CCNA2 acts as a novel biomarker in regulating the growth and apoptosis of colorectal cancer

Claudin 10 acts as a novel biomarker for the prognosis of patients with ovarian cancer

Ovarian cancer (OC) is one of the most fatal gynecological malignancies in the world and confers a poor 5-year survival rate. The present study was designed to discover novel prognostic markers for patients with OC in order to estimate disease metastasis or recurrence. Based on the large cohorts of transcriptome data from multicenter sources, a comprehensive analysis was performed to explore potential prognostic markers. A total of 269 differentially expressed genes were identified, of which 32 were upregulated and 237 downregulated in OC tissues compared with the corresponding expression in normal tissues. Kaplan-Meier analysis, log-rank test and nomogram analysis were employed to demonstrate that low expression levels of claudin 10 (CLDN10) were associated with a less favorable disease prognosis. The most promising prognostic marker for OC was subsequently selected. Additionally, the prognostic nomogram was constructed in order to assess the 5-year survival rate using CLDN10 expression as a prognostic marker for OC. Furthermore, gene set enrichment analysis and analysis of the tumor-associated competing endogenous RNA network were performed to elucidate the potential biological processes associated with CLDN10 expression. The current results indicated that CLDN10 may influence OC progression via transforming growth factor-β (TGF-β)- or WNT/β-catenin-induced epithelial-to-mesenchymal transition (EMT). The associations among CLDN10, microRNA-486-5p, TGF-β, WNT/β-catenin and EMT should be further investigated in future studies.

Screening for genes and subnetworks associated with atypical teratoid/rhabdoid tumors using bioinformatics analysis

Objectives: Atypical teratoid/rhabdoid tumors (AT/RTs) are rare, fast-growing lesions of central nervous system and their prognosis is poor. Nowadays, multimodal managements, including surgery, chemotherapy and radiation therapy are advocated; however, low survival rate and severe neurocognitive toxicity of chemotherapy as well as the irreversible long-term sequelae of irradiation in infants and young children with AT/RTs are alarming. The aim of our study is to provide valid biological information for more tailored advance therapy for these lesions.Methods: Gene expression profile of GSE94349 was downloaded from GEO database and was analyzed using limma R package. Function and enrichment analyses of DEGs were performed based on DAVID database. PPI network construction, hub gene selection and module analysis were conducted in Cytoscape software.Results: In this study, 224 up-regulated genes and 572 down-regulated genes were selected as DEGs. The up-regulated genes were mainly enriched in molecular function and cell component, which mainly included protein binding and nucleus, respectively. The down-regulated DEGs were significantly involved in cell component such as plasma membrane and integral component of membrane. Cell cycle and retrograde endocannabinoid signaling were the main KEGG pathway of up and down DEGs, respectively. CDK1, CCNA2, CDC20, TOP2A were identified as hub genes and two significant network modules were also obtained.Conclusions: Our study may help to further understand the molecular characteristics and provide more tailored targets for future treatment of AT/RTs. Hub genes CDK1, CCNA2, CDC20, TOP2A as well as cell cycle signaling pathway may be new more tailored targets for future treatment of AT/RTs.

Identification of key gene modules and hub genes of human mantle cell lymphoma by coexpression network analysis

Purpose

Mantle cell lymphoma (MCL) is a rare and aggressive subtype of non-Hodgkin lymphoma that is incurable with standard therapies. The use of gene expression analysis has been of interest, recently, to detect biomarkers for cancer. There is a great need for systemic coexpression network analysis of MCL and this study aims to establish a gene coexpression network to forecast key genes related to the pathogenesis and prognosis of MCL.

Methods

The microarray dataset GSE93291 was downloaded from the Gene Expression Omnibus database. We systematically identified coexpression modules using the weighted gene coexpression network analysis method (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis were performed on the modules deemed important. The protein-protein interaction networks were constructed and visualized using Cytoscape software on the basis of the STRING website; the hub genes in the top weighted network were identified. Survival data were analyzed using the Kaplan-Meier method and were compared using the log-rank test.

Results

Seven coexpression modules consisting of different genes were applied to 5,000 genes in the 121 human MCL samples using WGCNA software. GO and KEGG enrichment analysis identified the blue module as one of the most important modules; the most critical pathways identified were the ribosome, oxidative phosphorylation and proteasome pathways. The hub genes in the top weighted network were regarded as real hub genes (IL2RB, CD3D, RPL26L1, POLR2K, KIF11, CDC20, CCNB1, CCNA2, PUF60, SNRNP70, AKT1 and PRPF40A). Survival analysis revealed that seven genes (KIF11, CDC20, CCNB1, CCNA2, PRPF40A, CD3D and PUF60) were associated with overall survival time (p < 0.05).

Conclusions

The blue module may play a vital role in the pathogenesis of MCL. Five real hub genes (KIF11, CDC20, CCNB1, CCNA2 and PUF60) were identified as potential prognostic biomarkers as well as therapeutic targets with clinical utility for MCL.

CDK1 and CDC20 overexpression in patients with colorectal cancer are associated with poor prognosis: evidence from integrated bioinformatics analysis

BACKGROUND

Colorectal cancer (CRC) is one of the most common malignancies of the digestive system, which causes severe financial burden worldwide. However, the specific mechanisms involved in CRC are still unclear.

METHODS

To identify the significant genes and pathways involved in the initiation and progression of CRC, the microarray dataset GSE126092 was downloaded from Gene Expression Omnibus (GEO) database, and then, the data was analyzed to identify differentially expressed genes (DEGs). Subsequently, the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on these DEGs using the DAVID database, and the protein-protein interaction (PPI) network was constructed using the STRING database and analyzed using the Cytoscape software. Finally, hub genes were screened, and the survival analysis was performed on these hub genes using the Kaplan-Meier curves in the cBioPortal database.

RESULTS

In total, 937 DEGs were obtained, including 316 upregulated genes and 621 downregulated genes. GO analysis revealed that the DEGs were mostly enriched in terms of nuclear division, organelle fission, cell division, and cell cycle process. KEGG pathway analysis showed that the DEGs were mostly enriched in cell cycle, oocyte meiosis, cytokine-cytokine receptor interaction, and cGMP-PKG signaling pathway. The PPI network comprised 608 nodes and 3100 edges, and 4 significant modules and 10 hub genes with the highest degree were identified using the Cytoscape software. Finally, survival analysis showed that overexpression of CDK1 and CDC20 in patients with CRC were statistically associated with worse overall survival.

CONCLUSIONS

This bioinformatics analysis revealed that CDK1 and CDC20 might be candidate targets for diagnosis and treatment of CRC, which provided valuable clues for CRC.

Identification of Core Gene Expression Signature and Key Pathways in Colorectal Cancer

Objective

Colorectal cancer (CRC) is considered the most prevalent malignant tumor that contributes to high cancer-related mortality. However, the signaling pathways involved in CRC and CRC-driven genes are largely unknown. We sought to discover a novel biomarker in CRC.

Materials and Methods

All clinical CRC samples (n = 20) were from Renmin Hospital of Wuhan University. We first selected MAD2L1 by integrated bioinformatics analysis of a GSE dataset. Next, the expression of MAD2L1 in tissues and cell lines was verified by quantitative real-time PCR. The effects of MAD2L1 on cell growth, proliferation, the cell cycle, and apoptosis were examined by in vitro assays.

Results

We identified 683 shared DEGs (420 upregulated and 263 downregulated), and the top twenty genes (CDK1, CCNA2, TOP2A, PLK1, MAD2L1, AURKA, BUB1B, UBE2C, TPX2, RRM2, KIF11, NCAPG, MELK, NUSAP1, MCM4, RFC4, PTTG1, CHEK1, CEP55, DTL) were selected by integrated analysis. These hub genes were significantly overexpressed in CRC samples and were positively correlated. Our data revealed that the expression of MAD2L1 in CRC tissues is higher than that in normal tissues. MAD2L1 knockdown significantly suppressed CRC cell growth by impairing cell cycle progression and inducing cell apoptosis.

Conclusion

MAD2L1, as a novel oncogenic gene, plays a role in regulating cancer cell growth and apoptosis and could be used as a new biomarker for diagnosis and therapy in CRC.

Prognostic values of a novel multi-mRNA signature for predicting relapse of cholangiocarcinoma

Cholangiocarcinoma (CCA) is an epithelial cancer and has high death and recurrence rates, current methods cannot satisfy the need for predicting cancer relapse effectively. So, we aimed at conducting a multi-mRNA signature to improve the relapse prediction of CCA. We analyzed mRNA expression profiling in large CCA cohorts from the Gene Expression Omnibus (GEO) database (GSE76297, GSE32879, GSE26566, GSE31370, and GSE45001) and The Cancer Genome Atlas (TCGA) database. The Least absolute shrinkage and selection operator (LASSO) regression model was used to establish a 7-mRNA-based signature that was significantly related to the recurrence-free survival (RFS) in two test series. Based on the 7-mRNA signature, the cohort TCGA patients could be divided into high-risk or low-risk subgroups with significantly different RFS [p < 0.001, hazard ratio (HR): 48.886, 95% confidence interval (CI): 6.226-3.837E+02]. Simultaneously, the prognostic value of the 7-mRNA signature was confirmed in clinical samples of Ren Ji hospital (p < 0.001, HR: 4.558, 95% CI: 1.829-11.357). Further analysis including multivariable and sub-group analyses revealed that the 7-mRNA signature was an independent prognostic value for recurrence of patients with CCA. In conclusion, our results might provide an efficient tool for relapse prediction and were beneficial to individualized management for CCA patients.

Identification of crucial genes and pathways associated with colorectal cancer by bioinformatics analysis

Colorectal cancer (CRC) is a prevalent malignant tumour type arising from the colon and rectum. The present study aimed to explore the molecular mechanisms of the development and progression of CRC. Initially, differentially expressed genes (DEGs) between CRC tissues and corresponding non-cancerous tissues were obtained by analysing the GSE15781 microarray dataset. The Database for Annotation, Visualization and Integrated Discovery was then utilized for functional and pathway enrichment analysis of the DEGs. Subsequently, a protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Proteins database and visualized by Cytoscape software. Furthermore, CytoNCA, a Cytoscape plugin, was used for centrality analysis of the PPI network to identify crucial genes. Finally, UALCAN was employed to validate the expression of the crucial genes and to estimate their effect on the survival of patients with colon cancer by Kaplan-Meier curves and log-rank tests. A total of 1,085 DEGs, including 496 upregulated and 589 downregulated genes, were screened out. The DEGs identified were enriched in various pathways, including ‘metabolic pathway’, ‘cell cycle’, ‘DNA replication’, ‘nitrogen metabolism’, ‘p53 signalling’ and ‘fatty acid degradation’. PPI network analysis suggested that interleukin-6, MYC, NOTCH1, inhibin subunit βA (INHBA), CDK1, cyclin (CCN)B1 and CCNA2 were crucial genes, and their expression levels were markedly upregulated. Survival analysis suggested that upregulated INHBA significantly decreased the survival probability of patients with CRC. Conversely, upregulation of CCNB1 and CCNA2 expression levels were associated with increased survival probabalities. The identified DEGs, particularly the crucial genes, may enhance the current understanding of the genesis and progression of CRC, and certain genes, including INHBA, CCNB1 and CCNA2, may be candidate diagnostic and prognostic markers, as well as targets for the treatment of CRC.

Weighted gene co-expression network analysis identifies CCNA2 as a treatment target of prostate cancer through inhibiting cell cycle

Prostate cancer is a malignant tumor disease that seriously harms the lives of middle-aged and elderly men. Weighted gene co-expression analysis can be used to construct gene co-expression networks to explore gene sets and genes that are significantly correlated with clinical features. In this study, the transcriptome data of prostate cancer on TCGA was analyzed by weighted gene co-expression network, and the gene with a significant correlation with disease Gleason stage and tumor T stage was identified: CCNA2. CCNA2 was significantly associated with biochemical recurrence, disease-free survival and overall survival rate of prostate cancer. The ability of cancer cell proliferation, invasion and metastasis was decreased after down-regulated expression of CCNA2 in prostate cancer cell lines. Flow cytometry revealed that tumor cells were arrested in the S phase after down-regulated the expression of CCNA2. Taken together, we used WGCNA and obtain a gene CCNA2 which is significantly associated with the prognosis of prostate cancer, which may be an indicator of the prognosis of prostate cancer and a new therapeutic target.

RAPTOR promotes colorectal cancer proliferation by inducing mTORC1 and upregulating ribosome assembly factor URB1

Mammalian target of rapamycin complex 1 (mTORC1) is evolutionally conserved and frequently activated in various tumors, including colorectal cancer (CRC). It has been reported that the ribosome assembly factor Urb1 acts downstream of mTORC1/raptor signaling and contributes to digestive organ development in zebrafish. Previously, we highlighted that URB1 was overexpressed in CRC. Here, we assessed the mTORC1/regulatory associated protein with mTOR (RAPTOR)-URB1 axis in CRC tumorigenesis. We found that RAPTOR was overexpressed in CRC tissues and cell lines, was a favorable predictor in patients with CRC, and positively correlated with URB1. Silencing of RAPTOR suppressed CRC cell proliferation and migration and induced cell cycle arrest and apoptosis in vitro and inhibited xenograft growth in vivo. Moreover, ectopic overexpression of RAPTOR exerted an inverse biological phenotype. Knockdown of RAPTOR quenched mTORC1 activity and reduced the expression of URB1 and cyclinA2 (CCNA2). In contrast, overexpression of RAPTOR activated mTORC1 and upregulated URB1 and CCNA2. Furthermore, URB1 and CCNA2 expression were also impeded by rapamycin, which is a specific inhibitor of mTORC1. Thus, RAPTOR promoted CRC proliferation, migration, and cell cycle progression by inducing mTORC1 signaling and transcriptional activation of both URB1 and CCNA2. Taken together, we concluded that RAPTOR has the potential to serve as a novel biomarker and therapeutic target for CRC.

Integrated bioinformatics analysis of expression, related signal pathways, and prognostic significance of CCNA2 in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most diagnosed malignant carcinomas of the digestive system with a poor prognosis. In this study, the expression of CCNA2 gene expression in HCC was investigated by bioinformatics analysis and the feasibility of CCNA2 gene as a molecular marker for prognosis in HCC was assessed. The results of bioinformatics analysis were verified by immunohistochemistry assay.

AIM

To investigate the expression of CCNA2 in HCC, related signal pathways, and its relationship with prognosis.

METHODS

The expression levels of CCNA2 mRNA in HCC and paracancerous tissues were compared based on the TCCA database. CCNA2 protein interaction network was constructed based on the String database, and the related protein function and KEGG signal pathways were enriched. According to the expression level of CCNA2, the patients were divided into high and low expression groups, and the difference in overall survival (OS) and disease free survival (DFS) was compared between the two groups. The relationship between the expression of CCNA2 protein analyzed by immunohistochemistry and the clinicopathological features of 72 patients with HCC who underwent surgical treatment was analyzed.

RESULTS

The expression level of CCNA2 mRNA was significantly higher in HCC than in normal liver tissues. There are ten proteins that have a close interaction with CCNA2 protein, with edge = 50, and the regional clustering index is 0.931. The interaction network of the ten proteins that have a close interaction with CCNA2 protein was significantly enriched (P < 0.05). The expression of TOP2 mRNA was positively correlated with CCNA2 expression (r = 0.85, P < 0.05), while CCL14 expression was negatively correlated with CCNA2 expression (r = -0.54, P < 0.05). CCNA2 gene related signaling pathways are mainly enriched in cell cycle, viral carcinogenesis, hepatitis B, p53 signaling pathway, and PI3K-Akt signaling pathway. The prognosis analysis indicated that the OS (HR = 1.7, P = 0.0037) and DFS rates were significantly lower in the high expression group than in the low expression group (HR = 1.6, P = 0.0037). Immunohistochemistry showed that the high expression rate of CCNA2 in HCC was 34.7% (25/72). High expression of CCNA2 protein was significantly associated with tumor diameter (P < 0.05), DC infiltration (P < 0.05), and recurrence/metastasis 2 years after operation (P < 0.05).

CONCLUSION

CCNA2 gene is up-regulated in HCC, which can be used as a molecular marker for poor prognosis in HCC.

lncRNA Expression Reveals the Potential Regulatory Roles in Hepatocyte Proliferation during Rat Liver Regeneration

Liver regeneration is a tissue growth process after loss or injury of liver tissue, which is a compensatory hyperplasia rather than true regeneration, mainly depending on hepatocyte proliferation. Currently, a large number of studies on hepatocyte proliferation have been conducted. However, studies on the regulation of long noncoding RNA (lncRNA) on hepatocyte proliferation are still limited. To identify specially expressed lncRNA during rat liver regeneration, high-throughput sequencing technology was performed, and a total of 2446 lncRNAs and 4091 mRNAs were identified as significantly differentially expressed. Gene ontology (GO) enrichment analysis was performed to analyze the role of differentially expressed mRNAs, and 695 mRNAs were identified to be related to cell proliferation. Then, an lncRNA-mRNA coexpression network based on the differentially expressed lncRNAs and proliferation-related genes was constructed to analyze the potential function of lncRNAs on hepatocyte proliferation, and ten lncRNAs, NONRATT003557.2, NONRATT005357.2, NONRATT003292.2, NONRATT001466.2, NONRATT003289.2, NONRATT001047.2, NONRATT005180.2, NONRATT004419.2, NONRATT005336.2, and NONRATT005335.2, were selected as key regulatory factors, which may play crucial roles in hepatocyte proliferation during rat liver regeneration. Finally, a protein-protein interaction (PPI) network was established to illuminate the interaction between proliferation-related genes, and ten hub genes (Aurkb, Cdk1, Cdc20, Bub1b, Mad2l1, Kif11, Prc1, Ccna2, Top2a, and Ccnb1) were screened with the MCC method in the PPI network, which may be important biomarkers involved in the hepatocyte proliferation during rat liver regeneration. These results may provide clues for a more comprehensive understanding of the molecular mechanism of hepatocyte proliferation during rat liver regeneration.

A Humanized Yeast Phenomic Model of Deoxycytidine Kinase to Predict Genetic Buffering of Nucleoside Analog Cytotoxicity

Knowledge about synthetic lethality can be applied to enhance the efficacy of anticancer therapies in individual patients harboring genetic alterations in their cancer that specifically render it vulnerable. We investigated the potential for high-resolution phenomic analysis in yeast to predict such genetic vulnerabilities by systematic, comprehensive, and quantitative assessment of drug–gene interaction for gemcitabine and cytarabine, substrates of deoxycytidine kinase that have similar molecular structures yet distinct antitumor efficacy. Human deoxycytidine kinase (dCK) was conditionally expressed in the Saccharomyces cerevisiae genomic library of knockout and knockdown (YKO/KD) strains, to globally and quantitatively characterize differential drug–gene interaction for gemcitabine and cytarabine. Pathway enrichment analysis revealed that autophagy, histone modification, chromatin remodeling, and apoptosis-related processes influence gemcitabine specifically, while drug–gene interaction specific to cytarabine was less enriched in gene ontology. Processes having influence over both drugs were DNA repair and integrity checkpoints and vesicle transport and fusion. Non-gene ontology (GO)-enriched genes were also informative. Yeast phenomic and cancer cell line pharmacogenomics data were integrated to identify yeast–human homologs with correlated differential gene expression and drug efficacy, thus providing a unique resource to predict whether differential gene expression observed in cancer genetic profiles are causal in tumor-specific responses to cytotoxic agents.

Identification of Potential Crucial Genes and Key Pathways in Breast Cancer Using Bioinformatic Analysis

Background: The molecular mechanism of tumorigenesis remains to be fully understood in breast cancer. It is urgently required to identify genes that are associated with breast cancer development and prognosis and to elucidate the underlying molecular mechanisms. In the present study, we aimed to identify potential pathogenic and prognostic differentially expressed genes (DEGs) in breast adenocarcinoma through bioinformatic analysis of public datasets.

Methods: Four datasets (GSE21422, GSE29431, GSE42568, and GSE61304) from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) dataset were used for the bioinformatic analysis. DEGs were identified using LIMMA Package of R. The GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses were conducted through FunRich. The protein-protein interaction (PPI) network of the DEGs was established through STRING (Search Tool for the Retrieval of Interacting Genes database) website, visualized by Cytoscape and further analyzed by Molecular Complex Detection (MCODE). UALCAN and Kaplan–Meier (KM) plotter were employed to analyze the expression levels and prognostic values of hub genes. The expression levels of the hub genes were also validated in clinical samples from breast cancer patients. In addition, the gene-drug interaction network was constructed using Comparative Toxicogenomics Database (CTD).

Results: In total, 203 up-regulated and 118 down-regulated DEGs were identified. Mitotic cell cycle and epithelial-to-mesenchymal transition pathway were the major enriched pathways for the up-regulated and down-regulated genes, respectively. The PPI network was constructed with 314 nodes and 1,810 interactions, and two significant modules are selected. The most significant enriched pathway in module 1 was the mitotic cell cycle. Moreover, six hub genes were selected and validated in clinical sample for further analysis owing to the high degree of connectivity, including CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK, and they were all correlated to worse overall survival (OS) in breast cancer.

Conclusion: These results revealed that mitotic cell cycle and epithelial-to-mesenchymal transition pathway could be potential pathways accounting for the progression in breast cancer, and CDK1, CCNA2, TOP2A, CCNB1, KIF11, and MELK may be potential crucial genes. Further, it could be utilized as new biomarkers for prognosis and potential new targets for drug synthesis of breast cancer.

Resveratrol is a promising agent for colorectal cancer prevention and treatment: focus on molecular mechanisms

Colorectal cancer (CRC) is the third most common cancer and one of the main causes of cancer death entire the world. Environmental, dietary, and lifestyle factors including red meat consumption, cigarette smoking, alcohol intake and family history are the most important risk factors of CRC. Multiple pathways including inflammation, oxidative stress, and apoptosis are involved in its incidence and progression. Resveratrol, a polyphenolic compound, has different pharmacologic functions including anti-inflammation, cancer prevention, lipid-lowering effect, and hypoglycemic effect. Many studies have proved that resveratrol might also represent a chemo preventive effect on CRC. Thus, the aim of the current review is to depict the role of resveratrol in treatment of CRC in a molecular manner.