Ontology highlight
ABSTRACT:
PROVIDER: PRJNA937852 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23595731_1.fastq.gz | Fastqsanger.gz | |||
| SRR23595731_2.fastq.gz | Fastqsanger.gz | |||
| SRR23595732_1.fastq.gz | Fastqsanger.gz | |||
| SRR23595732_2.fastq.gz | Fastqsanger.gz | |||
| SRR23595733_1.fastq.gz | Fastqsanger.gz |
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Project description:Gut microbiome analysis as a predictive marker for the gastric cancer patients
| PRJNA639644 | ENA
Project description:Background: suitable diagnostic markers for cancers are urgently required in clinical practice. Long noncoding RNAs, which have been reported in many cancer types, are a potential new class of biomarkers for tumor diagnosis. Method: LncRNA gene expression profiles were analyzed in two pairs of human gastric cancer and adjacent non-tumor tissues by microarray analysis. Nine gastric cancer-associated lncRNAs were selected and assessed by quantitative real-time polymerase chain reaction in gastric tissues, and 5 of them were further analyzed in gastric cancer patients’plasma. Results: Five lncRNAs, including AK001058, INHBA-AS1, MIR4435-2HG, UCA1 and CEBPA-AS1 were validated to be increased in gastric cancer tissues. Furthermore, we found that plasma level of these five lncRNAs were significantly higher in gastric cancer patients compared with normal controls. By receiver operating characteristic analysis, we found that the combination of plasma lncRNAs with the area under the curve up to 0.921, including AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, is a better indicator of gastric cancer than their individual levels or other lncRNA combinations. Simultaneously, we found that the expression levels of a series of MIR4435-2HG fragments are different in gastric cancer plasma samples, but most of them higher than that in healthy control plasma samples. Conclusion: Our results demonstrate that certain lncRNAs, such as AK001058, INHBA-AS1, MIR4435-2HG, and CEBPA-AS1, are enriched in human gastric cancer tissues and significantly elevated in the plasma of patients with gastric cancer. These findings indicate that the combination of these four lncRNAs might be used as diagnostic or prognostic markers for gastric cancer patients.
2017-01-12 | GSE93512 | GEO
Project description:Gastric cancer is the second most common cause of cancer death worldwide, but incidence and mortality rates show large variations across different countries. Variation in risk factors between different populations, including environmental and host factors influencing gastric cancer risk, have been reported but little is known about the biological differences between gastric cancers from different geographic locations. We set out to study genomic instability patterns of gastric cancers obtained from patients from United Kingdom (UK) and South Africa (SA). DNA was isolated from 67 gastric adenocarcinomas, 33 UK patients, 9 Caucasian SA patients and 25 native SA patients. Microsatellite instability and chromosomal instability were analyzed by PCR and microarray comparative genomic hybridization, respectively. Data was analyzed by supervised univariate and multivariate analyses as well as unsupervised hierarchical cluster analysis. Tumors from Caucasian and native SA patients showed significantly more microsatellite instable tumors (p<0.05). For the microsatellite stable tumors, geographical origin of the patients correlated with cluster membership, derived from unsupervised hierarchical cluster analysis (p=0.001). Several chromosomal alterations showed significantly different frequencies in tumors from UK patients and native SA patients, but not between UK patients and Caucasian SA patients and between native and Caucasian SA patients. In conclusion, gastric cancers from South African and UK patients show differences in genetic instability patterns, indicating possible different biological mechanisms underlying the disease.
2011-06-28 | GSE22789 | GEO
Project description:Pancreatic cancer is the 3rd most prevalent cause of cancer related deaths in United states alone, with over 55000 patients being diagnosed in 2019 alone and nearly as many succumbing to it. Late detection, lack of effective therapy and poor understanding of pancreatic cancer systemically contributes to its poor survival statistics. Obesity and high caloric intake linked co-morbidities like type 2 diabetes (T2D) have been attributed as being risk factors for a number of cancers including pancreatic cancer. Studies on gut microbiome has shown that lifestyle factors as well as diet has a huge effect on the microbial flora of the gut. Further, modulation of gut microbiome has been seen to contribute to effects of intensive insulin therapy in mice on high fat diet. In another study, abnormal gut microbiota was reported to contribute to development of diabetes in Db/Db mice. Recent studies indicate that microbiome and microbial dysbiosis plays a role in not only the onset of disease but also in its outcome. In colorectal cancer, Fusobacterium has been reported to promote therapy resistance. Certain intra-tumoral bacteria have also been shown to elicit chemo-resistance by metabolizing anti-cancerous agents. In pancreatic cancer, studies on altered gut microbiome have been relatively recent. Microbial dysbiosis has been observed to be associated with pancreatic tumor progression. Modulation of microbiome has been shown to affect response to anti-PD1 therapy in this disease as well. However, most of the studies in pancreatic cancer and microbiome have remained focused om immune modulation. In the current study, we observed that in a T2D mouse model, the microbiome changed significantly as the hyperglycemia developed in these animals. Our results further showed that, tumors implanted in the T2D mice responded poorly to Gemcitabine/Paclitaxel (Gem/Pac) standard of care compared to those in the control group. A metabolomic reconstruction of the WGS of the gut microbiota further revealed that an enrichment of bacterial population involved in drug metabolism in the T2D group.
2020-11-30 | E-MTAB-8739 | biostudies-arrayexpress
Project description:We have performed gene expression microarray analysis to profile transcriptomic signatures between cancer and noncancerous patients Gastric cancer is currently the second leading cause of cancer deaths. Due to the difficulty of diagnosing patients in the early stages of gastric cancer, it is critical to develop a method that can diagnose the disease at the early stage to allow for better treatment options. In this study, we discovered salivary transcriptomic and miRNA biomarkers for the detection of gastric cancer and identified there are mRNA-miRNA correlations in saliva. RNA was extracted from saliva supernatant and mRNA candidates were identified that can distinguish gastric cancer from non-gastric cancer patients
2015-01-15 | E-GEOD-64951 | 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
Project description:Gastric cancer is an important health problem because it is difficult to diagnose and treat in advanced stage. This makes that the prognosis of gastric cancer patients remains scarce. Currently it is known that the cause of gastric cancer is attributed to chronic infection with Helicobacter pylori. Its persistent infection leads to development of chronic atrophic gastritis that is considered as a predecessor stage of intestinal-type gastric cancer. The understanding of the alteration of molecular mechanisms during the early stages of the development of gastric cancer, and the identification of their potential biomarkers can allow a rapid diagnosis that leads to an improvement diagnosis and increase the patient’s prognosis. We analyzed gene expression profiles of patients with chronic atrophic gastritis and gastric cancer through microarray analysis, functional enrichment analysis and validation of gene expression by quantitative PCR. Gene expression profiles in patients with chronic atrophic gastritis showed molecular changes of the gastric mucosa, which leads to intestinal metaplasia and subsequently, gastric cancer. In gastric cancer the gene expression profile showed the stage of tumor progression, the product of these genes are potential biomarkers of early stages of cancer that can be potential therapeutic targets. Accordingly, the transcriptome analysis revealed several gene groups are related to development of chronic atrophic gastritis, some of which were inhibited in gastric cancer patients. The increased expression of CLDN1, CLDN7, OLFM4, c-Myc and MMP-9 genes in chronic atrophic gastritis and gastric cancer point outs to their use as promising biomarkers for the early diagnosis of gastric cancer.
2019-12-18 | GSE116312 | GEO
Project description:Peritoneal carcinomatosis is a frequent finding in patients with primary gastric cancer, and it is associated with a poor prognosis. A major mechanism in peritoneal carcinomatosis is the dissemination of cancer cells into the abdominal cavity, mainly in diffuse gastric adenocarcinoma. The features that enable diffuse primary gastric tumours to develop peritoneal dissemination have been little investigated and are only incompletely understood. We therefore compared the gene expression profile in patients with diffuse primary gastric cancer with and without peritoneal carcinomatosis. Specimens from consecutive gastric cancer patients with and without peritoneal carcinomatosis were investigated using oligonucleotide microarrays. Keywords: Disease state analysis
2008-01-31 | GSE8657 | GEO