CDKN3 expression is negatively associated with pathological tumor stage and CDKN3 inhibition promotes cell survival in hepatocellular carcinoma

Olmutinib Reverses Thioacetamide-Induced Cell Cycle Gene Alterations in Mice Liver and Kidney Tissues, While Wheat Germ Treatment Exhibits Limited Efficacy at Gene Level

Background and Objectives: TAA is potent hepatic/renal toxicant. Conversely, WGO is a potent dietary supplement with impressive antioxidant properties. Olmutinib is an apoptotic chemotherapy drug that does not harm the liver or kidney. This study investigated the impact of olmutinib and wheat germ oil (WGO) on Thioacetamide (TAA)-induced gene alterations in mice liver and kidney tissues. Materials and Methods: Adult male C57BL/6 mice were exposed to 0.3% TAA in drinking water for 14 days, followed by the oral administration of olmutinib (30 mg/kg) and WGO (1400 mg/kg) for 5 consecutive days. Treatment groups included the following: groups I (control), II (TAA-exposed), III (TAA + olmutinib), IV (TAA + WGO), and V (TAA + olmutinib + WGO). Results: The findings revealed that TAA exposure increased MKi67 and CDKN3 gene expression in liver and kidney tissues. Olmutinib treatment effectively reversed these TAA-induced effects, significantly restoring MKi67 and CDKN3 gene expression. WGO also reversed MKi67 effects in the liver but exhibited limited efficacy in reversing CDKN3 gene alterations induced by TAA exposures in both the liver and kidney. TAA exposure showed the tissue-specific expression of TP53, with decreased expression in the liver and increased expression in the kidney. Olmutinib effectively reversed these tissue-specific alterations in TP53 expression. While WGO treatment alone could not reverse the gene alterations induced by TAA exposure, the co-administration of olmutinib and WGO exhibited a remarkable potentiation of therapeutic effects in both the liver and kidney. The gene interaction analysis revealed 77.4% of physical interactions and co-localization between MKi67, CDKN3, and TP53 expressions. Protein-protein interaction networks also demonstrated physical interactions between MKi67, TP53, and CDKN3, forming complexes or signaling cascades. Conclusions: It was predicted that the increased expression of the MKi67 gene by TAA leads to the increase in TP53, which negatively regulates the cell cycle via increased CDKN3 expression in kidneys and the restoration of TP53 levels in the liver. These findings contribute to our understanding of the effects of olmutinib and WGO on TAA-induced gene expression changes and highlight their contrasting effects based on cell cycle alterations.

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

Background

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

Objective

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

Methods

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

Results

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

Conclusion

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

Comprehensive Analysis Reveals the Potential Roles of CDKN3 in Pancancer and Verification in Endometrial Cancer

Background

Cyclin-dependent kinase inhibitor 3 (CDKN3) has been studied in many cancers. However, the comprehensive and systematic pancancer analysis of CDKN3 genes is still lacking.

Methods

Data were downloaded from online databases. R was used for analysis of the differential expression and gene alteration of CDKN3 and of the associations between CDKN3 expression and survival, signaling pathways, and drug sensitivity. Clinical samples and in vitro experiments were selected for verification.

Results

CDKN3 expression was higher in most types of cancers, and this phenotype was significantly correlated with poor survival. CDKN3 showed gene alterations and copy number alterations in many cancers and associated with some immune-related pathways and factors. Drug sensitivity analysis elucidated that CDKN3 could be a useful marker for therapy selection. Clinical samples elucidated CDKN3 expressed high in endometrial cancer tissue. In vitro studies showed that CDKN3 induced pro-tumor effect in immune environment and facilitated endometrial cancer cell proliferation and G1/S phase transition.

Conclusion

CDKN3 has been shown to be highly expressed in most types of cancers and promoted cancer cell progression. CDKN3 may serve as a novel marker in clinical diagnosis, treatment, and prognosis prediction in future.

Cyclin dependent kinase inhibitor 3 (CDKN3) upregulation is associated with unfavorable prognosis in clear cell renal cell carcinoma and shapes tumor immune microenvironment: A bioinformatics analysis

Cell cycle regulatory proteins plays a pivotal role in the development and progression of many human malignancies. Identification of their biological functions as well as their prognostic utility presents an active field of research. As a continuation of the ongoing efforts to elucidate the molecular characteristics of clear cell renal cell carcinoma (ccRCC); we present a comprehensive bioinformatics study targeting the prognostic and mechanistic role of cyclin-dependent kinase inhibitor 3 (CDKN3) in ccRCC. The ccRCC cohort from the Cancer Genome Atlas Program was accessed through the UCSC Xena browser to obtain CDKN3 mRNA expression data and their corresponding clinicopathological variables. The independent prognostic signature of CDKN3 was evaluated using univariate and multivariate Cox logistic regression analysis. Gene set enrichment analysis and co-expression gene functional annotations were used to discern CDKN3-related altered molecular pathways. The tumor immune microenvironment was evaluated using TIMER 2.0 and gene expression profiling interactive analysis. CDKN3 upregulation is associated with shortened overall survival (hazard ratio [HR] = 2.325, 95% confident interval [CI]: 1.703-3.173, P < .0001) in the Cancer Genome Atlas Program ccRCC cohort. Univariate (HR: 0.426, 95% CI: 0.316-0.576, P < .001) and multivariate (HR: 0.560, 95% CI: 0.409-0.766, P < .001) Cox logistic regression analyses indicate that CDKN3 is an independent prognostic variable of the overall survival. High CDKN3 expression is associated with enrichment within the following pathways including allograph rejection, epithelial-mesenchymal transition, mitotic spindle, inflammatory response, IL-6/JAK/STAT3 signaling, spermatogenesis, TNF-α signaling via NF-kB pathway, complement activation, KRAS signaling, and INF-γ signaling. CDKN3 is also associated with significant infiltration of a wide spectrum of immune cells and correlates remarkably with immune-related genes. CDKN3 is a poor prognostic biomarker in ccRCC that alters many molecular pathways and impacts the tumor immune microenvironment.

A systematic study on the treatment of hepatitis B-related hepatocellular carcinoma with drugs based on bioinformatics and key target reverse network pharmacology and experimental verification

BACKGROUND

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC). However, the mechanism of hepatitis B-related hepatocellular carcinoma (HBV-related HCC) is still unclear. Therefore, understanding the pathogenesis and searching for drugs to treat HBV-related HCC was an effective strategy to treat this disease.

PURPOSE

Bioinformatics was used to predict the potential targets of HBV-related HCC. The reverse network pharmacology of key targets was used to analyze the clinical drugs, traditional Chinese medicine (TCM) and small molecules of TCM in the treatment of HBV-related HCC.

METHODS

In this study, three microarray datasets totally containing 330 tumoral samples and 297 normal samples were selected from the GEO database. These microarray datasets were used to screen DEGs. And the expression profile and survival of 6 key genes were analyzed. In addition, Comparative Toxicogenomics Database and Coremine Medical database were used to enrich clinical drugs and TCM of HBV-related HCC by the 6 key targets. Then the obtained TCM were classified based on the Chinese Pharmacopoeia. Among these top 6 key genes, CDK1 and CCNB1 had the most connection nodes and the highest degree and were the most significantly expressed. In general, CDK1 and CCNB1 tend to form a complex, which is conducive to cell mitosis. Hence, this study mainly studied CDK1 and CCNB1. HERB database was used to predict small molecules TCM. The inhibition effect of quercetin, celastrol and cantharidin on HepG2.2.15 cells and Hep3B cells was verified by CCK8 experiment. The effects of quercetin, celastrol and cantharidin on CDK1 and CCNB1 of HepG2.2.15 cells and Hep3B cells were determined by Western Blot.

RESULTS

In short, 272 DEGs (53 upregulated and 219 downregulated) were identified. Among these DEGs, 6 key genes with high degree were identified, which were AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS. Kaplan-Meier plotter analysis showed that higher expression levels of AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS were associated with poor OS. According to the first 6 key targets, a variety of drugs and TCM were identified. These results showed that clinical drugs included targeted drugs, such as sorafenib, palbociclib and Dasatinib. and chemotherapy drugs, such as cisplatin and doxorubicin. TCM, such as the TCM flavor was mainly warm and bitter, and the main meridians were liver and lung. Small molecules of TCM included flavonoids, terpenoids, alkaloids and glycosides, such as quercetin, celastrol, cantharidin, hesperidin, silymarin, casticin, berberine and ursolic acid, which have great potential in anti-HBV-related HCC. For molecular docking of chemical components, the molecules with higher scores were flavonoids, alkaloids, etc. Three representative types of TCM small molecules were verified respectively, and it was found that quercetin, celastrol and cantharidin inhibited the proliferation of HepG2.2.15 cells and Hep3B cells along concentration gradient. Quercetin, celastrol and cantharidin decreased CDK1 expression in HepG2.2.15 and Hep3B cells, but for CCNB1, only cantharidin decreased CCNB1 expression in the two strains of cells.

CONCLUSION

In conclusion, AURKA, BIRC5, CCNB1, CDK1, CDKN3 and TYMS could be potential targets for the diagnosis and prognosis of HBV-related HCC. Clinical drugs include chemotherapeutic and targeted drug, traditional Chinese medicine is mainly bitter and warm TCM. Small molecular of TCM including flavonoids, terpenoids and glycosides and alkaloids, which have great potential in anti-HBV-related HCC. This study provides potential therapeutic targets and novel strategies for the treatment of HBV-related HCC.

Identification of key genes and carcinogenic pathways in hepatitis B virus-associated hepatocellular carcinoma through bioinformatics analysis

Backgrounds/Aims

Mechanisms for the development of hepatocellular carcinoma (HCC) in hepatitis B virus (HBV)-infected patients remain unclear. The aim of the present study was to identify genes and pathways involved in the development of HBV-associated HCC.

Methods

The GSE121248 gene dataset, which included 70 HCCs and 37 adjacent liver tissues, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) in HCCs and adjacent liver tissues were identified. Gene ontology and Kyoto Encyclopedia of Genes and Genome pathway enrichment analyses were then performed.

Results

Of 134 DEGs identified, 34 were up-regulated and 100 were down-regulated in HCCs. The 34 up-regulated DEGs were mainly involved in nuclear division, organelle fission, spindle and midbody formation, histone kinase activity, and p53 signaling pathway, whereas the 100 down-regulated DEGs were involved in steroid and hormone metabolism, collagen-coated extracellular matrix, oxidoreductase activity, and activity on paired donors, including incorporation or reduction of molecular oxygen, monooxygenase activity, and retinol metabolism. Analyses of protein-protein interaction networks with a high degree of connectivity identified significant modules containing 14 hub genes, including ANLN, ASPM, BUB1B, CCNB1, CDK1, CDKN3, ECT2, HMMR, NEK2, PBK, PRC1, RACGAP1, RRM2, and TOP2A, which were mainly associated with nuclear division, organelle fission, spindle formation, protein serine/threonine kinase activity, p53 signaling pathway, and cell cycle.

Conclusions

This study identified key genes and carcinogenic pathways that play essential roles in the development of HBV-associated HCC. This may provide important information for the development of diagnostic and therapeutic targets for HCC.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

CDKN3 expression predicates poor prognosis and regulates adriamycin sensitivity in hepatocellular carcinoma in vitro

Objective

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related deaths worldwide. This study investigated the relationship between cyclin-dependent kinase inhibitor (CDKN)3 and prognosis and pathological characteristics in HCC patients to determine whether it could be used as a prognostic factor and/or therapeutic target for HCC drug development.

Methods

We previously showed that CDKN3 is deregulated in HCC tumor samples. Here, bioinformatics analysis was used to assess the relationship between CDKN3 gene expression and the characteristics of HCC patients from Gene Expression Omnibus and The Cancer Genome Atlas databases. Additionally, CDKN3 expression was silenced by small interfering RNA to determine its effect on HCC cell proliferation and on HCC cell sensitivity to adriamycin chemotherapy.

Results

Bioinformatics analysis showed a negative correlation between CDKN3 expression and both disease-free survival and overall survival. CDKN3 silencing did not significantly suppress the proliferation of HCC cells, but did decrease their sensitivity to adriamycin.

Conclusions

CDKN3 may have a dual role during the development of HCC, and could be used as an independent prognostic factor and therapeutic target for HCC treatment.

Six genes involved in prognosis of hepatocellular carcinoma identified by Cox hazard regression

BACKGROUND

Hepatocellular carcinoma (HCC), derived from hepatocytes, is the main histological subtype of primary liver cancer and poses a serious threat to human health due to the high incidence and poor prognosis. This study aimed to establish a multigene prognostic model to predict the prognosis of patients with HCC.

RESULTS

Gene expression datasets (GSE121248, GSE40873, GSE62232) were used to identify differentially expressed genes (DEGs) between tumor and adjacent or normal tissues, and then hub genes were screened by protein-protein interaction (PPI) network and Cytoscape software. Seventeen genes among hub genes were significantly associated with prognosis and used to construct a prognostic model through COX hazard regression analysis. The predictive performance of this model was evaluated with TCGA data and was further validated with independent dataset GSE14520. Six genes (CDKN3, ZWINT, KIF20A, NUSAP1, HMMR, DLGAP5) were involved in the prognostic model, which separated HCC patients from TCGA dataset into high- and low-risk groups. Kaplan-Meier (KM) survival analysis and risk score analysis demonstrated that low-risk group represented a survival advantage. Univariate and multivariate regression analysis showed risk score could be an independent prognostic factor. The receiver operating characteristic (ROC) curve showed there was a better predictive power of the risk score than that of other clinical indicators. At last, the results from GSE14520 demonstrated the reliability of this prognostic model in some extent.

CONCLUSION

This prognostic model represented significance for prognosis of HCC, and the risk score according to this model may be a better prognostic factor than other traditional clinical indicators.

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

Aims

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

Methods

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

Results

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

Conclusion

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

Transcriptome analysis of neratinib treated HER2 positive cancer model vs untreated cancer unravels the molecular mechanism of action of neratinib

Human estrogen receptor positive cancer cells have mutations and make an excess of the HER2 protein and are far more aggressive than others cancers. Neratinib, an irreversible tyrosine kinase inhibitor is used to treat HER2 positive cancers. Neratinib targets HER2 and blocks its signal transduction resulting in inhibition of cell proliferation and induction of apoptosis without any information about the molecular mechanism involved. To understand the underlying molecular mechanism transcriptome analysis was carried out in normal vs cancer induced SWR/J nude mice. Cancer was induced in SWR/J nude mice with intraperitoneal injection of 5 × 10⁶ SKBR3 cells for 14 days. Histopathology confirmed the induction of cancer in liver and kidney after the tumor size was at least 0.5 cm. Genome wide Mouse U133 Array was used to analyze the effect of neratinib treatment on cancer. Validation of expression was done by qPCR and ELISA. Microscopic examination revealed that neratinib treatment has potential effects on cancerous liver. Transcriptome expression profiling showed 1481 transcripts differentially expressed by neratinib treatment. Transcriptome Analysis Console (TAC) showed that 532 upregulated transcripts were exclusively belonging to cell cycle, inflammation, olfaction, oxidative stress, HER, and EGFR1 while 949 downregulated transcripts were involved in immunology, drug resistance such as histocompatibility, T cell receptors, and immunoglobulins. The differentially expressed genes were considered significant under the criteria of an adjusted p-value < 0.02 and log2 ratios ≥ 1.0 and/or log2 ratios ≤  − 1.0 means two Fold change. qPCR assay and ELISA analysis was used to validate few genes involved in apoptosis and proliferation. This study provides new insights into the neratinib’s mode of action by cyclin-dependent kinase inhibitor-3 and calcium-activated chloride channel 3 as markers for treatment progress.

Predict New Therapeutic Drugs for Hepatocellular Carcinoma Based on Gene Mutation and Expression

Hepatocellular carcinoma (HCC) is the fourth most common primary liver tumor and is an important medical problem worldwide. However, the use of current therapies for HCC is no possible to be cured, and despite numerous attempts and clinical trials, there are not so many approved targeted treatments for HCC. So, it is necessary to identify additional treatment strategies to prevent the growth of HCC tumors. We are looking for a systematic drug repositioning bioinformatics method to identify new drug candidates for the treatment of HCC, which considers not only aberrant genomic information, but also the changes of transcriptional landscapes. First, we screen the collection of HCC feature genes, i.e., kernel genes, which frequently mutated in most samples of HCC based on human mutation data. Then, the gene expression data of HCC in TCGA are combined to classify the kernel genes of HCC. Finally, the therapeutic score (TS) of each drug is calculated based on the kolmogorov-smirnov statistical method. Using this strategy, we identify five drugs that associated with HCC, including three drugs that could treat HCC and two drugs that might have side-effect on HCC. In addition, we also make Connectivity Map (CMap) profiles similarity analysis and KEGG enrichment analysis on drug targets. All these findings suggest that our approach is effective for accurate predicting novel therapeutic options for HCC and easily to be extended to other tumors.

Screening of potential biomarkers and their predictive value in early stage non-small cell lung cancer: a bioinformatics analysis

Background

Non-small cell lung cancer (NSCLC) remains the first leading cause of death in malignancies worldwide. Despite the early screening of NSCLC by low-dose spiral computed tomography (CT) in high-risk individuals caused a 20% reduction in the mortality, there still exists imperative needs for the identification of novel biomarkers for the diagnosis and treatment of lung cancer.

Methods

mRNA microarray datasets GSE19188, GSE33532, and GSE44077 were searched, and the differentially expressed genes (DEGs) were obtained using GEO2R. Functional and pathway enrichment analyses were performed for the DEGs using DAVID database. Protein-protein interaction (PPI) network was plotted with STRING and visualized by Cytoscape. Module analysis of the PPI network was done through MCODE. The overall survival (OS) analysis of genes from MCODE was performed with the Kaplan Meier-plotter.

Results

A total of 221 DEGs were obtained, which were mainly enriched in the terms related to cell division, cell proliferation, and signal transduction. A PPI network was constructed, consisting of 221 nodes and 739 edges. A significant module including 27 genes was identified in the PPI network. Elevated expression of these genes was associated with poor OS of NSCLC patients, including UBE2T, UNF2, CDKN3, ANLN, CCNB2, and CKAP2L. The enriched functions and pathways included protein binding, ATP binding, cell cycle, and p53 signaling pathway.

Conclusions

The DEGs in NSCLC have the potential to become useful targets for the diagnosis and treatment of NSCLC.

CDKN3 promotes cell proliferation, invasion and migration by activating the AKT signaling pathway in esophageal squamous cell carcinoma

In China, esophageal squamous cell carcinoma (ESCC), capable of direct invasion and early metastasis, exhibits high mortality. Identification of the molecular basis driving ESCC progression and development of new diagnostic biomarkers are urgently needed. Cyclin-dependent kinase inhibitor 3 (CDKN3) performs crucial roles in the modulation of tumor development. The present study aimed to explore the functions and underlying mechanism of CDKN3 in regulating ESCC cell proliferation and invasion. The expression levels of CDKN3 in ESCC cells were evaluated by reverse transcription-quantitative PCR. Cell counting kit-8 and colony forming assays were used to evaluate cell viability. Wound-healing assay was performed to explore cell migration. Transwell invasion analysis was conducted to investigate the invasive capacity of ESCC cells. Protein levels were detected by western blot assay. The results demonstrated that the expression of CDKN3 was significantly upregulated in ESCC tissues, as predicted using the UALCAN and Gene Expression Omnibus databases. PCR and western blot assays confirmed that CDKN3 was upregulated in ESCC cell lines. Functional assays revealed that CDKN3 knockdown with small interfering RNA decreased the ability of ESCC cells to proliferate, invade and migrate and suppressed G1/S transition. Further mechanistic analyses demonstrated that CDKN3 promoted cell proliferation and invasion by activating the AKT signaling pathway in ESCC cells. To the best of our knowledge, the present study is the first to identify the functions of CDKN3 in ESCC and provide evidence that CDKN3 regulates tumor progression by activating the AKT signaling pathway. Therefore, CDKN3 may serve as a potential effective therapeutic target for ESCC treatment.

Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis

Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients.

Cyclin-Dependent Kinase Inhibitor 3 Promoted Cell Proliferation by Driving Cell Cycle from G1 to S Phase in Esophageal Squamous Cell Carcinoma

Background and aims. Cyclin-dependent kinase inhibitor 3 (CDKN3) has been found playing a varying role in carcinogenesis, but its biological function in esophageal squamous cell carcinoma (ESCC) is unclear. The aim of this study was to demonstrate the role of CDKN3 in ESCC. Materials and Methods: Real-time PCR and Western blot was performed in 15 pairs of ESCC tissues and adjacent normal esophageal tissues. Then cell proliferation ability, cloning ability, cell cycle status and migration and invasion ability were explored in CDKN3 overexpressed TE1 cell line and CDKN3 siRNA transfected TE1 and KYSE70 cell lines. Finally, cell cycle related proteins CyclinD1, CDK4, pAKT, P53, P21, and P27 were tested by Western blot. Results: mRNA level was higher in 11 ESCC tissues compared to adjacent normal tissues, and an increased protein expression was further detected in 8 of those 11 ESCC tissues. Functional assays showed that CDKN3 overexpression promoted ESCC cell proliferation, colony formation, migration and invasion, and facilitated G1/S transition. Opposite results were also got after transfected with CDKN3 siRNA. Cell cycle associated protein pAKT, CyclinD1, CDK4 and P27 were upregulated and P53, P21 and were downregulated under CDKN3 overexpression. All the protein levels were found changed in the opposite direction when CDKN3 expression was disturbed by siRNA. Conclusions: Our study suggested that CDKN3 acted as an oncogene in human ESCC and may accelerate the G1/S transition by affecting CyclinD-CDK4 complex via regulating pAKT-p53-p21 axis and p27 independent of AKT.

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

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

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

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide. However, the precise mechanisms of the development and progression of HCC remain unclear. The present study attempted to identify and functionally analyze the differentially expressed genes between HCC and cirrhotic tissues by using comprehensive bioinformatics analyses.

METHODS

The GSE63898 gene expression profile was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs were performed in DAVID. The STRING database was used to evaluate the interactions of DEGs and to construct a protein-protein interaction (PPI) network using Cytoscape software. Hub genes were selected using the cytoHubba plugin and were validated with the cBioPortal database.

RESULTS

A total of 301 DEGs were identified between HCC and cirrhotic tissues. The GO analysis results showed that these DEGs were significantly enriched in certain biological processes including negative regulation of growth and cell chemotaxis. Several significant pathways, including the p53 signaling pathway, were identified as being closely associated with these DEGs. The top 12 hub genes were screened and included TTK, NCAPG, TOP2A, CCNB1, CDK1, PRC1, RRM2, UBE2C, ZWINT, CDKN3, AURKA, and RACGAP1. The cBioPortal analysis found that alterations in hub genes could result in significantly reduced disease-free survival in HCC.

CONCLUSION

The present study identified a series of key genes and pathways that may be involved in the tumorigenicity and progression of HCC, providing a new understanding of the underlying molecular mechanisms of carcinogenesis in HCC.

Tumor-suppressive effects of microRNA-181d-5p on non-small-cell lung cancer through the CDKN3-mediated Akt signaling pathway in vivo and in vitro

The involvement of several microRNAs (miRs) in the initiation and development of tumors through the suppression of the target gene expression has been highlighted. The aberrant expression of miR-181d-5p and cyclin-dependent kinase inhibitor 3 (CDKN3) in non-small-cell lung cancer (NSCLC) was then screened by microarray analysis. In the present study, we performed a series of in vivo and in vitro experiments for the purpose of investigating their roles in NSCLC and the underlying mechanism. There was a high expression of CDKN3, whereas miR-181d-5p was downregulated in NSCLC. Quantitative RT-PCR, Western blot analysis, and dual-luciferase reporter gene assay further identified that CDKN3 could be negatively regulated by miR-181d-5p. Moreover, the upregulation of miR-181d-5p or silencing of CDKN3 could inactivate the Akt signaling pathway. A549 with the lowest miR-181d-5p and H1975 with the highest CDKN3 among the five NSCLC cell lines (H1299, A549, H1975, NCI-H157, and GLC-82) were adopted for in vitro experiments, in which expression of miR-181d-5p and CDKN3 was altered by transfection of miR-181d-5p mimic/inhibitor or siRNA-targeting CDKN3. Afterwards, cell proliferation, apoptosis, invasion, migration, and angiogenesis, as well as epithelial-mesenchymal transition (EMT), were evaluated, and tumorigenicity was assessed. In addition, an elevation in miR-181d-5p or depletion in CDKN3 led to significant reductions in proliferation, invasion, migration, angiogenesis, EMT, and tumorigenicity of NSCLC cells, coupling with increased cell apoptosis. In conclusion, this study highlights the tumor-suppressive effects of miR-181d-5p on NSCLC via Akt signaling pathway inactivation by suppressing CDKN3, thus providing a promising therapeutic strategy for the treatment of NSCLC.

Identifying Key Genes of Liver Cancer by Networking of Multiple Data Sets

Liver cancer is one of the deadliest cancers in the world. To find effective therapies for this cancer, it is indispensable to identify key genes, which may play critical roles in the incidence of the liver cancer. To identify key genes of the liver cancer with high accuracy, we integrated multiple microarray gene expression data sets to compute common differentially expressed genes, which will result more accurate than those from individual data set. To find the main functions or pathways that these genes are involved in, some enrichment analyses were performed including functional enrichment analysis, pathway enrichment analysis, and disease association study. Based on these genes, a protein-protein interaction network was constructed and analyzed to identify key genes of the liver cancer by combining the local and global influence of nodes in the network. The identified key genes, such as TOP2A, ESR1, and KMO, have been demonstrated to be key biomarkers of the liver cancer in many publications. All the results suggest that our method can effectively identify key genes of the liver cancer. Moreover, our method can be applied to other types of data sets to select key genes of other complex diseases.

Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma

AIM

To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways.

METHODS

The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software.

RESULTS

In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba.

CONCLUSION

In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.

CDKN3 expression is an independent prognostic factor and associated with advanced tumor stage in nasopharyngeal carcinoma

Background: Through data mining from the public transcriptome of NPC, cyclin-dependent kinase inhibitor 3 (CDKN3) was identified as a significantly upregulated gene in NPC. CDKN3 functions as a key factor in cell cycle regulation. This study was aimed to investigate the expression of CDKN3 in NPC tissues and its prognostic significance. Methods: Immunohistochemistry was performed for 124 NPC patients to assess the protein expression of CDKN3. The stainings of CDKN3 were scored by using H-score method. The relationships between CDKN3 expression status and clinicopathological parameters, disease-specific survival (DSS), distant metastasis-free survival (DMeFS), and local recurrence-free survival (LRFS) were statistically analyzed. Results: High expression of CDKN3 was significantly associated with higher primary nodal status (P=0.030) and higher TNM stage (P=0.019). In univariate analysis, high expression of CDKN3 predicted worse DSS (P<0.0001), DMeFS (P<0.0001), and LRFS (P<0.0001). In multivariate analysis, CDKN3 overexpression still acted as an independent prognostic factor for worse DSS (P<0.001; hazard ratio [HR]=11.999, 95% CI: 5.378-26.771), DMeFS (P<0.001; HR=15.069, 95% CI: 5.884-38.592), and LRFS (P<0.001; HR=5.000, 95% CI: 2.312-10.815). Conclusion: High expression of CDKN3 was an independent negative prognostic factor for NPC and was associated with advanced disease status. It might serve as potential therapeutic target and aid in risk stratification for patients with NPC.