Cytochrome P450 family members are associated with fast-growing hepatocellular carcinoma and patient survival: An integrated analysis of gene expression profiles

Decoding the Role of CYP450 Enzymes in Metabolism and Disease: A Comprehensive Review

Cytochrome P450 (CYP450) is a group of enzymes that play an essential role in Phase I metabolism, with 57 functional genes classified into 18 families in the human genome, of which the CYP1, CYP2, and CYP3 families are prominent. Beyond drug metabolism, CYP enzymes metabolize endogenous compounds such as lipids, proteins, and hormones to maintain physiological homeostasis. Thus, dysregulation of CYP450 enzymes can lead to different endocrine disorders. Moreover, CYP450 enzymes significantly contribute to fatty acid metabolism, cholesterol synthesis, and bile acid biosynthesis, impacting cellular physiology and disease pathogenesis. Their diverse functions emphasize their therapeutic potential in managing hypercholesterolemia and neurodegenerative diseases. Additionally, CYP450 enzymes are implicated in the onset and development of illnesses such as cancer, influencing chemotherapy outcomes. Assessment of CYP450 enzyme expression and activity aids in evaluating liver health state and differentiating between liver diseases, guiding therapeutic decisions, and optimizing drug efficacy. Understanding the roles of CYP450 enzymes and the clinical effect of their genetic polymorphisms is crucial for developing personalized therapeutic strategies and enhancing drug responses in diverse patient populations.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Urinary biomarkers for hepatocellular carcinoma: current knowledge for clinicians

Hepatocellular carcinoma (HCC) is the most predominant primary liver cancer, causing many illnesses and deaths worldwide. The insidious clinical presentation, difficulty in early diagnosis, and the highly malignant nature make the prognosis of HCC extremely poor. The complex and heterogeneous pathogenesis of HCC poses significant challenges to developing therapies. Urine-based biomarkers for HCC, including diagnostic, prognostic, and monitoring markers, may be valuable supplements to current tools such as serum α-fetoprotein (AFP) and seem promising for progress in precision medicine. Herein, we reviewed the major urinary biomarkers for HCC and assessed their potential for clinical application. Molecular types, testing platforms, and methods for building multimolecule models in the included studies have shown great diversity, thus providing abundant novel tools for future clinical transformation and applications.

PTGIS May Be a Predictive Marker for Ovarian Cancer by Regulating Fatty Acid Metabolism

Background

Ovarian cancer tends to metastasize to the omentum, which is an organ mainly composed of adipose tissue. Many studies have found that fatty acid metabolism is related to the occurrence and metastasis of cancers. Therefore, it is possible that fatty acid metabolism-related genes (FAMRG) affect the prognosis of ovarian cancer patients.

Methods

First, profiles of ovarian cancer and normal ovarian tissue transcriptomes were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A LASSO regression predictive model was developed via the "glmnet" R package. The nomogram was created via the "regplot." Gene Set Variation Analysis (GSVA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) analyses were conducted to determine the FAMRGs' roles. The percentage of immunocyte infiltration was calculated via CIBERSORT. Using "pRRophetic," the sensitivity of eight regularly used medications and immunotherapy was anticipated.

Results

125 genes were determined as different expression genes (DEGs). Based on RXRA, ECI2, PTGIS, and ACACB, a prognostic model is created and the risk score is calculated. Analyses of univariate and multivariate regressions revealed that the risk score was a distinct prognostic factor (univariate: HR: 2.855, 95% CI: 1.756-4.739, P < 0.001; multivariate: HR: 2.943, 95% CI: 1.800-4.812, P < 0.001). The nomogram demonstrated that it properly predicted the 1-year survival rate. The expression of memory B molecular units, follicular helper T molecular units, regulatory T molecular units, and M1 macrophages differed remarkably between the groups at high and low risk (P < 0.05). Adipocytokine signaling pathways, cancer pathways, and degradation of valine, leucine, and isoleucine vary between high- and low-risk populations. The findings of the GO enrichment revealed that the extracellular matrix and cellular structure were the two most enriched pathways. PTGIS, which is an important gene in fatty acid metabolism, was identified as the hub gene. This result was verified in ovarian cancer and ovarian tissues. The connection between the gene and survival was statistically remarkable (P = 0.015). The pRRophetic algorithm revealed that the low-risk group was more adaptable to cisplatin, doxorubicin, 5-fluorouracil, and etoposide (P < 0.001).

Conclusion

PTGIS may be an indicator of prognosis and a possible therapeutic target for the therapy of ovarian cancer patients. The fatty acid metabolism of immune cells may be controlled, which has an indirect effect on cancer cell growth.

Acyl-CoA Binding Domain Containing 4 Polymorphism rs4986172 and Expression Can Serve as Overall Survival Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma Patients After Hepatectomy

Background

The aim of our study was to evaluate the potential of expression and single nucleotide polymorphism of Acyl-CoA binding domain containing 4 (ACBD4) gene as prognosis biomarkers in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) after hepatectomy.

Methods

HBV-related HCC patients from the First Affiliated Hospital of Guangxi Medical University and GSE14520 were included in the current study, as well as The Cancer Genome Atlas (TCGA) HCC verification cohort. Prognostic analysis and multiple functional enrichment analysis methods were used to evaluate the prognostic value and potential biological functions of the ACBD4 gene in HBV-related HCC.

Results

We found that ACBD4 gene is highly expressed in normal liver tissues and markedly down-regulated in HBV-related HCC tissues. ACBD4 gene was significantly related to overall survival (OS) of HCC in TCGA and GSE14520 cohorts, and patients with low ACBD4 expression were markedly related to poor OS. Rs4986172 was observed as an OS biomarker after hepatectomy in the Guangxi HBV-related HCC cohort. The OS of rs4986172 GG genotype was worse than that of HCC patients with A allele (AA and AG genotypes). Multifunctional enrichment analysis suggested that ACBD4 gene is closely related to the metabolic, peroxisome proliferator-activated receptor and cytochrome P450 pathway. Through connectivity map, we also identified eight compounds that may be used as targeted therapeutic agents for ACBD4 gene in HBV-related HCC; these compounds were scopoletin, alfaxalone, bephenium hydroxynaphthoate, apramycin, 4,5-dianilinophthalimide, DL-thiorphan, aminohippuric acid and quinidine. Immune microenvironment analysis revealed that there were significant differences in immune scores of HBV-related HCC tumor tissues with different ACBD4 expression levels.

Conclusion

Our study reveals that ACBD4 expression and rs4986172 can be serve as biomarkers of OS in HBV-related HCC patients after hepatectomy.

Human Orphan Cytochromes P450: An Update

Orphan cytochromes P450 (CYP) are enzymes whose biological functions and substrates are unknown. However, the use of new experimental strategies has allowed obtaining more information about their relevance in the metabolism of endogenous and exogenous compounds. Likewise, the modulation of their expression and activity has been associated with pathogenesis and prognosis in different diseases. In this work, we review the regulatory pathways and the possible role of orphan CYP to provide evidence that allow us to stop considering some of them as orphan enzymes and to propose them as possible therapeutic targets in the design of new strategies for the treatment of diseases associated with CYP-mediated metabolism.

Identification and validation of ADME genes as prognosis and therapy markers for hepatocellular carcinoma patients

Purpose: ADME genes are genes involved in drug absorption, distribution, metabolism, and excretion (ADME). Previous studies report that expression levels of ADME-related genes correlate with prognosis of hepatocellular carcinoma (HCC) patients. However, the role of ADME gene expression on HCC prognosis has not been fully explored. The present study sought to construct a prediction model using ADME-related genes for prognosis of HCC.

Methods: Transcriptome and clinical data were retrieved from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC), which were used as training and validation cohorts, respectively. A prediction model was constructed using univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) analysis. Patients were divided into high- and low-risk groups based on the median risk score. The predictive ability of the risk signature was estimated through bioinformatics analyses.

Results: Six ADME-related genes (CYP2C9, ABCB6, ABCC5, ADH4, DHRS13, and SLCO2A1) were used to construct the prediction model with a good predictive ability. Univariate and multivariate Cox regression analyses showed the risk signature was an independent predictor of overall survival (OS). A single-sample gene set enrichment analysis (ssGSEA) strategy showed a significant relationship between risk signature and immune status. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed differentially expressed genes (DEGs) in the high- and low-risk groups were enriched in biological process (BP) associated with metabolic and cell cycle pathways.

Conclusion: A prediction model was constructed using six ADME-related genes for prediction of HCC prognosis. This signature can be used to improve HCC diagnosis, treatment, and prognosis in clinical use.

Exploratory Study Using Urinary Volatile Organic Compounds for the Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) biomarkers are lacking in clinical practice. We therefore explored the pattern and composition of urinary volatile organic compounds (VOCs) in HCC patients. This was done in order to assess the feasibility of a potential non-invasive test for HCC, and to enhance our understanding of the disease. This pilot study recruited 58 participants, of whom 20 were HCC cases and 38 were non-HCC cases. The non-HCC cases included healthy individuals and patients with various stages of non-alcoholic fatty liver disease (NAFLD), including those with and without fibrosis. Urine was analysed using gas chromatography-ion mobility spectrometry (GC-IMS) and gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS). GC-IMS was able to separate HCC from fibrotic cases with an area under the curve (AUC) of 0.97 (0.91-1.00), and from non-fibrotic cases with an AUC of 0.62 (0.48-0.76). For GC-TOF-MS, a subset of samples was analysed in which seven chemicals were identified and tentatively linked with HCC. These include 4-methyl-2,4-bis(p-hydroxyphenyl)pent-1-ene (2TMS derivative), 2-butanone, 2-hexanone, benzene, 1-ethyl-2-methyl-, 3-butene-1,2-diol, 1-(2-furanyl)-, bicyclo(4.1.0)heptane, 3,7,7-trimethyl-, [1S-(1a,3β,6a)]-, and sulpiride. Urinary VOC analysis using both GC-IMS and GC-TOF-MS proved to be a feasible method of identifying HCC cases, and was also able to enhance our understanding of HCC pathogenesis.

Cytochrome P450 2A6 is associated with macrophage polarization and is a potential biomarker for hepatocellular carcinoma

Cytochrome P450 2A6 (CYP2A6) is an important metabolic enzyme and is involved in the progression of hepatocellular carcinoma (HCC). However, its specific function and the mechanism of modulation remain to be elucidated. In this study, we found that CYP2A6 is dramatically downregulated in HCC. CYP2A6 expression is closely associated with pathological grading, histologic grade, hepatitis, vascular metastasis, liver inflammation, and worse prognosis. Reduced expression of CYP2A6 contributes to alternative activation of macrophage polarization and impairs macrophage maturation and phagocytosis. Mechanistically, CYP2A6 participates in arachidonic acid metabolism, initiates 20‐hydroxyeicosatetraenoic acid (HETE) generation, and inhibits epoxyeicosatrienoic acid (EET) generation. Disruption of the equilibrium between 20‐HETE and EETs can induce macrophage polarization, thereby modulating antitumor immunity.

CYP1A2 suppresses hepatocellular carcinoma through antagonizing HGF/MET signaling

Rationale: Hyperactivation of HGF/MET signaling pathway is a critical driver in liver tumorigenesis. Cytochrome P450 1A2 (CYP1A2) was significantly down-regulated in hepatocellular carcinoma (HCC). However, little is explored about its tumor suppressive role in HCC. In this study, we examined the functional mechanisms and clinical implication of CYP1A2 in HCC. Methods: The clinical impact of CYP1A2 was evaluated in HCC patients in Hong Kong cohort. The biological functions of CYP1A2 were investigated in vitro and in vivo. A series of biochemical experiments including Western blot assay, immunohistochemistry, quantitative reverse transcription-polymerase chain reaction, and Co-immunoprecipitation assay were conducted. Results: CYP1A2 expression was prominently silenced in HCC tumor tissues and the high expression of CYP1A2 was significantly correlated with lower AFP level, less vascular invasion, and better tumor-free survival in local cohort of HCC patients. The overexpression of CYP1A2 inhibited HCC cell viability and clonogenicity, reduced cell migration and invasion abilities in vitro, and suppressed tumorigenicity in vivo, whereas CYP1A2 knockdown exhibited the opposite effects. CYP1A2 significantly hindered HGF/MET signaling and Matrix metalloproteinases (MMPs) expression in HCC cells. Mechanically, CYP1A2 decreased HGF level and diminished HIF-1α expression, both of which are recognized as key regulators of MET activation. As the transcriptional activator of MET, HIF-1α was identified as a binding partner of CYP1A2. Direct binding of CYP1A2 with HIF-1α induced ubiquitin-mediated degradation of HIF-1α, inhibiting HIF-1α-mediated transcriptions. Conclusions: In conclusion, our results have identified CYP1A2 as a novel antagonist of HGF/MET signaling, and CYP1A2 may serve as an independent new biomarker for the prognosis of HCC patients.

Cytochrome P450 1A2 overcomes nuclear factor kappa B-mediated sorafenib resistance in hepatocellular carcinoma

Sorafenib resistance has become the main obstacle in the effective treatment of advanced hepatocellular carcinoma (HCC) patients. Activation of nuclear factor kappa B (NF-κB) is a newly identified mechanism that contributes to desensitized sorafenib. Cytochrome P450 1A2 (CYP1A2) functions as a tumor suppressor in HCC and its expression is negatively associated with NF-κB in the liver. This study aimed to study whether CYP1A2 could overcome sorafenib resistance. To investigate whether CYP1A2 and NF-κB p65 played roles in sorafenib desensitization, we established sorafenib-resistant (SR) HCC cells. SR cells decreased the expression of CYP1A2 along with the upregulation of NF-κB p65. CYP1A2 overexpression attenuated SR cell proliferation, increased sorafenib sensitivity, and inhibited the NF-κB pathway, whereas CYP1A2 silence showed opposite effects. Sorafenib, in combination with omeprazole, a CYP1A2 inducer, significantly hindered the growth and invasion of SR cells in vitro as well as decreased the tumor growth in vivo. The combination treatment markedly increased CYP1A2 expression and inhibited the sorafenib-induced NF-κB signaling. In addition, the overexpression of NF-κB p65 stimulated the SR cell growth and desensitized sorafenib in SR cells, where CYP1A2 overexpression reversed the phenomenon. Lastly, the majority of HCC tissue samples displayed decreased CYP1A2 but increased NF-κB p65 protein expression. Collectively, CYP1A2 can sensitize SR cells to sorafenib via inhibiting NF-κB p65 axis. Omeprazole in combination with sorafenib exerts a synergistic effect in alleviating acquired sorafenib resistance.

Identification of cancer stem cell characteristics in liver hepatocellular carcinoma by WGCNA analysis of transcriptome stemness index

Cancer stem cells (CSCs) are characterized by self-renewal and -differential potential as compared to common cancer cells and play an important role in the development and therapeutic resistance of liver hepatocellular carcinoma (LIHC). However, the specific pathogenesis of LIHC stem cells is still unclear, and the genes involved in the stemness of LIHC stem cells are currently unknown. In this study, we investigated novel biomarkers associated with LIHC and explored the expression characteristics of stem cell-related genes in LIHC. We found that mRNA expression-based stemness index (mRNAsi) was significantly overexpressed in liver cancer tissues. Further, mRNAsi expression in LIHC increased with the tumor pathological grade, with grade 4 tumors harboring the greatest stem cell features. Upon establishing mRNAsi scores based on mRNA expression of every gene, we found an association with poor overall survival in LIHC. Moreover, modules of interest were determined based on weighted gene co-expression network analysis (WGCNA) inclusion criteria, and three significant modules (red, green, and brown) and 21 key genes (DCN, ECM1, HAND2, PTGIS, SFRP1, SRPX, COLEC10, GRP182, ADAMTS7, CD200, CDH11, COL8A1, FAP, LZTS1, MAP1B, NAV1, NOTCH3, OLFML2A, PRR16, TMEM119, and VCAN) were identified. Functional analysis of these 21 genes demonstrated their enrichment in pathways involved in angiogenesis, negative regulation of DNA-binding transcription factor activity, apoptosis, and autophagy. Causal relationship with proteins indicated that the Wnt, Notch, and Hypoxia pathways are closely related to LIHC tumorigenesis. To our knowledge, this is the first report of a novel CSC biomarker, mRNAsi, to predict the prognosis of LIHC. Further, we identified 21 key genes through mRNA expression network analysis, which could be potential therapeutic targets to inhibit the stemness of cancer cells in LIHC.

Hepatocellular carcinoma: Gene expression profiling and regulation of xenobiotic-metabolizing cytochromes P450

Hepatocellular carcinoma (HCC) remains a highly prevalent and deadly disease, being among the top causes of cancer-related deaths worldwide. Despite the fact that the liver is the major site of biotransformation, studies on drug metabolizing enzymes in HCC are scarce. It is known that malignant transformation of hepatocytes leads to a significant alteration of their metabolic functions and overall deregulation of gene expression. Advanced stages of the disease are thus frequently associated with liver failure, and severe alteration of drug metabolism. However, the impact of dysregulation of metabolic enzymes on therapeutic efficacy and toxicity in HCC patients is largely unknown. Here we demonstrate a significant down-regulation in European Caucasian patients of cytochromes P450 (CYPs), the major xenobiotic-metabolizing enzymes, in HCC tumour samples as compared to their surrounding non-cancerous (reference) tissue. Moreover, we report for the first time the association of the unique CYP profiles with specific transcriptome changes, and interesting correlations with expression levels of nuclear receptors and with the histological grade of the tumours. Integrated analysis has suggested certain co-expression profiles of CYPs with lncRNAs that need to be further characterized. Patients with large tumours with down-regulated CYPs could be more vulnerable to drug toxicity; on the other hand, such tumours would eliminate drugs more slowly and should be more sensitive to pharmacotherapy (except in the case of pro-drugs where activation is necessary).

Mining TCGA Database for Tumor Microenvironment-Related Genes of Prognostic Value in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and lethal malignancies. Recent studies reveal that tumor microenvironment (TME) components significantly affect HCC growth and progression, particularly the infiltrating stromal and immune cells. Thus, mining of TME-related biomarkers is crucial to improve the survival of patients with HCC. Public access of The Cancer Genome Atlas (TCGA) database allows convenient performance of gene expression-based analysis of big data, which contributes to the exploration of potential association between genes and prognosis of a variety of malignancies, including HCC. The "Estimation of STromal and Immune cells in MAlignant Tumors using Expression data" algorithm renders the quantification of the stromal and immune components in TME possible by calculating the stromal and immune scores. Differentially expressed genes (DEGs) were screened by dividing the HCC cohort of TCGA database into high- and low-score groups according to stromal and immune scores. Further analyses of functional enrichment and protein-protein interaction networks show that the DEGs are mainly involved in immune response, cell adhesion, and extracellular matrix. Finally, seven DEGs have significant association with HCC poor outcomes. These genes contain FABP3, GALNT5, GPR84, ITGB6, MYEOV, PLEKHS1, and STRA6 and may be candidate biomarkers for HCC prognosis.