Comprehensive analysis of the association between tumor glycolysis and immune/inflammation function in breast cancer

WTAP mediates FOXP3 mRNA stability to promote SMARCE1 expression and augment glycolysis in colon adenocarcinoma

N6-methyladenosine (m6A) is the most abundant mRNA internal modification and has reportedly been linked to aerobic glycolysis, a hallmark event in tumor development. This work focuses on the role of the m6A methyltransferase WT1-associated protein (WTAP) in metabolic reprogramming and development of colon adenocarcinoma (COAD) and the molecules involved. The WTAP expression in COAD tissues and cells was detected. WTAP was knocked down in two COAD cell lines to figure out its role in the glycolytic activity and malignant phenotype of cancer cells. Cancer cells were further injected into nude mice subcutaneously or via tail vein to evaluate tumor growth and metastasis. The downstream molecules involved were explored using bioinformatics tools, and the molecular interactions were confirmed by immunoprecipitation, luciferase assays, and rescue experiments. WTAP was abundantly expressed in COAD samples. Knockdown of WTAP suppressed glucose consumption, lactate production, and glycolysis, which consequently suppressed cancer cell growth and dissemination in vitro and in vivo. WTAP promoted m6A methylation and stabilized forkhead box P3 (FOXP3) mRNA with the participation of the m6A "reader" YTHDF1. FOXP3 could further bind to SMARCE1 promoter for transcriptional activation. Rescue experiments showed that upregulation of FOXP3 or SMARCE1 restored the glycolytic activity in COAD cells and augmented the growth and mobility of cells both in vitro and in vivo. This study demonstrates that WTAP grants glycolytic activity to COAD and promotes tumor malignant development via the m6A modification of FOXP3 mRNA and the upregulation of SMARCE1.

Two novel predictive biomarkers for osteosarcoma and glycolysis pathways: A profiling study on HS2ST1 and SDC3

INTRODUCTION

Prognostic biomarkers for osteosarcoma (OS) are still very few, and this study aims to examine 2 novel prognostic biomarkers for OS through combined bioinformatics and experimental approach.

MATERIALS AND METHODS

Expression profile data of OS and paraneoplastic tissues were downloaded from several online databases, and prognostic genes were screened by differential expression analysis, Univariate Cox analysis, least absolute shrinkage and selection operator regression analysis, and multivariate Cox regression analysis to construct prognostic models. The accuracy of the model was validated using principal component analysis, constructing calibration plots, and column line plots. We also analyzed the relationship between genes and drug sensitivity. Gene expression profiles were analyzed by immunocytotyping. Also, protein expressions of the constructed biomarkers in OS and paraneoplastic tissues were verified by immunohistochemistry.

RESULTS

Heparan sulfate 2-O-sulfotransferase 1 (HS2ST1) and Syndecan 3 (SDC3, met all our requirements after screening. The constructed prognostic model indicated that patients in the high-risk group had a much lower patient survival rate than in the low-risk group. Moreover, these genes were closely related to immune cells (P < .05). Drug sensitivity analysis showed that the 2 genes modeled were strongly correlated with multiple drugs. Immunohistochemical analysis showed significantly higher protein expression of both genes in OS than in paraneoplastic tissues.

CONCLUSIONS

HS2ST1 and SDC3 are significantly dysregulated in OS, and the prognostic models constructed based on these 2 genes have much lower survival rates in the high-risk group than in the low-risk group. HS2ST1 and SDC3 can be used as glycolytic and immune-related prognostic biomarkers in OS.

A lipid metabolism-related risk signature for patients with gliomas constructed with TCGA and CGGA data

Lipid metabolism affects cell proliferation, differentiation, membrane homeostasis and drug resistance. An in-depth exploration of lipid metabolism in gliomas might provide a novel direction for gliomas treatment. A lipid metabolism-related risk signature was constructed in our study to assess the prognosis of patients with gliomas. Lipid metabolism-related genes were extracted. Differentially expressed genes (DEGs) were screened, and a risk signature was built. The ability of the risk signature to predict the outcomes of patients with gliomas was assessed using the log-rank test and Cox regression analysis. The relationships between immunological characteristics, drug sensitivity and the risk score were evaluated, and the risk-related mechanisms were also estimated. Twenty lipid metabolism-related DEGs associated with the patient prognosis were included in the risk signature. The survival rate of high-risk patients was worse than that of low-risk patients. The risk score independently predicted the outcomes of patients. Immunological parameters, drug sensitivity, immunotherapy benefits, and numerous molecular mechanisms were significantly associated with the risk score. A lipid metabolism-related risk signature might effectively assess the prognosis of patients with gliomas. The risk score might guide individualized treatment and further clinical decision-making for patients with gliomas.

Metabolomics in rheumatoid arthritis: Advances and review

Rheumatoid arthritis (RA) is an autoimmune disease accompanied by metabolic alterations. The metabolic profiles of patients with RA can be determined using targeted and non-targeted metabolomics technology. Metabolic changes in glucose, lipid, and amino acid levels are involved in glycolysis, the tricarboxylic acid cycle, the pentose phosphate pathway, the arachidonic acid metabolic pathway, and amino acid metabolism. These alterations in metabolic pathways and metabolites can fulfill bio-energetic requirements, promote cell proliferation, drive inflammatory mediator secretion, mediate leukocyte infiltration, induce joint destruction and muscle atrophy, and regulate cell proliferation, which may reflect the etiologies of RA. Differential metabolites can be used as biomarkers for the diagnosis, prognosis, and risk prediction, improving the specificity and accuracy of diagnostics and prognosis prediction. Additionally, metabolic changes associated with therapeutic responses can improve the understanding of drug mechanism. Metabolic homeostasis and regulation are new therapeutic strategies for RA. In this review, we provide a comprehensive overview of advances in metabolomics for RA.

Glycometabolism-related gene signature of hepatocellular carcinoma predicts prognosis and guides immunotherapy

Hepatocellular carcinoma (HCC) is a severe cancer endangering human health. We constructed a novel glycometabolism-related risk score to predict prognosis and immunotherapy strategies in HCC patients. The HCC data sets were obtained from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, and the glycometabolism-related gene sets were obtained from the Molecular Signature Database. The least absolute contraction and selection operator (LASSO) regression model was used to construct a risk score based on glycometabolism-related genes. A simple visual nomogram model with clinical indicators was constructed and its effectiveness in calibration, accuracy, and clinical value was evaluated. We also explored the correlation between glycometabolism-related risk scores and molecular pathways, immune cells, and functions. Patients in the low-risk group responded better to anti-CTLA-4 immune checkpoint treatment and benefited from immune checkpoint inhibitor (ICI) therapy. The study found that glycometabolism-related risk score can effectively distinguish the prognosis, molecular and immune-related characteristics of HCC patients, and may provide a new strategy for individualized treatment.

The HOXC-AS2/miR-876-5p/HKDC1 axis regulates endometrial cancer progression in a high glucose-related tumor microenvironment

The tumor microenvironment (TME) is related to chronic inflammation and is currently identified as a risk factor for the occurrence and development of endometrial cancer (EC). Pyroptosis is a new proinflammatory form of programmed cell death that plays a critical role in the progression of multiple diseases. However, the important role of pyroptosis in high‐glucose (HG)‐related EC and the underlying molecular mechanisms remain elusive. In the present study, transcriptome high‐throughput sequencing revealed significantly higher hexokinase domain‐containing 1 (HKDC1) expression in EC patients with diabetes than in EC patients with normal glucose. Mechanistically, HKDC1 regulates HG‐induced cell pyroptosis by modulating the production of reactive oxygen species and pyroptosis‐induced cytokine release in EC. In addition, HKDC1 regulates TME formation by enhancing glycolysis, promoting a metabolic advantage in lactate‐rich environments to further accelerate EC progression. Subsequently, miR‐876‐5p was predicted to target the HKDC1 mRNA, and HOXC‐AS2 was identified to potentially inhibit the miR‐876‐5p/HKDC1 axis in regulating cell pyroptosis in HG‐related EC. Collectively, we elucidated the regulatory role of the HOXC‐AS2/miR‐876‐5p/HKDC1 signal transduction axis in EC cell pyroptosis at the molecular level, which may provide an effective therapeutic target for patients with diabetes who are diagnosed with EC.

An HDAC9-associated immune-related signature predicts bladder cancer prognosis

Background

The close relationship between histone deacetylase 9 (HDAC9) and immunity has attracted attention. We constructed an immune signature for HDAC9, a vital epigenetic modification, to predict the survival status and treatment benefits in bladder cancer (BC).

Methods

An exhaustive analysis of HDAC9 and immunology via the tumor and immune system interaction database (TISIDB) was performed, and an immune prognostic risk signature was developed based on genes enriched in the top five immune-related pathways under high HDAC9 status. Comprehensive analysis of survival curves and Cox regression were used to estimate the effectiveness of the risk signature. The relationship between immunological characteristics and the risk score was evaluated, and the mechanisms were also explored.

Results

In the TISIDB, HDAC9 was closely related to various immunological characteristics. The risk signature was obtained based on genes related to prognosis enriched in the top five immune-related pathways under high HDAC9 status. The survival rate of the high-risk BC patients was poor. The risk score was closely related to multiple immunological characteristics, drug sensitivity, immunotherapy benefits and biofunctions.

Conclusion

An immune-related prognostic signature established for HDAC9 expression status could independently predict the prognosis of BC patients. The use of this signature could help clinicians make personalized treatment decisions.

Pyroptosis-related molecular classification and immune microenvironment infiltration in breast cancer: A novel therapeutic target

The underlying role of pyroptosis in breast cancer (BC) remains unknown. Herein, we investigated the correlations of 33 pyroptosis‐related genes (PRGs) with immune checkpoints and immune cell infiltrations in BC patients based on The Cancer Genome Atlas cohort (n = 996) and Gene Expression Omnibus cohort (n = 3,262). Enrichment analysis revealed that these PRGs mainly functioned in pyroptosis, inflammasomes and regulation of autophagy pathway. Four prognostic independent PRGs (CASP9, TIRAP, GSDMC and IL18) were identified. Then, cluster 1/2 was recognized using consensus clustering for these four PRGs. Patients from cluster 1 had a favourable prognosis and diverse immune cell infiltrations. A nomogram was developed based on age, TNM stage, tumour subtype and pyroptosis score. Patients with the high‐risk group exhibited worse 5‐year OS, and the result was consistent in the external cohort. Additionally, high‐risk group patients were associated with downregulated immune checkpoint expression. Further analysis suggested that the high‐risk group patients were associated with a higher IC50 of paclitaxel, doxorubicin, cisplatin, methotrexate and vinorelbine. In summarizing, the pyroptosis score‐based nomogram might serve as an independent prognostic predictor and could guide medication for chemotherapy. Additionally, it may bring novel insight into the regulation of tumour immune microenvironment in BC and help to achieve precision immunotherapy.

Expression and Prognostic Value of Glucose Transporter 3 in Diffuse Large B Cell Lymphoma

Background

Several reports have suggested that glucose transporter 3 (GLUT-3) promotes tumor metastasis. The aim of this study was to examine the relationship between the expression level of GLUT-3 and the prognosis of patients with diffuse large B cell lymphoma (DLBCL).

Methods

The GLUT-3 expression levels in 91 DLBCL patients were evaluated by immunohistochemistry. The relationships between GLUT-3 expression level and clinicopathological characteristics and progression-free survival (PFS) of DLBCL patients were analyzed. The use of validation cohorts confirmed the predictive value of GLUT-3 expression. The correlation between GLUT-3 and immune cell infiltration was investigated using the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts system and the analysis of the infiltrating score was obtained by single sample Gene Set Enrichment Analysis.

Results

Expression of GLUT-3, which is highly expressed in DLBCL patients, was significantly associated with elevated serum LDH level, recurrence and Ki-67 status. Kaplan–Meier analysis showed that high GLUT-3 expression levels in DLBCL were related to poor PFS. Univariate and multivariate analyses results showed that low GLUT-3 expression level was significantly but independently associated with favorable PFS in DLBCL patients. GLUT-3 expression was also correlated with immune cell infiltration and the analysis of the infiltrating score.

Conclusion

Our results indicate that GLUT-3 may act as a potential independent prognostic factor in DLBCL patients. The difference of the immune microenvironment in DLBCL patients may be predicted by the expression level of GLUT-3.

Ketogenic Diets and their Therapeutic Potential on Breast Cancer: A Systemic Review

Breast cancer remains a major cause of morbidity and mortality in women, and there is still a lack of complementary approaches to significantly improve the efficacy of standard therapies. For many kinds of cancers, the usual standard care is the combination of surgery, radiation, and chemotherapy. However, this standard therapy is not effective alone. Therefore, new approaches that increase therapeutic effectiveness are urgently needed. The ketogenic diet is a novel therapeutic approach for certain types of cancers, as indicated by several preclinical and clinical evidences. The ketogenic diet, which consists of a high-fat, low-carbohydrate diet with adequate protein, appears to sensitize most cancers to standard therapy by utilizing the reprogrammed metabolism of cancer cells, making it a promising candidate for adjuvant cancer treatment. The majority of preclinical and clinical studies argue that the use of a ketogenic diet in combination with standard therapies is based on its potential to improve the antitumor effects of conventional chemotherapy, its overall good safety and tolerability, and quality of life improvement. According to new evidence, a ketogenic diet lowers the level of glucose and insulin in the blood, which are necessary for tumor growth. Thus, the ketogenic diet has emerged as a potential treatment option for a variety of cancers, including breast cancer. Besides, implementation of a Ketogenic diet in the clinic could improve progression-free and overall survival for patients with breast cancer. This review summarizes the composition and metabolism of ketogenic diets and their potential mechanisms in breast carcinogenesis in addition to their therapeutic potential on breast cancer.

Identification of novel subtypes based on ssGSEA in immune-related prognostic signature for tongue squamous cell carcinoma

Background

Tongue squamous cell carcinoma (TSCC) is characterized by aggressive invasion and poor prognosis. Currently, immune checkpoint inhibitors may prolong overall survival compared with conventional treatments. However, PD1/PDL1 remain inapplicable in predicting the prognosis of TSCC; thus, it is urgent to explore the genetic characteristics of TSCC.

Materials and methods

We utilized single‐sample gene set enrichment analysis (ssGSEA) to classify TSCC patients from the TCGA database into clusters with different immune cell infiltrations. ESTIMATE (immune‐related scores) and CIBERSORT (immune cell distribution) analyses were used to evaluate the immune landscape among clusters. GO, KEGG, and GSEA analyses were performed to analyze the different underlying molecular mechanisms in the clusters. Based on the immune characteristics, we applied the LASSO Cox regression to select hub genes and construct a prognostic risk model. Finally, we established an interactive network among these hub genes by using Cytoscape, and a pan‐cancer analysis to further verify and decipher the innate function of these genes.

Results

Using ssGSEA, we constructed three functional clusters with different overall survival and immune‐cell infiltration. ESTIMATE and CIBERSORT analyses revealed the different distributions of immune cells (T cells, B cells, and macrophages) with diverse immune‐related scores (ESTIMATE, immune, stromal, and tumor purity scores). Moreover, pathways including those of the interferon‐gamma response, hypoxia, and glycolysis of the different subtypes were investigated to elucidate their involvement in mediating the heterogeneous immune characteristics. Subsequently, after LASSO Cox regression, a signature of 15 immune‐related genes was established that is more prognostically effective than the TNM stage. Furthermore, three hub genes—PGK1, GPI, and RPE—were selected using Cytoscape evaluation and verified by immunohistochemistry. PGK1, the foremost regulator, was a comprehensively profiled pan‐cancer, and a PGK1‐based interactive network was established.

Conclusion

Our results suggest that immune‐related genes and clusters in TSCC have the potential to guide individualized treatments.

Upregulated glycolysis correlates with tumor progression and immune evasion in head and neck squamous cell carcinoma

Altered metabolism is an emerging hallmark of cancer. Cancer cells preferentially utilize glycolysis for energy production, termed "aerobic glycolysis." In this study, we performed a comprehensive analysis of the glycolytic activity in head and neck squamous cell carcinoma (HNSCC) using data obtained from The Cancer Genome Atlas database. We first divided 520 patients with HNSCC into four groups based on the mRNA expression of 16 glycolysis-related genes. The upregulated glycolytic activity positively correlated with human papillomavirus-negative tumor type, advanced T factor, and unfavorable prognosis. The gene set enrichment analysis revealed upregulation of several hallmark pathways, including interferon-alpha response, myc targets, unfolded protein response, transforming growth factor-β signaling, cholesterol homeostasis, and interleukin 6-Janus kinase-signal transducer and activator of transcription 3 signaling, in the glycolysis-upregulated groups. Immune cell enrichment analysis revealed decreased infiltration of T cells, dendritic cells, and B cells in the glycolysis-upregulated groups, suggesting impaired tumor antigen presentation, T cell activation, and antibody production in the TME. Moreover, the expression profile of immune-related genes indicated increased immune evasion in the glycolysis-upregulated tumors. Collectively, these findings suggest that transcriptome analysis of glycolytic activity of tumors has the potential as a biomarker for tumor progression and immunological status in patients with HNSCC.

Metabolomics profiling predicts outcome of tocilizumab in rheumatoid arthritis: an exploratory study

INTRODUCTION

To study metabolic signatures can be used to identify predictive biomarkers for a patient's therapeutic response.

OBJECTIVES

We hypothesized that the characterization of a patients' metabolic profile, utilizing one-dimensional nuclear magnetic resonance (¹H-NMR), may predict a response to tocilizumab in patients with rheumatoid arthritis (RA).

METHODS

40 active RA patients meeting the 2010 ACR/EULAR classification criteria initiating treatment with tocilizumab were recruited. Clinical outcomes were determined at baseline, and after six and twelve months of treatment. EULAR response criteria at 6 and 12 months to categorize patients as responders and non-responders. Blood was collected at baseline and after six months of tocilizumab therapy. ¹H-NMR was used to acquire a spectra of plasma samples. Chenomx NMR suite 8.5 was used for metabolite identification and quantification. SPSS v.27 and MetaboAnalyst 4.0 were used for statistical and pathway analysis.

RESULTS

Isobutyrate, 3-hydroxybutyrate, lysine, phenylalanine, sn-glycero-3-phosphocholine, tryptophan and tyrosine were significantly elevated in responders at the baseline. OPLS-DA at baseline partially discriminated between RA responders and non-responders. A multivariate diagnostic model showed that concentrations of 3-hydroxybutyrate and phenylalanine improved the ability to specifically predict responders classifying 77.1% of the patients correctly. At 6 months, levels of methylamine, sn-glycero-3-phosphocholine and tryptophan tended to still be low in non-responders.

CONCLUSION

The relationship between plasma metabolic profiles and the clinical response to tocilizumab suggests that ¹H-NMR may be a promising tool for RA therapy optimization. More studies are needed to determine if metabolic profiling can predict the response to biological therapies in RA patients.

PGK1 Is a Key Target for Anti-Glycolytic Therapy of Ovarian Cancer: Based on the Comprehensive Analysis of Glycolysis-Related Genes

Reprogramming of energy metabolism is a key hallmark of cancer, which provides a new research perspective for exploring the development of cancer. However, the most critical target of anti-glycolytic therapy for ovarian cancer remains unclear. Therefore, in the present study, Oncomine, GEPIA, and HPA databases, combined with clinical specimens of different histological types of ovarian cancer were used to comprehensively evaluate the expression levels of glycolysis-related metabolite transporters and enzymes in ovarian cancer. We selected phosphoglycerate kinase 1 (PGK1), which showed the greatest prognostic value in the Kaplan-Meier Plotter database, for subsequent validation. Immunochemistry assays confirmed that PGK1 was highly expressed in ovarian cancer. The PGK1 expression level was an independent risk factor for the survival and prognosis of patients with ovarian cancer. Functional analysis showed that the PGK1 expression level was positively correlated with the infiltration of neutrophils. Cell experiments confirmed that inhibiting PGK1 expression in ovarian cancer cells could reduce the epithelial-mesenchymal transition (EMT) process, resulting in loss of cell migration and invasion ability. The small molecule NG52 dose-dependently inhibited the proliferation of ovarian cancer cells. In addition, NG52 reduced the EMT process and reversed the Warburg effect by inhibiting PGK1 activity. Therefore, PGK1 is an attractive molecular target for anti-glycolytic therapy of ovarian cancer.

Overexpression of GLUT3 promotes metastasis of triple-negative breast cancer by modulating the inflammatory tumor microenvironment

Triple-negative breast cancer (TNBC) exhibits a higher level of glycolytic capacity and are commonly associated with an inflammatory microenvironment, but the regulatory mechanism and metabolic crosstalk between the tumor and tumor microenvironment (TME) are largely unresolved. Here, we show that glucose transporter 3 (GLUT3) is particularly elevated in TNBC and associated with metastatic progression and poor prognosis in breast cancer patients. Expression of GLUT3 is crucial for promoting the epithelial-to-mesenchymal transition and enhancing invasiveness and distant metastasis of TNBC cells. Notably, GLUT3 is correlated with inflammatory gene expressions and is associated with M1 tumor-associated macrophages (TAMs), at least in part by C-X-C Motif Chemokine Ligand 8 (CXCL8). We found that expression of GLUT3 regulates CXCL8 production in TNBC cells. Secretion of CXCL8 participates in GLUT3-overexpressing TNBC cells-elicited activation of inflammatory TAMs, which further enhances GLUT3 expression and mobility of TNBC cells. Our findings demonstrate that aerobic glycolysis in TNBC not only promotes aggressiveness of tumor cells but also initiates a positive regulatory loop for enhancing tumor progression by modulating the inflammatory TME.

Development and validation of glycolysis-related prognostic score for prediction of prognosis and chemosensitivity of pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with aggressive biological behaviour. Its rapid proliferation and tumour growth require reprogramming of glucose metabolism or the Warburg effect. However, the association between glycolysis-related genes with clinical features and prognosis of PDAC is still unknown. Here, we used the meta-analysis to correlate the hazard ratios (HR) of 106 glycolysis genes from MSigDB by the cox proportional hazards regression analysis in 6 clinical data sets of PDAC patients to form a training cohort, and a single group of PDAC patients from the TCGA, ICGC, Arrayexpress and GEO databases to form the validation cohort. Then, a glycolysis-related prognosis (GRP) score based on 29 glycolysis prognostic genes was established in 757 PDAC patients from the training composite cohort and validated in 267 ICGC-CA validation cohort (all P < .05). In addition, including PADC, the prognostic value was also confirmed in other 7 out of 30 pan-cancer cohorts. The GRP score was significantly related to specific metabolism pathways, immune genes and immune cells in the patients with PADC (all P < .05). Finally, by combining with immune cells, the GRP score also well-predicted the chemosensitivity of patients with PADC in the TCGA cohort (AUC = 0.709). In conclusion, this study developed a GRP score for patients with PDAC in predicting prognosis and chemosensitivity for PDAC.

Construction of a prognostic value model in papillary renal cell carcinoma by immune-related genes

Papillary renal cell carcinoma (PRCC) is the second most common type of renal carcinoma following clear cell renal cell carcinoma, and the role of immune-related genes (IRGs) in tumorigenesis and metastasis is evident; its prognostic value in PRCC remains unclear. In this study, we downloaded the gene expression profiles and clinical data of patients with PRCC from The Cancer Genome Atlas (TCGA) database and obtained IRGs from the ImmPort database. A total of 371 differentially expressed IRGs (DEIRGs) were discovered between PRCC and normal kidney tissues. Prognostic DEIRGs (PDEIRGs) were identified by univariate Cox regression analysis. Then, we screened the four most representative PDEIRGs (IL13RA2, CCL19, BIRC5, and INHBE) and used them to construct a risk model to predict the prognosis of patients with PRCC. This model precisely stratified survival outcome and accurately identified mutation burden in PRCC. Thus, our results suggest that these four PDEIRGs are available prognostic predictors for PRCC. They could be used to assess the prognosis and to guide individualized treatments for patients with PRCC.

Correlations between glycolysis with clinical traits and immune function in bladder urothelial carcinoma

BACKGROUND

Glycolysis was a representative hallmark in the tumor microenvironment (TME), and we aimed to explore the correlations between glycolysis with immune activity and clinical traits in bladder urothelial carcinoma (BLCA).

METHODS

Our study obtained glycolysis scores for each BLCA samples from TCGA by a single-sample gene set enrichment analysis (ssGSEA) algorithm, based on a glycolytic gene set. The relationship between glycolysis with prognosis, clinical characteristics, and immune function were investigated subsequently.

RESULTS

We found that enhanced glycolysis was associated with poor prognosis and metastasis in BLCA. Moreover, glycolysis had a close correlation with immune function, and enhanced glycolysis increased immune activities. In other words, glycolysis had a positive correlation with immune activities. Immune checkpoints such as IDO1, CD274, were up-regulated in high-glycolysis group as well.

CONCLUSION

We speculated that in BLCA, elevated glycolysis enhanced immune function, which caused tumor cells to overexpress immune checkpoints to evade immune surveillance. Inhibition of glycolysis might be a promising assistant for immunotherapy in bladder cancer.

IL-17 in the Pathogenesis of Disease: Good Intentions Gone Awry

The IL-17 family is an evolutionarily old cytokine family consisting of six members (IL-17A through IL-17F). IL-17 family cytokines signal through heterodimeric receptors that include the shared IL-17RA subunit, which is widely expressed throughout the body on both hematopoietic and nonhematopoietic cells. The founding family member, IL-17A, is usually referred to as IL-17 and has received the most attention for proinflammatory roles in autoimmune diseases like psoriasis. However, IL-17 is associated with a wide array of diseases with perhaps surprisingly variable pathologies. This review focuses on recent advances in the roles of IL-17 during health and in disease pathogenesis. To decipher the functions of IL-17 in diverse disease processes it is useful to first consider the physiological functions that IL-17 contributes to health. We then discuss how these beneficial functions can be diverted toward pathogenic amplification of deleterious pathways driving chronic disease.

Identification of an immune-related long noncoding RNA signature that predicts prognosis in breast cancer patients

Aim: The purpose of this study was to identify an immune-related long noncoding RNA (lncRNA) signature that predicts the prognosis of breast cancer. Materials & methods: The expression profiles of breast cancer were downloaded from The Cancer Genome Atlas. Cox regression analysis was used to identify an immune-related lncRNA signature. Results: The five immune-related lncRNAs could be used to construct a breast cancer survival prognosis model. The receiver operating characteristic curve evaluation found that the accuracy of the model for predicting the 1-, 3- and 5-year prognosis of breast cancer was 0.688, 0.708 and 0.686. Conclusion: This signature may have an important clinical significance for improving predictive results and guiding the treatment of breast cancer patients.

Identification and validation of a six-gene signature associated with glycolysis to predict the prognosis of patients with cervical cancer

BACKGROUND

Cervical cancer (CC) is one of the most common gynaecological cancers. The gene signature is believed to be reliable for predicting cancer patient survival. However, there is no relevant study on the relationship between the glycolysis-related gene (GRG) signature and overall survival (OS) of patients with CC.

METHODS

We extracted the mRNA expression profiles of 306 tumour and 13 normal tissues from the University of California Santa Cruz (UCSC) Database. Then, we screened out differentially expressed glycolysis-related genes (DEGRGs) among these mRNAs. All patients were randomly divided into training cohort and validation cohort according to the ratio of 7: 3. Next, univariate and multivariate Cox regression analyses were carried out to select the GRG with predictive ability for the prognosis of the training cohort. Additionally, risk score model was constructed and validated it in the validation cohort.

RESULTS

Six mRNAs were obtained that were associated with patient survival. The filtered mRNAs were classified into the protective type (GOT1) and the risk type (HSPA5, ANGPTL4, PFKM, IER3 and PFKFB4). Additionally, by constructing the prognostic risk score model, we found that the OS of the high-risk group was notably poorer, which showed good predictive ability both in training cohort and validation cohort. And the six-gene signature is a prognostic indicator independent of clinicopathological features. Through the verification of PCR, the results showed that compared with the normal cervial tissuses, the expression level of six mRNAs were significantly higher in the CC tissue, which was consistent with our findings.

CONCLUSIONS

We constructed a glycolysis-related six-gene signature to predict the prognosis of patients with CC using bioinformatics methods. We provide a thorough comprehension of the effect of glycolysis in patients with CC and provide new targets and ideas for individualized treatment.

Prognostic model of invasive ductal carcinoma of the breast based on differentially expressed glycolysis-related genes

Background

Invasive ductal carcinoma (IDC) is a common pathological type of breast cancer that is characterized by high malignancy and rapid progression. Upregulation of glycolysis is a hallmark of tumor growth, and correlates with the progression of breast cancer. We aimed to establish a model to predict the prognosis of patients with breast IDC based on differentially expressed glycolysis-related genes (DEGRGs).

Methods

Transcriptome data and clinical data of patients with breast IDC were from The Cancer Genome Atlas (TCGA). Glycolysis-related gene sets and pathways were from the Molecular Signatures Database (MSigDB). DEGRGs were identified by comparison of tumor tissues and adjacent normal tissues. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to screen for DEGRGs with prognostic value. A risk-scoring model based on DEGRGs related to prognosis was constructed. Receiver operating characteristic (ROC) analysis and calculation of the area under the curve (AUC) were used to evaluate the performance of the model. The model was verified in different clinical subgroups using an external dataset (GSE131769). A nomogram that included clinical indicators and risk scores was established. Gene function enrichment analysis was performed, and a protein-protein interaction network was developed.

Results

We analyzed data from 772 tumors and 88 adjacent normal tissues from the TCGA database and identified 286 glycolysis-related genes from the MSigDB. There were 185 DEGRGs. Univariate Cox regression and LASSO regression indicated that 13 of these genes were related to prognosis. A risk-scoring model based on these 13 DEGRGs allowed classification of patients as high-risk or low-risk according to median score. The duration of overall survival (OS) was longer in the low-risk group (P < 0.001), and the AUC was 0.755 for 3-year OS and 0.726 for 5-year OS. The results were similar when using the GEO data set for external validation (AUC for 3-year OS: 0.731, AUC for 5-year OS: 0.728). Subgroup analysis showed there were significant differences in OS among high-risk and low-risk patients in different subgroups (T1-2, T3-4, N0, N1-3, M0, TNBC, non-TNBC; all P < 0.01). The C-index was 0.824, and the AUC was 0.842 for 3-year OS and 0.808 for 5-year OS from the nomogram. Functional enrichment analysis demonstrated the DEGRGs were mainly involved in regulating biological functions.

Conclusions

Our prognostic model, based on 13 DEGRGs, had excellent performance in predicting the survival of patients with IDC of the breast. These DEGRGs appear to have important biological functions in the progression of this cancer.

Gremlin-1 activates Akt/STAT3 signaling, which increases the glycolysis rate in breast cancer cells

Gremlin-1 (GREM1), one of the antagonists of bone morphogenetic proteins (BMPs), has recently been reported to be overexpressed in a variety of cancers including breast cancer. GREM1 is involved in tumor promotion, but little is known about its role in the glycolysis of cancer cells. In this study, we investigated the role of GREM1 in glycolysis of breast cancer cells and its underlying molecular mechanisms. We first observed that glucose uptake and lactate production were increased in GREM1-overexpressing breast cancer cells. GREM1 increased the expression of hexokinase-2 (HK2), which catalyzes the phosphorylation of glucose, the first step in glycolysis. In addition, GREM1 activated STAT3 transcription factor through the ROS-Akt signaling pathway. The ROS-Akt-STAT3 axis activated by GREM1 was involved in promoting glucose uptake by increasing the expression of HK2 in breast cancer cells. Therefore, our study suggested a new mechanism by which GREM1 is involved in breast cancer promotion by increasing glycolysis in breast cancer cells.

Cryptanshinone Inhibits the Glycolysis and Inhibits Cell Migration Through PKM2/β-Catenin Axis in Breast Cancer

Background

Breast cancer is one of the most prevalent gynecologic malignancies worldwide. Despite the high sensitivity in response to chemotherapy, drug resistance occurred frequently in clinical treatment. Cryptotanshinone (CTS) is a herbal medicine and has been identified as an anti-inflammatory and anti-oxidative drug.

Methods

In vitro assays, including the cell proliferation assay, colony formation assay, Western blot analysis, transwell migration/invasion assays, and cell scratch assay were used to explore the biological activities and working mechanism of CTS. Breast cancer cells were also transfected with PKM2 expressing vectors to define the molecular mechanisms involved in CTS-mediated anti-tumor activity.

Results

We found that CTS shows anti-proliferative effects and decreases the clonogenic ability of breast cancer cells. We also found that CTS inhibited the migration and invasion activity of MCF-7 and MDA-MB-231 cells by different analyzed methods. CTS also downregulated the levels of glycolysis-related proteins, such as PKM2, LDHA, and HK2. In addition, overexpression of PKM2 recovered CTS-mediated suppression of cell proliferation, colony formation, and cell mobility of breast cancer cells. We also found PKM2 was significantly overexpressed in tumor tissues and invasive ductal breast carcinoma compared to normal tissues and patients with high PKM2 expression had worse overall survival and metastasis-free survival outcomes.

Conclusion

CTS inhibited the proliferation, migration, and invasion of breast cancer cells. The involved mechanism may refer to the downregulation of the PKM2/β-catenin axis.