Immunotherapy For Gastric Cancer Research Articles

Identification of lactylation related model to predict prognostic, tumor infiltrating immunocytes and response of immunotherapy in gastric cancer.

The epigenetic regulatory chemical lactate is a product of glycolysis. It can regulate gene expression through histone lactylation, thereby promoting tumor proliferation, metastasis, and immunosuppression. In this study, a lactylation-related model for gastric cancer (GC) was constructed, and its relationships to prognosis, immune cell infiltration, and immunotherapy were investigated. By contrasting normal tissues and tumor tissues, four lactylation-related pathways that were substantially expressed in GC tissues were found in the GSEA database. Six lactylation-related genes were screened for bioinformatic analysis. The GC data sets from the TCGA and GEO databases were downloaded and integrated to perform cluster analysis, and the lactylation related model was constructed by secondary clustering. The fingding demonstrated that the lactylation score has a strong correlation with the overall survival rate from GC and the progression of GC. Mechanistic experiments showed that abundant immune cell infiltration (macrophages showed the highest degree of infiltration) and increased genetic instability are traits of high lactylation scores. Immune checkpoint inhibitors (ICIs) demonstrated a reduced response rate in GC with high lactylation scores. At the same time, tumors with high lactylation scores had high Tumor Immune Dysfunction and Exclusion scores, which means that they had a higher risk of immune evasion and dysfunction. These findings indicate that the lactylation score can be used to predict the malignant progression and immune evasion of GC. This model also can guide the treatment response to ICIs of GC. The constructed model of the lactate gene is also expected to become a potential therapeutic target for GC and diagnostic marker.

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer.

Autophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified. We endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature. Two distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy. Altogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

LncRNA and its role in gastric cancer immunotherapy.

Gastric cancer (GC) is a potential dominant disease in tumor immunotherapy checkpoint inhibitors, and adoptive cell therapy have brought great hope to GC patients. However, only some patients with GC can benefit from immunotherapy, and some patients develop drug resistance. More and more studies have shown that long non-coding RNAs (lncRNAs) may be important in GC immunotherapy's prognosis and drug resistance. Here, we summarize the differential expression of lncRNAs in GC and their impact on the curative effect of GC immunotherapy, discuss potential mechanisms of activity in GC immunotherapy resistance regulated by lncRNAs. This paper reviews the differential expression of lncRNA in GC and its effect on immunotherapy efficacy in GC. In terms of genomic stability, inhibitory immune checkpoint molecular expression, the cross-talk between lncRNA and immune-related characteristics of GC was summarized, including tumor mutation burden (TMB), microsatellite instability (MSI), and Programmed death 1 (PD-1). At the same time, this paper reviewed the mechanism of tumor-induced antigen presentation and upregulation of immunosuppressive factors, as well as the association between Fas system and lncRNA, immune microenvironment (TIME) and lncRNA, and summarized the functional role of lncRNA in tumor immune evasion and immunotherapy resistance.

The value of metabolic LncRNAs in predicting prognosis and immunotherapy efficacy of gastric cancer.

As a unique feature of malignant tumors, abnormal metabolism can regulate the immune microenvironment of tumors. However, the role of metabolic lncRNAs in predicting the prognosis and immunotherapy of gastric cancer (GC)has not been explored. We downloaded the metabolism-related genes from the GSEA website and identified the metabolic lncRNAs. Co-expression analysis and Lasso Cox regression analysis were utilized to construct the risk model. To value the reliability and sensitivity of the model, Kaplan-Meier analysis and receiver operating characteristic curves were applied. The immune checkpoints, immune cell infiltration and tumor mutation burden of low- and high-risk groups were compared. Tumor Immune Dysfunction and Exclusion (TIDE) score was conducted to evaluate the response of GC patients to immunotherapy. Twenty-three metabolic lncRNAs related to the prognosis of GC were obtained. Three cluster patterns based on metabolic lncRNAs could distinguish GC patients with different overall survival time (OS) effectively (p<0.05). The risk score model established by seven metabolic lncRNAs was verified as an independent prognostic indicator for predicting the OS of GC. The AUC value of the risk model was higher than TNM staging. The high-risk patients were accompanied by significantly increased expression of immune checkpoint molecules (including PD-1, PD-L1 and CTLA4) and increased tumor tolerant immune cells, but significantly decreased tumor mutation burden (TMB). Consistently, TIDE values of low-risk patients were significantly lower than that of high-risk patients. The metabolic lncRNAs risk model can reliably and independently predict the prognosis of GC. The feature that simultaneously map the immune status of tumor microenvironment and TMB gives risk model great potential to serve as an indicator of immunotherapy.

Application of immune checkpoint inhibitors in immunotherapy for gastric cancer.

Gastric cancer is the fifth most common cancer worldwide. With the development of immunotherapy, especially the application of immune checkpoint inhibitors (ICIs), the prognosis of advanced gastric cancer has improved. At present, ICIs combined with other therapies or dual ICI strategies in the treatment of advanced gastric cancer have shown clinicaleffectivenessand controllable safety. In addition, predictive biomarkers facilitate the precise selection of patients. Therefore, it is crucial to explore rational combinations and reliable predictive biomarkers for ICI therapy. This article reviews the recent advances in ICIs and relevant predictive biomarkers in the treatment of gastric cancer.

Visual analysis of global research on immunotherapy for gastric cancer: A literature mining from 2012 to 2022

ABSTRACT Gastric cancer (GC) is one of the most common malignancies. Immunotherapy becomes an indispensable part of GC. This study conducts bibliometric analysis of immunotherapy for GC to clarify the research status and identify potential new research directions. VOS viewer and CiteSpace visualization software were used to demonstrate collaborations and correlations. A total of 1141 English publications from 2012 to 2022 were included. The number of publications increased year by year. The publications were mainly from China (n = 579, 50.70%), followed by the United States. Fudan University published the most publications (n = 48, 4.21%). Frontiers in Oncology and Journal of Clinical Oncology ranked first in cited and co-cited journals, respectively. Kim Kyoung-Mee published the most publications on immunotherapy for GC (n = 14). The clustering of timeline view and co-cited references show the hotspot transformation on immunotherapy for GC. Initially, the hot topic was “cytokine-induced killer cells” and “myeloid-derived suppressor cells.” In recent years, the focus has turned to “targeted therapy.” “CAR-T” has become the hottest topic, and GC has entered precision therapy phase. Screening patients who can benefit from immunotherapy is key to improving prognosis. The combination of immunotherapy with other treatment options, such as chemotherapy and targeted therapy, is currently the focus of research. Chimeric antigen receptor T cell will be further studied in the future.

Histone deacetylase-mediated tumor microenvironment characteristics and synergistic immunotherapy in gastric cancer.

Background: Studies have shown that the expression of histone deacetylases (HDACs) is significantly related to the tumor microenvironment (TME) in gastric cancer. However, the expression of a single molecule or several molecules does not accurately reflect the TME characteristics or guide immunotherapy in gastric cancer. Methods: We constructed an HDAC score (HDS) based on the expression level of HDACs. The single-cell transcriptome was used to analyze the underlying factors contributing to differences in immune infiltration between patients with a high and low HDS. In vitro and in vivo experiments validated the strategy of transforming cold tumors into hot tumors to guide immunotherapy. Results: According to the expression characteristics of HDACs, we constructed an HDS model to characterize the TME. We found that patients with a high HDS had stronger immunogenicity and could benefit more from immunotherapy than those with a low score. The AUC value of the HDS combined with the combined positive score (CPS)for predicting the efficacy of immunotherapy was as high as 0.96. By single-cell and paired bulk transcriptome sequencing analysis, we found that the infiltration levels of CD4+ T cells, CD8+ T cells and NK cells were significantly decreased in the low HDS group, which may be induced by MYH11+ fibroblasts, CD234+ endothelial cells and CCL17+ pDCs via the MIF signaling pathway. Inhibition of the MIF signaling pathway was confirmed to potentially enhance immune infiltration. In addition, our analysis revealed that GPX4 inhibitors might be effective for patients with a low HDS. GPX4 knockout significantly inhibited PD-L1 expression and promoted the infiltration and activation of CD8+ T cells. Conclusion: We constructed an HDS model based on the HDAC expression characteristics of gastric cancer. This model was used to evaluate TME characteristics and predict immunotherapy efficacy. Inhibition of the MIF signaling pathway in the TME and GPX4 expression in tumor cells may be an important strategy for cold tumor synergistic immunotherapy for gastric cancer.

Recent Progress in Immunotherapy for Gastric Cancer

Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide. Under the standard of care, patients with advanced GC (AGC) have a median survival time of approximately 12-15 months. With the emergence of immunotherapy as a key therapeutic strategy in medical oncology, relevant changes are expected in the systemic treatment of GC. In the phase III ATTRACTION-2 trial, nivolumab, a monoclonal anti-programmed cell death 1 (PD-1) antibody, as a third- or later-line treatment improved overall survival (OS) compared with placebo in patients with AGC. Furthermore, nivolumab in combination with 5-fluorouracil and platinum as a first-line treatment improved OS in patients with human epidermal growth factor receptor-2 (HER2)-negative AGC in the global phase III CheckMate-649 study. Another anti-PD-1 antibody, pembrolizumab, in combination with trastuzumab and cytotoxic chemotherapy as a first-line treatment, significantly improved the overall response rate in patients with HER2-positive AGC. Therefore, immune checkpoint inhibitors (ICIs) are essential components of the current treatment of GC. Subsequent treatments after ICI combination therapy, such as ICI rechallenge or combination therapy with agents having other modes of action, are being actively investigated to date. On the basis of the success of immunotherapy in the treatment of AGC, various clinical trials are underway to apply this therapeutic strategy in the perioperative and postoperative settings for patients with early GC. This review describes recent progress in immunotherapy and potential immunotherapy biomarkers for GC.

Biological informed graph neural network for tumor mutation burden prediction and immunotherapy-related pathway analysis in gastric cancer

Tumor mutation burden (TMB) has emerged as an essential biomarker for assessing the efficacy of cancer immunotherapy. However, due to the inherent complexity of tumors, TMB is not always correlated with the responsiveness of immune checkpoint inhibitors (ICIs). Thus, refining the interpretation and contextualization of TMB is a requisite for enhancing clinical outcomes. In this study, we conducted a comprehensive investigation of the relationship between TMB and multi-omics data across 33 human cancer types. Our analysis revealed distinct biological changes associated with varying TMB statuses in STAD, COAD, and UCEC. While multi-omics data offer an opportunity to dissect the intricacies of tumors, extracting meaningful biological insights from such massive information remains a formidable challenge. To address this, we developed and implemented the PGLCN, a biologically informed graph neural network based on pathway interaction information. This model facilitates the stratification of patients into subgroups with distinct TMB statuses and enables the evaluation of driver biological processes through enhanced interpretability. By integrating multi-omics data for TMB prediction, our PGLCN model outperformed previous traditional machine learning methodologies, demonstrating superior TMB status prediction accuracy (STAD AUC: 0.976 ± 0.007; COAD AUC: 0.994 ± 0.007; UCEC AUC: 0.947 ± 0.023) and enhanced interpretability (BA-House: 1.0; BA-Community: 0.999; BA-Grid: 0.994; Tree-Cycles: 0.917; Tree-Grids: 0.867). Furthermore, the biological interpretability inherent to PGLCN identified the Toll-like receptor family and DNA repair pathways as potential combined biomarkers in conjunction with TMB status in gastric cancer. This finding suggests a potential synergistic targeting strategy with immunotherapy for gastric cancer, thus advancing the field of precision oncology.

Autophagy-Related Gene CXCR4 is a Potential Predictor of Prognosis and Immunotherapy Response for Gastric Cancer.

Autophagy is involved in the survival, differentiation, and activation of immune cell subsets. In this study, we determined the prognostic value and biological functions of autophagy genes in gastric cancer (GC). The RNA sequencing dataset for gastric cancer was obtained. Differences in prognosis and enrichment pathways in non-negative matrix factorization (NMF) subclasses were analyzed. Next, we analyzed CXC chemokine receptor 4 (CXCR4) by differential expression, clinical value, immune effects, tumor mutation burden (TMB) values, somatic variants, and biological functions. NMF identified three subclasses. Among the three subclasses, there were differences in prognosis, immune cell infiltration, immune checkpoint genes, and enrichment pathways. Moreover, CXCR4 level was elevated in most tumors, and high CXCR4 level was related to poor prognosis in GC patients. CXCR4 expression was significantly correlated with B cells, eosinophils, macrophages, and plasma cells. In in vitro experiment, CXCR4 promoted GC cell proliferation. Our results showed that CXCR4 is a promising biomarker for predicting prognosis and response to immunotherapy in GC.

Apolipoprotein C1 promotes tumor progression in gastric cancer.

Gastric cancer (GC) is a malignancy with the worst prognosis that seriously threatens human health, especially in East Asia. Apolipoprotein C1 (apoc1) belongs to the apolipoprotein family. In addition, apoc1 has been associated with various tumors. However, its role in GC remains unclear. Firstly, we quantified its expression in GC and adjacent tumor tissues, using The Cancer Genome Atlas (TCGA). Next, we assessed cell invasion and migration abilities. Finally, we revealed the role of apoc1 in the tumor microenvironment (TME), immune cell infiltration and drug sensitivity. Firstly, in TCGA database, it has been shown that elevated expression of apoc1 was identified in various cancers, including GC, then we found that high expression of apoc1 was significantly correlated with poor prognosis in GC. Histologically, apoc1 expression is proportional to grade, cancer stage, and T stage. The experimental results showed that apoc1 promoted cell invasion and migration. Then GO, KEGG, and GSEA pathway analyses indicated that apoc1 may be involved in the WNT pathway and immune regulation. Furthermore, we found out the tumor-infiltrating immune cells related to apoc1 in the tumor microenvironment (TME) using TIMER. Finally, we investigated the correlation between apoc1 expression and drug sensitivity, PD-1 and CTLA-4 therapy. These results suggest that apoc1 participates in the evolution of GC, and may represent a potential target for detection and immunotherapy in GC.

Research Progress of Immunotherapy for Gastric Cancer.

Gastric cancer (GC) is one of the most common gastrointestinal tract cancers worldwide, which has high incidence and mortality rates and poor prognosis. Although multidisciplinary comprehensive therapies consisting of surgery, chemotherapy, radiotherapy, and targeted therapy have made great progress in GC treatment, a satisfactory curative effect still cannot be achieved in many circumstances, and the 5-year survival of patients with GC remains to be very low. In China, about 75% of patients with GC are diagnosed in the advanced stage and thus miss the opportunity of surgical resection. Although the conventional treatment of GC has improved the survival time of advanced patients to a certain extent, the clinical efficacy has encountered a bottleneck and cannot bring higher survival benefits to patients. With the development of immunologic and molecular biologic technologies, immunotherapy has gradually become a new essential treatment for GC, which has attracted extensive attention in the field of oncology. The US Food and Drug Administration (USFDA) and China Food and Drug Administration (CFDA) have approved a variety of immune-related drugs for the treatment of GC, and all of which have achieved good efficacy. In this review, we summarize the recent development in nonspecific enhancer therapy, adoptive immunocell therapy, tumor vaccine therapy, oncolytic virus therapy, and immune checkpoint inhibitor therapy, and their roles in the treatment of GC.

Body composition parameters for predicting the efficacy of neoadjuvant chemotherapy with immunotherapy for gastric cancer.

Immune checkpoint inhibitors are increasingly used in neoadjuvant therapy for locally advanced gastric cancer. However, the effect of body composition on the efficacy of neoadjuvant therapy has not been reported. The computed tomography (CT) images and clinicopathological data of 101 patients with locally advanced gastric cancer who received neoadjuvant chemotherapy combined with immunotherapy (NCI) from 2019 to 2021 were collected. The CT image of L3 vertebral body section was selected, and the body composition before and after the neoadjuvant treatment was calculated using the SliceOmatic software, mainly including skeletal muscle index (SMI), subcutaneous adipose index (SAI), and visceral adipose index (VAI). The relationship between body composition and the efficacy and adverse events of NCI was analyzed. Of the 101 patients, 81 with evaluable data were included in the analysis. Of the included patients, 77.8% were male; the median age of all the patients was 62 years, and the median neoadjuvant therapy cycle was three. After the neoadjuvant therapy, 62.9% of the tumors were in remission (residual tumor cells ≤ 50%), and 37.1% of the tumors had no remission (residual tumor cells>50%). Moreover, 61.7% of the patients had treatment-related adverse events (TRAEs), and 18.5% had immune-related adverse events (irAEs). After neoadjuvant therapy, the body mass index (from 23 to 22.6 cm2/m2, p=0.042), SAI (from 34.7 to 32.9 cm2/m2, p=0.01) and VAI (from 32.4 to 26.8 cm2/m2, p=0.005) were significantly lower than those before treatment, while the SMI had no significant change (44.7 vs 42.5 cm2/m2, p=0.278). The multivariate logistics regression analysis revealed that low SMI (odds ratio [OR]: 3.23,95% confidence interval [CI]: 1.06-9.81, p=0.047), SMI attenuation (△SMI) ≥ 1.8(OR: 1.45,95%CI: 1.20-3.48, p=0.048), and clinical node positivity (OR: 6.99,95%CI: 2.35-20.82, p=0.001) were independent risk factors for non-remission. Additionally, high SAI is an independent risk factor for irAEs (OR: 14, 95%CI: 1.73-112.7, p=0.013). Low SMI and △SMI≥1.8 are independent risk factors for poor tumor regression in patients with advanced gastric cancer receiving NCI. Patients with a high SAI are more likely to develop irAEs.

The loss of epithelial Smad4 drives immune evasion via CXCL1 while displaying vulnerability to combinatorial immunotherapy in gastric cancer

SMAD4 is frequently mutated and inactivated in human gastric cancer (GC). Although the epithelial cell-autonomous functions of Smad4 have been extensively studied, its contribution to tumor immunity is largely undetermined. Here, we report that the loss of Smad4 expression in GC cells endows them with the ability to evade tumor immunity. Unlike their Smad4-proficient counterparts, Smad4-deficient stomach organoids can evade host immunity to form tumors in immunocompetent mice. Smad4-deficient GC cells show expanded CD133+ cancer stem-like cells while suppressing dendritic cell (DC) differentiation and cytotoxic Tcells with granulocytic myeloid-derived suppressor cell (G-MDSC) accumulation through a secretome containing CXCL1. Moreover, Smad4 deficiency increases programmed cell death ligand-1 (PD-L1) and decreases 4-1BBL expressions, indicating a change in immunogenicity. Combinatorial immune checkpoint blockade (ICB) of anti-PD-L1 and anti-CTLA-4 or agonistic anti-4-1BB antibodies effectively treats ICB monotherapy-resistant Smad4-deficient allografts, exposing a specific vulnerability. Collectively, these data provide a rational basis for ICB strategies in treating advanced GC with Smad4 deficiency.

PD-L1: Biological mechanism, function, and immunotherapy in gastric cancer.

Gastric cancer (GC) is one of the main causes of cancer incidence rate and mortality worldwide. As the main breakthrough direction, the application of immune checkpoint inhibitors makes patients with GC have better prognosis, where PD-L1/PD-1 inhibitors in immunotherapy have good anti-tumor immune efficacy. Further understanding of the regulatory mechanism of PD-L1 in GC may bring substantial progress to the immunotherapy. In this review, we provide information on the endogenous and exogenous regulatory mechanisms of PD-L1 and its biological functions combined with current clinical trials of PD-L1/PD-1 inhibitors in GC. The malignant biological phenotypes caused by PD-L1 and the corresponding clinical combined treatment scheme have been reported. Identifying the biomarkers of the potential efficacy of immunotherapy and specifying the clinical immunotherapy scheme in combination with molecular characteristics of patients may maximize clinical benefits and better prognosis.

Isothiocyanates attenuate immune checkpoint blockage therapy in gastric cancer via induction of PD-L1 expression

The PD-1/PD-L1 immune checkpoint blockade therapy has shown revolutionary efficacy in the treatment of multiple cancers including gastric cancer. Isothiocyanates play important roles in cancer cell suppression and immunomodulation. However, the effects of isothiocyanates on immune checkpoint inhibitors are poorly understood in gastric cancer. The influence of three major isothiocyanates (sulforaphane, phenylethyl isothiocyanate, and benzhydryl isothiocyanate) on gastric cancer cell growth and PD-L1 expression was investigated. Syngeneic mouse models were administered by isothiocyanates and anti-PD-L1 monoclonal antibody, and the anti-tumor effects were assessed. The expression of PD-L1, proportion of lymphocytes and serum cytokine levels were detected to explore the underlying mechanisms. We found that PD-L1 expression was significantly induced by isothiocyanates which was associated with TAp63α up-regulation. We further revealed that TAp63α promoted PD-L1 through transcriptional activation. Combination treatment of isothiocyanates and anti-PD-L1 therapy weakened the sensitivity of gastric cancer cells to anti-PD-L1 drug. Moreover, in vivo studies illustrated that the interference effects of isothiocyanates on anti-PD-L1 antibody were related to PD-L1 expression and decreased infiltrating T lymphocytes in tumor bearing mouse hosts. Our findings provide novel insights as isothiocyanates could interfere with the successful application of immunotherapy in gastric cancer.

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer.

Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients.

Prognostic Impact of Hepatic Steatosis Evaluated by CT on Immunotherapy for Gastric Cancer: Associations with Sarcopenia, Systemic Inflammation, and Hormones

Introduction: Immune checkpoint inhibitors (ICIs) are expected to improve the prognosis of gastric cancer (GC). Also, hepatic steatosis has been reported to be associated with cancer cachexia and is expected to be a cancer biomarker. The purpose of this study was to evaluate prognostic impact of hepatic steatosis in ICI therapy for GC. Methods: Unresectable or recurrent GC treated with ICIs was investigated. Using unenhanced CT, the liver-to-spleen CT attenuation ratio (LSR) was calculated as a parameter of hepatic steatosis. LSR was compared with the presence of sarcopenia and inflammatory markers including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and monocyte-to-lymphocyte ratio (MLR). These parameters were also compared with disease-specific survival (DSS) and progression-free survival (PFS). Associations of LSR with insulin-like growth factor 1 (IGF-1) and growth hormone were also evaluated. Results: A total of 70 patients were investigated. LSR of sarcopenia patients was significantly lower than that of non-sarcopenic ones (p = 0.02). LSR showed significant negative correlations with NLR, PLR, and MLR (p = 0.003, 0.03, 0.01, respectively). Lower LSR was significantly associated with a higher level of serum IGF-1 (p = 0.03). In univariate analysis, LSR was significantly correlated with DSS and PFS (both p < 0.0001), and multivariate analysis demonstrated that LSR was the independent prognostic factor for both DSS and PFS (both p = 0.01). ROC analysis demonstrated that LSR >1.263 was a good predictive marker for favorable DSS (>5.3 months) with an AUC of 0.80. Conclusion: Hepatic steatosis can be a promising prognostic biomarker for ICI therapy of GC, associated with sarcopenia and the elevation of inflammatory markers. Our data suggested that GC with steatohepatitis might be less responsive to ICI therapy.

A SERPINE1-Based Immune Gene Signature Predicts Prognosis and Immunotherapy Response in Gastric Cancer.

Immune checkpoint inhibitors (ICIs) therapy has been successfully utilized in the treatment of multiple tumors, but only a fraction of patients with gastric cancer (GC) could greatly benefit from it. A recent study has shown that the tumor microenvironment (TME) can greatly affect the effect of immunotherapy in GC. In this study, we established a novel immune risk signature (IRS) for prognosis and predicting response to ICIs in GC based on the TCGA-STAD dataset. Characterization of the TME was explored and further validated to reveal the underlying survival mechanisms and the potential therapeutic targets of GC. The GC patients were stratified into high- and low-risk groups based on the IRS. Patients in the high-risk group, associated with poorer outcomes, were characterized by significantly higher immune function. Further analysis showed higher T cell immune dysfunction and probability of potential immune escape. In vivo, we detected the expressions of SERPINE1 by the quantitative real-time polymerase chain reaction (qPCR)in tumor tissues and adjacent normal tissues. In vitro, knockdown of SERPINE1 significantly attenuated malignant biological behaviors of tumor cells in GC. Our signature can effectively predict the prognosis and response to immunotherapy in patients with GC.

Prognostic nutritional index for predicting the clinical outcomes of patients with gastric cancer who received immune checkpoint inhibitors.

Although the application of immunotherapy in gastric cancer has achieved satisfactory clinical effects, many patients have no response. The aim of this retrospective study is to investigate the predictive ability of the prognostic nutrition index (PNI) to the prognosis of patients with gastric cancer who received immune checkpoint inhibitors (ICIs). Participants were 146 gastric cancer patients with ICIs (PD-1/PD-L1 inhibitors) or chemotherapy. All patients were divided into a low PNI group and a high PNI group based on the cut-off evaluated by the receiver operating characteristic (ROC) curve. We contrasted the difference in progression-free survival (PFS) and overall survival (OS) in two groups while calculating the prognosis factors for PFS and OS by univariate and multivariate analyses. Moreover, the nomogram based on the results of the multivariate analysis was constructed to estimate the 1- and 3-year survival probabilities. There were 41 (28.1%) cases in the low PNI group and 105 (71.9%) cases in the high PNI group. The median survival time for PFS in the low PNI group and high PNI group was 12.30 months vs. 33.07 months, and 18.57 months vs. not reached in the two groups for OS. Patients in low PNI group were associated with shorter PFS and OS in all patients [Hazard ratio (HR) = 1.913, p = 0.013 and HR = 2.332, p = 0.001]. Additionally, in subgroup analysis, low PNI group cases also had poorer PFS and OS, especially in patients with ICIs. In addition, the multivariate analysis found that carbohydrate antigen 724 (CA724) and TNM stage were independent prognostic factors for PFS. At the same time, indirect bilirubin (IDBIL), CA724, PNI, and TNM stage were independent prognostic factors for OS. Prognostic nutrition index was an accurate inflammatory and nutritional marker, which could predict the prognosis of patients with gastric cancer who received ICIs. PNI could be used as a biomarker for ICIs to identify patients with gastric cancer who might be sensitive to ICIs.