Next-generation sequencing analysis of molecular subtypes of gastric cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE180887 | GEO | 2021/07/29
REPOSITORIES: GEO
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Project description:Objective Cancer immunotherapy aims to unleash the immune system's potential against cancer cells, providing sustained relief for tumors responsive to immune checkpoint inhibitors (ICIs). While promising in gastric cancer (GC) trials, the efficacy of ICIs diminishes in the context of peritoneal dissemination. Our objective is to identify strategies to enhance the impact of ICI treatment specifically for cases involving peritoneal dissemination in GC. Design The therapeutic efficacy of anti-PD1, CTLA4 treatment alone, or in combination, was assessed using the YTN16 peritoneal dissemination tumor model mice. Peritoneum and peritoneal exudate cells were collected for subsequent analysis. Immunohistochemical staining, flow cytometry, and bulk RNA-sequence analyses were conducted to evaluate the tumor microenvironment (TME). A JAK inhibitor (JAKi) was introduced based on the pathway analysis results. Results Anti-PD1 and anti-CTLA4 combination treatment (dual ICI treatment) demonstrated therapeutic efficacy in certain mice, primarily mediated by CD8+ T cells. However, in mice resistant to dual ICI treatment, even with CD8+ T cell infiltration, most of the T cells exhibited exhaustion phenotype. Notably, resistant tumors displayed abnormal activation of the JAK-STAT pathway compared to the untreated group, with observed infiltration of macrophages, neutrophils, and Tregs in the TME. The concurrent administration of JAKi rescued CD8+ T cells function and reshaped the immunosuppressive TME, resulting in enhanced efficacy of the dual ICI treatment. Conclusion Dual ICI treatment exerts its anti-tumor effects by increasing tumor- specific CD8+ T cell infiltration, and the addition of JAKi further improves ICI resistant by reshaping the immunosuppressive TME.
2024-05-28 | GSE250238 | GEO
Project description:Gastric cancer (GC) is a one of the most common gastroenterological carcinoma, but its prognostic and immunological characteristics is not fully illustrated. Here we build an immune-associated gene prognostic model to predict the prognosis of GC. We performed RNA sequencing of 33 GC patients to verified the prognostic model.
2024-07-05 | GSE236522 | GEO
Project description:The paper "Metabolomic Machine Learning Predictor for Diagnosis and Prognosis of Gastric Cancer" addresses the need for non-invasive diagnostic tools for gastric cancer (GC). Traditional methods like endoscopy are invasive and expensive. The authors conducted a targeted metabolomics analysis of 702 plasma samples to develop machine learning models for GC diagnosis and prognosis. The diagnostic model, using 10 metabolites, achieved a sensitivity of 0.905, outperforming conventional protein marker-based methods. The prognostic model effectively stratified patients into risk groups, surpassing traditional clinical models.
I have successfully reproduced the diagnosis model from the paper. This machine learning-based system differentiates GC patients from non-GC controls using metabolomics data from plasma samples analyzed by liquid chromatography-mass spectrometry (LC-MS). The model focuses on 10 metabolites, including succinate, uridine, lactate, and serotonin. Employing LASSO regression and a random forest classifier, the model achieved an AUROC of 0.967, with a sensitivity of 0.854 and specificity of 0.926. This model significantly outperforms traditional diagnostic methods and underscores the potential of integrating machine learning with metabolomics for early GC detection and treatment.
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Project description:Background: Helicobacter pylori (HP) infection may initiate and promote progression of gastric carcinogenesis. ONECUT2 shows promise for tumor diagnosis, prognosis, and treatment. This study explored ONECUT2's role and specific mechanism underlying HP infection-associated gastric carcinogenesis to suggest a basis for targeting ONECUT2 as a therapeutic strategy for gastric cancer (GC). Methods: Public data, single-cell RNA sequencing, spatial transcriptome analysis, RNA sequencing, GC tissue specimens, and clinical survival data were analyzed. Human GC organoids, HP infection, 3D sphere-forming, and extreme-dilution nude mouse models were constructed. Western blotting, qPCR, immunohistochemical staining, dual-luciferase reporter assays, phosphokinase microarray, and immunofluorescence were used. Results: Multidimensional data supported an association between ONECUT2, HP infection, and GC pathogenesis. HP infection upregulated ONECUT2 transcriptional activity via NFκB. In vitro and in vivo experiments demonstrated that ONECUT2 increases stemness in GC cells. ONECUT2 was also shown to inhibit PPP2R4 transcription, resulting in reduced PP2A activity, which in turn increased AKT/β-catenin phosphorylation. AKT/β-catenin phosphorylation facilitates β-catenin translocation to the nucleus, initiating transcription of downstream stemness-associated genes in GC cell. HP infection could upregulate the phosphorylation of AKT and β-catenin triggered by ONECUT2 downregulation via induction of ONECUT2. Clinical survival analysis indicated that high ONECUT2 expression might be an indicator of poor prognosis in GC. Conclusion: This study highlights a critical role played by ONECUT2 in promoting HP infection-associated GC by enhancing cell stemness through the PPP2R4/AKT/β-catenin signaling pathway. These findings suggest promising therapeutic strategies and potential therapeutic targets for GC treatment. Keywords: Helicobacter pylori; gastric cancer; prognosis; tumor stemness; ONECUT2
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Project description:Molecular knowledge of normal gastric tissues and gastric cancers remains incomplete. Here, we used single-cell RNA-seq to study the cell diversity of gastric tissues and gastric cancers. The expression landscape of normal gastric cell types and several candidate stem cell markers were obtained. Surprisingly, nearly all cell types in the antrum could transdifferentiate to intestinal metaplasia (IM). We also explored intra-tumoral heterogeneity and identified four common features of gastric cancer. In addition, we classified tumor cells into three major subtypes, which are associated with their prognosis. Finally, the proportions of mesenchymal and endothelial cells in the tumor microenvironment (TME) were negatively correlated with the prognosis of gastric cancer. Therefore, our work provides comprehensive molecular characterizations of both gastric development and gastric cancer at single-cell resolution and has significant potential to inspire better treatment strategies for gastric cancer. Keywords: Expression profiling by high throughput sequencing
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2023-06-10 | GSE234129 | GEO
Project description:Gastric cancer is the 3rd most lethal cancer worldwide, and the genomic status of its cancer cells has not translated into effective prognostic or therapeutic strategies. We therefore hypothesize that outcomes may depend on the tumor microenvironment (TME), and in particular cancer-associated fibroblasts (CAFs). However, very little is known about the role of CAFs in gastric cancer. To address this, we map the transcriptional landscape of human gastric cancer stroma by microdissection and RNA sequencing of CAFs from gastric cancer patients. We find a stromal gene signature associated with poor disease outcome. This signature is regulated by the transcription factor Heat Shock Factor 1 (HSF1), that activates CAF-derived extracellular vesicles (EVs) that transfer components including Inhibin Subunit Beta A (INHBA) and Thrombospondin 2 (THBS2), to the TME to promote cancer. Together, our work provides the first transcriptional map of human gastric cancer stroma, and highlights HSF1 and its transcriptional targets as potential diagnostic and therapeutic targets in the genomically stable tumor microenvironment.
2021-02-28 | GSE162301 | GEO
Project description:Gastric cancer is the 3rd most lethal cancer worldwide, and the genomic status of its cancer cells has not translated into effective prognostic or therapeutic strategies. We therefore hypothesize that outcomes may depend on the tumor microenvironment (TME), and in particular cancer-associated fibroblasts (CAFs). However, very little is known about the role of CAFs in gastric cancer. To address this, we map the transcriptional landscape of human gastric cancer stroma by microdissection and RNA sequencing of CAFs from gastric cancer patients. We find a stromal gene signature associated with poor disease outcome. This signature is regulated by the transcription factor Heat Shock Factor 1 (HSF1), that activates CAF-derived extracellular vesicles (EVs) that transfer components including Inhibin Subunit Beta A (INHBA) and Thrombospondin 2 (THBS2), to the TME to promote cancer. Together, our work provides the first transcriptional map of human gastric cancer stroma, and highlights HSF1 and its transcriptional targets as potential diagnostic and therapeutic targets in the genomically stable tumor microenvironment.
2021-02-28 | GSE165211 | GEO
Project description:Gastric cancer (GC) is a challenging malignant disease among gastrointestinal tumors. The clinical treatments for GC include surgery, neoadjuvant therapy and adjuvant chemotherapy. As patients’ response to chemotherapeutic drugs varies, an approach to predict the efficacy of chemotherapeutic treatment is urgently needed. Here, a biorepository of 17 human gastric tumor organoids (GTOs) and 9 normal organoids (GNOs) derived from GC patients was established, which contains the intestinal, diffuse and mixed-type of GC. GTOs retained morphologic, histopathological and genetic features of the tumors from which they were derived, and patient-derived organoid xenograft model indicated tumorigenicity of organoids. GTOs responses to clinically respective chemotherapy treatment positively correlated with the individual patient’s clinical response. Meanwhile, GNOs, which maintained original characteristics of corresponding tissues, could be separated from GTOs. The use of GNOs predicted the cytotoxic effect of chemotherapy on the organism. Our study implies that patient-derived organoids predict GC patient responses to chemotherapy and provide a solution for personalized treatment.
2024-03-10 | GSE235912 | GEO
Project description:In this study, we used single-cell RNA sequencing (scRNA-seq) to further elucidate the composition of the gastric cancer and adjacent normal tissue microenvironment and found that TNF and ACTG1 may be key regulators of regulatory T cells (Tregs) in gastric cancer. A total of seven cell types were identified, including B cells, T cells, NK cells, mast cells, fibroblasts, endothelial cells, and epithelial cells. T cells were further divided into 8 subgroups. Among them, Tregs showed the greatest difference between GC and adjacent normal tissues. Next, we clarified the function and signaling pathway of Tregs in the GC tumor microenvironment, obtained the key regulatory proteins TNF and ACTG1, and explored their clinical significance and possible effects on Tregs in the GC tumor microenvironment. This scRNA-seq study may reveal the composition of tumor microenvironment and clarify the role of Tregs in the development of gastric cancer, providing new methods for GC treatment.
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