CDK1, CCNB1, CDC20, BUB1, MAD2L1, MCM3, BUB1B, MCM2, and RFC4 May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

Shared Genomic Features Between Lung Adenocarcinoma and Type 2 Diabetes: A Bioinformatics Study

BACKGROUND

Lung adenocarcinoma (LUAD) is a common histopathological variant of non-small cell lung cancer. Individuals with type 2 diabetes (T2DM) face an elevated risk of developing LUAD. We examined the common genomic characteristics between LUAD and T2DM through bioinformatics analysis.

METHODS

We acquired the GSE40791, GSE25724, GSE10072, and GSE71416 datasets. Differentially expressed genes (DEGs) were identified through R software, particularly its version 4.1.3 and analyzed via gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Subsequently, we analyzed the relationship between immune cell infiltration and DEGs. we constructed a protein-protein interaction network using STRING and visualized it with Cytoscape. Moreover, gene modules were identified utilizing the MCODE plugin, and hub genes were selected through the CytoHubba plugin. Additionally, we evaluated the predictive significance of hub genes using receiver operating characteristic curves and identified the final central hub genes. Finally, we forecasted the regulatory networks of miRNA and transcription factors for the central hub genes.

RESULTS

A total of 748 DEGs were identified. Analysis of immune infiltration showed a notable accumulation of effector-memory CD8 T cells, T follicular helper cells, type 1 T helper cells, activated B cells, natural killer cells, macrophages, and neutrophils in both LUAD and T2DM. Moreover, these DEGs were predominantly enriched in immune-related pathways, including the positive regulation of I-κB kinase/NF-κB signaling, positive regulation of immunoglobulin production, cellular response to interleukin-7, and cellular response to interleukin-4. The TGF-β signaling pathway was significantly important among them. Additionally, seven hub genes were identified, including ATR, RFC4, MCM2, NUP155, NUP107, NUP85, and NUP37. Among them, ATR, RFC4, and MCM2 were identified as pivotal hub genes. Additionally, hsa-mir147a, hsa-mir16-5p, and hsa-mir-1-3p were associated with LUAD and T2DM. SP1 (specific protein 1) and KDM5A (lysine-specific demethylase 5A) regulated MCM2, ATR, and RFC4.

CONCLUSIONS

Our study elucidates the common mechanisms of immune response, TGF-β signaling pathway, and natural killer cells in LUAD and T2DM, and identifies ATR, RFC4, and MCM2 as key potential biomarkers and therapeutic targets for the comorbidity of these two conditions.

MCM3 promotes hepatocellular carcinoma progression via Epithelial-mesenchymal Transition through AKT/Twist signaling pathway

INTRODUCTION AND OBJECTIVES

Hepatocellular carcinoma (HCC), a leading cause of cancer fatalities, challenges clinicians with high recurrence and metastasis rates, urging the need for novel prognostic markers and therapeutic avenues. Minichromosome maintenance complex component 3 (MCM3) has been implicated in various cancers, but its role in HCC is not well-characterized.

MATERIALS AND METHODS

We investigated MCM3 expression in HCC through cell line and patient sample analyses, functional assays to determine its effect on cellular behaviors, and signal pathway exploration.

RESULTS

Elevated MCM3 expression was identified in both HCC cell lines and patient tissues, correlating with microvascular invasion, advanced cancer stage, and reduced survival. Functionally, MCM3 fueled HCC cellular proliferation, migration, invasion, and epithelial-mesenchymal transition (EMT) in vitro and expedited tumor growth in vivo. Mechanistically, MCM3 was found to potentiate EMT by upregulating Twist via the AKT signaling pathway.

CONCLUSIONS

MCM3 emerges as an oncogenic influencer in HCC, driving disease progression through the AKT/Twist axis. Its expression patterns hold prognostic value, and targeting MCM3 may offer a novel therapeutic strategy for HCC.

Exploring potential key genes and pathways associatedwith hepatocellular carcinoma prognosis through bioinformatics analysis, followed by experimental validation

BACKGROUND

Liver Hepatocellular Carcinoma (LIHC) is a prevalent and aggressive liver cancer with limited therapeutic options. Identifying key genes involved in LIHC can enhance our understanding of its molecular mechanisms and aid in the development of targeted therapies. This study aims to identify differentially expressed genes (DEGs) and key hub genes in LIHC using bioinformatics approaches and experimental validation.

METHOD

We analyzed two LIHC-related datasets, GSE84598 and GSE19665, from the Gene Expression Omnibus (GEO) database to identify DEGs. Differential expression analysis was performed using the limma package in R to identify DEGs between cancerous and non-cancerous liver tissues. A Protein-Protein Interaction (PPI) network was constructed using STRING to determine key hub genes. Further validation of these hub genes was conducted through UALCAN, OncoDB, and the Human Protein Atlas (HPA) databases for mRNA and protein expression levels. Promoter methylation and mutational analyses were performed using cBioPortal. Kaplan-Meier survival analysis assessed the impact of hub gene expression on patient survival. Correlations with immune cell abundance and drug sensitivity were explored using GSCA. Finally, AURKA was knocked down in HepG2 cells, and cell proliferation, colony formation, and wound healing assays were performed.

RESULTS

Analysis identified 180 DEGs, with four key hub genes, including AURKA, BUB1B, CCNA2, and PTTG1 showing significant overexpression and hypomethylation in LIHC tissues. AURKA knockdown in HepG2 cells led to decreased cell proliferation, reduced colony formation, and impaired wound healing, confirming its role in LIHC progression. These hub genes were also hypomethylated and their elevated expression correlated with poor overall survival.

CONCLUSION

AURKA, BUB1B, CCNA2, and PTTG1 are crucial for LIHC pathogenesis and may serve as potential biomarkers or therapeutic targets. Our findings provide new insights into LIHC mechanisms and suggest promising avenues for future research and therapeutic development.

BUB1 as a novel marker for predicting the immunotherapy efficacy and prognosis of breast cancer

Background

Budding uninhibited by benzimidazole 1 (BUB1) is a highly conserved serine/threonine kinase, showing prominent importance for proper function during mitosis. However, little is known about BUB1 mRNA expression in breast cancer (BRCA) and its correlation with prognosis and immune infiltration. Hence, we aimed to unveil its potential as groundbreaking biomarkers for immunotherapy efficacy and the prognosis of BRCA.

Methods

Database for Annotation, Visualization, and Integrated Discovery (DAVID) is a potent tool for identifying significant clusters of genes and pathways in the resulting dataset. In this study, gene set enrichment analysis of BUB1 was conducted using DAVID. The clinical characteristics of patients with or without altered BUB1 mRNA expression were compared using cBioPortal. Tumor Immune Estimation Resource (TIMER) is a known as database for comprehensive analysis of tumor-infiltrating immune cells in various cancers. In the present study, the relationship between BUB1 expression and the abundance of immune infiltrates was explored using TIMER in BRCA. Immunohistochemistry staining was performed to analyze the protein expression of BUB1 in tumor tissue specimens. We used PrognoScan and Kaplan-Meier Plotter to evaluate the prognosis of patients with different BUB1 expression levels.

Results

The expression of BUB1 in various tumor tissues was higher than that in adjacent normal tissues. BUB1 was mainly localized to the nucleoplasm and additionally localized to the cytosol. Functional enrichment analyses revealed that the cell cycle was the most significant pathway. Abnormal BUB1 mRNA expression was more frequently detected in invasive ductal carcinoma with higher histological grades and BRCAs with estrogen receptor (ER)-negative, human epidermal growth receptor 2 (HER2)-negative, and basal-like phenotypes. The BUB1 expression was correlated positively with tumor purity, B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, while BUB1 had no significant correlation with macrophages. The results of immunohistochemical staining from clinical samples further confirmed that BUB1 was overexpressed in BRCA compared to benign tumor (fibroadenoma of breast) (P<0.01). BRCA patients with lower BUB1 expression had a better prognosis than those with higher BUB1 expression in overall survival (OS) curves, distant metastasis-free survival (DMFS) curves, and relapse-free survival (RFS) curves (P<0.05).

Conclusions

Our results suggest that BUB1 is a potential molecular biomarker for evaluating the prognosis and predicting the effectiveness of immunotherapy for BRCA.

Prognostic analysis and risk assessment based on RNA editing in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model's utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

CDC20 Holds Novel Regulation Mechanism in RPA1 during Different Stages of DNA Damage to Induce Radio-Chemoresistance

Targeting CDC20 can enhance the radiosensitivity of tumor cells, but the function and mechanism of CDC20 on DNA damage repair response remains vague. To examine that issue, tumor cell lines, including KYSE200, KYSE450, and HCT116, were utilized to detect the expression, function, and underlying mechanism of CDC20 in radio-chemoresistance. Western blot and immunofluorescence staining were employed to confirm CDC20 expression and location, and radiation could upregulate the expression of CDC20 in the cell nucleus. The homologous recombination (HR) and non-homologous end joining (NHEJ) reporter gene systems were utilized to explore the impact of CDC20 on DNA damage repair, indicating that CDC20 could promote HR repair and radio/chemo-resistance. In the early stages of DNA damage, CDC20 stabilizes the RPA1 protein through protein-protein interactions, activating the ATR-mediated signaling cascade, thereby aiding in genomic repair. In the later stages, CDC20 assists in the subsequent steps of damage repair by the ubiquitin-mediated degradation of RPA1. CCK-8 and colony formation assay were used to detect the function of CDC20 in cell vitality and proliferation, and targeting CDC20 can exacerbate the increase in DNA damage levels caused by cisplatin or etoposide. A tumor xenograft model was conducted in BALB/c-nu/nu mice to confirm the function of CDC20 in vivo, confirming the in vitro results. In conclusion, this study provides further validation of the potential clinical significance of CDC20 as a strategy to overcome radio-chemoresistance via uncovering a novel role of CDC20 in regulating RPA1 during DNA damage repair.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

Reproductive endocrine disruption effect and mechanism in male zebrafish after life cycle exposure to environmental relevant triclosan

Triclosan (TCS) is a wide-spectrum antibacterial agent that is found in various water environments. It has been reported to have estrogenic effects. However, the impact of TCS exposure on the reproductive system of zebrafish (Danio rerio) throughout their life cycle is not well understood. In this study, zebrafish fertilized eggs were exposed to 0, 10, and 50 μg/L TCS for 120 days. The study investigated the effects of TCS exposure on brain and testis coefficients, the expression of genes related to the hypothalamus-pituitary-gonadal (HPG) axis, hormone levels, vitellogenin (VTG) content, histopathological sections, and performed RNA sequencing of male zebrafish. The results revealed that life cycle TCS exposure had significant effects on zebrafish reproductive parameters. It increased the testis coefficient, while decreasing the brain coefficient. TCS exposure also led to a decrease in mature spermatozoa and altered the expression of genes related to the HPG axis. Furthermore, TCS disrupted the balance of sex hormone levels and increased VTG content of male zebrafish. Transcriptome sequencing analysis indicated that TCS affected reproductive endocrine related pathways, including PPAR signaling pathway, cell cycle, GnRH signaling pathway, steroid biosynthesis, cytokine-cytokine receptor interaction, and steroid hormone biosynthesis. Protein-protein interaction (PPI) network analysis confirmed the enrichment of hub genes in these pathways, including bub1bb, ccnb1, cdc20, cdk1, mcm2, mcm5, mcm6, plk1, and ttk in the brain, as well as fabp1b.1, fabp2, fabp6, ccr7, cxcl11.8, hsd11b2, and hsd3b1 in the testis. This study sheds light on the reproductive endocrine-disrupting mechanisms of life cycle exposure to TCS.

[Identification of key genes in Wilms tumor based on high-throughput RNA sequencing and their impacts on prognosis and immune responses]

OBJECTIVE

To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients.

METHODS

High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor.

RESULTS

Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5- and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells.

CONCLUSION

TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

The prognostic and immunological role of MCM3 in pan-cancer and validation of prognosis in a clinical lower-grade glioma cohort

Background: Previous studies have shown that MCM3 plays a key role in initiating DNA replication. However, the mechanism of MCM3 function in most cancers is still unknown. The aim of our study was to explore the expression, prognostic role, and immunological characteristics of MCM3 across cancers. Methods: We explored the expression pattern of MCM3 across cancers. We subsequently explored the prognostic value of MCM3 expression by using univariate Cox regression analysis. Spearman correlation analysis was performed to determine the correlations between MCM3 and immune-related characteristics, mismatching repair (MMR) signatures, RNA modulator genes, cancer stemness, programmed cell death (PCD) gene expression, tumour mutation burden (TMB), microsatellite instability (MSI), and neoantigen levels. The role of MCM3 in predicting the response to immune checkpoint blockade (ICB) therapy was further evaluated in four immunotherapy cohorts. Single-cell data from CancerSEA were analysed to assess the biological functions associated with MCM3 in 14 cancers. The clinical correlation and independent prognostic significance of MCM3 were further analysed in the TCGA and CGGA lower-grade glioma (LGG) cohorts, and a prognostic nomogram was constructed. Immunohistochemistry in a clinical cohort was utilized to validate the prognostic utility of MCM3 expression in LGG. Results: MCM3 expression was upregulated in most tumours and strongly associated with patient outcomes in many cancers. Correlation analyses demonstrated that MCM3 expression was closely linked to immune cell infiltration, immune checkpoints, MMR genes, RNA modulator genes, cancer stemness, PCD genes and the TMB in most tumours. There was an obvious difference in outcomes between patients with high MCM3 expression and those with low MCM3 expression in the 4 ICB treatment cohorts. Single-cell analysis indicated that MCM3 was mainly linked to the cell cycle, DNA damage and DNA repair. The expression of MCM3 was associated with the clinical features of LGG patients and was an independent prognostic indicator. Finally, the prognostic significance of MCM3 in LGG was validated in a clinical cohort. Conclusion: Our study suggested that MCM3 can be used as a potential prognostic marker for cancers and may be associated with tumour immunity. In addition, MCM3 is a promising predictor of immunotherapy responses.

Identification of expression profiles and prognostic value of RFCs in colorectal cancer

Colorectal cancer (CRC) ranks among the most prevalent cancers globally, with its incidence closely tied to DNA damage. The Replication Factor C (RFC) complexes comprises five protein subunits: RFC1, RFC2, RFC3, RFC4, and RFC5. These RFC complexes play crucial roles in DNA replication, repair pathways, activities post DNA damage, and ATP-dependent processes during DNA synthesis. However, the impact of RFC complexes proteins on CRC prognosis remains unclear. To explore this, we employed a computational analysis approach, utilizing platforms such as the DepMap portal, GEPIA, DAVID Bioinformatics for KEGG pathway analysis, Human Protein Atlas (HPA), STRING, and TIMER. Our results indicate that the mRNA levels of RFC1 and RFC5 were the least expressed among CRC cell lines compared to other RFC complex subunits. Notably, low RFC1 and RFC5 expression was correlated with poor prognosis in terms of CRC patients' overall survival (OS). Immunohistochemical results from the Human Protein Atlas demonstrated medium staining for RFC1, RFC2, and RFC5 in CRC tissues. Furthermore, the low expression of RFC1 and RFC5 showed a significant correlation with high expression levels of miR-26a-5p and miR-636, impacting cell proliferation through mismatch repair, DNA replication, and the nucleotide excision repair pathway. Although the precise functions of RFC1 in cancer are still unknown, our findings suggest that the small-molecule single target, CHEMBL430483, and multiple target molecules could be potential treatments for CRC. In conclusion, the elevated expression of miR-26a-5p and miR-636 targeting RFC1 and RFC5 expression holds promise as a potential biomarker for early-stage CRC detection. These insights provide novel directions and strategies for CRC therapies.

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.

Reconstruction of the regulatory hypermethylation network controlling hepatocellular carcinoma development during hepatitis C viral infection

Hepatocellular carcinoma (HCC) has been associated with hepatitis C viral (HCV) infection as a potential risk factor. Nonetheless, the precise genetic regulatory mechanisms triggered by the virus, leading to virus-induced hepatocarcinogenesis, remain unclear. We hypothesized that HCV proteins might modulate the activity of aberrantly methylated HCC genes through regulatory pathways. Virus-host regulatory pathways, interactions between proteins, gene expression, transport, and stability regulation, were reconstructed using the ANDSystem. Gene expression regulation was statistically significant. Gene network analysis identified four out of 70 HCC marker genes whose expression regulation by viral proteins may be associated with HCC: DNA-binding protein inhibitor ID - 1 (ID1), flap endonuclease 1 (FEN1), cyclin-dependent kinase inhibitor 2A (CDKN2A), and telomerase reverse transcriptase (TERT). It suggested the following viral protein effects in HCV/human protein heterocomplexes: HCV NS3(p70) protein activates human STAT3 and NOTC1; NS2-3(p23), NS5B(p68), NS1(E2), and core(p21) activate SETD2; NS5A inhibits SMYD3; and NS3 inhibits CCN2. Interestingly, NS3 and E1(gp32) activate c-Jun when it positively regulates CDKN2A and inhibit it when it represses TERT. The discovered regulatory mechanisms might be key areas of focus for creating medications and preventative therapies to decrease the likelihood of HCC development during HCV infection.

A novel T-cell proliferation-associated gene predicts prognosis and reveals immune infiltration in patients with oral squamous cell carcinoma

OBJECTIVE

Oral squamous cell carcinoma (OSCC) is a highly malignant tumour, and the prediction of its prognosis remains challenging. The prognostic value of T-lymphocyte proliferation regulators in OSCC remains to be explored.

DESIGN

We integrated mRNA expression profiles and relevant clinical information of OSCC patients from The Cancer Genome Atlas database. The expression and function of T-lymphocyte proliferation regulators and their relationship with overall survival (OS) were analysed. The T-lymphocyte proliferation regulator signature was screened using univariate Cox regression and least absolute shrinkage and selection operator coefficients and used to construct models for prognosis and staging prediction as well as for immune infiltration analysis. Final validation was performed using single-cell sequencing database and immunohistochemical staining.

RESULTS

Most T-lymphocyte proliferation regulators in the TCGA cohort exhibited different expression levels between OSCC and paracancerous tissues. A prognostic model constructed using the T-lymphocyte proliferation regulator signature (RAN, CDK1, and CDK2) was used to categorise patients into high- and low-risk groups. The OS was significantly lower in the high-risk group than the low-risk group (p < 0.01). The predictive ability of the T-lymphocyte proliferation regulator signature was validated by receiver operating characteristic curve analysis. Immune infiltration analysis revealed different immune statuses in both groups.

CONCLUSIONS

We established a new T-lymphocyte proliferation regulator signature that can predict the prognosis of OSCC. The results of this study will contribute to studies of T-cell proliferation and the immune microenvironment in OSCC to improve prognosis and immunotherapeutic response.

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

NCAPD2 promotes breast cancer progression through E2F1 transcriptional regulation of CDK1

Breast cancer (BC) is a serious threat to women's health worldwide. Non-SMC condensin I complex subunit D2 (NCAPD2) is a regulatory subunit of the coagulin I complex, which is mainly involved in chromosome coagulation and separation. The clinical significance, biological behavior, and potential molecular mechanism of NCAPD2 in BC were investigated in this study. We found that NCAPD2 was frequently overexpressed in BC, and it had clinical significance in predicting the prognosis of BC patients. Moreover, loss-of-function assays demonstrated that NCAPD2 knockdown restrained the progression of BC by inhibiting proliferation and migration and enhancing apoptosis in vitro. It was further confirmed that the downregulation of NCAPD2 inhibited tumor growth in vivo. NCAPD2 promoted the progression of BC through the extracellular signal-regulated kinase 5 (ERK5) signaling pathway. Additionally, NCAPD2 could transcriptionally activate CDK1 by interacting with E2F transcription factor 1 (E2F1) in MDA-MB-231 cells. Overexpression of CDK1 alleviated the inhibitory effects of NCAPD2 knockdown in BC cells. In summary, the NCAPD2/E2F1/CDK1 axis may play a role in promoting the progression of BC, which may provide a blueprint for molecular therapy.

CD5L-associated gene analyses highlight the dysregulations, prognostic effects, immune associations, and drug-sensitivity predicative potentials of LCAT and CDC20 in hepatocellular carcinoma

BACKGROUND

The dysregulation of CD5L has been reported in hepatocellular carcinoma (HCC). However, its functions in HCC were controversial. In this study, we aimed to identify CD5L-associated pathways and markers and explore their values in HCC diagnosis, prognosis and treatment.

METHODS

HCC datasets with gene expression profiles and clinical data in TCGA and ICGC were downloaded. The immune/stroma cell infiltrations were estimated with xCell. CD5L-associated pathways and CD5L-associated genes (CD5L-AGs) were identified with gene expression comparisons and gene set enrichment analysis (GSEA). Cox regression, Kaplan-Meier survival analysis, and least absolute shrinkage and selection operator (LASSO) regression analysis were performed. The correlations of the key genes with immune/stroma infiltrations, immunoregulators, and anti-cancer drug sensitivities in HCC were investigated. At protein level, the key genes dysregulations, their correlations and prognostic values were validated in clinical proteomic tumor analysis consortium (CPTAC) database. Serum CD5L and LCAT activity in 50 HCC and 30 normal samples were evaluated and compared. The correlations of serum LCAT activity with alpha-fetoprotein (AFP), albumin (ALB) and high-density lipoprotein (HDL) in HCC were also investigated.

RESULTS

Through systemic analyses, 14 CD5L-associated biological pathways, 256 CD5L-AGs and 28 CD5L-associated prognostic and diagnostic genes (CD5L-APDGs) were identified. A risk model consisting of LCAT and CDC20 was constructed for HCC overall survival (OS), which could discriminate HCC OS status effectively in both the training and the validation sets. CD5L, LCAT and CDC20 were shown to be significantly correlated with immune/stroma cell infiltrations, immunoregulators and 31 anti-cancer drug sensitivities in HCC. At protein level, the dysregulations of CD5L, LCAT and CDC20 were confirmed. LCAT and CDC20 were shown to be significantly correlated with proliferation marker MKI67. In serum, no significance of CD5L was shown. However, the lower activity of LCAT in HCC serum was obvious, as well as its significant positive correlations ALB and HDL concentrations.

CONCLUSIONS

CD5L, LCAT and CDC20 were dysregulated in HCC both at mRNA and protein levels. The LCAT-CDC20 signature might be new predicator for HCC OS. The associations of the three genes with HCC microenvironment and anti-cancer drug sensitivities would provide new clues for HCC immunotherapy and chemotherapy.

CDC20 May Serve as a Potential Biomarker-Based Risk Score System in Predicting the Prognosis of Patients with Hepatocellular Carcinoma

Background

The specificity and sensitivity of hepatocellular carcinoma (HCC) diagnostic markers are limited, hindering the early diagnosis and treatment of HCC patients. Therefore, improving prognostic biomarkers for patients with HCC is urgently needed.

Methods

HCC-related datasets were downloaded from the public databases. Differentially expressed genes (DEGs) between HCC and adjacent nontumor liver tissues were then identified. Moreover, the intersection of DEGs in four datasets (GSE138178, GSE77509, GSE84006, and TCGA) was used in the functional enrichment, and module genes were obtained by a coexpression network. Cox and Kaplan-Meier analyses were used to identify overall survival- (OS-) related genes from module genes. Area under the curve (AUC) > 0.9 of OS-related genes was then carried out in order to perform the protein-protein interaction network. The feature genes were identified by least absolute shrinkage and selection operator (LASSO). Furthermore, the hub gene was identified through the univariate Cox model, after which the correlation analysis between the hub gene and pathways was explored. Finally, infiltration in immune cell types in HCC was analyzed.

Results

A total of 2,227 upregulated genes and 1,501 downregulated DEGs were obtained in all four datasets, which were mainly found to be involved in the cell cycle and retinol metabolism. Accordingly, 998 OS-related genes were screened to construct the LASSO model. Finally, 8 feature genes (BUB1, CCNB1, CCNB2, CCNA2, AURKB, CDC20, OIP5, and TTK) were obtained. CDC20 was shown to serve as a poor prognostic gene in HCC and was mainly involved in the cell cycle. Moreover, a positive correlation was noted between the high degree of infiltration with Th2 and CDC20.

Conclusion

High expression of CDC20 predicted poor survival, as potential target in the treatment for HCC.

ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG May Serve as Diagnostic and Prognostic Biomarkers in Endometrial Carcinoma

Uterine Corpus Endometrial Carcinoma (UCEC), the most common gynecologic malignancy in developed countries, remains to be a major public health problem. Further studies are surely needed to elucidate the tumorigenesis of UCEC. Herein, intersecting 203 differentially expressed genes (DEGs) were identified with the GSE17025, GSE63678, and The Cancer Genome Atlas-UCEC datasets. The Gene Ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis and protein-protein interaction (PPI) network were performed on those 203 DEGs. Intriguingly, 6 of the top 10 nodes in the PPI network were related to unfavorable prognosis, that is, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG. The mRNA and protein expression levels of the 6 hub genes were elevated in UCEC tissues compared to normal tissues. Higher expression of the 6 hub genes was associated with poor prognostic clinicopathological characteristics. The receiver operating characteristic curve suggested the significant diagnostic ability of the 6 hub genes for UCEC. Then, underlying pathogeneses of UCEC including promoter methylation level, TP53 mutation status, genomic genetic variation, and immune cells infiltration were analyzed. The mRNA expression level of the 6 hub genes was also higher in cervical squamous cell carcinoma and endocervical adenocarcinoma, uterine carcinosarcoma, and ovarian serous cystadenocarcinoma tissues than in corresponding normal tissues. In conclusion, ASPM, CDC20, DLGAP5, BUB1B, CDCA8, and NCAPG may be considered diagnostic and prognostic biomarkers in UCEC.

Bioinformatics-based identification of key genes and pathways associated with colorectal cancer diagnosis, treatment, and prognosis

Colorectal cancer (CRC) is known to display a high risk of metastasis and recurrence. The main objective of our investigation was to shed more light on CRC pathogenesis by screening CRC datasets for the identification of key genes and signaling pathways, possibly leading to new approaches for the diagnosis and treatment of CRC. We downloaded the colorectal cancer datasets from the Gene Expression Omnibus (GEO) database site. We used GEO2R to screen for differentially expressed genes (DEGs) of which those with a fold change >1 were considered as up-regulated and those with a fold change <-1 were considered as down-regulated on the basis of a P < .05. "Gene ontology (GO)" and "Kyoto Encyclopedia of Genes and Genomes (KEGG)" data were analyzed by the "DAVID" software. The online search tool "STRING" was used to search for interacting genes or proteins and we used Cytoscape (v3.8.0) to generate a PPI network map and to identify key genes. Finally, survival analysis and stage mapping of key genes were performed using "GEPIA" with the aim of elucidating their potential impact on CRC. Our study revealed 120 intersecting genes of which 55 were up- and 65 were downregulated, respectively. GO analysis revealed that these genes were involved in cell proliferation, exosome secretion, G2/M transition, cytosol, protein binding, and protein kinase activity. KEGG pathway analysis showed that these genes were involved in cell cycle and mineral absorption. The Cytoscape PPI map showed 17 nodes and 262 edges, and 10 hub genes were identified by top 10 degrees. Survival analysis demonstrated that the AURKA, CCNB1, and CCNA2 genes were strongly associated with the survival rate of CRC patients. In addition, CCNB1, CCNA2, CDK1, CKS2, MAD2L1, and DLGAP5 could be correlated to pathological CRC staging. In this research, we identified key genes that may explain the molecular mechanism of occurrence and progression of CRC but may also contribute to an improvement in the clinical staging and prognosis of CRC patients.

Dissecting novel mechanisms of hepatitis B virus related hepatocellular carcinoma using meta-analysis of public data

BACKGROUND

Hepatitis B virus (HBV) is a cause of hepatocellular carcinoma (HCC). Interestingly, this process is not necessarily mediated through cirrhosis and may in fact involve oncogenic processes. Prior studies have suggested specific oncogenic gene expression pathways were affected by viral regulatory proteins. Thus, identifying these genes and associated pathways could highlight predictive factors for HCC transformation and has implications in early diagnosis and treatment.

AIM

To elucidate HBV oncogenesis in HCC and identify potential therapeutic targets.

METHODS

We employed our Search, Tag, Analyze, Resource platform to conduct a meta-analysis of public data from National Center for Biotechnology Information’s Gene Expression Omnibus. We performed meta-analysis consisting of 155 tumor samples compared against 185 adjacent non-tumor samples and analyzed results with ingenuity pathway analysis.

RESULTS

Our analysis revealed liver X receptors/retinoid X receptor (RXR) activation and farnesoid X receptor/RXR activation as top canonical pathways amongst others. Top upstream regulators identified included the Ras family gene rab-like protein 6 (RABL6). The role of RABL6 in oncogenesis is beginning to unfold but its specific role in HBV-related HCC remains undefined. Our causal analysis suggests RABL6 mediates pathogenesis of HBV-related HCC through promotion of genes related to cell division, epigenetic regulation, and Akt signaling. We conducted survival analysis that demonstrated increased mortality with higher RABL6 expression. Additionally, homeobox A10 (HOXA10) was a top upstream regulator and was strongly upregulated in our analysis. HOXA10 has recently been demonstrated to contribute to HCC pathogenesis in vitro. Our causal analysis suggests an in vivo role through downregulation of tumor suppressors and other mechanisms.

CONCLUSION

This meta-analysis describes possible roles of RABL6 and HOXA10 in the pathogenesis of HBV-related HCC. RABL6 and HOXA10 represent potential therapeutic targets and warrant further investigation.

A cancer stem cell associated gene signature for predicting overall survival of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most prevalent type of primary liver cancer characterized by high mortality and morbidity rate. The lack of effective treatments and the high frequency of recurrence lead to poor prognosis of patients with HCC. Therefore, it is important to develop robust prediction tools for predicting the prognosis of HCC. Recent studies have shown that cancer stem cells (CSC) participate in HCC progression. The aim of this study was to explore the prognostic value of CSC-related genes and establish a prediction model based on data from The Cancer Genome Atlas (TCGA) database. In this study, 475 CSC-related genes were obtained from the Molecular Signature Database and 160 differentially expressed CSC-related genes in HCC patients were identified using the limma R package in the TCGA database. A total of 79 CSC-related genes were found to be associated with overall survival (OS). Using the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regressions, a 3-gene signature (RAB10, TCOF1, and PSMD14) was constructed. Receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves were constructed to test the prediction performance of the signature. Performance of the signature was validated using the International Cancer Genome Consortium (ICGC) dataset. In addition, immune feature and functional enrichment analyses were carried out to explore the underlying mechanisms. Moreover, a co-expression network was constructed using the weighted gene correlation network analysis (WGCNA) method to select genes significantly associated with risk scores in HCC in the TCGA dataset. The SGO2 gene was found to be significantly associated with risk scores of HCC. In vitro experiments revealed that it can promote HCC cell proliferation. Therefore, SGO2 may be a potential therapeutic target for HCC treatment. The constructed nomogram can help clinicians make decisions about HCC treatment.

DNA Damage Repair Classifier Defines Distinct Groups in Hepatocellular Carcinoma

Simple Summary

DNA repair pathways have been implicated in hepatocellular carcinoma outcomes. We found that hepatocellular carcinomas (HCC) could be separated into two groups (high and low) based on the overall expression of genes involved in DNA repair. Among the low repair group, there were three subgroups, one of which shared features of the high repair group. Given the important role of liver in metabolism and detoxification and its regenerative capacity, proliferation and DNA damage responses are critical in subdividing major biological categories of liver tumors. High repair samples showed more proliferative and regenerative signatures and had poorer outcomes versus the low repair that were more associated with the genes involved in normal liver biology. These biological groups suggest that dysregulation in endogenous liver processes promotes a pro-tumorigenic microenvironment that may facilitate tumor progression or identify tumors that require more substantial clinical intervention.

Abstract

DNA repair pathways have been associated with variability in hepatocellular carcinoma (HCC) clinical outcomes, but the mechanism through which DNA repair varies as a function of liver regeneration and other HCC characteristics is poorly understood. We curated a panel of 199 genes representing 15 DNA repair pathways to identify DNA repair expression classes and evaluate their associations with liver features and clinicopathologic variables in The Cancer Genome Atlas (TCGA) HCC study. We identified two groups in HCC, defined by low or high expression across all DNA repair pathways. The low-repair group had lower grade and retained the expression of classical liver markers, whereas the high-repair group had more clinically aggressive features, increased p53 mutant-like gene expression, and high liver regenerative gene expression. These pronounced features overshadowed the variation in the low-repair subset, but when considered separately, the low-repair samples included three subgroups: L1, L2, and L3. L3 had high DNA repair expression with worse progression-free (HR 1.24, 95% CI 0.81–1.91) and overall (HR 1.63, 95% CI 0.98–2.71) survival. High-repair outcomes were also significantly worse compared with the L1 and L2 groups. HCCs vary in DNA repair expression, and a subset of tumors with high regeneration profoundly disrupts liver biology and poor prognosis.

Arecoline promotes proliferation and migration of human HepG2 cells through activation of the PI3K/AKT/mTOR pathway

Background

Arecoline is a well-known risk factor for oral submucosal fibrosis and cancer. However, the mechanistic correlation between arecoline and hepatocellular cancer remains elusive. Here, we investigated the effect of arecoline on the proliferation and migration of human HepG2 hepatoma cells and its potential oncogenic mechanisms.

Methods

Bioinformatic technologies were used to identify the deferentially expressed miRNAs (DE-miRNAs) and hub target genes of arecoline-induced cancers. These DE-miRNAs, hub genes and pathway were proved in arecoline-treated HepG2 cells.

Results

A total of 86 DE-miRNAs and 460 target genes were identified. These target genes are associated with DNA-templated regulation of transcription and other biological processes. Significant molecular functions were protein binding, calcium ion binding, and enrichment in the nucleus and cytoplasm. These genes are involved in the PI3K-AKT pathway. CDK1, CCND1, RAF1, CDKN1B and BTRC were defined as the top 5 hub target genes, and patients with high expression of CDK1 showed poor prognosis. Compared with control group, 2.5 µM arecoline treatment increased the proliferation and migration ability of the HepG2 cells. Treatment with 2.5 µM arecoline increased the levels of miR-21-3p, miR-21-5p and miR-1267, upregulated the expression of PI3K-AKT pathway factors, CDK1, CCND1 but decreased RAF1 expression.

Conclusion

A low concentration arecoline can induce the proliferation and migration of HepG2 cells, with the potential mechanism of action linked to high levels of exosomal miR-21 and miR-1267, activation of the PI3K-AKT pathway, upregulation of CDK1 and CCND1, and downregulation of RAF1.

Identification and Validation of a Potential Stemness-Associated Biomarker in Hepatocellular Carcinoma

Background

Cancer stem cells (CSCs) are typically related to metastasis, recurrence, and drug resistance in malignant tumors. However, the biomarker and mechanism of CSCs need further exploration. This study is aimed at comprehensively depicting the stemness characteristics and identify a potential stemness-associated biomarker in hepatocellular carcinoma (HCC).

Methods

The data of HCC patients from The Cancer Genome Atlas (TCGA) were collected and divided based on the mRNA expression-based stemness index (mRNAsi) in this study. Weighted gene coexpression network analysis (WGCNA) and the protein-protein interaction (PPI) network were performed, and the genes were screened through the Cytoscape software. Then, we constructed a prognostic expression signature using the multivariable Cox analysis and verified using the GEO and ICGC databases. Even more importantly, we used the three-dimensional (3D) fibrin gel to enrich the tumor-repopulating cells (TRCs) to validate the expression of the signature in CSCs by quantitative RT-PCR.

Results

mRNAsi was significantly elevated in tumor and high-mRNAsi score was associated with poor overall survival in HCC. The positive stemness-associated (blue) module with 737 genes were screened based on WGCNA, and Budding uninhibited by benzimidazoles 1 (BUB1) was identified as the hub gene highly related to stemness in HCC. Then, the prognostic value and stemness characteristics were well validated in the ICGC and GSE14520 cohorts. Further analysis showed the expression of BUB1 was elevated in TRCs.

Conclusion

BUB1, as a potential stemness-associated biomarker, could serve as a therapeutic CSCs-target and predicted the clinical outcomes of patients with HCC.

Identification of Hub Genes for Early Diagnosis and Predicting Prognosis in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is among the most common digestive system malignancies worldwide, and its pathogenesis and gene signatures remain unclear. This study explored the genetic characteristics and molecular mechanisms underlying colon cancer development. Three gene expression data sets were obtained from the Gene Expression Omnibus (GEO) database. GEO2R was used to determine differentially expressed genes (DEGs) between COAD and normal tissues. Then, the intersection of the data sets was obtained. Metascape was used to perform the functional enrichment analyses. Next, STRING was used to build protein-protein interaction (PPI) networks. Hub genes were identified and analysed using Cytoscape. Next, survival analysis and expression analysis of the hub genes were performed. ROC curve analysis was performed for further test of the diagnostic efficacy. Finally, alterations in the hub genes were predicted and analysed by cBioPortal. Altogether, 436 DEGs were detected. The DEGs were mainly enriched in cell cycle phase transition, nuclear division, meiotic nuclear division, and cytokinesis. Based on PPI networks, 20 hub genes were selected. Among them, 6 hub genes (CCNB1, CCNA2, AURKA, NCAPG, DLGAP5, and CENPE) showed significant prognostic value in colon cancer (P < 0.05), while 5 hub genes (CDK1, CCNB1, CCNA2, MAD2L1, and DLGAP5) were associated with early colon cancer diagnosis and ROC curve analysis showed good diagnostic accuracy. In conclusion, integrated bioinformatics analysis was used to identify hub genes that reveal the potential mechanism of carcinogenesis and progression of colon cancer. The hub genes might be novel biomarkers for early diagnosis, treatment, and prognosis of colon cancer.

Profiling and integrated analysis of differentially expressed circRNAs in cervical cancer

Circular RNAs (circRNAs) are a new type of regulatory RNAs, which have been identified to play critical role in various tumors. However, the profiles and roles of circRNAs in cervical cancer (CCa) have not been fully understood and need to be further explored. In the present study, we performed circRNA array and mRNA-sequencing (mRNA-Seq) to profile the differentially expressed circRNAs and mRNAs in CCa tissues. A total of 397 differentially expressed circRNAs and 2138 differentially expressed mRNAs were detected, respectively. Subsequently, a circRNA-miRNA-mRNA regulatory network was constructed and indicated that hsa_circ_0026377 was downregulated in CCa. Overexpression of hsa_circ_0026377 inhibited HeLa and SiHa cells proliferation, migration and invasion. Collectively, this study provided new insights into the circRNA profiles in CCa and suggested that hsa_circ_0026377 might play important roles in CCa development.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated. Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G-related prognostic gene signature with immune infiltration in HCC. Methods: The TCGA datasets were used as a training and GEO dataset "GSE76427" for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2. Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment. Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

The Deubiquitinase USP39 Promotes Esophageal Squamous Cell Carcinoma Malignancy as a Splicing Factor

Esophageal squamous cell carcinoma (ESCC) is an aggressive epithelial malignancy and the underlying molecular mechanisms remain elusive. Here, we identify that the ubiquitin-specific protease 39 (USP39) drives cell growth and chemoresistance by functional screening in ESCC, and that high expression of USP39 correlates with shorter overall survival and progression-free survival. Mechanistically, we provide evidence for the role of USP39 in alternative splicing regulation. USP39 interacts with several spliceosome components. Integrated analysis of RNA-seq and RIP-seq reveals that USP39 regulates the alternative splicing events. Taken together, our results indicate that USP39 functions as an oncogenic splicing factor and acts as a potential therapeutic target for ESCC.

Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.

Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.

Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.

Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

Clinical implications and molecular mechanisms of Cyclin-dependent kinases 4 for patients with hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) was frequently considered as a kind of malignant tumor with a poor prognosis. Cyclin-dependent kinases (CDK) 4 was considered to be cell-cycle-related CDK gene. In this study, we explored the clinical significance of CDK4 in HCC patients.

Methods

Data of HCC patients were obtained from The Cancer Genome Atlas database (TCGA) and the Gene Expression Omnibus (GEO) database. Kaplan–Meier analysis and Cox regression model were performed to calculate median survival time (MST) and the hazard ration (HR), respectively. The joint-effect analysis and prognostic risk score model were constructed to demonstrate significance of prognosis-related genes. The differential expression of prognostic genes was further validated using reverse transcription-quantitative PCR (RT-qPCR) of 58 pairs of HCC samples.

Results

CDK1 and CDK4 were considered prognostic genes in TCGA and GSE14520 cohort. The result of joint-effect model indicated patients in CDK1 and CDK4 low expression groups had a better prognosis in TCGA (adjusted HR = 0.491; adjusted P = 0.003) and GSE14520 cohort (adjusted HR = 0.431; adjusted P = 0.002). Regarding Kaplan–Meier analysis, high expression of CDK1 and CDK4 was related to poor prognosis in both the TCGA (P < 0.001 and = 0.001 for CDK1 and CDK4, respectively) and the GSE14520 cohort (P = 0.006 and = 0.033 for CDK1 and CDK4, respectively). However, only CDK4 (P = 0.042) was validated in RT-qPCR experiment, while CDK1 (P = 0.075) was not.

Conclusion

HCC patients with high CDK4 expression have poor prognosis, and CDK4 could be a potential candidate diagnostic biomarker for HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02152-w.

Characterization of diagnostic and prognostic significance of cell cycle-linked genes in hepatocellular carcinoma

Background

The high degree of heterogeneity of hepatocellular carcinoma (HCC) imposes a significant challenge to predict the prognosis. Currently, increasing evidence has indicated that cell cycle-linked genes are strongly linked to occurrence and progress of HCC. Herein, we purposed to create a prediction model on the basis of cell cycle-linked genes.

Methods

The transcriptome along with clinicopathological data abstracted from The Cancer Genome Atlas (TCGA) were used as a training cohort. Lasso regression analysis was employed to create a prediction model in TCGA cohort. The data of samples obtained from the International Cancer Genome Consortium (ICGC) data resource were applied in the verification of the model. A series of bioinformatics analyzed the relationship of the risk signature with overall survival (OS), biological function, and clinicopathological features.

Results

Six cell cycle-linked genes (PLK1, CDC20, HSP90AA1, CHEK1, HDAC1, and NDC80) were chosen to create the prognostic model, demonstrating a good prognostic capacity. Further analyses indicated that the model could independently assess the OS of HCC patients. A single-sample gene set enrichment analysis (ssGSEA) indicated that the risk signature was remarkably linked to immune status. Additionally, there was a remarkable association of the risk signature with TP53 mutation frequency, as well as immune checkpoint molecule expression levels.

Conclusions

We created a prediction model using six cell cycle-linked genes to predict HCC prognosis. The six genes are expected to be novel markers for HCC diagnosis, as well as treatment.

Thiabendazole Inhibits Glioblastoma Cell Proliferation and Invasion Targeting Mini-chromosome Maintenance Protein 2

Thiabendazole (TBZ), approved by the US Food and Drug Administration (FDA) for human oral use, elicits a potential anticancer activity on cancer cells in vitro and in animal models. Here, we evaluated the efficacy of TBZ in the treatment of human glioblastoma multiforme (GBM). TBZ reduced the viability of GBM cells (P3, U251, LN229, A172, and U118MG) relative to controls in a dose- and time-dependent manner. However, normal human astrocytes (NHA) exhibited a greater IC50 than tumor cell lines and were thus more resistant to its cytotoxic effects. 5-Ethynyl-2'-deoxyuridine (EdU)-positive cells and the number of colonies formed were decreased in TBZ-treated cells (at 150 μM, P < 0.05 and at 150 μM, P < 0.001, respectively). This decrease in proliferation was associated with a G2/M arrest as assessed with flow cytometry, and the downregulation of G2/M check point proteins. In addition, TBZ suppressed GBM cell invasion. Analysis of RNA sequencing data comparing TBZ-treated cells with controls yielded a group of differentially expressed genes, the functions of which were associated with the cell cycle and DNA replication. The most significantly downregulated gene in TBZ-treated cells was mini-chromosome maintenance protein 2 (MCM2). SiRNA knockdown of MCM2 inhibited proliferation, causing a G2/M arrest in GBM cell lines and suppressed invasion. Taken together, our results demonstrated that TBZ inhibited proliferation and invasion in GBM cells through targeting of MCM2. SIGNIFICANCE STATEMENT: TBZ inhibits the proliferation and invasion of glioblastoma cells by downregulating the expression of MCM2. These results support the repurposing of TBZ as a possible therapeutic drug in the treatment of GBM.

Expression and Prognostic Role of E2F2 in Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a common clinical malignancy. Recent studies reported that E2F transcription factor 2 (E2F2) plays a significant role in tumor progression. However, its expression and biological function in HCC are still unclear. Therefore, we explored the relationship between E2F2 expression and tumor progression in HCC.

Methods

In this study, we utilized some online tools to explore the E2F2 expression in pan-carcinoma and HCC. The association of E2F2 expression with the clinical characteristics and prognosis of HCC was further studied. In addition, we explored the co-expressed genes of E2F2 and mined the positively/negatively corrected significant genes and excavated the possible functions. Meanwhile, the hub gene set was constructed based on protein-protein interaction (PPI) network, and the relationship between E2F2 and immunity was discovered.

Results

We observed that the expression level of E2F2 was generally upregulated in HCC. However, E2F2 expression was not significantly different between HCC and normal tissues in regard to the disease stage 4. Furthermore, we also observed the poor prognosis in patients with high E2F2 expression. The co-expressed genes of E2F2 were identified and further detected. Thereafter, we identified the positively/negatively corrected significant genes and constructed the hub gene network of E2F2 based on PPI network. We also found that E2F2 expression was positively correlated with the infiltration levels of CD4+ T, CD8+ T cells, macrophages, neutrophils, and dendritic cells.

Conclusion

Our findings suggested that E2F2 could be a potential prognostic factor for HCC, which could provide a therapeutic target for the molecular treatment of HCC.

Bioinformatic Evidence Reveals that Cell Cycle Correlated Genes Drive the Communication between Tumor Cells and the Tumor Microenvironment and Impact the Outcomes of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is an aggressive cancer type with poor prognosis; thus, there is especially necessary and urgent to screen potential prognostic biomarkers for early diagnosis and novel therapeutic targets. In this study, we downloaded target data sets from the GEO database, and obtained codifferentially expressed genes using the limma R package and identified key genes through the protein–protein interaction network and molecular modules, and performed GO and KEGG pathway analyses for key genes via the clusterProfiler package and further determined their correlations with clinicopathological features using the Oncomine database. Survival analysis was completed in the GEPIA and the Kaplan–Meier plotter database. Finally, correlations between key genes, cell types infiltrated in the tumor microenvironment (TME), and hypoxic signatures were explored based on the TIMER database. From the results, 11 key genes related to the cell cycle were determined, and high levels of these key genes' expression were focused on advanced and higher grade status HCC patients, as well as in samples of TP53 mutation and vascular invasion. Besides, the 11 key genes were significantly associated with poor prognosis of HCC and also were positively related to the infiltration level of MDSCs in the TME and the HIF1A and VEGFA of hypoxic signatures, but a negative correlation was found with endothelial cells (ECs) and hematopoietic stem cells. The result determined that 11 key genes (RRM2, NDC80, ECT2, CCNB1, ASPM, CDK1, PRC1, KIF20A, DTL, TOP2A, and PBK) could play a vital role in the pathogenesis of HCC, drive the communication between tumor cells and the TME, and act as probably promising diagnostic, therapeutic, and prognostic biomarkers in HCC patients.

Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

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.

Bioinformatics analysis of constructing a HCV-related hepatocellular carcinoma miRNA-mRNA regulation network

Hepatocellular carcinoma (HCC) is one of the tumors with a higher mortality rate globally, which significantly threatens people's health. Hepatitis C virus (HCV) infection is a major driving factor of HCC. This study aims to determine the key microRNA (miRNA), hub genes, and related pathways, construct potential miRNA-mRNA regulatory networks, and clarify the new molecular mechanism of HCV-related HCC. In this study, 16 differentially expressed miRNAs (DE miRNAs) were identified. The prediction of potential transcription factors and target genes not only found that SP1 and ERG1 may potentially regulate most of the screened DE miRNAs, but it also obtained 2923 and 1782 predicted target genes for the up-regulation and down-regulation of DE miRNAs, respectively. Subsequently, the introduction of differentially expressed genes dataset GSE62232 for target gene verification yielded 98 and 147 potential up-regulation and down-regulation target genes. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that they were mainly enriched in the cell cycle process, that is, subsequently, 20 hub genes were screened out through the protein-protein interaction network, and related genes were further evaluated using the GEPIA database. Based on the above analysis, the miRNA-hub gene regulatory network was constructed. In short, this research's hub genes and miRNAs closely related to HCV-related HCC were screened and identified through bioinformatics analysis and then built their connection. These results are expected to find potential therapeutic targets for HCV-related HCC.

Multi-Omic Analyses of the m5C Regulator ALYREF Reveal Its Essential Roles in Hepatocellular Carcinoma

The ALYREF protein acts as a crucial epigenetic regulator in several cancers. However, the specific expression levels and functional roles of ALYREF in cancers are largely unknown, including for hepatocellular carcinoma (HCC). In a pan-cancer tissue analysis that included HCC, we assessed the expression of ALYREF compared to normal tissues using The Cancer Genome Atlas database. Associations between ALYREF gene expression and the clinical characteristics of HCC patient samples were assessed using the UALCAN database. Kaplan-Meier plots were performed to assess HCC patient prognosis, and the TIMER database was used to explore associations between ALYREF expression and immune-cell infiltrations. The same methods were used to assess eIF4A3 expression in HCC patient samples. In addition, ALYREF- and elF4A3-related differentially expressed genes (DEGs) were determined using LinkedOmics, associated protein functionalities were predicted for positively associated DEGs, and both the TargetScan and miRDB databases were used to predict potential upstream miRNAs for control of ALYREF and eIF4A3 expression. We found that ALYREF gene expression was dysregulated in several cancers and was significantly elevated in HCC patient tissue samples and HCC cell lines. The overexpression of ALYREF was significantly related to both advanced tumor-node-metastasis stages and poor HCC prognosis. Furthermore, we found that eIF4A3 expression was significantly correlated with ALYREF expression, and that upregulated eIF4A3 was significantly associated with poor HCC patient outcomes. In the protein-protein interaction network, we identified eight hub genes based on the positively associated DEGs in common between ALYREF and eIF4A3, and the high expression levels of these hub genes were positively associated with patient clinical outcomes. In addition, we identified miR-4666a-5p and miR-6124 as potential regulators of ALYREF and eIF4A3 expression. These findings suggest that increased ALYREF expression may function as a novel biomarker for both HCC diagnosis and prognosis predictions.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background

Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.

Methods

The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.

Results

In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.

Conclusion

Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00837-6.

KDM5B promotes self-renewal of hepatocellular carcinoma cells through the microRNA-448-mediated YTHDF3/ITGA6 axis

Histone methylation plays important roles in mediating the onset and progression of various cancers, and lysine-specific demethylase 5B (KDM5B), as a histone demethylase, is reported to be an oncogene in hepatocellular carcinoma (HCC). However, the mechanism underlying its tumorigenesis remains undefined. Hence, we explored the regulatory role of KDM5B in HCC cells, aiming to identify novel therapeutic targets for HCC. Gene Expression Omnibus database and StarBase were used to predict important regulatory pathways related to HCC. Then, the expression of KDM5B and microRNA-448 (miR-448) in HCC tissues was detected by RT-qPCR and Western blot analysis. The correlation between KDM5B and miR-448 expression was analysed by Pearson's correlation coefficient and ChIP experiments, and the targeting of YTH N6-methyladenosine RNA binding protein 3 (YTHDF3) by miR-448 was examined by luciferase assay. Additionally, the effect of KDM5B on the proliferation, migration, invasion and apoptosis as well as tumorigenicity of transfected cells was assessed using ectopic expression and depletion experiments. KDM5B was highly expressed in HCC cells and was inversely related to miR-448 expression. KDM5B demethylated H3K4me3 on the miR-448 promoter and thereby inhibited the expression of miR-448, which in turn targeted YTHDF3 and integrin subunit alpha 6 (ITGA6) to promote the malignant phenotype of HCC. Moreover, KDM5B accelerated HCC progression in nude mice via the miR-448/YTHDF3/ITGA6 axis. Our study uncovered that KDM5B regulates the YTHDF3/ITGA6 axis by inhibiting the expression of miR-448 to promote the occurrence of HCC.

Global analysis of gene expression signature and diagnostic/prognostic biomarker identification of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. The landscape of HCC's molecular alteration signature has been explored over the last few decades. Even so, more comprehensive research is still needed to improve understanding of tumorigenesis and progression of HCC, as well as to identify potential biomarkers for the malignancy. In this research, a comprehensive bioinformatics analysis was conducted based on the publicly available databases from both the Cancer Genome Atlas (TCGA) program and the gene expression omnibus (GEO) database. R/Bioconductor was used to analyze differentially expressed genes (DEGs) between HCC tumor and normal control (NC) samples, and then a protein-protein interaction (PPI) network of DEGs was established through the STRING platform. Finally, the application of specific candidate genes as diagnostic or prognostic biomarkers of HCC was explored and evaluated by ROC and survival analysis. A total of 310 DEGs were detected in the HCC tumor samples. Thirty-six hub DEGs in the PPI network and 10 candidates of the 36 genes showed significant alterations in tumor expression, including CDKN3, TOP2A, UBE2C, CDC20, PBK, ASPM, KIF20A, NCAPG, CCNB2, CYP3A4. The 10-gene signature had relatively significant effects when distinguishing tumors from normal samples (sensitivity >70%, specificity >70%, AUC >0.8, p < 0.001). Eight candidate genes were negatively correlated with the overall survival rate of the patients (p < 0.05) and were all up-regulated in HCC tumor samples. The age and gender factors had no significant impact on the overall survival rate of HCC patients (p > 0.05), and the TNM stage status factor had a significant negative prognosis correlation (p < 0.05). This research provides evidence for a better understanding of tumorigenesis and progression of HCC and helps to explore candidate targets for disease diagnosis and treatment.

CDK10 in Gastrointestinal Cancers: Dual Roles as a Tumor Suppressor and Oncogene

Cyclin-dependent kinase 10 (CDK10) is a CDC2-related serine/threonine kinase involved in cellular processes including cell proliferation, transcription regulation and cell cycle regulation. CDK10 has been identified as both a candidate tumor suppressor in hepatocellular carcinoma, biliary tract cancers and gastric cancer, and a candidate oncogene in colorectal cancer (CRC). CDK10 has been shown to be specifically involved in modulating cancer cell proliferation, motility and chemosensitivity. Specifically, in CRC, it may represent a viable biomarker and target for chemoresistance. The development of therapeutics targeting CDK10 has been hindered by lack a specific small molecule inhibitor for CDK10 kinase activity, due to a lack of a high throughput screening assay. Recently, a novel CDK10 kinase activity assay has been developed, which will aid in the development of small molecule inhibitors targeting CDK10 activity. Discovery of a small molecular inhibitor for CDK10 would facilitate further exploration of its biological functions and affirm its candidacy as a therapeutic target, specifically for CRC.

Linking Coregulated Gene Modules with Polycyclic Aromatic Hydrocarbon-Related Cancer Risk in the 3D Human Bronchial Epithelium

Exposure to polycyclic aromatic hydrocarbons (PAHs) often occurs as complex chemical mixtures, which are linked to numerous adverse health outcomes in humans, with cancer as the greatest concern. The cancer risk associated with PAH exposures is commonly evaluated using the relative potency factor (RPF) approach, which estimates PAH mixture carcinogenic potential based on the sum of relative potency estimates of individual PAHs, compared to benzo[a]pyrene (BAP), a reference carcinogen. The present study evaluates molecular mechanisms related to PAH cancer risk through integration of transcriptomic and bioinformatic approaches in a 3D human bronchial epithelial cell model. Genes with significant differential expression from human bronchial epithelium exposed to PAHs were analyzed using a weighted gene coexpression network analysis (WGCNA) two-tiered approach: first to identify gene sets comodulated to RPF and second to link genes to a more comprehensive list of regulatory values, including inhalation-specific risk values. Over 3000 genes associated with processes of cell cycle regulation, inflammation, DNA damage, and cell adhesion processes were found to be comodulated with increasing RPF with pathways for cell cycle S phase and cytoskeleton actin identified as the most significantly enriched biological networks correlated to RPF. In addition, comodulated genes were linked to additional cancer-relevant risk values, including inhalation unit risks, oral cancer slope factors, and cancer hazard classifications from the World Health Organization's International Agency for Research on Cancer (IARC). These gene sets represent potential biomarkers that could be used to evaluate cancer risk associated with PAH mixtures. Among the values tested, RPF values and IARC categorizations shared the most similar responses in positively and negatively correlated gene modules. Together, we demonstrated a novel manner of integrating gene sets with chemical toxicity equivalence estimates through WGCNA to understand potential mechanisms.

Cytokinesis regulators as potential diagnostic and therapeutic biomarkers for human hepatocellular carcinoma

Cytokinesis, the final step of mitosis, is critical for maintaining the ploidy level of cells. Cytokinesis is a complex, highly regulated process and its failure can lead to genetic instability and apoptosis, contributing to the development of cancer. Human hepatocellular carcinoma is often accompanied by a high frequency of aneuploidy and the DNA ploidy pattern observed in human hepatocellular carcinoma results mostly from impairments in cytokinesis. Many key regulators of cytokinesis are abnormally expressed in human hepatocellular carcinoma, and their expression levels are often correlated with patient prognosis. Moreover, preclinical studies have demonstrated that the inhibition of key cytokinesis regulators can suppress the growth of human hepatocellular carcinoma. Here, we provide an overview of the current understanding of the signaling networks regulating cytokinesis, the key cytokinesis regulators involved in the initiation and development of human hepatocellular carcinoma, and their applications as potential diagnostic and therapeutic biomarkers.

Bioinformatics and machine learning approach identifies potential drug targets and pathways in COVID-19

Current coronavirus disease-2019 (COVID-19) pandemic has caused massive loss of lives. Clinical trials of vaccines and drugs are currently being conducted around the world; however, till now no effective drug is available for COVID-19. Identification of key genes and perturbed pathways in COVID-19 may uncover potential drug targets and biomarkers. We aimed to identify key gene modules and hub targets involved in COVID-19. We have analyzed SARS-CoV-2 infected peripheral blood mononuclear cell (PBMC) transcriptomic data through gene coexpression analysis. We identified 1520 and 1733 differentially expressed genes (DEGs) from the GSE152418 and CRA002390 PBMC datasets, respectively (FDR < 0.05). We found four key gene modules and hub gene signature based on module membership (MMhub) statistics and protein-protein interaction (PPI) networks (PPIhub). Functional annotation by enrichment analysis of the genes of these modules demonstrated immune and inflammatory response biological processes enriched by the DEGs. The pathway analysis revealed the hub genes were enriched with the IL-17 signaling pathway, cytokine-cytokine receptor interaction pathways. Then, we demonstrated the classification performance of hub genes (PLK1, AURKB, AURKA, CDK1, CDC20, KIF11, CCNB1, KIF2C, DTL and CDC6) with accuracy >0.90 suggesting the biomarker potential of the hub genes. The regulatory network analysis showed transcription factors and microRNAs that target these hub genes. Finally, drug-gene interactions analysis suggests amsacrine, BRD-K68548958, naproxol, palbociclib and teniposide as the top-scored repurposed drugs. The identified biomarkers and pathways might be therapeutic targets to the COVID-19.

Bioinformatics Analysis of Candidate Genes and Pathways Related to Hepatocellular Carcinoma in China: A Study Based on Public Databases

Background and Objective: Hepatocellular carcinoma (HCC) is a highly aggressive malignant tumor of the digestive system worldwide. Chronic hepatitis B virus (HBV) infection and aflatoxin exposure are predominant causes of HCC in China, whereas hepatitis C virus (HCV) infection and alcohol intake are likely the main risk factors in other countries. It is an unmet need to recognize the underlying molecular mechanisms of HCC in China.

Methods: In this study, microarray datasets (GSE84005, GSE84402, GSE101685, and GSE115018) derived from Gene Expression Omnibus (GEO) database were analyzed to obtain the common differentially expressed genes (DEGs) by R software. Moreover, the gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the protein-protein interaction (PPI) network was constructed, and hub genes were identified by the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape, respectively. The hub genes were verified using Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Kaplan-Meier Plotter online databases were performed on the TCGA HCC dataset. Moreover, the Human Protein Atlas (HPA) database was used to verify candidate genes’ protein expression levels.

Results: A total of 293 common DEGs were screened, including 103 up-regulated genes and 190 down-regulated genes. Moreover, GO analysis implied that common DEGs were mainly involved in the oxidation-reduction process, cytosol, and protein binding. KEGG pathway enrichment analysis presented that common DEGs were mainly enriched in metabolic pathways, complement and coagulation cascades, cell cycle, p53 signaling pathway, and tryptophan metabolism. In the PPI network, three subnetworks with high scores were detected using the Molecular Complex Detection (MCODE) plugin. The top 10 hub genes identified were CDK1, CCNB1, AURKA, CCNA2, KIF11, BUB1B, TOP2A, TPX2, HMMR and CDC45. The other public databases confirmed that high expression of the aforementioned genes related to poor overall survival among patients with HCC.

Conclusion: This study primarily identified candidate genes and pathways involved in the underlying mechanisms of Chinese HCC, which is supposed to provide new targets for the diagnosis and treatment of HCC in China.

A gene module identification algorithm and its applications to identify gene modules and key genes of hepatocellular carcinoma

To further improve the effect of gene modules identification, combining the Newman algorithm in community detection and K-means algorithm framework, a new method of gene module identification, GCNA-Kpca algorithm, was proposed. The core idea of the algorithm was to build a gene co-expression network (GCN) based on gene expression data firstly; Then the Newman algorithm was used to initially identify gene modules based on the topology of GCN, and the number of clusters and clustering centers were determined; Finally the number of clusters and clustering centers were input into the K-means algorithm framework, and the secondary clustering was performed based on the gene expression profile to obtain the final gene modules. The algorithm took into account the role of modularity in the clustering process, and could find the optimal membership module for each gene through multiple iterations. Experimental results showed that the algorithm proposed in this paper had the best performance in error rate, biological significance and CNN classification indicators (Precision, Recall and F-score). The gene module obtained by GCNA-Kpca was used for the task of key gene identification, and these key genes had the highest prognostic significance. Moreover, GCNA-Kpca algorithm was used to identify 10 key genes in hepatocellular carcinoma (HCC): CDC20, CCNB1, EIF4A3, H2AFX, NOP56, RFC4, NOP58, AURKA, PCNA, and FEN1. According to the validation, it was reasonable to speculate that these 10 key genes could be biomarkers for HCC. And NOP56 and NOP58 are key genes for HCC that we discovered for the first time.