Identification of prognostic biomarkers for patients with hepatocellular carcinoma after hepatectomy

ADH4-a potential prognostic marker for hepatocellular carcinoma with possible immune-related implications

OBJECTIVE

This study aims to explore ADH4 expression in hepatocellular carcinoma (HCC), its prognostic impact, and its immune correlation to provide novel insights into HCC prognostication and treatment.

METHODS

HCC prognostic marker genes were rigorously selected using GEO database, Lasso regression, GEPIA, Kaplan-Meier and pROC analyses. The expression of interested markers (ADH4, DNASE1L3, RDH16, LCAT, HGFAC) in HCC and adjacent tissues was assessed by Immunohistochemistry (IHC). We observed that ADH4 exhibited low expression levels in liver cancer tissues and high expression levels in normal liver tissues. However, the remaining four genes did not manifest any statistically significant differences between hepatocellular carcinoma (HCC) tissue and adjacent non-cancerous tissue. Consequently, ADH4 became the primary focus of our research. ADH4 expression was validated by signed-rank tests and unpaired Wilcoxon rank sum tests across pan-cancer and HCC datasets. Clinical significance and associations with clinicopathological variables were determined using Kaplan-Meier, logistic regression and Cox analyses on TCGA data. The ADH4-related immune responses were explored by Spearman correlation analysis using TIMER2 data. CD68, CD4, and CD19 protein levels were confirmed by IHC in HCC and non-cancerous tissues.

RESULTS

ADH4 showed significant downregulation in various cancers, particularly in HCC. Moreover, low ADH4 expression was associated with clinicopathological variables and served as an independent prognostic marker for HCC patients. Additionally, ADH4 affects a variety of biochemical functions and may influence cancer development, prognosis, and treatment by binding to immune cells. Furthermore, at the immune level, the low expression pattern of ADH4 is TME-specific, indicating that ADH4 has the potential to be used as a target for cancer immunotherapy.

CONCLUSION

This study highlights the diagnostic, prognostic and immunomodulatory roles of ADH4 in HCC. ADH4 could serve as a valuable biomarker for HCC diagnosis and prognosis, as well as a potential target for immunotherapeutic interventions.

Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma

Liver cancer, a global menace, ranked as the sixth most prevalent and third deadliest cancer in 2020. The challenge of early diagnosis and treatment, especially for hepatocellular carcinoma (HCC), persists due to late-stage detections. Understanding HCC's complex pathogenesis is vital for advancing diagnostics and therapies. This study combines bioinformatics and machine learning, examining HCC comprehensively. Three datasets underwent meticulous scrutiny, employing various analytical tools such as Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein interaction assessment, and survival analysis. These rigorous investigations uncovered twelve pivotal genes intricately linked with HCC's pathophysiological intricacies. Among them, CYP2C8, CYP2C9, EPHX2, and ESR1 were significantly positively correlated with overall patient survival, while AKR1B10 and NQO1 displayed a negative correlation. Moreover, the Adaboost prediction model yielded an 86.8 % accuracy, showcasing machine learning's potential in deciphering complex dataset patterns for clinically relevant predictions. These findings promise to contribute valuable insights into the elusive mechanisms driving liver cancer (HCC). They hold the potential to guide the development of more precise diagnostic methods and treatment strategies in the future. In the fight against this global health challenge, unraveling HCC's intricacies is of paramount importance.

BrcaDx: precise identification of breast cancer from expression data using a minimal set of features

Background: Breast cancer is the foremost cancer in worldwide incidence, surpassing lung cancer notwithstanding the gender bias. One in four cancer cases among women are attributable to cancers of the breast, which are also the leading cause of death in women. Reliable options for early detection of breast cancer are needed. Methods: Using public-domain datasets, we screened transcriptomic profiles of breast cancer samples, and identified progression-significant linear and ordinal model genes using stage-informed models. We then applied a sequence of machine learning techniques, namely, feature selection, principal components analysis, and k-means clustering, to train a learner to discriminate "cancer" from "normal" based on expression levels of identified biomarkers. Results: Our computational pipeline yielded an optimal set of nine biomarker features for training the learner, namely, NEK2, PKMYT1, MMP11, CPA1, COL10A1, HSD17B13, CA4, MYOC, and LYVE1. Validation of the learned model on an independent test dataset yielded a performance of 99.5% accuracy. Blind validation on an out-of-domain external dataset yielded a balanced accuracy of 95.5%, demonstrating that the model has effectively reduced the dimensionality of the problem, and learnt the solution. The model was rebuilt using the full dataset, and then deployed as a web app for non-profit purposes at: https://apalania.shinyapps.io/brcadx/. To our knowledge, this is the best-performing freely available tool for the high-confidence diagnosis of breast cancer, and represents a promising aid to medical diagnosis.

Identification of afatinib-associated ADH1B and potential small-molecule drugs targeting ADH1B for hepatocellular carcinoma

Background: Afatinib is an irreversible epidermal growth factor receptor tyrosine kinase inhibitor, and it plays a role in hepatocellular carcinoma (LIHC). This study aimed to screen a key gene associated with afatinib and identify its potential candidate drugs. Methods: We screened afatinib-associated differential expressed genes based on transcriptomic data of LIHC patients from The Cancer Genome Atlas, Gene Expression Omnibus, and the Hepatocellular Carcinoma Database (HCCDB). By using the Genomics of Drug Sensitivity in Cancer 2 database, we determined candidate genes using analysis of the correlation between differential genes and half-maximal inhibitory concentration. Survival analysis of candidate genes was performed in the TCGA dataset and validated in HCCDB18 and GSE14520 datasets. Immune characteristic analysis identified a key gene, and we found potential candidate drugs using CellMiner. We also evaluated the correlation between the expression of ADH1B and its methylation level. Furthermore, Western blot analysis was performed to validate the expression of ADH1B in normal hepatocytes LO2 and LIHC cell line HepG2. Results: We screened eight potential candidate genes (ASPM, CDK4, PTMA, TAT, ADH1B, ANXA10, OGDHL, and PON1) associated with afatinib. Patients with higher ASPM, CDK4, PTMA, and TAT exhibited poor prognosis, while those with lower ADH1B, ANXA10, OGDHL, and PON1 had unfavorable prognosis. Next, ADH1B was identified as a key gene negatively correlated with the immune score. The expression of ADH1B was distinctly downregulated in tumor tissues of pan-cancer. The expression of ADH1B was negatively correlated with ADH1B methylation. Small-molecule drugs panobinostat, oxaliplatin, ixabepilone, and seliciclib were significantly associated with ADH1B. The protein level of ADH1B was significantly downregulated in HepG2 cells compared with LO2 cells. Conclusion: Our study provides ADH1B as a key afatinib-related gene, which is associated with the immune microenvironment and can be used to predict the prognosis of LIHC. It is also a potential target of candidate drugs, sharing a promising approach to the development of novel drugs for the treatment of LIHC.

Modulating effect of Cu(II) complexes with enamine and tetrazole derivatives on CYP2C and CYP3A and their cytotoxic and antiproliferative properties in HepG2 spheroids

CYP2C and CYP3A cytochromes are induced by a variety of compounds and affect the pharmacokinetics and pharmacodynamics of a large number of drugs. Currently, the possibility of using copper coordination compounds in antitumor therapy is being actively studied. Evaluation of potential interactions between new molecules and P450 cytochromes is necessary at an early stage of drug design.The aim. To study the modulating effect of Cu(II) complexes with enamine and tetrazole derivatives on CYP2C9, CYP2C19 and CYP3A4 and their cytotoxic and antiproliferative properties on normal human lung fibroblasts MRC-5 and a 3D model of hepatocellular carcinoma HepG2.Materials and methods. Cytotoxic and antiproliferative activities of copper(II) complexes – [CuL2] (1), [Cu2(bipy)2(PT)4] (2), [Cu2(phen)2(PT)4] (3), {[Cu(phen)(MT)2]∙H2O}n (4) (L – anion of 2-anilinomethylidene-5,5-dimethylcyclohexane-1,3-dione; PT – 5-phenyltetrazolate anion; MT – 5-methyltetrazolate anion; bipy – 2,2’-bipyridine; phen – 1,10-phenanthroline) – were examined in 2D and 3D models using fluorescence-based phenotypic screening. The modulating effect on CYP2C9, CYP2C19 and CYP3A4 was studied using fluorescence-based targeted screening. The results of CYP3A4 expression were confirmed by real-time reverse transcription polymerase chain reaction (RT-PCR).Results. Complex (1) increases the CYP3A4 expression and does not affect CYP2C9 and CYP2C19 expression. Complex (2) has no modulating effect on CYP2C and CYP3A. Complexes with 1,10-phenatrolin (3) and (4) induce CYP3A4, inhibit CYP2C9 and do not affect CYP2C19 expression. All compounds have a dose-dependent cytotoxic effect on HepG2 and MRC-5: the compound with 5-methyltetrazolate anion (4) has the same effect on cell lines, compounds with 5-phenyltetrazolate anion (2) and (3) have selective effect. Complexes with 1,10-phenatrolin are effective on both 2D and 3D models.Conclusion. The [Cu2(phen)2(FT)4] complex (3) can be used as a basis for creating an antitumor compound, but further modification of the structure is required to increase the selectivity to tumor cells.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

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.

Downregulation of CYP39A1 Serves as a Novel Biomarker in Hepatocellular Carcinoma with Worse Clinical Outcome

Background

CYP39A1 is a poorly characterized metabolic enzyme that has been investigated in a few tumors. However, the role of CYP39A1 in hepatocellular carcinoma (HCC) has not yet been clarified. In this study, the expression and clinical significance of CYP39A1 in HCC were explored.

Methods

CYP39A1 protein expression was detected in Akt/c-Met-induced HCC mice and 14 paired fresh HCC samples as well as another 159 HCC and matched noncancerous tissues. Meanwhile, the mRNA expression was analyzed by GEO and TCGA analysis and validated in 14 paired fresh HCC tissues. Furthermore, the relationships between CYP39A1 expression and clinicopathologic features as well as prognosis were analyzed. HCC cell growth changes were analyzed by cell viability assays after CYP39A1 overexpression and then validated after CYP39A1 knockout by DepMap database analysis.

Results

CYP39A1 protein expression was lower expressed in HCC mouse models, and its mRNA and protein expression were also downregulated in HCC compared with noncancerous liver tissues. Higher CYP39A1 expression was associated with well differentiation. Moreover, survival analysis indicated that lower CYP39A1 expression was associated with poorer overall survival. In addition, HepG2 and SMMC-7721 cell viability were inhibited after CYP39A1 overexpression. Genome-wide CRISPR/Cas9 proliferation screening indicated that knockout of CYP39A1 could promote HCC cell growth. Likewise, p-NF-κB and Nrf2 were suppressed after CYP39A1 overexpression. It is worth mentioning that total bile acid, total bilirubin, and direct bilirubin were significantly increased in the patients with low CYP39A1 expression.

Conclusions

Downregulation of CYP39A1 is associated with HCC carcinogenesis, tumor differentiation, and poor overall survival, suggesting that CYP39A1 may serve as a tumor suppressor gene and novel biomarker for HCC patients.

A Mutation-Related Long Noncoding RNA Signature of Genome Instability Predicts Immune Infiltration and Hepatocellular Carcinoma Prognosis

Background: Long noncoding RNAs (lncRNAs) have been discovered to play a regulatory role in genomic instability (GI), which participates in the carcinogenesis of various cancers, including hepatocellular carcinoma (HCC). We endeavored to establish a GI-derived lncRNA signature (GILncSig) as a potential biomarker and explore its impact on immune infiltration and prognostic significance. Methods: Combining expression and somatic mutation profiles from The Cancer Genome Atlas database, we identified GI-related lncRNAs and conducted functional analyses on co-expressed genes. Based on Cox regression analysis, a GILncSig was established in the training cohort (n = 187), and an independent testing patient cohort (n = 183) was used to validate its predictive ability. Kaplan-Meier method and receiver operating characteristic curves were adopted to evaluate the performance. The correlation between GI and immune infiltration status was investigated based on the CIBERSORT algorithm and single sample gene set enrichment analysis. In addition, a comprehensive nomogram integrating the GILncSig and clinicopathological variables was constructed to efficiently assess HCC patient prognosis in clinical applications. Results: A total of 88 GI-related lncRNAs were screened out and the functional analyses indicated diversified effects on HCC progression. The GILncSig was established using four independent lncRNAs (AC116351.1, ZFPM2-AS1, AC145343.1, and MIR210HG) with significant prognostic value (p < 0.05). Following evaluation with the GILncSig, low-risk patients had significantly better clinical outcomes than high-risk patients in the training cohort (p < 0.001), which was subsequently validated in the independent testing cohort. High-risk group exhibited more immunocyte infiltration including B cells memory, macrophages M0 and neutrophils and higher expression of HLA gene set and immune checkpoint genes. Compared to existing HCC signatures, the GILncSig showed better prognosis predictive performance [area under the curve (AUC) = 0.709]. Furthermore, an integrated nomogram was constructed and validated to efficiently and reliably evaluate HCC patient prognosis (3-years survival AUC = 0.710 and 5-years survival AUC = 0.707). Conclusion: The GILncSig measuring GI and impacting immune infiltration serves as a potential biomarker and independent predictor of HCC patient prognosis. Our results highlight further investigation of GI and HCC molecular mechanisms.

CYP2C8 Suppress Proliferation, Migration, Invasion and Sorafenib Resistance of Hepatocellular Carcinoma via PI3K/Akt/p27kip1 Axis

Background

Cytochrome P450 2C8 (CYP2C8) gene is one of the members of the cytochrome P450 enzymes (CYPs) gene family. The aim of this study was to reveal the function of CYP2C8 in hepatocellular carcinoma (HCC) and its effect on the sorafenib resistance.

Methods

Differential expression analysis in multiple HCC datasets all suggested that CYP2C8 expression was significantly decreased in HCC tissues, compared with para-carcinoma liver tissues. The expression level of CYP2C8 was subsequently compared between HCC tissues and para-carcinoma liver tissues of 70 patients form Guangxi, China, with the result consistent with the above. Survival analysis and ROC analysis indicated that CYP2C8 was equipped with satisfactory diagnostic and prognostic value in HCC. To examine the effect of CYP2C8 on the malignant phenotype of HCC cells, stable transcriptional cell lines with CYP2C8 over-expression were established, and then Cell Counting Kit-8 (CCK8) assay, colony formation assay, cell cycle assay, cell invasion assay and wound healing assay were performed.

Results

The results of aforementioned assays suggested that CYP2C8 over-expression restricted the proliferation, clonality, migration, invasion and cell cycle of HCC cells but had no significant effect on cell apoptosis. The enrichment analysis in terms of sequencing data of HCC cell lines with stable CYP2C8 over-expression suggested that CYP2C8 might be related to PI3K/Akt/p27^Kip1 axis. The inhibition of CYP2C8 over-expression on PI3K/Akt/p27^Kip1 axis was subsequently demonstrated with Western blot assay. In the rescue experiment, it was observed that both P27 inhibitor and PI3K agonist counteracted the repressed malignant phenotype caused by CYP2C8 over-expression, which further demonstrated that CYP2C8 played a role in HCC cells via PI3K/Akt/p27^Kip1 axis.

Discussion

The results demonstrated that CYP2C8 enhances the anticancer activity of sorafenib in vitro assays and in tumor xenograft model, with Ki-67 down-regulation and PI3K/Akt/p27Kip1 axis inhibition. In conclusion, these findings hinted that CYP2C8 restricted malignant phenotype and sorafenib resistance in HCC via PI3K/Akt/p27^kip1 axis.

A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma

Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice.

Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks.

Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy.

Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery.

Fibroblast Growth Factor 21 Response in a Preclinical Alcohol Model of Acute-on-Chronic Liver Injury

BACKGROUND AND AIMS

Fibroblast growth factor (FGF) 21 has recently been shown to play a potential role in bile acid metabolism. We aimed to investigate the FGF21 response in an ethanol-induced acute-on-chronic liver injury (ACLI) model in Abcb4-/- mice with deficiency of the hepatobiliary phospholipid transporter.

METHODS

Total RNA was extracted from wild-type (WT, C57BL/6J) and Abcb4-/- (KO) mice, which were either fed a control diet (WT-Cont and KO-Cont groups; n = 28/group) or ethanol diet, followed by an acute ethanol binge (WT-EtOH and KO-EtOH groups; n = 28/group). A total of 58 human subjects were recruited into the study, including patients with alcohol-associated liver disease (AALD; n = 31) and healthy controls (n = 27). The hepatic and ileal expressions of genes involved in bile acid metabolism, plasma FGF levels, and bile acid and its precursors 7α- and 27-hydroxycholesterol (7α- and 27-OHC) concentrations were determined. Primary mouse hepatocytes were isolated for cell culture experiments.

RESULTS

Alcohol feeding significantly induced plasma FGF21 and decreased hepatic Cyp7a1 levels. Hepatic expression levels of Fibroblast growth factor receptor 1 (Fgfr1), Fgfr4, Farnesoid X-activated receptor (Fxr), and Small heterodimer partner (Shp) and plasma FGF15/FGF19 levels did not differ with alcohol challenge. Exogenous FGF21 treatment suppressed Cyp7a1 in a dose-dependent manner in vitro. AALD patients showed markedly higher FGF21 and lower 7α-OHC plasma levels while FGF19 did not differ.

CONCLUSIONS

The simultaneous upregulation of FGF21 and downregulation of Cyp7a1 expressions upon chronic plus binge alcohol feeding together with the invariant plasma FGF15 and hepatic Shp and Fxr levels suggest the presence of a direct regulatory mechanism of FGF21 on bile acid homeostasis through inhibition of CYP7A1 by an FGF15-independent pathway in this ACLI model. Lay Summary: Alcohol challenge results in the upregulation of FGF21 and repression of Cyp7a1 expressions while circulating FGF15 and hepatic Shp and Fxr levels remain constant both in healthy and pre-injured livers, suggesting the presence of an alternative FGF15-independent regulatory mechanism of FGF21 on bile acid homeostasis through the inhibition of Cyp7a1.

Is HSD17B13 Genetic Variant a Protector for Liver Dysfunction? Future Perspective as a Potential Therapeutic Target

As diet and lifestyle have changed, fatty liver disease (FLD) has become more and more prevalent. Many genetic risk factors, such as variants of PNPLA3, TM6SF2, GCKR, and MBOAT7, have previously been uncovered via genome wide association studies (GWAS) to be associated with FLD. In 2018, a genetic variant (rs72613567, T > TA) of hydroxysteroid 17-β dehydrogenase family 13 (HSD17B13) was first associated with a lower risk of developing alcoholic liver disease and non-alcoholic fatty liver disease (NAFLD) in minor allele carriers. Other HSD17B13 variants were also later linked with either lower inflammation scores among NAFLD patients or protection against NAFLD (rs6834314, A > G and rs9992651, G > A) respectively. HSD17B13 is a lipid droplet-associated protein, but its function is still ambiguous. Compared to the other genetic variants that increase risk for FLD, HSD17B13 variants serve a protective role, making this gene a potential therapeutic target. However, the mechanism by which these variants reduce the risk of developing FLD is still unclear. Because studies in cell lines and mouse models have produced conflicting results, human liver tissue modeling using induced pluripotent stem cells may be the best way to move forward and solve this mystery.

Predicting the Key Genes Involved in Aortic Valve Calcification Through Integrated Bioinformatics Analysis

Background: Valvular heart disease is obtaining growing attention in the cardiovascular field and it is believed that calcific aortic valve disease (CAVD) is the most common valvular heart disease (VHD) in the world. CAVD does not have a fully effective treatment to delay its progression and the specific molecular mechanism of aortic valve calcification remains unclear.

Materials and Methods: We obtained the gene expression datasets GSE12644 and GSE51472 from the public comprehensive free database GEO. Then, a series of bioinformatics methods, such as GO and KEGG analysis, STING online tool, Cytoscape software, were used to identify differentially expressed genes in CAVD and healthy controls, construct a PPI network, and then identify key genes. In addition, immune infiltration analysis was used via CIBERSORT to observe the expression of various immune cells in CAVD.

Results: A total of 144 differential expression genes were identified in the CAVD samples in comparison with the control samples, including 49 up-regulated genes and 95 down-regulated genes. GO analysis of DEGs were most observably enriched in the immune response, signal transduction, inflammatory response, proteolysis, innate immune response, and apoptotic process. The KEGG analysis revealed that the enrichment of DEGs in CAVD were remarkably observed in the chemokine signaling pathway, cytokine-cytokine receptor interaction, and PI3K-Akt signaling pathway. Chemokines CXCL13, CCL19, CCL8, CXCL8, CXCL16, MMP9, CCL18, CXCL5, VCAM1, and PPBP were identified as the hub genes of CAVD. It was macrophages that accounted for the maximal proportion among these immune cells. The expression of macrophages M0, B cells memory, and Plasma cells were higher in the CAVD valves than in healthy valves, however, the expression of B cells naïve, NK cells activated, and macrophages M2 were lower.

Conclusion: We detected that chemokines CXCL13, CXCL8, CXCL16, and CXCL5, and CCL19, CCL8, and CCL18 are the most important markers of aortic valve disease. The regulatory macrophages M0, plasma cells, B cells memory, B cells naïve, NK cells activated, and macrophages M2 are probably related to the occurrence and the advancement of aortic valve stenosis. These identified chemokines and these immune cells may interact with a subtle adjustment relationship in the development of calcification in CAVD.

SNORA23 inhibits HCC tumorigenesis by impairing the 2'-O-ribose methylation level of 28S rRNA

OBJECTIVE

The dysregulation of ribosome biogenesis is associated with the progression of numerous tumors, including hepatocellular carcinoma (HCC). Small nucleolar RNAs (snoRNAs) regulate ribosome biogenesis by guiding the modification of ribosomal RNAs (rRNAs). However, the underlying mechanism of this process in HCC remains elusive.

METHODS

RNA immunoprecipitation and sequencing were used to analyze RNAs targeted by ribosome proteins. The biological functions of SNORA23 were examined in HCC cells and a xenograft mouse model. To elucidate the underlying mechanisms, the 2'-O-ribose methylation level of rRNAs was evaluated by qPCR, and the key proteins in the PI3K/Akt/mTOR pathway were detected using Western blot.

RESULTS

Twelve snoRNAs were found to co-exist in 4 cancer cell lines using RPS6 pull-down assays. SNORA23 was downregulated in HCC and correlated with the poor prognoses of HCC patients. SNORA23 inhibited the proliferation, migration, and invasion of HCC cells both in vitro and in vivo. We also found that SNORA23 regulated ribosome biogenesis by impairing 2'-O-ribose methylation of cytidine4506 of 28S rRNA. Furthermore, SNORA23, which is regulated by the PI3K/Akt/mTOR signaling pathway, significantly inhibited the phosphorylation of 4E binding protein 1. SNORA23 and rapamycin blocked the PI3K/AKT/mTOR signaling pathway and impaired HCC growth in vivo.

CONCLUSIONS

SNORA23 exhibited antitumor effects in HCC and together with rapamycin, provided a promising therapeutic strategy for HCC treatment.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

Transcriptomic identification of HBx-associated hub genes in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies around the world. Among the risk factors involved in liver carcinogenesis, hepatitis B virus (HBV) X protein (HBx) is considered to be a key regulator in hepatocarcinogenesis. Whether HBx promotes or protects against HCC remains controversial, therefore exploring new HBx-associated genes is still important.

METHODS

HBx was overexpressed in HepG2, HepG2.2.15 and SMMC-7721 cell lines, primary mouse hepatocytes and livers of C57BL/6N mice. High-throughput RNA sequencing profiling of HepG2 cells with HBx overexpression and related differentially-expressed genes (DEGs), pathway enrichment analysis, protein-protein interaction networks (PPIs), overlapping analysis were conducted. In addition, Gene Expression Omnibus (GEO) and proteomic datasets of HBV-positive HCC datasets were used to verify the expression and prognosis of selected DEGs. Finally, we also evaluated the known oncogenic role of HBx by oncogenic array analysis.

RESULTS

A total of 523 DEGs were obtained from HBx-overexpressing HepG2 cells. Twelve DEGs were identified and validated in cells transiently transfected with HBx and three datasets of HBV-positive HCC transcription profiles. In addition, using the Kaplan-Meier plotter database, the expression levels of the twelve different genes were further analyzed to predict patient outcomes.

CONCLUSION

Among the 12 identified HBx-associated hub genes, HBV-positive HCC patients expressing ARG1 and TAT showed a good overall survival (OS) and relapse-free survival (RFS). Thus, ARG1 and TAT expression could be potential prognostic markers.

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.

CYP2C8 regulated by GAS5/miR-382-3p exerts anti-cancerous properties in liver cancer

A cornucopia of literatures has characterized the involvement of a host of functional molecules in liver cancer. Herein, according to online datasets, we found that cytochrome P450 family 2 subfamily C member 8 (CYP2C8) was downregulated in liver cancer, and high CYP2C8 expression was associated with favorable overall survival. Lower levels of CYP2C8 were confirmed in liver cancer cells. CYP2C8 overexpression efficiently attenuated liver cancer cell proliferation and promoted apoptosis. We then discovered that miR-382-3p directly targeted CYP2C8 to inhibit its expression in liver cancer cells based on bioinformatic prediction and experimental confirmation. Moreover, a cytoplasmic long noncoding RNA (lncRNA), growth arrest-specific 5 (GAS5), sponged and down-regulated miR-382-3p, thus positively modulating CYP2C8 expression. Rescue assays indicated that GAS5 overexpression gave rise to decreased proliferation and increased apoptosis of liver cancer cells, while CYP2C8 knockdown counteracted GAS5-mediated anti-carcinogenic effects. In summary, our work offered a solid experimental foundation for understanding the functional role of CYP2C8 and the mechanism of GAS5/miR-382-3p/CYP2C8 axis in cell proliferation and apoptosis of liver cancer.

The Perspective of Diagnostic and Prognostic Values of Lipoxygenases mRNA Expression in Colon Adenocarcinoma

Background

This study was mainly to explore and study the potential application of lipoxygenases (ALOX) family genes in the diagnostic and prognostic values of colon adenocarcinoma (COAD).

Methods

Data sets related to the ALOX genes of COAD were obtained from The Cancer Genome Atlas and the University of California, Santa Cruz Xena browser. Then, the relevant biological information was downloaded from the public data platform. Finally, the bioinformatics technologies and clinical verification were employed to comprehensively analyze the potential values of ALOX genes.

Results

The Pearson correlation analysis indicated that there were correlations among ALOXE3, ALOX5, ALOX12, and ALOX12B. The diagnostic receiver operating characteristic (ROC) curves suggested that ALOXE3 and ALOX12 had significant diagnosis in COAD: ALOXE3; P<0.001, area under curve (AUC) 95%CI:=0.818 (0.773–0.862) and ALOX12; P<0.001, AUC 95%CI=0.774 (0.682–0.807). Besides, the verification study indicated that ALOX12 had a diagnostic value in COAD. Finally, our multivariate survival analysis and comprehensive prognosis of ALOX genes in COAD suggested that the ALOXE3 and ALOX12 were associated with COAD overall survival: ALOXE3; P=0.025, HR 95%CI=1.765 (1.074–2.901), ALOX12; P=0.046, HR 95%CI=1.680 (1.009–2.796), and the low expression of ALOXE3 and ALOX12 had a favorable prognosis of COAD (all P<0.05); on the contrary, the high regulation of them increased the risk of death.

Conclusion

In our study, we observed that the mRNA expressions of ALOX genes were associated with the diagnosis and prognosis of COAD. The results of the diagnostic analysis suggested that ALOX12 might have a diagnosis value in COAD. Besides, our comprehensive prognosis analysis indicated that ALOXE3 combined ALOX12 might serve as potential prognosis biomarkers for COAD.

Clinical significance of long non-coding RNA DUXAP8 and its protein coding genes in hepatocellular carcinoma

Backgrounds: Hepatocellular carcinoma (HCC) is a lethal malignancy worldwide that is difficult to diagnose during the early stages and its tumors are recurrent. Long non-coding RNAs (lncRNAs) have increasingly been associated with tumor biomarkers for diagnosis and prognosis. This study attempts to explore the potential clinical significance of lncRNA DUXAP8 and its co-expression related protein coding genes (PCGs) for HCC.

Method: Data from a total of 370 HCC patients from The Cancer Genome Atlas were utilized for the analysis. DUXAP8 and its top 10 PCGs were explored for their diagnostic and prognostic implications for HCC. A risk score model and nomogram were constructed for prognosis prediction using prognosis-related genes and DUXAP8. Molecular mechanisms of DUXAP8 and its PCGs involved in HCC initiation and progression were investigated. Then, potential target drugs were identified using genome-wide DUXAP8-related differentially expressed genes in a Connectivity Map database.

Results: The top 10 PCGs were identified as: RNF2, MAGEA1, GABRA3, MKRN3, FAM133A, MAGEA3, CNTNAP4, MAGEA6, MALRD1, and DGKI. Diagnostic analysis indicated that DUXAP8, MEGEA1, MKRN3, and DGKI show diagnostic implications (all area under curves ≥0.7, p≤0.05). Prognostic analysis indicated that DUXAP8 and RNF2 had prognostic implications for HCC (adjusted p=0.014 and 0.008, respectively). The risk score model and nomogram showed an advantage for prognosis prediction. A total of 3 target drugs were determined: cinchonine, bumetanide and amiprilose and they may serve as potential therapeutic targets for HCC.

Conclusion: Functioning as an oncogene, DUXAP8 is overexpressed in tumor tissue and may serve as both a diagnostic and prognosis biomarker for HCC. MEGEA1, MKRN3, and DGKI maybe potential diagnostic biomarkers and DGKI may also be potentially prognostic biomarkers for HCC.