Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE196033 | GEO | 2023/08/24
REPOSITORIES: GEO
Similar Datasets
Project description:The differential expression of metabolic genes between effector and exhausted CD8+ Tcells has been characterized in the people living with HIV (PLWH) using 768 genes from the NanoString Human Metabolic Pathways Panel.
2024-07-01 | GSE270720 | GEO
Project description:Identification and subsequent validation of transcriptomic signature associated with metabolic status in endometrial cancer
| PRJNA803360 | ENA
Project description:Aberrant metabolism has been identified as a main driver of cancer. Profiling of metabolism-related pathways in cancer furthers the understanding of tumor plasticity and identification of potential metabolic vulnerabilities. In this prospective controlled study, we established transcriptomic profiles of metabolism-related pathways in endometrial cancer (EC) using a novel method, NanoString nCounter Technology. Fifty-seven ECs and 30 normal endometrial specimens were studied using the NanoString Metabolic Panel, further validated by qRT-PCR with a very high similarity. Statistical analyses were by GraphPad PRISM and Weka software. The analysis identified 11 deregulated genes (FDR ≤ 0.05; |FC|≥ 1.5) in EC: SLC7A11; SLC7A5; RUNX1; LAMA4; COL6A3; PDK1; CCNA1; ENO1; PKM; NR2F1; and NAALAD2. Gene ontology showed direct association of these genes with 'central carbon metabolism (CCM) in cancer'. Thus, 'CCM in cancer' appears to create one of the main metabolic axes in EC. Further, transcriptomic data were functionally validated with drug repurposing on three EC cell lines, with several drug candidates suggested. These results lay the foundation for personalized therapeutic strategies in this cancer. Metabolic plasticity represents a promising diagnostic and therapeutic option in EC.
| S-EPMC10447446 | biostudies-literature
Project description:Purpose: We sought to develop and evaluate a diagnostic classifier of UC subtype with the goal of accurate classification from clinically available specimens. Methods: Tumor samples from 52 patients with high-grade UC were profiled for BASE47 genes concurrently by RNAseq as well as NanoString. After design and technical validation of a BASE47 NanoString probeset, results from the RNAseq and NanoString were used to translate diagnostic criteria to the Nanostring platform. Evaluation of repeatability and accuracy was performed to derive a final Nanostring based classifier. Diagnostic classification resulting from the NanoString BASE47 classifier was validated on an independent dataset (n=63). The training and validation datasets accurately classified 87% and 93% of samples, respectively. Results: We have derived a NanoString-platform BASE47 classifier that accurately predicts basal-like and luminal-like subtypes in high grade urothelial cancer. We have further validated our new NanoString BASE47 classifier on an independent dataset and confirmed high accuracy when compared with our original Transcriptome BASE47 classifier. Conclusions: The NanoString BASE47 classifier provides a faster turnaround time, a lower cost per sample to process, and maintains the accuracy of the original subtype classifier for better clinical implementation.
2020-11-03 | GSE160693 | GEO
Project description:The aim of this study was to evaluate whether microRNA profiles of grade 1 – 2 or 3 endometrioid adenocarcinomas can be related to nodal status and used as a tool to adapt surgical staging in early-stage EC. MicroRNA expression were assessed in twelve formalin-fixed paraffin-embedded (FFPE) EC primary tumors with positive lymph node and in 36 FFPE EC primary tumors with negative lymph node, matched for grade, stage and lymphovascular space involvement status.
2016-06-01 | GSE75968 | GEO
Project description:Natural control of HIV infection in Elite Controllers (EC) is a characteristic of <1% of HIV infected individuals. Extensive research has tried to elucidate the mechanism of control without any concrete results due to a large heterogeneity in the EC group, both between the sexes and how they obtain control. Therefore, we sought to identify signaling pathways associated with the EC phenotype by proteomics (all male) and transcriptomic (male and female) analysis. Further, we integrated the proteomics and transcriptomic analysis in the male cohort. We found that HIF signaling and downstream glycolysis are specific traits of the EC phenotype. Within this pathway ENO1 was upregulated in EC on a protein level irrespective of sex. We also performed targeted transcriptomic analysis where we identified HIF target genes as specifically de-regulation of in EC group. Namely, we observed an increase in the antiviral transcript GPS2, while CXCR4 and EIF5A were down regulated. Although we could not detect any difference in protein levels of HIF-1α in total PBMCs, we observed a higher portion of HIF-1α and HIF-1β in the nuclei of CD4+ and CD8+ T cells indicating an increased activation of HIF signaling in the male EC. Furthermore, the intracellular glucose levels were elevated in EC even as metabolite transporter expression of Glut1 and MCT-1 was decreased in lymphocytes showing that the EC have a unique metabolic uptake and secretion profile. Combined our data indicate the role HIF-1 signaling and glycolysis has in natural control of HIV infection.
2021-04-01 | PXD023990 | Pride
Project description:The gut-uterus axis plays a pivotal role in the pathogenesis of endometrial cancer (EC). However, the correlations between the endometrial microbiome and endometrial tumor transcriptome in patients with EC and the impact of the endometrial microbiota on hematological indicators have not been thoroughly clarified. In this prospective study, endometrial tissue samples collected from EC patients (n = 30) and healthy volunteers (n = 10) were subjected to 16S rRNA sequencing of the microbiome. The 30 paired tumor and adjacent nontumor endometrial tissues from the EC group were subjected to RNAseq. Result: We found that Pelomonas and Prevotella were enriched in the EC group with a high tumor burden. Further transcriptome analysis identified 8 robust associations between Prevotella and fibrin degradation-related genes expressed within ECs. Conclusions: Our results suggest that the increasing abundance of Prevotella in endometrial tissue combined with high serum DD and FDP contents may be important factors associated with tumor burden.
2021-10-13 | GSE183185 | GEO
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
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
Project description:Protein kinases (collectively termed the kinome) represent one of the most tractable drug targets in the pursuit of new and effective cancer treatments. However, less than 20% of the kinome is currently being explored as primary targets for cancer therapy, leaving the majority of the kinome untargeted for drug therapy. Chemical proteomics approaches such as Multiplexed Inhibitor Beads and Mass Spectrometry (MIB-MS) have been developed that measure the abundance of a significant proportion of the kinome, providing a strategy to interrogate kinome landscapes and dynamics. Kinome profiling of cancer cell lines using MIB-MS has been extensively characterized, however, the application of this method to measure tissue kinome(s) has not been thoroughly explored. Here, we present a quantitative proteomics workflow specifically designed for kinome profiling of tissues that pairs MIB-MS with a newly designed s-SILAC kinome standard. Using this workflow, we mapped the kinome landscape of endometrial carcinoma (EC) tumors and normal endometrial (NE) tissues and identified several kinases overexpressed in EC tumors, including Serine/Arginine-Rich Splicing Factor kinase, (SRPK1). Immunohistochemical (IHC) analysis of EC tumor TMAs confirmed MIB-MS findings and showed SRPK1 protein levels were highly expressed in endometrioid and uterine serous cancer (USC) histological subtypes. Querying large-scale genomics studies of EC tumors revealed high expression of SRPK1 correlated with poor survival. Inhibition of SRPK1 in USC cells altered mRNA splicing, downregulating several oncogenes including MYC and Survivin resulting in apoptosis. Taken together, we present a SILAC-based MIB-MS kinome profiling platform for measuring kinase abundance in tumor tissues, and demonstrate its application to identify SRPK1 as a plausible kinase drug target for the treatment of EC.
2020-09-30 | PXD020357 | Pride