Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

Analysis of the implication of steroid 5 alpha-reductase 3 on prognosis and immune microenvironment in Liver Hepatocellular Carcinoma

INTRODUCTION

This study combined the bioinformatics and in vitro experiment-related technologies to analyze the impact of steroid 5 alpha-reductase 3 (SRD5A3) on the prognosis and immune microenvironment of Liver Hepatocellular Carcinoma (LIHC).

METHOD

Gene expression and clinical data were obtained from public databases. The prognosis was evaluated using survival, multifactor Cox, enrichment, and mutation analyses. This was then verified through in vitro experiments.

RESULTS

The expression level of SRD5A3 in LIHC tissues was significantly higher than that in the adjacent tissues. Kaplan-Meier survival analysis showed that high SRD5A3 expression was associated with poor overall survival (OS) and short progression-free survival in patients with LIHC. Multivariate Cox regression analysis revealed that positive SRD5A3 expression was an independent risk factor for OS in patients with LIHC. Expression of SRD5A3 was negatively correlated with immune cell infiltration of CD4+ T, CD8+ T, and B cells. GO and KEGG enrichment analyses showed that SRD5A3 was significantly enriched in signaling- and tumor metastasis-related pathways. Nomogram and calibration curve showed that the predicted performance of the model was consistent with the actual results. In vitro results confirmed that SRD5A3 knockdown inhibited the migration, invasion, and proliferation of LIHC cells.

CONCLUSIONS

SRD5A3 is actively expressed in LIHC, and positive expression of SRD5A3 is an independent risk factor for different prognoses in patients with LIHC. SRD5A3 can promote the proliferation, migration, and invasion of liver cancer cells and is related to short immune infiltration in patients with LIHC.

Baseline SUVmax is correlated with tumor hypoxia and patient outcomes in nasopharyngeal carcinoma

To evaluate the prognostic significance of the maximum standardized uptake value (SUVmax) in nasopharyngeal carcinoma (NPC), establish a gene signature that correlates with SUVmax, and explore the underlying biological behaviors associated with these correlations for the prediction of clinical outcomes. A cohort of 726 patients with NPC was examined to identify correlations between SUVmax and various clinical variables. RNA sequencing was performed to identify genes related to SUVmax, and these genes were used to develop an SUV signature. Additionally, transcriptome enrichment analysis was conducted to investigate the potential biological behaviors underlying the observed correlations. Higher SUVmax was associated with an increased tumor burden and worse prognosis. The SUV signature, which consisted of 10 genes, was positively correlated with SUVmax, and it predicted worse survival outcomes. This signature was highly expressed in malignant epithelial cells and associated with hypoxia and resistance to radiotherapy. Additionally, the signature was negatively correlated with immune function. SUVmax is a valuable prognostic indicator in NPC, with higher values predicting worse outcomes. The SUV signature offers further prognostic insights, linking glucose metabolism to tumor aggressiveness, treatment resistance, and immune function, and it could represent a potential biomarker for NPC.

Glycolysis-related genes predict prognosis and indicate immune microenvironment features in gastric cancer

BACKGROUND

Gastric cancer (GC) is a major contributor to cancer-related mortality. Glycolysis plays a pivotal role in tumor microenvironment (TME) reprogramming. In this research, the functions of glycolysis-associated genes (GRGs) were evaluated to predict the outcome and reveal the characteristics of the immune microenvironment in individuals with stomach cancer.

METHODS

The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort provided gene expression and clinical data for gastric cancer (GC) patients, which were further authenticated using datasets sourced from the Gene Expression Omnibus (GEO). By referencing the Molecular Signatures Database (MSigDB), a total of 326 GRGs were pinpointed. The various subtypes of GC were outlined through consensus clustering, derived from the expression patterns of these GRGs. Utilizing multivariate Cox regression analysis, a multigene risk score model was formulated. Both the CIBERSORT and ESTIMATE algorithms played a pivotal role in assessing the immune microenvironment. To delve into the biological functions of the key genes, wound healing, transwell invasion, and MTT assays were conducted.

RESULTS

Based on the expression patterns of GRGs, patients were categorized into two distinct groups: the metabolic subtype, designated as cluster A, and the immune subtype, labeled as cluster B. Patients belonging to cluster B exhibited a poorer prognosis. A prognostic risk score model, formulated upon the expression levels of six key GRGs - ME1, PLOD2, NUP50, CXCR4, SLC35A3, and SRD35A3 - emerged as a viable tool for predicting patient outcomes. The downregulation of CXCR4 notably diminished the glycolytic capacity of gastric cancer (GC) cells, alongside their migratory, invasive, and proliferative capabilities. Intriguingly, despite the adverse prognostic implications associated with both the immune subtype (cluster B) and the high-risk cohort, these groups exhibited a favorable immune microenvironment coupled with elevated expression of immune checkpoint genes. Our investigations revealed a positive correlation between high CXCR4 expression and low ME1 expression with the infiltration of CD8+ T cells, as well as an enhanced responsiveness to treatment with an anti-PD-1 immune checkpoint inhibitor.

CONCLUSIONS

In this study, we discovered that the expression profiles of GRGs hold the potential to forecast the prognosis of gastric cancer (GC) patients, thereby possibly aiding in clinical treatment decision-making.

A glycolysis-related signature to improve the current treatment and prognostic evaluation for breast cancer

Background

As a heterogeneous malignancy, breast cancer (BRCA) shows high incidence and mortality. Discovering novel molecular markers and developing reliable prognostic models may improve the survival of BCRA.

Methods

The RNA-seq data of BRCA patients were collected from the training set The Cancer Genome Atlas (TCGA)-BRCA and validation set GSE20685 in the Gene Expression Omnibus (GEO) databases. The "GSVA" R package was used to calculate the glycolysis score for each patient, based on which all the patients were divided into different glycolysis groups. The "limma" package was employed to perform differentially expression genes (DEGs) analysis. Key signature genes were selected by performing un/multivariate and least absolute shrinkage and selection operator (LASSO) C regression and used to develop a RiskScore model. The ESTIMATE and MCP-Counter algorithms were used for quantifying immune infiltration level. The functions of the genes were validated using Western blot, colony formation, transwell and wound-healing assay.

Results

The glycolysis score and prognostic analysis showed that high glycolysis score was related to tumorigenesis pathway and a poor prognosis in BRCA as overactive glycolysis inhibited the normal functions of immune cells. Subsequently, we screened five key prognostic genes using the LASSO Cox regression analysis and used them to establish a RiskScore with a high classification efficiency. Based on the results of the RiskScore, it was found that patients in the high-risk group had significantly unfavorable immune infiltration and prognostic outcomes. A nomogram integrating the RiskScore could well predict the prognosis for BRCA patients. Knockdown of PSCA suppressed cell proliferation, invasion and migration of BRCA cells.

Conclusion

This study developed a glycolysis-related signature with five genes to distinguish between high-risk and low-risk BRCA patients. A nomogram developed on the basis of the RiskScore was reliable to predict BRCA survival. Our model provided clinical guidance for the treatment of BRCA patients.

A stemness-based signature with inspiring indications in discriminating the prognosis, immune response, and somatic mutation of endometrial cancer patients revealed by machine learning

Endometrial cancer (EC) is a fatal gynecologic tumor. Bioinformatic tools are increasingly developed to screen out molecular targets related to EC. Our study aimed to identify stemness-related prognostic biomarkers for new therapeutic strategies in EC. In this study, we explored the prognostic value of cancer stem cells (CSCs), characterized by self-renewal and unlimited proliferation, and its correlation with immune infiltrates in EC. Transcriptome and somatic mutation profiles of EC were downloaded from TCGA database. Based on their stemness signature and DEGs, EC patients were divided into two subtypes via consensus clustering, and patients in Stemness Subtype I presented significantly better OS and DFS than Stemness Subtype II. Subtype I also displayed better clinicopathological features, and genomic variations demonstrated different somatic mutation from subtype II. Additionally, two stemness subtypes had distinct tumor immune microenvironment patterns. In the end, three machine learning algorithms were applied to construct a 7-gene stemness subtype risk model, which were further validated in an external independent EC cohort in our hospital. This novel stemness-based classification could provide a promising prognostic predictor for EC and may guide physicians in selecting potential responders for preferential use of immunotherapy. This novel stemness-dependent classification method has high value in predicting the prognosis, and also provides a reference for clinicians in selecting sensitive immunotherapy methods for EC patients.

Dichloroacetate for Cancer Treatment: Some Facts and Many Doubts

Rarely has a chemical elicited as much controversy as dichloroacetate (DCA). DCA was initially considered a dangerous toxic industrial waste product, then a potential treatment for lactic acidosis. However, the main controversies started in 2008 when DCA was found to have anti-cancer effects on experimental animals. These publications showed contradictory results in vivo and in vitro such that a thorough consideration of this compound's in cancer is merited. Despite 50 years of experimentation, DCA's future in therapeutics is uncertain. Without adequate clinical trials and health authorities' approval, DCA has been introduced in off-label cancer treatments in alternative medicine clinics in Canada, Germany, and other European countries. The lack of well-planned clinical trials and its use by people without medical training has discouraged consideration by the scientific community. There are few thorough clinical studies of DCA, and many publications are individual case reports. Case reports of DCA's benefits against cancer have been increasing recently. Furthermore, it has been shown that DCA synergizes with conventional treatments and other repurposable drugs. Beyond the classic DCA target, pyruvate dehydrogenase kinase, new target molecules have also been recently discovered. These findings have renewed interest in DCA. This paper explores whether existing evidence justifies further research on DCA for cancer treatment and it explores the role DCA may play in it.

Metabolic adaptation towards glycolysis supports resistance to neoadjuvant chemotherapy in early triple negative breast cancers

BACKGROUND

Neoadjuvant chemotherapy (NAC) is the standard of care for patients with early-stage triple negative breast cancers (TNBC). However, more than half of TNBC patients do not achieve a pathological complete response (pCR) after NAC, and residual cancer burden (RCB) is associated with dismal long-term prognosis. Understanding the mechanisms underlying differential treatment outcomes is therefore critical to limit RCB and improve NAC efficiency.

METHODS

Human TNBC cell lines and patient-derived organoids were used in combination with real-time metabolic assays to evaluate the effect of NAC (paclitaxel and epirubicin) on tumor cell metabolism, in particular glycolysis. Diagnostic biopsies (pre-NAC) from patients with early TNBC were analyzed by bulk RNA-sequencing to evaluate the predictive value of a glycolysis-related gene signature.

RESULTS

Paclitaxel induced a consistent metabolic switch to glycolysis, correlated with a reduced mitochondrial oxidative metabolism, in TNBC cells. In pre-NAC diagnostic biopsies from TNBC patients, glycolysis was found to be upregulated in non-responders. Furthermore, glycolysis inhibition greatly improved response to NAC in TNBC organoid models.

CONCLUSIONS

Our study pinpoints a metabolic adaptation to glycolysis as a mechanism driving resistance to NAC in TNBC. Our data pave the way for the use of glycolysis-related genes as predictive biomarkers for NAC response, as well as the development of inhibitors to overcome this glycolysis-driven resistance to NAC in human TNBC patients.

A novel prognostic model of breast cancer based on cuproptosis-related lncRNAs

OBJECTIVE

Breast cancer (BC) is a deadly form of malignancy responsible for the death of a large number of women every year. Cuproptosis is a newly discovered form of cell death that may have implications for the prognosis of BC. Long non-coding RNAs (lncRNAs) have been shown to be involved in the progression and development of BC. Here within, a novel model capable of predicting the prognosis of patients with BC was established based on cuproptosis-related lncRNAs.

METHODS

Data of breast cancer patients was downloaded, including clinical information from The Cancer Genome Atlas (TCGA) database and lncRNAs related to cuproptosis were isolated. In total, nine lncRNAs related to copper death were obtained by Cox regression model based on Least Absolute Shrinkage and Selector Operation (LASSO) algorithm for model construction. The model was verified by overall survival (OS), progression-free survival (PFS) and receiver operating characteristic (ROC) curve. The differences in immune function, tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) between patients with different risk scores were analyzed.

RESULTS

Based on cuproptosis-related lncRNAs, a prognostic model for predicting BC was constructed. Each patient was assigned a risk score based on our model formula. We found that patients with higher risk scores had significantly lower OS and PFS, increased TMB, and higher sensitivity to immunotherapy.

CONCLUSIONS

The model established in this study based on cuproptosis-related lncRNAs may be capable of improving the OS of patients with BC.

Expression of Claudin-9 (CLDN9) in Breast Cancer, the Clinical Significance in Connection with Its Subcoat Anchorage Proteins ZO-1 and ZO-3 and Impact on Drug Resistance

(1) Introduction: Claudin-9 (CLDN9) is a member of the claudin protein family, a critical transmembrane protein family for tight junctions that are implemented in the progression of numerous cancer types. The present study investigated the role that CLDN9, along with the subcoat proteins, Zonula Occludens (ZOs), plays in clinical breast cancer and subsequent impact on drug response of patients. (2) Methods: CLDN9 protein and CLDN9 transcript were determined and correlated with clinical and pathological indicators, together with the status of hormonal receptors. The levels of CLDN9 transcript were also assessed against the therapeutic responses of the patients to chemotherapies by using a dataset from the TCGA database. Breast cancer cell models, representing different molecular subtypes of breast cancer, with differential expression of CLDN9 were created and used to assess the biological impact and response to chemotherapeutic drugs. (3) Results: Breast cancer tissues expressed significantly higher levels of the CLDN9, with the high levels being associated with shorter survival. CLDN9 was significantly correlated with its anchorage proteins ZO-1 and ZO-3. Integrated expression of CLDN9, ZO-1 and ZO-3 formed a signature that was significantly linked to overall survival (OS) (p = 0.013) and relapse-free survival (RFS) (p = 0.024) in an independent matter. CLDN9 transcript was significantly higher in patients who were resistant to chemotherapies (p < 0.000001). CLDN9 connection to chemoresistance was particularly prominent in patients of ER-positive (ER(+)), Her-2-negative((Her-2(-)), ER(+)/Her-2(-) and triple-negative breast cancers (TNBCs), but not in patients with HER-2-positive tumors. In Her-2-negative MCF7 and MDA-MB-231 cancer cells, loss of CLDN9 significantly increased sensitivity to several chemotherapeutic drugs including paclitaxel, gemcitabine and methotrexate, which was not seen in Her-2(+) SKBR3 cells. However, suppressing Her-2 using neratinib, a permanent Her-2 inhibitor, sensitized cellular response to these chemodrugs in cells with CLDN9 knockdown. (4) Conclusions: CLDN9 is an important prognostic indicator for patients with breast cancer and also a pivotal factor in assessing patient responses to chemotherapies. Her-2 is a negating factor for the treatment response prediction value by CLDN9 and negating Her-2 and CLDN9 may enhance breast cancer cellular response to chemotherapeutic drugs.

Comprehensive analysis for the immune related biomarkers of platinum-based chemotherapy in ovarian cancer

BACKGROUND

Ovarian cancer (OC) is one of the most lethal gynecological malignancies. This study aimed to identify biomarkers that were sensitive to platinum-based chemotherapeutic agents and can be used in immunotherapy and explore the importance of their mechanisms of action.

METHODS

RNA-seq profiles and clinicopathological data for OC samples were obtained from The Cancer Genome Atlas (TCGA) and cBioPortal platform, respectively. Platinum-sensitive and platinum-resistant OC samples in the TCGA cohort were selected based on the clinical information. RNA-seq data for 70 OC samples withSingle-sample gene set enrichment analysis (ssGSEA) and unsupervised clustering were used to classify OC patients from the TCGA cohort into clusters with different proportions of infiltrating immune cells. ESTIMATE analysis was used to assess the immune landscape among clusters. Differential expression, univariate Cox regression, and LASSO regression analyses were performed to construct prognostic model. Spearman correlation analysis was conducted to investigate the correlations among immune checkpoint inhibitors (ICIs) and risk score, half-maximal drug inhibitory concentration (IC50) and risk score.

RESULTS

Using ssGSEA and unsupervised clustering, OC samples were divided into two clusters with different immune cell infiltration. Then, 1715 differentially expressed immune-related genes (DEIRGs) were identified between two clusters, 984 differentially expressed platinum-sensitive related genes (DEPSRGs) between 149 platinum-sensitive and 63 platinum-resistant OC samples were identified, and 5384 differentially expressed genes (DEGs) between 380 OC and 194 normal samples were detected from the TCGA cohort. Six biomarkers (GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB) were detected to establish a prognostic model. The OC patients in the TCGA cohort were classified into high- and low-risk groups. The receive operating characteristic (ROC) curve was plotted and demonstrated that the prognostic model performed well with the area under ROC curve (AUC) greater than 0.6. The expressions of 5 ICIs, including CD200, TNFRSF18, CD160, CD200R1, and CD274 (PD-L1), were significantly different between two risk groups, and the risk score was significant negative associated with CTLA4, TNFRSF4, TNFRSF18, and CD274. Moreover, there were significant differences in IC50 of 10 chemo drugs between two risk groups, patients in the high-risk group could be more resistant to po0tinib, dasatinib, and neratinib.

CONCLUSION

In summary, this study constructed a novel prognostic model based on six prognostic biomarkers, including GMPPB, SRPK1, STC1, PRSS16, HPDL, and SPTSSB, which can be utilized for predicting the prognosis of OC patients. These biomarkers were the potential therapeutic targets.

A novel glycolysis-related gene signature for predicting the prognosis of multiple myeloma

Background: Metabolic reprogramming is an important hallmark of cancer. Glycolysis provides the conditions on which multiple myeloma (MM) thrives. Due to MM's great heterogeneity and incurability, risk assessment and treatment choices are still difficult. Method: We constructed a glycolysis-related prognostic model by Least absolute shrinkage and selection operator (LASSO) Cox regression analysis. It was validated in two independent external cohorts, cell lines, and our clinical specimens. The model was also explored for its biological properties, immune microenvironment, and therapeutic response including immunotherapy. Finally, multiple metrics were combined to construct a nomogram to assist in personalized prediction of survival outcomes. Results: A wide range of variants and heterogeneous expression profiles of glycolysis-related genes were observed in MM. The prognostic model behaved well in differentiating between populations with various prognoses and proved to be an independent prognostic factor. This prognostic signature closely coordinated with multiple malignant features such as high-risk clinical features, immune dysfunction, stem cell-like features, cancer-related pathways, which was associated with the survival outcomes of MM. In terms of treatment, the high-risk group showed resistance to conventional drugs such as bortezomib, doxorubicin and immunotherapy. The joint scores generated by the nomogram showed higher clinical benefit than other clinical indicators. The in vitro experiments with cell lines and clinical subjects further provided convincing evidence for our study. Conclusion: We developed and validated the utility of the MM glycolysis-related prognostic model, which provides a new direction for prognosis assessment, treatment options for MM patients.

Tumor Microenvironment CD8 T and Treg Cells-related Genes Signature Distinguishes Distinct Prognosis and Targeted Therapies Response in Endometrial Cancer

Although most endometrial cancer (EC) patients have a favorable prognosis, the overall survival (OS) of metastatic and recurrent EC could hardly be improved by the current chemoradiotherapy. We aimed to reveal the tumor microenvironment immune infiltration characteristics to elucidate the underlying mechanism of EC progression and guide clinical decisions. In the Cancer Genome Atlas (TCGA) cohort, Kaplan-Meier survival curves confirmed Tregs and CD8 T cells were prognosis-protective factors in OS of EC ( P <0.05). Weighted gene coexpression network analysis identified 2 gene modules closely correlated with Tregs and CD8 T-cell infiltration. We randomly split the TCGA EC cohort into the training and testing cohorts at a ratio of 7:3. An immune-related prognosis risk index (IRPRI), including NR3C1, E2F1, OTOG, TTK, PPP1R16B, and FOXP3, was established by univariate, Least Absolute Shrinkage and Selection Operator, and multivariate Cox regression with area under the curve >0.67. Distinct clinical, immune, and mutation characteristics existed between IRPRI groups by multiomics analysis. Cell proliferation and DNA damage repair-related pathways were activated, and immune-related pathways were inactivated in the IRPRI-high group. Furthermore, patients in the IRPRI-high group had lower tumor mutation burden, programmed death-ligand 1 expression, and Tumor Immune Dysfunction and Exclusion scores, indicating a poor response to immune checkpoint inhibitors therapy ( P <0.05), which was also validated in the TCGA testing cohort and independent cohorts, GSE78200, GSE115821, and GSE168204. Also, the higher mutation frequencies of BRCA1, BRCA2, and genes enrolled in homologous recombination repair in the IRPRI-low group predicted a good response to PARP inhibitors. Finally, a nomogram integrating the IRPRI group and prognosis significant clinicopathological factors for EC OS prediction was developed and validated with good discrimination and calibration.

Androgens in endometrial carcinoma: the killer or helper?

PURPOSE

The aim of this review is to discuss the role of androgens in the progression of endometrial carcinoma (EC) with particular focus on the different kinds of androgenic hormones, androgen receptor (AR) and intracrine androgen metabolism.

METHODS

A comprehensive literature search within PubMed was performed. Selected publications related to androgens and EC were reviewed.

RESULTS

There are different kinds of androgenic hormones, and different kinds of androgens may have different effects. Elevated androgens (especially testosterone) have been associated with an increased EC risk in postmenopausal women. 5α-reductases (5α-Reds) and 17β-hydroxysteroid dehydrogenase type 2 (17βHSD2) pathway may inhibit the progression of EC mediated by dihydrotestosterone (DHT), but aromatases stimulate further progression of EC. The most of studies accessing the prognostic value of AR have found that AR expression may be a favorable prognostic indicator.

CONCLUSION

Androgens may have both oncogenic and tumor suppressive roles. Androgen-specific biases in metabolism and the expression of AR may contribute to the different prognosis of patients with EC.

Glucose metabolic reprogramming and its therapeutic potential in obesity-associated endometrial cancer

Endometrial cancer (EC) is a common gynecological cancer that endangers women health. Although substantial progresses of EC management have been achieved in recent years, the incidence of EC still remains high. Obesity has been a common phenomenon worldwide that increases the risk of EC. However, the mechanism associating obesity and EC has not been fully understood. Metabolic reprogramming as a remarkable characteristic of EC is currently emerging. As the primary factor of metabolic syndrome, obesity promotes insulin resistance, hyperinsulinemia and hyperglycaemia. This metabolic disorder remodels systemic status, which increases EC risk and is related with poor prognosis. Glucose metabolism in EC cells is complex and mediated by glycolysis and mitochondria to ensure energy requirement. Factors that affect glucose metabolism may have an impact on EC initiation and progression. In this study, we review the glucose metabolic reprogramming of EC not only systemic metabolism but also inherent tumor cell metabolism. In particular, the role of glucose metabolic regulation in malignant properties of EC will be focused. Understanding of metabolic profile and glucose metabolism-associated regulation mechanism in EC may provide novel perspective for treatment.

A compendium of Androgen Receptor Variant 7 target genes and their role in Castration Resistant Prostate Cancer

Persistent androgen receptor (AR) signalling is the main driver of prostate cancer (PCa). Truncated isoforms of the AR called androgen receptor variants (AR-Vs) lacking the ligand binding domain often emerge during treatment resistance against AR pathway inhibitors such as Enzalutamide. This review discusses how AR-Vs drive a more aggressive form of PCa through the regulation of some of their target genes involved in oncogenic pathways, enabling disease progression. There is a pressing need for the development of a new generation of AR inhibitors which can repress the activity of both the full-length AR and AR-Vs, for which the knowledge of differentially expressed target genes will allow evaluation of inhibition efficacy. This review provides a detailed account of the most common variant, AR-V7, the AR-V7 regulated genes which have been experimentally validated, endeavours to understand their relevance in aggressive AR-V driven PCa and discusses the utility of the downstream protein products as potential drug targets for PCa treatment.

Differential Regulation of Genes by the Glucogenic Hormone Asprosin in Ovarian Cancer

Background: Ovarian cancer (OvCa) is one of the most lethal forms of gynaecological malignancy. Altered energy metabolism and increased aerobic glycolysis in OvCa are hallmarks that demand attention. The glucogenic hormone asprosin is often dysregulated in metabolic disorders such as insulin resistance, diabetes (type 2 and gestational), and preeclampsia. Despite association with metabolic disorders, its role in energy metabolism within the tumour microenvironment is yet to be explored. Here, we study the role of asprosin in OvCa using transcriptomics and expand on functional studies with clinical samples. Methods: RNA sequencing, functional gene enrichment analysis, Western blotting and ImageStream. Results: Following treatment with 100 nM of asprosin, the serous OvCa cell line, SKOV-3, displayed 160 and 173 gene regulatory changes, at 4 and 12 h respectively, when compared with control samples (p < 0.05 and Log2FC > 1). In addition to energy metabolism and glucose-related pathways, asprosin was shown to alter pathways associated with cell communication, TGF-β signalling, and cell proliferation. Moreover, asprosin was shown to induce phosphorylation of ERK1/2 in the same in vitro model. Using liquid biopsies, we also report for novel expression of asprosin’s predicted receptors OR4M1 and TLR4 in cancer-associated circulating cells; with significant reduction seen between pre-chemotherapy and end of first line chemotherapy, in addition to patients under maintenance with bevacizumab +/− olaparib for OR4M1. Conclusions: In relation to OvCa, asprosin appears to regulate numerous signalling pathways in-vitro. The prognostic potential of OR4M1 in liquid biopsies should also be explored further.

A novel inflammation‑associated prognostic signature for clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) are typically situated in a complex inflammatory and immune microenvironment, which has been reported to contribute to the unfavorable prognosis of patients with ccRCC. There would be beneficial clinical implications for elucidating the roles of its molecular characteristics in the inflammatory microenvironment. This is because it would facilitate the development of reliable biomarkers for pre-stratification prior to the designation of individualized treatment strategies. In the present study, RNA-sequencing data from 607 patients were retrospectively analyzed to elucidate the profile of inflammatory molecules. Based on this, an inflammatory prognostic signature (IPS) was developed and further validated using clinical ccRCC samples. Subsequently, the associated mechanisms in terms of the immune microenvironment and molecular pathways were then investigated. This proposed IPS was found to exhibit superior accuracy compared with the criterion of a good prognostic model for the prediction of patient prognosis from ccRCC [area under the receiver operating characteristic curve (AUC)=0.811] in addition to being an independent factor for prognostic risk stratification [hazard ratio: 11.73 (95% CI, 26.98-5.10); log-rank test, P<0.001]. Pathologically, ccRCC cells identified as high-risk according to their IPS presented with a more malignant tumor structure, including voluminous eosinophilic cytoplasm, acinar/lamellar/tubular growth patterns and atypic nuclei. High-risk ccRCC also exhibited higher infiltration levels by four types of immune cells, including T regulatory cells, but lower infiltration levels by mast cells. Pathways associated with immune-inflammation interaction, including the IL-17 pathway, were found to be upregulated in IPS-identified high-risk ccRCC. Furthermore, by combining the IPS with clinical factors, an integrated prognostic index was developed and validated for increasing the accuracy of patient risk-stratification for ccRCC (AUC=0.911). In conclusion, the complex regulatory mechanisms and molecular characteristics involved in ccRCC-inflammation interaction, coupled with their prognostic potential, were systematically elucidated in the present study. This may have important implications in furthering the understanding into the molecular mechanisms underlying this ccRCC-inflammation interaction, which can in turn be exploited for identifying high-risk patients with ccRCC prior to designing their clinical treatment strategy.

Construction and validation of a prognostic model based on 11 lymph node metastasis-related genes for overall survival in endometrial cancer

BACKGROUND

Endometrial cancer (EC) is one of the most common malignant tumors in female reproductive system. The incidence of lymph node metastasis (LNM) is only about 10% in clinically suspected early-stage EC patients. Discovering prognostic models and effective biomarkers for early diagnosis is important to reduce the mortality rate.

METHODS

A least absolute shrinkage and selection operator (LASSO) regression was conducted to identify the characteristic dimension decrease and distinguish porgnostic LNM related genes signature. Subsequently, a novel prognosis-related nomogram was constructed to predict overall survival (OS). Survival analysis was carried out to explore the individual prognostic significance of the risk model and key gene was validated in vitro.

RESULTS

In total, 89 lymph node related genes (LRGs) were identified. Based on the LASSO Cox regression, 11 genes were selected for the development of a risk evaluation model. The Kaplan-Meier curve indicated that patients in the low-risk group had considerably better OS (p = 3.583e-08). The area under the ROC curve (AUC) of this model was 0.718 at 5 years of OS. Then, we developed an OS-associated nomogram that included the risk score and clinicopathological features. The concordance index of the nomogram was 0.769. The survival verification performed in three subgroups from the nomogram demonstrated the validity of the model. The AUC of the nomogram was 0.787 at 5 years OS. Proliferation and metastasis of HMGB3 were explored in EC cell line. External validation with 30 patients in our hospital showed that patients with low-risk scores had a longer OS (p-value = 0.03). Finally, we revealed that the most frequently mutated genes in the low-risk and high-risk groups are PTEN and TP53, respectively.

CONCLUSIONS

Our results suggest that LNM plays an important role in the prognosis, and HMGB3 was potential as a biomarker for EC patients.

Exploring the Interplay between Metabolism and Tumor Microenvironment Based on Four Major Metabolism Pathways in Colon Adenocarcinoma

Tumor metabolism plays a critical role in tumor progression. However, the interaction between metabolism and tumor microenvironment (TME) has not been comprehensively revealed in colon adenocarcinoma (COAD). We used unsupervised consensus clustering to establish three molecular subtypes (clusters) based on the enrichment score of four major metabolism pathways in TCGA-COAD dataset. GSE17536 was used as a validation dataset. Single-cell RNA sequencing data (GSE161277) was employed to further verify the reliability of subtyping and characterize the correlation between metabolism and TME. Three clusters were identified and they performed distinct prognosis and molecular features. Clust3 had the worst overall survival and the highest enrichment score of glycolysis. 86 differentially expressed genes (DEGs) were identified, in which 11 DEGs were associated with favorable prognosis and 75 DEGs were associated with poor prognosis. Striking correlations were observed between hypoxia and glycolysis, clust3 and hypoxia, and clust3 and angiogenesis (P < 0.001).We constructed a molecular subtyping system which was effective and reliable for predicting COAD prognosis. The 86 identified key DEGs may be greatly involved in COAD progression, and they provide new perspectives and directions for further understanding the mechanism of metabolism in promoting aggressive phenotype by interacting with TME.

GPC1 promotes the growth and migration of colorectal cancer cells through regulating the TGF-β1/SMAD2 signaling pathway

In this study, we analyzed GPC family genes in colorectal cancer (CRC) and the possible mechanism of action of GPC1 in CRC. CRC patient data were extracted from The Cancer Genome Atlas, and the prognostic significance of GPC1 expression and its association with clinicopathological features were identified by Kolmogorov–Smirnov test. CRC patients with high GPC1 expression had poor overall survival compared with patients with low GPC1 expression. In vitro experiments demonstrated that knockdown of GPC1 significantly inhibited the proliferation and migration and promoted cell apoptosis in CRC cell lines. Gene Ontology analysis of differential genes indicated that GPC1 may influence the TGF-β1 signaling pathway. Additional experiments revealed that silencing GPC1 suppressed the levels of TGF-β1 and p-SMAD2 but increased the expression of SMAD2. Taken together, these findings suggest that GPC1 may function as a tumor promoter in CRC cells through promoting TGF-β signaling pathway. Our results also indicate that GPC1 may serve as a critical effector in CRC progression and a new potential target for CRC therapy.

Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

Backgrounds

Epithelial-mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).

Methods

EMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.

Results

A total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N ⁶-methyladenosine (m⁶A) RNA expression, microsatellite instability level, and stemness indices.

Conclusion

We constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

The critical role of B4GALT4 in promoting microtubule spindle assembly in HCC through the regulation of PLK1 and RHAMM expression

Beta 1,4-galactosyltransferase (B4GALT)-family glycosyltransferases are involved in multiple biological processes promoting cancer progression, regulating the dynamic network of cancer cell proliferation and apoptosis, and are associated with metastasis. However, their roles in the dysregulation of expressions and functions in hepatocellular carcinoma (HCC) remain unclear. Herein, bioinformatic approaches have been applied to investigate their expression profiles, and to obtain correlations between gene expressions and clinicopathological parameters as well as downstream target genes in HCC. Multiple databases were used to screen the expressions of B4GALT family members in tumor tissues, and to evaluate their prognostic value among HCC patients in different aspects. Results indicated an overall upregulation of B4GALTs' transcription levels in tumor tissues and a strong correlation with poor prognosis. Through Gene Ontology analysis, gene set enrichment analysis, and verification of single-cell RNA sequencing data, we established a connection between the B4GALT family and microtubule spindle assembly, which particularly highlighted the role of B4GALT4 in this phenomenon. B4GALT4 knockdown downregulated the production of lumican, and repressed the expressions of polo-like kinase 1 and RHAMM by regulating the transforming growth factor-beta pathway, thus suggesting that B4GALT4 is a critical promotor for HCC. We believe that these studies will provide valuable insight into the role of B4GALT family members in HCC and lead to the development of new strategies to improve the outcomes for patients with HCC.

Identification of a glycolysis-related gene signature for survival prediction of ovarian cancer patients

Background

Ovarian cancer (OV) is deemed the most lethal gynecological cancer in women. The aim of this study was to construct an effective gene prognostic model for predicting overall survival (OS) in patients with OV.

Methods

The expression profiles of glycolysis‐related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed using training and test sets.

Results

A gene risk signature based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4) was identified to predict the survival outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high‐grade OV, in the TCGA dataset, with areas under the curve (AUC) of 0.709 and 0.762 for 3‐ and 5‐year survival, respectively. Similar results were found in the test sets, and the AUCs of 3‐, 5‐year OS were 0.714 and 0.772 in the combined test set. And our signature was an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was developed.

Conclusion

Our study established a nine‐GRG risk model and nomogram to better predict OS in patients with OV. The risk model represents a promising and independent prognostic predictor for patients with OV. Moreover, our study on GRGs could offer guidance for the elucidation of underlying mechanisms in future studies.

Identification of a novel glycolysis-related signature to predict the prognosis of patients with breast cancer

Background

Breast cancer (BC) has a high incidence and mortality rate in females. Its conventional clinical characteristics are far from accurate for the prediction of individual outcomes. Therefore, we aimed to develop a novel signature to predict the survival of patients with BC.

Methods

We analyzed the data of a training cohort from the Cancer Genome Atlas (TCGA) database and a validation cohort from the Gene Expression Omnibus (GEO) database. After the applications of Gene Set Enrichment Analysis (GSEA) and Cox regression analyses, a glycolysis-related signature for predicting the survival of patients with BC was developed; the signature contained AK3, CACNA1H, IL13RA1, NUP43, PGK1, and SDC1. Furthermore, on the basis of expression levels of the six-gene signature, we constructed a risk score formula to classify the patients into high- and low-risk groups. The receiver operating characteristic (ROC) curve and the Kaplan-Meier curve were used to assess the predicted capacity of the model. Later, a nomogram was developed to predict the outcomes of patients with risk score and clinical features over a period of 1, 3, and 5 years. We further used Human Protein Atlas (HPA) database to validate the expressions of the six biomarkers in tumor and sample tissues, which were taken as control.

Results

We constructed a six-gene signature to predict the outcomes of patients with BC. The patients in the high-risk group showed poor prognosis than those in the low-risk group. The area under the curve (AUC) values were 0.719 and 0.702, showing that the prediction performance of the signature is acceptable. Additionally, Cox regression analysis revealed that these biomarkers could independently predict the prognosis of BC patients with BC without being affected by clinical factors. The expression levels of all six biomarkers in BC tissues were higher than that in normal tissues; however, AK3 was an exception.

Conclusion

We developed a six-gene signature to predict the prognosis of patients with BC. Our signature has been proved to have the ability to make an accurate prediction and might be useful in expanding the hypothesis in clinical research.

A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.

METHODS

Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.

RESULTS

Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.

CONCLUSION

The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.

Over-expression of SRD5A3 and its prognostic significance in breast cancer

Objective

The study aimed to compare the Steroid 5 alpha-reductase 3 (SRD5A3) expression levels in breast cancer (BC) and normal tissues, to investigate the prognostic value of SRD5A3 mRNA expression in BC patients and to identify the SRD5A3-related signaling pathways using bioinformatics approaches.

Methods

We evaluated the expression levels of SRD5A3 and survival data in BC patients using different bioinformatic databases. Further, Cox regression analysis was conducted to predict the independent prognostic factors for BC. Moreover, the association of SRD5A3 with clinicopathological factors was measured through LinkedOmics database. And the potential role of SRD5A3 was determined by Gene Ontology and KEGG pathway enrichment analysis. Finally, protein network of SRD5A3 was constructed and genetic alterations were analyzed.

Results

Bioinformatic data indicated that both mRNA and protein expression levels of SRD5A3 were higher in BC group than those in the normal group (P < 0.05). Besides, BC patients with higher SRD5A3 mRNA expression levels had a lower overall survival (all P < 0.05). Cox regression analysis further demonstrated the independent prognostic value of SRD5A3 in BC (P = 0.015). SRD5A3 mRNA expression was significantly associated with N stage (P < 0.001), age (P < 0.05), and histologic subtype (P < 0.001) but had no significant relationship with other clinical characteristics (all P > 0.05). Moreover, the functional enrichment analysis revealed that the SRD5A3 was involved in metabolism-related pathways (all P < 0.05).

Conclusions

SRD5A3 was highly expressed in BC tissues and high SRD5A3 expression was related to poorer prognosis. SRD5A3 serves as an oncogene and might function as a potential biomarker for prognosis and a therapeutic target for BC.

Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer

INTRODUCTION

Uterine corpus endometrial carcinoma (UCEC) causes thousands of deaths per year. To improve the overall survival of patients with UCEC, there is a need to identify prognostic biomarkers and potential drugs.

OBJECTIVES

The aim of this study was twofold: the identification of prognostic gene signatures from expression profiles of pattern recognition receptor (PRR) genes and identification of the most effective existing drugs using the prognostic gene signature.

METHODS

This study was based on the expression profile of PRR genes of 541 patients with UCEC obtained from The Cancer Genome Atlas. Key prognostic signatures were identified using various approaches, including survival analysis, network, and clustering. Hub genes were identified by constructing a co-expression network. Representative genes were identified using k-means and k-medoids-based clustering. Univariate Cox proportional hazard (PH) analysis was used to identify survival-associated genes. 'cmap2' was used to identify potential drugs that can suppress/enhance the expression of prognostic genes.

RESULTS

Models were developed using hub genes and achieved a maximum hazard ratio (HR) of 1.37 (p = 0.294). Then, a clustering-based model was developed using seven genes (HR 9.14; p = 1.49 × 10^-12). Finally, a nine gene-based risk stratification model was developed (CLEC1B, CLEC3A, IRF7, CTSB, FCN1, RIPK2, NLRP10, NLRP9, and SARM1) and achieved HR 10.70; p = 1.1 × 10^-12. The performance of this model improved significantly in combination with the clinical stage and achieved HR 15.23; p = 2.21 × 10^-7. We also developed a model for predicting high-risk patients (survival ≤ 4.3 years) and achieved an area under the receiver operating characteristic curve (AUROC) of 0.86.

CONCLUSION

We identified potential immunotherapeutic agents based on prognostic gene signature: hexamethonium bromide and isoflupredone. Several novel candidate drugs were suggested, including human interferon-α-2b, paclitaxel, imiquimod, MESO-DAP1, and mifamurtide. These biomolecules and repurposed drugs may be utilised for prognosis and treatment for better survival.

Elevated expression of LPCAT1 predicts a poor prognosis and is correlated with the tumour microenvironment in endometrial cancer

Background

Endometrial cancer (EC) is one of the three malignant reproductive tumours that threaten womens lives and health. Glycerophospholipids (GPLs) are important bioactive lipids involved in various physiological and pathological processes, including cancer. Immune infiltration of the tumour microenvironment (TME) is positively associated with the overall survival in EC. Exploring GPL-related factors associated with the TME in endometrial cancer can aid in the prognosis of patients and provide new therapeutic targets.

Methods

Differentially expressed GPL-related genes were identified from TCGA-UCEC datasets and the Molecular Signatures Database (MSigDB). Univariate Cox regression analysis was used to select GPL-related genes with prognostic value. The Random forest algorithm, LASSO algorithm and PPI network were used to identify critical genes. ESTIMATEScore was calculated to identify genes associated with the TME. Then, differentiation analysis and survival analysis of LPCAT1 were performed based on TCGA datasets. GSE17025 and immunohistochemistry (IHC) verified the results of the differentiation analysis. An MTT assay was then conducted to determine the proliferation of EC cells. GO and KEGG enrichment analyses were performed to explore the underlying mechanism of LPCAT1. In addition, we used the ssGSEA algorithm to explore the correlation between LPCAT1 and cancer immune infiltrates.

Results

Twenty-three differentially expressed GPL-related genes were identified, and eleven prognostic genes were selected by univariate Cox regression analysis. Four significant genes were identified by two different algorithms and the PPI network. Only LPCAT1 was significantly correlated with the tumour microenvironment. Then, we found that LPCAT1 was highly expressed in tumour samples compared with that in normal tissues, and lower survival rates were observed in the groups with high LPCAT1 expression. Silencing of LPCAT1 inhibited the proliferation of EC cells. Moreover, the expression of LPCAT1 was positively correlated with the histologic grades and types. The ROC curve indicated that LPCAT1 had good prognostic accuracy. Receptor ligand activity, pattern specification process, regionalization, anterior/posterior pattern specification and salivary secretion pathways were enriched as potential targets of LPCAT1. By using the ssGSEA algorithm, fifteen kinds of tumor-infiltrating cells (TICs) were found to be correlated with LPCAT1 expression.

Conclusion

These findings suggested that LPCAT1 may act as a valuable prognostic biomarker and be correlated with immune infiltrates in endometrial cancer, which may provide novel therapy options for and improved treatment of EC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01965-1.

Development of an Aerobic Glycolysis Index for Predicting the Sorafenib Sensitivity and Prognosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a deadly tumor with high heterogeneity. Aerobic glycolysis is a common indicator of tumor growth and plays a key role in tumorigenesis. Heterogeneity in distinct metabolic pathways can be used to stratify HCC into clinically relevant subgroups, but these have not yet been well-established. In this study, we constructed a model called aerobic glycolysis index (AGI) as a marker of aerobic glycolysis using genomic data of hepatocellular carcinoma from The Cancer Genome Atlas (TCGA) project. Our results showed that this parameter inferred enhanced aerobic glycolysis activity in tumor tissues. Furthermore, high AGI is associated with poor tumor differentiation and advanced stages and could predict poor prognosis including reduced overall survival and disease-free survival. More importantly, the AGI could accurately predict tumor sensitivity to Sorafenib therapy. Therefore, the AGI may be a promising biomarker that can accurately stratify patients and improve their treatment efficacy.

Identification of Novel Gene Signature Associated with Cell Glycolysis to Predict Survival in Hepatocellular Carcinoma Patients

Purpose

As hepatocellular carcinoma (HCC) is a complex disease, it is hard to classify HCC with a specific biomarker. This study used data from TCGA to create a genetic signature for predicting the prognosis of HCC patients.

Methods

In a group of HCC patients (n = 424) from TCGA, mRNA profiling was carried out. To recognize gene sets that differed significantly between HCC and normal tissues, an enrichment study of genes was carried out. Cox relative hazard regression models have been used to identify genes that are significantly associated with overall survival. To test the function of a prognostic risk parameter, the following multivariate Cox regression analysis was used. The log-rank test and Kaplan–Meier survival estimates were used to test the significance of risk parameters for predictive prognoses.

Results

Eight genes have been identified as having a significant link to overall survival (PAM, NUP155, GOT2, KDELR3, PKM, NSDHL, ENO1, and SRD5A3). The 377 HCC patients were divided into eight-gene signature-based high/low-risk subgroups. The eight-gene signature's prognostic ability was unaffected by a number of factors.

Conclusion

To predict the survival of patients with HCC, an eight-gene signature associated with cellular glycolysis was then identified. The findings shed light on cellular glycolysis processes and the diagnosis of patients with low HCC prognoses.

Identification of a novel glycolysis-related gene signature for predicting the prognosis of osteosarcoma patients

Glycolysis ensures energy supply to cancer cells, thereby facilitating tumor progression. Here, we identified glycolysis-related genes that could predict the prognosis of patients with osteosarcoma. We examined 198 glycolysis-related genes that showed differential expression in metastatic and non-metastatic osteosarcoma samples in the TARGET database, and identified three genes (P4HA1, ABCB6, and STC2) for the establishment of a risk signature. Based on the signature, patients in the high-risk group had poor outcomes. An independent Gene Expression Omnibus database GSE21257 was selected as the validation cohort. Receiver operating characteristic curve analysis was performed and the accuracy of predicting the 1- and 3-year survival rates was shown by the areas under the curve. The results were 0.884 and 0.790 in the TARGET database, and 0.740 and 0.759 in the GSE21257, respectively. Furthermore, we applied ESTIMATE algorithm and performed single sample gene set enrichment analysis to compare tumor immunity between high- and low-risk groups. We found that the low-risk group had higher immune scores and immune infiltration levels than the high-risk group. Finally, we chose P4HA1 as a representative gene to verify the function of risk genes in vitro and in vivo and found that P4HA1 could promote the metastasis of osteosarcoma cells. Our study established a novel glycolysis-related risk signature that could predict the prognosis of patients with osteosarcoma.

hsa_circ_0001610 knockdown modulates miR-646-STAT3 axis to suppress endometrial carcinoma progression

BACKGROUND

Endometrial carcinoma (EC) development is associated with dysregulated circular RNA profiles. The purpose of the current research is to study the role and mechanism of hsa_circ_0001610 (circ_0001610) in EC progression.

METHODS

circ_0001610, microRNA (miR)-646, and signal transducer and activator of transcription 3 (STAT3) expression levels were measured in EC. Functional analyses were performed using Cell Counting Kit-8, colony formation, transwell, wound healing, flow cytometry, glycolysis, and xenograft analyses. Binding association was evaluated with dual-luciferase reporter assay.

RESULTS

circ_0001610 levels were upregulated in EC samples (n = 30) and cells. circ_0001610 interference restrained cell proliferation, migration, and invasion, and promoted apoptosis. circ_0001610 downregulation constrained glycolysis through reducing glucose consumption, lactate production, and levels of adenosine triphosphate, extracellular acidification, hexokinase 2, and lactate dehydrogenase A, and increasing oxygen consumption rate. miR-646 is targeted by circ_0001610, and miR-646 inhibition attenuated interference of circ_0001610-mediated suppression of EC development. STAT3 was modulated by miR-646, and miR-646 upregulation restrained EC progression by decreasing STAT3. circ_0001610 silencing reduced STAT3 levels by sponging miR-646 and reduced the growth of xenograft tumor established by EC cells.

CONCLUSION

circ_0001610 knockdown represses EC progression through modulating the miR-646-STAT3 axis.

A Novel Transcription Factor-Based Prognostic Signature in Endometrial Cancer: Establishment and Validation

Background

Endometrial cancer (EC) is a common malignancy of the female reproductive system worldwide. Increasing evidence has suggested that many transcription factors are aberrantly expressed in various cancers. This study aimed to develop a transcription factor-based prognostic signature for EC.

Methods

Gene expression data and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression and Multivariate Cox regression analysis was used to construct a prognostic signature. Then, the efficacy of the prognostic signature was validated in a training cohort, testing cohort and then the entire cohort. Correlations between clinical features and the model were also analyzed, and a nomogram based on the multivariate Cox analysis was developed. Furthermore, we verified the effect of a key transcription factor, E2F1, on biological functions of EC in vitro.

Results

We developed a nine-transcription factor (MSX1, HOXB9, E2F1, DLX4, BNC2, DLX2, PDX1, POU3F2, and FOXP3) prognostic signature. Compared with those in the low-risk group, patients in the high-risk group had worse clinical outcomes. The area under the curve (AUC) of this prognostic signature for 5-year survival was 0.806 in the training cohort, 0.710 in the testing cohort and 0.761 in the entire cohort. Gene set enrichment analysis (GSEA) revealed a correlation between the prognostic signature and various cancer signaling pathways, and a hub transcription factor regulatory network was constructed. The prognostic signature was confirmed to have independent predictive value. Finally, a nomogram based on the prognostic signature and clinical independent prognostic factors was also established and performed well according to the calibration curves. Further, knockdown of E2F1 inhibited invasion and metastasis of EC cells.

Conclusion

Our study developed and validated a transcription factor-based prognostic signature that accurately predicts prognosis of EC patients. Moreover, E2F1 may represent a potential target for the treatment of EC.

Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Background

Accumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.

Methods

mRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).

Results

Through the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.

Conclusions

Our study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.

TRIM29 alters bioenergetics of pancreatic cancer cells via cooperation of miR-2355-3p and DDX3X recruitment to AK4 transcript

TRIM29 is dysregulated in pancreatic cancer and implicated in maintenance of stem-cell-like characters of pancreatic cancer cells. However, the exact mechanisms underlying oncogenic function of TRIM29 in pancreatic cancer cells remain largely unclarified. Using a global screening procedure, the current study found that adenylate kinase 4 (AK4) was profoundly reduced by TRIM29 knockdown. In addition, our data demonstrated that TRIM29 knockdown altered bioenergetics and suppressed proliferation and invasion of pancreatic cancer cells via downregulation of AK4 at the posttranscriptional level. The current study demonstrated that upregulation of microRNA-2355-3p (miR-2355-3p) upregulated AK4 expression via facilitating DDX3X recruitment to the AK4 transcript, and TRIM29 knockdown thereby destabilized the AK4 transcript via miR-2355-3p downregulation. Collectively, our study uncovers posttranscriptional stabilization of the AK4 transcript by miR-2355-3p interaction to facilitate DDX3X recruitment. Regulation of AK4 by TRIM29 via miR-2355-3p thereby provides additional information for further identification of attractive targets for therapy with pancreatic cancer.

Prognostic Correlation of Glycolysis-Related Gene Signature in Patients with Laryngeal Cancer

BACKGROUND

Aerobic glycolysis is one of the metabolic characteristics of tumor cells, which is regulated by many genes. The aim of our study was to construct glycolysis-related gene signature to accurately predict the prognosis of laryngeal cancer (LC) patients.

METHODS

We analyzed the mRNA expression profiles of LC patients from The Cancer Genome Atlas (TCGA). Eleven glycolysis-related gene sets were analyzed by gene set enrichment analysis (GSEA). In order to acquire the gene signature related to prognosis, we used univariate and multivariate Cox regression analysis.

RESULTS

We confirmed that a gene signature composed of two genes (STC2, LHPP) can predict the overall survival (OS) of patients with LC. Based on each patient's risk score, we found that the survival results of patients in the high-risk group were significantly lower than those in the low-risk group (log-rank test P-value=0.002). Multivariate Cox regression analysis confirmed that gene signature could independently predict OS in LC patients (HR = 1.981, 95% CI 1.446-2.714 P<0.001). In addition, a nomogram including the age, sex, grade and risk score was constructed. The nomogram demonstrated good accuracy for OS prediction, with a C-index of 0.752.

CONCLUSION

The glycolysis-related two-gene risk score model could be used as a biomarker for LC prognosis.

MicroRNAs Regulate Metabolic Phenotypes During Multicellular Tumor Spheroids Progression

During tumor progression, cancer cells rewire their metabolism to face their bioenergetic demands. In recent years, microRNAs (miRNAs) have emerged as regulatory elements that inhibit the translation and stability of crucial mRNAs, some of them causing direct metabolic alterations in cancer. In this study, we investigated the relationship between miRNAs and their targets mRNAs that control metabolism, and how this fine-tuned regulation is diversified depending on the tumor stage. To do so, we implemented a paired analysis of RNA-seq and small RNA-seq in a breast cancer cell line (MCF7). The cell line was cultured in multicellular tumor spheroid (MCTS) and monoculture conditions. For MCTS, we selected two-time points during their development to recapitulate a proliferative and quiescent stage and contrast their miRNA and mRNA expression patterns associated with metabolism. As a result, we identified a set of new direct putative regulatory interactions between miRNAs and metabolic mRNAs representative for proliferative and quiescent stages. Notably, our study allows us to suggest that miR-3143 regulates the carbon metabolism by targeting hexokinase-2. Also, we found that the overexpression of several miRNAs could directly overturn the expression of mRNAs that control glycerophospholipid and N-Glycan metabolism. While this set of miRNAs downregulates their expression in the quiescent stage, the same set is upregulated in proliferative stages. This last finding suggests an additional metabolic switch of the above mentioned metabolic pathways between the quiescent and proliferative stages. Our results contribute to a better understanding of how miRNAs modulate the metabolic landscape in breast cancer MCTS, which eventually will help to design new strategies to mitigate cancer phenotype.

Transcriptomic Analysis of Glycolysis-Related Genes Reveals an Independent Signature of Bladder Carcinoma

Background

Bladder carcinoma (BC) is one of the most prevalent and malignant tumors. Multiple gene signatures based on BC metabolism, especially regarding glycolysis, remain unclear. Thus, we developed a glycolysis-related gene signature to be used for BC prognosis prediction.

Methods

Transcriptomic and clinical data were divided into a training set and a validation set after they were downloaded and analyzed from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Gene-set enrichment analysis (GSEA) and differential analysis were used to screen differentially expressed genes (DEGs), while univariate Cox regression and lasso-penalized Cox regression were employed for signature establishment. To evaluate the prognostic power of the signature, receiver operating characteristic (ROC) curve and Kaplan-Meier (KM) survival analysis were also used. Additionally, we developed a nomogram to predict patients' survival chances using the identified prognostic gene signature. Further, gene mutation and protein expression, as well as the independence of signature genes, were also analyzed. Finally, we also performed qPCR and western blot to detect the expression and potential pathways of signature genes in BC samples.

Results

Ten genes were selected for signature construction among 71 DEGs, including nine risk genes and one protection gene. KM survival analysis revealed that the high-risk group had poor survival and the low-risk group had increased survival. ROC curve analysis and the nomogram validated the accurate prediction of survival using a gene signature composed of 10 glycolysis-related genes. Western blot and qPCR analysis demonstrated that the expression trend of signature genes was basically consistent with previous results. These 10 glycolysis-related genes were independent and suitable for a signature.

Conclusion

Our current study indicated that we successfully built and validated a novel 10-gene glycolysis-related signature for BC prognosis.

Identification of a Novel Glycolysis-Related Gene Signature for Predicting Breast Cancer Survival

To identify a glycolysis-related gene signature for the evaluation of prognosis in patients with breast cancer, we analyzed the data of a training set from TCGA database and four validation cohorts from the GEO and ICGC databases which included 1,632 patients with breast cancer. We conducted GSEA, univariate Cox regression, LASSO, and multiple Cox regression analysis. Finally, an 11-gene signature related to glycolysis for predicting survival in patients with breast cancer was developed. And Kaplan–Meier analysis and ROC analyses suggested that the signature showed a good prognostic ability for BC in the TCGA, ICGC, and GEO datasets. The analyses of univariate Cox regression and multivariate Cox regression revealed that it’s an important prognostic factor independent of multiple clinical features. Moreover, a prognostic nomogram, combining the gene signature and clinical characteristics of patients, was constructed. These findings provide insights into the identification of breast cancer patients with a poor prognosis.

The effect of a novel glycolysis-related gene signature on progression, prognosis and immune microenvironment of renal cell carcinoma

Background

Glycolysis is a central metabolic pathway for tumor cells. However, the potential roles of glycolysis-related genes in renal cell carcinoma (RCC) have not been investigated.

Methods

Seven glycolysis-related gene sets were selected from MSigDB and were analyzed through GSEA. Using TCGA database, the glycolysis-related gene signature was constructed. Prognostic analyses were based on the Kaplan–Meier method. The cBioPortal database was employed to perform the mutation analyses. The CIBERSORT algorithm and TIMER database were used to determine the immunological effect of glycolytic gene signature. The expressions in protein level of eight glycolytic risk genes were determined by HPA database. Finally, qPCR, MTT and Transwell invasion assays were conducted to validate the roles of core glycolytic risk genes (CD44, PLOD1 and PLOD2) in RCC.

Results

Four glycolysis-related gene sets were significantly enriched in RCC samples. The glycolytic risk signature was constructed (including CD44, PLOD2, KIF20A, IDUA, PLOD1, HMMR, DEPDC1 and ANKZF1) and identified as an independent RCC prognostic factor (HR = 1.204). Moreover, genetic alterations of glycolytic risk genes were uncommon in RCC (10.5%) and glycolytic risk signature can partially affect immune microenvironment of RCC. Six glycolytic risk genes (except for IDUA and HMMR) were over-expression in A498 and 786-O renal cancer cells through qPCR test. MTT and Transwell assays revealed that silencing of CD44, PLOD1 and PLOD2 suppressed the proliferation and invasion of renal cancer cells.

Conclusions

The glycolysis-related risk signature is closely associated with RCC prognosis, progression and immune microenvironment. CD44, PLOD1 and PLOD2 may serve as RCC oncogenes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07702-7.

A glycolysis-related gene signature predicts prognosis of patients with esophageal adenocarcinoma

Background: Esophageal adenocarcinoma (EAC) is a growing problem with a rapidly rising incidence and carries a poor prognosis. We aimed to develop a glycolysis-related gene signature to predict the prognostic outcome of patients with EAC.

Results: Five genes (CLDN9, GFPT1, HMMR, RARS and STMN1) were correlated with prognosis of EAC patients. Patients were classified into high-risk and low-risk groups calculated by Cox regression analysis, based on the five gene signature risk score. The five-gene signature was an independent biomarker for prognosis and patients with low risk scores showed better prognosis. Nomogram incorporating the gene signature and clinical prognostic factors was effective in predicting the overall survival.

Conclusion: An innovative identified glycolysis-related gene signature and an effective nomogram reliably predicted the prognosis of EAC patients.

Methods: The Cancer Genome Atlas database was investigated for the gene expression profile of EAC patients. Glycolytic gene sets difference between EAC and normal tissues were identified via Gene set enrichment analysis (GSEA). Univariate and multivariate Cox analysis were utilized to construct a prognostic gene signature. The signature was evaluated by receiver operating characteristic curves and Kaplan–Meier curves. A prognosis model integrating clinical parameters with the gene signature was established with nomogram.

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.

Steroid 5 alpha-reductase 3 (SRD5A3) promotes tumor growth and predicts poor survival of human hepatocellular carcinoma (HCC)

Steroid 5 alpha-reductase 3 (SRD5A3) is an important molecule in glycosylation metabolism and steroid hormone formation. It is differentially expressed in human fetal liver, endometrial cancer and prostate cancer; however, its prognostic value and biological function in hepatocellular carcinoma (HCC) remain unclear. Here, bioinformatics analysis was employed to explore the expression and prognostic significance of SRD5A3 in various cancers including HCC. Additionally, clinical specimens of HCC were applied to analyze the expression of SRD5A3. SRD5A3-underexpressed HCC cell lines were established to test the effect of SRD5A3 on cell proliferation in in vitro and in vivo. We found that the elevated expression of SRD5A3 was common in many cancers with poor prognosis. Moreover, public datasets and our specimens revealed that SRD5A3 was also upregulated in HCC tissues and associated with clinical stage and patient’s gender. Kaplan-Meier survival analysis showed that higher SRD5A3 level predicted poor overall survival, progression-free survival, relapse-free survival and disease specific survival in HCC patients. Further experiments showed that the lack of SRD5A3 inhibited the growth of HCC. Collectively, these findings indicate that SRD5A3 functions as an oncogene and might serve as a potential biomarker for prognosis and a therapeutic target for HCC.

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.

Integrated analysis of immune-related genes in endometrial carcinoma

Background

Exploring novel and sensitive targets is urgent due to the high morbidity of endometrial cancer (EC). The purpose of our study was to explore the transcription factors and immune-related genes in EC and further identify immune-based lncRNA signature as biomarker for predicting survival prognosis.

Methods

Transcription factors, aberrantly expressed immune-related genes and immune-related lncRNAs were explored through bioinformatics analysis. Cox regression and the least absolute shrinkage and selection operator (LASSO) analysis were conducted to identify the immune and overall survival (OS) related lncRNAs. The accuracy of model was evaluated by Kaplan-Meier method and receiver operating characteristic (ROC) analysis, and the independent prognostic indicator was identified with Cox analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) were conducted to detect the accuracy of our results.

Results

A network of 29 transcription factors and 17 immune-related genes was constructed. Furthermore, four immune-prognosis-related lncRNAs were screened out. Kaplan-Meier survival analysis and time-dependent ROC analysis revealed a satisfactory predictive potential of the 4-lncRNA model. Consistency was achieved among the results from the training set, testing set and entire cohort. The distributed patterns between the high- and low-risk groups could be distinguished in principal component analysis. Comparisons of the risk score and clinical factors confirmed the four-lncRNA-based signature as an independent prognostic indicator. Last, the reliability of the results was verified by qRT-PCR in 29 cases of endometrial carcinoma and in cells.

Conclusions

Overall, our study constructed a network of transcription factors and immune-related genes and explored a four immune-related lncRNA signature that could serve as a novel potential biomarker of EC.

Seven-Gene Signature Based on Glycolysis Is Closely Related to the Prognosis and Tumor Immune Infiltration of Patients With Gastric Cancer

Background: Gastric cancer (GC) is one of the most common malignancies worldwide, exhibiting a high morbidity, and mortality. As the various treatment methods for gastric cancer are limited by disadvantages, many efforts to improve the efficacy of these treatments are being taken. Metabolic recombination is an important characteristic of cancer and has gradually caused a recent upsurge in research. However, systematic analysis of the interaction between glycolysis and GC patient prognosis and its potential associations with immune infiltration is lacking but urgently needed. Methods: We obtained the gene expression data and clinical materials of GC derived from The Cancer Genome Atlas (TCGA) dataset. Univariate and multivariate Cox proportional regression analyses were performed to select the optimal prognosis-related genes for subsequent modeling. We then validated our data in the GEO database and further verified the gene expression using the Oncomine database and PCR experiments. Besides, Gene set variation analysis (GSVA) analysis was employed to further explore the differences in activation status of biological pathways between the high and low risk groups. Furthermore, a nomogram was adopted to predict the individualized survival rate of GC patients. Finally, a violin plot and a TIMMER analysis were performed to analyse the characteristics of immune infiltration in the microenvironment. Results: A seven-gene signature, including STC1, CLDN9, EFNA3, ZBTB7A, NT5E, NUP50, and CXCR4, was established. Based on this seven-gene signature, the patients in the training set and testing sets could be divided into high-risk and low-risk groups. In addition, a nomogram based on risk and age showed good calibration and moderate discrimination. The results proved that the seven-gene signature had a strong capacity to predict the GC patient prognosis. Collectively, the violin plot and TIMMER analysis demonstrated that an immunosuppressive tumor microenvironment caused by hyperglycolysis led to poor prognosis. Conclusion: Taken together, these results established a genetic signature for gastric cancer based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of gastric cancer and the exploration of new clinical treatment strategies.

The AKR1C3/AR-V7 complex maintains CRPC tumour growth by repressing B4GALT1 expression

Multiple mechanisms contribute to the survival and growth of metastatic castration-resistant prostate cancer (mCRPC) cells without androgen, including androgen receptor splice variants (AR-V) and de novo intratumoral androgen synthesis. AKR1C3 is a critical androgenic enzyme that plays different roles in mCRPC, such as an EMT driver or AR coactivator. However, the relationship and regulatory mechanisms between AKR1C3 and AR-V remain largely unknown. In this study, we observed a positive correlation between AKR1C3 and AR-V7 staining in tissues from prostate rebiopsy at mCRPC. Mechanistically, AKR1C3 interacts with AR-V7 protein in CRPC cells, which can reciprocally inhibit AR-V7 and AKR1C3 protein degradation. Biologically, this complex is essential for in vitro and in vivo tumour growth of CRPC cells after androgen deprivation as it represses B4GALT1, a unique tumour suppressor gene in PCa. Together, this study reveals AKR1C3/AR-V7 complex as a potential therapeutic target in mCRPC.

N-glycosylation of the human β1,4-galactosyltransferase 4 is crucial for its activity and Golgi localization

β1,4-galactosyltransferase 4 (B4GalT4) is one of seven B4GalTs that belong to CAZy glycosyltransferase family 7 and transfer galactose to growing sugar moieties of proteins, glycolipids, glycosaminoglycans as well as single sugar for lactose synthesis. Herein, we identify two asparagine-linked glycosylation sites in B4GalT4. We found that mutation of one site (Asn220) had greater impact on enzymatic activity while another (Asn335) on Golgi localization and presence of N-glycans at both sites is required for production of stable and enzymatically active protein and its secretion. Additionally, we confirm B4GalT4 involvement in synthesis of keratan sulfate (KS) by generating A375 B4GalT4 knock-out cell lines that show drastic decrease in the amount of KS proteoglycans and no significant structural changes in N- and O-glycans. We show that KS decrease in A375 cells deficient in B4GalT4 activity can be rescued by overproduction of either partially or fully glycosylated B4GalT4 but not with N-glycan-depleted B4GalT4 version.