Identification of key genes and pathways in hepatocellular carcinoma: A preliminary bioinformatics analysis

Identification of CDK1 as a Biomarker for the Treatment of Liver Fibrosis and Hepatocellular Carcinoma Through Bioinformatics Analysis

Cyclin-dependent kinase 1 (CDK1) has emerged as a critical regulator of cell cycle progression, yet its role in liver fibrosis-associated hepatocellular carcinoma (LF-HCC) remains underexplored. This study aimed to systematically evaluate CDK1's prognostic significance, immune regulatory functions, and therapeutic potential in LF-HCC pathogenesis. Integrated bioinformatics approaches were applied to multi-omics datasets from GEO, TCGA, and TIMER databases. Differentially expressed genes were identified through enrichment analysis and protein-protein interaction networks. Survival outcomes were assessed via Kaplan-Meier analysis, while immune cell infiltration patterns were quantified using CIBERSORT. Molecular docking simulations evaluated CDK1's binding affinity with pharmacologically active compounds (alvocidib, seliciclib, alsterpaullone) using AutoDock Vina. CDK1 demonstrated significant overexpression in LF-HCC tissues compared to normal controls (p < 0.001). Elevated CDK1 expression correlated with reduced overall survival (HR = 2.41, 95% CI:1.78-3.26, p = 0.003) and advanced tumor staging (p = 0.007). Immune profiling revealed strong associations between CDK1 levels and immunosuppressive cell infiltration, particularly regulatory T cells (r = 0.63, p = 0.001) and myeloid-derived suppressor cells (r = 0.58, p = 0.004). Molecular docking confirmed high-affinity binding of CDK1 to kinase inhibitors through conserved hydrogen-bond interactions (binding energy ≤ -8.5 kcal/mol), with alvocidib showing optimal binding stability. This multimodal analysis establishes CDK1 as both a prognostic biomarker and immunomodulatory regulator in LF-HCC pathogenesis. The enzyme's dual role in driving tumor progression and reshaping the immune microenvironment positions it as a promising therapeutic target. Computational validation of CDK1 inhibitors provides a rational basis for developing precision therapies against LF-HCC, bridging translational gaps between biomarker discovery and clinical application.

Discovery of Genomic Targets and Therapeutic Candidates for Liver Cancer Using Single-Cell RNA Sequencing and Molecular Docking

Liver cancer is one of the most common malignancies and the second leading cause of cancer-related deaths worldwide, particularly in developing countries, where it poses a significant financial burden. Early detection and timely treatment remain challenging due to the complex mechanisms underlying the initiation and progression of liver cancer. This study aims to uncover key genomic features, analyze their functional roles, and propose potential therapeutic drugs identified through molecular docking, utilizing single-cell RNA sequencing (scRNA-seq) data from liver cancer studies. We applied two advanced hybrid methods known for their robust identification of differentially expressed genes (DEGs) regardless of sample size, along with four top-performing individual methods. These approaches were used to analyze four scRNA-seq datasets, leading to the identification of essential DEGs. Through a protein-protein-interaction (PPI) network, we identified 25 hub-of-hub genes (hHubGs) and 20 additional hHubGs from two naturally occurring gene clusters, ultimately validating a total of 36 hHubGs. Functional, pathway, and survival analyses revealed that these hHubGs are strongly linked to liver cancer. Based on molecular docking and binding-affinity scores with 36 receptor proteins, we proposed 10 potential therapeutic drugs, which we selected from a pool of 300 cancer meta-drugs. The choice of these drugs was further validated using 14 top-ranked published receptor proteins from a set of 42. The proposed candidates include Adozelesin, Tivozanib, NVP-BHG712, Nilotinib, Entrectinib, Irinotecan, Ponatinib, and YM201636. This study provides critical insights into the genomic landscape of liver cancer and identifies promising therapeutic candidates, serving as a valuable resource for advancing liver cancer research and treatment strategies.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

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

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

Circulating micro-RNAs Differentially Expressed in Korean Alzheimer's Patients With Brain Aβ Accumulation Activate Amyloidogenesis

BACKGROUND

Roles for extracellular vesicles (EVs) enriched with micro-RNAs (miRNAs) have been proposed in Alzheimer's disease (AD) pathogenesis, leading to the discovery of blood miRNAs as AD biomarkers. However, the diagnostic utility of specific miRNAs is not consistent. This study aimed to discover blood miRNAs that are differentially expressed in Korean AD patients, evaluate their clinical performance, and investigate their role in amyloidogenesis.

METHODS

We discovered miRNAs differentially expressed in AD (N = 8) from cognitively normal participants (CN, N = 7) or Parkinson's disease (PD) patients (N = 8). We evaluated the clinical performance of these miRNAs in plasma of subgroup (N = 99) and in plasma EVs isolated from the total cohort (N = 251). The effects of miRNAs on amyloidogenesis and on the regulation of their target genes were investigated in vitro.

RESULTS

Among 17 upregulated and one downregulated miRNAs in AD (>twofold), miR-122-5p, miR-210-3p, and miR-590-5p were differentially expressed compared with CN or PD. However, the diagnostic performance of the selected plasma or EV miRNAs in total participants were limited (area under the curve < 0.8). Nevertheless, levels of 3 miRNAs in plasma or plasma EVs of participants who were amyloid positron emission tomography (Aβ-PET) positive were significantly higher than those from the Aβ-PET negative participants (p < .05). The selected miRNAs induced Aβ production (p < .05) through activation of β-cleavage of amyloid precursor protein (CTF-β; p < .01), and downregulated their target genes (ADAM metallopeptidase domain 10, Brain-derived neurotrophic factor, and Jagged canonical notch ligand 1; p < .05), which was further supported by pathway enrichment analysis of target genes of the miRNAs.

CONCLUSION

In conclusion, despite of the limited diagnostic utility of selected miRNAs as plasma or plasma EV biomarkers, the discovered miRNAs may play a role in amyloidogenesis during AD onset and progression.

APEX1 promotes the oncogenicity of hepatocellular carcinoma via regulation of MAP2K6

Objective: Apurinic/apyrimidinic endonuclease 1 (APEX1), a key enzyme responsible for DNA base excision repair, has been linked to development and progression of cancers. In this work, we aimed to explore the role of APEX1 in hepatocellular carcinoma (HCC) and elucidate its molecular mechanism.

Methods: The expression of APEX1 in HCC tissues and matched adjacent normal tissues (n = 80 cases) was evaluated by immunohistochemistry. Web-based tools UALCAN and the Kaplan-Meier plotter were used to analyze the Cancer Genome Atlas database to compare expression of APEX1 mRNA to 5-year overall survival. APEX1 was stably silenced in two HCC cell lines, Hep 3B and Bel-7402, with shRNA technology. An in vivo tumorigenesis model was established by subcutaneously injecting sh-APEX1-transfected Bel-7402 cells into mice, and tumor growth was determined. We performed high-throughput transcriptome sequencing in sh-APEX1-treated HCC cells to identify the key KEGG signaling pathways induced by silencing of APEX1.

Results: APEX1 was significantly upregulated and predicted poor clinical overall survival in HCC patients. Silencing APEX1 inhibited the proliferation of HCC cells in vivo and in vitro, and it repressed invasion and migration and increased apoptosis and the percentage of cells in G1. Differentially expressed genes upon APEX1 silencing included genes involved in TNF signaling. A positive correlation between the expression of APEX1 and MAP2K6 was noted, and overexpressing MAP2K6 overcame cancer-related phenotypes associated with APEX1 silencing.

Conclusion: APEX1 enhances the malignant properties of HCC via MAP2K6. APEX1 may represent a valuable prognostic biomarker and therapeutic target in HCC.

Deciphering Key Genes and miRNAs Associated With Hepatocellular Carcinoma via Network-Based Approach

Hepatocellular carcinoma (HCC)is a common type of liver cancer and has a high mortality world-widely. The diagnosis, prognoses, and therapeutics are very poor due to the unclear molecular mechanism of progression of the disease. To unveil the molecular mechanism of progression of HCC, we extract a large sample of mRNA expression levels from the GEO database where a total of 167 samples were used for study, and out of them, 115 samples were from HCC tumor tissue. This study aims to investigate the module of differentially expressed genes (DEGs)which are co-expressed only in HCC sample data but not in normal tissue samples. Thereafter, we identified the highly significant module of significant co-expressed genes and formed a PPI network for these genes. There were only six genes (namely, MSH3, DMC1, ALPP, IL10, ZNF223, and HSD17B7)obtained after analysis of the PPI network. Out of six only MSH3, DMC1, HSD17B7, and IL10 were found enriched in GO Term & Pathway enrichment analysis and these candidate genes were mainly involved in cellular process, metabolic and catalytic activity, which promote the development & progression of HCC. Lastly, the composite 3-node FFL reveals the driver miRNAs and TFs associated with our key genes.

Identification of Molecular Subgroups in Liver Cirrhosis by Gene Expression Profiles

Background: Liver cirrhosis is characterized by high mortality, bringing a serious health and economic burden to the world. The clinical manifestations of liver cirrhosis are complex and heterogeneous. According to subgroup characteristics, identifying cirrhosis has become a challenge. Objectives: The purpose of this study was to evaluate the difference between different subgroups of cirrhosis. The ultimate goal of research on these different phenotypes was to discover groups of patients with unique treatment characteristics, and formulate targeted treatment plans that improve the prognosis of the disease and improve the patients’ quality of life. Methods: We obtained the relevant gene chip by searching the gene expression omnibus (GEO) database. According to the gene expression profile, 79 patients with liver cirrhosis were divided into four subgroups, which showed different expression patterns. Therefore, we used weighted gene coexpression network analysis (WGCNA) to find differences between subgroups. Results: The characteristics of the WGCNA module indicated that subjects in subgroup I might exhibit inflammatory characteristics; subjects in subgroup II might exhibit metabolically active characteristics; arrhythmogenic right ventricular cardiomyopathy and neuroactive ligand-receptive somatic interaction pathways were significantly enriched in subgroup IV. We did not find a significantly upregulated pathway in the third subgroup. Conclusions: In this study, a new type of clinical phenotype classification of liver cirrhosis was derived by consensus clustering. This study found that patients in different subgroups may have unique gene expression patterns. This new classification method helps researchers explore new treatment strategies for cirrhosis based on clinical phenotypic characteristics.

Comprehensive Pan-Cancer Analysis of the Prognostic and Immunological Roles of the METTL3/lncRNA-SNHG1/miRNA-140-3p/UBE2C Axis

Growing evidence has demonstrated that UBE2C plays a critical role in cancer progression, but there is no study focusing on the prognosis, upstream regulation mechanism, and immunological roles of UBE2C across diverse tumor types. In this study, we found that UBE2C was elevated in this human pan-cancer analysis, and high expression of UBE2C was correlated with poor prognosis. In addition, UBE2C expression was markedly associated with tumor mutation burden (TMB), microsatellite instability (MSI), immune cell infiltration, and diverse drug sensitivities. Finally, we showed that the METTL3/SNHG1/miRNA-140-3p axis could potentially regulate UBE2C expression. N(6)-Methyladenosine (m6A) modifications improved the stability of methylated SNHG1 transcripts by decreasing the rate of RNA degradation, which lead to upregulation of SNHG1 in non-small cell lung cancer (NSCLC). In vitro functional experiments showed that SNHG1, as a competing endogenous RNA, sponges miR-140-3p to increase UBE2C expression in NSCLC cell lines. Our study elucidates the clinical importance and regulatory mechanism of the METTL3/SNHG1/miRNA-140-3p/UBE2C axis in NSCLC and provides a prognostic indicator, as well as a promising therapeutic target for patients with NSCLC.

Multiomics Profiling of Alzheimer's Disease Serum for the Identification of Autoantibody Biomarkers

New biomarkers of Alzheimer's disease (AD) with a diagnostic value in preclinical and prodromal stages are urgently needed. AD-related serum autoantibodies are potential candidate biomarkers. Here, we aimed at identifying AD-related serum autoantibodies using protein microarrays and mass spectrometry-based methods. To this end, an untargeted complementary screening using high-density (42,100 antigens) and low-density (384 antigens) planar protein-epitope signature tag (PrEST) arrays and an immunoprecipitation protocol coupled to mass spectrometry analysis were used for serum autoantibody profiling. From the untargeted screening phase, 377 antigens corresponding to 338 proteins were selected for validation. Out of them, IVD, CYFIP1, and ADD2 seroreactivity was validated using 128 sera from AD patients and controls by PrEST-suspension bead arrays, and ELISA or luminescence Halotag-based bead immunoassay using full-length recombinant proteins. Importantly, IVD, CYFIP1, and ADD2 showed in combination a noticeable AD diagnostic ability. Moreover, IVD protein abundance in the prefrontal cortex was significantly two-fold higher in AD patients than in controls by western blot and immunohistochemistry, whereas CYFIP1 and ADD2 were significantly down-regulated in AD patients. The panel of AD-related autoantigens identified by a comprehensive multiomics approach may provide new insights of the disease and should help in the blood-based diagnosis of Alzheimer's disease. Mass spectrometry raw data are available in the ProteomeXchange database with the access number PXD028392.

Integrated in silico analysis for the identification of key genes and signaling pathways in copper oxide nanoparticles toxicity

Copper oxide nanoparticles (CuO-NPs) are used in various industrial and commercial products due to their enhanced physicochemical properties. The vast consumption increases their exposure in the environment, thereby affecting the ecosystem. Even with the rise in research towards understanding their toxicity, the major signaling cascades and key genes involved in CuO-NPs remain elusive due to the various attributes involved (size, shape, charge, coating in terms of nanoparticles, and dose, duration, and species used in the experiment). The focus of the study is to identify the key signaling cascades and genes involved in CuO-NPs toxicity irrespective of these attributes. CuO-NPs related microarray expression profiles were screened from GEO database and were subjected to toxicogenomic analysis to elucidate the toxicity mechanism. In silico tools were used to obtain the DEGs, followed by GO and KEGG functional enrichment analysis. The identified DEGs were then analyzed to determine major signaling pathways and key genes. Module and centrality parameter analysis was performed to identify the key genes. Further, the miRNAs and transcription factors involved in regulating the genes were predicted, and their interactive pathways were constructed. A total of 44 DEGs were commonly present in all the analysed datasets and all of them were downregulated. GO analysis reveals that most of the genes were enriched in functions related to cell division and chemotaxis. Cell-cycle, chemokine, cytokine-cytokine receptor interaction, and p53 signaling pathways were the key pathways with Cdk1 as the major biomarker altered irrespective of the variables (dosage, duration, species used, and surface coating). Overall, our integrated toxicogenomic analysis reveal that Cdk1 regulated cell cycle and cytokine-cytokine signaling cascades might be responsible for CuO-NPs toxicity. These findings will help us in understanding the mechanisms involved in NPs toxicity.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

A Five-Gene-Based Prognostic Signature for Hepatocellular Carcinoma

Objective: This study intends to identify potential prognostic marker genes associated with the prognosis of patients suffering from hepatocellular carcinoma (HCC) based on TCGA and GEO analysis.

Methods: TCGA-LIHC cohort was downloaded and the data related to HCC were extracted from The Cancer Genome Atlas (TCGA) database and subjected to differential analysis. HCC-related gene expression datasets were retrieved from the GEO database, followed by differential analysis. After intersection of the results of TCGA and GEO databases, gene interaction analysis was performed to obtain the core genes. To identify the genes related to the prognosis of HCC patients, we conducted univariate and multivariate Cox analyses.

Results: Based on differential analysis of TCGA database, 854 genes were differentially expressed in HCC, any of which might link to the occurrence and progression of HCC. Meanwhile, joint analysis of HCC-related gene expression datasets in the GEO database screened 214 genes. Five core genes CDC20, TOP2A, RRM2, UBE2C and AOX1 were significantly associated with the prognosis of HCC patients and the risk model based on these five genes effectively predicted the prognosis of HCC patients.

Conclusion: Collectively, our data suggest that CDC20, TOP2A, RRM2, UBE2C and AOX1 may be the key genes affecting the prognosis of patients with HCC. The five-gene signature could accurately predict the prognosis of HCC patients.

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

Background

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

Methods

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

Results

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

Conclusions

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

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

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

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

Alcohol‑associated hepatocellular carcinoma (HCC) is a subtype of HCC with poor prognosis. The present study aimed to identify key biomarkers for alcohol‑associated HCC. The gene data profiles and corresponding clinical traits of patients with alcohol‑associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Firstly, good genes and good samples were identified, which were subsequently used to conduct weighted gene co‑expression network analysis (WGCNA). Hub genes in the significant modules were selected following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and from constructing a protein‑protein interaction (PPI) network. Real hub genes among hub genes were determined following progression, survival analysis and gene set enrichment analysis (GSEA), as well as reverse transcription‑quantitative PCR and immunohistochemical staining of non‑alcohol‑ and alcohol‑associated HCC samples. In total, 64 good samples of alcohol‑associated HCC with height score <160 were selected, from which 15,195 good genes were identified and used to conduct WGCNA; 8 gene co‑expressed modules were identified using WGCNA, while 3 modules (including pink, magenta and turquoise) were significantly associated with Child‑Pugh score, T‑stage and body weight. Following GO and KEGG analysis and construction of the PPI network, a total of 30 hub genes were identified in the aforementioned 3 gene co‑expressed modules, while 16 hub genes (including AURKB, BUB1, BUB1B, CCNB1, CCNB2, CDC20, CDCA8, CDK1, PLK1, RPS5, RPS7, RPS8, RPS14, RPS27, RPSA and TOP2A) were associated with the development of alcohol‑associated HCC, and had a significant prognosis value. Among these genes, only RPS8 was highly expressed in alcohol‑associated HCC, but not in non‑alcohol‑associated HCC, while RPS5 was not significantly associated in either alcohol‑ or non‑alcohol‑associated HCC. GSEA demonstrated that 10 pathways, including RNA polymerase and ribosome pathways were enriched in alcohol‑associated HCC samples where RPS8 was highly expressed. Taken together, the results of the present study demonstrate that RPS8 may be a novel biomarker for the diagnosis of patients with alcohol‑associated HCC.

Identification of Key Biomarkers in Bladder Cancer: Evidence from a Bioinformatics Analysis

Bladder cancer (BCa) is one of the most common malignancies and has a relatively poor outcome worldwide. However, the molecular mechanisms and processes of BCa development and progression remain poorly understood. Therefore, the present study aimed to identify candidate genes in the carcinogenesis and progression of BCa. Five GEO datasets and TCGA-BLCA datasets were analyzed by statistical software R, FUNRICH, Cytoscape, and online instruments to identify differentially expressed genes (DEGs), to construct protein‒protein interaction networks (PPIs) and perform functional enrichment analysis and survival analyses. In total, we found 418 DEGs. We found 14 hub genes, and gene ontology (GO) analysis revealed DEG enrichment in networks and pathways related to cell cycle and proliferation, but also in cell movement, receptor signaling, and viral carcinogenesis. Compared with noncancerous tissues, TPM1, CRYAB, and CASQ2 were significantly downregulated in BCa, and the other hub genes were significant upregulated. Furthermore, MAD2L1 and CASQ2 potentially play a pivotal role in lymph nodal metastasis. CRYAB and CASQ2 were both significantly correlated with overall survival (OS) and disease-free survival (DFS). The present study highlights an up to now unrecognized possible role of CASQ2 in cancer (BCa). Furthermore, CRYAB has never been described in BCa, but our study suggests that it may also be a candidate biomarker in BCa.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Upregulation of EPS8L3 is associated with tumorigenesis and poor prognosis in patients with liver cancer

Epidermal growth factor receptor kinase substrate 8 (EPS8) plays critical roles in a variety of solid tumors. However, the biologic functions and clinical significance of EPS8‑like 3 (EPS8L3), an EPS8‑related protein, in liver cancer remain unclear. To measure EPS8L3 expression in liver cancer cell lines, reverse transcription‑quantitative PCR and western blot analyses were performed. The correlation between 338 patients with liver cancer and various clinicopathological factors obtained from the Oncomine database were evaluated using the χ2 test. Survival of patients with different expression of EPS8L3 was determined using Kaplan‑Meier survival analysis with a log rank test, and Cox regression analysis was performed to estimate the prognostic significance of EPS8L3 expression. Additionally, cell proliferation and migration were determined using Cell Counting Kit‑8 and wound healing assays. The results revealed that EPS8L3 expression was significantly upregulated in liver cancer tissues and cell lines (P<0.01), and that the expression of EPS8L3 was closely associated with grade (P=0.024) and mortality (P=0.011). Furthermore, survival analysis suggested patients with high EPS8L3 expression exhibited shorter survival compared with those with low EPS8L3 expression. Cox regression analysis indicated that EPS8L3 could be regarded as a prognostic biomarker in patients with liver cancer (hazard ratio, 1.58; 95% confidence interval, 1.085‑2.301; P=0.017). Additionally, in vitro assays revealed that EPS8L3 depletion significantly inhibited liver cancer cell proliferation and migration, and reduced the levels of phosphorylated PI3K and AKT in the PI3K/AKT signaling pathway. Collectively, the results of the present study, for the first time to the best of our knowledge, demonstrated that EPS8L3 serves as an oncogene in liver cancer development; therefore, EPS8L3 may be a valuable prognostic predictor for patients with liver cancer.

MicroRNA‑181c suppresses growth and metastasis of hepatocellular carcinoma by modulating NCAPG

Purpose: Numerous studies have shown that the expression of microRNA-181c (miR-181c) is inhibited in various cancers, which suggests that it has a cancer suppressive effect. In the current study, we evaluated the regulation and characteristics of miR-181c in human hepatocellular carcinoma (HCC).

Materials and methods: Samples of tumor tissues and adjacent non-tumor tissues were collected from 52 patients with HCC, and expression levels of miR-181c in these samples were investigated via quantitative real-time polymerase chain reaction. HCC cell migration and invasion were investigated via wound healing assays and transwell assays. HCC cell apoptosis rates were assessed via flow cytometry, and HCC proliferation was assessed via 5-ethynyl-20-deoxyuridine assays. In vivo tumors were initiated by subcutaneously inoculating HCC cells into nude mice. And various biomarkers were investigated via western blotting.

Results: In microarray datasets and tumor tissues, significant downregulation of miR-181c was apparent compared with non-tumorous adjacent tissues. Expression of miR-181c in HCC cells was also significantly lower than it was in normal human liver cells. miR-181c regulated the migration, invasion, apoptosis, and proliferation of HCC cell lines in vitro, and tumor development in vivo. Observations also suggest that miR-181c regulates NCAPG in HCC cells, and its expression affects cellular invasion, migration, proliferation, and apoptosis. There was a negative correlation between miR-181c expression and NCAPG in HCC tissue samples.

Conclusion: miR-181c exhibits tumor-suppression via the regulation of NCAPG levels.