Plasmalemmal Vesicle Associated Protein (PLVAP) as a therapeutic target for treatment of hepatocellular carcinoma

MiR-424-5p suppresses tumor growth and progression by directly targeting CHEK1 and activating cell cycle pathway in Hepatocellular Carcinoma

Objectives

The aim of this study is to elucidate the functional mechanism of the miRNA-424-5p/CHEK1 pathway in hepatocellular carcinoma (HCC), thereby offering novel insights for the development of targeted therapeutic strategies for HCC.

Methods

We employed a combination of bioinformatics analysis and data from the GEO to construct a regulatory network between miRNA and mRNA. Real-time quantitative polymerase chain reaction (RT-qPCR) was utilized to assess the expression levels of miR-424-5p and CHEK1. Protein expression of CHEK1 was determined using Western blot analysis. The targeting relationship between miR-424-5p and CHEK1 was validated through a dual-luciferase reporter assay. Furthermore, the effects of miR-424-5p on HCC cell proliferation, migration, and invasion were evaluated using the Cell Counting Kit-8 assay, wound healing assay, and Transwell invasion assay, respectively. Apoptosis of HCC cells was measured by flow cytometry.

Results

Bioinformatics analysis revealed that miR-424-5p was significantly downregulated, while CHEK1 was upregulated respectively in GEO dataset. Furthermore, this inverse expression pattern was observed in both HCC tissues and cell lines. Specifically, downregulation of miR-424-5p was found to promote the proliferation, migration, and invasion of HCC cells, while also inhibiting their apoptosis. The dual-luciferase reporter assay confirmed a direct targeting relationship between miR-424-5p and CHEK1. Inhibition of miR-424-5p was shown to counteract the suppressive effects on HCC cell proliferation, migration, and invasion that result from CHEK1 silencing. Additionally, experimental verification indicated that the activation of the cell cycle pathway is implicated in the oncogenic function of miR-424-5p/CHEK1 in HCC.

Conclusions

The present study demonstrates that miR-424-5p exerts a suppressive effect on HCC cell proliferation, migration, and invasion by downregulating the expression of CHEK1. This finding may offer a theoretical foundation for improving the prognosis and developing novel therapeutic strategies for HCC patients.

Single-cell transcriptional profiling of clear cell renal cell carcinoma reveals a tumor-associated endothelial tip cell phenotype

Clear cell renal cell carcinoma (ccRCC) is the most prevalent form of renal cancer, accounting for over 75% of cases. The asymptomatic nature of the disease contributes to late-stage diagnoses and poor survival. Highly vascularized and immune infiltrated microenvironment are prominent features of ccRCC, yet the interplay between vasculature and immune cells, disease progression and response to therapy remains poorly understood. Using droplet-based single-cell RNA sequencing we profile 50,236 transcriptomes from paired tumor and healthy adjacent kidney tissues. Our analysis reveals significant heterogeneity and inter-patient variability of the tumor microenvironment. Notably, we discover a previously uncharacterized vasculature subpopulation associated with epithelial-mesenchymal transition. The cell-cell communication analysis reveals multiple modes of immunosuppressive interactions within the tumor microenvironment, including clinically relevant interactions between tumor vasculature and stromal cells with immune cells. The upregulation of the genes involved in these interactions is associated with worse survival in the TCGA KIRC cohort. Our findings demonstrate the role of tumor vasculature and stromal cell populations in shaping the ccRCC microenvironment and uncover a subpopulation of cells within the tumor vasculature that is associated with an angiogenic phenotype.

AKR1B10 expression characteristics in hepatocellular carcinoma and its correlation with clinicopathological features and immune microenvironment

Hepatocellular carcinoma (HCC) represents a major global health threat with diverse and complex pathogenesis. Aldo-keto reductase family 1 member B10 (AKR1B10), a tumor-associated enzyme, exhibits abnormal expression in various cancers. However, a comprehensive understanding of AKR1B10's role in HCC is lacking. This study aims to explore the expression characteristics of AKR1B10 in HCC and its correlation with clinicopathological features, survival prognosis, and tumor immune microenvironment, further investigating its role and potential regulatory mechanisms in HCC. This study conducted comprehensive analyses using various bioinformatics tools and databases. Initially, differentially expressed genes related to HCC were identified from the GEO database, and the expression of AKR1B10 in HCC and other cancers was compared using TIMER and GEPIA databases, with validation of its specificity in HCC tissue samples using the HPA database. Furthermore, the relationship of AKR1B10 expression with clinicopathological features (age, gender, tumor size, staging, etc.) of HCC patients was analyzed using the TCGA database's LIHC dataset. The impact of AKR1B10 expression levels on patient prognosis was evaluated using Kaplan-Meier survival analysis and the Cox proportional hazards model. Additionally, the correlation of AKR1B10 expression with tumor biology-related signaling pathways and tumor immune microenvironment was studied using databases like GSEA, Targetscan, and others, identifying microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that regulate AKR1B10 expression to explore potential regulatory mechanisms. Elevated AKR1B10 expression was significantly associated with gender, primary tumor size, and fibrosis stage in HCC tissues. High AKR1B10 expression indicated poor prognosis and served as an independent predictor for patient outcomes. Detailed mechanism analysis revealed a positive correlation between high AKR1B10 expression, immune cell infiltration, and pro-inflammatory cytokines, suggesting a potential DANCR-miR-216a-5p-AKR1B10 axis regulating the tumor microenvironment and impacting HCC development and prognosis. The heightened expression of AKR1B10 in HCC is not only related to significant clinical-pathological traits but may also influence HCC progression and prognosis by activating key signaling pathways and altering the tumor immune microenvironment. These findings provide new insights into the role of AKR1B10 in HCC pathogenesis and highlight its potential as a biomarker and therapeutic target.

TPR is a prognostic biomarker and potential therapeutic target associated with immune infiltration in hepatocellular carcinoma

Liver cancer is the fourth leading cause of cancer-related mortality worldwide and hepatocellular carcinoma (HCC) is the most common primary liver cancer. In the present study, it was demonstrated that translocated promoter region (TPR) was upregulated in tumor tissues and associated with prognosis and immune infiltration in HCC. The clinical outcome of patients with HCC with aberrant expression of TPR was examined using multiple databases, including Gene Expression Omnibus, The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression, Kaplan-Meier (KM) Plotter and Xiantao tool. The clinicopathologic characteristics of patients from TCGA database that were associated with overall survival were assessed using Cox regression and KM analysis. The potential hallmarks associated with TPR expression were further predicted by Metascape and Gene Set Enrichment Analysis, and the relationship between TPR and immune infiltration was explored using the Tumor-Immune System Interactions Database and the Tumor Immune Estimation Resource. The results demonstrated that TPR expression was higher in HCC and its overexpression was associated with a worse prognosis, alongside a correlation with several clinical features. Furthermore, cell differentiation, a prospective new hallmark of cancer, was differentially enriched in the high TPR expression phenotype pathway. Moreover, TPR may also modulate the tumor immune microenvironment as it was significantly associated with immunoregulators and chemokines, as well as different tumor infiltration immune cells. According to the in vitro experiments, TPR silencing inhibited the phosphorylation of AKT and the proliferation of HCC cells. In summary, TPR may be a new marker and target for HCC therapy.

TGFβR1 inhibition drives hepatocellular carcinoma proliferation through induction of toll-like-receptor signalling

Transforming growth factor (TGF)-β and toll-like receptors (TLRs) have been shown to independently modulate the proliferation of hepatocellular carcinoma (HCC). Since a direct cross-talk between these two signalling pathways in HCC has not been clearly described before, we aimed here to explore the possibility of such interaction. A human HCC tissue array (n = 20 vs. four control samples), human HCC samples (n = 10) and steatohepatitis-driven murine HCC samples (control, NASH and HCC; n = 6/group) were immunostained for TGFβR1, pSMAD2, TRAF6, IRAK1 and PCNA. The results were confirmed by immunoblotting. Effects of constant activation of the SMAD pathway by constitutive expression of ALK5 or knockdown of mediators of TLR signalling, IRAK1 and MyD88, on HCC proliferation, were investigated in the HCC cell line (HUH-7) after treatment with TGFβ1 cytokine or TGFβR1 kinase inhibitor (LY2157299) using PCNA and MTS assay. TGFβR1 expression is decreased in human and murine HCC and associated with downregulated pSMAD2, but increased IRAK1, TRAF6 and PCNA staining. TGFβR1 kinase inhibition abolished the cytostatic effects of TGFβ1 and led to the induction of IRAK1, pIRAK1 and elevated mRNA levels of TLR-9. Overexpression of ALK5 and knockdown of MyD88 or IRAK1 augmented the cytostatic effects of TGFβ1 on HUH-7. In another epithelial HCC cell line, that is, HepG2, TGFβR1 kinase inhibitor similarly elevated cellular proliferation. There is a balance between the canonical SMAD-driven tumour-suppressing arm and the non-canonical tumour-promoting arm of TGFβ signalling. Disruption of this balance, by inhibition of the canonical pathway, induces HCC proliferation through TLR signalling.

The transcription factor ChREBP Orchestrates liver carcinogenesis by coordinating the PI3K/AKT signaling and cancer metabolism

Cancer cells integrate multiple biosynthetic demands to drive unrestricted proliferation. How these cellular processes crosstalk to fuel cancer cell growth is still not fully understood. Here, we uncover the mechanisms by which the transcription factor Carbohydrate responsive element binding protein (ChREBP) functions as an oncogene during hepatocellular carcinoma (HCC) development. Mechanistically, ChREBP triggers the expression of the PI3K regulatory subunit p85α, to sustain the activity of the pro-oncogenic PI3K/AKT signaling pathway in HCC. In parallel, increased ChREBP activity reroutes glucose and glutamine metabolic fluxes into fatty acid and nucleic acid synthesis to support PI3K/AKT-mediated HCC growth. Thus, HCC cells have a ChREBP-driven circuitry that ensures balanced coordination between PI3K/AKT signaling and appropriate cell anabolism to support HCC development. Finally, pharmacological inhibition of ChREBP by SBI-993 significantly suppresses in vivo HCC tumor growth. Overall, we show that targeting ChREBP with specific inhibitors provides an attractive therapeutic window for HCC treatment.

Screening immune-related blood biomarkers for DKD-related HCC using machine learning

Background

Diabetes mellitus is a significant health problem worldwide, often leading to diabetic kidney disease (DKD), which may also influence the occurrence of hepatocellular carcinoma (HCC). However, the relationship and diagnostic biomarkers between DKD and HCC are unclear.

Methods

Using public database data, we screened DKD secretory RNAs and HCC essential genes by limma and WGCNA. Potential mechanisms, drugs, and biomarkers for DKD-associated HCC were identified using PPI, functional enrichment, cMAP, and machine learning algorithms, and a diagnostic nomogram was constructed. Then, ROC, calibration, and decision curves were used to evaluate the diagnostic performance of the nomograms. In addition, immune cell infiltration in HCC was explored using CIBERSORT. Finally, the detectability of critical genes in blood was verified by qPCR.

Results

104 DEGs associated with HCC using WGCNA were identified. 101 DEGs from DKD were predicated on secreting into the bloodstream with Exorbase datasets. PPI analysis identified three critical modules considered causative genes for DKD-associated HCC, primarily involved in inflammation and immune regulation. Using lasso and RM, four hub genes associated with DKD-associated HCC were identified, and a diagnostic nomogram confirmed by DCA curves was established. The results of immune cell infiltration showed immune dysregulation in HCC, which was associated with the expression of four essential genes. PLVAP was validated by qPCR as a possible blood-based diagnostic marker for DKD-related HCC.

Conclusion

We revealed the inflammatory immune pathways of DKD-related HCC and developed a diagnostic nomogram for HCC based on PLVAP, C7, COL15A1, and MS4A6A. We confirmed with qPCR that PLVAP can be used as a blood marker to assess the risk of HCC in DKD patients.

Exploring the Molecular Mechanisms of Huaier on Modulating Metabolic Reprogramming of Hepatocellular Carcinoma: A Study based on Network Pharmacology, Molecular Docking and Bioinformatics

BACKGROUND

Huaier (Trametes robiniophila Murr), a traditional Chinese medicine, is widely used in China as a complementary and alternative therapy to treat hepatocellular carcinoma (HCC). Past studies have shown that Huaier can arrest the cell cycle, promote apoptosis and inhibit the proliferation of cancer cells. However, how it regulates the metabolism of HCC is still unclear.

OBJECTIVE

This study explores the metabolic-related function of Huaier in treating HCC with an in-silico approach.

METHODS

A network pharmacology and bioinformatics-based approach was employed to investigate the molecular pathogenesis of metabolic reprogramming in HCC with Huaier. The compounds of Huaier were obtained from public databases. Oral bioavailability and drug likeness were screened using the TCMSP platform. The differential gene expressions between HCC and non-tumor tissue were calculated and used to find the overlap from the targets of Huaier. The enrichment analysis of the overlapped targets by Metascape helped filter out the metabolism-related targets of Huaier in treating HCC. Protein-protein interaction (PPI) network construction and topological screening revealed the hub nodes. The prognosis and clinical correlation of these targets were validated from the cancer genome atlas (TCGA) database, and the interactions between the hub nodes and active ingredients were validated by molecular docking.

RESULTS

The results showed that Peroxyergosterol, Daucosterol, and Kaempferol were the primary active compounds of Huaier involved in the metabolic reprogramming of HCC. The top 6 metabolic targets included AKR1C3, CYP1A1, CYP3A4, CYP1A2, CYP17A1, and HSD11B1. The decreased expression of CYP3A4 and increased expression of AKR1C3 were related to the poor overall survival of HCC patients. The molecular docking validated that Peroxyergosterol and Kaempferol exhibited the potential to modulate CYP3A4 and AKR1C3 from a computational perspective.

CONCLUSION

This study provided a workflow for understanding the mechanism of Huaier in regulating the metabolic reprogramming of HCC.

Integrative analysis of mitochondrial metabolic reprogramming in early-stage colon and liver cancer

Gastrointestinal malignancies, including colon adenocarcinoma (COAD) and liver hepatocellular carcinoma (LIHC), remain leading causes of cancer-related deaths worldwide. To better understand the underlying mechanisms of these cancers and identify potential therapeutic targets, we analyzed publicly accessible Cancer Genome Atlas datasets of COAD and LIHC. Our analysis revealed that differentially expressed genes (DEGs) during early tumorigenesis were associated with cell cycle regulation. Additionally, genes related to lipid metabolism were significantly enriched in both COAD and LIHC, suggesting a crucial role for dysregulated lipid metabolism in their development and progression. We also identified a subset of DEGs associated with mitochondrial function and structure, including upregulated genes involved in mitochondrial protein import and respiratory complex assembly. Further, we identified mitochondrial 3-hydroxy-3-methylglutaryl-CoA synthase (HMGCS2) as a crucial regulator of cancer cell metabolism. Using a genome-scale metabolic model, we demonstrated that HMGCS2 suppression increased glycolysis, lipid biosynthesis, and elongation while decreasing fatty acid oxidation in colon cancer cells. Our study highlights the potential contribution of dysregulated lipid metabolism, including ketogenesis, to COAD and LIHC development and progression and identifies potential therapeutic targets for these malignancies.

PLVAP protein expression correlated with microbial composition, clinicopathological features, and prognosis of patients with stomach adenocarcinoma

PURPOSE

Plasmalemma vesicle-associated protein (PLVAP) is involved in many immune‑related signals; however, its role in stomach adenocarcinoma (STAD) remains to be elucidated. This study investigated PLVAP expression in tumor tissues and defined the value in STAD patients.

METHODS

A total of 96 patient paraffin-embedded STAD specimens and 30 paraffin-embedded adjacent non-tumor specimens from the Ninth Hospital of Xi'an were consecutively recruited in analyses. All RNA‑sequence data were available from the Cancer Genome Atlas database (TCGA). PLVAP protein expression was detected using immunohistochemistry. Microbial community analysis was performed by 16S rRNA gene sequencing using Illumina MiSeq. PLVAP mRNA expression was explored with the Tumor Immune Estimation Resource (TIMER), GEPIA, and UALCAN databases. The effect of PLVAP mRNA on prognosis was analyzed via GEPIA, and Kaplan-Meier plotter database. GeneMANIA and STRING databases were used to predict gene/protein interactions and functions. The relationships between PLVAP mRNA expression and tumor-infiltrated immune cells were analyzed via the TIMER and GEPIA databases.

RESULTS

Significantly elevated transcriptional and proteomic PLVAP expressions were found in STAD samples. Increased PLVAP protein and mRNA expression were significantly associated with advanced clinicopathological parameters and correlated with shorter disease-free survival (DFS) and overall survival (OS) in TCGA (P < 0.001). The microbiota in the PLVAP-rich (3+) group was significantly different from that in the PLVAP-poor (1+) group (P < 0.05). The results from TIMER showed that high PLVAP mRNA expression had significant positive correlations with CD4 + T cell (r = 0.42, P < 0.001).

CONCLUSION

PLVAP is a potential biomarker to predict the prognosis of patients with STAD, and the high level of PLVAP protein expression was closely related to bacteria. The relative abundance of Fusobacteriia was positvely associated with the level of PLVAP. In conclusion, positive staining for PLVAP was useful for predicting the poor prognosis of STAD with Fusobacteriia infection.

Understanding tumour endothelial cell heterogeneity and function from single-cell omics

Anti-angiogenic therapies (AATs) are used to treat different types of cancers. However, their success is limited owing to insufficient efficacy and resistance. Recently, single-cell omics studies of tumour endothelial cells (TECs) have provided new mechanistic insight. Here, we overview the heterogeneity of human TECs of all tumour types studied to date, at the single-cell level. Notably, most human tumour types contain varying numbers but only a small population of angiogenic TECs, the presumed targets of AATs, possibly contributing to the limited efficacy of and resistance to AATs. In general, TECs are heterogeneous within and across all tumour types, but comparing TEC phenotypes across tumours is currently challenging, owing to the lack of a uniform nomenclature for endothelial cells and consistent single-cell analysis protocols, urgently raising the need for a more consistent approach. Nonetheless, across most tumour types, universal TEC markers (ACKR1, PLVAP and IGFBP3) can be identified. Besides angiogenesis, biological processes such as immunomodulation and extracellular matrix organization are among the most commonly predicted enriched signatures of TECs across different tumour types. Although angiogenesis and extracellular matrix targets have been considered for AAT (without the hoped success), the immunomodulatory properties of TECs have not been fully considered as a novel anticancer therapeutic approach. Therefore, we also discuss progress, limitations, solutions and novel targets for AAT development.

The role of PLVAP in endothelial cells

Endothelial cells play a major part in the regulation of vascular permeability and angiogenesis. According to their duty to fit the needs of the underlying tissue, endothelial cells developed different subtypes with specific endothelial microdomains as caveolae, fenestrae and transendothelial channels which regulate nutrient exchange, leukocyte migration, and permeability. These microdomains can exhibit diaphragms that are formed by the endothelial cell-specific protein plasmalemma vesicle-associated protein (PLVAP), the only known protein component of these diaphragms. Several studies displayed an involvement of PLVAP in diseases as cancer, traumatic spinal cord injury, acute ischemic brain disease, transplant glomerulopathy, Norrie disease and diabetic retinopathy. Besides an upregulation of PLVAP expression within these diseases, pro-angiogenic or pro-inflammatory responses were observed. On the other hand, loss of PLVAP in knockout mice leads to premature mortality due to disrupted homeostasis. Generally, PLVAP is considered as a major factor influencing the permeability of endothelial cells and, finally, to be involved in the regulation of vascular permeability. Following these observations, PLVAP is debated as a novel therapeutic target with respect to the different vascular beds and tissues. In this review, we highlight the structure and functions of PLVAP in different endothelial types in health and disease.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

Exploring the mechanisms underlying the therapeutic effect of the Radix Bupleuri-Rhizoma Cyperi herb pair on hepatocellular carcinoma using multilevel data integration and molecular docking

Traditional Chinese medicine (TCM) is a promising and effective treatment for cancer with minimal side effects through a multi-active ingredient multitarget network. Radix Bupleuri and Rhizoma Cyperi are listed as herbs dispersing stagnated liver Qi in China. They have been used clinically to treat liver diseases for many years and recent pharmacological studies have shown that they inhibit the proliferation of hepatocellular carcinoma (HCC). However, the pharmacological mechanisms, potential targets, and clinical value of the Radix Bupleuri-Rhizoma Cyperi herb pair (CXP) for suppressing HCC growth have not been fully elucidated. We identified 44 CXP targets involved in the treatment of HCC using the GEO dataset and HERB database. An analysis of the Traditional Chinese Medicine System Pharmacology Database (TCMSP) showed that CXP exerts synergistic effects through 4 active ingredients, including quercetin, stigmasterol, isorhamnetin, and kaempferol. GO and KEGG analyses revealed that CXP mainly regulates HCC progression through metabolic pathways, the p53 signaling pathway, and the cell cycle. Additionally, we applied The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) database to perform the expression patterns, clinical features, and prognosis of 6 genes (CCNB1, CDK1, CDK4, MYC, CDKN2A, and CHEK1) in cell cycle pathways to reveal that CXP suppresses HCC clinical therapeutic value. Moreover, based on molecular docking, we further verified that CXP exerts its anti-HCC activity through the interaction of multiple active components with cell cycle-related genes. We systematically revealed the potential pharmacological mechanisms and targets of CXP in HCC using multilevel data integration and molecular docking strategies.

Protein Regulator of Cytokinesis 1 (PRC1) Upregulation Promotes Immune Suppression in Liver Hepatocellular Carcinoma

Liver hepatocellular carcinoma (LIHC) is a malignant cancer with widespread prevalence. The suppressive immune environment causes largely refractory to current treatment. The protein regulator of cytokinesis 1 (PRC1) is an essential gene for cytokinesis and is involved in cancer pathogenesis. However, the functions of PRC1 have been barely clarified, especially in LIHC. Here, we investigated the expression, prognostic value, and functions of PRC1 in LIHC. Pan-cancer analysis revealed the overexpression of PRC1 in the Cancer Genome Atlas (TCGA) database. Four LIHC datasets from the Gene Expression Omnibus (GEO) database confirmed the PRC1 overexpression in LIHC. The mRNA and protein levels of PRC1 in LIHC cells were higher than in normal liver cells. The overexpression of PRC1 predicted progressed clinical stage and poor prognosis of LIHC. We further investigated the functions of PRC1 by performing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses, and Gene Set Enrichment Analysis (GSEA) of its coexpressing genes. High PRC1 expression was associated with increased genome instability of LIHC. Moreover, PRC1 was positively correlated with the infiltration of suppressive immune cells like T regulatory cells (Tregs) and polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and was negatively correlated with the effector immune cells' infiltration, including B cells and CD8+ T cells. In addition, PRC1 was positively correlated with the expression of tumor immune checkpoint molecules. Taken together, PRC1 overexpression contributes to the genome instability and the suppressive immune microenvironment of LIHC. Thus, PRC1 has the potential to be a prognostic marker and therapeutic target of LIHC.

PLVAP is associated with glioma-associated malignant processes and immunosuppressive cell infiltration as a promising marker for prognosis

Previous reports have confirmed the significance of plasmalemma vesicle-associated protein (PLVAP) in the progression of multiple tumors; however, there are few studies examining its immune properties in the context of gliomas. Here, we methodically investigated the pathophysiological characteristics and clinical manifestations of gliomas. A total of 699 patients diagnosed with gliomas in the cancer genome atlas along with 325 glioma patients in the Chinese glioma genome atlas were collected for the training and validation sets. We analyzed and visualized the total statistics using RStudio. PLVAP was markedly upregulated among high grade gliomas, O⁶-methylguanine-DNA methyltransferase promoter unmethylated subforms, isocitrate dehydrogenase wild forms, 1p19q non-codeletion subforms, and mesenchyme type gliomas. The receiver operating characteristics analysis illustrated the favorable applicability of PLVAP in regard to estimating mesenchyme subform gliomas. Subsequent Kaplan–Meier curves together with multivariable Cox analyses upon survival identified high-expression PLVAP as a distinct prognostic variable for patients with gliomas. Gene ontology analysis of PLVAP among gliomas has documented the predominant role of this protein in glioma-associated immunobiological processes and also in inflammatory responses. We consequently examined the associations of PLVAP with immune-related meta-genes, and PLVAP was positively correlated with hematopoietic cell kinase, lymphocyte-specific protein tyrosine kinase, major histocompatibility complex (MHC) I, MHC II, signal transducer and activator of transcription 1, and interferon and was negatively correlated with immunoglobulin G. Moreover, association analyses between PLVAP and glioma-infiltrating immunocytes indicated that the infiltrating degrees of most immune cells exhibited positive correlations with PLVAP expression, particularly immunosuppressive subsets such as tumor-related macrophages, myeloid-derived suppressor cells, and regulatory T lymphocytes. In summary, we originally demonstrated that PLVAP is markedly associated with immunosuppressive immune cell infiltration degrees, unfavorable survival, and adverse pathology types among gliomas, thus identifying PLVAP as a practicable marker and a promising target for glioma-based precise diagnosis and therapeutic strategies.

Immortalized human choroid plexus endothelial cells enable an advanced endothelial-epithelial two-cell type in vitro model of the choroid plexus

The choroid plexus (CP) is a highly vascularized structure containing endothelial and epithelial cells located in the ventricular system of the central nervous system (CNS). The role of the fenestrated CP endothelium is under-researched and requires the generation of an immortalized CP endothelial cell line with preserved features. Transduction of primary human CP endothelial cells (HCPEnC) with the human telomerase reverse transcriptase (hTERT) resulted in immortalized HCPEnC (iHCPEnC), which grew as monolayer with contact inhibition, formed capillary-like tubes in Matrigel, and showed no colony growth in soft agar. iHCPEnC expressed pan-endothelial markers and presented characteristic plasmalemma vesicle-associated protein-containing structures. Cultivation of iHCPEnC and human epithelial CP papilloma (HIBCPP) cells on opposite sides of cell culture filter inserts generated an in vitro model with a consistently enhanced barrier function specifically by iHCPEnC. Overall, iHCPEnC present a tool that will contribute to the understanding of CP organ functions, especially endothelial-epithelial interplay.

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Generation of an immortalized human choroid plexus endothelial cell line (iHCPEnC)

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iHCPEnC immortalized by telomerase maintain essential endothelial properties

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The mRNA expression profile distinguishes iHCPEnC from other endothelial cell types

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iHCPEnC enhance the barrier function of a choroid plexus epithelium in coculture

A hybrid feature selection algorithm and its application in bioinformatics

Feature selection is an independent technology for high-dimensional datasets that has been widely applied in a variety of fields. With the vast expansion of information, such as bioinformatics data, there has been an urgent need to investigate more effective and accurate methods involving feature selection in recent decades. Here, we proposed the hybrid MMPSO method, by combining the feature ranking method and the heuristic search method, to obtain an optimal subset that can be used for higher classification accuracy. In this study, ten datasets obtained from the UCI Machine Learning Repository were analyzed to demonstrate the superiority of our method. The MMPSO algorithm outperformed other algorithms in terms of classification accuracy while utilizing the same number of features. Then we applied the method to a biological dataset containing gene expression information about liver hepatocellular carcinoma (LIHC) samples obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). On the basis of the MMPSO algorithm, we identified a 18-gene signature that performed well in distinguishing normal samples from tumours. Nine of the 18 differentially expressed genes were significantly up-regulated in LIHC tumour samples, and the area under curves (AUC) of the combination seven genes (ADRA2B, ERAP2, NPC1L1, PLVAP, POMC, PYROXD2, TRIM29) in classifying tumours with normal samples was greater than 0.99. Six genes (ADRA2B, PYROXD2, CACHD1, FKBP1B, PRKD1 and RPL7AP6) were significantly correlated with survival time. The MMPSO algorithm can be used to effectively extract features from a high-dimensional dataset, which will provide new clues for identifying biomarkers or therapeutic targets from biological data and more perspectives in tumor research.

Plasmalemmal Vesicle-Associated Protein Is Associated with Endothelial Cells Sprouting from the Peribiliary Capillary Plexus in Human Cirrhotic Liver

INTRODUCTION

Plasmalemmal vesicle-associated protein (PLVAP) is an endothelial-specific integral membrane glycoprotein that localizes to caveolae and fenestrae in animal models; however, little is known about PLVAP in endothelial cells (ECs) in hepatic sinusoids during liver cirrhosis (LC). Here, we aimed to elucidate PLVAP localization and expression in the human liver during LC progression.

METHODS

PLVAP protein expression was detected in specimens from normal control livers and hepatitis C-related cirrhotic livers using immunohistochemistry, Western blotting, and immunoelectron microscopy.

RESULTS

PLVAP mainly localized to the peribiliary capillary plexus (PCP) and was rarely observed in hepatic artery branches and portal venules in control tissue, but was aberrantly expressed in capillarized sinusoids and proliferated capillaries in fibrotic septa within cirrhotic liver tissue. Ultrastructural analysis indicated that PLVAP localized to thin ECs in some caveolae, whereas PLVAP localized primarily to caveolae-like structures and proliferative sinusoid capillary EC vesicles in cirrhotic liver tissue. Western blot analysis confirmed that PLVAP was overexpressed at the protein level in advanced cirrhotic liver tissue.

CONCLUSION

PLVAP was strongly expressed in the caveolae of proliferated capillaries directly connected with sinusoids linked with the PCP, suggesting that it plays a role in angiogenesis and sinusoidal remodeling in LC.

Plasmalemma vesicle-associated protein promotes angiogenesis in cholangiocarcinoma via the DKK1/CKAP4/PI3K signaling pathway

Cholangiocarcinoma (CCA) is aggressive and has poor clinical outcomes because of typically delayed diagnosis and a lack of effective non-surgical therapeutic options. Recent studies have shown that plasmalemma vesicle-associated protein (PLVAP) is related to angiogenesis in various tumors, and in vivo PLVAP targeting therapy has been proven effective against hepatocellular carcinoma and pancreatic cancer. The goal of this study was to determine the potential therapeutic utility of targeting PLVAP and thus angiogenesis in CCA and explore the underlying molecular mechanisms. We found that the PLVAP expression levels were significantly higher in CCA tissues when compared with matched adjacent non-tumor tissues obtained from a total of 90 CCA patients; higher expression levels of PLVAP were associated with shorter overall survival of patients. In addition, overexpression of PLVAP was associated with higher micro-vessel density in CCA tissues. In a PLVAP overexpressing CCA patient-derived xenograft model, a novel humanized anti-PLVAP antibody in combination with Gemcitabine plus Cisplatin was significantly inhibited tumor growth. Molecular analysis of CCA cells co-cultured with human umbilical vascular endothelial cells or human hepatic sinusoidal endothelial cells showed that Dickkopf-related protein 1 (DKK1) secreted by CCA cells activated the PI3K/Akt pathway after binding to its receptor, cytoskeleton-associated protein 4 (CKAP4), resulting in the upregulation of PLVAP. Thus, CCA cells increased the angiogenic potency of endothelial cells in a paracrine fashion. Consistently, patients bearing CKAP4 and PLVAP overexpressing tumors had a poor prognosis. In conclusion, the DKK1/CKAP4/PI3K/PLVAP pathway increases angiogenesis in CCA and is therefore a potential anti-angiogenic target.

A new perspective on the immune escape mechanism in HCC: onco-foetal reprogramming

We found a shared immunosuppressive microenvironment between foetal liver and hepatocellular carcinoma (HCC) which includes the re-emergence of foetal-associated endothelial cells (PLVAP/VEGFR2) and foetal-like (FOLR2) tumour-associated macrophages in HCC, mediated via VEGF-NOTCH signalling. The discoveries suggest possible novel targets for therapeutic interventions in HCC.

Polymeric immunoglobulin receptor (PIGR) exerts oncogenic functions via activating ribosome pathway in hepatocellular carcinoma

Objective: This report aimed to investigate the potential mechanism of polymeric immunoglobulin receptor (PIGR) in promoting cancer development in hepatocellular carcinoma (HCC). Methods: PIGR expression was investigated in Gene Expression Omnibus (GEO), Oncomine, The Cancer Genome Atlas (TCGA) and The Human Protein Atlas (HPA) databases. Relationships between PIGR and HCC survival and clinico-pathological features were conducted in TCGA. RNAseq of PIGR overexpression and knockdown samples in Bel-7404 cells were performed for identifying potential mechanisms. Results: PIGR was significantly overexpressed in tumors compared to nontumors and in HCC serum peripheral blood mononuclear cells (PBMC) than in healthy individuals (all p < 0.05). In TCGA, PIGR was highly altered in 14% HCC patients. PIGR upregulation was significantly associated with poor disease-free survival (p < 0.05). More patients recurred/progressed in PIGR altered group compared to unaltered group (p < 0.01). PIGR was significantly higher in HCC patients with incomplete cirrhosis (p < 0.001) and established cirrhosis (p < 0.05). Fewer patients had N₀ lymph node stage in PIGR altered group than those in the unaltered group (p < 0.05). PIGR RNAseq revealed that ribosome signaling was the common pathway in PIGR overexpression and PIGR knockdown samples. RNAseq analysis indicated that RPL10, RPL10A, RPL12, RPL19, RPL36, RPL38, RPL41, RPL6, RPL8, RPS12, RPS14, RPS15A, RPS2, RPS27A and RPSA were significantly upregulated in PIGR overexpression group and downregulated in PIGR underexpression group (all p < 0.05). Conclusions: Aberrant PIGR was associated with HCC recurrence, and PIGR stimulated ribosome pathway might be a potential mechanism.

Identification of Hub Genes Associated With Hepatocellular Carcinoma Using Robust Rank Aggregation Combined With Weighted Gene Co-expression Network Analysis

Background

Bioinformatics provides a valuable tool to explore the molecular mechanisms underlying pathogenesis of hepatocellular carcinoma (HCC). To improve prognosis of patients, identification of robust biomarkers associated with the pathogenic pathways of HCC remains an urgent research priority.

Methods

We employed the Robust Rank Aggregation method to integrate nine qualified HCC datasets from the Gene Expression Omnibus. A robust set of differentially expressed genes (DEGs) between tumor and normal tissue samples were screened. Weighted gene co-expression network analysis was applied to cluster DEGs and the key modules related to clinical traits identified. Based on network topology analysis, novel risk genes derived from key modules were mined and biological verification performed. The potential functions of these risk genes were further explored with the aid of miRNA–mRNA regulatory networks. Finally, the prognostic ability of these genes was assessed by constructing a clinical prediction model.

Results

Two key modules showed significant association with clinical traits. In combination with protein–protein interaction analysis, 29 hub genes were identified. Among these genes, 19 from one module showed a pattern of upregulation in HCC and were associated with the tumor node metastasis stage, and 10 from the other module displayed the opposite trend. Survival analyses indicated that all these genes were significantly related to patient prognosis. Based on the miRNA-mRNA regulatory network, 29 genes strongly linked to tumor activity were identified. Notably, five of the novel risk genes, ABAT, DAO, PCK2, SLC27A2, and HAO1, have rarely been reported in previous studies. Gene set enrichment analysis for each gene revealed regulatory roles in proliferation and prognosis of HCC. Least absolute shrinkage and selection operator regression analysis further validated DAO, PCK2, and HAO1 as prognostic factors in an external HCC dataset.

Conclusion

Analysis of multiple datasets combined with global network information presents a successful approach to uncover the complex biological mechanisms of HCC. More importantly, this novel integrated strategy facilitates identification of risk hub genes as candidate biomarkers for HCC, which could effectively guide clinical treatments.

The Role of Sinusoidal Endothelial Cells in the Axis of Inflammation and Cancer Within the Liver

Liver sinusoidal endothelial cells (LSEC) form a unique barrier between the liver sinusoids and the underlying parenchyma, and thus play a crucial role in maintaining metabolic and immune homeostasis, as well as actively contributing to disease pathophysiology. Whilst their endocytic and scavenging function is integral for nutrient exchange and clearance of waste products, their capillarisation and dysfunction precedes fibrogenesis. Furthermore, their ability to promote immune tolerance and recruit distinct immunosuppressive leukocyte subsets can allow persistence of chronic viral infections and facilitate tumour development. In this review, we present the immunological and barrier functions of LSEC, along with their role in orchestrating fibrotic processes which precede tumourigenesis. We also summarise the role of LSEC in modulating the tumour microenvironment, and promoting development of a pre-metastatic niche, which can drive formation of secondary liver tumours. Finally, we summarise closely inter-linked disease pathways which collectively perpetuate pathogenesis, highlighting LSEC as novel targets for therapeutic intervention.

CCNB2, TOP2A, and ASPM Reflect the Prognosis of Hepatocellular Carcinoma, as Determined by Weighted Gene Coexpression Network Analysis

Background

Hepatocellular carcinoma (HCC) is characterized by increased mortality and poor prognosis. We aimed to identify potential prognostic markers by weighted gene coexpression network analysis (WGCNA), to assist clinical outcome prediction and improve treatment decisions for HCC patients.

Methods

Prognosis-related gene modules were first established by WGCNA. Venn diagrams obtained intersection genes of module genes and differentially expressed genes. The Kaplan-Meier overall survival curves and disease-free survival curves of intersection genes were further analyzed on the Gene Expression Profiling Interactive Analysis website. Chi-square tests were performed to explore the associations between prognostic gene expressions and clinicopathological features.

Results

CCNB2, TOP2A, and ASPM were identified as both prognosis-related genes and differentially expressed genes. TOP2A (HR: 1.7, P = 0.003) and ASPM (HR: 1.8, P < 0.001) exhibited a significant difference between the high- and low-expression groups in the overall survival analysis, while CCNB2 (HR: 1.4, P = 0.052) was not statistically significant. CCNB2 (HR: 1.5, P = 0.006), TOP2A (HR: 1.7, P < 0.001), and ASPM (HR: 1.6, P = 0.003) were all statistically significant in the disease-free survival analysis. All three genes were significantly associated with race and fetoprotein values (P < 0.05). CCNB2 expression was associated with tumor stage (P = 0.01), and ASPM expression was associated with new tumor events (P = 0.03).

Conclusion

Overexpression of CCNB2, TOP2A, and ASPM are associated with poor prognosis, and these genes could serve as potential prognostic markers and therapeutic targets for HCC.

Bioinformatics Analysis based on Multiple Databases Identifies Hub Genes Associated with Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common liver cancer and the mechanisms of hepatocarcinogenesis remain elusive.

Objective

This study aims to mine hub genes associated with HCC using multiple databases.

Methods

Data sets GSE45267, GSE60502, GSE74656 were downloaded from GEO database. Differentially expressed genes (DEGs) between HCC and control in each set were identified by limma software. The GO term and KEGG pathway enrichment of the DEGs aggregated in the datasets (aggregated DEGs) were analyzed using DAVID and KOBAS 3.0 databases. Protein-protein interaction (PPI) network of the aggregated DEGs was constructed using STRING database. GSEA software was used to verify the biological process. Association between hub genes and HCC prognosis was analyzed using patients' information from TCGA database by survminer R package.

Results

From GSE45267, GSE60502 and GSE74656, 7583, 2349, and 553 DEGs were identified respectively. A total of 221 aggregated DEGs, which were mainly enriched in 109 GO terms and 29 KEGG pathways, were identified. Cell cycle phase, mitotic cell cycle, cell division, nuclear division and mitosis were the most significant GO terms. Metabolic pathways, cell cycle, chemical carcinogenesis, retinol metabolism and fatty acid degradation were the main KEGG pathways. Nine hub genes (TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK) were selected by PPI network and all of them were associated with prognosis of HCC patients.

Conclusion

TOP2A, NDC80, CDK1, CCNB1, KIF11, BUB1, CCNB2, CCNA2 and TTK were hub genes in HCC, which may be potential biomarkers of HCC and targets of HCC therapy.

Genome-wide screening diagnostic biomarkers and the construction of prognostic model of hepatocellular carcinoma

Although methods in diagnosis and therapy of hepatocellular carcinoma (HCC) have made significant progress in decades, the overall survival (OS) of HCC remains dissatisfactory, so it is particularly important to find better diagnostic and prognostic biomarkers. In this study, we found a more reliable potential diagnostic biomarkers and constructed a more accurate prognostic evaluation model based on integrated transcriptome sequencing analysis of multiple independent data sets. First, we performed quality evaluation and differential analysis on seven Gene Expression Omnibus (GEO) data sets, and then comprehensively analyzed the differentially expressed genes with a robust rank aggregation algorithm. Next, Least absolute shrinkage and selection operator (LASSO) regression was used to establish an 8-gene prognostic risk score (RS) model. Finally, the prognostic model was further validated in the GEO data set. Also, RS has independence on other clinicopathological characteristics but has similarities in prognostic assessment compared with the T stage. Moreover, the combination of T stage and prognostic RS model based on the 8-gene had a better prognostic evaluation effect. In brief, our research suggest that the prognostic risk model of 8 genes has important clinical significance in HCC patients, and can further enrich the prognostic guidance value of the traditional T stage.

Fenestral diaphragms and PLVAP associations in liver sinusoidal endothelial cells are developmentally regulated

Endothelial cells contain several nanoscale domains such as caveolae, fenestrations and transendothelial channels, which regulate signaling and transendothelial permeability. These structures can be covered by filter-like diaphragms. A transmembrane PLVAP (plasmalemma vesicle associated protein) protein has been shown to be necessary for the formation of diaphragms. The expression, subcellular localization and fenestra-forming role of PLVAP in liver sinusoidal endothelial cells (LSEC) have remained controversial. Here we show that fenestrations in LSEC contain PLVAP-diaphragms during the fetal angiogenesis, but they lose the diaphragms at birth. Although it is thought that PLVAP only localizes to diaphragms, we found luminal localization of PLVAP in adult LSEC using several imaging techniques. Plvap-deficient mice revealed that the absence of PLVAP and diaphragms did not affect the morphology, the number of fenestrations or the overall vascular architecture in the liver sinusoids. Nevertheless, PLVAP in fetal LSEC (fenestrations with diaphragms) associated with LYVE-1 (lymphatic vessel endothelial hyaluronan receptor 1), neuropilin-1 and VEGFR2 (vascular endothelial growth factor receptor 2), whereas in the adult LSEC (fenestrations without diaphragms) these complexes disappeared. Collectively, our data show that PLVAP can be expressed on endothelial cells without diaphragms, contradict the prevailing concept that biogenesis of fenestrae would be PLVAP-dependent, and reveal previously unknown PLVAP-dependent molecular complexes in LSEC during angiogenesis.

Integrated analysis of the impact of age on genetic and clinical aspects of hepatocellular carcinoma

Despite the rapid growing and aging of populations worldwide, our knowledge on hepatocellular carcinoma (HCC) is still age-standardized rather than age-specific, with only few studies exploring the topic from a genetic point of view. Here, we analyze clinical and genetic aspects of HCC in patients of different age groups with the major attention directed to children (≤20 y) and elderly groups (≥80 y). A number of significant differences were found in elderly patients compared to children group, including smaller tumor size (P=0.001) and improved survival rates (P=0.002). Differences in gene mutations, copy number variants, and mRNA expressions were identified between the groups, with alteration rates for some genes like AKR1B10 increasing significantly with the age of patients. Immunohistochemistry testing of AKR1B10 showed a significant difference in expression levels at the age of 40 (30.77% high expression rate in patients younger than 40 compared to 51.57% in older patients) (P=0.043). Expression levels also differed between HCC tissues (49.64%) and near-tumor tissues (6.58%) (P<0.001). These findings contribute to the limited data available regarding the age-specific aspects of HCC patients, and support the need to address potential differences in the diagnosis, treatment, and prevention strategies of HCC.

Identification of Flap endonuclease 1 as a potential core gene in hepatocellular carcinoma by integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is a common yet deadly form of malignant cancer. However, the specific mechanisms involved in HCC diagnosis have not yet fully elucidated. Herein, we screened four publically available Gene Expression Omnibus (GEO) expression profiles (GSE14520, GSE29721, GSE45267 and GSE60502), and used them to identify 409 differentially expressed genes (DEGs), including 142 and 267 up- and down-regulated genes, respectively. The DAVID database was used to look for functionally enriched pathways among DEGs, and the STRING database and Cytoscape platform were used to generate a protein-protein interaction (PPI) network for these DEGs. The cytoHubba plug-in was utilized to detect 185 hub genes, and three key clustering modules were constructed with the MCODE plug-in. Gene functional enrichment analyses of these three key clustering modules were further performed, and nine core genes including BIRC5, DLGAP5, DTL, FEN1, KIAA0101, KIF4A, MCM2, MKI67, and RFC4, were identified in the most critical cluster. Subsequently, the hierarchical clustering and expression of core genes in TCGA liver cancer tissues were analyzed using the UCSC Cancer Genomics Browser, and whether elevated core gene expression was linked to a poor prognosis in HCC patients was assessed using the GEPIA database. The PPI of the nine core genes revealed an interaction between FEN1, MCM2, RFC4, and BIRC5. Furthermore, the expression of FEN1 was positively correlated with that of three other core genes in TCGA liver cancer tissues. FEN1 expression in HCC and other tumor types was assessed with the FIREBROWSE and ONCOMINE databases, and results were verified in HCC samples and hepatoma cells. FEN1 levels were also positively correlated with tumor size, distant metastasis and vascular invasion. In conclusion, we identified nine core genes associated with HCC development, offering novel insight into HCC progression. In particular, the aberrantly elevated FEN1 may represent a potential biomarker for HCC diagnosis and treatment.

Current Status of Gene Therapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the fifth most common cancer and the second leading cause of cancer related deaths world-wide. Liver transplantation, surgical resection, trans-arterial chemoembolization, and radio frequency ablation are effective strategies to treat early stage HCC. Unfortunately, HCC is usually diagnosed at an advanced stage and there are not many treatment options for late stage HCC. First-line therapy for late stage HCC includes sorafenib and lenvatinib. However, these treatments provide only an approximate three month increase in survival. Besides, they cannot specifically target cancer cells that lead to a wide array of side effects. Patients on these drugs develop resistance within a few months and have to rely on second-line therapy that includes regorafenib, pembrolizumab, nivolumab, and cabometyx. These disadvantages make gene therapy approach to treat HCC an attractive option. The two important questions that researchers have been trying to answer in the last 2-3 decades are what genes should be targeted and what delivery systems should be used. The objective of this review is to analyze the changing landscape of HCC gene therapy, with a focus on these two questions.

Transcriptome Analysis Revealed a Highly Connected Gene Module Associated With Cirrhosis to Hepatocellular Carcinoma Development

Introduction

Cirrhosis is one of the most important risk factors for development of hepatocellular carcinoma (HCC). Recent studies have shown that removal or well control of the underlying cause could reduce but not eliminate the risk of HCC. Therefore, it is important to elucidate the molecular mechanisms that drive the progression of cirrhosis to HCC.

Materials and Methods

Microarray datasets incorporating cirrhosis and HCC subjects were identified from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were determined by GEO2R software. Functional enrichment analysis was performed by the clusterProfiler package in R. Liver carcinogenesis-related networks and modules were established using STRING database and MCODE plug-in, respectively, which were visualized with Cytoscape software. The ability of modular gene signatures to discriminate cirrhosis from HCC was assessed by hierarchical clustering, principal component analysis (PCA), and receiver operating characteristic (ROC) curve. Association of top modular genes and HCC grades or prognosis was analyzed with the UALCAN web-tool. Protein expression and distribution of top modular genes were analyzed using the Human Protein Atlas database.

Results

Four microarray datasets were retrieved from GEO database. Compared with cirrhotic livers, 125 upregulated and 252 downregulated genes in HCC tissues were found. These DEGs constituted a liver carcinogenesis-related network with 272 nodes and 2954 edges, with 65 nodes being highly connected and formed a liver carcinogenesis-related module. The modular genes were significantly involved in several KEGG pathways, such as “cell cycle,” “DNA replication,” “p53 signaling pathway,” “mismatch repair,” “base excision repair,” etc. These identified modular gene signatures could robustly discriminate cirrhosis from HCC in the validation dataset. In contrast, the expression pattern of the modular genes was consistent between cirrhotic and normal livers. The top modular genes TOP2A, CDC20, PRC1, CCNB2, and NUSAP1 were associated with HCC onset, progression, and prognosis, and exhibited higher expression in HCC compared with normal livers in the HPA database.

Conclusion

Our study revealed a highly connected module associated with liver carcinogenesis on a cirrhotic background, which may provide deeper understanding of the genetic alterations involved in the transition from cirrhosis to HCC, and offer valuable variables for screening and surveillance of HCC in high-risk patients with cirrhosis.

Examination of Independent Prognostic Power of Gene Expressions and Histopathological Imaging Features in Cancer

Cancer prognosis is of essential interest, and extensive research has been conducted searching for biomarkers with prognostic power. Recent studies have shown that both omics profiles and histopathological imaging features have prognostic power. There are also studies exploring integrating the two types of measurements for prognosis modeling. However, there is a lack of study rigorously examining whether omics measurements have independent prognostic power conditional on histopathological imaging features, and vice versa. In this article, we adopt a rigorous statistical testing framework and test whether an individual gene expression measurement can improve prognosis modeling conditional on high-dimensional imaging features, and a parallel analysis is conducted reversing the roles of gene expressions and imaging features. In the analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma and liver hepatocellular carcinoma data, it is found that multiple individual genes, conditional on imaging features, can lead to significant improvement in prognosis modeling; however, individual imaging features, conditional on gene expressions, only offer limited prognostic power. Being among the first to examine the independent prognostic power, this study may assist better understanding the "connectedness" between omics profiles and histopathological imaging features and provide important insights for data integration in cancer modeling.

DNA topoisomerase 1 and 2A function as oncogenes in liver cancer and may be direct targets of nitidine chloride

The aim of the present study was to determine the role of topoisomerase 1 (TOP1) and topoisomerase 2A (TOP2A) in liver cancer (LC), and to investigate the inhibitory effect of nitidine chloride (NC) on these two topoisomerases. Immunohistochemistry (IHC) staining and microarray or RNA sequencing data mining showed markedly higher expression of TOP1 and TOP2A at the protein and mRNA levels in LC tissues compared with that in control non-tumor tissues. The prognostic values of TOP1 and TOP2A expression were also estimated based on data from The Cancer Genome Atlas. The elevated expression levels of TOP1 and TOP2A were closely associated with poorer overall survival and disease-free survival rates. When patients with LC were divided into high- and low-risk groups according to their prognostic index, TOP1 and TOP2A were highly expressed in the high-risk group. Bioinformatics analyses conducted on the co-expressed genes of TOP1 and TOP2A revealed that the topoisomerases were involved in several key cancer-related pathways, including the 'p53 pathway', 'pathway in cancer' and 'apoptosis signaling pathway'. Reverse transcription-quantitative polymerase chain reaction and IHC performed on triplicate tumor tissue samples from LC xenografts in control or NC-treated nude mice showed that NC treatment markedly reduced the protein and mRNA expression of TOP1 and TOP2A in LC tissues. Molecular docking studies further confirmed the direct binding of NC to TOP1 and TOP2A. In conclusion, the present findings indicate that TOP1 and TOP2A are oncogenes in LC and could serve as potential biomarkers for the prediction of the prognosis of patients with LC and for identification of high-risk cases, thereby optimizing individual treatment management. More importantly, the findings support TOP1 and TOP2A as potential drug targets of NC for the treatment of LC.

Upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in Tumor Tissues Predicted Worse Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma Patients

OBJECTIVE

To evaluate the association between upregulated differentially expressed genes (DEGs) and the outcomes of patients with hepatocellular carcinoma (HCC).

METHODS

Using Gene Expression Omnibus (GEO) datasets including GSE45436, GSE55092, GSE60502, GSE84402, and GSE17548, we detected upregulated DEGs in tumors. KEGG, GO, and Reactome enrichment analysis of the DEGs was conducted to clarify their function. The impact of the upregulated DEGs on patients' survival was analyzed based on TCGA profile.

RESULTS

161 shared upregulated DEGs were identified among GSE45436, GSE55092, GSE60502, and GSE84402 profiles. Cell cycle was the shared pathway/biological process in the gene sets investigation among databases of KEGG, GO, and Reactome. After being validated in GSE17548, 13 genes including BUB1B, CCNA2, CCNB1, CCNE2, CDC20, CDC6, CDC7, CDK1, CDK4, CDKN2A, CHEK1, MAD2L1, and MCM3 in cell cycle pathway were shared in the three databases for enrichment. The expression of BUB1B, CCNB1, CDC7, CDC20, and MCM3 was upregulated in HCC tissues when compared with adjacent normal tissues in 6.67%, 7.5%, 8.06%, 5.56%, and 9.72% of HCC patients, respectively. Overexpression of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in HCC tissues accounted for poorer overall survival (OS) and disease-free survival (DFS) in HCC patients (all log rank P < 0.05). BUB1B, CCNB1, CDC7, CDC20, and MCM3 were all overexpressed in HCC patients with neoplasm histologic grade G3-4 compared to those with G1-2 (all P < 0.05). BUB1B, CCNB1, and CDC20 were significantly upregulated in HCC patients with vascular invasion (all P < 0.05). Additionally, levels of BUB1B, CCNB1, CDC7, and CDC20 were significantly higher in HCC patients deceased, recurred, or progressed (all P < 0.05).

CONCLUSION

Correlated with advanced histologic grade and/or vascular invasion, upregulation of BUB1B, CCNB1, CDC7, CDC20, and MCM3 in HCC tissues predicted worse OS and DFS in HCC patients. These genes could be novel therapeutic targets for HCC treatment.

Integrative Analysis of Microarray Data to Reveal Regulation Patterns in the Pathogenesis of Hepatocellular Carcinoma

Background/Aims

The integration of multiple profiling data and the construction of a transcriptional regulatory network may provide additional insights into the molecular mechanisms of hepatocellular carcinoma (HCC). The present study was conducted to investigate the deregulation of genes and the transcriptional regulatory network in HCC.

Methods

An integrated analysis of HCC gene expression datasets was performed in Gene Expression Omnibus. Functional annotation of the differentially expression genes (DEGs) was conducted. Furthermore, transcription factors (TFs) were identified, and a global transcriptional regulatory network was constructed.

Results

An integrated analysis of eight eligible gene expression profiles of HCC led to 1,835 DEGs. Consistent with the fact that the cell cycle is closely related to various tumors, the functional annotation revealed that genes involved in the cell cycle were significantly enriched. A transcriptional regulatory network was constructed using the 62 TFs, which consisted of 872 TF-target interactions between 56 TFs and 672 DEGs in the context of HCC. The top 10 TFs covering the most downstream DEGs were ZNF354C, NFATC2, ARID3A, BRCA1, ZNF263, FOXD1, GATA3, FOXO3, FOXL1, and NR4A2. This network will appeal to future investigators focusing on the development of HCC.

Conclusions

The transcriptional regulatory network can provide additional information that is valuable in understanding the underlying molecular mechanism in hepatic tumorigenesis.

Gene expression profiling, pathway analysis and subtype classification reveal molecular heterogeneity in hepatocellular carcinoma and suggest subtype specific therapeutic targets

A very low 5-year survival rate among hepatocellular carcinoma (HCC) patients is mainly due to lack of early stage diagnosis, distant metastasis and high risk of postoperative recurrence. Hence ascertaining novel biomarkers for early diagnosis and patient specific therapeutics is crucial and urgent. Here, we have performed a comprehensive analysis of the expression data of 423 HCC patients (373 tumors and 50 controls) downloaded from The Cancer Genome Atlas (TCGA) followed by pathway enrichment by gene ontology annotations, subtype classification and overall survival analysis. The differential gene expression analysis using non-parametric Wilcoxon test revealed a total of 479 up-regulated and 91 down-regulated genes in HCC compared to controls. The list of top differentially expressed genes mainly consists of tumor/cancer associated genes, such as AFP, THBS4, LCN2, GPC3, NUF2, etc. The genes over-expressed in HCC were mainly associated with cell cycle pathways. In total, 59 kinases associated genes were found over-expressed in HCC, including TTK, MELK, BUB1, NEK2, BUB1B, AURKB, PLK1, CDK1, PKMYT1, PBK, etc. Overall four distinct HCC subtypes were predicted using consensus clustering method. Each subtype was unique in terms of gene expression, pathway enrichment and median survival. Conclusively, this study has exposed a number of interesting genes which can be exploited in future as potential markers of HCC, diagnostic as well as prognostic and subtype classification may guide for improved and specific therapy.

GeNNet: an integrated platform for unifying scientific workflows and graph databases for transcriptome data analysis

There are many steps in analyzing transcriptome data, from the acquisition of raw data to the selection of a subset of representative genes that explain a scientific hypothesis. The data produced can be represented as networks of interactions among genes and these may additionally be integrated with other biological databases, such as Protein-Protein Interactions, transcription factors and gene annotation. However, the results of these analyses remain fragmented, imposing difficulties, either for posterior inspection of results, or for meta-analysis by the incorporation of new related data. Integrating databases and tools into scientific workflows, orchestrating their execution, and managing the resulting data and its respective metadata are challenging tasks. Additionally, a great amount of effort is equally required to run in-silico experiments to structure and compose the information as needed for analysis. Different programs may need to be applied and different files are produced during the experiment cycle. In this context, the availability of a platform supporting experiment execution is paramount. We present GeNNet, an integrated transcriptome analysis platform that unifies scientific workflows with graph databases for selecting relevant genes according to the evaluated biological systems. It includes GeNNet-Wf, a scientific workflow that pre-loads biological data, pre-processes raw microarray data and conducts a series of analyses including normalization, differential expression inference, clusterization and gene set enrichment analysis. A user-friendly web interface, GeNNet-Web, allows for setting parameters, executing, and visualizing the results of GeNNet-Wf executions. To demonstrate the features of GeNNet, we performed case studies with data retrieved from GEO, particularly using a single-factor experiment in different analysis scenarios. As a result, we obtained differentially expressed genes for which biological functions were analyzed. The results are integrated into GeNNet-DB, a database about genes, clusters, experiments and their properties and relationships. The resulting graph database is explored with queries that demonstrate the expressiveness of this data model for reasoning about gene interaction networks. GeNNet is the first platform to integrate the analytical process of transcriptome data with graph databases. It provides a comprehensive set of tools that would otherwise be challenging for non-expert users to install and use. Developers can add new functionality to components of GeNNet. The derived data allows for testing previous hypotheses about an experiment and exploring new ones through the interactive graph database environment. It enables the analysis of different data on humans, rhesus, mice and rat coming from Affymetrix platforms. GeNNet is available as an open source platform at https://github.com/raquele/GeNNet and can be retrieved as a software container with the command docker pull quelopes/gennet.

Identification of the Consistently Altered Metabolic Targets in Human Hepatocellular Carcinoma

BACKGROUND & AIMS

Cancer cells rely on metabolic alterations to enhance proliferation and survival. Metabolic gene alterations that repeatedly occur in liver cancer are largely unknown. We aimed to identify metabolic genes that are consistently deregulated, and are of potential clinical significance in human hepatocellular carcinoma (HCC).

METHODS

We studied the expression of 2,761 metabolic genes in 8 microarray datasets comprising 521 human HCC tissues. Genes exclusively up-regulated or down-regulated in 6 or more datasets were defined as consistently deregulated. The consistent genes that correlated with tumor progression markers (ECM2 and MMP9) (Pearson correlation P < .05) were used for Kaplan-Meier overall survival analysis in a patient cohort. We further compared proteomic expression of metabolic genes in 19 tumors vs adjacent normal liver tissues.

RESULTS

We identified 634 consistent metabolic genes, ∼60% of which are not yet described in HCC. The down-regulated genes (n = 350) are mostly involved in physiologic hepatocyte metabolic functions (eg, xenobiotic, fatty acid, and amino acid metabolism). In contrast, among consistently up-regulated metabolic genes (n = 284) are those involved in glycolysis, pentose phosphate pathway, nucleotide biosynthesis, tricarboxylic acid cycle, oxidative phosphorylation, proton transport, membrane lipid, and glycan metabolism. Several metabolic genes (n = 434) correlated with progression markers, and of these, 201 predicted overall survival outcome in the patient cohort analyzed. Over 90% of the metabolic targets significantly altered at the protein level were similarly up- or down-regulated as in genomic profile.

CONCLUSIONS

We provide the first exposition of the consistently altered metabolic genes in HCC and show that these genes are potentially relevant targets for onward studies in preclinical and clinical contexts.

Plasmalemma vesicle-associated protein: A crucial component of vascular homeostasis

Endothelial subcellular structures, including caveolae, fenestrae and transendothelial channels, are crucial for regulating microvascular function. Plasmalemma vesicle-associated protein (PLVAP) is an endothelial cell-specific protein that forms the stomatal and fenestral diaphragms of blood vessels and regulates basal permeability, leukocyte migration and angiogenesis. Loss of PLVAP in mice leads to premature mortality due to disrupted homeostasis. Evidence from previous studies suggested that PLVAP is involved in cancer, traumatic spinal cord injury, acute ischemic brain disease, transplant glomerulopathy, Norrie disease and diabetic retinopathy. Specifically, PLVAP expression has been demonstrated to be upregulated in these diseases, accompanied by pro-angiogenic or pro-inflammatory responses. Therefore, PLVAP is considered a novel therapeutic target, in addition to an endothelial cell marker. The present review summarizes the structure and functions of PLVAP, and its roles in pathophysiological processes.

Glyco-genes change expression in cancer through aberrant methylation

BACKGROUND

Most eukaryotic proteins are modified by covalent addition of glycan molecules that considerably influence their function. Aberrant glycosylation is profoundly involved in malignant transformation, tumor progression and metastasis. Some glycan structures are tumor-specific and reflect disturbed glycan biosynthesis pathways.

METHODS

We analyzed DNA methylation and expression of 86 glyco-genes in melanoma, hepatocellular, breast and cervical cancers using data from publicly available databases. We also analyzed methylation datasets without the available matching expression data for glyco-genes in lung cancer, and progression of melanoma into lymph node and brain metastases.

RESULTS

Ten glyco-genes (GALNT3, GALNT6, GALNT7, GALNT14, MGAT3, MAN1A1, MAN1C1, ST3GAL2, ST6GAL1, ST8SIA3) showing changes in both methylation and expression in the same type of cancer belong to GalNAc transferases, GlcNAc transferases, mannosidases and sialyltransferases, which is in line with changes in glycan structures already reported in the same type of tumors. Some of those genes were additionally identified as potentially valuable for disease prognosis. The MGAT5B gene, so far identified as specifically expressed in brain, emerged as a novel candidate gene that is epigenetically dysregulated in different cancers other than brain cancer. We also report for the first time aberrant expression of the GALNT and MAN genes in cancer by aberrant promoter methylation.

CONCLUSIONS

Aberrant expression of glyco-genes due to aberrant promoter methylation could be a way leading to characteristic glycosylation profiles commonly described in cancer.

GENERAL SIGNIFICANCE

Methylation status in promoters of candidate glyco-genes might serve as prognostic markers for specific tumors and point to potential novel targets for epigenetic drugs. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.