A metabolomic signature of FIGO stage I and II endometrial cancer

Gu, Mingli; Chen, Xin; Sun, Yihong; Wang, Linguo; Shu, Huizhen; Qing, Cheng

doi:10.4149/neo_2021_210306n288

Cited by 9 publications

(7 citation statements)

References 31 publications

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“…Individual metabolites showed potential as prognostic biomarkers and separated EC patients with/without LVI (AUC = 0.83; Supplementary Table S8 ). Other studies on serum/plasma metabolome in EC patients reported only different levels of metabolites in EC patients ( 133 , 135 , 137 , 141 ) and associations of individual metabolites with EC ( 131 , 132 ).…”

Section: Resultsmentioning

confidence: 94%

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

et al. 2023

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Endometrial cancer is the most common gynaecological malignancy in developed countries. Over 382,000 new cases were diagnosed worldwide in 2018, and its incidence and mortality are constantly rising due to longer life expectancy and life style factors including obesity. Two major improvements are needed in the management of patients with endometrial cancer, i.e., the development of non/minimally invasive tools for diagnostics and prognostics, which are currently missing. Diagnostic tools are needed to manage the increasing number of women at risk of developing the disease. Prognostic tools are necessary to stratify patients according to their risk of recurrence pre-preoperatively, to advise and plan the most appropriate treatment and avoid over/under-treatment. Biomarkers derived from proteomics and metabolomics, especially when derived from non/minimally-invasively collected body fluids, can serve to develop such prognostic and diagnostic tools, and the purpose of the present review is to explore the current research in this topic. We first provide a brief description of the technologies, the computational pipelines for data analyses and then we provide a systematic review of all published studies using proteomics and/or metabolomics for diagnostic and prognostic biomarker discovery in endometrial cancer. Finally, conclusions and recommendations for future studies are also given.

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Section: Resultsmentioning

confidence: 94%

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

et al. 2023

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“…Metabolomics studies also revealed that arachidonic acid, stearic acid, and linoleic acid metabolites enrolled in lipid metabolism were lowered in advanced-stage and nonendometrioid EC samples. 60 , 61 , 62 Building upon the insights gained from multi-omics analysis, we hypothesized that focusing on subtype-specific metabolic characteristics that significantly influence tumor behavior within the MPS subtypes may provide useful targeted therapy strategies in EC, but these need further validation in the future.…”

Section: Discussionmentioning

confidence: 99%

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Liu,

Wang,

Zhang

et al. 2024

Molecular Therapy - Nucleic Acids

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“…Troisi et al reported that lactic acid, progesterone, homocysteine, 3‐hydroxybutyrate, linoleic acid, stearic acid, myristic acid, threonine, and valine were important metabolites in the class separation of EC 12 . Gu et al also found that D‐glucose thiamine upregulated and cholesterol, arachidonic acid, palmitic acid, oleic acid, stearic acid, and linoleic acid downregulated in EC patients, and these potential biomarkers have essential functions in regulating vital metabolic pathways associated with stage I and II EC 13 …”

Section: Introductionmentioning

confidence: 97%

“…12 Gu et al also found that D-glucose thiamine upregulated and cholesterol, arachidonic acid, palmitic acid, oleic acid, stearic acid, and linoleic acid downregulated in EC patients, and these potential biomarkers have essential functions in regulating vital metabolic pathways associated with stage I and II EC. 13 Noncoding RNA (ncRNA), with a length of more than 200 nucleotides, is involved in the regulation of epigenetic 14 cell metabolism and tumor transformation. 15 Recently, some studies have shown that ncRNA plays a very important role in the occurrence and deterioration of various cancers.…”

Section: Introductionmentioning

confidence: 99%

Potential serum metabolites and long‐chain noncoding RNA biomarkers for endometrial cancer tissue

Hao

Lin

Liu

et al. 2022

J of Obstet and Gynaecol

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Background Endometrial carcinoma (EC) is one of the most common tumors in the female reproductive system. There are nearly 200 000 new cases every year. It is the third most common gynecological malignant tumor leading to female death. The incidence rate is closely related to lifestyle, and the incidence rate varies in different regions. The incidence rate of EC is ranking the first in the female reproductive system cancer just second only to breast, lung, and colorectal cancer in North America and Europe and the incidence rate of EC is only second, followed by breast cancer and cervical cancer in China. Purpose The potential metabolic markers of endometrial cancer were screened by liquid chromatograph mass spectrometer (LC‐MS), and the tissues of patients with hysteromyoma and endometrial cancer were sequenced to explore the relationship between the disease and change in the content of long‐chain noncoding RNA (lncRNA). Methods Serum and tissue samples were collected from patients with endometrial dysplasia, endometrial cancer stage I, and endometrial cancer stage III. The metabolites in all serum samples were extracted, and the metabolites in all samples were detected by LC–MS/MS technology. The Pareto‐scaling method was used for normalization, and the MetaboAnalyst 4.0 software was used for different analyses. The T test between groups showed that p ≤ 0.05 was regarded as the metabolite with a difference. Further, the function of differential metabolites was determined by metabolite function enrichment and co‐expression analysis. Meanwhile, the differentially expressed lncRNA was detected by Illumina second‐generation high‐throughput sequencing technology, and the expression was analyzed by DEGseq software. Different lncRNA were screened according to p < 0.05. LncRNA with significant differences were screened by p < 0.01, q < 0.001, fold change ≥2, and false discovery rate (FDR) ≤0.001. Results Through synthesis of T test, cluster heatmap, and ROC curve analysis, five biomarkers with potential diagnostic ability were obtained, including 2,3‐Pyridinedicarboxylic acid (area under the curve (AUC) = 0.69), Hematommic acid, ethyl ester (AUC = 0.69), Maltitol (AUC = 0.69), 13(S)‐HODE (AUC = 0.88), and D‐Mannitol (AUC = 0.69) had potential diagnostic ability between EC phase I versus EC phase III. At the same time, lncRNA sequencing results showed that when endometrial atypical hyperplasia continued to change, including LINC00511, PVT1, and IQCH‐AS1 (downregulated), and only changed significantly in the endometrial dysplasia group, including MALAT1, CARMN (downregulated) and LINC00648, BISPR, LINC01534, and LINC00930 (upregulated). Moreover, both differential metabolites and differential lncRNA were annotated to the lipid metabolism pathway, suggesting that this pathway played an important role in the occurrence and development of endometrial carcinoma. Conclusions It can combine the results of metabolomics and lncRNA sequencing to assist in the early diagnosis of endometrial precancerous lesions and endomet...

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A metabolomic signature of FIGO stage I and II endometrial cancer

Cited by 9 publications

References 31 publications

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Potential serum metabolites and long‐chain noncoding RNA biomarkers for endometrial cancer tissue

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