Screening Hub Genes as Prognostic Biomarkers of Hepatocellular Carcinoma by Bioinformatics Analysis

Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Background

Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.

Methods

Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.

Results

A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.

Conclusion

This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.

Construction of Novel lncRNA-miRNA-mRNA Network Associated With Recurrence and Identification of Immune-Related Potential Regulatory Axis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignant diseases globally. Despite continuous improvement of treatment methods, high postoperative recurrence rate remains an urgent problem. In order to determine the mechanism underlying recurrence of liver cancer and identify prognostic genes, data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were integrated and analyzed. Differentially expressed genes (DEGs) between HCC tissue and normal liver tissue were identified, and a protein–protein interaction network was constructed to find hub genes. Clinical correlation analysis and disease-free survival (DFS) analysis were performed using the R language and GEPIA to identify relapse-related genes. Correlation analysis was used to identify a potential regulatory axis. Dual-luciferase reporter gene assay was used to confirm the reliability of the long non-coding RNA (lncRNA)–microRNA (miRNA)–mRNA regulatory axis. Immune infiltration analysis was performed using the TIMER database. Correlations between immune gene markers and ASF1B were verified using quantitative real-time polymerase chain reaction (RT-qPCR). In this work, we found that nine lncRNAs and five mRNAs were significantly overexpressed in HCC tissues from patients with recurrence. SNHG3, LINC00205, ASF1B, AURKB, CCNB1, CDKN3, and DTL were also closely related to HCC grade and stage. Survival analysis showed that these seven DEGs were significantly correlated with poor DFS. Correlation analysis identified SNHG3–miR-214-3p–ASF1B as a potential regulatory axis. Dual-luciferase reporter gene assay showed that SNHG3 and ASF1B directly bound to miR-214-3p. ASF1B was negatively regulated by miRNA-214-3p, and overexpression of SNHG3 could inhibit the expression of miRNA-214-3p. In addition, ASF1B was positively correlated with immune infiltration. A reduction in ASF1B could markedly inhibit the expression of CD86, CD8, STAT1, STAT4, CD68, and PD1 in HCC cells. Flow cytometry showed that SNHG3 promoted the PD-1 expression by regulating ASF1B. Meanwhile, elevated ASF1B predicted poor prognosis of HCC patients in subgroups with decreased B cells, CD8+ T cells, or neutrophils, and those with enriched CD4+ T cells. In conclusion, we found that a novel lncRNA SNHG3/miR-214-3p/ASF1B axis could promote the recurrence of HCC by regulating immune infiltration.

Identification of Multiple Hub Genes and Pathways in Hepatocellular Carcinoma: A Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor of the digestive system, and its early asymptomatic characteristic increases the difficulty of diagnosis and treatment. This study is aimed at obtaining some novel biomarkers with diagnostic and prognostic meaning and may find out potential therapeutic targets for HCC. We screen differentially expressed genes (DEGs) from the HCC gene expression profile GSE14520 using GEO2R. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted by using the clusterProfiler software while a protein-protein interaction (PPI) network was performed based on the STRING database. Then, prognosis analysis of hub genes was conducted using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was utilized to further verify the expression of hub genes and explore the correlation between gene expression and clinicopathological parameters. A total of 1053 DEGs were captured, containing 497 upregulated genes and 556 downregulated genes. GO and KEGG analysis indicated that the downregulated DEGs were mainly enriched in the fatty acid catabolic process while upregulated DEGs were primarily enriched in the cell cycle. Simultaneously, ten hub genes (CYP3A4, UGT1A6, AOX1, UGT1A4, UGT2B15, CDK1, CCNB1, MAD2L1, CCNB2, and CDC20) were identified by the PPI network. Five prognosis-related hub genes (CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20) were uncovered by the survival analysis based on TCGA database. The ten hub genes were further validated by qRT-PCR using samples obtained from our hospital. The prognosis-related hub genes such as CYP3A4, CDK1, CCNB1, MAD2L1, and CDC20 could be considered potential diagnosis biomarkers and prognosis targets for HCC. We also use Oncomine for further verification, and we found CCNB1, CCNB2, CDK1, and CYP3A4 which were highly expressed in HCC. Meanwhile, CCNB1, CCNB2, and CDK1 are highly expressed in almost all cancer types, which may play an important role in cancer. Still, further functional study should be conducted to explore the underlying mechanism and biological effect in the near future.

Integrative analysis identifies key mRNA biomarkers for diagnosis, prognosis, and therapeutic targets of HCV-associated hepatocellular carcinoma

Hepatitis C virus-associated HCC (HCV-HCC) is a prevalent malignancy worldwide and the molecular mechanisms are still elusive. Here, we screened 240 differentially expressed genes (DEGs) of HCV-HCC from Gene expression omnibus (GEO) and the Cancer Genome Atlas (TCGA), followed by weighted gene coexpression network analysis (WGCNA) to identify the most significant module correlated with the overall survival. 10 hub genes (CCNB1, AURKA, TOP2A, NEK2, CENPF, NUF2, CDKN3, PRC1, ASPM, RACGAP1) were identified by four approaches (Protein-protein interaction networks of the DEGs and of the significant module by WGCNA, and diagnostic and prognostic values), and their abnormal expressions, diagnostic values, and prognostic values were successfully verified. A four hub gene-based prognostic signature was built using the least absolute shrinkage and selection operator (LASSO) algorithm and a multivariate Cox regression model with the ICGC-LIRI-JP cohort (N =112). Kaplan-Meier survival plots (P = 0.0003) and Receiver Operating Characteristic curves (ROC = 0.778) demonstrated the excellent predictive potential for the prognosis of HCV-HCC. Additionally, upstream regulators including transcription factors and miRNAs of hub genes were predicted, and candidate drugs or herbs were identified. These findings provide a firm basis for the exploration of the molecular mechanism and further clinical biomarkers development of HCV-HCC.

Identification of Hub Genes and Their Correlation With Immune Infiltration Cells in Hepatocellular Carcinoma Based on GEO and TCGA Databases

Hepatocellular carcinoma (HCC) is a primary liver cancer with extremely high mortality in worldwide. HCC is hard to diagnose and has a poor prognosis due to the less understanding of the molecular pathological mechanisms and the regulation mechanism on immune cell infiltration during hepatocarcinogenesis. Herein, by performing multiple bioinformatics analysis methods, including the RobustRankAggreg (RRA) rank analysis, weighted gene co-expression network analysis (WGCNA), and a devolution algorithm (CIBERSORT), we first identified 14 hub genes (NDC80, DLGAP5, BUB1B, KIF20A, KIF2C, KIF11, NCAPG, NUSAP1, PBK, ASPM, FOXM1, TPX2, UBE2C, and PRC1) in HCC, whose expression levels were significantly up-regulated and negatively correlated with overall survival time. Moreover, we found that the expression of these hub genes was significantly positively correlated with immune infiltration cells, including regulatory T cells (Treg), T follicular helper (TFH) cells, macrophages M0, but negatively correlated with immune infiltration cells including monocytes. Among these hub genes, KIF2C and UBE2C showed the most significant correlation and were associated with immune cell infiltration in HCC, which was speculated as the potential prognostic biomarker for guiding immunotherapy.

Development and Validation of a Metabolic-related Prognostic Model for Hepatocellular Carcinoma

BACKGROUND AND AIMS

Growing evidence suggests that metabolic-related genes have a significant impact on the occurrence and development of hepatocellular carcinoma (HCC). However, the prognostic value of metabolic-related genes for HCC has not been fully revealed.

METHODS

mRNA sequencing and clinical data were obtained from The Cancer Genome Atlas and the GTEx Genotype-Tissue Expression comprehensive database. Differentially expressed metabolic-related genes in tumor tissues (n=374) and normal tissues (n=160) were identified by the Wilcoxon test. Time-dependent receiver operating characteristic curve analysis, univariate multivariate Cox regression analysis and Kaplan-Meier survival analysis were used to evaluate the predictive effectiveness and independence of the prognostic model. Two independent cohorts (International Cancer Genome Consortiums and GSE14520) were applied to verify the prognostic model.

RESULTS

Our study included a total of 793 patients with HCC. We constructed a risk score consisting of five metabolic-genes (BDH1, RRM2, CYP2C9, PLA2G7, and TXNRD1). For the overall survival rate, the low-risk group had a considerably higher rate than the high-risk group. Univariate and multivariate Cox regression analyses indicated that the risk score was an independent predictor for the prognosis of HCC.

CONCLUSIONS

We constructed and validated a novel prognostic model, which may provide support for the precise treatment of HCC.

Prognostic and clinicopathological value of BUB1B expression in patients with lung adenocarcinoma: a meta-analysis

BACKGROUND

Abnormal BUB1B expression has been proven to be related to the poor prognosis of various tumors. This meta-analysis aimed to identify the prognostic role of BUB1B in patients with lung adenocarcinoma (LUAD).

RESEARCH DESIGN AND METHODS

Relevant studies from the PubMed, Embase, Web of Science, and Cochrane Library databases and two public databases that stored sequencing data were retrieved. The standardized mean difference (SMD) and 95% confidence intervals (CIs) for the association between the BUB1B expression level and clinical characteristics were calculated. Pooled hazard ratios (HRs) and 95% CIs were calculated to estimate the association between BUB1B expression and survival outcomes.

RESULTS

A total of 16 studies involving 2771 LUAD patients with BUB1B expression were included in this meta-analysis. Patients with older age showed low BUB1B expression. High BUB1B expression was associated with male sex, a smoking history, and an advanced TNM stage. High BUB1B expression was predictive of poor overall survival (OS) and progression-free survival (PFS). In addition, no publication bias was found.

CONCLUSIONS

This meta-analysis demonstrates that BUB1B is a significant biomarker for a poor prognosis and poor clinicopathological outcomes in patients with LUAD.

Bioinformatics analysis of lncRNA‑associated ceRNA network in melanoma

Melanoma is an extremely malignant tumor with early metastasis and high mortality. Little is known about the process of by which melanoma occurs, as its mechanism is very complex and only limited data are available on its long non-coding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs). The purpose of this study was to screen out potential prognostic molecules and identify a ceRNA network related to the occurrence of melanoma. We screened 169 differentially expressed mRNAs (DEmRNAs) from E-MTAB-1862 and GSE3189; gene ontology (GO) enrichment analysis showed that these genes were closely related to the development of skin. In the protein-protein interaction network, we screened out a total of 19 hub genes. Furthermore, we predicted the microRNAs (miRNAs) that regulate hub genes using the miRWalk database and then intersected these with GSE35579, resulting in nine DEmiRNAs. We also predicted the lncRNAs that regulate the miRNAs using the LncBasev.2 database. According to the ceRNA hypothesis, and based on the intersection of the DElncRNAs with merged GTEx and TCGA data, we obtained 20 DElncRNAs. A total of four DEmRNAs, nine DEmiRNAs, and 20 DElncRNAs were included in the ceRNA network. Based on Cox stepwise regression and survival analysis, we identified five biomarkers, ZSCAN16-AS1, LINC00520, XIST, DTL, and let-7a-5p, and obtained risk scores. The results showed that most of the differentially expressed genes were related to epithelial-mesenchymal transition (EMT) in melanoma. Finally, we obtained a LINC00520/let-7a-5p/DTL molecular regulatory network. These results suggest that ceRNA networks have an important role in evaluating the prognosis of patients with melanoma and provide a new experimental basis for exploring the EMT process in the development of melanoma.

Identification of Core Genes Related to Progression and Prognosis of Hepatocellular Carcinoma and Small-Molecule Drug Predication

Background

Hepatocellular carcinoma (HCC) is one of the most leading causes of cancer death with a poor prognosis. However, the underlying molecular mechanisms are largely unclear, and effective treatment for it is limited. Using an integrated bioinformatics method, the present study aimed to identify the key candidate prognostic genes that are involved in HCC development and identify small-molecule drugs with treatment potential.

Methods and Results

In this study, by using three expression profile datasets from Gene Expression Omnibus database, 1,704 differentially expressed genes were identified, including 671 upregulated and 1,033 downregulated genes. Then, weighted co-expression network analysis revealed nine modules are related with pathological stage; turquoise module was the most associated module. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway analyses (KEGG) indicated that these genes were enriched in cell division, cell cycle, and metabolic related pathways. Furthermore, by analyzing the turquoise module, 22 genes were identified as hub genes. Based on HCC data from gene expression profiling interactive analysis (GEPIA) database, nine genes associated with progression and prognosis of HCC were screened, including ANLN, BIRC5, BUB1B, CDC20, CDCA5, CDK1, NCAPG, NEK2, and TOP2A. According to the Human Protein Atlas and the Oncomine database, these genes were highly upregulated in HCC tumor samples. Moreover, multivariate Cox regression analysis showed that the risk score based on the gene expression signature of these nine genes was an independent prognostic factor for overall survival and disease-free survival in HCC patients. In addition, the candidate small-molecule drugs for HCC were identified by the CMap database.

Conclusion

In conclusion, the nine key gene signatures related to HCC progression and prognosis were identified and validated. The cell cycle pathway was the core pathway enriched with these key genes. Moreover, several candidate molecule drugs were identified, providing insights into novel therapeutic approaches for HCC.

Development of a prognostic index based on immunogenomic landscape analysis in glioma

Background

Glioma is the most common intracranial tumor. The inflammatory response actively participates in the malignancy of gliomas. There is still limited knowledge about the biological function of immune‐related genes (IRGs) and their potential involvement in the malignancy of gliomas.

Methods

We screened differentially expressed and survival‐associated IRGs, and explored their potential molecular characteristics. Then we developed a prognostic index derived from seven hub IRGs. A prognostic nomogram was built to indicate the prognostic value of the prognostic index and seven IRGs. We characterized the immune infiltration landscape to analyze tumor‐immune interactions. The real‐time quantitative polymerase chain reaction assay was performed to validate bioinformatics results.

Results

The differentially expressed IRGs are involved in cell chemotaxis, cytokine activity, and the chemokine‐mediated signaling pathway. The prognostic index derived from seven IRGs had clinical prognostic value in glioma, and positively correlated with the malignant clinicopathological characteristics. A nomogram further indicated that the prognostic index and seven hub IRGs had clinical prognostic value for gliomas. We revealed that the prognostic index could reflect the state of the glioma immune microenvironment.

Conclusion

This study demonstrates the importance of an IRG‐based prognostic index as a potential biomarker for predicting malignancy in gliomas.

Identification of Hub Genes Associated With Immune Infiltration and Predict Prognosis in Hepatocellular Carcinoma via Bioinformatics Approaches

Aims

In the cancer-related research field, there is currently a major need for a greater number of valuable biomarkers to predict the prognosis of hepatocellular carcinoma (HCC). In this study, we aimed to screen hub genes related to immune cell infiltration and explore their prognostic value for HCC.

Methods

We analyzed five datasets (GSE46408, GSE57957, GSE74656, GSE76427, and GSE87630) from the Gene Expression Omnibus database to screen the differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes; then, the hub genes were identified. Functional enrichment of the genes was performed on the Metascape website. Next, the expression of these hub genes was validated in several databases, including Oncomine, Gene Expression Profiling Interactive Analysis 2 (GEPIA2), and Human Protein Atlas. We explored the correlations between the hub genes and infiltrated immune cells in the TIMER2.0 database. The survival curves were generated in GEPIA2, and the univariate and multivariate Cox regression analyses were performed using TIMER2.0.

Results

The top ten hub genes [DNA topoisomerase II alpha (TOP2A), cyclin B2 (CCNB2), protein regulator of cytokinesis 1 (PRC1), Rac GTPase-activating protein 1 (RACGAP1), aurora kinase A (AURKA), cyclin-dependent kinase inhibitor 3 (CDKN3), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle-associated 5 (CDCA5), abnormal spindle microtubule assembly (ASPM), and non-SMC condensin I complex subunit G (NCAPG)] were identified in subsequent analysis. These genes are most markedly enriched in cell division, suggesting their close association with tumorigenesis. Multi-database analyses validated that the hub genes were upregulated in HCC tissues. All hub genes positively correlated with several types of immune infiltration, including B cells, CD4⁺ T cells, macrophages, and dendritic cells. Furthermore, these hub genes served as independent prognostic factors, and the expression of these hub genes combing with the macrophage levels could help predict an unfavorable prognosis of HCC.

Conclusion

In sum, these hub genes (TOP2A, CCNB2, PRC1, RACGAP1, AURKA, CDKN3, NUSAP1, CDCA5, ASPM, and NCAPG) may be pivotal markers for prognostic prediction as well as potentially work as targets for immune-based intervention strategies in HCC.

Identification of a novel circRNA, hsa_circ_0065898, that regulates tumor growth in cervical squamous cell carcinoma

Background

Circular RNAs (circRNAs) were reported to play an important role in regulating tumor pathogenesis. The molecular mechanism of circRNAs in cervical squamous cell carcinoma (CSCC) remains poorly understood. We aimed to identify the circRNAs differentially expressed, and to investigate the role of a novel circRNA, hsa_circ_0065898, in regulating proliferation, migration, and invasion in CSCC.

Methods

The online Kaplan-Meier Plotter was used to analyze the relationship between miRNA expression and overall survival. Bioinformatics tools, such as R, Cytoscape, and Perl, were used to analyze the Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI) network, and regulatory network. The expression level of hsa_circ_0065898 in CSCC cell lines was evaluated using quantitative polymerase chain reaction in vitro. The cell counting kit-8 (CCK-8) and transwell assays were used to assess cell proliferation, migration, and invasion.

Results

circRNA expression data (GSE102686) was downloaded from the Gene Expression Omnibus database, and this included data from 5 CSCC patients and 5 normal tissues. 13 differentially expressed circRNAs were identified, which included 9 upregulated circRNAs and 4 downregulated circRNAs. GO enrichment analysis showed that the target genes of miRNAs associated with hsa_circ_0065898 were enriched in ubiquitin-protein transferase activity, ubiquitin-like protein transferase activity, core promoter sequence-specific DNA binding, mRNA 3’-UTR AU-rich region binding, core promoter binding, and so on. KEGG showed that the Hippo and p53 signaling pathways played significant role in the pathway network. Hsa_circ_0065898 was significantly overexpressed in the CSCC cell lines. Hsa_circ_0065898 facilitated cell proliferation, migration, and invasion in CSCC.

Conclusions

This study identified differentially expressed circRNAs and constructed the regulatory network of hsa_circ_0065898 targeting microRNAs and mRNAs. We demonstrated that hsa_circ_0065898 promoted CSCC cell proliferation, migration, and invasion. Hence, hsa_circ_0065898 might be useful as a biomarker for CSCC diagnosis and targeted therapy.

Identification of four novel prognosis biomarkers and potential therapeutic drugs for human colorectal cancer by bioinformatics analysis

Colorectal cancer (CRC) is one of the most deadly cancers in the world with few reliable biomarkers that have been selected into clinical guidelines for prognosis of CRC patients. In this study, mRNA microarray datasets GSE113513, GSE21510, GSE44076, and GSE32323 were obtained from the Gene Expression Omnibus (GEO) and analyzed with bioinformatics to identify hub genes in CRC development. Differentially expressed genes (DEGs) were analyzed using the GEO2R tool. Gene ontology (GO) and KEGG analyses were performed through the DAVID database. STRING database and Cytoscape software were used to construct a protein-protein interaction (PPI) network and identify key modules and hub genes. Survival analyses of the DEGs were performed on GEPIA database. The Connectivity Map database was used to screen potential drugs. A total of 865 DEGs were identified, including 374 upregulated and 491 downregulated genes. These DEGs were mainly associated with metabolic pathways, pathways in cancer, cell cycle and so on. The PPI network was identified with 863 nodes and 5817 edges. Survival analysis revealed that HMMR, PAICS, ETFDH, and SCG2 were significantly associated with overall survival of CRC patients. And blebbistatin and sulconazole were identified as candidate drugs. In conclusion, our study found four hub genes involved in CRC, which may provide novel potential biomarkers for CRC prognosis, and two potential candidate drugs for CRC.

PBK/TOPK Inhibitor Suppresses the Progression of Prolactinomas

BACKGROUND

Prolactinoma is the most common type of pituitary tumors, and its resultant tumor occupying and hormone disturbance greatly damage the health of patients. In this study, we investigated a protein kinase-PDZ Binding Kinase (PBK)/T-LAK Cell-Originated Protein Kinase (TOPK) as a candidate protein regulating prolactin (PRL) secretion and tumor growth of prolactinomas.

METHODS

Downloaded prolactinoma transcriptome dataset from Gene Expression Omnibus (GEO) database, and screened differentially expressed genes (DEGs) between normal pituitary tissues and prolactinoma tissues. Then, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were performed, a protein-protein interaction (PPI) network was constructed and the hub genes were identified. After a literature search, TOPK was presumed as an candidate target regulating the prolactinoma. We found a specific inhibitor of TOPK to investigate its effects on the proliferation, migration, apoptosis and PRL secretion of pituitary tumor cells. Finally, the regulation of TOPK inhibitor on its downstream target-p38 Mitogen Activated Protein Kinase (p38 MAPK) was detected to explore the potential mechanism.

RESULTS

A total of 361 DEGs were identified, and 20 hub genes were screened out. TOPK inhibitor HI-TOPK-032 could suppress the proliferation & migration and induce apoptosis of pituitary tumor cells in vitro, and reduce PRL secretion and tumor growth in vivo. HI-TOPK-032 also inhibited the phosphorylation level of the downstream target p38 MAPK, suggesting that TOPK inhibitors regulate the development of prolactinoma by mediating p38 MAPK.

CONCLUSION

Our study of identification and functional validation of TOPK suggests that this candidate can be a promising molecular target for prolactinoma treatment.

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

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

KIF14 and KIF23 Promote Cell Proliferation and Chemoresistance in HCC Cells, and Predict Worse Prognosis of Patients with HCC

Background

Hepatocellular carcinoma (HCC) is one of the most common human malignant tumors. The prognosis of HCC patients is still unsatisfying. In this study, we performed the integrated bioinformatics analysis to identify potential biomarkers and biological pathways in HCC.

Methods

Gene expression profiles were obtained from the Gene Expression Omnibus database (GSE55048, GSE55758, and GSE56545) for the screening of the common differentially expressed genes (DEGs) between HCC tissues and matched non-tumor tissues. DEGs were subjected to Gene Ontology, KEGG pathway, and Reactome pathway analysis. The hub genes were identified by using protein–protein interaction (PPI) network analysis. The hub genes in HCC were further subjected to overall survival analysis of HCC patients. The hub genes were further validated by in vitro functional assays.

Results

A total of 544 common differentially expressed genes were screened from three datasets. Gene Ontology, KEGG and Reactome analysis results showed that DEGs are significantly associated with the biological process of cell cycle, cell division, and DNA replication. PPI network analysis identified 20 hub genes from the DEGs. These hub genes except CENPE were all significantly up-regulated in the HCC tissues when compared to non-tumor tissues. The Kaplan–Meier survival analysis results showed that the high expression of the 20 hub genes was associated with shorter survival of the HCC patients. Further validation studies showed that knockdown of KIF14 and KIF23 both suppressed the proliferative potential, increased the caspase-3/-7 activity, up-regulated Bax expression, and promoted the invasive and migratory abilities in the HCC cells. In addition, knockdown of KIF14 and KIF23 enhanced chemosensitivity to cisplatin and sorafenib in the HCC cells. Finally, the high expression of KIF14 and KIF23 was associated with shorter progression-free survival, recurrence-free survival, and disease-specific survival of patients with HCC.

Conclusion

In conclusion, the present study performed the integrated bioinformatics analysis and showed that KIF14 and KIF23 silence attenuated cell proliferation, invasion, and migration, and promoted chemosensitivity of HCC cells. KIF14 and KIF23 may serve as potential biomarkers for predicting the worse prognosis of patients with HCC.

Butyrate-containing structured lipids inhibit RAC1 and epithelial-to-mesenchymal transition markers: a chemopreventive mechanism against hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is one of the most aggressive human cancers. The rising incidence of HCC worldwide and its resistance to pharmacotherapy indicate that the prevention of HCC development may be the most impactful strategy to improve HCC-related morbidity and mortality. Among the broad range of chemopreventive agents, the use of dietary and nutritional agents is an attractive and promising approach; however, a better understanding of the mechanisms of their potential cancer suppressive action is needed to justify their use. In the present study, we investigated the underlying molecular pathways associated with the previously observed suppressive effect of butyrate-containing structured lipids (STLs) against liver carcinogenesis using a rat "resistant hepatocyte" model of hepatocarcinogenesis that resembles the development of HCC in humans. Using whole transcriptome analysis, we demonstrate that the HCC suppressive effect of butyrate-containing STLs is associated with the inhibition of the cell migration, cytoskeleton organization, and epithelial-to-mesenchymal transition (EMT), mediated by the reduced levels of RACGAP1 and RAC1 proteins. Mechanistically, the inhibition of the Racgap1 and Rac1 oncogenes is associated with cytosine DNA and histone H3K27 promoter methylation. Inhibition of the RACGAP1/RAC1 oncogenic signaling pathways and EMT may be a valuable approach for liver cancer prevention.

Oleate acid-stimulated HMMR expression by CEBPα is associated with nonalcoholic steatohepatitis and hepatocellular carcinoma

Non-alcoholic steatohepatitis (NASH) is a type of nonalcoholic fatty liver disease and has become a major risk factor for hepatocellular carcinoma (HCC). However, the underlying pathophysiological mechanisms are still elusive. Here, we identify hyaluronan-mediated motility receptor (HMMR) as a critical gene associated with NASH/HCC by combination of bioinformatic analysis and functional experiments. Analysis of differentially expressed genes (DEGs) between normal controls and NASH/HCC identified 5 hub genes (HMMR, UBE2T, TYMS, PTTG1 and GINS2). Based on the common DEGs, analyses of univariate and multivariate Cox regression and the area under the curve (AUC) value of the receiver operating characteristic (ROC) indicate that HMMR is the most significant gene associated with NASH/HCC among five hub genes. Oleate acid (OA), one of fatty acids that induce cellular adipogenesis, stimulates HMMR expression via CCAAT/enhancer-binding protein α (CEBPα). CEBPα increases the expression of HMMR through binding to its promoter. HMMR promotes HCC cell proliferation in vitro via activation of G1/S and G2/M checkpoint transitions, concomitant with a marked increase of the positive cell cycle regulators, including cyclin D1, cyclin E, and cyclin B1. Knockdown of HMMR suppresses HCC tumor growth in nude mice. Our study identifies an important role of HMMR in NASH/HCC, and suggests that HMMR may be a useful target for therapy and prognostic prediction of NASH/HCC patients.

Identification of osalmid metabolic profile and active metabolites with anti-tumor activity in human hepatocellular carcinoma cells

BACKGROUNDS

Ribonucleotide reductase (RR) catalyzes the essential step in the formation of all four deoxynucleotides. Upregulated activity of RR plays an active role in tumor progression. As the regulatory subunit of RR, ribonucleotide reductase subunit M2 (RRM2) is regarded as one of the effective therapeutic targets for DNA replication-dependent diseases, such as cancers. Recent studies have revealed that osalmid significantly inhibits the activity of RRM2, but the metabolic profile of osalmid remains unknown.

OBJECTIVE

The aim of this study was to clarify the metabolic profile including metabolites, isoenzymes and metabolic pathways of osalmid. The anti-human hepatocellular carcinoma activity and mechanism of metabolites were further investigated.

MATERIALS AND METHODS

Ultra high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF-MS) was used for identifying metabolites and for characterizing phase I and phase II metabolic pathways with recombinant enzymes or in human liver microsomes of osalmid. The eHiTS docking system was used for potential RRM2 inhibitor screening among metabolites. Cytotoxicity assays were performed for evaluating cell proliferation inhibitory activity of metabolites. Cell cycle assays and cell apoptosis assays were assessed by flow cytometry. Western blotting analysis of RRM2, cyclin D1, p21, p53, phosphorylated p53, Bcl-2 and Bax was performed to explore the anti-hepatocellular carcinoma mechanism of the active metabolites.

RESULTS

Ten metabolites of osalmid were identified, and none of them have been reported previously. Hydroxylation, glucuronidation, sulfonation, acetylation and degradation were recognized as the main metabolic processes of osalmid. Isozymes of CYP1A2, CYP2C9, UGT1A1, UGT1A6, UGT1A9, UGT2B7 and UGT2B15 were involved in phase I and phase II metabolism of osalmid. Metabolites M7, M8 and M10 showed higher binding affinities with the RRM2 active site than osalmid. Metabolite M7 exhibited potent inhibitory activity to hepatocellular carcinoma cell lines by both competitive inhibition and down-regulation of RRM2. Moreover, M7 significantly induced cell cycle arrest and apoptosis by activating p53-related pathways.

CONCLUSIONS

The metabolic profile of osalmid was identified. M7 significantly inhibited human hepatocellular carcinoma progression by inhibiting RRM2 activity. Furthermore, M7 induced cell cycle arrest and apoptosis by activating p53-related signaling pathways.

Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of malignant neoplasm of the liver with high morbidity and mortality. Extensive research into the pathology of HCC has been performed; however, the molecular mechanisms underlying the development of hepatitis B virus-associated HCC have remained elusive. Thus, the present study aimed to identify critical genes and pathways associated with the development and progression of HCC. The expression profiles of the GSE121248 dataset were downloaded from the Gene Expression Omnibus database and the differentially expressed genes (DEGs) were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analyses were performed by using the Database for Annotation, Visualization and Integrated Discovery. Subsequently, protein-protein interaction (PPI) networks were constructed for detecting hub genes. In the present study, 1,153 DEGs (777 upregulated and 376 downregulated genes) were identified and the PPI network yielded 15 hub genes. GO analysis revealed that the DEGs were primarily enriched in ‘protein binding’, ‘cytoplasm’ and ‘extracellular exosome’. KEGG analysis indicated that DEGs were accumulated in ‘metabolic pathways’, ‘chemical carcinogenesis’ and ‘fatty acid degradation’. After constructing the PPI network, cyclin-dependent kinase 1, cyclin B1, cyclin A2, mitotic arrest deficient 2 like 1, cyclin B2, DNA topoisomerase IIα, budding uninhibited by benzimidazoles (BUB)1, TTK protein kinase, non-SMC condensin I complex subunit G, NDC80 kinetochore complex component, aurora kinase A, kinesin family member 11, cell division cycle 20, BUB1B and abnormal spindle microtubule assembly were identified as hub genes based on the high degree of connectivity by using Cytoscape software. In addition, overall survival (OS) and disease-free survival (DFS) analyses were performed using the Gene Expression Profiling Interactive Analysis online database, which revealed that the increased expression of all hub genes were associated with poorer OS and DFS outcomes. Receiver operating characteristic curves were constructed using GraphPad prism 7.0 software. The results confirmed that 15 hub genes were able to distinguish HCC form normal tissues. Furthermore, the expression levels of three key genes were analyzed in tumor and normal samples of the Human Protein Atlas database. The present results may provide further insight into the underlying mechanisms of HCC and potential therapeutic targets for the treatment of this disease.

Potential mechanism of RRM2 for promoting Cervical Cancer based on weighted gene co-expression network analysis

Cervical cancer is the most common gynecologic malignant tumor, with a high incidence in 50-55-year-olds. This study aims to investigate the potential molecular mechanism of RRM2 for promoting the development of cervical cancer based on The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). RRM2 was found to be significant upregulated in cervical tissue (P<0.05) by extracting the expression of RRM2 from TCGA, GSE63514, GSE7410, GSE7803 and GSE9750. Survival analysis indicated that the overall survival was significantly worse in the patients with high-expression of RRM2 (P<0.05). The top 1000 positively/negatively correlated genes with RRM2 by Pearson Correlation test were extracted. The gene co-expression network by Weighted Gene Co-Expression Network Analysis (WGCNA) with these genes and the clinical characteristics (lymphocyte infiltration, monocyte infiltration, necrosis, neutrophil infiltration, the number of normal/stromal/tumor cells and the number of tumor nuclei) was constructed. By screening the hub nodes from the co-expression network, results suggested that RRM2 may co-express with relevant genes to regulate the number of stromal/tumor cells and the process of lymphocyte infiltration to promote the progression of cervical cancer. RRM2 is likely to become a novel potential diagnostic and prognostic biomarker of cervical cancer and provide evidence to support the study of mechanisms for cervical cancer.