Stomach Adenocarcinoma Patients Research Articles

A degradome-related signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma based on machine learning procedure.

Stomach adenocarcinoma (STAD) is one of the subtype of gastric cancer with high invasiveness, extreme heterogeneity, high morbidity, and high mortality. The degradome is the most abundant class of cellular enzymes that play an essential role in regulating cellular activity and carcinogenesis. An integrative machine learning procedure including 10 methods was performed to develop a prognostic degradome-based prognostic signature (DPS) in TCGA, GSE15459, GSE26253, and GSE62254 datasets. Investigations of the DPS concerning immune infiltration, immunotherapy benefits, and drug priority were orchestrated. The DPS developed by Enet [alpha = 0.3] method was regarded as the optimal prognostic model. The DPS had a stable and powerful performance in predicting the clinical outcome of STAD and served as an independent risk factor in training and testing cohorts. The C-index of DPS was higher than that of age, sex, and clinical stage. STAD patients with low DPS scores had a higher abundance of B cells, CD8+ T cells, higher cytolytic scores, and T cell co-stimulation scores. Moreover, low DPS score indicated a lower tumor immune dysfunction and exclusion score, lower T cell dysfunction and exclusion score, higher PD1&CTLA4 immunophenoscore, and higher tumor mutation burden score in STAD, demonstrating a better immunotherapy response. STAD patients with a high DPS score had a lower IC50 value of common chemotherapy and targeted therapy regimens (Cisplatin, Docetaxel, Gefitinib, etc). Our study developed an optimal DPS for STAD. The DPS could predict the prognosis, risk stratification and guide treatment for STAD patients.

Disulfidptosis-related lncRNA signature reveals immune microenvironment and novel molecular subtyping of stomach adenocarcinoma

The main challenge in treating stomach adenocarcinoma (STAD) is chemotherapy resistance, which is characterized by changes in the immune microenvironment. Disulfidptosis, a novel form of programmed cell death, is involved in STAD but its mechanism is not fully understood. Long non-coding RNAs (LncRNAs) may play a role in regulating disulfidptosis and influencing the immune microenvironment and chemotherapy resistance in STAD. This study aims to establish disulfidptosis-related lncRNA (DRL) features and explore their significance in the immune microenvironment and chemotherapy resistance in STAD patients. By analyzing RNA sequencing and clinical data from STAD patients and extracting disulfidptosis-related genes, we identified DRLs through co-expression, single-factor and multi-factor Cox regression, and Lasso regression analyses. We also investigated differences in the immune microenvironment, immune function, immune checkpoint gene expression, and chemotherapy resistance between different risk groups using various algorithms. A prognostic risk model consisting of 2 DRLs was constructed, with a strong predictive value for patient survival, outperforming other clinical-pathological factors in predicting 3-year and 5-year survival. Immune-related analysis revealed a strong positive correlation between T cell CD4+ cells and risk score across all algorithms, and higher expression of immune checkpoint genes in the high-risk group. In addition, high-risk patients showed better sensitivity to Erlotinib, Oxaliplatin, and Gefitinib. Furthermore, three novel molecular subtypes of STAD were identified based on the 2-DRLs features, with evaluation of the immune microenvironment and chemotherapy drug sensitivity for each subgroup, which holds significant implications for achieving precise treatment in STAD. Overall, our 2-DRLs prognostic model demonstrates high predictive value for patient survival in STAD, potentially providing new targets for individualized immune and chemical therapy.

Differential genetic mutations and immune cell infiltration in high- and low-risk STAD: Implications for prognosis and immunotherapy efficacy.

This study investigates genetic mutations and immune cell dynamics in stomach adenocarcinoma (STAD), focusing on identifying prognostic markers and therapeutic targets. Analysis of TCGA-STAD samples revealed C > A as the most common single nucleotide variant (SNV) in both high and low-risk groups. Key mutated driver genes included TTN, TP53 and MUC16, with frame-shift mutations more prevalent in the low-risk group and missense mutations in the high-risk group. Interaction analysis of hub genes such as C1QA and CD68 showed significant correlations, impacting immune cell infiltration patterns. Using ssGSEA, we found higher immune cell infiltration (B cells, CD4+ T cells, CD8+ T cells, DC cells, NK cells) in the high-risk group, correlated with increased risk scores. xCell algorithm results indicated distinct immune infiltration levels between the groups. The study's risk scoring model proved effective in prognosis prediction and immunotherapy efficacy assessment. Key molecules like CD28, CD27 and SLAMF7 correlated significantly with risk scores, suggesting potential targets for high-risk STAD patients. Drug sensitivity analysis showed a negative correlation between risk scores and sensitivity to certain treatments, indicating potential therapeutic options for high-risk STAD patients. We also validated the carcinogenic role of RPL14 in gastric cancer through phenotypic experiments, demonstrating its influence on cancer cell proliferation, invasion and migration. Overall, this research provides crucial insights into the genetic and immune aspects of STAD, highlighting the importance of a risk scoring model for personalized treatment strategies and clinical decision-making in gastric cancer management.

Machine learning-based cell death signature for predicting the prognosis and immunotherapy benefit in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a one of most common malignancies with high mortality-to-incidence ratio. Programmed cell death (PCD) exerts vital functions in the progression of cancer. The role of PCD-related genes (PRGs) in STAD are not fully clarified. Using TCGA, GSE15459, GSE26253, GSE62254 and GSE84437 datasets, PCD-related signature (PRS) was constructed with an integrative procedure including 10 machine learning methods. The role of PRS in predicting the immunotherapy benefits was evaluated by several predicting score and 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210). The model developed by Lasso + CoxBoost algorithm having a highest average C-index of 0.66 was considered as the optimal PRS. As an independent risk factor for STAD patients, PRS had a good performance in predicting the overall survival rate of patients, with an AUC of 1-, 3-, and 5-year ROC curve being 0.771, 0.751 and 0.827 in TCGA cohort. High PRS score demonstrated a lower gene set score of some immune-activated cells and immune-activated activities. Patient with high PRS score had a higher TIDE score, higher immune escape score, lower PD1&CTLA4 immunophenoscore, lower TMB score, lower response rate and poor prognosis, indicating a less immunotherapy response. The IC50 value of some drugs correlated with chemotherapy and targeted therapy was higher in high PRS score group. Our investigation developed an optimal PRS in STAD and it acted as an indicator for predicting the prognosis, stratifying risk and guiding treatment for STAD patients.

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

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. 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). 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. 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.

Establishment of the molecular subtypes and a risk model for stomach adenocarcinoma based on genes related to reactive oxygen species

BackgroundOxidative stress promotes the development of stomach adenocarcinoma (STAD) and resistance of STAD patients to chemotherapy. This study developed a risk classification and prognostic model for STAD based on genes related to oxidative stress. MethodsUnivariate Cox regression and least absolute shrinkage and selection operator (Lasso) regression analysis were performed using transcriptome data of STAD from The Cancer Genome Atlas (TCGA) and reactive oxygen species (ROS)-related genes from Gene Set Enrichment Analysis (GSEA) website to develop a risk model. Genetic landscape, pathway characteristics and immune characteristics between the two risk groups were assessed to evaluate patients’ response to anti-tumor therapy. Further, a nomogram was created to evaluate the clinical outcomes of STAD patients. The mRNA levels of genes were detected by reverse transcription quantitative PCR (RT-qPCR). ResultsTwo ROS-related molecular subtypes (subtype C1 and C2) were classified, with subtype C2 having unfavorable prognosis, higher immune score, and greater infiltration of macrophages, myeloid-derived suppressor cells (MDSCs), mast cells, regulatory T cells, and C–C chemokine receptor (CCR). Five ROS-related genes (ASCL2, COMP, NOX1, PEG10, and VPREB3) were screened to develop a prognostic model, the robustness of which was validated in TCGA and external cohorts. RT-qPCR analysis showed that ASCL2, COMP, NOX1, and PEG10 were upregulated, while the mRNA level of VPREB3 was downregulated in gastric cancer cells. The risk score showed a negative relation to tumor mutation burden (TMB). Low-risk patients exhibited higher mutation frequencies of TTN, SYNE1, and ARID1A, higher response rate to immunotherapy and were more sensitive to 32 traditional chemotherapeutic drugs, while high-risk patients were sensitive to 13 drugs. Calibration curve and DCA confirmed the accuracy and reliability of the nomogram. ConclusionThese findings provided novel understanding on the mechanism of ROS in STAD. The current study developed a ROS-related signature to help predict the prognosis of patients suffering from STAD and to guide personalized treatment.

Uncovering the ceRNA Network Related to the Prognosis of Stomach Adenocarcinoma Among 898 Patient Samples.

Stomach adenocarcinoma (STAD) patients are often associated with significantly high mortality rates and poor prognoses worldwide. Among STAD patients, competing endogenous RNAs (ceRNAs) play key roles in regulating one another at the post-transcriptional stage by competing for shared miRNAs. In this study, we aimed to elucidate the roles of lncRNAs in the ceRNA network of STAD, uncovering the molecular biomarkers for target therapy and prognosis. Specifically, a multitude of differentially expressed lncRNAs, miRNAs, and mRNAs (i.e., 898 samples in total) was collected and processed from TCGA. Cytoplasmic lncRNAs were kept for evaluating overall survival (OS) time and constructing the ceRNA network. Differentially expressed mRNAs in the ceRNA network were also investigated for functional and pathological insights. Interestingly, we identified one ceRNA network including 13 lncRNAs, 25 miRNAs, and 9 mRNAs. Among them, 13 RNAs were found related to the patient survival time; their individual risk score can be adopted for prognosis inference. Finally, we constructed a comprehensive ceRNA regulatory network for STAD and developed our own risk-scoring system that can predict the OS time of STAD patients by taking into account the above.

Identification of necroptosis-associated miRNA signature for predicting prognosis and immune landscape in stomach adenocarcinoma

PurposeThis study aims to identify the potential necroptosis related genes (NRGs)-associated miRNAs signature and explore the impact on the prognosis of stomach adenocarcinoma (STAD). MethodsEmploying rigorous methodologies, we utilized univariate Cox, Lasso and multivariate Cox regression analyses to develop a prognostic signature. Kaplan-Meier (K-M) and ROC curves were applied to assess the prognostic value of signature in a training group and an independent test group. Furthermore, we conducted Gene Set Enrichment Analysis (GSEA) for enrichment of tumor-related pathways. The risk score was calculated for each patient based on the expression of miRNAs which were enrolled in the signature. Patients were stratified into high- and low-risk groups. The immune cell infiltration and immunotherapy were compared between the two groups. Finally, the diagnostic potential of the miRNA was explored by RT-qPCR. ResultsWe constructed a prognostic model based on 6 NRGs-associated miRNAs. K-M plots underscored superior survival outcomes in the low-risk group. GSEA results revealed the enrichment of several tumor-related pathways in the high-risk group. Notably, CD8+ T cells, Tregs and activated memory CD4+ T cells exhibited negative correlations with the risk score. Additionally, a few immune checkpoint genes, such as CTLA4, PD1 and PD-L1, were significantly upregulated in the low-risk group. Furthermore, the serum expression levels of all these 6 miRNAs were significantly elevated in STAD patients. ConclusionsOur study identified a robust risk score derived from a signature of 6 NRGs-associated miRNAs, demonstrating high efficacy for prognosis of STAD. These results not only contributed to our understanding of STAD pathogenesis, but also held promise for potential clinical applications, particularly in the realm of personalized immunotherapy for STAD patients.

Pan-cancer analysis of HS6ST2: associations with prognosis, tumor immunity, and drug resistance.

In this comprehensive study spanning 33 malignancies, we explored the differential expression and prognostic significance of Heparan sulfate 6-O-sulfotransferase 2 (HS6ST2). TIMER2, UALCAN, and GEPIA2 were used for the expression analysis. cBioPortal was used for mutational analysis. CancerSEA, STRING, and DAVID, were employed for the single cell sequencing data analysis, protein-protein interaction network development, and gene enrichment analyses, respectively. GSCAlite and RT-qPCR were used for drug sensitivity and expression validation analysis. HS6ST2 exhibited significant (P < 0.05) overexpression in multiple cancers. Prognostically, elevated HS6ST2 expression was significantly associated with poor overall survival (OS) in patients with cervical squamous cell carcinoma (CESC), kidney chromophobe (KICH), lung adenocarcinoma (LUAD), and stomach adenocarcinoma (STAD), emphasizing its potential as a prognostic indicator in these cancers. Moreover, HS6ST2 expression correlated with pathological stages in CESC, KICH, LUAD, and STAD patients. Exploration of genetic alterations using cBioPortal unveiled distinct mutational landscapes, with low mutation frequencies in CESC, KICH, LUAD, and STAD. Additionally, reduced DNA methylation in CESC, KICH, LUAD, and STAD suggested a potential link between hypomethylation and heightened HS6ST2 expression. Analysis of immune cell infiltration revealed a positive correlation between HS6ST2 expression and the infiltration of CD8+ T and CD4+ T cells in CESC, KICH, LUAD, and STAD, highlighting its involvement in the tumor immunology processes. Single-cell functional states analysis demonstrated associations between HS6ST2 and diverse cellular processes. Moreover, gene enrichment analysis revealed the involvement HS6ST2 in crucial cellular activities. GSCAlite analysis underscored the potential of HS6ST2 as a therapeutic target, showing associations with drug sensitivity. Finally, experimental validation through reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry in LUAD tissues confirmed elevated HS6ST2 expression. Overall, this study provides a comprehensive understanding of HS6ST2 in CESC, KICH, LUAD, and STAD, emphasizing its potential as a prognostic biomarker and therapeutic target.

ASF1B acted as a prognostic biomarker for stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) has a high mortality rate due to the lack of highly sensitive biomarkers. Therefore, the search for potential tumor markers is of great value. ASF1B is a prognostic marker for a variety of tumors, while the prognostic value and immune microenvironment of ASF1B in STAD remain unclear, and to be determined. Kaplan-Meier analysis was performed to analyze the prognostic role of ASF1B in STAD. Functional enrichment of ASF1B was explored with GO and KEGG pathway analysis. We also explored the correlation between ASF1B expression and immune infiltration in STAD. ASF1B was significantly upregulated in STAD tissues and high expression of ASF1B indicated a poor overall survival, progression-free survival, and first progression rate in STAD. The functional enrichment analysis of ASF1B and related genes showed high enrichment in the cell cycle and DNA repair, and the ASF1B high expression group was also mainly enriched in pathways such as the cell cycle. Analysis of tumor immune infiltration showed that ASF1B expression was significantly associated with the majority of immune cell infiltration in STAD. Moreover, STAD patients with high ASF1B expression had a higher tumor mutation burden score, microsatellite instability score, PD-1 immunophenoscore, and immune checkpoint expression. Our results suggest that ASF1B was an independent prognostic factor for STAD as well as a potential target for immunotherapy.

Immunobiological signatures and the emerging role of SPP1 in predicting tumor heterogeneity, malignancy, and clinical outcomes in stomach adenocarcinoma.

Immunotherapy, as a form of immunobiological therapy, represents a promising approach for enhancing patients' immune responses. This work aims to present innovative ideas and insights for prognostic assessment and clinical treatment of stomach adenocarcinoma (STAD) by leveraging immunobiological signatures. We employed weighted gene co-expression network analysis (WGCNA) and unsupervised clustering analysis to identify hub genes. These hub genes were utilized to construct a prognostic risk model, and their impact on the tumor microenvironment (TME) and DNA variations was assessed using large-scale STAD patient cohorts. Additionally, we conducted transfection experiments with plasmids to investigate the influence of SPP1 on the malignancy of HGC27 and NCI-N87 cells. Unsupervised clustering of 12 immune-related genes (IRGs) revealed three distinct alteration patterns with unique molecular phenotypes, clinicopathological characteristics, prognosis, and TME features. Using LASSO and multivariate Cox regression analyses, we identified three hub genes (MMP12, SPP1, PLAU) from the IRGs to establish a risk signature. This IRG-related risk model significantly stratified the prognosis risk among STAD patients in the training (n = 522), testing (n = 521), and validation (n = 300) cohorts. Notably, there were discernible differences in therapy responses and TME characteristics, such as tumor purity and lymphocyte infiltration, between the risk model groups. Subsequently, a nomogram that incorporates the IRG signature and clinicopathological factors demonstrated superior sensitivity and specificity in predicting outcomes for STAD patients. Furthermore, down-regulation of SPP1, as observed after siRNA transfection, significantly inhibited the proliferation and migration abilities of HGC27 and NCI-N87 cells. In summary, this study highlights the critical role of immune-related signatures in STAD and offers novel insights into prognosis indicators and immunotherapeutic targets for this condition. SPP1 emerges as an independent prognostic factor for STAD and appears to regulate STAD progression by influencing the immune microenvironment.

Development of an immunogenic cell death prognostic signature for predicting clinical outcome and immune infiltration characterization in stomach adenocarcinoma.

Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remain unclear. The MSigDB database collecting ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients. Stomach adenocarcinoma (STAD) is a common gastric histological cancer type with a high mortality rate. Immunogenic cell death (ICD) plays a key factor during carcinogenesis progress, whereas the prognostic value and role of ICD-related genes (ICDRGs) in STAD remains unclear. The MSigDB database collected ICDRGs were selected by univariate Cox regression analysis and LASSO algorithm to establish a novel risk model. The Kaplan-Meier survival analysis indicated a significant difference of OS rate of patients by risk score stratification. ESTIMATE, CIBERSORT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape by risk stratification. Subgroup analysis and tumor mutation burden analysis were also analyzed to identify characteristics between groups. Differences in therapeutic responsiveness to chemotherapeutic drugs and targeted drugs were also analyzed between high-risk group and low-risk group. The impact of one ICDRG, GPX1, on the proliferation, migration and invasiveness of was confirmed by in vitro experiments in GC cells to test the reliability of bioinformatics results. This study gives evidence of the involvement of ICD process in STAD and provides a new perspective for further accurate assessment of prognosis and therapeutic efficacy in STAD patients.

DNA cytosine deamination is associated with recurrent Somatic Copy Number Alterations in stomach adenocarcinoma.

Stomach Adenocarcinoma (STAD) is a leading cause of death worldwide. Somatic Copy Number Alterations (SCNAs), which result in Homologous recombination (HR) deficiency in double-strand break repair, are associated with the progression of STAD. However, the landscape of frequent breakpoints of SCNAs (hotspots) and their functional impacts remain poorly understood. In this study, we aimed to explore the frequency and impact of these hotspots in 332 STAD patients and 1,043 cancer cells using data from the Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia (CCLE). We studied the rates of DSB (Double-Strand Breaks) loci in STAD patients by employing the Non-Homogeneous Poisson Distribution (λ), based on which we identified 145 DSB-hotspots with genes affected. We further verified DNA cytosine deamination as a critical process underlying the burden of DSB in STAD. Finally, we illustrated the clinical impact of the significant biological processes. Our findings highlighted the relationship between DNA cytosine deamination and SCNA in cancer was associated with recurrent Somatic Copy Number Alterations in STAD.

Identification of memory B-cell-associated miRNA signature to establish a prognostic model in gastric adenocarcinoma

BackgroundMemory B cells and microRNAs (miRNAs) play important roles in the progression of gastric adenocarcinoma (GAC), also known as stomach adenocarcinoma (STAD). However, few studies have investigated the use of memory B-cell-associated miRNAs in predicting the prognosis of STAD.MethodsWe identified the marker genes of memory B cells by single-cell RNA sequencing (scRNA-seq) and identified the miRNAs associated with memory B cells by constructing an mRNA‒miRNA coexpression network. Then, univariate Cox, random survival forest (RSF), and stepwise multiple Cox regression (StepCox) algorithms were used to identify memory B-cell-associated miRNAs that were significantly related to overall survival (OS). A prognostic risk model was constructed and validated using these miRNAs, and patients were divided into a low-risk group and a high-risk group. In addition, the differences in clinicopathological features, tumour microenvironment, immune blocking therapy, and sensitivity to anticancer drugs in the two groups were analysed.ResultsFour memory B-cell-associated miRNAs (hsa-mir-145, hsa-mir-125b-2, hsa-mir-100, hsa-mir-221) with significant correlations to OS were identified and used to construct a prognostic model. Time-dependent receiver operating characteristic (ROC) curve analysis confirmed the feasibility of the model. Kaplan‒Meier (K‒M) survival curve analysis showed that the prognosis was poor in the high-risk group. Comprehensive analysis showed that patients in the high-risk group had higher immune scores, matrix scores, and immune cell infiltration and a poor immune response. In terms of drug screening, we predicted eight drugs with higher sensitivity in the high-risk group, of which CGP-60474 was associated with the greatest sensitivity.ConclusionsIn summary, we identified memory B-cell-associated miRNA prognostic features and constructed a novel risk model for STAD based on scRNA-seq data and bulk RNA-seq data. Among patients in the high-risk group, STAD showed the highest sensitivity to CGP-60474. This study provides prognostic insights into individualized and precise treatment for STAD patients.

An immunogenic cell death-related signature predicts prognosis and immunotherapy response in stomach adenocarcinoma.

The immunogenic cell death (ICD) is a specific type of regulatory cell death (RCD), which induces adaptive immunity against antigens of dead cells. ICDs have received increasing attention for their potential role in tumor microenvironment reprogramming and immunotherapy. However, the relationship between ICD-related features and stomach adenocarcinoma (STAD) prognosis, immune cell infiltration and immunotherapy remains unclear. Patients were divided into different ICD-related subtypes by consensus clustering. The differences in prognosis, Tumor microenvironment (TME), and immune checkpoint expression between different ICD-related subtypes were systematically assessed. Additionally, we constructed an ICD-related gene risk score (ICDRS). We systematically analyzed the correlation between ICDRS and prognosis, TME, immunotherapy response and drug sensitivity of gastric cancer. In addition, we explored the role of TGM2 in promoting gastric cancer progression through in vitro experiments. We identified three ICD-associated subtypes by consensus clustering. The ICD gene was highly expressed in Cluster B. Compared with the other two subtypes, Cluster B had better prognosis, higher immune response signaling activity, massive immune cell infiltration and lower tumor purity. Immune checkpoint (ICP) and human leukocyte antigen (HLA) related genes were also highly expressed in Cluster B. In addition, we found that ICDRS is an effective indicator for predicting the prognosis and immune efficacy of STAD. The low ICDRS group has the characteristics of good prognosis, high tumor mutation burden (TMB), high microsatellite instability (MSI), and sensitivity to immunotherapy, while the high ICDRS group is prone to immune escape and immunotherapy resistance. In addition, we found that down-regulating TGM2 gene can inhibit the proliferation and migration of gastric cancer cells through in vitro experiments. Our study found that the model based on ICD features is helpful to clarify the TME characteristics of STAD, and has important clinical significance for evaluating the prognosis and immunotherapy response of STAD patients. TGM2 plays an important role in the progression of STAD, suggesting that TGM2 can be used as a new target for the treatment of STAD.

The impact of the Hedgehog signal pathway on the tumor immune microenvironment of gastric adenocarcinoma by integrated analysis of scRNA-seq and RNA-seq datasets.

The Hedgehog signaling is a highly conserved pathway to regulate cell growth and proliferation, and plays an essential role in stomach adenocarcinoma (STAD) and other cancer types. However, previous studies were primarily conducted in terms of mRNA or vitro cell culture. It would be more convincing to integrate single-cell RNA sequencing (scRNA-seq) data because it is a more precise approach for genomic research. The expression profile, genetic alteration, and activity of the Hedgehog signaling pathway were investigated in both scRNA-seq and RNA-seq datasets of STAD. Communications between cancer cells and fibroblasts were determined by the cell-chat algorithm, and the Hedgehog-related gene signature was constructed to predict the survival of STAD. Patients were categorized into high- and low-risk groups according to the median of the signature. Further analysis explored the difference in survival outcome, tumor immune microenvironment (TIME), and drug sensitivity between the two groups, aiming to guide the use of chemotherapy and immunotherapy in STAD patients. Hedgehog signal pathway was over-activated in STAD. GAS1, GLI1, and SCEBU2 were recognized as hub genes in the prognostic signature of STAD, and served as robust risk factors to induce a poor survival outcome. Patients in the high-risk group demonstrated an exhausted TIME pattern, with rather low sensitivity toward molecular-targeted drugs. This study depicted the influence of the Hedgehog pathway on the survival outcome, TIME, and drug sensitivity of STAD, and provides novel insights for the treatment of STAD.

A cuproptosis-related gene signature and associated regulatory axis in stomach adenocarcinoma based on bioinformatics analysis.

Stomach adenocarcinoma (STAD) is a highly aggressive and extremely heterogeneous gastric cancer characterized by high morbidity and mortality. Cuproptosis, a copper (Cu)-triggered modality of mitochondrial cell death, could regulate tumor proliferation and metastasis. Least absolute shrinkage and selection operator cox regression analysis was constructed to develop a prognostic cuproptosis-related signature. A lncRNA-miRNA-mRNA regulatory axis was performed to explore cuproptosis-related mechanism for STAD. The expression of FDX1, LIPT1, DLD, DLAT, PDHA1, PDHB, MTF1, GLS, and CDKN2A was upregulated in STAD versus normal tissue. We also summarized single nucleotide variants and copy number variation landscape of cuproptosis-related gene in STAD. Further analysis demonstrated that STAD patients with high expression of CDKN2A, DLD, GLS, and MTF1 and low expression of DLAT, FDX1, PDHA1 and PDHB had a poor overall survival (OS) and post progression survival (PPS) rate. By performing least absolute shrinkage and selection operator cox regression analysis, we constructed a cuproptosis-related prognostic signature for STAD. Further analysis demonstrated a significant correlation between FDX1 expression and immune cell infiltration, tumor mutational burden (TMB) score, microsatellite instability (MSI) score and drug sensitivity. Univariate and multivariate analysis indicated FDX1 expression and clinical stage as independent factors affecting the prognosis of STAD patients. We also identified a lncRNA MALAT1/miR-328-3p/FDX1 regulatory axis for STAD. Multi-omics approaches were performed to develop a cuproptosis-related signature with 2 genes (FDX1 and MTF1) for STAD. We also identified a lncRNA MALAT1/miR-328-3p/FDX1 regulatory axis for STAD.

Comprehensive analysis of the oncogenic roles of vascular endothelial growth factors and their receptors in stomach adenocarcinoma

BackgroundVascular endothelial growth factors (VEGFs) and their receptors (VEGFRs) play complicated oncogenic roles in multiple tumors by initiating and promoting tumor angiogenesis and lymphangiogenesis. The main goal of our study was to comprehensively investigate the oncogenic roles of VEGFs and VEGFRs in stomach adenocarcinoma (STAD). MethodsThe present study applied multiple bioinformatic tools to comprehensively explore the expression levels, prognostic values, genetic alterations and immune infiltrations of VEGFs and VEGFRs in STAD patients. ResultsWe found that VEGFA, VEGFC, placenta growth factor, FLT1, KDR, FLT4, and Neuropilin 1 were overexpressed in STAD, while the expression of VEGFB and VEGFD were decreased. Survival analysis revealed that higher transcription levels of VEGF/VEGFRs were obviously correlated with worse clinical outcome in STAD patients. Additionally, high alteration frequencies of VEGFs and VEGFRs (27%) were observed in STAD patients, and alterations of VEGFs and VEGFRs improved their prognosis. The expression of VEGFs and VEGFRs was remarkably associated with immune cell infiltration and immune checkpoint expression in STAD patients. ConclusionOur study systematically explored the transcriptome profiles and distinct prognostic values of VEGFs and their receptors in STAD and contributed to a better understanding of the oncogenic roles of VEGF/VEGFR members in STAD.

METTL3 regulates N6-methyladenosine modification of ANGPTL3 mRNA and potentiates malignant progression of stomach adenocarcinoma

BackgroundN6-methyladenosine (m6A) is associated with mammalian mRNA biogenesis, decay, translation and metabolism, and also contributes greatly to gastrointestinal tumor formation and development. Therefore, the specific mechanisms and signaling pathways mediated by methyltransferase-like 3 (METTL3), which catalyzes the formation of m6A chemical labeling in stomach adenocarcinoma (STAD), are still worth exploring.MethodsQuantitative real-time PCR (qRT-PCR) was constructed to detect the expression of METTL3 in gastric cancer cell lines and patient tissues. The biological function of METTL3 was investigated in vitro/in vivo by Cell Counting Kit-8, colony formation assay, Transwell assay and nude mouse tumorigenesis assay. Based on the LinkedOmics database, the genes co-expressed with METTL3 in the TCGA STAD cohort were analyzed to clarify the downstream targets of METTL3. Methylated RNA immunoprecipitation-qPCR (MeRIP-qPCR) and RNA stability analysis were employed to explore the mechanism of METTL3 in gastric cancer progression.ResultsWe analyzed TCGA data and found that METTL3 was frequently elevated in STAD, and demonstrated that METTL3 was present at high levels in clinical STAD tissues and cells. High METTL3 expression was more likely to have advanced TNM tumors and distant metastasis. On the other hand, METTL3 silencing effectively impeded the higher oncogenic capacity of AGS and HGC27 cells in vivo and in vitro, as reflected by slowed cell growth and diminished migration and invasion capacities. Continued mining of the TCGA dataset identified the co-expression of angiopoietin-like 3 (ANGPTL3) and METTL3 in STAD. Lower level of ANGPTL3 was related to increased level of METTL3 in STAD samples and shorter survival times in STAD patients. ANGPTL3 enrichment limited the growth and metastasis of STAD cells. Besides, ANGPTL3 mRNA levels could be decreased by METTL3-dominated m6A modifications, a result derived from a combination of MeRIP-qPCR and RNA half-life experiments. Importantly, the inhibitory effect of METTL3 silencing on cancer could be reversed to some extent by ANGPTL3 inhibition.ConclusionsOverall, our findings suggested that METTL3 functioned an oncogenic role in STAD by reducing ANGPTL3 expression in an m6A-dependent manner. The discovery of the METTL3-ANGPTL3 axis and its effect on STAD tumor growth will contribute to further studies on the mechanisms of gastric adenocarcinoma development.

Glutamine metabolism genes prognostic signature for stomach adenocarcinoma and immune infiltration: potential biomarkers for predicting overall survival.

Stomach adenocarcinoma (STAD), caused by mutations in stomach cells, is characterized by poor overall survival. Chemotherapy is commonly administered for stomach cancer patients following surgical resection. An imbalance in tumor metabolic pathways is connected to tumor genesis and growth. It has been discovered that glutamine (Gln) metabolism plays a crucial role in cancer. Metabolic reprogramming is associated with clinical prognosis in various cancers. However, the role of glutamine metabolism genes (GlnMgs) in the fight against STAD remains poorly understood. GlnMgs were determined in STAD samples from the TCGA and GEO datasets. The TCGA and GEO databases provide information on stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and clinical characteristics. Lasso regression was performed to build the prediction model. The relationship between gene expression and Gln metabolism was investigated using co-expression analysis. GlnMgs, found to be overexpressed in the high-risk group even in the absence of any symptomatology, demonstrated strong predictive potential for STAD outcomes. GSEA highlighted immunological and tumor-related pathways in the high-risk group. Immune function and m6a gene expression differed significantly between the low- and high-risk groups. AFP, CST6, CGB5, and ELANE may be linked to the oncology process in STAD patients. The prognostic model, CNVs, single nucleotide polymorphism (SNP), and medication sensitivity all revealed a strong link to the gene. GlnMgs are connected to the genesis and development of STAD. These corresponding prognostic models aid in predicting the prognosis of STAD GlnMgs and immune cell infiltration in the tumor microenvironment (TME) may be possible therapeutic targets in STAD. Furthermore, the glutamine metabolism gene signature presents a credible alternative for predicting STAD outcomes, suggesting that these GlnMgs could open a new field of study for STAD-focused therapy Additional trials are needed to validate the results of the current study.