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

Comprehensive analysis of cuproptosis-related genes involved in prognosis and tumor microenvironment infiltration of colorectal cancer

Background

Colorectal cancer (CRC) is a common malignancy, with high incidence and high mortality rates. Cuproptosis, a novel form of copper-induced programmed cell death, contributes to tumor progression. However, whether cuproptosis-related genes (CRGs) play a role in CRC remains unclear. This study aims to elucidate the role of CRGs in CRC development, patient prognosis, and immune response.

Methods

We performed bioinformatics analysis of the differential expression of CRGs between CRC and normal tissues. Least absolute shrinkage and selection operator (LASSO), and univariate and multivariate Cox analyses were employed to identify risk factors, which were used to construct a risk score model. Patients with CRC were categorized into high- and low-risk groups based on their median risk scores. Receiver operating characteristic curve analysis was used to verify the predictive accuracy of the risk model. A nomogram was developed for CRC through univariate and multivariate Cox regression analyses. The chemotherapeutic drug sensitivity was compared between patients with high and low CDKN2A/DLAT expression using the Wilcoxon rank-sum test. Spearman's correlation and TISIDB database analyses were conducted to determine relationships between CDKN2A or DLAT and immune cell infiltration.

Results

Eight of ten identified CRGs exhibited significant differential expression between CRC and normal tissues. Among the eight significant differential expression CRGs, CDKN2A and DLAT were identified as independent risk factors for predicting overall survival (OS) in CRC. Patients with CRC in the low-risk group had longer OS than those in the high-risk group. The risk score model had good predictive accuracy for OS. Based on CDKN2A, DLAT and some clinical characteristics, a prognostic nomogram was developed to predict OS for CRC patients and showed good predictive ability. CDKN2A and DLAT expressions were significantly associated with chemotherapeutic drug sensitivity and immune cell infiltration in CRC, and the molecular subtypes and immune subtypes differed between CDKN2A and DLAT.

Conclusions

Our research revealed the prognostic value of CRGs, particularly CDKN2A and DLAT, in CRC and demonstrated the relationship between CDKN2A/DLAT and immune infiltration in CRC, thereby contributing to the outcome evaluation of patients with CRC and identifying novel targets for CRC immunotherapy.

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 cuproptosis-related gene DLAT as a novel prognostic marker and its relevance to immune infiltration in low-grade gliomas

DLAT has been recognized as a cuproptosis-related gene that is crucial for cuproptosis in earlier research. The study is to look at how DLAT affects individuals with low-grade glioma's prognosis and immune infiltration. The Genotype-Tissue Expression (GTEx) database and the TCGA database were used in this work to download RNAseq data in TPM format. DLAT was found to be overexpressed in LGG by comparing DLAT expression levels between LGG and normal brain tissue, and the expression of DLAT was verified by immunohistochemistry and semi-quantitative analysis. Then, the functional enrichment analysis revealed that the biological functional pathways and possible signal transduction pathways involved were primarily focused on extracellular matrix organization, transmembrane transporter complex, ion channel complex, channel activity, neuroactive ligand-receptor interaction, complement and coagulation cascades, and channel activity. The level of immune cell infiltration by plasmacytoid dendritic cells and CD8 T cells was subsequently evaluated using single-sample gene set enrichment analysis, which showed that high DLAT expression was inversely connected with that level of infiltration. The link between the methylation and mRNA transcription of DLAT was then further investigated via the MethSurv database, and the results showed that DLAT's hypomethylation status was linked to a poor outcome. Finally, by evaluating the prognostic value of DLAT using the Cox regression analysis and Kaplan-Meier technique, a column line graph was created to forecast the overall survival (OS) rate at 1, 3, and 5 years after LGG identification. The aforementioned results demonstrated that high DLAT expression significantly decreased OS and DSS, and that overexpression of DLAT in LGG was significantly linked with WHO grade, IDH status, primary therapy outcome, overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) events. DLAT was discovered as a separate predictive sign of OS in the end. DLAT might thus represent a brand-new predictive biomarker.

ANKZF1 knockdown inhibits glioblastoma progression by promoting intramitochondrial protein aggregation through mitoRQC

Protein homeostasis is fundamental to the development of tumors. Ribosome-associated quality-control (RQC) is able to add alanine and threonine to the stagnant polypeptide chain C-terminal (CAT-tail) when protein translation is hindered, while Ankyrin repeat and zinc-finger domain-containing-protein 1 (ANKZF1) can counteract the formation of the CAT-tail, preventing the aggregation of polypeptide chains. In particular, ANKZF1 plays an important role in maintaining mitochondrial protein homeostasis by mitochondrial RQC (mitoRQC) after translation stagnation of precursor proteins targeting mitochondria. However, the role of ANKZF1 in glioblastoma is unclear. Therefore, the current study was aimed to investigate the effects of ANKZF1 in glioblastoma cells and a nude mouse glioblastoma xenograft model. Here, we reported that knockdown of ANKZF1 in glioblastoma cells resulted in the accumulation of CAT-tail in mitochondria, leading to the activated mitochondrial unfolded protein response (UPRmt) and inhibits glioblastoma malignant progression. Excessive CAT-tail sequestered mitochondrial chaperones HSP60, mtHSP70 and proteases LONP1 as well as mitochondrial respiratory chain subunits ND1, Cytb, mtCO2 and ATP6, leading to mitochondrial oxidative phosphorylation dysfunction, membrane potential impairment, and mitochondrial apoptotic pathway activation. Our study highlights ANKZF1 as a valuable target for glioblastoma intervention and provides an innovative insight for the treatment of glioblastoma through the regulating of mitochondrial protein homeostasis.

Integrated analysis identifies cuproptosis-related gene DLAT and its competing endogenous RNAs network to predict the prognosis of pancreatic adenocarcinoma patients

Pancreatic adenocarcinoma (PAAD) is a highly malignant tumor with poor prognosis. However, the relationship between cuproptosis-related genes (CRGs) and its competing endogenous RNA (ceRNA) network with the prognosis of PAAD patients remains unclear. To investigate this relationship, we calculated the difference in CRGs between PAAD tissues and normal tissues using the 'limma' R package. Additionally, we employed least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct a prognostic signature for CRGs. Survival analysis of patients with PAAD was performed using Kaplan-Meier analysis. Furthermore, we used bioinformatics tools to screen for CRGs-related MicroRNA (miRNA) and lncRNAs. To validate these findings, we conducted real-time quantitative polymerase chain reaction (RT-qPCR), CCK-8, colony formation, and Transwell assays to assess the effect of DLAT in vitro. Our results revealed a cuproptosis-related prognostic signature consisting of 3 prognostic genes (DLAT, LIAS, and LIPT1). Notably, patients with a high-risk score for the CRGs signature exhibited poor prognosis in terms of overall survival (OS) (P < .05). The receiver operating characteristic (ROC) curve was used to evaluate the prognostic signature of CRGs. The results showed that the 1-year, 3-year, and 5-year area under the curve values for predicting OS were 0.62, 0.66, and 0.79, respectively. Additionally, the CRGs-related ceRNA network revealed the regulatory axis of LINC00857/has-miR-1179/DLAT in PAAD. In vitro experiments demonstrated that knockdown of LINC00857 and DLAT inhibited the growth and invasion of PAAD cells. This study identified a CRG-related prognostic signature consisting of 3 biomarkers (DLAT, LIAS, and LIPT1) for PAAD. Furthermore, ceRNA network analysis suggested the involvement of the LINC00857/has-miR-1179/DLAT axis in the development of PAAD. Overall, this study provides theoretical support for the investigation of diagnostic and prognostic biomarkers as well as potential therapeutic targets in PAAD.

LINC02253 promote the malignant phenotype of Colon adenocarcinoma cells by up-regulating WWP1-mediated SMAD3 ubiquitination

OBJECTIVES

Colon adenocarcinoma (COAD) represents a type of common malignant tumor originating in the digestive tract. Long non-coding RNAs (lncRNAs) have been identified to engage in regulating the initiation and development of COAD. LncRNA LINC02253 has been reported abnormal expressed in COAD, but the underlying mechanism has not been discussed so far. This study aimed to determine the role and the molecular biology mechanism of LINC02253 in COAD progression and unearthed its specific molecular mechanism.

MATERIALS AND RESULTS

RT-qPCR and Western blot assays were conducted to detect gene expression. Function assays were performed to evaluate the effect of gene expression on COAD cell phenotype. Mechanism analyses were done to verify the association among genes after bioinformatics analysis. The obtained data revealed that LINC02253 demonstrated a high expression in COAD tissues and cells. This gene served as an oncogene, permitting to stimulate proliferation and suppress apoptosis of COAD cells. Mechanically, it was found that LINC02253 recruited FUS to stabilize WWP1 mRNA and WWP1 could mediate SMAD3 ubiquitination, thereby promoting the malignant phenotype formation of COAD cells.

CONCLUSIONS

LINC02253 was uncovered to exert an oncogenic role, enhancing the proliferation of COAD cells and repressing the cell apoptosis by recruiting FUS and encouraging WWP1-mediated SMAD3 ubiquitination.

Roles of cuproptosis-related gene DLAT in various cancers: a bioinformatic analysis and preliminary verification on pro-survival autophagy

Background

Studies have shown that the expressions and working mechanisms of Dihydrolipoamide S-acetyltransferase (DLAT) in different cancers vary. It is necessary to analyze the expressions and regulatory roles of DLAT in tumors systematically.

Methods

Online public-platform literature on the relationships between DLAT expression levels and tumor prognosis, methylation status, genetic alteration, drug sensitivity, and immune infiltration has been reviewed. The literature includes such documents as The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA), Tumor Immune Estimation Resource 2.0 (TIMER2.0), Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and Receiver Operating Characteristic plotter (ROC plotter). The molecular mechanisms of DLAT were explored with the Gene Set Enrichment Analysis (GSEA). The relationship between down-regulated DLAT and autophagy in two liver hepatocellular carcinoma (LIHC) cell lines was confirmed with the western blot method, colony formation assay, and transmission electron microscopy. Tissue microarrays were validated through the immunohistochemical staining of DLAT.

Results

DLAT is upregulated in the LIHC, lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), and stomach adenocarcinoma (STAD) tumors but is down-regulated in the head and neck squamous cell carcinoma (HNSC) and kidney renal clear cell carcinoma (KIRC) tumors in comparison with normal tissues. For LIHC patients treated with 5-Fluorouracil and Lenvatinib, the DLAT levels of those in the drug-resistant group are significantly high. In LIHC cells, autophagy will be inhibited, and cell death will be induced when DLAT breaks down. Moreover, there exist positive correlations between DLAT expression levels and infiltration of B cells, DC cells, Tregs, and CD8+ T cells in kidney chromophobe (KICH), breast invasive carcinoma (BRCA), prostate adenocarcinoma (PRAD), LIHC and HPV+ HNSC. In LIHC, markers of Tregs are positively correlated with DLAT. Compared with those of normal tissues, the staining intensity of DLAT and the amount of Tregs marker CD49d in LIHC increase.

Conclusions

Through this study, the expressions of DLAT in various cancer types can be understood comprehensively. It suggests that DLAT may be a prognostic marker for LIHC, LUAD, LUSC, STAD and KIRC. A high DLAT expression in LIHC may promote tumorigenesis by stimulating autophagy and inhibiting anti-tumor immunity.

Role of cuproptosis-related gene in lung adenocarcinoma

Backgrounds

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, which is the leading cause of cancer death. Dysregulation of cell proliferation and death plays a crucial role in the development of LUAD. As of recently, the role of a new form of cell death, cuproptosis, and it has attracted more and more attention. As of yet, it is not clear whether cuproptosis is involved in the progression of LUAD.

Methods

An integrated set of bioinformatics tools was utilized to analyze the expression and prognostic significance of cuproptosis-related genes. Meanwhile, a robust risk signature was developed using machine learning based on prognostic cuproptosis-related genes and explored the value of prognostic cuproptosis-related signature for clinical applications, functional enrichment and immune landscape. Lastly, the dysregulation of the cuproptosis-related genes in LUAD was validated by in vitro experiment.

Results

In this study, first, cuproptosis-related genes were found to be differentially expressed in LUAD patients of public databases, and nine of them had prognostic value. Next, a cuproptosis-related model with five features (DLTA, MTF1, GLS, PDHB and PDHA1) was constructed to separate the patients into high- and low-risk groups based on median risk score. Internal validation set and external validation set were used for model validation and evaluation. What's more, Enrichment analysis of differential genes and the WGCNA identified that cuproptosis-related signatures affected tumor prognosis by influencing tumor immunity. Small molecule compounds were predicted based on differential expressed genes to improve poor prognosis in the high-risk group and a nomogram was constructed to further advance clinical applications. In closing, our data showed that FDX1 affected the prognosis of lung cancer by altering the expression of cuproptosis-related signature.

Conclusion

A new cuproptosis-related signature for survival prediction was constructed and validated by machine learning algorithm and in vitro experiments to reflect tumor immune infiltration in LUAD patients. The purpose of this article was to provide a potential diagnostic and therapeutic strategy for LUAD.

Glycolysis-related gene dihydrolipoamide acetyltransferase promotes poor prognosis in hepatocellular carcinoma through the Wnt/β-catenin and PI3K/Akt signaling pathways

Background

Recent research suggests that dihydrolipoamide acetyltransferase (DLAT), which is a copper-induced cell death-related gene, is involved in multiple biological events in tumors. This study sought to investigate the relationship between DLAT and hepatocellular carcinoma (HCC).

Methods

In the Cancer Genome Atlas (TCGA) database, we first identified the differentially expressed gene (i.e., DLAT), then confirmed DLAT expression, and found a link between it and the prognosis of HCC patients. An internal validation nomogram was built based on a multivariate Cox regression analysis. Data from the Tumor Immune Estimation Resource (TIMER) database was used to examine the association between DLT and immunological cells. A gene set enrichment analysis (GSEA) was conducted to investigate the probable mechanism of action. Finally, in vitro cytological research was conducted to further examin the involvement of DLAT in HCC-related unfavorable biological events.

Results

The database screenings showed that DLAT was a differentially expressed molecule; that is, DLAT was more highly expressed in the cancer tissues than normal tissues. TCGA results and Kaplan-Meier-plotter data sets showed that HCC patients with reduced DLAT expression had greater disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI). The prediction model had a concordance index of 0.659 (0.614-0.704), which indicates high accuracy. According to the TIMER database, tumor cells in the HCC microenvironment may be able to bypass the immune system due to the expression of DLAT. The in vitro cytological tests showed that DLAT knockdown significantly decreased the proliferation and invasion of the HCC cells. It also inhibited the activity of the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K)/protein kinase B (Akt) and Wnt/β-catenin signaling pathways.

Conclusions

Decreased DLAT expression significantly prolongs the OS, PFI, and DSS of HCC patients. DLAT may be employed as a new predictive biomarker for HCC, and may be linked to the immune system in HCC patients. The tumor microenvironment (TME) may have a significant effect on the ability of tumor cells to evade the immune system. The PI3K/Akt and Wnt/β-catenin signaling pathways may affect the prognosis of HCC by interfering with DLAT. Given these findings, HCC may be an ideal target for the development of anti-cancer therapies.

Aberrant gene expression pattern in the glycolysis-cholesterol synthesis axis is linked with immune infiltration and prognosis in prostate cancer: A bioinformatics analysis

Aberrant lipid metabolism is an early event in tumorigenesis and has been found in a variety of tumor types, especially prostate cancer (PCa). Therefore, We hypothesize that PCa can be stratified into metabolic subgroups based on glycolytic and cholesterogenic related genes, and the different subgroups are closely related to the immune microenvironment. Bioinformatics analysis of genomic, transcriptomic, and clinical data from a comprehensive cohort of PCa patients was performed. Datasets included the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) dataset, GSE70768, our previously published PCa cohort. The unsupervised cluster analysis was employed to stratify PCa samples based on the expression of metabolic-related genes. Four molecular subtypes were identified, named Glycolytic, Cholesterogenic, Mixed, and Quiescent. Each metabolic subtype has specific features. Among the 4 subtypes, the cholesterogenic subtype exhibited better median survival, whereas patients with high expression of glycolytic genes showed the shortest survival. The mitochondrial pyruvate carriers (MPC) 1 exhibited expression difference between PCa metabolic subgroups, but not for MPCs 2. Glycolytic subtypes had lower immune cell scores, while Cholesterogenic subgroups had higher immune cell scores. Our results demonstrated that metabolic classifications based on specific glycolytic and cholesterol-producing pathways provide new biological insights into previously established subtypes and may guide develop personalized therapies for unique tumor metabolism characteristics.

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.

Cuproptosis-Related genes in the prognosis of colorectal cancer and their correlation with the tumor microenvironment

Colorectal cancer (CRC) is a common tumor disease of the digestive system with high incidence and mortality. Cuproptosis has recently been found to be a new form of cell death. The clinical significance of cuproptosis-related genes (CRGs) in CRC is not clear. In this study, The Cancer Genome Atlas Colon and Rectal Cancer dataset was used to analyze the relationship between CRGs and clinical characteristics of CRC by differential expression analysis and Kaplan–Meier survival (K-M) analysis. Based on CRGs, prognosis model and risk score of CRC was constructed in COADREAD by multivariate Cox analysis. Receiver operating curves (ROC) analysis, K-M analysis and calibration analysis in GDC TCGA Colon Cancer dataset were applied to validating model. Subsequently, the relationship between risk score of CRC and immune microenvironment was analyzed by multiple immune score algorithms. Finally, we found that most CRGs were differentially expressed between tumors and normal tissues. Some CRGs were differentially expressed among different clinical characteristics. K-M analysis showed that the CRGs were related to overall survival (OS), disease-specific survival, and progression-free survival. Subsequently, DLAT and CDKN2A were identified as risk factors for OS in CRC by multivariate Cox analysis, and the risk score was established. K–M analysis showed that there was a significant difference in OS between the high-risk and low-risk groups, which were grouped by risk score median. ROC analysis showed that the risk score performs well in predicting the 1-year, 3-year and 5-year OS. Enrichment analysis showed that the differentially expressed genes between the high- and low-risk groups were enriched in immune-related signaling pathways. Further analysis showed that there were significant differences in the levels of immune cells and stromal cells between the high- and low-risk groups. The high-risk group had higher levels of immune cells and interstitial cells. At the same time, the high-risk group had a higher immune escape ability, and the predicted immune treatment response in the high-risk group was poor. In conclusion, CRGs can be used as prognostic factors in CRC and are closely related to the levels of immune cells and stromal cells in the tumor microenvironment.

Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD.

Methods: The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes.

Results: The mRNAsi values of PAAD samples were significantly higher than those of normal samples (p &lt; 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors.

Conclusion: We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.

Effects of Glycolysis-Related Genes on Prognosis and the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a common and deadly malignancy worldwide. Current treatment methods for hepatocellular carcinoma have many disadvantages; thus, it is urgent to improve the efficacy of these therapies. Glycolysis is critical in the occurrence and development of tumors. However, survival and prognosis biomarkers related to glycolysis in HCC patients remain to be fully identified.

Methods: Glycolysis-related genes (GRGs) were downloaded from “The Molecular Signatures Database” (MSigDB), and the mRNA expression profiles and clinical information of HCC patients were obtained from TCGA. Consensus clustering was performed to classify the HCC patients into two subgroups. We used the least absolute shrinkage and selection operator (LASSO) regression analysis to construct the risk signature model. Kaplan–Meier (K-M) survival analysis was performed to evaluate the prognostic significance of the risk model, and the receiver operating characteristic (ROC) curve analysis was used to evaluate the prediction accuracy. The independent prediction ability of the risk model was validated by univariate and multivariate Cox regression analyses. The differences of immune infiltrates and relevant oncogenic signaling between different risk groups were compared. Finally, biological experiments were performed to explore the functions of screened genes.

Results: HCC patients were classified into two subgroups, according to the expression of prognostic-related GRGs. Almost all GRGs categorized in cluster 2 showed upregulated expressions, whereas GRGs in cluster 1 conferred survival advantages. GSEA identified a positive correlation between cluster 2 and the glycolysis process. Ten genes were selected for risk signature construction. Patients were assigned to high-risk and low-risk groups based on the median risk score, and K-M survival analysis indicated that the high-risk group had a shorter survival time. Additionally, the risk gene signature can partially affect immune infiltrates within the HCC microenvironment, and many oncogenic pathways were enriched in the high-risk group, including glycolysis, hypoxia, and DNA repair. Finally, in vitro knockdown of ME1 suppressed proliferation, migration, and invasion of hepatocellular carcinoma cells.

Conclusion: In our study, we successfully constructed and verified a novel glycolysis-related risk signature for HCC prognosis prediction, which is meaningful for classifying HCC patients and offers potential targets for the treatment of hepatocellular carcinoma.

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.

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

Background

Hepatocellular carcinoma (HCC) is the most common primary liver cancer in the world. Although great advances in HCC diagnosis and treatment have been achieved, due to the complicated mechanisms in tumor development and progression, the prognosis of HCC is still dismal. Recent studies have revealed that the Warburg effect is related to the development, progression and treatment of various cancers; however, there have been a few explorations of the relationship between glycolysis and HCC prognosis.

Methods

mRNA expression profiling was downloaded from public databases. Gene set enrichment analysis (GSEA) was used to explore glycolysis-related genes (GRGs), and the LASSO method and Cox regression analysis were used to identify GRGs related to HCC prognosis and to construct predictive models associated with overall survival (OS) and disease-free survival (DFS). The relationship between the predictive model and the tumor mutation burden (TMB) and tumor immune microenvironment (TIME) was explored. Finally, real-time PCR was used to validate the expression levels of the GRGs in clinical samples and different cell lines.

Results

Five GRGs (ABCB6, ANKZF1, B3GAT3, KIF20A and STC2) were identified and used to construct gene signatures to predict HCC OS and DFS. Using the median value, HCC patients were divided into low- and high-risk groups. Patients in the high-risk group had worse OS/DFS than those in the low-risk group, were related to higher TMB and were associated with a higher rate of CD4+ memory T cells resting and CD4+ memory T cells activated. Finally, real-time PCR suggested that the five GRGs were all dysregulated in HCC samples compared to adjacent normal samples.

Conclusions

We identified five GRGs associated with HCC prognosis and constructed two GRGs-related gene signatures to predict HCC OS and DFS. The findings in this study may contribute to the prediction of prognosis and promote HCC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09209-9.

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

Background

Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients.

Results

Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ.

Conclusions

We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00377-0.

Identification of a Hypoxia-Related Gene Model for Predicting the Prognosis and Formulating the Treatment Strategies in Kidney Renal Clear Cell Carcinoma

Purpose

The present study aimed to establish a hypoxia related genes model to predict the prognosis of kidney clear cell carcinoma (KIRC) patients using data accessed from The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database.

Methods

Patients’ data were downloaded from the TCGA and ICGC databases, and hypoxia related genes were accessed from the Molecular Signatures Database. The differentially expressed genes were evaluated and then the differential expressions hypoxia genes were screened. The TCGA cohort was randomly divided into a discovery TCGA cohort and a validation TCGA cohort. The discovery TCGA cohort was used for constructing the hypoxia genes risk model through Lasso regression, univariate and multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were used to assess the reliability and sensitivity of our model. Then, we established a nomogram to predict the probable one-, three-, and five-year overall survival rates. Lastly, the Tumor Immune Dysfunction and Exclusion (TIDE) score of patients was calculated.

Results

We established a six hypoxia-related gene prognostic model of KIRC patients in the TCGA database and validated in the ICGC database. The patients with high riskscore present poorer prognosis than those with low riskscore in the three TCGA cohorts and ICGC cohort. ROC curves show our six-gene model with a robust predictive capability in these four cohorts. In addition, we constructed a nomogram for KIRC patients in the TCGA database. Finally, the high risk-group had a high TIDE score than the patients with low riskscore.

Conclusions

We established a six hypoxia-related gene risk model for independent prediction of the prognosis of KIRC patients was established and constructed a robust nomogram. The different riskscores might be a biomarker for immunotherapy strategy.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the worst-prognosis malignant tumors. This study used bioinformatic analysis of the transcriptome sequencing data of HNSCC and the patients' survival and clinical data to construct a prediction signature of glycolysis-related genes as the prognostic risk markers.

Methods

Gene expression profile data about HNSCC tissues (n = 498) and normal tissues in the head and neck (n = 44) were got from The Cancer Genome Atlas (TCGA), as well as patients' survival and clinical data. Then, we obtained core genes; their expression in head and neck squamous cell carcinoma tissues is significantly different from that in normal head and neck tissues. The predicted glycolysis-related genes are screened through univariate Cox regression analysis, and then, the prognostic risk markers were constructed through further correction of multivariate Cox regression analysis. The Kaplan-Meier curve and receiver operating characteristic curve are used to analyze the potential value of these risk markers in diagnosis and prognosis. We also evaluated that the glycolysis-related prognostic risk markers composed of 6 oncogenes are correlated with clinical features, such as age, gender, grade, and clinical stage of the tumor, by univariate and multivariate Cox regression analyses.

Results

Differentially expressed glycolytic genes in HNSCC tissues and normal head and neck tissues were screened from TCGA databases using the bioinformatic method. We confirmed a set of six glycolytic genes that were significantly associated with OS in the test series. According to our analysis, the prognostic risk markers composed of HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 may be an independent risk factor for the prognosis of HNSCC.

Conclusions

Through this analysis, we constructed new prognostic risk markers related to glycolysis as a prognostic risk marker for patients with HNSCC and provided new ideas and molecular targets for the research and individualized treatment of HNSCC.