Multi Omics and Mechanistic Investigation Reveals CBFB as a Prognostic Biomarker in Gastric Cancer

Multi Omics and Mechanistic Investigation Reveals CBFB as a Prognostic Biomarker in Gastric Cancer | 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 Multi Omics and Mechanistic Investigation Reveals CBFB as a Prognostic Biomarker in Gastric Cancer Qi ZHOU, Lu WANG, Meng-Han WANG, Yao-Hong YUAN, Jian GUO, Bo CHEN, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7940951/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background : Gastric cancer (GC) is an aggressive malignancy with poor prognosis due to complex pathogenesis, underscoring the need for biomarkers and targets. CBFB has regulatory roles across cancers and shows promise, but its prognostic significance and mechanisms in GC remain unclear. Methods : We analyzed GC transcriptomes by WGCNA to identify modules; machine learning prioritized core regulators. GSEA predicted signaling pathways, and molecular docking validated CBFB–STAT3 interactions. Single-cell RNA-seq was processed with Seurat for QC, clustering, and annotation; CellChat quantified intercellular ligand–receptor crosstalk. Functional validation employed CCK-8 proliferation and Transwell migration/invasion assays; Western blot assessed pathway activity. Results : Multi-omics analyses implicate CBFB in GC pathogenesis. WGCNA highlighted 40 ribosome biogenesis–related genes, with machine learning prioritizing CBFB as a key regulator. GSEA linked CBFB to the JAK/STAT pathway (NES 1.95). Docking showed high-affinity CBFB–STAT3 binding (ΔG −11.5 kcal/mol). scRNA-seq showed higher CBFB in tumor parenchyma, enriched in mast cells; CellChat revealed enhanced mast cell–endothelial crosstalk via COL4A1/COL4A2–CD44 in CBFB-high groups (P<0.01). Functionally, CBFB knockdown reduced proliferation by ~40% and invasion/migration by ~30% (P<0.01); Western blot showed decreased STAT3 phosphorylation and reduced MMP9, implicating a CBFB–STAT3–MMP9 axis in GC progression. Conclusion : CBFB acts as a pivotal GC oncogene, with higher expression predicting poor prognosis and showing strong diagnostic potential. Mechanistically, CBFB may promote progression by engaging STAT3; single-cell data indicate overexpression in mast cells with enhanced mast cell–endothelial crosstalk. Functional data support CBFB as a therapeutic target in GC. Biological Markers Core Binding Factor Beta Subunit (CBFB) JAK-STAT Pathway Molecular Targeted Therapy Stomach Neoplasms Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. INTRODUCTION Gastric cancer (GC), the fourth most frequent cause of cancer-related mortality‌ and the fifth most prevalent cancer worldwide‌, accounted for ‌over one million new cases and 770,000 deaths in 2020, highlighting its substantial public health burden [1-2] . A ‌substantial proportion of patients present with advanced-stage disease, severely compromising therapeutic efficacy [3] . Nevertheless, advances in bioinformatics and functional genomics have accelerated the identification of GC-associated biomarkers, which are critical for ‌enhancing early diagnosis and predicting survival outcomes and recurrence risks after treatment [4] . Ribosome biogenesis is crucial for cellular growth and division, and its dysregulation plays a critical role in the initiation and progression of tumors [5-6] . In cancer cells, this process is frequently heightened to satisfy the increased demand for protein synthesis that accompanies rapid cell growth [7-8] . Aberrations in genes governing ribosome biogenesis can disrupt the translation of mRNA, thereby promoting tumor development [9-10] . For instance, in gastric cancer, ribosomal protein L40 (rpL40) has been demonstrated to facilitate malignant progression by modulating mRNA translational output [11] . Moreover, interleukin-6 (IL-6) not only stimulates ribosome biogenesis but also suppresses the expression and function of the tumor suppressor protein p53, further driving tumor formation [12] . Among ribosome biogenesis-related genes, the core-binding factor β (CBFB) gene emerged as a key candidate warranting in-depth investigation. CBFB, a crucial component of the core-binding factor (CBF) complex, is essential for transcriptional regulation and cellular differentiation. It works in conjunction with Runx family proteins to form heterodimers, thereby increasing their ability to bind to DNA, a process essential for normal skeletal development [13] . In breast cancer, CBFB facilitates osteotropic metastasis through exosomal mechanisms and modulates oxidative stress-related proteins to augment tumor invasiveness [14] . Acute myeloid leukemia (AML) pathogenesis is driven by CBFB-MYH11 fusion proteins arising from chromosomal rearrangements [15] . While the function of CBFB in gastric cancer remains underexplored, emerging evidence implicates long non-coding RNA LINC01234 in competitively regulating CBFB expression, thereby influencing the development and progression of gastric cancer [16] . These findings underscore the potential of CBFB as a ribosome biogenesis-associated oncogenic driver in gastric cancer, warranting targeted research to refine prognostic markers and therapeutic interventions. This study systematically integrated gene co-expression network analysis, single-cell analysis, and molecular docking to delineate the functional mechanisms of CBFB in gastric cancer. By combining multi-omics data with experimental validation, the findings demonstrate the significant functions of CBFB in the progression of gastric tumors, offering foundational evidence for translational applications. The research objectives focus on deciphering the regulatory roles of CBFB to identify novel therapeutic targets, with potential implications for improving clinical management in gastric cancer patients. 2. MATERIALS AND METHODS 2.1 Data Sources Relevant data were obtained from the GEO database (www.ncbi.nlm.nih.gov) utilizing the search terms (“gastric” OR “stomach”) AND (“cancer” OR “carcinoma” OR “neoplasm*”). The datasets GSE184336 and GSE29272 were identified. Specifically, GSE184336 encompasses whole-transcriptome sequencing data comprising 231 gastric cancer tissues and 230 normal gastric tissues. GSE29272 consists of gene expression array data pertaining to cardia and non-cardia gastric tumors, along with normal glands, with a focus on identifying distinct and commonly dysregulated genes across these two gastric cancer subtypes. Furthermore, a set of genes associated with “ribosome biogenesis” was retrieved from GeneCards (http://www.genecards.org), resulting in a total of 6234 ribosome biogenesis-related genes. Single-cell RNA sequencing data (GSE184198) containing paired normal tissue and tumor samples (n=1 each) were acquired from the GEO database. Concurrently, bulk RNA-seq data from the TCGA-STAD project (https://portal.gdc.cancer.gov) were systematically retrieved and preprocessed for downstream analysis. Differential expression analysis was performed using a significance criterion of P < 0.05 and |log2 FC| ≥ 1, with results visualized via volcano plots generated in R software. Subsequent analyses included evaluation of target gene expression levels, survival curves, and receiver operating characteristic (the ROC curves) for the target genes in stomach adenocarcinoma (STAD). 2.2 WGCNA and Precise Identification of Differentially Expressed Genes (DEGs) The WGCNA analysis was performed using the “WGCNA” package in R on gene expression data from the GEO datasets. The core modules were selected according to their Pearson correlation coefficients and the strongest association with clinical features. Module construction involved average linkage hierarchical clustering based on topological overlap matrix (TOM) dissimilarity, followed by dynamic tree cutting implementation. For GSE184336, the minimum number of genes per module was set to 130, and modules with feature gene dissimilarity < 0.5 were merged. The dataset GSE1843336 included 461 samples (normal controls and gastric cancer), from which 19834 genes were retained after data cleaning and filtering. Regarding GSE29272 non-cardia tumor subgroup analysis, the minimum number of genes per module was set to 100, and modules with feature gene dissimilarity < 0.3 were merged. The GSE29272 dataset contained 144 samples (non-cardia normal controls and gastric cancer cases), from which 6,619 passed quality filtering. Venn diagrams were generated using VENNY to illustrate the overlap among DEGs. 2.3 Enrichment Analysis Functional enrichment analysis was performed on hub module genes to elucidate their biological functions, incorporating Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The DAVID bioinformatics resource facilitated GO-based functional characteristics of differentially expressed genes (DEGs) and their products. Concurrent KEGG pathway analysis identified associated signaling pathways, with a statistical significance threshold of P < 0.05. 2.4 Machine Learning Algorithms Machine learning approaches comprising Random Forest (implemented via randomForest R package), LASSO regression (glmnet package), and Support Vector Machines (SVM; e1071 package) were employed for biomarker identification. Random Forest facilitated precise feature selection through decision tree construction, while SVM addressed nonlinear classification with gene importance ranking. LASSO regression optimized the feature set through regularization. This approach identified two target genes, CBFB and HEATR1, thereby strengthened the model’s reliability and transparency. In addition, statistical methods such as Student’s t-test and the Wilcoxon rank-sum test were used to evaluate biomarkers, with false discovery rate (FDR) correction applied to minimize false positives. 2.5 Gene Set Enrichment Analysis (GSEA) Samples were stratified based on CBFB expression levels (high and low). To identify related pathways and mechanisms, the c2.cp.kegg.v7.4.symbols.gmt gene set was used to subset from the Molecular Signatures Database. GSEA was run on gene expression profiles with their phenotypes, using these parameters: minimum gene set size 5, maximum 5,000, 1,000 permutations, significance thresholds of P < 0.05, and FDR < 0.25. 2.6 Protein-Protein Docking using GRAMM Protein-protein docking was performed using GRAMM, maintaining fixed ligand-receptor conformations to identify interfacial binding sites. Surface-binding sites were located on the protein interface. Key molecules from the CBFB and JAK/STAT pathways had their sequences fetched from UniProtKB. Optimal protein structures were then obtained via SWISS-MRED Wait—correcting: SWISS-MODEL is the tool; export formats in PDB for docking. Docking outcomes with binding free energies below -4 kcal/mol were considered biologically relevant. 2.7 Single-Cell Data Analysis 2.7.1 Quality Control of Single-Cell Data Transcriptomic data processing was initiated in Seurat with sequential filtering steps: UMI counts, expressed genes, and mitochondrial gene expression ratios, which indicate potential apoptosis. Quality control implemented MAD for outlier removal, supplemented by DoubletFinder-mediated doublet identification and exclusion. 2.7.2 Dimensionality Reduction, Clustering, and Annotation of Single-Cell Data Expression profiles underwent LogNormalize standardization (10,000 reads/cell) with subsequent logarithmic transformation, while CellCycleScoring calculated cell cycle scores. FindVariableFeatures was performed, followed by ScaleData normalization accounting for technical variations (mitochondrial/ribosomal genes and cell cycle effects). Principal component analysis (PCA) was performed on the expression matrix, with selected components used for further analysis. The Harmony algorithm addressed batch effects, followed by UMAP for dimensionality reduction. Cell type annotations and marker genes were compiled from sources like CellMarker, PanglaoDB, literature, and SingleR software. 2.7.3 Cellular Communication CellChat was used to enable quantitative reconstruction of intercellular communication networks by integrating single-cell data with normalized expression profiles and predefined cell subtypes. This computational framework systematically evaluates cell interactions, providing mechanistic insights into signaling activity modulation under disease conditions. 2.8 Validation Using the Human Protein Atlas Database and Immunohistochemistry Experiments CBFB protein expression patterns were comparatively analyzed in STAD and adjacent non-cancerous tissues through Human Protein Atlas, and IHC staining was performed on 30 paired gastric cancer and matched non-tumor samples. Slides were baked at 70°C for 60 minutes, deparaffinized, rehydrated, and subjected to antigen retrieval, with endogenous peroxidase blocked for 10 minutes. Primary antibodies (anti-CBFB 1:100; anti-Phospho-STAT3 1:100; anti-MMP9 1:100; all from Proteintech) were incubated overnight at 4°C. The next day, slides were treated with a biotinylated secondary antibody, incubated at 37°C for 20 minutes, then with an HRP-conjugated polymer for 20 minutes. DAB chromogenic development preceded hematoxylin counterstaining, dehydration, and mounting. Digital imaging employed a Nikon microscope with quantitative analysis of positively stained areas performed in ImageJ. Tissue samples were obtained from surgical resections at the Second Affiliated Hospital of Baotou Medical College, with pathological diagnoses confirmed by our department. 2.9 Cell Culture The human gastric cancer cell lines AGS and HGC-27 were obtained from Wuhan Pricella Biotechnology. AGS cells were cultured in Ham’s F-12 with 10% FBS and 1% penicillin-streptomycin (P/S); HGC-27 cells were cultured in RPMI-1640 with 10% FBS and 1% P/S. Standard culture conditions involved incubation at 37°C with 5% CO₂ humidified atmosphere, with medium replacement performed every 2 days and subculturing conducted at 3–4 day intervals according to growth. Cell counts were performed with a cell counter for accuracy, and each sample was prepared in triplicate for reproducibility. 2.10 Cell Transfection The gastric cancer cell lines AGS and HGC-27 were plated in 6-well plates to achieve approximately 60% confluence at the time of transfection. Each well received 42.5 μL of buffer transferred into sterile, enzyme-free Eppendorf tubes, followed by addition of 3.75 μL of siRNA, then pipetted up and down to mix. GP-Transfect-Mate transfection reagent (GenePharma, G04026, China) was subsequently introduced at 7.5 μL per reaction with immediate vortexing before application to cell monolayers. Post-transfection protocols included 24-hour incubation without medium change and 48-hour culture prior to protein extraction for Western blotting. The si-CBFB was synthesized by GenePharma Biotechnology Co., Ltd. 2.11 Western Blotting Protein analysis was performed following established Western blotting protocols. Cells were lysed on ice in RIPA buffer with protease and phosphatase inhibitors for 30 minutes, followed by centrifugation (12,000 rpm, 15 minutes, 4°C). Protein concentrations were determined using BCA assay prior to equal loading for SDS-PAGE separation and subsequent transfer onto PVDF membranes. After blocking, membranes were incubated overnight at 4°C with primary antibodies against CBFB (1:1000, Proteintech, #67885-1-Ig), Phospho-STAT3 (1:2000, Proteintech, #28945-1-AP), and MMP9 (1:2000, Proteintech, #10375-2-AP), followed by 1 hour with HRP-conjugated secondary antibodies at 1:2000. Proteins were detected with ECL and imaged on a Bio-Rad ChemiDoc. Quantitative analysis involved background subtraction and β-actin normalization of ImageJ-processed band intensities. 2.12 Cell Function Experiment 2.12.1 CCK-8 Cell proliferation was assessed in 96-well plates seeded 3,000 cells/well with complete medium. Following cell attachment, 10 μL of the Cell Counting Kit-8 (CCK-8; Dojindo, CK04, Japan) solution was added to each well. The plates were then incubated in the dark for one hour, and absorbance was measured at 450 nm using a Bio-Rad microplate reader (Japan) at 12, 24, 48, and 72 hours. 2.12.2 Colony Formation Assay Cells plated at 400 to 700 cells/well in 6-well plates were maintained in complete medium under standard culture conditions (37°C with 5% CO2) for 10 to 14 days with medium replacement every four days. These colonies were fixed in 4% paraformaldehyde for 15 minutes, stained with 0.1% crystal violet, and counted and analyzed with ImageJ software. 2.12.3 Cell Scratch Test Confluent monolayers (90%) in 6-well plates were mechanically scratched using sterile 10 μL pipette tips perpendicular to the long axis. Non-adherent cells were then rinsed away with PBS, and serum-free medium was added. Images were captured at 0, 24, and 48 hours to assess cell migration. 2.12.4 Transwell Chamber Experiment Cell suspensions (2×105 cells/mL in serum-free medium) were loaded into Matrigel-coated transwell inserts (200 μL/insert; 8.0 μm pores, Catalog No. 3422, Corning, USA) with 600 μL complete medium (10% FBS) in lower chambers. After 24 hours, cells on the upper surface were removed with a PBS-moistened cotton swab. Cells that migrated to the lower membrane surface were fixed in 4% paraformaldehyde for 20 minutes, then stained with crystal violet for 20 minutes at room temperature. Cells were microscopically imaged and quantified using ImageJ software. 2.13 Statistical Analysis Statistical analyses were performed using R (version 4.4.1) for data processing. Survival analysis was evaluated through Kaplan-Meier curves with log-rank testing, while distribution normality and variance homogeneity were verified by Shapiro-Wilk test (α = 0.05) and Levene’s test (α = 0.05), respectively. When normality wasn’t met, suitable nonparametric tests or data transformations were applied. For two-group comparisons, two-tailed unpaired t-tests were used; for more than two groups, one-way ANOVA was employed. Categorical differences were analyzed by chi-square or Fisher’s exact test. Bonferroni correction addressed multiple comparisons as needed. Significance was set at P < 0.05, with the following asterisks: * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001. Three independent biological replicates were evaluated, each with three technical replicates, and results are shown as mean ± SEM. Graphs and analyses were generated with GraphPad Prism (v9.01), with additional tools from Xiantao and Sangerbox. 2.14 Ethics This study protocol received ethical approval from the Ethics Committee of the University Hospital (Approval No. 2024-ZX-052), with written informed consent obtained from all participants prior to study enrollment. 3. RESULTS 3.1 WGCNA Results Co-expression networks were constructed using the WGCNA algorithm, with soft-thresholding powers empirically determined as β=6 for GSE184336 (Figure 2A-B) and β=18 for GSE29272 (Figures 3A-B), generating weighted networks (Figure 2C, 3C), respectively. In GSE184336, nine modules were identified, among which the salmon, pink, and brown modules (581 genes) (Figures 2D-G) demonstrated the strongest correlations with gastric cancer progression. Similarly, analysis of GSE29272 yielded six modules, with the black, blue, magenta, and red modules (825 genes) (Figures 3D-H) reflecting the most significant associations within biologically relevant networks. 3.2 Analysis of TCGA-STAD Dataset Differentially expressed genes (DEGs) were identified and normalized using standardized thresholds (|log2 FC| > 1, P < 0.05) (Figure 4A) and visualized through Venny-generated Venn diagrams, illustrating 581 genes from GSE184336, 825 genes from GSE29272, 4636 genes from TCGA-STAD, and 6234 ribosomal biogenesis-related genes obtained from the GeneCards database. A total of 40 overlapping genes related to ribosomal biogenesis in gastric cancer were identified (Figure 4B). 3.3 Enrichment Analysis Results GO and KEGG pathway analyses of the intersecting genes revealed significant enrichment in: (1) Biological processes (BP) including cellular component biogenesis and protein-containing complex assembly; (2) Cellular components (CC) localized to nuclear lumen and protein-containing complex; (3) Molecular functions (MF) involving RNA binding and nucleic acid binding; (4) KEGG pathways related to ribosome biogenesis in eukaryotes and RNA transport (Figure 4C-F). These findings highlight the diverse functions and pathways associated with the intersecting genes. 3.4 Machine Learning Algorithm Results for Feature Selection Comprehensive feature selection was performed on the 40 candidate genes through integrated Random Forest, SVM, and LASSO analyses. In the Random Forest results, the five most influential features were AQP4, CA9, CBFB, HEATR1, and NUP62, with AQP4 exerting the strongest effect (Figure 5A). The SVM analysis placed CA9, CBFB, HEATR1, RUVBL1, and CDC123 at the top in terms of importance (Figure 5B). LASSO regression for diagnostic and prognostic evaluation revealed additional key genes (Figures 5C-E) with Venn analysis confirming CBFB and HEATR1 as consensus core targets across all methodologies (Figure 5F). 3.5 Prognostic and Diagnostic Analyses of CBFB and HEATR1 RNA-seq data for STAD were acquired from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov) and processed for downstream analysis. Matched transcript data were compared against available TCGA datasets. Using R, we assessed survival curves, ROC curves, and the expression of CBFB and HEATR1 in STAD patients. The results showed that high CBFB expression significantly correlated with reduced 30-month survival, while HEATR1 showed no statistically meaningful prognostic correlation (Figures 6A-B). Expression levels of CBFB and HEATR1 were notably higher in STAD tissues than in normal controls (P < 0.001; Figure 6C). ROC analysis supported the diagnostic relevance of these genes, with CBFB and HEATR1 yielding AUC values of 0.961 and 0.926, respectively (Figure 6D). Collectively, these results highlight the expression pattern of CBFB in STAD and its potential clinical relevance, warranting further investigation. Consequently, CBFB was prioritized for subsequent functional studies. 3.6 Gene Set Enrichment Analysis (GSEA) of Pathways Single-gene pathway enrichment analysis of CBFB identified significant association with JAK/STAT signaling pathway in cancer (GSEA: NES = 1.95, p = 0.0021, and FDR = 0.0034) (Figure 6E), suggesting its potential involvement in gastric cancer pathogenesis. 3.7 Protein-Protein Docking Using GRAMM Protein-protein docking analysis using GRAMM generated 11 distinct conformations between CBFB and key molecules within the JAK/STAT pathway. The binding free energies were as follows: JAK1 = -7.4 kcal/mol, JAK2 = -10.0 kcal/mol, JAK3 = -9.1 kcal/mol, TYK2 = -5.1 kcal/mol, STAT1 = -3.3 kcal/mol, STAT2 = -3.5 kcal/mol, STAT3 = -11.5 kcal/mol, STAT4 = -5.7 kcal/mol, STAT5A = -8.8 kcal/mol, STAT5B = -2.4 kcal/mol, and STAT6 = -5.4 kcal/mol (Figure 7). The highest-affinity interaction occurred between CBFB and STAT3, forming a strong binding interaction with notably high affinity, indicating a stable complex. Additionally, multiple interaction sites between the two proteins were observed. Their surfaces made contact through various interactions, including hydrogen bonds that reinforced structural stability. This specific binding pattern suggests preferential CBFB-STAT3 molecular recognition within the JAK/STAT signaling cascade. 3.8 Single-Cell Analysis 3.8.1 Quality Control and Clustering of Single-Cell Data To ensure data quality across samples, cells with abnormal values or fewer than 200 detected genes were filtered, followed by doublet removal using DoubletFinder (15,520 high-quality cells retained). The resulting violin and scatter plots (Figure 8A-B) will be generated. Subsequent computational processing identified 2,000 highly variable genes prior to normalization, scaling, principal component analysis (PCA), and batch correction via Harmony integration (Figure 8C-D). 3.8.2. Cell Annotation and Cell Communication UMAP obtained 14 subgroups (Figure 9A) annotated as B cells、T Cells、Neutrophil、Plasma cells、mast cells、Fibroblast、Endothelial cells、Epithelial cells、Macrophages. These 9 cell categories (Figure 9B). Bubble plot of 9 cell markers (Figure 9C) and cell proportion bar chart corresponding to grouping (Figure 9D). Seurat-based evaluation through FeaturePlot and DotPlot visualizations revealed markedly higher CBFB expression in tumor cells than in normal cells. Among immune populations, CBFB showed significant overexpression in mast cells, with DotPlot indicating roughly a twofold higher level relative to other cell types (P < 10-5) (Figure 9E-F). CellChat analysis of intercellular communication networks quantified interaction frequency and weighted communication strength between high- and low-CBFB expression groups (Figure 9G), with differential intensity patterns visualized separately (Figure 9H). Pathway-specific heatmaps identified distinct signaling profiles across cell types (Figure 9I). Notably, endothelial cells appeared to engage more readily with other cell populations. Ligand–receptor interaction mapping further delineated communication pathways between endothelial cells (as signal sources) and target cells in both CBFB-high and CBFB-low groups, underscoring potential signaling relationships (Figure 9J). 3.9 HPA Database Validation and Immunohistochemical Experiment Validation Consistent with Human Protein Atlas (HPA) staining patterns, CBFB, pSTAT3, and MMP9 proteins were demonstrated to be elevated in STAD tissues (Figure 10A). IHC was conducted on clinical specimens of adjacent normal gastric tissue and gastric cancer tissue. Statistical analysis revealed differential expression of CBFB, pSTAT3, and MMP9 in gastric cancer compared with adjacent normal tissue with statistical significance (P < 0.05, **P < 0.01, ***P < 0.001; Figure 10B-C). 3.10 Functional Experiments of CBFB in Gastric Cancer Cell Lines Based on its prognostic association and high expression in gastric cancer, CBFB was targeted for functional studies through siRNA-mediated knockdown in AGS and HGC-27 human gastric cancer cell lines using the GP-Transfect-Mate reagent (GenePharma, G04026, China). This experimental approach evaluated the impact of CBFB suppression on malignant phenotypes, with knockdown efficiency verified by Western blot analysis (Figure 11A). 3.10.1 Cell Proliferation Function CCK-8 proliferation assays revealed significantly reduced growth kinetics in CBFB-silenced gastric cancer cells than in controls (Figure 11B), corroborated by decreased colony formation capacity in both AGS and HGC-27 lines (P < 0.01; Figure 11C). 3.10.2 Invasion and Migration Functions Motility analysis through wound healing revealed impaired migration upon CBFB suppression, evidenced by delayed wound closure compared to the controls (P < 0.01; Figure 11D). Similarly, the Transwell invasion assay indicated a significant drop in cells that degraded the matrix and traversed the membrane into the lower chamber after CBFB knockdown (P < 0.01; Figure 11E). 3.11 CBFB and JAK/STAT Pathways Western blot analysis of JAK/STAT pathway components in CBFB-silenced AGS and HGC-27 cells demonstrated a marked reduction in p-STAT3 levels (P < 0.01), accompanied by a 22–35% decrease in downstream MMP9 expression relative to the controls (P < 0.01; Figure 11F). This aligns with prior work on the STAT3-MMP9 transcriptional axis [17-18] , implying that CBFB may participate in invasion-related signaling in gastric cancer by positively regulating STAT3 activity. 4. DISCUSSION Gastric cancer maintains global prevalence as a leading malignancy, exhibiting elevated incidence and mortality rates in male populations. Late-stage diagnosis frequently restricts surgical and chemotherapeutic efficacy, leading to poor overall and disease-free survival and a grim prognosis [4] . Recent progress in bioinformatics and functional genomics has helped identify gastric cancer-associated biomarkers, which facilitate prediction of therapeutic responses to chemotherapy, targeted therapies, and immunotherapy, and their levels can inform post-treatment survival and recurrence risk [19] . Early detection, along with a deeper understanding of molecular mechanisms, can support more personalized treatment, improve outcomes, and boost survival. Ribosome biogenesis critically supports malignant transformation by facilitating ribosomal RNA production and protein synthesis, thereby regulating fundamental cellular processes including proliferation, apoptosis, and cell-cycle control. Studies show that ribosomal proteins modulate these processes, affecting apoptosis, cell-cycle arrest, growth, and tumor development through both MDM2/p53-dependent and -independent pathways, while concurrently maintaining genome integrity by regulating DNA-damage responses and repair mechanisms [20] . Biochemical studies demonstrate that the interaction of CBFB with heterogeneous nuclear ribonucleoprotein K (hnRNPK) forms functional complexes that activate ribosome assembly and enhance mRNA translation efficiency via the initiation factor eIF4B, thereby increasing protein-synthesis capacity [21] . In addition, CBFB forms a heterodimer with RUNX1, which binds ribosomal DNA promoter regions [10] . The CBFβ-RUNX1 complex recruits RNA polymerase I-associated factors including PAF53 to activate rDNA transcription and rRNA biosynthesis [22-23] . The CBFB gene exhibits context-dependent oncogenic functions across cancers. In breast cancer, higher CBFB levels are linked to more metastatic potential, mainly through effects on the RUNX protein family. Acute myeloid leukemia (AML) pathogenesis involves the CBFB-MYH11 fusion protein as a major driver. Mitochondrial translation regulation represents another functional dimension, where CBFB deficiency disrupts oxidative phosphorylation, induces Warburg metabolism, and activates autophagy-mitophagy pathways [24-25] . In gastric cancer, Chen and colleagues report a LINC01234-miR-204-5p-CBFB ceRNA network, wherein LINC01234 overexpression competitively binds miR-204-5p to alleviate CBFB suppression, establishing this regulatory axis as a potential tumorigenic mechanism [16] . Integrated analysis of gastric cancer transcriptomic datasets enabled construction of gene co-expression networks to identify molecular regulators. Machine learning-based selection of differentially expressed genes revealed significant association with ribosome biogenesis pathways, with CBFB emerging as a gene demonstrating both diagnostic and prognostic relevance. Single-gene GSEA enrichment linked CBFB to the JAK/STAT signaling pathway. Taken together, these results suggest that CBFB may drive gastric cancer progression by modulating JAK/STAT signaling. Elevated CBFB expression in tumor tissues was confirmed by Western blot (P < 0.01), with functional knockdown demonstrating significant suppression of cellular proliferation and migration capacities. Molecular docking analysis revealed robust CBFB-STAT3 interaction, suggesting potential enhancement of STAT3 transcriptional activity through associations with RUNX proteins. STAT3 governs essential processes like proliferation, differentiation, and survival, with activity controlled by phosphorylation and nuclear translocation. Togi and colleagues identified ARL3 as a novel STAT3-binding partner that enhances phosphorylation and nuclear accumulation, affecting transcriptional activity, which hints that CBFB could engage STAT3 via parallel mechanisms [26] . Activation of the IL-6/JAK/STAT3 axis is a known driver of gastric cancer cell proliferation and migration, and its inhibition reduces invasiveness and growth [27-28] , collectively indicating potential activation of CBFB in the JAK/STAT pathway through direct or indirect routes, promoting proliferation, migration, and invasion of gastric cancer cells. Specifically, silencing CBFB in gastric cancer reduces STAT3 phosphorylation and downregulates downstream targets such as MMP9, contributing to impaired proliferation, migration, and invasion. Single-cell resolution analysis demonstrates that the expression patterns of CBFB within the gastric cancer microenvironment are notably specific to certain cell types and exhibit spatial complexity. Specifically, quantification via DotPlot analysis identifies mast cells as the primary CBFB-expressing population, with expression levels significantly exceeding those observed in endothelial and fibroblast cells. Further analysis using CellChat indicates that in the group with high CBFB expression, mast cells secrete crucial extracellular matrix (ECM) components such as COL4A1, COL4A2, LAMA5, LAMC1, LAMB2, and APP, which establish high-affinity interactions (P < 0.01) with CD44/CD74 receptors on endothelial cells. This interaction network has the potential to activate downstream signaling pathways, specifically the STAT3-MMP9 axis. These findings extend previous reports regarding the autonomous functions of CBFB [29-30] , aligning with insights from earlier functional experiments that showed knocking down CBFB diminishes invasive capacity primarily by inhibiting mast cell-mediated ECM signaling to endothelial cells [31] . The data support a novel intercellular regulatory model: CBFB-mediated mast cell ECM secretion initiates CD44-dependent STAT3 activation cascade, offering new perspectives for targeting the tumor microenvironment [32] . Future research could directly validate this causal interaction through primary mast-endothelial co-culture models. Collectively, the evidence positions CBFB as a critical regulator of gastric cancer progression via STAT3-MMP9 axis activation and microenvironmental reprogramming, warranting further exploration into its mechanistic contributions to malignant transformation and potential as a therapeutic target. Several methodological constraints require acknowledgment. Firstly, the small sample size may reduce statistical power, limiting insights into the role of CBFB in gastric cancer. Secondly, single-cell analysis used only one tumor–normal pair, which may not reflect the full patient diversity of CBFB-related cellular interactions. While bioinformatic analyses implicate this ribosome-associated gene in gastric cancer, experimental confirmation of pivotal ribosomal regulatory factors still remains outstanding. To establish causality, future work should use approaches such as ribosome profiling (Ribo-seq) and CRISPR screens. The lack of more advanced in vivo models also constrains validation of the role of CBFB in gastric cancer progression. Subsequent investigations should incorporate expanded cohorts, multi-center validation, and animal models to clearly delineate the biological function of CBFB and assess its potential as a therapeutic target. Conclusion : This study highlights CBFB as a gastric cancer-associated gene with biomarker potential by integrating bioinformatics analyses with experimental validation, demonstrating consistent overexpression and functional linkage to activation of the JAK/STAT pathway. Mechanistic investigations reveal CBFB-mediated regulation of malignant phenotypes including tumor cell proliferation, migration, and invasion, underscoring its relevance to gastric cancer development and progression. While these findings offer insights for early diagnosis and targeted therapy, full mechanistic elucidation and clinical translation require additional validation. Declarations AUTHOR CONTRIBUTIONS Q. Z. and L. W.: Designed studies, performed experiments and data analysis, and wrote original manuscripts. M. W.:Designed studies, performed experiments. Y. Y.: Collecting and organizing data; J. G. and B. C.: Analysis and interpretation of data. J. Z.: Study concept and design. M. W.: Directed the research process and completed major revisions of the manuscript. Ethics Approval This study has received approval from the local Ethics Committee of The Second Affiliated Hospital of Baotou Medical College (Approval No. 2024-ZX-052). All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013). Consent To Participate All participants have provided informed consent by signing the requisite documentation. FUNDING INFORMATION Supported by the Inner Mongolia Autonomous Region Public Hospital Research Joint Fund, No. 2024GLLH0592; Baotou Health Science and Technology Plan, No. 2024wsjkkj59 and Qingmiao Plan of Baotou Medical College, No. BYJJ-ZRQM202230. CONFLICT OF INTEREST STATEMENT The authors declare that they have no conflict of interest. Clinical trial number: not applicable. DATA AVAILABILITY STATEMENT The datasets supporting this research include GSE184336, GSE29272 and the Cancer Genome Atlas-stomach adenocarcinoma dataset. GSE191275 and GSE15459 are available from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo); The Cancer Genome Atlas-stomach adenocarcinoma is available from the Cancer Genome Atlas database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga). References Zhang Z, Chen Z, Que Z, Fang Z, Zhu H, Tian J. Chinese Medicines and Natural Medicine as Immunotherapeutic Agents for Gastric Cancer: Recent Advances. Cancer Rep (Hoboken). 2024;7(9):e2134. 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 Cancer J Clin. 2021;71(3):209-249. Ahadi A. Dysregulation of miRNAs as a signature for diagnosis and prognosis of gastric cancer and their involvement in the mechanism underlying gastric carcinogenesis and progression. IUBMB Life. 2020;72(5):884-898. Ye DM, Xu G, Ma W, et al. Significant function and research progress of biomarkers in gastric cancer. Oncol Lett. 2020;19(1):17-29. Penzo M, Montanaro L, Treré D, Derenzini M. The Ribosome Biogenesis-Cancer Connection. Cells. 2019;8(1). Derenzini M, Montanaro L, Trerè D. Ribosome biogenesis and cancer. Acta Histochem. 2017;119(3):190-197. Catez F, Dalla Venezia N, Marcel V, Zorbas C, Lafontaine D, Diaz JJ. Ribosome biogenesis: An emerging druggable pathway for cancer therapeutics. Biochem Pharmacol. 2019;159:74-81. Pecoraro A, Pagano M, Russo G, Russo A. Ribosome Biogenesis and Cancer: Overview on Ribosomal Proteins. Int J Mol Sci. 2021;22(11). Goudarzi KM, Lindström MS. Role of ribosomal protein mutations in tumor development (Review). Int J Oncol. 2016;48(4):1313-1324. Sulima SO, Hofman I, De Keersmaecker K, Dinman JD. How Ribosomes Translate Cancer. Cancer Discov. 2017;7(10):1069-1087. Park J, Wu J, Szkop KJ, et al. SMYD5 methylation of rpL40 links ribosomal output to gastric cancer. Nature. 2024;632(8025):656-663. Brighenti E, Calabrese C, Liguori G, et al. Interleukin 6 downregulates p53 expression and activity by stimulating ribosome biogenesis: a new pathway connecting inflammation to cancer. Oncogene. 2014;33(35):4396-4406. Qin X, Jiang Q, Matsuo Y, et al. Cbfb regulates bone development by stabilizing Runx family proteins. J Bone Miner Res. 2015;30(4):706-714. Hsu CH, Ma HP, Ong JR, et al. Cancer-Associated Exosomal CBFB Facilitates the Aggressive Phenotype, Evasion of Oxidative Stress, and Preferential Predisposition to Bone Prometastatic Factor of Breast Cancer Progression. Dis Markers. 2022;2022:8446629. Cho BS, Min GJ, Park SS, et al. Prognostic values of D816V KIT mutation and peri-transplant CBFB-MYH11 MRD monitoring on acute myeloid leukemia with CBFB-MYH11. Bone Marrow Transplant. 2021;56(11):2682-2689. Chen X, Chen Z, Yu S, et al. Long Noncoding RNA LINC01234 Functions as a Competing Endogenous RNA to Regulate CBFB Expression by Sponging miR-204-5p in Gastric Cancer. Clin Cancer Res. 2018;24(8):2002-2014. Kamiya T, Mizuno N, Hayashi K, et al. Methoxylated Flavones from Casimiroa edulis La Llave Suppress MMP9 Expression via Inhibition of the JAK/STAT3 Pathway and TNFα-Dependent Pathways. J Agric Food Chem. 2024;72(26):14678-14683. Hu L, Huang B, Bai S, et al. SO(2) derivatives induce dysfunction in human trophoblasts via inhibiting ROS/IL-6/STAT3 pathway. Ecotoxicol Environ Saf. 2021;210:111872. Hoadley KA, Yau C, Wolf DM, et al. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell. 2014;158(4):929-944. Xu X, Xiong X, Sun Y. The role of ribosomal proteins in the regulation of cell proliferation, tumorigenesis, and genomic integrity. Sci China Life Sci. 2016;59(7):656-672. Malik N, Yan H, Moshkovich N, et al. The transcription factor CBFB suppresses breast cancer through orchestrating translation and transcription. Nat Commun. 2019;10(1):2071. Cordonnier G, Mandoli A, Radhouane A, et al. CBFβ-SMMHC regulates ribosomal gene transcription and alters ribosome biogenesis. Leukemia. 2017;31(6):1443-1446. Hyde RK, Zhao L, Alemu L, Liu PP. Runx1 is required for hematopoietic defects and leukemogenesis in Cbfb-MYH11 knock-in mice. Leukemia. 2015;29(8):1771-1778. Malik N, Yan H, Yang HH, et al. CBFB cooperates with p53 to maintain TAp73 expression and suppress breast cancer. PLoS Genet. 2021;17(5):e1009553. Malik N, Kim YI, Yan H, et al. Dysregulation of Mitochondrial Translation Caused by CBFB Deficiency Cooperates with Mutant PIK3CA and Is a Vulnerability in Breast Cancer. Cancer Res. 2023;83(8):1280-1298. Togi S, Muromoto R, Hirashima K, et al. A New STAT3-binding Partner, ARL3, Enhances the Phosphorylation and Nuclear Accumulation of STAT3. J Biol Chem. 2016;291(21):11161-11171. Lin J, Wang D, Zhou J, et al. MIEN1 on the 17q12 amplicon facilitates the malignant behaviors of gastric cancer via activating IL-6/JAK2/STAT3 pathway. Int J Biochem Cell Biol. 2024;176:106666. Yang Y, Zhang Q, Liang J, et al. STAM2 knockdown inhibits proliferation, migration, and invasion by affecting the JAK2/STAT3 signaling pathway in gastric cancer. Acta Biochim Biophys Sin (Shanghai). 2021;53(6):697-706. Khan M, Huang X, Ye X, et al. Necroptosis-based glioblastoma prognostic subtypes: implications for TME remodeling and therapy response. Ann Med. 2024;56(1):2405079. Pereira BA, Lister NL, Hashimoto K, et al. Tissue engineered human prostate microtissues reveal key role of mast cell-derived tryptase in potentiating cancer-associated fibroblast (CAF)-induced morphometric transition in vitro. Biomaterials. 2019;197:72-85. Panda VK, Mishra B, Nath AN, et al. Osteopontin: A Key Multifaceted Regulator in Tumor Progression and Immunomodulation. Biomedicines. 2024;12(7). Zheng T, Zhou H, Zheng Z, et al. The pathological significance and potential mechanism of ARHGEF6 in lung adenocarcinoma. Comput Biol Med. 2023;158:106894. Additional Declarations No competing interests reported. Supplementary Files FulluncroppedGelsandBlotsimage.doc Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 01 Dec, 2025 Reviews received at journal 21 Nov, 2025 Reviews received at journal 20 Nov, 2025 Reviewers agreed at journal 13 Nov, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 11 Nov, 2025 Editor assigned by journal 11 Nov, 2025 Submission checks completed at journal 07 Nov, 2025 First submitted to journal 07 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7940951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":548293132,"identity":"90c41a08-81f1-41ad-ad39-5200f20417c8","order_by":0,"name":"Qi ZHOU","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"ZHOU","suffix":""},{"id":548293133,"identity":"0a2e1a91-e4ef-41b3-a793-480d11763aec","order_by":1,"name":"Lu WANG","email":"","orcid":"","institution":"The Second Affiliated Hospital of Baotou Medical College","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"WANG","suffix":""},{"id":548293135,"identity":"d3e64b3d-5231-4fbf-ab96-15a8024a4492","order_by":2,"name":"Meng-Han WANG","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Meng-Han","middleName":"","lastName":"WANG","suffix":""},{"id":548293136,"identity":"a5ec7928-9f25-4c17-8b70-ef91951eb132","order_by":3,"name":"Yao-Hong YUAN","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Yao-Hong","middleName":"","lastName":"YUAN","suffix":""},{"id":548293138,"identity":"bab05d92-7fb0-40f9-a8fb-46af200bae4b","order_by":4,"name":"Jian GUO","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"GUO","suffix":""},{"id":548293139,"identity":"8adfdda9-8443-47ca-a607-d4ad114b1b3d","order_by":5,"name":"Bo CHEN","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"CHEN","suffix":""},{"id":548293140,"identity":"3c3a4edd-ec36-4b34-a906-672f72080e19","order_by":6,"name":"Ji-Zhe ZHANG","email":"","orcid":"","institution":"Baotou Medical College Graduate school","correspondingAuthor":false,"prefix":"","firstName":"Ji-Zhe","middleName":"","lastName":"ZHANG","suffix":""},{"id":548293141,"identity":"7d4b5060-7611-4d36-82c6-8d2def1310e1","order_by":7,"name":"Mi-Zhu WANG","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA80lEQVRIiWNgGAWjYNACA4YEBvbGhgMfKiTk5InXwnP44MMZZyyMDRuItCeBQSIt2Zi3rSKR4QAh84+fPfzqRkFdnsGBHDPJmfMkEhgbmB8+uoFPy5m8NOscA7ZigwNnzCQ+bpPIY2dgMzbOwaPFDGi4cY4BT+KGgz1AW7ZJFDM28LBJ49Vy/g1Ii0TihsM8ZtK8cyQSGw4Q0nIjx/hxjoFB4oZjbEDvNxChxf7GGzPmHIOExJlnmIGBfEzC2LCZgF8k+3OMP+f8qUvsu/8QGJU1dXLy7M0PH+PTAgRsEqh8ZvzKwUo+EFYzCkbBKBgFIxoAALA/UmMSPacgAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Affiliated Hospital of Baotou Medical College","correspondingAuthor":true,"prefix":"","firstName":"Mi-Zhu","middleName":"","lastName":"WANG","suffix":""}],"badges":[],"createdAt":"2025-10-25 08:02:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7940951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7940951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96590390,"identity":"8cfa2662-1c75-4cc7-b6a1-06cc771781d5","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":348640,"visible":true,"origin":"","legend":"\u003cp\u003egraphical abstract\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/43f199e9ba7dd5d0901a1538.png"},{"id":96605341,"identity":"71233fa0-151f-4038-bcbc-74ccd340b2df","added_by":"auto","created_at":"2025-11-24 09:22:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10020729,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) of GSE184336. (A-B) Determination of the optimal soft-thresholding power (β=6) through scale-free topology and mean connectivity analysis. (C) Cluster analysis of characteristic gene modules identified 9 distinct modules. (D-G) Heatmap illustrating correlations between phenotypes and characteristic gene modules, confirming salmon, pink and brown modules as hub modules. DEGs: differentially expressed genes; WGCNA: weighted gene co-expression network analysis.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/719b16d5b62d208e99920d48.png"},{"id":96590397,"identity":"587b8828-6117-422b-be8a-64c9eef99381","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":12690562,"visible":true,"origin":"","legend":"\u003cp\u003eWeighted gene co-expression network analysis (WGCNA) of GSE29272. (A-B) Determination of the optimal soft-thresholding power (β=18) based on scale-free topology and mean connectivity analyses. (C) Cluster analysis of characteristic gene modules identified 6 distinct modules. (D-H) Heatmap demonstrating correlations between phenotypic traits and characteristic gene modules, confirming the black, blue, magenta, and red modules as hub modules.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/c525549a021b2df23ce6c3bb.png"},{"id":96590395,"identity":"0879ffb8-16e3-4863-aa23-7fd7c63985f2","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9967436,"visible":true,"origin":"","legend":"\u003cp\u003eIntegrated analysis of gastric cancer genomics from The Cancer Genome Atlas (TCGA). (A) Volcano plot of differentially expressed genes (DEGs) between gastric adenocarcinoma (STAD) and normal tissues in TCGA. (B) Venn diagram showing overlap among WGCNA-derived modules, STAD DEGs, and ribosomal biogenesis-associated genes. (C) Gene Ontology (GO) biological process (BP) enrichment analysis of DEGs. (D) GO cellular component (CC) enrichment analysis of DEGs. (E) GO molecular function (MF) enrichment analysis of DEGs. (F) Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; BP: biological process; CC: cellular component; MF: molecular function.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/e25ba7d1da9757c7af8a2c44.png"},{"id":96590394,"identity":"80f21041-b35b-4515-b7fd-707fc2b93081","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3245608,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of target genes via machine learning algorithms from intersecting analysis sets. (A) Feature importance ranking in the random forest model. (B) Visualization of Support Vector Machine (SVM) analysis results. (C-E) Diagnostic and prognostic least absolute shrinkage and selection operator (LASSO) analysis based on The Cancer Genome Atlas (TCGA) gastric cancer data. (F) Venn diagram illustrating intersection of the four analytical methods. SVM: Support Vector Machine; LASSO: least absolute shrinkage and selection operator; TCGA: The Cancer Genome Atlas.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/ae59f676b0bdb466f25cc51c.png"},{"id":96590392,"identity":"9772dbd6-581e-4216-9637-4c133f10cdd4","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":212953,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic and diagnostic analyses of target genes. (A-B) Kaplan-Meier curves for CBFB and HEATR1 in high- vs. low-expression cohorts from The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) dataset. (C) Expression of CBFB/HEATR1 in unpaired and paired samples of TCGA-STAD. (D) Receiver operating characteristic (ROC) curves for CBFB/HEATR1 diagnostic performance (AUC: area under the curve). (E) Single-gene gene set enrichment analysis (GSEA) of CBFB pathway associations (NES: normalized enrichment score). (*P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001)\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/fd0318343e432fe1a7cdd6e3.png"},{"id":96605719,"identity":"3c67d7f2-ca48-41d3-aa4d-76e8c156f823","added_by":"auto","created_at":"2025-11-24 09:23:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":6508965,"visible":true,"origin":"","legend":"\u003cp\u003eGRAMM docking of CBFB with proteins in the JAK/STAT signaling pathway. (A) CBFB-JAK1; (B) CBFB-JAK2; (C) CBFB-JAK3; (D) CBFB-TYK2; (E) CBFB-STAT1; (F) CBFB-STAT2; (G) CBFB-STAT3; (H) CBFB-STAT4; (I) CBFB-STAT5A; (J) CBFB-STAT5B; (K) CBFB-STAT6.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/2bc43e5af8aec4693a4b7c73.png"},{"id":96590393,"identity":"64b34e47-bd7f-4ec2-8c1f-6aa9f9395c95","added_by":"auto","created_at":"2025-11-24 06:21:00","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3659062,"visible":true,"origin":"","legend":"\u003cp\u003eData quality control and clustering in single-cell RNA sequencing analysis. (A) Single-cell quality control metrics.Bar plots display the number of cells, detected genes per cell (nFeature_RNA), and sequencing depth (nCount_RNA) across samples. (B) Technical parameter correlations.Left: Sequencing depth (nCount_RNA, log10-transformed) vs. mitochondrial gene percentage (mito%).Center: Mitochondrial gene content (mito%, y-axis) vs. total RNA counts (nCount_RNA, x-axis).Right: Relationship between sequencing depth (nCount_RNA) and detected genes (nFeature_RNA). (C) Identify genes with significant differences between cells and draw characteristic variance maps, as well as variance ranking maps for each principal component. (D) The display of principal component analysis and the distribution of principal components, as well as the display of Harry and its distribution, with dots representing cells and colors representing samples.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/ad9e3833358d18cb6e0d1581.png"},{"id":96605900,"identity":"4a995957-963c-4bf6-b412-ffcae7cd1f84","added_by":"auto","created_at":"2025-11-24 09:24:19","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":15415437,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of CBFB and cell-cell communication in single-cell data. (A) UMAP projection of cells clustered into 14 distinct populations based on significant principal components from PCA . (B) Annotation of 14 clusters into 9 cell types using canonical marker genes: B cells, T cells, Neutrophils, Plasma cells, Mast cells, Fibroblasts, Endothelial cells, Epithelial cells, and Macrophages . (C) Dot plot displaying marker gene expression (rows) across annotated cell types (columns). Dot size represents the proportion of expressing cells, and color intensity reflects average expression levels . (D) Stacked bar plot comparing the proportional abundance of each cell type between experimental groups. (E) Scatter plot (UMAP) illustrating CBFB expression across all cells, with gradient coloring from low (blue) to high (red) expression. (F) The expression of CBFB gene in normal and tumor cells; Bubble plot overlaying CBFB expression levels on annotated cell types, highlighting cell-type-specific expression patterns. (G) Differential ligand-receptor interaction activity between CBFB high- and low-expression groups, quantified using interaction scores . (H) Cell-cell communication network diagram. Edge width corresponds to interaction probability/strength, and nodes represent cell types . (I) Heatmap depicting pathway-specific communication patterns between cell types, with hierarchical clustering revealing context-dependent signaling modules . (J) Bubble plot visualizing ligand-receptor pairs enriched in CBFB high-expression cells, annotated with interaction pathways and statistical significance.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/78c9273d19095c543865ebce.png"},{"id":96590400,"identity":"21337af4-6467-475d-9794-ed681c5fabf4","added_by":"auto","created_at":"2025-11-24 06:21:01","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":76748740,"visible":true,"origin":"","legend":"\u003cp\u003eImmunohistochemical analysis of CBFB/STAT3/MMP9 in human protein atlas and clinical specimens. (A) Representative IHC images of CBFB, STAT3, and MMP9 expression in normal gastric mucosa versus gastric cancer tissues from the Human Protein Atlas (HPA). (B) IHC staining of CBFB, pSTAT3 (phosphorylated STAT3), and MMP9 in paired adjacent normal and gastric cancer tissues (200X magnification). (C) Quantification of CBFB/pSTAT3/MMP9-positive cells (mean ± SD) in adjacent normal versus gastric cancer tissues.\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/9c0de553aac19aa0e0e79a18.png"},{"id":96590399,"identity":"6169b7c0-22c2-4495-9476-35538ca8a6dc","added_by":"auto","created_at":"2025-11-24 06:21:01","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":53890413,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of CBFB biological functions and signaling pathways. (A) Western blot verification of si-CBFB knockdown efficiency in AGS and HGC-27 cell lines. (B) Line graph of cell proliferation after CBFB knockdown measured by Cell Counting Kit-8 assay. (C) Colony formation analysis and quantitative results of colony formation assay after CBFB knockdown in AGS and HGC-27 cells. (D) Results of wound healing assay following CBFB knockdown. (E) Transwell invasion assay results after CBFB knockdown. (F) Reduced expression levels of STAT3 in the JAK/STAT pathway and its downstream target gene protein MMP9 caused by CBFB knockdown in AGS and HGC-27 cell lines. NC: Negative control; GC: Gastric cancer.\u003c/p\u003e","description":"","filename":"image11.png","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/12e0525a85968b3cf006eff9.png"},{"id":96590403,"identity":"99c1dd6c-a434-4d08-95a0-10d5a85e0499","added_by":"auto","created_at":"2025-11-24 06:21:03","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":104468480,"visible":true,"origin":"","legend":"","description":"","filename":"FulluncroppedGelsandBlotsimage.doc","url":"https://assets-eu.researchsquare.com/files/rs-7940951/v1/72f3f45e2f19371b0703a7b7.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi Omics and Mechanistic Investigation Reveals CBFB as a Prognostic Biomarker in Gastric Cancer","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eGastric cancer (GC), the fourth most frequent cause of cancer-related mortality\u0026zwnj; and the fifth most prevalent cancer worldwide\u0026zwnj;, accounted for \u0026zwnj;over one million new cases and 770,000 deaths in 2020, highlighting its substantial public health burden\u003csup\u003e[1-2]\u003c/sup\u003e. A \u0026zwnj;substantial proportion of patients present with advanced-stage disease, severely compromising therapeutic efficacy\u003csup\u003e[3]\u003c/sup\u003e. Nevertheless, advances in bioinformatics and functional genomics have accelerated the identification of GC-associated biomarkers, which are critical for \u0026zwnj;enhancing early diagnosis and predicting survival outcomes and recurrence risks after treatment\u003csup\u003e[4]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eRibosome biogenesis is crucial for cellular growth and division, and its dysregulation plays a critical role in the initiation and progression of tumors\u003csup\u003e[5-6]\u003c/sup\u003e. In cancer cells, this process is frequently heightened to satisfy the increased demand for protein synthesis that accompanies rapid cell growth\u003csup\u003e[7-8]\u003c/sup\u003e. Aberrations in genes governing ribosome biogenesis can disrupt the translation of mRNA, thereby promoting tumor development\u003csup\u003e[9-10]\u003c/sup\u003e. For instance, in gastric cancer, ribosomal protein L40 (rpL40) has been demonstrated to facilitate malignant progression by modulating mRNA translational output\u003csup\u003e[11]\u003c/sup\u003e. Moreover, interleukin-6 (IL-6) not only stimulates ribosome biogenesis but also suppresses the expression and function of the tumor suppressor protein p53, further driving tumor formation\u003csup\u003e[12]\u003c/sup\u003e. Among ribosome biogenesis-related genes, the core-binding factor \u0026beta; (CBFB) gene emerged as a key candidate warranting in-depth investigation.\u003c/p\u003e\n\u003cp\u003eCBFB, a crucial component of the core-binding factor (CBF) complex, is essential for transcriptional regulation and cellular differentiation. It works in conjunction with Runx family proteins to form heterodimers, thereby increasing their ability to bind to DNA, a process essential for normal skeletal development\u003csup\u003e[13]\u003c/sup\u003e. In breast cancer, CBFB facilitates osteotropic metastasis through exosomal mechanisms and modulates oxidative stress-related proteins to augment tumor invasiveness\u003csup\u003e[14]\u003c/sup\u003e. Acute myeloid leukemia (AML) pathogenesis is driven by CBFB-MYH11 fusion proteins arising from chromosomal rearrangements\u003csup\u003e[15]\u003c/sup\u003e. While the function of CBFB in gastric cancer remains underexplored, emerging evidence implicates long non-coding RNA LINC01234 in competitively regulating CBFB expression, thereby influencing the development and progression of gastric cancer\u003csup\u003e[16]\u003c/sup\u003e. These findings underscore the potential of CBFB as a ribosome biogenesis-associated oncogenic driver in gastric cancer, warranting targeted research to refine prognostic markers and therapeutic interventions.\u003c/p\u003e\n\u003cp\u003eThis study systematically integrated gene co-expression network analysis, single-cell analysis, and molecular docking to delineate the functional mechanisms of CBFB in gastric cancer. By combining multi-omics data with experimental validation, the findings demonstrate the significant functions of CBFB in the progression of gastric tumors, offering foundational evidence for translational applications. The research objectives focus on deciphering the regulatory roles of CBFB to identify novel therapeutic targets, with potential implications for improving clinical management in gastric cancer patients.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003e2.1 Data Sources\u003c/p\u003e\n\u003cp\u003eRelevant data were obtained from the GEO database (www.ncbi.nlm.nih.gov) utilizing the search terms (\u0026ldquo;gastric\u0026rdquo; OR \u0026ldquo;stomach\u0026rdquo;) AND (\u0026ldquo;cancer\u0026rdquo; OR \u0026ldquo;carcinoma\u0026rdquo; OR \u0026ldquo;neoplasm*\u0026rdquo;). The datasets GSE184336 and GSE29272 were identified. Specifically, GSE184336 encompasses whole-transcriptome sequencing data comprising 231 gastric cancer tissues and 230 normal gastric tissues. GSE29272 consists of gene expression array data pertaining to cardia and non-cardia gastric tumors, along with normal glands, with a focus on identifying distinct and commonly dysregulated genes across these two gastric cancer subtypes. Furthermore, a set of genes associated with \u0026ldquo;ribosome biogenesis\u0026rdquo; was retrieved from GeneCards (http://www.genecards.org), resulting in a total of 6234 ribosome biogenesis-related genes. Single-cell RNA sequencing data (GSE184198) containing paired normal tissue and tumor samples (n=1 each) were acquired from the GEO database. Concurrently, bulk RNA-seq data from the TCGA-STAD project (https://portal.gdc.cancer.gov) were systematically retrieved and preprocessed for downstream analysis. Differential expression analysis was performed using a significance criterion of P \u0026lt; 0.05 and |log2 FC| \u0026ge; 1, with results visualized via volcano plots generated in R software. Subsequent analyses included evaluation of target gene expression levels, survival curves, and receiver operating characteristic (the ROC curves) for the target genes in stomach adenocarcinoma (STAD).\u003c/p\u003e\n\u003cp\u003e2.2 WGCNA and Precise Identification of Differentially Expressed Genes (DEGs)\u003c/p\u003e\n\u003cp\u003eThe WGCNA analysis was performed using the \u0026ldquo;WGCNA\u0026rdquo; package in R on gene expression data from the GEO datasets. The core modules were selected according to their Pearson correlation coefficients and the strongest association with clinical features. Module construction involved average linkage hierarchical clustering based on topological overlap matrix (TOM) dissimilarity, followed by dynamic tree cutting implementation. For GSE184336, the minimum number of genes per module was set to 130, and modules with feature gene dissimilarity \u0026lt; 0.5 were merged. The dataset GSE1843336 included 461 samples (normal controls and gastric cancer), from which 19834 genes were retained after data cleaning and filtering. Regarding GSE29272 non-cardia tumor subgroup analysis, the minimum number of genes per module was set to 100, and modules with feature gene dissimilarity \u0026lt; 0.3 were merged. The GSE29272 dataset contained 144 samples (non-cardia normal controls and gastric cancer cases), from which 6,619 passed quality filtering. Venn diagrams were generated using VENNY to illustrate the overlap among DEGs.\u003c/p\u003e\n\u003cp\u003e2.3 Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis was performed on hub module genes to elucidate their biological functions, incorporating Gene Ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The DAVID bioinformatics resource facilitated GO-based functional characteristics of differentially expressed genes (DEGs) and their products. Concurrent KEGG pathway analysis identified associated signaling pathways, with a statistical significance threshold of P \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e2.4 Machine Learning Algorithms\u003c/p\u003e\n\u003cp\u003eMachine learning approaches comprising Random Forest (implemented via randomForest R package), LASSO regression (glmnet package), and Support Vector Machines (SVM; e1071 package) were employed for biomarker identification. Random Forest facilitated precise feature selection through decision tree construction, while SVM addressed nonlinear classification with gene importance ranking. LASSO regression optimized the feature set through regularization. This approach identified two target genes, CBFB and HEATR1, thereby strengthened the model\u0026rsquo;s reliability and transparency. In addition, statistical methods such as Student\u0026rsquo;s t-test and the Wilcoxon rank-sum test were used to evaluate biomarkers, with false discovery rate (FDR) correction applied to minimize false positives.\u003c/p\u003e\n\u003cp\u003e2.5 Gene Set Enrichment Analysis (GSEA)\u003c/p\u003e\n\u003cp\u003eSamples were stratified based on CBFB expression levels (high and low). To identify related pathways and mechanisms, the c2.cp.kegg.v7.4.symbols.gmt gene set was used to subset from the Molecular Signatures Database. GSEA was run on gene expression profiles with their phenotypes, using these parameters: minimum gene set size 5, maximum 5,000, 1,000 permutations, significance thresholds of P \u0026lt; 0.05, and FDR \u0026lt; 0.25.\u003c/p\u003e\n\u003cp\u003e2.6 Protein-Protein Docking using GRAMM\u003c/p\u003e\n\u003cp\u003eProtein-protein docking was performed using GRAMM, maintaining fixed ligand-receptor conformations to identify interfacial binding sites. Surface-binding sites were located on the protein interface. Key molecules from the CBFB and JAK/STAT pathways had their sequences fetched from UniProtKB. Optimal protein structures were then obtained via SWISS-MRED Wait\u0026mdash;correcting: SWISS-MODEL is the tool; export formats in PDB for docking. Docking outcomes with binding free energies below -4 kcal/mol were considered biologically relevant.\u003c/p\u003e\n\u003cp\u003e2.7 Single-Cell Data Analysis\u003c/p\u003e\n\u003cp\u003e2.7.1 Quality Control of Single-Cell Data\u003c/p\u003e\n\u003cp\u003eTranscriptomic data processing was initiated in Seurat with sequential filtering steps: UMI counts, expressed genes, and mitochondrial gene expression ratios, which indicate potential apoptosis. Quality control implemented MAD for outlier removal, supplemented by DoubletFinder-mediated doublet identification and exclusion.\u003c/p\u003e\n\u003cp\u003e2.7.2 Dimensionality Reduction, Clustering, and Annotation of Single-Cell Data\u003c/p\u003e\n\u003cp\u003eExpression profiles underwent LogNormalize standardization (10,000 reads/cell) with subsequent logarithmic transformation, while CellCycleScoring calculated cell cycle scores. FindVariableFeatures was performed, followed by ScaleData normalization accounting for technical variations (mitochondrial/ribosomal genes and cell cycle effects). Principal component analysis (PCA) was performed on the expression matrix, with selected components used for further analysis. The Harmony algorithm addressed batch effects, followed by UMAP for dimensionality reduction. Cell type annotations and marker genes were compiled from sources like CellMarker, PanglaoDB, literature, and SingleR software.\u003c/p\u003e\n\u003cp\u003e2.7.3 Cellular Communication\u003c/p\u003e\n\u003cp\u003eCellChat was used to enable quantitative reconstruction of intercellular communication networks by integrating single-cell data with normalized expression profiles and predefined cell subtypes. This computational framework systematically evaluates cell interactions, providing mechanistic insights into signaling activity modulation under disease conditions.\u003c/p\u003e\n\u003cp\u003e2.8 Validation Using the Human Protein Atlas Database and Immunohistochemistry Experiments\u003c/p\u003e\n\u003cp\u003eCBFB protein expression patterns were comparatively analyzed in STAD and adjacent non-cancerous tissues through Human Protein Atlas, and IHC staining was performed on 30 paired gastric cancer and matched non-tumor samples. Slides were baked at 70\u0026deg;C for 60 minutes, deparaffinized, rehydrated, and subjected to antigen retrieval, with endogenous peroxidase blocked for 10 minutes. Primary antibodies (anti-CBFB 1:100; anti-Phospho-STAT3 1:100; anti-MMP9 1:100; all from Proteintech) were incubated overnight at 4\u0026deg;C. The next day, slides were treated with a biotinylated secondary antibody, incubated at 37\u0026deg;C for 20 minutes, then with an HRP-conjugated polymer for 20 minutes. DAB chromogenic development preceded hematoxylin counterstaining, dehydration, and mounting. Digital imaging employed a Nikon microscope with quantitative analysis of positively stained areas performed in ImageJ. Tissue samples were obtained from surgical resections at the Second Affiliated Hospital of Baotou Medical College, with pathological diagnoses confirmed by our department.\u003c/p\u003e\n\u003cp\u003e2.9 Cell Culture\u003c/p\u003e\n\u003cp\u003eThe human gastric cancer cell lines AGS and HGC-27 were obtained from Wuhan Pricella Biotechnology. AGS cells were cultured in Ham\u0026rsquo;s F-12 with 10% FBS and 1% penicillin-streptomycin (P/S); HGC-27 cells were cultured in RPMI-1640 with 10% FBS and 1% P/S. Standard culture conditions involved incubation at 37\u0026deg;C with 5% CO₂ humidified atmosphere, with medium replacement performed every 2 days and subculturing conducted at 3\u0026ndash;4 day intervals according to growth. Cell counts were performed with a cell counter for accuracy, and each sample was prepared in triplicate for reproducibility.\u003c/p\u003e\n\u003cp\u003e2.10 Cell Transfection\u003c/p\u003e\n\u003cp\u003eThe gastric cancer cell lines AGS and HGC-27 were plated in 6-well plates to achieve approximately 60% confluence at the time of transfection. Each well received 42.5 \u0026mu;L of buffer transferred into sterile, enzyme-free Eppendorf tubes, followed by addition of 3.75 \u0026mu;L of siRNA, then pipetted up and down to mix. GP-Transfect-Mate transfection reagent (GenePharma, G04026, China) was subsequently introduced at 7.5 \u0026mu;L per reaction with immediate vortexing before application to cell monolayers. Post-transfection protocols included 24-hour incubation without medium change and 48-hour culture prior to protein extraction for Western blotting. The si-CBFB was synthesized by GenePharma Biotechnology Co., Ltd.\u003c/p\u003e\n\u003cp\u003e2.11 Western Blotting\u003c/p\u003e\n\u003cp\u003eProtein analysis was performed following established Western blotting protocols. Cells were lysed on ice in RIPA buffer with protease and phosphatase inhibitors for 30 minutes, followed by centrifugation (12,000 rpm, 15 minutes, 4\u0026deg;C). Protein concentrations were determined using BCA assay prior to equal loading for SDS-PAGE separation and subsequent transfer onto PVDF membranes. After blocking, membranes were incubated overnight at 4\u0026deg;C with primary antibodies against CBFB (1:1000, Proteintech, #67885-1-Ig), Phospho-STAT3 (1:2000, Proteintech, #28945-1-AP), and MMP9 (1:2000, Proteintech, #10375-2-AP), followed by 1 hour with HRP-conjugated secondary antibodies at 1:2000. Proteins were detected with ECL and imaged on a Bio-Rad ChemiDoc. Quantitative analysis involved background subtraction and \u0026beta;-actin normalization of ImageJ-processed band intensities.\u003c/p\u003e\n\u003cp\u003e2.12 Cell Function Experiment\u003c/p\u003e\n\u003cp\u003e2.12.1 CCK-8\u003c/p\u003e\n\u003cp\u003eCell proliferation was assessed in 96-well plates seeded 3,000 cells/well with complete medium. Following cell attachment, 10 \u0026mu;L of the Cell Counting Kit-8 (CCK-8; Dojindo, CK04, Japan) solution was added to each well. The plates were then incubated in the dark for one hour, and absorbance was measured at 450 nm using a Bio-Rad microplate reader (Japan) at 12, 24, 48, and 72 hours.\u003c/p\u003e\n\u003cp\u003e2.12.2 Colony Formation Assay\u003c/p\u003e\n\u003cp\u003eCells plated at 400 to 700 cells/well in 6-well plates were maintained in complete medium under standard culture conditions (37\u0026deg;C with 5% CO2) for 10 to 14 days with medium replacement every four days. These colonies were fixed in 4% paraformaldehyde for 15 minutes, stained with 0.1% crystal violet, and counted and analyzed with ImageJ software.\u003c/p\u003e\n\u003cp\u003e2.12.3 Cell Scratch Test\u003c/p\u003e\n\u003cp\u003eConfluent monolayers (90%) in 6-well plates were mechanically scratched using sterile 10 \u0026mu;L pipette tips perpendicular to the long axis. Non-adherent cells were then rinsed away with PBS, and serum-free medium was added. Images were captured at 0, 24, and 48 hours to assess cell migration.\u003c/p\u003e\n\u003cp\u003e2.12.4 Transwell Chamber Experiment\u003c/p\u003e\n\u003cp\u003eCell suspensions (2\u0026times;105 cells/mL in serum-free medium) were loaded into Matrigel-coated transwell inserts (200 \u0026mu;L/insert; 8.0 \u0026mu;m pores, Catalog No. 3422, Corning, USA) with 600 \u0026mu;L complete medium (10% FBS) in lower chambers. After 24 hours, cells on the upper surface were removed with a PBS-moistened cotton swab. Cells that migrated to the lower membrane surface were fixed in 4% paraformaldehyde for 20 minutes, then stained with crystal violet for 20 minutes at room temperature. Cells were microscopically imaged and quantified using ImageJ software.\u003c/p\u003e\n\u003cp\u003e2.13 Statistical Analysis\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using R (version 4.4.1) for data processing. Survival analysis was evaluated through Kaplan-Meier curves with log-rank testing, while distribution normality and variance homogeneity were verified by Shapiro-Wilk test (\u0026alpha; = 0.05) and Levene\u0026rsquo;s test (\u0026alpha; = 0.05), respectively. When normality wasn\u0026rsquo;t met, suitable nonparametric tests or data transformations were applied. For two-group comparisons, two-tailed unpaired t-tests were used; for more than two groups, one-way ANOVA was employed. Categorical differences were analyzed by chi-square or Fisher\u0026rsquo;s exact test. Bonferroni correction addressed multiple comparisons as needed. Significance was set at P \u0026lt; 0.05, with the following asterisks: * P \u0026lt; 0.05, ** P \u0026lt; 0.01, *** P \u0026lt; 0.001, **** P \u0026lt; 0.0001. Three independent biological replicates were evaluated, each with three technical replicates, and results are shown as mean \u0026plusmn; SEM. Graphs and analyses were generated with GraphPad Prism (v9.01), with additional tools from Xiantao and Sangerbox.\u003c/p\u003e\n\u003cp\u003e2.14 Ethics\u003c/p\u003e\n\u003cp\u003eThis study protocol received ethical approval from the Ethics Committee of the University Hospital (Approval No. 2024-ZX-052), with written informed consent obtained from all participants prior to study enrollment.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e3.1 WGCNA Results\u003c/p\u003e\n\u003cp\u003eCo-expression networks were constructed using the WGCNA algorithm, with soft-thresholding powers empirically determined as \u0026beta;=6 for GSE184336 (Figure 2A-B) and \u0026beta;=18 for GSE29272 (Figures 3A-B), generating weighted networks (Figure 2C, 3C), respectively. In GSE184336, nine modules were identified, among which the salmon, pink, and brown modules (581 genes) (Figures 2D-G) demonstrated the strongest correlations with gastric cancer progression. Similarly, analysis of GSE29272 yielded six modules, with the black, blue, magenta, and red modules (825 genes) (Figures 3D-H) reflecting the most significant associations within biologically relevant networks.\u003c/p\u003e\n\u003cp\u003e3.2 Analysis of TCGA-STAD Dataset\u003c/p\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified and normalized using standardized thresholds (|log2 FC| \u0026gt; 1, P \u0026lt; 0.05) (Figure 4A) and visualized through Venny-generated Venn diagrams, illustrating 581 genes from GSE184336, 825 genes from GSE29272, 4636 genes from TCGA-STAD, and 6234 ribosomal biogenesis-related genes obtained from the GeneCards database. A total of 40 overlapping genes related to ribosomal biogenesis in gastric cancer were identified (Figure 4B).\u003c/p\u003e\n\u003cp\u003e3.3 Enrichment Analysis Results\u003c/p\u003e\n\u003cp\u003eGO and KEGG pathway analyses of the intersecting genes revealed significant enrichment in: (1) Biological processes (BP) including cellular component biogenesis and protein-containing complex assembly; (2) Cellular components (CC) localized to nuclear lumen and protein-containing complex; (3) Molecular functions (MF) involving RNA binding and nucleic acid binding; (4) KEGG pathways related to ribosome biogenesis in eukaryotes and RNA transport (Figure 4C-F). These findings highlight the diverse functions and pathways associated with the intersecting genes.\u003c/p\u003e\n\u003cp\u003e3.4 Machine Learning Algorithm Results for Feature Selection\u003c/p\u003e\n\u003cp\u003eComprehensive feature selection was performed on the 40 candidate genes through integrated Random Forest, SVM, and LASSO analyses. In the Random Forest results, the five most influential features were AQP4, CA9, CBFB, HEATR1, and NUP62, with AQP4 exerting the strongest effect (Figure 5A). The SVM analysis placed CA9, CBFB, HEATR1, RUVBL1, and CDC123 at the top in terms of importance (Figure 5B). LASSO regression for diagnostic and prognostic evaluation revealed additional key genes (Figures 5C-E) with Venn analysis confirming CBFB and HEATR1 as consensus core targets across all methodologies (Figure 5F).\u003c/p\u003e\n\u003cp\u003e3.5 Prognostic and Diagnostic Analyses of CBFB and HEATR1\u003c/p\u003e\n\u003cp\u003eRNA-seq data for STAD were acquired from The Cancer Genome Atlas (TCGA; https://portal.gdc.cancer.gov) and processed for downstream analysis. Matched transcript data were compared against available TCGA datasets. Using R, we assessed survival curves, ROC curves, and the expression of CBFB and HEATR1 in STAD patients. The results showed that high CBFB expression significantly correlated with reduced 30-month survival, while HEATR1 showed no statistically meaningful prognostic correlation (Figures 6A-B). Expression levels of CBFB and HEATR1 were notably higher in STAD tissues than in normal controls (P \u0026lt; 0.001; Figure 6C). ROC analysis supported the diagnostic relevance of these genes, with CBFB and HEATR1 yielding AUC values of 0.961 and 0.926, respectively (Figure 6D). Collectively, these results highlight the expression pattern of CBFB in STAD and its potential clinical relevance, warranting further investigation. Consequently, CBFB was prioritized for subsequent functional studies.\u003c/p\u003e\n\u003cp\u003e3.6 Gene Set Enrichment Analysis (GSEA) of Pathways\u003c/p\u003e\n\u003cp\u003eSingle-gene pathway enrichment analysis of CBFB identified significant association with JAK/STAT signaling pathway in cancer (GSEA: NES = 1.95, p = 0.0021, and FDR = 0.0034) (Figure 6E), suggesting its potential involvement in gastric cancer pathogenesis.\u003c/p\u003e\n\u003cp\u003e3.7 Protein-Protein Docking Using GRAMM\u003c/p\u003e\n\u003cp\u003eProtein-protein docking analysis using GRAMM generated 11 distinct conformations between CBFB and key molecules within the JAK/STAT pathway. The binding free energies were as follows: JAK1 = -7.4 kcal/mol, JAK2 = -10.0 kcal/mol, JAK3 = -9.1 kcal/mol, TYK2 = -5.1 kcal/mol, STAT1 = -3.3 kcal/mol, STAT2 = -3.5 kcal/mol, STAT3 = -11.5 kcal/mol, STAT4 = -5.7 kcal/mol, STAT5A = -8.8 kcal/mol, STAT5B = -2.4 kcal/mol, and STAT6 = -5.4 kcal/mol (Figure 7). The highest-affinity interaction occurred between CBFB and STAT3, forming a strong binding interaction with notably high affinity, indicating a stable complex. Additionally, multiple interaction sites between the two proteins were observed. Their surfaces made contact through various interactions, including hydrogen bonds that reinforced structural stability. This specific binding pattern suggests preferential CBFB-STAT3 molecular recognition within the JAK/STAT signaling cascade.\u003c/p\u003e\n\u003cp\u003e3.8 Single-Cell Analysis\u003c/p\u003e\n\u003cp\u003e3.8.1 Quality Control and Clustering of Single-Cell Data\u003c/p\u003e\n\u003cp\u003eTo ensure data quality across samples, cells with abnormal values or fewer than 200 detected genes were filtered, followed by doublet removal using DoubletFinder (15,520 high-quality cells retained). The resulting violin and scatter plots (Figure 8A-B) will be generated. Subsequent computational processing identified 2,000 highly variable genes prior to normalization, scaling, principal component analysis (PCA), and batch correction via Harmony integration (Figure 8C-D).\u003c/p\u003e\n\u003cp\u003e3.8.2. Cell Annotation and Cell Communication\u003c/p\u003e\n\u003cp\u003eUMAP obtained 14 subgroups (Figure 9A) annotated as B cells、T Cells、Neutrophil、Plasma cells、mast cells、Fibroblast、Endothelial cells、Epithelial cells、Macrophages. These 9 cell categories (Figure 9B). Bubble plot of 9 cell markers (Figure 9C) and cell proportion bar chart corresponding to grouping (Figure 9D). Seurat-based evaluation through FeaturePlot and DotPlot visualizations revealed markedly higher CBFB expression in tumor cells than in normal cells. Among immune populations, CBFB showed significant overexpression in mast cells, with DotPlot indicating roughly a twofold higher level relative to other cell types (P \u0026lt; 10-5) (Figure 9E-F). CellChat analysis of intercellular communication networks quantified interaction frequency and weighted communication strength between high- and low-CBFB expression groups (Figure 9G), with differential intensity patterns visualized separately (Figure 9H). Pathway-specific heatmaps identified distinct signaling profiles across cell types (Figure 9I). Notably, endothelial cells appeared to engage more readily with other cell populations. Ligand\u0026ndash;receptor interaction mapping further delineated communication pathways between endothelial cells (as signal sources) and target cells in both CBFB-high and CBFB-low groups, underscoring potential signaling relationships (Figure 9J).\u003c/p\u003e\n\u003cp\u003e3.9 HPA Database Validation and Immunohistochemical Experiment Validation\u003c/p\u003e\n\u003cp\u003eConsistent with Human Protein Atlas (HPA) staining patterns, CBFB, pSTAT3, and MMP9 proteins were demonstrated to be elevated in STAD tissues (Figure 10A). IHC was conducted on clinical specimens of adjacent normal gastric tissue and gastric cancer tissue. Statistical analysis revealed differential expression of CBFB, pSTAT3, and MMP9 in gastric cancer compared with adjacent normal tissue with statistical significance (P \u0026lt; 0.05, **P \u0026lt; 0.01, ***P \u0026lt; 0.001; Figure 10B-C).\u003c/p\u003e\n\u003cp\u003e3.10 Functional Experiments of CBFB in Gastric Cancer Cell Lines\u003c/p\u003e\n\u003cp\u003eBased on its prognostic association and high expression in gastric cancer, CBFB was targeted for functional studies through siRNA-mediated knockdown in AGS and HGC-27 human gastric cancer cell lines using the GP-Transfect-Mate reagent (GenePharma, G04026, China). This experimental approach evaluated the impact of CBFB suppression on malignant phenotypes, with knockdown efficiency verified by Western blot analysis (Figure 11A).\u003c/p\u003e\n\u003cp\u003e3.10.1 Cell Proliferation Function\u003c/p\u003e\n\u003cp\u003eCCK-8 proliferation assays revealed significantly reduced growth kinetics in CBFB-silenced gastric cancer cells than in controls (Figure 11B), corroborated by decreased colony formation capacity in both AGS and HGC-27 lines (P \u0026lt; 0.01; Figure 11C).\u003c/p\u003e\n\u003cp\u003e3.10.2 Invasion and Migration Functions\u003c/p\u003e\n\u003cp\u003eMotility analysis through wound healing revealed impaired migration upon CBFB suppression, evidenced by delayed wound closure compared to the controls (P \u0026lt; 0.01; Figure 11D). Similarly, the Transwell invasion assay indicated a significant drop in cells that degraded the matrix and traversed the membrane into the lower chamber after CBFB knockdown (P \u0026lt; 0.01; Figure 11E).\u003c/p\u003e\n\u003cp\u003e3.11 CBFB and JAK/STAT Pathways\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWestern blot analysis of JAK/STAT pathway components in CBFB-silenced AGS and HGC-27 cells demonstrated a marked reduction in p-STAT3 levels (P \u0026lt; 0.01), accompanied by a 22\u0026ndash;35% decrease in downstream MMP9 expression relative to the controls (P \u0026lt; 0.01; Figure 11F). This aligns with prior work on the STAT3-MMP9 transcriptional axis\u003csup\u003e[17-18]\u003c/sup\u003e, implying that CBFB may participate in invasion-related signaling in gastric cancer by positively regulating STAT3 activity.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eGastric cancer maintains global prevalence as a leading malignancy, exhibiting elevated incidence and mortality rates in male populations. Late-stage diagnosis frequently restricts surgical and chemotherapeutic efficacy, leading to poor overall and disease-free survival and a grim prognosis\u003csup\u003e[4]\u003c/sup\u003e. Recent progress in bioinformatics and functional genomics has helped identify gastric cancer-associated biomarkers, which facilitate prediction of therapeutic responses to chemotherapy, targeted therapies, and immunotherapy, and their levels can inform post-treatment survival and recurrence risk\u003csup\u003e[19]\u003c/sup\u003e. Early detection, along with a deeper understanding of molecular mechanisms, can support more personalized treatment, improve outcomes, and boost survival.\u003c/p\u003e\n\u003cp\u003eRibosome biogenesis critically supports malignant transformation by facilitating ribosomal RNA production and protein synthesis, thereby regulating fundamental cellular processes including proliferation, apoptosis, and cell-cycle control. Studies show that ribosomal proteins modulate these processes, affecting apoptosis, cell-cycle arrest, growth, and tumor development through both MDM2/p53-dependent and -independent pathways, while concurrently maintaining genome integrity by regulating DNA-damage responses and repair mechanisms\u003csup\u003e[20]\u003c/sup\u003e. Biochemical studies demonstrate that the interaction of CBFB with heterogeneous nuclear ribonucleoprotein K (hnRNPK) forms functional complexes that activate ribosome assembly and enhance mRNA translation efficiency via the initiation factor eIF4B, thereby increasing protein-synthesis capacity\u003csup\u003e[21]\u003c/sup\u003e. In addition, CBFB forms a heterodimer with RUNX1, which binds ribosomal DNA promoter regions\u003csup\u003e[10]\u003c/sup\u003e. The CBF\u0026beta;-RUNX1 complex recruits RNA polymerase I-associated factors including PAF53 to activate rDNA transcription and rRNA biosynthesis\u003csup\u003e[22-23]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe CBFB gene exhibits context-dependent oncogenic functions across cancers. In breast cancer, higher CBFB levels are linked to more metastatic potential, mainly through effects on the RUNX protein family. Acute myeloid leukemia (AML) pathogenesis involves the CBFB-MYH11 fusion protein as a major driver. Mitochondrial translation regulation represents another functional dimension, where CBFB deficiency disrupts oxidative phosphorylation, induces Warburg metabolism, and activates autophagy-mitophagy pathways\u003csup\u003e[24-25]\u003c/sup\u003e. In gastric cancer, Chen and colleagues report a LINC01234-miR-204-5p-CBFB ceRNA network, wherein LINC01234 overexpression competitively binds miR-204-5p to alleviate CBFB suppression, establishing this regulatory axis as a potential tumorigenic mechanism\u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIntegrated analysis of gastric cancer transcriptomic datasets enabled construction of gene co-expression networks to identify molecular regulators. Machine learning-based selection of differentially expressed genes revealed significant association with ribosome biogenesis pathways, with CBFB emerging as a gene demonstrating both diagnostic and prognostic relevance. Single-gene GSEA enrichment linked CBFB to the JAK/STAT signaling pathway. Taken together, these results suggest that CBFB may drive gastric cancer progression by modulating JAK/STAT signaling.\u003c/p\u003e\n\u003cp\u003eElevated CBFB expression in tumor tissues was confirmed by Western blot (P \u0026lt; 0.01), with functional knockdown demonstrating significant suppression of cellular proliferation and migration capacities. Molecular docking analysis revealed robust CBFB-STAT3 interaction, suggesting potential enhancement of STAT3 transcriptional activity through associations with RUNX proteins. STAT3 governs essential processes like proliferation, differentiation, and survival, with activity controlled by phosphorylation and nuclear translocation. Togi and colleagues identified ARL3 as a novel STAT3-binding partner that enhances phosphorylation and nuclear accumulation, affecting transcriptional activity, which hints that CBFB could engage STAT3 via parallel mechanisms\u0026nbsp;\u003csup\u003e[26]\u003c/sup\u003e. Activation of the IL-6/JAK/STAT3 axis is a known driver of gastric cancer cell proliferation and migration, and its inhibition reduces invasiveness and growth\u0026nbsp;\u003csup\u003e[27-28]\u003c/sup\u003e, collectively indicating potential activation of CBFB in the JAK/STAT pathway through direct or indirect routes, promoting proliferation, migration, and invasion of gastric cancer cells. Specifically, silencing CBFB in gastric cancer reduces STAT3 phosphorylation and downregulates downstream targets such as MMP9, contributing to impaired proliferation, migration, and invasion.\u003c/p\u003e\n\u003cp\u003eSingle-cell resolution analysis demonstrates that the expression patterns of CBFB within the gastric cancer microenvironment are notably specific to certain cell types and exhibit spatial complexity. Specifically, quantification via DotPlot analysis identifies mast cells as the primary CBFB-expressing population, with expression levels significantly exceeding those observed in endothelial and fibroblast cells. Further analysis using CellChat indicates that in the group with high CBFB expression, mast cells secrete crucial extracellular matrix (ECM) components such as COL4A1, COL4A2, LAMA5, LAMC1, LAMB2, and APP, which establish high-affinity interactions (P \u0026lt; 0.01) with CD44/CD74 receptors on endothelial cells. This interaction network has the potential to activate downstream signaling pathways, specifically the STAT3-MMP9 axis. These findings extend previous reports regarding the autonomous functions of CBFB \u003csup\u003e[29-30]\u003c/sup\u003e, aligning with insights from earlier functional experiments that showed knocking down CBFB diminishes invasive capacity primarily by inhibiting mast cell-mediated ECM signaling to endothelial cells \u003csup\u003e[31]\u003c/sup\u003e. The data support a novel intercellular regulatory model: CBFB-mediated mast cell ECM secretion initiates CD44-dependent STAT3 activation cascade, offering new perspectives for targeting the tumor microenvironment \u003csup\u003e[32]\u003c/sup\u003e. Future research could directly validate this causal interaction through primary mast-endothelial co-culture models. Collectively, the evidence positions CBFB as a critical regulator of gastric cancer progression via STAT3-MMP9 axis activation and microenvironmental reprogramming, warranting further exploration into its mechanistic contributions to malignant transformation and potential as a therapeutic target.\u003c/p\u003e\n\u003cp\u003eSeveral methodological constraints require acknowledgment. Firstly, the small sample size may reduce statistical power, limiting insights into the role of CBFB in gastric cancer. Secondly, single-cell analysis used only one tumor\u0026ndash;normal pair, which may not reflect the full patient diversity of CBFB-related cellular interactions. While bioinformatic analyses implicate this ribosome-associated gene in gastric cancer, experimental confirmation of pivotal ribosomal regulatory factors still remains outstanding. To establish causality, future work should use approaches such as ribosome profiling (Ribo-seq) and CRISPR screens. The lack of more advanced in vivo models also constrains validation of the role of CBFB in gastric cancer progression. Subsequent investigations should incorporate expanded cohorts, multi-center validation, and animal models to clearly delineate the biological function of CBFB and assess its potential as a therapeutic target.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study highlights CBFB as a gastric cancer-associated gene with biomarker potential by integrating bioinformatics analyses with experimental validation, demonstrating consistent overexpression and functional linkage to activation of the JAK/STAT pathway. Mechanistic investigations reveal CBFB-mediated regulation of malignant phenotypes including tumor cell proliferation, migration, and invasion, underscoring its relevance to gastric cancer development and progression. While these findings offer insights for early diagnosis and targeted therapy, full mechanistic elucidation and clinical translation require additional validation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQ. Z. and L. W.: Designed studies, performed experiments and data analysis, and wrote original manuscripts. M. W.:Designed studies, performed experiments. Y. Y.: Collecting and organizing data; J. G. and B. C.: Analysis and interpretation of data. J. Z.: Study concept and design. M. W.: Directed the research process and completed major revisions of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has received approval from the local Ethics Committee of The Second Affiliated Hospital of Baotou Medical College (Approval No. 2024-ZX-052). All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent To Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants have provided informed consent by signing the requisite documentation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING INFORMATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the Inner Mongolia Autonomous Region Public Hospital Research Joint Fund, No. 2024GLLH0592; Baotou Health Science and Technology Plan, No. 2024wsjkkj59 and Qingmiao Plan of Baotou Medical College, No. BYJJ-ZRQM202230.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting this research include GSE184336, GSE29272 and the Cancer Genome Atlas-stomach adenocarcinoma dataset. GSE191275 and GSE15459 are available from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo); The Cancer Genome Atlas-stomach adenocarcinoma is available from the Cancer Genome Atlas database (https://www.cancer.gov/ccg/research/genome-sequencing/tcga).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZhang Z, Chen Z, Que Z, Fang Z, Zhu H, Tian J. Chinese Medicines and Natural Medicine as Immunotherapeutic Agents for Gastric Cancer: Recent Advances. Cancer Rep (Hoboken). 2024;7(9):e2134.\u003c/li\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 Cancer J Clin. 2021;71(3):209-249.\u003c/li\u003e\n\u003cli\u003eAhadi A. Dysregulation of miRNAs as a signature for diagnosis and prognosis of gastric cancer and their involvement in the mechanism underlying gastric carcinogenesis and progression. 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CBF\u0026beta;-SMMHC regulates ribosomal gene transcription and alters ribosome biogenesis. Leukemia. 2017;31(6):1443-1446.\u003c/li\u003e\n\u003cli\u003eHyde RK, Zhao L, Alemu L, Liu PP. Runx1 is required for hematopoietic defects and leukemogenesis in Cbfb-MYH11 knock-in mice. Leukemia. 2015;29(8):1771-1778.\u003c/li\u003e\n\u003cli\u003eMalik N, Yan H, Yang HH, et al. CBFB cooperates with p53 to maintain TAp73 expression and suppress breast cancer. PLoS Genet. 2021;17(5):e1009553.\u003c/li\u003e\n\u003cli\u003eMalik N, Kim YI, Yan H, et al. Dysregulation of Mitochondrial Translation Caused by CBFB Deficiency Cooperates with Mutant PIK3CA and Is a Vulnerability in Breast Cancer. Cancer Res. 2023;83(8):1280-1298.\u003c/li\u003e\n\u003cli\u003eTogi S, Muromoto R, Hirashima K, et al. A New STAT3-binding Partner, ARL3, Enhances the Phosphorylation and Nuclear Accumulation of STAT3. J Biol Chem. 2016;291(21):11161-11171.\u003c/li\u003e\n\u003cli\u003eLin J, Wang D, Zhou J, et al. 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Osteopontin: A Key Multifaceted Regulator in Tumor Progression and Immunomodulation. Biomedicines. 2024;12(7).\u003c/li\u003e\n\u003cli\u003eZheng T, Zhou H, Zheng Z, et al. The pathological significance and potential mechanism of ARHGEF6 in lung adenocarcinoma. Comput Biol Med. 2023;158:106894.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biological Markers, Core Binding Factor Beta Subunit (CBFB), JAK-STAT Pathway, Molecular Targeted Therapy, Stomach Neoplasms","lastPublishedDoi":"10.21203/rs.3.rs-7940951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7940951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Gastric cancer (GC) is an aggressive malignancy with poor prognosis due to complex pathogenesis, underscoring the need for biomarkers and targets. CBFB has regulatory roles across cancers and shows promise, but its prognostic significance and mechanisms in GC remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed GC transcriptomes by WGCNA to identify modules; machine learning prioritized core regulators. GSEA predicted signaling pathways, and molecular docking validated CBFB–STAT3 interactions. Single-cell RNA-seq was processed with Seurat for QC, clustering, and annotation; CellChat quantified intercellular ligand–receptor crosstalk. Functional validation employed CCK-8 proliferation and Transwell migration/invasion assays; Western blot assessed pathway activity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Multi-omics analyses implicate CBFB in GC pathogenesis. WGCNA highlighted 40 ribosome biogenesis–related genes, with machine learning prioritizing CBFB as a key regulator. GSEA linked CBFB to the JAK/STAT pathway (NES 1.95). Docking showed high-affinity CBFB–STAT3 binding (ΔG −11.5 kcal/mol). scRNA-seq showed higher CBFB in tumor parenchyma, enriched in mast cells; CellChat revealed enhanced mast cell–endothelial crosstalk via COL4A1/COL4A2–CD44 in CBFB-high groups (P\u0026lt;0.01). Functionally, CBFB knockdown reduced proliferation by ~40% and invasion/migration by ~30% (P\u0026lt;0.01); Western blot showed decreased STAT3 phosphorylation and reduced MMP9, implicating a CBFB–STAT3–MMP9 axis in GC progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: CBFB acts as a pivotal GC oncogene, with higher expression predicting poor prognosis and showing strong diagnostic potential. Mechanistically, CBFB may promote progression by engaging STAT3; single-cell data indicate overexpression in mast cells with enhanced mast cell–endothelial crosstalk. Functional data support CBFB as a therapeutic target in GC.\u003c/p\u003e","manuscriptTitle":"Multi Omics and Mechanistic Investigation Reveals CBFB as a Prognostic Biomarker in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 06:20:55","doi":"10.21203/rs.3.rs-7940951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-01T10:44:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-22T02:43:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-20T17:10:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64089553657749720683291413982741146696","date":"2025-11-13T16:46:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"243514985167420757073111158083038902406","date":"2025-11-12T08:39:38+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-11T14:59:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-11T10:22:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-07T16:29:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-11-07T16:21:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7bae1757-5a0b-4a79-b02e-e68aace74ab2","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T05:09:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 06:20:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7940951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7940951","identity":"rs-7940951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}