Deregulation of the Genes that Are Involved in Drug Absorption, Distribution, Metabolism, and Excretion in Hepatocellular Carcinoma

Potential role of hypobaric hypoxia environment in treating pan-cancer

Cancer incidence and mortality are lower among high-altitude residents, suggesting that hypobaric hypoxia (HH) might protect against cancer. Our study aimed to develop a pan-cancer prognosis risk model using ADME genes, which are influenced by low oxygen, to explore HH's impact on overall survival (OS) across various cancers. We constructed and validated the model with gene expression and survival data from 8628 samples, using three gene expression databases. AltitudeOmics confirmed HH's significant effects. We employed single-gene prognostic analysis, weighted gene co-expression network analysis, and stepwise Cox regression to identify biomarkers and refine the model. Drugs interacting with the model were explored using LINCS L1000, AutoDockTools, and STITCH. Eight ADME genes significantly altered by HH were identified, revealing their prognostic value across cancers. The model showed lower risk scores linked to better prognosis in 25 cancers, with reduced overall gene expression and decreased tumor mortality risk. Higher T cell infiltration was observed in the low-risk group. Additionally, three potential drugs to modulate our model were identified. This study presents a novel pan-cancer survival prognosis model based on ADME genes influenced by HH, offering new insights into cancer prevention and treatment.

Integrating network pharmacology and experimental validation via PPAR signaling to ameliorate rheumatoid arthritis: Insights from Corydalis Decumbentis Rhizoma (Xiatianwu)

Corydalis Decumbentis Rhizoma (Xiatianwu, XTW) exhibits a positive effect in treating rheumatoid arthritis (RA). However, the precise molecular mechanisms underlying its effects remain unclear. In this study, TNF-α was used to induce inflammation and establish and in vitro RA model. Network pharmacology was employed to identify the important active components and targets in the treatment of XTW on RA. CCK-8 was used to investigate the cell viability. GW9662 (a PPARG antagonist) was applied to validate the network pharmacology prediction. ELISA was used to measure pro-inflammatory cytokines (IL-6, IL-1β, and INF-γ) and oxidative stress markers (MMP-2, MDA, and ROS). HPLC-MS was conducted to validate the four important active ingredients (bicuculline, ferulic acid, berberine, and jatrorrhizine) in XTW. Western blotting was carried out to detect the protein levels of PPAR-γ. In vitro experiments demonstrated that XTW exerted dose-dependent anti-RA effects by downregulating pro-inflammatory cytokines and oxidative stress markers. Through Network pharmacology, three targets (RXRA, PPARG, and PPARA) and four active ingredients (bicuculline, ferulic acid, berberine, and jatrorrhizine) were demonstrated important in the treatment of XTW on RA. Besides, PPAR signaling pathway may be a therapeutic target for XTW treating RA. Further experiments confirmed that XTW administration significantly inhibited inflammation and oxidative stress by upregulating the PPAR signaling pathway. In conclusion, XTW modulates RXRA, PPARG, and PPARA through the PPAR signaling pathway, thereby mitigating inflammation and oxidative stress in RA.

ADME gene-driven prognostic model for bladder cancer: a breakthrough in predicting survival and personalized treatment

BACKGROUND

Genes that participate in the absorption, distribution, metabolism, excretion (ADME) processes occupy a central role in pharmacokinetics. Meanwhile, variability in clinical outcomes and responses to treatment is notable in bladder cancer (BLCA).

METHODS

Our study utilized expansive datasets from TCGA and the GEO to explore prognostic factors in bladder cancer. Utilizing both univariate Cox regression and the lasso regression techniques, we identified ADME genes critical for patient outcomes. Utilizing genes identified in our study, a model for assessing risk was constructed. The evaluation of this model's predictive precision was conducted using Kaplan-Meier survival curves and assessments based on ROC curves. Furthermore, we devised a predictive nomogram, offering a straightforward visualization of crucial prognostic indicators. To explore the potential factors mediating the differences in outcomes between high and low risk groups, we performed comprehensive analyses including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)-based enrichment analyses, immune infiltration variations, somatic mutation landscapes, and pharmacological sensitivity response assessment etc. Immediately following this, we selected core genes based on the PPI network and explored the prognostic potential of the core genes as well as immune modulation, and pathway activation. And the differential expression was verified by immunohistochemistry and qRT-PCR. Finally we explored the potential of the core genes as pan-cancer biomarkers.

RESULTS

Our efforts culminated in the establishment of a validated 17-gene ADME-centered risk prediction model, displaying remarkable predictive accuracy for BLCA prognosis. Through separate cox regression analyses, the importance of the model's risk score in forecasting BLCA outcomes was substantiated. Furthermore, a novel nomogram incorporating clinical variables alongside the risk score was introduced. Comprehensive studies established a strong correlation between the risk score and several key indicators: patterns of immune cell infiltration, reactions to immunotherapy, landscape of somatic mutation and profiles of drug sensitivity. We screened the core prognostic gene CYP2C8, explored its role in tumor bioregulation and validated its upregulated expression in bladder cancer. Furthermore, we found that it can serve as a reliable biomarker for pan-cancer.

CONCLUSION

The risk assessment model formulated in our research stands as a formidable instrument for forecasting BLCA prognosis, while also providing insights into the disease's progression mechanisms and guiding clinical decision-making strategies.

The liver proteome of individuals with a natural UGT2B17 complete deficiency

Glucuronidation is a crucial pathway for the metabolism and detoxification of drugs and endobiotics, and primarily occurs in the liver. UGT2B17 is one of the 22 glycosyltransferases (UGT) that catalyze this reaction. In a large proportion of the population, UGT2B17 is absent due to complete gene deletion. We hypothesized that a UGT2B17 human deficiency affects the composition and function of the liver proteome, potentially provoking compensatory responses, and altering interconnected pathways and regulatory networks. The objective was to elucidate the liver proteome of UGT2B17-deficient individuals. Liver specimens from UGT2B17-deficient and proficient individuals were compared by mass spectrometry-based proteomics using data-independent acquisition. In UGT2B17-deficient livers, 80% of altered proteins showed increased abundance with a notable enrichment in various metabolic and chemical defense pathways, cellular stress and immune-related responses. Enzymes involved in the homeostasis of steroids, nicotinamide, carbohydrate and energy metabolism, and sugar pathways were also more abundant. Some of these changes support compensatory mechanisms, but do not involve other UGTs. An increased abundance of non-metabolic proteins suggests an adaptation to endoplasmic reticulum stress, and activation of immune responses. Data implies a disrupted hepatocellular homeostasis in UGT2B17-deficient individuals and offers new perspectives on functions and phenotypes associated with a complete UGT2B17 deficiency.

Identifying ADME-related gene signature for immune landscape and prognosis in KIRC by single-cell and spatial transcriptome analysis

Kidney renal clear cell carcinoma (KIRC) is the most prevalent subtype of kidney cancer. Although multiple therapeutic agents have been proven effective in KIRC, their clinical application has been hindered by a lack of reliable biomarkers. This study focused on the prognostic value and function of drug absorption, distribution, metabolism, and excretion- (ADME-) related genes (ARGs) in KIRC to enhance personalized therapy. The critical role of ARGs in KIRC microenvironment was confirmed by single cell RNA-seq analysis and spatial transcriptome sequencing analysis for the first time. Then, an ADME-related prognostic signature (ARPS) was developed by the bulk RNA-seq analysis. The ARPS, created through Cox regression, LASSO, and stepAIC analyses, identified eight ARGs that stratified patients into high-risk and low-risk groups. High-risk patients had significantly poorer overall survival. Multivariate analysis confirmed the independent predictive ability of ARPS, and an ARPS-based nomogram was constructed for clinical application. Gene ontology and KEGG pathway analyses revealed immune-related functions and pathways enriched in these groups, with low-risk patients showing better responses to immunotherapy. Finally, the expression of ARGs was validated by qRT-PCR and Western blotting experiments. These findings underscore the prognostic significance of ARPS in KIRC and its potential application in guiding personalized treatment strategies.

Research on molecular characteristics of ADME-related genes in kidney renal clear cell carcinoma

Genes involved in drug absorption, distribution, metabolism, and excretion (ADME) are named ADME genes. However, the comprehensive role of ADME genes in kidney renal clear cell carcinoma (KIRC) remains unclear. Using the clinical and gene expression data of KIRC patients downloaded from The Cancer Genome Atlas (TCGA), ArrayExpress, and the Gene Expression Omnibus (GEO) databases, we cluster patients into two patterns, and the population with a relatively poor prognosis demonstrated higher level of immunosuppressive cell infiltration and higher proportion of glycolytic subtypes. Then, 17 ADME genes combination identified through the least absolute shrinkage and selection operator algorithm (LASSO, 1000 times) was utilized to calculate the ADME score. The ADME score was found to be an independent predictor of prognosis in KIRC and to be tightly associated with the infiltration level of immune cells, metabolic properties, tumor-related signaling pathways, genetic variation, and responses to chemotherapeutics. Our work revealed the characteristics of ADME in KIRC. Assessing the ADME profiles of individual patients can deepen our comprehension of tumor microenvironment (TME) features in KIRC and can aid in developing more personalized and effective therapeutic strategies.

Identification of an ADME-related gene for forecasting the prognosis and responding to immunotherapy in sarcomas

There are more than 170 subtypes of sarcomas (SARC), which pose a challenge for diagnosis and patient management. Relatively simple or complex karyotypes play an indispensable role in the early diagnosis and effective treatment of SARC. The genes related to absorption, distribution, metabolism, and excretion (ADME) of a drug can serve as prognostic biomarkers of cancer and potential drug targets. In this study, a risk score signature was created. The SARC cohort was downloaded from The Cancer Genome Atlas (TCGA) database, and divided into high-risk group and low-risk group according to the median value of risk score. Compared with high-risk group, low-risk group has a longer survival time, which is also verified in osteosarcoma cohort from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. In addition, the relationship between the signature and immunophenotypes, including status of immune cell infiltration and immune checkpoint expression, was explored. Then, we found that high-risk group is in immunosuppressive status. Finally, we verified that PPARD played a role as a carcinogen in osteosarcoma, which provided a direction for targeted treatment of osteosarcoma in the future. Generally speaking, the signature can not only help clinicians predict the prognosis of patients with SARC, but also provide a theoretical basis for developing more effective targeted drugs in the future.

The burden of rare variants in DPYS gene is a novel predictor of the risk of developing severe fluoropyrimidine-related toxicity

BACKGROUND

Despite a growing number of publications highlighting the potential impact on the therapy outcome, rare genetic variants (minor allele frequency < 1%) in genes associated to drug adsorption, distribution, metabolism, and elimination are poorly studied. Previously, rare germline DPYD missense variants were shown to identify a subset of fluoropyrimidine-treated patients at high risk for severe toxicity. Here, we investigate the impact of rare genetic variants in a panel of 54 other fluoropyrimidine-related genes on the risk of severe toxicity.

METHODS

The coding sequence and untranslated regions of 54 genes related to fluoropyrimidine pharmacokinetics/pharmacodynamics were analyzed by next-generation sequencing in 120 patients developing grade 3-5 toxicity (NCI-CTC vs3.0) and 104 matched controls. Sequence Kernel Association Test (SKAT) analysis was used to select genes with a burden of genetic variants significantly associated with risk of severe toxicity. The statistical association of common and rare genetic variants in selected genes was further investigated. The functional impact of genetic variants was assessed using two different in silico prediction tools (Predict2SNP; ADME Prediction Framework).

RESULTS

SKAT analysis highlighted DPYS and PPARD as genes with a genetic mutational burden significantly associated with risk of severe fluoropyrimidine-related toxicity (Bonferroni adjusted P = 0.024 and P = 0.039, respectively). Looking more closely at allele frequency, the burden of rare DPYS variants was significantly higher in patients with toxicity compared with controls (P = 0.047, Mann-Whitney test). Carrying at least one rare DPYS variant was associated with an approximately fourfold higher risk of severe cumulative (OR = 4.08, P = 0.030) and acute (OR = 4.21, P = 0.082) toxicity. The burden of PPARD rare genetic variants was not significantly related to toxicity. Some common variants with predictive value in DPYS and PPARD were also identified: DPYS rs143004875-T and PPARD rs2016520-T variants predicted an increased risk of severe cumulative (P = 0.002 and P = 0.001, respectively) and acute (P = 0.005 and P = 0.0001, respectively) toxicity.

CONCLUSION

This work demonstrated that the rare mutational burden of DPYS, a gene strictly cooperating with DPYD in the catabolic pathway of fluoropyrimidines, is a promising pharmacogenetic marker for precision dosing of fluoropyrimidines. Additionally, some common genetic polymorphisms in DPYS and PPARD were identified as promising predictive markers that warrant further investigation.

Based on disulfidptosis-related glycolytic genes to construct a signature for predicting prognosis and immune infiltration analysis of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) comprises several distinct molecular subtypes with varying prognostic implications. However, a comprehensive analysis of a prognostic signature for HCC based on molecular subtypes related to disulfidptosis and glycolysis, as well as associated metabolomics and the immune microenvironment, is yet to be fully explored.

Methods

Based on the differences in the expression of disulfide-related glycolytic genes (DRGGs), patients with HCC were divided into different subtypes by consensus clustering. Establish and verify a risk prognosis signature. Finally, the expression level of the key gene SLCO1B1 in the signature was evaluated using immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR) in HCC. The association between this gene and immune cells was explored using multiplex immunofluorescence. The biological functions of the cell counting kit-8, wound healing, and colony formation assays were studied.

Results

Different subtypes of patients have specific clinicopathological features, prognosis and immune microenvironment. We identified seven valuable genes and constructed a risk-prognosis signature. Analysis of the risk score revealed that compared to the high-risk group, the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways, consistent with the tumor subtypes. Furthermore, IHC and qRT-PCR analyses showed decreased expression of SLCO1B1 in HCC tissues. Functional experiments revealed that SLCO1B1 overexpression inhibited the proliferation, migration, and invasion of HCC cells.

Conclusion

We developed a prognostic signature that can assist clinicians in predicting the overall survival of patients with HCC and provides a reference value for targeted therapy.

Relevance of the organic anion transporting polypeptide 1B3 (OATP1B3) in the personalized pharmacological treatment of hepatocellular carcinoma

Although pharmacological treatment is the best option for most patients with advanced hepatocellular carcinoma (HCC), its success is very limited, partly due to reduced uptake and enhanced efflux of antitumor drugs. Here we have explored the usefulness of vectorizing drugs towards the organic anion transporting polypeptide 1B3 (OATP1B3) to enhance their efficacy against HCC cells. In silico studies (RNA-Seq data, 11 cohorts) and immunohistochemistry analyses revealed a marked interindividual variability, together with general downregulation but still expression of OATP1B3 in the plasma membrane of HCC cells. The measurement of mRNA variants in 20 HCC samples showed the almost absence of the cancer-type variant (Ct-OATP1B3) together with marked predominance of the liver-type variant (Lt-OATP1B3). In Lt-OATP1B3-expressing cells, the screening of 37 chemotherapeutical drugs and 17 tyrosine kinase receptors inhibitors (TKIs) revealed that 10 classical anticancer drugs and 12 TKIs were able to inhibit Lt-OATP1B3-mediated transport. Lt-OATP1B3-expressing cells were more sensitive than Mock parental cells (transduced with empty lentiviral vectors) to some Lt-OATP1B3 substrates (paclitaxel and the bile acid-cisplatin derivative Bamet-UD2), but not to cisplatin, which is not transported by Lt-OATP1B3. This enhanced response was abolished by competition with taurocholic acid, a known Lt-OATP1B3 substrate. Tumors subcutaneously generated in immunodeficient mice by Lt-OATP1B3-expressing HCC cells were more sensitive to Bamet-UD2 than those derived from Mock cells. In conclusion, Lt-OATP1B3 expression should be screened before deciding the use of anticancer drugs substrates of this carrier in the personalized treatment of HCC. Moreover, Lt-OATP1B3-mediated uptake must be considered when designing novel anti-HCC targeted drugs.

A pyroptosis-related gene signature for prognosis prediction in hepatocellular carcinoma

Introduction

Hepatocellular carcinoma (HCC) is one of the most invasive cancers with a low 5-year survival rate. Pyroptosis, a specialized form of cell death, has shown its association with cancer progression. However, its role in the prognosis of HCC has not been fully understood.

Methods

In our study, clinical information and mRNA expression for 1076 patients with HCC were obtained from the five public cohorts. Pyroptotic clusters were generated by unsupervised clustering based on 40 pyroptosis-related genes (PRGs) in the TCGA and ICGC cohort. A pyroptosis-related signature was constructed using least absolute shrinkage and selection operator (LASSO) regression according to differentially expressed genes (DEGs) of pyroptotic clusters. The signature was then tested in the validation cohorts (GES10142 and GSE14520) and subsequently validated in the CPTAC cohort (n=159) at both mRNA and protein levels. Response to sorafenib was explored in GSE109211.

Results

Three clusters were identified based on the 40 PRGs in the TCGA cohort. A total of 24 genes were selected based on DEGs of the above three pyroptotic clusters to construct the pyroptotic risk score. Patients with the high-risk score showed shorter overall survival (OS) compared to those with the low-risk score in the training set (P&lt;0.001; HR, 3.06; 95% CI, 2.22-4.24) and the test set (P=0.008; HR, 1.61; 95% CI, 1.13-2.28). The predictive ability of the risk score was further confirmed in the CPTAC cohort at both mRNAs (P&lt;0.001; HR, 2.99; 95% CI, 1.67-5.36) and protein levels (P&lt;0.001; HR, 2.97; 95% CI 1.66-5.31). The expression of the model genes was correlated with immune cell infiltration, angiogenesis-related genes, and sensitivity to antiangiogenic therapy (P&lt;0.05).

Discussion

In conclusion, we established a prognostic signature of 24 genes based on pyroptosis clusters for HCC patients, providing insight into the risk stratification of HCC.

Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma

Background

Currently, the most predominant type of liver cancer is hepatocellular carcinoma (HCC), which is also the fourth leading cause of cancer-related death in the global population. Pyroptosis is an emerging form of cell death that affects the prognosis of cancer patients by modulating tumor cell migration, proliferation and invasion. However, the evaluation of pyroptosis in the prognosis of HCC is still insufficient.

Methods

A total of 365 HCC patients from the TCGA-LIHC cohort were classified into two distinct subtypes using consensus clustering of pyroptosis-related genes (PRGs). Following univariate Cox analysis of differentially expressed genes between subtypes, we established a prognostic model (PRGs-score, PRGS) by LASSO Cox analysis. We further tested the predictive power of the prognostic model in the ICGC (LIRI-JP) and GEO (GSE14520) cohorts. The tumor microenvironment (TME) was studied using the CIBERSORT. The enrichment scores for immune cells and immune functions in low- and high-PRGS groups were assessed using ssGSEA. The IMvigor210 cohort was used to investigate the immunotherapy efficacy. Furthermore, we validated the expression of prognostic genes in PRGS by RT-qPCR in vitro.

Results

The subtyping of HCC based on PRGs exhibited distinct clinical characteristics. We developed a prognostic model PRGS by differentially expressed genes between different subtypes. The results showed that PRGS could well forecast the survival of HCC patients in different cohorts and was associated with the immune microenvironment. Moreover, PRGS was considered to be an independent prognostic risk factor and superior to other pyroptosis-related signatures. Low-PRGS implied greater immune cell infiltration and better overall survival with immunotherapy. The results of RT-qPCR also showed that prognostic genes were significantly dysregulated in HCC.

Conclusions

PRGS has promising application in forecasting the prognosis of HCC patients, and its relationship with the immune microenvironment provides a basis for the subsequent treatment and research of HCC.

Comparative RNA-Sequencing Analysis Reveals High Complexity and Heterogeneity of Transcriptomic and Immune Profiles in Hepatocellular Carcinoma Tumors of Viral (HBV, HCV) and Non-Viral Etiology

Background and Objectives: Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, is the leading cause of cancer-related mortality. It arises and progresses against fibrotic or cirrhotic backgrounds mainly due to infection with hepatitis viruses B (HBV) or C (HCV) or non-viral causes that lead to chronic inflammation and genomic changes. A better understanding of molecular and immune mechanisms in HCC subtypes is needed. Materials and Methods: To identify transcriptional changes in primary HCC tumors with or without hepatitis viral etiology, we analyzed the transcriptomes of 24 patients by next-generation sequencing. Results: We identified common and unique differentially expressed genes for each etiological tumor group and analyzed the expression of SLC, ATP binding cassette, cytochrome 450, cancer testis, and heat shock protein genes. Metascape functional enrichment analysis showed mainly upregulated cell-cycle pathways in HBV and HCV and upregulated cell response to stress in non-viral infection. GeneWalk analysis identified regulator, hub, and moonlighting genes and highlighted CCNB1, ACTN2, BRCA1, IGF1, CDK1, AURKA, AURKB, and TOP2A in the HCV group and HSF1, HSPA1A, HSP90AA1, HSPB1, HSPA5, PTK2, and AURKB in the group without viral infection as hub genes. Immune infiltrate analysis showed that T cell, cytotoxic, and natural killer cell markers were significantly more highly expressed in HCV than in non-viral tumors. Genes associated with monocyte activation had the highest expression levels in HBV, while high expression of genes involved in primary adaptive immune response and complement receptor activity characterized tumors without viral infection. Conclusions: Our comprehensive study underlines the high degree of complexity of immune profiles in the analyzed groups, which adds to the heterogeneous HCC genomic landscape. The biomarkers identified in each HCC group might serve as therapeutic targets.

The Somatic Mutation Landscape of UDP-Glycosyltransferase (UGT) Genes in Human Cancers

The human UDP-glycosyltransferase (UGTs) superfamily has a critical role in the metabolism of anticancer drugs and numerous pro/anti-cancer molecules (e.g., steroids, lipids, fatty acids, bile acids and carcinogens). Recent studies have shown wide and abundant expression of UGT genes in human cancers. However, the extent to which UGT genes acquire somatic mutations within tumors remains to be systematically investigated. In the present study, our comprehensive analysis of the somatic mutation profiles of 10,069 tumors from 33 different TCGA cancer types identified 3427 somatic mutations in UGT genes. Overall, nearly 18% (1802/10,069) of the assessed tumors had mutations in UGT genes with huge variations in mutation frequency across different cancer types, ranging from over 25% in five cancers (COAD, LUAD, LUSC, SKCM and UCSC) to less than 5% in eight cancers (LAML, MESO, PCPG, PAAD, PRAD, TGCT, THYM and UVM). All 22 UGT genes showed somatic mutations in tumors, with UGT2B4, UGT3A1 and UGT3A2 showing the largest number of mutations (289, 307 and 255 mutations, respectively). Nearly 65% (2260/3427) of the mutations were missense, frame-shift and nonsense mutations that have been predicted to code for variant UGT proteins. Furthermore, about 10% (362/3427) of the mutations occurred in non-coding regions (5' UTR, 3' UTR and splice sites) that may be able to alter the efficiency of translation initiation, miRNA regulation or the splicing of UGT transcripts. In conclusion, our data show widespread somatic mutations of UGT genes in human cancers that may affect the capacity of cancer cells to metabolize anticancer drugs and endobiotics that control pro/anti-cancer signaling pathways. This highlights their potential utility as biomarkers for predicting therapeutic efficacy and clinical outcomes.

Identification of a novel prognostic ADME-related signature associated with tumor immunity for aiding therapy in head and neck squamous cell carcinoma

The genes that control drug absorption, distribution, metabolism, and excretion (ADME) are also involved in carcinogenesis, cancer progression, and chemoresistance. However, no studies have systematically investigated the clinical significance and underlying functions of ADME genes in head and neck squamous cell carcinoma. Herein, we comprehensively explored the ADME genes in this disease, constructed and validated as a prognostic ADME gene signature (ADMEGS), using three ADME genes (ABCB1, ALDH1B1, and PON2) utilizing multiple datasets, including the training and test sets of The Cancer Genome Atlas and the Gene Expression Omnibus validation set. Moreover, we analyzed the relationship between the ADMEGS and clinical parameters, tumor immunity, and therapeutic response. We found that the ADMEGS was significantly correlated with the clinical, T, and N stages. Additionally, we were able to effectively differentiate tumor immune scores, immune cell infiltration statuses, and treatment responses based on the ADMEGS. As such, ADMEGS may be promising predictors for clinical outcome, tumor immunity, and treatment response.

A transcriptional regulatory network of HNF4α and HNF1α involved in human diseases and drug metabolism

HNF4α and HNF1α are core transcription factors involved in the development and progression of a variety of human diseases and drug metabolism. They play critical roles in maintaining the normal growth and function of multiple organs, mainly the liver, and in the metabolism of endogenous and exogenous substances. The twelve isoforms of HNF4α may exhibit different physiological functions, and HNF4α and HNF1α show varying or even opposing effects in different types of diseases, particularly cancer. Additionally, the regulation of CYP450, phase II drug-metabolizing enzymes, and drug transporters is affected by several factors. This article aims to review the role of HNF4α and HNF1α in human diseases and drug metabolism, including their structures and physiological functions, affected diseases, regulated drug metabolism genes, influencing factors, and related mechanisms. We also propose a transcriptional regulatory network of HNF4α and HNF1α that regulates the expression of target genes related to disease and drug metabolism.

A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy.

Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs.

Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability.

Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.

The role of hepatocyte nuclear factor 4α (HNF4α) in tumorigenesis

Hepatocyte Nuclear Factor 4 Alpha (HNF4α) is a master transcription factor mainly expressed in the liver, kidney, intestine and endocrine pancreas. It regulates multiple target genes involved in embryonic development and metabolism. HNF4α-related diseases include non-alcoholic fatty liver disease (NAFLD), obesity, hypertension, hyperlipidemia, metabolic syndrome and diabetes mellitus. Recently, HNF4α has been emerging as a key player in a variety of cancers. In this review, we summarized the role and mechanism of HNF4α in different types of cancers, especially in liver and colorectal cancer, aiming to provide additional guidance for intervention of these diseases.

Development and Validation of an ADME-Related Gene Signature for Survival, Treatment Outcome and Immune Cell Infiltration in Head and Neck Squamous Cell Carcinoma

ADME genes are a set of genes which are involved in drug absorption, distribution, metabolism, and excretion (ADME). However, prognostic value and function of ADME genes in head and neck squamous cell carcinoma (HNSCC) remain largely unclear. In this study, we established an ADME-related prognostic model through the least absolute shrinkage and selection operator (LASSO) analysis in the Cancer Genome Atla (TCGA) training cohort and its robustness was validated by TCGA internal validation cohort and a Gene Expression Omnibus (GEO) external cohort. The 14-gene signature stratified patients into high- or low-risk groups. Patients with high-risk scores exhibited significantly poorer overall survival (OS) and disease-free survival (DFS) than those with low-risk scores. Receiver operating characteristic (ROC) curve analysis was used to confirm the signature’s predictive efficacy for OS and DFS. Furthermore, gene ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway analyses showed that immune-related functions and pathways were enriched, such as lymphocyte activation, leukocyte cell-cell adhesion and T-helper cell differentiation. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) and other analyses revealed that immune cell (especially B cell and T cell) infiltration levels were significantly higher in the low-risk group. Moreover, patients with low-risk scores were significantly associated with immunotherapy and chemotherapy treatment benefit. In conclusion, we constructed a novel ADME-related prognostic and therapeutic biomarker associated with immune cell infiltration of HNSCC patients.

Nano-bio interactions: A major principle in the dynamic biological processes of nano-assemblies

Controllable nano-assembly with stimuli-responsive groups is emerging as a powerful strategy to generate theranostic nanosystems that meet unique requirements in modern medicine. However, this prospective field is still in a proof-of-concept stage due to the gaps in our understanding of complex-(nano-assemblies)-complex-(biosystems) interactions. Indeed, stimuli-responsive assembly-disassembly is, in and of itself, a process of nano-bio interactions, the key steps for biological fate and functional activity of nano-assemblies. To provide a comprehensive understanding of these interactions in this review, we first propose a 4W1H principle (Where, When, What, Which and How) to delineate the relevant dynamic biological processes, behaviour and fate of nano-assemblies. We further summarize several key parameters that govern effective nano-bio interactions. The effects of these kinetic parameters on ADMET processes (absorption, distribution, metabolism, excretion and transformation) are then discussed. Furthermore, we provide an overview of the challenges facing the evaluation of nano-bio interactions of assembled nanodrugs. We finally conclude with future perspectives on safe-by-design and application-driven-design of nano-assemblies. This review will highlight the dynamic biological and physicochemical parameters of nano-bio interactions and bridge discrete concepts to build a full spectrum understanding of the biological outcomes of nano-assemblies. These principles are expected to pave the way for future development and clinical translation of precise, safe and effective nanomedicines with intelligent theranostic features.

CYP2E1 plays a suppressive role in hepatocellular carcinoma by regulating Wnt/Dvl2/β-catenin signaling

Objective

Knowledge of the role of CYP2E1 in hepatocarcinogenesis is largely based on epidemiological and animal studies, with a primary focus on the role of CYP2E1 in metabolic activation of procarcinogens. Few studies have directly assessed the effects of CYP2E1 on HCC malignant phenotypes.

Methods

The expression of CYP2E1 in HCC tissues was determined by qRT-PCR, western blotting and immunohistochemistry. Overexpression of CYP2E1 in HCC cell was achieved by lentivirus transfection. The function of CYP2E1 were detected by CCK-8, wound healing, transwell assays, xenograft models and pulmonary metastasis model. TOP/FOPFlash reporter assay, western blotting, functional rescue experiments, Co-immunoprecipitation and reactive oxygen species detection were conducted to reveal the underlying mechanism of the tumor suppressive role of CYP2E1.

Results

CYP2E1 expression is down-regulated in HCC tissues, and this downregulation was associated with large tumor diameter, vascular invasion, poor differentiation, and shortened patient survival time. Ectopic expression of CYP2E1 inhibits the proliferation, invasion and migration and epithelial-to-mesenchymal transition of HCC cells in vitro, and inhibits tumor formation and lung metastasis in nude mice. Mechanistic investigations show that CYP2E1 overexpression significantly inhibited Wnt/β-catenin signaling activity and decreased Dvl2 expression in HCC cells. An increase in Dvl2 expression restored the malignant phenotype of HCC cells. Notably, CYP2E1 promoted the ubiquitin-mediated degradation of Dvl2 by strengthening the interaction between Dvl2 and the E3 ubiquitin ligase KLHL12 in CYP2E1-stable HCC cells. CYP2E1-induced ROS accumulation was a critical upstream event in the Wnt/β-Catenin pathway in CYP2E1-overexpressing HCC cells.

Conclusions

These results provide novel insight into the role of CYP2E1 in HCC and the tumor suppressor role of CYP2E1 can be attributed to its ability to manipulate Wnt/Dvl2/β-catenin pathway via inducing ROS accumulation, which provides a potential target for the prevention and treatment of HCC.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03396-6.

Complex roles of nicotinamide N-methyltransferase in cancer progression

Nicotinamide N-methyltransferase (NNMT) is an intracellular methyltransferase, catalyzing the N-methylation of nicotinamide (NAM) to form 1-methylnicotinamide (1-MNAM), in which S-adenosyl-l-methionine (SAM) is the methyl donor. High expression of NNMT can alter cellular NAM and SAM levels, which in turn, affects nicotinamide adenine dinucleotide (NAD⁺)-dependent redox reactions and signaling pathways, and remodels cellular epigenetic states. Studies have revealed that NNMT plays critical roles in the occurrence and development of various cancers, and analysis of NNMT expression levels in different cancers from The Cancer Genome Atlas (TCGA) dataset indicated that NNMT might be a potential biomarker and therapeutic target for tumor diagnosis and treatment. This review provides a comprehensive understanding of recent advances on NNMT functions in different tumors and deciphers the complex roles of NNMT in cancer progression.

Establishment of non-small-cell lung cancer risk prediction model based on prognosis-associated ADME genes

PURPOSE

ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In the present study, a non-small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores.

METHODS

An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail.

RESULTS

A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model's effectiveness. Univariate and multivariate Cox regression analyses showed that the model's risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions.

CONCLUSION

The risk prediction model established with seven ADME genes in the present study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.

The Expression Profiles and Deregulation of UDP-Glycosyltransferase (UGT) Genes in Human Cancers and Their Association with Clinical Outcomes

The human UDP-glycosyltransferase (UGTs) superfamily has 22 functional enzymes that play a critical role in the metabolism of small lipophilic compounds, including carcinogens, drugs, steroids, lipids, fatty acids, and bile acids. The expression profiles of UGT genes in human cancers and their impact on cancer patient survival remains to be systematically investigated. In the present study, a comprehensive analysis of the RNAseq and clinical datasets of 9514 patients from 33 different TCGA (the Genome Cancer Atlas) cancers demonstrated cancer-specific UGT expression profiles with high interindividual variability among and within individual cancers. Notably, cancers derived from drug metabolizing tissues (liver, kidney, gut, pancreas) expressed the largest number of UGT genes (COAD, KIRC, KIRP, LIHC, PAAD); six UGT genes (1A6, 1A9, 1A10, 2A3, 2B7, UGT8) showed high expression in five or more different cancers. Kaplan-Meier plots and logrank tests revealed that six UGT genes were significantly associated with increased overall survival (OS) rates [UGT1A1 (LUSC), UGT1A6 (ACC), UGT1A7 (ACC), UGT2A3 (KIRC), UGT2B15 (BLCA, SKCM)] or decreased OS rates [UGT2B15 (LGG), UGT8 (UVM)] in specific cancers. Finally, differential expression analysis of 611 patients from 12 TCGA cancers identified 16 UGT genes (1A1, 1A3, 1A6, 1A7, 1A8, 1A9, 1A10, 2A1, 2A3, 2B4, 2B7, 2B11, 2B15, 3A1, 3A2, UGT8) that were up/downregulated in at least one cancer relative to normal tissues. In conclusion, our data show widespread expression of UGT genes in cancers, highlighting the capacity for intratumoural drug metabolism through the UGT conjugation pathway. The data also suggests the potentials for specific UGT genes to serve as prognostic biomarkers or therapeutic targets in cancers.

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.

Non-coding RNA crosstalk with nuclear receptors in liver disease

The dysregulation of nuclear receptors (NRs) underlies the pathogenesis of a variety of liver disorders. Non-coding RNAs (ncRNAs) are defined as RNA molecules transcribed from DNA but not translated into proteins. MicroRNAs (miRNAs) and long non-coding RNAs (lncRNAs) are two types of ncRNAs that have been extensively studied for regulating gene expression during diverse cellular processes. NRs as therapeutic targets in liver disease have been exemplified by the successful application of their pharmacological ligands in clinics. MiRNA-based reagents or drugs are emerging as flagship products in clinical trials. Advancing our understanding of the crosstalk between NRs and ncRNAs is critical to the development of diagnostic and therapeutic strategies. This review summarizes recent findings on the reciprocal regulation between NRs and ncRNAs (mainly on miRNAs and lncRNAs) and their implication in liver pathophysiology, which might be informative to the translational medicine of targeting NRs and ncRNAs in liver disease.

Identification of hub genes associated with obesity-induced hepatocellular carcinoma risk based on integrated bioinformatics analysis

Obesity, which has become one of the biggest public health problems of the twenty-first century, accompanies many chronic conditions, including cancer. On the other hand, liver cancer, which is known to be associated with obesity, is considered another serious threat to public health. However, the underlying drivers of the development of obesity-associated hepatocellular carcinoma (HCC) remain blurry. The current study attempted to identify the key genes and pathways in the obesity-induced development of HCC using integrated bioinformatics analyses. Obesity and HCC-associated gene expression datasets were downloaded from Gene Expression Omnibus (GEO) and analyzed to identify overlapping differentially expressed genes (DEGs) and hub genes. The prognostic potentials, survival analysis, and expression levels of hub genes were further assessed. Moreover, the correlation between hub genes and the immune cells infiltration was analyzed. The findings of this research revealed that both mRNA and protein expression levels of the four hub genes (IGF1, ACADL, CYP2C9, and G6PD) involved in many important metabolic pathways are remarkably altered in both obese individuals and patients with HCC. The results demonstrated that these dysregulated genes in both obesity and HCC may serve as considerable targets for the prevention and treatment of HCC development in obese individuals.

Comprehensive Identification of Bridge Genes to Explain the Progression from Chronic Hepatitis B Virus Infection to Hepatocellular Carcinoma

Background

Hepatitis B virus infection co-occurs in 33% of individuals with hepatocellular carcinoma worldwide. However, the molecular link between hepatitis B virus and hepatocellular carcinoma is unknown. Thus, we aimed to elucidate molecular linkages underlying pathogenesis through in-depth data mining analysis.

Materials and Methods

Differentially expressed genes were identified from patients with chronic hepatitis B virus infection, hepatocellular carcinoma, or both. Gene set enrichment analysis revealed signaling pathways involving differentially expressed genes. Protein-protein interaction networks, protein crosstalk, and enrichment were analyzed to determine whether differentially expressed gene products might serve as a bridge from hepatitis B virus infection to hepatocellular carcinoma pathogenesis. Prognostic potential and transcriptional and post-transcriptional regulators of bridge genes were also examined.

Results

We identified vital bridge factors in hepatitis B virus infection-associated hepatocellular carcinoma. Differentially expressed genes were clustered into modules based on relative protein function. Signaling pathways associated with cancer, inflammation, immune system, and microenvironment showed significant crosstalk between modules. Thirty-two genes were dysregulated in hepatitis B virus infection-mediated hepatocellular carcinoma. CPEB3, RAB26, SLCO1B1, ST3GAL6 and XK had higher connectivity in the modular network, suggesting significant associations with survival. CDC20 and NUP107 were identified as driver genes as well as markers of poor prognosis.

Conclusion

Our results suggest that the sustained inflammatory environment created by hepatitis B virus infection is a risk factor for hepatocellular carcinoma. The identification of hepatitis B virus infection-related hepatocellular carcinoma bridge genes provides testable hypotheses about the pathogenesis of hepatocellular carcinoma.

A Novel Epigenetic Machine Learning Model to Define Risk of Progression for Hepatocellular Carcinoma Patients

Although extensive advancements have been made in treatment against hepatocellular carcinoma (HCC), the prognosis of HCC patients remains unsatisfied. It is now clearly established that extensive epigenetic changes act as a driver in human tumors. This study exploits HCC epigenetic deregulation to define a novel prognostic model for monitoring the progression of HCC. We analyzed the genome-wide DNA methylation profile of 374 primary tumor specimens using the Illumina 450 K array data from The Cancer Genome Atlas. We initially used a novel combination of Machine Learning algorithms (Recursive Features Selection, Boruta) to capture early tumor progression features. The subsets of probes obtained were used to train and validate Random Forest models to predict a Progression Free Survival greater or less than 6 months. The model based on 34 epigenetic probes showed the best performance, scoring 0.80 accuracy and 0.51 Matthews Correlation Coefficient on testset. Then, we generated and validated a progression signature based on 4 methylation probes capable of stratifying HCC patients at high and low risk of progression. Survival analysis showed that high risk patients are characterized by a poorer progression free survival compared to low risk patients. Moreover, decision curve analysis confirmed the strength of this predictive tool over conventional clinical parameters. Functional enrichment analysis highlighted that high risk patients differentiated themselves by the upregulation of proliferative pathways. Ultimately, we propose the oncogenic MCM2 gene as a methylation-driven gene of which the representative epigenetic markers could serve both as predictive and prognostic markers. Briefly, our work provides several potential HCC progression epigenetic biomarkers as well as a new signature that may enhance patients surveillance and advances in personalized treatment.

A novel four-gene of iron metabolism-related and methylated for prognosis prediction of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a liver disease with a complex underlying mechanism, and patients with HCC have low survival rates. Iron metabolism plays a crucial role in the pathogenesis of HCC; however, the prognostic value of iron metabolism-related and methylated genes for HCC needs to be further explored. In the present study, we identified differentially expressed genes (DEGs) that play a role in iron metabolism and DNA methylation in HCC from The Cancer Genome Atlas. Four of these DEGs, whose expression levels are correlated with HCC prognosis, namely, RRM2, FTCD, CYP2C9, and ATP6V1C1, were further used to construct a prognostic model for HCC, wherein the risk score was calculated using the gene expression of the four DEGs. This could be used to predict the overall survival of HCC patients for 1, 3, and 5 years. Results of a multivariate Cox regression analysis further indicated that the risk score was an independent variable correlated with the prognosis of HCC patients. The identified gene signature was further validated using an independent cohort of HCC patients from the International Cancer Genome Consortium. Weighted gene co-expression network analysis and gene set enrichment analysis were performed to identify potential regulatory mechanisms of the gene signature in HCC. Taken together, we identified key prognostic factors of iron metabolism-related and methylated genes for HCC, providing a potential treatment strategy for HCC.

Moonlighting in drug metabolism

Drug metabolizing enzymes catalyze the biotransformation of many of drugs and chemicals. The drug metabolizing enzymes are distributed among several evolutionary families and catalyze a range of detoxication reactions, including oxidation/reduction, conjugative, and hydrolytic reactions that serve to detoxify potentially toxic compounds. This detoxication function requires that drug metabolizing enzymes exhibit substrate promiscuity. In addition to their catalytic functions, many drug metabolizing enzymes possess functions unrelated to or in addition to catalysis. Such proteins are termed 'moonlighting proteins' and are defined as proteins with multiple biochemical or biophysical functions that reside in a single protein. This review discusses the diverse moonlighting functions of drug metabolizing enzymes and the roles they play in physiological functions relating to reproduction, vision, cell signaling, cancer, and transport. Further research will likely reveal new examples of moonlighting functions of drug metabolizing enzymes.

Changes and sex- and age-related differences in the expression of drug metabolizing enzymes in a KRAS-mutant mouse model of lung cancer

Background

This study aimed to systematically profile the alterations and sex- and age-related differences in the drug metabolizing enzymes (DMEs) in a KRAS-mutant mouse model of lung cancer (KRAS mice).

Methodology

In this study, the LC-MS/MS approach and a probe substrate method were used to detect the alterations in 21 isoforms of DMEs, as well as the enzymatic activities of five isoforms, respectively. Western blotting was applied to study the protein expression of four related receptors.

Results

The proteins contents of CYP2C29 and CYP3A11, were significantly downregulated in the livers of male KRAS mice at 26 weeks (3.7- and 4.4-fold, respectively, p < 0.05). SULT1A1 and SULT1D1 were upregulated by 1.8- to 7.0- fold at 20 (p = 0.015 and 0.017, respectively) and 26 weeks (p = 0.055 and 0.031, respectively). There were positive correlations between protein expression and enzyme activity for CYP2E1, UGT1A9, SULT1A1 and SULT1D1 (r² ≥ 0.5, p < 0.001). Western blotting analysis revealed the downregulation of AHR, FXR and PPARα protein expression in male KRAS mice at 26 weeks. For sex-related differences, CYP2E1 was male-predominant and UGT1A2 was female-predominant in the kidney. UGT1A1 and UGT1A5 expression was female-predominant, whereas UGT2B1 exhibited male-predominant expression in liver tissue. For the tissue distribution of DMEs, 21 subtypes of DMEs were all expressed in liver tissue. In the intestine, the expression levels of CYP2C29, CYP27A1, UGT1A2, 1A5, 1A6a, 1A9, 2B1, 2B5 and 2B36 were under the limitation of quantification. The subtypes of CYP7A1, 1B1, 2E1 and UGT1A1, 2A3, 2B34 were detected in kidney tissue.

Conclusions

This study, for the first time, unveils the variations and sex- and age-related differences in DMEs in C57 BL/6 (WT) mice and KRAS mice.

The Expression Profiles of ADME Genes in Human Cancers and Their Associations with Clinical Outcomes

ADME genes are a group of genes that are involved in drug absorption, distribution, metabolism, and excretion (ADME). The expression profiles of ADME genes within tumours is proposed to impact on cancer patient survival; however, this has not been systematically examined. In this study, our comprehensive analyses of pan-cancer datasets from the Cancer Genome Atlas (TCGA) revealed differential intratumoral expression profiles for ADME genes in 21 different cancer types. Most genes also showed high interindividual variability within cancer-specific patient cohorts. Using Kaplan-Meier plots and logrank tests, we showed that intratumoral expression levels of twenty of the thirty-two core ADME genes were associated with overall survival (OS) in these cancers. Of these genes, five showed significant association with unfavourable OS in three cancers, including SKCM (ABCC2, GSTP1), KIRC (CYP2D6, CYP2E1), PAAD (UGT2B7); sixteen showed significant associations with favourable OS in twelve cancers, including BLCA (UGT2B15), BRCA (CYP2D6), COAD (NAT1), HNSC (ABCB1), KIRC (ABCG2, CYP3A4, SLC22A2, SLC22A6), KIRP (SLC22A2), LIHC (CYP2C19, CYP2C8, CYP2C9, CYP3A5, SLC22A1), LUAD (SLC15A2), LUSC (UGT1A1), PAAD (ABCB1), SARC (ABCB1), and SKCM (ABCB1, DYPD). Overall, these data provide compelling evidence supporting ADME genes as prognostic biomarkers and potential therapeutic targets. We propose that intratumoral expression of ADME genes may impact cancer patient survival by multiple mechanisms that can include metabolizing/transporting anticancer drugs, activating anticancer drugs, and metabolizing/transporting a variety of endogenous molecules involved in metabolically fuelling cancer cells and/or controlling pro-growth signalling pathways.

Epigenetic Regulation of Differentially Expressed Drug-Metabolizing Enzymes in Cancer

Drug metabolism is a biotransformation process of drugs, catalyzed by drug-metabolizing enzymes (DMEs), including phase I DMEs and phase II DMEs. The aberrant expression of DMEs occurs in the different stages of cancer. It can contribute to the development of cancer and lead to individual variations in drug response by affecting the metabolic process of carcinogen and anticancer drugs. Apart from genetic polymorphisms, which we know the most about, current evidence indicates that epigenetic regulation is also central to the expression of DMEs. This review summarizes differentially expressed DMEs in cancer and related epigenetic changes, including DNA methylation, histone modification, and noncoding RNAs. Exploring the epigenetic regulation of differentially expressed DMEs can provide a basis for implementing individualized and rationalized medication. Meanwhile, it can promote the development of new biomarkers and targets for the diagnosis, treatment, and prognosis of cancer. SIGNIFICANCE STATEMENT: This review summarizes the aberrant expression of DMEs in cancer and the related epigenetic regulation of differentially expressed DMEs. Exploring the epigenetic regulatory mechanism of DMEs in cancer can help us to understand the role of DMEs in cancer progression and chemoresistance. Also, it provides a basis for developing new biomarkers and targets for the diagnosis, treatment, and prognosis of cancer.

Epigenetic effects of low-level sodium arsenite exposure on human liver HepaRG cells

Chronic exposure to inorganic arsenic is associated with a variety of adverse health effects, including lung, bladder, kidney, and liver cancer. Several mechanisms have been proposed for arsenic-induced tumorigenesis; however, insufficient knowledge and many unanswered questions remain to explain the integrated molecular pathogenesis of arsenic carcinogenicity. In the present study, using non-tumorigenic human liver HepaRG cells, we investigated epigenetic alterations upon prolonged exposure to a noncytotoxic concentration of sodium arsenite (NaAsO₂). We demonstrate that continuous exposure of HepaRG cells to 1 µM sodium arsenite (NaAsO₂) for 14 days resulted in substantial cytosine DNA demethylation and hypermethylation across the genome, among which the claudin 14 (CLDN14) gene was hypermethylated and the most down-regulated gene. Another important finding was a profound loss of histone H3 lysine 36 (H3K36) trimethylation, which was accompanied by increased damage to genomic DNA and an elevated de novo mutation frequency. These results demonstrate that continuous exposure of HepaRG cells to a noncytotoxic concentration of NaAsO₂ results in substantial epigenetic abnormalities accompanied by several carcinogenesis-related events, including induction of epithelial-to-mesenchymal transition, damage to DNA, inhibition of DNA repair genes, and induction of de novo mutations. Importantly, this study highlights the intimate mechanistic link and interplay between two fundamental cancer-associated events, epigenetic and genetic alterations, in arsenic-associated carcinogenesis.

miRNA-based biomarkers, therapies, and resistance in Cancer

MicroRNAs (miRNAs), small non-coding RNAs (ncRNAs) of about 22 nucleotides in size, play important roles in gene regulation, and their dysregulation is implicated in human diseases including cancer. A variety of miRNAs could take roles in the cancer progression, participate in the process of tumor immune, and function with miRNA sponges. During the last two decades, the connection between miRNAs and various cancers has been widely researched. Based on evidence about miRNA, numerous potential cancer biomarkers for the diagnosis and prognosis have been put forward, providing a new perspective on cancer screening. Besides, there are several miRNA-based therapies among different cancers being conducted, advanced treatments such as the combination of synergistic strategies and the use of complementary miRNAs provide significant clinical benefits to cancer patients potentially. Furthermore, it is demonstrated that many miRNAs are engaged in the resistance of cancer therapies with their complex underlying regulatory mechanisms, whose comprehensive cognition can help clinicians and improve patient prognosis. With the belief that studies about miRNAs in human cancer would have great clinical implications, we attempt to summarize the current situation and potential development prospects in this review.

Molecular Bases of Drug Resistance in Hepatocellular Carcinoma

The poor outcome of patients with non-surgically removable advanced hepatocellular carcinoma (HCC), the most frequent type of primary liver cancer, is mainly due to the high refractoriness of this aggressive tumor to classical chemotherapy. Novel pharmacological approaches based on the use of inhibitors of tyrosine kinases (TKIs), mainly sorafenib and regorafenib, have provided only a modest prolongation of the overall survival in these HCC patients. The present review is an update of the available information regarding our understanding of the molecular bases of mechanisms of chemoresistance (MOC) with a significant impact on the response of HCC to existing pharmacological tools, which include classical chemotherapeutic agents, TKIs and novel immune-sensitizing strategies. Many of the more than one hundred genes involved in seven MOC have been identified as potential biomarkers to predict the failure of treatment, as well as druggable targets to develop novel strategies aimed at increasing the sensitivity of HCC to pharmacological treatments.

Serum exosomal long noncoding RNAs lnc-FAM72D-3 and lnc-EPC1-4 as diagnostic biomarkers for hepatocellular carcinoma

Long noncoding RNAs (lncRNAs), such as LINC00462, HOTAIR, and MALAT1, are significantly upregulated in hepatocellular carcinoma (HCC) tissues. However, lncRNA expression in the serum of HCC patients is still unclear. To identify candidate lncRNAs for HCC diagnosis, we purified exosomal total RNA from the serum of healthy volunteers (controls) and hepatitis, cirrhosis, and HCC patients. To assess the function of lncRNAs, small interfering RNAs and overexpression vectors were designed and cell viability and cell apoptosis assays conducted. The exosomes of the control group had a larger number of lncRNAs with a high amount of alternative splicing compared to hepatic disease patients. qPCR assays showed that lnc-FAM72D-3, lnc-GPR89B-15, lncZEB2-19, and lnc-EPC1-4 are differentially expressed in HCC. Furthermore, the expression level of lnc-EPC1-4 correlated with age. While the expression levels of lnc-GPR89B-15 and lnc-EPC1-4 correlated with serum alpha-fetoprotein level. lnc-FAM72D-3 knockdown decreased cell viability and promoted cell apoptosis, indicating that lnc-FAM72D-3 functions as an oncogene in HCC. In contrast, lnc-EPC1-4 overexpression inhibited cell proliferation and induced cell apoptosis, indicating that it functions as a tumor suppressor gene. Collectively, these findings show that lnc-FAM72D-3 and lnc-EPC1-4 play a novel role that might contribute to hepatocarcinogenesis and identify potential candidate biomarkers for HCC diagnosis.

A robust twelve-gene signature for prognosis prediction of hepatocellular carcinoma

Background

The prognosis of hepatocellular carcinoma (HCC) patients remains poor. Identifying prognostic markers to stratify HCC patients might help to improve their outcomes.

Methods

Six gene expression profiles (GSE121248, GSE84402, GSE65372, GSE51401, GSE45267 and GSE14520) were obtained for differentially expressed genes (DEGs) analysis between HCC tissues and non-tumor tissues. To identify the prognostic genes and establish risk score model, univariable Cox regression survival analysis and Lasso-penalized Cox regression analysis were performed based on the integrated DEGs by robust rank aggregation method. Then Kaplan-Meier and time-dependent receiver operating characteristic (ROC) curves were generated to validate the prognostic performance of risk score in training datasets and validation datasets. Multivariable Cox regression analysis was used to identify independent prognostic factors in liver cancer. A prognostic nomogram was constructed based on The Cancer Genome Atlas (TCGA) dataset. Finally, the correlation between DNA methylation and prognosis-related genes was analyzed.

Results

A twelve-gene signature including SPP1, KIF20A, HMMR, TPX2, LAPTM4B, TTK, MAGEA6, ANX10, LECT2, CYP2C9, RDH16 and LCAT was identified, and risk score was calculated by corresponding coefficients. The risk score model showed a strong diagnosis performance to distinguish HCC from normal samples. The HCC patients were stratified into high-risk and low-risk group based on the cutoff value of risk score. The Kaplan-Meier survival curves revealed significantly favorable overall survival in groups with lower risk score (P < 0.0001). Time-dependent ROC analysis showed well prognostic performance of the twelve-gene signature, which was comparable or superior to AJCC stage at predicting 1-, 3-, and 5-year overall survival. In addition, the twelve-gene signature was independent with other clinical factors and performed better in predicting overall survival after combining with age and AJCC stage by nomogram. Moreover, most of the prognostic twelve genes were negatively correlated with DNA methylation in HCC tissues, which SPP1 and LCAT were identified as the DNA methylation-driven genes.

Conclusions

We identified a twelve-gene signature as a robust marker with great potential for clinical application in risk stratification and overall survival prediction in HCC patients.

De Novo Transcriptome Assembly and Annotation of Liver and Brain Tissues of Common Brushtail Possums (Trichosurus vulpecula) in New Zealand: Transcriptome Diversity after Decades of Population Control

The common brushtail possum (Trichosurus vulpecula), introduced from Australia in the mid-nineteenth century, is an invasive species in New Zealand where it is widespread and forms the largest self-sustained reservoir of bovine tuberculosis (Mycobacterium bovis) among wild populations. Conservation and agricultural authorities regularly apply a series of population control measures to suppress brushtail possum populations. The evolutionary consequence of more than half a century of intensive population control operations on the species’ genomic diversity and population structure is hindered by a paucity of available genomic resources. This study is the first to characterise the functional content and diversity of brushtail possum liver and brain cerebral cortex transcriptomes. Raw sequences from hepatic cells and cerebral cortex were assembled into 58,001 and 64,735 transcripts respectively. Functional annotation and polymorphism assignment of the assembled transcripts demonstrated a considerable level of variation in the core metabolic pathways that represent potential targets for selection pressure exerted by chemical toxicants. This study suggests that the brushtail possum population in New Zealand harbours considerable variation in metabolic pathways that could potentially promote the development of tolerance against chemical toxicants.

An Integrated Approach Exploring the Synergistic Mechanism of Herbal Pairs in a Botanical Dietary Supplement: A Case Study of a Liver Protection Health Food

Herbal pairs are used as a bridge between single herb and polyherbal formulas in Traditional Chinese Medicine (TCM) to provide rationale for complicated TCM formulas. The effectiveness and rationality of TCM herbal pairs have been widely applied as a strategy for dietary supplements. However, due to the complexity of the phytochemistry of individual and combinations of herbal materials, it is difficult to reveal their effective and synergistic mechanisms from a molecular or systematic point of view. In order to address this question, UPLC-Q-TOF/MS analysis and System Pharmacology tools were applied to explore the mechanism of action, using a White Peony (Paeoniae Radix Alba) and Licorice (Glycyrrhizae Radix et Rhizoma)-based dietary supplement. A total of sixteen chemical constituents of White Peony and Licorice were isolated and identified, which interact with 73 liver protection-related targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were then performed along with network analysis. Results showed that the synergistic mechanism of the White Peony and Licorice herbal pair was associated with their coregulation of bile secretion and ABC transporter pathways. In addition, Licorice exhibits a specific response to drug and xenobiotic metabolism pathways, whereas White Peony responds to Toll-like receptor signaling, C-type lectin receptor signaling, IL-17 signaling, and TNF signaling pathways, resulting in the prevention of hepatocyte apoptosis and the reduction of immune and inflammation-mediated liver damage. These findings suggest that a White Peony and Licorice herbal pair supplement would have a liver-protecting benefit through complimentary and synergistic mechanisms. This approach provides a new path to explore herbal compatibility in dietary supplements derived from TCM theory.

Transcriptome analysis revealed key prognostic genes and microRNAs in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. However, the molecular mechanisms involved in HCC remain unclear and are in urgent need of elucidation. Therefore, we sought to identify biomarkers in the prognosis of HCC through an integrated bioinformatics analysis.

Methods

Messenger RNA (mRNA) expression profiles were obtained from the Gene Expression Omnibus database and The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA-LIHC) for the screening of common differentially expressed genes (DEGs). Function and pathway enrichment analysis, protein-protein interaction network construction and key gene identification were performed. The significance of key genes in HCC was validated by overall survival analysis and immunohistochemistry. Meanwhile, based on TCGA data, prognostic microRNAs (miRNAs) were decoded using univariable and multivariable Cox regression analysis, and their target genes were predicted by miRWalk.

Results

Eleven hub genes (upregulated ASPM, AURKA, CCNB2, CDC20, PRC1 and TOP2A and downregulated AOX1, CAT, CYP2E1, CYP3A4 and HP) with the most interactions were considered as potential biomarkers in HCC and confirmed by overall survival analysis. Moreover, AURKA, PRC1, TOP2A, AOX1, CYP2E1, and CYP3A4 were considered candidate liver-biopsy markers for high risk of developing HCC and poor prognosis in HCC. Upregulation of hsa-mir-1269b, hsa-mir-518d, hsa-mir-548aq, hsa-mir-548f-1, and hsa-mir-6728, and downregulation of hsa-mir-139 and hsa-mir-4800 were determined to be risk factors of poor prognosis, and most of these miRNAs have strong potential to help regulate the expression of key genes.

Conclusions

This study undertook the first large-scale integrated bioinformatics analysis of the data from Illumina BeadArray platforms and the TCGA database. With a comprehensive analysis of transcriptional alterations, including mRNAs and miRNAs, in HCC, our study presented candidate biomarkers for the surveillance and prognosis of the disease, and also identified novel therapeutic targets at the molecular and pathway levels.

Overexpression of Hepatocyte Chemerin-156 Lowers Tumor Burden in a Murine Model of Diethylnitrosamine-Induced Hepatocellular Carcinoma

The tumor inhibitory potential of the highly active chemerin-156 isoform was described in orthotopic models of hepatocellular carcinoma (HCC). The majority of HCC arises in the fibrotic liver, which was not reproduced in these studies. Here, a potential therapeutic activity of chemerin-156 was evaluated in diethylnitrosamine (DEN)-induced liver cancer, which mimics fibrosis-associated HCC. Mice were infected with adeno-associated virus (AAV) six months after DEN injection to overexpress chemerin-156 in the liver, and animals injected with non-recombinant-AAV served as controls. Three months later, the animals were killed. Both groups were comparable with regard to liver steatosis and fibrosis. Of note, the number of very small tumors was reduced by chemerin-156. Anyhow, the expression of inflammatory and profibrotic genes was similar in larger tumors of control and chemerin-156-AAV-infected animals. Although genes with a role in lipid metabolism, like 3-hydroxy-3-methylglutaryl-coenzym-A--reductase, were overexpressed in tumors of animals with high chemerin-156, total hepatic cholesterol, diacylglycerol and triglyceride levels, and distribution of individual lipid species were normal. Chemerin-156-AAV-infected mice had elevated hepatic and systemic chemerin. Ex vivo activation of the chemerin receptor chemokine-like receptor 1 increased in parallel with serum chemerin, illustrating the biological activity of the recombinant protein. In the tumors, chemerin-155 was the most abundant variant. Chemerin-156 was not detected in tumors of the controls and was hardly found in chemerin-156-AAV infected animals. In conclusion, the present study showed that chemerin-156 overexpression caused a decline in the number of small lesions but did not prevent the growth of pre-existing neoplasms.

The human organic cation transporter OCT1 and its role as a target for drug responses

The human organic cation uptake transporter OCT1, encoded by the SLC22A1 gene, is highly expressed in the liver and reported to possess a broad substrate specificity. OCT1 operates by facilitated diffusion and allows the entry of nutrients into cells. Recent findings revealed that OCT1 can mediate the uptake of drugs for treating various diseases such as cancers. The levels of OCT1 expression correlate with the responses towards many drugs and functionally defective OCT1 lead to drug resistance. It has been recently proposed that OCT1 should be amongst the crucial drug targets used for pharmacogenomic analyses. Several single nucleotide polymorphisms exist and are distributed across the entire OCT1 gene. While there are differences in the OCT1 gene polymorphisms between populations, there are at least five variants that warrant consideration in any genetic screen. To date, and despite two decades of research into OCT1 functional role, it still remains uncertain what are the define substrates for this uptake transporter, although studies from mice revealed that one of the substrates is vitamin B1. It is also unclear how OCT1 recognizes a broad array of ligands and whether this involves specific modifications and interactions with other proteins. In this review, we highlight the current findings related to OCT1 with the aim of propelling further studies on this key uptake transporter.

Thyroid hormones induce doxorubicin chemosensitivity through enzymes involved in chemotherapy metabolism in lymphoma T cells

Thyroid hormones (THs) – 3,3′,5-triiodo-L-thyronine (T3) and L-thyroxine (T4) – are important regulators of the metabolism and physiology of most normal tissues. Cytochrome P450 family 3A members are drug metabolizing enzymes involved in the activation and detoxification of several drugs. CYP3A4 is the major enzyme involved in the metabolism of chemotherapeutic drugs. In this work, we demonstrate that THs induce a significant increase in CYP3A4 mRNA levels, protein expression and metabolic activity through the membrane receptor integrin αvβ3 and the activation of signalling pathways through Stat1 and NF-κB. We reasoned that TH-induced CYP3A4 modulation may act as an important regulator in the metabolism of doxorubicin (Doxo). Experiments in vitro demonstrated that in CYP3A4-knocked down cells, no TH-mediated chemosensitivity to Doxo was observed. We also found that THs modulate these functions by activating the membrane receptor integrin αvβ3. In addition, we showed that the thyroid status can modulate CYP450 mRNA levels in tumor and liver tissues, and the tumor volume in response to chemotherapy in vivo. In fact, Doxo treatment in hypothyroid mice was associated with lower tumors, displaying lower levels of CYP enzymes, than euthyroid mice. However, higher mRNA levels of CYP enzymes were found in livers from Doxo treated hypothyroid mice respect to control. These results present a new mechanism by which TH could modulate chemotherapy response. These findings highlight the importance of evaluating thyroid status in patients during application of T-cell lymphoma therapeutic regimens.