Similar Datasets
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
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:Background: Circular RNAs (circRNAs) have attracted increasing attention in recent years for their potential application as disease biomarkers due to their high abundance and stability. In this study, we attempted to screen circRNAs that can be used to predict postoperative recurrence and survival in patients with gastric cancer (GC). Methods: High-throughput RNA sequencing was used to identify differentially expressed circRNAs in GC patients with different prognoses. The expression level of circRNAs in the training set (n=136) and validation set (n=167) was detected by quantitative real-time PCR (qRT-PCR). Kaplan–Meier estimator, receiver operating characteristic (ROC) curve and cox regression analysis were used to evaluate the prognostic value of circRNAs on recurrence-free survival (RFS) and overall survival (OS) in GC patients. CeRNA network prediction, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed for the circRNAs with prognostic significance. Results: A total of 259 differentially expressed circRNAs were identified in GC patients with different RFS. We found two circRNAs (hsa_circ_0005092 and hsa_circ_0002647) that highly expressed in GC patients with good prognoses, and subsequently established a predictive model for postoperative recurrence and prognosis evaluation, named circPanel. Patients with circPanellow might have shorter recurrence-free survival (RFS) and overall survival (OS). We also performed circRNA-miRNA-mRNA network prediction and functional analysis for hsa_circ_0005092 and hsa_circ_0002647. Conclusions: CircPanel has the potential to be a prognostic biomarker in GC patients with greater accuracy than a single circRNA and certain traditional tumor markers (e.g., CEA, CA19-9 and CA724).
2021-08-12 | GSE181769 | GEO
Project description:METTL3-mediated RNA N6-methyladenosine (m6A) is the most prevalent modification participates in tumor initiation and progression via regulating expression of their target genes in cancers. However, its role in tumor cell metabolism remains poorly appreciated. In this study, we conducted a multi-omics analysis including m6A microarray and quantitative proteomics to explore the potential effect and mechanism of METTL3 on the metabolism in gastric cancer cells. Our results found that significant alterations in the protein and m6A modification profile which induced by METTL3 overexpression in GC cells. Gene Ontology (GO) enrichment results showed that down-regulated proteins were significantly enriched in intracellular mitochondrial oxidative phosphorylation (OXPHOS), and the Protein-Protein Interaction (PPI) network analysis found that these differentially expressed proteins were significantly associated with OXPHOS. Subsequently, a prognostic model constructed based on the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and the high-risk group showed a worse prognosis in GC patients. Meanwhile, the Gene Set Enrichment Analysis (GSEA) showed a significant enrichment in the energy metabolism signaling pathway. Then, combined with the results of the m6A microarray analysis, the intersection molecules of DEPs and differential methylation genes (DMGs) were significantly correlated with the genes involved in OXPHOS. Besides, there were also significant differences in prognosis and GSEA enrichment between the two clusters of GC patients classified according to consensus clustering algorithm. Finally, we focused on highly expressed, highly methylated molecules regulated by METTL3 and identified three (AVEN, DAZAP2, DNAJB1) genes that were significantly associated with poor prognosis in patients with GC. These results indicated that METTL3-regulated DEPs in GC cells were significantly associated with OXPHOS. After combined with m6A microarray analysis, the results suggested that these proteins might be involved in cell energy metabolism through m6A modifications thus influencing the prognosis of GC patients. Overall, our study revealed that METTL3 involved in cell metabolism through an m6A-dependent mechanism in GC cells, and indicated a potential biomarker for prognostic prediction in GC.
2023-03-10 | PXD036773 | Pride
Project description:We elucidated the functional significance and molecular mechanisms of DUSP5P1 lncRNA (dual specificity phosphatase 5 pseudogene 1) in gastric carcinogenesis. We demonstrated that gastric cancer (GC) patients with high DUSP5P1 expression had shortened survival in two independent cohorts. DUSP5P1 promoted GC cell migration and invasion in vitro and metastasis in vivo. Mechanistically, DUSP5P1 activated ARHGAP5 transcription by directly binding to the promoter of ARHGAP5 with a binding motif of TATGTG. RNA-seq revealed that ARHGAP5 activated focal adhesion and MAPK signalling pathways to promote GC metastasis. DUSP5P1 also dysregulated platinum drug resistance pathway. Consistently, DUSP5P1 overexpression in GC cells antagonized cytotoxic effect of Oxaliplatin, and shDUSP5P1 plus Oxaliplatin exerted synergistic effect on inhibiting GC metastasis in vitro and in vivo. DUSP5P1 depletion also suppressed the growth of platinum drug-resistant PDO models. In conclusion, DUSP5P1 promoted GC metastasis by directly modulating ARHGAP5 expression to activate focal adhesion and MAPK pathways, serves as therapeutic target for platinum drug resistant GC, and is an independent prognostic factor in GC.
2022-11-18 | GSE213581 | GEO
Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin, genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using highly-sensitive transcriptome profiling of untreated DLBCL tissues, we here assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells, in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.
2021-06-18 | GSE178394 | GEO
Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common B-cell malignancy with varying prognosis after the gold standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP). Several prognostic models have been established by focusing primarily on characteristics of lymphoma cells themselves, including cell-of-origin, genomic alterations, and gene/protein expressions. However, the prognostic impact of the lymphoma microenvironment and its association with characteristics of lymphoma cells are not fully understood. Using highly-sensitive transcriptome profiling of untreated DLBCL tissues, we here assess the clinical impact of lymphoma microenvironment on the clinical outcomes and pathophysiological, molecular signatures in DLBCL. The presence of normal germinal center (GC)-microenvironmental cells, including follicular T cells, macrophage/dendritic cells, and stromal cells, in lymphoma tissue indicates a positive therapeutic response. Our prognostic model, based on quantitation of transcripts from distinct GC-microenvironmental cell markers, clearly identified patients with graded prognosis independently of existing prognostic models. We observed increased incidences of genomic alterations and aberrant gene expression associated with poor prognosis in DLBCL tissues lacking GC-microenvironmental cells relative to those containing these cells. These data suggest that the loss of GC-associated microenvironmental signature dictates clinical outcomes of DLBCL patients reflecting the accumulation of “unfavorable” molecular signatures.
2021-09-01 | GSE178965 | GEO
Project description:Anoctamin 5 as a key mediator in tumor progression, it may being a promising prognostic biomarker for a novel therapeutic target of GC.
2021-11-29 | GSE189601 | GEO
Project description:The prognosis after curative resection of gastric cancer (GC) remains unsatisfactory, and thus, the development of treatments involving alternative molecular and genetic targets is critical. Circular RNAs (circRNAs), new stars of the non-coding RNA network, have been identified as critical regulators in various cancers. Here, we aimed to determine the circRNA expression profile and investigate the functional and prognostic significance of circRNA in GC. Using next-generation sequencing profiling, we first characterized an abundant circRNA, hsa_circ_0008549, derived from the OSBPL10 gene, and named it circOSBPL10. The expression of circOSBPL10 was found via quantitative real-time RT-PCR (qRT-PCR) to be upregulated in GC tissues, and silencing of circOSBPL10 significantly inhibited gastric cancer cell growth, migration and invasion in multiple experiments. We further confirmed that miR-136-5p is a downstream target of circOSBPL10 using RNA pull-down and luciferase reporter assays. Rescue experiments confirmed that circOSBPL10 regulates biological functions in GC cells via a circOSBPL10-miR-136-5p-WNT2 axis. In vivo experiments showed that circOSBPL10 promotes tumor growth and metastasis in mice. Furthermore, the level of circOSBPL10 was observed to be a prognostic marker of the overall survival and disease-free survival of patients with GC. Taken together, our findings reveal that circOSBPL10 may serve as a new proliferation factor and prognostic marker in gastric cancer.
2018-11-22 | GSE122796 | GEO
Project description:Circular (circ) RNAs have been widely reported to be involved in gastric cancer (GC) pathogenesis and coiled coil domain containing 6 (CCDC6) is recognized as fused partner of multiple oncogenes; however, the underlying mechanisms of how circRNAs regulate CCDC6 expression in the progression and prognosis of GC remain unclear. Here, we discovered a novel circRNA derived from the DNA2 gene locus (circDNA2) through joint analysis of circRNA microarrays. By performing qRT-PCR and FISH assays with a human tissue microarray, circDNA2 was identified to be highly expressed in GC tissues and associated with lymphatic invasion of GC patients. Knockdown of circDNA2 suppressed the proliferation of GC cells by reducing CCDC6 expression in vitro. Mechanically, circDNA2 acted as a sponge for microRNA (miR)-149-5p, which was validated to target CCDC6 by dual luciferase reporter assays and rescue experiments. Both miR-149-5p low expression and CCDC6 high expression were related to unfavorable prognosis of GC patients. Moreover, GC patients with low miR-149-5p expression had shorter overall survival and higher risk of chemotherapy resistance compared with these with high miR-149-5p expression. In summary, our findings reveal that circDNA2 contributes to the growth and lymphatic metastasis of GC through upregulating CCDC6 expression via sponging miR-149-5p. The circDNA2/miR-149-5p/CCDC6 axis might be developed as a therapeutic target and prognostic indicator for GC patients.
2024-02-25 | GSE167512 | GEO