Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

MicroRNA profile of endometrial cancer from Indian patients-Identification of potential biomarkers for prognosis

ABSTRACT: Endometrial cancer is one of the major cancers in women throughout the world. If diagnosed early, these cancers are treatable and the prognosis is usually good. However, one major problem in treating endometrial cancer is accurate diagnosis and staging. Till date, the choice method for diagnosis and staging is histopathology. Although there are few molecular markers identified, they are not always sufficient in making accurate diagnosis and deciding on therapeutic strategy. As a result, very often patients are under treated or over treated. In this study, our group has profiled microRNAs from Indian patients using NGS-based approach. We have identified differentially expressed microRNAs in endometrial cancer. These microRNAs have also been compared to data from TCGA, which represent other populations and also correlated to relevance in overall survival. Using in-silico approaches, mRNA targets of the miRNAs have been predicted. After comparing with TCGA, we have identified 16 miRNA-mRNA pairs which could be potential prognostic biomarkers for endometrial cancer. This is the first miRNA profiling report from Indian cohort and one of the very few studies which have identified potential biomarkers of prognosis in endometrial cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE268888 | GEO | 2024/11/13

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Identification of long non-coding RNA expression patterns useful for molecular-based classification of type I endometrial cancers

Project description:Endometrial cancer represents the most frequent gynecologic malignant disease. Although several genetic alterations have been associated with increased risk, to date diagnosis and prognosis still rely on morphological features of the tumor, such as histological type, grading and invasiveness. As molecular-based classification is desirable for optimal treatment and prognosis of these cancers, , we explored the potential of lncRNAs as molecular biomarkers. To this end, we first identified by RNA sequencing (RNA-Seq) a set of lncRNAs differentially expressed in cancer vs normal endometrial tissues, a result confirmed also by analysis of RNA-Seq data of normal and cancerous endometrium from The Cancer Genome Atlas (TCGA). A significant association of a subset of these differentially expressed lncRNAs with tumor grade was then determined in 405 TCGA endometrial cancer profiles. Integrating endometrial cancer-specific expression profiles of long and small non-coding RNAs a functional association network was then identified. These results described for the first time a functional ‘core’ network, comprising small and long RNAs, whose deregulation is associated with endometrial neoplastic transformation, representing a set of cancer biomarkers that can be monitored and targeted for diagnosis, follow-up and therapy of these tumors.

2019-04-30 | E-MTAB-7039 | biostudies-arrayexpress

A 4-gene signature associated with recurrence risk in patients with low and intermediate-risk endometrial cancer

Project description:The molecular profile of endometrial cancer has become an important tool in determining patient prognosis and their optimal adjuvant treatment. In addition to The Cancer Genome Atlas (TCGA), simpler tools have been developed, such as the Proactive Molecular Risk Classifier for Endometrial Cancer (ProMisE). We attempted to determine a genetic signature to build a recurrence risk score in patients diagnosed with low- and intermediate-risk endometrial cancer. A case-control study was conducted. The eligible patients were women diagnosed with recurrence low- and intermediate-risk endometrial cancer between January 2009 and December 2014 at a single institution; the recurrence patients were matched to two nonrecurrence patients with the same diagnosis by age and surgical staging. Following RNA isolation of 51 cases, 17 recurrence and 34 nonrecurrence patients, the expression profile was determined using the nCounter® PanCancer Pathways Panel, which contains 770 genes. The expression profile was successfully characterized in 49/51 (96.1%) cases. We identified 12 genes differentially expressed between the recurrence and nonrecurrence groups. The ROC curve for each gene was generated, and all had AUCs higher than 0.7. After backward stepwise logistic regression, four genes were highlighted: FN1, DUSP4, LEF1, and SMAD9. The recurrence risk score was calculated, leading to a ROC curve of the 4-gene model with an AUC of 0.93, sensitivity of 100%, and specificity of 72.7%. We identified a four-gene signature that is associated the risk of recurrence in patients with low- and intermediate-risk endometrial cancer. This finding suggests a new prognostic factor in this poorly explored group of patients with endometrial cancer.

2021-07-27 | GSE178671 | GEO

Sentinel Lymph Node and Endometrial Cancer Grade in depth Proteomic Analyses : A Novel Approach for Molecular Diagnosis

Project description:Endometrial cancer is the most common gynecological cancer worldwide. In women from developed countries, endometrial cancer is usually detected after the age of 60 years. Nearly 90% of the diagnosed cases are sporadic, while 10% are attributed to genetic factors. Thus, it is necessary to develop a diagnostic platform. Currently, the lymph node status is a major prognostic factor in assessing endometrial cancer. However, despite its high sensitivity, it is still in debate. In order to increase its selectivity and specificity, in-depth proteomic study has been performed on patients sentinel lymph node (SNL) samples and on grade I to grade III endometrial carcinoma (EC) samples. We identified specific markers of bad diagnosis that clearly correlate SNL to EC i.e. PRSS3, PTX3, ASS1, YBX2, RBM25, MUC5B. These markers have also been detected in TCGA. By contrast, we also detected good prognosis markers for the overall survival such as CD74, GOLM1, SLC9A3R2, SP100 proteins. Markers for EC and SNL cancer-grading have also been identified. Taken together, it is now possible to propose a novel approach for molecular diagnosis, prognosis and patient’s stratification in EC which will be of excellent utility in therapeutic decision.

2021-09-09 | PXD020410 | Pride

Serum lipidomics study looking for specific endometrial polyps biomarkers distinguishing from endometrial cancer or hyperplasia

Project description:<p>Endometrial diseases are common gynecological diseases that disorder women of childbearing age and perimenopausal women, including endometrial polyps (EP), endometrial cancer (EC), and endometrial hyperplasia (EH). Clinically, biopsy or imaging methods are usually used to screen and diagnose endometrial diseases, but due to their invasiveness and heterogeneity, a noninvasive, convenient, objective and accurate biomarker is needed for the differential diagnosis of EP and EC or EH. In the present study, serum samples from 396 patients with endometrial disease and 225 healthy volunteers were analyzed by UPLC-Q-TOF/MS non-targeted lipidomics. A combination of multivariate (Orthogonal partial least-squares discriminant analysis) and univariate (Student t-test) analyses were used to identify and qualify 6, 8, and 7 potential biomarkers in serum from patients with EP, EC, and EH, respectively. With the aid of logistic regression algorithm and receiver operating characteristic (ROC) curve analysis, A biomarker panel including four specific EP biomarkers, 6-Keto-PGF1α, PA(37:4), LysoPC(20:1) and PS (36:0), had good classification and diagnostic ability in distinguishing EP from EC or EH. The biomarker panel can be used as a rapid diagnostic method to assist imaging examination to effectively differentially diagnose endometrial diseases, and provide a reference for clinicians in the identification and diagnosis of endometrial diseases. </p>

2022-01-14 | MTBLS3444 | MetaboLights

Genomic characterization of five commonly used endometrial cancer cell lines

Project description:An in-depth examination of five endometrial cancer cell lines in which mutation profiles, copy number variation, micro-satellite instability and homolgous recombination repair ability were determined and assessed in the context of TCGA endometrial cancer groups.

2020-05-27 | GSE151207 | GEO

Multi-omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer

Project description:Endometrial cancer (EC) is one of the most common gynecological cancers. Finding biomarkers of EC can support its diagnosis in a non- or minimally-invasive way. In this work, we integrated metabolomics and proteomics for the analysis of multiple types of biosamples to gain a more comprehensive view on the altered metabolism in EC patients, and to demonstrate the potential of metabolite biomarker-based EC diagnosis. Based on the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, EC related significant pathways, such as amino acid metabolism and nucleotide metabolism, were identified. Then, the significance of the pathways was further demonstrated by metabolomic analysis of urine and intrauterine brushings samples. We also demonstrated the potential of EC diagnosis by metabolite biomarkers in urine or intrauterine brushings samples. This study provides a network explanation of the metabolic changes of EC with the verification by multi-omics and multi-biosamples, and further demonstrates the potential of non- or minimally-invasive biopsy for EC diagnosis in the future.

2022-03-28 | MTBLS3935 | MetaboLights

Montagud2022 - Prostate cancer Boolean model

Project description:Prostate cancer is the second most occurring cancer in men worldwide, and with the advances made with screening for prostate-specific antigen, it has been prone to early diagnosis and over-treatment. To better understand the mechanisms of tumorigenesis and possible treatment responses, we developed a mathematical model of prostate cancer which considers the major signalling pathways known to be deregulated. The model includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT, MAPK, mTOR, SHH, the cell cycle, the epithelial-mesenchymal transition (EMT), apoptosis and DNA damage pathways. The final model accounts for 133 nodes and 449 edges. We applied a methodology to personalise this Boolean model to molecular data to reflect the heterogeneity and specific response to perturbations of cancer patients, using TCGA and GDSC datasets.

2022-06-22 | MODEL2106070001 | BioModels

Expression data of Human Gastric Adenocarcinoma

Project description:Anatomical staging is a critical, although imperfect, instrument to assess gastric cancer prognosis and define indication for surgery and adjuvant therapy. Despite recent advances, treatment results, as a whole, remain less than satisfactory. Thus, biomarkers are sorely needed to improve risk categorization and define new molecular targets for therapy. We used microarrays to identify genes differentially expressed according to prognosis of the patients. A cohort of 15 patients was prospectively identified with a histological diagnosis of gastric adenocarcinoma, submitted to surgery with curative intent. Two prognostic patient groups were defined, according to overall survival (short x long), with a 24 month cut-off. These groups were matched by known prognostic factors: TNM staging, histological subtype (intestinal/diffuse) and gender.

2012-09-01 | E-GEOD-38749 | biostudies-arrayexpress

CGH profiles of primary colorecal cancer: metastasis-free, liver and peritoneal metastasis

Project description:Accurate staging of colorectal cancer (CRC) with clinicopathological parameters is important for predicting prognosis and guiding treatment but provides us no information about organ site of metastases. Patterns of genomic aberrations in primary colorectal tumors may reveal a chromosomal signature for organ specific metastases.

2010-12-03 | GSE20496 | GEO

A Prostate Cancer Proteomics Database for SWATH-MS based Protein Quantification

Project description:Prostate cancer is the third most frequent cancer in men worldwide, with a notable increase in prevalence over the last two decades. The PSA is the only well-established protein biomarker for prostate cancer diagnosis, staging, and sur-veillance. It frequently leads to inaccurate diagnosis and overtreatment since it is an organ-specific biomarker rather than a tumour-specific biomarker. As a result, one of the primary goals of prostate cancer proteome research is to iden-tify novel biomarkers that can be used with or instead of PSA, particularly in non-invasive blood samples. Thousands of peptides or assays were detected in blood samples from patients with low to high-grade prostate cancer and healthy individuals, allowing data processing of sequential window acquisition of all theoretical mass spectra (SWATH-MS). By assisting in the detection of prostate cancer biomarkers in blood samples, this useful resource will improve our un-derstanding of the role of proteomics in prostate cancer diagnosis and risk assessment.

2021-12-20 | PXD028651 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data