Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population

Xia, Rong; Tang, Hua; Shen, Jiacheng; Xu, Shanshan; Liang, Yan; Zhang, Yuxin; Gong, Xing; Min, Yang Gi; Zhang, Di; Tao, Chenzhe; Wang, Shoulin; Zhang, Yi; Yang, Jinyou; Wang, Chao

doi:10.1186/s12935-021-01857-4

“…Selenophosphate synthetase 1 (SEPHS1) promotes the expression of SMADs in liver cancer cells and stimulates the migration and invasion of tumors induced by TGF-β, which is negatively correlated with the OS and RFS of HCC patients ( Yang et al, 2021 ). Glucosamine-6-phosphate deaminase 1 (GNPDA1) and procollagen-lysine (PLOD2) are upregulated in liver cancer and promote tumor proliferation and migration, which are correlated with poor prognosis ( Du et al, 2017 ; Xia et al, 2021 ). Mutation of Beta-1, 4-galactosyltransferase 2 (B4GALT2) leads to abnormal glycosylation of proteins, promoting the development of colon cancer ( Venkitachalam et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

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

Zhao

¹

,

Zhang

²

,

Wang

³

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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“…GPD1 encodes glycerol-3phosphate dehydrogenase 1 (GPD1), which played a pivotal role in lipid metabolism through catalyzing the transformation of NADH and dihydroxyacetone phosphate to NAD+ and glycerol-3-phosphate reversibly (Yoneten et al, 2019). Studies suggested that GPD1 was closely related to prognosis of gastrointestinal cancer, breast cancer, and glioblastoma (Xie et al, 2020;Xia et al, 2021). Survival analysis revealed that no matter in the training cohort or the verification cohort, the established risk model exhibited potent predictive performance for the survival of osteosarcoma patients.…”

Section: Discussionmentioning

confidence: 99%

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

Hu

¹

,

Lei

²

,

Hu

³

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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“…In 2019, the 5-year overall survival rate after gastric cancer in China was approximately 20% [ 3 ], whereas that of colorectal cancer was 60% [ 4 ]. Consequently, accurate markers for therapy response are required to improve the prognosis of GI cancer patients [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Expression of Four Autophagy-Related Genes Accurately Predicts the Prognosis of Gastrointestinal Cancer in Asian Patients

Tang

¹

,

Liang

²

,

Xu

³

et al. 2021

Self Cite

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Gastrointestinal (GI) cancers are among the most fatal diseases in the world. Numerous studies have demonstrated the relationship between autophagy and development of gastrointestinal cancers. However, whether autophagy-related genes can predict prognosis of GI cancers in individuals of Asian ancestry has not been defined. This study, evaluated the prognostic value of autophagy-related genes in gastrointestinal cancer. Expression profile of autophagy-related genes for 296 gastrointestinal cancer patients of Asian ancestry was downloaded from the TCGA database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL, and TCGA-READ). The prognostic value of the autophagy-related genes was evaluated using univariate Cox, LASSO, and multivariate Cox regression analyses. The risk score of the autophagy-related gene signature was calculated to assess its predictive prognostic value for GI cancers. Forty-seven differentially expressed autophagy-related genes, in Asian patients with gastrointestinal cancers, were identified. Of the 47 genes, 4 were associated with prognosis of GI cancer (SQSTM1, BIRC5, NRG3, and CXCR4). A prognostic model for GI cancer, based on the expression of the above 4 genes in the training set, showed that cancer patients were stratified into high-risk and low-risk groups ( P < 0.05 ). The utility of the model for overall survival (OS) of GI cancer patients was consistent across the entire set, training set, and test set (entire set: P = 4.568 × 10 − 4 ; train set: P = 5.718 × 10 − 3 ; test set: P = 3.516 × 10 − 2 ). The sensitivity and specificity of the ROC curve of the above prognostic model in predicting the 5-year prognosis of GI cancer was satisfactory (entire set: 0.728; train set: 0.727; test set: 0.733). Analysis of clinical samples validated the overexpression of the 4 genes (SQSTM1, BIRC5, NRG3, and CXCR4) in tumor tissues relative to paired normal tissues, consistent with bioinformatic findings. Expression of the 4 autophagy-related genes (SQSTM1, BIRC5, NRG3, and CXCR4) can accurately predict the prognosis of gastrointestinal tumors in Asian patients.

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Prognostic value of a novel glycolysis-related gene expression signature for gastrointestinal cancer in the Asian population

Cited by 14 publications

References 74 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

Expression of Four Autophagy-Related Genes Accurately Predicts the Prognosis of Gastrointestinal Cancer in Asian Patients

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