Metabolism‐associated molecular classification of hepatocellular carcinoma

Yang, Chen; Huang, Xiaowen; Liu, Zhicheng; Qin, Wenxin; Wang, Cun

doi:10.1002/1878-0261.12639

Cited by 153 publications

(143 citation statements)

References 51 publications

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“…Therefore, in this study, we determined a lower metabolic level, and that greater tumor heterogeneity of the PCa samples was associated with a worse prognosis for the patient. This was in accordance with that reported for liver cancer (60). These specifications may partly explain why tumors from the C1 subset had the worst prognosis in PCa.…”

Section: Discussionsupporting

confidence: 92%

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Zhang

Liang

et al. 2020

Front. Oncol.

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BackgroundDespite being the second most common tumor in men worldwide, the tumor metabolism-associated mechanisms of prostate cancer (PCa) remain unclear. Herein, this study aimed to investigate the metabolism-associated characteristics of PCa and to develop a metabolism-associated prognostic risk model for patients with PCa.MethodsThe activity levels of PCa metabolic pathways were determined using mRNA expression profiling of The Cancer Genome Atlas Prostate Adenocarcinoma cohort via single-sample gene set enrichment analysis (ssGSEA). The analyzed samples were divided into three subtypes based on the partitioning around medication algorithm. Tumor characteristics of the subsets were then investigated using t-distributed stochastic neighbor embedding (t-SNE) analysis, differential analysis, Kaplan–Meier survival analysis, and GSEA. Finally, we developed and validated a metabolism-associated prognostic risk model using weighted gene co-expression network analysis, univariate Cox analysis, least absolute shrinkage and selection operator, and multivariate Cox analysis. Other cohorts (GSE54460, GSE70768, genotype-tissue expression, and International Cancer Genome Consortium) were utilized for external validation. Drug sensibility analysis was performed on Genomics of Drug Sensitivity in Cancer and GSE78220 datasets. In total, 1,039 samples and six cell lines were concluded in our work.ResultsThree metabolism-associated clusters with significantly different characteristics in disease-free survival (DFS), clinical stage, stemness index, tumor microenvironment including stromal and immune cells, DNA mutation (TP53 and SPOP), copy number variation, and microsatellite instability were identified in PCa. Eighty-four of the metabolism-associated module genes were narrowed to a six-gene signature associated with DFS, CACNG4, SLC2A4, EPHX2, CA14, NUDT7, and ADH5 (p <0.05). A risk model was developed, and external validation revealed the strong robustness our risk model possessed in diagnosis and prognosis as well as the association with the cancer feature of drug sensitivity.ConclusionsThe identified metabolism-associated subtypes reflected the pathogenesis, essential features, and heterogeneity of PCa tumors. Our metabolism-associated risk model may provide clinicians with predictive values for diagnosis, prognosis, and treatment guidance in patients with PCa.

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Section: Discussionsupporting

confidence: 92%

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Zhang

Liang

et al. 2020

Front. Oncol.

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“…At the same time, C3 displayed intermediate metabolic activity. Our classification in CRC coincided with that in HCC, a work published recently (16). Clinical feature analyses showed that most samples in C2 were in advanced pathological stage.…”

Section: Discussionsupporting

confidence: 81%

“…C2 exhibited high immune infiltration and sensitivity toward immune blockade as well as chemotherapy. What's more, a meaningful 90-gene classifier was provided, which may help to predict the prognosis of HCC patients and prospective therapies (16). However, research on metabolism-relevant molecular classification of CRC has not yet been reported.…”

Section: Introductionmentioning

confidence: 99%

Metabolism-Associated Molecular Classification of Colorectal Cancer

et al. 2020

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The high heterogeneity of colorectal cancer (CRC) is the main clinical challenge for individualized therapies. Molecular classification will contribute to drug discovery and personalized management optimizing. Here, we aimed to characterize the molecular features of CRC by a classification system based on metabolic gene expression profiles. 435 CRC samples from the Genomic Data Commons data portal were chosen as training set while 566 sample in GSE39582 were selected as testing set. Then, a non-negative matrix factorization clustering was performed, and three subclasses of CRC (C1, C2, and C3) were identified in both training set and testing set. Results showed that subclass C1 displayed high metabolic activity and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high immune signatures as well as high expression of immune checkpoint genes. C2 had the worst prognosis among the three subtypes. Subclass C3 displayed intermediate metabolic activity, high gene mutation numbers and good prognosis. Finally, a 27-gene metabolism-related signature was identified for prognosis prediction. Our works deepened the understanding of metabolic hallmarks of CRC, and provided valuable information for “multi-molecular” based personalized therapies.

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“…A univariate Cox proportional hazards regression model assisted in estimating the hazard ratios exhibited by univariate analyses. A two-tailed P value less than 0.05 exhibited statistical signi cance [22].…”

Section: Discussionmentioning

confidence: 98%

Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

Huo

Zang

Dong

et al. 2020

Preprint

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Background: In recent years, the relationship between tumor associated macrophages (TAMs) and solid tumors has become a research hotspot. The study aims at exploring the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma(HCC), in order to provide a new way of treatment for HCC.Materials and methods: The study selected 343 HCC patients with complete survival information(survival time >= 1month) in the Cancer Genome Atlas (TCGA) as the study objects. Kaplan-Meier survival analysis assisted in figuring out the relationship between macrophage infiltration level and overall survival (OS), and Pearson correlation test to identify metabolic reprogramming genes(MRGs) related to tumor macrophage abundance. Lasso regression algorithm were conducted on prognosis related MRGs screened by Univariate Cox regression analysis and Kaplan-Meier survival analysis to construct the riskscore, another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) were used for external validation regarding the prognostic signature.Results: A risk score composed of 8 metabolic genes can accurately predict the OS of training cohort(TCGA) and testing cohort(ICGC). It is important that the risk score could widely used for people with different clinical characteristics, and is an independent predictor independent of other clinical factors affecting prognosis. As expected, high-risk group exhibited an obviously higher macrophage abundance relative to low-risk group, and the risk score presented a positive relation to the expression level of three commonly used immune checkpoints(PD1,PDL1,CTLA4).Conclusion: Our study constructed and validated a novel eight‑gene signature for predicting HCC patients’ OS, which possibly contributed to making clinical treatment decisions.

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Metabolism‐associated molecular classification of hepatocellular carcinoma

Cited by 153 publications

References 51 publications

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Identification of Metabolism-Associated Prostate Cancer Subtypes and Construction of a Prognostic Risk Model

Metabolism-Associated Molecular Classification of Colorectal Cancer

Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

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