Glycolysis-related Gene Research Articles

High expression of glycolysis-related PGM2 gene in relation to poor prognosis and deficient immune cells infiltration in lung adenocarcinoma: a study based on bioinformatics analysis.

BackgroundLung adenocarcinoma (LUAD) is the most important subtype of lung cancer and usually metastasizes. Patients with LUAD usually had a poor prognosis. Identifying viable molecular markers for diagnostic and prognostic prediction among individuals with LUAD is critical for the future management of this disease. This study aimed to determine and verify a correlation between the glycolysis-related phosphoglucomutase 2 (PGM2) gene and dissatisfactory results and deficient infiltration of immune cells in LUAD.MethodsThe expression of PGM2 in LUAD and adjoining normal tissues was screened from The Cancer Genome Atlas (TCGA) data and human protein atlas (HPA), and validatied by quantative reverse transcription polymerase chain reaction (qRT-PCR). We examined the correlation between PGM2 expression and clinicopathologic characteristics (including pathological stage, gender, M stage, smoker, age, N stage, race, and number pack years smoked) by multivariable approaches and Kaplan-Meier survival curves. The proteins network with PGM2 was built using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database. The correlation between PGM2 expression and infiltration of immune cells, along with the corresponding gene marker sets, was investigated through the Gene Expression Profiling Interactive Analysis (GEPIA) and Tumor Immune Estimation Resource (TIMER) databases. We evaluated the possible correlation between PGM2 expression and progression-free interval (PFI), disease-specific survival (DSS), and overall survival (OS) in LUAD patients.ResultsExpression of PGM2 was up-regulated in LUAD tissues (P=0.003). According to the multivariate logistic regression analysis, the elevated expression level of PGM2 exhibited a remarkable correlation with advanced tumor-node-metastasis (TNM) stage, high-grade malignancy, and primary therapeutic outcome . Overexpression of PGM2 was shown to be correlated with an unfavorable prognosis including OS (P=0.004, HR =1.54), DSS (P=0.003, HR =1.77), and PFI (P=0.003, HR =1.5) in LUAD. The proteins PGM1 and UGP2 were shown to have a significant correlation with PGM2. Additionally, PGM2 was associated with the lack of infiltrating immune cells as well as their associated gene marker sets in LUAD.ConclusionsOverexpression of PGM2 was shown to be associated with the progression and an unfavorable prognosis of LUAD, as well as with inefficient immune cell infiltration. PGM2 was expected to be a potential biological marker for predicting the prognosis of patients with LUAD.

Identification and validation of a glycolysis-related gene signature for depicting clinical characteristics and its relationship with tumor immunity in patients with colon cancer.

Colon cancer (CC) is one of the most common gastrointestinal malignant tumors with a high mortality rate. Glycolysis is an important pathway for tumors to obtain energy. However, its role in CC remains largely unknown. In present study, we analyzed glycolysis-related gene expression to depict clinical characteristics and its relationship with tumor immunity in CC to find potential target treatments. A prognostic model based on 13 glycolysis-related genes was established by univariate and multivariate Cox regression analyses. The efficacy of the gene model was tested via survival analysis, receiver operating characteristic analysis, and principal component analysis. Furthermore, our findings revealed and validated 13 glycolysis-related genes (NUP107, SEC13, ALDH7A1, ALG1, CHPF, FAM162A, FBP2, GALK1, IDH1, TGFA, VLDLR, XYLT2, and OGDHL), which constituted a prognostic prediction model. The model exhibited clinical implication potential, had a relatively high accuracy, and was closely associated with the patients' clinical features. In particular, the tumor stage could be clearly distinguished by glycolysis-related gene signatures. Finally, a significant difference between glycolysis-related gene colon cancer immunity and sensitive immune drugs was observed. Our glycolysis-related gene model could provide the basis for potential early individualized treatment. The 13 glycolysis-related gene signature was a reliable predictive tool for the prognosis of colon cancer. Our findings could help patients select targets for individualized treatment and immunotherapy strategies. The study findings advance our understanding of the potential mechanism of glycolysis in colon cancer.

Nutrition promotion of brewer's spent grain by symbiotic fermentation adding Bacillus velezensis and Levilactobacillus brevis

Brewer's spent grain (BSG) is the main byproduct of the beer industry and is commonly used as a low-value animal feed or food ingredient. This study investigated the role of symbiotic fermentation with Bacillus velezensis and Levilactobacillus brevis in improving the nutritional value of BSG. The addition of B. velezensis increased the cell growth of L. brevis by over thirty-fold higher than that in the single-strain system. Simultaneously, cellulase and protease activities were substantially increased, and more soluble sugars were produced. Total amino acid, glutamic acid, and γ-aminobutyric acid (GABA) levels increased by 52.2%, 155.1%, and 144.1%, respectively. Conversely, lactic acid production decreased. Genetic analysis showed that the glycolysis-related gene pykA, pentose phosphate pathway gene gnd, and GABA-related gene gad were upregulated, while the heterolactin-related gene xpkA was downregulated, explaining that B. velezensis might inhibit heterolactin fermentation while enhancing GABA biosynthesis. This study provides a potential strategy for the high-value utilization of BSG in the food or feed industry.

CXCL12 secreted by pancreatic stellate cells accelerates gemcitabine resistance of pancreatic cancer by enhancing glycolytic reprogramming

ABSTRACT Pancreatic stellate cells (PSCs) are the primary cell components of pancreatic cancer (PC) and are involved in tumor growth, metastasis and resistance. However, the role and the mechanism of PSCs in gemcitabine (GEM) resistance to PC still need more investigation. We found that CXCL12 mRNA and secreted CXCL12 protein were higher in PSCs after GEM treatment. The conditioned medium (CM) from GEM-treated PSCs reduced the GEM sensitivity of PC cells. Blocking of CXCL12 in CM by anti-CXCL12 antibody partly restored the GEM sensitivity of PC cells. Blocking of CXCL12 decreased glucose consumption, lactate production, ECAR, and glycolysis-related gene expression in PC cells. The PI3K/AKT/mTOR pathway was activated by the binding of CXCL12 and CXCR4. Moreover, CXCR4 mRNA and protein expressions in PC cells were increased after GEM treatment. Our results indicated the cross-talk between PSCs and PC cells during GEM chemotherapy. CXCL12 secreted by PSCs reduces GEM sensitivity of PC cells by binding to CXCR4 and activating PI3K/AKT/mTOR-glycolysis pathway in PC. Our findings would lay the foundation for solving GEM resistance in PC.

Trained Immunity in Primary Sjögren's Syndrome: Linking Type I Interferons to a Pro-Atherogenic Phenotype.

BackgroundTrained immunity – or innate immune memory – can be described as the long-term reprogramming of innate immune cells towards a hyperresponsive state which involves intracellular metabolic changes. Trained immunity has been linked to atherosclerosis. A subgroup of patients with primary Sjögren’s syndrome (pSS) exhibits systemic type I interferon (IFN) pathway activation, indicating innate immune hyperactivation. Here, we studied the link between type I IFNs and trained immunity in an in vitro monocytic cell model and peripheral blood mononuclear cells (PBMCs) from pSS patients.MethodsThe training stimuli heat killed Candida albicans, muramyl dipeptide, IFNβ, and patient serum were added to THP-1 cells for 24 hours, after which the cells were washed, rested for 48 hours and subsequently re-stimulated with LPS, Pam3Cys, poly I:C, IFNβ or oxLDL for 4-24 hours. PBMCs from pSS patients and healthy controls were stimulated with LPS, Pam3Cys, poly I:C or IFNβ for 0.5-24 hours.ResultsTraining with IFNβ induced elevated production of pro-atherogenic cytokines IL-6, TNFα and CCL2, differential cholesterol- and glycolysis-related gene expression, and increased glucose consumption and oxLDL uptake upon re-stimulation. Type I IFN production was increased in Candida albicans- and IFNβ-trained cells after LPS re-stimulation, but was reduced after poly I:C re-stimulation. Training with muramyl dipeptide and IFNβ, but not Candida albicans, affected the IFN-stimulated gene expression response to IFNβ re-stimulation. PBMCs from pSS patients consumed more glucose compared with healthy control PBMCs and tended to produce more TNFα and type I IFNs upon LPS stimulation, but less type I IFNs upon poly I:C stimulation.ConclusionsType I IFN is a trainer inducing a trained immunity phenotype with pro-atherogenic properties in monocytes. Conversely, trained immunity also affects the production of type I IFNs and transcriptional response to type I IFN receptor re-stimulation. The phenotype of pSS PBMCs is consistent with trained immunity. This connection between type I IFN, trained immunity and cholesterol metabolism may have important implications for pSS and the pathogenesis of (subclinical) atherosclerosis in these patients.

Clinical significance and immunogenomic landscape analysis of glycolysis-associated prognostic model to guide clinical therapy in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a common malignant tumor with a poor prognosis and high mortality rate worldwide. Glucose metabolism disorder is one of the most important characteristics of HCC. However, as the primary risk factors for the prognosis of HCC patients are unclear, the survival prognosis and therapy response of patients cannot be accurately predicted. First, gene sets of 29 cancer hallmarks were collected from public databases. The z-score of various cancer hallmarks were quantitively analyzed by a single-sample gene set enrichment analysis (ssGSEA) of HCC patients. Next, a glycolysis-related gene signature (GRS) was constructed using a series of bioinformatics methods, which were used to predict the survival prognosis of HCC patients and the immunotherapy benefits. The prediction accuracy of the GRS was validated in different HCC cohorts and clinical subgroups. Additionally, a decision tree and nomogram were also established based on the GRS and other clinical variables. Finally, the genomic alterations and tumor immune microenvironment of the HCC patients were examined. Among the 29 cancer hallmarks, glycolysis was the most predominant risk factor for a poor prognosis in HCC. We subsequently constructed a novel GRS comprising 12 glycolysis-related genes. The high-GRS patients had a poorer survival prognosis than the low-GRS patients. The GRS exhibited a powerful ability to predict survival prognosis in different HCC cohorts and clinical feature subgroups. Additionally, the decision tree and nomogram aided in the risk stratification and prognosis evaluations of HCC patients. Further, we found that a high GRS was characterized by a severe tumor stage, pathological grade, and other clinical features. There were significant differences in the genomic alterations, immune cells, and immune checkpoints between the low- and high-GRS patients, especially in relation to the tumor protein p53 mutation and immunosuppressive cells. Notably, we also found that the GRS could be used to identify HCC patients who are more sensitive to chemotherapy and immunotherapy. In summary, the GRS may be a useful tool for predicting the prognosis and guiding the clinical therapy of HCC patients.

Astragalus polysaccharides protects against acute leptospirosis by glycolysis-depended priming effect

Leptospirosis, caused by pathogenic leptospira, is a neglected infectious disease that causes acute kidney injury, bleeding disorders, and even death. People can become infected with leptospirosis when they travel into epidemic areas. Except for vaccines and antibiotics, there are few reports of other drugs about prevention of leptospirosis. In this study, we show that the natural molecular compound, astragalus polysaccharides (APS), prevents against acute leptospirosis in hamsters. Pretreatment with APS improved the survival rate of hamsters with more minor organ damage and lower leptospira burden. After pretreatment with APS, the expression levels of leptospira-induced TLR2, TLR4, and TNF-α were enhanced. The priming effect of APS was studied in vitro. The data showed that leptospira-induced expressions of TNF-α and IL-1β were higher in APS-primed peritoneal macrophage, with enhanced glucose consumption and lactate production. Transcriptomic analysis revealed that pretreatment with APS down regulated respiratory chain and mitochondrial function, up regulated glycolysis related gene expressions. After pretreatment with glycolysis inhibitor (2-DG), the priming effect of APS in leptospira infection was inhibited. Our results indicated that pretreatment with natural molecular compound, APS, protected against acute leptospirosis in hamsters by priming effect through enhanced glycolysis.

SIX1, a glycolysis related gene, regulates tumor progression via c-Myc activation in breast cancer cells

SIX1, a glycolysis related gene, regulates tumor progression via c-Myc activation in breast cancer cells

A Glycolysis-Related Gene Signature Correlates With the Characteristics of the Tumor Immune Microenvironment and Predicts Prognosis in Patients With Hepatocellular Carcinoma.

Aim: To develop a glycolysis-related gene signature that correlated with the characteristics of the tumor immune microenvironment and had good predictive power for overall survival (OS) in hepatocellular carcinoma (HCC). Methods: Gene expression profiles, RNA sequencing data, clinical characteristics and survival information for 407 patients with HCC and 58 healthy controls were downloaded from the TCGA database. GSEA 4.1.0 software was used to evaluate the glycolysis-related pathways enriched in HCC compared to normal liver tissue. Univariate Cox, Least Absolute Shrinkage, Selection Operator, and two-step multivariate Cox analyses were used to construct a glycolysis-related gene signature for prognostic prediction. The glycolysis-related gene signature was combined with clinical characteristics to generate a nomogram. Tumor-infiltrating immune cell profiles and PD-L1 protein expression in HCC tissues were investigated. Results: The gene expression profiles of HCC tissues were enriched in glycolysis-related pathways. A glycolysis-related gene signature was used to categorize patients as high-risk or low-risk, where high-risk patients had significantly worse OS. Receiver operating characteristic curves confirmed the predictive capability of the glycolysis-related gene signature for OS (AUC >0.80). There was a significant difference in M0 macrophage (p = 0.017), dendritic cell (p = 0.043), B cell (p = 0.0018), CD4 T cell (p = 0.003), Treg (p = 0.01) and mast cell (p = 0.02) content and PD-L1 protein expression (p = 0.019) between HCC tissues in patients in the high-risk and low-risk groups. Conclusion: We established a glycolysis-related gene signature for OS in HCC that was predictive in training and test TCGA cohorts and correlated with the characteristics of the HCC tumor immune microenvironment. The glycolysis-related gene signature may guide clinical decision-making concerning patient selection for immunotherapy in HCC.

A Prognostic Model of Differentiated Thyroid Cancer Based on Up-Regulated Glycolysis-Related Genes

ObjectiveThis study aims to identify reliable prognostic biomarkers for differentiated thyroid cancer (DTC) based on glycolysis-related genes (GRGs), and to construct a glycolysis-related gene model for predicting the prognosis of DTC patients.MethodsWe retrospectively analyzed the transcriptomic profiles and clinical parameters of 838 thyroid cancer patients from 6 public datasets. Single factor Cox proportional risk regression analysis and Least Absolute Shrinkage and Selection Operator (LASSO) were applied to screen genes related to prognosis based on 2528 GRGs. Then, an optimal prognostic model was developed as well as evaluated by Kaplan-Meier and ROC curves. In addition, the underlying molecular mechanisms in different risk subgroups were also explored via The Cancer Genome Atlas (TCGA) Pan-Cancer study.ResultsThe glycolysis risk score (GRS) outperformed conventional clinicopathological features for recurrence-free survival prediction. The GRS model identified four candidate genes (ADM, MKI67, CD44 and TYMS), and an accurate predictive model of relapse in DTC patients was established that was highly correlated with prognosis (AUC of 0.767). In vitro assays revealed that high expression of those genes increased DTC cancer cell viability and invasion. Functional enrichment analysis indicated that these signature GRGs are involved in remodelling the tumour microenvironment, which has been demonstrated in pan-cancers. Finally, we generated an integrated decision tree and nomogram based on the GRS model and clinicopathological features to optimize risk stratification (AUC of the composite model was 0.815).ConclusionsThe GRG signature-based predictive model may help clinicians provide a prognosis for DTC patients with a high risk of recurrence after surgery and provide further personalized treatment to decrease the chance of relapse.

Construction and Verification of a Glycolysis-Associated Gene Signature for the Prediction of Overall Survival in Low Grade Glioma.

The overall survival of patients with lower grade glioma (LGG) that might develop into high-grade malignant glioma shows marked heterogeneity. The currently used clinical evaluation index is not sufficient to predict precise prognostic outcomes accurately. To optimize survival risk stratification and the personalized management of patients with LGG, there is an urgent need to develop an accurate risk prediction model. The TCGA-LGG dataset, downloaded from The Cancer Genome Atlas (TCGA) portal, was used as a training cohort, and the Chinese Glioma Genome Atlas (CGGA) dataset and Rembrandt dataset were used as validation cohorts. The levels of various cancer hallmarks were quantified, which identified glycolysis as the primary overall survival-related risk factor in LGGs. Furthermore, using various bioinformatic and statistical methods, we developed a strong glycolysis-related gene signature to predict prognosis. Gene set enrichment analysis showed that in our model, high-risk glioma correlated with the chemoradiotherapy resistance and poor survival. Moreover, based on established risk model and other clinical features, a decision tree and a nomogram were built, which could serve as useful tools in the diagnosis and treatment of LGGs. This study indicates that the glycolysis-related gene signature could distinguish high-risk and low‐risk patients precisely, and thus can be used as an independent clinical feature.

Glycolysis-Related Gene Signature Can Predict Survival and Immune Status of Hepatocellular Carcinoma.

Concise and precise prognostic models are urgently needed due to the intricate genetic variations among hepatocellular carcinoma (HCC) cells. Disorder or change in glycolysis metabolism has been considered one of the "hallmarks" of cancer. However, the prognostic value of glycolysis-related genes in HCC remains elusive. A multigene prognostic model was constructed by least absolute shrinkage and selection operator Cox regression analysis in the The Cancer Genome Atlas (TCGA) cohort with 365 HCC patients and validated in the International Cancer Genome Consortium (ICGC) cohort with 231 HCC patients. The Kaplan-Meier methodology and time-dependent receiver operating characteristic curve were employed to confirm its predictive capability. A predictive nomogram was established based on the stepwise multivariate regression model. The differential expression of prognostic genes between HCC tissues and normal tissues was verified by quantitative real-time polymerase chain reaction (PCR) and immunohistochemistry in an independent sample cohort with 30 HCC patients. The glycolysis-related gene signature and the nomogram model exhibited robust validity in predicting prognosis. The risk score was an independent predictor for overall survival (OS). Expression levels of immune checkpoint genes and cell cycle genes were significantly elevated in the high-risk group. The high-risk group presented high levels of immune exclusion. The risk score can distinguish the effect of immunotherapy in the IMvigor210 cohort. The prognostic gene expression showed a significant difference between HCC tissues and adjacent nontumorous tissues in the independent sample cohort. The currently established glycolysis-related gene signature can accurately predict prognosis and reflect immune status, which may be a therapeutic alternative.

Tenascin C regulates cancer cell glycolysis and tumor progression in prostate cancer.

Tenascin C is a potential biomarker of cancer-associated fibroblasts and has been significantly associated with poor prognosis in patients with prostate cancer. However, the effects of Tenascin C in prostate cancer cell glycolysis largely remain unclear. Thus, this study aimed to investigate the Tenascin C expression in prostate cancer and its correlation to glycolysis-related protein and gene expression, clinicopathological parameters, and survival of patients. We performed immunohistochemical staining for Tenascin C in 141 cases of primary prostate cancer. Based on public data sets, we explored the association of Tenascin C with angiogenesis-related genes, M2 macrophage-related gene, androgen receptor levels, PI3K/AKT/NF-κB pathway genes, and glycolytic enzyme expression. The glucose uptake, lactate production, and glycolytic enzyme levels were detected by glycolysis assay and western blotting. Our results showed that Tenascin C expression is upregulated in prostate cancer tissues compared with benign prostatic hyperplasia tissues. High Tenascin C expression in prostate cancer cells was positively associated with lymph node metastasis, advanced clinical stage, the expression of CD105, CD206, and androgen receptor levels. The Kaplan-Meier curves showed a significant association of Tenascin C expression with the patient's overall survival. Tenascin C expression was positively associated with PI3K p85, pAKT-ser308, and NF-κB p65 protein expression in prostate cancer samples. Moreover, siRNA-mediated knockdown of Tenascin C expression inhibited cell glucose uptake, lactate production, and glycolytic-enzyme expression in prostate cancer cells invitro. Together, our findings suggest that Tenascin C is a prognostic marker for patients with prostate cancer and that its effects might be mediated via regulation of the glycolysis process of prostate cancer cells.

Investigation of an FGFR-Signaling-Related Prognostic Model and Immune Landscape in Head and Neck Squamous Cell Carcinoma.

Background: There is accumulating evidence on the clinical importance of the fibroblast growth factor receptor (FGFR) signal, hypoxia, and glycolysis in the immune microenvironment of head and neck squamous cell carcinoma (HNSCC), yet reliable prognostic signatures based on the combination of the fibrosis signal, hypoxia, and glycolysis have not been systematically investigated. Herein, we are committed to establish a fibrosis–hypoxia–glycolysis–related prediction model for the prognosis and related immune infiltration of HNSCC. Methods: Fibrotic signal status was estimated with microarray data of a discovery cohort from the TCGA database using the UMAP algorithm. Hypoxia, glycolysis, and immune-cell infiltration scores were imputed using the ssGSEA algorithm. Cox regression with the LASSO method was applied to define prognostic genes and develop a fibrosis–hypoxia–glycolysis–related gene signature. Immunohistochemistry (IHC) was conducted to identify the expression of specific genes in the prognostic model. Protein expression of several signature genes was evaluated in HPA. An independent cohort from the GEO database was used for external validation. Another scRNA-seq data set was used to clarify the related immune infiltration of HNSCC. Results: Six genes, including AREG, THBS1, SEMA3C, ANO1, IGHG2, and EPHX3, were identified to construct a prognostic model for risk stratification, which was mostly validated in the independent cohort. Multivariate analysis revealed that risk score calculated by our prognostic model was identified as an independent adverse prognostic factor (p < .001). Activated B cells, immature B cells, activated CD4+ T cells, activated CD8+ T cells, effector memory CD8+ T cells, MDSCs, and mast cells were identified as key immune cells between high- and low-risk groups. IHC results showed that the expression of SEMA3C, IGHG2 were slightly higher in HNSCC tissue than normal head and neck squamous cell tissue. THBS1, ANO1, and EPHX3 were verified by IHC in HPA. By using single-cell analysis, FGFR-related genes and highly expressed DEGs in low-survival patients were more active in monocytes than in other immune cells. Conclusion: A fibrosis–hypoxia–glycolysis–related prediction model provides risk estimation for better prognoses to patients diagnosed with HNSCC.

Comprehensive Analysis of the Glycolysis-Related Gene Prognostic Signature and Immune Infiltration in Endometrial Cancer.

Glucose metabolic reprogramming and immune imbalance play important roles in the progression of cancers. The purpose of this study is to develop a glycolysis-related prognostic signature for endometrial cancer (EC) and analyze its relationship with immune function. The mRNA expression profiling of the glycolysis-related genes and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA). We identified a glycolysis-related gene prognostic signature for predicting the prognosis of EC by using The Least Absolute Shrinkage and Selection Operator (LASSO) regression and found the patients in the high-risk group had worse survival prognosis. Multivariate Cox regression analysis showed that the gene signature was an independent prognostic factor for EC. The ROC curve confirmed the accuracy of the prognostic signature (AUC = 0.730). Then, we constructed a nomogram to predict the 1–5 years survival rate of EC patients. The association between the gene signature and immune function was analyzed based on the “ESTIMATE” and “CIBERSORT” algorithm, which showed the immune and ESTIMATE scores of patients in the high-risk group were lower, while the low immune and ESTIMATE scores were associated with a worse prognosis of patients. The imbalance of immune cells was also found in the high-risk group. Further, the protein of CDK1, a gene in the signature, was found to be closely related to prognosis of EC and inhibition of CDK1 could inhibit migration and promote apoptosis of EC cells. This study reveals a link between glycolysis-related gene signature and immunity, and provides personalized therapeutic targets for EC.

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

BackgroundHepatocellular 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.MethodsmRNA 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.ResultsFive 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.ConclusionsWe 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.

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer

BackgroundAerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients.ResultsThree gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ.ConclusionsWe found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Identification of a glycolysis-related gene signature for predicting pancreatic cancer survival.

Pancreatic cancer (PC) is one of the most common malignant tumors of the digestive tract. Its clinical symptoms are obscure and atypical. It is difficult to diagnose and treat. Tumor cells mainly obtain energy through glycolysis to promote their growth. Inhibiting glycolysis can inhibit proliferation and kill tumor cells. Using bioinformatics method, we investigate the relationships between glycolysis-related genes and PC tumor samples' epidemiologic information comprehensively. Different expression levels of 27 genes were identified. Using bioinformatics methods, we plotted two subgroup curves based on glycolysis-related gene expression level. Potential predictive genes were screened and their prognostic values were analyzed. Survival among high-risk group and low risk group had significant difference. Receiver operating characteristic (ROC) curve analysis indicated that area under curve (AUC) of 10 genes was greater than 0.8. These genes could be used for clinical diagnosis and prediction for PC. Two potential predictors [Kinesin Family Member 20A (KIF20A) and MET Proto-Oncogene, Receptor Tyrosine Kinase (MET)] that met the independent predictive value were selected. In univariate analysis, we screened out 3 regulators MET, protein kinase CAMP-activated catalytic subunit alpha (PRKACA) and KIF20A. According to the 3 regulatory factors, the prognostic signals of PC were constructed, by which the samples with good prognosis and poor prognosis can be clearly distinguished independently of potential confounding factors. Our results indicate that for PC, glycolysis -related genes could be promising therapeutic targets or prognostic indicators.

A novel risk score model based on glycolysis-related genes and a prognostic model for predicting overall survival of osteosarcoma patients.

This study aims to construct a novel risk score model based on glycolysis-related genes in osteosarcoma and to build and validate a prognostic model for predicting overall survival of patients with osteosarcoma. The transcriptome data and corresponding clinical data of patients with osteosarcoma were obtained from The Cancer Genome Atlas (TCGA) as the training set, and from Gene Expression Omnibus (GEO) database as the validation set. Univariate Cox regression analysis was used to screen the prognostic glycolysis-related genes. The risk coefficient of each glycolysis-related gene was calculated using LASSO regression analysis. Using the median risk score as the cut-off point, patients were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis was used to determine whether there was a significant difference in the overall survival between the two groups. The nomogram was constructed according to the results of multivariate Cox regression. The C-index was calculated, the calibration chart, clinical decision curve and receiver operating characteristic curve were drawn to evaluate the predictive performance of the nomogram. We performed Gene Ontology and Kyoto encyclopedia of genes and genomics enrichment analysis to explore the potential mechanism of prognostic-related glycolysis genes in osteosarcoma. A total of 88 and 53 cases were obtained from the TCGA and GEO database, respectively. A total of 10 key glycolytic genes related to prognosis were screened out. The Kaplan-Meier survival curve revealed that the overall survival of the high-risk group was significantly shorter than that of the low-risk group. The C indices of the training set and the verification set were 0.882 and 0.828, respectively. Our findings will provide further understanding of clinical prognostic outcomes of osteosarcoma patients.

Enhanced glycolysis in granulosa cells promotes the activation of primordial follicles through mTOR signaling

In mammals, nonrenewable primordial follicles are activated in an orderly manner to maintain the longevity of reproductive life. Mammalian target of rapamycin (mTOR)-KIT ligand (KITL) signaling in pre-granulosa cells and phosphatidylinositol 3-kinase (PI3K)-protein kinase B (Akt)-forkhead Box O3a (FOXO3a) signaling in oocytes are important for primordial follicle activation. The activation process is accompanied by the enhancement of energy metabolism, but the causal relationship is unclear. In the present study, the levels of glycolysis-related proteins GLUT4, HK1, PFKL, and PKM2 were significantly increased in granulosa cells but were decreased in oocytes during the mouse primordial-to-primary follicle transition. Both short-term pyruvate deprivation in vitro and acute fasting in vivo increased the glycolysis-related gene and protein levels, decreased AMPK activity, and increased mTOR activity in mouse ovaries. The downstream pathways Akt and FOXO3a were phosphorylated, resulting in mouse primordial follicle activation. The blockade of glycolysis by 2-deoxyglucose (2-DG), but not the blockade of the communication network between pre-granulosa cells and oocyte by KIT inhibitor ISCK03, decreased short-term pyruvate deprivation-promoted mTOR activity. Glycolysis was also increased in human granulosa cells during the primordial-to-primary follicle transition, and short-term pyruvate deprivation promoted the activation of human primordial follicles by increasing the glycolysis-related protein levels and mTOR activity in ovarian tissues. Taken together, the enhanced glycolysis in granulosa cells promotes the activation of primordial follicles through mTOR signaling. These findings provide new insight into the relationship between glycolytic disorders and POI/PCOS.