Deconvolution of diffuse gastric cancer and the suppression of CD34 on the BALB/c nude mice model

Subtyping of gastric cancer based on basement membrane genes that stratifies the prognosis, immune infiltration and therapeutic response

Gastric cancer (GC) is highly heterogeneous and prone to metastasis, which are obstacles to the effectiveness of treatment. The basement membrane (BM) acts as a barrier to tumor cell invasion and metastasis. It is critical to investigate the relationship between BM status, metastasis, and patient prognosis. In several large cohorts, we investigated BM gene expression-based molecular classification and risk-prognosis models for GC, examined tumor microenvironment (TME) differences among different molecular subtypes, and developed risk models in predicting prognosis, immunotherapy effectiveness, and chemotherapy resistance. Three GC subtypes (BMclusterA/B/C) based on BM gene expression status were discovered. Each of the three GC subtypes has unique immune infiltration and activated oncogenic signals. Moreover, a 6-gene score (BMscore) predictive model was developed. The low BMscore group had a high tumor mutation burden, high immunogenicity, and low RHOJ expression levels, implying that individuals with GC in this category may be more susceptible to immunotherapy and treatment. The EMT subtype showed a considerably higher BMscore than the other subtypes in the Asian Organization for Research on Cancer (ACRG) molecular classification. Endothelial cells, smooth muscle cells, and fibroblasts may be engaged in regulating BM reorganization in GC progression, according to single-cell transcriptome analyses. In conclusion, we defined a novel molecular classification of GC based on BM genes, developed a prognostic risk model, and elucidated the cell subpopulations involved in BM remodeling at the single-cell level. This study has deepened the understanding of the relationship between GC metastasis and BM alterations, achieved prognostic stratification, and guided therapy.

Utilizing machine learning to integrate single-cell and bulk RNA sequencing data for constructing and validating a novel cell adhesion molecules related prognostic model in gastric cancer

BACKGROUND

Cell adhesion molecules (CAMs) play a vital role in cell-cell interactions, immune response modulation, and tumor cell migration. However, the unique role of CAMs in gastric cancer (GC) remains largely unexplored.

METHODS

This study characterized the genetic alterations and mRNA expression of CAMs. The role of CD34, a representative molecule, was validated in 375 GC tissues. The activity of the CAM pathway was further tested using single-cell and bulk characterization. Next, data from 839 patients with GC from three cohorts was analyzed using univariate Cox and random survival forest methods to develop and validate a CAM-related prognostic model.

RESULTS

Most CAM-related genes exhibited multi-omics alterations and were associated with clinical outcomes. There was a strong correlation between increased CD34 expression and advanced clinical staging (P = 0.026), extensive vascular infiltration (P = 0.003), and unfavorable prognosis (Log-rank P = 0.022). CD34 expression was also found to be associated with postoperative chemotherapy and tumor immunotherapy response. Furthermore, the CAM pathway was significantly activated and mediated poor prognosis. Additionally, eight prognostic signature genes (PSGs) were identified in the training cohort. There was a substantial upregulation of the expression of immune checkpoints and a pronounced infiltration of immune cells in GC tissues with high PSG score, which is consistent with the prediction of increased sensitivity to immunotherapy. Moreover, 9 compounds from the CTRPv2 database and 13 from the Profiling Relative Inhibition Simultaneously in Mixture (PRISM) database were identified as potential therapeutic drugs for patients with GC with high PSG score.

CONCLUSION

Thorough understanding of CAM pathways regulation and the innovative PSG score model hold significant implications for medical diagnosis, potentially enhancing personalized treatment strategies and improving patient outcomes in GC management.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer

BACKGROUND

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer.

METHODS

The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted.

RESULTS

Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor.

CONCLUSIONS

In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

Identification of collagen subtypes of gastric cancer for distinguishing patient prognosis and therapeutic response

Background

Gastric cancer is a highly heterogeneous disease, presenting a major obstacle to personalized treatment. Effective markers of the immune checkpoint blockade response are needed for precise patient classification. We, therefore, divided patients with gastric cancer according to collagen gene expression to indicate their prognosis and treatment response.

Methods

We collected data for 1250 patients with gastric cancer from four cohorts. For the TCGA-STAD cohort, we used consensus clustering to stratify patients based on expression levels of 44 collagen genes and compared the prognosis and clinical characteristics between collagen subtypes. We then identified distinct transcriptomic and genetic alteration signatures for the subtypes. We analyzed the associations of collagen subtypes with the responses to chemotherapy, immunotherapy, and targeted therapy. We also established a platform-independent collagen-subtype predictor. We verified the findings in three validation cohorts (GSE84433, GSE62254, and GSE15459) and compared the collagen subtyping method with other molecular subtyping methods.

Results

We identified two subtypes of gastric adenocarcinoma: a high-expression collagen subtype (CS-H) and a low-expression collagen subtype (CS-L). Collagen subtype was an independent prognostic factor, with better overall survival in the CS-L subgroup. The inflammatory response, angiogenesis, and phosphoinositide 3-kinase (PI3K)/Akt pathways were transcriptionally active in the CS-H subtype, while DNA repair activity was significantly greater in the CS-L subtype. PIK3CA was frequently amplified in the CS-H subtype, while PIK3C2A, PIK3C2G, and PIK3R1 were frequently deleted in the CS-L subtype. CS-H subtype tumors were more sensitive to fluorouracil, while CS-L subtype tumors were more sensitive to immune checkpoint blockade. CS-L subtype was predicted to be more sensitive to HER2-targeted drugs, and CS-H subtype was predicted to be more sensitive to vascular endothelial growth factor and PI3K pathway-targeting drugs. Collagen subtyping also has the potential to be combined with existing molecular subtyping methods for better patient classification.

Conclusions

We classified gastric cancers into two subtypes based on collagen gene expression and validated these subtypes in three validation cohorts. The collagen subgroups differed in terms of prognosis, clinical characteristics, transcriptome, and genetic alterations. The subtypes were closely related to patient responses to chemotherapy, immunotherapy, and targeted therapy.

Identification of cancer-associated fibroblast-related Ectodysplasin-A as a novel indicator for prognosis and immune response in gastric cancer

Studies have indicated cancer-associated fibroblasts (CAFs) could have a significant impact in gastric cancer (GC) progression and chemotherapy resistance. However, the gene related to cancer fibroblasts that can be used as biomarkers to judge the occurrence of gastric cancer has not been fully explored. Based on two Gene Expression Omnibus (GEO) datasets, we focus on differentially expressed genes which may act as CAFs markers related to GC. Through COX regression, LASSO regression and Kaplan-Meier survival analysis, we discovered three upregulated genes (GLT8D2, GNAS and EDA) associated with poor GC patients' survival. By single-cell analysis and nomogram, we found that EDA may affect fibroblast production and disease prognosis in GC patients. EDA expression showed a positive correlation with 5-Fluorouracil IC50 values. Immunohistochemistry (IHC) and real time PCR indicated elevated EDA levels in GC tissues and cells. Enrichment analysis revealed that EDA was closely linked to immune system regulation. IHC and single-cell analysis indicated that EDA gene was associated with cancer fibroblasts marker FGF12 and influence cell interferon-gamma response, which may play a role in regulating immune-related characteristics. In summary, we concluded that EDA may be used as a new therapeutic CAFs marker for GC.

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma

Background

Gastric cancer (GC), a multifaceted gastrointestinal malignancy, is the fourth most prevalent contributor to cancer-related fatalities globally. As a member of the ATP-binding cassette (ABC) family, transporter associated with antigen processing 1 (TAP1) is crucial for conveying antigen peptides from the cytoplasm to the lumen of the endoplasmic reticulum and subsequently loading them onto the major histocompatibility complex (MHC) class I molecules. Recent studies have established the biological significance of TAP1 in upholding tumor survival and facilitating immune evasion by remodeling the tumor microenvironment (TME) and orchestrating immune infiltration. The study was conducted to elucidate the association of TAP1 expression with immunological characteristics, and sought to exploit the value of TAP1 as a biomarker reflecting the inflamed TME and immunotherapeutic response.

Methods

RNA-sequencing profiles and clinical annotations were obtained from The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort and Gene Expression Omnibus (GEO) portal. Preprocessing was conducting using the limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules and TAP1 co-expressed genes (CEGs) based on correlation patterns. Consensus clustering and silhouette analysis determined the optimal number of TAP1-related groups. Gene expression profiles were integrated and classified using the pamr package. The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate immunological characteristics. Differential expression analysis was conducted using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Single-cell RNA sequencing (scRNA-seq) datasets were analyzed using the Seurat toolkit to characterize cell types.

Results

Within this investigation, no significant differences in TAP1 expression were observed among patients exhibiting various clinicopathological features, indicating that TAP1 expression was not specific to molecular subtypes. Subsequent analysis revealed a positive correlation between TAP1 and diverse immunological traits, encompassing immunomodulators, tumor-infiltrating immune cells, as well as immune checkpoints across multiple datasets. Besides, within a GC immunotherapy cohort, individuals displaying high TAP1 expression demonstrated an increased likelihood of achieving complete remission (CR) post-treatment, suggesting heightened sensitivity to immunotherapy. In the clinical cohort, TAP1 overexpression in GC patients was positively correlated with CD8.

Conclusions

TAP1 appears linked to an inflamed TME and serves as a prospective biomarker for discerning immunological attributes and gauging immunotherapeutic responses in GC, particularly in identifying immune-reactive tumors.

Identification of immunotherapy-related subtypes, characterization of tumor microenvironment infiltration, and development of a prognostic signature in gastric carcinoma

BACKGROUND

Recent advances in immunotherapy have elicited a considerable amount of attention as viable therapeutic options for several cancer types, the present study aimed to explore the immunotherapy-related genes (IRGs) and develop a prognostic risk signature in gastric carcinoma (GC) based on these genes.

METHODS

IRGs were identified by comparing immunotherapy responders and non-responders in GC. Then, GC patients were divided into distinct subtypes by unsupervised clustering method based on IRGs, and the differences in immune characteristics and prognostic stratification between these subtypes were analyzed. An immunotherapy-related risk score (IRRS) signature was developed and validated for risk classification and prognosis prediction based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Besides, the predictive ability of the IRRS in immunotherapy response was also determined.

RESULTS

A total of 63 IRGs were identified, and 371 GC patients were stratified into two molecular subgroups with significantly different prognosis and immune characteristics. Then, an IRRS signature comprised of three IRGs (CENP8, NRP1, and SERPINE1) was constructed to predict the prognosis of GC patients in TCGA cohort. Importantly, external validation in multiple GEO cohorts further confirmed the universal applicability of the IRRS in distinct populations. Furthermore, we found that the IRRS was significantly correlated with patient's responsiveness to immunotherapy, GC patients with low IRRS are more likely to benefit from existing immunotherapy.

CONCLUSIONS

The risk score could serve as a robust prognostic biomarker, provide therapeutic benefits for immunotherapy and may be helpful for clinical decision making in GC patients.

The potential contribution of aberrant cathepsin K expression to gastric cancer pathogenesis

The role of cathepsin K (CTSK) expression in the pathogenesis and progression of gastric cancer (GC) remains unclear. Hence, the primary objective of this study is to elucidate the precise expression and biological role of CTSK in GC by employing a combination of bioinformatics analysis and in vitro experiments. Our findings indicated a significant upregulation of CTSK in GC. The bioinformatics analysis revealed that GC patients with a high level of CTSK expression exhibited enrichment of hallmark gene sets associated with angiogenesis, epithelial-mesenchymal transition (EMT), inflammatory response, KRAS signaling up, TNFα signaling via KFκB, IL2-STAT5 signaling, and IL6-JAK-STAT3 signaling. Additionally, these patients demonstrated elevated levels of M2-macrophage infiltration, which was also correlated with a poorer prognosis. The results of in vitro experiments provided confirmation that the over-expression of CTSK leads to an increase in the proliferative and invasive abilities of GC cells. However, further evaluation was necessary to determine the impact of CTSK on the migration capability of these cells. Our findings suggested that CTSK has the potential to facilitate the initiation and progression of GC by augmenting the invasive capacity of GC cells, engaging in tumor-associated EMT, and fostering the establishment of an immunosuppressive tumor microenvironment (TME).

Targeting cellular senescence as a therapeutic vulnerability in gastric cancer

AIMS

Cellular senescence (CS) represents an intracellular defense mechanism responding to stress signals and can be leveraged as a "vulnerability" in cancer treatment. This study aims to construct a CS atlas for gastric cancer (GC) and uncover potential therapeutics for GC patients.

MATERIALS AND METHODS

38 senescence-associated regulators with prognostic significance in GC were obtained from the CellAge database to construct Gastric cancer-specific Senescence Score (GSS). Using eXtreme Sum algorism, GSS-based drug repositioning was conducted to identify drugs that could antagonize GSS in CMap database. In vitro experiments were conducted to test the effect of combination of palbociclib and exisulind in eliminating GC cells.

KEY FINDINGS

Patients with high GSS exhibited CS-related features, such as CS markers upregulation, adverse clinical outcomes and hypomethylation status. scRNA-seq data showed malignant cells with high GSS exhibited enhanced senescence state and more immunosuppressive signals such as PVR-CD96 compared with malignant cells with low GSS. In addition, the GSS-High cancer associated fibroblasts might secrete cytokines and chemokines such as IL-6, CXCL1, CXCL12, and CCL2 to from an immunosuppressive microenvironment, and GSS could serve as an indicator for immunotherapy resistance. Exisulind exhibited the greatest potential to reverse GSS. In vitro experiments demonstrated that exisulind could induce apoptosis and suppress the proliferation of palbociclib-induced senescent GC cells.

SIGNIFICANCE

Overall, GSS offers a framework for better understanding of correlation between senescence and GC, which might provide new insights into the development of novel therapeutics in GC.

Enhanced epithelial-mesenchymal transition signatures are linked with adverse tumor microenvironment, angiogenesis and worse survival in gastric cancer

Epithelial-mesenchymal transition (EMT) is a crucial mechanism that facilitates cancer cell metastasis. Despite its importance, the clinical significance of EMT in gastric cancer (GC) patients has yet to be clearly demonstrated. For gauging the extent of EMT in GC, we employed gene set variation analysis to score 807 patient samples from two large cohorts: TCGA and GSE84437. In both cohorts, EMT high GC showed a significant association with worse overall survival (hazard ratio (HR) = 1.74, p = 0.011 and HR = 2.01, p < 0.001, respectively). This association was stronger when considering the EMT signature score compared to the individual expressions of EMT-related genes (CDH1, CDH2, VIM, and FN1). While the EMT signature level did not differ among various cancers, high EMT signature specifically correlated with survival in GC alone. Mucinous and diffuse histological types exhibited higher EMT levels compared to others (p < 0.001), and the EMT signature level was correlated with tumor depth and AJCC stage (all p < 0.001). Interestingly, the EMT score was an independent factor for overall and disease-specific survival (multivariate; p = 0.006 and 0.032, respectively). EMT high GC displayed a lower fraction of Th1 cells and a higher fraction of dendritic cells, M1 macrophages and several stromal cells. EMT high GC exhibited an inverse correlation with cell proliferation-related gene sets. While they significantly enriched multiple pro-cancerous gene sets, such as TGF-β signaling, hypoxia, and angiogenesis. The presence of EMT signature in a bulk tumor was linked to TGF-β signaling, hypoxia, and angiogenesis, and was also associated with poorer survival outcomes in GC patients.

The Impact of Transcriptional Profiling Cadherin Family and Therapeutic Approaches of Gastric Cancer: A Translational Outlook on Multi-omics Data Analysis

The classical cadherin gene has been linked to a variety of human malignancies, including gastric cancer. However, the link between cadherin genes and gastric cancer outcome is still unclear. This study used multi-omics data to examine the cadherin genes that were differentially regulated in gastric cancer. Differential expression of genes, epigenetic, molecular alterations, and protein expression analyses was conducted. Male SD rats were given N-methyl-N-nitrosourea (MNU) to induce stomach carcinoma in order to verify the activation of cadherin genes. CDH5, CDH6, CDH11, and CDH24 levels were found to be considerably higher in gastric cancer and may serve as useful indicators of stomach adenocarcinoma (STAD). Cadherin genes with variable expression had considerably more promoter methylation in cancers than in normal tissues. In individuals with gastric cancer, high expression of these cadherin genes was related to lower total mortality and disease-free survival rates. Furthermore, compared to normal rats, gastric cancer-induced rats had significantly higher expression and distribution of CDH5, CDH6, CDH11, and CDH24. This study sheds new light on the diagnosis and prognosis of gastric cancer by identifying potential prognostic markers such as CDH5, CDH6, CDH11, and CDH24. The multi-omics approach provided a potential tool for target-based therapy by accurately predicting the outcome of stomach cancer. Researchers may gain more knowledge about the role of cadherin genes in the development and dissemination of tumors to the activated rat model of gastric cancer.

Prognostic value of RNA methylation-related genes in gastric adenocarcinoma based on bioinformatics

Background

Gastric cancer (GC) is a malignant tumor that originates from the epithelium of the gastric mucosa and has a poor prognosis. Stomach adenocarcinoma (STAD) covers 95% of total gastric cancer. This study aimed to identify the prognostic value of RNA methylation-related genes in gastric cancer.

Methods

In this study, The Cancer Genome Atlas (TCGA)-STAD and GSE84426 cohorts were downloaded from public databases. Patients were classified by consistent cluster analysis based on prognosis-related differentially expressed RNA methylation genes Prognostic genes were obtained by differential expression, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses. The prognostic model was established and validated in the training set, test set and validation set respectively. Independent prognostic analysis was implemented. Finally, the expression of prognostic genes was affirmed by reverse transcription quantitative PCR (RT-qPCR).

Results

In total, four prognostic genes (ACTA2, SAPCD2, PDK4 and APOD) related to RNA methylation were identified and enrolled into the risk signature. The STAD patients were divided into high- and low-risk groups based on the medium value of the risk score, and patients in the high-risk group had a poor prognosis. In addition, the RNA methylation-relevant risk signature was validated in the test and validation sets, and was authenticated as a reliable independent prognostic predictor. The nomogram was constructed based on the independent predictors to predict the 1/3/5-year survival probability of STAD patients. The gene set enrichment analysis (GSEA) result suggested that the poor prognosis in the high-risk subgroup may be related to immune-related pathways. Finally, the experimental results indicated that the expression trends of RNA methylation-relevant prognostic genes in gastric cancer cells were in agreement with the result of bioinformatics.

Conclusion

Our study established a novel RNA methylation-related risk signature for STAD, which was of considerable significance for improving prognosis of STAD patients and offering theoretical support for clinical therapy.

Prediction and verification of the prognostic biomarker SLC2A2 and its association with immune infiltration in gastric cancer

Gastric cancer (GC) is the fifth most common cause of cancer-associated deaths; however, its treatment options are limited. Despite clinical improvements, chemotherapy resistance and metastasis are major challenges in improving the prognosis and quality of life of patients with GC. Therefore, effective prognostic biomarkers and targets associated with immunological interventions need to be identified. Solute carrier family 2 member 2 (SLC2A2) may serve a role in tumor development and invasion. The present study aimed to evaluate SLC2A2 as a prospective prognostic marker and chemotherapeutic target for GC. SLC2A2 expression in several types of cancer and GC was analyzed using online databases, and the effects of SLC2A2 expression on survival prognosis in GC were investigated. Clinicopathological parameters were examined to explore the association between SLC2A2 expression and overall survival (OS). Associations between SLC2A2 expression and immune infiltration, immune checkpoints and IC50 were estimated using quantification of the tumor immune contexture from human RNA-seq data, the Tumor Immune Estimation Resource 2.0 database and the Genomics of Drug Sensitivity in Cancer database. Differential SLC2A2 expression and the predictive value were validated using the Human Protein Atlas, Gene Expression Omnibus, immunohistochemistry and reverse transcription-quantitative PCR. SLC2A2 expression was downregulated in most types of tumor but upregulated in GC. Functional enrichment analysis revealed an association between SLC2A2 expression and lipid metabolism and the tumor immune microenvironment. According to Gene Ontology term functional enrichment analysis, SLC2A2-related differentially expressed genes were enriched predominantly in 'chylomicron assembly', 'plasma lipoprotein particle assembly', 'high-density lipoprotein particle', 'chylomicron', 'triglyceride-rich plasma lipoprotein particle', 'very-low-density lipoprotein particle'. 'intermembrane lipid transfer activity', 'lipoprotein particle receptor binding', 'cholesterol transporter activity' and 'intermembrane cholesterol transfer activity'. In addition, 'cholesterol metabolism', and 'fat digestion and absorption' were significantly enriched in the Kyoto Encyclopedia of Genes and Genomes pathway analysis. Patients with GC with high SLC2A2 expression had higher levels of neutrophil and M2 macrophage infiltration and a significant inverse correlation was observed between SLC2A2 expression and MYC targets, tumor mutation burden, microsatellite instability and immune checkpoints. Furthermore, patients with high SLC2A2 expression had worse prognosis, including OS, disease-specific survival and progression-free interval. Multivariate regression analysis demonstrated that SLC2A2 could independently prognosticate GC and the nomogram model showed favorable performance for survival prediction. SLC2A2 may be a prospective prognostic marker for GC. The prediction model may improve the prognosis of patients with GC in clinical practice, and SLC2A2 may serve as a novel therapeutic target to provide immunotherapy plans for GC.

Leveraging synthetic lethality to uncover potential therapeutic target in gastric cancer

Since trastuzumab was approved in 2012 for the first-line treatment of gastric cancer (GC), no significant advancement in GC targeted therapies has occurred. Synthetic lethality refers to the concept that simultaneous dysfunction of a pair of genes results in a lethal effect on cells, while the loss of an individual gene does not cause this effect. Through exploiting synthetic lethality, novel targeted therapies can be developed for the individualized treatment of GC. In this study, we proposed a computational strategy named Gastric cancer Specific Synthetic Lethality inference (GSSL) to identify synthetic lethal interactions in GC. GSSL analysis was used to infer probable synthetic lethality in GC using four accessible clinical datasets. In addition, prediction results were confirmed by experiments. GSSL analysis identified a total of 34 candidate synthetic lethal pairs, which included 33 unique targets. Among the synthetic lethal gene pairs, TP53-CHEK1 was selected for further experimental validation. Both computational and experimental results indicated that inhibiting CHEK1 could be a potential therapeutic strategy for GC patients with TP53 mutation. Meanwhile, in vitro experimental validation of two novel synthetic lethal pairs TP53-AURKB and ARID1A-EP300 further proved the universality and reliability of GSSL. Collectively, GSSL has been shown to be a reliable and feasible method for comprehensive analysis of inferring synthetic lethal interactions of GC, which may offer novel insight into the precision medicine and individualized treatment of GC.

Gastric cancer with enhanced myogenesis is associated with less cell proliferation, enriched epithelial-to-mesenchymal transition and angiogenesis, and poor clinical outcomes

Gastric cancer (GC) remains a lethal disease, with over 26,000 new cases and more than 11,000 deaths annually in the US. Thus, a deeper understanding of GC biology is critical to improve survival. Myogenesis is the formation of muscle fibers, which is a mesodermal tissue. In cancer, epithelial-to-mesenchymal transition (EMT) is a known phenomenon that promotes metastasis and poor survival. Given that myogenesis produces mesenchymal cells, we hypothesized that GC with increased myogenesis is linked to aggressive tumor behaviors and less favorable outcomes. In this study, three GC patient cohorts: TCGA (n=375), GSE26253 (n=432), and GSE84437 (n=482), were analyzed. The "MYOGENESIS" set in the Hallmark collection which comprises 200 myogenesis-related genes was analyzed to perform gene set variation analysis to create a score to quantify the myogenesis activity. Our results showed that T category of AJCC cancer staging that reflects the tumor invasion to stomach wall consistently correlated with myogenesis activity in two GC cohorts. High myogenesis GC was associated with lower cell proliferation, evidenced by reduced proliferation scores, decreased Ki67 gene expression, and less enrichment of E2F Targets, G2M checkpoint, MYC Targets V1, and V2 gene sets. High myogenesis tumors showed increased stromal cells (fibroblasts and adipocytes) infiltration within the tumor microenvironment, as well as less silent and non-silent mutation rates and copy number alterations. Higher lymphocyte infiltration, leukocyte fraction, T-cell receptor richness, and B-cell receptor richness were associated with high myogenesis GC. However, infiltration of CD4 cells, T helper type 1 and 2 cells, Natural Killer cells, regulatory T cells, and plasma cells was lower, with increased infiltration of dendritic cells in high myogenesis GC. High myogenesis GC enriched EMT, Hedgehog, TGF-β, and KRAS gene sets. Furthermore, it was associated with enhanced angiogenesis, evidenced by enrichment of Angiogenesis, Coagulation, and Hypoxia gene sets, and increased infiltration of microvascular and lymphatic endothelial cells and pericytes. High myogenesis GC consistently correlated with worse overall survival in all three cohorts, and worse disease-specific and progression-free survival in the TCGA cohort. Hence, our findings suggest that GC with enhanced myogenesis is associated with decreased cell proliferation, increased EMT and angiogenesis, and worse prognosis.

Intratumoral microbiome is associated with gastric cancer prognosis and therapy efficacy

The role of the intratumoral microbiome in gastric cancer (GC) has not been comprehensively assessed. Here, we explored the relationship between the microbial community and GC prognosis and therapy efficacy. Several cancer-associated microbial characteristics were identified, including increased α-diversity, differential β-diversity, and decreased Helicobacter pylori abundance. After adjusting for clinical features, prognostic analysis revealed 2 phyla, 14 genera, and 5 species associated with the overall survival of patients with GC. Additionally, 2 phyla, 14 genera, and 6 species were associated with adjuvant chemotherapy (ACT) efficacy in patients with stage II - III GC. Furthermore, we classified GC microbiome structures into three microbial subtypes (MS1, MS2 and MS3) with distinguishing features. The MS1 subtype exhibited high immune activity and enrichment of microbiota related to immunotherapy and butyric acid-producing, as well as potential benefits in immunotherapy. MS2 featured the highest α-diversity and activation of the TFF pathway, MS3 was characterized by epithelial-mesenchymal transition and was associated with poor prognosis and reduced ACT efficacy. Collectively, the results of this study provide valuable insights into the microbial characteristics associated with GC prognosis and therapy efficacy.

Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC

BACKGROUND

Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC).

MATERIALS AND METHODS

The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset.

RESULTS

According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset.

CONCLUSION

The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.

Regulation of Hippo-YAP/CTGF signaling by combining an HDAC inhibitor and 5-fluorouracil in gastric cancer cells

Histone deacetylase (HDAC) inhibitors diminish carcinogenesis, metastasis, and cancer cell proliferation by inducing death in cancer cells. Tissue regeneration and organ development are highly dependent on the Hippo signaling pathway. Targeting the dysregulated hippo pathway is an excellent approach for cancer treatment. According to the results of this study, the combination of panobinostat, a histone deacetylase inhibitor, and 5-fluorouracil (5-FU), a chemotherapy drug, can act synergistically to induce apoptosis in gastric cancer cells. The combination of panobinostat and 5-FU was more effective in inhibiting cell viability than either treatment alone by elevating the protein levels of cleaved PARP and cleaved caspase-9. By specifically targeting E-cadherin, vimentin, and MMP-9, the combination of panobinostat and 5-FU significantly inhibited cell migration. Additionally, panobinostat significantly increased the anticancer effects of 5-FU by activating Hippo signaling (Mst 1 and 2, Sav1, and Mob1) and inhibiting the Akt signaling pathway. As a consequence, there was a decrease in the amount of Yap protein. The combination therapy of panobinostat with 5-FU dramatically slowed the spread of gastric cancer in a xenograft animal model by deactivating the Akt pathway and supporting the Hippo pathway. Since combination treatment exhibits much higher anti-tumor potential than 5-FU alone, panobinostat effectively potentiates the anti-tumor efficacy of 5-FU. As a result, it is believed that panobinostat and 5-FU combination therapy will be useful as supplemental chemotherapy in the future.

A methylation-related signature for predicting prognosis and sensitivity to first-line therapies in gastric cancer

Background

Methylation modification patterns play a crucial role in human cancer progression, especially in gastrointestinal cancers. We aimed to use methylation regulators to classify patients with gastric adenocarcinoma and build a model to predict prognosis, promoting the application of precision medicine.

Methods

We obtained RNA sequencing data and clinical data from The Cancer Genome Atlas (TCGA) database (n=335) and Gene Expression Omnibus (GEO) database (n=865). Unsupervised consensus clustering was used to identify subtypes of gastric adenocarcinoma. We performed functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular feature analysis to determine the clinical application for different subtypes. The univariate Cox regression analysis and the LASSO regression analysis were subsequently used to identify prognosis-related methylation regulators and construct a risk model.

Results

Through unsupervised consensus clustering, patients were divided into two subtypes (cluster A and cluster B) with different clinical outcomes. Cluster B included patients with a better prognosis outcome and who were more likely to respond to immunotherapy. We then successfully built a predictive model and found five methylation-related genes (CHAF1A, CPNE8, PHLDA3, SPARC, and EHF) potentially significant to the prognosis of patients. The 1-, 3-, and 5-year areas under the curve of the risk model were 0.712, 0.696, and 0.759, respectively. The risk score was an independent prognostic factor and had the highest concordance index among common clinical indicators. Meanwhile, the tumor microenvironment, sensitivity of chemotherapeutic drugs, molecular features, and oncogenic dedifferentiation differed significantly across the risk groups and subtypes.

Conclusions

We classified patients with gastric adenocarcinoma based on methylation regulators, which has positive implications for first-line clinical treatment. The prognostic model could predict the prognosis of patients and help to promote the development of precision medicine.

The role of senescence genes in the treatment, prognosis, and tumor microenvironment of gastric cancer

AIM

Gastric cancer (GC) has a high incidence and poor prognosis. Senescence genes are suggested to participate in immune cell infiltration, thus affecting the immunotherapy of GC. In this research, we established a senescence-related GC model to explore and verify the role of senescence genes in the prognosis, treatment, and tumor microenvironment (TME) of GC.

METHODS

The TCGA GC (TCGA-STAD) dataset was used to screen key senescence genes from differentially expressed genes (DEGs). A prognostic risk model was trained utilizing the TCGA-STAD dataset and validated using an external GEO dataset. The CIBERSORT algorithm was run to explore the relationship between senescence genes and TME. The chemotherapy drug sensitivities in GC patients were calculated utilizing R package pRRophetic.

RESULTS

A total of 37 senescence-related DEGs were obtained. Five key senescence-related genes were further screened to establish a senescence-related risk model based on Cox regression. The survival status of GC patients in the high-risk group was found to be worse than that in the low-risk group. According to the results of gene set enrichment analysis, the senescence-related risk was mainly associated with cytokine activity, immune mechanism, and related pathways. By analyzing the sensitivity of common chemotherapy drugs in GC patients, it was revealed that the sensitivities of high-risk patients to Dasatinib, Lapatinib, and Pazopanib were lower than those of low-risk patients. The CIBERSORT algorithm was executed to analyze the TME in the high-risk group, revealing elevated levels of CD8 T cells, Macrophages M2, and resting Mast cells. In addition, decreased levels of resting memory CD4 T cells , resting NK cells, activated Dendritic cells, and activated Mast cells were also observed.

CONCLUSION

Senescence genes were related to the prognosis, response to chemotherapy drugs, and TME of GC. Our senescence-related risk model could forecast the survival of patients, their response to chemotherapy drugs, and the TME to a certain extent.

Pan-cancer analysis of cuproptosis-promoting gene signature from multiple perspectives

Cuproptosis is a newly discovered cell death form with a unique mechanism. Seven genes have been identified to facilitate the process. To explore the roles of cuproptosis in different cancers, we first used Gene Expression Profiling, Interactive Analysis, version 2, and cBioPortal to analyze expression, prognosis and mutation conditions in different cancers from The Cancer Genome Atlas (TCGA). Then, we conducted single sample gene set enrichment analysis to combine the signature of the cuproptosis-promoting genes for all TCGA cancers. Moreover, we performed a survival analysis to explore if cuproptosis-score could independently influence clinical outcomes. Next, we compared pathway enrichment, immune infiltration, gene set activity and gene mutation between different cuproptosis-score groups. Finally, based on the intersected genes from difference analysis and weighted gene co-expression network analysis, consensus clustering and Least Absolute Shrinkage and Selection Operator Cox regression were performed and nomograms were constructed. Cuproptosis-score was associated with a favorable prognosis in eight TCGA cancers. Cancer-associated fibroblasts, B cells, neutrophils and mast cells were generally less abundant, and ferroptosis activity was higher in high cuproptosis-score groups. The novel classifications could differentiate patients' overall survival, and the risk models could effectively predict patients' outcomes in kidney, renal clear cell carcinoma, liver hepatocellular carcinoma, mesothelioma and stomach adenocarcinoma. Cuproptosis activity was closely related to the prognosis of several cancers. Its effects on the immune microenvironment and its relationship with other cell death modes, especially ferroptosis, may become the focus of further research.

Association of genes in hereditary metabolic diseases with diagnosis, prognosis, and treatment outcomes in gastric cancer

Background

Aberrant metabolism is a major hallmark of cancers and hereditary diseases. Genes associated with inborn metabolic errors may also play roles in cancer development. This study evaluated the overall impact of these genes on gastric cancer (GC).

Methods

In total, 162 genes involved in 203 hereditary metabolic diseases were identified in the Human Phenotype Ontology database. Clinical and multi-omic data were acquired from the GC cohort of the Affiliated Hospital of Jiangsu University and other published cohorts. A 4-gene and 32-gene signature was established for diagnosis and prognosis or therapeutic prediction, respectively, and corresponding abnormal metabolism scores (AMscores) were calculated.

Results

The diagnostic AMscore showed high sensitivity (0.88-1.00) and specificity (0.89-1.00) to distinguish between GC and paired normal tissues, with area under the receiver operating characteristic curve (AUC) ranging from 0.911 to 1.000 in four GC cohorts. The prognostic or predictive AMscore was an independent predictor of overall survival (OS) in five GC cohorts and a predictor of the OS and disease-free survival benefit of postoperative chemotherapy or chemoradiotherapy in one GC cohort with such data. The AMscore adversely impacts immune biomarkers, including tumor mutation burden, tumor neoantigen burden, microsatellite instability, programmed death-ligand 1 protein expression, tumor microenvironment score, T cell receptor clonality, and immune cell infiltration detected by multiplex immunofluorescence staining. The AUC of the AMscore for predicting immunotherapy response ranging from 0.780 to 0.964 in four cohorts involving GC, urothelial cancer, melanoma, and lung cancer. The objective response rates in the low and high AMscore subgroups were 78.6% and 3.2%, 40.4% and 7%, 52.6% and 0%, and 72.7% and 0%, respectively (all p<0.001). In cohorts with survival data, a high AMscore was hazardous for OS or progression-free survival, with hazard ratios ranged from 5.79 to 108.59 (all p<0.001). Importantly, the AMscore significantly improved the prediction of current immune biomarkers for both response and survival, thus redefining the advantaged and disadvantaged immunotherapy populations.

Conclusions

Signatures based on genes associated with hereditary metabolic diseases and their corresponding scores could be used to guide the diagnosis and treatment of GC. Therefore, further validation is required.

Oxidative stress and autophagy-mediated immune patterns and tumor microenvironment infiltration characterization in gastric cancer

Recent years have seen a sharp rise in the amount of research on the connection between oxidative stress, autophagy, and cancer cells. However, the significant functions of oxidative stress and autophagy-related genes (OARGs) in gastric cancer (GC) are yet to be investigated integrally. Therefore, it will be a new and promising concept to search for novel OARG-related biomarkers to predict the prognosis and treatment response of GC. First, we assessed changes in prognosis and tumor microenvironment (TME) characteristics across the various oxidative stress and autophagy-related modification patterns based on a detailed analysis of 17 OARGs with prognostic significance of 808 GC samples. We identified three distinct OARG alteration patterns which displayed unique biological characteristics and immune cell infiltration features. Using principal component analysis methods, the OARGscore was developed to evaluate the OARG modification patterns of certain tumors. The negative connection between OARGscore and immune cells was statistically significant. Increased survival, a higher incidence of mutations, and a better response to immunotherapy were all predicted to be related to patients' high-OARGscore. In addition, the candidate chemotherapeutic drugs were predicted using the oncoPredict program. The low-OARGscore group was predicted to benefit more from Ribociclib, Alisertib, Niraparib, Epirubicin, Olaparib, and Axitinib, while patients in the high-OARGscore group were predicted to benefit more from Afatinib, Oxaliplatin, Paclitaxel, 5-Fluorouracil, Dabrafenib and Lapatinib. Our findings offer a specific method for predicting a patient's prognosis and susceptibility to immunotherapy, as well as a promising insight of oxidative stress and autophagy in GC.

Microdissection of cancer-associated fibroblast infiltration subtypes unveils the secreted SERPINE2 contributing to immunosuppressive microenvironment and immuotherapeutic resistance in gastric cancer: A large-scale study integrating bulk and single-cell transcriptome profiling

In the era of immunotherapy, the suboptimal response rate and the development of acquired resistance among the initial beneficiaries continue to present significant challenges across multiple malignancies, including gastric cancer (GC). Considering that the interactions of tumor stroma, especially the cancer-associated fibroblasts (CAFs), with immune and tumor cells, play indispensable roles in tumor progression, tumor microenvironment remodeling and therapeutic responsiveness, in-depth exploration on the roles of CAFs and pivotal mediators of their functions may provide novel clues to increase the effectiveness of current immunotherapeutic drugs and further achieve synergistic antitumor response. Herein, through the consensus clustering of canonical biomarkers, three GC subclasses with different abundance of CAFs were virtually microdissected in four integrated bulk cohorts encompassing 2148 GC patients from 11 independent datasets. An extensive immunogenomic analysis revealed that tumors with high CAFs infiltration were characterized with unfavorable outcomes, aggressive phenotypes, decreased tumor immunogenicity, high risk of immune evasion and thus immunotherapeutic resistance. By leveraging large-scale single-cell transcriptomic profiling, a series of CAF-secreted proteins were identified, among which the SERPINE2 was confirmed to be restrictively enriched in stromal fibroblasts of GC tissues and contribute to promoting a protumor milieu and fostering an immunosuppressive microenvironment via bioinformatics computations and tissue microarray analysis. Moreover, pan-cancer investigations generalized the immunological roles of SERPINE2, especially in pan-gastrointestinal malignancies, with multiple real-world immunotherapy cohorts further confirming its implications on predicting immunotherapeutic efficacy. In conclusion, these findings suggest that the CAF-derived SERPINE2 is a promising immune-oncology target with therapeutic implications to further synergize the immunotherapeutic combinations.

Dynamic single-cell mapping unveils Epstein‒Barr virus-imprinted T-cell exhaustion and on-treatment response

Epstein‒Barr virus (EBV)-associated gastric cancer (GC) manifests an intriguing immunotherapy response. However, the cellular basis for EBV-imprinted tumour immunity and on-treatment response remains undefined. This study aimed to finely characterize the dynamic tumour immune contexture of human EBV (+) GC treated with immunochemotherapy by longitudinal scRNA-seq and paired scTCR/BCR-seq. EBV (+) GC exhibits an inflamed-immune phenotype with increased T-cell and B-cell infiltration. Immunochemotherapy triggers clonal revival and reinvigoration of effector T cells which step to determine treatment response. Typically, an antigen-specific ISG-15+CD8+ T-cell population is highly enriched in EBV (+) GC patients, which represents a transitory exhaustion state. Importantly, baseline intratumoural ISG-15+CD8+ T cells predict immunotherapy responsiveness among GC patients. Re-emerged clonotypes of pre-existing ISG-15+CD8+ T cells could be found after treatment, which gives rise to a CXCL13-expressing effector population in responsive EBV (+) tumours. However, LAG-3 retention may render the ISG-15+CD8+ T cells into a terminal exhaustion state in non-responsive EBV (+) tumours. In accordance, anti-LAG-3 therapy could effectively reduce tumour burden in refractory EBV (+) GC patients. Our results delineate a distinct implication of EBV-imprinted on-treatment T-cell immunity in GC, which could be leveraged to optimize the rational design of precision immunotherapy.

Construction and validation of a signature for T cell-positive regulators related to tumor microenvironment and heterogeneity of gastric cancer

Background

Positive regulators of T cell function play a vital role in the proliferation and differentiation of T cells. However, their functions in gastric cancer have not been explored so far.

Methods

The TCGA-STAD dataset was utilized to perform consensus clustering in order to identify subtypes related to T cell-positive regulators. The prognostic differentially expressed genes of these subtypes were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. To validate the robustness of the identified signature, verification analyses were conducted across the TCGA-train, TCGA-test, and GEO datasets. Additionally, a nomogram was constructed to enhance the clinical efficacy of this predictive tool. Transwell migration, colony formation, and T cell co-culture assays were used to confirm the function of the signature gene in gastric cancer and its influence on T cell activation.

Results

Two distinct clusters of gastric cancer, related to T cell-positive regulation, were discovered through the analysis of gene expression. These clusters exhibited notable disparities in terms of survival rates (P = 0.028), immune cell infiltration (P< 0.05), and response to immunotherapy (P< 0.05). Furthermore, a 14-gene signature was developed to classify gastric cancer into low- and high-risk groups, revealing significant differences in survival rates, tumor microenvironment, tumor mutation burden, and drug sensitivity (P< 0.05). Lastly, a comprehensive nomogram model was constructed, incorporating risk factors and various clinical characteristics, to provide an optimal predictive tool. Additionally, an assessment was conducted on the purported molecular functionalities of low- and high-risk gastric cancers. Suppression of DNAAF3 has been observed to diminish the migratory and proliferative capabilities of gastric cancer, as well as attenuate the activation of T cells induced by gastric cancer within the tumor microenvironment.

Conclusion

We identified an ideal prognostic signature based on the positive regulators of T cell function in this study.

A Novel Gene List Identifies Tumors with a Stromal-Mesenchymal Phenotype and Worse Prognosis in Gastric Cancer

BACKGROUND

Molecular biomarkers that predict disease progression can help identify tumor subtypes and shape treatment plans. In this study, we aimed to identify robust biomarkers of prognosis in gastric cancer based on transcriptomic data obtained from primary gastric tumors.

METHODS

Microarray, RNA sequencing, and single-cell RNA sequencing-based gene expression data from gastric tumors were obtained from public databases. Freshly frozen gastric tumors (n = 42) and matched FFPE (formalin-fixed, paraffin-embedded) (n = 40) tissues from a Turkish gastric cancer cohort were used for quantitative real-time PCR and immunohistochemistry-based assessments of gene expression, respectively.

RESULTS

A novel list of 20 prognostic genes was identified and used for the classification of gastric tumors into two major tumor subgroups with differential stromal gene expression ("Stromal-UP" (SU) and "Stromal-DOWN" (SD)). The SU group had a more mesenchymal profile with an enrichment of extracellular matrix-related gene sets and a poor prognosis compared to the SD group. Expression of the genes within the signature correlated with the expression of mesenchymal markers ex vivo. A higher stromal content in FFPE tissues was associated with shorter overall survival.

CONCLUSIONS

A stroma-rich, mesenchymal subgroup among gastric tumors identifies an unfavorable clinical outcome in all cohorts tested.

Bulk and single-cell transcriptome profiling reveal the metabolic heterogeneity in gastric cancer

Metabolic reprogramming has been defined as a key hall mark of human tumors. However, metabolic heterogeneity in gastric cancer has not been elucidated. Here we separated the TCGA-STAD dataset into two metabolic subtypes. The differences between subtypes were elaborated in terms of transcriptomics, genomics, tumor-infiltrating cells, and single-cell resolution. We found that metabolic subtype 1 is predominantly characterized by low metabolism, high immune cell infiltration. Subtype 2 is mainly characterized by high metabolism and low immune cell infiltration. From single-cell resolution, we found that the high metabolism of subtype 2 is dominated by epithelial cells. Not only epithelial cells, but also various immune cells and stromal cells showed high metabolism in subtype 2 and low metabolism in subtype 1. Our study established a classification of gastric cancer metabolic subtypes and explored the differences between subtypes from multiple dimensions, especially the single-cell resolution.

The combined signatures of the tumour microenvironment and nucleotide metabolism-related genes provide a prognostic and therapeutic biomarker for gastric cancer

The tumour microenvironment (TME) is vital to tumour development and influences the immunotherapy response. Abnormal nucleotide metabolism (NM) not only promotes tumour cell proliferation but also inhibits immune responses in the TME. Therefore, this study aimed to determine whether the combined signatures of NM and the TME could better predict the prognosis and treatment response in gastric cancer (GC). 97 NM-related genes and 22 TME cells were evaluated in TCGA-STAD samples, and predictive NM and TME characteristics were determined. Subsequent correlation analysis and single-cell data analysis illustrated a link between NM scores and TME cells. Thereafter, NM and TME characteristics were combined to construct an NM-TME classifier. Patients in the NMlow/TMEhigh group exhibited better clinical outcomes and treatment responses, which could be attributed to the differences in immune cell infiltration, immune checkpoint genes, tumour somatic mutations, immunophenoscore, immunotherapy response rate and proteomap. Additionally, the NMhigh/TMElow group benefited more from Imatinib, Midostaurin and Linsitinib, while patients in the NMlow/TMEhigh group benefited more from Paclitaxel, Methotrexate and Camptothecin. Finally, a highly reliable nomogram was developed. In conclusion, the NM-TME classifier demonstrated a pretreatment predictive value for prognosis and therapeutic responses, which may offer novel strategies for strategizing patients with optimal therapies.

Construction of an RNA modification-related gene predictive model associated with prognosis and immunity in gastric cancer

BACKGROUND

Gastric cancer (GC) is one of the most common causes of cancer-related fatalities worldwide, and its progression is associated with RNA modifications. Here, using RNA modification-related genes (RNAMRGs), we aimed to construct a prognostic model for patients with GC.

METHODS

Based on RNAMRGs, RNA modification scores (RNAMSs) were obtained for GC samples from The Cancer Genome Atlas and were divided into high- and low-RNAMS groups. Differential analysis and weighted correlation network analysis were performed for the differential expressed genes (DEGs) to obtain the key genes. Next, univariate Cox regression, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were performed to obtain the model. According to the model risk score, samples were divided into high- and low-risk groups. Enrichment analysis and immunoassays were performed for the DEGs in these groups. Four external datasets from Gene Expression Omnibus data base were used to test the accuracy of the predictive model.

RESULTS

We identified SELP and CST2 as key DEGs, which were used to generate the predictive model. The high-risk group had a worse prognosis compared to the low-risk group (p < 0.05). Enrichment analysis and immunoassays revealed that 144 DEGs related to immune cell infiltration were associated with the Wnt signaling pathway and included hub genes such as ELN. Overall mutation levels, tumor mutation burden, and microsatellite instability were lower, but tumor immune dysfunction and exclusion scores were greater (p < 0.05) in the high-risk group than in the low-risk group. The validation results showed that the prediction model score can accurately predict the prognosis of GC patients. Finally, a nomogram was constructed using the risk score combined with the clinicopathological characteristics of patients with GC.

CONCLUSION

This risk score from the prediction model related to the tumor microenvironment and immunotherapy could accurately predict the overall survival of GC patients.

METTL3-mediated Hsa_circ_0000390 downregulation enhances the proliferation, migration, and invasion of colorectal cancer cells by increasing Notch1 expression

Circular RNAs (circRNAs), as noncoding RNAs, have gained widespread attention in cancers. Circ_0000390 has been verified to be downregulated in gastric cancer, while its function and regulatory mechanism in cancer is largely unknown. The purpose of this study is to investigate the expression, functions, and potential mechanism of circ_0000390 in colorectal cancer (CRC). Circ_0000390 expression in CRC tissues was first identified with RT-qPCR. Besides, the function of circ_0000390 was assessed through gain-of-function and rescued experiments in CRC cells and mouse xenograft models. Our results showed that circ_0000390 was lowly expressed in CRC tissues, and circ_0000390 could downregulate Notch1 and be downregulated by METTL3. Functionally, results showed circ_0000390 overexpression suppressed the proliferation, cell migration, and invasion of CRC cells, which also could be reversed by Notch1 overexpression. Additionally, METTL3 overexpression could accelerate the proliferation, cell migration, and invasion of CRC cells, which also was weakened by circ_0000390 overexpression in CRC cells in vitro and in vivo. This study suggested that circ_0000390 might be anti-tumor factor in CRC and METTL3/Notch1 might be a therapeutic targets for CRC.

Cytoplasmic localization of IRF5 induces Wnt5a/E-cadherin degradation and promotes gastric cancer cells metastasis

IRF5, a nucleoplasm shuttling protein, is a pivotal transcription factor regulating immune system activity. It's well known that immunosuppression is involved in the development of gastric cancer. However, no data exist for the expression and function of IRF5 in gastric cancer. This study demonstrated that IRF5 was cytoplasm-enriched in gastric cancer cells. IRF5 promoted gastric cancer cell migration, which involved the inhibition of Wnt5a and E-cadherin proteins expression. IRF5 (LA) localized in nucleus had no significant effect on Wnt5a and E-cadherin expressions, while mutation of IRF5 (ΔNLS), which prevents IRF5 nuclear translocation, had more impact on these inhibitory effects. In addition, degradation rates of both Wnt5a and E-cadherin were enhanced by resiquimod, an IRF5 agonist. Further in vivo experiments indicated that IRF5 knockout of gastric cancer cells repressed their pulmonary metastasis in nude mice. Finally, the expression and clinical significance of IRF5 were analyzed using gastric cancer tissue microarrays, which suggested that the expression of IRF5 varied procedurally in different progressive stages of gastric cancer. Our data revealed that IRF5 cytoplasmic localization were associated with Wnt5a and E-cadherin degradation and gastric cancer cell metastasis. Inhibiting IRF5 expression and/or its cytoplasmic localization may provide a novel target for gastric cancer therapy.

Cancer functional states-based molecular subtypes of gastric cancer

BACKGROUND

The treatment of gastric cancer remains a challenge.

METHODS

We divided gastric cancer into three subtypes based on 14 cancer functional states. We investigated differences between subtypes through multi-omics data, especially at the single-cell level, which allowed us to analyze differences from the perspective of each type of cell rather than the whole.

RESULTS

The cluster 1 is characterized by high levels of tumor progression-related cancer functional status, worst survival outcomes, low metabolic level, high infiltration of immunosuppressive cells, high copy number variations (CNV), and low tumor mutational burden (TMB). The cluster 2 is characterized by low levels of tumor progression-related cancer functional status, favorable prognosis, moderate metabolic level, low immune cell infiltration, high CNV, and moderate TMB. Then, the cluster 3 is characterized by the high level of all cancer functional status, high metabolic level, low CNV, high TMB, high infiltration of immune cells with high cytotoxicity, and better response to immunotherapy. We also established a prognostic model based on cancer functional status and validated its robustness.

CONCLUSIONS

Collectively, our study identified gastric cancer subtypes and provided new insights into the clinical treatment of gastric cancer.

Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics

Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical-pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.

Unveiling DNA damage repair-based molecular subtypes, tumor microenvironment and pharmacogenomic landscape in gastric cancer

Objective: The current molecular classification system for gastric cancer covers genomic, molecular, and morphological characteristics. Non-etheless, classification of gastric cancer based upon DNA damage repair is still lacking. Here, we defined DNA damage repair-based subtypes across gastric cancer and identified clinicopathological, tumor microenvironment and pharmacogenomic features. Methods: Unsupervised clustering analysis was executed in the TCGA-STAD cohort based upon the transcriptional expression profiling of DNA damage repair genes. LASSO computational approach was adopted for generating a DNA damage repair-relevant gene signature. The identified subtypes or signature were externally verified in the GSE84426 or GSE84433 cohort. The transcriptional levels of immunomodulators, abundance of immune cells and somatic mutations were measured, respectively. Immunotherapeutic response, and drug sensitivity were investigated. The DNA damage repair-relevant genes were further experimentally verified. Results: Two DNA damage repair-based subtypes were identified, with the notable heterogeneity in prognostic stratification, tumor microenvironment and somatic mutations. The gene signature was generated for risk stratification and prognostic prediction, which was in relation to immunomodulators and immune cells. High-risk cases were more likely to respond to immunotherapy, with distinct pharmacogenomic landscapes between low- and high-risk groups. Higher levels of PAPPA2, MPO, MAGEA11, DEPP1, CPZ, and COLEC12 and lower level of CYTL1 were proven in gastric cancer cells versus controls. Silencing CYTL1 facilitated intracellular ROS accumulation and suppressed migration in gastric cancer cells. Conclusion: Collectively, the DNA damage repair-based classification is a suitable complement to existing molecular classification system, and the quantitative gene signature provides a robust tool in selecting specific therapeutic options.

Identification of a novel reactive oxygen species (ROS)-related genes model combined with RT-qPCR experiments for prognosis and immunotherapy in gastric cancer

Reactive oxygen species play a crucial role in the prognosis and tumor microenvironment (TME) of malignant tumors. An ROS-related signature was constructed in gastric cancer (GC) samples from TCGA database. ROS-related genes were obtained from the Molecular Signatures Database. Consensus clustering was used to establish distinct ROS-related subtypes related to different survival and immune cell infiltration patterns. Sequentially, prognostic genes were identified in the ROS-related subtypes, which were used to identify a stable ROS-related signature that predicted the prognosis of GC. Correlation analysis revealed the significance of immune cell iniltration, immunotherapy, and drug sensitivity in gastric cancers with different risks. The putative molecular mechanisms of the different gastric cancer risks were revealed by functional enrichment analysis. A robust nomogram was established to predict the outcome of each gastric cancer. Finally, we verified the expression of the genes involved in the model using RT-qPCR. In conclusion, the ROS-related signature in this study is a novel and stable biomarker associated with TME and immunotherapy responses.

Identification and characterization of a novel molecular classification incorporating oxidative stress and metabolism-related genes for stomach adenocarcinoma in the framework of predictive, preventive, and personalized medicine

BACKGROUND

Stomach adenocarcinoma (STAD) is one of the primary contributors to deaths that are due to cancer globally. At the moment, STAD does not have any universally acknowledged biological markers, and its predictive, preventive, and personalized medicine (PPPM) remains sufficient. Oxidative stress can promote cancer by increasing mutagenicity, genomic instability, cell survival, proliferation, and stress resistance pathways. As a direct and indirect result of oncogenic mutations, cancer depends on cellular metabolic reprogramming. However, their roles in STAD remain unclear.

METHOD

743 STAD samples from GEO and TCGA platforms were selected. Oxidative stress and metabolism-related genes (OMRGs) were acquired from the GeneCard Database. A pan-cancer analysis of 22 OMRGs was first performed. We categorized STAD samples by OMRG mRNA levels. Additionally, we explored the link between oxidative metabolism scores and prognosis, immune checkpoints, immune cell infiltration, and sensitivity to targeted drugs. A series of bioinformatics technologies were employed to further construct the OMRG-based prognostic model and clinical-associated nomogram.

RESULTS

We identified 22 OMRGs that could evaluate the prognoses of patients with STAD. Pan-cancer analysis concluded and highlighted the crucial part of OMRGs in the appearance and development of STAD. Subsequently, 743 STAD samples were categorized into three clusters with the enrichment scores being C2 (upregulated) > C3 (normal) > C1 (downregulated). Patients in C2 had the lowest OS rate, while C1 had the opposite. Oxidative metabolic score significantly correlates with immune cells and immune checkpoints. Drug sensitivity results reveal that a more tailored treatment can be designed based on OMRG. The OMRG-based molecular signature and clinical nomogram have good accuracy for predicting the adverse events of patients with STAD. Both transcriptional and translational levels of ANXA5, APOD, and SLC25A15 exhibited significantly higher in STAD samples.

CONCLUSION

The OMRG clusters and risk model accurately predicted prognosis and personalized medicine. Based on this model, high-risk patients might be identified in the early stage so that they can receive specialized care and preventative measures, and choose targeted drug beneficiaries to deliver individualized medical services. Our results showed oxidative metabolism in STAD and led to a new route for improving PPPM for STAD.

Dynamic gene screening enabled identification of a 10-gene panel for early detection and progression assessment of gastric cancer

Early diagnosis and progression assessment are critical for the timely detection and treatment of gastric cancer (GC) patients. Identification of diagnostic biomarkers for early detection of GC represents an unmet clinical need, and how these markers further influence GC progression is explored rarely. We performed dynamic gene screening based on high-throughput data analysis from patients with precancerous lesions and early gastric cancer (EGC) and identified a 10-gene panel by the lasso regression model. This panel demonstrated good diagnostic performance in TCGA (AUC = 0.95, sensitivity = 86.67 %, specificity = 90.63 %) and GEO (AUC = 0.84, sensitivity = 91.67 %, specificity = 78.13 %) cohorts. Moreover, three GC subtypes were clustered based on this panel, in which cluster 2 (C2) demonstrated the highest tumor progression level with a high expression of 10 genes, showing a decreased tumor mutation burden, significantly enriched epithelial-mesenchymal transition hallmark and increased immune exclusion/exhausted features. Finally, the cell localization of these panel genes was explored in scRNA-seq data based on more than 40,000 cells. The 10-gene panel is expected to be a new clinical early detection signature for GC and may aid in progression assessment and personalized treatment of patients.

Comprehensive analysis of hypoxia-related genes for prognosis, immune features, and drugs treatment strategy in gastric cancer using bulk and single-cell RNA-sequencing

Hypoxia is one of the malignant characteristics of solid tumors and is related to the multiple malignant characteristics of the tumor. No study has not yet reported a systematical analysis of the characteristics of hypoxia from single-cell resolution in gastric cancer. In our research, we investigated the hypoxia features of various types of cells in single-cell resolution, identified hypoxia-related genes by the weighted gene co-expression network analysis method. Through the hypoxia-related genes from single-cell levels, we screened out 13 genes and established a prognostic model. This model performs well in the training dataset and multiple independent verification data sets. We thought that tumor hypoxia might affect the DNA methylation of cells and promote the transcription of genes associated with malignant features, thereby promoting tumor progression. We found that the more tumor associated genes in the high-risk group showed hypomethylation and high hypoxia-risk score group have more tumor-related genes, more immunosuppressive immune cells and more enrichment of cancer -related pathways. The lower risk group is more sensitive to three chemotherapy drugs for gastric cancer. Our study illustrates the crucial role of hypoxia in gastric cancer. Hypoxia-related gene prognostic model has been established and has good performance. Hypoxia-related risk score can also be used to guide a patient's drug treatment strategy.

Correlation analysis of lipid metabolism genes with the immune microenvironment in gastric cancer and the construction of a novel gene signature

BACKGROUND

Lipid metabolism reprogramming plays an important role in cell growth, proliferation, angiogenesis and invasion of cancer. However, the prognostic value of lipid metabolism during gastric cancer (GC) progression and the relationship with the immune microenvironment are still unclear. The aim of this study was to clarify the correlation between lipid metabolism genes and GC immunity.

METHOD

We obtained 350 patients from The Cancer Genome Atlas (TCGA) and 355 patients from Gene Expression Omnibus (GEO) databases. Lipid metabolism-related gene datasets were obtained from the Reactome and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Molecular subtypes were obtained by Consensus clustering, and subtype immune status was analyzed using ESTIMATE, TIMER and microenvironmental cell population counter (MCP Counter) algorithm for immune analysis. Functional analyses included the application of Gene Set Enrichment Analysis (GSEA), KEGG, gene ontology (GO), and Protein-Protein Interaction Networks (PPI) to evaluate the molecular mechanisms of different subtypes. Weighted gene co-expression network analysis (WGCNA) was used to identify genes associated with immunity. The LASSO algorithm and multivariate Cox regression analysis were used to construct prognostic risk models.

RESULT

Based on the lipid metabolism genes found in GC, patients with GC can be divided into two subgroups with significantly different survival. The subgroup with a better prognosis presented higher immune scores and immune infiltrating cell abundance. 1170 immune-related genes were screened by WGCNA, and further screening by PPI network analysis revealed that PTPRC, CD4, ITGB2 and LCP2 were closely associated with immune cells. Combined with the TIDE score results, it was found that the population with high expression of the above genes might be more sensitive to immunotherapy. In addition, a survival prediction model for GC was developed based on five survival-related lipid metabolism genes, PIAS4, PLA2R1, PRKACA, SLCO1A2 and STARD4. The ROC analysis over time showed that the risk prediction score model had good stability.

CONCLUSION

Lipid metabolism gene expression is correlated with the immune microenvironment in GC patients and can accurately predict their prognosis. Studies on lipid metabolism and GC immunity can help to screen the population for immunotherapy benefits.

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma

METHODS

Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints.

RESULTS

The patients were divided into low-risk and high-risk groups according to the k-means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80e - 06; adjusted hazard ratio = 2.386, 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository.

CONCLUSIONS

A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.

An antigen processing and presentation signature for prognostic evaluation and immunotherapy selection in advanced gastric cancer

Objective

The aim of the study was to propose a signature based on genes associated with antigen processing and presentation (APscore) to predict prognosis and response to immune checkpoint inhibitors (ICIs) in advanced gastric cancer (aGC).

Background

How antigen presentation-related genes affected the immunotherapy response and whether they could predict the clinical outcomes of the immune checkpoint inhibitor (ICI) in aGC remain largely unknown.

Methods

In this study, an aGC cohort (Kim cohort, RNAseq, N=45) treated by ICIs, and 467 aGC patients from seven cohorts were conducted to investigate the value of the APscore predicting the prognosis and response to ICIs. Subsequently, the associations of the APscore with the tumor microenvironment (TME), molecular characteristics, clinical features, and somatic mutation variants in aGC were assessed. The area under the receiver operating characteristic curve (AUROC) of the APscore was analyzed to estimate response to ICIs. Cox regression or Log-rank test was used to estimate the prognosis of aGC patients.

Results

The APscore constructed by principal component analysis algorithms was an effective predictive biomarker of the response to ICIs in the Kim cohort and 467 aGC patients (Kim: AUC =0.85, 95% CI: 0.69–1.00; 467 aGC: AUC =0.69, 95% CI: 0.63–0.74). The APscore also was a prognostic biomarker in 467 aGC patients (HR=1.73, 95% CI: 1.21−2.46). Inhibitory immunity, decreased TMB and low stromal scores were observed in the high APscore group, while activation of immunity, increased TMB, and high stromal scores were observed in the low APscore group. Next, we evaluated the value of several central genes in predicting the prognosis and response to ICIs in aGC patients, and verified them using immunogenic, transcriptomic, genomic, and multi-omics methods. Lastly, a predictive model built successfully discriminated patients with vs. without immunotherapy response and predicted the survival of aGC patients.

Conclusions

The APscore was a new biomarker for identifying high-risk aGC patients and patients with responses to ICIs. Exploration of the APscore and hub genes in multi-omics GC data may guide treatment decisions.

Identification of a chemotherapy-associated gene signature for a risk model of prognosis in gastric adenocarcinoma through bioinformatics analysis

BACKGROUND

Over the past few years, the overall survival rate of patients with gastric adenocarcinoma who have received different chemotherapy regimens has increased. However, not all gastric cancer patients who receive chemotherapy have a longer survival. We need better predictive biomarkers. This study is to construct a new risk model of chemotherapy-associated genes in gastric adenocarcinoma (GA) for prognostication.

METHODS

RNA-seq data and clinical information of GSE26901 (containing 44 chemotherapy samples and 65 patients without chemotherapy) in Gene Expression Omnibus (GEO) and stomach adenocarcinoma (STAD, containing 360 cancer tissue samples and 50 paired normal tissue samples) in The Cancer Genome Atlas (TCGA) were selected for screening differentially expressed genes (DEGs). Multivariate Cox regression was conducted to screen prognosis-associated genes and its link to patients' prognosis were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the key genes, a risk scoring equation for the prognosis model was established, and constructed survival prognosis model. The model was tested for predictive ability through training set (TCGA datasets) and validation set (GSE84437). The correlations of the risk score with clinical pathological features, immune score and drug sensitivity score were evaluated.

RESULTS

In total, 179 overlapping genes were obtained by screening DEGs. Univariate Cox analysis revealed 36 prognosis-related genes, and LASSO regression analysis revealed 8 key genes (KCNJ2, GATA5, CLDN1, SERPINE1, FCER2, PMEPA1, TMEM37 and CRTAC1). Kaplan-Meier (K-M) analysis uncovered a relatively short overall survival time in the high-risk group. The model was verified to possess favourable predictive ability. In addition, the nomogram model were demonstrated good predictability with area under the curve (AUC) for 1-5 years in training set were 0.78, 0.78, 0.76, 0.79 and 0.81. The high-risk group was less likely to get benefits from immunotherapy and less sensitive to cisplatin.

CONCLUSIONS

According to the results of our training set and validation set, the risk model based on the eight chemotherapy-related gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of GA patients which can be a promising prognostic parameter for GA. However, its efficacy remains to be proved in clinical practice.

Association of the TGFβ gene family with microenvironmental features of gastric cancer and prediction of response to immunotherapy

In the complex tumor microenvironment, TGFβ is a pleiotropic cytokine involved in regulating cellular processes such as cancer cell proliferation, apoptosis and metastasis. TGFβ defines three subtypes (TGFβ1, TGFβ2, and TGFβ3), of which TGFβ is highly expressed in many cancers, especially those showing high dissemination potential. In addition, increased expression of TGFβ in multiple cancers is usually positively correlated with epithelial mesenchymal transition (EMT) and coordinated with the expression of genes driving EMT-related genes. TGFβ signaling in the tumor microenvironment inhibits the antitumor function of multiple immune cell populations, including T cells and natural killer cells, and the resulting immunosuppression severely limits the efficacy of immune checkpoint inhibitors and other immunotherapeutic approaches. As a major pathway to enhance the efficacy of cancer immunotherapy effects, the role of TGFβ signaling inhibitors have been evaluated in many clinical trials. However, the potential functions and mechanisms of TGFβ1, TGFβ2 and TGFβ3 in gastric cancer progression and tumor immunology are unclear. In this study, we comprehensively analyzed TGFβ1, TGFβ2 and TGFβ3 and gastric cancer microenvironmental features, including immune cell infiltration, EMT, hypoxia, mutation, immunotherapy and drug treatment, based on HMUCH sequencing data (GSE184336) and public databases. We also validated the protein expression levels of TGFβ in gastric cancer tissues as well as the role of TGFβ factor in cytology experiments. This report reveals the important role of the TGFβ gene family in gastric cancer and provides possible relationships and potential mechanisms of TGFβ in gastric cancer.

Gastric Cancer Subtypes in Tumour and Nontumour Tissues by Immunologic and Hallmark Gene Sets

A previous research study on differentiating gastric cancer (GC) into distinct subtypes or prognostic models was mostly based on GC tissues, which neglected the role of nontumour tissues in GC subtypes. The purpose of the research was to identify GC subtypes on the basis of tumour and adjacent nontumour tissues to assess the prognosis of GC patients. We characterized three GC subtypes on the basis of the immunologic and hallmark gene sets in GC and adjacent nontumour tissues: among them, the GC patients with subtype I had the longest survival time compared to patients with other subtypes. The classification was closely associated with T stage and pathological stage of GC patients. A prognostic model containing two gene sets was constructed by LASSO analysis. Kaplan–Meier analysis showed that patients in the high-risk group survived longer than those in the low-risk group and the two prognostic genes sets in the model were strongly correlated with survival status. Then, GO and KEGG analyses and PPI network show that nontumour and tumour tissues are influencing the prognosis of GC patients in separate manners. In summary, we emphasized the prognostic value of nontumour tissue in GC patients and proposed a novel insight that both changes in tumour and nontumour tissues should be taken into account when selecting a treatment strategy for GC.

miR-22 Suppresses EMT by Mediating Metabolic Reprogramming in Colorectal Cancer through Targeting MYC-Associated Factor X

Colorectal cancer (CRC) is one of the most frequent gastrointestinal cancers. MicroRNAs (miRNAs) have been proved to be unusually expressed in CRC progression and thus alter multiple pathological processes in CRC cells. However, the specific roles and mechanisms of miR-22 in CRC have not been clearly reported. MicroRNA-22 (miR-22) and MYC-associated factor X (MAX) expressions were determined by RT-qPCR in CRC tissues and cells. The targeted regulatory effects of miR-22 and MAX were confirmed by luciferase reporter and coimmunoprecipitation assays. Also, gain- and loss-of-function and rescue experiments were used to elucidate the function and mechanism of miR-22 and MAX in CRC cells and the mouse xenograft model. We discovered that miR-22 was hypermethylated and downregulated, while MAX was upregulated in CRC. miR-22 markedly inhibited migration, invasion, glycolysis, and cancer stem cell transcription factors in CRC cells. In addition, it was found that miR-22 can directly target MAX. Additional functional experiments confirmed that MAX overexpression can rescue the effects of miR-22 on the behavior of CRC cells. This study suggested that miR-22, as a cancer suppressor, participates in CRC progression by targeting MAX, which might provide basic information for therapeutic targets for CRC.

Higher intra-tumoral expression of pro-coagulation genes is a predictor of angiogenesis, epithelial mesenchymal transition and worse patient survival in gastric cancer

Coagulation regulates angiogenesis in cancer, and is associated with tumor development and metastasis. To date, there have been no studies quantifying the state of intra-tumoral coagulation. We measured intra-tumoral coagulation gene expression using the "Hallmark-COAGULATION" gene set in the MSigDB, performing gene set variation analysis and then assigning a "coagulation score" to quantify gene expression. Clinical, histologic, and genetic data were analyzed in 807 gastric cancer patients from the TCGA_STAD and GSE84437 databases. Tumors with increased expression of pro-coagulation genes were consistently associated with higher AJCC T-categories (p = 0.018), lymph node metastasis (p = 0.036), and stage (p = 0.006) in both cohorts. Patients with high coagulation scores were found to have worse disease-specific survival and overall survival (OS) (p = 0.019 and 0.011, respectively) in TCGA, and worse OS in GSE84437 cohort (p = 0.012). Higher expression of pro-coagulation genes correlated with increased intra-tumoral angiogenesis, as well as increased proportions of lymphatic and microvascular endothelial cells, endothelial cells, and pericytes, calculated by xCell algorithm. High coagulation scores were significantly associated with low tumor mutation burden, but not with intratumor heterogeneity and homologous recombination deficiency. Gastric cancers with high coagulation scores contained higher amounts of M1 macrophages and dendritic cells, and low numbers of Th1 cells (all P<0.001). Genes for epithelial mesenchymal transition (EMT), myogenesis, apical junction, transforming growth factor (TGF)-β signaling, and angiogenesis were enriched in high coagulation score-gastric cancers (all false discovery rate <0.25). In conclusion, gastric cancers expressing higher levels of pro-coagulation genes demonstrate increased angiogenesis, EMT, TGF-β signaling and worse patient prognosis.

CBXs-related prognostic gene signature correlates with immune microenvironment in gastric cancer

Background: Chromobox (CBX) proteins are important Polycomb family proteins in the development of gastric cancer. Nonetheless, the relationship between CBXs and gastric cancer microenvironment remains unclear.

Methods: Multiple databases were used for the analysis of CBXs expression and clinical value in gastric cancer patients. A Cox regression analysis was used to evaluate the prognostic importance of CBXs. Thereafter, regression analysis of LASSO Cox was used to construct the prognostic model. Spearman's correlation between risk score and immune infiltration was analyzed using the McP-counter algorithm. A predicted nomogram was developed to predict the overall survival of gastric cancer patients after 1, 2, and 3 years.

Results: In contrast with normal tissues, mRNA and protein expression levels of CBX2/3 were significantly high in gastric cancer tissues, whereas those of CBX6/7 were low. CBXs significantly correlated with immune subtypes and molecular subtypes. A prognostic gene model based on five CBX genes (CBX1, CBX2, CBX3, CBX7, and CBX8) predicted the overall survival of gastric cancer patients. A significant correlation was noted between the risk score of the CBXs-related prognostic gene model and immune-cell infiltration. Low risk patients could achieve a better response to immune checkpoint inhibitors. A predictive nomogram constructed using the above five CBX genes revealed that overall survival rates over 1, 2, and 3 years could be reasonably predicted. Therefore, the roles of CBXs were associated with chromatin modifications and histone methylation, etc.

Conclusion: In summary, we identified a prognostic CBXs model comprising five genes (CBX1, CBX2, CBX3, CBX7, and CBX8) for gastric cancer patients through bioinformatics analysis.