Identification of molecular target genes and key pathways in hepatocellular carcinoma by bioinformatics analysis

ASPM stabilizes the NOTCH intracellular domain 1 and promotes oncogenesis by blocking FBXW7 binding in hepatocellular carcinoma cells

Notch signaling is aberrantly activated in approximately 30% of hepatocellular carcinoma (HCC), significantly contributing to tumorigenesis and disease progression. Expression of the major Notch receptor, NOTCH1, is upregulated in HCC cells and correlates with advanced disease stages, although the molecular mechanisms underlying its overexpression remain unclear. Here, we report that expression of the intracellular domain of NOTCH1 (NICD1) is upregulated in HCC cells due to antagonism between the E3-ubiquitin ligase F-box/WD repeat-containing protein 7 (FBXW7) and the large scaffold protein abnormal spindle-like microcephaly-associated protein (ASPM) isoform 1 (ASPM-i1). Mechanistically, FBXW7-mediated polyubiquitination and the subsequent proteasomal degradation of NICD1 are hampered by the interaction of NICD1 with ASPM-i1, thereby stabilizing NICD1 and rendering HCC cells responsive to stimulation by Notch ligands. Consistently, downregulating ASPM-i1 expression reduced the protein abundance of NICD1 but not its FBXW7-binding-deficient mutant. Reinforcing the oncogenic function of this regulatory module, the forced expression of NICD1 significantly restored the tumorigenic potential of ASPM-i1-deficient HCC cells. Echoing these findings, NICD1 was found to be strongly co-expressed with ASPM-i1 in cancer cells in human HCC tissues (P < 0.001). In conclusion, our study identifies a novel Notch signaling regulatory mechanism mediated by protein-protein interaction between NICD1, FBXW7, and ASPM-i1 in HCC cells, representing a targetable vulnerability in human HCC.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

Identification of characteristic genes and construction of regulatory network in gallbladder carcinoma

BACKGROUND

Gallbladder carcinoma (GBC) is a highly malignant tumor with a poor overall prognosis. This study aimed to identify the characteristic microRNAs (miRNAs) of GBC and the competing endogenous RNA (ceRNA) regulatory mechanisms.

METHODS

The microarray data of GBC tissue samples and normal gallbladder (NGB) tissue samples from the Gene Expression Omnibus (GEO) database was downloaded. GBC-related differentially expressed miRNAs (DE-miRNAs) were identified by inter-group differential expression analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were used to screen the characteristic miRNA based on the intersect between least absolute shrinkage and selection operator (LASSO) and Support vector machine-recursive feature elimination (SVM-RFE). Based on the differential expression analysis of GEO database, the ceRNA network of characteristic miRNA was predicted and constructed. The biological functions of the ceRNA network were revealed by carrying out the gene enrichment analysis was implemented. We further screened the key genes of ceRNA network and constructed a protein-protein interaction (PPI) network, and predicted and generated the transcription factors (TFs) network of signature miRNAs. The expression of characteristic miRNA in clinical samples was verified by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

A total of 131 GBC-related DE-miRNAs were obtained. The hsa-miR-4770 was defined as characteristic miRNA for GBC. The ceRNA network containing 211 mRNAs, one miRNA, two lncRNAs, and 48 circRNAs was created. Gene enrichment analysis suggested that the downstream genes were mainly involved in actin filament organization, cell-substrate adhesion, cell-matrix adhesion, reactive oxygen species metabolic process, glutamine metabolic process and extracellular matrix (ECM)-receptor interaction pathway. 10 key genes in the network were found to be most correlated with disease, and involved in cell cycle-related processes, p53, and extrinsic apoptotic signaling pathways. The qRT-PCR result demonstrated that hsa-miR-4770 is down-regulated in GBC, and the expression trend is consistent with the public database.

CONCLUSIONS

We identified hsa-miR-4770 as the characteristic miRNA for GBC. The ceRNA network of hsa-miR-4770 may play key roles in GBC. This study provided some basis for potential pathogenesis of GBC.

Analysis of mRNA-miRNA interaction network reveals the role of CAFs-derived exosomes in the immune regulation of oral squamous cell carcinoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) have significant tumor regulatory functions, and CAFs-derived exosomes (CAFs-Exo) released from CAFs play an important role in the progression of oral squamous cell carcinoma (OSCC). However, a lack of comprehensive molecular biological analysis leaves the regulatory mechanisms of CAFs-Exo in OSCC unclear.

METHODS

We used platelet derived growth factor-BB (PDGF-BB) to induce the transformation of human oral mucosa fibroblast (hOMF) into CAFs, and extracted exosomes from the supernatant of CAFs and hOMF. We validated the effect of CAFs-Exo on tumor progression by exosomes co-culture with Cal-27 and tumor-forming in nude mice. The cellular and exosomal transcriptomes were sequenced, and immune regulatory genes were screened and validated using mRNA-miRNA interaction network analysis in combination with publicly available databases.

RESULTS

The results showed that CAFs-Exo had a stronger ability to promote OSCC proliferation and was associated with immunosuppression. We discovered that the presence of immune-related genes in CAFs-Exo may regulate the expression of PIGR, CD81, UACA, and PTTG1IP in Cal-27 by analyzing CAFs-Exo sequencing data and publicly available TCGA data. This may account for the ability of CAFs-Exo to exert immunomodulation and promote OSCC proliferation.

CONCLUSIONS

CAFs-Exo was found to be involved in tumor immune regulation through hsa-miR-139-5p, ACTR2 and EIF6, while PIGR, CD81, UACA and PTTG1IP may be potentially effective targets for the treatment of OSCC in the future.

Maternal embryonic leucine zipper kinase in tumor cell and tumor microenvironment: Emerging player and promising therapeutic opportunities

Maternal embryonic leucine zipper kinase (MELK) is a member of the AMPK (AMP-activated protein kinase) protein family, which is widely and highly expressed in multiple cancer types. Through direct and indirect interactions with other proteins, it mediates various cascades of signal transduction processes and plays an important role in regulating tumor cell survival, growth, invasion and migration and other biological functions. Interestingly, MELK also plays an important role in the regulation of the tumor microenvironment, which can not only predict the responsiveness of immunotherapy, but also affect the function of immune cells to regulate tumor progression. In addition, more and more small molecule inhibitors have been developed for the target of MELK, which exert important anti-tumor effects and have achieved excellent results in a number of clinical trials. In this review, we outline the structural features, molecular biological functions, potential regulatory mechanisms and important roles of MELK in tumors and tumor microenvironment, as well as substances targeting MELK. Although many molecular mechanisms of MELK in the process of tumor regulation are still unknown, it is worth affirming that MELK is a potential tumor molecular therapeutic target, and its unique superiority and important role provide clues and confidence for subsequent basic research and scientific transformation.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

BTEAC Catalyzed Ultrasonic-Assisted Synthesis of Bromobenzofuran-Oxadiazoles: Unravelling Anti-HepG-2 Cancer Therapeutic Potential through In Vitro and In Silico Studies

In this work, BTEAC (benzyl triethylammonium chloride) was employed as a phase transfer catalyst in an improved synthesis (up to 88% yield) of S-alkylated bromobenzofuran-oxadiazole scaffolds BF1-9. These bromobenzofuran-oxadiazole structural hybrids BF1-9 were evaluated in vitro against anti-hepatocellular cancer (HepG2) cell line as well as for their in silico therapeutic potential against six key cancer targets, such as EGFR, PI3K, mTOR, GSK-3β, AKT, and Tubulin polymerization enzymes. Bromobenzofuran structural motifs BF-2, BF-5, and BF-6 displayed the best anti-cancer potential and with the least cell viabilities (12.72 ± 2.23%, 10.41 ± 0.66%, and 13.08 ± 1.08%), respectively, against HepG2 liver cancer cell line, and they also showed excellent molecular docking scores against EGFR, PI3K, mTOR, and Tubulin polymerization enzymes, which are major cancer targets. Bromobenzofuran-oxadiazoles BF-2, BF-5, and BF-6 displayed excellent binding affinities with the active sites of EGFR, PI3K, mTOR, and Tubulin polymerization enzymes in the molecular docking studies as well as in MMGBSA and MM-PBSA studies. The stable bindings of these structural hybrids BF-2, BF-5, and BF-6 with the enzyme targets EGFR and PI3K were further confirmed by molecular dynamic simulations. These investigations revealed that 2,5-dimethoxy-based bromobenzofuran-oxadiazole BF-5 (10.41 ± 0.66% cell viability) exhibited excellent cytotoxic therapeutic efficacy. Moreover, computational studies also suggested that the EGFR, PI3K, mTOR, and Tubulin polymerization enzymes were the probable targets of this BF-5 scaffold. In silico approaches, such as molecular docking, molecular dynamics simulations, and DFT studies, displayed excellent association with the experimental biological data of bromobenzofuran-oxadiazoles BF1-9. Thus, in silico and in vitro results anticipate that the synthesized bromobenzofuran-oxadiazole hybrid BF-5 possesses prominent anti-liver cancer inhibitory effects and can be used as lead for further investigation for anti-HepG2 liver cancer therapy.

Loss of WNK1 Suppressed the Malignant Behaviors of Hepatocellular Carcinoma Cells by Promoting Autophagy and Activating AMPK Pathway

Background

WNK lysine deficient protein kinase 1 (WNK1) has been shown to be highly expressed in hepatocellular carcinoma (HCC) samples and related to poor prognosis of HCC patients based on bioinformatics analysis. However, the specific function of WNK1 in HCC has not been analyzed. This study is aimed at exploring the function of WNK1 in HCC progression as well as its related molecular mechanism.

Methods

After knockdown of WNK1 by small interference RNA, cell counting kit-8, colony formation, western blot, Transwell, and wound healing assays were employed to evaluate the biological behaviors of HCC cells. Immunofluorescent staining was applied to detect the effect of WNK1 on LC3 II. GSK690693 or si-AMPK was applied to block AMPK pathway. The expression of autophagy and AMPK pathway related molecules was examined by western blot assay.

Results

WNK1 was highly expressed in HCC cell lines and loss of WNK1 inhibited HCC cell proliferation, cell cycle, migration, and invasion. Additionally, we demonstrated that loss of WNK1 promoted the autophagy and activated AMPK pathway in HCC cells. While, GSK690693 treatment or si-AMPK transfection suppressed the autophagy and promoted HCC cells proliferation. However, WNK1 knockdown counteracted the effect of GSK690693 or si-AMPK in regulating HCC cell proliferation. Finally, we demonstrated that WNK1 regulated the malignant behaviors of HCC cells by modulating autophagy and AMPK pathway.

Conclusions

The above results indicated that WNK1 may be a worthwhile target to be considered for therapy of HCC.

Identification and Validation of Hub Genes with Poor Prognosis in Hepatocellular Carcinoma by Integrated Bioinformatical Analysis

Background

Hepatocellular carcinoma (HCC) is the reason for the world’s second largest cancer-related death. It is clinically valuable to study the molecular mechanisms of HCC occurrence and development for formulating more effective diagnosis and treatment strategies.

Methods

The five microarray data sets GSE45267, GSE101685, GSE84402, GSE62232 and GSE45267 were downloaded from Gene Expression Omnibus (GEO) database, including 165 HCC tissues and 73 normal tissues. Differential expressed genes (DEGs) between HCC tissues and normal tissues were determined by GEO2R. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and the protein–protein interaction network (PPI) network analysis were employed to identify DEGs and to evaluate the clinical significance in prognosis of HCC.

Results

A total of 152 genes differentially expressed in HCC tissues and normal tissues were identified. GO and KEGG functional enrichment analysis revealed that 39 up-regulated genes were mainly enriched in mitosis, cell cycle and oocyte meiosis, while those down-regulated genes (113) were concentrated in exogenous drug catabolism and the metabolism of cytochrome P450 on exogenous drugs. Totally, 19 hub genes were chosen by PPI network and module analysis and verified by The Cancer Genome Atlas (TCGA) database. Finally, 8 hub genes were selected, including CDK1, CYP2C8, CCNB1, AURKA, CYP2C9, BUB1B, MAD2L1 and TTK, which were associated with the overall survival rate of HCC patients.

Conclusion

This study presented eight target genes connected to the prognosis of HCC patients. Those mainly exists in cell cycle and drug catabolism, which may be latent targets for clinical treatment.

NCAPG facilitates colorectal cancer cell proliferation, migration, invasion and epithelial-mesenchymal transition by activating the Wnt/β-catenin signaling pathway

BACKGROUND

The condensation complex gene non-SMC condensin I complex subunit G(NCAPG), a cell cycle-associated condensin, is over-expressed in various cancers. However, its biological function in colorectal cancer (CRC) has yet to be deciphered. In this study, we investigated the role of NCAPG in CRC progression.

METHODS

Tissues and cells were used to measure NCAPG expression levels and their association with clinicopathological characteristics. NCAPG silencing and overexpression in CRC cells were used to measure its effect on proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) progression. In addition, mRNA, and protein expression levels of key EMT biomarkers were measured. The underlying mechanism of NCAPG modulating CRC progression was further explored using western blotting, co-immunoprecipitation (CO-IP), and immunofluorescence (IF) assays.

RESULTS

NCAPG was over-expressed in CRC tissues and cell lines. High expression levels were associated with differentiation levels, lymph metastasis, and vascular invasion in patients. NCAPG silencing suppressed, while NCAPG overexpression promoted the proliferative, migration, and invasive capacity of HCT116 and SW480 cells. Mechanistically, we discovered that NCAPG participated in regulating the EMT process and the Wnt/β-catenin signaling pathway to facilitate CRC invasion and metastasis. Additional experiments demonstrated that NCAPG activated the Wnt/β-catenin signaling pathway by binding to β-catenin in CRC cells.

CONCLUSION

NCAPG acts as an oncogene involved in the development and progression of CRC by binding to β-catenin to activate the Wnt/β-catenin signaling pathway.

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

OBJECTIVE

The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis.

METHODS

GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression.

RESULTS

We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

The effect of PPP2CA expression on the prognosis of patients with hepatocellular carcinoma and its molecular biological characteristics

BACKGROUND

To investigate the role of the PPP2CA gene in the prognosis of patients with hepatocellular carcinoma (HCC) and its molecular biological characteristics.

METHODS

We performed comparison of the expression of PPP2CA in HCC and non-HCC tissues of HCC patients who underwent surgery for the first time in the Tumor Hospital of Guangxi Medical University from July 2017 to July 2019, and retrospectively analyzed the relevant clinical data and prognosis. The GSE76427 data set and bioinformatics and public databases were used to compare the expression of PPP2CA between HCC and non-cancer tissues. Gene Ontology (GO) analysis was performed of PPP2CA and its differential genes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. A protein-protein interaction (PPI) network of PPP2CA and its differentially expressed genes (DEGs) was constructed from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and visualized by Cytoscape software.

RESULTS

The immunohistochemistry (IHC) of tissue sections confirmed that PPP2CA was highly expressed in most HCC tissues; the high expression of PPP2CA was significantly correlated with microvascular invasion (MVI) and portal vein tumor thrombi (P<0.05). Participants in the PPP2CA high expression group had worse overall survival (OS; P=0.04) and recurrence-free survival (RFS; P=0.019). The PPP2CA gene and 71 DEGs were mainly enriched in the nuclear division, organelle fission, nuclear chromosome separation, and chromatid separation process, and KEGG analysis revealed enrichment in drug metabolism-cytochrome metabolism of xenobiotics by P450 and cytochrome P450. Finally, through the PPI network, CCNA2, AURKB, TOP2A, NCAPG, MCM2, CDC20, CCMB2, AURKA, and MGST1 were identified as the top 9 highly connected hub genes.

CONCLUSIONS

The PPP2CA gene is highly expressed in HCC tissues. The high expression of PPP2CA is significantly associated with poor prognosis. Through the analysis of DEGs, GO and KEGG pathway analysis, it was found that PPP2CA may act on liver cancer through multiple targets and multiple pathways, and PPP2CA plays a promoting role in HCC.

Identification and Analysis of Potential Autophagy-Related Biomarkers in Endometriosis by WGCNA

Background: Endometriosis is a serious gynecological disorder characterized by debilitating pain, infertility and the establishment of innervated endometriosis lesions outside the uterus. Early detection and accurate diagnosis are pivotal in endometriosis. The work screened autophagy-related genes (ATGs) as potential biomarkers to reveal new molecular subgroups for the early diagnosis of endometriosis.

Materials and Methods: The gene lists of ATGs from five databases were integrated. Then, weighted gene co-expression network analysis (WGCNA) was used to map the genes to the gene profile of endometriosis samples in GSE51981 to obtain functional modules. GO and KEGG analyses were performed on the ATGs from the key modules. Differentially expressed ATGs were identified by the limma R package and further validated in the external datasets of GSE7305 and GSE135485. The DESeq2 R package was utilized to establish multifactorial network. Subsequently, one-way analysis of variance (ANOVA) was performed to identify new molecular subgroups. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blotting were used to confirm the differential expression of hub ATGs, and the receiver operating characteristic (ROC) curve analysis and Spearman correlation analysis were applied to assess the diagnostic value of hub ATGs in 40 clinical samples and human primary endometrial stromal cells (ESCs).

Results: We screened 4 key modules and 12 hub ATGs and found the key genes to be strongly correlated with endometriosis. The pathways of ATGs were mainly enriched in autophagy, apoptosis, ubiquitin-protein ligase binding, and MAPK signaling pathway. The expression levels of EZH2 (Enhancer of Zeste homolog 2) and RND3 (also known as RhoE) had statistically significant changes with higher values in the endometriosis group compared with the controls, both in the tissue samples and primary ESCs. Besides, they also showed higher specificity and sensitivity by the receiver operating characteristic analysis and Spearman correlation analysis for the diagnosis of endometriosis. The TF-mRNA-miRNA-lncRNA multifactorial network was successfully constructed. Four new molecular subgroups were identified, and we preliminarily showed the ability of IQCG to independently differentiate subgroups.

Conclusion: EZH2 and RND3 could be candidate biomarkers for endometriosis, which would contribute to the early diagnosis and intervention in endometriosis.

Asynchronous DNA Replication of Biallelically Expressed Genes in Human Peripheral Blood Lymphocytes as a Prognostic Sign of Cancer

The aim of the study was to identify and quantify lymphocytes with asynchronous replication of the AURKA and TP53 genes in cancer patients versus controls and to assess the diagnostic capabilities of this approach.

Identifying key genes and screening therapeutic agents associated with diabetes mellitus and HCV-related hepatocellular carcinoma by bioinformatics analysis

Objective

Incidence of both Type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC) are rapidly increasing worldwide. One of the leading causes of HCC is hepatitis C virus (HCV), which is a resource of blood-borne viral infection. HCV increases the risk for HCC probably by promoting fibrosis and cirrhosis. Association among T2DM and HCV related HCC remains significant, indicating that such association is clinically reliable and robust. Lawson was the first who uncovered HCC in person suffered from T2DM. Until now, genetic association between HCV related HCC and T2DM is poorly known. Current work was designed to figure out the molecular mechanisms of both diseases by identifying the hub genes and therapeutic drugs using integrated bioinformatics analysis.

Methods

Four microarray datasets were downloaded from GEO database and analyzed using R in order to obtain different expressed genes (DEGs). Protein–protein interaction (PPI) networks was constructed using STRING tool and visualized by Cytoscape. Moreover, hub genes were identified on the basis of their degree of connectivity. Finally, Networkanalyst and DGIdb were used for the identification of transcription factors (TFs) and selection of candidate drugs, respectively.

Results

A total of 53 DEGs were identified, of which 41 were upregulated genes and 12 were downregulated genes. PPI network obtained from STRING were subjected to Cytoscape plugin cytoHubba, and top 10 genes (AURKA, JUN, AR, MELK, NCOA2, CENPF, NCAPG, PCK1, RAD51AP1, and GTSE1) were chosen as the target hub genes based on the highest degree of connectivity. Furthermore, 47 drugs of AURKA, JUN, AR, MELK, and NCOA2 were found having therapeutic potential to treat HCV-HCC in patients with T2DM.

Conclusion

This study updates the information and yield a new perspective in context of understanding the pathogenesis and development of HCV related HCC in affected persons with T2DM. In vivo and in vitro investigation of hub genes and pathway interaction is essential to delineate the specific roles of the novel hub genes, which may help to reveal the genetic association between HCV-HCC and T2DM. In future, hub genes along with their candidate drugs might be capable of improving the personalized detection and therapies for both diseases.

Identification of hub genes and candidate drugs in hepatocellular carcinoma by integrated bioinformatics analysis

BACKGROUND

Hepatocellular carcinoma (HCC) is the third cancer-related cause of death in the world. Until now, the involved mechanisms during the development of HCC are largely unknown. This study aims to explore the driven genes and potential drugs in HCC.

METHODS

Three mRNA expression datasets were used to analyze the differentially expressed genes (DEGs) in HCC. The bioinformatics approaches include identification of DEGs and hub genes, Gene Ontology terms analysis and Kyoto encyclopedia of genes and genomes enrichment analysis, construction of protein-protein interaction network. The expression levels of hub genes were validated based on The Cancer Genome Atlas, Gene Expression Profiling Interactive Analysis, and the Human Protein Atlas. Moreover, overall survival and disease-free survival analysis of HCC patients were further conducted by Kaplan-Meier plotter and Gene Expression Profiling Interactive Analysis. DGIdb database was performed to search the candidate drugs for HCC.

RESULTS

A total of 197 DEGs were identified. The protein-protein interaction network was constructed using Search Tool for the Retrieval of Interacting Genes software, 10 genes were selected by Cytoscape plugin cytoHubba and served as hub genes. These 10 genes were all closely related to the survival of HCC patients. DGIdb database predicted 29 small molecules as the possible drugs for treating HCC.

CONCLUSION

Our study provides some new insights into HCC pathogenesis and treatments. The candidate drugs may improve the efficiency of HCC therapy in the future.

Underlying mechanism of sorafenib resistance in hepatocellular carcinoma: a bioinformatics study based on validated resistance-related genes

Background

Sorafenib, the first approved targeted therapy for advanced hepatocellular carcinoma (HCC), is often reported to comprised survival-benefit due to resistance. An underlying mechanism of resistance was proposed using bioinformatics analysis based on differentially expressed genes (DEGs) from microarrays. However, most DEGs were invalidated at both the expression level, and the role in causing resistance. Therefore, we conducted a bioinformatics analysis based on experimentally determined sorafenib-resistance-related genes (SRRGs) to elucidate the mechanism of sorafenib resistance.

Methods

The SRRGs, which have been experimentally determined to promote or inhibit resistance, were collected from published studies. The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform Gene Ontology (GO) and pathway enrichment analysis, respectively. A corresponding protein-protein interaction network (PPI) was created using the Cytoscape software program, and network hub genes were proposed.

Results

A total of 145 SRRGs, with 117 promoting and 28 inhibiting resistance, were identified. Cell proliferation, migration, development, response to oxygen levels, epithelial-to-mesenchymal transition (EMT), cell skeleton, protein function, and autophagy were all proposed as crucial gene functions related to resistance. The pathways related to cell proliferation or apoptosis, immune function, endocrine metabolism, stem cell function, and differentiation were identified as key resistance-related pathways. A total of 81 hub genes were proposed, including the following top 10 genes: TP53, AKT1, EGFR, STAT3, VEGFA, JUN, MAPK1, IL6, PTEN, and CTNNB1.

Conclusions

In conclusion, this study gathered experimentally validated genes that determine sorafenib resistance in HCC, provided an overview of the underlying mechanisms of resistance, and further validated sorafenib resistance in HCC.

Identification of Key Modules and Hub Genes Involved in Esophageal Squamous Cell Carcinoma Tumorigenesis Using WCGNA

Introduction:

The mechanistic basis for the development of esophageal squamous cell carcinoma (ESCC) remains poorly understood. The goal of the present study was thus to characterize mRNA and long noncoding RNA (lncRNA) expression profiles associated with ESCC in order to identify key hub genes associated with the pathogenesis of this cancer.

Materials and Methods:

The GSE26866 and GSE45670 datasets from the Gene Expression Omnibus (GEO) database were used to conduct a weighted gene co-expression network analysis (WGCNA), after which Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Cytoscape was additionally used to construct lncRNA-mRNA networks, after which hub genes were identified and validated through the assessment of TCGA datasets and clinical samples.

Results:

Two gene modules were found to be closely linked to ESCC tumorigenesis. These genes were enriched in cell cycle, MAPK signaling, JAK-STAT signaling, pyrimidine metabolism, arachidonic acid metabolism, and P53 signaling pathway activity, all of which are directly linked with the development of cancer. In total, we identified and validated 9 hub genes associated with ESCC (DDX18, DNMT1, NCAPG, WDHD1, PRR11, VOPP1, ZKSCAN5, LC35C2, and PHACTR2).

Conclusion:

In summary, we identified key gene modules and hub genes associated with ESCC development, and we constructed a lncRNA-mRNA network pertaining to this cancer type. These results provide a foundation for future research regarding the mechanistic basis of ESCC.

ASPM promotes hepatocellular carcinoma progression by activating Wnt/β-catenin signaling through antagonizing autophagy-mediated Dvl2 degradation

Hepatocellular carcinoma (HCC) is one of the most fatal cancers worldwide. In this article, we show that expression of abnormal spindle‐like microcephaly‐associated protein (ASPM) is up‐regulated in liver cancer samples, and this up‐regulation is significantly associated with tumor aggressiveness and reduced survival times of patients. Down‐regulation of ASPM expression inhibits the proliferation, invasion, migration and epithelial‐to‐mesenchymal transition of HCC cells in vitro and inhibits tumor formation in nude mice. ASPM interacts with disheveled‐2 (Dvl2) and antagonizes autophagy‐mediated Dvl2 degradation by weakening the functional interaction between Dvl2 and the lipidated form of microtubule‐associated proteins 1A/1B light chain 3A (LC3II), thereby increasing Dvl2 protein abundance and leading to Wnt/β‐catenin signaling activation in HCC cells. Thus, our results define ASPM as a novel oncoprotein in HCC and indicate that disruption of the Wnt–ASPM–Dvl2–β‐catenin signaling axis might have potential clinical value.

Identification of genes predicting unfavorable prognosis in hepatitis B virus-associated hepatocellular carcinoma

Background

To identify potential key genes predicting unfavorable prognosis in hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC).

Methods

Gene expression profiles of GSE121248, GSE62232, and GSE55092 from the GEO database were obtained and analyzed. Differentially expressed genes (DEGs) between HBV-associated HCC tissues and adjacent normal tissues were screened by the limma package and Venn diagram software. Functional assessment of DEGs was performed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were selected by the protein-protein interaction (PPI) network and further validated by GSE14520 clinical data.

Results

A total of 26 up-regulated genes and 76 down-regulated genes were identified by analyzing three databases. GO and KEGG analysis demonstrated that these genes were involved in cell division, metabolism-related biological processes, the p53 pathway, and the cell cycle, among others. PPI network suggested that 14 hub DEGs (TOP2A, HMMR, DTL, CCNB1, NEK2, PBK, RACGAP1, PRC1, CDK1, RRM2, ECT2, BUB1B, ANLN, and ASPM) were most dysregulated and had potential to distinguish between HBV-associated HCC and noncancerous tissues. Further survival analysis of hub genes demonstrated that high expression of TOP2A was significantly associated with poor clinical outcomes of HBV-associated HCC.

Conclusions

TOP2A might serve as a key gene for prognosis and as a therapeutic target for HBV-associated HCC.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

Identification of Biomarkers Related to Immune Cell Infiltration in Hepatocellular Carcinoma Using Gene Co-Expression Network

Hepatocellular carcinoma (HCC) is a common cancer with poor prognosis. Due to the lack of effective biomarkers and its complex immune microenvironment, the effects of current HCC therapies are not ideal. In this study, we used the GSE57957 microarray data from Gene Expression Omnibus database to construct a co-expression network. The weighted gene co-expression network analysis and CIBERSORT algorithm, which quantifies cellular composition of immune cells, were used to identify modules related to immune cells. Four hub genes (EFTUD2, GAPDH, NOP56, PA2G4) were identified by co-expression network and protein-protein interactions network analysis. We examined these genes in TCGA database, and found that the four hub genes were highly expressed in tumor tissues in multiple HCC groups, and the expression levels were significantly correlated with patient survival time, pathological stage and tumor progression. On the other hand, methylation analysis showed that the up-regulation of EFTUD2, GAPDH, NOP56 might be due to the hypomethylation status of their promoters. Next, we investigated the correlations between the expression levels of four hub genes and tumor immune infiltration using Tumor Immune Estimation Resource (TIMER). Gene set variation analysis suggested that the four hub genes were associated with numerous pathways that affect tumor progression or immune microenvironment. Overall, our results showed that the four hub genes were closely related to tumor prognosis, and may serve as targets for treatment and diagnosis of HCC. In addition, the associations between these genes and immune infiltration enhanced our understanding of tumor immune environment and provided new directions for the development of drugs and the monitoring of tumor immune status.

A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

Background

Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated.

Methods

To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan–Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model.

Results

Compared with normal tissues, 43 highly upregulated and 8 downregulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated 2-year or 5-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram.

Conclusion

Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02175-9.

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Expression of FOXM1 and Aurora-A predicts prognosis and sorafenib efficacy in patients with hepatocellular carcinoma

BACKGROUND:

Effective prognostic biomarkers and powerful target-therapeutic drugs are needed for improving the treatment of Hepatocellular carcinoma (HCC).

OBJECTIVE:

This study aimed to evaluate the expression of FOXM1 and Aurora-A and their prognostic value in HCC.

METHODS:

We determined the differentially expressed genes signature in HCC using the Gene Set Enrichment Analysis (GSEA), and then evaluated the expression of FOXM1 and Aurora-A in TCGA and KMUH cohort. Associations between co-expression of FOXM1 and Aurora-A and clinical variables were calculated. Overall survival (OS) and recurrence-free survival (RFS) were estimated with different FOXM1 and Aurora-A expression status.

RESULTS:

FOXM1-related gene sets were mostly associated with cell cycle regulation in HCC tissues. We found a positive correlation between the expression of FOXM1 and Aurora-A. Overexpression of FOXM1 and Aurora-A was associated with larger tumor size, advanced stage, higher grade, and double-positive for HBV and HCV. The coordinated overexpression of FOXM1 and Aurora-A was the most significant independent prognostic factor for OS and RFS. Furthermore, the concomitant high expression of FOXM1 and Aurora-A predicted the worst OS of sorafenib-treated patients with HCC.

CONCLUSIONS:

The co-expression of FOXM1 and Aurora-A could be a reliable biomarker to predict the sorafenib response and prognosis of HCC patients.

System-Wide Pollution of Biomedical Data: Consequence of the Search for Hub Genes of Hepatocellular Carcinoma Without Spatiotemporal Consideration

Biomedical institutions rely on data evaluation and are turning into data factories. Big-data storage centers, supercomputing systems, and increased algorithmic efficiency allow us to analyze the ever-increasing amount of data generated every day in biomedical research centers. In network science, the principal intrinsic problem is how to integrate the data and information from different experiments on genes or proteins. Data curation is an essential process in annotating new functional data to known genes or proteins, undertaken by a biobank curator, which is then reflected in the calculated networks. We provide an example of how protein-protein networks today have space-time limits. The next step is the integration of data and information from different biobanks. Omics data and networks are essential parts of this step but also have flawed protocols and errors. Consider data from patients with cancer: from biopsy procedures to experimental tests, to archiving methods and computational algorithms, these are continuously handled so require critical and continuous "updates" to obtain reproducible, reliable, and correct results. We show, as a second example, how all this distorts studies in cellular hepatocellular carcinoma. It is not unlikely that these flawed data have been polluting biobanks for some time before stringent conditions for the veracity of data were implemented in Big data. Therefore, all this could contribute to errors in future medical decisions.

TAF1A and ZBTB41 serve as novel key genes in cervical cancer identified by integrated approaches

Cervical cancer (CC) is the second most common cancer and the leading cause of cancer mortality in women. Numerous studies have found that the development of CC was associated with multiple genes. However, the mechanisms on gene level are enigmatic, hindering the understanding of its functional roles. This study sought to identify prognostic biomarkers of CC, and explore their biological functions. Here we conducted an integrated analysis to screen potential vital genes. Candidate genes were further tested by experiments in clinical specimens and cancer cell line. Then, molecular modeling was used to predict the three-dimensional structure of candidate genes’ proteins, and the interaction pattern was analyzed by docking simulation technique. Among the potential genes identified, we found that TAF1A and ZBTB41 were highly correlated. Furthermore, there was a definite interaction between the proteins of TAF1A and ZBTB41, which was affected by the activity of the p53 signaling pathway. In conclusion, our findings identified TAF1A and ZBTB41 could serve as biomarkers of CC. We confirmed their biological function and deciphered their interaction for the first time, which may be helpful for developing further researches.

NCAPG Induces Cell Proliferation in Cardia Adenocarcinoma via PI3K/AKT Signaling Pathway

Purpose

Previous studies have shown that non-SMC condensin I complex subunit G (NCAPG) overexpression is correlated to poor prognosis of multiple cancer types. Herein, we explored the underlying mechanism of NCAPG-mediated cardia adenocarcinoma (CA) proliferation and cell cycle regulation.

Methods

The protein profiling technology was used to analyze the gene expression in 20 CA and adjacent tissue samples. Differential genes were identified by bioinformatic analysis. Western blot and qRT-PCR-based analysis assessed the NCAPG expression levels in multiple CA cell lines. CA cell lines, SGC-7901 and AGS, were transfected with Lip 2000, and stably transfected cell lines were screened for NCAPG overexpression and downregulation. MTT and clone formation assays were employed to detect cell proliferation, and cell cycle phases were analyzed using flow cytometry. Western blot was performed to determine the NCAPG gene expression levels. Finally, we studied the tumorigenic effects of NCAPG in the mouse model and validated the cell experiment results using immunohistochemistry.

Results

A significant overexpression of NCAPG was found in CA tissues and CA cell lines. The outcomes of MTT and clone formation assays showed that NCAPG upregulation promoted cell proliferation. The outcomes of these analyses were further validated using nude mice as an in vivo tumor model. As per the outcome of Western blot and flow cytometry analysis, NCAPG regulated the G1 phase through the cyclins (CDK4, CDK6, and cyclin D1) overexpression and cell cycle inhibitors (P21 and P27) downregulation. Overexpressed NCAPG and silenced NCAPG, both in vitro and in vivo, resulted in abnormal activation of the PI3K/AKT signaling pathway in CA cells. We observed that NCAPG overexpression increased the levels of phosphorylated PI3K, AKT, and GSK3β; however, their total protein levels remained unchanged in CA cells.

Conclusion

As a CA oncogene, NCAPG promoted cell proliferation and regulated cell cycle through PI3K/AKT signaling pathway activation.

A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

An increasing number of studies have shown that abnormal metabolism processes are closely correlated with the genesis and progression of colorectal cancer (CRC). In this study, we systematically explored the prognostic value of metabolism-related genes (MRGs) for CRC patients. A total of 289 differentially expressed MRGs were screened based on The Cancer Genome Atlas (TCGA) and the Molecular Signatures Database (MSigDB), and 72 differentially expressed transcription factors (TFs) were obtained from TCGA and the Cistrome Project database. The clinical samples obtained from TCGA were randomly divided at a ratio of 7 : 3 to obtain the training group (n = 306) and the test group (n = 128). After univariate and multivariate Cox regression analyses, we constructed a prognostic model based on 6 MRGs (AOC2, ENPP2, ADA, GPD1L, ACADL, and CPT2). Kaplan-Meier survival analysis of the training group, validation group, and overall samples proved that the model had statistical significance in predicting the outcomes of patients. Independent prognosis analysis suggested that this risk score might serve as an independent prognosis factor for CRC patients. Moreover, we combined the prognostic model and the clinical characteristics in a nomogram to predict the overall survival of CRC patients. Furthermore, gene set enrichment analysis (GSEA) was conducted to identify the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in the high- and low-risk groups, which might provide novel therapeutic targets for CRC patients. We discovered through the protein-protein interaction (PPI) network and TF-MRG regulatory network that 7 hub genes were retrieved from the PPI network and 4 kinds of differentially expressed TFs (NR3C1, MYH11, MAF, and CBX7) positively regulated 4 prognosis-associated MRGs (GSTM5, PTGIS, ENPP2, and P4HA3).

Identification of hub genes involved in the occurrence and development of hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy, which is a major cause of cancer morbidity and mortality worldwide. Thus, the aim of the present study was to identify the hub genes and underlying pathways of HCC via bioinformatics analyses. The present study screened three datasets, including GSE112790, GSE84402 and GSE74656 from the Gene Expression Omnibus (GEO) database, and downloaded the RNA-sequencing of HCC from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) in both the GEO and TCGA datasets were filtered, and the screened DEGs were subsequently analyzed for functional enrichment pathways. A protein-protein interaction (PPI) network was constructed, and hub genes were further screened to create the Kaplan-Meier curve using cBioPortal. The expression levels of hub genes were then validated in different datasets using the Oncomine database. In addition, associations between expression and tumor grade, hepatitis virus infection status, satellites and vascular invasion were assessed. A total of 126 DEGs were identified, containing 70 upregulated genes and 56 downregulated genes from the GEO and TCGA databases. By constructing the PPI network, the present study identified hub genes, including cyclin B1 (CCNB1), cell-division cycle protein 20 (CDC20), cyclin-dependent kinase 1, BUB1 mitotic checkpoint serine/threonine kinase β (BUB1B), cyclin A2, nucleolar and spindle associated protein 1, ubiquitin-conjugating enzyme E2 C (UBE2C) and ZW10 interactor. Furthermore, upregulated CCNB1, CDC20, BUB1B and UBE2C expression levels indicated worse disease-free and overall survival. Moreover, a meta-analysis of tumor and healthy tissues in the Oncomine database demonstrated that BUB1B and UBE2C were highly expressed in HCC. The present study also analyzed the data of HCC in TCGA database using univariate and multivariate Cox analyses, and demonstrated that BUB1B and UBE2C may be used as independent prognostic factors. In conclusion, the present study identified several genes and the signaling pathways that were associated with tumorigenesis using bioinformatics analyses, which could be potential targets for the diagnosis and treatment of HCC.

Time serial transcriptome reveals Cyp2c29 as a key gene in hepatocellular carcinoma development

Objective: Hepatocellular carcinoma (HCC) is a severely lethal cancer that usually originates from chronic liver injury and inflammation. Although progress on diagnosis and treatment is obvious, the cause of HCC remains unclear. In this study, we sought to determine key genes in HCC development. Methods: To identify key regulators during HCC progression, we performed transcriptome sequencing to obtain time series gene expression data from a mouse model with diethylnitrosamine-induced liver tumors and further verified gene expression and function in vitro and in vivo. Results: Among the differentially expressed genes, Cyp2c29 was continuously downregulated during HCC progression. Overexpression of Cyp2c29 suppressed NF-κB activation and proinflammatory cytokine production by increasing the production of 14,15-epoxyeicosatrienoic acid in vitro. Furthermore, overexpression of Cyp2c29 in vivo protected against liver inflammation in mouse models of liver injury induced by both acetaminophen and CCl4. Two human homologs of mouse Cyp2c29, CYP2C8 and CYP2C9, were found to be downregulated in human HCC progression, and their expression was positively correlated with overall survival in patients with HCC (significance: P = 0.046 and 0.0097, respectively). Conclusions: Collectively, through systematic analysis and verification, we determined that Cyp2c29 is a novel gene involved in liver injury and inflammation, which may be a potential biomarker for HCC prevention and prognosis determination.

NCAPG Is a Promising Therapeutic Target Across Different Tumor Types

Background

With the advent of CRISPR-Cas9 genome editing tool in gene therapy, identification of aberrantly expressed genes is of great value across various cancer types. Since a large number of patients may benefit from molecular targeted gene therapy. The purpose of this study was to identify aberrantly expressed genes across various cancer types, analyze prospective mechanisms and their correlation with survival outcomes.

Results

NCAPG was highly expressed in The Cancer Genome Atlas (TCGA) database, which includes the transcriptomes of 6,647 cancer and 647 normal tissue samples from 16 cancer types. Furthermore, a predicted NCAPG overexpression rate was also observed at the protein level in 16 tumor types. Importantly, high NCAPG level was significantly associated with unfavorable survival in various cancer types such as hepatocellular carcinoma (HCC), breast, lung or ovarian cancer. The multivariate analyses demonstrated that NCAPG, TNM, and Barcelona Clinic Liver Cancer (BCLC) staging were independent risk factors for mortality of patients with HCC. Moreover, functional and pathway enrichment analysis suggested that NCAPG was closely correlated with the pathways of cell cycle, cellular senescence, and mismatch repair. By weighted gene co-expression network analysis (WGCNA), we identified NCAPG as a hub gene in the turquoise module mostly related to the survival time of HCC samples.

Conclusion

To our knowledge, this study represents a comprehensive RNA-Seq analysis of several tumor types, revealing NCAPG as a promising molecular target. NCAPG overexpression may play important roles in carcinogenesis and progression of tumors via regulating tumor-related pathways, thereby broadening the understanding of the pathogenic mechanisms and highlighting the possibility of developing novel targeted therapeutics.

Comprehensive analysis of biomarkers for prostate cancer based on weighted gene co-expression network analysis

BACKGROUND

Prostate cancer (PCa) is one of the leading causes of cancer-related death. In the present research, we adopted a comprehensive bioinformatics method to identify some biomarkers associated with the tumor progression and prognosis of PCa.

METHODS

Differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were applied for exploring gene modules correlative with tumor progression and prognosis of PCa. Clinically Significant Modules were distinguished, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to Annotation, Visualization and Integrated Discovery (DAVID). Protein-protein interaction (PPI) networks were used in selecting potential hub genes. RNA-Seq data and clinical materials of prostate cancer from The Cancer Genome Atlas (TCGA) database were used for the identification and validation of hub genes. The significance of these genes was confirmed via survival analysis and immunohistochemistry.

RESULTS

2688 DEGs were filtered. Weighted gene co-expression network was constructed, and DEGs were divided into 6 modules. Two modules were selected as hub modules which were highly associated with the tumor grades. Functional enrichment analysis was performed on genes in hub modules. Thirteen hub genes in these hub modules were identified through PPT networks. Based on TCGA data, 4 of them (CCNB1, TTK, CNN1, and ACTG2) were correlated with prognosis. The protein levels of CCNB1, TTK, and ACTG2 had a degree of differences between tumor tissues and normal tissues.

CONCLUSION

Four hub genes were identified as candidate biomarkers and potential therapeutic targets for further studies of exploring molecular mechanisms and individual therapy on PCa.

Loss of alanine-glyoxylate and serine-pyruvate aminotransferase expression accelerated the progression of hepatocellular carcinoma and predicted poor prognosis

BACKGROUND

Accumulated studies reported abnormal gene expression profiles of hepatocellular carcinoma (HCC) by cDNA microarray. We tried to merge cDNA microarray data from different studies to search for stably changed genes, and to find out better diagnostic and prognostic markers for HCC.

METHODS

A systematic review was performed by searching publications indexed in Pubmed from March 1, 2001 to July 1, 2016. Studies that reporting cDNA microarray profiles in HCC, containing both tumor and nontumor data and published in English-language were retrieved. The differentially expressed genes from eligible studies were summarized and ranked according to the frequency. High frequency genes were subjected to survival analyses. The expression and prognostic value of alanine-glyoxylate and serine-pyruvate aminotransferase (AGXT) was further evaluated in HCC datasets in Oncomine and an independent HCC tissue array cohort. The role of AGXT in HCC progression was evaluated by proliferation and migration assays in a human HCC cell line.

RESULTS

A total of 43 eligible studies that containing 1917 HCC patients were included, a list of 2022 non redundant abnormally expressed genes in HCC were extracted. The frequencies of reported genes were ranked. We finally obtained a list of only five genes (AGXT; ALDOB; CYP2E1; IGFBP3; TOP2A) that were differentially expressed in tumor and nontumor tissues across studies and were significantly correlated to HCC prognosis. Only AGXT had not been reported in HCC. Reduced expression of AGXT reflected poor differentiation of HCC and predicts poor survival. Knocking down of AGXT enhanced cell proliferation and migration of HCC cell line.

CONCLUSIONS

The present study supported the feasibility and necessity of systematic review on discovering new and reliable biomarkers for HCC. We also identified a list of high frequency prognostic genes and emphasized a critical role of AGXT deletion during HCC progression.

Feature selection with the Fisher score followed by the Maximal Clique Centrality algorithm can accurately identify the hub genes of hepatocellular carcinoma

This study aimed to select the feature genes of hepatocellular carcinoma (HCC) with the Fisher score algorithm and to identify hub genes with the Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was performed to examine the enrichment of terms. Gene set enrichment analysis (GSEA) was used to identify the classes of genes that are overrepresented. Following the construction of a protein-protein interaction network with the feature genes, hub genes were identified with the MCC algorithm. The Kaplan–Meier plotter was utilized to assess the prognosis of patients based on expression of the hub genes. The feature genes were closely associated with cancer and the cell cycle, as revealed by GO, KEGG and GSEA enrichment analyses. Survival analysis showed that the overexpression of the Fisher score–selected hub genes was associated with decreased survival time (P < 0.05). Weighted gene co-expression network analysis (WGCNA), Lasso, ReliefF and random forest were used for comparison with the Fisher score algorithm. The comparison among these approaches showed that the Fisher score algorithm is superior to the Lasso and ReliefF algorithms in terms of hub gene identification and has similar performance to the WGCNA and random forest algorithms. Our results demonstrated that the Fisher score followed by the application of the MCC algorithm can accurately identify hub genes in HCC.

Identification of potential hub genes related to the progression and prognosis of hepatocellular carcinoma through integrated bioinformatics analysis

Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related deaths among cancer patients. Genes correlated with the progression and prognosis of HCC are critically needed to be identified. In the present study, 3 Gene Expression Omnibus (GEO) datasets (GSE46408, GSE65372 and GSE84402) were used to analyze the differentially expressed genes (DEGs) between HCC and non-tumor liver tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to clarify the functional roles of DEGs. A protein-protein interaction network was established to screen the hub genes associated with HCC. The prognostic values of hub genes in HCC patients were analyzed using The Cancer Genome Atlas (TCGA) database. The expression levels of hub genes were validated based on ONCOMINE, TCGA and Human Protein Atlas (HPA) databases. Notably, 56 upregulated and 33 downregulated DEGs were markedly enriched under various GO terms and four KEGG terms. Among these DEGs, 10 hub genes with high connectivity degree were identified, including cyclin B1, cyclin A2, cyclin B2, condensin complex subunit 3, PDZ binding kinase, nucleolar and spindle-associated protein 1, aurora kinase A, ZW10 interacting kinetochore protein, protein regulator of cytokinesis 1 and kinesin family member 4A. The upregulated expression levels of these hub genes in HCC tissues were further confirmed by ONCOMINE, TCGA, and HPA databases. Additionally, the increased mRNA expression of each hub gene was related to the unfavorable disease-free survival and overall survival of HCC patients. The present study identified ten genes associated with HCC, which may help to provide candidate targets for the diagnosis and treatment of HCC.

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.

Based on Integrated Bioinformatics Analysis Identification of Biomarkers in Hepatocellular Carcinoma Patients from Different Regions

Accumulating statistics have shown that liver cancer causes the second highest mortality rate of cancer-related deaths worldwide, of which 80% is hepatocellular carcinoma (HCC). Given the underlying molecular mechanism of HCC pathology is not fully understood yet, identification of reliable predictive biomarkers is more applicable to improve patients' outcomes. The results of principal component analysis (PCA) showed that the grouped data from 1557 samples in Gene Expression Omnibus (GEO) came from different populations, and the mean tumor purity of tumor tissues was 0.765 through the estimate package in R software. After integrating the differentially expressed genes (DEGs), we finally got 266 genes. Then, the protein-protein interaction (PPI) network was established based on these DEGs, which contained 240 nodes and 1747 edges. FOXM1 was the core gene in module 1 and highly associated with FOXM1 transcription factor network pathway, while FTCD was the core gene in module 2 and was enriched in the metabolism of amino acids and derivatives. The expression levels of hub genes were in line with The Cancer Genome Atlas (TCGA) database. Meanwhile, there were certain correlations among the top ten genes in the up- and downregulated DEGs. Finally, Kaplan–Meier curves and receiver operating characteristic (ROC) curves were plotted for the top five genes in PPI. Apart from CDKN3, the others were closely concerned with overall survival. In this study, we detected the potential biomarkers and their involved biological processes, which would provide a new train of thought for clinical diagnosis and treatment.

Downregulation of exosomal CLEC3B in hepatocellular carcinoma promotes metastasis and angiogenesis via AMPK and VEGF signals

Background

C-Type Lectin Domain Family 3 Member B (CLEC3B), is down-regulated in serum and tumor tissues in different cancers including hepatocellular carcinoma (HCC). However, the functions of CLEC3B in HCC remains elucidated. The aim of this study is to analyze the roles of CLEC3B in HCC.

Methods

The expression of genes was evaluated by immunohistochemistry, western blot, real-time PCR, enzyme-linked immunosorbent assays, and analysis on TCGA-LIHC database and gene expression omnibus. Transmission electron microscopy and immunofluorescence were applied to detect CLEC3B in exosomes. The function of exosomal CLEC3B in tumor progression were performed in vivo and in vitro.

Results

We determined that down-regulated CLEC3B in HCC indicated a poor prognosis. Exosomes derived from HCC with down-regulated CLEC3B promoted migration, invasion, epithelial–mesenchymal transition of both tumor cells and endothelial cells (ECs). Moreover, the downregulation CLEC3B in exosomes suppressed VEGF secretion in both HCC cells and ECs, and eventually inhibited angiogenesis. Mechanistically, CLEC3B-mediated VEGF expression in tumor cells and ECs depends on the activation of AMPK signal pathway.

Conclusion

This study demonstrates that CLEC3B acts as a novel independent prognostic factor, and CLEC3B in exosomes might be a potential therapeutic target for hepatocellular carcinoma.

Graphical abstract

Electronic supplementary material

The online version of this article (10.1186/s12964-019-0423-6) contains supplementary material, which is available to authorized users.

SUCO as a Promising Diagnostic Biomarker of Hepatocellular Carcinoma: Integrated Analysis and Experimental Validation

Background

Hepatocellular carcinoma (HCC) is not frequently diagnosed until the late stage due to its concealed symptoms. Therefore, the identification of biomarkers that have effective diagnostic performance and act as potential key therapeutic targets for HCC becomes urgent.

Material/Methods

Comprehensive analysis of accumulated data downloaded from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases was used to obtain more reliable potential diagnostic biomarkers of HCC and to explore related molecular mechanisms. Meta-analysis and summary receiver operating characteristic (SROC) curve analysis were performed to evaluate the differential expression of SUCO gene in HCC and identify the capability of SUCO in distinguishing HCC-tissues from normal liver-tissues.

Results

SUCO was found to be upregulated in HCC-tissues and exhibited a favorable value in diagnosing HCC. Bioinformatics analysis showed that SUCO might play important roles in HCC progression, and was significantly related to cell cycle, cell metabolism, and proliferation.

Conclusions

This study was the first to demonstrate that SUCO was overexpressed in HCC-tissues, and that high expression of SUCO was significantly related to poor overall survival in HCC patients. SUCO might be a potential diagnostic biomarker for HCC patients, which promotes the tumorigenesis and progression of HCC.

miR-1249-3p accelerates the malignancy phenotype of hepatocellular carcinoma by directly targeting HNRNPK

Background

microRNAs (miRNAs) have been implicated to play crucial roles in carcinogenesis. miR‐1249‐3p was reported to be abnormally expressed in multiple human cancers. However, its biological role and the associated underlying mechanisms in hepatocellular carcinoma (HCC) remain largely unknown.

Methods

miR‐1249‐3p expression level in HCC cell lines and normal cell line was measured by quantitative real‐time PCR. Role of miR‐1249‐3p on HCC cell proliferation, colony formation, and invasion was examined by cell counting kit‐8 assay, colony formation assay, and transwell invasion assay, respectively. Luciferase activity reporter assay and western blot were performed to validate whether heterogeneous nuclear ribonucleoprotein K (HNRNPK) was a direct target of miR‐1249‐3p. Effect of miR‐1249‐3p on overall survival of HCC patients was analyzed at KM Plotter website.

Results

We found miR‐1249‐3p expression level was increased, while HNRNPK expression level was decreased in HCC cell lines compared with normal cell line. Knockdown miR‐1249‐3p expression inhibits HCC cell proliferation, colony formation, and cell invasion through regulating HNRNPK in vitro. We also showed high miR‐1249‐3p expression was a predictor for poor overall survival of HCC patients.

Conclusions

These findings about miR‐1249‐3p/HNRNPK pair provide a novel therapeutic method for HCC patients.

Analysis of potential key genes in very early hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the major pathological type of primary liver cancer, one of the leading causes of cancer death worldwide. In addition, the long-term survival rates of HCC still remain low. Therefore, we attempted to identify the potential key genes in the occurrence of HCC by comparing the expression profiles of very early HCC tissue samples with that of chronic cirrhotic tissue samples by integrating the bioinformatics analysis in this study.

Methods

Gene expression profiles of 19 very early HCC and 19 cirrhotic tissue samples were selected from GSE63898. Differentially expressed genes (DEGs) were also identified by using online tool GEO2R. Furthermore, the GO and KEGG enrichment analysis of the DGEs were conducted on DAVID datasets. Then a protein–protein interaction (PPI) network was constructed and the modules were analyzed based on STRING database and Cytoscape software. The hub genes were screened by applying the cytoHubba plugin and then analyzed with the Kaplan Meier plotter.

Results

A total of 118 DEGs were identified between very early HCC and cirrhotic tissue samples. These DGEs were strongly associated with several biological processes, such as negative regulation of growth and p53 signaling pathway. A PPI network was constructed and top eight hub genes, including CDKN3, CDK1, CCNB1, TOP2A, CCNA2, CCNB2, PRC1, and RRM2, were determined. High expressions of CDK1, CCNB1, TOP2A, CCNA2, PRC1, RRM2, CDKN3, and CCNB2 were associated with poorer overall survivals (OS) in HCC patients.

Conclusion

We had compared the expression profiles between the very early HCC and cirrhotic tissue samples by using bioinformatics analysis tools, which might help us better to understand the molecular mechanism of the initiation of HCC and even to find novel targets for HCC therapy.

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.

Identification of Potential Key Genes for Hepatitis B Virus-Associated Hepatocellular Carcinoma by Bioinformatics Analysis

Hepatitis B virus-associated (HBV(+)) hepatocellular carcinoma (HCC) accounts for a large proportion of liver cancer with poor clinical outcomes and treatment options. However, the underlying molecular mechanisms are still poorly understood. To explore potential key genes in the development of HBV(+)HCC, four series of data (GSE14520, GSE94660, GSE25599, and GSE55092) derived from Gene Expression Omnibus database were analyzed. Totally, 84 upregulated and 46 downregulated common differentially expressed genes (DEGs) were discovered. Gene ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses showed that these DEGs were mainly enriched in cell division and DNA replication biological processes, nucleoplasm and microtubule cellular components, protein-binding molecular functions, and cell cycle and DNA replication pathways. Through protein-protein interaction analysis, 10 hub DEGs with the highest degree of connectivity were indicated, including TOP2A, CDC20, MAD2L1, BUB1B, RFC4, CCNB1, CDKN3, CCNB2, TPX2, and FEN1. Kaplan-Meier analysis revealed that high expression of TOP2A and CDC20 was associated with poor overall survival, relapse-free survival, and high serum alpha-fetoprotein level in HBV(+)HCC. In conclusion, TOP2A and CDC20 were two potential key genes for HBV(+)HCC. Their value in the diagnosis and treatment of HBV(+)HCC requires further investigation.

MicroRNA-144 inhibits cell proliferation, migration and invasion in human hepatocellular carcinoma by targeting CCNB1

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies with a high morbidity and mortality worldwide. MicroRNAs are key regulators of HCC genesis. However, the regulatory role and underlying mechanisms of microRNA in HCC is still limited.

Methods

Cyclin B1 (CCNB1) mRNA levels were examined in non-tumor and liver cancer of The Cancer Genome Atlas (TCGA) cohort. CCNB1 was knockdown to evaluate the HCC cell proliferation, migration and invasion. MicroRNA-144 targeting CCNB1 was identified with TargetScan analysis and confirmed with reporter assay. Overexpression of MicroRNA-144 was achieved using microRNA mimics and function of microRNA-144 was tested in vitro HCC cell line proliferation and in vivo tumor formation experiments.

Results

Here, we found that the high level expression of CCNB1 was closely associated with poor prognosis in HCC patients. Knockdown of CCNB1 by RNA interference significantly inhibited cell proliferation, migration and invasion in HCC. Furthermore, we found that miR-144 directly targeted CCNB1 and inhibited CCNB1 expression. Moreover, in vivo experiments of subcutaneous tumor formation further demonstrated that miR-144 delayed tumor formation by negative regulation of CCNB1.

Conclusion

Therefore, we conclude that microRNA-144/CCNB1 axis plays an important role in human HCC. Therapies targeting microRNA-144 could potentially improve HCC treatment.

Electronic supplementary material

The online version of this article (10.1186/s12935-019-0729-x) contains supplementary material, which is available to authorized users.

Comprehensive analysis of potential prognostic genes for the construction of a competing endogenous RNA regulatory network in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an extremely common malignant tumor with worldwide prevalence. The aim of this study was to identify potential prognostic genes and construct a competing endogenous RNA (ceRNA) regulatory network to explore the mechanisms underlying the development of HCC.

METHODS

Integrated analysis was used to identify potential prognostic genes in HCC with R software based on the GSE14520, GSE17548, GSE19665, GSE29721, GSE60502, and the Cancer Genome Atlas databases. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enrichment analyses were performed to explore the molecular mechanisms of potential prognostic genes. Differentially expressed miRNAs (DEMs) and lncRNAs (DELs) were screened based on the Cancer Genome Atlas database. An lncRNA-miRNA-mRNA ceRNA regulatory network was constructed based on information about interactions derived from the miRcode, TargetScan, miRTarBase, and miRDB databases.

RESULTS

A total of 152 potential prognostic genes were screened that were differentially expressed in HCC tissue and significantly associated with overall survival of HCC patients. There were 13 key potential prognostic genes in the ceRNA regulatory network: eleven upregulated genes (CCNB1, CEP55, CHEK1, EZH2, KPNA2, LRRC1, PBK, RRM2, SLC7A11, SUCO, and ZWINT) and two downregulated genes (ACSL1 and CDC37L1) whose expression might be regulated by eight DEMs and 61 DELs. Kaplan-Meier curve analysis showed that nine DELs (AL163952.1, AL359878.1, AP002478.1, C2orf48, C10orf91, CLLU1, CLRN1-AS1, ERVMER61-1, and WARS2-IT1) in the ceRNA regulatory network were significantly associated with HCC-patient prognoses.

CONCLUSION

This study identified potential prognostic genes and constructed an lncRNA- miRNA-mRNA ceRNA regulatory network of HCC, which not only has important clinical significance for early diagnoses but also provides effective targets for HCC treatments and could provide new insights for HCC-interventional strategies.

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

BACKGROUND/AIMS

The biological heterogeneity of hepatocellular carcinoma (HCC) makes prognosis difficult. Although many molecular tools have been developed to assist in stratification and prediction of patients by using microarray analysis, the classification and prediction are still improvable because the high-through microarray contains a large amount of information. Meanwhile, gene expression patterns and their prognostic value for HCC have not been systematically investigated. In order to explore new molecular diagnostic and prognostic biomarkers, the gene expression profiles between HCCs and adjacent nontumor tissues were systematically analyzed in the present study.

MATERIALS AND METHODS

In this study, gene expression profiles were obtained by repurposing five Gene Expression Omnibus databases. Differentially expressed genes were identified by using robust rank aggregation method. Three datasets (GSE14520, GSE36376, and GSE54236) were used to validate the associations between cytochrome P450 (CYP) family genes and HCC. GSE14520 was used as the training set. GSE36376 and GSE54236 were considered as the testing sets.

RESULTS

From the training set, a four-CYP gene signature was constructed to discriminate between HCC and nontumor tissues with an area under curve (AUC) of 0.991. Accuracy of this four-gene signature was validated in two testing sets (AUCs for them were 0.973 and 0.852, respectively). Moreover, this gene signature had a good performance to make a distinction between fast-growing HCC and slow-growing HCC (AUC = 0.898), especially for its high sensitivity of 95%. At last, CYP2C8 was identified as an independent risk factor of recurrence-free survival (hazard ratio [HR] =0.865, 95% confidence interval [CI], 0.754-0.992, P = 0.038) and overall survival (HR = 0.849; 95% CI, 0.716-0.995, P = 0.033).

CONCLUSIONS

In summary, our results confirmed for the first time that a four-CYP gene (CYP1A2, CYP2E1, CYP2A7, and PTGIS) signature is associated with fast-growing HCC, and CYP2C8 is associated with patient survival. Our findings could help to identify HCC patients at high risk of rapid growth and recurrence.

Cyclin B2 Overexpression in Human Hepatocellular Carcinoma is Associated with Poor Prognosis

BACKGROUND AND AIMS

Cyclin B2 (CCNB2) has been reported to be highly expressed in a few malignancies. However, the biological function of CCNB2 in hepatocellular carcinoma (HCC) is largely unknown. We aimed to investigate the effect of CCNB2 in HCC.

METHODS

The expression of CCNB2 in HCC and normal liver tissues and connection of its expression with prognosis and clinical parameters were studied. The effect of knocking down CCNB2 on cell proliferation, migration, cell cycle distribution, and apoptosis were estimated in BEL-7404 cells.

RESULTS

Compared to normal liver tissues, the level of CCNB2 was higher in HCC tissues from the Gene Expression Profiling Interactive Analysis (GEPIA). The 5 year overall survival and disease-free survival of HCC patients with high CCNB2 levels were shorter than that of those with low CCNB2 levels. Immunohistochemistry analysis also discovered the expression differences of CCNB2 in HCC and normal liver tissues and showed that CCNB2 expression was significantly associated with tumor number, tumor size, tumor thrombus, and alanine aminotransferase level. CCNB2 expression was higher in HCC cell lines (BEL-7404, Hep3B, BEL-7402, and SMMC-7721) than that in the normal hepatic cell line (HL-7702). Knockdown of CCNB2 inhibited cell proliferation and migration, promoted cell apoptosis, and caused S phase arrest in BEL-7404 cells. Finally, CCNB2 was associated with Polo Like Kinase 1 (PLK1) in the GEPIA database and BEL-7404 cells.

CONCLUSIONS

CCNB2 may serve as a prognostic factor and participated in the development and progression and promote cell proliferation and migration through CCNB2/PLK1 pathway in HCC.

Large-scale analysis reveals a novel risk score to predict overall survival in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a major cause of cancer mortality and an increasing incidence worldwide; however, there are very few effective diagnostic approaches and prognostic biomarkers.

Materials and methods

One hundred forty-nine pairs of HCC samples from Gene Expression Omnibus (GEO) were obtained to screen differentially expressed genes (DEGs) between HCC and normal samples. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, Gene ontology enrichment analyses, and protein–protein interaction network were used. Cox proportional hazards regression analysis was used to identify significant prognostic DEGs, with which a gene expression signature prognostic prediction model was identified in The Cancer Genome Atlas (TCGA) project discovery cohort. The robustness of this panel was assessed in the GSE14520 cohort. We verified details of the gene expression level of the key molecules through TCGA, GEO, and qPCR and used immunohistochemistry for substantiation in HCC tissues. The methylation states of these genes were also explored.

Results

Ninety-eight genes, consisting of 13 upregulated and 85 downregulated genes, were screened out in three datasets. KEGG and Gene ontology analysis for the DEGs revealed important biological features of each subtype. Protein–protein interaction network analysis was constructed, consisting of 64 nodes and 115 edges. A subset of four genes (SPINK1, TXNRD1, LCAT, and PZP) that formed a prognostic gene expression signature was established from TCGA and validated in GSE14520. Next, the expression details of the four genes were validated with TCGA, GEO, and clinical samples. The expression panels of the four genes were closely related to methylation states.

Conclusion

This study identified a novel four-gene signature biomarker for predicting the prognosis of HCC. The biomarkers may also reveal molecular mechanisms underlying development of the disease and provide new insights into interventional strategies.