BBC3 is a biomarker related to gastric cancer prognosis and immunity

BBC3 is a biomarker related to gastric cancer prognosis and immunity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article BBC3 is a biomarker related to gastric cancer prognosis and immunity liu liu, Guangyao Li, Weiwei Zhang, Xiang Li, Yuan Yao, Zhengjun Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6335473/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background BBC3, also known as PUMA, is a key apoptosis-related protein, and its dysregulated expression in various cancers is closely linked to tumorigenesis and progression. However, the functional role of BBC3 in gastric cancer remains underexplored. Methods This study aims to investigate the relationship between BBC3 and the prognosis as well as immune infiltration in gastric cancer, providing a potential molecular foundation for its immunotherapy, including clinical and pathological parameters, tumor immunity, copy number variation (CNV), methylation, and enrichment analysis. Furthermore, We employed single-cell sequencing technology to investigate the role of BBC3 in the immune microenvironment of gastric cancer. Immunohistochemistry was used to confirm the expression of the BBC3 gene in gastric cancer tissues. Furthermore, a ceRNA network regulating BBC3 was constructed. Results The results of differential expression analysis revealed that BBC3 expression was higher in gastric cancer tissues compared to adjacent non-cancerous tissues. CNV and methylation were associated with abnormal BBC3 mRNA expression in tumor tissues. Survival analysis showed a significant correlation between BBC3 levels and the prognosis of gastric cancer, with elevated BBC3 expression being strongly associated with better clinical outcomes for patients. Functional enrichment analysis revealed that BBC3 is significantly enriched in pathways associated with tumorigenesis and immune suppression. Using single-cell sequencing data, we identified the distribution of BBC3 in gastric cancer tissues and its expression in immune cells within these tissues. The ceRNA network we constructed elucidated the molecular role of BBC3 in gastric cancer prognosis. Conclusion BBC3 is linked to tumor immunity and could serve as a biomarker for prognosis in gastric cancer patients. Gastric cancer Immune microenvironment Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Introduction Gastric cancer (GC) remains one of the most prevalent malignant tumors worldwide, with its incidence and mortality rates second only to colorectal cancer, ranking sixth globally. In 2020, over 1 million new GC cases and 769,000 deaths were estimated [ 1 ]. Despite significant advances in GC treatment, including surgical resection, chemotherapy, and radiotherapy, the 5-year overall survival (OS) rate for patients with advanced disease remains below 30%[ 2 ]. Typically, delayed diagnosis, lymph node metastasis, and the limited effectiveness of current treatments are the main factors contributing to the poor prognosis of GC, resulting in a high mortality rate[ 3 ]. In recent years, with rapid advances in genomics and high-throughput sequencing technologies, GC can now be studied at high resolution and molecular levels. RNA sequencing (RNA-seq) technology has become increasingly applied in gastric cancer research, revealing new gene mutations, chromosomal alterations, and epigenetic changes associated with gastric cancer progression. Although bulk RNA-seq technology shows promise, it only provides the average expression levels of all cell types within the sample and cannot capture the expression profiles of individual cell types or the ligand-receptor interactions between different cell types[ 4 ]. The emergence of single-cell technology effectively overcomes these limitations, allowing the assessment of gene expression in thousands of cells at single-cell resolution [ 5 ]. It also facilitates the analysis of immune infiltration and tumor heterogeneity in the tumor microenvironment (TME)[ 6 ]. This study integrated RNA-seq data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to construct a single-cell transcriptomic map of gastric cancer samples and identify multiple cell types. The aim was to predict the relationship between BBC3 and gastric cancer prognosis, thereby developing a prognostic model. BBC3, also known as PUMA, is an important apoptosis-related protein, and its abnormal expression in various tumors is closely linked to tumorigenesis and progression. Research has shown that BBC3 regulates the proliferation and death of cancer cells in non-small cell lung cancer[ 7 ]. The miR-222 inhibitor can enhance BBC3 protein expression by binding to it, thereby inhibiting the proliferation, migration, and invasion of liver cancer cells and promoting apoptosis in these cells[ 8 ]. Overexpression of BBC3 induces the dissociation of thioredoxin from ASK1, activating the JNK/BCL-2/BCL-XL pathway and promoting apoptosis in ovarian cancer cells[ 9 ]. BBC3 inhibits the growth of colon cancer through targeting the mTOR pathway. According to existing research, chaperone-mediated autophagy (CMA) prevents cell apoptosis by degrading BBC3/BCRIA[ 5 ]. Long non-coding RNA TSLC8 (lncRNA TSLC8) inhibits colorectal cancer by stabilizing BBC3[ 10 ]. This suggests that BBC3 plays a crucial role in apoptosis induced by various stress signals mediated by multiple transcription factors. BBC3 Ser10 phosphorylation is crucial for tumor necrosis factor-induced apoptosis. This provides valuable insights for further research into the mechanisms by which BBC3 contributes to apoptosis in gastric cancer cells. However, the mechanism of BBC3 in gastric cancer has been less studied. Therefore, this study aims to investigate the role of BBC3 in the development and progression of gastric cancer, providing new insights and strategies for its treatment and prevention. Materials and Methods Data Acquisition Clinical and RNA sequencing data from 407 GC patients were obtained from the TCGA database ( https://portal.gdc.cancer.gov/ ), including 375 GC tissue samples and 32 normal gastric tissue samples. Data on T stage, age, N stage, gender, M stage, pathological stage, and lymphatic invasion were also included. The scRNA-seq data of GC were obtained from the GSE251950 dataset, which is available in the GEO database ( http://www.ncbi.nlm.nih.gov/geo )[ 11 ]. Analysis of DEmRNA DEmRNAs were obtained using the 'Limma' package with an absolute log2 fold change (|logFC|) > 1.0 and P.adj < 0.05. We identified mRNAs co-expressed with the target gene using R programming. The 'ggplot2' package was used to visualize the volcano plot of the mRNAs. BBC3 Expression Analysis The HPA ( https://www.proteinatlas.org ) database was used to explore the mRNA and protein expression levels of BBC3 in normal human tissues. The expression levels of BBC3 in various cancer tissues were obtained using the 'Gene_DE' module from TIMER 2.0 ( http://timer.cistrome.org/ )[ 12 ]. Copy number variation and methylation contribute to driving the abnormal expression of BBC3 in gastric cancer Gene Set Cancer Analysis (GSCA; http://bioinfo.life.hust.edu.cn/GSCA#/ ) is a powerful bioinformatics tool that primarily integrates mRNA expression, mutation, immune infiltration, and methylation data from the TCGA database [ 13 ]. The 'Mutation' module in the GSCA database is used to analyze the CNV and methylation of BBC3 and their correlation with mRNA expression levels. Xiantao ( https://www.xiantaozi.com/ ) is a comprehensive and user-friendly bioinformatics analysis platform[ 14 ]. The relationship between BBC3 and the expression of DNA methyltransferase genes was investigated using the Xiantao platform. Survival analysis Proportional hazards assumption tests and survival regression fitting were performed using the 'survival' package, and the results were visualized with the 'survminer' and 'ggplot2' packages[ 15 ]. The optimal cut-off for grouping was determined using the 'surv_cutpoint' function in the 'survminer' package. Immune-related analysis of BBC3 Using the ssGSEA algorithm from the R package GSVA [1.46.0][ 18 ], immune infiltration of 24 immune cell types was calculated using the markers of these cells, with corresponding cloud-based data[ 19 ]. This analysis was conducted to evaluate the immune cell types in GC tissue samples.Spearman correlation analysis was performed to evaluate the relationship between BBC3 expression and immune cells, as well as immune checkpoints—programmed cell death protein 1 (PD-1), PDCD1, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and programmed cell death ligand 1 (PD-L1).We used the 'ggplot2' [3.4.4], 'stats' [4.2.1], and 'car' [3.1-0] packages to visualize the correlation. The Welch t-test was used to evaluate the enrichment of immune infiltrating cells in GC patients with high BBC3 expression compared to those with low BBC3 expression. Generation of the ceRNA axis Differentially expressed lncRNAs (DE-lncRNAs) between tumor and healthy samples were identified with the following criteria: |log2(fold change)| > 1, P value < 0.05. The target miRNAs of lncRNAs were predicted using the miRcode database ( http://www.example.com )[ 20 ], and the target miRNAs of prognostic DEGs (differentially expressed genes) were predicted using the Starbase database ( https://rnasysu.com/encori/ )[ 21 ]. Common miRNAs predicted by both miRcode and Starbase, along with their corresponding lncRNAs and prognostic DEGs, were imported into Cytoscape software to construct the ceRNA network[ 22 ]. scRNA-seq Data Integration and Dimensionality Reduction Clustering The FindVariableFeatures function from the Seurat package was used to extract genes with the highest coefficient of variation between cells from the normalized data. The data were then scaled and subjected to principal component analysis (PCA)[ 23 ]. t-distributed stochastic neighbor embedding (t-SNE) was applied for data visualization. Differentially expressed genes between different cell populations were identified using the FindAllMarkers function, which led to the identification of BBC3[ 24 ]. Cell types were annotated with the SingleRbao package, and t-SNE visualization was performed using TSNEPlot[ 25 ]. Cell-cell communication analysis The R package CellChat enables the inference, visualization, and analysis of intercellular communication in scRNA-seq data, and can illustrate the interactions between ligands, receptors, and their cofactors[ 26 ]. To explore potential communication between T cells and other cell types, ligand-receptor interactions between different cell populations were analyzed using the R package CellChat. Immunohistochemistry of paraffin sections A total of 10 pairs of gastric cancer (STAD) and adjacent non-cancerous tissue samples were collected from the specimen bank of Wuhu Second Hospital, all from gastric cancer patients diagnosed between September 2022 and October 2023. These patients had not undergone radiotherapy or chemotherapy prior to surgery. This study was approved by the Ethics Committee of Wuhu Second Hospital, and all patients provided written informed consent (2023-KY-010). The same immunohistochemical method and scoring system described in published articles were used[ 27 – 29 ], with a 1:400 dilution. Scoring was carried out using ImageJ software, followed by statistical analysis and visualization based on the obtained scores. Statistical analysis In R (version 4.2.1), the Mann-Whitney U test (Wilcoxon rank sum test), Shapiro-Wilk normality test, and Levene's test were used to assess BBC3 expression in normal gastric and GC tissues[ 30 ]. Appropriate statistical methods were chosen based on the data format and characteristics (using the 'stats' and 'car' packages). Statistical analysis was not performed if the assumptions were not met. The data were visualized using the 'ggplot2' package. Results Acquisition of DEmRNA Previous studies have revealed that BBC3 plays a role in the proliferation, migration, and invasion of liver cancer cells and shows significant correlation with prognosis in lung squamous cell carcinoma patients. However, the prognostic value of BBC3 in gastric cancer and its involvement in immune responses remain unclear. Therefore, we utilized the TCGA public database to identify BBC3 as a differentially expressed mRNA (DEmRNA) in gastric cancer, which contained 375 differentially expressed mRNAs, as shown in Fig. 1 . The expression of BBC3 across different normal human tissues. To investigate the expression levels of BBC3 in different types of normal human tissues, we assessed its mRNA and protein expression using the Human Protein Atlas (HPA) database. As shown in Fig. 2 a, the tissues with the highest BBC3 expression, in descending order, are skeletal muscle, cerebellum, skin, pancreas, lung, salivary glands, spleen, liver, cerebral cortex, kidney, and stomach. Next, we evaluated its protein expression and found that BBC3 expression levels vary significantly across different tissues (Fig. 2 b). BBC3 mRNA and protein exhibited distinct expression patterns in normal tissues, which may be due to the low specificity of the BBC3 antibody, though this has not been experimentally validated. Immunohistochemistry revealed that BBC3 is expressed in both the nucleus and cytoplasm of normal gastric tissue (Fig. 2 c) and gastric cancer tissue (Fig. 2 d), with representative staining patterns at different expression levels shown. BBC3 shows higher expression in gastric cancer than in normal gastric tissues, and high expression predicts better prognosis. We accessed the TIMER2.0 website to examine the expression changes of BBC3 in tumor tissues and corresponding normal tissues from the TCGA database, as shown in Fig. 3 a. We visualized BBC3 expression using the TCGA database, as shown in Figs. 3 b and 3 c. STAD tissues exhibit higher levels of BBC3 expression compared to normal gastric tissue. BBC3 may serve as a potential diagnostic biomarker, with an AUC of 0.926 (Fig. 3 d). Survival analysis shows that STAD patients with high BBC3 expression have better overall survival, as shown in Fig. 3 e. BBC3 expression is correlated with clinical and pathological parameters. As shown in Table 1 , we used the chi-square test to assess the association between clinical pathological factors and BBC3 expression. The analysis revealed that BBC3 expression is associated with the T stage (P = 0.017) and pathological stage (P = 0.031) in STAD patients. As shown in Figs. 4 a and 4 b, using the Kruskal-Wallis rank sum test, BBC3 expression is correlated with the T stage in both normal gastric tissues and STAD patients (P < 0.05), as well as across different T stages in STAD patients (P < 0.05). BBC3 expression is also correlated with the pathological stage in STAD (P < 0.05). The overall survival of STAD subgroups with high and low BBC3 expression is shown in Figs. 4 (c-e). The analysis revealed that the T4 subgroup (HR = 0.50, 95% CI: 0.26–0.94, P = 0.03), the stage IV subgroup (HR = 0.35, 95% CI: 0.14–0.83, P = 0.018), and the N3 subgroup (HR = 0.49, 95% CI: 0.26–0.91, P = 0.024) are associated with higher overall survival. Furthermore, higher BBC3 expression is associated with better prognosis. To investigate the impact of BBC3 expression and clinical pathological parameters on survival, we performed univariate and multivariate COX regression analyses. In the univariate COX regression analysis, variables with P < 0.05, including T stage, N stage, M stage, pathological stage, and age, were found to be statistically significant. In the multivariate COX regression model, variables with P < 0.05, including N stage, M stage, age, and increased BBC3 expression, were shown to be statistically significant. In this study, BBC3 expression (P < 0.001), N3 stage (P = 0.014), M stage (P = 0.02), and age (P < 0.001) were identified as independent factors affecting the overall survival rate of STAD patients, as shown in Table 2 . Table 1 BBC3 expression associated with clinicopathological characteristics (chi-square test) Characteristics Low expression of BBC3 High expression of BBC3 P value n 187 188 Pathologic T stage, n (%) 0.017 T1&T2 59 (16.1%) 40 (10.9%) T3&T4 122 (33.2%) 146 (39.8%) Pathologic N stage, n (%) 0.477 N0 55 (15.4%) 56 (15.7%) N1 41 (11.5%) 56 (15.7%) N2 37 (10.4%) 38 (10.6%) N3 40 (11.2%) 34 (9.5%) Pathologic M stage, n (%) 0.293 M0 162 (45.6%) 168 (47.3%) M1 15 (4.2%) 10 (2.8%) Pathologic stage, n (%) 0.031 Stage I 31 (8.8%) 22 (6.2%) Stage II 44 (12.5%) 67 (19%) Stage III 72 (20.5%) 78 (22.2%) Stage IV 24 (6.8%) 14 (4%) Gender, n (%) 0.410 Female 63 (16.8%) 71 (18.9%) Male 124 (33.1%) 117 (31.2%) Age, n (%) 0.227 65 98 (26.4%) 109 (29.4%) Table 2 Univariate and multivariate analyses of clinicopathological parameters in patients with GC Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Pathologic T stage 362 T1&T2 96 Reference Reference T3&T4 266 1.719 (1.131–2.612) 0.011 1.501 (0.884–2.550) 0.133 Pathologic N stage 352 N0 107 Reference Reference N1 97 1.629 (1.001–2.649) 0.049 1.526 (0.833–2.796) 0.172 N2 74 1.655 (0.979–2.797) 0.060 1.627 (0.778–3.401) 0.196 N3 74 2.709 (1.669–4.396) < 0.001 2.556 (1.209–5.404) 0.014 Pathologic M stage 352 M0 327 Reference Reference M1 25 2.254 (1.295–3.924) 0.004 2.087 (1.120–3.890) 0.020 Pathologic stage 347 Stage I&Stage II 160 Reference Reference Stage III&Stage IV 187 1.947 (1.358–2.793) < 0.001 0.908 (0.476–1.731) 0.769 Age 367 65 204 1.620 (1.154–2.276) 0.005 1.878 (1.301–2.711) < 0.001 Gender 370 Female 133 Reference Male 237 1.267 (0.891–1.804) 0.188 BBC3 370 Low 184 Reference Reference High 186 0.565 (0.406–0.786) < 0.001 0.558 (0.389–0.799) 0.001 Copy number variations and methylation contribute to the abnormal expression of BBC3 in gastric cancer. To further investigate the mechanism of abnormal BBC3 mRNA expression, we analyzed the relationship between gene copy number variation (CNV) and mRNA expression. GSEA analysis revealed that BBC3 expression in STAD patients is significantly positively correlated with CNV (Fig. 5 a), suggesting that CNV may contribute to abnormal BBC3 expression in gastric cancer patients. Additionally, the abnormal expression of BBC3 in gastric cancer may be associated with structural variations in DNA fragments within the human genome. DNA methylation is an epigenetic process that significantly regulates gene transcription. Therefore, we found a significant association between DNA methylation levels and mRNA expression in STAD (Fig. 5 b). To further investigate the underlying mechanism of BBC3 methylation in STAD, we evaluated the correlation between BBC3 and three DNA methyltransferase genes (DNMT1, DNMT3A, and DNMT3B). We found that in STAD, BBC3 expression is positively correlated with DNMT1, while it is negatively correlated with DNMT3A and DNMT3B expression (Fig. 5 c). The association between BBC3 expression and tumor immunity. As depicted in Fig. 6 , we used the ssGSEA algorithm to evaluate the relationship between the relative abundance of 24 immune cells and BBC3 expression in gastric cancer. As illustrated in Figs. 7 A-I, BBC3 expression is correlated with various immune cell types, including CD8 + T cells (P < 0.001, correlation coefficient R = 0.335), macrophages (P = 0.003, R = 0.151), DC cells (P = 0.028, R = 0.114), cytotoxic lymphocytes (P < 0.001, R = 0.296), NK cells (P = 0.006, R = 0.142), NK CD56dim cells (P < 0.001, R = 0.364), T cells (P < 0.001, R = 0.204), Th2 cells (P < 0.001, R = 0.256), and TReg cells (P < 0.001, R = 0.287). We used the Wilcoxon rank-sum test to assess the enrichment of immune cells in the high and low BBC3 expression groups. The results showed that, in comparison to the low BBC3 expression group, the high BBC3 expression group exhibited higher enrichment of CD8 T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, TReg cells, macrophages, and Th2 cells (Figs. 8 a-i). Spearman's correlation analysis showed a positive correlation between the expression of BBC3 and the expressions of CD274 (PD-L1), CTLA4, LAG3, and PDCD-1 (Figs. 9 a through 9 d). Functional enrichment analysis Our previous findings suggest that BBC3 expression is closely associated with the prognosis and immunity of gastric cancer patients. Given the strong correlation observed between BBC3 expression and immune infiltration in STAD, we selected STAD as a model to validate the potential function of BBC3 using LinkedOmics. Gene set enrichment analysis (GSEA) of BBC3-related genes in STAD revealed that KEGG pathway analysis identified several major enriched pathways (Fig. 10 ), including ribosomal biogenesis, antigen processing and presentation, the cytosolic DNA sensing pathway, necroptosis, cytosolic DNA-sensing pathway, the proteasome pathway, the intestinal immune network for IgA production, apoptosis, DNA replication, and other signaling pathways. The single-cell transcriptome atlas of gastric cancer In this study, we analyzed two samples in total from the GSE184198 dataset, consisting of tumor and matched normal tissue samples from a single patient. The samples were subjected to preliminary screening to remove low-quality data, and as a result, 8816 cells were retained for subsequent analysis. Subsequently, we employed the 'anchor' method for data integration to eliminate batch effects and performed standardization, centering, and PCA dimensionality reduction, retaining the first 15 principal components. Clustering visualization was then performed using the t-SNE method. Clustering analysis grouped 8816 cells into 15 clusters, as shown in Figure A. Cell type annotation was performed using the Single R package, revealing 9 cell types (Figure B). Figure C shows the distribution of BBC3 in gastric cancer tissues, and Figure D illustrates the differential expression of BBC3 in different cell types within gastric cancer tissues. The expression levels, from highest to lowest, were as follows: CD8 + T Cell, CD4 + T Cell, HSC, B Cell, Epithelial Cell, Monocytes, Fibroblasts, Endothelial Cells, and Neutrophils. Cell-cell interaction analysis To investigate the communication patterns between various cell types in gastric cancer tissues, CellChat was employed to identify ligand-receptor pairs and their molecular interactions. As shown in Figs. 12 a, the network represents intercellular interaction quantity. Nodes denote different cell types, with larger circles indicating higher cell abundance for the corresponding type. Thicker lines denote more interactions between cell types, and line colors match the colors of ligand cells. Figure 12 b shows the network of intercellular interaction strength. Nodes indicate cell types, line thickness reflects interaction intensity, and colors match ligand cells. Figure 13 (a-i) compares intercellular interaction networks across different group. As shown in Fig. 14 , the x-axis represents the interacting cells, the y-axis represents the ligand-receptor pairs, and the size of the circles correlates with the significance of the p-value, with larger circles indicating smaller p-values. The color of the circles indicates the likelihood of interaction, with redder colors reflecting a higher probability of interaction. From this bubble plot, we can observe that the ligand-receptor pair CCL5-ACKR1 contributes most significantly to the interaction between CD8 + T cells and endothelial cells. Establishment of a ceRNA network for BBC3 LncRNAs and circRNAs are often regarded as competing endogenous RNAs (ceRNAs) that bind to miRNAs. ceRNA analysis involves examining the entire ceRNA regulatory network; typically, circRNA-miRNA-mRNA or lncRNA-miRNA-mRNA analysis is regarded as the core of the ceRNA regulatory network. As ceRNAs (such as lncRNAs or circRNAs) competitively bind to miRNAs, the transcription levels of miRNA-regulated genes will increase. To further investigate the potential regulatory role of BBC3 in STAD prognosis, we constructed a ceRNA network incorporating DE-lncRNAs and BBC3. The target miRNAs of DE-lncRNAs were identified using the miRcode database, and the target miRNAs of BBC3 were identified using the Starbase database. The ceRNA network, which includes 5 lncRNAs, 158 miRNAs, and BBC3, revealed the molecular mechanism of BBC3 in the prognosis of STAD, Supplementary Table 1. Expression of BBC3 protein in gastric cancer tissues To fully investigate the expression of BBC3 in gastric cancer, 10 STAD and adjacent non-cancerous tissue samples were analyzed using immunohistochemistry. The study results showed that BBC3 expression was significantly increased in cancer tissues compared to adjacent non-cancerous tissues (Fig. 16 ). Discussion For gastric cancer (GC), molecular targeted therapy and immunotherapy have shown therapeutic efficacy in recent years[ 31 – 35 ]. For example, the development of immune checkpoint inhibitors has demonstrated clinical efficacy. However, due to the occurrence of adverse events[ 36 ], most GC patients have not benefited from immune checkpoint inhibitors. According to relevant studies, molecular chaperone-mediated autophagy prevents cell apoptosis by the degradation of BBC3/BCRIA[ 5 ], and the long non-coding RNA TSLC8 inhibits colorectal cancer progression by stabilizing BBC3[ 10 ]. This suggests that BBC3 plays a crucial role in cell apoptosis induced by various stress signals through the mediation of multiple transcription factors. This provides important guidance for our further research on the mechanism by which BBC3 contributes to gastric cancer cell apoptosis. Currently, scRNA-seq is widely used to characterize the fundamental characteristics of tumor-infiltrating immune cells and has revealed the regulation of immune cell subpopulations within the TME (tumor microenvironment)[ 37 , 38 ]. Therefore, further identification of immune-related genes is necessary to systematically investigate the relationship between STAD and the TME, with the goal of improving the prognosis of GC patients. These findings highlight the need for in-depth studies to elucidate the molecular pathways mediated by BBC3 during STAD progression. From the TCGA and GEO databases, we acquired clinical and RNA-seq data from GC patients and employed bioinformatics methods to identify differentially expressed mRNAs (DE-mRNAs) in GC. Ultimately, BBC3 was found to be elevated in gastric cancer tissues relative to normal tissues. Based on data from the UCSC XENA, TCGA, and HPA databases, the expression of BBC3 is higher in GC tissue compared to normal gastric tissue. The expression of BBC3 in gastric cancer is associated with clinicopathological parameters (T stage) and poor prognosis. Furthermore, BBC3 shows a high diagnostic accuracy for gastric cancer. Multivariate regression analysis revealed that BBC3 is an independent prognostic factor for GC patients, suggesting that BBC3 may be beneficial as a biomarker for both diagnosis and prognosis in GC patients. Our findings indicate that in pan-cancer, the expression of BBC3 mRNA shows significant correlations with CNV and methylation. Cancer immunotherapy stimulates and enhances the immune system's ability to recognize, target, and eliminate tumor cells[ 39 ]. Anti-tumor immunity strategy has been accomplished through different modalities including cellular immunotherapy, specific vaccines, monoclonal antibodies, and oncolytic virotherapy[ 40 ]. Immune-related gene expression signatures have emerged as potential predictive biomarkers for immunotherapy efficacy in malignancies[ 41 ]. BBC3 is closely associated with several immune checkpoint genes, and its expression levels may indirectly reflect the abundance of this immune infiltrating factor in the tumor microenvironment (TME). The expression of immune-related genes is considered a predictive biomarker for immunotherapy in various cancers. Correlation analysis revealed that BBC3 expression is correlated with PD-L1 (CD274), CTLA4, PD-1 (PDCD1), LAG3, and immune cells such as CD8 + T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, Treg cells, macrophages, and Th2 cells. Furthermore, the enrichment of immune cells (CD8 + T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, Treg cells, macrophages, and Th2 cells) in the high BBC3 expression group is higher than in the low expression group. Kaplan-Meier curves indicate that high expression of BBC3 is associated with a better prognosis in STAD patients. This may explain the association between BBC3 overexpression and great prognosis in patients with cancer. Based on GSEA and KEGG pathway enrichment analysis, BBC3 and its co-expressed mRNAs are enriched in several signaling pathways, including ribosomal expression, antigen processing and presentation, cytosolic DNA sensing pathway, necroptosis, proteasome, intestinal immune network for IgA production, apoptosis, DNA replication, and other signaling pathways. We found that pathways associated with immunity, as well as cancer cell proliferation and migration, including the intestinal immune network for IgA production[ 42 , 43 ], may explain the potential mechanisms by which BBC3 promotes immune infiltration and affects cancer cell proliferation and migration. These results provide confirmatory support for the role of BBC3 as an immune biomarker. And then, the GSE184198 scRNA-seq dataset was used to analyze the heterogeneity of STAD. After annotation, a total of 9 cell types were identified, including CD8 + T cells, CD4 + T cells, HSC, B cells, epithelial cells, monocytes, fibroblasts, endothelial cells, and neutrophils. Through t-SNE dimensionality reduction, we can visualize the differences in the expression of the BBC3 gene across different cell populations. Furthermore, using the R package ggplot2, we generated dot plots illustrating the expression of BBC3 across different immune cell types. We observed that BBC3 exhibits significantly higher expression levels in CD4 + T cells and CD8 + T cells compared to other cell populations. The cell-cell communication analysis results indicate that there is direct and strong communication between various cell subtypes, primarily mediated by the MIF signaling pathway. Therefore, these findings provide evidence supporting the involvement of BBC3 in the immune microenvironment of gastric cancer. Next, we constructed a ceRNA network comprising 5 lncRNAs, 158 miRNAs, and BBC3, which explored the molecular mechanism of BBC3 in STAD prognosis. Finally, the IHC experimental result shown BBC3 expressed higher in STAD. Our results indicated that in the microenvironment of GC, BBC3 was associated closely with immune infiltration. Limitations Although this study has yielded valuable findings, it is important to acknowledge several limitations. Relying on retrospective data from public databases such as TCGA and GEO introduces inherent biases in these datasets, including incomplete clinical annotations and batch effects. Although we validated the expression of BBC3 through immunohistochemistry, the relatively small number of paired gastric cancer and adjacent tissue samples (10 pairs) may limit the broader applicability of our results. Second, while bioinformatics tools such as ssGSEA and GSCA facilitate comprehensive immune analyses, the computational predictions they generate still require experimental validation to confirm their accuracy. Third, the study did not utilize in vitro or in vivo models to investigate the mechanistic role of BBC3 in gastric cancer (GC) progression, and its direct impact on apoptosis, immune evasion, or therapeutic resistance remains unresolved. Finally, although the prognostic value of BBC3 demonstrated statistical significance, prospective multicenter validation remains necessary to assess its clinical utility across diverse populations and GC subtypes. Addressing these limitations in future studies will enhance the translational relevance of BBC3 as a biomarker and therapeutic target for GC. Conclusions In summary, this study explored the immune cell profile and TME of BBC3 using scRNA-seq technology, revealing potential key prognostic genes. For the first time, this study elucidates the heterogeneity of BBC3 in functional enrichment and cell-cell communication in STAD. Our findings demonstrate that BBC3 is associated with tumor immunity, showing upregulated expression in gastric cancer. Its expression correlates with prognosis, copy number variation (CNV), methylation, and clinicopathological parameters, suggesting its potential as a therapeutic target for gastric cancer. Declarations Acknowledgements We are grateful to the patients who provided the pathological specimens, our peers for sharing the sample data and survival data. Author contributions Material preparation, data collection and analysis were performed by L L. L L conducted the visualization. The first draft of the manuscript was written by L L. G Y L revised the manuscript. Z J Z conceived and designed the study presented in this paper. Y Y and X L and W W Z performed the immunohistochemical experiments for this study. All the authors have read and approved the final manuscript. Funding Anhui Provincial Health Commission Provincial Financial Support for Youth Programs (Grant No. AHWJ2023A30159) Data availability The data used in this study were sourced from publicly available databases. No datasets were generated or analyzed during the current study. Ethics approval and consent to participate The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of The Second People’s Hospital of Wuhu(2023-KY-010). All patients gave informed consent. Competing interests The authors declare no competing interests. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021 2021/1/1;71(3):209-49. doi: 10.3322/caac.21660 Yoon H, Kim N. Diagnosis and Management of High Risk Group for Gastric Cancer. 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FRONT CELL DEV BIOL. 2022 2022/5/25;10. doi: 10.3389/fcell.2022.852135 Shen T, Liu J, Wang C, et al. Targeting Erbin in B Cells for Therapy of Lung Metastasis of Colorectal Cancer. SIGNAL TRANSDUCT TAR. 2021 2024/1/2;6. doi: 10.1038/s41392-021-00501-x Yang Z, Tao Y, Xu X, et al. Bufalin inhibits cell proliferation and migration of hepatocellular carcinoma cells via APOBEC3F induced intestinal immune network for IgA production signaling pathway. BIOCHEM BIOPH RES CO. [Journal Article; Research Support, Non-U.S. Gov't]. 2018 2018/9/10;503(3):2124-31. doi: 10.1016/j.bbrc.2018.07.169 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-6335473","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447993019,"identity":"94ccb423-9857-4ff8-8b15-1be75f9edbd4","order_by":0,"name":"liu liu","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"liu","middleName":"","lastName":"liu","suffix":""},{"id":447993020,"identity":"eda2eeda-cd61-4290-84b8-f4d8c0d0376c","order_by":1,"name":"Guangyao Li","email":"","orcid":"","institution":"The Second People’s Hospital of Wuhu","correspondingAuthor":false,"prefix":"","firstName":"Guangyao","middleName":"","lastName":"Li","suffix":""},{"id":447993021,"identity":"ebbf603b-4cda-4c76-a073-9e6cf9c20c3b","order_by":2,"name":"Weiwei Zhang","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Weiwei","middleName":"","lastName":"Zhang","suffix":""},{"id":447993022,"identity":"aa140d4e-6975-46c0-b74e-ad1cacdac347","order_by":3,"name":"Xiang Li","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Li","suffix":""},{"id":447993023,"identity":"84c43968-3117-4bb0-a50b-211f95ca65a7","order_by":4,"name":"Yuan Yao","email":"","orcid":"","institution":"The Second People’s Hospital of Wuhu","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Yao","suffix":""},{"id":447993024,"identity":"e57d49c3-eb43-46ef-9c5b-b31619063b24","order_by":5,"name":"Zhengjun Zhang","email":"data:image/png;base64,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","orcid":"","institution":"The Second People’s Hospital of Wuhu","correspondingAuthor":true,"prefix":"","firstName":"Zhengjun","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-03-29 18:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6335473/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6335473/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82123068,"identity":"7eeae88b-a2ea-4391-a3bc-28812e563dd2","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1362422,"visible":true,"origin":"","legend":"\u003cp\u003eDistributions of mRNA in GC. Volcano plot of 375 DEmRNA. Upregulated mRNA are red, while downregulated mRNA are blue.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/b147f98ffc62d6a0a55358c4.png"},{"id":82124683,"identity":"a66b2713-4bf4-46d2-8643-f13250eb5cc6","added_by":"auto","created_at":"2025-05-07 03:39:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586822,"visible":true,"origin":"","legend":"\u003cp\u003eBBC3 expression in various human normal tissues. (a) BBC3 mRNA expression profiles in normal human tissues. (b) BBC3 protein expression data in human normal tissues. (c) Representative IHC images of BBC3 expression in normal stomach. (d) Representative IHC images of BBC3 expression in gastric cancer.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/fbaf84cb9d458668eed65513.png"},{"id":82123079,"identity":"3f41c5ba-9b58-41d6-948e-580ac7474d9b","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":477452,"visible":true,"origin":"","legend":"\u003cp\u003eA good prognosis was linked to upregulation of BBC3 in GC. (a) The expression of BBC3 in pan-cancer from UCSC XENA data base. (b–c) BBC3 expression in GC from TCGA database. (d) The ROC curve of BBC3. (e) Overall survival curve of BBC3 from TCGA database. **, P＜0.01, ***, P＜0.001\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/2eb10ca93a155d43fca424fd.png"},{"id":82125868,"identity":"817b2921-448a-49ff-806a-e0d3864e5ddc","added_by":"auto","created_at":"2025-05-07 03:55:48","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":499404,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of BBC3 expression with clinicopathological parameters. (a) There was a correlation between BBC3 expression and the T stage of GC patients. (b) There was a correlation between BBC3 expression and the pathologic stage of GC patients. (c-e) Overall survival were better in subgroups of GC with higher BBC3 expression. *, P＜0.05,**, P＜0.01, ***, P＜0.001\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/d627b04380136ddb6920361e.png"},{"id":82123069,"identity":"b88da9e1-23f7-425b-8b57-268049283e6f","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":327136,"visible":true,"origin":"","legend":"\u003cp\u003eCNA and methylation contribute to driving the abnormal expression of BBC3 in gastric cancer. (a) Correlation of CNV and BBC3 mRNA expression in the GSCA database. A significant positive correlation was observed in patients with STAD. (b)In gastric cancer, BBC3 mRNA expression was significantly associated with methylation levels. (c) Correlation of BBC3 mRNA with three methyltransferases, namely DNMT1, DNMT3A and DNMT3B.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/4c024a7995312555623a91ba.png"},{"id":82123077,"identity":"ac97bfa2-01ee-4a45-8844-551b23c643cf","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":801741,"visible":true,"origin":"","legend":"\u003cp\u003eBBC3 expression and tumor immunity. (a) In the bar graph, BBC3 expression was correlated with 24 immune infiltration cells. The horizontal axis represents correlations, and the vertical axis represents immune cells.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/14a26668fe26aa0b9310a850.png"},{"id":82124685,"identity":"9d27b25c-f014-4975-8782-bfaee590b7a6","added_by":"auto","created_at":"2025-05-07 03:39:48","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":738178,"visible":true,"origin":"","legend":"\u003cp\u003eBBC3 expression was positively correlated with (a) CD8 T cells, (b) Macrophages, (c) DC cells, (d) Cytotoxic cells, (e) NK cells, (f) NK CD56dim cells, (g) T cells, (h) Th2 cells, and (i) TReg cells.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/e6104fcaabe14b7c5ef12152.png"},{"id":82125280,"identity":"f0d02ec9-2501-4c85-9bec-d07bd41fed3f","added_by":"auto","created_at":"2025-05-07 03:47:48","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":351113,"visible":true,"origin":"","legend":"\u003cp\u003eThe relationship between BBC3 expression and immune cells. Compared to low expression of BBC3, the BBC3 high expression group of GC had greater concentrations of (a) CD8 Tcells, (b) Cytotoxic cells, (c) DC cells, (d) T cells, (e) CD56dim cells, (f) Th1 cells, (g) TReg cells,(h) Macrophages, and (i)Th2 cells*P ＜0.05, **, P ＜0.01, ***, P ＜0.001\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/fed62300d355b337f642a4eb.png"},{"id":82124686,"identity":"9763f9f3-6d03-43cd-a0fd-1253e1826010","added_by":"auto","created_at":"2025-05-07 03:39:48","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":308880,"visible":true,"origin":"","legend":"\u003cp\u003eTumor immune checkpoints and BBC3 expression. There were significant correlations between BBC3 and (a)PD-L1(CD274), (b) CTLA4, (c)LAG3and(d) PD-1 (PDCD1)\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/45d16c1877f856feca27dbc6.png"},{"id":82124696,"identity":"269887b2-2137-4691-8c2e-7f445e454dae","added_by":"auto","created_at":"2025-05-07 03:39:48","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":292681,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA of BBC3 in the TCGA STAD cohort. (a-e) KEGG enrichment analyses showed that Apoptosis, Necroptosos, Cytosolic DNA-sensing pathway, Antigen processing and presentation were enriched.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/864195621c2be536474f1408.png"},{"id":82125283,"identity":"2eaa6acc-9648-4d30-8e54-8102efe61253","added_by":"auto","created_at":"2025-05-07 03:47:48","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":430404,"visible":true,"origin":"","legend":"\u003cp\u003eIntegration and clustering of BBC3 scRNA-Seq data. (a)t-SNE of the 15 cell clusters. (b) The 9 cell types were identified by marker genes. (c)Feature plots showing BBC3 expressions across the cell types. (d)The violin plot showing the expression levels of BBC3 in different cell types\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/f6fe1e5f93f850d5f28ff91c.png"},{"id":82123083,"identity":"e3941c1b-9c51-48ef-b03b-70204cced76b","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":268061,"visible":true,"origin":"","legend":"\u003cp\u003eCell-cell communication analysis. (a)Network diagram illustrating the count of ligand-receptor interactions between different cell types. (b)Network diagram illustrating the weight of ligand-receptor interactions between different cell types\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/6c98602ea63c3449e45db0a1.png"},{"id":82123094,"identity":"7fee68e5-a026-4fa4-8b2c-dcaaadea1323","added_by":"auto","created_at":"2025-05-07 03:31:49","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":218921,"visible":true,"origin":"","legend":"\u003cp\u003eThe cellular communication between each cell and other cells. (a) The cellular communication between CD4+T cells and other cells. (b) The cellular communication between Endothelial cells and other cells. (c) The cellular communication between B cells and other cells. (d) The cellular communication between CD8+T cells and other cells. (e) The cellular communication between Fibroblasts cell and other cells. (f) The cellular communication between Epithelial cell and other cells. (g) The cellular communication between Monocytes and other cells. (h) The cellular communication between HSC and other cells. (i) The cellular communication between Neutrophils and other cells.\u003c/p\u003e","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/c0f852d7ec9a395663170850.png"},{"id":82124688,"identity":"3e5c5cae-5fcd-42de-9e48-741a352742d4","added_by":"auto","created_at":"2025-05-07 03:39:48","extension":"jpeg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":161674,"visible":true,"origin":"","legend":"\u003cp\u003eThe bubble plot of cellular communication, through which ligand-receptor pairs do cells communicate with each other\u003c/p\u003e","description":"","filename":"floatimage14.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/b100febbe4f5bd828f5d81f1.jpeg"},{"id":82125873,"identity":"f5904751-c1ca-428d-992f-525522f38c10","added_by":"auto","created_at":"2025-05-07 03:55:49","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":1760900,"visible":true,"origin":"","legend":"\u003cp\u003eceRNA network. yellow, blue, and red represent the miRNAs, lncRNAs, and mRNAs of the risk model genes, respectively.\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/beb40608d900f0bf4081d28b.png"},{"id":82123095,"identity":"a64182ed-9bf2-4c4a-ad28-8c57a5265905","added_by":"auto","created_at":"2025-05-07 03:31:49","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":1264015,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative immunohistochemical staining of BBC3 in STAD tissues. (a) Positive expression of BBC3 in GC tissue, Magnification ×100. (b) Positive expression of BBC3 in GC tissue, Magnification ×400. (c) Positive expression of BBC3 in adjacent non-cancerous tissues, Magnification ×100. (d) Positive expression of BBC3 in adjacent non-cancerous tissues, Magnification ×400. (e) Different expression of BBC3 in STAD tissue and matched adjacent noncancerous tissues. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"floatimage16.png","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/b2ca59aacf1d70cdd1d80007.png"},{"id":86700720,"identity":"7e7fb416-c073-468b-94b1-78fbd12bb11d","added_by":"auto","created_at":"2025-07-14 16:12:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11053442,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/ced85504-e39d-4662-a371-d67a445d44f1.pdf"},{"id":82123070,"identity":"684be0a8-83b9-4bea-b9ae-a04aa8f127f3","added_by":"auto","created_at":"2025-05-07 03:31:48","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":13080,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6335473/v1/f023cc4560664fc1021f345b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"BBC3 is a biomarker related to gastric cancer prognosis and immunity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) remains one of the most prevalent malignant tumors worldwide, with its incidence and mortality rates second only to colorectal cancer, ranking sixth globally. In 2020, over 1\u0026nbsp;million new GC cases and 769,000 deaths were estimated [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite significant advances in GC treatment, including surgical resection, chemotherapy, and radiotherapy, the 5-year overall survival (OS) rate for patients with advanced disease remains below 30%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Typically, delayed diagnosis, lymph node metastasis, and the limited effectiveness of current treatments are the main factors contributing to the poor prognosis of GC, resulting in a high mortality rate[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In recent years, with rapid advances in genomics and high-throughput sequencing technologies, GC can now be studied at high resolution and molecular levels. RNA sequencing (RNA-seq) technology has become increasingly applied in gastric cancer research, revealing new gene mutations, chromosomal alterations, and epigenetic changes associated with gastric cancer progression. Although bulk RNA-seq technology shows promise, it only provides the average expression levels of all cell types within the sample and cannot capture the expression profiles of individual cell types or the ligand-receptor interactions between different cell types[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The emergence of single-cell technology effectively overcomes these limitations, allowing the assessment of gene expression in thousands of cells at single-cell resolution [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It also facilitates the analysis of immune infiltration and tumor heterogeneity in the tumor microenvironment (TME)[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This study integrated RNA-seq data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) to construct a single-cell transcriptomic map of gastric cancer samples and identify multiple cell types. The aim was to predict the relationship between BBC3 and gastric cancer prognosis, thereby developing a prognostic model.\u003c/p\u003e \u003cp\u003eBBC3, also known as PUMA, is an important apoptosis-related protein, and its abnormal expression in various tumors is closely linked to tumorigenesis and progression. Research has shown that BBC3 regulates the proliferation and death of cancer cells in non-small cell lung cancer[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The miR-222 inhibitor can enhance BBC3 protein expression by binding to it, thereby inhibiting the proliferation, migration, and invasion of liver cancer cells and promoting apoptosis in these cells[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Overexpression of BBC3 induces the dissociation of thioredoxin from ASK1, activating the JNK/BCL-2/BCL-XL pathway and promoting apoptosis in ovarian cancer cells[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. BBC3 inhibits the growth of colon cancer through targeting the mTOR pathway. According to existing research, chaperone-mediated autophagy (CMA) prevents cell apoptosis by degrading BBC3/BCRIA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Long non-coding RNA TSLC8 (lncRNA TSLC8) inhibits colorectal cancer by stabilizing BBC3[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests that BBC3 plays a crucial role in apoptosis induced by various stress signals mediated by multiple transcription factors. BBC3 Ser10 phosphorylation is crucial for tumor necrosis factor-induced apoptosis. This provides valuable insights for further research into the mechanisms by which BBC3 contributes to apoptosis in gastric cancer cells. However, the mechanism of BBC3 in gastric cancer has been less studied. Therefore, this study aims to investigate the role of BBC3 in the development and progression of gastric cancer, providing new insights and strategies for its treatment and prevention.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Acquisition\u003c/h2\u003e \u003cp\u003eClinical and RNA sequencing data from 407 GC patients were obtained 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), including 375 GC tissue samples and 32 normal gastric tissue samples. Data on T stage, age, N stage, gender, M stage, pathological stage, and lymphatic invasion were also included. The scRNA-seq data of GC were obtained from the GSE251950 dataset, which is available in the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of DEmRNA\u003c/h3\u003e\n\u003cp\u003eDEmRNAs were obtained using the 'Limma' package with an absolute log2 fold change (|logFC|)\u0026thinsp;\u0026gt;\u0026thinsp;1.0 and P.adj\u0026thinsp;\u0026lt;\u0026thinsp;0.05. We identified mRNAs co-expressed with the target gene using R programming. The 'ggplot2' package was used to visualize the volcano plot of the mRNAs.\u003c/p\u003e\n\u003ch3\u003eBBC3 Expression Analysis\u003c/h3\u003e\n\u003cp\u003eThe HPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database was used to explore the mRNA and protein expression levels of BBC3 in normal human tissues. The expression levels of BBC3 in various cancer tissues were obtained using the 'Gene_DE' module from TIMER 2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.cistrome.org/\u003c/span\u003e\u003cspan address=\"http://timer.cistrome.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eCopy number variation and methylation contribute to driving the abnormal expression of BBC3 in gastric cancer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGene Set Cancer Analysis (GSCA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinfo.life.hust.edu.cn/GSCA#/\u003c/span\u003e\u003cspan address=\"http://bioinfo.life.hust.edu.cn/GSCA#/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a powerful bioinformatics tool that primarily integrates mRNA expression, mutation, immune infiltration, and methylation data from the TCGA database [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The 'Mutation' module in the GSCA database is used to analyze the CNV and methylation of BBC3 and their correlation with mRNA expression levels. Xiantao (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.xiantaozi.com/\u003c/span\u003e\u003cspan address=\"https://www.xiantaozi.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is a comprehensive and user-friendly bioinformatics analysis platform[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The relationship between BBC3 and the expression of DNA methyltransferase genes was investigated using the Xiantao platform.\u003c/p\u003e\n\u003ch3\u003eSurvival analysis\u003c/h3\u003e\n\u003cp\u003eProportional hazards assumption tests and survival regression fitting were performed using the 'survival' package, and the results were visualized with the 'survminer' and 'ggplot2' packages[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The optimal cut-off for grouping was determined using the 'surv_cutpoint' function in the 'survminer' package.\u003c/p\u003e\n\u003ch3\u003eImmune-related analysis of BBC3\u003c/h3\u003e\n\u003cp\u003eUsing the ssGSEA algorithm from the R package GSVA [1.46.0][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], immune infiltration of 24 immune cell types was calculated using the markers of these cells, with corresponding cloud-based data[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This analysis was conducted to evaluate the immune cell types in GC tissue samples.Spearman correlation analysis was performed to evaluate the relationship between BBC3 expression and immune cells, as well as immune checkpoints\u0026mdash;programmed cell death protein 1 (PD-1), PDCD1, cytotoxic T lymphocyte-associated protein 4 (CTLA-4), and programmed cell death ligand 1 (PD-L1).We used the 'ggplot2' [3.4.4], 'stats' [4.2.1], and 'car' [3.1-0] packages to visualize the correlation. The Welch t-test was used to evaluate the enrichment of immune infiltrating cells in GC patients with high BBC3 expression compared to those with low BBC3 expression.\u003c/p\u003e\n\u003ch3\u003eGeneration of the ceRNA axis\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed lncRNAs (DE-lncRNAs) between tumor and healthy samples were identified with the following criteria: |log2(fold change)| \u0026gt; 1, P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The target miRNAs of lncRNAs were predicted using the miRcode database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.example.com\u003c/span\u003e\u003cspan address=\"http://www.example.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], and the target miRNAs of prognostic DEGs (differentially expressed genes) were predicted using the Starbase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rnasysu.com/encori/\u003c/span\u003e\u003cspan address=\"https://rnasysu.com/encori/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Common miRNAs predicted by both miRcode and Starbase, along with their corresponding lncRNAs and prognostic DEGs, were imported into Cytoscape software to construct the ceRNA network[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq Data Integration and Dimensionality Reduction Clustering\u003c/h2\u003e \u003cp\u003eThe FindVariableFeatures function from the Seurat package was used to extract genes with the highest coefficient of variation between cells from the normalized data. The data were then scaled and subjected to principal component analysis (PCA)[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. t-distributed stochastic neighbor embedding (t-SNE) was applied for data visualization. Differentially expressed genes between different cell populations were identified using the FindAllMarkers function, which led to the identification of BBC3[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Cell types were annotated with the SingleRbao package, and t-SNE visualization was performed using TSNEPlot[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell communication analysis\u003c/h2\u003e \u003cp\u003eThe R package CellChat enables the inference, visualization, and analysis of intercellular communication in scRNA-seq data, and can illustrate the interactions between ligands, receptors, and their cofactors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. To explore potential communication between T cells and other cell types, ligand-receptor interactions between different cell populations were analyzed using the R package CellChat.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry of paraffin sections\u003c/h2\u003e \u003cp\u003eA total of 10 pairs of gastric cancer (STAD) and adjacent non-cancerous tissue samples were collected from the specimen bank of Wuhu Second Hospital, all from gastric cancer patients diagnosed between September 2022 and October 2023. These patients had not undergone radiotherapy or chemotherapy prior to surgery. This study was approved by the Ethics Committee of Wuhu Second Hospital, and all patients provided written informed consent (2023-KY-010). The same immunohistochemical method and scoring system described in published articles were used[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], with a 1:400 dilution. Scoring was carried out using ImageJ software, followed by statistical analysis and visualization based on the obtained scores.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn R (version 4.2.1), the Mann-Whitney U test (Wilcoxon rank sum test), Shapiro-Wilk normality test, and Levene's test were used to assess BBC3 expression in normal gastric and GC tissues[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Appropriate statistical methods were chosen based on the data format and characteristics (using the 'stats' and 'car' packages). Statistical analysis was not performed if the assumptions were not met. The data were visualized using the 'ggplot2' package.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eAcquisition of DEmRNA\u003c/h2\u003e \u003cp\u003ePrevious studies have revealed that BBC3 plays a role in the proliferation, migration, and invasion of liver cancer cells and shows significant correlation with prognosis in lung squamous cell carcinoma patients. However, the prognostic value of BBC3 in gastric cancer and its involvement in immune responses remain unclear. Therefore, we utilized the TCGA public database to identify BBC3 as a differentially expressed mRNA (DEmRNA) in gastric cancer, which contained 375 differentially expressed mRNAs, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe expression of BBC3 across different normal human tissues.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate the expression levels of BBC3 in different types of normal human tissues, we assessed its mRNA and protein expression using the Human Protein Atlas (HPA) database. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, the tissues with the highest BBC3 expression, in descending order, are skeletal muscle, cerebellum, skin, pancreas, lung, salivary glands, spleen, liver, cerebral cortex, kidney, and stomach. Next, we evaluated its protein expression and found that BBC3 expression levels vary significantly across different tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). BBC3 mRNA and protein exhibited distinct expression patterns in normal tissues, which may be due to the low specificity of the BBC3 antibody, though this has not been experimentally validated. Immunohistochemistry revealed that BBC3 is expressed in both the nucleus and cytoplasm of normal gastric tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) and gastric cancer tissue (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed), with representative staining patterns at different expression levels shown.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBBC3 shows higher expression in gastric cancer than in normal gastric tissues, and high expression predicts better prognosis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe accessed the TIMER2.0 website to examine the expression changes of BBC3 in tumor tissues and corresponding normal tissues from the TCGA database, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea. We visualized BBC3 expression using the TCGA database, as shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec. STAD tissues exhibit higher levels of BBC3 expression compared to normal gastric tissue. BBC3 may serve as a potential diagnostic biomarker, with an AUC of 0.926 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed). Survival analysis shows that STAD patients with high BBC3 expression have better overall survival, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBBC3 expression is correlated with clinical and pathological parameters.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, we used the chi-square test to assess the association between clinical pathological factors and BBC3 expression. The analysis revealed that BBC3 expression is associated with the T stage (P\u0026thinsp;=\u0026thinsp;0.017) and pathological stage (P\u0026thinsp;=\u0026thinsp;0.031) in STAD patients. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, using the Kruskal-Wallis rank sum test, BBC3 expression is correlated with the T stage in both normal gastric tissues and STAD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as across different T stages in STAD patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). BBC3 expression is also correlated with the pathological stage in STAD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The overall survival of STAD subgroups with high and low BBC3 expression is shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (c-e). The analysis revealed that the T4 subgroup (HR\u0026thinsp;=\u0026thinsp;0.50, 95% CI: 0.26\u0026ndash;0.94, P\u0026thinsp;=\u0026thinsp;0.03), the stage IV subgroup (HR\u0026thinsp;=\u0026thinsp;0.35, 95% CI: 0.14\u0026ndash;0.83, P\u0026thinsp;=\u0026thinsp;0.018), and the N3 subgroup (HR\u0026thinsp;=\u0026thinsp;0.49, 95% CI: 0.26\u0026ndash;0.91, P\u0026thinsp;=\u0026thinsp;0.024) are associated with higher overall survival. Furthermore, higher BBC3 expression is associated with better prognosis.\u003c/p\u003e \u003cp\u003eTo investigate the impact of BBC3 expression and clinical pathological parameters on survival, we performed univariate and multivariate COX regression analyses. In the univariate COX regression analysis, variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, including T stage, N stage, M stage, pathological stage, and age, were found to be statistically significant. In the multivariate COX regression model, variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, including N stage, M stage, age, and increased BBC3 expression, were shown to be statistically significant. In this study, BBC3 expression (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), N3 stage (P\u0026thinsp;=\u0026thinsp;0.014), M stage (P\u0026thinsp;=\u0026thinsp;0.02), and age (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were identified as independent factors affecting the overall survival rate of STAD patients, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003eBBC3 expression associated with clinicopathological characteristics (chi-square test)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow expression of BBC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh expression of BBC3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.017\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59 (16.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (10.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (39.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (15.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (10.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (11.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34 (9.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162 (45.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (47.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (8.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (19%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78 (22.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e63 (16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (18.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e88 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (20.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e98 (26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \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\u003eUnivariate and multivariate analyses of clinicopathological parameters in patients with GC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTotal(N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic T stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT1\u0026amp;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT3\u0026amp;T4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.719 (1.131\u0026ndash;2.612)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.501 (0.884\u0026ndash;2.550)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic N stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.629 (1.001\u0026ndash;2.649)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.049\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.526 (0.833\u0026ndash;2.796)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.655 (0.979\u0026ndash;2.797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.627 (0.778\u0026ndash;3.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.709 (1.669\u0026ndash;4.396)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.556 (1.209\u0026ndash;5.404)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic M stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e352\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.254 (1.295\u0026ndash;3.924)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.087 (1.120\u0026ndash;3.890)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.020\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePathologic stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u0026amp;Stage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u0026amp;Stage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.947 (1.358\u0026ndash;2.793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.908 (0.476\u0026ndash;1.731)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;= 65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.620 (1.154\u0026ndash;2.276)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.878 (1.301\u0026ndash;2.711)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.267 (0.891\u0026ndash;1.804)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBBC3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.565 (0.406\u0026ndash;0.786)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.558 (0.389\u0026ndash;0.799)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\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 \u003cb\u003eCopy number variations and methylation contribute to the abnormal expression of BBC3 in gastric cancer.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo further investigate the mechanism of abnormal BBC3 mRNA expression, we analyzed the relationship between gene copy number variation (CNV) and mRNA expression. GSEA analysis revealed that BBC3 expression in STAD patients is significantly positively correlated with CNV (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), suggesting that CNV may contribute to abnormal BBC3 expression in gastric cancer patients. Additionally, the abnormal expression of BBC3 in gastric cancer may be associated with structural variations in DNA fragments within the human genome.\u003c/p\u003e \u003cp\u003eDNA methylation is an epigenetic process that significantly regulates gene transcription. Therefore, we found a significant association between DNA methylation levels and mRNA expression in STAD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). To further investigate the underlying mechanism of BBC3 methylation in STAD, we evaluated the correlation between BBC3 and three DNA methyltransferase genes (DNMT1, DNMT3A, and DNMT3B). We found that in STAD, BBC3 expression is positively correlated with DNMT1, while it is negatively correlated with DNMT3A and DNMT3B expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe association between BBC3 expression and tumor immunity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, we used the ssGSEA algorithm to evaluate the relationship between the relative abundance of 24 immune cells and BBC3 expression in gastric cancer. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-I, BBC3 expression is correlated with various immune cell types, including CD8\u0026thinsp;+\u0026thinsp;T cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, correlation coefficient R\u0026thinsp;=\u0026thinsp;0.335), macrophages (P\u0026thinsp;=\u0026thinsp;0.003, R\u0026thinsp;=\u0026thinsp;0.151), DC cells (P\u0026thinsp;=\u0026thinsp;0.028, R\u0026thinsp;=\u0026thinsp;0.114), cytotoxic lymphocytes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.296), NK cells (P\u0026thinsp;=\u0026thinsp;0.006, R\u0026thinsp;=\u0026thinsp;0.142), NK CD56dim cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.364), T cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.204), Th2 cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.256), and TReg cells (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026thinsp;=\u0026thinsp;0.287). We used the Wilcoxon rank-sum test to assess the enrichment of immune cells in the high and low BBC3 expression groups. The results showed that, in comparison to the low BBC3 expression group, the high BBC3 expression group exhibited higher enrichment of CD8 T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, TReg cells, macrophages, and Th2 cells (Figs.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea-i).\u003c/p\u003e \u003cp\u003eSpearman's correlation analysis showed a positive correlation between the expression of BBC3 and the expressions of CD274 (PD-L1), CTLA4, LAG3, and PDCD-1 (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ea through \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis\u003c/h2\u003e \u003cp\u003eOur previous findings suggest that BBC3 expression is closely associated with the prognosis and immunity of gastric cancer patients. Given the strong correlation observed between BBC3 expression and immune infiltration in STAD, we selected STAD as a model to validate the potential function of BBC3 using LinkedOmics.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis (GSEA) of BBC3-related genes in STAD revealed that KEGG pathway analysis identified several major enriched pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e), including ribosomal biogenesis, antigen processing and presentation, the cytosolic DNA sensing pathway, necroptosis, cytosolic DNA-sensing pathway, the proteasome pathway, the intestinal immune network for IgA production, apoptosis, DNA replication, and other signaling pathways.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe single-cell transcriptome atlas of gastric cancer\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed two samples in total from the GSE184198 dataset, consisting of tumor and matched normal tissue samples from a single patient. The samples were subjected to preliminary screening to remove low-quality data, and as a result, 8816 cells were retained for subsequent analysis. Subsequently, we employed the 'anchor' method for data integration to eliminate batch effects and performed standardization, centering, and PCA dimensionality reduction, retaining the first 15 principal components. Clustering visualization was then performed using the t-SNE method. Clustering analysis grouped 8816 cells into 15 clusters, as shown in Figure A. Cell type annotation was performed using the Single R package, revealing 9 cell types (Figure B). Figure C shows the distribution of BBC3 in gastric cancer tissues, and Figure D illustrates the differential expression of BBC3 in different cell types within gastric cancer tissues. The expression levels, from highest to lowest, were as follows: CD8\u0026thinsp;+\u0026thinsp;T Cell, CD4\u0026thinsp;+\u0026thinsp;T Cell, HSC, B Cell, Epithelial Cell, Monocytes, Fibroblasts, Endothelial Cells, and Neutrophils.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCell-cell interaction analysis\u003c/h2\u003e \u003cp\u003eTo investigate the communication patterns between various cell types in gastric cancer tissues, CellChat was employed to identify ligand-receptor pairs and their molecular interactions. As shown in Figs.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003ea, the network represents intercellular interaction quantity. Nodes denote different cell types, with larger circles indicating higher cell abundance for the corresponding type. Thicker lines denote more interactions between cell types, and line colors match the colors of ligand cells. Figure\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eb shows the network of intercellular interaction strength. Nodes indicate cell types, line thickness reflects interaction intensity, and colors match ligand cells. Figure\u0026nbsp;\u003cspan refid=\"Fig13\" class=\"InternalRef\"\u003e13\u003c/span\u003e(a-i) compares intercellular interaction networks across different group. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e14\u003c/span\u003e, the x-axis represents the interacting cells, the y-axis represents the ligand-receptor pairs, and the size of the circles correlates with the significance of the p-value, with larger circles indicating smaller p-values. The color of the circles indicates the likelihood of interaction, with redder colors reflecting a higher probability of interaction. From this bubble plot, we can observe that the ligand-receptor pair CCL5-ACKR1 contributes most significantly to the interaction between CD8\u0026thinsp;+\u0026thinsp;T cells and endothelial cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of a ceRNA network for BBC3\u003c/h2\u003e \u003cp\u003eLncRNAs and circRNAs are often regarded as competing endogenous RNAs (ceRNAs) that bind to miRNAs. ceRNA analysis involves examining the entire ceRNA regulatory network; typically, circRNA-miRNA-mRNA or lncRNA-miRNA-mRNA analysis is regarded as the core of the ceRNA regulatory network. As ceRNAs (such as lncRNAs or circRNAs) competitively bind to miRNAs, the transcription levels of miRNA-regulated genes will increase. To further investigate the potential regulatory role of BBC3 in STAD prognosis, we constructed a ceRNA network incorporating DE-lncRNAs and BBC3. The target miRNAs of DE-lncRNAs were identified using the miRcode database, and the target miRNAs of BBC3 were identified using the Starbase database. The ceRNA network, which includes 5 lncRNAs, 158 miRNAs, and BBC3, revealed the molecular mechanism of BBC3 in the prognosis of STAD, Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eExpression of BBC3 protein in gastric cancer tissues\u003c/h2\u003e \u003cp\u003eTo fully investigate the expression of BBC3 in gastric cancer, 10 STAD and adjacent non-cancerous tissue samples were analyzed using immunohistochemistry. The study results showed that BBC3 expression was significantly increased in cancer tissues compared to adjacent non-cancerous tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor gastric cancer (GC), molecular targeted therapy and immunotherapy have shown therapeutic efficacy in recent years[\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. For example, the development of immune checkpoint inhibitors has demonstrated clinical efficacy. However, due to the occurrence of adverse events[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], most GC patients have not benefited from immune checkpoint inhibitors. According to relevant studies, molecular chaperone-mediated autophagy prevents cell apoptosis by the degradation of BBC3/BCRIA[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and the long non-coding RNA TSLC8 inhibits colorectal cancer progression by stabilizing BBC3[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This suggests that BBC3 plays a crucial role in cell apoptosis induced by various stress signals through the mediation of multiple transcription factors. This provides important guidance for our further research on the mechanism by which BBC3 contributes to gastric cancer cell apoptosis. Currently, scRNA-seq is widely used to characterize the fundamental characteristics of tumor-infiltrating immune cells and has revealed the regulation of immune cell subpopulations within the TME (tumor microenvironment)[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Therefore, further identification of immune-related genes is necessary to systematically investigate the relationship between STAD and the TME, with the goal of improving the prognosis of GC patients. These findings highlight the need for in-depth studies to elucidate the molecular pathways mediated by BBC3 during STAD progression.\u003c/p\u003e \u003cp\u003eFrom the TCGA and GEO databases, we acquired clinical and RNA-seq data from GC patients and employed bioinformatics methods to identify differentially expressed mRNAs (DE-mRNAs) in GC. Ultimately, BBC3 was found to be elevated in gastric cancer tissues relative to normal tissues. Based on data from the UCSC XENA, TCGA, and HPA databases, the expression of BBC3 is higher in GC tissue compared to normal gastric tissue. The expression of BBC3 in gastric cancer is associated with clinicopathological parameters (T stage) and poor prognosis. Furthermore, BBC3 shows a high diagnostic accuracy for gastric cancer. Multivariate regression analysis revealed that BBC3 is an independent prognostic factor for GC patients, suggesting that BBC3 may be beneficial as a biomarker for both diagnosis and prognosis in GC patients. Our findings indicate that in pan-cancer, the expression of BBC3 mRNA shows significant correlations with CNV and methylation.\u003c/p\u003e \u003cp\u003eCancer immunotherapy stimulates and enhances the immune system's ability to recognize, target, and eliminate tumor cells[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Anti-tumor immunity strategy has been accomplished through different modalities including cellular immunotherapy, specific vaccines, monoclonal antibodies, and oncolytic virotherapy[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Immune-related gene expression signatures have emerged as potential predictive biomarkers for immunotherapy efficacy in malignancies[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. BBC3 is closely associated with several immune checkpoint genes, and its expression levels may indirectly reflect the abundance of this immune infiltrating factor in the tumor microenvironment (TME). The expression of immune-related genes is considered a predictive biomarker for immunotherapy in various cancers. Correlation analysis revealed that BBC3 expression is correlated with PD-L1 (CD274), CTLA4, PD-1 (PDCD1), LAG3, and immune cells such as CD8\u0026thinsp;+\u0026thinsp;T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, Treg cells, macrophages, and Th2 cells. Furthermore, the enrichment of immune cells (CD8\u0026thinsp;+\u0026thinsp;T cells, lymphocytes, DC cells, T cells, NK CD56dim cells, Th1 cells, Treg cells, macrophages, and Th2 cells) in the high BBC3 expression group is higher than in the low expression group. Kaplan-Meier curves indicate that high expression of BBC3 is associated with a better prognosis in STAD patients. This may explain the association between BBC3 overexpression and great prognosis in patients with cancer.\u003c/p\u003e \u003cp\u003eBased on GSEA and KEGG pathway enrichment analysis, BBC3 and its co-expressed mRNAs are enriched in several signaling pathways, including ribosomal expression, antigen processing and presentation, cytosolic DNA sensing pathway, necroptosis, proteasome, intestinal immune network for IgA production, apoptosis, DNA replication, and other signaling pathways. We found that pathways associated with immunity, as well as cancer cell proliferation and migration, including the intestinal immune network for IgA production[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], may explain the potential mechanisms by which BBC3 promotes immune infiltration and affects cancer cell proliferation and migration. These results provide confirmatory support for the role of BBC3 as an immune biomarker.\u003c/p\u003e \u003cp\u003eAnd then, the GSE184198 scRNA-seq dataset was used to analyze the heterogeneity of STAD. After annotation, a total of 9 cell types were identified, including CD8\u0026thinsp;+\u0026thinsp;T cells, CD4\u0026thinsp;+\u0026thinsp;T cells, HSC, B cells, epithelial cells, monocytes, fibroblasts, endothelial cells, and neutrophils. Through t-SNE dimensionality reduction, we can visualize the differences in the expression of the BBC3 gene across different cell populations. Furthermore, using the R package ggplot2, we generated dot plots illustrating the expression of BBC3 across different immune cell types. We observed that BBC3 exhibits significantly higher expression levels in CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells compared to other cell populations. The cell-cell communication analysis results indicate that there is direct and strong communication between various cell subtypes, primarily mediated by the MIF signaling pathway. Therefore, these findings provide evidence supporting the involvement of BBC3 in the immune microenvironment of gastric cancer.\u003c/p\u003e \u003cp\u003eNext, we constructed a ceRNA network comprising 5 lncRNAs, 158 miRNAs, and BBC3, which explored the molecular mechanism of BBC3 in STAD prognosis. Finally, the IHC experimental result shown BBC3 expressed higher in STAD. Our results indicated that in the microenvironment of GC, BBC3 was associated closely with immune infiltration.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eAlthough this study has yielded valuable findings, it is important to acknowledge several limitations. Relying on retrospective data from public databases such as TCGA and GEO introduces inherent biases in these datasets, including incomplete clinical annotations and batch effects. Although we validated the expression of BBC3 through immunohistochemistry, the relatively small number of paired gastric cancer and adjacent tissue samples (10 pairs) may limit the broader applicability of our results. Second, while bioinformatics tools such as ssGSEA and GSCA facilitate comprehensive immune analyses, the computational predictions they generate still require experimental validation to confirm their accuracy. Third, the study did not utilize in vitro or in vivo models to investigate the mechanistic role of BBC3 in gastric cancer (GC) progression, and its direct impact on apoptosis, immune evasion, or therapeutic resistance remains unresolved. Finally, although the prognostic value of BBC3 demonstrated statistical significance, prospective multicenter validation remains necessary to assess its clinical utility across diverse populations and GC subtypes. Addressing these limitations in future studies will enhance the translational relevance of BBC3 as a biomarker and therapeutic target for GC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study explored the immune cell profile and TME of BBC3 using scRNA-seq technology, revealing potential key prognostic genes. For the first time, this study elucidates the heterogeneity of BBC3 in functional enrichment and cell-cell communication in STAD. Our findings demonstrate that BBC3 is associated with tumor immunity, showing upregulated expression in gastric cancer. Its expression correlates with prognosis, copy number variation (CNV), methylation, and clinicopathological parameters, suggesting its potential as a therapeutic target for gastric cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the patients who provided the pathological specimens, our peers for sharing the sample data and survival data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003eMaterial preparation, data collection and analysis were performed by L L. \u0026nbsp;L L conducted the visualization. The first draft of the manuscript was written by L L. G Y L revised the manuscript. Z J Z conceived and designed the study presented in this paper. Y Y and X L and W W Z performed the immunohistochemical experiments for this study. All the authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eAnhui Provincial Health Commission Provincial Financial Support for Youth Programs (Grant No. AHWJ2023A30159)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The data used in this study were sourced from publicly available databases. No datasets were generated or analyzed during the current study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of The Second People\u0026rsquo;s Hospital of Wuhu(2023-KY-010). All patients gave informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians. 2021 2021/1/1;71(3):209-49. doi: 10.3322/caac.21660\u003c/li\u003e\n\u003cli\u003eYoon H, Kim N. Diagnosis and Management of High Risk Group for Gastric Cancer. GUT LIVER. 2015 2015/1/15;9(1):5-17. doi: 10.5009/gnl14118\u003c/li\u003e\n\u003cli\u003eWang T, Zhao Y, Peng L, et al. Tumour-activated neutrophils in gastric cancer foster immune suppression and disease progression through GM-CSF-PD-L1 pathway. GUT. 2017;66(11):1900-11. doi: 10.1136/gutjnl-2016-313075\u003c/li\u003e\n\u003cli\u003eWang H, Liu Y, Ding J, et al. 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[Journal Article; Research Support, Non-U.S. Gov\u0026apos;t]. 2018 2018/9/10;503(3):2124-31. doi: 10.1016/j.bbrc.2018.07.169\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, Immune microenvironment, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-6335473/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6335473/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eBBC3, also known as PUMA, is a key apoptosis-related protein, and its dysregulated expression in various cancers is closely linked to tumorigenesis and progression. However, the functional role of BBC3 in gastric cancer remains underexplored.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study aims to investigate the relationship between BBC3 and the prognosis as well as immune infiltration in gastric cancer, providing a potential molecular foundation for its immunotherapy, including clinical and pathological parameters, tumor immunity, copy number variation (CNV), methylation, and enrichment analysis. Furthermore, We employed single-cell sequencing technology to investigate the role of BBC3 in the immune microenvironment of gastric cancer. Immunohistochemistry was used to confirm the expression of the BBC3 gene in gastric cancer tissues. Furthermore, a ceRNA network regulating BBC3 was constructed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results of differential expression analysis revealed that BBC3 expression was higher in gastric cancer tissues compared to adjacent non-cancerous tissues. CNV and methylation were associated with abnormal BBC3 mRNA expression in tumor tissues. Survival analysis showed a significant correlation between BBC3 levels and the prognosis of gastric cancer, with elevated BBC3 expression being strongly associated with better clinical outcomes for patients. Functional enrichment analysis revealed that BBC3 is significantly enriched in pathways associated with tumorigenesis and immune suppression. Using single-cell sequencing data, we identified the distribution of BBC3 in gastric cancer tissues and its expression in immune cells within these tissues. The ceRNA network we constructed elucidated the molecular role of BBC3 in gastric cancer prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBBC3 is linked to tumor immunity and could serve as a biomarker for prognosis in gastric cancer patients.\u003c/p\u003e","manuscriptTitle":"BBC3 is a biomarker related to gastric cancer prognosis and immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 03:31:43","doi":"10.21203/rs.3.rs-6335473/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"243fa40f-553b-4742-bb51-ba7bb9c8dcf6","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-14T16:12:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-07 03:31:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6335473","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6335473","identity":"rs-6335473","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}