Front Immunol. 2026 May 8;17:1742982. doi: 10.3389/fimmu.2026.1742982. eCollection 2026.

ABSTRACT

BACKGROUND: Filaggrin (FLG) plays an important role in the progression of malignant tumors; however, its expression characteristics and biological functions in gastric cancer (GC) remain unclear.

METHODS: Cancer-related datasets were retrieved from public repositories, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory networks involving FLG. Differential expression analysis, genetic alteration analysis, and clinicopathological and survival analyses were performed to evaluate the role of FLG in GC. In addition, Gene Set Enrichment Analysis (GSEA), immune infiltration analysis, and in vitro functional experiments were conducted to investigate the biological effects and potential mechanisms of FLG in GC.

RESULTS: FLG was aberrantly expressed across multiple cancer types and was significantly associated with clinical characteristics and prognosis in GC. Further analyses showed that FLG was involved in genetic alterations and was closely associated with the immune microenvironment in GC. Functional experiments demonstrated that FLG promoted the invasion and metastasis of GC cells. Mechanistically, GSEA and experimental validation indicated that FLG exerted its tumor-promoting effects, at least in part, through activation of the epithelial-mesenchymal transition (EMT) signaling pathway.

CONCLUSION: This study clarifies the biological role of FLG in GC and highlights its potential as a novel prognostic biomarker and therapeutic target. These findings provide new insights into the molecular mechanisms underlying GC progression and may contribute to the development of more effective diagnostic and therapeutic strategies.

PMID:42183248 | PMC:PMC13194527 | DOI:10.3389/fimmu.2026.1742982

Curr Oncol Rep. 2026 May 22;28(1):58. doi: 10.1007/s11912-026-01793-4.

ABSTRACT

PURPOSE OF REVIEW: To provide an updated overview of the role of the human microbiome in the initiation, progression, and therapeutic response of gastrointestinal tumors, emphasizing molecular, immunological, and metabolic mechanisms, as well as its potential as a target for novel therapeutic strategies.

RECENT FINDINGS: Emerging evidence demonstrates that microbiome dysbiosis contributes to carcinogenesis across gastrointestinal malignancies, including colorectal, gastric, hepatic, and pancreatic cancers. Microbial-derived metabolites, such as short-chain fatty acids and secondary bile acids, modulate key signaling pathways involved in cell proliferation, apoptosis, and genomic stability. In addition, the microbiome influences the tumor microenvironment and immune responses, shaping variability in treatment outcomes. Both preclinical and clinical studies have shown that microbiome composition affects the efficacy and toxicity of chemotherapy and immunotherapy. Notably, specific microbial signatures are being explored as non-invasive biomarkers for early detection and prognostic stratification, while microbiome modulation strategies, such as diet, probiotics, antibiotics, and fecal microbiota transplantation, have demonstrated potential to enhance therapeutic response. The bidirectional interaction between the microbiome and the host plays a central role in gastrointestinal tumorigenesis and treatment response. Although this field holds significant promise for precision oncology, its clinical translation remains limited by interindividual variability, methodological heterogeneity, and insufficient longitudinal evidence. Future efforts should focus on standardization, validation of microbiome-based biomarkers, and integration of multi-omics and artificial intelligence approaches to enable clinically actionable applications.

PMID:42171841 | DOI:10.1007/s11912-026-01793-4

Front Cell Dev Biol. 2026 May 1;14:1807136. doi: 10.3389/fcell.2026.1807136. eCollection 2026.

ABSTRACT

BACKGROUND: Therapeutic vulnerability in gastric cancer is profoundly influenced by the tumor microenvironment (TME), yet reliable and clinically actionable preoperative indicators remain insufficient.

METHODS: We developed and validated an artificial intelligence-driven multi-omics TME score (DLRS/TMEscore) by integrating CT-derived imaging features with transcriptomic, immunohistochemical, and molecular profiling. The score was evaluated for its associations with survival outcomes, benefit from adjuvant chemotherapy, and response to anti-PD-1 therapy.

RESULTS: The DLRS/TMEscore reproducibly stratified disease-free and overall survival across independent cohorts. Patients in the low-risk subgroup derived substantial benefit from adjuvant chemotherapy, whereas those in the high-risk subgroup demonstrated attenuated benefit. Among individuals receiving immunotherapy, the score enriched objective responders and predicted more durable clinical outcomes, outperforming established biomarkers including PD-L1 combined positive score (CPS) and microsatellite instability (MSI). In addition, DLRS/TMEscore correlated with multiple surgical parameters, such as operative complexity, resection margin status, nodal involvement, and postoperative recovery, indicating relevance in perioperative risk assessment.

CONCLUSION: This AI-enabled multi-omics framework offers a robust and interpretable approach for characterizing microenvironment-defined therapeutic vulnerability, supporting preoperative risk stratification and individualized systemic treatment strategies in gastric cancer.

PMID:42148312 | PMC:PMC13176314 | DOI:10.3389/fcell.2026.1807136

Chin J Cancer Res. 2026 Apr 30;38(2):234-251. doi: 10.21147/j.issn.1000-9604.2026.02.09.

ABSTRACT

OBJECTIVE: Gastric cancer (GC) is heterogeneous, and current mismatch repair (MMR)-based classifications incompletely predict response to immune checkpoint inhibitors (ICIs).

METHODS: RNA sequencing (RNA-seq) and immune infiltration profiles from 189 resected GC were used to derive four refined immune-MMR subtypes (R1-R4) by integrating MMR status, survival, and tumor microenvironment (TME) features. Multi-omics profiling and pathway analysis defined subtype biology. External transcriptomic cohorts and an ICI-treated cohort were classified with Nearest Template Prediction (NTP). Immune response-associated genes were identified from responder vs. non-responder comparisons within the ICI-sensitive subtype and validated by multiplex immunohistochemistry (mIHC).

RESULTS: R1 showed the best prognosis and highest immunotherapy response with objective response rate (ORR) 54.5%, while R4 had the worst prognosis. R2 represented an immune-unresponsive deficient mismatch repair (dMMR) subset, and R3 captured an immune-active proficient mismatch repair (pMMR) subgroup with moderate therapy sensitivity. Multi-omics integration revealed subtype-specific pathways (e.g., ECM remodeling in R1, metabolic reprogramming in R2). Reclassification of pMMR tumors based on transcriptional similarity to R1 identified a New R3 subset with enhanced immune features and higher ICI response. Eight immune response-associated genes (e.g., CXCL10, CXCL11, ELN, GAD1, IL32, MT1E, OR2I1P, SLC3A1) were identified and validated by mIHC for predictive relevance.

CONCLUSIONS: This immune-based molecular framework refines risk stratification beyond conventional MMR categories, identifies ICI-sensitive subsets among both dMMR and pMMR tumors, and proposes candidate biomarkers for patient selection.

PMID:42147371 | PMC:PMC13171420 | DOI:10.21147/j.issn.1000-9604.2026.02.09

Front Oncol. 2026 May 1;16:1785100. doi: 10.3389/fonc.2026.1785100. eCollection 2026.

ABSTRACT

BACKGROUND: Acquired resistance to cisplatin-based chemotherapy is common in patients with gastric cancer (GC) and significantly limits treatment efficacy. The aim of this study was to investigate molecular features associated with GC chemoresistance using an integrative multi-level analytical framework combined with Mendelian randomization (MR), followed by cellular validation of key candidates.

METHODS: Transcriptome datasets GSE14210 and GSE31811 were obtained from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs), followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore potential pathways. A total of 113 machine learning model combinations were applied for feature selection. MR analysis integrating expression quantitative trait loci (eQTLs) and genome-wide association study (GWAS) data was conducted to assess causal relationships between candidate genes and chemoresistance. The single-cell dataset GSE183904 was used to examine cell-type-specific expression patterns. Cisplatin-resistant NCI-N87/DDP cells were then established in vitro, and qRT-PCR, Western blotting, and drug sensitivity assays were performed to evaluate gene expression and function. Pathway inhibitors were applied to test the reversal of resistance.

RESULTS: A total of 827 DEGs were identified, mainly enriched in immune response, ECM interactions, metabolic reprogramming, and signaling pathways such as PI3K-Akt and MAPK. Among the machine learning models, the Stepglm[both] + Random Forest (RF) model achieved the best performance [area under the curve (AUC) = 0.865] and identified several core candidate genes. MR analysis supported potential risk associations for TRABD, RXRA, DEFA4, PARP16, SLC12A9, and TMEM132A, with PARP16 consistently highlighted across transcriptomic, machine learning, and MR analyses. In vitro experiments showed that PARP16 expression was elevated by approximately 3.1-fold in NCI-N87/DDP cells, accompanied by activation of the unfolded protein response (UPR) and suppression of apoptosis, and an elevated cisplatin IC₅₀ of 11.82 μg/mL. Inhibition of the PARP16-UPR axis significantly reduced the IC₅₀ to 4.67 μg/mL and restored DNA damage and apoptosis, demonstrating synergistic effects.

CONCLUSIONS: PARP16 emerged as a key candidate associated with chemoresistance in GC. Its elevated expression in stem-like cell populations and resistant cell models was associated with UPR activation, and targeting the PARP16-UPR axis restored cisplatin sensitivity. Targeting the PARP16-UPR axis effectively reverses resistance, providing new insights and potential therapeutic strategies for overcoming chemoresistance in GC.

PMID:42147232 | PMC:PMC13175845 | DOI:10.3389/fonc.2026.1785100

Front Microbiol. 2026 Apr 28;17:1782471. doi: 10.3389/fmicb.2026.1782471. eCollection 2026.

ABSTRACT

Digestive system cancers (DSCs) represent a substantial global health burden. In recent years, the role of gut microbiota in the DSCs has garnered considerable attention, but its change pattern during tumor progression and the specific mechanisms are still not fully understood. We conducted a comprehensive systematic review to characterize patterns of gut microbiota dysbiosis across different DSC types and assess their clinical significance. We systematically searched four English and three Chinese databases up to January 2025 to identify systematic reviews focused on the dynamic characteristics of the gut microbiota during gastrointestinal tumorigenesis. Microbiota biodiversity and taxonomic composition were extracted to identify specific signatures associated with DSCs. The ROBIS tool was used to evaluate the methodological quality of the included studies. Ultimately, 59 studies involving six distinct DSC types were included. Data synthesis and comparison revealed distinct microbiota profiles across DSCs. At the phylum level, Bacillota was decreased in esophageal cancer (EC) and pancreatic ductal adenocarcinoma (PDAC), Pseudomonadota was augmented in EC but exhibited divergent trajectories in colorectal cancer (CRC) and PDAC. Genus-level analyses revealed Veillonella enrichment in EC and PDAC, and Fusobacterium outgrowth in EC, gastric cancer (GC) and CRC. Parvimonas and Streptococcus showed a concordant ascending trend in GC and CRC. Prevotella was overrepresented in EC and GC. This synthesis delineates a qualitative landscape of gut microbiota imbalances associated with various DSCs, highlighting the potential for these microbial shifts to serve as markers for early detection and targeted therapy. Multiomics integration and prospective cohort studies should be prioritized to accelerate clinical translation.

PMID:42131199 | PMC:PMC13161176 | DOI:10.3389/fmicb.2026.1782471

Biomed Rep. 2026 Apr 24;24(6):74. doi: 10.3892/br.2026.2147. eCollection 2026 Jun.

ABSTRACT

Hepatocellular carcinoma (HCC) shows a marked predominance in men, yet the molecular basis for this sex disparity remains unclear. The present study leveraged multi-omics data and machine learning algorithms to identify key genes associated with sex-specific differences in HCC and to screen for putative candidate compounds, aiming to provide new insights for sex-specific therapy. The mRNA expression data of male and female patients with HCC and paracancerous tissues were obtained from the GEO and TCGA databases. To mitigate overfitting, data were partitioned into independent training and testing sets. Candidate genes were screened by differential expression analysis and weighted gene co-expression network analysis. A total of four complementary algorithms, random forest, support vector machines, generalized linear models and extreme gradient boosting were used to identify key genes with high predictive capability. CYP17A1 and IRX3 were identified as the top differentially expressed core genes associated with HCC in men. Pan-cancer analysis showed that CYP17A1 was lowly expressed in the majority of tumors, but significantly highly expressed in HCC, rectal adenocarcinoma and gastric cancer (P<0.001). Functional cell-based assays showed that knockout of CYP17A1 inhibited the proliferation, migration and invasion ability of HCC cells (P<0.001). Immunohistochemistry showed that CYP17A1 protein expression was significantly increased in HCC tissues from male patients when compared with that in paracancerous tissues (P<0.001), whereas there was no significant difference in female patient tissues (P>0.05). Notably, while IRX3 was identified computationally, its functional role remains to be experimentally validated. Molecular docking predicted a potential interaction between the natural compound Saikosaponin A and the CYP17A1 protein, and cellular assays revealed that it dose-dependently inhibits HCC cell malignant phenotypes. The present study suggests that CYP17A1 is associated with sex differences in HCC, potentially via the androgen signaling axis. Furthermore, IRX3 emerges as a novel hypothesis-generating candidate gene. Finally, the findings of the present study highlight Saikosaponin A as a putative therapeutic candidate for male patients with HCC, warranting further target-dependency investigations.

PMID:42125766 | PMC:PMC13158723 | DOI:10.3892/br.2026.2147

Clin Exp Med. 2026 May 10. doi: 10.1007/s10238-026-02160-0. Online ahead of print.

ABSTRACT

The prognostic values of cuproptosis-related genes (CRGs) in gastric cancer with lymph node metastasis (GCLM), especially in the tumor immune microenvironment (TIME), remain unclear. We analyzed the expression, mutation, immunity, drug sensitivity, and prognostic value of CRGs in GCLM using TCGA and GEO cohorts. Consensus clustering was performed to identify CRG subtypes, with differences characterized by multi-omics analysis. A CRG-based prognostic risk score and immune score were constructed for individualized assessment, and the role of CRGs was validated through in vitro and in vivo experiments. Consensus clustering revealed that CRGs were significantly enriched in biological processes related to mitosis and energy metabolism, as well as in immune-related and cancer-associated pathways. Four distinct CRG subtypes were identified, showing marked differences in expression profiles, prognosis, genetic alterations, TIME, and chemotherapeutic drug sensitivity. We developed an exploratory CRG-based prognostic risk score for preliminary individualized assessment, and the functional relevance of CRGs in GCLM was further validated through in vitro experiments. Among these, FDX1, LIAS, DLAT, MTF1, and GLS were identified as key determinants of overall survival in patients with GCLM, with FDX1 emerging as a potential independent prognostic factor. Notably, upregulation of FDX1 significantly suppressed lymph node metastasis of gastric cancer cells in a mouse popliteal lymph node metastasis model. Our data uncovers FDX1 might be a potential favorable prognostic factors in GCLM patients. These findings may improve our understanding of CRGs in GCLM and provide new in-sights for assessing prognosis and developing more effective treatment strategies.

PMID:42107026 | DOI:10.1007/s10238-026-02160-0

Hum Genomics. 2026 May 9. doi: 10.1186/s40246-026-00979-y. Online ahead of print.

ABSTRACT

Cellular senescence plays a context-dependent role in gastric cancer (GC), functioning both through tumor-suppressive arrest and the tumor-promoting senescence-associated secretory phenotype. However, its systematic integration into prognostic models remains limited. Here, we develop a novel interpretable framework to identify and validate a robust senescence-related gene signature for GC prognosis. We first introduce a dual-model interpretable feature selection strategy that integrates a biologically informed Kolmogorov-Arnold Network with a tabular foundation model to identify cancer-associated senescence genes. From the initial candidates, an ensemble of ten machine learning algorithms distills a core 4-gene signature to construct a Senescence Risk Score (SRS). The SRS proves to be a powerful and independent prognostic indicator, effectively stratifies patients into high- and low-risk groups with distinct overall survival across multiple cohorts. High-risk patients exhibit an "immune-hot" but potentially dysfunctional tumor microenvironment, characterized by enriched immune cell infiltration, elevated checkpoint expression, and distinct metabolic reprogramming favoring pathways such as angiogenesis and epithelial-mesenchymal transition (EMT). Furthermore, the SRS correlates with differential somatic mutation profiles and suggests potential sensitivity to specific chemotherapeutic agents. In vitro functional assays confirmed the oncogenic role of SERPINE1, a top-ranked core gene, in promoting GC cell proliferation. Regulatory network analysis revealed potential upstream transcription factors and miRNAs governing the signature. Collectively, we present a validated senescence-related prognostic signature that enables effective risk stratification of patients with gastric cancer.

PMID:42106891 | DOI:10.1186/s40246-026-00979-y

Front Cell Infect Microbiol. 2026 Apr 20;16:1763765. doi: 10.3389/fcimb.2026.1763765. eCollection 2026.

ABSTRACT

BACKGROUND: Advanced gastric cancer (AGC) exhibits a high incidence in Northwest China, largely attributed to region-specific dietary patterns and environmental exposures. Its pathogenesis involves complex host-microbiota crosstalk, which has not yet been comprehensively elucidated through integrated multi-omics approaches. Herein, we employed trasncriptomic and shotgun metagenomic sequencing on paired tumoral and peritumoal mucosal tissues from 88 AGC patients in Northwest China. Our aim was to systematically characterize host gene expression profiles, the composition and functional potential of the gastric mucosal microbiota, and their intricate interrelationships.

RESULTS: Transcriptomic profiling clearly distinguished tumoral from peritumoral regions (PERMANOVA, R² = 0.24, P = 0.0001), with 8870 differentially expressed genes (DEGs) identified between the two tissue types. Tumor tissues harbored 8377 up-regulated DEG, which were enriched in extracellular matrix (ECM) organization, cell cycle regulation, signaling transduction, and inflammatory pathways (e.g., PI3K-Akt, IL-17 signaling). In contrast, peritumoral tissues showed 493 up-regulated DEGs primarily associated with metabolic processes. Host gene expression was significantly modulated by Lauren classification in tumoral mucosa (P = 0.025) and by Helicobacter pylori (Hp) infection in peritumoral tissues (P = 0.0424). Hp-infected tissues exhibited 65 up-regulated DEGs linked to transcriptional misregulation in cancer, inflammation, immune activation and mitochondrial pathways. Lauren subtypes displayed distinct transcriptomic signatures: intestinal-type AGC was enriched in metabolic processes, diffuse-type in immune and signal transduction pathways, and mixed-type in Ras/MAPK/ErbB and NF-κB signaling pathways. Correlation analysis between the 8870 DEGs and seven differentially abundant bacterial species (e.g., Serratia surfactantfaciens, Pseudomonas protegens, Prevotella jejuni, and Streptococcus infantis) revealed 13199 significant correlations. Among these, S. surfactantfaciens and P. protegens exhibited the strongest connectivity with host genes. Functionally, the correlated DEGs were involved in ECM structure, cell cycle progression, immune and inflammatory responses, cellular proliferation and differentiation, and metabolic processes.

CONCLUSIONS: Our findings demonstrated phenotype- and tissue-specific regulation of host gene expression in AGC and revealed extensive host-microbe interactions. This work fills a critical gap in multi-omics research on AGC in the Northwest Chinese population and suggests potential diagnostic and therapeutic targets for AGC.

PMID:42088021 | PMC:PMC13136179 | DOI:10.3389/fcimb.2026.1763765

Microorganisms. 2026 Mar 26;14(4):746. doi: 10.3390/microorganisms14040746.

ABSTRACT

Effective oral interventions for alcohol-induced metabolic stress and liver injury remain limited. Pre-absorptive gastrointestinal alcohol handling is gaining interest as a non-pharmacological strategy to reduce hepatic burden. In this study, we developed a formulation-integrated, food-compatible lyophilized recombinant whole-cell catalyst based on Escherichia coli Nissle 1917 engineered to express alcohol dehydrogenase and acetaldehyde dehydrogenase. Rather than focusing exclusively on strain-level genetic modification, the engineered cells were protected by lyophilization combined with a food-grade chitosan-alginate layer-by-layer coating, forming an artificial cell wall designed to enhance survivability during oral delivery. The formulation resisted simulated gastric acid, sodium taurocholate, and ethanol, retained enzymatic activity after storage, and demonstrated formulation stability. In alcohol-exposed mice, oral administration reduced blood ethanol and acetaldehyde levels, improved liver biochemical parameters, attenuated hepatic steatosis, and partially restored oxidative stress indicators. Integrated multi-omics analyses indicated coordinated gut-associated metabolic and inflammatory responses to alcohol and intervention, rather than a single dominant pathway. These findings provide hypothesis-generating evidence; causality remains to be established. Overall, this study demonstrates a proof-of-concept, food-compatible lyophilized recombinant whole-cell catalyst that integrates enzymatic function with formulation stability and gastrointestinal resilience, highlighting an applied, food-compatible microbial framework for exploring alcohol-related metabolic stress.

PMID:42075143 | PMC:PMC13119499 | DOI:10.3390/microorganisms14040746

Comput Biol Chem. 2026 Apr 23;124(Pt 1):109095. doi: 10.1016/j.compbiolchem.2026.109095. Online ahead of print.

ABSTRACT

Immune checkpoint inhibitors like pembrolizumab exhibit variable efficacy in metastatic gastric cancer (GC). This study aimed to identify molecular drivers of pembrolizumab response, explore mechanisms of immune checkpoint inhibitors (ICIs) efficacy, and develop a prognostic signature. Transcriptomic analysis of pembrolizumab-treated GC (TIGER database) identified 165 response-associated differentially expressed genes (DEGs). Functional annotation and single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) revealed that responder-upregulated genes (R-DEGs) were enriched in immune activation pathways and mainly localized to CD8 + T/NK cells. In contrast, non-responder-upregulated genes (D-DEGs) were linked to extracellular matrix (ECM) remodeling and mainly expressed in fibroblasts/endothelial cells. CellChat analysis demonstrated that key DEGs mediate immune-stromal crosstalk via MHC-I and collagen/laminin signaling. A prognostic signature (Lasso-StepCox[forward] Riskscore; LSR: APOD, APOH, BATF2, GJA1, MAGED1, SLC5A1, SLCO2A1, VWF, VCAN) was derived and validated in four independent GC cohorts from the GEO and Cancer Genome Atlas (TCGA) database. Multi-omics analyses showed that LSR-high tumors exhibited aggressive clinicopathological features, increased stromal components, reduced cytotoxic immune infiltration, diminished tumor mutational burden (TMB), and poorer prognosis. Immunohistochemistry (IHC) and spatial transcriptomics in GC showed that stromal VWF/VCAN expression correlates with reduced CD8⁺ T cell granzyme B expression, suggesting T cell dysfunction. High VWF expression in GC predicted poor survival, and a combined VWF/VCAN score showed enhanced prognostic stratification. This study highlights stromal-immune crosstalk as a driver of pembrolizumab resistance and provides a signature as a clinical tool for prognosis and personalized therapy in metastatic GC.

PMID:42068630 | DOI:10.1016/j.compbiolchem.2026.109095

Front Oncol. 2026 Apr 15;16:1791971. doi: 10.3389/fonc.2026.1791971. eCollection 2026.

ABSTRACT

OBJECTIVE: This study aimed to integrate multi-omics analyses with genetic causal inference to identify key genes associated with cisplatin resistance in gastric cancer and to evaluate the potential mechanism by which curcumin enhances cisplatin sensitivity through relevant pathways.

METHODS: Cisplatin resistance-related transcriptomic datasets(GSE14210 and GSE31811) and a gastric cancer single-cell transcriptomic dataset (GSE183904) were obtained from the Gene Expression Omnibus(GEO)database. Differential expression analysis was performed to identify resistance-associated differentially expressed genes(DEGs),followed by GO and KEGG enrichment analyses. Putative curcumin targets were collected and intersected with DEGs to obtain candidate genes. Mendelian randomization (MR) analysis was conducted using the TwoSampleMR framework to evaluate the genetic association between RXRA expression and gastric cancer risk, with robustness and sensitivity analyses based on multiple MR methods. RXRA expression was further evaluated, along with pathway activity assessment using GSEA and GSVA, and molecular docking was performed to explore the potential binding of curcumin to RXRA. In vitro experiments were performed using the cisplatin-resistant gastric cancer cell lineNCI-N87/DDP. Drug effects and chemosensitization under combination treatment were assessed by CCK-8 assays, synergy was evaluated using the combination index(CI),and changes in key proteins in thePI3K/AKT pathway were measured by Western blotting.

RESULTS: A total of 595 DEGs associated with cisplatin resistance were identified. Functional enrichment analyses indicated that these DEGs were mainly involved in extracellular matrix remodeling and adhesion, secretion and vesicular transport, and signaling pathways including PI3K-Akt.The intersection of curcumin targets with DEGs highlighted RXRA as a key candidate gene. MR results indicated that genetically predicted increased RXRA expression was significantly associated with elevated gastric cancer risk (OR = 4.216,95%CI:1.201-14.797,P=0.025). GSEA and GSVA suggested that high RXRA expression was associated with altered activity of pathways related to lysosome, proteasome, oxidative phosphorylation, and the pentose phosphate pathway. Single-cell analysis indicated that RXRA was mainly expressed in tissue stem cells and fibroblasts. Molecular docking predicted a feasible interaction between curcumin and RXRA. In vitro experiments demonstrated that curcumin inhibited the viability of resistant cells and showed a synergistic trend when combined with cisplatin. Western blotting revealed decreased p-PI3K and p-AKT levels following curcumin treatment, supporting an inhibitory effect on the PI3K/AKT pathway.

CONCLUSION: These findings highlight RXRA as a candidate gene associated with cisplatin resistance-related programs in gastric cancer. Curcumin may enhance cisplatin sensitivity by influencing RXRA-associated transcriptional networks and suppressing PI3K/AKT signaling. This study provides new candidate targets and experimental evidence for mechanistic investigation and combination treatment strategies to overcome cisplatin resistance in gastric cancer.

PMID:42063729 | PMC:PMC13124633 | DOI:10.3389/fonc.2026.1791971

J Transl Med. 2026 Apr 30;24(1):638. doi: 10.1186/s12967-026-08173-3.

ABSTRACT

BACKGROUND: Metabolic reprogramming is a hallmark of gastric cancer and is essential for sustaining rapid proliferation and malignant progression. The serine synthesis pathway (SSP), a key branch of glycolysis coupled to one-carbon metabolism (OCM), plays a central role in nucleotide biosynthesis, redox homeostasis, and epigenetic regulation. Although aberrant SSP activation has been implicated in gastric cancer, its upstream regulatory mechanisms remain poorly defined. Long non-coding RNAs (lncRNAs) have emerged as critical modulators of oncogenic signaling and metabolism. This study aimed to elucidate the role of the lncRNA SNHG12 in gastric cancer progression and to determine whether it drives metabolic reprogramming through the Wnt/β-catenin-SSP axis.

METHODS: SNHG12 expression and clinical relevance were analyzed using public datasets, clinical gastric cancer specimens, and cell lines. Gain- and loss-of-function experiments were performed to assess the effects of SNHG12 on proliferation, apoptosis, migration, and invasion. Transcriptomic profiling, targeted metabolomics, and integrative multi-omics analyses were used to characterize metabolic alterations. Pharmacological inhibition of SSP (NCT503) and Wnt/β-catenin signaling (IWR-1) was applied in vitro and in vivo. A subcutaneous xenograft mouse model was used to validate tumor-promoting effects and therapeutic responses.

RESULTS: SNHG12 was significantly upregulated in gastric cancer tissues and cell lines and was associated with poor overall and progression-free survival. Functionally, SNHG12 promoted gastric cancer cell proliferation, migration, and invasion while suppressing apoptosis. Transcriptomic and targeted metabolomic analyses revealed broad metabolic alterations associated with SNHG12, including changes in serine/one-carbon metabolism, purine biosynthesis, and glutathione-related pathways. Mechanistically, SNHG12 increased Wnt/β-catenin reporter activity, promoted β-catenin nuclear accumulation, and was accompanied by increased expression of key SSP-associated enzymes, including PHGDH, PSAT1, and SHMT2. Pharmacological inhibition of SSP or Wnt/β-catenin signaling partially reversed SNHG12-induced malignant phenotypes in vitro and suppressed tumor growth in xenograft models.

CONCLUSIONS: This study identifies SNHG12 as an important regulator of metabolic reprogramming in gastric cancer. Our data support a model in which SNHG12 promotes gastric cancer cell proliferation, invasion, and migration through SSP regulation, and suggest that its effects on the SSP may be mediated, at least in part, through modulation of SSP-associated enzymes via the Wnt/β-catenin pathway. These findings support SNHG12 as a candidate biomarker and a potential therapeutic target for combined metabolic and signaling-based interventions in gastric cancer.

PMID:42063161 | PMC:PMC13151230 | DOI:10.1186/s12967-026-08173-3

Front Immunol. 2026 Apr 14;17:1767512. doi: 10.3389/fimmu.2026.1767512. eCollection 2026.

ABSTRACT

Gastric cancer (GC) is plagued by profound intratumoral heterogeneity and a complex tumor microenvironment (TME), which are the core obstacles to precise diagnosis and treatment. Conventional bulk multi-omics technologies average molecular signals across tissues, thus masking cellular heterogeneity; single-cell multi-omics resolves cellular diversity but dissociates cells from their native spatial context, leading to the loss of critical information on intercellular crosstalk and molecular spatial distribution. These limitations result in an incomplete understanding of GC pathogenesis and TME regulatory networks. Spatial multi-omics technologies, integrating genomics, transcriptomics, proteomics, and metabolomics with high-resolution spatial localization, address these key scientific problems by preserving the native tissue architecture and elucidating the spatiotemporal dynamics of molecular and cellular events in GC. This review systematically synthesizes the latest advances in the application of four major spatial multi-omics modalities in GC research over the past 15 years, with a critical evaluation of the technical performance, methodological shortcomings, and clinical translation potential of existing studies. Unlike previous reviews that only summarize research findings, this work uniquely integrates technical principles, mechanistic discoveries, and clinical translation of spatial multi-omics in GC, deeply analyzes the practical barriers to clinical application, and systematically elaborates the integration of spatial multi-omics with artificial intelligence (AI). We also identify unresolved challenges in the field and propose future development directions, providing a comprehensive and in-depth reference for the advancement of GC precision medicine based on spatial multi-omics.

PMID:42058209 | PMC:PMC13120937 | DOI:10.3389/fimmu.2026.1767512

World J Surg Oncol. 2026 Apr 7. doi: 10.1186/s12957-026-04321-7. Online ahead of print.

NO ABSTRACT

PMID:41947143 | DOI:10.1186/s12957-026-04321-7

J Gastric Cancer. 2026 Apr;26(2):260-278. doi: 10.5230/jgc.2026.26.e19.

ABSTRACT

PURPOSE: Patient-derived cells (PDCs) and patient-derived organoids (PDOs) are complementary preclinical models widely used in translational cancer research. However, their molecular and functional differences have not been systematically characterized. This study established and analyzed paired PDC and PDO models derived from the same gastric cancer ascites to delineate platform-dependent molecular and functional profiles.

MATERIALS AND METHODS: Malignant ascites or pleural fluid obtained from 6 patients with advanced gastric cancer were used to establish paired PDC and PDO models. All pairs underwent comprehensive multi-omics profiling, integrating genomic, transcriptomic, and proteomic data. Phenotypic characterization included morphological, histological, proliferative, and cell cycle analyses. Drug sensitivity assays were performed using 4 chemotherapeutic agents commonly used to treat gastric cancer.

RESULTS: The 6 paired PDC and PDO models exhibited distinct morphological characteristics. Whole-genome analyses demonstrated high concordance among primary tumors, PDCs, and PDOs, confirming tumor representation across platforms. Multi-omics profiling identified platform-dependent molecular signatures; PDOs were enriched for extracellular matrix remodeling and stemness, whereas PDCs displayed proliferation- and immune-related signatures. Clinically relevant biomarkers, including HER2 and MET alterations, were concordant with primary tumors. Notably, drug responses differed between platforms and patients, indicating platform-dependent and patient-specific chemosensitivity.

CONCLUSIONS: Paired PDC and PDO models derived from the same patients preserved core patient-specific tumor characteristics while exhibiting distinct molecular and functional profiles. These findings underscore the culture platform as a critical determinant of experimental outcomes and therapeutic responses. Therefore, careful selection of an appropriate preclinical model is essential to accurately address biological questions and optimize precision oncology strategies.

PMID:41942359 | PMC:PMC13053824 | DOI:10.5230/jgc.2026.26.e19

Phytomedicine. 2026 Mar 27;155:158126. doi: 10.1016/j.phymed.2026.158126. Online ahead of print.

ABSTRACT

BACKGROUND: Mitochondrial damage can induce the release of mitochondrial DNA (mtDNA), leading to oxidative stress and activation of immune responses. Targeting mitochondrial dysfunction may thus represent a therapeutic strategy for gastric cancer. Isobavachalcone (IBC), a prenylated chalcone derived from Psoralea corylifolia L., has demonstrated antitumor activity, but its mechanism of action remains unclear, limiting its clinical application.

PURPOSE: This study aimed to investigate the antitumor effects of IBC in gastric cancer and to elucidate the underlying molecular mechanisms, with a focus on mitochondrial damage and immune activation.

STUDY DESIGN: The study combined in vitro and in vivo assays with multi-omics sequencing and network pharmacology to identify IBC's therapeutic target and downstream signaling pathways.

METHODS: Gastric cancer cells and mouse models were treated with IBC to assess its inhibitory effects. Multi-omics approaches and network pharmacology were used to identify potential targets. ROS production, mitochondrial membrane integrity, and immune pathway activation were evaluated via biochemical and molecular assays.

RESULTS: IBC significantly suppresses gastric cancer growth both in vitro and in vivo. Integrated analysis identifies dihydroorotate dehydrogenase (DHODH) as a direct target of IBC. DHODH deficiency can induce mitochondrial membrane remodeling and STING pathway activation. Inhibition of DHODH by IBC induces ROS accumulation, mitochondrial membrane remodeling, and activation of the STING pathway, promoting antitumor immune responses. This study demonstrates that IBC enhances antitumor immunity in gastric cancer through mitochondrial damage-mediated mechanisms.

CONCLUSION: IBC exerts dual antitumor and immunostimulatory effects in gastric cancer by targeting DHODH, inducing mitochondrial damage, and activating the STING pathway, highlighting its promising therapeutic potential in gastric cancer.

PMID:41931998 | DOI:10.1016/j.phymed.2026.158126

PLoS One. 2026 Apr 3;21(4):e0346173. doi: 10.1371/journal.pone.0346173. eCollection 2026.

ABSTRACT

Cancer remains a leading cause of mortality worldwide and a significant barrier to improving quality of life across all populations. The protein kinase D family, including PRKD3, has been demonstrated to play a crucial role in cancer development through its involvement in regulating key cellular processes. Although growing evidence highlights the role of PRKD3 in the tumorigenesis of certain cancers, a comprehensive pan-cancer analysis of PRKD3 remains unavailable. To address this, we performed an integrative pan-cancer analysis of PRKD3 using multi-omics datasets from The Cancer Genome Atlas, the Genotype-Tissue Expression project, and cBioPortal. We examined PRKD3 expression, copy number variation, mutation, and DNA methylation, and evaluated their associations with clinicopathological features, patient survival, and diagnostic potential across 33 cancer types. Immune relevance was further assessed through correlations with immune infiltration, checkpoint gene expression, and immunotherapy response-related genomic biomarkers. Our results revealed that PRKD3 expression was highly heterogeneous, showing significant upregulation in liver cancer, gastric cancer, and adrenocortical carcinoma, and downregulation in others. Elevated expression was consistently associated with poor prognosis and increased stromal, neutrophil, and cancer-associated fibroblast infiltration in adrenocortical carcinoma, liver cancer, and stomach cancer, whereas paradoxical associations with favorable outcomes were observed in kidney clear cell carcinoma. PRKD3 expression also correlated with immune checkpoint molecules including PD-1, PD-L1, and CTLA-4, supporting an immunosuppressive role, while context-dependent associations with TMB and MSI highlighted its potential influence on tumor immunogenicity and responsiveness to immune checkpoint blockade. Collectively, these findings identify PRKD3 as a potential context-dependent modulator of tumor biology, prognosis, and immune interactions, underscoring its potential as a biomarker of diagnostic, prognostic, and therapeutic relevance in precision oncology.

PMID:41931575 | PMC:PMC13048501 | DOI:10.1371/journal.pone.0346173

Eur J Cancer. 2026 Mar 24;239:116699. doi: 10.1016/j.ejca.2026.116699. Online ahead of print.

ABSTRACT

INTRODUCTION: The latest generation of liquid biopsies incorporates multi-omic features, including genomics, methylomics, and fragmentomics. Machine learning (ML) approaches have been proposed to synthesize these complex biological data for the development of diagnostic classifiers. This study aims to evaluate the integration of ML with circulating cell-free DNA (cfDNA) analysis for early cancer detection.

METHODS: Medline, Embase, Cochrane, and Web of Science were searched in July 2025. Eligible studies combined ML and cfDNA features to distinguish cancer patients (stages I-III) from non-cancer controls. Summary diagnostic performance metrics and their 95% confidence intervals (CI) were calculated.

RESULTS: The study included 109 articles permitting analyses for lung (n = 34), liver (n = 29), colorectal (n = 28), pancreatic (n = 16), breast (n = 17), esophageal (n = 12), ovarian (n = 13), gastric (n = 9), head and neck (n = 4), and mixed (n = 27) cancer types. Specificity was consistently high across all tumor types and stages (94%-99%). Sensitivity ranged from 72% to 92% for stage I-III, 44-91% for stage I, 71-98% for stage II and 83-99% for stage III. In the pooled study population, neural networks (90%, 95% CI: 81%-95%), random forest (86%, 95% CI: 77%-92%) and heterogeneous ensemble learning (85%, 95% CI: 79%-89%) demonstrated the highest sensitivity. The stratified analysis by classifier feature revealed 86% (95% CI: 80%-90%) sensitivity for fragmentation and 81% (95% CI: 76%-85%) for methylation, with 92%-96% specificity.

CONCLUSION: ML and cfDNA profiling show potential for early cancer detection, with ensemble methods, neural networks and random forests achieving the best overall performance. Fragmentomic features provide the highest sensitivity.

PMID:41930854 | DOI:10.1016/j.ejca.2026.116699