Expression data from gastric cancer and paried normal tissues
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE79973 | GEO | 2016/04/07
SECONDARY ACCESSION(S): PRJNA317544
REPOSITORIES: GEO
Similar Datasets
Project description:Gastric cancer (GC) is one of the most common cancer worldwide. Specific and reliable molecular markers are limited; it is critical to identify new biomarkers for GC to aid in early diagnosis, treatment strategy, and prognosis evaluation. Microarray technology makes it possible to measure the expression levels of thousands of genes, and identifying meaningful and useful molecular targets from these large data. Total RNA was extracted from 10 pairs of GC tissue and adjacent non-tumor mucosa using TRIzol® reagent (Invitrogen, Carlsbad, CA, US) following the manufacturer's instructions. The RNA integrity number (RIN), detected by an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, US), was used to determine the RNA integrity. Total RNA meeting the specified quality criteria (RIN ⥠7.0 and 28S/18S ⥠0.7) were further purified using an RNase-Free DNase Set (Qiagen, Hilden, Germany) and an RNeasy® Micro Kit (Qiagen, Hilden, Germany) according to the manufacturer's instructions. Purified total RNA was then used for obtaining biotin-labeled cRNA by applying the GeneChip® 3'IVT Express Kit (Affymetrix, Santa Clara, CA, US), according to the manufacturer's instructions. Then, the GeneChip® Hybridization, Wash, and Stain Kit (Affymetrix, Santa Clara, CA, US) was used for performing the array hybridization and wash, with the use of a GeneChip® Hybridization Oven 645 (Affymetrix, Santa Clara, CA, US) and a GeneChip® Fluidics Station 450 (Affymetrix, Santa Clara, CA, US). All arrays were scanned using a GeneChip® Scanner 3000 (Affymetrix, Santa Clara, CA, US) and Command Console Software 3.1 (Affymetrix, Santa Clara, CA, US) with the default settings. Raw data were normalized by the MAS 5.0 algorithm in GeneSpring Software 11.0 (Agilent Technologies, Santa Clara, CA, US). The significance analysis of microarray (SAM) was used to identify genes that were differentially expressed between the GC and normal tissues. Genes were considered to be differentially expressed when the Tumor versus Normal signal log ratio values were â¥2 or â¤0.5.
2016-04-07 | E-GEOD-79973 | biostudies-arrayexpress
Project description:Gastric cancer (GC) remains one of the most prevalent tumor worldwide, and ranks third in cancer-related deaths globally. Long non-coding RNAs (lncRNAs) have been reported to play significant role in the progression and metastasis in gastric cancer (GC), however, the molecular mechanism are largely elusive. We aim to identify up-regulated lncRNA in GC and study their function in promoting tumor progression and metastasis.
2023-11-13 | GSE193109 | GEO
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:Pathological examination of gastroscopy biopsy specimens will make false diagnosis for gastric cancer (GC) due to inaccurate sampling locations and/or insufficient sampling amount. We extracted a robust qualitative transcriptional signature, based on the within-sample relative expression orderings (REOs) of gene pairs, to discriminate both GC tissues and adjacent-normal tissues from non-GC gastritis and normal gastric tissues.The qualitative transcriptional signature can be robustly applied at the individual level to aid the diagnosis of early GC.
2018-07-10 | GSE116782 | GEO
Project description:Increasing evidence has clarified that the tumor microenvironment (TME) is closely related to the prognosis and therapeutic efficacy of gastric cancer (GC). However, there is no reliable TME evaluation system used to accurately predict the prognosis and therapeutic efficacy of gastric cancer. In this study, immune cell enrichment analysis was used to construct an immune microenvironment score (IMS).We further demonstrated that IMS may be used as an indicator to predict the efficacy of immunotherapy in patients with GC.
2021-07-29 | GSE180887 | GEO
Project description:Gastric cancer is one of the most common causes of cancer-related death worldwide, and the molecular mechanisms involved in gastric carcinogenesis are still not fully understood. To gain molecular understanding of carcinogenesis, progression, and diversity of gastric cancer, 111 human gastric cancer tissues and 21 noncancerous gastric tissues were analyzed by high-density oligonucleotide microarray in this study. These results provide not only a new molecular basis for understanding biological properties of gastric cancer, but also useful resources for future development of therapeutic targets and diagnostic markers for gastric cancer.
2017-01-15 | GSE54129 | GEO
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:Although recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for handling diffuse type GC are identified. Here, we aim to identify a signature classifying high risk diffuse type GC. To identify molecular subtypes of diffuse type GC, we generated RNA-seq based transcriptome data, which were generated using normal mucosa and tumor cells from 140 fresh frozen tissues including diffuse type GCs (n = 107). Unsupervised hierarchical cluster analysis of the RNA-seq data revealed three distinct subgroups of GC. Based on these subtypes, we generated a signature reflecting the best characteristics of aggressive diffuse type GC. When estimating prognostic value, the signature showed a strong prediction ability and an independent clinical utility in diffuse type GC patients. Our signature represents a promising diagnostic tool for the identification of diffuse type GC patients that have a high risk of poor clinical behavior.
2020-03-18 | GSE113255 | GEO
Project description:Gastric cancer (GC) is the third leading cause of cancer-related death worldwide, continuously requiring innovative target therapies. The sialyl-lewis A (SLeA) antigen offers tremendous potential for precise cancer targeting. This glycan is rarely expressed by healthy tissues and blood cells but is often present in cancer cells of high metastatic potential as well as in the metastasis. To improve further on the gastric cancer nature of this glycan we have zoomed in on the SLeA-glycoproteome envisaging the identification of possible targetable biomarkers. SLeA-glycoproteins were isolated by immunoprecipitation from well-known cell models of gastric cancer reflecting different histological subtypes of the disease (KATO-III, OCUM-1 and N87 cells). Glycoproteins with affinity for E-selectin were also isolated and analyzed. Proteomics study employed a typical nanoLC-ESI-Orbitrap-MS/MS platform. The well-known molecular heterogeneity of these cell lines was reflected in the markedly different glycoproteomics signatures observed. Nevertheless, a restricted panel of SLeA-expressing glycoproteins with affinity for E-selectin remained conserved between the cell lines, consisting of a promising list of potentially targetable biomarkers for bladder cancer.
2020-05-11 | PXD016968 | Pride
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