A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Zhao, Yiqiao; Tao, Zijia; Chen, Xiaonan

doi:10.3389/fonc.2020.570281

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…Beta-1,3-glucuronyltransferase 3, encoded by the B3GAT3 gene, is crucial for proteoglycan (PG) biosynthesis [ 38 ]. Since PG could affect the cell cycle and was related to tumor cell invasion and metastasis [ 39 , 40 ], the potential role of B3GAT3 in tumor development, progression and prognosis was explored [ 41 ]. Recently, Zhang et al [ 42 ] found that B3GAT3 was upregulated in HCC samples, and the dysregulation of B3GAT3 was associated with poorer pathological characteristics, tumor invasion, migration and a more adverse prognosis.…”

Section: Discussionmentioning

confidence: 99%

Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

Kong

Guo

et al. 2022

BMC Cancer

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Background Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis. Methods mRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines. Results Five GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples. Conclusions We identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment.

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Section: Discussionmentioning

confidence: 99%

Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

Kong

Guo

et al. 2022

BMC Cancer

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“…In addition, the results of this study revealed that low expression of PTRPG could predict poor prognosis. According to recent reports, other genes, such as MID1IP1 (30), ABCD1 (31), CPT2 (32), and TP53INP2 (33), are closely associated with the progression of ccRCC. However, our study is the first to demonstrate that FAAH2 is inhibited in ccRCC and is an indicator of poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes

et al. 2022

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Renal cell carcinoma (RCC) is a malignant tumor that is characterized by the accumulation of intracellular lipid droplets. The prognostic value of fatty acid metabolism-related genes (FMGs) in RCC remains unclear. Alongside this insight, we collected data from three RCC cohorts, namely, The Cancer Genome Atlas (TCGA), E-MTAB-1980, and GSE22541 cohorts, and identified a total of 309 FMGs that could be associated with RCC prognosis. First, we determined the copy number variation and expression levels of these FMGs, and identified 52 overall survival (OS)-related FMGs of the TCGA-KIRC and the E-MTAB-1980 cohort data. Next, 10 of these genes—FASN, ACOT9, MID1IP1, CYP2C9, ABCD1, CPT2, CRAT, TP53INP2, FAAH2, and PTPRG—were identified as pivotal OS-related FMGs based on least absolute shrinkage and selection operator and Cox regression analyses. The expression of some of these genes was confirmed in patients with RCC by immunohistochemical analyses. Kaplan–Meier analysis showed that the identified FMGs were effective in predicting the prognosis of RCC. Moreover, an optimal nomogram was constructed based on FMG-based risk scores and clinical factors, and its robustness was verified by time-dependent receiver operating characteristic analysis, calibration curve analysis, and decision curve analysis. We have also described the biological processes and the tumor immune microenvironment based on FMG-based risk score classification. Given the close association between fatty acid metabolism and cancer-related pain, our 10-FMG signature may also serve as a potential therapeutic target with dual effects on ccRCC prognosis and cancer pain and, therefore, warrants further investigation.

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“…B3GAT3 participates in the biosynthesis of the glycosaminoglycan (GAG) linker region of proteoglycan (PG) ( Barré, et al, 2006 ). Given its important role in tumour metabolism, which was used repeatedly as a candidate gene for constructing prognostic models ( Zhao, et al, 2021 ; Zhao, et al, 2020 ; Bingxiang, et al, 2021 ), its function of promoting the process of tumour EMT in HCC was confirmed by experimental verification ( Zhang, et al, 2019 ). Based on the analysis above, the cancer-promoting effects of dysregulated expression are in accordance with our prediction results.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Zhou

Wang

et al. 2022

Front. Genet.

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Although many prognostic models have been developed to help determine personalized prognoses and treatments, the predictive efficiency of these prognostic models in hepatocellular carcinoma (HCC), which is a highly heterogeneous malignancy, is less than ideal. Recently, aberrant glycosylation has been demonstrated to universally participate in tumour initiation and progression, suggesting that dysregulation of glycosyltransferases can serve as novel cancer biomarkers. In this study, a total of 568 RNA-sequencing datasets of HCC from the TCGA database and ICGC database were analysed and integrated via bioinformatic methods. LASSO regression analysis was applied to construct a prognostic signature. Kaplan–Meier survival, ROC curve, nomogram, and univariate and multivariate Cox regression analyses were performed to assess the predictive efficiency of the prognostic signature. GSEA and the “CIBERSORT” R package were utilized to further discover the potential biological mechanism of the prognostic signature. Meanwhile, the differential expression of the prognostic signature was verified by western blot, qRT–PCR and immunohistochemical staining derived from the HPA. Ultimately, we constructed a prognostic signature in HCC based on a combination of six glycosyltransferases, whose prognostic value was evaluated and validated successfully in the testing cohort and the validation cohort. The prognostic signature was identified as an independent unfavourable prognostic factor for OS, and a nomogram including the risk score was established and showed the good performance in predicting OS. Further analysis of the underlying mechanism revealed that the prognostic signature may be potentially associated with metabolic disorders and tumour-infiltrating immune cells.

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A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Cited by 10 publications

References 35 publications

Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

Identification of a glycolysis-related gene signature for predicting prognosis in patients with hepatocellular carcinoma

Prognosis and pain dissection of novel signatures in kidney renal clear cell carcinoma based on fatty acid metabolism-related genes

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

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