis experiments. To further elucidate the influence of ACTG2 on the biological behavior of gastric cancer (GC) cells, we conducted in vitro cellular experiments for validation. In vitro functional assays demonstrated that ACTG2 overexpression in gastric cancer cells significantly suppressed proliferation, induced apoptosis, and impaired invasive/migratory capacities compared to controls, with effects validated by CCK-8, flow cytometry, and Transwell wound healing assays. This study is not without limitations. Firstly, all data were derived from various databases processing TCGA information, which may introduce statistical biases and lack specificity among databases. Secondly, further experimental data are required to substantiate the potential mechanisms of ACTG2 expression in GC progression and cellular behavior, as well as its regulation of immune cell infiltration and immune checkpoint genes. Comprehensive validation necessitates additional data analysis and both in vivo and in vitro experiments. 5 Conclusions In conclusion, ACTG2 is significantly underexpressed in gastric cancer (GC), and its low expression is closely associated with poor prognosis. ACTG2 may play a role in GC progression by modulating tumor immune infiltration. The overexpression of ACTG2 inhibits GC cell proliferation, invasion, and migration, while promoting apoptosis. Consequently, ACTG2 emerges as a promising biomarker for predicting prognosis in GC and as a potential therapeutic target. Abbreviations GC gastric cancer TME tumor microenvironment HCC hepatocellular carcinoma SI-NETs small intestinal neuroendocrine tumors CRC colorectal cancer DEGs differentially expressed genes EPC Edge Percolated Component MNC Maximum Neighborhood Component DMNC Density of Maximum Neighborhood Component MCC Maximal Clique Centrality OS overall survival PFS progression-free survival PPS post-progression survival K-M Kaplan-Meier α-SMA α-smooth muscle actin EMT epithelial-mesenchymal transition Declarations Acknowledgements The datasets utilized in this research were sourced from publicly accessible databases, and we extend our sincere gratitude to all researchers for making these data publicly available. Author Contributions All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yang Shuai, Zhao Wenwen, Xiao Jingwen, Feng Qingqing, Zhao Wenfei Zhao Lili and Tian Jin. The first draft of the manuscript was written by Hongmei Wei and Jun Xiao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding This study was funded by the Study on the Impact of ACTG2 on the Invasion,Metastasis, and Immune Microenvironment of Gastric Cancer(2023-WJZD198) and Investigating the Mechanism of Yiwei Xiaoji Formula-Mediated IL6 Promotion of Tumor-Infiltrating Lymphocytes (TILs) in Regulating Macrophage Polarization to Enhance Gastric Cancer Immunity via the TLR4/MAPK Pathway(Z20240222). Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests Data availability All data generated or analyzed during this study are included in this published article and in Supplementary Information files. References Bray, F.; Laversanne, M.; Sung, H. Y. A.; Ferlay, J.; Siegel, R. L.; Soerjomataram, I.; Jemal, A. 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Megacystis-Microcolon-Intestinal Hypoperistalsis Syndrome (MMIHS): Series of 4 Cases Caused by Mutation of ACTG2 (Actin Gamma 2, Smooth Muscle) Gene. Case Reports in Gastrointestinal Medicine 2021 , 6612983. Edfeldt, K.; Björklund, P.; Åkerström, G.; Westin, G.; Hellman, P.; Stålberg, P. Different gene expression profiles in metastasizing midgut carcinoid tumors. Endocr. Relat. Cancer 2011, 18, 479-489. Miranda, T. B.; Cortez Cc Fau - Yoo, C. B.; Yoo Cb Fau - Liang, G.; Liang G Fau - Abe, M.; Abe M Fau - Kelly, T. K.; Kelly Tk Fau - Marquez, V. E.; Marquez Ve Fau - Jones, P. A.; Jones, P. A. DZNep is a global histone methylation inhibitor that reactivates developmental genes not silenced by DNA methylation. Mol. Cancer Ther. 2009, 9, 1579-1588. Edfeldt, K.; Hellman, P.; Westin, G.; Stalberg, P. A plausible role for actin gamma smooth muscle 2 (ATCG2) in small intestinal neuroendocrine tumorigenesis. BMC Endocr. Disord. 2016, 16. Adammek, M.; Greve, B.; Kässens, N.; Schneider, C.; Brüggemann, K.; Schüring, A. N.; Starzinski-Powitz, A.; Kiesel, L.; Götte, M. MicroRNA miR-145 inhibits proliferation, invasiveness, and stem cell phenotype of an in vitro endometriosis model by targeting multiple cytoskeletal elements and pluripotency factors. Fertil. Steril. 2013, 99, 1346-+. Liu, Y. D.; Li, C. F.; Lu, Y. P.; Liu, C.; Yang, W. Tumor microenvironment-mediated immune tolerance in development and treatment of gastric cancer. Front. Immunol. 2022, 13. Zeng, D. Q.; Wu, J. N.; Luo, H. Y.; Li, Y.; Xiao, J.; Peng, J. J.; Ye, Z. L.; Zhou, R.; Yu, Y. F.; Wang, G. F.; Huang, N.; Wu, J. H.; Rong, X. X.; Sun, L.; Sun, H. Y.; Qiu, W. J.; Xue, Y. C.; Bin, J. P.; Liao, Y. L.; Li, N. L.; Shi, M.; Kim, K. M.; Liao, W. J. Tumor microenvironment evaluation promotes precise checkpoint immunotherapy of advanced gastric cancer. J. ImmunoTher. Cancer 2021, 9. Li, B.; Severson, E.; Pignon, J. C.; Zhao, H. Q.; Li, T. W.; Novak, J.; Jiang, P.; Shen, H.; Aster, J. C.; Rodig, S.; Signoretti, S.; Liu, J. S.; Liu, X. S. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol. 2016, 17. Tsuchiya, H.; Shiota, G. Immune evasion by cancer stem cells. Regen Ther 2021, 17, 20-33. Additional Declarations No competing interests reported. Supplementary Files alltheoriginalwesternblots.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Jul, 2025 Reviews received at journal 25 Jun, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 04 Jun, 2025 Editor invited by journal 15 May, 2025 Submission checks completed at journal 14 May, 2025 First submitted to journal 03 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6582968","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466969019,"identity":"08ac0478-58c5-4767-bfa7-4efb9af8bc26","order_by":0,"name":"Yang Shuai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACNvnHBx9+qLCR42dvPvggoaKGsBY+hrRkY4kzacaSPceSDR6cOUZYixxDjpkAb9uhxA03fMwkH7YwE+EwhmNpDBJnDjA23GBLq0hsYGPgb+9OwK+FsfnYg4KKO8yMs5uP3UjcIQPUf3YDfi3MbOkGEmeesTHLHEu7kXiGjcFAIpeAFjYeMwnetsM8bBI5ZgWJbcxEaOGBaJHgAWphIE6LBBs4kA0keI4lSyScOcZD0C/yM5jBUVm//3jzwY8/Kmrk+Nt78WvBADykKR8Fo2AUjIJRgBUAAFEiTEq39caoAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao Medical College of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Shuai","suffix":""},{"id":466969020,"identity":"79a5bce9-97fa-4540-8a0c-2e482121d601","order_by":1,"name":"Zhao Wenwen","email":"","orcid":"","institution":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences(Qingdao Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Wenwen","suffix":""},{"id":466969021,"identity":"831a8a95-f51e-47de-8c6c-92f721b2a727","order_by":2,"name":"Xiao Jingwen","email":"","orcid":"","institution":"Qingdao University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Jingwen","suffix":""},{"id":466969023,"identity":"ffc5b028-6332-4bfb-9a03-c2c5aa3d6931","order_by":3,"name":"Feng Qingqing","email":"","orcid":"","institution":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences(Qingdao Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Qingqing","suffix":""},{"id":466969029,"identity":"898b232e-3f03-4204-a36e-cc342573e384","order_by":4,"name":"Zhao Wenfei","email":"","orcid":"","institution":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences(Qingdao Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Wenfei","suffix":""},{"id":466969031,"identity":"bfecdbb3-6366-4663-8236-a0e0d2885c19","order_by":5,"name":"Zhao Lili","email":"","orcid":"","institution":"Qingdao Hiser Hospital Affiliated of Qingdao University(Qingdao Traditional Chinese Medicine Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Zhao","middleName":"","lastName":"Lili","suffix":""},{"id":466969032,"identity":"3f93cf6b-d367-4dc7-85dc-c568bea7cac4","order_by":6,"name":"Tian Jin","email":"","orcid":"","institution":"Qingdao Hiser Hospital Affiliated of Qingdao University(Qingdao Traditional Chinese Medicine Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Tian","middleName":"","lastName":"Jin","suffix":""},{"id":466969036,"identity":"ae7da06c-1f00-494b-8a41-2c8d91df21b1","order_by":7,"name":"Xiao Jun","email":"","orcid":"","institution":"Qingdao Hiser Hospital Affiliated of Qingdao University(Qingdao Traditional Chinese Medicine Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"Jun","suffix":""},{"id":466969039,"identity":"b902f1a7-f75d-42e1-bc2d-2df2252496c2","order_by":8,"name":"Wei Hongmei","email":"","orcid":"","institution":"Qingdao Central Hospital, University of Health and Rehabilitation Sciences(Qingdao Central Hospital)","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Hongmei","suffix":""}],"badges":[],"createdAt":"2025-05-03 08:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6582968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6582968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84276686,"identity":"8a1fd03c-b1cd-408e-8f00-b81bb85f1605","added_by":"auto","created_at":"2025-06-10 05:42:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1486040,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eillustrates the process of gene screening, identification of key genes, and validation of gene expression. (A) A volcano plot depicts the differentially expressed genes between gastric cancer (GC) cases with distant metastasis (M1) and those without (M0), sourced from the TCGA database. (B) The protein-protein interaction network for the differentially expressed genes is presented. (C) ACTG2 is notably downregulated across various tumor types, showing a significant difference in expression between GC and adjacent normal tissues (p 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001). (E, F) Immunohistochemical staining results for adjacent normal tissues and GC tissues are displayed.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/bc6e36008078ab3cd528ce34.png"},{"id":84276684,"identity":"5efec674-a6ae-4103-9ed7-13dbfaebde21","added_by":"auto","created_at":"2025-06-10 05:42:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":358426,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between ACTG2 and Prognosis in GC. (A) Correlation between ACTG2 expression and Overall Survival (OS). (B) Correlation between ACTG2 expression and Progression-Free Survival (PFS). (C) Correlation between ACTG2 expression and Post-Progression Survival (PPS). (D) Correlation between ACTG2 expression and tumor grade in GC. (E) Correlation between ACTG2 expression and tumor stage in GC. (F) Correlation between ACTG2 expression and lymph node metastasis. (G, H) Independent prognostic analysis of age, gender, tumor grade, stage, and risk score. Note: ns, p \u0026gt; 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/4a0dd2f9bedd69f94aa1c749.png"},{"id":84276683,"identity":"06885228-3beb-40b9-bd0a-eb23f767af7d","added_by":"auto","created_at":"2025-06-10 05:42:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":951954,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Relationship Between ACTG2 Expression and Immune Infiltration. (A) Correlation between ACTG2 expression and six types of immune cells. (B) Relationship between ACTG2 expression and immune checkpoint genes. (C) ACTG2 expression and ESTIMATE scores.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/b556db01a8715014b42f481a.png"},{"id":84278024,"identity":"434320d5-a140-49f3-9f6d-64d61cd8df9b","added_by":"auto","created_at":"2025-06-10 06:04:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":461772,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of ACTG2 Expression and Verification Post-Transfection. (A, B, C) qRT-PCR and Western Blotting were used to assess the RNA and protein expression levels of ACTG2 in human normal gastric mucosal cells (GES-1) and human GC cells (SGC7901, HGC-27, and AGS). (D, E, F) After upregulation of ACTG2 expression, qRT-PCR and Western Blotting were performed to quantify the RNA and protein expression levels of ACTG2 in three groups of human GC cells (HGC-27). (G, H, I) Similar quantification was conducted for three groups of human GC cells (AGS) post-upregulation of ACTG2 expression. Note: Control group represents blank controls; NC group represents cells transfected with NC-empty plasmid; ACTG2 group represents cells transfected with human ACTG2 overexpression plasmid (ns, p \u0026gt; 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/cc956a5284ea6b3aa55b76f5.png"},{"id":84276698,"identity":"cae02d40-f5e9-48cb-902b-f659b58db671","added_by":"auto","created_at":"2025-06-10 05:42:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":324552,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell proliferation and apoptosis among different groups. (A) Statistical graph showing the proliferation of HGC-27 GC cells across three groups. (B) Proliferation of AGS GC cells in three groups. (C, D) Apoptosis and its statistical representation in HGC-27 GC cells across three groups. (E, F) Apoptosis and its statistical representation in AGS GC cells across three groups. Note: Control group represents blank controls; NC group represents cells transfected with NC-empty plasmid; ACTG2 group represents cells transfected with human ACTG2 overexpression plasmid (ns, p \u0026gt; 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/8a164024627645dadf7db456.png"},{"id":84276690,"identity":"8c43884b-68c1-44eb-af63-993ed968958a","added_by":"auto","created_at":"2025-06-10 05:42:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":758588,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCell invasion and migration among different groups. (A,B,C) Representative images and statistical graphs of Transwell invasion assays for HGC-27 and AGS GC cells across three groups. (D,E,F) Representative images and statistical graphs of Transwell migration assays for HGC-27 and AGS GC cells across three groups. (G,H,I,J) Representative images and statistical graphs of wound healing assays for HGC-27 and AGS GC cells across three groups. Note: Control group represents blank controls; NC group represents cells transfected with NC-empty plasmid; ACTG2 group represents cells transfected with human ACTG2 overexpression plasmid (ns, p \u0026gt; 0.05; *, p \u0026lt; 0.05; **, p \u0026lt; 0.01; ***, p \u0026lt; 0.001; ****, p \u0026lt; 0.0001).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/8be50c03128d613be86a4fcf.png"},{"id":84278616,"identity":"3060a2d0-6412-44a5-9092-684c62ba762f","added_by":"auto","created_at":"2025-06-10 06:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7840261,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/1138a959-e179-4118-a97e-0ea75abde74f.pdf"},{"id":84278165,"identity":"7dc25854-be80-4252-a530-53333c38f961","added_by":"auto","created_at":"2025-06-10 06:07:36","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":492894,"visible":true,"origin":"","legend":"","description":"","filename":"alltheoriginalwesternblots.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6582968/v1/30c4f1a5b3b67f133702334b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An Investigation into the Influence of ACTG2 on Gastric Cancer Prognosis and Cellular Biological Behaviors","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eGastric cancer (GC) is one of the most prevalent cancers worldwide. According to GLOBOCAN 2022 statistics, nearly 1\u0026nbsp;million new cases and 660,000 deaths are attributed to GC annually, making it the fifth most common cancer and the fifth leading cause of cancer-related deaths globally\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. China, as a high-incidence region, ranks fifth in incidence and third in mortality, with 359,000 new cases and 260,000 deaths each year\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite declining trends in both incidence and mortality rates, GC remains a significant global health concern due to its complex heterogeneity and multiple risk factors\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Early-stage GC often presents with no apparent symptoms, and the limited prevalence of early screening programs, coupled with inadequate public awareness and a lack of preventive measures in China, results in low detection rates for early-stage GC. While advancements in medical philosophy and technology have contributed to improved survival rates among GC patients, the propensity for metastasis, recurrence, and drug resistance underscores the critical need to understand the underlying molecular mechanisms of GC metastasis in order to develop more effective treatment strategies and improve outcomes for this deadly disease.\u003c/p\u003e \u003cp\u003eStudies have indicated that the progression and recurrence of cancer are influenced not only by the underlying genetic alterations within tumors but also by the tumor microenvironment (TME), where tumor-infiltrating immune cells play a pivotal role\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The continuous interplay between the TME and tumor cells significantly impacts tumor initiation, progression, metastasis, and treatment response. Cancer progression markers are, to a certain extent, dependent on the functions of the TME, which facilitates cancer cell invasion, metastasis, angiogenesis, and immune evasion\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. The intricate interactions between tumor cells and tumor-infiltrating immune cells are crucial to cancer development. Therefore, a comprehensive understanding of the molecular mechanisms by which the tumor immune microenvironment influences cancer progression provides new hope for combating this devastating disease.\u003c/p\u003e \u003cp\u003eACTG2 is a gene that encodes actin gamma-2, a critical structural protein composed of seven exons and six introns, spanning a total length of 14.4 kb and located on human chromosome 2q35. This gene is adjacent to several other genes associated with smooth muscle\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The protein it encodes consists of 377 amino acids and is responsible for forming myofibrils within cells. Actin, as a fundamental and ubiquitous structural protein present in all human cells, plays pivotal roles in various cellular functions, including cell division, migration, chromatin remodeling, and vesicle trafficking\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Despite its importance, the role of ACTG2 in digestive tract tumors has been infrequently studied, with limited research reported in hepatocellular carcinoma (HCC)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, small intestinal neuroendocrine tumors (SI-NETs)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and colorectal cancer (CRC)\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study employs a combined approach of bioinformatics and fundamental experimentation to conduct a comprehensive investigation into the expression levels, prognostic value, and biological functions of the ACTG2 gene in gastric cancer (GC). Notably, the perspectives discussed herein have not been previously reported in the literature. Our research aims to elucidate the role of ACTG2 in assessing prognosis and understanding the underlying mechanisms of GC progression. This work provides a theoretical foundation for predicting patient outcomes, identifying novel therapeutic targets, and developing effective treatment strategies for GC.\u003c/p\u003e"},{"header":"2 MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Source\u003c/h2\u003e \u003cp\u003eRelevant transcriptomic and clinical data from gastric cancer (GC) patients, including 412 GC tumor tissues and 36 normal tissues, were retrieved from the TCGA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These data were consolidated and curated using R and Perl, with incomplete entries excluded to ensure data integrity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Screening, Identification, and Analysis of Key Genes\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Differential Expression Analysis (DEGs)\u003c/h2\u003e \u003cp\u003eThe GC sample data from the TCGA database were categorized into two groups based on the presence or absence of distant metastasis: the non-distant metastasis group (M0) and the distant metastasis group (M1). Differential expression analysis of genes was performed between these two groups. Utilizing R software, specifically the BiocManager, pheatmap, and ggplot2 packages, a matrix was constructed to facilitate the analysis. The criteria for screening differentially expressed genes (DEGs) were established as follows: |log2FC| \u0026gt; 1 for the fold change in expression and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for statistical significance. A volcano plot was generated to visually represent these DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Construction of PPI Network and Screening of Core Genes in the Network\u003c/h2\u003e \u003cp\u003eDifferentially expressed genes were uploaded to the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to construct a protein-protein interaction (PPI) network specific to distant metastasis in gastric cancer (GC). Utilizing 11 topological analysis methods available through the CytoHubba plugin in Cytoscape software\u0026mdash;including Degree, Edge Percolated Component (EPC), Maximum Neighborhood Component (MNC), Density of Maximum Neighborhood Component (DMNC), Maximal Clique Centrality (MCC), BottleNeck, Eccentricity, Closeness, Radiality, Betweenness, and Stress\u0026mdash;scores were calculated for each gene. Genes exhibiting downregulated expression were selected, and their scores from these 11 analytical approaches were consolidated and ranked using Excel. The top five genes from this comprehensive ranking were subsequently scrutinized for in-depth analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Identification, Expression Analysis, and Validation of Key Genes\u003c/h2\u003e \u003cp\u003eFollowing a search for the five selected genes in PubMed and the exclusion of those with clear experimental data, the target gene was identified as ACTG2. The expression of ACTG2 across various cancer types was analyzed using the TIMER 2.0 database, with a specific emphasis on its expression in gastric cancer. The expression of ACTG2 in gastric cancer was further validated through in vitro cell experiments and immunohistochemical staining in the HpA database.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Survival Analysis\u003c/h2\u003e \u003cp\u003eUtilizing the Kaplan-Meier Plotter database, we examined the relationship between ACTG2 gene expression levels and overall survival (OS), progression-free survival (PFS), and post-progression survival (PPS) in gastric cancer patients, subsequently generating Kaplan-Meier (K-M) curves. Additionally, we leveraged the UALCAN database to investigate the association between ACTG2 expression and clinical characteristics, including gastric cancer grade, stage, and lymph node metastasis stage. Both univariate and multivariate Cox analyses were conducted to evaluate the prognostic significance of ACTG2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Immune Infiltration Analysis\u003c/h2\u003e \u003cp\u003eEmploying the Immune Association module in the TIMER 2.0 database, we assessed the correlation between the ACTG2 gene and the infiltration levels of various immune cell types in gastric cancer. Furthermore, we utilized SangerBox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sangerbox.com/\u003c/span\u003e\u003cspan address=\"http://sangerbox.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to analyze the relationships between immune checkpoint genes associated with ACTG2 in gastric cancer. We calculated the stromal score, immune score, and ESTIMATE score for each gastric cancer patient, and subsequently analyzed the correlations between these scores and ACTG2 expression.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.3 In Vitro Cellular Experiments\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Materials and Reagents\u003c/h2\u003e \u003cp\u003e The human gastric cancer cell lines SGC-7901, HGC-27, and AGS were acquired from the Cell Bank of Shanghai, China, while the human normal gastric mucosal cell line GES-1 was obtained from the Beijing Cancer Institute. The culture medium utilized was DMEM high glucose medium (GIBCO, catalog number 11965092), supplemented with fetal bovine serum (FBS) (GIBCO, catalog number A5669701) and 0.25% trypsin digestive solution (containing EDTA), which were sourced from Dalian Meilun Biotechnology Co., Ltd. Additionally, penicillin-streptomycin (10,000 U/mL) was obtained from GIBCO (catalog number 15140148). The cells were cultured in an incubator maintained at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e. Lipofectamine\u0026trade; 3000 was procured from Life Technologies (Thermo Fisher). The human ACTG2 overexpression plasmid was generated by cloning human ACTG2 cDNA into the eukaryotic expression vector pcDNA3.1, which was self-constructed. Opti-MEM reduced serum medium was also purchased from Life Technologies (Thermo Fisher).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 RNA Extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted from the cells using Trizol, and its quality and concentration were assessed prior to reverse transcription into cDNA. The primer sequences were as follows: for ACTG2, the forward primer sequence was 5'-GCGTGTAGCACCTGAAGAG-3', and the reverse primer sequence was 5'-GAATGGCGACGTACATGGCA-3'; for Tubulin, the forward primer sequence was 5'-CGGACCAATACGACCAAATCCG-3', and the reverse primer sequence was 5'-AGCCACATCGCTCAGACACC-3'. The reaction system was established according to the kit instructions. The reaction conditions were as follows: 95\u0026deg;C for 5 minutes, followed by 40 cycles of 95\u0026deg;C for 10 seconds, 55\u0026ndash;60\u0026deg;C for 20 seconds, and 72\u0026deg;C for 20 seconds. Tubulin served as an internal reference, and the relative expression level of the target gene was calculated using the 2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e method. The experiment was conducted in triplicate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Western Blotting\u003c/h2\u003e \u003cp\u003eCell lysates were prepared using RIPA lysis buffer, followed by protein separation through SDS-PAGE and transfer to PVDF membranes. After blocking with 5% non-fat milk, the blots were incubated overnight at 4℃ with primary antibodies. Following three washes, secondary antibodies were added, and the blots were incubated for one hour at room temperature. After an additional three washes, the blots were visualized using enhanced chemiluminescence reagents (ECL). The results were captured with a gel imaging system, with Tubulin serving as the internal reference. The grayscale values of each protein were analyzed using Image J software, allowing for the calculation of relative expression levels of different proteins.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Experimental Grouping\u003c/h2\u003e \u003cp\u003eGastric cancer cells HGC-27 and AGS, which were in optimal growth conditions, were selected. Approximately 1 ml of 0.25% trypsin was added, followed by 3 ml of complete medium to neutralize the trypsin. The cells were then pipetted repeatedly to create a single-cell suspension. Subsequently, the cells were centrifuged, sedimented, plated, and cultured in a cell incubator at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e. Lipofectamine 3000 was utilized for the transfection of pcDNA3.1 and pcDNA3.1-ACTG2 (human ACTG2 overexpression plasmid). The cells were categorized into different groups based on the transfection: Control group (blank control), NC group (transfected with pcDNA3.1), and ACTG2 group (transfected with pcDNA3.1-ACTG2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 CCK-8\u003c/h2\u003e \u003cp\u003eThe experiment was conducted with three groups: Control, NC, and ACTG2. Well-cultured human gastric cancer (GC) cells, specifically HGC-27 and AGS, were seeded into 96-well plates at a density of 1 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells per well. At designated time points (0, 6, 12, 24, 36, and 48 hours), 10 \u0026micro;L of CCK-8 solution was added to each well. The cells were then incubated for 3 hours in an incubator, after which the absorbance at 450 nm was measured using a microplate reader. This experiment was repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Flow Cytometry for Apoptosis Detection\u003c/h2\u003e \u003cp\u003eHuman GC cells (HGC-27 and AGS) collected from each group were washed twice with phosphate-buffered saline (PBS). Following centrifugation, the cells were resuspended in 500 \u0026micro;L of PBS and incubated with 5 \u0026micro;L of FITC-labeled Annexin V antibody and 5 \u0026micro;L of propidium iodide (PI) staining solution for 30 minutes at room temperature (RT). After centrifugation, the supernatant was discarded, and the cells were washed three times with PBS. Subsequently, the cells were analyzed using a BD FACSAria III flow cytometer, and the flow cytometry data were analyzed using FlowJo software. This experiment was also repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.3.7 Transwell\u003c/h2\u003e \u003cp\u003eHGC-27 and AGS cells were digested and resuspended in serum-free medium, adjusting the cell density to 1.5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/ml. A total of 300 \u0026micro;l of the cell suspension was added to each Transwell insert, while 600 \u0026micro;l of medium containing 10% FBS was added to the lower chamber. The cells were incubated at 37\u0026deg;C for 24 hours. For the invasion assay, the upper chamber of the Transwell insert was pre-coated with matrix gel, whereas no gel was used for the migration assay. Following incubation, the medium in the lower chamber was removed, and the cells were washed twice with PBS buffer. The cells were then fixed with 4% paraformaldehyde for 10 minutes and washed twice with PBS buffer again. Subsequently, the cells were stained with crystal violet for 10 minutes, washed twice with PBS buffer, and the cells in the upper chamber were wiped off using a cotton swab. The cells in the lower chamber were examined under a microscope, and three random fields were selected for photography, counting, and analysis. This experiment was repeated three times.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.3.8 Wound Healing Assay\u003c/h2\u003e \u003cp\u003eHGC-27 and AGS cells were plated onto 6-cm dishes at a density of approximately 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells per dish. The cells were cultured overnight until they reached confluence. A uniform straight line was then scratched across each well of the 6-cm dishes using the tip of a pipette. After scratching, the cells were washed three times with PBS to remove any floating cells, and serum-free medium was subsequently added. Photographs were taken at 0, 12, and 24 hours using a microscope, with PBS washes performed prior to each photography session to eliminate floating cells. The distance between the scratch lines and the number of migrating cells were measured and recorded. This experiment was repeated three times.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll experiments were independently repeated at least three times, and the resulting data were statistically analyzed using SPSS 26.0. Graphs and charts were generated using GraphPad Prism 9.5 software. For the quantitative data confirmed to follow a normal distribution, results were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̄ \u0026plusmn; s). Comparisons among multiple groups were performed using one-way and two-way ANOVA, while comparisons between two groups were analyzed using paired sample t-tests. The significance level was set at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003e \u003cb\u003e3.1 Screening and Identification of Key Genes Related to Distant Metastasis in GC Based on the TCGA Database\u003c/b\u003e \u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1.1 Downloading and Organization of TCGA Database Data, and Analysis of DEGs\u003c/h2\u003e \u003cp\u003eA total of 448 gastric cancer (GC) samples with complete RNA sequencing and metastasis information were downloaded from the TCGA database, consisting of 36 cases with distant metastasis and 412 cases without distant metastasis. These samples were categorized into two groups based on the presence or absence of distant metastasis: the distant metastasis group (M1) and the non-distant metastasis group (M0). Differential gene expression analysis was conducted on the samples from these two groups. The criteria for selecting differentially expressed genes (DEGs) were established as follows: |logFC| (logarithm of fold change)\u0026thinsp;\u0026gt;\u0026thinsp;1 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. A total of 55 DEGs were identified, comprising 19 upregulated genes and 36 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 PPI Network Construction and Identification of Key Gene\u003c/h2\u003e \u003cp\u003eUsing Cytoscape software, we employed 11 topological algorithms to calculate the scores for each gene within the PPI network (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Subsequently, the 36 downregulated genes were comprehensively ranked based on their scores using Excel software. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the top five genes from this ranking. Through a search of the PubMed literature, we preliminarily identified ACTG2 as the target gene of interest.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 ACTG2 is Lowly Expressed in GC\u003c/h2\u003e \u003cp\u003eThe ACTG2 gene has been significantly downregulated in various tumor types, including breast cancer, colon cancer, lung cancer, and rectal cancer, while it is notably upregulated in cholangiocarcinoma and hepatocellular carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC), which aligns with findings reported in the literature we reviewed. The figure highlights the expression of ACTG2 in gastric cancer (GC), where we observe significant downregulation compared to adjacent normal tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To validate the differential expression of ACTG2 between human normal gastric mucosal cells and GC cells, we conducted preliminary cellular experiments using a human normal gastric mucosal cell line (GES-1) and three GC cell lines (SGC-7901, HGC-27, and AGS). Compared to GES-1, ACTG2 was found to be lowly expressed in GC cells, with statistically significant differences observed in HGC-27 and AGS cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Furthermore, we utilized the HPA database to download immunohistochemical staining images, which revealed that ACTG2 expression levels were lower in GC tissues compared to adjacent normal tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F). These validations are consistent with the analytical results obtained from the database.\u003c/p\u003e \u003cp\u003e \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\u003eTop 5 Genes Ranked Comprehensively by Excel Based on 11 Topological Algorithms in Protein-Protein Interaction Network\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNode name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDMNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMNC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEPC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBottleNeck\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEcCentricity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCloseness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRadiality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eBetweenness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eStress\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMYD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e19.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e210.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e464.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACTG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYBPC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e89.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e260.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCOL22A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e114.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e324.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePAX4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e260.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e492.00\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 \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Low Expression of ACTG2 is Associated With Poor Prognosis in Gastric Cancer\u003c/h2\u003e \u003cp\u003eSurvival analysis was conducted using the Kaplan-Meier Plotter database, where ACTG2 expression values were dichotomized into high and low groups based on the median value. The survival curves were generated using the log-rank test. The results indicated that, for overall survival (OS) analysis, a total of 592 gastric cancer (GC) patients were included. The survival duration in the low ACTG2 expression group (191 patients) was significantly shorter than that in the high expression group (401 patients), with a statistically significant difference observed (HR\u0026thinsp;=\u0026thinsp;0.75, 95% CI\u0026thinsp;=\u0026thinsp;0.61\u0026ndash;0.92, p\u0026thinsp;=\u0026thinsp;0.0055) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). For progression-free survival (PFS), 358 GC patients were analyzed, revealing that the PFS for patients in the low ACTG2 expression group (141 patients) was shorter than that of the high expression group (217 patients), with a statistically significant difference (HR\u0026thinsp;=\u0026thinsp;0.62, 95% CI\u0026thinsp;=\u0026thinsp;0.49\u0026ndash;0.8, p\u0026thinsp;=\u0026thinsp;0.00018) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). In terms of post-progression survival (PPS), 221 GC patients were evaluated, demonstrating that the survival time of patients in the low ACTG2 expression group (158 patients) was shorter than that of the high expression group (63 patients), with this difference also being statistically significant (HR\u0026thinsp;=\u0026thinsp;0.63, 95% CI\u0026thinsp;=\u0026thinsp;0.46\u0026ndash;0.87, p\u0026thinsp;=\u0026thinsp;0.0048) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Collectively, these findings suggest that low expression of ACTG2 is associated with a poor prognosis in gastric cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 The Relationship Between ACTG2 Expression and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eAnalysis utilizing the UALCAN database revealed that ACTG2 expression was significantly lower in tumor tissues compared to adjacent normal tissues across all tumor grades (1\u0026ndash;3) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Specifically, ACTG2 expression was markedly reduced in patients with grade 3 tumors compared to those with grade 1 tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Similarly, grade 2 tumors exhibited lower ACTG2 expression compared to grade 3 tumors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Furthermore, ACTG2 expression was significantly downregulated in all tumor stages (1\u0026ndash;4) relative to adjacent normal tissues (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Notably, within tumor stages, ACTG2 expression was significantly lower in stage 1 tumors compared to stages 2 and 3 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Additionally, a decreasing trend in ACTG2 expression was observed with increasing lymph node metastasis stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). Independent prognostic analysis underscored the statistical significance of age, stage (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and risk score (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in both multivariate and univariate Cox regression models (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH). Collectively, these findings suggest that ACTG2 may serve as a predictive biomarker for prognosis in gastric cancer (GC) patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.1.6 The Relationship Between ACTG2 Expression and Immune Infiltration\u003c/h2\u003e \u003cp\u003eAnalysis of the TIMER 2.0 database's gene module revealed a significant positive correlation between ACTG2 expression and the infiltration levels of CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, neutrophils, monocytes, macrophages, and dendritic cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Utilizing the Sangerbox database to investigate immune checkpoints closely associated with ACTG2, we identified notable correlations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) between ACTG2 expression and 21 out of 60 immune checkpoint pathway genes (Inhibitory\u0026thinsp;=\u0026thinsp;24, Stimulatory\u0026thinsp;=\u0026thinsp;36) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Furthermore, we calculated the stromal score, immune score, and estimate score for each gastric cancer (GC) patient based on ACTG2 expression. The results indicated a positive correlation between ACTG2 expression and all three scores: stromal score (r\u0026thinsp;=\u0026thinsp;0.70, p\u0026thinsp;=\u0026thinsp;3.5E-59), immune score (r\u0026thinsp;=\u0026thinsp;0.25, p\u0026thinsp;=\u0026thinsp;4.4E-7), and estimate score (r\u0026thinsp;=\u0026thinsp;0.52, p\u0026thinsp;=\u0026thinsp;3.8E-28) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\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\u003eCorrelation Between ACTG2 Expression and Immune Cells\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=\"char\" char=\".\" 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\u003eTypes\u0026nbsp;of\u0026nbsp;Immune\u0026nbsp;Cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epurity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.57e-03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD8\u0026thinsp;+\u0026thinsp;T\u0026nbsp;cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCD4\u0026thinsp;+\u0026thinsp;T\u0026nbsp;cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8e-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eneutrophils\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.81e-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emonocytes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.04e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emacrophages\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.10e-18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edendritic\u0026nbsp;cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.16e-06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.2 In Vitro Cellular Experiments\u003c/h2\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 ACTG2 is Lowly Expressed in GC Cells\u003c/h2\u003e \u003cp\u003eUsing qRT-PCR and Western blotting, we consistently observed low expression levels of ACTG2 across various gastric cancer (GC) cell lines (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). Based on these findings, we selected two GC cell lines, HGC-27 and AGS, characterized by significantly lower ACTG2 expression, for subsequent experiments targeting the ACTG2 gene.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Overexpression Vector Upregulates ACTG2 Expression and Verification\u003c/h2\u003e \u003cp\u003eWe employed a human ACTG2 overexpression plasmid to upregulate ACTG2 expression in the GC cell lines HGC-27 and AGS. Subsequent quantification of the upregulated ACTG2 expression levels through qRT-PCR and Western blotting confirmed successful overexpression. Specifically, qRT-PCR analysis revealed a significant increase in RNA expression levels, while Western blotting exhibited markedly thicker bands and a notable elevation in ACTG2 protein levels following transfection with the human ACTG2 overexpression plasmid in both HGC-27 and AGS cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI), thereby validating the successful overexpression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Overexpression of ACTG2 Inhibits Proliferation of GC Cells\u003c/h2\u003e \u003cp\u003eThe CCK-8 cell proliferation assay was employed to investigate the impact of ACTG2 overexpression on the proliferation of GC cells. The results demonstrated that the proliferation ability of GC cells HGC-27 and AGS was significantly reduced compared to the negative control cell lines after ACTG2 overexpression (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Overexpression of ACTG2 Promotes Apoptosis in GC Cells\u003c/h2\u003e \u003cp\u003eTo further elucidate the effect of ACTG2 overexpression on apoptosis in GC cells, we utilized Annexin-V/PI double staining and flow cytometry. The results indicated that compared to the Control and NC groups, the ACTG2 group exhibited distinct peaks of early and late apoptosis, with statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). This validates the potential of ACTG2 to induce apoptosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Overexpression of ACTG2 Inhibits Migration and Invasion of GC Cells\u003c/h2\u003e \u003cp\u003eTo evaluate the invasive capabilities of gastric cancer (GC) cells, Transwell chambers coated with Matrigel were utilized in the upper chamber. GC cells (HGC-27 and AGS) were allowed to invade through the Matrigel-coated membrane to reach the lower chamber, where the number of invasive cells was quantified using crystal violet staining. The ACTG2 group demonstrated significantly reduced invasive capabilities compared to both the Control and NC groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Similarly, in uncoated Transwell chambers, GC cells (HGC-27 and AGS) migrated directly from the upper to the lower chamber, and the number of migrated cells was quantified by crystal violet staining. In line with the invasion results, the migration capacity of GC cells in the ACTG2 group was significantly lower than that of the Control and NC groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Furthermore, the wound healing assay further confirmed that the migration abilities of GC cells (HGC-27 and AGS) in the ACTG2 group were inferior to those in the Control and NC groups, with statistically significant differences observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eI, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eJ).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 DISCUSSION","content":"\u003cp\u003eGastric cancer (GC) represents a prevalent and perilous tumor type globally, necessitating urgent and comprehensive investigations into its pathogenesis and therapeutic strategies. Early-stage GC often presents with minimal symptoms, with radical surgical resection being the primary treatment option. However, due to insufficient early screening programs in China, the majority of GC patients are diagnosed at advanced stages, characterized by late tumor staging and substantial tumor burden. Systemic chemotherapy, which includes fluoropyrimidines, platinum agents, and taxanes, serves as the cornerstone treatment for advanced GC. Unfortunately, both monotherapy and combination therapies have failed to extend median survival beyond 12 months, leading to a poor prognosis, with a 5-year survival rate below 10%\u003csup\u003e11\u003c/sup\u003e. The high degree of heterogeneity in GC predisposes patients to metastasis, local recurrence, and drug resistance, further contributing to an unfavorable prognosis\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Therefore, it is essential to actively investigate the molecular mechanisms underlying GC progression.\u003c/p\u003e \u003cp\u003eTumor metastasis, the primary determinant of cancer-related mortality, presents a formidable challenge in gastric cancer (GC) management. Despite therapeutic advancements, metastatic GC remains associated with dismal 5-year survival rates, underscoring the urgent need to decipher the molecular drivers of this process and highlighting the significance of ACTG2's potential role in GC progression.\u003c/p\u003e \u003cp\u003eAs a critical component of the actin cytoskeleton, ACTG2 exhibits functional versatility through its involvement in cellular motility, contractility, and structural maintenance. Based on its biological characteristics and existing literature, we hypothesize its direct involvement in tumor cell migration - a hypothesis particularly relevant to gastrointestinal malignancies where emerging preclinical evidence supports this mechanistic link.\u003c/p\u003e \u003cp\u003eEdfeldt et al. discovered significantly lower ACTG2 mRNA expression in metastatic small intestinal neuroendocrine tumors (SI-NETs) compared to primary tumors. Treatment with the epigenetic modulator DZNep or miR-145 overexpression in CNDT2.5 cells induced ACTG2 expression, with DZNep concurrently upregulating miR-145 - suggesting a potential regulatory axis. Consistent with breast cancer findings, miR-145 overexpression enhanced ACTG2 expression in SI-NETs, with functional implications for tumor biology\u003csup\u003e\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Tang et al. identified reduced ACTG2 expression in colorectal cancer (CRC) tissues/cells, demonstrating post-transcriptional regulation by miR-3918 through RNA pull-down and luciferase assays. They established a competing endogenous RNA (ceRNA) network where MIR497HG sequesters miR-3918, thereby increasing ACTG2 expression and inhibiting CRC progression - effects reversed by ACTG2 knockdown\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In hepatocellular carcinoma (HCC)\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, Wu et al. showed that ACTG2 overexpression promotes migration/invasion in vitro and in vivo, while shRNA-mediated knockdown reduces metastatic potential. Mechanistically, ACTG2 silencing decreased Notch1 expression in SMMC-7721 cells, and Notch1 pathway activation rescued ACTG2-mediated migration defects, establishing a functional link between ACTG2 and EMT-related metastasis.\u003c/p\u003e \u003cp\u003eThese findings underscore the crucial role of ACTG2 in cancer metastasis, with no reports in gastric cancer (GC). Utilizing data from The Cancer Genome Atlas (TCGA), which includes 448 GC samples (36 of which exhibit distant metastasis),and pinpointed low-expressed genes, ultimately selecting ACTG2 as the key gene. Analysis using TIMER 2.0 confirmed the significant downregulation of ACTG2 across various cancers, including GC. Preliminary experiments and immunohistochemical staining from the Human Protein Atlas (HPA) database further validated the downregulation of ACTG2 in GC. Patients with metastatic GC experience poor prognosis and reduced survival, highlighting the critical need for novel prognostic biomarkers and therapeutic targets for this malignancy. Consequently, we performed a survival prognosis analysis focusing on ACTG2. Using Kaplan-Meier Plotter, we identified low ACTG2 expression as significantly associated with reduced overall survival, progression-free survival, and post-progression survival in gastric cancer, with UALCAN analysis further correlating low ACTG2 with advanced tumor grade, stage, and lymph node metastasis\u0026mdash;findings validated by Cox regression demonstrating its independent prognostic value beyond age, stage, and risk score. Collectively, these findings position ACTG2 as a promising prognostic gene for GC.\u003c/p\u003e \u003cp\u003ePrevious studies have implicated tumor-infiltrating immune cells as fundamental contributors to immune tolerance and evasion in gastric cancer (GC) \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This raises the question: Can ACTG2 influence GC progression by modulating immune cells within the tumor microenvironment (TME)? To address this, we investigated the immunological implications of ACTG2. Utilizing the Immune Association module of the TIMER 2.0 database, we discovered a strong correlation between ACTG2 expression and the infiltration levels of various immune cells, including CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, neutrophils, monocytes, macrophages, and dendritic cells. This suggests that ACTG2, within the context of GC, modulates these immune cells through immunological signals, thereby altering their functionality and impacting the overall TME. Furthermore, we identified 60 immune checkpoint pathway genes (24 inhibitory and 36 stimulatory) in GC patients that significantly correlated with ACTG2 expression. Calculations of stromal, immune, and estimate scores based on ACTG2 expression revealed positive correlations with all three scores, indicating ACTG2's regulatory role in immune cell infiltration and checkpoint genes within the GC TME. These findings underscore ACTG2's involvement in immune evasion and suppression, providing a theoretical foundation for GC immunotherapy and suggesting its potential as a therapeutic target\u003csup\u003e\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Nonetheless, the correlation between ACTG2 and the immune microenvironment warrants further validation through immunohistochemistry and TME analysis experiments.\u003c/p\u003e \u003cp\u003eTo further elucidate the influence of ACTG2 on the biological behavior of gastric cancer (GC) cells, we conducted in vitro cellular experiments for validation. In vitro functional assays demonstrated that ACTG2 overexpression in gastric cancer cells significantly suppressed proliferation, induced apoptosis, and impaired invasive/migratory capacities compared to controls, with effects validated by CCK-8, flow cytometry, and Transwell wound healing assays. This study is not without limitations. Firstly, all data were derived from various databases processing TCGA information, which may introduce statistical biases and lack specificity among databases. Secondly, further experimental data are required to substantiate the potential mechanisms of ACTG2 expression in GC progression and cellular behavior, as well as its regulation of immune cell infiltration and immune checkpoint genes. Comprehensive validation necessitates additional data analysis and both in vivo and in vitro experiments.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn conclusion, ACTG2 is significantly underexpressed in gastric cancer (GC), and its low expression is closely associated with poor prognosis. ACTG2 may play a role in GC progression by modulating tumor immune infiltration. The overexpression of ACTG2 inhibits GC cell proliferation, invasion, and migration, while promoting apoptosis. Consequently, ACTG2 emerges as a promising biomarker for predicting prognosis in GC and as a potential therapeutic target.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGC gastric cancer\u003c/p\u003e\n\u003cp\u003eTME tumor microenvironment\u003c/p\u003e\n\u003cp\u003eHCC hepatocellular carcinoma\u003c/p\u003e\n\u003cp\u003eSI-NETs small intestinal neuroendocrine tumors\u003c/p\u003e\n\u003cp\u003eCRC colorectal cancer\u003c/p\u003e\n\u003cp\u003eDEGs differentially expressed genes\u003c/p\u003e\n\u003cp\u003eEPC Edge Percolated Component\u003c/p\u003e\n\u003cp\u003eMNC Maximum Neighborhood Component\u003c/p\u003e\n\u003cp\u003eDMNC Density of Maximum Neighborhood Component\u003c/p\u003e\n\u003cp\u003eMCC Maximal Clique Centrality\u003c/p\u003e\n\u003cp\u003eOS overall survival\u003c/p\u003e\n\u003cp\u003ePFS progression-free survival\u003c/p\u003e\n\u003cp\u003ePPS post-progression survival\u003c/p\u003e\n\u003cp\u003eK-M Kaplan-Meier\u003c/p\u003e\n\u003cp\u003e\u0026alpha;-SMA \u0026alpha;-smooth muscle actin\u003c/p\u003e\n\u003cp\u003eEMT epithelial-mesenchymal transition\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets utilized in this research were sourced from publicly accessible databases, and we extend our sincere gratitude to all researchers for making these data publicly available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yang Shuai, Zhao Wenwen, Xiao Jingwen, Feng Qingqing, Zhao Wenfei Zhao Lili and Tian Jin. The first draft of the manuscript was written by Hongmei Wei and Jun Xiao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Study on the Impact of ACTG2 on the Invasion,Metastasis, and Immune Microenvironment of Gastric Cancer(2023-WJZD198) and Investigating the Mechanism of Yiwei Xiaoji Formula-Mediated IL6 Promotion of Tumor-Infiltrating Lymphocytes (TILs) in Regulating Macrophage Polarization to Enhance Gastric Cancer Immunity via the TLR4/MAPK Pathway(Z20240222).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and in Supplementary Information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F.; Laversanne, M.; Sung, H. 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Immune evasion by cancer stem cells. \u003cem\u003eRegen Ther \u003c/em\u003e\u003cstrong\u003e2021,\u003c/strong\u003e 17, 20-33.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"ACTG2, GC, Prognosis, Cellular Biological Behaviors","lastPublishedDoi":"10.21203/rs.3.rs-6582968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6582968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study investigates the impact of ACTG2 expression on poor prognosis in gastric cancer (GC) patients, as well as the biological behavior of GC cells.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eDifferential gene expression analysis was conducted on GC samples with distant metastasis and those without, utilizing the TCGA database. We constructed a protein interaction network for the differentially expressed genes and identified the target gene ACTG2. Subsequently, we explored the correlation between ACTG2 gene expression levels and prognosis, as well as clinical features. A GC cell line exhibiting significantly low ACTG2 expression was selected, and ACTG2 expression was upregulated using a human ACTG2 overexpression plasmid. The effects of ACTG2 on cancer cell proliferation, apoptosis, invasion, and migration were verified through CCK-8, flow cytometry, Transwell, and cell scratch assays.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 55 differentially expressed genes associated with distant metastasis of cancer were identified from the TCGA database. The expression of ACTG2 was significantly reduced in cancer tissues and showed a significant correlation with overall survival, progression-free survival, and post-progression survival of cancer patients. Further analysis confirmed that ACTG2 expression is positively correlated with the infiltration levels of CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells, neutrophils, monocytes, macrophages, and dendritic cells, demonstrating the potential role of ACTG2 in immune regulation. Notably, the ACTG2 gene is significantly downregulated in the cancer cell lines HGC-27 and AGS. After upregulating ACTG2 expression, the proliferation, invasion, and migration abilities of HGC-27 and AGS cells were significantly reduced, while their apoptosis ability was significantly enhanced.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe reduced expression of ACTG2 in gastric cancer (GC) may correlate with a poor prognosis, highlighting its potential significance as a prognostic biomarker. Furthermore, the overexpression of ACTG2 has been shown to inhibit the proliferation, migration, and invasion of GC cells, thereby underscoring its potential role in the pathogenesis and progression of GC.\u003c/p\u003e","manuscriptTitle":"An Investigation into the Influence of ACTG2 on Gastric Cancer Prognosis and Cellular Biological Behaviors","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-10 05:41:58","doi":"10.21203/rs.3.rs-6582968/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-01T05:47:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-25T13:48:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298059089403418713900569257187285622884","date":"2025-06-25T13:01:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-15T11:03:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"21398306519715597449502893331967177473","date":"2025-06-05T08:11:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-04T17:59:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-06-04T17:52:19+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-15T09:21:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-14T10:26:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-03T08:30:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"419ad29a-b57c-4990-bc7f-069f0000d6a6","owner":[],"postedDate":"June 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":49578909,"name":"Biological sciences/Cancer"},{"id":49578910,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":49578911,"name":"Biological sciences/Cancer/Gastrointestinal cancer"},{"id":49578912,"name":"Biological sciences/Cancer/Metastases"},{"id":49578913,"name":"Biological sciences/Cancer/Tumour biomarkers"}],"tags":[],"updatedAt":"2025-09-17T10:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-10 05:41:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6582968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6582968","identity":"rs-6582968","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}