Prognostic and predictive role of a metabolic rate-limiting enzyme signature in hepatocellular carcinoma

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Objective

Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

Methods

We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.

Results

We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.

Conclusion

The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

Withaferin A suppressed hepatocellular carcinoma progression through inducing IGF2BP3/FOXO1/JAK2/STAT3 pathway-mediated ROS production

OBJECTIVE

This study aimed to investigate the underlying molecular mechanisms of Withaferin A (WA) in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

The gene and protein expression were analyzed using RT-qPCR and western blot, respectively. The proliferation of HCC cells was evaluated by CCK-8 assays. The migrative ability of HCC cells was measured by transwell assays.

RESULTS

We revealed that WA suppressed the proliferation and migration of HCC cells and inhibited IGF2BP3 (insulin like growth factor 2 mRNA binding protein 3) expression. IGF2BP3 abundance reversed the reactive oxygen species (ROS) accumulation and suppression of HCC cell proliferation and migration induced by WA. Besides, IGF2BP3 suppressed ROS production to promote the growth and migration of HCC cells. Furthermore, we found that IGF2BP3 exerted its tumor-promotive and ROS-suppressive effect on HCC cells by regulating the expression of FOXO1 (forkhead box O1). In addition, IGF2BP3-stimulated activation of JAK2 (Janus kinase 2)/STAT3 (signal transducer and activator of transcription 3) phosphorylation effectively decreased the transcription of FOXO1. FOXO1 abundance decreased the phosphorylation of JAK2 and STAT3 by increasing ROS level, forming a feedback loop for the inhibition of JAK2/STAT3 signaling activated by IGF2BP3.

CONCLUSIONS

WA-induced ROS inhibited HCC cell growth and migration through the inhibition of IGF2BP3 to deactivate JAK2/STAT3 signaling, resulting in increased FOXO1 expression to further stimulate ROS production and inhibit JAK2/STAT3 signaling.

Combining bulk and single-cell RNA-sequencing data to develop an NK cell-related prognostic signature for hepatocellular carcinoma based on an integrated machine learning framework

BACKGROUND

The application of molecular targeting therapy and immunotherapy has notably prolonged the survival of patients with hepatocellular carcinoma (HCC). However, multidrug resistance and high molecular heterogeneity of HCC still prevent the further improvement of clinical benefits. Dysfunction of tumor-infiltrating natural killer (NK) cells was strongly related to HCC progression and survival benefits of HCC patients. Hence, an NK cell-related prognostic signature was built up to predict HCC patients' prognosis and immunotherapeutic response.

METHODS

NK cell markers were selected from scRNA-Seq data obtained from GSE162616 data set. A consensus machine learning framework including a total of 77 algorithms was developed to establish the gene signature in TCGA-LIHC data set, GSE14520 data set, GSE76427 data set and ICGC-LIRI-JP data set. Moreover, the predictive efficacy on ICI response was externally validated by GSE91061 data set and PRJEB23709 data set.

RESULTS

With the highest C-index among 77 algorithms, a 11-gene signature was established by the combination of LASSO and CoxBoost algorithm, which classified patients into high- and low-risk group. The prognostic signature displayed a good predictive performance for overall survival rate, moderate to high predictive accuracy and was an independent risk factor for HCC patients' prognosis in TCGA, GEO and ICGC cohorts. Compared with high-risk group, low-risk patients showed higher IPS-PD1 blocker, IPS-CTLA4 blocker, common immune checkpoints expression but lower TIDE score, which indicated low-risk patients might be prone to benefiting from ICI treatment. Moreover, a real-world cohort, PRJEB23709, also revealed better immunotherapeutic response in low-risk group.

CONCLUSIONS

Overall, the present study developed a gene signature based on NK cell-related genes, which offered a novel platform for prognosis and immunotherapeutic response evaluation of HCC patients.

A T-cell-related signature for prognostic stratification and immunotherapy response in hepatocellular carcinoma based on transcriptomics and single-cell sequencing

BACKGROUND

Hepatocellular carcinoma (HCC) is the fifth most frequently diagnosed malignancy and the third leading cause of cancer death globally. T cells are significantly correlated with the progression, therapy and prognosis of cancer. Limited systematic studies regarding the role of T-cell-related markers in HCC have been performed.

METHODS

T-cell markers were identified with single-cell RNA sequencing (scRNA-seq) data from the GEO database. A prognostic signature was developed with the LASSO algorithm in the TCGA cohort and verified in the GSE14520 cohort. Another three eligible immunotherapy datasets, GSE91061, PRJEB25780 and IMigor210, were used to verify the role of the risk score in the immunotherapy response.

RESULTS

With 181 T-cell markers identified by scRNA-seq analysis, a 13 T-cell-related gene-based prognostic signature (TRPS) was developed for prognostic prediction, which divided HCC patients into high-risk and low-risk groups according to overall survival, with AUCs of 1 year, 3 years, and 5 years of 0.807, 0.752, and 0.708, respectively. TRPS had the highest C-index compared with the other 10 established prognostic signatures, suggesting a better performance of TRPS in predicting the prognosis of HCC. More importantly, the TRPS risk score was closely correlated with the TIDE score and immunophenoscore. The high-risk score patients had a higher percentage of SD/PD, and CR/PR occurred more frequently in patients with low TRPS-related risk scores in the IMigor210, PRJEB25780 and GSE91061 cohorts. We also constructed a nomogram based on the TRPS, which had high potential for clinical application.

CONCLUSION

Our study proposed a novel TRPS for HCC patients, and the TRPS could effectively indicate the prognosis of HCC. It also served as a predictor for immunotherapy.

Metabolism-related signatures is correlated with poor prognosis and immune infiltration in hepatocellular carcinoma via multi-omics analysis and basic experiments

Background

Metabolism is an ordered series of biological processes that occur in an organism. Altered cellular metabolism is often closely associated with the development of cancer. The aim of this research was to construct a model by multiple metabolism-related molecules to diagnose and assess the prognosis of patients.

Method

WGCNA analysis was used to screen out differential genes. GO, KEGG are used to explore potential pathways and mechanisms. The lasso regression model was used to filter out the best indicators to construct the model. Single-sample GSEA (ssGSEA) assess immune cells abundance, immune terms in different Metabolism Index (MBI) groups. Human tissues and cells were used to verify the expression of key genes.

Result

WGCNA clustering grouped genes into 5 modules, of which 90 genes from the MEbrown module were selected for subsequent analysis. GO analysis was found that BP mainly has mitotic nuclear division, while KEGG pathway is enriched to Cell cycle, Cellular senescence. Mutation analysis revealed that the frequency of TP53 mutations was much higher in samples from the high MBI group than in the low MBI group. Immunoassay revealed that patients with higher MBI have higher macrophage and Regulatory T cells (Treg) abundance, while NK cells were lowly expressed in the high MBI group. RT-qPCR and immunohistochemistry (IHC) revealed that the hub genes expression is higher in cancer tissues. The expression in hepatocellular carcinoma cells was also much higher than that in normal hepatocytes.

Conclusion

In conclusion, a metabolism-related model was constructed that can be used to estimate the prognosis of hepatocellular carcinoma, and the clinical treatment of different hepatocellular carcinoma patients with medications was guided.

Prognostic and Immunological Potential of Ribonucleotide Reductase Subunits in Liver Cancer

Background

Ribonucleotide reductase (RR) consists of two subunits, the large subunit RRM1 and the small subunit (RRM2 or RRM2B), which is essential for DNA replication. Dysregulations of RR were implicated in multiple types of cancer. However, the abnormal expressions and biologic functions of RR subunits in liver cancer remain to be elucidated.

Methods

TCGA, HCCDB, CCLE, HPA, cBioPortal, and GeneMANIA were utilized to perform bioinformatics analysis of RR subunits in the liver cancer. GO, KEGG, and GSEA were used for enrichment analysis.

Results

The expressions of RRM1, RRM2, and RRM2B were remarkably upregulated among liver cancer tissue both in mRNA and protein levels. High expression of RRM1 and RRM2 was notably associated with high tumor grade, high stage, short overall survival, and disease-specific survival. Enrichment analyses indicated that RRM1 and RRM2 were related to DNA replication, cell cycle, regulation of nuclear division, DNA repair, and DNA recombination. Correlation analysis indicated that RRM1 and RRM2 were significantly associated with several subsets of immune cell, including Th2 cells, cytotoxic cells, and neutrophils. RRM2B expression was positively associated with immune score and stromal score. Chemosensitivity analysis revealed that sensitivity of nelarabine was positively associated with high expressions of RRM1 and RRM2. The sensitivity of rapamycin was positively associated with high expressions of RRM2B.

Conclusion

Our findings demonstrated high expression profiles of RR subunits in liver cancer, which may provide novel insights for predicting the poor prognosis and increased chemosensitivity of liver cancer in clinic.

Construction and validation of a prognostic model for hepatocellular carcinoma: Inflammatory ferroptosis and mitochondrial metabolism indicate a poor prognosis

Background

An increasing number of innovations have been discovered for treating hepatocellular carcinoma (HCC or commonly called HCC) therapy, Ferroptosis and mitochondrial metabolism are essential mechanisms of cell death. These pathways may act as functional molecular biomarkers that could have important clinical significance for determining individual differences and the prognosis of HCC. The aim of this study was to construct a stable and reliable comprehensive model of genetic features and clinical factors associated with HCC prognosis.

Methods

In this study, we used RNA-sequencing (fragments per kilobase of exon model per million reads mapped value) data from the Cancer Genome Atlas (TCGA) database to establish a prognostic model. We enrolled 104 patients for further validation. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analyses (KEGG) analysis were used for the functional study of differentially expressed genes. Pan-cancer analysis was performed to evaluate the function of the Differentially Expressed Genes (DEGs). Thirteen genes were identified by univariate and least absolute contraction and selection operation (LASSO) Cox regression analysis. The prognostic model was visualized using a nomogram.

Results

We found that eight genes, namely EZH2, GRPEL2, PIGU, PPM1G, SF3B4, TUBG1, TXNRD1 and NDRG1, were hub genes for HCC and differentially expressed in most types of cancer. EZH2, GRPEL2 and NDRG1 may indicate a poor prognosis of HCC as verified by tissue samples. Furthermore, a gene set variation analysis algorithm was created to analyze the relationship between these eight genes and oxidative phosphorylation, mitophagy, and FeS-containing proteins, and it showed that ferroptosis might affect inflammatory-related pathways in HCC.

Conclusion

EZH2, GRPEL2, NDRG1, and the clinical factor of tumor size, were included in a nomogram for visualizing a prognostic model of HCC. This nomogram based on a functional study and verification by clinical samples, shows a reliable performance of patients with HCC.

High Expression of RRM1 Mediated by ncRNAs Correlates with Poor Prognosis and Tumor Immune Infiltration of Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is one of several tumors with poor prognosis and causes a significant social burden. A growing number of studies have shown that RRM1 plays a crucial role in the development and progression of multiple human cancers. However, the specific role and mechanism of RRM1 have not been fully defined in HCC.

Methods

TCGA and GTEx data were used for the first time to conduct a pan-cancer analysis of RRM1 expression and prognosis, and identified RRM1 as a possible potential oncogene in HCC. At the same time, a combination of analyses (including expression analysis, correlation analysis or survival analysis) identified non-coding RNAs (ncRNAs) that contribute to RRM1 overexpression.

Results

MIR4435-2HG/miR-22-3p and SNHG6/miR-101-3p were identified as the most promising RRM1 upstream ncRNA-related pathways in HCC. In addition, RRM1 levels were significantly and positively correlated with tumor immune cell infiltration, immune cell biomarker or immune checkpoint expression.

Conclusion

These results suggest that high expression of RRM1 mediated by ncRNAs is associated with poor prognosis and tumor immune infiltration in HCC.

Prognostic and predictive role of a metabolic rate-limiting enzyme signature in hepatocellular carcinoma

OBJECTIVES

Abnormal expression of metabolic rate-limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate-limiting enzymes associated with the prognosis of HCC.

MATERIALS AND METHODS

HCC animal model and TCGA project were used to screen out differentially expressed metabolic rate-limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate-limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort).

RESULTS

A classifier based on three rate-limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653-0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798-0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes.

CONCLUSIONS

Our results highlighted the prognostic value of rate-limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.