Alterations in glycolytic/cholesterogenic gene expression in hepatocellular carcinoma

Gypenoside L inhibits hepatocellular carcinoma by targeting the SREBP2-HMGCS1 axis and enhancing immune response

Hepatocellular carcinoma (HCC) is a malignant tumor that occurs in the liver, with a high degree of malignancy and relatively poor prognosis. Gypenoside L has inhibitory effects on liver cancer cells. However, its mechanism of action is still unclear. This study aims to investigate the inhibitory effects of gypenoside L on HCC in vitro and in vivo, and explore its potential mechanisms. The results showed that gypenoside L reduced the cholesterol and triglyceride content in HepG2 and Huh-7 cells, inhibited cell proliferation, invasion and metastasis, arrested cell cycle at G0/G1 phase, promoted cell apoptosis. Mechanistically, it targeted the transcription factor SREPB2 to inhibit the expression of HMGCS1 protein and inhibited the downstream proteins HMGCR and MVK, thereby regulating the mevalonate (MVA) pathway. Overexpression HMGCS1 led to significant alterations in the cholesterol metabolism pathway of HCC, which mediated HCC cell proliferation and conferred resistance to the therapeutic effect of gypenoside L. In vivo, gypenoside L effectively suppressed HCC growth in tumor-bearing mice by reducing cholesterol production, exhibiting favorable safety profiles and minimal toxic side effects. Gypenoside L modulated cholesterol homeostasis, enhanced expression of inflammatory factors by regulating MHC I pathway-related proteins to augment anticancer immune responses. Clinical samples from HCC patients also exhibited high expression levels of MVA pathway-related genes in tumor tissues. These findings highlight gypenoside L as a promising agent for targeting cholesterol metabolism in HCC while emphasizing the effectiveness of regulating the SREBP2-HMGCS1 axis as a therapeutic strategy.

The relationship and clinical significance of lactylation modification in digestive system tumors

Lactylation, an emerging post-translational modification, plays a pivotal role in the initiation and progression of digestive system tumors. This study presents a comprehensive review of lactylation in digestive system tumors, underscoring its critical involvement in tumor development and progression. By focusing on metabolic reprogramming, modulation of the tumor microenvironment, and the molecular mechanisms regulating tumor progression, the potential of targeting lactylation as a therapeutic strategy is highlighted. The research reveals that lactylation participates in gene expression regulation and cell signaling by affecting the post-translational states of histones and non-histone proteins, thereby influencing metabolic pathways and immune evasion mechanisms in tumor cells. Furthermore, this study assesses the feasibility of lactylation as a therapeutic target, providing insights for clinical treatment of gastrointestinal cancers. Future research should concentrate on elucidating the mechanisms of lactylation, developing efficient lactylation inhibitors, and validating their therapeutic efficacy in clinical trials, which could transform current cancer treatment and immunotherapy approaches. In summary, this review emphasizes the crucial role of lactylation in tumorigenesis and progression through a detailed analysis of its molecular mechanisms and clinical significance.

L-arginine combination with 5-fluorouracil inhibit hepatocellular carcinoma cells through suppressing iNOS/NO/AKT-mediated glycolysis

L-arginine can produce nitric oxide (NO) under the action of inducible nitric oxide synthase (iNOS), while 5-fluorouracil (5-FU) can induce the increase of iNOS expression. The present study was to investigate the mechanism of L-arginine combined with 5-FU regulating glucose metabolism of hepatocellular carcinoma (HCC) through iNOS/NO/AKT pathway. The combination of L-arginine and 5-FU resulted in decreased cell survival and exhibited synergistic cytotoxic effects in HepG2 and SMMC7721 cells. Meanwhile, L-arginine increased 5-FU inhibitory effect on HepG2 and SMMC7721 cells by increasing NO production. Co-treatment with L-arginine and 5-FU resulted in a significant decrease in both G6PDH and LDH enzymatic activities, as well as reduced levels of ATP and LD compared to treatment with L-arginine or 5-FU alone. Moreover, the combination of L-arginine and 5-FU resulted in a decrease in the expression of GLUT1, PKM2, LDHA, p-PI3K and p-AKT. Furthermore, the combination demonstrated a synergistic effect in downregulating the expression of HIF-1α and β-catenin, which were further diminished upon the addition of shikonin, a specific inhibitor of PKM2. LY294002 treatment further reduced the expression of GLUT1, PKM2, and LDHA proteins induced by combined L-arginine and 5-FU treatment compared to the combined group. However, the reduction in p-PI3K, p-AKT, and GLUT1 expression caused by L-arginine and 5-FU combination was also reversed in HepG2 and SMMC7721 cells with iNOS knockdown, respectively. Additionally, the combination of L-arginine and 5-FU led to a greater reduction in the enzymatic activity of ALT, AST, G6PDH and LDH, as well as a significant reduction in hepatic index, AFP, AFP-L3, ATP and LD levels in a rat model of HCC. Moreover, the simultaneous administration of L-arginine and 5-FU significantly improved the gross morphology of the liver, reduced nuclear atypia, inhibited the proliferation of cancer cells, and decreased the expression levels of p-PI3K, p-AKT, GLUT1, PKM2, and LDHA, while iNOS expression was increased in the combination group. Taking together, L-arginine and 5-FU combination resulted in the inhibition of enzymes in aerobic glycolysis via the iNOS/NO/AKT pathway, which led to the suppression of glucose metabolism and downregulation of nuclear transcription factors, thereby impeding the proliferation of hepatocellular carcinoma cells.

MYC in liver cancer: mechanisms and targeted therapy opportunities

MYC, a major oncogenic transcription factor, regulates target genes involved in various pathways such as cell proliferation, metabolism and immune evasion, playing a critical role in the tumor initiation and development in multiple types of cancer. In liver cancer, MYC and its signaling pathways undergo significant changes, exerting a profound impact on liver cancer progression, including tumor proliferation, metastasis, dedifferentiation, metabolism, immune microenvironment, and resistance to comprehensive therapies. This makes MYC an appealing target, despite it being previously considered an undruggable protein. In this review, we discuss the role and mechanisms of MYC in liver physiology, chronic liver diseases, hepatocarcinogenesis, and liver cancer progression, providing a theoretical basis for targeting MYC as an ideal therapeutic target for liver cancer. We also summarize and prospect the strategies for targeting MYC, including direct and indirect approaches to abolish the oncogenic function of MYC in liver cancer.

Glycolysis-cholesterol metabolic axis in immuno-oncology microenvironment: emerging role in immune cells and immunosuppressive signaling

Cell proliferation and function require nutrients, energy, and biosynthesis activity to duplicate repertoires for each daughter. It is therefore not surprising that tumor microenvironment (TME) metabolic reprogramming primarily orchestrates the interaction between tumor and immune cells. Tumor metabolic reprogramming affords bioenergetic, signaling intermediates, and biosynthesis requirements for both malignant and immune cells. Different immune cell subsets are recruited into the TME, and these manifestations have distinct effects on tumor progression and therapeutic outcomes, especially the mutual contribution of glycolysis and cholesterol metabolism. In particularly, glycolysis-cholesterol metabolic axis interconnection plays a critical role in the TME modulation, and their changes in tumor metabolism appear to be a double-edged sword in regulating various immune cell responses and immunotherapy efficacy. Hence, we discussed the signature manifestation of the glycolysis-cholesterol metabolic axis and its pivotal role in tumor immune regulation. We also highlight how hypothetical combinations of immunotherapy and glycolysis/cholesterol-related metabolic interventions unleash the potential of anti-tumor immunotherapies, as well as developing more effective personalized treatment strategies.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data

BACKGROUND

We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.

METHODS

Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.

RESULTS

OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.

CONCLUSIONS

Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Clinical Significance of Glycolytic Metabolic Activity in Hepatocellular Carcinoma

High metabolic activity is a hallmark of cancers, including hepatocellular carcinoma (HCC). However, the molecular features of HCC with high metabolic activity contributing to clinical outcomes and the therapeutic implications of these characteristics are poorly understood. We aimed to define the features of HCC with high metabolic activity and uncover its association with response to current therapies. By integrating gene expression data from mouse liver tissues and tumor tissues from HCC patients (n = 1038), we uncovered three metabolically distinct HCC subtypes that differ in clinical outcomes and underlying molecular biology. The high metabolic subtype is characterized by poor survival, the strongest stem cell signature, high genomic instability, activation of EPCAM and SALL4, and low potential for benefitting from immunotherapy. Interestingly, immune cell analysis showed that regulatory T cells (Tregs) are highly enriched in high metabolic HCC tumors, suggesting that high metabolic activity of cancer cells may trigger activation or infiltration of Tregs, leading to cancer cells' evasion of anti-cancer immune cells. In summary, we identified clinically and metabolically distinct subtypes of HCC, potential biomarkers associated with these subtypes, and a potential mechanism of metabolism-mediated immune evasion by HCC cells.

Screening for Lipid-Metabolism-Related Genes and Identifying the Diagnostic Potential of ANGPTL6 for HBV-Related Early-Stage Hepatocellular Carcinoma

Lipid metabolic reprogramming is one of the hallmarks of hepatocarcinogenesis and development. Therefore, lipid-metabolism-related genes may be used as potential biomarkers for hepatocellular carcinoma (HCC). This study aimed to screen for genes with dysregulated expression related to lipid metabolism in HCC and explored the clinical value of these genes. We screened differentially expressed proteins between tumorous and adjacent nontumorous tissues of hepatitis B virus (HBV)-related HCC patients using a Nanoscale Liquid Chromatography–Tandem Mass Spectrometry platform and combined it with transcriptomic data of lipid-metabolism-related genes from the GEO and HPA databases to identify dysregulated genes that may be involved in lipid metabolic processes. The potential clinical values of these genes were explored by bioinformatics online analysis tools (GEPIA, cBioPortal, SurvivalMeth, and TIMER). The expression levels of the secreted protein (angiopoietin-like protein 6, ANGPTL6) in serum were analyzed by ELISA. The ability of serum ANGPTL6 to diagnose early HCC was assessed by ROC curves. The results showed that serum ANGPTL6 could effectively differentiate between HBV-related early HCC patients with normal serum alpha-fetoprotein (AFP) levels and the noncancer group (healthy participants and chronic hepatitis B patients) (AUC = 0.717, 95% CI: from 0.614 to 0.805). Serum ANGPTL6 can be used as a potential second-line biomarker to supplement serum AFP in the early diagnosis of HBV-related HCC.

Establishment and validation of a cholesterol metabolism-related prognostic signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents the most important type of liver cancer, the 5-year survival rate for advanced HCC is 2%. The heterogeneity of HCC makes previous models fail to achieve satisfactory results. The role of Cholesterol-based metabolic reprogramming in cancer has attracted more and more attention. In this study, we screened cholesterol metabolism-related genes (CMRGs) based on a systematical analysis from TCGA and GEO database. Then, we constructed a prognostic signature based on the screened 5 CMRGs: FDPS, FABP5, ANXA2, ACADL and HMGCS2. The clinical value of the five CMRGs was validated by TCGA database and HPA database. HCC patients were assigned to the high-risk and low-risk groups on the basis of median risk score calculated by the five CMRGs. We evaluated the signature in TCGA database and validated in ICGC database. The results revealed that the prognostic signature had good prognostic performance, even among different clinicopathological subgroups. The function analysis linked CMRGs with KEGG pathway, such as cell adhesion molecules, drug metabolism-cytochrome P450 and other related pathways. In addition, patients in the high-risk group exhibited characteristics of high TP53 mutation, high immune checkpoints expression and high immune cell infiltration. Furthermore, based on the prognostic signature, we identified 25 most significant small molecule drugs as potential drugs for HCC patients. Finally, a nomogram combined risk score and TNM stage was constructed. These results indicated our prognostic signature has an excellent prediction performance. This study is expected to provide a potential diagnostic and therapeutic strategies for HCC.

Identification of Four Metabolic Subtypes of Glioma Based on Glycolysis-Cholesterol Synthesis Genes

Based on alterations in gene expression associated with the production of glycolysis and cholesterol, this research classified glioma into prognostic metabolic subgroups. In this study, data from the CGGA325 and The Cancer Genome Atlas (TCGA) datasets were utilized to extract single nucleotide variants (SNVs), RNA-seq expression data, copy number variation data, short insertions and deletions (InDel) mutation data, and clinical follow-up information from glioma patients. Glioma metabolic subtypes were classified using the ConsensusClusterPlus algorithm. This study determined four metabolic subgroups (glycolytic, cholesterogenic, quiescent, and mixed). Cholesterogenic patients had a higher survival chance. Genome-wide investigation revealed that inappropriate amplification of MYC and TERT was associated with improper cholesterol anabolic metabolism. In glioma metabolic subtypes, the mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) presented deletion and amplification, respectively. Differentially upregulated genes in the glycolysis group were related to pathways, including IL-17, HIF-1, and TNF signaling pathways and carbon metabolism. Downregulated genes in the glycolysis group were enriched in terpenoid backbone biosynthesis, nitrogen metabolism, butanoate metabolism, and fatty acid metabolism pathway. Cox analysis of univariate and multivariate survival showed that risks of glycolysis subtypes were significantly higher than other subtypes. Those results were validated in the CGGA325 dataset. The current findings greatly contribute to a comprehensive understanding of glioma and personalized treatment.

A Cholesterol Homeostasis-Related Gene Signature Predicts Prognosis of Endometrial Cancer and Correlates With Immune Infiltration

Background: Endometrial cancer (EC) is one of the most common gynecological malignancies in women. Cholesterol metabolism has been confirmed to be closely related to tumor proliferation, invasion and metastasis. However, the correlation between cholesterol homeostasis-related genes and prognosis of EC remains unclear.

Methods: EC patients from the Cancer Genome Atlas (TCGA) database were randomly divided into training cohort and test cohort. Transcriptome analysis, univariate survival analysis and LASSO Cox regression analysis were adopted to construct a cholesterol homeostasis-related gene signature from the training cohort. Subsequently, Kaplan-Meier (KM) plot, receiver operating characteristic (ROC) curve and principal component analysis (PCA) were utilized to verify the predictive performance of the gene signature in two cohorts. Additionally, enrichment analysis and immune infiltration analysis were performed on differentially expressed genes (DEGs) between two risk groups.

Results: Seven cholesterol homeostasis-related genes were selected to establish a gene signature. KM plot, ROC curve and PCA in two cohorts demonstrated that the gene signature was an efficient independent prognostic indicator. The enrichment analysis and immune infiltration analysis indicated that the high-risk group generally had lower immune infiltrating cells and immune function.

Conclusion: We constructed and validated a cholesterol homeostasis-related gene signature to predict the prognosis of EC, which correlated to immune infiltration and expected to help the diagnosis and precision treatment of EC.

Identification of four gastric cancer subtypes based on genetic analysis of cholesterogenic and glycolytic pathways

Warburg phenomenon refers to the development of unique metabolic patterns during the growth of tumor cells. This study stratified gastric cancer into prognostic metabolic subgroups according to changes in gene expressions related to glycolysis and cholesterol synthesis. The RNA-seq expression data, single nucleotide variants (SNV), short insertions and deletions (InDel) mutation data, copy number variation (CNV) data and clinical follow-up information data of gastric cancer tissues were downloaded from The Cancer Genome Atlas (TCGA) database. ConsensusClusterPlus was used to stratify the metabolic subtypes of gastric cancer. Four metabolic subtypes (Cholesterogenic, Glycolytic, Mixed and Quiescent) of gastric cancer were identified, and patients with cholesterogenic tumors had the longest disease-specific survival (DSS). Genome-wide analysis showed that aberrant amplification of TP53 and MYC in gastric cancer was associated with abnormal cholesterol anabolic metabolism. The mRNA levels of mitochondrial pyruvate carriers 1 and 2 (MPC1/2) differed among the four subtypes. Tumors in the glycolytic group showed a higher PDCD1. A genomic signature based on tumor metabolism of different cancer types was established. This study showed that genes related to glucose and lipid metabolism play an important role in gastric cancer and facilitate a personalized treatment of gastric cancer.

Immune signature-based hepatocellular carcinoma subtypes may provide novel insights into therapy and prognosis predictions

Background

Hepatocellular carcinoma (HCC) has a poor prognosis and has become the sixth most common malignancy worldwide due to its high incidence. Advanced approaches to therapy, including immunotherapeutic strategies, have played crucial roles in decreasing recurrence rates and improving clinical outcomes. The HCC microenvironment is important for both tumour carcinogenesis and immunogenicity, but a classification system based on immune signatures has not yet been comprehensively described.

Methods

HCC datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Cancer Genome Consortium (ICGC) were used in this study. Gene set enrichment analysis (GSEA) and the ConsensusClusterPlus algorithm were used for clustering assessments. We scored immune cell infiltration and used linear discriminant analysis (LDA) to improve HCC classification accuracy. Pearson's correlation analyses were performed to assess relationships between immune signature indices and immunotherapies. In addition, weighted gene co-expression network analysis (WGCNA) was applied to identify candidate modules closely associated with immune signature indices.

Results

Based on 152 immune signatures from HCC samples, we identified four distinct immune subtypes (IS1, IS2, IS3, and IS4). Subtypes IS1 and IS4 had more favourable prognoses than subtypes IS2 and IS3. These four subtypes also had different immune system characteristics. The IS1 subtype had the highest scores for IFNγ, cytolysis, angiogenesis, and immune cell infiltration among all subtypes. We also identified 11 potential genes, namely, TSPAN15, TSPO, METTL9, CD276, TP53I11, SPINT1, TSPO, TRABD2B, WARS2, C9ORF116, and LBH, that may represent potential immunological biomarkers for HCC. Furthermore, real-time PCR revealed that SPINT1, CD276, TSPO, TSPAN15, METTL9, and WARS2 expression was increased in HCC cells.

Conclusions

The present gene-based immune signature classification and indexing may provide novel perspectives for both HCC immunotherapy management and prognosis prediction.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02033-4.

Down-regulation of RBP4 indicates a poor prognosis and correlates with immune cell infiltration in hepatocellular carcinoma

Recent research has indicated that metabolically related genes play crucial roles in the pathogenesis of hepatocellular carcinoma (HCC). We evaluated the associations between novel biomarkers and retinol-binding protein 4 (RBP4) for predicting clinical HCC outcomes, hub-related genes, pathway regulation, and immune cells infiltration. Bioinformatic analyses based on data from The Cancer Genome Atlas were performed using online analysis tools. RBP4 expression was low in HCC and was also down-regulated in pan-cancers compared with normal tissues. RBP4 expression was also significantly different based on age (41–60 years old versus 61–80 years old), and low RBP4 expression levels were associated with advanced tumor stages and grades. Higher RBP4 expression was associated with better overall survival time in HCC patients, and we identified a deletion-mutation rate of 1.4% in RBP4. We also identified ten co-expressed genes most related to RBP4 and explored the relationships between six hub genes (APOB, FGA, FGG, SERPINC1, APOA1, and F2) involved in RBP4 regulation. A pathway enrichment analysis for RBP4 indicated complement and coagulation cascades, metabolic pathways, antibiotic biosynthesis pathways, peroxisome proliferator-activated receptor signaling pathways, and pyruvate metabolism pathways. These results suggest that RBP4 may be a novel biomarker for HCC prognosis, and an indicator of low immune response to the disease.

LDLR inhibition promotes hepatocellular carcinoma proliferation and metastasis by elevating intracellular cholesterol synthesis through the MEK/ERK signaling pathway

Objective

Adaptive rewiring of cancer energy metabolism has received increasing attention. By binding with LDLs, LDLRs make most of the circulating cholesterol available for cells to utilize. However, it remains unclear how LDLR works in HCC development by affecting cholesterol metabolism.

Methods

Database analyses and immunohistochemical staining were used to identify the clinical significance of LDLR in HCC. A transcriptome analysis was used to reveal the mechanism of LDLR aberration in HCC progression. A liver orthotopic transplantation model was used to evaluate the role of LDLR in HCC progression in vivo.

Results

Downregulation of LDLR was identified as a negative prognostic factor in human HCC. Reduced expression of LDLR in HCC cell lines impaired LDL uptake but promoted proliferation and metastasis in vitro and in vivo. Mechanistically, increasing intracellular de novo cholesterol biosynthesis was the chief contributor to malignant behaviors caused by LDLR inhibition, which could be rescued by simvastatin. Activation of the MEK/ERK pathway by LDLR downregulation partially contributed to intracellular cholesterol synthesis in HCC.

Conclusions

Downregulation of LDLR may elevate intracellular cholesterol synthesis to accelerate proliferation and motility through a mechanism partially attributed to stimulation of the MEK/ERK signaling pathway. Repression of intracellular cholesterol synthesis with statins may constitute a targetable liability in the context of lower LDLR expression in HCC.

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Downregulation of LDLR is identified as a negative prognostic factor in human HCC.

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LDLR inhibition facilitates the proliferation and metastasis of HCC cells.

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Increased cholesterol synthesis chiefly contributes to the malignant behaviors caused by LDLR reduction.

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Blockade of cholesterol synthesis by simvastatin attenuates HCC progression under lower LDLR.

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Activation of the MEK/ERK pathway by LDLR downregulation promotes cholesterol synthesis in HCC.

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

BACKGROUND

Globally, gastrointestinal (GI) cancer is one of the most prevalent malignant tumors. However, studies have not established glycolysis-related gene signatures that can be used to construct accurate prognostic models for GI cancers in the Asian population. Herein, we aimed at establishing a novel glycolysis-related gene expression signature to predict the prognosis of GI cancers.

METHODS

First, we evaluated the mRNA expression profiles and the corresponding clinical data of 296 Asian GI cancer patients in The Cancer Genome Atlas (TCGA) database (TCGA-LIHC, TCGA-STAD, TCGA-ESCA, TCGA-PAAD, TCGA-COAD, TCGA-CHOL and TCGA-READ). Differentially expressed mRNAs between GI tumors and normal tissues were investigated. Gene Set Enrichment Analysis (GSEA) was performed to identify glycolysis-related genes. Then, univariate, LASSO regression and multivariate Cox regression analyses were performed to establish a key prognostic glycolysis-related gene expression signature. The Kaplan-Meier and receiver operating characteristic (ROC) curves were used to evaluate the efficiency and accuracy of survival prediction. Finally, a risk score to predict the prognosis of GI cancers was calculated and validated using the TCGA data sets. Furthermore, this risk score was verified in two Gene Expression Omnibus (GEO) data sets (GSE116174 and GSE84433) and in 28 pairs of tissue samples.

RESULTS

Prognosis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1) among the differentially expressed glycolysis-related genes were screened and identified. The five-gene expression signature was used to assign patients into high- and low-risk groups (p < 0.05) and it showed a satisfactory prognostic value for overall survival (OS, p = 6.383 × 10^-6). The ROC curve analysis revealed that this model has a high sensitivity and specificity (0.757 at 5 years). Besides, stratification analysis showed that the prognostic value of the five-gene signature was independent of other clinical characteristics, and it could markedly discriminate between GI tumor tissues and normal tissues. Finally, the expression levels of the five prognosis-related genes in the clinical tissue samples were consistent with the results from the TCGA data sets.

CONCLUSIONS

Based on the five glycolysis-related genes (NUP85, HAX1, GNPDA1, HDLBP and GPD1), and in combination with clinical characteristics, this model can independently predict the OS of GI cancers in Asian patients.