Construction of a lipid metabolism‐related and immune‐associated prognostic signature for hepatocellular carcinoma

Hu, Bo; Yang, Xiaobo; Sang, Xinting

doi:10.1002/cam4.3353

Cited by 49 publications

(40 citation statements)

References 44 publications

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“…Univariate and multivariate Cox analyses confirmed the independent prognostic value of the risk signature. Several studies in recent years have identified metabolism-related risk signatures for effectively predicting OS and have uncovered the importance of metabolism-related genes in the process of HCC development ( Hu et al, 2020 ; Wu et al, 2021 ). Zhu’s study built a lipid metabolism-related prognostic signature and revealed that lipid metabolism-related genes are closely correlated with clinical characteristics, immune cells, and multiple biological functions of HCC ( Zhu et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC

Zhao

Zhang

Wang

et al. 2021

Front. Cell Dev. Biol.

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Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment.Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software.Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation.Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

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Section: Discussionmentioning

confidence: 99%

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC

Zhao

Zhang

Wang

et al. 2021

Front. Cell Dev. Biol.

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“…Hilvo et al (2011) demonstrated that the lipids in the breast cancer tissue were correlated with tumor progression and patient survival. Moreover, previous studies have demonstrated that lipid metabolism related genes (LMRGs) had potent prognostic potential in multiple types of tumors, including ovarian carcinomas (Zheng et al, 2020), lung adenocarcinoma (LAUD) (Li et al, 2020), pancreatic cancer (Ye et al, 2021), hepatocellular carcinoma (HCC) (Hu et al, 2020), renal cell carcinoma (RCC) (Bao et al, 2019) and diffuse gliomas (Wu et al, 2019). As such, targeting lipid metabolism has been regarded as a novel therapeutic strategy for tumor treatment (Visweswaran et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Lei

et al. 2021

Front. Cell Dev. Biol.

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ObjectivesOsteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.MethodsClinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.ResultsTwo molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.ConclusionExpression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.

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“…Lipids have been gradually included in the emerging prognostic indices, such as Prognostic Nutritional Index (PNI) [ 35 ], Controlling Nutritional Status (CONUT) score [ 36 ] and Naples prognostic score (NPS) [ 37 ]. In addition, lipid metabolism-related signature also showed prognostic significance for some cancers [ 38 ]. The hypothesis that tumors are driven by dysregulated metabolism largely explained the significance of metabolomics in tumor prognosis [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Derivation and validation of a lipid-covered prognostic model for mature T-cell lymphomas

Lü

Shi

et al. 2021

Cancer Cell Int

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Background Mature T-cell lymphomas (MTCLs), a group of diseases with high aggressiveness and vulnerable prognosis, lack for the accurate prognostic stratification systems at present. Novel prognostic markers and models are urgently demanded. Aberrant lipid metabolism is closely related to the tumor progression but its prognostic significance in MTCLs remains unexplored. This study aims to investigate the relationship between dysregulated lipid metabolism and survival prognosis of MTCLs and establish a novel and well-performed prognostic scoring system for MTCL patients. Methods A total of 173 treatment-naive patients were enrolled in this study. Univariate and multivariate Cox regression analysis were performed to assess the prognostic significance of serum lipid profiles and screen out independent prognostic factors, which constituted a novel prognostic model for MTCLs. The performance of the novel model was assessed in the training and validation cohort, respectively, by examining its calibration, discrimination and clinical utility. Results Among the 173 included patients, 115 patients (01/2006–12/2016) constituted the training cohort and 58 patients (01/2017–06/2020) formed the validation cohort. Univariate analysis revealed declined total cholesterol (TC, P = 0.000), high-density lipoprotein cholesterol (HDL-C, P = 0.000) and increased triglycerides (TG, P = 0.000) correlated to inferior survival outcomes. Multivariate analysis revealed extranodal involved sites ≥ 2 (hazard ratio [HR]: 2.439; P = 0.036), β2-MG ≥ 3 mg/L (HR: 4.165; P = 0.003) and TC < 3.58 mmol/L (HR: 3.338; P = 0.000) were independent predictors. Subsequently, a novel prognostic model, EnBC score, was constructed with these three factors. Harrell’s C-index of the model in the training and validation cohort was 0.840 (95% CI 0.810–0.870) and 0.882 (95% CI 0.822–0.942), respectively, with well-fitted calibration curves. The model divided patients into four risk groups with distinct OS [median OS: not available (NA) vs. NA vs. 14.0 vs. 4.0 months, P < 0.0001] and PFS (median PFS: 84.0 vs. 19.0 vs. 8.0 vs. 1.5 months, P < 0.0001). Time-dependent receiver operating characteristic curve and decision curve analysis further revealed that EnBC score provided higher diagnostic capacity and clinical benefit, compared with International Prognostic Index (IPI). Conclusion Firstly, abnormal serum lipid metabolism was demonstrated significantly related to the survival of MTCL patients. Furthermore, a lipid-covered prognostic scoring system was established and performed well in stratifying patients with MTCLs.

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Construction of a lipid metabolism‐related and immune‐associated prognostic signature for hepatocellular carcinoma

Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Cited by 49 publications

References 44 publications

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Derivation and validation of a lipid-covered prognostic model for mature T-cell lymphomas

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