Construction of a Glycolysis-related long noncoding RNA signature for predicting survival in endometrial cancer

Jiang, Yuan; Chen, Jie; Ling, Jiwei; Zhu, Xiaonian; Jiang, Pinping; Tang, Xiaohu; Zhou, Huaqiang; Li, Rong

doi:10.7150/jca.50413

Cited by 31 publications

(31 citation statements)

References 15 publications

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“…Previous studies have established a prognostic model for UCEC, but they have not specifically targeted grade III high-risk patients with a poor prognosis. 18 , 21 , 22 , 23 , 24 , 47 , 48 , 49 Among the four prognostic risk models selected for comparison, our model had the highest C-index, indicating that its overall performance was superior. Taken together, our model was a better predictor of patients’ outcome.…”

Section: Discussionmentioning

confidence: 92%

“…In this genomic era, a large number of genome-sequencing technologies and data have emerged, 16 and researchers began to focus on the prognosis prediction of UCEC using The Cancer Genome Atlas (TCGA)-UCEC cohort. 17 , 18 , 19 , 20 Wang et al. 17 constructed an autophagy-related long-noncoding RNA signature to predict the prognosis of UCEC.…”

Section: Introductionmentioning

confidence: 99%

“…Jiang et al. 18 and Liu et al. 19 constructed a glycolysis-related gene signature to predict the prognosis of UCEC.…”

Section: Introductionmentioning

confidence: 99%

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Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Zheng

Gou

et al. 2021

Molecular Therapy - Oncolytics

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Uterine corpus endometrial carcinoma locally infiltrates numerous immune cells and other tumor immune microenvironment components. These cells are involved in malignant tumor growth and proliferation and the process of resistance toward immunotherapies. Here, we aimed to develop a tumor immune microenvironment-related prognostic signature for high-risk grade III endometrial carcinoma based on The Cancer Genome Atlas. The signature was systematically correlated with immune infiltration characteristics of the tumor microenvironment. The seven-gene Riskscore signature was robust and performed well in training, testing, and Gene Expression Omnibus-independent cohorts. A nomogram comprising the gene signature accurately predicted patient prognosis, with our model performing better than other endometrial cancer-related signatures. Analysis of the IMvigor210 immunotherapy cohort revealed that subgroups with a low Riskscore had a better prognosis than subgroups with a high Riskscore. Subgroups with a low Riskscore exhibited immune cell infiltration and inflammatory profiles, whereas subgroups with a high Riskscore experienced progressive disease. The receiver operating characteristic curve indicated that risk score, neoantigen, and tumor mutation burden models together accurately predicted treatment response. Taken together, we developed a tumor microenvironment-based seven-gene prognostic stratification system to predict the prognosis of patients with high-risk endometrial cancer and guide more effective immunotherapy strategies.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Zheng

Gou

et al. 2021

Molecular Therapy - Oncolytics

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“…We compared the prediction performance of the novel immune-related pseudogene signature with four recently published signatures: nine-lncRNA signature (LINC02387, FUT8-AS1, UBXN10-AS1, LINC00473, AL353194.1, FAM222A-AS1, AP002761.3, AL731566.2, and AP001021.2) derived from Xu’s research [ 9 ], nine-mRNA signature (CYP4F3, LYPLA2, CEL, PHGDH, GPAT3, HNMT, UCK2, CKM, and ACACB) derived from Jiang’s study [ 32 ], nine-mRNA signature (TP53, RAE1, RFC2, TAF10, DDB2, UMPS, TAF12, ERCC2, SEC61A1) derived from Liu’s research [ 33 ], and five-lncRNA signature (AL121906.2, BOLA3-AS1, LINC01833, AC016405.3, and RAB11B-AS1) from Jiang’s study [ 34 ] using the same TCGA EC patient cohort. As illustrated in Fig.…”

Section: Resultsmentioning

confidence: 99%

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

Tang

Zhuge

2021

BioMed Eng OnLine

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Background Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC). Methods Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT. Results Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways. Conclusion We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

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“…Given the key role of lncRNAs in regulating energy metabolism in cancer, their function in anaerobic glycolysis in cancer cells has been the focus of many studies [ 32 , 54 ]. A prognostic risk model based on glycolysis-related lncRNAs has been established in endometrial cancer [ 55 ]. However, the effect of glycolysis-related lncRNAs on the prognosis of ccRCC remains unclear.…”

Section: Discussionmentioning

confidence: 99%

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

Zhong

Liu

2021

Bioscience Reports

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Background: This study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC). Methods: A coexpression analysis of glycolysis-related mRNAs–lncRNAs in ccRCC from The Cancer Genome Atlas was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model. Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways. Conclusion: The 7 identified glycolysis-related lncRNAs constitute a lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

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Construction of a Glycolysis-related long noncoding RNA signature for predicting survival in endometrial cancer

Cited by 31 publications

References 15 publications

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

Development of a seven-gene tumor immune microenvironment prognostic signature for high-risk grade III endometrial cancer

An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

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