Integrative analysis of multi-omic data for the characteristics of the Endometrial Cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo Sapiens
SUBMITTER: 
Rui Wang
PROVIDER: PXD045217 | iProX | Fri Sep 08 00:00:00 BST 2023
REPOSITORIES: iProX
Publications
Integrative Analysis of Multi-Omic Data for the Characteristics of Endometrial Cancer.
Li Tong T Ruan Zhijun Z Song Chunli C Yin Feng F Zhang Tuanjie T Shi Liyun L Zuo Min M Lu Linlin L An Yuhao Y Wang Rui R Ye Xiyang X
ACS omega 20240313 12
Endometrial cancer (EC) is a frequently diagnosed gynecologic cancer. Identifying reliable prognostic genes for predicting EC onset is crucial for reducing patient morbidity and mortality. Here, a comprehensive strategy with transcriptomic and proteomic data was performed to measure EC's characteristics. Based on the publicly available RNA-seq data, death-associated protein kinase 3, recombination signal-binding protein for the immunoglobulin kappa J region, and myosin light chain 9 were screene ...[more]
Similar Datasets
Project description:Endometrial cancer (EC) is a frequently diagnosed gynecologic cancer. Identifying reliable prognostic genes for predicting EC onset is crucial for reducing patient morbidity and mortality. Here, a comprehensive strategy with transcriptomic and proteomic data was performed to measure EC's characteristics. Based on the publicly available RNA-seq data, death-associated protein kinase 3, recombination signal-binding protein for the immunoglobulin kappa J region, and myosin light chain 9 were screened out as potential biomarkers that affect the EC patients' prognosis. A linear model was further constructed by multivariate Cox regression for the prediction of the risk of being malignant. From further integrative analysis, exosomes were found to have a highly enriched role that might participate in EC occurrence. The findings were validated by qRT-polymerase chain reaction (PCR) and western blotting. Collectively, we constructed a prognostic-gene-based model for EC prediction and found that exosomes participate in EC incidents, revealing significantly promising support for the diagnosis of EC.
| S-EPMC10975631 | biostudies-literature
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:While endometrial cancer (EC) has an overall favorable prognosis, some patients do poorly and may benefit from refinements of current classification systems. Molecular classification stratifies ECs into four prognostic groups: POLEmut, MMRd (mismatch repair deficient), p53abn, and NSMP (no specific molecular profile), where POLEmut has the best prognosis and p53abn has the worst prognosis. We used proteomic profiling to provide additional prognostic or predictive information for EC patients, across or within molecular subtypes. Global proteome profiling of formalin fixed, paraffin embedded samples, that had clinicopathologic and outcome data, was performed on 184 ECs encompassing all four molecular subtypes, including replicate samples of the same tumor, and both biopsy and final hysterectomy specimens. To ensure representation of each subtype, we profiled an approximately equal distribution in the 148 unique tumors; 34 (23%) POLEmut, 40 (23%) MMRd, 35 (24%) p53abn and 39 (26%) NSMP, rather than the population-based distributions. There was high reproducibility in the proteomic profiles of intra-tumor replicate samples, and between matched biopsy and hysterectomy tumor samples. Consensus clustering identified four clusters with different prognosis, named ‘Adhesion’, ‘Immune’, ‘Proliferation’, and ‘Metabolic’ based on proteins enriched in each cluster. We correlated protein expression with common mutations, molecular subtypes, and outcomes. Proteomic analysis of EC identified candidate prognostic markers that may further refine current molecular classification and help guide treatment decisions. These data offer insights into new therapeutic interventions that could be developed to target proteins and pathways identified by EC proteomic profiling.
2025-10-06 | PXD057700 | Pride
Project description:Endometrial cancer (EC) incidence and mortality continues to rise. Molecular profiling of EC promises improvement of risk assessment and treatment selection. Though, we still lack robust and accurate models to predict those at risk of failing treatment. The objective of this pilot study is to create models with clinical and genomic data that will discriminate patients with EC at risk of disease recurrence. We performed a pilot, retrospective, case-control study evaluating patients with EC, endometrioid type: 7 with recurrence of disease (cases), and 54 without (controls). RNA was extracted from frozen specimens and sequenced (RNAseq). Genomic features from RNAseq included transcriptome expression, genomic, and structural variation. Feature selection for variable reduction was performed with univariate ANOVA with cross-validation. Selected variables, informative for EC recurrence, were introduced in multivariate lasso regression models. Validation of models was performed in machine learning platforms (ML) and independent datasets (TCGA). The best performing prediction models (out of >170) contained the same lncRNA features (AUC of 0.9, and 95% CI: 0.75, 1.0). Models were validated with excellent performance in ML platforms and good performance in an independent dataset. Prediction models of EC recurrence containing lncRNA features have better performance than models with clinical data alone
2022-11-02 | GSE216872 | GEO
Project description:A specific form of endometrial cancer (EC) can develop in breast cancer patients previously treated with tamoxifen (ET), an antagonist of estrogen receptor (ER) α that inhibits proliferation of ER positive breast cancer. ET tumors have a different phenotype than endometrial tumors which typically develop de novo without previous exposure to tamoxifen (EN). Here we aimed to identify specific protein markers that could serve as specific molecular targets in either phenotype. A set of total 45 formalin-fixed paraffin-embedded (FFPE) endometrial tumor tissue and adjacent myometrial tissue samples were analyzed using LC-MS/MS in SWATH-MS mode. We found that calcyphosin (CAPS) levels were elevated in EN tumors compared to ET tumors. The higher CAPS level in EC tissue invading to myometrium support its relationship to EC aggressiveness. Further, stathmin (STMN1) levels were found significantly elevated in ET vs. EN tumors and significantly associated with patient survival. This finding connects elevated levels of this cell cycle regulating, proliferation-associated protein with tamoxifen exposure. In a summary, using SWATH-MS we show that CAPS and STMN1 should be recognized as clinicopathologically different EC markers of which STMN1 is specifically connected with a previous tamoxifen exposition.
2020-05-05 | PXD017217 | Pride
Project description:Rationale: Sepsis is a leading cause of morbidity and mortality; early diagnosis and prediction of progression is difficult to determine. The integration of metabolomic and transcriptomic data in an experimental model of sepsis may be a novel method to identify molecular signatures of clinical sepsis. Objectives: Develop a biomarker panel for earlier diagnosis and prognostic characterization of sepsis patients to inform personalized clinical management and improve understanding of the pathophysiology of sepsis progression. Methods: Mild to severe sepsis, lung injury and death was recapitulated in Macaca fascicularis by intravenous inoculation of Escherichia coli. Plasma samples were obtained at time of challenge and at one, three, and five days later or time of euthanasia. Necropsy was performed and blood, lung, kidney and spleen samples were obtained. An integrative analysis of comprehensive metabolomic and transcriptomic datasets was performed to identify and parameterize a biomarker panel. Measurements and Main Results: Pathogen invasion, respiratory distress, lethargy and mortality was dose dependent. Severe infection and death were associated with metabolomic and transcriptomic changes indicative of mitochondrial, peroxisomal and liver dysfunction. Analysis of reciprocal pulmonary transcriptome and plasma metabolome data revealed an integrated host response that suggested dysregulated fatty acid catabolism resulting from peroxisome-proliferator activated receptor signaling. A representative 4-metabolite model effectively diagnosed sepsis in primates (AUC 0.966) and in two human sepsis cohorts (AUC=0.78 and 0.82). Conclusion: A model to guide early management of patients with sepsis was developed by analysis of reciprocal metabolomic and transcriptomic data in primates that diagnosed sepsis in humans. Transcriptomic analysis of lungs from Cynomolgus macaques challenged with E. coli
2015-07-03 | E-GEOD-59075 | biostudies-arrayexpress
Project description:Emerging data indicate that B-cells play a key role in cancer suppression, particularly when aggregated in tertiary lymphoid-like structures (TLS). Here, we used single-cell and bulk RNA sequencing of endometrial cancers (ECs) and found L1-cell adhesion molecule L1CAM to be significantly associated with TLS. Immunohistochemical (IHC) expression of L1CAM was restricted to the germinal centre (GC) of mature TLS, where it co-localised with follicular dendritic cell (FDC) marker CD21. Tumours surrounded by L1CAM-positive TLS were identified across all molecular EC subgroups, but enriched in mismatch repair deficient (MMRd) and polymerase-epsilon mutant (POLEmut) EC, especially when TP53 mutations were also present. Importantly, L1CAM-positive TLS appeared an independent favourable predictor of EC recurrence and death, and significantly improved the current prognostic molecular EC classification based on clinicopathological and molecular factors. Our data suggest a pivotal role of TLS in predicting outcome of EC patients, and establishes L1CAM as a simple and widely available biomarker of TLS in clinical care.
2021-07-15 | GSE180091 | GEO
Project description:Endometrial cancer had a relatively high prevalence of MMR deficiency. MMR-D/MSI-H endometrial cancer patients are suggested to be potential beneficiaries of PD-1/PD-L1 inhibitor therapy. Here, we explored the prognostic value of MSI subtype in endometrial cancer and its correlation with immune environment. Based on expression and clinical data of 78 POLE, 123 MSI and 299 Other EC samples from the TCGA-UCEC project, we found that the MSI tumors were identified more often in early stage, had a lower age, better patient survival, enriched CD8+ T cells, and regulatory T cells and less M2 macrophages and activated dendritic cells than the Other group, and shared a relatively similar expression profile with POLE group by differential analysis. In addition, we established the immune landscape of an MMR-D endometrial cancer tissue using unbiased single-cell RNA-seq analysis of 3371 cells. By immunohistochemistry analysis, we found that the MMR-D tumors showed a higher trend of CD20+ B cells infiltration. Our study might expand our understanding of the role of immune subsets in MSI endometrial carcinomas and provide guidance of immunotherapy for endometrial cancer.
2022-03-09 | GSE193430 | GEO
Project description:Background: Endometrial cancer (EC) is a prevalent gynecological malignancy globally, with a rising incidence trend. While classic androgens have been implicated with EC risk, the role of their 11-oxygenated metabolites is poorly understood. Here, we studied 11-oxyandrogen formation from steroid precursors in EC for the first time. Methods: We performed in vitro studies on a panel of four EC cell lines of varying differentiation degree and molecular subtype and a control cell line of normal endometrium to assess 11-oxyandrogen formation from steroid precursors. We also characterized the transcriptomic effects of dihydrotestosterone (DHT) and 11-keto-DHT on Ishikawa and RL95-2. Key molecular players in 11-oxyandrogen metabolism and action were explored in endometrial tumors using public transcriptomic datasets. Results: We discovered that within endometrial tumors, the formation of 11-oxyandrogens does not occur from classic androgen precursors. However, we observed distinct regulatory mechanisms at a pre-receptor level in normal endometrium compared to cancerous tissue, and between low- and high-grade tumors. Specifically, in vitro models of low-grade EC formed higher levels of bioactive 11-keto-testosterone from 11-oxyandrogen precursors compared to models of noncancerous endometrium and high-grade, TP53-mutated EC. Moreover, the potent androgen, DHT and its 11-keto homologue induced mild transcriptomic effects on androgen receptor (AR)-expressing EC model, Ishikawa. Finally, using public transcriptomic datasets, we found HSD11B2 and SRD5A2, coding for key enzymes in steroid metabolism, to be associated with better disease-specific survival, whereas higher intra-tumoral AR expression correlated with lower recurrence in TP53-wt tumors. Conclusions: The intra-tumoral metabolism of 11-oxyandrogen precursors is characteristic for low-grade EC of non-TP53-alt molecular subtypes. Our findings support further exploration of circulating 11-oxyandrogens as prognostic biomarkers in EC.
2025-07-26 | GSE303681 | GEO
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