Strong expression of cyclin B2 mRNA correlates with a poor prognosis in patients with non-small cell lung cancer

Role of Cyclins and Cytoskeletal Proteins in Endometriosis: Insights into Pathophysiology

Endometriosis is a gynecological condition where endometrium-like tissue grows outside the uterus, posing challenges in understanding and treatment. This article delves into the deep cellular and molecular processes underlying endometriosis, with a focus on the crucial roles played by cyclins and cytoskeletal proteins in its pathogenesis, particularly in the context of Epithelial-Mesenchymal Transition (EMT). The investigation begins by examining the activities of cyclins, elucidating their diverse biological roles such as cell cycle control, proliferation, evasion of apoptosis, and angiogenesis among ectopic endometrial cells. A comprehensive analysis of cytoskeletal proteins follows, emphasizing their fundamental biological roles and their specific significance to endometriotic cell features. This review sheds light on the interconnected pathways through which cyclins and cytoskeletal proteins converge, contributing to the genesis and progression of endometriosis. Understanding these molecular complexities not only provides insight into the underlying causes of the disease but also holds promise for the development of specific therapeutic approaches, ushering in a new era in the management of this devastating disorder.

Cyclin B2 impairs the p53 signaling in nasopharyngeal carcinoma

BACKGROUND

Cyclin B2 (CCNB2), a member of the cyclin family, is an oncogene in multiple cancers, including nasopharyngeal carcinoma (NPC). However, the epigenetics mechanism for CCNB2 overexpression in NPC remains unclear. This study dissects the regulatory role of CCNB2 in NPC and the molecular mechanism.

METHODS

Differentially methylated genes (DMG) and differentially expressed genes (DEG) were screened out in GSE52068 and GSE13597 databases, respectively, and candidate targets were identified by the Venn diagram. GO annotation and pathway enrichment analyses were performed on selected DMG and DEG, and a PPI network was constructed to pinpoint hub genes. PCR and qMSP were conducted to detect the expression and methylation of CCNB2 in cells. The siRNA targeting CCNB2 was transfected into NPC cells, and the migration, proliferation, cell cycle, epithelial-mesenchymal transition (EMT), tumorigenesis, and metastasis were examined. The upstream factor responsible for CCNB2 overexpression in NPC was explored. The p53 activity in NPC cells was assessed using western blot analysis.

RESULTS

CCNB2 showed hypomethylation and overexpression in NPC. CCNB2 silencing inhibited cell migration, proliferation, cell cycle entry, and EMT. JMJD6 was overexpressed in NPC and upregulated CCNB2 through demethylation. JMJD6 reversed the effects of CCNB2 downregulation, resulting in elevated cellular activity in vitro and tumorigenic and metastatic activities in vivo. CCNB2 blocked the p53 pathway, while the p53 pathway inhibitor reversed the effect of CCNB2 silencing to increase the activity of NPC cells.

CONCLUSIONS

JMJD6 enhanced CCNB2 transcription by demethylating CCNB2, thereby repressing the p53 pathway and promoting NPC progression.

Identification of Potential Key Genes and Prognostic Biomarkers of Lung Cancer Based on Bioinformatics

Objective

To analyze and identify the core genes related to the expression and prognosis of lung cancer including lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) by bioinformatics technology, with the aim of providing a reference for clinical treatment.

Methods

Five sets of gene chips, GSE7670, GSE151102, GSE33532, GSE43458, and GSE19804, were obtained from the Gene Expression Omnibus (GEO) database. After using GEO2R to analyze the differentially expressed genes (DEGs) between lung cancer and normal tissues online, the common DEGs of the five sets of chips were obtained using a Venn online tool and imported into the Database for Annotation, Visualization, and Integrated Discovery (DAVID) database for Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The protein-protein interaction (PPI) network was constructed by STRING online software for further study, and the core genes were determined by Cytoscape software and KEGG pathway enrichment analysis. The clustering heat map was drawn by Excel software to verify its accuracy. In addition, we used the University of Alabama at Birmingham Cancer (UALCAN) website to analyze the expression of core genes in P53 mutation status, confirmed the expression of crucial core genes in lung cancer tissues with Gene Expression Profiling Interactive Analysis (GEPIA) and GEPIA2 online software, and evaluated their prognostic value in lung cancer patients with the Kaplan-Meier online plotter tool.

Results

CHEK1, CCNB1, CCNB2, and CDK1 were selected. The expression levels of these four genes in lung cancer tissues were significantly higher than those in normal tissues. Their increased expression was negatively correlated with lung cancer patients (including LUAD and LUSC) prognosis and survival rate.

Conclusion

CHEK1, CCNB1, CCNB2, and CDK1 are the critical core genes of lung cancer and are highly expressed in lung cancer. They are negatively correlated with the prognosis of lung cancer patients (including LUAD and LUSC) and closely related to the formation and prediction of lung cancer. They are valuable predictors and may be predictive biomarkers of lung cancer.

Bioinformatics analysis reveals three key genes and four survival genes associated with youth-onset NSCLC

Youth-onset non-small cell lung cancer (NSCLC) is a heterogeneous disease. It has a unique clinicopathology and special genetic background. In this study, three key genes, CDC20, CCNB2, and BUB1, have been identified in youth-onset NSCLC tumor tissues based on the TCGA and GEO cohorts. Functional enrichment analysis reveals that the “oocyte meiosis,” “cell cycle,” and the “P53 signaling pathway” are significantly enriched. Additionally, four survival genes, including AKAP12, CRIM1, FEN1, and SLC7A11, that affect the prognosis of youth-onset NSCLC patients are identified in this study. Finally, we construct a risk model to predict the overall survival of youth-onset NSCLC patients, the AUC of the risk model in 1, 3, and 5 years of overall survival is 0.808, 0.844, and 0.728. This study aims to provide a novel idea to explore the pathogenic genes of youth-onset NSCLC.

Bioinformatics and System Biology Approach to Reveal the Interaction Network and the Therapeutic Implications for Non-Small Cell Lung Cancer Patients With COVID-19

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the leading cause of coronavirus disease-2019 (COVID-19), is an emerging global health crisis. Lung cancer patients are at a higher risk of COVID-19 infection. With the increasing number of non-small-cell lung cancer (NSCLC) patients with COVID-19, there is an urgent need of efficacious drugs for the treatment of COVID-19/NSCLC.

Methods: Based on a comprehensive bioinformatic and systemic biological analysis, this study investigated COVID-19/NSCLC interactional hub genes, detected common pathways and molecular biomarkers, and predicted potential agents for COVID-19 and NSCLC.

Results: A total of 122 COVID-19/NSCLC interactional genes and 21 interactional hub genes were identified. The enrichment analysis indicated that COVID-19 and NSCLC shared common signaling pathways, including cell cycle, viral carcinogenesis, and p53 signaling pathway. In total, 10 important transcription factors (TFs) and 44 microRNAs (miRNAs) participated in regulations of 21 interactional hub genes. In addition, 23 potential candidates were predicted for the treatment of COVID-19 and NSCLC.

Conclusion: This study increased our understanding of pathophysiology and screened potential drugs for COVID-19 and NSCLC.

Screening of Potential Key Biomarkers for Ewing Sarcoma: Evidence from Gene Array Analysis

Background

Ewing’s sarcoma (ES) is a common bone cancer in children and adolescents. There are ethnic differences in the incidence and treatment effects. People have made great efforts to clarify the cause; however, the molecular mechanism of ES is still poorly understood.

Methods

We download the microarray datasets GSE68776, GSE45544 and GSE17674 from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of the three datasets were screened and enrichment analysis was performed. STRING and Cytoscape were used to carry out module analysis, building a protein–protein interaction (PPI) network. Finally, a series of analyses such as survival analysis and immune infiltration analysis were performed on the selected genes.

Results

A total of 629 differentially expressed genes were screened, including 206 up-regulated genes and 423 down-regulated genes. The pathways and rich-functions of DEGs include protein activation cascade, carbohydrate binding, cell-cell adhesion junctions, mitotic cell cycle, p53 pathway, and cancer pathways. Then, a total of 10 hub genes were screened out. Biological process analysis showed that these genes were mainly enriched in mitotic nuclear division, protein kinase activity, cell division, cell cycle, and protein phosphorylation.

Conclusion

Survival analysis and multiple gene comparison analysis showed that CDCA8, MAD2L1 and FANCI may be involved in the occurrence and prognosis of ES. The purpose of our study is to clarify the DEG and key genes, which will help us know more about the molecular mechanisms of ES, provide potential pathway or targets for the diagnosis and treatment.

Multi modular toxicity assessment of nephrotoxicity in podophyllotoxin exposure rats on account of toxicological evidence chain (TEC) concept

Early diagnosis of kidney injuries caused by herbs is necessary to enable effective treatments, prevent kidney failure and promote the internationalization and modernization of herbal medicine. Whereas the toxic assessment evidence has not integrated yet, and the evaluation method has not been unanimously agreed. For example, the gold standard assessing toxicity in animals remains to be histopathology, but serum biochemical indexes are the primary measures for monitoring organs dysfunction in humans. In this study, using Sprague Dawley rats, we investigated whether integrated analyses of transcriptomic and metabolomic data with toxicological evidence chain (TEC) concept could identify indicators of injury and provide new insights into the mechanisms of nephrotoxicity. Firstly, the objective phenotype of the animals was observed in detail and the toxicity performance was collected after administration. Subsequently, histopathological examination and serum biochemical toxicity evidence were collected. Next, we obtained concurrent measurements of transcriptomic changes in kidneys, and changes along with metabolic profiles in serum, after exposure to PT(Podophyllotoxin) to acquire evidence at the molecular level. Last but not least, the GTEA (Grades of Toxicological Evidence Assessment) based on GRADE(Grading of Recommendations Assessment, Development, and Evaluation) system was used to evaluate toxic evidence which can be assigned to a toxic level. The orally gavaged rats with PT have been confirmed with dose-dependent kidney damage from 5 to 15 mg/kg after 4 d. Our findings suggest that the main pathological changes occurred in Glycerophosphatidylcholine metabolism, Arachidonic acid metabolism, Energy metabolism, Tyrosine metabolism, Tryptophan metabolism and so on.Moreover, the alteration of the potential metabolites lipid (i.e. LPC, palmitic acid) and sulfate could serve as plausible markers of PT-induced kidney injury. Our approach provides a mechanistic framework for the refinement of the grading standard of toxicity evidence, which is applicable to other toxicants originated from herbal medicine based on multi-omics data.

Integrative Analysis for Elucidating Transcriptomics Landscapes of Systemic Lupus Erythematosus

Systemic lupus erythematosus (SLE) is a complex and heterogeneous autoimmune disease that the immune system attacks healthy cells and tissues. SLE is difficult to get a correct and timely diagnosis, which makes its morbidity and mortality rate very high. The pathogenesis of SLE remains to be elucidated. To clarify the potential pathogenic mechanism of SLE, we performed an integrated analysis of two RNA-seq datasets of SLE. Differential expression analysis revealed that there were 4,713 and 2,473 differentially expressed genes, respectively, most of which were up-regulated. After integrating differentially expressed genes, we identified 790 common differentially expressed genes (DEGs). Gene functional enrichment analysis was performed and found that common differentially expressed genes were significantly enriched in some important immune-related biological processes and pathways. Our analysis provides new insights into a better understanding of the pathogenic mechanisms and potential candidate markers for systemic lupus erythematosus.

N6-methyladenosine regulates ATM expression and downstream signaling

N6-methyladenosine (m6A) is the most abundant modification in eukaryotic mRNAs, which plays an important role in regulating multiple biological processes. ATM is a major protein kinase that regulates the DNA damage response. Here, we identified that ATM is a m6A-modificated gene. METTL3 (a m6A "writer") and FTO (a m6A "eraser") oppositely regulated ATM expression and its downstream signaling. Mechanically, m6A "readers" YTHDFs and eIF3A suppressed ATM expression in the post-transcriptional levels. We also revealed the oncogenic potential of METTL3 and YTHDF1 related to ATM modulation. This is the first report that ATM, a master in the DNA damage response, is modified by m6A epigenetic modification, and METTL3 disrupts the ATM stability via m6A modification, thereby affecting the DNA-damage response.

ALG3 contributes to the malignant properties of OSCC cells by regulating CDK-Cyclin pathway

In this study, we planned to investigate the function and potential mechanisms of Alpha-1,3-mannosyltransferase (ALG3) in oral squamous cell carcinoma (OSCC). Data from TCGA were used to analyze ALG3 expression and its effect on the prognosis of patients with OSCC. KEGG enrichment analysis was applied to explore the pathways related to ALG3. ALG3 expression was measured by qPCR and Western blot. Cell counting kit-8, colony formation, and transwell assays were implemented to detect the effects of ALG3 on malignant biological properties of OSCC cells. The expression of key proteins related to CDK-Cyclin pathway was detected by Western blot. The expression of ALG3 in OSCC samples was higher than that of the control samples, and the increase of ALG3 expression was related to unfavorable prognosis of OSCC patients. Additionally, the elevated expression of ALG3 was associated with pathological stage, lymph node metastasis, and primary lesion in OSCC patients. ALG3 depletion blocked the growth and movement of OSCC cells, while over-expression ALG3 reversed these phenomena. Moreover, exhaustion of ALG3 resulted in decreased expression of MCM7/CCNB2/CDK1/PCNA, while these phenomena were inversed after ALG3 up-regulation. The enhancement of ALG3 expression promoted the aggressive biological behaviors of OSCC cells probably by promoting CDK-Cyclin pathway.

Screening of hub genes associated with prognosis in non-small cell lung cancer by integrated bioinformatics analysis

Background

Lung cancer is an intractable disease and the second leading cause of cancer-related deaths and morbidity in the world. This study conducted a bioinformatics analysis to identify critical genes associated with poor prognosis in non-small cell lung cancer (NSCLC).

Methods

We downloaded three datasets (GSE33532, GSE27262, and GSE18842) from the gene expression omnibus (GEO), and used the GEO2R online tools to identify the differentially expressed genes (DEGs). We then used the Search Tool for Retrieval of Interacting Genes (STRING) database to establish a protein-protein interaction (PPI) network and used the Cytoscape software to perform a module analysis of the PPI network. A Kaplan-Meier plotter was used to perform the overall survival (OS) analysis, and the Gene Expression Profiling Interactive Analysis (GEPIA) database was used for expression level analysis of hub genes. Further, the UALCAN database was used to validate the relationship between the gene expression level of each hub gene and clinical characteristics.

Results

We identified 254 DEGs, which were composed of 66 up-regulated genes and 188 down-regulated genes. Out of these, five DEGs were identified as hub genes (CDC20, BUB1, CCNB2, CCNB1, UBE2C) by constructing a PPI network. The use of a Kaplan-Meier plotter to generate patient survival curves suggested a strong relationship between the five hub genes with worse OS. Validation of the above results using the GEPIA database showed that all the hub genes were highly expressed in NSCLC tissues. Using the UALACN data mining platform, we found that the five hub genes are correlated with tumor stage and the status of node metastasis in NSCLC patients.

Conclusions

We identified five hub DEGs that might provide perspectives in the explorations of pathogenesis and treatments for NSCLC.

Identification of key differentially expressed mRNAs and microRNAs in non-small cell lung cancer using bioinformatics analysis

Non-small cell lung cancer (NSCLC) is a leading cause of mortality worldwide. However, the pathogenesis of NSCLC remains to be fully elucidated. Therefore, the present study aimed to explore the differential expression of mRNAs and microRNAs (miRNAs/miRs) in NSCLC and to determine how these RNA molecules interact with one another to affect disease progression. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified from the GSE18842, GSE32863 and GSE29250 datasets downloaded from the Gene Expression Omnibus (GEO database). Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. STRING, Cytoscape and MCODE were applied to construct a protein-protein interaction (PPI) network and to screen hub genes. The interactions between miRNAs and mRNAs were predicted using miRWalk 3.0 and a miRNA-mRNA regulatory network was constructed. The prognostic value of the identified hub genes was then evaluated via Kaplan-Meier survival analyses using datasets from The Cancer Genome Atlas. A total of 782 DEGs and 46 DEMs were identified from the 3 GEO datasets. The enriched pathways and functions of the DEGs and target genes of the DEMs included osteoclast differentiation, cell adhesion, response to a drug, plasma membrane, extracellular exosome and protein binding. A subnetwork composed of 11 genes was extracted from the PPI network and the genes in this subnetwork were mainly involved in the cell cycle, cell division and DNA replication. A miRNA-gene regulatory network was constructed with 247 miRNA-gene pairs based on 6 DEMs and 210 DEGs. Kaplan-Meier survival analysis indicated that the expression of ubiquitin E2 ligase C, cell division cycle protein 20, DNA topoisomerase IIα, aurora kinase A and B, cyclin B2, maternal embryonic leucine zipper kinase, slit guidance ligand 3, phosphoglucomutase 5, endomucin, cysteine dioxygenase type 1, dihydropyrimidinase-like 2, miR-130b, miR-1181 and miR-127 was significantly associated with overall survival of patients with lung adenocarcinoma. In the present study, a miRNA-mRNA regulatory network in NSCLC was established, which may provide future avenues for scientific exploration and therapeutic targeting of NSCLC.

CCNB2 and AURKA overexpression may cause atypical mitosis in Japanese cortisol-producing adrenocortical carcinoma with TP53 somatic variant

Background

Many genomic analyses of cortisol-producing adrenocortical carcinoma (ACC) have been reported, but very few have come from East Asia. The first objective of this study is to verify the genetic difference with the previous reports by analyzing targeted deep sequencing of 7 Japanese ACC cases using next-generation sequencing (NGS). The second objective is to compare the somatic variant findings identified by NGS analysis with clinical and pathological findings, aiming to acquire new knowledge about the factors that contribute to the poor prognosis of ACC and to find new targets for the treatment of ACC.

Method

DNA was extracted from ACC tissue of seven patients and two reference blood samples. Targeted deep sequencing was performed using the MiSeq system for 12 genes, and the obtained results were analyzed using MuTect2. The hypothesis was obtained by integrating the somatic variant findings with clinical and pathological data, and it was further verified using The Cancer Genome Atlas (TCGA) dataset for ACC.

Results

Six possible pathogenic and one uncertain significance somatic variants including a novel PRKAR1A (NM_002734.4):c.545C>A (p.T182K) variant were found in five of seven cases. By integrating these data with pathological findings, we hypothesized that cases with TP53 variants were more likely to show atypical mitotic figures. Using TCGA dataset, we found that atypical mitotic figures were associated with TP53 somatic variant, and mRNA expression of CCNB2 and AURKA was significantly high in TP53 mutated cases and atypical mitotic figure cases.

Conclusion

We believe this is the first report that discusses the relationship between atypical mitotic figures and TP53 somatic variant in ACC. We presumed that overexpression of CCNB2 and AURKA mRNA may cause atypical mitosis in TP53 somatic mutated cases. Because AURKA is highly expressed in atypical mitotic cases, it may be an appropriate indicator for AURKA inhibitors.

PTPN11 Knockdown Prevents Changes in the Expression of Genes Controlling Cell Cycle, Chemotherapy Resistance, and Oncogene-Induced Senescence in Human Thyroid Cells Overexpressing BRAF V600E Oncogenic Protein

The MAPK (RAS/BRAF/MEK/ERK) signaling pathway is a kinase cascade involved in the regulation of cell proliferation, differentiation, and survival in response to external stimuli. The V600E mutation in the BRAF gene has been detected in various tumors, resulting in a 500-fold increase in BRAF kinase activity. However, monotherapy with selective BRAF V600E inhibitors often leads to reactivation of MAPK signaling cascade and emergence of drug resistance. Therefore, new targets are being developed for the inhibition of components of the aberrantly activated cascade. It was recently discovered that resistance to BRAF V600E inhibitors may be associated with the activity of the tyrosine phosphatase SHP-2 encoded by the PTPN11 gene. In this paper, we analyzed transcriptional effects of PTPN11 gene knockdown and selective suppression of BRAF V600E in a model of thyroid follicular epithelium. We found that the siRNA-mediated knockdown of PTPN11 after vemurafenib treatment prevented an increase in the expression CCNA1 and NOTCH4 genes involved in the formation of drug resistance of tumors. On the other hand, downregulation of PTPN11 expression blocked the transcriptional activation of genes (p21, p15, p16, RB1, and IGFBP7) involved in cell cycle regulation and oncogene-induced senescence in response to BRAF V600E expression. Therefore, it can be assumed that SHP-2 participates not only in emergence of drug resistance in cancer cells, but also in oncogene-induced cell senescence.

Common and specific gene signatures among three different endometriosis subtypes

Aims

To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE).

Methods

Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764.

Results

A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment.

Conclusion

By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

Potential Prognostic Predictors and Molecular Targets for Skin Melanoma Screened by Weighted Gene Co-expression Network Analysis

AIMS AND OBJECTIVES

Among skin cancers, malignant skin melanoma is the leading cause of death. Identification of gene markers of malignant skin melanoma associated with survival may provide new clues for prognosis prediction and treatment. This research aimed to screen out potential prognostic predictors and molecular targets for malignant skin melanoma.

INTRODUCTION

Information regarding gene expression in skin melanoma and patients' clinical traits was obtained from the Gene Expression Omnibus database. Weighted gene co-expression network analysis (WGCNA) was applied to build co-expression modules and investigate the association between the modules and clinical traits. Moreover, functional enrichment analysis was performed for clinically significant co-expression modules. Hub genes of these modules were validated via Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas (http:// www.proteinatlas.org).

METHODS

First, using WGCNA, 9 co-expression modules were constructed by the top 25% differentially expressed genes (4406 genes) from 77 human melanoma samples. Two co-expression modules (magenta and blue modules) were significantly correlated with survival months (r = -0.27, p = 0.02; r = 0.27, p = 0.02, respectively). The results of functional enrichment analysis demonstrated that the magenta module was mainly enriched in the cell cycle process and the blue module was mainly enriched in the immune response process. Additionally, the GEPIA and Human Protein Atlas results suggested that the hub genes CCNB2, ARHGAP30, and SEMA4D were associated with relapse-free survival and overall survival (all p-values < 0.05) and were differentially expressed in melanoma tumors and normal skin.

RESULTS AND CONCLUSION

The results provided the framework of co-expression gene modules of skin melanoma and screened out CCNB2, ARHGAP30, and SEMA4D associated with survival as potential prognostic predictors and molecular targets of treatment.

Identification of key protein-coding genes in lung adenocarcinomas based on bioinformatic analysis

Background

Lung cancer is one of the most common cancers and the primary cause of cancer-related deaths in the world. The 5-year survival of lung cancer patients is lower than 15%. As a common subtype of lung cancer, lung adenocarcinoma still has a high morbidity and mortality, although many strategies have been made, such as surgical operation, chemotherapy, targeted therapy. The use of gene expression microarray has provided a feasible and effective approach for the study on lung cancer. However, the biomarkers and potential therapeutic targets of lung adenocarcinomas are still not completely identified. Our study is aimed to find biomarkers and therapeutic targets of lung adenocarcinomas by identifying the key protein-coding gene in lung adenocarcinomas by bioinformatical approaches.

Methods

We selected and obtained messenger RNA microarray datasets from Gene Expression Omnibus database to identify differentially expressed genes between lung adenocarcinomas and normal lung tissue. The differentially expressed genes were clarified by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the protein-protein interaction (PPI) network and statistical analyses. Subsequently, quantitative real-time PCR was used to verify the results of bioinformatic analysis.

Results

We obtained 1,264, 896 and 408 differentially expressed genes from GSE32863, GSE43458 and GSE63459, respectively. The 242 common differentially expressed genes in three datasets were related to cell adhesion molecules, ECM-receptor interaction, leukocyte transendothelial migration according to KEGG analysis. GO analysis showed that these common differentially expressed genes were enriched in tumor-related functions. ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T and KIAA0101 have the strongest protein-protein interaction relationships based on protein-protein interaction networks. Survival analysis showed that these nine genes were closely related to the survival of lung adenocarcinomas. The further qRT-PCR assays indicated that seven key genes (ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T) display differential profile between clinical lung adenocarcinoma specimens and their matched normal tissues.

Conclusions

ASPM, CCNB2, CDC20, CDC45, MELK, TOP2A and UBE2T may be key protein coding genes in lung adenocarcinoma, and deserve further study to verify their feasibility and effectiveness as biomarkers and therapeutic targets for lung adenocarcinomas.

Screening and identification of key biomarkers in lung squamous cell carcinoma by bioinformatics analysis

The high mortality rate of lung squamous cell carcinoma (LUSC) is in part due to the lack of early detection of its biomarkers. The identification of key molecules involved in LUSC is therefore required to improve clinical diagnosis and treatment outcomes. The present study used the microarray datasets GSE31552, GSE6044 and GSE12428 from the Gene Expression Omnibus database to identify differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were conducted to construct the protein-protein interaction network of DEGs and hub genes module using STRING and Cytoscape. The 67 DEGs identified consisted of 42 upregulated genes and 25 downregulated genes. The pathways predicted by KEGG and GO enrichment analyses of DEGs mainly included cell cycle, cell proliferation, glycolysis or gluconeogenesis, and tetrahydrofolate metabolic process. Further analysis of the University of California Santa Cruz and ONCOMINE databases identified 17 hub genes. Overall, the present study demonstrated hub genes that were closely associated with clinical tissue samples of LUSC, and identified TYMS, CCNB2 and RFC4 as potential novel biomarkers of LUSC. The findings of the present study contribute to an improved understanding of the molecular mechanisms of carcinogenesis and progression of LUSC, and assist with the identification of potential diagnostic and therapeutic targets of LUSC.

miR-190, CDK1, MCM10 and NDC80 predict the prognosis of the patients with lung cancer

Lung cancer (LC), which includes small-cell lung carcinoma (SCLC) and non-small-cell lung carcinoma (NSCLC), is common and has a high fatality rate. This study aimed to reveal the prognostic mechanisms of LC. GSE30219 was extracted from the Gene Expression Omnibus (GEO) database, and included 293 LC samples and 14 normal lung samples. Differentially expressed genes (DEGs) were identified using the Limma package, and subjected to pathway enrichment analysis using DAVID. MicroRNAs (miRNAs) targeting the DEGs were predicted using Webgestalt. Cytoscape software was used to build a protein-protein interaction (PPI) network and to identify significant network modules. Survival analysis was conducted using Survminer and Survival packages, and validation was performed using The Cancer Genome Atlas (TCGA) dataset. The good and poor prognosis groups contained 518 DEGs. miR-190, miR-493, and miR-218 for the upregulated genes and miR-302, miR-200, and miR-26 for the downregulated genes were predicted. Three network modules (module 1, 2, and 3) were identified from the PPI network. CDK1, MCM10, and NDC80 were the core nodes of module 1, 2, and 3, respectively. In module 1, CDK1 interacted with both CCNB1 and CCNB2. Additionally, CDK1, CCNB1, CCNB2, MCM10, and NDC80 expression levels correlated with clinical survival and were identified as DEGs in both GSE30219 and the TCGA dataset. miR-190, miR-493, miR-218, miR-200, and miR-302 might act in LC by targeting the DEGs. CDK1, CCNB1, CCNB2, MCM10, and NDC80 might also influence the prognosis of LC.

Clinical significance and biological roles of cyclins in gastric cancer

Background and aim

Cyclins have been reported to be overexpressed with poor prognosis in several human cancers. However, limited numbers of studies evaluated the expressions and prognostic roles of cyclins in gastric cancer (GC). We aim to evaluate the expressions and prognostic roles of cyclins. Also, further efforts were made to explore biological function of the differentially expressed cyclins.

Methods

Cyclins expressions were analyzed by Oncomine and The Cancer Genome Atlas datasets, and the prognostic roles of cyclins in GC patients were investigated by the Kaplan–Meier Plotter database. Then, a comprehensive PubMed literature search was performed to identify expression and prognosis of cyclins in GC. Biological functions of the differentially expressed cyclins were explored through Enrich R platform, and KEGG and transcription factor were analyzed.

Results

The expression levels of CCNA2 (cyclin A2), CCNB1 (cyclin B1), CCNB2 (cyclin B2), and CCNE1 (cyclin E1) mRNAs were identified to be significantly higher in GC tissues than in normal tissues in both Oncomine and The Cancer Genome Atlas datasets. High expressions of CCNA2, CCNB1, and CCNB2 mRNAs were identified to be related with poor overall survival in Kaplan–Meier Plotter dataset. Evidence from clinical studies showed that CCNB1 was related with overall survival in GC patients. Cyclins were associated with several biological pathways, including cell cycle, p53 signaling pathway, FoxO signaling pathway, viral carcinogenesis, and AMPK signaling pathway. Enrichment analysis also showed that cyclins interacted with some certain transcription factors, such as FOXM1, SIN3A, NFYA, and E2F4.

Conclusion

Based on our results, high expressions of cyclins were related with poor prognosis in GC patients. The above information might be useful for better understanding the clinical and biological roles of cyclins mRNA and guiding individualized treatments for GC patients.

Identification of key genes and pathways for esophageal squamous cell carcinoma by bioinformatics analysis

The aim of the present study was to identify the differentially expressed genes (DEGs) in esophageal squamous-cellcarcinoma (ESCC) and provide potential therapeutic targets. The microarray dataset GSE20347 was downloaded from the Gene Expression Omnibus (GEO) database, and included 17 tissue samples and 13 normal adjacent tissue samples from patients with ESCC. A total of 22,277 DEGs were identified. A heat map for the DEGs was constructed with the Morpheus online tool and the top 200 genes (100 upregulated and 100 downregulated) were selected for further bioinformatics analysis, including analysis of gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, protein-protein interaction networks and Spearman's correlation tests. The results of the GO analysis indicated that the upregulated DEGs were most significantly enriched in membrane-bounded vesicles in the cellular component (CC) category, but were not significantly enriched in any GO terms of the categories biological process (BP) or molecular function (MF); furthermore, the downregulated DEGs were most significantly enriched in regulation of DNA metabolic processes, nucleotide binding and chromosomes in the categories BP, MF and CC, respectively. The KEGG analysis indicated that the downregulated DEGs were enriched in the regulation of cell cycle pathways. The top 10 hub proteins in the protein-protein interaction network were cyclin-dependent kinase 4, budding uninhibited by benzimidazoles 1, cyclin B2, heat shock protein 90AA1, aurora kinase A, H2A histone family member Z, replication factor C subunit 4, and minichromosome maintenance complex component 2, −4 and −7. These proteins are mainly involved in regulating tumor progression. The genes in the four top modules were mainly implicated in regulating cell cycle pathways. Secreted Ly-6/uPAR-related protein (SLURP) was the hub gene, and SLURP and its interacting genes were most enriched in the chromosomal part in the CC category, organelle organization in the BP category and protein binding in the MF category, and were involved in pathways including DNA replication, cell cycle and P53 signaling. The expression of SLURP-1 in fifteen patients with esophageal carcinoma was detected using quantitative polymerase chain reaction analysis, and the results indicated that SLURP-1 expression was significantly decreased in the tumor samples relative to that in normal adjacent tissues. These results suggest that several hub proteins and the hub gene SLURP-1 may serve as potential therapeutic targets, and that gene dysfunction may be involved in the tumorigenesis of ESCC.

Target gene screening and evaluation of prognostic values in non-small cell lung cancers by bioinformatics analysis

BACKGROUND

Non-small cell lung cancer (NSCLC) is the major leading cause of cancer-related deaths worldwide. This study aims to explore molecular mechanism of NSCLC.

METHODS

Microarray dataset was obtained from the Gene Expression Omnibus (GEO) database, and analyzed by using GEO2R. Functional and pathway enrichment analysis were performed based on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Then, STRING, Cytoscape and MCODE were applied to construct the Protein-protein interaction (PPI) network and screen hub genes. Following, overall survival (OS) analysis of hub genes was performed by using the Kaplan-Meier plotter online tool. Moreover, miRecords was also applied to predict the targets of the differentially expressed microRNAs (DEMs).

RESULTS

A total of 228 DEGs were identified, and they were mainly enriched in the terms of cell adhesion molecules, leukocyte transendothelial migration and ECM-receptor interaction. A PPI network was constructed, and 16 hub genes were identified, including TEK, ANGPT1, MMP9, VWF, CDH5, EDN1, ESAM, CCNE1, CDC45, PRC1, CCNB2, AURKA, MELK, CDC20, TOP2A and PTTG1. Among the genes, expressions of 14 hub genes were associated with prognosis of NSCLC patients. Additionally, a total of 11 DEMs were also identified.

CONCLUSION

Our results provide some potential underlying biomarkers for NSCLC. Further studies are required to elucidate the pathogenesis of NSCLC.

Co-expression network analysis identified six hub genes in association with progression and prognosis in human clear cell renal cell carcinoma (ccRCC)

Human clear cell renal cell carcinoma (ccRCC) is one of the most common types of malignant adult kidney tumors. We constructed a weighted gene co-expression network to identify gene modules associated with clinical features of ccRCC (n = 97). Six hub genes (CCNB2, CDC20, CEP55, KIF20A, TOP2A and UBE2C) were identified in both co-expression and protein-protein interaction (PPI) networks, which were highly correlated with pathologic stage. The significance of expression of the hub genes in ccRCC was ranked top 4 among all cancers and correlated with poor prognosis. Functional analysis revealed that the hub genes were significantly enriched in cell cycle regulation and cell division. Gene set enrichment analysis suggested that the samples with highly expressed hub gene were correlated with cell cycle and p53 signaling pathway. Taken together, six hub genes were identified to be associated with progression and prognosis of ccRCC, and they might lead to poor prognosis by regulating p53 signaling pathway.

Screening and identification of key biomarkers in hepatocellular carcinoma: Evidence from bioinformatic analysis

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. Intense efforts have been made to elucidate the pathogeny, but the molecular mechanisms of HCC are still not well understood. To identify the candidate genes in the carcinogenesis and progression of HCC, microarray datasets GSE19665, GSE33006 and GSE41804 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein-protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. A total of 273 DEGs were identified, consisting of 189 downregulated genes and 84 upregulated genes. The enriched functions and pathways of the DEGs include protein activation cascade, complement activation, carbohydrate binding, complement and coagulation cascades, mitotic cell cycle and oocyte meiosis. Sixteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in cell division, cell cycle and nuclear division. Survival analysis showed that BUB1, CDC20, KIF20A, RACGAP1 and CEP55 may be involved in the carcinogenesis, invasion or recurrence of HCC. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms underlying the carcinogenesis and progression of HCC, and provide candidate targets for diagnosis and treatment of HCC.

HOTAIR upregulates an 18-gene cell cycle-related mRNA network in glioma

HOTAIR is a tumor promoting long non-coding RNA (lncRNA) with roles in multiple cancers. However, the role of HOTAIR in glioma has not been well charaterized. Genes that positively correlated with HOTAIR were identified from the Chinese Glioma Genome Atlas and constructed into an interacting network. In total, 18 genes with P-values <0.01 were further extracted and constructed into a subnetwork. Real-time PCR, western blot and immunofluorescence analyses were employed to examine the expression of the genes after HOTAIR overexpression or knockdown. Intracranial glioblastoma multiform (GBM) models were used to test the potential of HOTAIR as a glioma therapy target. It was discovered that the 18 genes that most significantly correlated with HOTAIR expression formed a cell cycle-related mRNA network, which is positively regulated by HOTAIR. Furthermore, HOTAIR knockdown inhibited mouse intracranial GBM model formation. HOTAIR positively regulates a cell cycle-related mRNA network in glioma, and could be a potential therapeutic target for treating glioma.

Elevated transcriptional levels of aldolase A (ALDOA) associates with cell cycle-related genes in patients with NSCLC and several solid tumors

BACKGROUND

Aldolase A (ALDOA) is one of the glycolytic enzymes primarily found in the developing embryo and adult muscle. Recently, a new role of ALDOA in several cancers has been proposed. However, the underlying mechanism remains obscure and inconsistent. In this study, we tried to investigate ALDOA-associated (AA) genes using available microarray datasets to help elucidating the role of ALDOA in cancer.

RESULTS

In the dataset of patients with non-small-cell lung cancer (NSCLC, E-GEOD-19188), 3448 differentially expressed genes (DEGs) including ALDOA were identified, in which 710 AA genes were found to be positively associated with ALDOA. Then according to correlation coefficients between each pair of AA genes, ALDOA-associated gene co-expression network (GCN) was constructed including 182 nodes and 1619 edges. 11 clusters out of GCN were detected by ClusterOne plugin in Cytoscape, and only 3 of them have more than three nodes. These three clusters were functionally enriched. A great number of genes (43/79, 54.4%) in the biggest cluster (Cluster 1) primarily involved in biological process like cell cycle process (P_a = 6.76E-26), mitotic cell cycle (P_a = 4.09E-19), DNA repair (P_a = 1.13E-04), M phase of meiotic cell cycle (P_a = 0.006), positive regulation of ubiquitin-protein ligase activity during mitotic cell cycle (P_a = 0.014). AA genes with highest degree and betweenness were considered as hub genes of GCN, namely CDC20, MELK, PTTG1, CCNB2, CDC45, CCNB1, TK1 and PSMB2, which could distinguish cancer from normal controls with ALDOA. Their positive association with ALDOA remained after removing the effect of HK2 and PKM, the two rate limiting enzymes in glycolysis. Further, knocking down ALDOA blocked breast cancer cells in the G0/G1 phase under minimized glycolysis. All suggested that ALDOA might affect cell cycle progression independent of glycolysis. RT-qPCR detection confirmed the relationship of ALDOA with CDC45 and CCNB2 in breast tumors. High expression of the hub genes indicated poor outcome in NSCLC. ALDOA could improve their predictive power.

CONCLUSIONS

ALDOA could contribute to the progress of cancer, at least partially through its association with genes relevant to cell cycle independent of glycolysis. AA genes plus ALDOA represent a potential new signature for development and prognosis in several cancers.

Integrated Analysis of DNA Methylation and mRNA Expression Profiles Data to Identify Key Genes in Lung Adenocarcinoma

Introduction. Lung adenocarcinoma (LAC) is the most frequent type of lung cancer and has a high metastatic rate at an early stage. This study is aimed at identifying LAC-associated genes. Materials and Methods. GSE62950 downloaded from Gene Expression Omnibus included a DNA methylation dataset and an mRNA expression profiles dataset, both of which included 28 LAC tissue samples and 28 adjacent normal tissue samples. The differentially expressed genes (DEGs) were screened by Limma package in R, and their functions were predicted by enrichment analysis using TargetMine online tool. Then, protein-protein interaction (PPI) network was constructed using STRING and Cytoscape. Finally, LAC-associated methylation sites were identified by CpGassoc package in R and mapped to the DEGs to obtain LAC-associated DEGs. Results. Total 913 DEGs were identified in LAC tissues. In the PPI networks, MAD2L1, AURKB, CCNB2, CDC20, and WNT3A had higher degrees, and the first four genes might be involved in LAC through interaction. Total 8856 LAC-associated methylation sites were identified and mapped to the DEGs. And there were 29 LAC-associated methylation sites located in 27 DEGs (e.g., SH3GL2, BAI3, CDH13, JAM2, MT1A, LHX6, and IGFBP3). Conclusions. These key genes might play a role in pathogenesis of LAC.

Identification of key genes in hepatocellular carcinoma and validation of the candidate gene, cdc25a, using gene set enrichment analysis, meta-analysis and cross-species comparison

The aim of the present study was to determine key pathways and genes involved in the pathogenesis of hepatocellular carcinoma (HCC) through bioinformatic analyses of HCC microarray data based on cross-species comparison. Microarray data of gene expression in HCC in different species were analyzed using gene set enrichment analysis (GSEA) and meta-analysis. Reverse transcription-quantitative polymerase chain reaction and western blotting were performed to determine the mRNA and protein expression levels of cdc25a, one of the identified candidate genes, in human, rat and tree shrew samples. The cell cycle pathway had the largest overlap between the GSEA and meta-analysis. Meta-analyses showed that 25 genes, including cdc25a, in the cell cycle pathway were differentially expressed. Cdc25a mRNA levels in HCC tissues were higher than those in normal liver tissues in humans, rats and tree shrews, and the expression level of cdc25a in HCC tissues was higher than in corresponding paraneoplastic tissues in humans and rats. In human HCC tissues, the cdc25a mRNA level was significantly correlated with clinical stage, portal vein tumor thrombosis and extrahepatic metastasis. Western blotting showed that, cdc25a protein levels were significantly upregulated in HCC tissues in humans, rats and tree shrews. In conclusion, GSEA and meta-analysis can be combined to identify key molecules and pathways involved in HCC. This study demonstrated that the cell cycle pathway and the cdc25a gene may be crucial in the pathogenesis and progression of HCC.

CCNB2 overexpression is a poor prognostic biomarker in Chinese NSCLC patients

Cyclin B2 (CCNB2), a member of cyclin family proteins, serves a key role in progression of G2/M transition. The clinical value of CCNB2 in non-small cell lung cancer is still unknown. The aim of our study is to identify the role of CCNB2 in NSCLC patients. The status of CCNB2 in NSCLC tissues and normal lung tissues was observed in Gene Expression Omnibus (GEO: GSE19804). CCNB2 mRNA and protein expressions were detected in NSCLC and normal lung tissues by using Real-time PCR and immunohistochemistry. The association of CCNB protein expression with clinical characteristics of 186 NSCLC patients was analyzed by immunohistochemistry. Based on microarray data (GEO: GSE19804), we observed that CCNB2 was significantly overexpressed in NSCLC tissues compared with paired adjacent normal lung tissue. Furthermore, we verified mRNA and protein levels of CCNB2 expression were both increased in NSCLC tissues. We found high levels of CCNB2 protein were positively associated with the status of differentiated degree, tumor size, lymph node metastasis, distant metastasis, and clinical stage. Meanwhile, CCNB2 protein overexpression was an independent unfavorable prognostic factor for the overall survival of patients with NSCLC. In conclusion, overexpression of CCNB2 protein is associated with clinical progression and poor prognosis in NSCLC.