Identification of Five Glycolysis-Related Gene Signature and Risk Score Model for Colorectal Cancer

LGALS4 inhibits glycolysis and promotes apoptosis of colorectal cancer cells via β‑catenin signaling

Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths worldwide. Glycolysis serves a crucial role in the development of CRC. The aim of the present study was to investigate the function of lectin galactoside-binding soluble 4 (LGALS4) in the regulation of glycolysis and its therapeutic potential in CRC. In the present study, 175 overlapping differentially expressed genes were identified by comprehensive analysis of The Cancer Genome Atlas database and the GSE26571 CRC dataset from the Gene Expression Omnibus database. LGALS4 was identified as the central gene by prognostic analysis using the mimetic map construction method and least absolute shrinkage and selection operator Cox regression. In vitro experiments were performed to evaluate the effects of LGALS4 overexpression on CRC cell phenotype and aerobic glycolysis, as well as its relationship with β-catenin signaling. LGALS4 was significantly downregulated in CRC, with an average 3-fold decrease compared with LGALS4 expression levels in normal tissues. LGALS4 was also significantly associated with patient survival. LGALS4 overexpression inhibited CRC cell growth, induced cell cycle arrest and enhanced 5-fluorouracil (5-FU)-induced apoptosis. Specifically, LGALS4 overexpression resulted in a ~50% decrease in cell proliferation and a ~2-fold increase in apoptosis. In addition, LGALS4 overexpression inhibited aerobic glycolysis and reduced glucose-dependent and glycolytic activity in CRC cells. The downregulatory effect of LGALS4 on glycolysis-related genes was further enhanced by the addition of the β-catenin inhibitor XAV-939. LGALS4 expression decreased CRC progression by inhibiting glycolysis and affecting β-catenin signaling. Overexpression of LGALS4 reduced the proliferation and glycolytic capacity of CRC cells and also enhanced their sensitivity to 5-FU. These results may potentially provide new perspectives for CRC treatment and targets for future clinical intervention strategies.

Exploring glycolytic enzymes in disease: potential biomarkers and therapeutic targets in neurodegeneration, cancer and parasitic infections

Glycolysis, present in most organisms, is evolutionarily one of the oldest metabolic pathways. It has great relevance at a physiological level because it is responsible for generating ATP in the cell through the conversion of glucose into pyruvate and reducing nicotinamide adenine dinucleotide (NADH) (that may be fed into the electron chain in the mitochondria to produce additional ATP by oxidative phosphorylation), as well as for producing intermediates that can serve as substrates for other metabolic processes. Glycolysis takes place through 10 consecutive chemical reactions, each of which is catalysed by a specific enzyme. Although energy transduction by glucose metabolism is the main function of this pathway, involvement in virulence, growth, pathogen-host interactions, immunomodulation and adaptation to environmental conditions are other functions attributed to this metabolic pathway. In humans, where glycolysis occurs mainly in the cytosol, the mislocalization of some glycolytic enzymes in various other subcellular locations, as well as alterations in their expression and regulation, has been associated with the development and progression of various diseases. In this review, we describe the role of glycolytic enzymes in the pathogenesis of diseases of clinical interest. In addition, the potential role of these enzymes as targets for drug development and their potential for use as diagnostic and prognostic markers of some pathologies are also discussed.

A Glycolysis and gluconeogenesis-related model for breast cancer prognosis

BackgroundBreast cancer is a malignant tumor with high morbidity and mortality, which seriously endangers the health of women around the world. Biomarker-based exploration will be effective for better diagnosis, prediction and targeted therapy.ObjectiveTo construct biomarker models related to glycolysis and gluconeogenesis in breast cancer.MethodsThe gene expression of 932 breast cancer patients in the Cancer Genome Atlas (TCGA) database was analyzed by Gene Set Variation Analysis (GSVA) using glycolysis and gluconeogenesis-related pathways. Differential expression genes were searched for by the T-test. Univariate Cox proportional hazards model (COX) regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Multivariate COX regression were used to find clinically significant genes for prognostic survival. After that, the constructed gene signature was externally validated through the Gene Expression Omnibus (GEO). Finally, a nomogram was constructed to predict the survival of patients. In addition, analyzing the role of biomarkers in pan-cancer.ResultsA risk scoring model associated with glycolysis and gluconeogenesis was developed and validated. A nomogram was created to predict 2-, 3-, and 5- survival.ConclusionsThe predictive model accurately predicted the prognosis of breast cancer patients.

Pan-cancer analysis and the oncogenic role of Glypican 1 in hepatocellular carcinoma

Recent studies indicate that Glypican 1 (GPC-1) is aberrantly expressed and plays a key role in certain cancers, but little is known in the hepatocellular carcinoma. Raw data from TCGA, GTEx and TIMER databases were utilized to comprehensively analyze GPC-1 expression landscape in pan-cancer, and the biological function of GPC-1 was investigated in liver cancer cells. The results revealed that GPC-1 is highly expressed in HCC, negatively correlated with survival, and also positively correlated with immune infiltration and clinical stage. Furthermore, GPC-1 promoted cell proliferation and inhibited apoptosis in the HCC cell lines. WGCNA analysis and HCCDB database revealed that Akt acted as a key molecule related to GPC-1, influencing biological functions and regulating cell malignant behaviors via the AKT signaling pathway. In conclusion, our findings provide a relatively comprehensive understanding of the oncogenic role of GPC-1 in HCC, implying that GPC-1 could serve as an innovative therapeutic target.

Predictive significance of glycolysis-associated lncRNA profiles in colorectal cancer progression

BACKGROUND

The Warburg effect is a hallmark characteristic of colorectal cancer (CRC). Despite extensive research, the role of long non-coding RNAs (lncRNAs) in influencing the Warburg effect remains incompletely understood. Our study aims to identify lncRNAs that may modulate the Warburg effect by functioning as competing endogenous RNAs (ceRNAs).

METHODS

Utilizing bioinformatics approaches, we extracted glycolysis-associated gene data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and identified 101 glycolysis-related lncRNAs in CRC. We employed Univariable Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, and Multivariable Cox regression to develop a prognostic model comprising four glycolysis-linked lncRNAs. We then constructed a prognostic nomogram integrating this lncRNA model with other relevant clinical parameters.

RESULTS

The prognostic efficacy of our four-lncRNA signature and its associated nomogram was validated in both training and validation cohorts. Functional assays demonstrated significant glycolysis and hexokinase II (HK2) inhibition following the silencing of RUNDC3A - AS1, a key lncRNA in our prognostic signature, highlighting its regulatory importance in the Warburg effect.

CONCLUSIONS

Our research illuminates the critical role of glycolysis-centric lncRNAs in CRC. The developed prognostic model and nomogram underscore the pivotal prognostic and regulatory significance of the lncRNA RUNDC3A - AS1 in the Warburg effect in colorectal cancer.

Functional annotation with expression validation identifies novel metastasis-relevant genes from post-GWAS risk loci in sporadic colorectal carcinomas

BACKGROUND

Colorectal cancer (CRC) is the third highest incidence cancer and is the leading cause of cancer mortality worldwide. Metastasis to distal organ is the major cause of cancer mortality. However, the underlying genetic factors are unclear. This study aimed to identify metastasis-relevant genes and pathways for better management of metastasis-prone patients.

METHODS

A case-case genome-wide association study comprising 2677 sporadic Chinese CRC cases (1282 metastasis-positive vs 1395 metastasis-negative) was performed using the Human SNP6 microarray platform and analysed with the correlation/trend test based on the additive model. SNP variants with association testing -log10 p value ≥5 were imported into Functional Mapping and Annotation (FUMA) for functional annotation.

RESULTS

Glycolysis was uncovered as the top hallmark gene set. Transcripts from two of the five genes profiled, hematopoietic substrate 1 associated protein X 1 (HAX1) and hyaluronan-mediatedmotility receptor (HMMR), were significantly upregulated in the metastasis-positive tumours. In contrast to disease-risk variants, HAX1 appeared to act synergistically with HMMR in significantly impacting metastasis-free survival. Examining the subtype datasets with FUMA and Ingenuity Pathway Analysis (IPA) identified distinct pathways demonstrating sexual dimorphism in CRC metastasis.

CONCLUSIONS

Combining genome-wide association testing with in silico functional annotation and wet-bench validation identified metastasis-relevant genes that could serve as features to develop subtype-specific metastasis-risk signatures for tailored management of patients with stage I-III CRC.

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.

Identification and Validation of a Hypoxia and Glycolysis Prognostic Signatures in Lung Adenocarcinoma

Background: Lung adenocarcinoma (LUAD) represents a prevalent subtype of non-small cell lung cancer with a complex molecular landscape. Dysregulated cellular energetics, notably the interplay between hypoxia and glycolysis, has emerged as a hallmark feature of LUAD tumorigenesis and progression. In this study, we aimed to identify hypoxia and glycolysis related gene signatures and construct a prognostic model to enhance the clinical management of LUAD. Methods: A gene signature associated with hypoxia and glycolysis was established within the The Cancer Genome Atlas (TCGA) cohort and subsequently validated in the GSE31210 cohort. Additionally, a nomogram was formulated to aid in predictive modeling. Subsequently, an evaluation of the tumor microenvironment and immune checkpoints expression levels was conducted to discern disparities between low risk and high risk groups. Lastly, an exploration for drugs with potential effectiveness was carried out. Results: Our analyses revealed a distinct hypoxia and glycolysis related gene signature consisting of 6 genes significantly associated with LUAD patient survival. Integration of these genes into the prognostic model demonstrated superior predictive accuracy for patient outcomes. Furthermore, we developed a user-friendly nomogram that effectively translates the model's prognostic information into a practical tool for clinical decision-making. Conclusion: This study elucidates the critical role of hypoxia and glycolysis related genes in LUAD and offers a novel prognostic model with promising clinical utility. This model has the potential to refine risk stratification and guide personalized therapeutic interventions, ultimately improving the prognosis of LUAD patients.

Hypoxia-induced P4HA1 overexpression promotes post-ischemic angiogenesis by enhancing endothelial glycolysis through downregulating FBP1

BACKGROUND

Angiogenesis is essential for tissue repair in ischemic diseases, relying on glycolysis as its primary energy source. Prolyl 4-hydroxylase subunit alpha 1 (P4HA1), the catalytic subunit of collagen prolyl 4-hydroxylase, is a glycolysis-related gene in cancers. However, its role in glycolysis-induced angiogenesis remains unclear.

METHODS

P4HA1 expression was modulated using adenoviruses. Endothelial angiogenesis was evaluated through 5-ethynyl-2'-deoxyuridine incorporation, transwell migration, and tube formation assays in vitro. In vivo experiments measured blood flow and capillary density in the hindlimb ischemia (HLI) model. Glycolytic stress assays, glucose uptake, lactate production, and quantitative reverse transcription-polymerase chain reaction (RT-PCR) were employed to assess glycolytic capacity. Transcriptome sequencing, validated by western blotting and RT-PCR, was utilized to determine underlying mechanisms.

RESULTS

P4HA1 was upregulated in endothelial cells under hypoxia and in the HLI model. P4HA1 overexpression promoted angiogenesis in vitro and in vivo, while its knockdown had the opposite effect. P4HA1 overexpression reduced cellular α-ketoglutarate (α-KG) levels by consuming α-KG during collagen hydroxylation. Downregulation of α-KG reduced the protein level of a DNA dioxygenase, ten-eleven translocation 2 (TET2), and its recruitment to the fructose-1,6-biphosphatase (FBP1) promoter, resulting in decreased FBP1 expression. The decrease in FBP1 enhanced glycolytic metabolism, thereby promoting endothelial angiogenesis.

CONCLUSIONS

Hypoxia-induced endothelial P4HA1 overexpression enhanced angiogenesis by promoting glycolytic metabolism reprogramming through the P4HA1/α-KG/TET2/FBP1 pathway. The study's findings underscore the significance of P4HA1 in post-ischemic angiogenesis, suggesting its therapeutic potential for post-ischemic tissue repair.

Glucose metabolism-based signature predicts prognosis and immunotherapy strategies for colon adenocarcinoma

BACKGROUND

The global prevalence and metastasis rates of colon adenocarcinoma (COAD) are high, and therapeutic success is limited. Although previous research has primarily explored changes in gene phenotypes, the incidence rate of COAD remains unchanged. Metabolic reprogramming is a crucial aspect of cancer research and therapy. The present study aims to develop cluster and polygenic risk prediction models for COAD based on glucose metabolism pathways to assess the survival status of patients and potentially identify novel immunotherapy strategies and related therapeutic targets.

METHODS

COAD-specific data (including clinicopathological information and gene expression profiles) were sourced from The Cancer Genome Atlas (TCGA) and two Gene Expression Omnibus (GEO) datasets (GSE33113 and GSE39582). Gene sets related to glucose metabolism were obtained from the MSigDB database. The Gene Set Variation Analysis (GSVA) method was utilized to calculate pathway scores for glucose metabolism. The hclust function in R, part of the Pheatmap package, was used to establish a clustering system. The mutation characteristics of identified clusters were assessed via MOVICS software, and differentially expressed genes (DEGs) were filtered using limma software. Signature analysis was performed using the least absolute shrinkage and selection operator (LASSO) method. Survival curves, survival receiver operating characteristic (ROC) curves and multivariate Cox regression were analyzed to assess the efficacy and accuracy of the signature for prognostic prediction. The pRRophetic program was employed to predict drug sensitivity, with data sourced from the Genomics of Drug Sensitivity in Cancer (GDSC) database.

RESULTS

Four COAD subgroups (i.e., C1, C2, C3 and C4) were identified based on glucose metabolism, with the C4 group having higher survival rates. These four clusters were bifurcated into a new Clust2 system (C1 + C2 + C3 and C4). In total, 2175 DEGs were obtained (C1 + C2 + C3 vs. C4), from which 139 prognosis-related genes were identified. ROC curves predicting 1-, 3- and 5-year survival based on a signature containing nine genes showed an area under the curve greater than 0.7. Meanwhile, the study also found this feature to be an important predictor of prognosis in COAD and accordingly assessed the risk score, with higher risk scores being associated with a worse prognosis. The high-risk and low-risk groups responded differently to immunotherapy and chemotherapeutic agents, and there were differences in functional enrichment pathways.

CONCLUSIONS

This unique signature based on glucose metabolism may potentially provide a basis for predicting patient prognosis, biological characteristics and more effective immunotherapy strategies for COAD.

LINC00174 Promotes Colon Cancer Progression by Regulating Inflammation and Glycolysis by Targeting the MicroRNA-2467-3p/Enolase 3 Axis

OBJECTIVE

To elucidate the mechanism by which LINC00174 promotes colon cancer progression by targeting the microRNA-2467-3p (miR-2467-3p)/enolase 3 (ENO3) axis to regulate inflammation and glycolysis.

METHODS

The expression of LINC00174 and ENO3 in colon cancer tissues, its relationship with survival rate, and correlation were analyzed using bioinformatic analysis. The effects of LINC00174 overexpression and silencing on the biological behavior of and inflammation in colon cancer cells were analyzed via transfection experiments. The target relationships between miR-2467-3p or LINC00174 and ENO3 were verified using sequence prediction and the dual-luciferase reporter assay, respectively. Furthermore, LINC00174- and/or miR-2467-3p-overexpressing cells were prepared to determine the effects on ENO3 protein levels and glycolysis. Finally, the effects of LINC00174 and/or miR-2467-3p overexpression on colon cancer, ENO3 protein levels, and inflammation were analyzed using a tumor-bearing mice model.

RESULTS

LINC00174 and ENO3 were overexpressed and associated with a lower survival rate. LINC00174 was positively correlated with ENO3 in colon cancer tissues. Furthermore, the overexpression of LINC00174 in colon cancer cell lines promoted the proliferation, migration, and invasion of colon cancer cells and inflammation but inhibited apoptosis. The overexpression of miR-2467-3p inhibited ENO3 protein levels, which was attenuated via LINC00174 overexpression. Furthermore, it inhibited the biological behavior of and inflammation and glycolysis in colon cancer cells and blocked their LINC00174-induced promotion. Moreover, using animal experiments, the regulatory effects of LINC00174 on tumor growth, ENO3 protein levels, and inflammation via miR-2467-3p were confirmed.

CONCLUSION

LINC00174 promotes the glycolysis, inflammation, proliferation, migration, and invasion of colon cancer cells and inhibits apoptosis. The cancer-promoting mechanism of LINC00174 is related to targeting miR-2467-3p to promote ENO3 protein levels.

Machine learning-based glycolysis-associated molecular classification reveals differences in prognosis, TME, and immunotherapy for colorectal cancer patients

Background

Aerobic glycolysis is a process that metabolizes glucose under aerobic conditions, finally producing pyruvate, lactic acid, and ATP for tumor cells. Nevertheless, the overall significance of glycolysis-related genes in colorectal cancer and how they affect the immune microenvironment have not been investigated.

Methods

By combining the transcriptome and single-cell analysis, we summarize the various expression patterns of glycolysis-related genes in colorectal cancer. Three glycolysis-associated clusters (GAC) were identified with distinct clinical, genomic, and tumor microenvironment (TME). By mapping GAC to single-cell RNA sequencing analysis (scRNA-seq), we next discovered that the immune infiltration profile of GACs was similar to that of bulk RNA sequencing analysis (bulk RNA-seq). In order to determine the kind of GAC for each sample, we developed the GAC predictor using markers of single cells and GACs that were most pertinent to clinical prognostic indications. Additionally, potential drugs for each GAC were discovered using different algorithms.

Results

GAC1 was comparable to the immune-desert type, with a low mutation probability and a relatively general prognosis; GAC2 was more likely to be immune-inflamed/excluded, with more immunosuppressive cells and stromal components, which also carried the risk of the poorest prognosis; Similar to the immune-activated type, GAC3 had a high mutation rate, more active immune cells, and excellent therapeutic potential.

Conclusion

In conclusion, we combined transcriptome and single-cell data to identify new molecular subtypes using glycolysis-related genes in colorectal cancer based on machine-learning methods, which provided therapeutic direction for colorectal patients.

Identification of a hypoxia-related gene prognostic signature in colorectal cancer based on bulk and single-cell RNA-seq

Colorectal cancer (CRC) is the most common and fatal tumor in the gastrointestinal system. Its incidence and mortality rate have increased in recent years. Hypoxia, a persistent physiological tumor feature, plays a vital role in CRC tumorigenesis, metastasis, and tumor microenvironment (TME). Therefore, we constructed a hypoxia-related gene (HRG) nomogram to predict overall survival (OS) and explored the role of HRGs in the CRC TME. The Cancer Genome Atlas (TCGA) dataset was used as the training set, and two Gene Expression Omnibus datasets (GSE39582 and GSE103479) were used as the testing sets. HRGs were identified using the Gene Set Enrichment Analysis (GSEA) database. An HRG prognostic model was constructed in the training set using the least absolute shrinkage and selection operator regression algorithm and validated in the testing sets. Then, we analyzed tumor-infiltrating cells (TICs) using the cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm. Furthermore, single-cell next-generation RNA sequencing (RNA-seq) was used to investigate HRG expression in different TICs in the GSE139555 dataset. Finally, reverse transcription polymerase chain reactions (RT-PCR) were used to validate HRG mRNA expression in ten pairs of CRC normal and cancer tissue samples. A six HRG prognostic signature was constructed, with a superior OS prediction ability in CRC patients (area under the receiver operating characteristic curve (AUC) at one year: 0.693, AUC at three years: 0.712, and AUC at five years: 0.780). GSEA enrichment analysis identified six pathways enriched in the high-risk group. The TIC analysis indicated that the high-risk group had lower T-cell expression and higher neutrophil expression than the low-risk group. Furthermore, immune-related genes had an inseparable relationship with the HRG prognostic signature. Based on single-cell RNA-seq data, we found elevated hexokinase 1 (HK1) and glucose-6-phosphate isomerase (GPI) gene expression in natural killer (NK) and CD8⁺ T cells. RT-PCR in ten CRC normal-tumor tissue pairs showed that expression of the signature's six HRGs varied differently in cancerous and paracancerous tissues. The constructed HRG signature successfully predicted the OS of whole-stage CRC patients. In addition, we showed that the signature's six HRGs were closely associated with the TME in CRC, where hypoxia inhibits the antitumor function of T cells.

A novel NSUN5/ENO3 pathway promotes the Warburg effect and cell growth in clear cell renal cell carcinoma by 5-methylcytosine-stabilized ENO3 mRNA

OBJECTIVES

Clear cell renal cell carcinoma (ccRCC) cells often reprogram their metabolisms. Enolase 3 (ENO3) is closely related to the Warburg effect observed in cells during tumor progression. However, the expression and function of ENO3 in ccRCC cells remain unclear. Therefore, this study investigated the expression and functional significance of ENO3 in the Warburg effect observed in ccRCC cells.

METHODS

In this study, B-mode and microflow imaging ultrasound examinations were performed to evaluate patients with ccRCC. The extracellular acidification rate test and glucose uptake and lactate production assays were used to examine the Warburg effect in ccRCC cells. Western blotting, quantitative reverse transcription polymerase chain reaction, and immunochemistry were used to detect the expression of ENO3 and NOP2/Sun RNA methyltransferase 5 (NSUN5).

RESULTS

ENO3 upregulation in ccRCC tumor tissues was accompanied by an increase in tumor size. Importantly, ENO3 participated in the Warburg effect observed in ccRCC cells, and high levels of ENO3 indicated a poor prognosis for patients. Loss of ENO3 reduced glucose uptake, lactate production, and extracellular acidification rate as well as inhibited ccRCC cell proliferation. Furthermore, NSUN5 was involved in the ENO3-regulated Warburg effect and ccRCC cell progression. Mechanically, NSUN5 was upregulated in ccRCC tissues, and NSUN5 upregulation mediated 5-methylcytosine modification of messenger RNA (mRNA) in ccRCC cells to promote mRNA stability and ENO3 expression.

CONCLUSIONS

Collectively, the destruction of the NSUN5/ENO3 axis prevents ccRCC growth in vivo and in vitro, and targeting this pathway may be an effective strategy against ccRCC progression.

A novel signature model based on mitochondrial-related genes for predicting survival of colon adenocarcinoma

Background

Colon cancer is the foremost reason of cancer-related mortality worldwide. Colon adenocarcinoma constitutes 90% of colon cancer, and most patients with colon adenocarcinoma (COAD) are identified until advanced stage. With the emergence of an increasing number of novel pathogenic mechanisms and treatments, the role of mitochondria in the development of cancer, has been studied and reported with increasing frequency.

Methods

We systematically analyzed the effect of mitochondria-related genes in COAD utilizing RNA sequencing dataset from The Cancer Genome Atlas database and 1613 mitochondrial function-related genes from MitoMiner database. Our approach consisted of differentially expressed gene, gene set enrichment analysis, gene ontology terminology, Kyoto Encyclopedia of Genes and Genomes, independent prognostic analysis, univariate and multivariate analysis, Kaplan–Meier survival analysis, immune microenvironment correlation analysis, and Cox regression analysis.

Results

Consequently, 8 genes were identified to construct 8 mitochondrial-related gene model by applying Cox regression analysis, CDC25C, KCNJ11, NOL3, P4HA1, QSOX2, Trap1, DNAJC28, and ATCAY. Meanwhile, we assessed the connection between this model and clinical parameters or immune microenvironment. Risk score was an independent predictor for COAD patients’ survival with an AUC of 0.687, 0.752 and 0.762 at 1-, 3- and 5-year in nomogram, respectively. The group with the highest risk score had the lowest survival rate and the worst clinical stages. Additionally, its predictive capacity was validated in GSE39582 cohort.

Conclusion

In summary, we established a prognostic pattern of mitochondrial-related genes, which can predict overall survival in COAD, which may enable a more optimized approach for the clinical treatment and scientific study of COAD. This gene signature model has the potential to improve prognosis and treatment for COAD patients in the future, and to be widely implemented in clinical settings. The utilization of this mitochondrial-related gene signature model may be benefit in the treatments and medical decision-making of COAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12911-022-02020-3.

The Metabolic Landscape in Osteoarthritis

Articular cartilage function depends on the temporal and zonal distribution of coordinated metabolic regulation in chondrocytes. Emerging evidence shows the importance of cellular metabolism in the molecular control of the cartilage and its dysregulation in degenerative diseases like osteoarthritis (OA). Compared to most other tissues, chondrocytes are sparsely located in the extracellular matrix, lacking the typical proximity of neural, vascular, and lymphatic tissue. Making up under 5% of the total tissue weight of cartilage, chondrocytes have a relative deficiency of access to nutrients and oxygen, as well as limited pathways for metabolite removal. This makes cartilage a unique tissue with hypocellularity, prolonged metabolic rate, and tissue turnover. Studies in the past decade have shown that several pathways of central carbon metabolism are essential for cartilage homeostasis. Here, we summarised the literature findings on the role of cellular metabolism in determining the chondrocyte function and how this metabolic dysregulation led to cartilage aging in OA and provided an outlook on how the field may evolve in the coming years. Although the various energy metabolism pathways are inextricably linked with one another, for the purpose of this review, we initially endeavoured to examine them individually and in relative isolation. Subsequently, we comment on what is known regarding the integration and linked signalling pathways between these systems and the therapeutic opportunities for targeting OA metabolism.

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.

Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

BACKGROUND

Most prognostic signatures for colorectal cancer (CRC) are developed to predict overall survival (OS). Gene signatures predicting recurrence-free survival (RFS) are rarely reported, and postoperative recurrence results in a poor outcome. Thus, we aim to construct a robust, individualized gene signature that can predict both OS and RFS of CRC patients.

METHODS

Prognostic genes that were significantly associated with both OS and RFS in GSE39582 and TCGA cohorts were screened via univariate Cox regression analysis and Venn diagram. These genes were then submitted to least absolute shrinkage and selection operator (LASSO) regression analysis and followed by multivariate Cox regression analysis to obtain an optimal gene signature. Kaplan-Meier (K-M), calibration curves and receiver operating characteristic (ROC) curves were used to evaluate the predictive performance of this signature. A nomogram integrating prognostic factors was constructed to predict 1-, 3-, and 5-year survival probabilities. Function annotation and pathway enrichment analyses were used to elucidate the biological implications of this model.

RESULTS

A total of 186 genes significantly associated with both OS and RFS were identified. Based on these genes, LASSO and multivariate Cox regression analyses determined an 8-gene signature that contained ATOH1, CACNB1, CEBPA, EPPHB2, HIST1H2BJ, INHBB, LYPD6, and ZBED3. Signature high-risk cases had worse OS in the GSE39582 training cohort (hazard ratio [HR] = 1.54, 95% confidence interval [CI] = 1.42 to 1.67) and the TCGA validation cohort (HR = 1.39, 95% CI = 1.24 to 1.56) and worse RFS in both cohorts (GSE39582: HR = 1.49, 95% CI = 1.35 to 1.64; TCGA: HR = 1.39, 95% CI = 1.25 to 1.56). The area under the curves (AUCs) of this model in the training and validation cohorts were all around 0.7, which were higher or no less than several previous models, suggesting that this signature could improve OS and RFS prediction of CRC patients. The risk score was related to multiple oncological pathways. CACNB1, HIST1H2BJ, and INHBB were significantly upregulated in CRC tissues.

CONCLUSION

A credible OS and RFS prediction signature with multi-cohort and cross-platform compatibility was constructed in CRC. This signature might facilitate personalized treatment and improve the survival of CRC patients.

Stanniocalcin 2 (STC2): a universal tumour biomarker and a potential therapeutical target

Stanniocalcin 2 (STC2) is a glycoprotein which is expressed in a broad spectrum of tumour cells and tumour tissues derived from human breast, colorectum, stomach, esophagus, prostate, kidney, liver, bone, ovary, lung and so forth. The expression of STC2 is regulated at both transcriptional and post-transcriptional levels; particularly, STC2 is significantly stimulated under various stress conditions like ER stress, hypoxia and nutrient deprivation. Biologically, STC2 facilitates cells dealing with stress conditions and prevents apoptosis. Importantly, STC2 also promotes the development of acquired resistance to chemo- and radio- therapies. In addition, multiple groups have reported that STC2 overexpression promotes cell proliferation, migration and immune response. Therefore, the overexpression of STC2 is positively correlated with tumour growth, invasion, metastasis and patients' prognosis, highlighting its potential as a biomarker and a therapeutic target. This review focuses on discussing the regulation, biological functions and clinical importance of STC2 in human cancers. Future perspectives in this field will also be discussed.

ENO3 promotes colorectal cancer progression by enhancing cell glycolysis

BACKGROUND

Colorectal cancer (CRC) is among the leading cause of cancer-related morbidity and mortality worldwide. Aerobic glycolysis, as a metabolic hallmark of cancer, plays an important role in CRC progression. Enolase 3 (ENO3) is a glycolytic enzyme that catalyzes 2-phosphoglycerate into phosphoenolpyruvate, while its role in CRC is still unknown.

METHODS

Bioinformatics analysis was performed to examine the expression changes and roles of ENO3 in CRC patients from public databases. Then, ENO3 expression was validated in CRC tissues using Quantitative real-time PCR (qRT-PCR), immunohistochemical (IHC) analysis, and western blot. Overexpression and silencing models were constructed using plasmid and lentivirus transfection. Cell viability, proliferation, and migration in vitro were applied to evaluate the protumoral effects of ENO3 on CRC. RNA sequencing and GO enrichment analysis of differentially expressed genes (DEGs) were performed to explore the underlying molecular mechanisms of ENO3 in CRC progression. The ATP and lactate production level were detected to assess cell glycolysis.

RESULTS

ENO3 was significantly up-regulated in CRC. High ENO3 expression was positively correlated with poor prognosis and higher clinical stages of CRC patients. ROC curve demonstrated the diagnostic value of ENO3 for CRC with the AUC of 0.802. Gain- and loss-of function experiments demonstrated that ENO3 significantly enhanced the proliferation and migration ability of CRC cells in vitro. After ENO3 knockdown, RNA sequencing screened out a list of DEGs which were enriched in the regulation of the glycolytic process. The detection of lactate production and ATP level verified the role of ENO3 in the glycolytic process.

CONCLUSION

Our findings illustrate that ENO3 could promote the progression of CRC by the enhancement of cell glycolysis, indicating the potential value of ENO3 as a novel biomarker and therapeutic target for CRC.

ENO3 Inhibits Growth and Metastasis of Hepatocellular Carcinoma via Wnt/β-Catenin Signaling Pathway

β-enolase (ENO3) is a metalloenzyme that functions during glycolysis and has been revealed ectopic expression in different cancers. However, the function and underlying modulatory mechanisms of ENO3 in hepatocellular carcinoma (HCC) are still elusive. Here, we discovered that ENO3 was remarkably down-regulated in human HCC tissue in contrast to those in noncancerous tissue. Moreover, low expression of ENO3 was related to the poor prognosis of HCC patients. Overexpression of ENO3 suppressed proliferative, migratory, and invasive abilities of HCC cells both in vitro and in vivo, whereas knocking down ENO3 led to the opposite effect. In addition, we revealed that ENO3 repressed the epithelial-mesenchymal transition (EMT) process with its biomarker variations. Mechanistic research unveiled that ENO3 suppressed the Wnt/β-catenin signal, which subsequently modulated the transcription of its target genes associated with the proliferation and metastasis capacity of HCC cells. Taken together, our study uncovered that ENO3 acted as a tumor inhibitor in HCC development and implied ENO3 as a promising candidate for HCC treatment.

Development of an Aging-Related Gene Signature for Predicting Prognosis, Immunotherapy, and Chemotherapy Benefits in Rectal Cancer

Objective: Aging is the major risk factor for human cancers, including rectal cancer. Targeting the aging process provides broad-spectrum protection against cancers. Here, we investigate the clinical implications of aging-related genes in rectal cancer.

Methods: Dysregulated aging-related genes were screened in rectal cancer from TCGA project. A LASSO prognostic model was conducted, and the predictive performance was evaluated and externally verified in the GEO data set. Associations of the model with tumor-infiltrating immune cells, immune and stromal score, HLA and immune checkpoints, and response to chemotherapeutic agents were analyzed across rectal cancer. Biological processes underlying the model were investigated through GSVA and GSEA methods. Doxorubicin (DOX)-induced or replicative senescent stromal cells were constructed, and AGTR1 was silenced in HUVECs. After coculture with conditioned medium of HUVECs, rectal cancer cell growth and invasion were investigated.

Results: An aging-related model was established, consisting of KL, BRCA1, CLU, and AGTR1, which can stratify high- and low-risk patients in terms of overall survival, disease-free survival, and progression-free interval. ROC and Cox regression analyses confirmed that the model was a robust and independent predictor. Furthermore, it was in relation to tumor immunity and stromal activation as well as predicted the responses to gemcitabine and sunitinib. AGTR1 knockdown ameliorated stromal cell senescence and suppressed senescent stromal cell-triggered rectal cancer progression.

Conclusion: Our findings suggest that the aging-related gene signature was in relation to tumor immunity and stromal activation in rectal cancer, which might predict survival outcomes and immuno- and chemotherapy benefits.

Identification of a Ferroptosis-Related Long Noncoding RNA Prognostic Signature and Its Predictive Ability to Immunotherapy in Hepatocellular Carcinoma

Background: Immune checkpoint blockers (ICBs) are increasingly being used to treat patients with advanced hepatocellular carcinoma (HCC), but only a third of these patients are sensitive to ICBs. Emerging evidence suggests that ferroptosis could be a novel target for antitumor treatment, and combined treatment with ferroptosis inducers might enhance sensitivity to immunotherapy. However, there is a lack of information on the crosstalk between ferroptosis-related lncRNAs and anti-tumor immunity. Therefore, we aim to explore prognostic value of ferroptosis-related lncRNAs and clarify potential role in ICBs of HCC.

Methods: We obtained mRNA and lncRNA expression data from two independent cohorts (TCGA and GEO database). Univariate Cox, the least absolute shrinkage and selection operator (Lasso) algorithm and multivariate Cox analysis were used to construct a lncRNA signature, which was evaluated using the area under the receiver operating characteristic curve (AUC) and Kaplan–Meier curves. Tumor-infiltrating cell (TIC) profiling and the tumor immune dysfunction and exclusion (TIDE) algorithm were used to validate the signature model and immunotherapy. Finally, we adopted RT-PCR assay to evaluate the differential expression of lncRNAs in HCC tissues in our hospital.

Results: The ferroptosis-related lncRNA signature included five lncRNAs, most of which were positively correlated with clinical stage and grade. The signature could stratify patients into two risk groups, with the high-risk group associated with a shorter overall survival (OS, p < 0.05) in TCGA-LIHC and GSE76427. Besides, the AUCs of the 1-, 3-, and 5-years OS were 0.772, 0.707, and 0.666, respectively. Gene set enrichment analysis (GESA) of lncRNAs revealed enrichment of oncogenic and immune-related pathways. The TIC profiling indicated a close correlation between the signature and immune cells. Furthermore, the high-risk group had a better response to immunotherapy than low-risk group. RT-PCR demonstrated these five lncRNAs were upregulated in cancerous tissue than normal tissues.

Conclusions: The ferroptosis-related lncRNA signature could accurately predict the OS of HCC patients and may serve as an independent clinical factor for patients’ outcomes. Ferroptosis-related lncRNAs may remodel the tumor microenvironment (TME) and affect the anti-cancer ability of ICBs, and therefore, could potentially act as an indicator for the response to immunotherapy in HCC.

Glycolysis related gene expression signature in predicting prognosis of laryngeal squamous cell carcinoma

Researches have suggested that aerobic glycolysis can reflect the development and progression of most carcinomas. We aimed to investigate whether glycolysis-related genes (GRGs) are associated with overall survival in laryngeal squamous cell carcinoma (LSCC). Here, we identified differentially expressed GRGs in TCGA dataset and microarray sample of GSE27020 from GEO database. A set of two glycolytic gene signatures, including DDIT4 and PLOD2 was screened through Cox and Lasso regression. The risk score was calculated using the gene expression of the two GRGs. The high-risk group presented a poor prognosis through Kaplan–Meier method. The ROC curve indicated good prediction performance in survival based on the validation of four cohorts. Univariate and multivariate Cox regression analyses suggested that two-gene signature could be an independent risk factor in LSCC. A total of 17 LSCC patients were enrolled to clarify the genetic expression through using reverse transcription-polymerase chain reaction (RT-PCR). A visualized nomogram was then constructed to predict 1-, 3-, and 5-year overall survival. Taken together, two novel glycolytic gene signatures were discovered and validated, providing a potential therapeutic and overall survival (OS)-prediction biomarker for LSCC.

Human sperm-associated antigen 4 as a potential prognostic biomarker of lung squamous cell carcinoma

OBJECTIVE

Lung cancer (LC) is one of the most prevalent malignant tumors worldwide. As a subtype of LC, lung squamous cell carcinoma (LUSC) has a 5-year survival rate of less than 15%. In this study, we aimed to evaluate the prognostic value of a glycolysis-related gene signature in LUSC patients.

METHODS

We obtained RNA-Seq data from The Cancer Genome Atlas (TCGA) database. Prognosis-related genes were screened out by Gene Set Enrichment Analysis (GSEA) and Cox proportional regression models. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the mRNA expression levels in relevant tissues.

RESULTS

We found that sperm-associated antigen 4 (SPAG4) overexpression was an independent risk factor for overall survival (OS) in LUSC. Patients with high-risk scores had higher mortality rates than those with low-risk scores. Moreover, by using RT-qPCR, we validated that SPAG4 mRNA was overexpressed in LUSC tissue samples compared with their paired para-cancerous histological normal tissues.

CONCLUSIONS

Analysis of aberrantly overexpressed SPAG4 may provide a further useful approach to complement existing methods and predict prognosis in LUSC patients.