Identification of significant gene and pathways involved in HBV-related hepatocellular carcinoma by bioinformatics analysis

Analysis of host factor networks during hepatitis B virus infection in primary human hepatocytes

BACKGROUND

Chronic hepatitis B virus (HBV) infection affects around 250 million people worldwide, causing approximately 887,000 deaths annually, primarily owing to cirrhosis and hepatocellular carcinoma (HCC). The current approved treatments for chronic HBV infection, such as interferon and nucleos(t)ide analogs, have certain limitations as they cannot completely eradicate covalently closed circular DNA (cccDNA). Considering that HBV replication relies on host transcription factors, focusing on host factors in the HBV genome may provide insights into new therapeutic targets against HBV. Therefore, understanding the mechanisms underlying viral persistence and hepatocyte pathogenesis, along with the associated host factors, is crucial. In this study, we investigated novel therapeutic targets for HBV infection by identifying gene and pathway networks involved in HBV replication in primary human hepatocytes (PHHs). Importantly, our study utilized cultured primary hepatocytes, allowing transcriptomic profiling in a biologically relevant context and enabling the investigation of early HBV-mediated effects.

METHODS

PHHs were infected with HBV virion particles derived from HepAD38 cells at 80 HBV genome equivalents per cell (Geq/cell). For transcriptomic sequencing, PHHs were harvested 1, 2-, 3-, 5-, and 7 days post-infection (dpi). After preparing the libraries, clustering and sequencing were conducted to generate RNA-sequencing data. This data was processed using Bioinformatics tools and software to analyze DEGs and obtain statistically significant results. Furthermore, qRT-PCR was performed to validate the RNA-sequencing results, ensuring consistent findings.

RESULTS

We observed significant alterations in the expression patterns of 149 genes from days 1 to 7 following HBV infection (R2 > 0.7, q < 0.05). Functional analysis of these genes identified RNA-binding proteins involved in mRNA metabolism and the regulation of alternative splicing during HBV infection. Results from qRT-PCR experiments and the analysis of two validation datasets suggest that RBM14 and RPL28 may serve as potential biomarkers for HBV-associated HCC.

CONCLUSIONS

Transcriptome analysis of gene expression changes during HBV infection in PHHs provided valuable insights into chronic HBV infection. Additionally, understanding the functional involvement of host factor networks in the molecular mechanisms of HBV replication and transcription may facilitate the development of novel strategies for HBV treatment.

Identification of ribosomal protein S21 as a potential prognostic and immunotherapy biomarker for hepatocellular carcinoma

BACKGROUND

Recent studies show that ribosomal protein S21 (RPS21) plays a role in the development and progression of various malignancies. However, the biological value of RPS21 in hepatocellular carcinoma (HCC) and its association with immunotherapy remain unknown.

METHODS

Here, we examined the differential expression of RPS21 between HCC and normal liver tissues, using the TCGA, ICGC and GEO databases, followed by verification by reverse-transcription quantitative polymerase chain reaction (RT-qPCR) in LO2, SMMC7721, HepG2, and MHCC-97H cell lines. Kaplan-Meier and Cox regression analyses were applied to investigate how RPS21 expression influenced overall survival, and a nomogram was established to predict prognosis among HCC patients. We further analyzed how RPS21 expression was related to tumor immune microenvironment, immunotherapy efficiency, and genomic alterations, and investigated potential underlying mechanisms.

RESULTS

RPS21 upregulation was observed in HCC tissues and cell lines, compared to normal controls. Survival analysis revealed that RPS21 overexpression was significantly associated with poor clinical outcomes (all p < 0.05). Functional enrichment analyses indicated that differentially expressed genes relative to RPS21 expression were mainly involved in tumor response, proliferation, and metabolism. Additionally, RPS21 expression was positively correlated with the infiltration of activated CD4+ T cells and tumor mutational burden (all p < 0.05). Moreover, RPS21 was co-expressed with immune-related genes and immune checkpoint genes. Analyses of drug sensitivity predict that HCC patients with low RPS21 expression were more sensitive to targeted immunotherapy.

CONCLUSIONS

The present results suggested that RPS21 might be a promising prognostic marker and a potential immunotherapy target for patients with HCC.

Identification of hub genes related to metastasis and prognosis of osteosarcoma and establishment of a prognostic model with bioinformatic methods

Osteosarcoma (OS) is the most common primary malignant bone tumor occurring in children and adolescents. Improvements in our understanding of the OS pathogenesis and metastatic mechanism on the molecular level might lead to notable advances in the treatment and prognosis of OS. Biomarkers related to OS metastasis and prognosis were analyzed and identified, and a prognostic model was established through the integration of bioinformatics tools and datasets in multiple databases. 2 OS datasets were downloaded from the Gene Expression Omnibus database for data consolidation, standardization, batch effect correction, and identification of differentially expressed genes (DEGs); following that, gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs; the STRING database was subsequently used for protein-protein interaction (PPI) network construction and identification of hub genes; hub gene expression was validated, and survival analysis was conducted through the employment of the TARGET database; finally, a prognostic model was established and evaluated subsequent to the screening of survival-related genes. A total of 701 DEGs were identified; by gene ontology and KEGG pathway enrichment analyses, the overlapping DEGs were enriched for 249 biological process terms, 13 cellular component terms, 35 molecular function terms, and 4 KEGG pathways; 13 hub genes were selected from the PPI network; 6 survival-related genes were identified by the survival analysis; the prognostic model suggested that 4 genes were strongly associated with the prognosis of OS. DEGs related to OS metastasis and survival were identified through bioinformatics analysis, and hub genes were further selected to establish an ideal prognostic model for OS patients. On this basis, 4 protective genes including TPM1, TPM2, TPM3, and TPM4 were yielded by the prognostic model.

Identification of hub genes and pathways in hepatitis B virus-associated hepatocellular carcinoma: A comprehensive in silico study

Background and Aim

The hepatitis B virus (HBV) is one of the most common causes of liver cancer in the world. This study aims to provide a better understanding of the mechanisms involved in the development and progression of HBV-associated hepatocellular carcinoma (HCC) by identifying hub genes and the pathways related to their functions.

Methods

GSE83148 and GSE94660 were selected from the Gene Expression Omnibus (GEO) database, differentially expressed genes (DEGs) with an adjusted p-value < 0.05 and a |logFC| ≥1 were identified. Common DEGs of two data sets were identified using the GEO2R tool. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene ontology (GO) databases were used to identify pathways. Protein-protein interactions (PPIs) analysis was performed by using the Cytoscap and Gephi. A Gene Expression Profiling Interactive Analysis (GEPIA) analysis was carried out to confirm the target genes.

Results

One hundred and ninety-eight common DEGs and 49 hub genes have been identified through the use of GEO and PPI, respectively. The GO and KEGG pathways analysis showed DEGs were enriched in the G1/S transition of cell cycle mitotic, cell cycle, spindle, and extracellular matrix structural constituent. The expression of four genes (TOP2A, CDK1, CCNA2, and CCNB2) with high scores in module 1 were more in tumor samples and have been identified by GEPIA analysis.

Conclusion

In this study, the hub genes and their related pathways involved in the development of HBV-associated HCC were identified. These genes, as potential diagnostic biomarkers, may provide a potent opportunity to detect HBV-associated HCC at the earliest stages, resulting in a more effective treatment.

The role of kinesin family members in hepatobiliary carcinomas: from bench to bedside

As a major component of the digestive system malignancies, tumors originating from the hepatic and biliary ducts seriously endanger public health. The kinesins (KIFs) are molecular motors that enable the microtubule-dependent intracellular trafficking necessary for mitosis and meiosis. Normally, the stability of KIFs is essential to maintain cell proliferation and genetic homeostasis. However, aberrant KIFs activity may destroy this dynamic stability, leading to uncontrolled cell division and tumor initiation. In this work, we have made an integral summarization of the specific roles of KIFs in hepatocellular and biliary duct carcinogenesis, referring to aberrant signal transduction and the potential for prognostic evaluation. Additionally, current clinical applications of KIFs-targeted inhibitors have also been discussed, including their efficacy advantages, relationship with drug sensitivity or resistance, the feasibility of combination chemotherapy or other targeted agents, as well as the corresponding clinical trials. In conclusion, the abnormally activated KIFs participate in the regulation of tumor progression via a diverse range of mechanisms and are closely associated with tumor prognosis. Meanwhile, KIFs-aimed inhibitors also carry out a promising tumor-targeted therapeutic strategy that deserves to be further investigated in hepatobiliary carcinoma (HBC).

RAD51AP1 as an Immune-Related Prognostic Biomarker and Therapeutic Response Predictor in Hepatocellular Carcinoma

Background

RAD51 associated protein 1 (RAD51AP1) is shown to regulate cell proliferation and cancer progression. However, the immune-infiltrating correlation and the therapeutics guidance of RAD51AP1 in hepatocellular carcinoma (HCC) still need further investigation.

Methods

In this study, comprehensive bioinformatic analysis of RAD51AP1 on differential expression, clinicopathologic correlation, prognostic value, and function enrichment were performed in The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO; GSE14520 and GSE76427), and International Cancer Genome Consortium (ICGC) datasets. Besides, the Guangxi cohort containing 50 pairs HCC and adjacent non-cancerous samples from First Affiliated Hospital of Guangxi Medical University was served as validation cohort. Moreover, we explored the predictive value of RAD51AP1 to therapeutics response and its underlying correlation with HCC immunoinfiltration.

Results

RAD51AP1 was significantly overexpressed in HCC tissues and had a high diagnostic value of HCC. The shorter survival time and poorer clinical features were showed when RAD51AP1 upregulated, and then a nomogram featuring RAD51AP1 expression and other clinicopathologic factors was established to predict prognosis. In CIBERSORT analysis, higher T cells follicular helper but lower T cells CD4+ memory resting infiltration levels were exhibited when RAD51AP1 upregulated. The ssGSEA analysis demonstrated that high-RAD51AP1 expression subgroup had higher macrophages, Th2 and Treg cells infiltration levels, but lower type II IFN response function. Furthermore, high-RAD51AP1 expression subgroup exhibited the upregulated expression levels of immune-related checkpoint genes, but lower IPS and TIDE scores which suggested a possibly better immunotherapy response. The drug sensitivity analysis showed the high-expression subgroup may be more susceptible to Bexarotene, Doxorubicin, Gemcitabine and Tipifarnib.

Conclusion

Taken together, RAD51AP1 is a potential diagnostic and prognostic biomarker. It may be related to the immunosuppressive microenvironment and could be an underlying HCC treatment strategy. However, the conclusions still require further validation studies.

[Research Advances of RAD51AP1 in Tumor Progression and Drug Resistance]

The genomic instability may lead to an initiation of cancer in many organisms. Homologous recombination repair (HRR) is vital in maintaining cellular genomic stability. RAD51 associated protein 1 (RAD51AP1), which plays a crucial role in HRR and primarily participates in forming D-loop, was reported as an essential protein for maintaining cellular genomic stability. However, recent studies showed that RAD51AP1 was significantly overexpressed in various cancer types and correlated with poor prognosis. These results suggested that RAD51AP1 may play a significant pro-cancer effect in multiple cancers. The underlying mechanism is still unclear. Cancer stemness-maintaining effects of RAD51AP1 might be considered as the most reliable mechanism. Meanwhile, RAD51AP1 also promoted resistance to radiation therapy and chemotherapy in many cancers. Thus, researches focused on RAD51AP1, and its regulatory molecules may provide new targets for overcoming cancer progression and treatment resistance. Here, we reviewed the latest research on RAD51AP1 in cancers and summarized its differential expression and prognostic implications. In this review, we also outlined the potential mechanisms of its pro-cancer and drug resistance-promoting effects to provide several potential directions for further research.
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Comparative Transcriptional Signature Analysis of Peripheral Blood Mononuclear Cells in Early Stage of Hepatitis B-related Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is a prevalent and life-threatening tumor with high morbidity and mortality. Proper prediction and prognosis are incredibly stressed to diagnose HCC and increase patient survival. Objectives: This research aims to evaluate gene expression levels of pre-differentiated transcripts for those suffering from chronic hepatitis B (CHB) and HCC. Methods: To examine the previously analyzed peripheral blood mononuclear cells (PBMCs) transcriptomic array data, we selected seven differentially expressed genes (DEGs) in normal versus CHB and CHB versus HCC (CD44, SP3, USP8, E2F2, UFM1, IFN regulative factor binding protein 2 (IRF2BP2), and T-cell intracellular antigen 1 (TIA1)). The study included individuals with treatment-naïve CHB (n = 30) and primary HCC (n = 25) and healthy controls (n = 15). Subsequently, the expression of genes was assayed using qRT-PCR. A phylogenetic evaluation was performed using direct sequencing of HBsAg. Results: In HCC patients, 60% (n = 15) were HBeAg-positive. HBeAg was negative in all CHB patients, but all were anti-HBe-positive. The hepatitis B virus (HBV) load of HCC patients was more than that of CHB subjects. All patients were of the Iranian race and HBV D genotype. The expression of five transcriptional markers (CD44, SP3, USP8, E2F2, and UFM1) was higher in HCC patients than in CHB and healthy subjects, which was similar to the initial microarray data analysis. Conclusions: Transcriptional signatures may be related to the pathogenesis of HCC and used as diagnostic biological markers for the initial monitoring and prediction of HCC.

Novel potential biomarkers for severe alcoholic liver disease

Background

Alcoholic liver disease (ALD) is a leading cause of advanced liver disease; however, minor clinical symptoms in the early stage frequently result in delayed diagnosis and therapy. Invasive liver biopsy, the gold standard for diagnosing ALD, is unsuitable for repetitive analysis. This study aims to identify potential serum biomarkers that could contribute to non-invasive disease screening and monitoring.

Methods

Label-free LC-MS/MS quantitative proteomics analysis was performed to identify differentially expressed proteins in the discovery cohort, followed by bioinformatics analysis based on the KEGG, GO, and String databases. Prioritized proteins were validated subsequently by quantitative assays. The area under the receiver operating characteristic curve (AUROC) was used to assess the diagnosis performance of potential biomarkers.

Results

A total of 161 differentially expressed proteins were identified in the discovery cohort, of which 123 were up-regulated and 38 were down-regulated. B2M, IGFALS, and IGFBP3 were evaluated, and all demonstrated excellent diagnosis performance with AUROCs of over 0.9 when distinguishing patients with severe ALD from healthy controls. The AUROC values of B2M, IGFBP3, and IGFALS were 0.7131, 0.8877, and 0.9896 for differentiating severe ALD from non-severe ALD to indicate disease severity. B2M could distinguish patients with non-severe ALD and HC participants with an AUROC value of 0.8985. The efficiency of multiple combinations of these biomarkers was superior to that of the existing liver fibrosis evaluation indices used to monitor disease progression, with AUROC values of over 0.9. IGFALS showed a positive correlation with ALT/AST (r=0.4648, P=0.0009) and may be developed as a therapeutic target.

Conclusion

This proteomic study identified three novel candidate proteins as promising circulating biomarkers for clinical diagnosis and disease progression and also provided the proteomic atlas for ALD pathophysiological mechanisms.

Prognosis-Related Molecular Subtypes and Immune Features Associated with Hepatocellular Carcinoma

Bioinformatics tools were used to identify prognosis-related molecular subtypes and biomarkers of hepatocellular carcinoma (HCC). Differential expression analysis of four datasets identified 3330 overlapping differentially expressed genes (DEGs) in the same direction in all four datasets. Those genes were involved in the cell cycle, FOXO signaling pathway, as well as complement and coagulation cascades. Based on non-negative matrix decomposition, two molecular subtypes of HCC with different prognoses were identified, with subtype C2 showing better overall survival than subtype C1. Cox regression and Kaplan-Meier analysis showed that 217 of the overlapping DEGs were closely associated with HCC prognosis. The subset of those genes showing an area under the curve >0.80 was used to construct random survival forest and least absolute shrinkage and selection operator models, which identified seven feature genes (SORBS2, DHRS1, SLC16A2, RCL1, IGFALS, GNA14, and FANCI) that may be involved in HCC occurrence and prognosis. Based on the feature genes, risk score and recurrence models were constructed, while a univariate Cox model identified FANCI as a key gene involved mainly in the cell cycle, DNA replication, and mismatch repair. Further analysis showed that FANCI had two mutation sites and that its gene may undergo methylation. Single-sample gene set enrichment analysis showed that Th2 and T helper cells are significantly upregulated in HCC patients compared to controls. Our results identify FANCI as a potential prognostic biomarker for HCC.

Machine Learning-based Prediction of HBV-related Hepatocellular Carcinoma and Detection of Key Candidate Biomarkers

OBJECTIVE

This study aimed to classify open-access gene expression data of patients with hepatitis B virus-related hepatocellular carcinoma (HBV + HCC) and chronic HBV without HCC (HBV alone) using the XGBoost method, one of the machine learning methods, and reveal important genes that may cause HCC.

METHODS

This case-control study used the open-access gene expression data of patients with HBV + HCC and HBV alone. Data from 17 patients with HBV + HCC and 36 patients with HBV were included. XGBoost was constructed for the classification via 10-fold cross-validation. Accuracy, balanced accuracy, sensitivity, selectivity, positive-predictive value, and negative-predictive value performance metrics were evaluated for model performance.

RESULTS

According to the feature-selection method, 18 genes were selected, and modeling was performed with these input variables. Accuracy, balanced accuracy, sensitivity, specificity, positive-predictive value, negative-predictive value, and F1 score obtained from XGBoost model were 98.1%, 98.6%, 100%, 97.2%, 94.4%, 100%, and 97.1%, respectively. Based on the predictor importance findings acquired from XGBoost, the RNF26, FLJ10233, ACBD6, RBM12, PFAS, H3C11, and GKP5 can be employed as potential biomarkers of HBV-related HCC.

CONCLUSIONS

In this study, genes that may be possible biomarkers of HBV-related HCC were determined using a machine learning-based prediction approach. After the reliability of the obtained genes are clinically verified in subsequent research, therapeutic procedures can be established based on these genes, and their usefulness in clinical practice may be documented.

UBE2T regulates FANCI monoubiquitination to promote NSCLC progression by activating EMT

Fanconi anemia complementation group I (FANCI) is a critical protein for maintaining DNA stability. However, the exact role of FANCI in tumors remains to be elucidated. The present study aimed to explore the role and potential mechanism of action of FANCI in non-small cell lung cancer (NSCLC). To quantify the expression levels of FANCI and ubiquitin-conjugating enzyme E2T (UBE2T) in NSCLC tissues, reverse-transcription quantitative PCR and western blotting were employed. Cell Counting Kit-8, wound healing and Transwell assays along with flow cytometry analysis and tumor xenograft were used to investigate the biological effects of FANCI in NSCLC in vitro and in vivo. The binding of FANCI with UBE2T was confirmed using a co-immunoprecipitation assay. Epithelial-to-mesenchymal transition (EMT) protein markers were quantified via western blotting. The results showed that FANCI expression level was higher in NSCLC tumor tissues, compared with adjacent tissues. In A549 and H1299 cells, knockdown of FANCI inhibited cell proliferation, migration, invasion, cell cycle and EMT in vitro. Tumor growth was repressed in vitro, upon downregulation of FANCI expression. UBE2T was observed to directly bind to FANCI and regulate its monoubiquitination. Overexpression of UBE2T reversed the effects induced by FANCI knockdown in NSCLC cells. Furthermore, it was noted that FANCI interacted with WD repeat domain 48 (WDR48). Overexpression of WDR48 reversed the effects of FANCI on cell proliferation, migration and EMT. In conclusion, FANCI was identified to be a putative oncogene in NSCLC, wherein FANCI was monouniubiquitinated by UBE2T to regulate cell growth, migration and EMT through WDR48. The findings suggested that FANCI could be used as a prognostic biomarker and therapeutic target for NSCLC.

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.

Identification of hub genes for early detection of bone metastasis in breast cancer

BACKGROUND

Globally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.

METHODS

GSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.

CONCLUSIONS

2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.

Absent in melanoma 1-like (AIM1L) serves as a novel candidate for overall survival in hepatocellular carcinoma

Identifying biomarkers for hepatocellular carcinoma (HCC) survival is of great importance for the early detection, monitoring, and predicting for prognosis. This study aimed to investigate the candidate biomarkers for predicting overall survival (OS) in HCC patients. Using RTCGAToolbox, top 50 upregulated differential expressed genes (DEGs) were identified. The least absolute shrinkage and selection operator (LASSO) and Cox models were used to select powerful candidate genes, and log rank method was used to address the survivor functions of potential biomarkers. Selected by LASSO model, ANLN, TTK, AIM1L and person neoplasm cancer status might be candidate parameters associated with OS in HCC patients. After adjusting person neoplasm cancer status, ANLN and TTK levels in Cox model, AIM1L was identified as a risk factor for predicting OS in HCC patients (HR = 1.5, P = 0.037). Validated in The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) series, AIM1L was significantly overexpressed in tumor tissues compared to nontumor tissues (all P < 0.0001). HCC patients with high AIM1L in tumor tissues had significantly unfavorable OS compared to those with low AIM1L in TCGA, ICGC, Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan-Meier Plotter datasets (all P < 0.05). Conclusively, AIM1L is upregulated in tumor samples and serves as a novel candidate for predicting unfavorable OS in HCC patients.

Identification of Key Pathways and Genes in SARS-CoV-2 Infecting Human Intestines by Bioinformatics Analysis

COVID-19 is a serious infectious disease that has recently swept the world, and research on its causative virus, SARS-CoV-2, remains insufficient. Therefore, this study uses bioinformatics analysis techniques to explore the human digestive tract diseases that may be caused by SARS-CoV-2 infection. The gene expression profile data set, numbered GSE149312, is from the Gene Expression Omnibus (GEO) database and is divided into a 24-h group and a 60-h group. R software is used to analyze and screen out differentially expressed genes (DEGs) and then gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses are performed. In KEGG, the pathway of non-alcoholic fatty liver disease exists in both the 24-h group and 60-h group. STRING is used to establish a protein–protein interaction (PPI) network, and Cytoscape is then used to visualize the PPI and define the top 12 genes of the node as the hub genes. Through verification, nine statistically significant hub genes are identified: AKT1, TIMP1, NOTCH, CCNA2, RRM2, TTK, BUB1B, KIF20A, and PLK1. In conclusion, the results of this study can provide a certain direction and basis for follow-up studies of SARS-CoV-2 infection of the human digestive tract and provide new insights for the prevention and treatment of diseases caused by SARS-CoV-2.

MiR-16-5p suppresses breast cancer proliferation by targeting ANLN

BACKGROUND

In recent years, gene expression-based analysis has been used for disease biomarker discovery, providing ways for better diagnosis, leading to improvement of clinical treatment efficacy. This study aimed to explore the role of miR-16-5p and ANLN in breast cancer (BC).

METHODS

Cohort datasets of BC were obtained from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) and analyzed by bioinformatics tools. qRT-PCR and western blotting were applied to validate ANLN and its protein expression. A dual-luciferase reporter assay was used to prove the regulatory relationship of miR-16-5p and ANLN. Finally, MTT, wound healing, Transwell invasion and flow cytometry analyses of the cell cycle and apoptosis were performed to assess cell proliferation, migration, invasion, cell cycle and apoptosis, respectively.

RESULTS

A total of 195 differentially expressed genes (DEGs) and 50 overlapping microRNAs (miRNAs) were identified. Among these DEGs and miRNAs, ANLN, associated with poor overall survival in BC, overlapped in the GSE29431, GSE42568, TCGA and GEPIA2 databases. Moreover, ANLN was highly expressed, while miR-16-5p was lower in BC cells than in breast epithelial cells. Then, we confirmed that ANLN was directly targeted by miR-16-5p in BC cells. Over-expression of miR-16-5p and knock-down of ANLN remarkably inhibited cell proliferation and migration as well as cell invasion, arrested the cells in G2/M phase and induced apoptosis in BC cells.

CONCLUSIONS

These findings suggest that miR-16-5p restrains proliferation, migration and invasion while affecting cell cycle and promotes apoptosis by regulating ANLN, thereby providing novel candidate biomarkers for the diagnosis and treatment of BC.

Integrated Transcriptomic Analysis Revealed Hub Genes and Pathways Involved in Sorafenib Resistance in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), a high mortality malignancy, has become a worldwide public health concern. Acquired resistance to the multikinase inhibitor sorafenib challenges its clinical efficacy and the survival benefits it provides to patients with advanced HCC. This study aimed to identify critical genes and pathways associated with sorafenib resistance in HCC using integrated bioinformatics analysis. Differentially expressed genes (DEGs) were identified using four HCC gene expression profiles (including 34 sorafenib-resistant and 29 sorafenib-sensitive samples) based on the robust rank aggregation method and R software. Gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool. A protein–protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING), and small molecules reversing sorafenib resistance were searched for using the connectivity map (CMAP) database. Pearson correlation and survival analyses of hub genes were performed using cBioPortal and Gene Expression Profiling and Interactive Analysis (GEPIA). Finally, the expression levels of hub genes in sorafenib-resistant HCC cells were verified using quantitative polymerase chain reaction (q-PCR). A total of 165 integrated DEGs (66 upregulated and 99 downregulated in sorafenib resistant samples compared sorafenib sensitive ones) primarily enriched in negative regulation of endopeptidase activity, extracellular exosome, and protease binding were identified. Some pathways were commonly shared between the integrated DEGs. Seven promising therapeutic agents and 13 hub genes were identified. These findings provide a strategy and theoretical basis for overcoming sorafenib resistance in HCC patients.

[CDK1, CCNB1 and NDC80 are associated with prognosis and progression of hepatitis B virus-associated hepatocellular carcinoma: a bioinformatic analysis]

OBJECTIVE

To identify the key genes involved in the transformation of hepatitis B virus (HBV) into hepatocellular carcinoma (HCC) and explore the underlying molecular mechanisms.

METHODS

We analyzed the mRNA microarray data of 119 HBV-related HCC tissues and 252 HBV-related non-tumor tissues in GSE55092, GSE84044 and GSE121248 from the GEO database, and the "sva" R package was used to remove the batch effects. Integration analysis was performed to identify the differentially expressed genes (DEGs) in HBV-related liver cancer and liver tissues with HBV infection. The significant DEGs were functionally annotated using GO and KEGG analyses, and the most important modules and hub genes were explored with STRING analysis. Kaplan-Meier and Oncomine databases were used to verify the HCC gene expression data in the TCGA database to explore the correlations of the hub genes with the occurrence, progression and prognosis of HCC. We also examined the expressions of the hub genes in 17 pairs of surgical specimens of HCC and adjacent tissues using RT-qPCR.

RESULTS

We identified a total of 121 DEGs and 3 genetic markers in HCC (P < 0.01). These DEGs included cyclin1 (CDK1), cyclin B1 (CCNB1), and nuclear division cycle 80 (NDC80), which participated in cell cycle, pyrimidine metabolism and DNA replication and were highly correlated (P < 0.05). Analysis of the UALCAN database confirmed high expressions of these 3 genes in HCC tissues, which were correlated with a low survival rate of the patients, as shown by Kaplan-Meier analysis of the prognostic data from the UALCAN database. CDK1, CCNB1 and NDC80 were all correlated with the clinical grading of HCC (P < 0.05). The results of RT-qPCR on the surgical specimens verified significantly higher expressions of CDK1, CCNB1 and NDC80 mRNA in HCC tissues than in the adjacent tissues.

CONCLUSION

CDK1, CCNB1 and NDC80 genes can be used as prognostic markers of HBV-related HCC and may serve as potential targets in preclinical studies and clinical treatment of HCC.

Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND

The majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC.

METHODS

GSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC.

CONCLUSIONS

Five abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

Identification of Key Genes Associated With the Process of Hepatitis B Inflammation and Cancer Transformation by Integrated Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) has become the main cause of cancer death worldwide. More than half of hepatocellular carcinoma developed from hepatitis B virus infection (HBV). The purpose of this study is to find the key genes in the transformation process of liver inflammation and cancer and to inhibit the development of chronic inflammation and the transformation from disease to cancer.

Methods

Two groups of GEO data (including normal/HBV and HBV/HBV-HCC) were selected for differential expression analysis. The differential expression genes of HBV-HCC in TCGA were verified to coincide with the above genes to obtain overlapping genes. Then, functional enrichment analysis, modular analysis, and survival analysis were carried out on the key genes.

Results

We identified nine central genes (CDK1, MAD2L1, CCNA2, PTTG1, NEK2) that may be closely related to the transformation of hepatitis B. The survival and prognosis gene markers composed of PTTG1, MAD2L1, RRM2, TPX2, CDK1, NEK2, DEPDC1, and ZWINT were constructed, which performed well in predicting the overall survival rate.

Conclusion

The findings of this study have certain guiding significance for further research on the transformation of hepatitis B inflammatory cancer, inhibition of chronic inflammation, and molecular targeted therapy of cancer.

The Matrisome Genes From Hepatitis B-Related Hepatocellular Carcinoma Unveiled

Chronic hepatitis B virus (HBV) infection changes the composition of the extracellular matrix (ECM) and enables the onset and progression of hepatocellular carcinoma (HCC). The ensemble of ECM proteins and associated factors is a major component of the tumor microenvironment. Our aim was to unveil the matrisome genes from HBV-related HCC. Transcriptomic and clinical profiles from 444 patients with HBV-related HCC were retrieved from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) repositories. Matrisome genes associated with HBV-related hepatocarcinogenesis, matrisome gene modules, HCC subgroups, and liver-specific matrisome genes were systematically analyzed, followed by identification of their biological function and clinical relevance. Eighty matrisome genes, functionally enriched in immune response, ECM remodeling, or cancer-related pathways, were identified as associated with HBV-related HCC, which could robustly discriminate HBV-related HCC tumor from nontumor samples. Subsequently, four significant matrisome gene modules were identified as showing functional homogeneity linked to cell cycle activity. Two subgroups of patients with HBV-related HCC were classified based on the highly correlated matrisome genes. The high-expression subgroup (15.0% in the TCGA cohort and 17.9% in the GEO cohort) exhibited favorable clinical prognosis, activated metabolic activity, exhausted cell cycle, strong immune infiltration, and lower tumor purity. Four liver-specific matrisome genes (F9, HPX [hemopexin], IGFALS [insulin-like growth-factor-binding protein, acid labile subunit], and PLG [plasminogen]) were identified as involved in HBV-related HCC progression and prognosis. Conclusion: This study identified the expression and function of matrisome genes from HBV-related hepatocarcinogenesis, providing major insight to understand HBV-related HCC and develop potential therapeutic opportunities.

CENPL, ISG20L2, LSM4, MRPL3 are four novel hub genes and may serve as diagnostic and prognostic markers in breast cancer

As a highly prevalent disease among women worldwide, breast cancer remains in urgent need of further elucidation its molecular mechanisms to improve the patient outcomes. Identifying hub genes involved in the pathogenesis and progression of breast cancer can potentially help to unveil mechanism and also provide novel diagnostic and prognostic markers. In this study, we integrated multiple bioinformatic methods and RNA in situ detection technology to identify and validate hub genes. EZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4, MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among these, many studies on EZH2 in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These four novel hub genes were up-regulated in tumor tissues and associated with cancer progression. The receiver operating characteristic analysis and Kaplan-Meier survival analysis indicated that these four hub genes are promising candidate genes that can serve as diagnostic and prognostic biomarkers for breast cancer. Moreover, these four newly identified hub genes as aberrant molecules in the maintenance of breast cancer development, their exact functional mechanisms deserve further in-depth study.

HMGCS2 in metabolic pathways was associated with overall survival in hepatocellular carcinoma: A LASSO-derived study

This integrated bioinformatic study aimed to investigate potential prognostic candidates in hepatocellular carcinoma (HCC). In the GSE14520, GSE101685, and The Cancer Genome Atlas (TCGA) datasets, differentially expressed genes (DEGs) were identified and functional pathways of common DEGs were enriched. The least absolute shrinkage and selection operator (LASSO) model was used to screen the potential parameters associated with overall survival (OS) in HCC patients. Metabolic pathways were the most significantly enriched functional pathways of common DEGs in these three datasets. After LASSO model analysis, HMGCS2, UGP2, BCLC staging and TNM staging were screened as potential prognostic candidates for OS in HCC patients in GSE14520. HMGCS2 in the metabolic pathway was significantly downregulated in tumor tissues and peripheral blood mononuclear cells in HCC patients (all p < 0.05). Cox regression model indicated that HMGCS2 might be associate with OS in HCC patients in GSE14520 and in the TCGA (p = 0.029 and p = 0.05, respectively). Kaplan-Meier analysis demonstrated that HMGCS2 downregulation in tumors contributed to an unfavorable OS in HCC patients, both in GSE14520 and in the TCGA (p = 0.0001 and p = 0.0002, respectively). Additionally, HMGCS2 was significantly downregulated in HCC patients with high alpha-fetoprotein (AFP), main tumor size >5 cm, multinodular, advanced tumor staging including BCLC, TNM and CLIP (all p < 0.05). HMGCS2 was involved in metabolic pathways, and downregulated HMGCS2 in tumors was associated with unfavorable OS in HCC patients.

Identification of Potential Hub Genes Related to Diagnosis and Prognosis of Hepatitis B Virus-Related Hepatocellular Carcinoma via Integrated Bioinformatics Analysis

Hepatocellular carcinoma (HCC) is a common malignant cancer with poor survival outcomes, and hepatitis B virus (HBV) infection is most likely to contribute to HCC. But the molecular mechanism remains obscure. Our study intended to identify the candidate potential hub genes associated with the carcinogenesis of HBV-related HCC (HBV-HCC), which may be helpful in developing novel tumor biomarkers for potential targeted therapies. Four transcriptome datasets (GSE84402, GSE25097, GSE94660, and GSE121248) were used to screen the 309 overlapping differentially expressed genes (DEGs), including 100 upregulated genes and 209 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment were used to explore the biological function of DEGs. A PPI network based on the STRING database was constructed and visualized by the Cytoscape software, consisting of 209 nodes and 1676 edges. Then, we recognized 17 hub genes by CytoHubba plugin, which were further validated on additional three datasets (GSE14520, TCGA-LIHC, and ICGC-LIRI-JP). The diagnostic effectiveness of hub genes was assessed with receiver operating characteristic (ROC) analysis, and all hub genes displayed good performance in discriminating TNM stage I patient samples and normal tissue ones. For prognostic analysis, two prognostic key genes (TOP2A and KIF11) out of the 17 hub genes were screened and used to develop a prognostic signature, which showed good potential for overall survival (OS) stratification of HBV-HCC patients. Gene Set Enrichment Analysis (GSEA) was performed in order to better understand the function of this prognostic gene signature. Finally, the miRNA-mRNA regulatory relationships of all hub genes in human liver were predicted using miRNet. In conclusion, the current study gives further insight on the pathogenesis and carcinogenesis of HBV-HCC, and the identified DEGs provide a promising direction for improving the diagnostic, prognostic, and therapeutic outcomes of HBV-HCC.

Integrated Analysis of an lncRNA-Associated ceRNA Network Reveals Potential Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a common malignant tumor worldwide. In this study, we aimed to explore the potential biomarkers and key regulatory pathways related to HCC using integrated bioinformatic analysis and validation. The microarray data of GSE12717 and GSE54238 were downloaded from the Gene Expression Omnibus database. A competing endogenous RNA (ceRNA) network was constructed based on potential long-noncoding RNA (lncRNA)-microRNA (miRNA)-mRNA interactions. A total of 191 mRNAs, 8 miRNAs, and 5 lncRNAs were selected to construct the ceRNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to predict their biological functions. The PI3K-Akt signaling pathway was significantly enriched. Kaplan-Meier survival analysis based on the Gene Expression Profiling Interactive Analysis (GEPIA) database was conducted for the weighted mRNAs and lncRNAs. The results showed that SRC, GMPS, CDK2, FEN1, EZH2, ZWINT, MTHFD1L, GINS2, and MAPKAPK5-AS1 were significantly upregulated in tumor tissues. The relative expression levels of these genes were significantly upregulated in HCC patients based on the StarBase database. For further validation, the expression levels of these genes were detected by real-time quantitative reverse transcription-polymerase chain reaction in 20 HCC tumor tissues and paired paracancerous tissues. Receiver operating characteristic analysis revealed that CDK2, MTHFD1L, SRC, ZWINT, and MAPKAPK5-AS1 had significant diagnostic value in HCC, but further studies are needed to explore their mechanisms in HCC.

ANLN Directly Interacts with RhoA to Promote Doxorubicin Resistance in Breast Cancer Cells

Background

Chemotherapy resistance is the leading cause of cancer treatment failure. This research was conducted to explore a potential link between actin-binding protein anillin (ANLN) and doxorubicin resistance in breast cancer.

Materials and Methods

We compared ANLN expression and 50% inhibition concentration (IC50) of doxorubicin in human breast cancer cells (MDA-MB-231) and human breast cancer cells with doxorubicin resistance (MDA-MB-231/ADM). Co-immunoprecipitation was used to investigate the interaction between ANLN and RhoA. The cell viability, apoptosis, gene and protein expression were estimated by MTT, flow cytometry, quantitative real-time PCR and western blot.

Results

The doxorubicin resistance in MDA-MB-231/ADM cells (IC50 = 19.40 ± 1.16 μg/mL) was significantly higher than that in MDA-MB-231 cells (IC50 = 1.65 ± 0.23 μg/mL). ANLN was up-regulated in MDA-MB-231/ADM cells compared to MDA-MB-231 cells. Furthermore, ANLN overexpression promoted cell viability and inhibited apoptosis of MDA-MB-231 cells. The gene and protein expression of multidrug resistance (MDR1) and cancer resistance protein (BCRP) were enhanced by ANLN overexpression in MDA-MB-231 cells. ANLN silencing suppressed cell viability and the expression of MDR1 and BCRP and facilitated apoptosis in MDA-MB-231/ADM cells. Moreover, ANLN promoted RhoA activation by interacting with RhoA. ANLN up-regulation enhanced cell viability and the expression of MDR1 and BCRP and decreased apoptosis of MDA-MB-231 cells. The influence conferred by ANLN overexpression was effectively abolished by C3 transferase.

Conclusion

This work revealed that ANLN promoted doxorubicin resistance in breast cancer cells by activating RhoA. Thus, our study suggests a novel target for breast cancer treatment.

MCM10 Acts as a Potential Prognostic Biomarker and Promotes Cell Proliferation in Hepatocellular Carcinoma: Integrated Bioinformatics Analysis and Experimental Validation

Background

Hepatocellular carcinoma (HCC) is one of the most common human malignant tumors. The prognosis of HCC patients is still unsatisfying. Thus, it is of great importance to identify novel molecules and functional pathways associated with the pathophysiology of HCC. In this study, we performed the integrated bioinformatics analysis and experiment validation to identify novel biomarkers in the prognosis and progression of HCC.

Materials and Methods

Gene expression profiles were obtained from Gene Expression Omnibus database (GSE33294) for the screening of the differentially expressed genes (DEGs) between HCC tissues and matched non-tumor tissues. The DEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). The key genes in HCC were further subjected to overall survival analysis of HCC patients. The in vitro functional studies were performed to validate the biological functions of the key gene in HCC cell progression.

Results

A total of 2,334 DEGs were screened from GSE33294 dataset, including 1,120 up-regulated and 1,214 down-regulated genes. GO, KEGG and GSEA results showed that DEGs are significantly associated with the biological process of cell cycle, cell division and DNA replication. The Kaplan–Meier survival analysis results showed that the key genes from the minichromosome maintenance protein complex (MCM) family including MCM8, MCM10, MCM2, MCM3, MCM4, MCM6 and MCM7 were significantly correlated with the overall survival of the HCC patients. Further validation studies showed that MCM10 was significantly up-regulated in the HCC cell lines, and knockdown of MCM10 significantly suppressed cell proliferation as determined by the cell counting kit-8 and BrdU incorporation assays and increased the caspase-3 activity of HCC cells.

Conclusion

The comprehensive bioinformatics analysis identified several key genes that were associated with the prognosis of HCC patients. The validation study results indicated that MCM10 may be an important predictor for poorer prognosis of HCC patients and may act as an oncogene to promote HCC cell progression.

Identification of Energy Metabolism Genes for the Prediction of Survival in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) samples were clustered into three energy metabolism-related molecular subtypes (C1, C2, and C3) with different prognosis using the gene expression data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). HCC energy metabolism-related molecular subtype analysis was conducted based on the 594 energy metabolism genes. Differential expression analysis yielded 576 differentially expressed genes (DEGs) among the three subtypes, which were closely related to HCC progression. Six genes were finally selected from the 576 DEGs through LASSO-Cox regression and used in constructing a six-gene signature-associated prognostic risk model, which was validated using the TCGA internal and three GEO external validation cohorts. The risk model showed that high ANLN, ENTPD2, TRIP13, PLAC8, and G6PD expression levels were associated with bad prognosis, and high expression of ADH1C was associated with a good prognosis. The validation results showed that our risk model had a high distinguishing ability of prognosis in HCC patients. The four enriched pathways of the risk model were obtained by gene set enrichment analysis (GSEA) and found to be associated with the tumorigenesis and development of HCC, including the cell cycle, Wnt signaling pathway, drug metabolism cytochrome P450, and primary bile acid biosynthesis. The risk score calculated from the established risk model in 204 samples and other clinical characteristics were used in building a nomogram with a good prognostic prediction ability (C-index = 0.746, 95% CI = 0.714-0.777). The area under the curves (AUCs) of the nomogram model in 1-, 2-, and 3-years were 0.82, 0.77, and 0.79, respectively. Then, qRT-PCR and immunohistochemistry were used to validate the mRNA expression levels of the six genes, and significant differences in mRNA and gene expression were observed among the tumor and adjacent tissues. Overall, our study divided HCC patients into three energy metabolism-related molecular subtypes with different prognosis. Then, a risk model with a good performance in prognostic prediction was built using the TCGA dataset. This model can be used as an independent prognostic evaluation index for HCC patients.

Role of Anillin in Tumour: From a Prognostic Biomarker to a Novel Target

Anillin (ANLN), an actin-binding protein, reportedly plays a vital role in cell proliferation and migration, particularly in cytokinesis. Although there have been findings pointing to a contribution of ANLN to the development of cancer, the association of ANLN to cancer remains not fully understood. Here, we gather evidence to determine the applicability of ANLN as a prognostic tool for some types of cancer, and the impact that ANLN has on the hallmarks of cancer. We searched academic repositories including PubMed and Google Scholar to find and review studies related to cancer and ANLN. The conclusion is that ANLN could be a potent target for cancer treatment, but the roles ANLN, other than in cytokinesis and its influence on tumour microenvironment remodeling in cancer development, must be further elucidated, and specific ANLN inhibitors should be found.

Aberrant expression of two miRNAs promotes proliferation, hepatitis B virus amplification, migration and invasion of hepatocellular carcinoma cells: evidence from bioinformatic analysis and experimental validation

Background

As key negative regulators of gene expression, microRNAs (miRNAs) play an important role in the onset and progression of hepatocellular carcinoma (HCC). This study aimed to identify the miRNAs involved in HCC carcinogenesis and their regulated genes.

Methods

The Gene Expression Omnibus (GEO) dataset (GSE108724) was chosen and explored to identify differentially expressed miRNAs using GEO2R. For the prediction of potential miRNA target genes, the miRTarBase was explored. Enrichment analysis of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed by the DAVID online tool. The hub genes were screened out using the CytoHubba plug-in ranked by degrees. The networks between miRNAs and hub genes were constructed by Cytoscape software. MiRNA mimics and negative control were transfected into HCC cell lines and their effects on proliferation, hepatitis B virus DNA (HBV-DNA) replication, TP53 expression, migration, and invasion were investigated. The following methods were employed: MTT assay, quantitative PCR (qPCR) assay, western blotting, wound healing assay, and transwell assay.

Results

A total of 50 differentially expressed miRNAs were identified, including 20 upregulated and 30 downregulated miRNAs, in HCC tumor tissues compared to matched adjacent tumor-free tissues. The top three upregulated (miR-221-3p, miR-222-3p, and miR-18-5p) and downregulated (miR-375, miR-214-3p and miR-378d) miRNAs, ranked by |log₂ fold change (log₂FC)|, were chosen and their potential target genes were predicted. Two gene sets, targeted by the upregulated and the downregulated miRNAs, were identified respectively. GO and KEGG pathway analysis showed that the predicted target genes of upregulated and downregulated miRNAs were mainly enriched in the cell cycle and cancer-related pathways. The top ten hub nodes of gene sets ranked by degrees were identified as hub genes. Analysis of miRNA-hub gene network showed that miR-221-3p and miR-375 modulated most of the hub genes, especially involving regulation of TP53. The q-PCR results showed that miR-221-3p and miR-375 were markedly upregulated and downregulated, respectively, in HCC cells and HCC clinical tissue samples compared to non-tumoral tissues. Furthermore, miR-221-3p overexpression significantly enhanced proliferation, HBV-DNA replication, as well as the migration and invasion of HCC cells, whereas miR-375 overexpression resulted in opposite effects. Western blotting analysis showed that the overexpression of miR-221-3p and miR-375 reduced and increased TP53 expression, respectively.

Conclusion

The present study revealed that miR-211-3p and miR-375 may exert vital effects on cell proliferation, HBV-DNA replication, cell migration, and invasion through the regulation of TP53 expression in HCC.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.

Oncogenic Roles of RAD51AP1 in Tumor Tissues Related to Overall Survival and Disease-Free Survival in Hepatocellular Carcinoma

OBJECTIVE

This study aimed to investigate the associations between RAD51AP1 and the outcomes of hepatocellular carcinoma (HCC).

METHODS

RAD51AP1 expression levels were compared in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets. The Liver Hepatocellular Carcinoma (TCGA, Provisional) and GSE36376 datasets were used for survival analysis. RAD51AP1 associations with clinicopathological features were determined with the GSE36376 dataset.

RESULTS

RAD51AP1 mRNA expression was significantly upregulated in advanced liver fibrosis samples (S3-4 vs. S0-2 and G3-4 vs. G0-2) from hepatitis B virus (HBV)-related liver fibrosis patients and in tumor tissues and peripheral blood mononuclear cells (PBMCs) from HCC patients (all P < 0.05). HCC patients with high RAD51AP1 expression had significantly worse overall survival (OS) and disease-free survival (DFS) than those with low RAD51AP1 expression (P = 0.0034 and P = 0.0012, respectively) in the TCGA dataset, and these findings were validated with the GSE36376 dataset (P = 0.0074 and P = 0.0003, respectively). A Cox regression model indicated that RAD51AP1 was a risk factor for OS and DFS in HCC patients in GSE36376 (HR = 1.54, 95% CI = 1.02-2.32, P = 0.04 and HR = 1.71, 95% CI = 1.22-2.39, P = 0.002, respectively). Moreover, RAD51AP1 mRNA expression increased gradually with increasing tumor stage, including stratification by American Joint Committee on Cancer (AJCC) stages, Barcelona Clinic Liver Cancer (BCLC) stages and Edmondson grades. In addition, RAD51AP1 was overexpressed in HCC patients with intrahepatic metastasis, major portal vein invasion, vascular invasion and/or an alpha-fetoprotein (AFP) level > 300 ng/ml.

CONCLUSIONS

Contributing to an advanced tumor stage, intrahepatic metastasis, vascular invasion and AFP level elevation, RAD51AP1 upregulation was significantly associated with OS and DFS in HCC patients.