Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Yuan, Fan; Li, Xingchen; Tian, Li; Wang, Jianliu

doi:10.3389/fonc.2021.630905

Cited by 17 publications

(25 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…14 And Fan et al thoroughly investigated the implications of metabolism-related genes in endometrial cancer progression. 33 While those models based on transcriptome have reliable discrimination and calibration, it is not universal to apply them in clinical practice. Based on this, the prognostic models on the strength of the clinical variables perform superior convenience and EC patients do not need redundant examination such as molecular diagnosis or genomic sequence.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

Yu¹,

Wei²,

Zhang³

et al. 2021

CMAR

View full text Add to dashboard Cite

Existing prognostic models for endometrial cancer are short of facility and effective validation. In this study, we aim to develop and validate a novel prognostic model for endometrial cancer based on clinical characteristics. Methods: The clinical data such as age, BMI (body mass index), FIGO stage, surgical approach, myometrial invasion, grade, lymph node metastasis, pathology and menopause status were collected for constructing and validating the prognostic model from The Cancer Genome Atlas

show abstract

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

Yu¹,

Wei²,

Zhang³

et al. 2021

CMAR

View full text Add to dashboard Cite

show abstract

“…Fortunately, the screening of metabolic biomarkers can specifically detect abnormal changes in organisms to prevent malignant diseases with pathophysiological characteristics ( 13 ). The metabolic markers have been well displayed in a variety of tumors, such as hepatocellular carcinoma ( 14 ), colorectal cancer ( 15 ), endometrial cancer ( 16 ), and clear cell renal cell carcinoma ( 17 ), but research in PCa is still relatively scarce. Therefore, it is of great clinical significance to find a new metabolic marker to predict the prognosis of PCa.…”

Section: Introductionmentioning

confidence: 99%

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Liang

Yin

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

Prostate cancer is still the main male health problem in the world. The role of metabolism in the occurrence and development of prostate cancer is becoming more and more obvious, but it is not clear. Here we firstly identified a metabolism-related gene-based subgroup in prostate cancer. We used metabolism-related genes to divide prostate cancer patients from The Cancer Genome Atlas into different clinical benefit populations, which was verified in the International Cancer Genome Consortium. After that, we analyzed the metabolic and immunological mechanisms of clinical beneficiaries from the aspects of functional analysis of differentially expressed genes, gene set variation analysis, tumor purity, tumor microenvironment, copy number variations, single-nucleotide polymorphism, and tumor-specific neoantigens. We identified 56 significant genes for non-negative matrix factorization after survival-related univariate regression analysis and identified three subgroups. Patients in subgroup 2 had better overall survival, disease-free interval, progression-free interval, and disease-specific survival. Functional analysis indicated that differentially expressed genes in subgroup 2 were enriched in the xenobiotic metabolic process and regulation of cell development. Moreover, the metabolism and tumor purity of subgroup 2 were higher than those of subgroup 1 and subgroup 3, whereas the composition of immune cells of subgroup 2 was lower than that of subgroup 1 and subgroup 3. The expression of major immune genes, such as CCL2, CD274, CD276, CD4, CTLA4, CXCR4, IL1A, IL6, LAG3, TGFB1, TNFRSF4, TNFRSF9, and PDCD1LG2, in subgroup 2 was almost significantly lower than that in subgroup 1 and subgroup 3, which is consistent with the results of tumor purity analysis. Finally, we identified that subgroup 2 had lower copy number variations, single-nucleotide polymorphism, and neoantigen mutation. Our systematic study established a metabolism-related gene-based subgroup to predict outcomes of prostate cancer patients, which may contribute to individual prevention and treatment.

show abstract

“…The utilization of various mechanisms depends on intrinsic and extrinsic factors which contribute to heterogeneity in the metabolic profiles of cancer patients. Earlier studies on EC analyzed the expression of metabolic genes between normal and tumor samples based on differential gene expression analysis [42,43]. In this study, we applied an unsupervised technique to stratify EC samples into metabolic subtypes based on a genome-scale metabolic model (HMR2.0) and characterized each subtype with distinct survival outcomes, clinical features, metabolic pathways, and genomic alterations.…”

Section: Discussionmentioning

confidence: 99%

Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

Srivastava

Vinod

2023

Preprint

View full text Add to dashboard Cite

Endometrial cancer (EC) is the most common gynaecological cancer worldwide. Understanding the metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in the metabolism within tumor samples. We integrated the TCGA transcriptomics data of EC (RNA-Seq) with the human genome-scale metabolic model (HMR2.0) and performed unsupervised learning to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we characterized each subtype at the molecular level and correlated the subtype-specific metabolic changes occurring at the transcriptome level with the genomic alterations. EC patients are stratified into two robust metabolic subtypes (cluster-1 and cluster-2) that significantly correlate to patient survival, tumor stages, mutation, and copy number variations. We observed co-activation of pentose phosphate pathway and one-carbon metabolism along with genes involved in controlling estrogen levels in cluster-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in cluster-2 samples and present in the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.

show abstract

Identification of a Metabolism-Related Signature for the Prediction of Survival in Endometrial Cancer Patients

Cited by 17 publications

References 59 publications

Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

Development and Validation of a Novel Prognostic Model for Endometrial Cancer Based on Clinical Characteristics

Identification of Metabolism-Related Gene-Based Subgroup in Prostate Cancer

Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

Contact Info

Product

Resources

About