Glycolysis-Related Gene Signature Can Predict Survival and Immune Status of Hepatocellular Carcinoma

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.

Establishment of a prognostic model for pancreatic cancer based on mitochondrial metabolism related genes

AIM

Pancreatic ductal adenocarcinoma (PAAD) is recognized as an exceptionally aggressive cancer that both highly lethal and unfavorable prognosis. The mitochondrial metabolism pathway is intimately involved in oncogenesis and tumor progression, however, much remains unknown in this area. In this study, the bioinformatic tools have been used to construct a prognostic model with mitochondrial metabolism-related genes (MMRGs) to evaluate the survival, immune status, mutation profile, and drug sensitivity of PAAD patients.

METHOD

Univariate Cox regression and LASSO regression were used to screen the differentially expressed genes (DEGs), and multivariate Cox regression was used to develop the risk model. Kaplan-Meier estimator was employed to identify MMRGs signatures associated with overall survival (OS). ROC curves were utilized to evaluate the model's performance. Maftools, immunedeconv and CIBERSORT R packages were applied to analyze the gene mutation profiles and immune status. The corresponding sensitivity to pharmaceutical agents was assessed using oncoPredict R packages.

RESULTS

A prognostic model with five MMRGs was developed, which defined the patients as high-risk showed lower survival rates. There was good consistency among individuals categorized as high-risk, showing elevated rates of genetic alterations, particularly in the TP53 and KRAS genes. Furthermore, these patients exhibited increased levels of immunosuppression, characterized by an increased presence of macrophages, neutrophils, Th2 cells, and regulatory T cells. Additionally, high-risk patients showed increased sensitivity to Sabutoclax and Venetoclax.

CONCLUSION

By utilizing a gene signature associated with mitochondrial metabolism, a prognostic model has been established which could be a highly efficient method for predicting the outcomes of PAAD patients.

Development and Validation of a Carbohydrate Metabolism-Related Model for Predicting Prognosis and Immune Landscape in Hepatocellular Carcinoma Patients

OBJECTIVE

The activities and products of carbohydrate metabolism are involved in key processes of cancer. However, its relationship with hepatocellular carcinoma (HCC) is unclear.

METHODS

The cancer genome atlas (TCGA)-HCC and ICGC-LIRI-JP datasets were acquired via public databases. Differentially expressed genes (DEGs) between HCC and control samples in the TCGA-HCC dataset were identified and overlapped with 355 carbohydrate metabolism-related genes (CRGs) to obtain differentially expressed CRGs (DE-CRGs). Then, univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses were applied to identify risk model genes, and HCC samples were divided into high/low-risk groups according to the median risk score. Next, gene set enrichment analysis (GSEA) was performed on the risk model genes. The sensitivity of the risk model to immunotherapy and chemotherapy was also explored.

RESULTS

A total of 8 risk model genes, namely, G6PD, PFKFB4, ACAT1, ALDH2, ACYP1, OGDHL, ACADS, and TKTL1, were identified. Moreover, the risk score, cancer status, age, and pathologic T stage were strongly associated with the prognosis of HCC patients. Both the stromal score and immune score had significant negative/positive correlations with the risk score, reflecting the important role of the risk model in immunotherapy sensitivity. Furthermore, the stromal and immune scores had significant negative/positive correlations with risk scores, reflecting the important role of the risk model in immunotherapy sensitivity. Eventually, we found that high-/low-risk patients were more sensitive to 102 drugs, suggesting that the risk model exhibited sensitivity to chemotherapy drugs. The results of the experiments in HCC tissue samples validated the expression of the risk model genes.

CONCLUSION

Through bioinformatic analysis, we constructed a carbohydrate metabolism-related risk model for HCC, contributing to the prognosis prediction and treatment of HCC patients.

Identification of subclusters and prognostic genes based on glycolysis/gluconeogenesis in hepatocellular carcinoma

Background

This study aimed to examine glycolysis/gluconeogenesis-related genes in hepatocellular carcinoma (HCC) and evaluate their potential roles in HCC progression and immunotherapy response.

Methods

Data analyzed in this study were collected from GSE14520, GSE76427, GSE174570, The Cancer Genome Atlas (TCGA), PXD006512, and GSE149614 datasets, metabolic pathways were collected from MSigDB database. Differentially expressed genes (DEGs) were identified between HCC and controls. Differentially expressed glycolysis/gluconeogenesis-related genes (candidate genes) were obtained and consensus clustering was performed based on the expression of candidate genes. Bioinformatics analysis was used to evaluate candidate genes and screen prognostic genes. Finally, the key results were tested in HCC patients.

Results

Thirteen differentially expressed glycolysis/gluconeogenesis-related genes were validated in additional datasets. Consensus clustering analysis identified two distinct patient clusters (C1 and C2) with different prognoses and immune microenvironments. Immune score and tumor purity were significantly higher in C1 than in C2, and CD4+ memory activated T cell, Tfh, Tregs, and macrophage M0 were higher infiltrated in HCC and C1 group. The study also identified five intersecting DEGs from candidate genes in TCGA, GSE14520, and GSE141198 as prognostic genes, which had a protective role in HCC patient prognosis. Compared with the control group, the prognostic genes all showed decreased expression in HCC patients in RT-qPCR and Western blot analyses. Flow cytometry verified the abnormal infiltration level of immune cells in HCC patients.

Conclusion

Results showed that glycolysis/gluconeogenesis-related genes were associated with patient prognosis, immune microenvironment, and response to immunotherapy in HCC. It suggests that the model based on five prognostic genes may valuable for predicting the prognosis and immunotherapy response of HCC patients.

Glycolysis induces Th2 cell infiltration and significantly affects prognosis and immunotherapy response to lung adenocarcinoma

Glycolysis has a major role in cancer progression and can affect the tumor immune microenvironment, while its specific role in lung adenocarcinoma (LUAD) remains poorly studied. We obtained publicly available data from The Cancer Genome Atlas and Gene Expression Omnibus databases and used R software to analyze the specific role of glycolysis in LUAD. The Single Sample Gene Set Enrichment Analysis (ssGSEA) indicated a correlation between glycolysis and unfavorable clinical outcome, as well as a repression effect on the immunotherapy response of LUAD patients. Pathway enrichment analysis revealed a significant enrichment of MYC targets, epithelial-mesenchymal transition (EMT), hypoxia, G2M checkpoint, and mTORC1 signaling pathways in patients with higher activity of glycolysis. Immune infiltration analysis showed a higher infiltration of M0 and M1 macrophages in patients with elevated activity of glycolysis. Moreover, we developed a prognosis model based on six glycolysis-related genes, including DLGAP5, TOP2A, KIF20A, OIP5, HJURP, and ANLN. Both the training and validation cohorts demonstrated the high efficiency of prognostic prediction in this model, which identified that patients with high risk may have a poorer prognosis and lower sensitivity to immunotherapy. Additionally, we also found that Th2 cell infiltration may predict poorer survival and resistance to immunotherapy. The study indicated that glycolysis is significantly associated with poor prognosis in patients with LUAD and immunotherapy resistance, which might be partly dependent on the Th2 cell infiltration. Additionally, the signature comprised of six genes related to glycolysis showed promising predictive value for LUAD prognosis.

Comprehensive analysis of the amino acid metabolism-related gene signature for prognosis, tumor immune microenvironment, and candidate drugs in hepatocellular carcinoma

Introduction

Metabolic rewiring satisfies increased nutritional demands and modulates many oncogenic processes in tumors. Amino acid metabolism is abnormal in many malignancies. Metabolic reprogramming of amino acids not only plays a crucial role in sustaining tumor cell proliferation but also influences the tumor immune microenvironment. Herein, the aim of our study was to elucidate the metabolic signature of amino acids in hepatocellular carcinoma (HCC).

Methods

Transcriptome profiles of HCC were obtained from the TCGA and ICGC databases. Based on the expression of amino acid metabolism-related genes (AAMRGs), we clustered the HCC samples into two molecular subtypes using the non-negative matrix factorization algorithm. Then, we constructed the amino acid metabolism-related gene signature (AAMRGS) by Cox regression and LASSO regression. Afterward, the clinical significance of the AAMRGS was evaluated. Additionally, we comprehensively analyzed the differences in mutational profiles, immune cell infiltration, immune checkpoint expression, and drug sensitivity between different risk subgroups. Furthermore, we examined three key gene expressions in liver cancer cells by quantitative real-time PCR and conducted the CCK8 assay to evaluate the influence of two chemotherapy drugs on different liver cancer cells.

Results

A total of 81 differentially expressed AAMRGs were screened between the two molecular subtypes, and these AAMRGs were involved in regulating amino acid metabolism. The AAMRGS containing GLS, IYD, and NQO1 had a high value for prognosis prediction in HCC patients. Besides this, the two AAMRGS subgroups had different genetic mutation probabilities. More importantly, the immunosuppressive cells were more enriched in the AAMRGS-high group. The expression level of inhibitory immune checkpoints was also higher in patients with high AAMRGS scores. Additionally, the two AAMRGS subgroups showed different susceptibility to chemotherapeutic and targeted drugs. In vitro experiments showed that gemcitabine significantly reduced the proliferative capacity of SNU449 cells, and rapamycin remarkedly inhibited Huh7 proliferation. The five HCC cells displayed different mRNA expression levels of GLS, IYD, and NQO1.

Conclusions

Our study explored the features of amino acid metabolism in HCC and identified the novel AAMRGS to predict the prognosis, immune microenvironment, and drug sensitivity of HCC patients. These findings might help to guide personalized treatment and improve the clinical outcomes of HCC.

Construction of a B cell-related gene pairs signature for predicting prognosis and immunotherapeutic response in non-small cell lung cancer

BACKGROUND

Accumulating evidence indicates that the B cells play important roles in anti-tumor immunity and shaping tumor development. This study aimed to explore the expression profiles of B cell marker genes and construct a B cell-related gene pairs (BRGPs) signature associated with the prognosis and immunotherapeutic efficiency in non-small cell lung cancer (NSCLC) patients.

METHODS

B cell-related marker genes in NSCLC were identified using single-cell RNA sequencing data. TCGA and GEO datasets were utilized to identify the prognostic BRGPs based on a novel algorithm of cyclically single pairing along with a 0-or-1 matrix. BRGPs signature was then constructed using Lasso-Cox regression model. Its prognostic value, associated immunogenomic features, putative molecular mechanism and predictive ability to immunotherapy were investigated in NSCLC patients.

RESULTS

The BRGPs signature was composed of 23 BRGPs including 28 distinct B cell-related genes. This predictive signature demonstrated remarkable power in distinguishing good or poor prognosis and can serve as an independent prognostic factor for NSCLC patients in both training and validation cohorts. Furthermore, BRGPs signature was significantly associated with immune scores, tumor purity, clinicopathological characteristics and various tumor-infiltrating immune cells. Besides, we demonstrated that the tumor mutational burden scores and TIDE scores were positively correlated with the risk score of the model implying immune checkpoint blockade therapy may be more effective in NSCLC patients with high-risk scores.

CONCLUSIONS

This novel BRGPs signature can be used to assess the prognosis of NSCLC patients and may be useful in guiding immune checkpoint inhibitor treatment in our clinical practice.

Signature and Molecular Mechanism of Mitochondrial Energy Metabolism Pathway-Related Genes in Lung Adenocarcinoma

Objective

The mitochondrial energy metabolic pathway (MEMP) is the primary energy metabolism of tumor cells, and its disruption may promote cancer emergence, spreading, and immune escape. However, there is a lack of studies to determine the relationship between relevant functional mechanisms and lung adenocarcinoma (LUAD) prognosis.

Methods

Gene set enrichment analysis (GSEA) was employed to determine MEMP pathway-related genes. Then, a prognostic model was created using the MEMP key genes that were found by LASSO-Cox regression analysis. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided the training and validation sets. Furthermore, the infiltration of immune cells was examined by ssGSEA. Finally, a screening of candidate therapeutic compounds for LUAD patients was performed using DrugBank, Protein Data Bank (PDB), and AutoDock Vina databases.

Results

First, 266 MEMP pathway-related genes that exhibited aberrant activity in tumors were identified. Then, 19 MEMP key genes were used to build a prognostic model, which can successfully predict the survival rates of LUAD patients after 1, 3, and 5 years, respectively. The Kaplan-Meier curve showed that patients in the high-risk group had considerably lower survival outcomes than those in the low-risk group. Furthermore, it was discovered that the high-risk group had the majority of activated T cells, while the low-risk group tended to have more other activated immune cells. The majority of immunological checkpoints expressed themselves more strongly in the high-risk group as well. Finally, 11 prospective medication small molecules were obtained from the projected potential therapeutic drugs, with DB0980 being regarded as the most promising of them for the treatment of LUAD.

Conclusion

This current study developed reliable prognostic signature, called MEMP score, which provides new guidance for prognostic assessment, immunotherapy, and drug development in LUAD. Thereby, DB0980 appears to be the most likely approach for the treatment of LUAD.