Nociceptive neurons interact with gastric cancer cells via a CGRP/Ramp1 axis to promote tumor progression
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE243578 | GEO | 2024/11/24
REPOSITORIES: GEO
Similar Datasets
Project description:Nociceptive neurons interact with gastric cancer cells via a CGRP/Ramp1 axis to promote tumor progression
| PRJNA1018958 | ENA
Project description:Genome-wide mRNA expression profiles of 200 primary gastric tumors from the Singapore patient cohort. Gastric cancer (GC) is the second leading cause of global cancer mortality, with individual gastric tumors displaying significant heterogeneity in their deregulation of various oncogenic pathways. We aim to identify major oncogenic pathways in GC that robustly impact patient survival and treatment response. We used an in silico strategy based on gene expression signatures and connectivity analytics to map patterns of oncogenic pathway activation in 25 unique GC cell lines, and in 301 primary gastric cancers from three independent patient cohorts. Of 11 oncogenic pathways previously implicated in GC, we identified three predominant pathways (proliferation/stem cell, NF-kB, and Wnt/b-catenin) deregulated in the majority (>70%) of gastric tumors. Using a variety of proliferative, Wnt, and NF-kB-related assays, we experimentally validated the pathway predictions in multiple GC cell lines showing similar pathway activation patterns in vitro. Patients stratified at the level of individual pathways did not exhibit consistent differences in clinical outcome. However, patients grouped by oncogenic pathway combinations demonstrated robust and significant survival differences (e.g., high proliferation/high NF-kB vs. low proliferation/low NF-kB), suggesting that tumor behavior in GC is likely influenced by the combined effects of multiple oncogenic pathways. Our results demonstrate that GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Experiment Overall Design: Profiling of 200 primary gastric tumors on Affymetrix GeneChip Human Genome U133 Plus 2.0 Array. All tumors were collected with approvals from the National Cancer Centre, Singapore; the Research Ethics Review Committee; and signed patient informed consent.
2009-04-10 | E-GEOD-15459 | biostudies-arrayexpress
Project description:Gastric cancer (GC) is associated with high mortality rates and an unfavorable prognosis at advanced stages. In addition, there are no effective methods for diagnosing gastric cancer at an early stage or for predicting the outcome for the purpose of selecting patient-specific treatment options. Therefore, it is important to investigate new methods for GC diagnosis. We designed a custom microarray of gastric cancer. The customized microarray contained 1042 canceration and prognosis related genes identical to the probes on the Agilent microarray. DNA microarray profilling analysis was performed on gastric cancer tissues and premalignant tissues (20 samples per group).
2014-03-01 | E-MTAB-1881 | biostudies-arrayexpress
Project description:Gastric cancer (GC) represents a major health problem, remaining the fifth most common type of cancer and the third leading cause of cancer-related death worldwide with only few targeted therapies available to date. The positive effect of kinase inhibitors on many different types of cancers has prompted the investigation of these compounds also in GC. In a screen of FDA-approved kinase inhibitors, dasatinib was found as a potent inhibitor of gastric cancer cells migration and invasion. In order to identify the kinases involved proteomics analysis was performed identifying SRC kinases, Ephrins and DDR1 as potential molecular targets.
2020-05-26 | PXD012953 | Pride
Project description:Accumulating evidence has shown that circRNAs played critical roles in cancer and oncology. However, the role and mechanisms of circRNAs in gastric cancer (GC) remains not fully understood. CircRNA microarray analysis is one of reliable and convenient methods to detect differentially expressed circular RNAs. The aims of this study are to investigate the differentially expressed circRNAs in GC via human circRNA microarray analysis, and illuminate the role and underlying mechanisms of hsa_circ_0025506, i.e., circGPRC5A(e2), in GC.
2023-01-25 | GSE194384 | GEO
Project description:We performed RNA-Seq to confirm the changes in expression of genes in the relevant pathway in human gastric cancer cells (AGS cells) and AGS cells cocultured with macrophages (RAW264.7 cells). In the gene ontology enrichment analysis of the 160 genes that were increased in AGS cells cocultured with RAW264.7 cells, pathways related to immune response, cell adhesion, and cytokine response were enriched.
2022-12-12 | GSE220531 | GEO
Project description:miRNA expression profiles in the progression of the gastric cancer According to the development of intestinal gastric cancer(GC), the normal gastric mucosa gradually evolves into gastric cancer through CSG(Chronic superficial gastritis), CAG(Chronic atrophic gastritis), IM (intestinal metaplasia) and Dys(Dysplasia). H. pylori is the main risk factor for GC, but the mechanism is still unclear. In this study, we indentified the miRNA, lncRNAs and mRNAs expression profiles in GC progression, analyzed the fuctions and pathways and investigated the relationship between non coding RNAs and H. pylori infection. Our study provided new ideas for the study of the pathogenesis of GC and a basis for the early diagnosis and treatment of GC and precancerous lesions.
2023-12-17 | GSE163747 | GEO
Project description:The circRNAs expression profiles in 5 gastric cancer (GC) and their matched non-gastric cancer (non-GC) tissues were detected by human circRNA expression profile chips, and the differentially expression circRNAs between GC and matched non-GC tissues were then identified. A total of 713 circRNAs were differentially expressed in GC tissues vs. non-GC tissues (fold change≥2.0, p<0.05) among all the candidate circRNAs (62998) detected in both GC and non-GC tissues according to the T-test. 191/713 circRNAs were significantly upregulated in GC tissues, whereas 522/713 circRNAs were significantly downregulated in GC tissues. GO and KEGG pathway analyses showed that many dysregulated circRNAs may be functionally GC-related, thereby contributing to the development of GC.
2017-06-19 | GSE100170 | GEO
Project description:Regions of recurrent genomic amplification and deletion are frequently observed in primary gastric cancers (GC). However, identifying specific oncogenes and tumor suppressor genes within these regions can be challenging, as they often cover tens to hundreds of genes. Here, we combined high-resolution array-based comparative genomic hybridization (aCGH) with gene expression profiling to target genes lying within focal high-level amplifications in GC cell lines, and identified RAB23 as an amplified and overexpressed Chr 6p11p12 gene in Hs746T cells. High RAB23 protein expression was also observed in some lines lacking RAB23 amplification, suggesting additional mechanisms besides gene amplification for up-regulating RAB23. siRNA silencing of RAB23 reduced the invasive potential of both amplified and nonamplified GC cell lines. RAB23 gene amplifications were observed in 13% of primary gastric carcinomas. In two independent patient cohorts, RAB23 transcript and protein expression was significantly associated with diffuse-type gastric cancer (dGC) compared to intestinal-type gastric cancer (iGC), providing further evidence that dGC and iGC likely represent two molecularly distinct tumor types. Our study demonstrates that investigating focal chromosomal amplifications by combining highresolution aCGH with expression profiling is a powerful general strategy for identifying novel cancer genes in recurrent regions of chromosomal aberration. Keywords: gastric cancer cell lines, comparative genomic hybridization, gene expression profiling Affy 100K SNP profiling and 32K BAC Array profiling for 7 Gastric Cancer Cell Lines
2010-05-14 | E-GEOD-10611 | biostudies-arrayexpress
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.
2024-07-23 | MODEL2407230001 | BioModels