Pan-cancer analysis of CDKN2A alterations identifies a subset of gastric cancer with a cold tumor immune microenvironment.

BACKGROUND: Although CDKN2A alteration has been explored as a favorable factor for tumorigenesis in pan-cancers, the association between CDKN2A point mutation (MUT) and intragenic deletion (DEL) and response to immune checkpoint inhibitors (ICIs) is still disputed. This study aims to determine the associations of CDKN2A MUT and DEL with overall survival (OS) and response to immune checkpoint inhibitors treatment (ICIs) among pan-cancers and the clinical features of CDKN2A-altered gastric cancer. METHODS: This study included 45,000 tumor patients that underwent tumor sequencing across 33 cancer types from four cohorts, the MSK-MetTropism, MSK-IMPACT, OrigiMed2020 and TCGA cohorts. Clinical outcomes and genomic factors associated with response to ICIs, including tumor mutational burden, copy number alteration, neoantigen load, microsatellite instability, tumor immune microenvironment and immune-related gene signatures, were collected in pan-cancer. Clinicopathologic features and outcomes were assessed in gastric cancer. Patients were grouped based on the presence of CDKN2A wild type (WT), CDKN2A MUT, CDKN2A DEL and CDKN2A other alteration (ALT). RESULTS: Our research showed that CDKN2A-MUT patients had shorter survival times than CDKN2A-WT patients in the MSK MetTropism and TCGA cohorts, but longer OS in the MSK-IMPACT cohort with ICIs treatment, particularly in patients having metastatic disease. Similar results were observed among pan-cancer patients with CDKN2A DEL and other ALT. Notably, CDKN2A ALT frequency was positively related to tumor-specific objective response rates to ICIs in MSK MetTropism and OrigiMed 2020. Additionally, individuals with esophageal carcinoma or stomach adenocarcinoma who had CDKN2A MUT had poorer OS than patients from the MSK-IMPACT group, but not those with adenocarcinoma. We also found reduced levels of activated NK cells, T cells CD8 and M2 macrophages in tumor tissue from CDKN2A-MUT or DEL pan-cancer patients compared to CDKN2A-WT patients in TCGA cohort. Gastric cancer scRNA-seq data also showed that CDKN2A-ALT cancer contained less CD8 T cells but more exhausted T cells than CDKN2A-WT cancer. A crucial finding of the pathway analysis was the inhibition of three immune-related pathways in the CDKN2A ALT gastric cancer patients, including the interferon alpha response, inflammatory response, and interferon gamma response. CONCLUSIONS: This study illustrates the CDKN2A MUT and DEL were associated with a poor outcome across cancers. CDKN2A ALT, on the other hand, have the potential to be used as a biomarker for choosing patients for ICI treatment, notably in esophageal carcinoma and stomach adenocarcinoma.

PRMT6 Promotes the Immune Evasion of Gastric Cancer by Upregulating ANXA1.

Gastric cancer is a most malignancy in digestive tract worldwide. This study aimed to investigate the roles of protein arginine methyltransferase 6 (PRMT6) in gastric cancer. Immunohistochemistry was performed to detect PRMT6 expression in gastric tumors. Real-time transcriptase-quantitative polymerase chain reaction (RT-qPCR) was used to detected mRNA levels. Protein expression was determined using western blot. Gastric cancer cells were co-cultured with CD8+ T cells. Colony formation assay was performed to detect cell proliferation. Flow cytometry was performed to determine CD8+ T cell function and tumor cell apoptosis. PRMT6 was overexpressed in gastric tumors. High level of PRMT6 predicted poor outcomes of gastric cancer patients and inhibition of CD8+ T cell infiltration. PRMT6 promoted proliferation of CD8+ T cells and enhanced its tumor killing ability. Moreover, PRMT6 upregulated annexin A1 (ANXA1) and promoted ANXA1 protein stability. ANXA1 overexpression suppressed the proliferation of CD8+ T cells and promoted tumor cell survival. PRMT6 functions as an oncogene in gastric cancer. PRMT6-mediated protein stability inhibits the infiltration of CD8+ T cells, resulting in immune evasion of gastric cancer. The PRMT6-ANXA1 may be a promising strategy for gastric cancer.

The impact of laparoscopic radical gastrectomy on the inflammatory response and immune function of patients with gastric cancer.

The currest study aimed to measure the effects of laparoscopic radical gastrectomy on inflammatory response along with immune function in gastric cancer (GC) patients. Seventy patients with GC in our hospital were retrospectively chosen to be the study objects and separated into control group (CG, 35 cases) and observation group (OG, 35 cases). Patients in the OG received radical laparotomy. Patients in the OG received laparoscopic radical gastrectomy. The surgical indicators, postoperative recovery indicators, inflammatory factors, immune function, incidence of adverse reactions along with quality of life of patients in both groups were compared. In contrast to the CG, the operation time of the OG presented as shorter (P<0.05), and the amount of intraoperative blood loss together with postoperative VAS score in the OG presented lower (P<0.05), but the number of lymph nodes dissection presented not statistically significant between 2 groups (P>0.05). The postoperative exhaust time, feeding time as well as hospital stay in the OG presented shorter relative to the CG (P<0.05). The serum levels of CRP, and IL-6 together with TNF-α presented elevated in both groups after surgery, and those in the OG presented lower when compared with the CG (P<0.05). The serum levels of IgA, and IgG together with IgM presented declined in both groups after surgery, and those in the OG presented higher when compared with the CG (P<0.05). The incidence of postoperative complications in the OG presented reduction relative to the CG (P<0.05). The GLQI scores of the OG presented significantly higher relative to the CG at discharge (P<0.05). Compared with radical gastrectomy, laparoscopic radical gastrectomy is more suitable for the treatment of GC, which can reduce the inflammatory response and promote the immune function of GC patients.

The ratio of intratumoral CD15+ neutrophils to CD8+ lymphocytes predicts recurrence in patients with gastric cancer after curative resection.

BACKGROUND: An elevated neutrophil-to-lymphocyte ratio (NLR) in peripheral blood is an independent prognostic indicator of various cancers. AIMS: In this study, we aimed to investigate the prognostic relevance of the intratumoral immune cell balance in gastric cancer. METHODS AND RESULTS: The study included 82 patients who underwent curative resection for gastric cancer. The intratumoral cluster of differentiation (CD) 15- and CD8-positive cells were evaluated using immunohistochemical staining. Additionally, clinicopathological factors and prognoses were analyzed. Patients with high intratumoral CD15/CD8 ratios had significantly lower overall survival (OS) and relapse-free survival (RFS) compared to those with low CD15/CD8 ratios (p = .0026 and p < .0001, respectively). Additionally, a high CD15/CD8 ratio was associated with lymph node metastasis (p = .019). Patients with high NLR had a significantly lower RFS than those with low NLR (p = .0050). Multivariate analysis revealed that the intratumoral CD15/CD8 ratio, NLR, and venous invasion were independent prognostic indicators of RFS (CD15/CD8 ratio: p < .001, hazard ratio (HR) = 14.7, 95% confidence interval (CI) = 3.8-56.8; NLR: p = .010, HR = 5.4, 95% CI = 1.5-19.6; venous invasion: p = .005, HR = 7.4, 95% CI = 1.8-29.7). CONCLUSION: In summary, we found that the intratumoral CD15/CD8 ratio is an independent prognostic factor following gastric cancer resection and its increase is associated with lymph node metastasis and microscopic lymph vessel invasion. Immunological evaluation with additional aspects of innate immunity may be useful in predicting cancer prognosis.

Interpretable machine learning for predicting the response duration to Sintilimab plus chemotherapy in patients with advanced gastric or gastroesophageal junction cancer.

Background: Sintilimab plus chemotherapy has proven effective as a combination immunotherapy for patients with advanced gastric and gastroesophageal junction adenocarcinoma (GC/GEJC). A multi-center study conducted in China revealed a median progression-free survival (PFS) of 7.1 months. However, the prediction of response duration to this immunotherapy has not been thoroughly investigated. Additionally, the potential of baseline laboratory features in predicting PFS remains largely unexplored. Therefore, we developed an interpretable machine learning (ML) framework, iPFS-SC, aimed at predicting PFS using baseline (pre-treatment) laboratory features and providing interpretations of the predictions. Materials and methods: A cohort of 146 patients with advanced GC/GEJC, along with their baseline laboratory features, was included in the iPFS-SC framework. Through a forward feature selection process, predictive baseline features were identified, and four ML algorithms were developed to categorize PFS duration based on a threshold of 7.1 months. Furthermore, we employed explainable artificial intelligence (XAI) methodologies to elucidate the relationship between features and model predictions. Results: The findings demonstrated that LightGBM achieved an accuracy of 0.70 in predicting PFS for advanced GC/GEJC patients. Furthermore, an F1-score of 0.77 was attained for identifying patients with PFS durations shorter than 7.1 months. Through the feature selection process, we identified 11 predictive features. Additionally, our framework facilitated the discovery of relationships between laboratory features and PFS. Conclusion: A ML-based framework was developed to predict Sintilimab plus chemotherapy response duration with high accuracy. The suggested predictive features are easily accessible through routine laboratory tests. Furthermore, XAI techniques offer comprehensive explanations, both at the global and individual level, regarding PFS predictions. This framework enables patients to better understand their treatment plans, while clinicians can customize therapeutic approaches based on the explanations provided by the model.

GLT8D2 is a prognostic biomarker and regulator of immune cell infiltration in gastric cancer.

Because of the considerable tumor heterogeneity in gastric cancer (GC), only a limited group of patients experiences positive outcomes from immunotherapy. Herein, we aim to develop predictive models related to glycosylation genes to provide a more comprehensive understanding of immunotherapy for GC. RNA sequencing (RNA-seq) data and corresponding clinical outcomes were obtained from GEO and TCGA databases, and glycosylation-related genes were obtained from GlycoGene DataBase. We identified 48 differentially expressed glycosylation-related genes and established a prognostic model (seven prognosis genes including GLT8D2, GALNT6, ST3GAL6, GALNT15, GBGT1, FUT2, GXYLT2) based on these glycosylation-related genes using the results from Cox regression analysis. We found that these glycosylation-related genes revealed a robust correlation with the abundance of Tumor Infiltrating Lymphocytes (TILs), especially the GLT8D2 which is associated with many TILs. Finally, we employed immunohistochemistry and Multiplex Immunohistochemical to discover that GLT8D2 serves as a valuable prognostic biomarker in GC and is closely associated with macrophage-related markers. Collectively, we established a prognostic model based on glycosylation-related genes to provide a more comprehensive understanding of prediction for GC prognosis, and identified that GLT8D2 is closely correlated with adverse prognosis and may underscore its role in regulating immune cell infiltration in GC patients.

Inhibition of glycolysis enhances the efficacy of immunotherapy via PDK-mediated upregulation of PD-L1.

BACKGROUND: Immunotherapy for gastric cancer remains a challenge due to its limited efficacy. Metabolic reprogramming toward glycolysis has emerged as a promising avenue for enhancing the sensitivity of tumors to immunotherapy. Pyruvate dehydrogenase kinases (PDKs) play pivotal roles in regulating glycolysis. The importance of PDKs in the context of gastric cancer immunotherapy and their potential as therapeutic targets have not been fully explored. METHODS: PDK and PD-L1 expression was analyzed using data from the GSE66229 and The Cancer Genome Atlas (TCGA) cohorts. Additionally, the Immune Checkpoint Blockade Therapy Atlas (ICBatlas) database was utilized to assess PDK expression in an immune checkpoint blockade (ICB) therapy group. Subsequently, the upregulation of PD-L1 and the enhancement of anticancer effects achieved by targeting PDK were validated through in vivo and in vitro assays. The impact of PDK on histone acetylation was investigated using ChIP‒qPCR to detect changes in histone acetylation levels. RESULTS: Our analysis revealed a notable negative correlation between PD-L1 and PDK expression. Downregulation of PDK led to a significant increase in PD-L1 expression. PDK inhibition increased histone acetylation levels by promoting acetyl-CoA generation. The augmentation of acetyl-CoA production and concurrent inhibition of histone deacetylation were found to upregulate PD-L1 expression in gastric cancer cells. Additionally, we observed a significant increase in the anticancer effect of PD-L1 antibodies following treatment with a PDK inhibitor. CONCLUSIONS: Downregulation of PDK in gastric cancer cells leads to an increase in PD-L1 expression levels, thus potentially improving the efficacy of PD-L1 immune checkpoint blockade therapy.

Comprehensive analysis of T-cell regulatory factors and tumor immune microenvironment in stomach adenocarcinoma.

BACKGROUND: Gastric cancer (GC) is one of the most prevalent malignant tumors worldwide and is associated with high morbidity and mortality rates. However, the specific biomarkers used to predict the postoperative prognosis of patients with gastric cancer remain unknown. Recent research has shown that the tumor microenvironment (TME) has an increasingly positive effect on anti-tumor activity. This study aims to build signatures to study the effect of certain genes on gastric cancer. METHODS: Expression profiles of 37 T cell-related genes and their TME characteristics were comprehensively analyzed. A risk signature was constructed and validated based on the screened T cell-related genes, and the roles of hub genes in GC were experimentally validated. RESULTS: A novel T cell-related gene signature was constructed based on CD5, ABCA8, SERPINE2, ESM1, SERPINA5, and NMU. The high-risk group indicated lower overall survival (OS), poorer immune efficacy, and higher drug resistance, with SERPINE2 promoting GC cell proliferation, according to experiments. SERPINE2 and CXCL12 were significantly correlated, indicating poor OS via the Youjiang cohort. CONCLUSIONS: This study identified T cell-related genes in patients with stomach adenocarcinoma (STAD) for prognosis estimation and proposed potential immunotherapeutic targets for STAD.

Exploring the immune escape mechanisms in gastric cancer patients based on the deep AI algorithms and single-cell sequencing analysis.

Gastric cancer is a prevalent and deadly malignancy, and the response to immunotherapy varies among patients. This study aimed to develop a prognostic model for gastric cancer patients and investigate immune escape mechanisms using deep machine learning and single-cell sequencing analysis. Data from public databases were analysed, and a prediction model was constructed using 101 algorithms. The high-AIDPS group, characterized by increased AIDPS expression, exhibited worse survival, genomic variations and immune cell infiltration. These patients also showed immunotherapy tolerance. Treatment strategies targeting the high-AIDPS group identified three potential drugs. Additionally, distinct cluster groups and upregulated AIDPS-associated genes were observed in gastric adenocarcinoma cell lines. Inhibition of GHRL expression suppressed cancer cell activity, inhibited M2 polarization in macrophages and reduced invasiveness. Overall, AIDPS plays a critical role in gastric cancer prognosis, genomic variations, immune cell infiltration and immunotherapy response, and targeting GHRL expression holds promise for personalized treatment. These findings contribute to improved clinical management in gastric cancer.

Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.

BACKGROUND: Only a subset of patients with gastric cancer experience long-term benefits from immune checkpoint inhibitors (ICIs). Currently, there is a deficiency in precise predictive biomarkers for ICI efficacy. The aim of this study was to develop and validate a pathomics-driven ensemble model for predicting the response to ICIs in gastric cancer, using H&E-stained whole slide images (WSI). METHODS: This multicenter study retrospectively collected and analyzed H&E-stained WSIs and clinical data from 584 patients with gastric cancer. An ensemble model, integrating four classifiers: least absolute shrinkage and selection operator, k-nearest neighbors, decision trees, and random forests, was developed and validated using pathomics features, with the objective of predicting the therapeutic efficacy of immune checkpoint inhibition. Model performance was evaluated using metrics including the area under the curve (AUC), sensitivity, and specificity. Additionally, SHAP (SHapley Additive exPlanations) analysis was used to explain the model's predicted values as the sum of the attribution values for each input feature. Pathogenomics analysis was employed to explain the molecular mechanisms underlying the model's predictions. RESULTS: Our pathomics-driven ensemble model effectively stratified the response to ICIs in training cohort (AUC 0.985 (95% CI 0.971 to 0.999)), which was further validated in internal validation cohort (AUC 0.921 (95% CI 0.839 to 0.999)), as well as in external validation cohort 1 (AUC 0.914 (95% CI 0.837 to 0.990)), and external validation cohort 2 (0.927 (95% CI 0.802 to 0.999)). The univariate Cox regression analysis revealed that the prediction signature of pathomics-driven ensemble model was a prognostic factor for progression-free survival in patients with gastric cancer who underwent immunotherapy (p<0.001, HR 0.35 (95% CI 0.24 to 0.50)), and remained an independent predictor after multivariable Cox regression adjusted for clinicopathological variables, (including sex, age, carcinoembryonic antigen, carbohydrate antigen 19-9, therapy regime, line of therapy, differentiation, location and programmed death ligand 1 (PD-L1) expression in all patients (p<0.001, HR 0.34 (95% CI 0.24 to 0.50)). Pathogenomics analysis suggested that the ensemble model is driven by molecular-level immune, cancer, metabolism-related pathways, and was correlated with the immune-related characteristics, including immune score, Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data score, and tumor purity. CONCLUSIONS: Our pathomics-driven ensemble model exhibited high accuracy and robustness in predicting the response to ICIs using WSIs. Therefore, it could serve as a novel and valuable tool to facilitate precision immunotherapy.

GGT5: a potential immunotherapy response inhibitor in gastric cancer by modulating GSH metabolism and sustaining memory CD8+ T cell infiltration.

PURPOSE: The variable responses to immunotherapy observed in gastric cancer (GC) patients can be attributed to the intricate nature of the tumor microenvironment. Glutathione (GSH) metabolism significantly influences the initiation and progression of gastric cancer. Consequently, targeting GSH metabolism holds promise for improving the effectiveness of Immune checkpoints inhibitors (ICIs). METHODS: We investigated 16 genes related to GSH metabolism, sourced from the MSigDB database, using pan-cancer datasets from TCGA. The most representative prognosis-related gene was identified for further analysis. ScRNA-sequencing analysis was used to explore the tumor heterogeneity of GC, and the results were confirmed by  Multiplex immunohistochemistry (mIHC). RESULTS: Through DEGs, LASSO, univariate and multivariate Cox regression analyses, and survival analysis, we identified GGT5 as the hub gene in GSH metabolism with the potential to promote GC. Combining CIBERSORT, ssGSEA, and scRNA analysis, we constructed the immune architecture of GC. The subpopulations of T cells were isolated, revealing a strong association between GGT5 and memory CD8+ T cells. Furthermore, specimens from 10 GC patients receiving immunotherapy were collected. mIHC was used to assess the expression levels of GGT5 and memory CD8+ T cell markers. Our results established a positive correlation between GGT5 expression, the enrichment of memory CD8+ T cells, and a suboptimal response to immunotherapy. CONCLUSIONS: Our study identifies GGT5, a hub gene in GSH metabolism, as a potential therapeutic target for inhibiting the response to immunotherapy in GC patients. These findings offer new insights into strategies for optimizing immunotherapy of GC.

Gastric cancer immune microenvironment score predicts neoadjuvant chemotherapy efficacy and prognosis.

The efficacy of neoadjuvant chemotherapy (NACT) in patients with advanced gastric cancer (GC) varies greatly. Thus, we aimed to verify the predictive value of tumor-infiltrating immune cells (TIICs) on the treatment response to NACT and the prognosis of patients with advanced GC, and to explore the impact of NACT on the tumor immune microenvironment (TIME). Paired tumor tissues (pre- and post-NACT) from patients with advanced GC were collected for this study. TIICs were assessed using immunohistochemistry staining and analyzed using logistic regression to establish an immune microenvironment score for GC (ISGC score) and predict NACT efficacy. Kaplan-Meier curves were used to evaluate the survival outcome of patients. The results showed that TIME was dramatically heterogeneous between NACT response and nonresponse patients. In the validation cohort, the ISGC score demonstrated good predictive performance for treatment response to NACT. Moreover, high ISGC indicated better long-term survival in patients with advanced GC. Furthermore, tumor-infiltrated T cells (CD3+ and CD8+) and CD11c+ macrophages were significantly increased in the response group, while CD163+ macrophages and FOXP3+ Treg cells were decreased after NACT. However, opposite results were exhibited in the nonresponse group. Finally, we found that the percentage of programmed cell death ligand 1 (PD-L1)-positive tumors was 31% (32/104) pre-NACT and 49% (51/104) post-NACT, and almost all patients with elevated PD-L1 were in the NACT response group. The ISGC model accurately predicted NACT efficacy and classified patients with GC into different survival groups. NACT regulates the TIME in GC, which may provide strategies for personalized immunotherapy.

Development and verification of a manganese metabolism- and immune-related genes signature for prediction of prognosis and immune landscape in gastric cancer.

Background: Gastric cancer (GC) poses a global health challenge due to its widespread prevalence and unfavorable prognosis. Although immunotherapy has shown promise in clinical settings, its efficacy remains limited to a minority of GC patients. Manganese, recognized for its role in the body's anti-tumor immune response, has the potential to enhance the effectiveness of tumor treatment when combined with immune checkpoint inhibitors. Methods: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases was utilized to obtain transcriptome information and clinical data for GC. Unsupervised clustering was employed to stratify samples into distinct subtypes. Manganese metabolism- and immune-related genes (MIRGs) were identified in GC by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analysis. We conducted gene set variation analysis, and assessed the immune landscape, drug sensitivity, immunotherapy efficacy, and somatic mutations. The underlying role of NPR3 in GC was further analyzed in the single-cell RNA sequencing data and cellular experiments. Results: GC patients were classified into four subtypes characterized by significantly different prognoses and tumor microenvironments. Thirteen genes were identified and established as MIRGs, demonstrating exceptional predictive effectiveness in GC patients. Distinct enrichment patterns of molecular functions and pathways were observed among various risk subgroups. Immune infiltration analysis revealed a significantly greater abundance of macrophages and monocytes in the high-risk group. Drug sensitivity analysis identified effective drugs for patients, while patients in the low-risk group could potentially benefit from immunotherapy. NPR3 expression was significantly downregulated in GC tissues. Single-cell RNA sequencing analysis indicated that the expression of NPR3 was distributed in endothelial cells. Cellular experiments demonstrated that NPR3 facilitated the proliferation of GC cells. Conclusion: This is the first study to utilize manganese metabolism- and immune-related genes to identify the prognostic MIRGs for GC. The MIRGs not only reliably predicted the clinical outcome of GC patients but also hold the potential to guide future immunotherapy interventions for these patients.

Stage-Specific Alteration and Prognostic Relationship of Serum Fumarate Hydratase Autoantibodies in Gastric Cancer.

The relationship between energy production and cancer is attracting attention. This study aimed to investigate the clinicopathological significance of fumarate hydratase (FH), a tricarboxylic acid cycle enzyme, in gastric cancer using autoantibodies as biomarkers. The study analyzed 116 patients who underwent gastric cancer surgery and 96 healthy controls. Preoperative serum FH autoantibody (s-FH-Ab) titers were analyzed using an immunosorbent assay with an amplified luminescent proximity homogeneous assay. Receiver operating characteristic analysis was used to determine the cutoff s-FH-Ab titer. Clinicopathological factors and prognosis were compared between the high and low s-FH-Ab groups. The s-FH-Ab levels were significantly higher in the gastric cancer group than in the control group (p = 0.01). Levels were elevated even in patients with stage I gastric cancer compared with healthy controls (p = 0.02). A low s-FH-Ab level was significantly associated with distant metastasis (p = 0.01), peritoneal dissemination (p < 0.05), and poor overall survival (p < 0.01). Multivariate analysis revealed that low s-FH-Ab levels were an independent risk factor for poor prognosis (p < 0.01). Therefore, s-FH-Ab levels may be a useful biomarker for early diagnosis and the prediction of prognosis in patients with gastric cancer.

New genetic insights into immunotherapy outcomes in gastric cancer via single-cell RNA sequencing and random forest model.

OBJECTIVE: The high mortality rate of gastric cancer, traditionally managed through surgery, underscores the urgent need for advanced therapeutic strategies. Despite advancements in treatment modalities, outcomes remain suboptimal, necessitating the identification of novel biomarkers to predict sensitivity to immunotherapy. This study focuses on utilizing single-cell sequencing for gene identification and developing a random forest model to predict immunotherapy sensitivity in gastric cancer patients. METHODS: Differentially expressed genes were identified using single-cell RNA sequencing (scRNA-seq) and gene set enrichment analysis (GESA). A random forest model was constructed based on these genes, and its effectiveness was validated through prognostic analysis. Further, analyses of immune cell infiltration, immune checkpoints, and the random forest model provided deeper insights. RESULTS: High METTL1 expression was found to correlate with improved survival rates in gastric cancer patients (P = 0.042), and the random forest model, based on METTL1 and associated prognostic genes, achieved a significant predictive performance (AUC = 0.863). It showed associations with various immune cell types and negative correlations with CTLA4 and PDCD1 immune checkpoints. Experiments in vitro and in vivo demonstrated that METTL1 enhances gastric cancer cell activity by suppressing T cell proliferation and upregulating CTLA4 and PDCD1. CONCLUSION: The random forest model, based on scRNA-seq, shows high predictive value for survival and immunotherapy sensitivity in gastric cancer patients. This study underscores the potential of METTL1 as a biomarker in enhancing the efficacy of gastric cancer immunotherapy.

Comprehensive multi-omics analysis of pyroptosis for optimizing neoadjuvant immunotherapy in patients with gastric cancer.

Background: Pyroptosis plays a crucial role in immune responses. However, the effects of pyroptosis on tumor microenvironment remodeling and immunotherapy in gastric cancer (GC) remain unclear. Patients and Methods: Large-sample GEO data (GSE15459, GSE54129, and GSE62254) were used to explore the immunoregulatory roles of pyroptosis. TCGA cohort was used to elucidate multiple molecular events associated with pyroptosis, and a pyroptosis risk score (PRS) was constructed. The prognostic performance of the PRS was validated using postoperative GC samples from three public databases (n=925) and four independent Chinese medical cohorts (n=978). Single-cell sequencing and multiplex immunofluorescence were used to elucidate the immune cell infiltration landscape associated with PRS. Patients with GC who received neoadjuvant immunotherapy (n=48) and those with GC who received neoadjuvant chemotherapy (n=49) were enrolled to explore the value of PRS in neoadjuvant immunotherapy. Results: GC pyroptosis participates in immune activation in the tumor microenvironment and plays a powerful role in immune regulation. PRS, composed of four pyroptosis-related differentially expressed genes (BATF2, PTPRJ, RGS1, and VCAN), is a reliable and independent biomarker for GC. PRSlow is associated with an activated pyroptosis pathway and greater infiltration of anti-tumor immune cells, including more effector and CD4+ T cells, and with the polarization of tumor-associated macrophages in the tumor center. Importantly, PRSlow marks the effectiveness of neoadjuvant immunotherapy and enables screening of GC patients with combined positive score ≥1 who benefit from neoadjuvant immunotherapy. Conclusion: Our study demonstrated that pyroptosis activates immune processes in the tumor microenvironment. A low PRS correlates with enhanced infiltration of anti-tumor immune cells at the tumor site, increased pyroptotic activity, and improved patient outcomes. The constructed PRS can be used as an effective quantitative tool for pyroptosis analysis to guide more effective immunotherapeutic strategies for patients with GC.

An On-Treatment Decreased Trend of Serum IL-6 and IL-8 as Predictive Markers Quickly Reflects Short-Term Efficacy of PD-1 Blockade Immunochemotherapy in Patients with Advanced Gastric Cancer.

Objective: Immunotherapy has proven effective in treating advanced gastric cancer (AGC), yet its benefits are limited to a subset of patients. Our aim is to swiftly identify prognostic biomarkers using cytokines to improve the precision of clinical guidance and decision-making for PD-1 inhibitor-based cancer immunotherapy in AGC. Materials and Methods: The retrospective study compared 36 patients with AGC who received combined anti-PD-1 immunotherapy and chemotherapy (immunochemotherapy) with a control group of 20 patients who received chemotherapy alone. The concentrations of TNF-α, IL-1ß, IL-2R, IL-6, IL-8, IL-10, and IL-17 in the serum were assessed using chemiluminescence immunoassay at three distinct time intervals following the commencement of immunochemotherapy. Results: When compared to controls, patients undergoing immunochemotherapy demonstrated a generalized rise in cytokine levels after the start of treatment. However, patients who benefited from immunochemotherapy showed a decrease in IL-6 or IL-8 concentrations throughout treatment (with varied trends observed for IL-1ß, IL-2R, IL-10, IL-17, and TNF-α) was evident in patients benefiting from immunochemotherapy but not in those who did not benefit. Among these markers, the combination of IL-6, IL-8, and CEA showed optimal predictive performance for short-term efficacy of immunochemotherapy in AGC patients. Conclusion: Reductions in IL-6/IL-8 levels observed during immunochemotherapy correlated with increased responsiveness to treatment effectiveness. These easily accessible blood-based biomarkers are predictive and rapid and may play a crucial role in identifying individuals likely to derive benefits from PD-1 blockade immunotherapy.

Effect of anti-claudin 18.2 monoclonal antibody zolbetuximab alone or combined with chemotherapy or programmed cell death-1 blockade in syngeneic and xenograft gastric cancer models.

The development of targeted cancer therapies based on monoclonal antibodies against tumor-associated antigens has progressed markedly over recent decades. This approach is dependent on the identification of tumor-specific, normal tissue-sparing antigenic targets. The transmembrane protein claudin-18 splice variant 2 (CLDN18.2) is frequently and preferentially displayed on the surface of primary gastric adenocarcinomas, making it a promising monoclonal antibody target. Phase 3 studies of zolbetuximab, a chimeric immunoglobulin G1 monoclonal antibody targeting CLDN18.2, combined with 5-fluorouracil/leucovorin plus oxaliplatin (modified FOLFOX6) or capecitabine plus oxaliplatin (CAPOX) in advanced or metastatic first-line gastric or gastroesophageal junction (G/GEJ) adenocarcinoma have demonstrated favorable clinical results with zolbetuximab. In studies using xenograft or syngeneic models with gastric cancer cell lines, zolbetuximab mediated death of CLDN18.2-positive human cancer cell lines via antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity in vitro and demonstrated anti-tumor efficacy as monotherapy and combined with chemotherapy in vivo. Mice treated with zolbetuximab plus chemotherapy displayed a significantly higher frequency of tumor-infiltrating CD8+ T cells versus vehicle/isotype control-treated mice. Furthermore, zolbetuximab combined with an anti-mouse programmed cell death-1 antibody more potently inhibited tumor growth compared with either agent alone. These results support the potential of zolbetuximab as a novel treatment option for G/GEJ adenocarcinoma.

Extracellular vesicles mediated gastric cancer immune response: tumor cell death or immune escape?

Gastric cancer (GC) is a major global health issue, being the fifth most prevalent cancer and the third highest contributor to cancer-related deaths. Although treatment strategies for GC have diversified, the prognosis for advanced GC remains poor. Hence, there is a critical need to explore new directions for GC treatment to enhance diagnosis, treatment, and patient prognosis. Extracellular vesicles (EVs) have emerged as key players in tumor development and progression. Different sources of EVs carry different molecules, resulting in distinct biological functions. For instance, tumor-derived EVs can promote tumor cell proliferation, alter the tumor microenvironment and immune response, while EVs derived from immune cells carry molecules that regulate immune function and possess tumor-killing capabilities. Numerous studies have demonstrated the crucial role of EVs in the development, immune escape, and immune microenvironment remodeling in GC. In this review, we discuss the role of GC-derived EVs in immune microenvironment remodeling and EVs derived from immune cells in GC development. Furthermore, we provide an overview of the potential uses of EVs in immunotherapy for GC.

TM-Score predicts immunotherapy efficacy and improves the performance of the machine learning prognostic model in gastric cancer.

Immunotherapy is becoming increasingly important, but the overall response rate is relatively low in the treatment of gastric cancer (GC). The application of tumor mutational burden (TMB) in predicting immunotherapy efficacy in GC patients is limited and controversial, emphasizing the importance of optimizing TMB-based patient selection. By combining TMB and major histocompatibility complex (MHC) related hub genes, we established a novel TM-Score. This score showed superior performance for immunotherapeutic selection (AUC = 0.808) compared to TMB, MSI status, and EBV status. Additionally, it predicted the prognosis of GC patients. Subsequently, a machine learning model adjusted by the TM-Score further improved the accuracy of survival prediction (AUC > 0.8). Meanwhile, we found that GC patients with low TM-Score had a higher mutation frequency, higher expression of HLA genes and immune checkpoint genes, and higher infiltration of CD8+ T cells, CD4+ helper T cells, and M1 macrophages. This suggests that TM-Score is significantly associated with tumor immunogenicity and tumor immune environment. Notably, based on the RNA-seq and scRNA-seq, it was found that AKAP5, a key component gene of TM-Score, is involved in anti-tumor immunity by promoting the infiltration of CD4+ T cells, NK cells, and myeloid cells. Additionally, siAKAP5 significantly reduced MHC-II mRNA expression in the GC cell line. In addition, our immunohistochemistry assays confirmed a positive correlation between AKAP5 and human leukocyte antigen (HLA) expression. Furthermore, AKAP5 levels were higher in patients with longer survival and those who responded to immunotherapy in GC, indicating its potential value in predicting prognosis and immunotherapy outcomes. In conclusion, TM-Score, as an optimization of TMB, is a more precise biomarker for predicting the immunotherapy efficacy of the GC population. Additionally, AKAP5 shows promise as a therapeutic target for GC.