Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer

Exploration and experimental verification of triaptosis-related prognostic genes and cells 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 Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer Xirong Cui, Hongkun Qiu, Hailian Zhang, Huijing Xu, Mao Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9566914/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background Triaptosis is a newly characterized mode of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). Methods Transcriptomic and clinical data were obtained from TCGA-STAD and GSE62254, and single-cell RNA-seq data from GSE183904. Patients were stratified by triaptosis-related gene (TRG) scores calculated via ssGSEA. Differentially expressed genes were detected between TRG subgroups and between tumor and normal samples. Overlapping genes were subjected to Cox regression analyses to construct a prognostic signature, which was validated externally and in vitro. Associations between risk stratification and immune features and drug sensitivity were assessed. Single-cell analysis identified key cell populations linked to TRG activity and prognostic gene expression, followed by cell-communication analysis. Results A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. The GRB14 and VTN expression were markedly upregulated in GC cells. High-risk patients exhibited elevated stromal scores and distinct immune infiltration patterns, with 15 immune cell types differentially abundant between groups. Single-cell analysis revealed fibroblasts and pericytes as top TRG-active populations. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed reduced crosstalk with macrophages, indicating immune microenvironment remodeling. Conclusion The TRG-related prognostic signature effectively predicts GC outcomes and reflects distinct immune and genomic features, providing potential biomarkers for risk stratification and personalized therapy. gastric cancer single-cell RNA sequencing analysis triaptosis prognostic model fibroblasts Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Gastric cancer (GC) remains a major global malignancy with substantial mortality despite ongoing advances in prevention and treatment [ 1 ]. Clinically, GC often presents with nonspecific symptoms such as epigastric discomfort, weight loss, anemia, or early satiety, which contributes to delayed diagnosis, while the disease and its treatment can also impose a considerable psychosocial and quality-of-life burden on patients[ 1 , 2 ] .Current management has evolved from conventional surgery and chemotherapy toward biomarker-guided strategies, including targeted therapy and immunotherapy [ 3 ]. Nevertheless, the prognosis of advanced GC remains unsatisfactory because of marked molecular heterogeneity, complex stromal–immune interactions, and persistent therapeutic resistance [ 1 , 4 , 5 ].Therefore, developing new therapeutic targets for GC may provide new opportunities for prognostic stratification and therapeutic intervention. Among emerging forms of programmed cell death, triaptosis has recently been proposed as a distinct endosome-dependent death modality [ 6 ]. Current evidence indicates that triaptosis can be triggered by pro-oxidant stress, exemplified by menadione sodium bisulfite, which oxidatively perturbs PIK3C3/VPS34, disrupts endosomal homeostasis, and ultimately drives a noncanonical cell-death process characterized by vesicular dysfunction and cellular collapse [ 6 – 8 ]. In oncology, triaptosis has attracted increasing attention because it appears mechanistically different from apoptosis and several other established death pathways, raising the possibility that it may reveal vulnerabilities in tumor cells that have acquired resistance to conventional cell-death programs [ 7 , 8 ]. Consistent with this view, the initial discovery study demonstrated antitumor activity of triaptosis induction in preclinical prostate cancer models [ 7 ], whereas subsequent exploratory studies in melanoma and hepatocellular carcinoma suggested that triaptosis-related molecular patterns may be associated with prognosis, immune features, and therapeutic responsiveness [ 9 , 10 ]. However, compared with these emerging findings in other malignancies, the biological relevance and clinical significance of triaptosis in GC remain poorly defined, and direct evidence in this disease is still limited. These uncertainties highlight the need for analytical strategies capable of resolving triaptosis-related programs at cellular resolution within the complex GC microenvironment. Single-cell RNA sequencing (scRNA-seq) is a high-resolution transcriptomic technology that measures gene expression at the level of individual cells [ 11 ]. Compared with bulk RNA sequencing, scRNA-seq offers unique advantages in resolving cellular heterogeneity, identifying rare cell populations, and inferring cell-state transition trajectories [ 10 , 11 ].. In GC, recent studies have shown that scRNA-seq can effectively delineate the heterogeneity of malignant epithelial, immune, and stromal compartments and thereby provide new insights into tumor evolution and individualized therapy [ 12 , 13 ]. More specifically, single-cell analyses have revealed alternative T-cell exhaustion trajectories in GC [ 14 ], identified a pro-invasive cancer-associated fibroblast subgroup associated with poor prognosis [ 15 ], uncovered fibroblast–macrophage crosstalk related to immunosuppressive remodeling [ 16 ], and further demonstrated the heterogeneity and intercellular communication patterns of cancer-associated fibroblasts in GC[ 17 ]. These features make scRNA-seq particularly suitable for determining whether triaptosis-related molecular programs are enriched in specific cell populations and whether they participate in microenvironmental remodeling in GC. This study integrated transcriptome data from GC and identified prognostic genes associated with trimer apoptosis using bioinformatics. A prognostic model was then constructed and validated based on these genes. Mutation patterns, drug sensitivity, functional profiles, immunological characteristics, and clinical features were subsequently assessed across different risk groups. Single-cell expression analysis, intercellular communication analysis, pseudo-time series analysis, and RT-qPCR validation further elucidated the relevant cell populations. This integrative approach provides new insights into the mechanisms associated with trimer apoptosis in GC and offers candidate biomarkers for prognosis and personalized treatment. 2 Materials and Methods 2.1 Data collection Regarding publicly available transcriptomic resources, RNA sequencing data reflecting gene expression levels, clinical survival records, and phenotypic annotations for the TCGA-STAD cohort were acquired from the University of California Santa Cruz (UCSC) Xena platform ( https://xenabrowser.net/datapages/ ). The initial download comprised 448 specimens—comprising both tumor tissues (designated as “01A”) and histologically normal adjacent tissues (“11A”). After quality filtering and sample annotation verification, the final analytical cohort included 446 samples: 410 GC cases and 36 non-malignant controls. For subsequent survival modeling, a refined subset of 383 GC patients with fully documented, non-censored survival durations was extracted. Complementarily, the GSE62254 microarray dataset—generated on the GPL570 platform and encompassing 300 gastric tissue specimens with associated survival outcomes—was retrieved from the Gene Expression Omnibus (GEO) repository ( http://www.ncbi.nlm.nih.gov/geo/ ) [ 18 ]. At the single-cell resolution level, the GSE183904 dataset (platform: GPL24676) from GEO comprised 36 gastric tissue samples, of which 26 were derived from GC patients and 10 from healthy donors [ 19 ]. The panel of triaptosis-related genes (TRGs) was curated from two recent publications [ 9 , 10 ], which collectively reported 22 TRGs. 2.2 Correlation of the scores of TRG with the immune microenvironment and genomic characteristics of GC Utilizing ssGSEA approach, TRG expression activity was quantified across GC samples from TCGA-STAD cohort. An optimal threshold for stratifying patients was identified via the surv_cutpoint function, enabling binary classification into high- and low-TRG score subgroups. Survival outcomes were visualized using Kaplan–Meier (KM) curves, and survival difference between the two subgroups was assessed with the log-rank test. To characterize immune contexture differences, ssGSEA scores for 28 distinct immune cells were computed; intergroup disparities were evaluated by applying the Wilcoxon test. 2.3 Construction of the prognostic model Differential expression analysis of the TCGA-STAD cohort was executed utilizing the DESeq2 (v1.36.0) package[ 20 ] to find differentially expressed genes (DEGs1) between GC and healthy control samples (|log 2 fold change(FC)| > 1, P < 0.05), as well as DEGs2 between TRG score subgroups (adj. P 0.5). The overlap of the two DEG sets (DEGs1 and DEGs2) was assessed, and functional enrichment analysis—including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (adj. P < 0.05)—was performed on the overlapping genes by adopting clusterProfiler (v4.7.1.001) package ( P < 0.05)[ 21 ]. The overlapping genes in GC samples from the TCGA-STAD cohort were then subjected to univariate Cox proportional hazards regression. Genes exhibiting a hazard ratio (HR) significantly different from 1 ( P 0.05). These genes constituted the candidate prognostic gene set. The glmnet (v4.1-6) package was then employed to develop least absolute shrinkage and selection operator (LASSO) regression in order to improve feature selection[ 22 ], with optimal λ determined via ten-fold cross-validation. Finally, the genes selected by LASSO were subjected to multivariate Cox regression with backward stepwise elimination, yielding a parsimonious, robust prognostic model. 2.4 Validation of the prognostic model The TCGA-STAD and the GSE62254 were adopted to assess the prognostic model’s performance. The timeROC software was employed to plot the receiver operator characteristic (ROC) curves. A risk score formula was developed based on the prognostic model to determine the score for every patient, and risk curves were created. The patients were split into two risk groups (high/low) after the surv_cutpoint function of the survminer (v0.4.9) package[ 23 ]to identify the ideal cut-off value (minprop = 0.5). The KM survival curves for the two groups were made to examine the survival difference. 2.5 Correlation analysis of the prognostic model with clinical pathological characteristics The distribution and expression levels of clinical characteristics (such as gender, age, TNM stage, and pathological stage) and prognostic genes of GC samples in the TCGA-STAD cohort between various risk groups were shown by the pheatmap (v 1.0.12) package[ 24 ]. The distribution differences of each clinical characteristic subtype between various risk groups were examined by the chi-square test, and the distribution differences of risk scores between various clinical characteristic subgroups were also compared. 2.6 Immune infiltration characteristics and immune subtypes analyses The ESTIMATE algorithm was employed to assess and contrast immune, stromal, and ESTIMATE scores between two risk patients. Immune cell infiltration levels across 28 distinct immune cell types were quantified via ssGSEA, with intergroup differences statistically evaluated. Spearman’s correlation was applied to examine associations between prognostic gene expression and immune cell abundance. GC samples were further stratified into six immunological subtypes (C1–C4) by applying the ImmuneSubtypeClassifier (v0.1.0) package[ 25 ], and differential expression of prognostic genes across these subtypes was assessed through the Wilcoxon test. Moreover, GSVA (v1.44.5) package [ 26 ]was executed to derive enrichment scores for 13 immune-related functional pathways, followed by comparative analysis of these scores between the various risk groups. 2.7 Drug sensitivity and tumor mutation burden (TMB) analyses The oncoPredict (v1.2) [ 27 ]was implemented to predict drug sensitivity profiles for 198 chemotherapeutic and targeted treatments by leveraging pharmacogenomic information from the Genomics of Drug Sensitivity in Cancer (GDSC) database, with the differences in IC 50 between the various risk groups being evaluated. Maftools was employed to define the somatic mutation landscapes of GC patients in the TCGA-STAD cohort, and waterfall plots showing the top 20 most commonly mutated genes were created independently for each risk category. Additionally, the TMB between the two risk strata was calculated and compared. 2.8 Gene set variation analysis (GSVA) Leveraging the KEGG gene set (c2.cp.kegg_legacy.v2025.1.Hs.entrez.gmt) from the MSigDB database, the pathway enrichment score of TCGA-STAD samples was calculated by adopting the GSVA package. The pathway activity differences between the two risk strata were compared by adopting the limma (v3.52.4) package (|t| > 2, P < 0.05)[ 28 ]. 2.9 scRNA-seq analysis In the GSE183904 dataset, we carried out comprehensive quality assessment and preprocessing with the Seurat (v5.1.0) package[ 29 ]. Genes with expression detected in fewer than 200 cells were filtered out. Cells were discarded if they exhibited any of the following features: percent.mt ≥ 20%, nCount_RNA ≥ 40,000, or nFeature_RNA ≥ 6,000. Following log-normalization, highly variable genes were detected via the vst approach, and the top 2,000 such genes were retained for downstream dimensionality reduction. Principal component analysis (PCA) was then adopted, and the optimal number of principal components—determined jointly by permutation testing and examination of the “elbow” in the scree plot—was set to 30. Unsupervised clustering was conducted (resolution = 0.4), and cell embeddings were visualized by applying Uniform Manifold Approximation and Projection (UMAP). Cell type annotations were guided by prior knowledge from a published reference[ 30 ]. Doublets were computationally identified and removed through DoubletFinder (v2.0.4) package[ 31 ]. To quantify TRG activity across individual cells, five complementary scoring methods—AddModuleScore, SingScore, UCell, ssGSEA, and AUCell—were independently applied. Final per-cell TRG scores were derived as the mean of the five algorithm outputs, enabling robust cross-cell-type comparisons of TRG activity. Additionally, differential expression of prognosis genes in each annotated cell between GC and control samples was assessed. Core cells were selected based on the expression of comprehensive prognostic genes and TRG activity. Intercellular communication patterns were inferred by adopting CellChat (v1.6.1) package[ 32 ], while Monocle (v2.26.0)-based pseudotime analysis was employed to reconstruct temporal ordering and infer dynamic developmental trajectories of core cells[ 33 ]. 2.10 Cell culture RPMI-1640 media containing 10% fetal bovine serum and 1% penicillin-streptomycin was employed to cultivate the human normal cells GES-1 (90% RPMI-1640, immocell) and the GC cells MKN-45 (immocell) (37°C, 5% CO 2 ). 2.11 Reverse transcription-quantitative PCR (RT-qPCR) The FastPure Complex Tissue/Cell Total RNA Isolation Kit (Vazyme Biotech) was employed to isolate RNA from cultivated cells. Only samples with an OD 260 /OD 280 > 1.8 were kept for cDNA synthesis. RNA quality and concentration were assessed with a NanoDrop 500 spectrophotometer. ABScript III RT Master Mix (ABclonal), which incorporates on-board genomic DNA removal, was applied to carry out first-strand cDNA synthesis. The Genious 2X SYBR Green Fast RT-qPCR Mix (ABclonal) was applied for quantitative PCR with standard thermal cycling conditions. The reference gene for normalization was GAPDH (Primes listed in Table S1 ). Three separate technical replicates were examined for every cell line, and the relative mRNA expression levels were determined employing the 2 –ΔΔCt technique. 2.12 Statistical analysis R software was a tool for conducting all bioinformatics statistical analyses. The comparison among groups were evaluated via the Wilcoxon test for bioinformatics analysis and the t-test for RT-qPCR. P < 0.05 is considered statistically significant. 3 Results 3.1 Genes common in the TRG score and GC To explore the potential link between the TRG signature and GC, this study stratified GC patients from the TCGA-STAD cohort into two TRG score subgroups (high/low). Survival analysis indicated that individuals in the low-TRG score subgroup exhibited markedly poorer clinical outcomes (Fig. 1 A). Subsequent immune infiltration profiling uncovered substantial differences in the abundance of 17 distinct immune cell populations (e.g., activated B cell and activated dendritic cell) between the two subgroups, and all of them showed higher infiltration in the high-TRG score subgroup, implying a strong association of the TRG score with the tumor immune microenvironment in GC (Fig. 1 B-C). Differential expression analysis identified 2,519 DEGs2 distinguishing the high- versus low-TRG-score groups, comprising 1,605 upregulated and 914 downregulated genes (Fig. 1 D-E). In parallel, comparison of GC tissues with adjacent normal tissues yielded 2,491 DEGs1, including 1,017 upregulated and 1,474 downregulated genes (Fig. 1 F-G). Integration of these two DEG sets revealed 1,030 overlapping genes (Fig. 1 H). These genes were markedly associated with 320 biological processes (e.g., muscle contraction), 73 cellular components (e.g., contractile fibers), 23 molecular functions (e.g., heparin binding), and 25 KEGG pathways (e.g., the calcium signaling pathway) (Fig. 1 I-J, Table S2 ). 3.2 Robustness and generalizability of the prognostic risk model An additional refinement process was applied to the overlapping gene set. Initially, univariate Cox proportional hazards regression identified 29 genes significantly associated with patient prognosis (Fig. 2 A). Next, LASSO regression was employed to narrow down the candidate genes, yielding a parsimonious 13-gene signature with λ.min = 0.012 (Fig. 2 B). Finally, multivariate Cox regression analysis further distilled this set to a robust three-prognostic gene signature comprising ASPN, GRB14, and VTN (Fig. 2 C). A prognostic risk model was developed based on multivariate Cox proportional hazards regression: risk score = 0.1714 × ASPN + 0.1996 × GRB14 + 0.1111 × VTN. The model predicted 1-, 2-, and 3-year overall survival in the TCGA-STAD cohort with AUC values of 0.649, 0.671, and 0.731, respectively (Fig. 2 D). Similarly, in the independent validation cohort (GSE62254), all time-point–specific AUCs were ≥ 0.60 (Fig. 2 E). KM analysis further demonstrated markedly worse survival in the high-risk group across both cohorts—consistent with higher mortality in the former (Fig. 2 F-I). Moreover, the expression of the prognostic genes was verified through in vitro experiments. The RT-qPCR results showed that the mRNA expression levels of GRB14 and VTN were significantly upregulated in GC cells (Fig. 2 J). However, no valid value was detected for the ASPN gene. Collectively, these results affirm the model’s reliability as a prognostic instrument, demonstrating both stability across diverse conditions and broad applicability. 3.3 Correlation of the prognostic model with the stratification of clinical characteristics We examined how the prognostic model correlates with various clinicopathological features. The heatmap revealed that the three prognostic genes were highly expressed (ASPN, GRB14, and VTN) in the high-risk group (Fig. 3 A). The distribution of clinical characteristics such age, pathological stage, and T stage varied markedly between the two risk groups (Fig. 3 B-G). Additionally, the risk score also showed marked differences among different subgroups of T stage, N stage, and pathological stage (Fig. 3 H-M). 3.4 Immune landscape associated with the prognostic model in GC The association between the prognostic model (or its constituent genes) and immune-related functions was analyzed. Stromal and overall immune scores were markedly higher in the high-risk group (Fig. 4 A). Immune infiltration analysis revealed differential abundance of 15 immune cell types across risk strata: 4 cell types—including activated dendritic cells and activated CD4⁺ T cells—were depleted in high-risk patients, whereas 11 types—including macrophages and mast cells—were enriched (Fig. 4 B-C). ASPN expression correlated positively with most of these differential immune cells (strongest with natural killer cells), while GRB14 showed predominantly negative correlations (e.g., with central memory CD8⁺ T cells) (Fig. 4 D). Noteworthy differences in immune functional pathways—including APC co-inhibition, MHC class I presentation, and type II IFN response—were also observed between risk groups (Fig. 4 E). Furthermore, ASPN and VTN expression varied significantly across immunological subtypes (C1-C4) (Fig. 4 F-H). It reveals statistically significant differences in immune cell infiltration and functional profiles across risk groups, implicating a potential association between this gene signature and tumor immune regulation. 3.5 The functional characteristics, genetic mutation profiles and drug sensitivity differences of different risk stratifications To gain deeper insights into the biological, genomic, and pharmacological distinctions across risk subgroups, this study performed a comprehensive assessment. GSVA identified substantial pathway-level divergence—121 KEGG pathways showed markedly enrichment differences between two risk groups. Specifically, the high-risk group exhibited pronounced activation of pathways involved in extracellular matrix–receptor interaction, Notch signaling, and dilated cardiomyopathy, whereas the low-risk group demonstrated elevated activity in fructose and mannose metabolism as well as amyotrophic lateral sclerosis–associated pathways (Fig. 5 A, Table S3 ). Somatic mutation profiling revealed distinct mutational landscapes: TTN, TP53, and MUC16 were the most frequently altered genes in the low-risk group, while TP53, TTN, and LRP1B dominated the high-risk group (Fig. 5 B-C). Furthermore, TMB differed significantly between the two groups, implying divergent levels of genomic instability (Fig. 5 D). Drug response evaluation uncovered differential sensitivity to 47 therapeutic agents, quantified by IC 50 values: erlotinib, oxaliplatin, and afatinib showed greater efficacy in the low-risk group, whereas BMS-754807 and JQ1 displayed enhanced potency against high-risk tumors (Fig. 5 E, Table S4 ). These findings underscore marked intergroup heterogeneity in pathway regulation, mutational architecture, and treatment susceptibility among GC patients stratified by the three-TRG-based prognostic signature, highlighting its biological relevance and potential clinical utility. 3.6 The TRG activity and prognostic gene expression of fibroblasts in GC After conducting quality control, dimension reduction, and clustering analysis on the GSE183904 dataset, 26 cell clusters were obtained, and 11 major cell types were annotated, including mast cells, M1 macrophages, T cells, M2 macrophages, B cells, pericytes, plasma cells, chief cells, fibroblasts, epithelial cells, and endothelial cells (Fig. 6 A-B, Figure S1 A-E ). Meanwhile, 11,258 (7.5%) high-confidence twin cases were removed ( Figure S1 F ). Among all cell types, T cells accounted for the highest proportion (Fig. 6 C). The average TRG activity scores based on five algorithms showed that the TRG activity levels of M2 macrophages, fibroblasts, and pericytes ranked the top three (Fig. 6 D). The three prognostic genes (ASPN, GRB14, and VTN) were significantly differentially expressed in fibroblasts and epithelial cells between GC and control samples, and were higher than those in other cell types (Fig. 6 E). Given the crucial role of fibroblasts in TRG activity and prognostic gene expression, they were identified as the core cell type. Cell communication analysis was performed by stratifying fibroblasts into high- and low-TRG activity groups according to their TRG activity. Fibroblasts exhibiting elevated TRG activity demonstrated increased interaction frequency and strength with neighboring cell types—particularly pericytes—indicating a TRG-dependent enhancement of intercellular crosstalk. In contrast, tumor-derived fibroblasts showed attenuated communication with macrophages compared to those from normal tissues, implying substantial remodeling of fibroblast–immune cell interactions within the tumor microenvironment (Fig. 6 F-G). Pseudotime analysis of fibroblasts identified five differentiation stages, all represented in tumor samples (Fig. 6 H). 4 Discussion GC remains a highly heterogeneous malignancy in which clinical outcome is shaped not only by tumor-intrinsic molecular alterations but also by complex interactions within the tumor microenvironment[ 34 , 35 ]. This study is the first to construct a prognostic model for GC based on TRGs (ASPN, GRB14, VTN). Single-cell transcriptome analysis confirmed that fibroblasts serve as the core carriers of TRG activity and prognostic gene expression. Further research indicated that the high-risk subgroup demonstrated significant stromal remodeling, an immunosuppressive microenvironment, and an elevated TMB. Moreover, the differences in drug sensitivity among different risk groups offer a potential basis for the stratification of individualized treatment in GC. ASPN encodes asporin, a member of the small leucine-rich proteoglycan family that localizes predominantly to the extracellular matrix and participates in matrix organization, stromal remodeling, and signal modulation[ 36 ]. Recent studies have shown that ASPN is upregulated in GC, associated with poor prognosis, and functionally linked to enhanced invasion and migration, while also promoting macrophage M2 polarization, thereby connecting ASPN to both tumor aggressiveness and immune remodeling [ 37 , 38 ]. Additional mechanistic work has further implicated the MATN3–ASPN axis in epithelial–mesenchymal transition and metastatic progression in GC [ 39 ]. These observations are well aligned with our findings that ASPN was retained in the final risk model and showed broad positive correlations with differential immune-cell populations, supporting the view that ASPN may participate in a stromal–immune regulatory network in GC. GRB14 (Growth factor receptor-bound protein 14) is an adaptor protein of the Grb7 family, containing PH and SH2 domains, and is involved in receptor-mediated signaling [ 40 ]. In GC, recent evidence indicates that GRB14 is overexpressed, predicts poor outcome, and promotes proliferation, migration, invasion, and apoptosis resistance through activation of the PI3K/AKT pathway [ 41 ]. Given the well-established role of PI3K/AKT signaling in gastric-cancer progression and treatment resistance, the retention of GRB14 in our final model is biologically plausible and suggests that part of the prognostic signal may intersect with tumor-cell survival circuitry rather than stromal remodeling alone. VTN encodes vitronectin, a multifunctional adhesive glycoprotein involved in extracellular-matrix interaction, cell adhesion, and integrin-associated signaling [ 42 , 43 ]. It can mediate communication between the extracellular environment and intracellular signaling systems through cell-matrix contact [ 44 ] In GC, vitronectin overexpression has been associated with adverse clinicopathological behavior and poorer survival [ 42 ]. This observation is functionally plausible, given that extracellular-matrix remodeling and integrin-dependent signaling are increasingly recognized as major drivers of GC progression, immune evasion, and microenvironmental adaptation [ 43 , 45 ]. In our study, VTN also showed the strongest upregulation in RT-qPCR, further supporting its biological relevance in GC. Although this does not prove a direct mechanistic link to triaptosis, it does suggest that matrix-associated adhesion signaling may represent one route through which triaptosis-related molecular variation becomes coupled to aggressive tumor behavior. Our data indicated that the high-risk subgroup was characterized by a reorganized stromal–immune state. In particular, the combination of increased stromal score, macrophage enrichment, and relative depletion of activated T-cell-associated populations suggests an immunosuppressive microenvironment,which is biologically plausible in GC. Increasing evidence indicates that the GC microenvironment is highly heterogeneous and that stromal components, particularly cancer-associated fibroblasts and myeloid cells, actively shape immune escape, therapy resistance, and disease progression [ 35 , 46 ]. Macrophages are among the dominant infiltrating immune populations in GC and are widely recognized as drivers of tumor-promoting inflammation, immune suppression, angiogenesis, and resistance to treatment [ 47 ]. Against this background, the coexistence in our high-risk group of stromal enrichment and macrophage accumulation is unlikely to be incidental; rather, it supports the view that this risk pattern reflects a microenvironment permissive to tumor progression. The observed immune functional differences further strengthen this interpretation. Although these pathway-level findings should be interpreted cautiously, alterations involving APC co-inhibition, MHC class I-related activity, and type II IFN response are compatible with impaired antigen presentation and dysregulated immune effector signaling, both of which are hallmarks of tumor immune evasion [ 46 , 48 ] In GC specifically, immune escape has been linked to defective antigen processing and presentation, T-cell dysfunction, and microenvironment-driven suppression of effective antitumor responses [ 46 , 49 ]. Therefore, when considered together with the immune-cell redistribution observed in our cohort, these functional changes support the notion that the high-risk subgroup is characterized by a qualitatively immune-escaped state, rather than merely a quantitatively altered immune infiltrate. Furthermore, our analyses suggested that the triaptosis-related risk model stratified GC not only by survival, but also by pathway activity and predicted therapeutic vulnerability. In particular, the high-risk subgroup appeared to be characterized by enrichment of ECM-related and Notch-associated programs, whereas the two risk groups also displayed distinct predicted sensitivities to several targeted and cytotoxic agents. The enrichment of ECM–receptor interaction in the high-risk subgroup is biologically meaningful in GC, where ECM remodeling is increasingly recognized as a central driver of invasion, metastasis, immune evasion, and treatment resistance [ 43 ]. In GC, matrix-derived signals are not merely structural; they actively reshape tumor–stromal communication and can promote adaptive survival phenotypes through integrin-dependent pathways[ 50 ]. In parallel, the enrichment of Notch signaling further supports the interpretation that high-risk tumors may exist in a more plastic and aggressive state, because aberrant Notch activation has been implicated in gastric-cancer progression, stemness, therapeutic resistance, and even immune escape [ 51 , 52 ]. Therefore, these pathway-level differences suggest that the biological basis of the high-risk phenotype is not limited to isolated gene dysregulation, but may reflect a broader program of stromal dependence and adaptive signaling activation. The predicted drug-sensitivity differences provide a further layer of translational relevance. The greater predicted sensitivity of the low-risk subgroup to agents such as erlotinib, afatinib, and oxaliplatin suggests that this subgroup may retain relative vulnerability to EGFR/ErbB-directed therapy and platinum-based chemotherapy. This is biologically plausible, given that afatinib has shown activity in gastric and gastroesophageal adenocarcinomas in clinical and preclinical settings [ 53 ], and oxaliplatin remains a cornerstone of systemic treatment in GC [ 54 ]. By contrast, the enhanced predicted sensitivity of the high-risk subgroup to BMS-754807 and JQ1 is particularly interesting because these agents target pathways closely linked to adaptive survival. BMS-754807 inhibits IGF-1R/IR signaling, and this pathway has documented relevance in gastric-cancer growth and survival [ 55 , 56 ]. Likewise, the BET inhibitor JQ1 has demonstrated antitumor activity in gastric carcinoma models, including suppression of metastasis and improved efficacy in rational combination strategies [ 38 , 57 ]. Taken together, these observations suggest that the high-risk subgroup may be less amenable to conventional approaches but more dependent on targetable survival and chromatin-regulatory programs. Unlike M2 macrophages or pericytes, fibroblasts simultaneously exhibit elevated TRG activity and prominent expression of the three prognostic genes (ASPN, GRB14, and VTN), a pattern consistent with the recognized role of cancer-associated fibroblasts (CAFs) in extracellular matrix remodeling, angiogenesis, metastasis, and immunosuppression [ 34 , 58 ]. High-resolution studies further reveal that fibroblasts in GC are heterogeneous, and our observation of preferential fibroblast–pericyte communication under high-TRG conditions suggests a stromal state that supports vascular adaptation and tumor-promoting signaling, in line with evidence that tumor-derived exosomes can induce pericyte transition into CAF-like cells [ 59 , 60 ]. Conversely, the reduced fibroblast–macrophage communication relative to normal tissues implies not a global loss but a selective rewiring of stromal–immune crosstalk, mirroring recent reports of specific immunosuppressive fibroblast–myeloid networks in GC [ 5 , 16 ]. Finally, the pseudotime distribution of fibroblasts across multiple states underscores their dynamic heterogeneity [ 15 , 61 ]. Together, these data position fibroblasts as the central cellular carrier of the triaptosis-related signature, bridging bulk prognostic stratification with stromal remodeling in the GC microenvironment. This study has several strengths. By integrating several techniques established a triaptosis-related prognostic signature in GC. In addition to identifying ASPN, GRB14, and VTN as prognostically relevant genes, our findings highlighted fibroblasts as a core cell population associated with elevated TRG activity and prognostic-gene expression, providing new insight into the stromal context of triaptosis-related signals in GC. Moreover, the model was linked to immune remodeling, genomic heterogeneity, and differential drug sensitivity, which enhances its potential translational relevance. Several limitations should also be acknowledged. First, although the model was externally validated and partially supported by RT-qPCR, the current evidence remains largely associative, and the molecular mechanisms by which ASPN, GRB14, and VTN contribute to gastric-cancer progression or triaptosis-related processes remain unclear. Secondly, the predicted drug-sensitivity results require further experimental and clinical validation. 5 Conclusion In conclusion, we identified and validated a triaptosis-related three-gene signature (ASPN, GRB14, and VTN) that effectively predicts prognosis in GC. This signature was associated with distinct immune and genomics features, including stromal-immue remodeling and increased tumor mutational burden,and may provide potential biomarkers for risk sanctification and personalized therapeutic exploration. Declarations Ethics approval Not applicable. Competing interests The authors declare no competing interests. Funding Not applicable Author Contribution X.C: Writing – original draft, Software, Project administration, Methodology, Investigation, Data curation, Conceptualization. L.M: Writing – original draft, Methodology, Formal analysis. H.Q: Validation, Software, Formal analysis. H.Z: Validation, Software, Formal analysis. H.X: Writing – review & editing, Data curation. Acknowledgments We would like to sincerely thank the authors for their scientific contribution. Data Availability The datasets analyzed in this study are publicly available. TCGA-STAD data were obtained from the UCSC Xena platform, and the GSE62254 and GSE183904 datasets were downloaded from the Gene Expression Omnibus (GEO) database under the accession numbers GSE62254 and GSE183904. References Patel AK, Sethi NS, Park H (2026) Gastric Cancer: A Review. JAMA 335(5):439–450 Rupp SK, Stengel A (2021) Influencing Factors and Effects of Treatment on Quality of Life in Patients With Gastric Cancer-A Systematic Review. Front Psychiatry 12:656929 Yuan H, Bao M, Chen M, Fu J, Yu S (2025) Advances in Immunotherapy and Targeted Therapy for Gastric Cancer: A Comprehensive Review. Br J Hosp Med (Lond) 86(3):1–24 Kuwata T (2024) Molecular classification and intratumoral heterogeneity of gastric adenocarcinoma. 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Supplementary Files TableS1.xlsx TableS3.xlsx TableS2.xlsx FigureS1.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 18 May, 2026 Reviews received at journal 17 May, 2026 Reviews received at journal 15 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 01 May, 2026 Submission checks completed at journal 01 May, 2026 First submitted to journal 29 Apr, 2026 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. 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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-9566914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":638008731,"identity":"b94af85c-ee53-47f0-b41e-2d0d3a2a1f08","order_by":0,"name":"Xirong Cui","email":"","orcid":"","institution":"Neijiang First People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xirong","middleName":"","lastName":"Cui","suffix":""},{"id":638008732,"identity":"00f82dcd-d9c2-4842-8032-6fadf2c4008d","order_by":1,"name":"Hongkun Qiu","email":"","orcid":"","institution":"Shenyang Aerospace University","correspondingAuthor":false,"prefix":"","firstName":"Hongkun","middleName":"","lastName":"Qiu","suffix":""},{"id":638008733,"identity":"a2bb724a-22c2-42ec-8df5-d6e3a2028b51","order_by":2,"name":"Hailian Zhang","email":"","orcid":"","institution":"YanBian University","correspondingAuthor":false,"prefix":"","firstName":"Hailian","middleName":"","lastName":"Zhang","suffix":""},{"id":638008734,"identity":"0cbbeeec-1b81-439f-a64c-2b91527a2bf2","order_by":3,"name":"Huijing Xu","email":"","orcid":"","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Huijing","middleName":"","lastName":"Xu","suffix":""},{"id":638008735,"identity":"92bc7892-56a5-4e03-bdc4-92bb0011adbe","order_by":4,"name":"Mao Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYDACdsYGhgQwi7H54QcDGxCj8QBeLcxwLczHjCUq0kBaGghogbPYEiR4zhwGM/Fq4WdmbpN4ULNNzpx/jYGBZNt5u7Xth4G21NhE49Ii2czYJpFw7Lax5Yw3Bg8K224nbzuTCNRyLC23AYcWg8OMzQYJbLcTN9w4A7LldrLZAaAWxobDBLT8u10P0iLB23Yu2ez8Q4JaGh8ktt1OMDjfBvL+ATuzGwRsAfoFqKXvtuGGG+BATk4wuwG0JQGPX/jZ2x8c/PHttrzB+YOgqLSzNzuf/vDBhxobnFoQQCIBTCWCVSYQVA627wCYsidK8SgYBaNgFIwoAADQh2uBor3GSAAAAABJRU5ErkJggg==","orcid":"","institution":"Neijiang First People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Mao","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2026-04-29 14:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9566914/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9566914/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109263551,"identity":"4df26f37-b0b3-467b-a4d0-f5e3c015938e","added_by":"auto","created_at":"2026-05-14 12:05:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1299909,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of overlapping geneslinked to triaptosis in gastric cancer (GC). (\u003cstrong\u003eA\u003c/strong\u003e) Kaplan–Meier (KM) survival curves to evaluate survival differences between high- and low-TRG score subgroups in the TCGA-STAD cohort based on the optimal cutoff of enrichment scores. (\u003cstrong\u003eB\u003c/strong\u003e) Stacked bar chart of immune cell scores. (\u003cstrong\u003eC\u003c/strong\u003e) Histogramt showing the difference in the proportion of immune cells between high- and low-TRG score subgroups. (\u003cstrong\u003eD\u003c/strong\u003e) Volcano plot of differentially expressed genes (DEGs)2 between high- and low-TRG score subgroups in the TCGA-STAD cohort. (\u003cstrong\u003eE\u003c/strong\u003e) Heatmap of top 20 up-regulated and down-regulated DEGs2. (\u003cstrong\u003eF\u003c/strong\u003e) Volcano plot of DEGs1 between GC and control groups in the TCGA-STAD cohort. (\u003cstrong\u003eG\u003c/strong\u003e) Heatmap of top 20 up-regulated and down-regulated DEGs1. (\u003cstrong\u003eH\u003c/strong\u003e) Venn diagram of DEGs1 with DEGs2. (\u003cstrong\u003eI\u003c/strong\u003e) Top 10 significant Gene Ontology (GO) terms enriched by overlapping genes. (\u003cstrong\u003eJ\u003c/strong\u003e) Top 10 significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched by overlapping genes. ns: not significant, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/416b09e69053150f166e9afd.png"},{"id":109263553,"identity":"2f343fd3-f7fd-457c-b953-f31b55fe1531","added_by":"auto","created_at":"2026-05-14 12:05:53","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":449010,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of ASPN, GRB14, and VTNas prognostic genes. (\u003cstrong\u003eA\u003c/strong\u003e) Univariate Cox regression analysis on overlapping genes. (\u003cstrong\u003eB\u003c/strong\u003e) least absolute shrinkage and selection operator (LASSO) regression utilizing genes in univariate Cox regression analysis. (\u003cstrong\u003eC\u003c/strong\u003e) Multivariate Cox regression analysis on genes in LASSO. (\u003cstrong\u003eD\u003c/strong\u003e) Receiver operating characteristic (ROC) curves for 1-, 2-, and 3-year survival in the TCGA-STAD cohort. (\u003cstrong\u003eE\u003c/strong\u003e) ROC curves for 1-, 2-, and 3-year survival in the GSE62254 cohort. (\u003cstrong\u003eF\u003c/strong\u003e) KM survival curve between high- and low-risk groups in the TCGA-STAD cohort. (\u003cstrong\u003eG\u003c/strong\u003e) KM survival curve between high- and low-risk groups in the GSE62254 cohort. (\u003cstrong\u003eH\u003c/strong\u003e) Risk curve in the TCGA-STAD cohort. (\u003cstrong\u003eI\u003c/strong\u003e) Risk curve in the GSE62254 cohort. (\u003cstrong\u003eJ\u003c/strong\u003e) Reverse transcription-quantitative PCR (RT-qPCR) results of prognostic genes (GRB14 and VTN) expression in GES-1 and MKN-45 cells.** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/970cfbf68d8c5016d4c492b4.png"},{"id":109263555,"identity":"46ad8cbb-564b-4e66-8d69-b9c9412c9827","added_by":"auto","created_at":"2026-05-14 12:05:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":512361,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of the prognostic model with clinical pathological characteristics. (\u003cstrong\u003eA\u003c/strong\u003e) Heatmap of prognostic gene expression and clinical characteristics distribution in high- and low-risk groups. (\u003cstrong\u003eB\u003c/strong\u003e) The proportion of M-stage subgroups in high-risk and low-risk groups. (\u003cstrong\u003eC\u003c/strong\u003e) The proportion of N-stage subgroups in high-risk and low-risk groups. (\u003cstrong\u003eD\u003c/strong\u003e) The proportion of T-stage subgroups in high-risk and low-risk groups. (\u003cstrong\u003eE\u003c/strong\u003e) The proportion of stage subgroups in high-risk and low-risk groups. (\u003cstrong\u003eF\u003c/strong\u003e) The proportion of age subgroups in high-risk and low-risk groups. (\u003cstrong\u003eG\u003c/strong\u003e) The proportion of gender subgroups in high-risk and low-risk groups. (\u003cstrong\u003eH\u003c/strong\u003e) Violin plot showing the difference in risk score between M-stage subgroups. (\u003cstrong\u003eI\u003c/strong\u003e) Violin plot showing the difference in risk score across N-stage subgroups. (\u003cstrong\u003eJ\u003c/strong\u003e) Violin plot showing the difference in risk score across T-stage subgroups. (\u003cstrong\u003eK\u003c/strong\u003e) Violin plot showing the difference in risk score across stage subgroups. (\u003cstrong\u003eL\u003c/strong\u003e) Violin plot showing the difference in risk score between age subgroups. (\u003cstrong\u003eM\u003c/strong\u003e) Violin plot showing the difference in risk score between gender subgroups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/e927b62bc33ad43b1dfc6ce8.png"},{"id":109296211,"identity":"d2b19374-f3e7-437e-9e5e-1a0cd55a9fc5","added_by":"auto","created_at":"2026-05-15 08:46:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":444765,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of association between the prognostic model or its constituent genes and immune-related functions in GC. (\u003cstrong\u003eA\u003c/strong\u003e) Violin plot showing the difference in immune, stromal, and ESTIMATE scores between high- and low-risk groups in the TCGA-STAD cohort. (\u003cstrong\u003eB\u003c/strong\u003e) Stacked bar chart of immune cell scores. (\u003cstrong\u003eC\u003c/strong\u003e) Histogramt showing the difference in the proportion of immune cells between high- and low-risk groups in the TCGA-STAD cohort. (\u003cstrong\u003eD\u003c/strong\u003e) The correlation between differential immune cells and prognostic genes. (\u003cstrong\u003eE\u003c/strong\u003e) Histogramt showing the difference in immune functional pathways between high- and low-risk groups. (\u003cstrong\u003eF\u003c/strong\u003e) Violin plot showing the difference in ASPN expression across immunological subtypes (C1-C4). (\u003cstrong\u003eG\u003c/strong\u003e) Violin plot showing the difference in GRB14 expression across immunological subtypes (C1-C4). (\u003cstrong\u003eH\u003c/strong\u003e) Violin plot showing the difference in VTN expression across immunological subtypes (C1-C4). ns: not significant, * \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01, *** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/0cdea6f7ab8e5031df7e1804.png"},{"id":109263558,"identity":"e5a2a430-6726-43be-a94f-03502feb08a5","added_by":"auto","created_at":"2026-05-14 12:05:53","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":268476,"visible":true,"origin":"","legend":"\u003cp\u003eThe functional characteristics, genetic mutation profiles and drug sensitivity differences of different risk stratifications.\u003cstrong\u003e \u003c/strong\u003e(\u003cstrong\u003eA\u003c/strong\u003e) Gene set variation analysis (GSVA) of high- and low-risk groups. (\u003cstrong\u003eB\u003c/strong\u003e) Somatic mutation profiling in the low-risk group. (\u003cstrong\u003eC\u003c/strong\u003e) Somatic mutation profiling in the high-risk group. (\u003cstrong\u003eD\u003c/strong\u003e) Violin plot showing the difference in tumor mutation burden (TMB) between high- and low-risk groups. (\u003cstrong\u003eE\u003c/strong\u003e) Histogramt showing the difference in drug sensitivity between high- and low-risk groups. **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/35d828d15535894b8f026491.png"},{"id":109296572,"identity":"d890c563-d216-4a53-b264-38b98f69443e","added_by":"auto","created_at":"2026-05-15 08:48:16","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":388928,"visible":true,"origin":"","legend":"\u003cp\u003eThe TRG activity and prognostic gene expression of fibroblasts in GC. (\u003cstrong\u003eA\u003c/strong\u003e) Uniform Manifold Approximation and Projection (UMAP) clustering plot of 26 clusters in the single-cell RNA sequencing (scRNA-seq) GSE183904 dataset. (\u003cstrong\u003eB\u003c/strong\u003e) UMAP plot illustrating the distribution of 11 cell types. (\u003cstrong\u003eC\u003c/strong\u003e) The cellular composition of each cell type in GC and control groups. (\u003cstrong\u003eD\u003c/strong\u003e) Heatmap of TRG activity in each cell type. (\u003cstrong\u003eE\u003c/strong\u003e) Violin chart showing the expression of prognostic genes in each cell type between the GC and control groups. (\u003cstrong\u003eF\u003c/strong\u003e) Cellular communication network illustrating the number (left) and strength (right) of interactions among fibroblasts stratified by TRG activity and neighboring immune cells in control group. (\u003cstrong\u003eG\u003c/strong\u003e) Cellular communication network illustrating the number (left) and strength (right) of interactions among fibroblastsstratified by TRG activity and neighboring immune cells in GC group. (\u003cstrong\u003eH\u003c/strong\u003e)From left to right: Time trajectory of fibroblasts in pseudotime trajectory analysis; Cell state distribution of fibroblasts in pseudotime trajectory analysis; Time trajectory of fibroblasts in the GC group and the control group. ns: not significant, * \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003eP\u003c/em\u003e\u0026lt; 0.01, **** \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/220200ae6465b4e1886f4670.png"},{"id":109296474,"identity":"ccd81608-2539-41d8-bebd-43756037e21f","added_by":"auto","created_at":"2026-05-15 08:47:14","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10086,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/8fac6ecafedf2ef48dbb3485.xlsx"},{"id":109263552,"identity":"49c47cea-2f7f-4012-86a8-88f7aade4e16","added_by":"auto","created_at":"2026-05-14 12:05:53","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25823,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/9549e8995e4859edeb42873d.xlsx"},{"id":109296082,"identity":"301fbc2a-f052-4541-b583-b20de240894f","added_by":"auto","created_at":"2026-05-15 08:45:20","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":77050,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/6645d348267fada2162abf6c.xlsx"},{"id":109296206,"identity":"a3dd51ad-b90e-47b3-a8e5-7b2147b06f13","added_by":"auto","created_at":"2026-05-15 08:46:08","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":5100443,"visible":true,"origin":"","legend":"","description":"","filename":"FigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9566914/v1/1b51f14d97bfaeaefee0f74f.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eGastric cancer (GC) remains a major global malignancy with substantial mortality despite ongoing advances in prevention and treatment [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Clinically, GC often presents with nonspecific symptoms such as epigastric discomfort, weight loss, anemia, or early satiety, which contributes to delayed diagnosis, while the disease and its treatment can also impose a considerable psychosocial and quality-of-life burden on patients[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] .Current management has evolved from conventional surgery and chemotherapy toward biomarker-guided strategies, including targeted therapy and immunotherapy [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Nevertheless, the prognosis of advanced GC remains unsatisfactory because of marked molecular heterogeneity, complex stromal\u0026ndash;immune interactions, and persistent therapeutic resistance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].Therefore, developing new therapeutic targets for GC may provide new opportunities for prognostic stratification and therapeutic intervention.\u003c/p\u003e \u003cp\u003eAmong emerging forms of programmed cell death, triaptosis has recently been proposed as a distinct endosome-dependent death modality [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Current evidence indicates that triaptosis can be triggered by pro-oxidant stress, exemplified by menadione sodium bisulfite, which oxidatively perturbs PIK3C3/VPS34, disrupts endosomal homeostasis, and ultimately drives a noncanonical cell-death process characterized by vesicular dysfunction and cellular collapse [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In oncology, triaptosis has attracted increasing attention because it appears mechanistically different from apoptosis and several other established death pathways, raising the possibility that it may reveal vulnerabilities in tumor cells that have acquired resistance to conventional cell-death programs [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Consistent with this view, the initial discovery study demonstrated antitumor activity of triaptosis induction in preclinical prostate cancer models [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], whereas subsequent exploratory studies in melanoma and hepatocellular carcinoma suggested that triaptosis-related molecular patterns may be associated with prognosis, immune features, and therapeutic responsiveness [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, compared with these emerging findings in other malignancies, the biological relevance and clinical significance of triaptosis in GC remain poorly defined, and direct evidence in this disease is still limited. These uncertainties highlight the need for analytical strategies capable of resolving triaptosis-related programs at cellular resolution within the complex GC microenvironment.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) is a high-resolution transcriptomic technology that measures gene expression at the level of individual cells [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Compared with bulk RNA sequencing, scRNA-seq offers unique advantages in resolving cellular heterogeneity, identifying rare cell populations, and inferring cell-state transition trajectories [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].. In GC, recent studies have shown that scRNA-seq can effectively delineate the heterogeneity of malignant epithelial, immune, and stromal compartments and thereby provide new insights into tumor evolution and individualized therapy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. More specifically, single-cell analyses have revealed alternative T-cell exhaustion trajectories in GC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], identified a pro-invasive cancer-associated fibroblast subgroup associated with poor prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], uncovered fibroblast\u0026ndash;macrophage crosstalk related to immunosuppressive remodeling [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and further demonstrated the heterogeneity and intercellular communication patterns of cancer-associated fibroblasts in GC[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These features make scRNA-seq particularly suitable for determining whether triaptosis-related molecular programs are enriched in specific cell populations and whether they participate in microenvironmental remodeling in GC.\u003c/p\u003e \u003cp\u003eThis study integrated transcriptome data from GC and identified prognostic genes associated with trimer apoptosis using bioinformatics. A prognostic model was then constructed and validated based on these genes. Mutation patterns, drug sensitivity, functional profiles, immunological characteristics, and clinical features were subsequently assessed across different risk groups. Single-cell expression analysis, intercellular communication analysis, pseudo-time series analysis, and RT-qPCR validation further elucidated the relevant cell populations. This integrative approach provides new insights into the mechanisms associated with trimer apoptosis in GC and offers candidate biomarkers for prognosis and personalized treatment.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eRegarding publicly available transcriptomic resources, RNA sequencing data reflecting gene expression levels, clinical survival records, and phenotypic annotations for the TCGA-STAD cohort were acquired from the University of California Santa Cruz (UCSC) Xena platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/datapages/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/datapages/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The initial download comprised 448 specimens\u0026mdash;comprising both tumor tissues (designated as \u0026ldquo;01A\u0026rdquo;) and histologically normal adjacent tissues (\u0026ldquo;11A\u0026rdquo;). After quality filtering and sample annotation verification, the final analytical cohort included 446 samples: 410 GC cases and 36 non-malignant controls. For subsequent survival modeling, a refined subset of 383 GC patients with fully documented, non-censored survival durations was extracted. Complementarily, the GSE62254 microarray dataset\u0026mdash;generated on the GPL570 platform and encompassing 300 gastric tissue specimens with associated survival outcomes\u0026mdash;was retrieved from the Gene Expression Omnibus (GEO) repository (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. At the single-cell resolution level, the GSE183904 dataset (platform: GPL24676) from GEO comprised 36 gastric tissue samples, of which 26 were derived from GC patients and 10 from healthy donors [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The panel of triaptosis-related genes (TRGs) was curated from two recent publications [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], which collectively reported 22 TRGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Correlation of the scores of TRG with the immune microenvironment and genomic characteristics of GC\u003c/h2\u003e \u003cp\u003eUtilizing ssGSEA approach, TRG expression activity was quantified across GC samples from TCGA-STAD cohort. An optimal threshold for stratifying patients was identified via the surv_cutpoint function, enabling binary classification into high- and low-TRG score subgroups. Survival outcomes were visualized using Kaplan\u0026ndash;Meier (KM) curves, and survival difference between the two subgroups was assessed with the log-rank test. To characterize immune contexture differences, ssGSEA scores for 28 distinct immune cells were computed; intergroup disparities were evaluated by applying the Wilcoxon test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of the prognostic model\u003c/h2\u003e \u003cp\u003eDifferential expression analysis of the TCGA-STAD cohort was executed utilizing the DESeq2 (v1.36.0) package[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] to find differentially expressed genes (DEGs1) between GC and healthy control samples (|log\u003csub\u003e2\u003c/sub\u003efold change(FC)| \u0026gt; 1, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as well as DEGs2 between TRG score subgroups (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 0.5). The overlap of the two DEG sets (DEGs1 and DEGs2) was assessed, and functional enrichment analysis\u0026mdash;including Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways (adj.\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u0026mdash;was performed on the overlapping genes by adopting clusterProfiler (v4.7.1.001) package (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The overlapping genes in GC samples from the TCGA-STAD cohort were then subjected to univariate Cox proportional hazards regression. Genes exhibiting a hazard ratio (HR) significantly different from 1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, HR\u0026thinsp;\u0026ne;\u0026thinsp;1) were retained, provided they satisfied the proportional hazards assumption (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These genes constituted the candidate prognostic gene set. The glmnet (v4.1-6) package was then employed to develop least absolute shrinkage and selection operator (LASSO) regression in order to improve feature selection[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], with optimal λ determined via ten-fold cross-validation. Finally, the genes selected by LASSO were subjected to multivariate Cox regression with backward stepwise elimination, yielding a parsimonious, robust prognostic model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Validation of the prognostic model\u003c/h2\u003e \u003cp\u003eThe TCGA-STAD and the GSE62254 were adopted to assess the prognostic model\u0026rsquo;s performance. The timeROC software was employed to plot the receiver operator characteristic (ROC) curves. A risk score formula was developed based on the prognostic model to determine the score for every patient, and risk curves were created. The patients were split into two risk groups (high/low) after the surv_cutpoint function of the survminer (v0.4.9) package[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]to identify the ideal cut-off value (minprop\u0026thinsp;=\u0026thinsp;0.5). The KM survival curves for the two groups were made to examine the survival difference.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Correlation analysis of the prognostic model with clinical pathological characteristics\u003c/h2\u003e \u003cp\u003eThe distribution and expression levels of clinical characteristics (such as gender, age, TNM stage, and pathological stage) and prognostic genes of GC samples in the TCGA-STAD cohort between various risk groups were shown by the pheatmap (v 1.0.12) package[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The distribution differences of each clinical characteristic subtype between various risk groups were examined by the chi-square test, and the distribution differences of risk scores between various clinical characteristic subgroups were also compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Immune infiltration characteristics and immune subtypes analyses\u003c/h2\u003e \u003cp\u003eThe ESTIMATE algorithm was employed to assess and contrast immune, stromal, and ESTIMATE scores between two risk patients. Immune cell infiltration levels across 28 distinct immune cell types were quantified via ssGSEA, with intergroup differences statistically evaluated. Spearman\u0026rsquo;s correlation was applied to examine associations between prognostic gene expression and immune cell abundance. GC samples were further stratified into six immunological subtypes (C1\u0026ndash;C4) by applying the ImmuneSubtypeClassifier (v0.1.0) package[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and differential expression of prognostic genes across these subtypes was assessed through the Wilcoxon test. Moreover, GSVA (v1.44.5) package [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]was executed to derive enrichment scores for 13 immune-related functional pathways, followed by comparative analysis of these scores between the various risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Drug sensitivity and tumor mutation burden (TMB) analyses\u003c/h2\u003e \u003cp\u003eThe oncoPredict (v1.2) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]was implemented to predict drug sensitivity profiles for 198 chemotherapeutic and targeted treatments by leveraging pharmacogenomic information from the Genomics of Drug Sensitivity in Cancer (GDSC) database, with the differences in IC\u003csub\u003e50\u003c/sub\u003e between the various risk groups being evaluated. Maftools was employed to define the somatic mutation landscapes of GC patients in the TCGA-STAD cohort, and waterfall plots showing the top 20 most commonly mutated genes were created independently for each risk category. Additionally, the TMB between the two risk strata was calculated and compared.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Gene set variation analysis (GSVA)\u003c/h2\u003e \u003cp\u003eLeveraging the KEGG gene set (c2.cp.kegg_legacy.v2025.1.Hs.entrez.gmt) from the MSigDB database, the pathway enrichment score of TCGA-STAD samples was calculated by adopting the GSVA package. The pathway activity differences between the two risk strata were compared by adopting the limma (v3.52.4) package (|t| \u0026gt; 2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 scRNA-seq analysis\u003c/h2\u003e \u003cp\u003eIn the GSE183904 dataset, we carried out comprehensive quality assessment and preprocessing with the Seurat (v5.1.0) package[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Genes with expression detected in fewer than 200 cells were filtered out. Cells were discarded if they exhibited any of the following features: percent.mt\u0026thinsp;\u0026ge;\u0026thinsp;20%, nCount_RNA\u0026thinsp;\u0026ge;\u0026thinsp;40,000, or nFeature_RNA\u0026thinsp;\u0026ge;\u0026thinsp;6,000. Following log-normalization, highly variable genes were detected via the vst approach, and the top 2,000 such genes were retained for downstream dimensionality reduction. Principal component analysis (PCA) was then adopted, and the optimal number of principal components\u0026mdash;determined jointly by permutation testing and examination of the \u0026ldquo;elbow\u0026rdquo; in the scree plot\u0026mdash;was set to 30. Unsupervised clustering was conducted (resolution\u0026thinsp;=\u0026thinsp;0.4), and cell embeddings were visualized by applying Uniform Manifold Approximation and Projection (UMAP). Cell type annotations were guided by prior knowledge from a published reference[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Doublets were computationally identified and removed through DoubletFinder (v2.0.4) package[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. To quantify TRG activity across individual cells, five complementary scoring methods\u0026mdash;AddModuleScore, SingScore, UCell, ssGSEA, and AUCell\u0026mdash;were independently applied. Final per-cell TRG scores were derived as the mean of the five algorithm outputs, enabling robust cross-cell-type comparisons of TRG activity. Additionally, differential expression of prognosis genes in each annotated cell between GC and control samples was assessed. Core cells were selected based on the expression of comprehensive prognostic genes and TRG activity. Intercellular communication patterns were inferred by adopting CellChat (v1.6.1) package[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], while Monocle (v2.26.0)-based pseudotime analysis was employed to reconstruct temporal ordering and infer dynamic developmental trajectories of core cells[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Cell culture\u003c/h2\u003e \u003cp\u003eRPMI-1640 media containing 10% fetal bovine serum and 1% penicillin-streptomycin was employed to cultivate the human normal cells GES-1 (90% RPMI-1640, immocell) and the GC cells MKN-45 (immocell) (37\u0026deg;C, 5% CO\u003csub\u003e2\u003c/sub\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Reverse transcription-quantitative PCR (RT-qPCR)\u003c/h2\u003e \u003cp\u003eThe FastPure Complex Tissue/Cell Total RNA Isolation Kit (Vazyme Biotech) was employed to isolate RNA from cultivated cells. Only samples with an OD\u003csub\u003e260\u003c/sub\u003e/OD\u003csub\u003e280\u003c/sub\u003e\u0026thinsp;\u0026gt;\u0026thinsp;1.8 were kept for cDNA synthesis. RNA quality and concentration were assessed with a NanoDrop 500 spectrophotometer. ABScript III RT Master Mix (ABclonal), which incorporates on-board genomic DNA removal, was applied to carry out first-strand cDNA synthesis. The Genious 2X SYBR Green Fast RT-qPCR Mix (ABclonal) was applied for quantitative PCR with standard thermal cycling conditions. The reference gene for normalization was GAPDH (Primes listed in \u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). Three separate technical replicates were examined for every cell line, and the relative mRNA expression levels were determined employing the 2\u003csup\u003e\u0026ndash;ΔΔCt\u003c/sup\u003e technique.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.12 Statistical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eR software was a tool for conducting all bioinformatics statistical analyses. The comparison among groups were evaluated via the Wilcoxon test for bioinformatics analysis and the t-test for RT-qPCR. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genes common in the TRG score and GC\u003c/h2\u003e \u003cp\u003eTo explore the potential link between the TRG signature and GC, this study stratified GC patients from the TCGA-STAD cohort into two TRG score subgroups (high/low). Survival analysis indicated that individuals in the low-TRG score subgroup exhibited markedly poorer clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequent immune infiltration profiling uncovered substantial differences in the abundance of 17 distinct immune cell populations (e.g., activated B cell and activated dendritic cell) between the two subgroups, and all of them showed higher infiltration in the high-TRG score subgroup, implying a strong association of the TRG score with the tumor immune microenvironment in GC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). Differential expression analysis identified 2,519 DEGs2 distinguishing the high- versus low-TRG-score groups, comprising 1,605 upregulated and 914 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD-E). In parallel, comparison of GC tissues with adjacent normal tissues yielded 2,491 DEGs1, including 1,017 upregulated and 1,474 downregulated genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF-G). Integration of these two DEG sets revealed 1,030 overlapping genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH). These genes were markedly associated with 320 biological processes (e.g., muscle contraction), 73 cellular components (e.g., contractile fibers), 23 molecular functions (e.g., heparin binding), and 25 KEGG pathways (e.g., the calcium signaling pathway) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eI-J, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Robustness and generalizability of the prognostic risk model\u003c/h2\u003e \u003cp\u003eAn additional refinement process was applied to the overlapping gene set. Initially, univariate Cox proportional hazards regression identified 29 genes significantly associated with patient prognosis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Next, LASSO regression was employed to narrow down the candidate genes, yielding a parsimonious 13-gene signature with λ.min\u0026thinsp;=\u0026thinsp;0.012 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Finally, multivariate Cox regression analysis further distilled this set to a robust three-prognostic gene signature comprising ASPN, GRB14, and VTN (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). A prognostic risk model was developed based on multivariate Cox proportional hazards regression: risk score\u0026thinsp;=\u0026thinsp;0.1714 \u0026times; ASPN\u0026thinsp;+\u0026thinsp;0.1996 \u0026times; GRB14\u0026thinsp;+\u0026thinsp;0.1111 \u0026times; VTN. The model predicted 1-, 2-, and 3-year overall survival in the TCGA-STAD cohort with AUC values of 0.649, 0.671, and 0.731, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Similarly, in the independent validation cohort (GSE62254), all time-point\u0026ndash;specific AUCs were \u0026ge;\u0026thinsp;0.60 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). KM analysis further demonstrated markedly worse survival in the high-risk group across both cohorts\u0026mdash;consistent with higher mortality in the former (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF-I). Moreover, the expression of the prognostic genes was verified through in vitro experiments. The RT-qPCR results showed that the mRNA expression levels of GRB14 and VTN were significantly upregulated in GC cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eJ). However, no valid value was detected for the ASPN gene. Collectively, these results affirm the model\u0026rsquo;s reliability as a prognostic instrument, demonstrating both stability across diverse conditions and broad applicability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation of the prognostic model with the stratification of clinical characteristics\u003c/h2\u003e \u003cp\u003eWe examined how the prognostic model correlates with various clinicopathological features. The heatmap revealed that the three prognostic genes were highly expressed (ASPN, GRB14, and VTN) in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The distribution of clinical characteristics such age, pathological stage, and T stage varied markedly between the two risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB-G). Additionally, the risk score also showed marked differences among different subgroups of T stage, N stage, and pathological stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH-M).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Immune landscape associated with the prognostic model in GC\u003c/h2\u003e \u003cp\u003eThe association between the prognostic model (or its constituent genes) and immune-related functions was analyzed. Stromal and overall immune scores were markedly higher in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Immune infiltration analysis revealed differential abundance of 15 immune cell types across risk strata: 4 cell types\u0026mdash;including activated dendritic cells and activated CD4⁺ T cells\u0026mdash;were depleted in high-risk patients, whereas 11 types\u0026mdash;including macrophages and mast cells\u0026mdash;were enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB-C). ASPN expression correlated positively with most of these differential immune cells (strongest with natural killer cells), while GRB14 showed predominantly negative correlations (e.g., with central memory CD8⁺ T cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Noteworthy differences in immune functional pathways\u0026mdash;including APC co-inhibition, MHC class I presentation, and type II IFN response\u0026mdash;were also observed between risk groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Furthermore, ASPN and VTN expression varied significantly across immunological subtypes (C1-C4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF-H). It reveals statistically significant differences in immune cell infiltration and functional profiles across risk groups, implicating a potential association between this gene signature and tumor immune regulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5 The functional characteristics, genetic mutation profiles and drug sensitivity differences of different risk stratifications\u003c/h2\u003e \u003cp\u003eTo gain deeper insights into the biological, genomic, and pharmacological distinctions across risk subgroups, this study performed a comprehensive assessment. GSVA identified substantial pathway-level divergence\u0026mdash;121 KEGG pathways showed markedly enrichment differences between two risk groups. Specifically, the high-risk group exhibited pronounced activation of pathways involved in extracellular matrix\u0026ndash;receptor interaction, Notch signaling, and dilated cardiomyopathy, whereas the low-risk group demonstrated elevated activity in fructose and mannose metabolism as well as amyotrophic lateral sclerosis\u0026ndash;associated pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, \u003cb\u003eTable \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). Somatic mutation profiling revealed distinct mutational landscapes: TTN, TP53, and MUC16 were the most frequently altered genes in the low-risk group, while TP53, TTN, and LRP1B dominated the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-C). Furthermore, TMB differed significantly between the two groups, implying divergent levels of genomic instability (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). Drug response evaluation uncovered differential sensitivity to 47 therapeutic agents, quantified by IC\u003csub\u003e50\u003c/sub\u003e values: erlotinib, oxaliplatin, and afatinib showed greater efficacy in the low-risk group, whereas BMS-754807 and JQ1 displayed enhanced potency against high-risk tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, \u003cb\u003eTable \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e). These findings underscore marked intergroup heterogeneity in pathway regulation, mutational architecture, and treatment susceptibility among GC patients stratified by the three-TRG-based prognostic signature, highlighting its biological relevance and potential clinical utility.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6 The TRG activity and prognostic gene expression of fibroblasts in GC\u003c/h2\u003e \u003cp\u003eAfter conducting quality control, dimension reduction, and clustering analysis on the GSE183904 dataset, 26 cell clusters were obtained, and 11 major cell types were annotated, including mast cells, M1 macrophages, T cells, M2 macrophages, B cells, pericytes, plasma cells, chief cells, fibroblasts, epithelial cells, and endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-B, \u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-E\u003c/b\u003e). Meanwhile, 11,258 (7.5%) high-confidence twin cases were removed (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eF\u003c/b\u003e). Among all cell types, T cells accounted for the highest proportion (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). The average TRG activity scores based on five algorithms showed that the TRG activity levels of M2 macrophages, fibroblasts, and pericytes ranked the top three (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD). The three prognostic genes (ASPN, GRB14, and VTN) were significantly differentially expressed in fibroblasts and epithelial cells between GC and control samples, and were higher than those in other cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Given the crucial role of fibroblasts in TRG activity and prognostic gene expression, they were identified as the core cell type. Cell communication analysis was performed by stratifying fibroblasts into high- and low-TRG activity groups according to their TRG activity. Fibroblasts exhibiting elevated TRG activity demonstrated increased interaction frequency and strength with neighboring cell types\u0026mdash;particularly pericytes\u0026mdash;indicating a TRG-dependent enhancement of intercellular crosstalk. In contrast, tumor-derived fibroblasts showed attenuated communication with macrophages compared to those from normal tissues, implying substantial remodeling of fibroblast\u0026ndash;immune cell interactions within the tumor microenvironment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF-G). Pseudotime analysis of fibroblasts identified five differentiation stages, all represented in tumor samples (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eGC remains a highly heterogeneous malignancy in which clinical outcome is shaped not only by tumor-intrinsic molecular alterations but also by complex interactions within the tumor microenvironment[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This study is the first to construct a prognostic model for GC based on TRGs (ASPN, GRB14, VTN). Single-cell transcriptome analysis confirmed that fibroblasts serve as the core carriers of TRG activity and prognostic gene expression. Further research indicated that the high-risk subgroup demonstrated significant stromal remodeling, an immunosuppressive microenvironment, and an elevated TMB. Moreover, the differences in drug sensitivity among different risk groups offer a potential basis for the stratification of individualized treatment in GC.\u003c/p\u003e \u003cp\u003eASPN encodes asporin, a member of the small leucine-rich proteoglycan family that localizes predominantly to the extracellular matrix and participates in matrix organization, stromal remodeling, and signal modulation[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Recent studies have shown that ASPN is upregulated in GC, associated with poor prognosis, and functionally linked to enhanced invasion and migration, while also promoting macrophage M2 polarization, thereby connecting ASPN to both tumor aggressiveness and immune remodeling [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Additional mechanistic work has further implicated the MATN3\u0026ndash;ASPN axis in epithelial\u0026ndash;mesenchymal transition and metastatic progression in GC [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. These observations are well aligned with our findings that ASPN was retained in the final risk model and showed broad positive correlations with differential immune-cell populations, supporting the view that ASPN may participate in a stromal\u0026ndash;immune regulatory network in GC.\u003c/p\u003e \u003cp\u003eGRB14 (Growth factor receptor-bound protein 14) is an adaptor protein of the Grb7 family, containing PH and SH2 domains, and is involved in receptor-mediated signaling [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In GC, recent evidence indicates that GRB14 is overexpressed, predicts poor outcome, and promotes proliferation, migration, invasion, and apoptosis resistance through activation of the PI3K/AKT pathway [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Given the well-established role of PI3K/AKT signaling in gastric-cancer progression and treatment resistance, the retention of GRB14 in our final model is biologically plausible and suggests that part of the prognostic signal may intersect with tumor-cell survival circuitry rather than stromal remodeling alone.\u003c/p\u003e \u003cp\u003eVTN encodes vitronectin, a multifunctional adhesive glycoprotein involved in extracellular-matrix interaction, cell adhesion, and integrin-associated signaling [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. It can mediate communication between the extracellular environment and intracellular signaling systems through cell-matrix contact [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] In GC, vitronectin overexpression has been associated with adverse clinicopathological behavior and poorer survival [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. This observation is functionally plausible, given that extracellular-matrix remodeling and integrin-dependent signaling are increasingly recognized as major drivers of GC progression, immune evasion, and microenvironmental adaptation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In our study, VTN also showed the strongest upregulation in RT-qPCR, further supporting its biological relevance in GC. Although this does not prove a direct mechanistic link to triaptosis, it does suggest that matrix-associated adhesion signaling may represent one route through which triaptosis-related molecular variation becomes coupled to aggressive tumor behavior.\u003c/p\u003e \u003cp\u003eOur data indicated that the high-risk subgroup was characterized by a reorganized stromal\u0026ndash;immune state. In particular, the combination of increased stromal score, macrophage enrichment, and relative depletion of activated T-cell-associated populations suggests an immunosuppressive microenvironment,which is biologically plausible in GC. Increasing evidence indicates that the GC microenvironment is highly heterogeneous and that stromal components, particularly cancer-associated fibroblasts and myeloid cells, actively shape immune escape, therapy resistance, and disease progression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Macrophages are among the dominant infiltrating immune populations in GC and are widely recognized as drivers of tumor-promoting inflammation, immune suppression, angiogenesis, and resistance to treatment [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Against this background, the coexistence in our high-risk group of stromal enrichment and macrophage accumulation is unlikely to be incidental; rather, it supports the view that this risk pattern reflects a microenvironment permissive to tumor progression.\u003c/p\u003e \u003cp\u003eThe observed immune functional differences further strengthen this interpretation. Although these pathway-level findings should be interpreted cautiously, alterations involving APC co-inhibition, MHC class I-related activity, and type II IFN response are compatible with impaired antigen presentation and dysregulated immune effector signaling, both of which are hallmarks of tumor immune evasion [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] In GC specifically, immune escape has been linked to defective antigen processing and presentation, T-cell dysfunction, and microenvironment-driven suppression of effective antitumor responses [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Therefore, when considered together with the immune-cell redistribution observed in our cohort, these functional changes support the notion that the high-risk subgroup is characterized by a qualitatively immune-escaped state, rather than merely a quantitatively altered immune infiltrate.\u003c/p\u003e \u003cp\u003eFurthermore, our analyses suggested that the triaptosis-related risk model stratified GC not only by survival, but also by pathway activity and predicted therapeutic vulnerability. In particular, the high-risk subgroup appeared to be characterized by enrichment of ECM-related and Notch-associated programs, whereas the two risk groups also displayed distinct predicted sensitivities to several targeted and cytotoxic agents. The enrichment of ECM\u0026ndash;receptor interaction in the high-risk subgroup is biologically meaningful in GC, where ECM remodeling is increasingly recognized as a central driver of invasion, metastasis, immune evasion, and treatment resistance [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In GC, matrix-derived signals are not merely structural; they actively reshape tumor\u0026ndash;stromal communication and can promote adaptive survival phenotypes through integrin-dependent pathways[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. In parallel, the enrichment of Notch signaling further supports the interpretation that high-risk tumors may exist in a more plastic and aggressive state, because aberrant Notch activation has been implicated in gastric-cancer progression, stemness, therapeutic resistance, and even immune escape [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Therefore, these pathway-level differences suggest that the biological basis of the high-risk phenotype is not limited to isolated gene dysregulation, but may reflect a broader program of stromal dependence and adaptive signaling activation.\u003c/p\u003e \u003cp\u003eThe predicted drug-sensitivity differences provide a further layer of translational relevance. The greater predicted sensitivity of the low-risk subgroup to agents such as erlotinib, afatinib, and oxaliplatin suggests that this subgroup may retain relative vulnerability to EGFR/ErbB-directed therapy and platinum-based chemotherapy. This is biologically plausible, given that afatinib has shown activity in gastric and gastroesophageal adenocarcinomas in clinical and preclinical settings [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and oxaliplatin remains a cornerstone of systemic treatment in GC [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. By contrast, the enhanced predicted sensitivity of the high-risk subgroup to BMS-754807 and JQ1 is particularly interesting because these agents target pathways closely linked to adaptive survival. BMS-754807 inhibits IGF-1R/IR signaling, and this pathway has documented relevance in gastric-cancer growth and survival [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Likewise, the BET inhibitor JQ1 has demonstrated antitumor activity in gastric carcinoma models, including suppression of metastasis and improved efficacy in rational combination strategies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Taken together, these observations suggest that the high-risk subgroup may be less amenable to conventional approaches but more dependent on targetable survival and chromatin-regulatory programs.\u003c/p\u003e \u003cp\u003eUnlike M2 macrophages or pericytes, fibroblasts simultaneously exhibit elevated TRG activity and prominent expression of the three prognostic genes (ASPN, GRB14, and VTN), a pattern consistent with the recognized role of cancer-associated fibroblasts (CAFs) in extracellular matrix remodeling, angiogenesis, metastasis, and immunosuppression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. High-resolution studies further reveal that fibroblasts in GC are heterogeneous, and our observation of preferential fibroblast\u0026ndash;pericyte communication under high-TRG conditions suggests a stromal state that supports vascular adaptation and tumor-promoting signaling, in line with evidence that tumor-derived exosomes can induce pericyte transition into CAF-like cells [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Conversely, the reduced fibroblast\u0026ndash;macrophage communication relative to normal tissues implies not a global loss but a selective rewiring of stromal\u0026ndash;immune crosstalk, mirroring recent reports of specific immunosuppressive fibroblast\u0026ndash;myeloid networks in GC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Finally, the pseudotime distribution of fibroblasts across multiple states underscores their dynamic heterogeneity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Together, these data position fibroblasts as the central cellular carrier of the triaptosis-related signature, bridging bulk prognostic stratification with stromal remodeling in the GC microenvironment.\u003c/p\u003e \u003cp\u003eThis study has several strengths. By integrating several techniques established a triaptosis-related prognostic signature in GC. In addition to identifying ASPN, GRB14, and VTN as prognostically relevant genes, our findings highlighted fibroblasts as a core cell population associated with elevated TRG activity and prognostic-gene expression, providing new insight into the stromal context of triaptosis-related signals in GC. Moreover, the model was linked to immune remodeling, genomic heterogeneity, and differential drug sensitivity, which enhances its potential translational relevance.\u003c/p\u003e \u003cp\u003eSeveral limitations should also be acknowledged. First, although the model was externally validated and partially supported by RT-qPCR, the current evidence remains largely associative, and the molecular mechanisms by which ASPN, GRB14, and VTN contribute to gastric-cancer progression or triaptosis-related processes remain unclear. Secondly, the predicted drug-sensitivity results require further experimental and clinical validation.\u003c/p\u003e "},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, we identified and validated a triaptosis-related three-gene signature (ASPN, GRB14, and VTN) that effectively predicts prognosis in GC. This signature was associated with distinct immune and genomics features, including stromal-immue remodeling and increased tumor mutational burden,and may provide potential biomarkers for risk sanctification and personalized therapeutic exploration.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics approval\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u0026nbsp;\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eX.C:\u0026nbsp;Writing \u0026ndash; original draft, Software, Project administration, Methodology, Investigation, Data curation, Conceptualization.\u0026nbsp;L.M:\u0026nbsp;Writing \u0026ndash; original draft, Methodology, Formal analysis. H.Q:\u0026nbsp;Validation, Software, Formal analysis.\u0026nbsp;H.Z:\u0026nbsp;Validation, Software, Formal analysis.\u0026nbsp;H.X:\u0026nbsp;Writing \u0026ndash; review \u0026amp; editing, Data curation.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eWe would like to sincerely thank the authors for their scientific contribution.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed in this study are publicly available. TCGA-STAD data were obtained from the UCSC Xena platform, and the GSE62254 and GSE183904 datasets were downloaded from the Gene Expression Omnibus (GEO) database under the accession numbers GSE62254 and GSE183904.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePatel AK, Sethi NS, Park H (2026) Gastric Cancer: A Review. 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Front Mol Biosci 11:1340124\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":false,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"gastric cancer, single-cell RNA sequencing analysis, triaptosis, prognostic model, fibroblasts","lastPublishedDoi":"10.21203/rs.3.rs-9566914/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9566914/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTriaptosis is a newly characterized mode of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTranscriptomic and clinical data were obtained from TCGA-STAD and GSE62254, and single-cell RNA-seq data from GSE183904. Patients were stratified by triaptosis-related gene (TRG) scores calculated via ssGSEA. Differentially expressed genes were detected between TRG subgroups and between tumor and normal samples. Overlapping genes were subjected to Cox regression analyses to construct a prognostic signature, which was validated externally and in vitro. Associations between risk stratification and immune features and drug sensitivity were assessed. Single-cell analysis identified key cell populations linked to TRG activity and prognostic gene expression, followed by cell-communication analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. The GRB14 and VTN expression were markedly upregulated in GC cells. High-risk patients exhibited elevated stromal scores and distinct immune infiltration patterns, with 15 immune cell types differentially abundant between groups. Single-cell analysis revealed fibroblasts and pericytes as top TRG-active populations. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed reduced crosstalk with macrophages, indicating immune microenvironment remodeling.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe TRG-related prognostic signature effectively predicts GC outcomes and reflects distinct immune and genomic features, providing potential biomarkers for risk stratification and personalized therapy.\u003c/p\u003e","manuscriptTitle":"Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 12:05:48","doi":"10.21203/rs.3.rs-9566914/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-18T11:44:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T07:26:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:22:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T14:30:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"210328813875657720924434310229483741929","date":"2026-05-05T10:32:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"330242524903961059255522533974210498937","date":"2026-05-05T07:25:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84820512198536917110221429674337192234","date":"2026-05-05T07:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T07:13:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T13:12:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T13:12:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Biology Reports","date":"2026-04-29T13:55:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-biology-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mole","sideBox":"Learn more about [Molecular Biology Reports](https://www.springer.com/journal/11033)","snPcode":"11033","submissionUrl":"https://submission.nature.com/new-submission/11033/3","title":"Molecular Biology Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f5f95391-f145-4a2a-bea7-6578432824b1","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-18T11:44:16+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-17T07:26:08+00:00","index":38,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-15T17:22:41+00:00","index":36,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T14:30:06+00:00","index":32,"fulltext":""},{"type":"reviewerAgreed","content":"210328813875657720924434310229483741929","date":"2026-05-05T10:32:44+00:00","index":31,"fulltext":""},{"type":"reviewerAgreed","content":"330242524903961059255522533974210498937","date":"2026-05-05T07:25:59+00:00","index":29,"fulltext":""},{"type":"reviewerAgreed","content":"84820512198536917110221429674337192234","date":"2026-05-05T07:17:46+00:00","index":26,"fulltext":""},{"type":"reviewersInvited","content":"18","date":"2026-05-05T07:13:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-05-01T13:12:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-05-01T13:12:00+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-18T11:54:06+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 12:05:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9566914","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9566914","identity":"rs-9566914","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}