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

Identification of a mitophagy-related gene signature for predicting overall survival and response to immunotherapy in rectal cancer

BACKGROUND

Rectal cancer is a highly heterogeneous gastrointestinal tumor, and the prognosis for patients with treatment-resistant and metastatic rectal cancer remains poor. Mitophagy, a type of selective autophagy that targets mitochondria, plays a role in promoting or inhibiting tumors; however, the importance of mitophagy-related genes (MRGs) in the prognosis and treatment of rectal cancer is unclear.

METHODS

In this study, we used the differentially expressed genes (DEGs) and MRGs from the TCGA-READ dataset to identify differentially expressed mitophagy-related genes (MRDEGs). The mitophagy scores were then analyzed for differential expression and ROC. Seven module genes were identified using the weighted gene coexpression network analysis (WGCNA) approach and subsequently validated in the merged datasets GSE87211 and GSE90627. The model genes were obtained based on prognostic features, and the subgroups were distinguished by risk score. Gene enrichment, immune infiltration and immunotherapy response were also evaluated. Finally, validation of prognostic gene expression in rectal cancer was carried out using clinical samples, employing Immunohistochemistry (IHC).

RESULTS

We demonstrated that 22 MRGs were differentially expressed between normal and rectal cancer tissues. A prognostic model for rectal cancer MRGs was constructed using WGCNA and Cox regression, which exhibited good diagnostic performance. In this study, we identified four molecular markers (MYLK, FLNC, MYH11, and NEXN) as potential prognostic biomarkers for rectal cancer for the first time. Moreover, our findings indicate that the risk scores derived from the four MRGs are associated with tumor immunity. To further validate our findings, IHC analyses suggested that the expression of MYH11 in rectal cancer tissues was lower than in nontumorous rectal tissues.

CONCLUSION

MRGs could predict the prognosis and response to immunotherapy in patients with rectal cancer and might be able to personalize treatment for patients.

Construction of an Oxidative Stress Risk Model to Analyze the Correlation Between Liver Cancer and Tumor Immunity

BACKGROUND

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally. Despite advancements in immunotherapy, the prognosis for patients with HCC continues to be poor. As oxidative stress plays a significant role in the onset and progression of various diseases, including metabolism-related HCC, comprehending its mechanism in HCC is critical for effective diagnosis and treatment.

METHODS

This study utilized the TCGA dataset and a collection of oxidative stress genes to identify the expression of oxidative stress-related genes in HCC and their association with overall survival using diverse bioinformatics methods. A novel prognostic risk model was developed, and the TCGA cohort was divided into high-risk and low-risk groups based on each tumor sample's risk score. Levels of immune cell infiltration and the expression of immune checkpoint-related genes in different risk subgroups were analyzed to investigate the potential link between tumor immunity and oxidative stress-related features. The expression of model genes in actual samples was validated through immunohistochemistry, and their mRNA and protein expression levels were measured in cell cultures.

RESULTS

Four oxidative stress-related genes (EZH2, ANKZF1, G6PD, and HMOX1) were identified and utilized to create a predictive risk model for HCC patient overall survival, which was subsequently validated in an independent cohort. A correlation was found between the expression of these prognostic genes and the infiltration of tumor immune cells. Elevated expression of EZH2, ANKZF1, G6PD, and HMOX1 was observed in both HCC tissues and cell lines.

CONCLUSION

The combined assessment of EZH2, ANKZF1, G6PD, and HMOX1 gene expression can serve as an oxidative stress risk model for assessing HCC prognosis. Furthermore, there is a correlation between the expression of these risk model genes and tumor immunity.

Identification and validation of KIF20A for predicting prognosis and treatment outcomes in patients with breast cancer

Breast cancer is a leading cause of cancer-related deaths among women globally. It is imperative to explore novel biomarkers to predict breast cancer treatment response as well as progression. Here, we collected six breast cancer samples and paired normal tissues for high-throughput sequencing. By differential expression analysis, we found 1687 DEGs and identified the top 10 hub genes, including TOP2A, CDK1, BUB1B, KIF11, CCNA2, BUB1, CCNB1, KIF20A, DLGAP5 and CDC20. Univariate and multivariate Cox analyses on the METABRIC database and GSE96058 dataset demonstrated that KIF20A was an independent prognostic predictor for overall survival. KIF20A was positively correlated with cell cycle phases, including the cell cycle process, cycle G2 M phase transition and cell cycle DNA replication initiation. Single-cell analyses revealed that KIF20A was enriched in fibroblasts and endothelial within breast cancer stroma. Meanwhile, multidrug resistance (MDR) genes ABCB1, ABCC1 and ABCG2 were co-expressed with KIF20A in fibroblasts and endothelial cells within the stroma. MTABRIC database confirmed that high expression of KIF20A was positively correlated with treatment efficacy in patients with breast cancer. In conclusion, KIF20A could be served as a predictive biomarker for breast cancer prognosis and treatment outcomes. KIF20A may play a significant role by regulating cell cycle progression and modulating stromal progression in breast cancer. Our findings provided novel molecular insights that can guide personalized treatment strategies in breast cancer.

PCK1 as a target for cancer therapy: from metabolic reprogramming to immune microenvironment remodeling

Recently, changes in metabolites and metabolism-related enzymes related to tumor cell proliferation, metastasis, drug resistance, and immunosuppression have become a research hotspot, and researchers have attempted to determine the clinical correlation between specific molecular lesions and metabolic phenotypes. Convincing evidence shows that metabolic reprogramming is closely related to the proliferation, invasion, metastasis, and poor prognosis of malignant tumors. Therefore, targeting metabolic reprogramming is a new direction for cancer treatment. However, how molecular alterations in tumors contribute to metabolic diversity and unique targeting dependencies remains unclear. A full understanding of the underlying mechanisms of metabolic reprogramming in cancer may lead to better identification of therapeutic targets and the development of therapeutic strategies. Evidence for the importance of PCK1, a phosphoenolpyruvate carboxykinase 1, in tumorigenesis and development is accumulating. PCK1 can regulate cell proliferation and metastasis by remodeling cell metabolism. Additionally, PCK1 has "nonclassical" nonmetabolic functions, involving the regulation of gene expression, angiogenesis, epigenetic modification, and other processes, and has an impact on cell survival, apoptosis, and other biological activities, as well as the remodeling of the tumor immune microenvironment. Herein, we provide a comprehensive overview of the functions of PCK1 under physiological and pathological conditions and suggest that PCK1 is a potential target for cancer therapy. We also propose a future exploration direction for targeting PCK1 for cancer therapy from a clinical perspective. Finally, in view of the collective data, the results of our discussion suggest the potential clinical application of targeted PCK1 therapy in combination with chemotherapy and immunotherapy for cancer treatment.

Development of a diagnostic model based on glycolysis-related genes and immune infiltration in intervertebral disc degeneration

Background

The glycolytic pathway and immune response play pivotal roles in the intervertebral disc degeneration (IDD) progression. This study aimed to develop a glycolysis-related diagnostic model and analyze its relationship with the immune response to IDD.

Methods

GSE70362, GSE23130, and GSE15227 datasets were collected and merged from the Gene Expression Omnibus, and differential expression analysis was performed. Glycolysis-related differentially expressed genes (GLRDEGs) were identified, and a machine learning-based diagnostic model was constructed and validated, followed by Gene Set Enrichment Analysis (GSEA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and mRNA-miRNA and mRNA-transcription factor (TF) interaction networks were constructed. Immune infiltration was analyzed using single-sample GSEA (ssGSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm between high- and low-risk groups.

Results

In the combined dataset, samples from 31 patients with IDD and 55 normal controls were analyzed, revealing differential expression of 16 GLRDEGs between the two groups. Using advanced machine learning techniques (LASSO, support vector machine, and random forest algorithms), we identified eight common GLRDEGs (PXK, EIF3D, WSB1, ZNF185, IGFBP3, CKAP4, RPL15, and, SSR1) and developed a diagnostic model, which demonstrated high accuracy in distinguishing IDD from control samples (area under the curve, 0.935). We identified 42 mRNA-miRNA and 33 mRNA-TF interaction pairs. Using the RiskScore from the diagnostic model, the combined dataset was stratified into high- and low-risk groups. SsGSEA revealed significant differences in the infiltration abundances of the four immune cell types between the groups. The CIBERSORT algorithm revealed the strongest correlation between resting natural killer (NK) cells and ZNF185 in the low-risk group and between CD8+ T cells and SSR1 in the high-risk group.

Conclusions

Our study reveals a potential interplay between glycolysis-associated genes and immune infiltration in IDD pathogenesis. These findings contribute to our understanding of IDD and may guide development of novel diagnostic markers and therapeutic interventions.

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

Background

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

Methods

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

Results

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

Conclusion

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

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.

Glycolysis‑related lncRNA may be associated with prognosis and immune activity in grade II‑III glioma

Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.

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.

MCT4 blockade increases the efficacy of immune checkpoint blockade

BACKGROUND & AIMS

Intratumoral lactate accumulation and acidosis impair T-cell function and antitumor immunity. Interestingly, expression of the lactate transporter monocarboxylate transporter (MCT) 4, but not MCT1, turned out to be prognostic for the survival of patients with rectal cancer, indicating that single MCT4 blockade might be a promising strategy to overcome glycolysis-related therapy resistance.

METHODS

To determine whether blockade of MCT4 alone is sufficient to improve the efficacy of immune checkpoint blockade (ICB) therapy, we examined the effects of the selective MCT1 inhibitor AZD3965 and a novel MCT4 inhibitor in a colorectal carcinoma (CRC) tumor spheroid model co-cultured with blood leukocytes in vitro and the MC38 murine CRC model in vivo in combination with an antibody against programmed cell death ligand-1(PD-L1).

RESULTS

Inhibition of MCT4 was sufficient to reduce lactate efflux in three-dimensional (3D) CRC spheroids but not in two-dimensional cell-cultures. Co-administration of the MCT4 inhibitor and ICB augmented immune cell infiltration, T-cell function and decreased CRC spheroid viability in a 3D co-culture model of human CRC spheroids with blood leukocytes. Accordingly, combination of MCT4 and ICB increased intratumoral pH, improved leukocyte infiltration and T-cell activation, delayed tumor growth, and prolonged survival in vivo. MCT1 inhibition exerted no further beneficial impact.

CONCLUSIONS

These findings demonstrate that single MCT4 inhibition represents a novel therapeutic approach to reverse lactic-acid driven immunosuppression and might be suitable to improve ICB efficacy.

Seven bacterial response-related genes are biomarkers for colon cancer

BACKGROUND

Colon cancer (CC) is a common tumor that causes significant harm to human health. Bacteria play a vital role in cancer biology, particularly the biology of CC. Genes related to bacterial response were seldom used to construct prognosis models. We constructed a bacterial response-related risk model based on three Molecular Signatures Database gene sets to explore new markers for predicting CC prognosis.

METHODS

The Cancer Genome Atlas (TCGA) colon adenocarcinoma samples were used as the training set, and Gene Expression Omnibus (GEO) databases were used as the test set. Differentially expressed bacterial response-related genes were identified for prognostic gene selection. Univariate Cox regression analysis, least absolute shrinkage and selection operator-penalized Cox regression analysis, and multivariate Cox regression analysis were performed to construct a prognostic risk model. The individual diagnostic effects of genes in the prognostic model were also evaluated. Moreover, differentially expressed long noncoding RNAs (lncRNAs) were identified. Finally, the expression of these genes was validated using quantitative polymerase chain reaction (qPCR) in cell lines and tissues.

RESULTS

A prognostic signature was constructed based on seven bacterial response genes: LGALS4, RORC, DDIT3, NSUN5, RBCK1, RGL2, and SERPINE1. Patients were assigned a risk score based on the prognostic model, and patients in the TCGA cohort with a high risk score had a poorer prognosis than those with a low risk score; a similar finding was observed in the GEO cohort. These seven prognostic model genes were also independent diagnostic factors. Finally, qPCR validated the differential expression of the seven model genes and two coexpressed lncRNAs (C6orf223 and SLC12A9-AS1) in 27 pairs of CC and normal tissues. Differential expression of LGALS4 and NSUN5 was also verified in cell lines (FHC, COLO320DM, SW480).

CONCLUSIONS

We created a seven-gene bacterial response-related gene signature that can accurately predict the outcomes of patients with CC. This model can provide valuable insights for personalized treatment.

Rectum adenocarcinoma metabolic subtypes analysis and a risk prognostic model construction based on fatty acid metabolism genes

Fatty acid metabolism is an essential part of cancer research due to its role in cancer initiation and progression. However, its characteristics and prognostic value in rectum adenocarcinoma have not been systematically evaluated. We collected fatty acid metabolism gene expression profiles and clinical information from the cancer genome atlas and gene expression omnibus databases. After excluding individuals lacking clinical information and the presence of genetic mutations, we performed consistent clustering of the remaining patients and selected stable clustering results to group patients. Differentially expressed genes and gene set enrichment analysis were compared between subgroups, while metabolic signature identification and decoding the tumor microenvironment were performed. In addition, we explored the survival status of patients among different subgroups and identified signature genes affecting survival by least absolute shrinkage and selection operator regression. Finally, we selected signature genes to construct a risk prognostic model by multivariate Cox regression and evaluated model efficacy by univariate Cox regression and the receiver operating characteristic curve. By consensus clustering, patients were distinguished into 2 stable subpopulations, gene set enrichment analysis and metabolic signature identification effectively defined 2 completely different subtypes of fatty acid metabolism: fatty acid catabolic subtype and fatty acid anabolic subtype. Among them, patients with the fatty acid catabolic subtype had a poorer prognosis, with a significantly lower proportion of myeloid dendritic cells infiltration within the tumor microenvironment. Aquaporin 7 (hazard ratio, HR = 2.064 (1.4408-4.5038); P < .01), X inactive specific transcript (HR = (0.3758-0.7564), P = .045) and interleukin 4 induced 1 (HR = 1.34 (1.13-1.59); P = .034), were selected by multivariate Cox regression, which constructed a risk prognostic model. The independent hazard ratio of the model was 2.72 and the area under curve was higher than age, gender and tumor stage, showing better predictive efficacy. Our study revealed the heterogeneity of fatty acid metabolism in rectum adenocarcinoma, defined 2 completely distinct subtypes of fatty acid metabolism, and finally established a novel fatty acid metabolism-related risk prognostic model. The study contributes to the early risk assessment and monitoring of individual prognosis and provides data to support individualized patient treatment.

A nomogram and risk classification system forecasting the cancer-specific survival of lymph- node- positive rectal cancer patient after radical proctectomy

Background

The aim of the study was to develop and validate a nomogram for predicting cancer-specific survival (CSS) in lymph- node- positive rectal cancer patients after radical proctectomy.

Methods

In this study, we analyzed data collected from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2015. In addition, in a 7:3 randomized design, all patients were split into two groups (development and validation cohorts). CSS predictors were selected via univariate and multivariate Cox regressions. The nomogram was constructed by analyzing univariate and multivariate predictors. The effectiveness of this nomogram was evaluated by concordance index (C-index), calibration plots, and receiver operating characteristic (ROC) curve. Based on the total score of each patient in the development cohort in the nomogram, a risk stratification system was developed. In order to analyze the survival outcomes among different risk groups, Kaplan-Meier method was used.

Results

We selected 4,310 lymph- node- positive rectal cancer patients after radical proctectomy, including a development cohort (70%, 3,017) and a validation cohort (30%, 1,293). The nomogram correlation C-index for the development cohort and the validation cohort was 0.702 (95% CI, 0.687-0.717) and 0.690 (95% CI, 0.665-0.715), respectively. The calibration curves for 3- and 5-year CSS showed great concordance. The 3- and 5-year areas under the curve (AUC) of ROC curves in the development cohort were 0.758 and 0.740, respectively, and 0.735 and 0.730 in the validation cohort, respectively. Following the establishment of the nomogram, we also established a risk stratification system. According to their nomogram total points, patients were divided into three risk groups. There were significant differences between the low-, intermediate-, and high-risk groups (p< 0.05).

Conclusions

As a result of our research, we developed a highly discriminatory and accurate nomogram and associated risk classification system to predict CSS in lymph-node- positive rectal cancer patients after radical proctectomy. This model can help predict the prognosis of patients with lymph- node- positive rectal cancer.

The analysis of tumor-infiltrating immune cell and ceRNA networks in laryngeal squamous cell carcinoma

BACKGROUND

Laryngeal squamous cell carcinoma (LSCC) is one of the most common forms of head and neck cancers. However, few studies have focused on the correlation between competing endogenous RNA (ceRNAs) and immune cells in LSCC.

METHODS

RNAseq expression of LSCC and adjacent tissues were downloaded from The Cancer Genome Atlas to establish a ceRNA network. The key gene in ceRNA was screened by the cox regression analysis to establish a prognostic risk assessment model. The CIBERSORT algorithm was then used to screen important tumor-infiltrating cells related to LSCC. Finally, co-expression analysis was applied to explore the relationship between key genes in the ceRNA network and tumor-infiltrating cells. The external datasets were used to validate critical biomarkers.

RESULTS

We constructed a prognostic risk assessment model of key genes in the ceRNA network. As it turned out, Kaplan-Meier survival analysis showed significant differences in overall survival rates between high-risk and low-risk groups (P < .001). The survival rate of the high-risk group was drastically lower than that of the low-risk group, and the AUC of 1 year, 3 years, and 5 years were all above 0.7. In addition, some immune infiltrating cells were also found to be related to LSCC. In the co-expression analysis, there is a negative correlation between plasma cells and TUBB3 (r = -0.33, P = .0013). External dataset validation also supports this result.

CONCLUSION

In this study, we found that some key genes (SLC35C1, CLDN23, HOXB7, STC2, TMEM158, TNFRSF4, TUBB3) and immune cells (plasma cells) may correspond to the prognosis of LSCC.