Homo sapiens
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PROVIDER: PRJNA380115 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:LncRNA expression profiles for 6 human samples derived from patients with gastric cancer
| PRJNA208084 | 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:To identify the lncRNA profiles of gastric cancer samples, we performed microarray analysis using tumor samples and paired normal samples. 66 lncRNAs were statistically dysregulated more than 2-fold.
2016-07-26 | GSE84787 | GEO
Project description:CircRNA expression profiles for 6 human samples derived from patients with gastric cancer
| PRJNA350394 | ENA
Project description:Long noncoding RNAs (LncRNAs) are an important class if pervasive genes involved in a variety of biological functions. LncRNAs have been recently implicated as having oncogenic and tumor suppressor roles. To further investigate the function of lncRNA in gastric cancer, we use lncRNA microarray to describe LncRNAs profiles in 6 pairs of human gastric adenocarcinoma and the corresponding adjacent nontumorous tissues. The experimental samples are divided into two groups(normal and tumor) to compare lncRNA expression profiling of those
2013-12-09 | E-GEOD-53137 | 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:The lncRNA expression profiles in three pairs of hTERT-positive gastric cancer tissue sand hTERT-negative para-cancerous tissues. The para-cancerous tissue is at least 5cm away from the cancer tissue. The expression of hTERT of identified by immunohistochemistry before RNA extraction for lncRNA assay. LncRNAs/mRNAs in 3 gastric cancer tissue and 3 paired para-cancerous tissue (Control) by microarray using Arraystar Human LncRNA Microarray v2.0
2014-06-26 | E-GEOD-58828 | biostudies-arrayexpress
Project description:LncRNA and mRNA expression profiling for 7 human gastric cancr samples (3 tumor tissues and 3 tumor lymph node and 1 normal tissue) We have completed the metastasis-related Long Noncoding RNA expression profiling data microarray analysis of the 7 human gastric cancer related samples In the study presented here, a consecutively operated, well-defined cohort of three gastric cancer tissues and three metastatic lymph nodes tissues compared with the normal tissues and lymph nodes tissues, followed up more than five years, was used to acquire expression profiles of a total of 1942 lncRNA and 1976 mRNA, leading to the successful construction of supervised
2014-02-11 | E-GEOD-54835 | biostudies-arrayexpress
Project description:CircRNA expression profiles for 6 human plasma samples derived from patients with gastric cancer
| PRJNA361074 | ENA
Project description:To identify the lncRNA profiles of gastric cancer samples, we performed microarray analysis using tumor samples and paired normal samples. 66 lncRNAs were statistically dysregulated more than 2-fold. The samples were divided into two groups, paired normal samples (negative control group_Rep10) and tumor samplea (Experiment group_Rep10).
2016-07-26 | E-GEOD-84787 | biostudies-arrayexpress