FOXM1 promotes proliferation in human hepatocellular carcinoma cells by transcriptional activation of CCNB1

A Gluconeogenesis-Related Genes Model for Predicting Prognosis, Tumor Microenvironment Infiltration, and Drug Sensitivity in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is a prevalent malignancy within the digestive system, known for its poor prognosis. Gluconeogenesis, a critical metabolic pathway, is responsible for the synthesis of glucose in the normal liver. This study aimed to examine the role of gluconeogenesis-related genes (GRGs) in HCC and evaluate their impact on the tumor microenvironment infiltration and drug sensitivity in HCC.

Methods

We retrieved gene expression and clinical pathological data of HCC from The Cancer Genome Atlas (TCGA) database. This dataset was utilized to develop a prognosis model. The data from The International Cancer Genome Consortium (ICGC) served as an independent validation cohort. A least absolute shrinkage and selection operator (LASSO) regression analysis was applied to a curated panel of GRGs to construct and validate the predictive model. Furthermore, unsupervised consensus clustering, based on the expression levels of GRGs, categorized HCC patients into distinct subgroups.

Results

A four-gene prognostic model, referred to as GRGs, has been successfully developed with high accuracy and stability for the prediction of HCC patient prognosis. This model enables the stratification of patients into high or low risk groups based on individual risk scores, revealing significant differences in immune infiltration patterns and anti-tumor drug responses. Unsupervised consensus clustering analysis delineated four distinct subgroups of patients, each characterized by a unique prognosis and tumor immune microenvironment (TIME).

Conclusion

This study is the first to develop a prognostic model incorporating 4-GRGs that effectively predicts the prognosis, tumor microenvironment infiltration, and drug sensitivity in HCC patients. The model based on 4 GRGs may contribute to predict the prognosis, immunotherapy and chemotherapy response of HCC patients.

Deciphering the Molecular Complexity of Hepatocellular Carcinoma: Unveiling Novel Biomarkers and Therapeutic Targets Through Advanced Bioinformatics Analysis

BACKGROUND

Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research.

METHODS

The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP.

RESULTS

A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression.

CONCLUSION

The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation.

Study on the mechanism of quercetin in Sini Decoction Plus Ginseng Soup to inhibit liver cancer and HBV virus replication through CDK1

BACKGROUND

To explore the anti-tumor and anti-virus key active ingredients of Sini Decoction Plus Ginseng Soup (SNRS) and their mechanisms.

METHODS

The main ingredients of SNRS were analyzed by network pharmacology, and quercetin was identified as the key active ingredient. Then, we obtained the targets of quercetin by using Drugbank, PharmMapper, and SwissTargetPrediction databases. Then, the targets of HBV-related hepatocellular carcinoma (HBV-related HCC) were obtained by using Genecards database. In addition, using the gene expression profiles of HBV-related HCC patients in GEO database and the genes with the greatest survival difference in GEPIA 2 database identified the potential targets of quercetin. In addition, the mechanism of potential genes was studied through GO, KEGG analysis, and PPI network. Using AUC and survival analysis to evaluate the diagnostic and prognostic value of cyclin-dependent kinase 1 (CDK1) and CCNB1. Finally, the effects of quercetin on proliferation of Hep3B and HepG2215 cells and the level of CDK1 and CCNB1 were verified in vitro. ELISA was used to measure the expression levels of hepatitis B surface antigen (HBsAg) and hepatitis B e antigen (HBeAg) after the intervention by quercetin for 24 h and 48 h in HepG2215 cell.

RESULTS

The first 10 key ingredients of SNRS were identified, and quercetin was the most key ingredient. The 101 potential quercetin targets were identified for the treatment of HBV-related HCC. GO and KEGG showed that 101 potential target enrichment in cancer and cell cycle regulation. By Venn analysis, CDK1 and CCNB1 were intersection targets, which could be used as potential targets for the action of quercetin on HBV-related HCC. Moreover, the expression of CDK1 and CCNB1 was highly expressed in the high-risk group, while the OS rate was low. The 1-year, 3-year and 5-year area under the curve (AUC) curves of CDK1 and CCNB1 were 0.724, 0.676, 0.622 and 0.745, 0.678, 0.634, respectively. Moreover, experimental results also showed that quercetin inhibited cell proliferation and reduced CDK1 expression in Hep3B and HepG2215 cells. The expressions of HBsAg and HBeAg in HepG2215 cell supernatant and cell gradually decreased with the increase of intervention time of quercetin and CDK1 inhibitor.

CONCLUSIONS

Quercetin is a key ingredient of anti-HBV-related HCC activity and inhibits HBV replication in SNRS by inhibiting CDK1.

Antiproliferative Activity of N-Acylhydrazone Derivative on Hepatocellular Carcinoma Cells Involves Transcriptional Regulation of Genes Required for G2/M Transition

Liver cancer is the second leading cause of cancer-related death in males. It is estimated that approximately one million deaths will occur by 2030 due to hepatic cancer. Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer subtype and is commonly diagnosed at an advanced stage. The drug arsenal used in systemic therapy for HCC is very limited. Multikinase inhibitors sorafenib (Nexavar®) and lenvatinib (Lenvima®) have been used as first-line drugs with modest therapeutic effects. In this scenario, it is imperative to search for new therapeutic strategies for HCC. Herein, the antiproliferative activity of N-acylhydrazone derivatives was evaluated on HCC cells (HepG2 and Hep3B), which were chemically planned on the ALL-993 scaffold, a potent inhibitor of vascular endothelial growth factor 2 (VEGFR2). The substances efficiently reduced the viability of HCC cells, and the LASSBio-2052 derivative was the most effective. Further, we demonstrated that LASSBio-2052 treatment induced FOXM1 downregulation, which compromises the transcriptional activation of genes required for G2/M transition, such as AURKA and AURKB, PLK1, and CDK1. In addition, LASSBio-2052 significantly reduced CCNB1 and CCND1 expression in HCC cells. Our findings indicate that LASSBio-2052 is a promising prototype for further in vivo studies.

Identification of tumor antigens and immune subtypes of hepatocellular carcinoma for mRNA vaccine development

BACKGROUND

mRNA vaccines have been investigated in multiple tumors, but limited studies have been conducted on their use for hepatocellular carcinoma (HCC).

AIM

To identify candidate mRNA vaccine antigens for HCC and suitable subpopulations for mRNA vaccination.

METHODS

Gene expression profiles and clinical information of HCC datasets were obtained from International Cancer Genome Consortium and The Cancer Genome Atlas. Genes with somatic mutations and copy number variations were identified by cBioPortal analysis. The differentially expressed genes with significant prognostic value were identified by Gene Expression Profiling Interactive Analysis 2 website analysis. The Tumor Immune Estimation Resource database was used to assess the correlation between candidate antigens and the abundance of antigen-presenting cells (APCs). Tumor-associated antigens were overexpressed in tumors and associated with prognosis, genomic alterations, and APC infiltration. A consensus cluster analysis was performed with the Consensus Cluster Plus package to identify the immune subtypes. The weighted gene coexpression network analysis (WGCNA) was used to determine the candidate biomarker molecules for appropriate populations for mRNA vaccines.

RESULTS

AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 were identified as candidate HCC antigens for mRNA vaccine development. Four immune subtypes (IS1-IS4) and five immune gene modules of HCC were identified that were consistent in both patient cohorts. The immune subtypes showed distinct cellular and clinical characteristics. The IS1 and IS3 immune subtypes were immunologically "cold". The IS2 and IS4 immune subtypes were immunologically "hot", and the immune checkpoint genes and immunogenic cell death genes were upregulated in these subtypes. IS1-related modules were identified with the WGCNA algorithm. Ultimately, five hub genes (RBP4, KNG1, METTL7A, F12, and ABAT) were identified, and they might be potential biomarkers for mRNA vaccines.

CONCLUSION

AURKA, CCNB1, CDC25C, CDK1, TRIP13, PES1, MCM3, PPM1G, NEK2, KIF2C, PTTG1, KPNA2, and PRC1 have been identified as candidate HCC antigens for mRNA vaccine development. The IS1 and IS3 immune subtypes are suitable populations for mRNA vaccination. RBP4, KNG1, METTL7A, F12, and ABAT are potential biomarkers for mRNA vaccines.

Transcription factor FOXM1 promotes hepatocellular carcinoma malignant progression through activation of the WNT pathway by binding to SETDB1

BACKGROUND

FOXM1 is a transcription factor confirmed by studies to promote the development of hepatocellular carcinoma (HCC) and various other cancers, yet the molecular mechanism remains rather enigmatic. This study attempted to unveil the function and regulatory mechanism of FOXM1 in the progression of HCC.

METHODS

Bioinformatics methods first analyzed the expression of FOXM1 in HCC tissues and then screened target genes downstream of FOXM1. Possible pathways of the target gene were specified through Gene Set Enrichment Analysis (GSEA). After using qRT-PCR to measure the expression of FOXM1 and its downstream regulatory gene SETDB1 in HCC tissues, ChIP and dual-luciferase assays were employed and verified the binding relationship between FOXM1 and the promoter of SETDB1. Then the effects of the FOXM1/SETDB1/Wnt pathway on the proliferation, migration, and invasion of HCC cells were profiled by CCK-8, colony formation, wound healing, and transwell assays. WNT and EMT-related protein expression levels were detected by western blot and immunofluorescence assay, respectively.

RESULTS

The bioinformatics prediction showed that SETDB1 was the target downstream of FOXM1, and their binding relationship was verified by ChIP and dual-luciferase assays. Cell experiments showed that FOXM1 could enhance the proliferative, migratory, and invasive abilities of HCC cells through binding to SETDB1. Rescue assay suggested that the activation of key genes of the WNT pathway and EMT-related genes were part of the regulatory mechanism that FOXM1 bound to SETDB1.

CONCLUSION

This study found that FOXM1 could bind with SETDB1 and hence activate the WNT signaling pathway to promote the malignant progression of HCC. It indicated that FOXM1 could be the possible target for treating HCC.

Improvement of variables interpretability in kernel PCA

BACKGROUND

Kernel methods have been proven to be a powerful tool for the integration and analysis of high-throughput technologies generated data. Kernels offer a nonlinear version of any linear algorithm solely based on dot products. The kernelized version of principal component analysis is a valid nonlinear alternative to tackle the nonlinearity of biological sample spaces. This paper proposes a novel methodology to obtain a data-driven feature importance based on the kernel PCA representation of the data.

RESULTS

The proposed method, kernel PCA Interpretable Gradient (KPCA-IG), provides a data-driven feature importance that is computationally fast and based solely on linear algebra calculations. It has been compared with existing methods on three benchmark datasets. The accuracy obtained using KPCA-IG selected features is equal to or greater than the other methods' average. Also, the computational complexity required demonstrates the high efficiency of the method. An exhaustive literature search has been conducted on the selected genes from a publicly available Hepatocellular carcinoma dataset to validate the retained features from a biological point of view. The results once again remark on the appropriateness of the computed ranking.

CONCLUSIONS

The black-box nature of kernel PCA needs new methods to interpret the original features. Our proposed methodology KPCA-IG proved to be a valid alternative to select influential variables in high-dimensional high-throughput datasets, potentially unravelling new biological and medical biomarkers.

FOXM1 augments sorafenib resistance and promotes progression of hepatocellular carcinoma by epigenetically activating KIF23 expression

Sorafenib has been used to enhance the survival outcome of hepatocellular carcinoma (HCC) patients. But, occurrence resistance to sorafenib subtracts from its therapeutic benefits. Herein, we identified that FOXM1 was markedly upregulated in both tumor samples and sorafenib-resistant HCC tissues. We also demonstrated that patients with decreased FOXM1 expression had longer overall survival (OS) and progression-free survival (PFS) in the cohort of sorafenib-treated patients. For HCC cells resistant to sorafenib, the IC50 value of sorafenib and the expression of FOXM1 were increased. In addition, Downregulation of FOXM1 expression alleviated the occurrence of resistance to sorafenib and reduced the proliferative potential and viability of HCC cells. Mechanically, the suppression of the FOXM1 gene resulted in the downregulation of KIF23 levels. Moreover, downregulation of FOXM1 expression reduced the levels of RNA polymerase II (RNA pol II) and histone H3 lysine 27 acetylation (H3K27ac) on the KIF23 promoter, further epigenetically silencing the production of KIF23. More intriguingly, our results similarly revealed that FDI-6, a specific inhibitor of FOXM1, suppressed the proliferation of HCC cells resistant to sorafenib, as well as upregulation of FOXM1 or KIF23 abolished this effect. In addition, we found that FDI-6 combined with sorafenib significantly improved the therapeutic effect of sorafenib. Collectively, the present results revealed that FOXM augments sorafenib resistance and enhances HCC progression by upregulating KIF23 expression via an epigenetic mechanism, and targeting FOXM1 can be an effective treatment for HCC.

Identifying M1-like macrophage related genes for prognosis prediction in lung adenocarcinoma based on a gene co-expression network

Macrophages are one of the most important players in the tumor microenvironment. But the contribution of macrophages to lung adenocarcinoma (LUAD) is still controversial. The current study aimed to display an immune landscape to clarify the function of macrophages and detect prognostic hub genes in LUAD. The transcriptome data were adopted to screen differently expressed genes (DEGs) in The Cancer Genome Atlas database (TCGA). The cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithm was used to reveal the immune landscape. Weighted gene co-expression network analysis (WGCNA) analysis was performed to identify the hub module associated with macrophages. Function Enrichment analysis was conducted on hub module genes. Moreover, univariate and multivariate Cox regression analyses were performed to identify prognostic hub genes. Kaplan-Meier (KM) and Time-dependent receiver operating characteristic (ROC) curves were plotted to assess the prognostic capacity of the four prognostic hub genes. The GES1196959 dataset from the Gene Expression Omnibus (GEO) database was downloaded to verify the differential expression of the 4 prognostic hub genes.

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of lung cancer, which is the most prevalent and lethal malignancy worldwide. Cyclins have been reported to regulate the physiology of various types of tumors by controlling cell cycle progression. However, the key roles and regulatory networks associated with the majority of the cyclin family members in LUAD remain unclear. In total, 556 differentially expressed genes were screened from the GSE33532, GSE40791 and GSE19188 mRNA microarray datasets by R software. Subsequently, protein-protein interaction network containing 499 nodes and 4,311 edges, in addition to a significant module containing 76 nodes and 2,631 edges, were extracted through the MCODE plug-in of Cytoscape. A total of four cyclin family genes [cyclin (CCNA2, CCNB1, CCNB2 and CCNE2] were then found in this module. Further co-expression analysis and associated gene prediction revealed forkhead box M1 (FOXM1), the common transcription factor of CCNB2, CCNB1 and CCNA2. In addition, using GEPIA database, it was found that the high expression of these four genes were simultaneously associated with poorer prognosis in patients with LUAD. Experimentally, it was proved that these four hub genes were highly expressed in LUAD cell lines (Beas-2B and H1299) and LUAD tissues through qPCR, western blot analysis and immunohistochemical studies. The diagnostic value of these 4 hub genes in LUAD was analyzed by logistic regression, CCNA2 was deleted, following which a nomogram diagnostic model was constructed accordingly. The area under the curve values of CCNB1, CCNB2 and FOXM1 diagnostic models were calculated to be 0.92, 0.91 and 0.96 in the training set (Combined dataset of GSE33532, GSE40791 and GSE19188) and two validation sets (GSE10072 and GSE75037), respectively. To conclude, data from the present study suggested that the FOXM1/cyclin (CCNA2, CCNB1 and/or CCNB2) axis may serve a regulatory role in the development and prognosis of LUAD. Specifically, CCNB1, CCNB2 and FOXM1 have potential as diagnostic markers and/or therapeutic targets for LUAD treatment.

Dysregulated FOXM1 signaling in the regulation of cancer stem cells

Since the introduction of the cancer stem cell (CSC) paradigm, significant advances have been made in understanding the functional and biological plasticity of these elusive components in malignancies. Endowed with self-renewing abilities and multilineage differentiation potential, CSCs have emerged as cellular drivers of virtually all facets of tumor biology, including metastasis, tumor recurrence/relapse, and drug resistance. The functional and biological characteristics of CSCs, such as self-renewal, cell fate decisions, survival, proliferation, and differentiation are regulated by an array of extracellular factors, signaling pathways, and pluripotent transcriptional factors. Besides the well-characterized regulatory role of transcription factors OCT4, SOX2, NANOG, KLF4, and MYC in CSCs, evidence for the central role of Forkhead box transcription factor FOXM1 in the establishment, maintenance, and functions of CSCs is accumulating. Conventionally identified as a master regulator of the cell cycle, a comprehensive understanding of this molecule has revealed its multifarious oncogenic potential and uncovered its role in angiogenesis, invasion, migration, self-renewal, and drug resistance. This review compiles the large body of literature that has accumulated in recent years that provides evidence for the mechanisms by which FOXM1 expression promotes stemness in glioblastoma, breast, colon, ovarian, lung, hepatic, and pancreatic carcinomas. We have also compiled the data showing the association of stem cell mediators with FOXM1 using TCGA mRNA expression data. Further, the prognostic importance of FOXM1 and other stem cell markers is presented. The delineation of FOXM1-mediated regulation of CSCs can aid in the development of molecularly targeted pharmacological approaches directed at the selective eradication of CSCs in several human malignancies.

A Prognostic Nomogram for Hepatocellular Carcinoma Based on Wound Healing and Immune Checkpoint Genes

BACKGROUND AND AIMS

Wound healing and tumor progression share some common biological features; however, how variations in wound healing patterns affect hepatocellular carcinoma (HCC) prognosis remains unclear.

METHODS

We analyzed the wound healing patterns of 594 HCC samples from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) and correlated them with immune infiltration and the expression levels of immune checkpoint genes. A risk score, which we named the "heal.immune" score, was established via stepwise Cox estimation. We constructed a nomogram based on age, sex, TNM stage, and heal.immune score and explored its predictive value for HCC prognosis. Seventy-four clinical patients were enrolled in this study, and all were from Huashan Hospital of Fudan University between 2015 and 2017 to serve as an independent validation group.

RESULTS

We identified two distinct wound healing patterns in HCC. The biological processes of healing cluster 1 (C1) are related to metabolism, while those of healing cluster 2 (C2) are related to the inflammatory response and immune cell accumulation. A total of 565 wound healing-related genes (based on Gene Ontology) and 25 immune checkpoint genes were considered. By analyzing differentially expressed genes and implementing a stepwise Cox estimation analysis, six genes with p values less than 0.02 in a multivariate Cox estimation were chosen as the "heal.immune" gene set (FCER1G, PLAT, ITGA5, CCNB1, CD86 and CD40). The "heal.immune" gene set, as an OS risk factor, was further validated in Fudan cohort. We constructed a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in the TCGA cohort. The area under curve vales of the receiver characteristic operator curves were 0.82, 0.76 and 0.73 in the training group and 0.84, 0.76 and 0.72 in the test group.

CONCLUSIONS

We established a prognostic nomogram based on the heal.immune gene signature, which includes six wound healing- and immunity-related genes. This nomogram accurately predicts the OS of HCC patients.

Mechanism of Glycitein in the Treatment of Colon Cancer Based on Network Pharmacology and Molecular Docking

INTRODUCTION

The prevalence of colon cancer remains high across the world. The early diagnosis of colon cancer is challenging. Moreover, patients with colon cancer frequently suffer from poor prognoses.

METHODS

Differentially expressed genes (DEGs) in colon cancer were acquired based on TCGA-COAD dataset screening. DEGs were input into the Connectivity Map (CMap) database to screen small molecule compounds with the potential to reverse colon cancer pathological function. Glycitein ranked first among the screened small-molecule compounds. We downloaded the main targets of glycitein from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database and constructed protein-protein interaction (PPI) networks of those which were closely related to targets by the Search Tool for the Retrieval of Interaction Gene/Proteins (STRING). Five potential targets of glycitein for treating colon cancer were identified (CCNA2, ESR1, ESR2, MAPK14, and PTGS2). These targets were used as seeds for random walk with restart (RWR) analysis of PPI networks. Then, the interaction network of glycitein-colon cancer-related genes was constructed based on the top 50 genes in affinity coefficients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on the potential genes targeted by glycitein in colon cancer treatment and those that were closely bound up with targets.

RESULTS

GO analysis demonstrated that the enrichment of these genes was primarily discovered in biological functions including regulation of fibroblast proliferation, response to oxygen levels, and epithelial cell proliferation. The KEGG analysis results illustrated that the signaling pathways where these genes were mostly involved consisted of the mitogen-activated protein kinase signaling pathway, the phosphatidylinositol-3-kinase-Akt signaling pathway, and the p53 signaling pathway. Finally, stable binding of glycitein to five potential targets in colon cancer was verified by molecular docking.

CONCLUSION

This study elucidated the key targets and main pathways of glycitein on the basis of network pharmacology and preliminarily analyzed molecular mechanisms in the treatment of colon cancer. A scientific basis is provided for glycitein application in treating colon cancer.

Expression of FOXM1 and PLK1 predicts prognosis of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most frequently encountered malignant tumor types and to improve its treatment, effective prognostic biomarkers are urgently required. Cell cycle dysregulation is a significant feature of cancer progression. The aim of the present study was to estimate the expression levels of forkhead box protein M1 (FOXM1) and polo-like kinase 1 (PLK1), both of which have essential roles in cell cycle regulation, and determine their prognostic value in HCC. To this end, FOXM1 and PLK1 expression levels were assessed in The Cancer Genome Atlas and International Cancer Genome Consortium Japan HCC cohorts, and the associations between their co-expression were determined via Pearson's correlation analysis. Furthermore, the overall survival and disease-free survival in these cohorts for different FOXM1 and PLK1 expression statuses were analyzed. In vitro knockdown experiments were also performed using Huh7 cells. The results obtained indicated overexpression of FOXM1 and PLK1 in HCC tumor tissues as well as a positive correlation between FOXM1 and PLK1 expression. The results also suggested that both FOXM1 and PLK1 are required for HCC cell proliferation. In addition, upregulation of FOXM1 and PLK1 was indicated to be associated with poor prognosis of patients with HCC. However, only their coordinated overexpression was identified as an independent prognostic factor for HCC.

CCNB1, Negatively Regulated by miR-559, Promotes the Proliferation, Migration, and Invasion of Ovarian Carcinoma Cells

Cyclin B1 (CCNB1) is regarded as an oncogene in multiple tumors. This work aims to investigate the expression, function, and related mechanisms of CCNB1 in ovarian carcinoma (OC). Three microarray datasets (GSE14407, GSE18520, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database and screened for differentially expressed genes (DEGs) of OC tissues and normal ovarian tissues. CCNB1 expression in OC tissues and paracancerous tissues was detected by immunohistochemistry. Kaplan-Meier plotter database was utilized to analyze the correlation between CCNB1 expression and the prognosis of OC patients. After the loss-of-function and gain-of-function cell models were established, cell counting kit-8 (CCK-8), bromo-deoxyuridine (BrdU), and transwell experiments were employed to examine the proliferation, migration, and invasion of OC cells, respectively. The targeting relationship between miR-559 and CCNB1 was verified using the dual-luciferase reporter gene experiment. The expressions of CCNB1 mRNA and miR-559 were detected by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Western blot was used to quantify the protein expression of CCNB1. In addition, xenograft nude mouse models were established to examine the effects of CCNB1 on lung metastasis in vivo. CCNB1 expression was markedly increased in OC tissues and cell lines. The overall survival, progression-free survival, and post-progression survival of OC patients with high CCNB1 expression were significantly shorter. OC cell proliferation, migration, and invasion were enhanced by CCNB1 overexpression while CCNB1 knockdown led to opposite effects. MiR-559 expression was remarkably reduced in OC tissues and cell lines, and miR-559 markedly suppressed the malignant characteristics of OC cells. Besides, miR-559 directly targeted the 3' UTR of CCNB1 mRNA and reduced CCNB1 expression at both the mRNA and protein levels. Overexpression of CCNB1 accelerated lung metastasis of OC cells in vivo. CCNB1, of which expression is modulated by miR-559, facilitates proliferation, migration, and invasion of OC cells, therefore, working as a potential therapeutic target of OC. This work provides new insights into the clinical diagnosis and treatment of OC.

A comprehensive analysis of FOX family in HCC and experimental evidence to support the oncogenic role of FOXH1

Hepatocellular carcinoma (HCC) remains the second leading cause of cancer related deaths worldwide. Understanding about the molecular biology of HCC and development of targeted therapies are still the main focuses of this type of disease. Here, by connecting the expression levels of FOX proteins with their associated clinical characteristics using TCGA LIHC dataset, we found that 27/40 FOX proteins were highly expressed in HCC tumors compared to normal liver tissues and their expression levels were tightly associated with HCC tumor stage, tumor grade and overall survival. Our experimental results also confirmed that FOXH1 indeed played an oncogenic role in HCC development by promoting cell growth and cell migration/invasion. Mechanistic dissection demonstrated that FOXH1-induced cell growth and cell migration/invasion relied on mTOR signaling because inhibition of mTOR signaling by rapamycin could attenuate FOXH1-mediated phenotypic alterations of HCC cells. The results from orthotopic mouse model also validated that FOXH1 promoted HA22T tumor growth via triggering mTOR activation. Overall, this study not only comprehensively examines the clinical values of FOX proteins in HCC but also provides experimental evidence to support the role of FOXH1 in HCC development, building rationale to develop more effective therapies to treat HCC patients.

MiR-139-5p Targeting CCNB1 Modulates Proliferation, Migration, Invasion and Cell Cycle in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most frequent histological subtype of non-small cell lung cancer. Cyclin B1 (CCNB1) is the vital initiator and controller of mitosis. Studies have indicated that CCNB1 overexpression is closely associated with cell proliferation and tumorigenesis in many cancers. Thus, discovery of molecular mechanism of CCNB1 in LUAD is conducive to developing new diagnostic or therapeutic targets for LUAD. We acquired mature miRNA and mRNA expression information of LUAD from TCGA database, as well as related clinical data. CCNB1 expression in normal and LUAD tissue was analyzed. Relationship between CCNB1 and patient's survival and clinical stage was analyzed. Upstream regulatory gene miRNA of CCNB1 was predicted. qRT-PCR and western blot examined expression levels of CCNB1 and miR-139-5p in cells. CCK-8 tested cell proliferation. Scratch healing and Transwell determined cell migration and invasion. Flow cytometry analyzed the cell cycle. Dual-luciferase verified targeting relationship between the two genes. Compared to controls, CCNB1 expression was prominently high in LUAD patient samples, and associated with advanced tumor stages and shorter overall survival. MiR-139-5p expressed an evidently negative correlation with CCNB1 and was predicted to target CCNB1. MiR-139-5p mimics reduced CCNB1 mRNA and protein expression, and suppressed luciferase activity in a target-specific manner, as confirmed by a control construct with a mutated miR-139-5p binding site. CCNB1 overexpression fostered progression of LUAD cells. Mechanistically, miR-139-5p might negatively regulate CCNB1 in LUAD, thereby suppressing cell proliferation, migration, invasion and cell cycle.

FOXM1-induced TYMS upregulation promotes the progression of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and one of the major causes of cancer-related death. Thymidylate synthase (TYMS) catalyzes the methylation of deoxy guanosine to deoxy thymidylate, which is a crucial gene for DNA repair and replication. Thus, TYMS was reported to be closely associated with developing a variety of tumors, but it has been poorly studied in HCC.

Materials and methods

We used the cell counting kit-8 (CCK-8), BrdU, and CFSE assay to measure cell proliferation. The flow cytometry assay and the TUNEL assay were used for assessing cell apoptosis. The flow cytometry assay was used to analyze the cell cycle. The Transwell invasion assay and the wound healing assay were conducted to determine the invasive ability of the cells. RT-qPCR and Western blot analyses were performed to evaluate the mRNA and protein expression levels of specific genes, respectively.

Results

TYMS was found to be upregulated in both HCC cells and patient samples. High expression of TYMS was associated with an unfavorable prognosis in HCC patients based on the TCGA-LIHC dataset. Cell proliferation, apoptosis, and invasion assays revealed that TYMS promoted the proliferation and invasion of HCC cells as well as inhibited apoptosis. In addition, TYMS is a downstream target of FOXM1. TYMS knockdown reversed the 5-FU resistance caused by FOXM1 overexpression and re-sensitized HCC cells to 5-FU treatment.

Conclusion

This study suggested that TYMS serves as an oncogene in HCC, and targeting the FOXM1-TYMS axis may help improve the survival of HCC patients as well as provide new insights for treating advanced HCC patients.

Identification of Differentially Expressed Genes Associated with the Prognosis and Diagnosis of Hepatocellular Carcinoma by Integrated Bioinformatics Analysis

OBJECTIVE

The goal of this study was to understand the possible core genes associated with hepatocellular carcinoma (HCC) pathogenesis and prognosis.

METHODS

GEO contains datasets of gene expression, miRNA, and methylation patterns of diseased and healthy/control patients. The GSE62232 dataset was selected by employing the server Gene Expression Omnibus. A total of 91 samples were collected, including 81 HCC and 10 healthy samples as control. GSE62232 was analysed through GEO2R, and Functional Enrichment Analysis was performed to extract rational information from a set of DEGs. The Protein-Protein Relationship Networking search method has been used for extracting the interacting genes. MCC method was used to calculate the top 10 genes according to their importance. Hub genes in the network were analysed using GEPIA to estimate the effect of their differential expression on cancer progression.

RESULTS

We identified the top 10 hub genes through CytoHubba plugin. These included BUB1, BUB1B, CCNB1, CCNA2, CCNB2, CDC20, CDK1 and MAD2L1, NCAPG, and NDC80. NCAPG and NDC80 reported for the first time in this study while the remaining from a recently reported literature. The pathogenesis of HCC may be directly linked with the aforementioned genes. In this analysis, we found critical genes for HCC that showed recommendations for future prognostic and predictive biomarkers studies that could promote selective molecular therapy for HCC.

Six potential biomarkers for bladder cancer: key proteins in cell-cycle division and apoptosis pathways

BACKGROUND

The bladder cancer (BC) pathology is caused by both exogenous environmental and endogenous molecular factors. Several genes have been implicated, but the molecular pathogenesis of BC and its subtypes remains debatable. The bioinformatic analysis evaluates high numbers of proteins in a single study, increasing the opportunity to identify possible biomarkers for disorders.

METHODS

The aim of this study is to identify biomarkers for the identification of BC using several bioinformatic analytical tools and methods. BC and normal samples were compared for each probeset with T test in GSE13507 and GSE37817 datasets, and statistical probesets were verified with GSE52519 and E-MTAB-1940 datasets. Differential gene expression, hierarchical clustering, gene ontology enrichment analysis, and heuristic online phenotype prediction algorithm methods were utilized. Statistically significant proteins were assessed in the Human Protein Atlas database. GSE13507 (6271 probesets) and GSE37817 (3267 probesets) data were significant after the extraction of probesets without gene annotation information. Common probesets in both datasets (2888) were further narrowed by analyzing the first 100 upregulated and downregulated probesets in BC samples.

RESULTS

Among the total 400 probesets, 68 were significant for both datasets with similar fold-change values (Pearson r: 0.995). Protein-protein interaction networks demonstrated strong interactions between CCNB1, BUB1B, and AURKB. The HPA database revealed similar protein expression levels for CKAP2L, AURKB, APIP, and LGALS3 both for BC and control samples.

CONCLUSION

This study disclosed six candidate biomarkers for the early diagnosis of BC. It is suggested that these candidate proteins be investigated in a wet lab to identify their functions in BC pathology and possible treatment approaches.

Molecular Mechanism of Chrysin in Hepatocellular Carcinoma Treatment Based on Network Pharmacology and in Vitro Experiments

This study elucidated the potential molecular mechanism of chrysin in hepatocellular carcinoma (HCC) treatment using network pharmacology and in vitro experiments. Chrysin and candidate targets of HCC were obtained from the TCMSP and DrugBank databases, followed by mapping and screening of chrysin and HCC targets to identify the core targets of chrysin in HCC treatment. The interaction of chrysin and its targets, including CDK1, CDK5, as well as MMP9, were evaluated by molecular docking. The STRING database and Cytoscape (version 3.8.2) software were used to construct protein interactions and component-target networks of the core targets. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis of the core target genes were performed using the DAVID database. Network pharmacology results showed that chrysin treatment of HCC was mainly related to cell proliferation and cell cycle. Accordingly, the cell counting kit-8 method and flow cytometry were used to detect the cell viability and cell cycle of hepatocarcinoma cells HCCLM3 and BEL-7402 in vitro. A total of 142 compound targets of chrysin, 12,179 HCC-related targets, and 116 intersecting targets were screened. The first 20 GO biological annotations of 17 core targets and the first 20 KEGG pathways mainly involved cell proliferation and cell cycle. In vitro experiments showed that chrysin inhibits the proliferation of human hepatocarcinoma cells (HCCLM3 and BEL-7402) in a dose-dependent manner. Moreover, chrysin induced cell cycle arrest in HCCLM3 and BEL-7402 cells in the G2 phase, and the expression was downregulated of cyclin-dependent kinases (CDKs), CDK2 and CDK4. Chrysin can offset HCC mainly by regulating the cell cycle and inhibiting cell proliferation. The network pharmacology results were verified, providing the basis for further study on the mechanism of chrysin intervention in 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.

Artemisinin Mediates Its Tumor-Suppressive Activity in Hepatocellular Carcinoma Through Targeted Inhibition of FoxM1

The aberrant up-regulation of the oncogenic transcription factor Forkhead box M1 (FoxM1) is associated with tumor development, progression and metastasis in a myriad of carcinomas, thus establishing it as an attractive target for anticancer drug development. FoxM1 overexpression in hepatocellular carcinoma is reflective of tumor aggressiveness and recurrence, poor prognosis and low survival in patients. In our study, we have identified the antimalarial natural product, Artemisinin, to efficiently curb FoxM1 expression and activity in hepatic cancer cells, thereby exhibiting potential anticancer efficacy. Here, we demonstrated that Artemisinin considerably mitigates FoxM1 transcriptional activity by disrupting its interaction with the promoter region of its downstream targets, thereby suppressing the expression of numerous oncogenic drivers. Augmented level of FoxM1 is implicated in drug resistance of cancer cells, including hepatic tumor cells. Notably, FoxM1 overexpression rendered HCC cells poorly responsive to Artemisinin-mediated cytotoxicity while FoxM1 depletion in resistant liver cancer cells sensitized them to Artemisinin treatment, manifested in lower proliferative and growth index, drop in invasive potential and repressed expression of EMT markers with a concomitantly increased apoptosis. Moreover, Artemisinin, when used in combination with Thiostrepton, an established FoxM1 inhibitor, markedly reduced anchorage-independent growth and displayed more pronounced death in liver cancer cells. We found this effect to be evident even in the resistant HCC cells, thereby putting forth a novel combination therapy for resistant cancer patients. Altogether, our findings provide insight into the pivotal involvement of FoxM1 in the tumor suppressive activities of Artemisinin and shed light on the potential application of Artemisinin for improved therapeutic response, especially in resistant hepatic malignancies. Considering that Artemisinin compounds are in current clinical use with favorable safety profiles, the results from our study will potentiate its utility in juxtaposition with established FoxM1 inhibitors, promoting maximal therapeutic efficacy with minimal adverse effects in liver cancer patients.

Close interactions between lncRNAs, lipid metabolism and ferroptosis in cancer

Abnormal lipid metabolism including synthesis, uptake, modification, degradation and transport has been considered a hallmark of malignant tumors and contributes to the supply of substances and energy for rapid cell growth. Meanwhile, abnormal lipid metabolism is also associated with lipid peroxidation, which plays an important role in a newly discovered type of regulated cell death termed ferroptosis. Long noncoding RNAs (lncRNAs) have been proven to be associated with the occurrence and progression of cancer. Growing evidence indicates that lncRNAs are key regulators of abnormal lipid metabolism and ferroptosis in cancer. In this review, we mainly summarized the mechanism by which lncRNAs regulate aberrant lipid metabolism in cancer, illustrated that lipid metabolism can also influence the expression of lncRNAs, and discussed the mechanism by which lncRNAs affect ferroptosis. A comprehensive understanding of the interactions between lncRNAs, lipid metabolism and ferroptosis could help us to develop novel strategies for precise cancer treatment in the future.

Long noncoding RNA PTTG3P/miR-192-3p/CCNB1 axis is a potential biomarker of childhood asthma

BACKGROUND

Increasing evidence suggests that long non-coding RNAs (lncRNAs) affect the regulation of immune responses, airway inflammation, and other pathological processes involved in asthma. LncRNA PTTG3P is associated with the development of various tumors, but its role in childhood asthma remains unknown. In this study, we investigated the functions of the lncRNA PTTG3P in the progression of childhood asthma.

METHODS

Twenty-six healthy children and 26 asthmatic children were monitored for disease progression for 2 years. We obtained blood samples during the chronic phase of disease for lncRNA/mRNA expression microarray analysis. A competitive endogenous RNA network (PTTG3P/miR-192-3p/CCNB1) was identified using bioinformatics analyses. Real-time qPCR and western blot were used to quantify gene and protein expression levels, respectively. Cell counting kit‑8 and transwell assays were used to evaluate the proliferation and migration of bronchial epithelial (16HBE) cells. Double luciferase reporter gene assay was used to validate the predictive targets in PTTG3P, miR-192-3p, and CCNB1.

RESULTS

PTTG3P was highly expressed in the peripheral blood of asthmatic children. Knocking down PTTG3P inhibited epithelial-mesenchymal transition, proliferation, and migration of 16HBE cells. PTTG3P promoted progression of childhood asthma by targeting the miR-192-3p/CCNB1 axis.

CONCLUSIONS

Childhood asthma was associated with the PTTG3P/miR-192-3p/CCNB1 axis. This study provides potential diagnostic and treatment biomarkers for childhood asthma.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Increased BUB1B/BUBR1 expression contributes to aberrant DNA repair activity leading to resistance to DNA-damaging agents

There has been accumulating evidence for the clinical benefit of chemoradiation therapy (CRT), whereas mechanisms in CRT-recurrent clones derived from the primary tumor are still elusive. Herein, we identified an aberrant BUB1B/BUBR1 expression in CRT-recurrent clones in bladder cancer (BC) by comprehensive proteomic analysis. CRT-recurrent BC cells exhibited a cell-cycle-independent upregulation of BUB1B/BUBR1 expression rendering an enhanced DNA repair activity in response to DNA double-strand breaks (DSBs). With DNA repair analyses employing the CRISPR/cas9 system, we revealed that cells with aberrant BUB1B/BUBR1 expression dominantly exploit mutagenic nonhomologous end joining (NHEJ). We further found that phosphorylated ATM interacts with BUB1B/BUBR1 after ionizing radiation (IR) treatment, and the resistance to DSBs by increased BUB1B/BUBR1 depends on the functional ATM. In vivo, tumor growth of CRT-resistant T24R cells was abrogated by ATM inhibition using AZD0156. A dataset analysis identified FOXM1 as a putative BUB1B/BUBR1-targeting transcription factor causing its increased expression. These data collectively suggest a redundant role of BUB1B/BUBR1 underlying mutagenic NHEJ in an ATM-dependent manner, aside from the canonical activity of BUB1B/BUBR1 on the G2/M checkpoint, and offer novel clues to overcome CRT resistance.

Peptide-Based Vaccines for Hepatocellular Carcinoma: A Review of Recent Advances

Primary liver cancer is the sixth most commonly diagnosed cancer and the third leading cause of cancer-related deaths worldwide. After surgery, up to 70% of patients experience relapses. The current first-line therapy for advanced cases of hepatocellular carcinoma (HCC) comprises sorafenib and lenvatinib administered as single-drug therapies. Regorafenib, cabozantinib, and ramucirumab are administered as second-line therapies. Recently, it has been reported that using the immune checkpoint inhibitors atezolizumab (anti-PDL1 antibody) and bevacizumab (anti-VEGF antibody) leads to longer overall survival of unresectable cases, when compared with the use of sorafenib. The role of cancer immunity against HCC has attracted the attention of clinicians. In this review, we describe our phase I/II clinical trials of peptide vaccines targeting GPC3 in HCC and discuss the potential of peptide vaccines targeting common cancer antigens that are highly expressed in HCC, such as WT-I, AFP, ROBO1, and FOXM1. Further, we introduce recent cancer vaccines targeting neoantigens, which have attracted attention in recent times, as well as present our preclinical studies, the results of which might aid to initiate a neoantigen vaccine clinical trial, which would be the first of its kind in Japan.

MicroRNA-200b Regulates the Proliferation and Differentiation of Ovine Preadipocytes by Targeting p27 and KLF9

Simple Summary

The miR-200b has been shown to play an important role in preadipocyte proliferation and differentiation. Herein, we explored the role of miR-200b in ovine adipocyte development, using Oil Red O staining, cell viability analysis, EdU and RT-qPCR. The results showed that miR-200b facilitated proliferation and suppressed the differentiation of preadipocytes. The dual fluorescent reporter vector experiments showed that miR-200b directly targeted p27 and KLF9. Meanwhile, we demonstrated that p27 significantly inhibited the proliferation, while KLF9 significantly promoted the differentiation of preadipocytes.

Abstract

MicroRNAs (miRNAs) are crucial regulatory molecules in lipid deposition and metabolism. However, the effect of miR-200b on the regulation of proliferation and adipogenesis of ovine preadipocytes is unknown in the sheep (Ovis aries). In this study, the expression profiles of miR-200b were investigated in the seven tissues of Tibetan ewes and differentiated preadipocytes. The effect of miR-200b, as well as its target genes p27 and KLF9, on the proliferation of ovine preadipocytes and adipogenesis was also investigated, using cell viability analysis, EdU staining, Oil Red O staining and reverse transcription-quantitative PCR (RT-qRCR). The miR-200b was expressed in all the tissues investigated, and it was highly expressed in lung, liver, subcutaneous adipose and spleen tissues. The expression of miR-200b continuously decreased when the differentiation of ovine preadipocytes initiated. The miR-200b mimic dramatically accelerated the proliferation but inhibited differentiation of ovine preadipocytes. The miR-200b inhibitor resulted in an opposite effect on the proliferation and differentiation of ovine preadipocytes. The dual luciferase reporter assay results showed that miR-200b mimic significantly decreased the luciferase activity of p27 and KLF9 in HEK293 cells transfected with wild-type dual luciferase reporter vectors. This suggests that p27 and KLF9 are the target genes of miR-200b. In over-expressed-p27 preadipocytes, the number of EdU-labeled preadipocytes and the expression levels of proliferation marker genes CDK2, CDK4, CCND1 and PCNA significantly decreased. In addition, the transfection of over-expressed-KLF9 vector into adipocytes remarkably increased the accumulation of lipid droplets and the expression levels of differentiation marker genes aP2, PPARγ, LPL and GLUT4. These results suggest that miR-200b accelerated the proliferation but inhibited the adipogenic differentiation of ovine preadipocytes by targeting p27 and KLF9, respectively.

A Deep-Learning Model With the Attention Mechanism Could Rigorously Predict Survivals in Neuroblastoma

BACKGROUND

Neuroblastoma is one of the most devastating forms of childhood cancer. Despite large amounts of attempts in precise survival prediction in neuroblastoma, the prediction efficacy remains to be improved.

METHODS

Here, we applied a deep-learning (DL) model with the attention mechanism to predict survivals in neuroblastoma. We utilized 2 groups of features separated from 172 genes, to train 2 deep neural networks and combined them by the attention mechanism.

RESULTS

This classifier could accurately predict survivals, with areas under the curve of receiver operating characteristic (ROC) curves and time-dependent ROC reaching 0.968 and 0.974 in the training set respectively. The accuracy of the model was further confirmed in a validation cohort. Importantly, the two feature groups were mapped to two groups of patients, which were prognostic in Kaplan-Meier curves. Biological analyses showed that they exhibited diverse molecular backgrounds which could be linked to the prognosis of the patients.

CONCLUSIONS

In this study, we applied artificial intelligence methods to improve the accuracy of neuroblastoma survival prediction based on gene expression and provide explanations for better understanding of the molecular mechanisms underlying neuroblastoma.

Anti-malarial drug dihydroartemisinin downregulates the expression levels of CDK1 and CCNB1 in liver cancer

Liver cancer is the third leading cause of cancer-associated mortality worldwide. By the time liver cancer is diagnosed, it is already in the advanced stage. Therefore, novel therapeutic strategies need to be identified to improve the prognosis of patients with liver cancer. In the present study, the profiles of GSE84402, GSE19665 and GSE121248 were used to screen differentially expressed genes (DEGs). Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses for DEGs were conducted using the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was established to screen the hub genes associated with liver cancer. Additionally, the expression levels of hub genes were validated using the Gene Expression Profiling Interactive Analysis and Oncomine databases. In addition, the prognostic value of hub genes in patients with liver cancer was analyzed using Kaplan-Meier Plotter. It was demonstrated that 132 and 246 genes were upregulated and downregulated, respectively, in patients with liver cancer. Among these DEGs, 10 hub genes with high connected node values were identified, which were AURKA, BIRC5, BUB1B, CCNA2, CCNB1, CCNB2, CDC20, CDK1, DLGAP5 and MAD2L1. CDK1 and CCNB1 had the most connection nodes and the highest score and were therefore, the most significantly expressed. In addition, it was demonstrated that high expression levels of CDK1 and CCNB1 were associated with poor overall survival time of patients with liver cancer. Dihydroartemisinin (DHA) is a Food and Drug Administration-approved drug, which is derived from the traditional Chinese medicine Artemisia annua Linn. DHA inhibits cell proliferation in numerous cancer types, including liver cancer. In our previous study, it was revealed that DHA inhibited the proliferation of HepG2215 cells. In the present study, it was further demonstrated that DHA reduced the expression levels of CDK1 and CCNB1 in liver cancer. Overall, CDK1 and CCNB1 were the potential therapeutic targets of liver cancer, and DHA reduced the expression levels of CDK1 and CCNB1, and inhibited the proliferation of liver cancer cells.

Identification of novel hub genes associated with gastric cancer using integrated bioinformatics analysis

Background

Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.

Methods

Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.

Results

Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.

Conclusions

We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08358-7.

Identification of potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma via integrated bioinformatics analysis

Objective

The objective was to identify potential hub genes associated with the pathogenesis and prognosis of hepatocellular carcinoma (HCC).

Methods

Gene expression profile datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HCC and normal samples were identified via an integrated analysis. A protein–protein interaction network was constructed and analyzed using the STRING database and Cytoscape software, and enrichment analyses were carried out through DAVID. Gene Expression Profiling Interactive Analysis and Kaplan–Meier plotter were used to determine expression and prognostic values of hub genes.

Results

We identified 11 hub genes (CDK1, CCNB2, CDC20, CCNB1, TOP2A, CCNA2, MELK, PBK, TPX2, KIF20A, and AURKA) that might be closely related to the pathogenesis and prognosis of HCC. Enrichment analyses indicated that the DEGs were significantly enriched in metabolism-associated pathways, and hub genes and module 1 were highly associated with cell cycle pathway.

Conclusions

In this study, we identified key genes of HCC, which indicated directions for further research into diagnostic and prognostic biomarkers that could facilitate targeted molecular therapy for HCC.

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

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

Comprehensive Identification of Bridge Genes to Explain the Progression from Chronic Hepatitis B Virus Infection to Hepatocellular Carcinoma

Background

Hepatitis B virus infection co-occurs in 33% of individuals with hepatocellular carcinoma worldwide. However, the molecular link between hepatitis B virus and hepatocellular carcinoma is unknown. Thus, we aimed to elucidate molecular linkages underlying pathogenesis through in-depth data mining analysis.

Materials and Methods

Differentially expressed genes were identified from patients with chronic hepatitis B virus infection, hepatocellular carcinoma, or both. Gene set enrichment analysis revealed signaling pathways involving differentially expressed genes. Protein-protein interaction networks, protein crosstalk, and enrichment were analyzed to determine whether differentially expressed gene products might serve as a bridge from hepatitis B virus infection to hepatocellular carcinoma pathogenesis. Prognostic potential and transcriptional and post-transcriptional regulators of bridge genes were also examined.

Results

We identified vital bridge factors in hepatitis B virus infection-associated hepatocellular carcinoma. Differentially expressed genes were clustered into modules based on relative protein function. Signaling pathways associated with cancer, inflammation, immune system, and microenvironment showed significant crosstalk between modules. Thirty-two genes were dysregulated in hepatitis B virus infection-mediated hepatocellular carcinoma. CPEB3, RAB26, SLCO1B1, ST3GAL6 and XK had higher connectivity in the modular network, suggesting significant associations with survival. CDC20 and NUP107 were identified as driver genes as well as markers of poor prognosis.

Conclusion

Our results suggest that the sustained inflammatory environment created by hepatitis B virus infection is a risk factor for hepatocellular carcinoma. The identification of hepatitis B virus infection-related hepatocellular carcinoma bridge genes provides testable hypotheses about the pathogenesis of hepatocellular carcinoma.

Profiling of alternative polyadenylation and gene expression in PEDV-infected IPEC-J2 cells

Since 2010, porcine epidemic diarrhea virus (PEDV) has received global attention with the emergence of variant strains characterized with high pathogenicity. The pathogen-host interaction after PEDV infection is still unclear. To investigate this issue, high-throughput-based sequencing technology is one of the optimal choices. In this study, we used in vitro transcription sequencing alternative polyadenylation sites (IVT-SAPAS) method, which allowed accurate profiling of gene expression and alternative polyadenylation (APA) sites to profile APA switching genes and differentially expressed genes (DEGs) in IPEC-J2 cells during PEDV variant strain infection. We found 804 APA switching genes, including switching in tandem 3' UTRs and switching between coding region and 3' UTR, and 1,677 DEGs in host after PEDV challenge. These genes participated in variety of biological processes such as cellular process, metabolism and immunity reactions. Moreover, 413 genes, most of which are the "focus" genes in interaction networks, were found to be involved in both APA switching genes and DEGs, suggesting these genes were synchronously regulated by different mechanisms. In summary, our results gave a relatively comprehensive insight into dynamic host-pathogen interactions in the regulation of host gene transcripts during PEDV infection.

Aberrantly high activation of a FoxM1-STMN1 axis contributes to progression and tumorigenesis in FoxM1-driven cancers

Fork-head box protein M1 (FoxM1) is a transcriptional factor which plays critical roles in cancer development and progression. However, the general regulatory mechanism of FoxM1 is still limited. STMN1 is a microtubule-binding protein which can inhibit the assembly of microtubule dimer or promote depolymerization of microtubules. It was reported as a major responsive factor of paclitaxel resistance for clinical chemotherapy of tumor patients. But the function of abnormally high level of STMN1 and its regulation mechanism in cancer cells remain unclear. In this study, we used public database and tissue microarrays to analyze the expression pattern of FoxM1 and STMN1 and found a strong positive correlation between FoxM1 and STMN1 in multiple types of cancer. Lentivirus-mediated FoxM1/STMN1-knockdown cell lines were established to study the function of FoxM1/STMN1 by performing cell viability assay, plate clone formation assay, soft agar assay in vitro and xenograft mouse model in vivo. Our results showed that FoxM1 promotes cell proliferation by upregulating STMN1. Further ChIP assay showed that FoxM1 upregulates STMN1 in a transcriptional level. Prognostic analysis showed that a high level of FoxM1 and STMN1 is related to poor prognosis in solid tumors. Moreover, a high co-expression of FoxM1 and STMN1 has a more significant correlation with poor prognosis. Our findings suggest that a general FoxM1-STMN1 axis contributes to cell proliferation and tumorigenesis in hepatocellular carcinoma, gastric cancer and colorectal cancer. The combination of FoxM1 and STMN1 can be a more precise biomarker for prognostic prediction.

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.

METHODS

The bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.

RESULTS

We ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.

CONCLUSION

The present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

Autophagic protein ULK1 regulates FOXM1 signalling in human hepatoma cells

Hepatocellular cancer (HCC) is one of the leading causes of mortality worldwide. Unfortunately, a limited choice of anti-cancer drugs is available for treatment, owing to their minimal efficacy and development of acquired resistance. Autophagy, a cellular survival pathway, often exhibits a pleiotropic role in HCC progression. Studies show increased autophagy in established HCC, promoting the survival of HCC cells in the tumour microenvironment. Therefore, novel anti-autophagy drugs hold promise for preventing HCC progression. Here, using a non-biased transcriptomics analysis in HepG2 cells we demonstrate the existence of an autophagy-FOXM1 nexus regulating growth in HepG2 cells. Additionally, we show that suppression of autophagy by an Unc-51 Like Autophagy Activating Kinase 1(ULK1) inhibitor not only attenuates the expression of FOXM1 and its transcriptional targets, but also has a synergistic effect on the inhibition of HepG2 growth when combined with FOXM1 inhibitors. Thus, the autophagic protein, ULK1, is a promising candidate for preventing HCC progression. Collectively, our results provide new insight into the role of autophagy in HCC growth and are a proof-of concept for combinatorial therapy using ULK1 and FOXM1 inhibitors.

[Spindle assembly checkpoint complex-related genes TTK and MAD2L1 are over-expressed in lung adenocarcinoma: a big data and bioinformatics analysis]

OBJECTIVE

To screen the key genes related to the prognosis of lung adenocarcinoma through big data analysis and explore their clinical value and potential mechanism.

METHODS

We analyzed GSE18842, GSE27262, and GSE33532 gene expression profile data obtained from the Gene Expression Omnibus (GEO). Bioinformatics methods were used to screen the differentially expressed genes in lung adenocarcinoma tissues and KEGG and GO enrichment analysis was performed, followed by PPI interaction network analysis, module analysis, differential expression analysis, and prognosis analysis. The expressions of MAD2L1 and TTK by immunohistochemistry were verified in 35 non-small cell lung cancer specimens and paired adjacent tissues.

RESULTS

We identified a total of 256 genes that showed significant differential expressions in lung adenocarcinoma, including 66 up-regulated and 190 down-regulated genes. Thirty-two up-regulated core genes were screened by functional analysis, and among them 29 were shown to significantly correlate with a poor prognosis of patients with lung adenocarcinoma. All the 29 genes were highly expressed in lung adenocarcinoma tissues compared with normal lung tissues and were mainly enriched in cell cycle pathways. Seven of these key genes were closely related to the spindle assembly checkpoint (SAC) complex and responsible for regulating cell behavior in G2/M phase. We selected SAC-related proteins TTK and MAD2L1 to test their expressions in clinical tumor samples, and detected their overexpression in lung adenocarcinoma tissues as compared with the adjacent tissues.

CONCLUSIONS

Seven SAC complex-related genes, including TTK and MAD2L1, are overexpressed in lung adenocarcinoma tissues with close correlation with the prognosis of the patients.

Screening and verifying key genes with poor prognosis in colon cancer through bioinformatics analysis

Background

Colon cancer (CC) is one of the tumors with high morbidity and mortality in the world, and has a trend of younger generation. The molecular level of CC has not been fully elaborated. The purpose of this study is to screen and identify important genes with poor prognosis and their mechanisms at different levels.

Methods

GSE74602 and GSE10972 gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. There were 58 normal tissues and 58 CC tissues. Differentially expressed genes (DEGs) were screened out by using the GEO2R tool and Venn diagram. Then, the DAVID online database was used to perform the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Six hub genes with the highest correlation were screened out after the modular analysis of the protein-protein interaction (PPI) network by using Cytoscape’s MCODE plug-in. Finally, the overall survival of key hub genes and potential pathways were verified in GEPIA and UALCAN database.

Results

A total of 78 up-regulated DEGs were enriched in the mitotic nuclear division, cell division, cell proliferation, anaphase-promoting complex-dependent catabolic process and G2/M transition of the mitotic cell cycle. In total, 130 down-regulated DEGs were enriched in muscle contraction, bicarbonate transport, cellular response to zinc ion, negative regulation of growth, negative regulation of leukocyte apoptotic process and one-carbon metabolic process. CDK1, CCNB1, CDC20, AURKA, CCNA2 and TOP2A were the top six hub genes, mainly enriched in cell cycle pathways. Among them, CCNB1, CDK1, CDC20, CCNA2 were enriched in the G2/M phase. GEPIA and UALCAN database confirmed that CCNA2 and CCNB1 had a significant relationship with the poor prognosis of CC patients. Meanwhile, there was a positive correlation between the two.

Conclusions

Screening out genes with abnormal expression in CC help understand the initiation and progression of CC at the molecular level and explore candidate biomarkers for diagnosis, treatment and prognosis.

Rosmarinic Acid Methyl Ester Regulates Ovarian Cancer Cell Migration and Reverses Cisplatin Resistance by Inhibiting the Expression of Forkhead Box M1

Rosmarinic acid methyl ester (RAME), a derivative of rosmarinic acid (RA), is reported to have several therapeutic effects, including anti-tumor effects against cervical cancer. However, its anti-tumor effects in ovarian cancer is unclear. In this study, we studied the molecular pathways associated with the anti-tumor effects of RAME in ovarian cancer. To identify the effects of RAME in ovarian cancer, RNA sequencing was performed in RAME-treated ovarian cancer cells; we found that RAME treatment downregulated the genes closely involved with the target genes of the transcription factor Forkhead box M1 (FOXM1). It was reported that FOXM1 is overexpressed in a variety of cancer cells and is associated with cell proliferation and tumorigenesis. Therefore, we hypothesized that FOXM1 is a key target of RAME; this could result in its anti-tumor effects. Treatment of ovarian cancer cells with RAME-inhibited cell migration and invasion, as shown by wound healing and transwell migration assays. To examine whether RAME represses the action of FOXM1, we performed quantitative RT-PCR and ChIP-qPCR. Treatment of ovarian cancer cells with RAME decreased the mRNA expression of FOXM1 target genes and the binding of FOXM1 to its target genes. Moreover, FOXM1 expression was increased in cisplatin-resistant ovarian cancer cells, and combination treatment with RAME and cisplatin sensitized the cisplatin-resistant ovarian cancer cells, which was likely due to FOXM1 inhibition. Our research suggests that RAME is a promising option in treating ovarian cancer patients, as it revealed a novel molecular pathway underlying its anti-tumor effects.

SP1-independent inhibition of FOXM1 by modified thiazolidinediones

This research article describes an approach to modify the thiazolidinedione scaffold to produce test drugs capable of binding to, and inhibit, the in vitro transcriptional activity of the oncogenic protein FOXM1. This approach allowed us to obtain FOXM1 inhibitors that bind directly to the FOXM1-DNA binding domain without targeting the expression levels of Sp1, an upstream transcription factor protein known to activate the expression of FOXM1. Briefly, we modified the chemical structure of the thiazolidinedione scaffold present in anti-diabetic medications such as pioglitazone, rosiglitazone and the former anti-diabetic drug troglitazone, because these drugs have been reported to exert inhibition of FOXM1 but hit other targets as well. After the chemical synthesis of 11 derivatives possessing a modified thiazolidinedione moiety, we screened all test compounds using in vitro protocols to measure their ability to (a) dissociate a FOXM1-DNA complex (EMSA assay); (b) decrease the expression of FOXM1 in triple negative-breast cancer cells (WB assay); (c) downregulate the expression of FOXM1 downstream targets (luciferase reporter assays and qPCR); and inhibit the formation of colonies of MDA-MB-231 cancer cells (colony formation assay). We also identified a potential binding mode associated with these compounds in which compound TFI-10, one of the most active molecules, exerts binding interactions with Arg289, Trp308, and His287. Unlike the parent drug, troglitazone, compound TFI-10 does not target the in vitro expression of Sp1, suggesting that it is possible to design FOXM1 inhibitors with a better selectivity profile.

Screening of significant biomarkers with poor prognosis in hepatocellular carcinoma via bioinformatics analysis

Hepatocellular carcinoma (HCC) is a malignant tumor with unsatisfactory prognosis. The abnormal genes expression is significantly associated with initiation and poor prognosis of HCC. The aim of the present study was to identify molecular biomarkers related to the initiation and development of HCC via bioinformatics analysis, so as to provide a certain molecular mechanism for individualized treatment of hepatocellular carcinoma.Three datasets (GSE101685, GSE112790, and GSE121248) from the GEO database were used for the bioinformatics analysis. Differentially expressed genes (DEGs) of HCC and normal liver samples were obtained using GEO2R online tools. Gene ontology term and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis were conducted via the Database for Annotation, Visualization, and Integrated Discovery online bioinformatics tool. The protein-protein interaction (PPI) network was constructed by the Search Tool for the Retrieval of Interacting Genes database and hub genes were visualized by Cytoscape. Survival analysis and RNA sequencing expression were conducted by UALCAN and Gene Expression Profiling Interactive Analysis.A total of 115 shared DEGs were identified, including 30 upregulated genes and 85 downregulated genes in HCC samples. P53 signaling pathway and cell cycle were the major enriched pathways for the upregulated DEGs whereas metabolism-related pathways were the major enriched pathways for the downregulated DEGs. The PPI network was established with 105 nodes and 249 edges and 3 significant modules were identified via molecular complex detection. Additionally, 17 candidate genes from these 3 modules were significantly correlated with HCC patient survival and 15 of 17 genes exhibited high expression level in HCC samples. Moreover, 4 hub genes (CCNB1, CDK1, RRM2, BUB1B) were identified for further reanalysis of KEGG pathway, and enriched in 2 pathways, the P53 signaling pathway and cell cycle pathway.Overexpression of CCNB1, CDK1, RRM2, and BUB1B in HCC samples was correlated with poor survival in HCC patients, which could be potential therapeutic targets for HCC.

Molecular differences across invasive lung adenocarcinoma morphological subgroups

Background

Lung adenocarcinomas (ADCs) show heterogeneous morphological patterns that are classified into five subgroups: lepidic predominant, papillary predominant, acinar predominant, micropapillary predominant and solid predominant. The morphological classification of ADCs has been reported to be associated with patient prognosis and adjuvant chemotherapy response. However, the molecular mechanisms underlying the morphology differences among different subgroups remain largely unknown.

Methods

Using the molecular profiling data from The Cancer Genome Atlas (TCGA) lung ADC (LUAD) cohort, we studied the molecular differences across invasive ADC morphological subgroups.

Results

We showed that the expression of proteins and mRNAs, but not the gene mutations copy number alterations (CNA), were significantly associated with lung ADC morphological subgroups. In addition, expression of the FOXM1 gene (which is negatively associated with patient survival) likely plays an important role in the morphological differences among different subgroups. Moreover, we found that protein abundance of PD-L1 were associated with the malignancy of subgroups. These results were validated in an independent cohort.

Conclusions

This study provides insights into the molecular differences among different lung ADC morphological subgroups, which could lead to potential subgroup-specific therapies.

Integrated Bioinformatic Analysis Identifies Networks and Promising Biomarkers for Hepatitis B Virus-Related Hepatocellular Carcinoma

Chronic infection with hepatitis B virus (HBV) has long been recognized as a dominant hazard factor for hepatocellular carcinoma (HCC) and accounts for at least half of HCC instances globally. However, the underlying molecular mechanism of HBV-linked HCC has not been completely elucidated. Here, three microarray datasets, totally containing 170 tumoral samples and 181 adjacent normal tissues from the liver of patients suffering from HBV-related HCC assembled from the Gene Expression Omnibus (GEO) database, were subjected to integrated analysis of differentially expressed genes (DEGs). Subsequently, the analysis of function and pathway enrichment as well as the protein-protein interaction network (PPI) was performed. The ten hub genes screened out from the PPI network were further subjected to expression profile and survival analysis. Overall, 329 DEGs (67 upregulated and 262 downregulated) were identified. Ten DEGs with the highest degree of connectivity included cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), cyclin B2 (CCNB2), PDZ-binding kinase (PBK), abnormal spindle microtubule assembly (ASPM), nuclear division cycle 80 (NDC80), aurora kinase A (AURKA), targeting protein for xenopus kinesin-like protein 2 (TPX2), kinesin family member 2C (KIF2C), and centromere protein F (CENPF). Kaplan-Meier analysis unveiled that overexpression levels of KIF2C and TPX2 were relevant to both the poor overall survival and relapse-free survival. In summary, the hub genes validated in the present study may provide promising targets for the diagnosis, prognosis, and therapy of HBV-associated HCC. Additionally, our work uncovers various crucial biological components (e.g., extracellular exosome) and signaling pathways that participate in the progression of HCC induced by HBV, serving comprehensive knowledge of the mechanisms regarding HBV-related HCC.

RP11-295G20.2 facilitates hepatocellular carcinoma progression via the miR-6884-3p/CCNB1 pathway

Objective: An increasing number of studies have indicated that long noncoding RNAs (lncRNAs) play an important role in the pathogenesis of hepatocellular carcinoma (HCC). In this study, we aimed to clarify the roles of RP11-295G20.2 in HCC progression and the underlying molecular mechanisms.

Results: Bioinformatics analyses based TCGA data suggested that RP11-295G20.2 was significantly upregulated in HCC tissues and increased RP11-295G20.2 expression level correlated with poor overall survival of patients with HCC. The results of RT-PCR further showed that RP11-295G20.2 was upregulated in HCC tissues and cell lines. Functionally, RP11-295G20.2 knockdown significantly inhibited the proliferation, colony formation, invasion and migration, but induced the apoptosis of HCC cells. In line with this, downregulation of RP11-295G20.2 in HCC lines markedly suppressed the tumor growth in vivo. Mechanistically, RP11-295G20.2 could upregulate CCNB1 through targeting miR-6884-3p. More importantly, our rescue experiments revealed that miR-6884-3p/CCNB1 axis was involved in RP11-295G20.2-meditated tumorigenic behaviors of HCC cells.

Conclusions: RP11-295G20.2 can contribute to HCC progression at least partly via the miR-6884-3p/CCNB1 axis, suggesting that RP11-295G20.2 may be a potential target for HCC therapy.

Methods: RT-qPCR was employed to examine the expression levels of RP11-295G20.2, miR-6884-3p, and CCNB1 in HCC tissues and cell lines. CCK8 assay, transwell assay, colony formation assay and flow cytometry analysis were performed to evaluate the biological function of RP11-295G20.2 in HCC cells. The xenograft tumor assay was used to assess the effect of RP11-295G20.2 on the in vivo growth of HCC cells. The luciferase reporter assay, RIP assay and Spearman's correlation analysis were performed to explore the potential mechanisms underlying the roles of RP11-295G20.2 in HCC progression.

miR-4429 Regulates the Proliferation, Migration, Invasion, and Epithelial-Mesenchymal Transition of Cervical Cancer by Targeting FOXM1

BACKGROUND

miR-4429 acts as an inhibitor in many malignant tumors and participates in the biological processes of them, but the clinical value and potential molecular mechanism of miR-4429 in cervical cancer (CC) are still under investigation.

OBJECTIVE

To analyze the clinical value and molecular mechanism of miR-4429 in CC.

MATERIALS AND METHODS

A qRT-PCR assay was employed to determine the levels of miR-4429 and forkhead-box M1 (FOXM1) in CC tissues, CC cell lines (SiHa, CaSki, ME-180, and C33A) and human normal immortalized epithelial cell lines (HaCaT). The proliferation, migration, invasion, and apoptosis abilities of ME-180 and C33A cells were detected, and the epithelial-to-mesenchymal transition (EMT)-related proteins in the cells were also determined.

RESULTS

MiR-4429 acted as a tumor suppressor gene in CC tissues and cells and was linked to lymph node metastasis and International Federation of Gynecology and Obstetrics (FIGO) staging. The survival analysis revealed that lymph node metastasis, high FIGO staging, and low miR-4429 expression were all related to the unfavorable prognosis of the patients, and the dual-luciferase reporter assay revealed that FOXM1 was the target of miR-4429. Both overexpression of miR-4429 and knock-down of FOXM1 inhibited the proliferation, migration, invasion, and EMT of CCCs, and accelerated the apoptosis of them. Conversely, both knockdown of miR-4429 and overexpression of FOXM1 promoted those biological behaviors of the cells. Moreover, the rescue experiment revealed that the overexpression of FOXM1 reversed the influences of miR-4429 overexpression on the proliferation, migration, invasion, and EMT of CCCs.

CONCLUSION

miR-4429 acts as a tumor suppressor in CC and can directly target FOXM1 to regulate the proliferation, migration, invasion, apoptosis and EMT of CCCs, so miR-4429 is expected to be a new therapeutic target for CC.