Identification of key pathways and genes in colorectal cancer using bioinformatics analysis

Identification of Key Genes and Pathways for Enchondromas by Bioinformatics Analysis

Background:

The risk of malignant transformation of enchondromas (EC) toward central chondrosarcoma is increased up to 35%, while the exact etiology of EC is unknown. The purpose of this research was to authenticate gene signatures during EC and reveal their potential mechanisms in occurrence and development of EC.

Methods:

The gene expression profiles was acquired from Gene Expression Omnibus database (no. GSE22855). The gene ontology (GO), protein–protein interaction (PPI) network and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were utilized to identify differentially expressed genes (DEGs).

Results:

Finally, 242 DEGs were appraisal, containing 200 overregulated genes and 42 downregulated genes. The outcomes of GO analysis indicated that upregulated DEGs were mainly enriched in several biological processes containing response to hypoxia, calcium ion, and negative regulation extrinsic apoptotic signaling pathway. Furthermore, the upregulated DEGs were enriched in extracellular matrix (ECM)–receptor interaction, protein processing in endoplasmic reticulum and ribosome, which was analyzed by KEGG pathway. From the PPI network, the top 10 hub genes were identified, which were related to significant pathways containing ribosome, protein processing in endoplasmic reticulum, and ECM-receptor interaction.

Conclusion:

In conclusion, the present study may be helpful for understanding the diagnostic biomarkers of EC.

A network pharmacology-based strategy for predicting anti-inflammatory targets of ephedra in treating asthma

Asthma, the most common chronic respiratory disease in the world, is involved in a sustained inflammatory response caused by a variety of immune cells. Ephedra with multi-target, multi-pathway functions is an effective treatment for asthma. However, the ingredients and anti-inflammatory targets of ephedra in treating asthma are unclear. Therefore, there is a need for further research. Ephedra-related and anti-inflammatory targets were found and then combined to get intersection, which represented potential anti-inflammatory targets of ephedra. Moreover, compound-anti-inflammatory target and asthma-target protein-protein interaction network were merged to get the protein-protein interaction network intersection and core genes in asthma-target protein-protein interaction network. For the anti-inflammatory targets of ephedra in treating asthma, Gene Ontology and pathway analysis were executed to confirm gene functions of ephedra in antagonizing inflammation of asthma. Finally, molecular docking, qRT-PCR, WB and ELISA were performed to assess the binding activities between the compounds and anti-inflammatory targets of ephedra in treating asthma. Critical compounds and anti-inflammatory targets of ephedra in treating asthma were identified, including quercetin, luteolin, kempferol, naringenin, beta-sitosterol, SELE, IL-2 and CXCL10. The biological processes of anti-inflammatory targets of ephedra in treating asthma were involved in immune response, inflammatory response, cell-cell signaling and response to lipopolysaccharide. Moreover, 22 pathways were obtained and we proved that critical compounds inhabited the expression of SELE, IL-2 and CXCL10 at mRNA and protein levels.

Comprehensive RNA Sequencing in Adenoma-Cancer Transition Identified Predictive Biomarkers and Therapeutic Targets of Human CRC

Specific molecular biomarkers for predicting the transition from colorectal adenoma to cancer have been identified, however, circular RNA (circRNA)-related signatures remain to be clarified. We carried out high-throughput RNA sequencing to determine the expression profiles of circRNAs, microRNAs (miRNAs), and mRNAs in human colorectal cancer (CRC), adenoma, and adjacent normal tissues. We identified 84 circRNAs, 41 miRNAs, and 398 mRNAs that were commonly differentially expressed in CRC and adenoma tissues compared with normal tissues. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) analyses identified numerous cancer-related hub genes that might serve as potential therapeutic targets in CRC. Competing endogenous RNA (ceRNA) networks, including three circRNAs, three miRNAs, and 28 mRNAs were constructed, suggesting their potential role in cancer progression. Representative differentially expressed RNAs were validated by the Cancer Genome Atlas (TCGA) database and real-time PCR experiments. Receiver operating characteristic (ROC) curve analysis identified three circRNAs (hsa_circ_0049487, hsa_circ_0066875, and hsa_circ_0007444) as possible novel biomarkers predicting the transition from colonic adenoma to cancer. Overall, our findings may provide novel perspectives to clarify the mechanisms of the transition from premalignant adenoma to cancer and identify specific circRNA-related signatures with possible applications for the early diagnosis of and as potential therapeutic targets in CRC.

Identification of Key Regulatory Genes and Pathways in Prefrontal Cortex of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder partly induced by dysregulation of different brain regions. Prefrontal cortex (PFC) dysregulation has been reported to associate with mental symptoms such as delusion, apathy, and depression in AD patients. However, the internal mechanisms have not yet been well-understood. This study aims to identify the potential therapeutic target genes and related pathways in PFC of AD. First, differential expression analyses were performed on transcriptome microarray of PFC between AD specimens and non-AD controls. Second, protein-protein interaction networks were constructed based on the identified differentially expressed genes to explore candidate therapeutic target genes. Finally, these candidate genes were validated through biological experiments. The enrichment analyses showed that the differentially expressed genes were significantly enriched in protein functions and pathways related to AD. Furthermore, the top ten hub genes in the protein-protein interaction network (ELAVL1, CUL3, MAPK6, FBXW11, YWHAE, YWHAZ, GRB2, CLTC, YWHAQ, and PDHA1) were proved to be directly or indirectly related to AD. Besides, six genes (PDHA1, CLTC, YWHAE, MAPK6, YWHAZ, and GRB2) of which were validated to significantly altered in AD mice by biological experiments. Importantly, the most significantly changed gene, PDHA1, was proposed for the first time that may be serve as a target gene in AD treatment. In summary, several genes and pathways that play critical roles in PFC of AD patients have been uncovered, which will provide novel insights on molecular targets for treatment and diagnostic biomarkers of AD.

Identification of key pathways and genes in nonalcoholic fatty liver disease using bioinformatics analysis

INTRODUCTION

Nonalcoholic fatty liver disease (NAFLD) is one of the most common types of liver disease in the world. However, the molecular mechanisms regulating the development of NAFLD have remained unclear.

MATERIAL AND METHODS

In the present study, we analyzed two public datasets (GSE48452 and GSE89632) to identify differentially expressed mRNAs in the progression of NAFLD. Next, we performed bioinformatics analysis to explore key pathways underlying NAFLD development.

RESULTS

Gene Ontology (GO) analysis showed that differentially expressed genes (DEGs) were mainly involved in regulating a series of metabolism-related pathways (including proteolysis and lipid metabolism), cell proliferation and adhesion, the inflammatory response, and the immune response. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that DEGs in NAFLD were mainly enriched in the insulin signaling pathway, peroxisome proliferator-activated receptor (PPAR) signaling pathway, and p53 signaling pathway. We also constructed protein-protein interaction (PPI) networks for these DEGs. Interestingly, we observed that key hub nodes in PPI networks were also associated with the progression of hepatocellular carcinoma (HCC).

CONCLUSIONS

Taken together, our analysis revealed that a series of pathways, such as metabolism and PPAR signaling pathways, were involved in NAFLD development. Moreover, we observed that many DEGs in NAFLD were also dysregulated in HCC. Although further validation is still needed, we believe this study could provide useful information to explore the potential candidate biomarkers for diagnosis, prognosis, and drug targets of NAFLD.

Gene Expression Analysis of Human Papillomavirus-Associated Colorectal Carcinoma

PURPOSE

Human papillomavirus (HPV) antigens had been found in colorectal cancer (CRC) tissue, but little evidence demonstrates the association of HPV with oncogene mutations in CRC. We aim to elucidate the mutated genes that link HPV infection and CRC carcinogenesis.

METHODS

Cancerous and adjacent noncancerous tissues were obtained from CRC patients. HPV antigen was measured by using the immunohistochemical (IHC) technique. The differentially expressed genes (DEGs) in HPV-positive and HPV-negative tumor tissues were measured by using TaqMan Array Plates. The target genes were validated with the qPCR method.

RESULTS

15 (31.9%) cases of CRC patients were observed to be HPV positive, in which HPV antigen was expressed in most tumor tissues rather than in adjacent noncancerous tissues. With TaqMan Array Plates analyses, we found that 39 differentially expressed genes (DEGs) were upregulated, while 17 DEGs were downregulated in HPV-positive CRC tissues compared with HPV-negative tissues. Four DEGs (MMP-7, MYC, WNT-5A, and AXIN2) were upregulated in tumor vs. normal tissues, or adenoma vs. normal tissue in TCGA, which was overlapped with our data. In the confirmation test, MMP-7, MYC, WNT-5A, and AXIN2 were upregulated in cancerous tissue compared with adjacent noncancerous tissue. MYC, WNT-5A, and AXIN2 were shown to be upregulated in HPV-positive CRC tissues when compared to HPV-negative tissues.

CONCLUSION

HPV-encoding genome may integrate into the tumor genomes that involved in multiple signaling pathways. Further genomic and proteomic investigation is necessary for obtaining a more comprehensive knowledge of signaling pathways associated with the CRC carcinogenesis.

Identification of key genes in non-small cell lung cancer by bioinformatics analysis

Background

Non-small cell lung cancer (NSCLC) is one of the most common malignant tumors in the world, and it has become the leading cause of death of malignant tumors. However, its mechanisms are not fully clear. The aim of this study is to investigate the key genes and explore their potential mechanisms involving in NSCLC.

Methods

We downloaded gene expression profiles GSE33532, GSE30219 and GSE19804 from the Gene Expression Omnibus (GEO) database and analyzed them by using GEO2R. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes were used for the functional and pathway enrichment analysis. We constructed the protein-protein interaction (PPI) network by STRING and visualized it by Cytoscape. Further, we performed module analysis and centrality analysis to find the potential key genes. Finally, we carried on survival analysis of key genes by GEPIA.

Results

In total, we obtained 685 DEGs. Moreover, GO analysis showed that they were mainly enriched in cell adhesion, proteinaceous extracellular region, heparin binding. KEGG pathway analysis revealed that transcriptional misregulation in cancer, ECM-receptor interaction, cell cycle and p53 signaling pathway were involved in. Furthermore, PPI network was constructed including 249 nodes and 1,027 edges. Additionally, a significant module was found, which included eight candidate genes with high centrality features. Further, among the eight candidate genes, the survival of NSCLC patients with the seven high expression genes were significantly worse, including CDK1, CCNB1, CCNA2, BIRC5, CCNB2, KIAA0101 and MELK. In summary, these identified genes should play an important role in NSCLC, which can provide new insight for NSCLC research.

Identifying candidate diagnostic markers for early stage of non-small cell lung cancer

We performed a series of bioinformatics analysis on a set of important gene expression data with 76 samples in early stage of non-small cell lung cancer, including 40 adenocarcinoma samples, 16 squamous cell carcinoma samples and 20 normal samples. In order to identify the specific markers for diagnosis, we compared the two subtypes with the normal samples respectively to determine the gene expression characteristics. Through the multi-dimensional scaling classification, we found that the samples were clustered well according to the disease cases. Based on the classification results and using empirical Bayes moderation and treat method, 486 important genes associated with the disease were identified. We constructed gene functions and gene pathways to verify our result and explain the pathogenicity factor and process. We generated a protein-protein interaction network based on the mutual interaction between the selected genes and found that the top thirteen hub genes were highly associated with lung cancer or some other cancers including five newly found genes through our method. The results of this study indicated that contrast on the gene expression between different subtypes and normal samples provides important information for the detection of non-small cell lung cancer and helps exploration of the disease pathogenesis.

Identification of key genes for predicting colorectal cancer prognosis by integrated bioinformatics analysis

Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.

Macrophage transcriptome modification induced by hypoxia and lactate

The production of lactate under hypoxic conditions or by cancer cells was reported to promote the M2 polarization of tumor-associated macrophages. However, the exact effect of lactate on macrophages, particularly under hypoxic conditions, has remained largely elusive. In the present study, an in-depth bioinformatics analysis of previously published transcriptome data of macrophages was performed. A total of 6, 101 and 764 upregulated genes were identified in the lactate, hypoxia and hypoxia-lactate groups, respectively, whereas 4, 41 and 588 genes were downregulated in the same respective groups. Furthermore, differentially expressed genes (DEGs) of the hypoxia and hypoxia-lactate groups were significantly enriched in the hypoxia-inducible factor 1 (HIF-1) signaling pathway and the Hedgehog pathway. Upregulation of the mTOR and Hedgehog pathways in the hypoxia-lactate group was identified by gene set enrichment analysis. Furthermore, a set of HIF-1 pathway-associated genes was identified to be positively correlated with hypoxia using weighted gene co-expression network analysis. Lactate was indicated to inhibit the cell cycle in a hypoxia-independent manner. The DEGs of the hypoxia and hypoxia-lactate groups, including C-C motif chemokine receptor type 1 and 5, were enriched in the cytokine-cytokine receptor interaction pathway. In conclusion, under normoxic conditions, lactate exerted a weak effect on macrophages, while the combination of lactate and hypoxia markedly promoted the M2-polarization of macrophages via the HIF-1, Hedgehog and mTOR pathways. Lactate and hypoxia may also contribute to the formation of the spatial structure of tumor niches by inhibiting the proliferation of resident macrophages and by regulating the recruitment of peripheral macrophages.

Identifying hub genes of clear cell renal cell carcinoma associated with the proportion of regulatory T cells by weighted gene co-expression network analysis

BACKGROUND

Numerous patients with clear cell renal cell carcinoma (ccRCC) experience drug resistance after immunotherapy. Regulatory T (Treg) cells may work as a suppressor for anti-tumor immune response.

PURPOSE

We performed bioinformatics analysis to better understand the role of Treg cells in ccRCC.

RESULTS

Module 10 revealed the most relevance with Treg cells. Functional annotation showed that biological processes and pathways were mainly related to activation of the immune system and the processes of immunoreaction. Four hub genes were selected: LCK, MAP4K1, SLAMF6, and RHOH. Further validation showed that the four hub genes well-distinguished tumor and normal tissues and were good prognostic biomarkers for ccRCC.

CONCLUSION

The identified hub genes facilitate our knowledge of the underlying molecular mechanism of how Treg cells affect ccRCC in anti-tumor immune therapy.

METHODS

The CIBERSORT algorithm was performed to evaluate tumor-infiltrating immune cells based on the Cancer Genome Atlas cohort. Weighted gene co-expression network analysis was conducted to explore the modules related to Treg cells. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein-protein interaction network was built. Samples from the International Cancer Genomics Consortium database was used as a validation set.

Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein-protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.

Identification of potential diagnostic and prognostic biomarkers for prostate cancer

Prostate cancer (PCa) is one of the most common malignant tumors worldwide. The aim of the present study was to determine potential diagnostic and prognostic biomarkers for PCa. The GSE103512 dataset was downloaded, and the differentially expressed genes (DEGs) were screened. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) analyses of DEGs were performed. The result of GO analysis suggested that the DEGs were mostly enriched in ‘carboxylic acid catabolic process’, ‘cell apoptosis’, ‘cell proliferation’ and ‘cell migration’. KEGG analysis results indicated that the DEGs were mostly concentrated in ‘metabolic pathways’, ‘ECM-receptor interaction’, the ‘PI3K-Akt pathway’ and ‘focal adhesion’. The PPI analysis results showed that Golgi membrane protein 1 (GOLM1), melanoma inhibitory activity member 3 (MIA3), ATP citrate lyase (ACLY) and G protein subunit β2 (GNB2) were the key genes in PCa, and the Module analysis revealed that they were associated with ‘ECM-receptor interaction’, ‘focal adhesion’, the ‘PI3K-Akt pathway’ and the ‘metabolic pathway’. Subsequently, the gene expression was confirmed using Gene Expression Profiling Interactive Analysis and the Human Protein Atlas. The results demonstrated that GOLM1 and ACLY expression was significantly upregulated (P<0.05) in PCa compared with that in normal tissues. Receiver operating characteristic and survival analyses were performed. The results showed that area under the curve values of these genes all exceeded 0.85, and high expression of these genes was associated with poor survival in patients with PCa. In conclusion, this study identified GOLM1 and ACLY in PCa, which may be potential diagnostic and prognostic biomarker of PCa.

Screening key genes and signaling pathways in colorectal cancer by integrated bioinformatics analysis

The aim of the present study was to identify potential key genes associated with the progression and prognosis of colorectal cancer (CRC). Differentially expressed genes (DEGs) between CRC and normal samples were screened by integrated analysis of gene expression profile datasets, including the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to identify the biological role of DEGs. In addition, a protein‑protein interaction network and survival analysis were used to identify the key genes. The profiles of GSE9348, GSE22598 and GSE113513 were downloaded from the GEO database. A total of 405 common DEGs were identified, including 236 down‑ and 169 upregulated. GO analysis revealed that the downregulated DEGs were mainly enriched in 'detoxification of copper ion' [biological process, (BP)], 'oxidoreductase activity, acting on CH‑OH group of donors, NAD or NADP as acceptor' [molecular function, (MF)] and 'brush border' [cellular component, (CC)]. Upregulated DEGs were mainly involved in 'nuclear division' (BP), 'snoRNA binding' (MF) and 'nucleolar part' (CC). KEGG pathway analysis revealed that DEGs were mainly involved in 'mineral absorption', 'nitrogen metabolism', 'cell cycle' and 'caffeine metabolism'. A PPI network was constructed with 268 nodes and 1,027 edges. The top one module was selected, and it was revealed that module‑related genes were mainly enriched in the GO terms 'sister chromatid segregation' (BP), 'chemokine activity' (MF), and 'condensed chromosome (CC)'. The KEGG pathway was mainly enriched in 'cell cycle', 'progesterone‑mediated oocyte maturation', 'chemokine signaling pathway', 'IL‑17 signaling pathway', 'legionellosis', and 'rheumatoid arthritis'. DNA topoisomerase II‑α (TOP2A), mitotic arrest deficient 2 like 1 (MAD2L1), cyclin B1 (CCNB1), checkpoint kinase 1 (CHEK1), cell division cycle 6 (CDC6) and ubiquitin conjugating enzyme E2 C (UBE2C) were indicated as hub genes. Furthermore, survival analysis revealed that TOP2A, MAD2L1, CDC6 and CHEK1 may serve as prognostic biomarkers in CRC. The present study provided insights into the molecular mechanism of CRC that may be useful in further investigations.

Identification of key genes and pathways in seminoma by bioinformatics analysis

Background: Seminoma accounts for the most part of cases of testicular germ cell tumor, which is the most common malignancy among males between ages 15 and 44 years. Understanding the molecular mechanism of tumorigenesis is important for better clinical diagnosis and treatment.

Purpose: We performed bioinformatics analysis to better understand seminoma at the genetic level and to explore potential candidate genes or molecules for diagnosis, treatment, and prognosis.

Methods: A gene expression profile (GSE8607), containing 40 seminoma samples and three healthy testes samples, was analyzed to identify differentially expressed genes (DEGs) associated with the occurrence of seminoma. Functional annotation was then performed using gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Cytoscape with Search Tool for the Retrieval of Interacting Genes was used to construct a protein–protein interaction (PPI) network of the DEGs and select hub genes. Moreover, validation of expression level and Kaplan–Meier analysis for overall survival were conducted to those hub genes.

Results: A total of 1,636 DEGs were identified between seminoma and healthy samples, including 701 up-regulated in seminoma that were enriched in the regulation of immune responses, defense responses, receptor activity, and signal transducer activity; 935 were down-regulated in seminoma and were associated with reproductive processes, kinase activity, and carbohydrate derivative binding. Five hub genes were selected from the PPI network according to the degree of connectivity: IL6, VEGFA, IL10, CCR5, and CXCR4. Among them, high expression levels of CCR5 and CXCR4 were associated with poor prognosis for seminoma patients. Four modules selected from the PPI network revealed that seminoma was connected with the Janus kinase-signal transducers and activators of transcription signaling pathway, chemokine signaling pathway, endocytosis, and cytokine–cytokine receptor interaction.

Conclusion: These identified DEGs and hub genes facilitate our knowledge of the underlying molecular mechanism of seminoma and have the potential to be used as diagnostic biomarkers or therapeutic targets for seminoma.

Comprehensive analysis of microarray expression profiles of circRNAs and lncRNAs with associated co-expression networks in human colorectal cancer

Increasing data demonstrate that circular RNAs (circRNAs) and long non-coding RNAs (lncRNAs) play important roles in tumorigenesis. However, the mechanisms in colorectal cancer (CRC) remain unclear. Here, hundreds of significantly expressed circRNAs, and thousands of lncRNAs as well as mRNAs were identified. By qRT-PCR, one abnormal circRNA, lncRNA, and three mRNAs were verified in 24 pairs of tissues and blood samples, respectively. Then, by GO analysis, we found that the gene expression profile of linear counterparts of upregulated circRNAs in human CRC tissues preferred positive regulation of GTPase activity, cellular protein metabolic process, and protein binding, while that of downregulated circRNAs of CRC preferred positive regulation of cellular metabolic process, acetyl-CoA metabolic process, and protein kinase C activity. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that p53 signaling pathway was an important pathway in upregulated protein-coding genes, whereas cyclic guanosine monophosphate-protein kinase G (cGMP-PKG) signaling pathway was the top enriched KEGG pathway for downregulated transcripts. Furthermore, lncRNA-mRNA co-expression analysis demonstrated that downregulated lncRNA uc001tma.3 was negatively with CDC45 and positively with ELOVL4, BVES, FLNA, and HSPB8, while upregulated lncRNA NR_110882 was positively with FZD2. In addition, lncRNA-transcription factor (TF) co-expression analysis showed that the most relevant TFs were forkhead box protein A1 (FOXA1), transcription initiation factor TFIID submint 7 (TAF7), and adenovirus early region 1A(E1A)-associated protein p300 (EP300). Our findings offer a fresh view on circRNAs and lncRNAs and provide the foundation for further study on the potential roles of circRNAs and lncRNAs in colorectal cancer.

Systematic identification of key genes and pathways in clear cell renal cell carcinoma on bioinformatics analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of adult renal neoplasm and has a poor prognosis owing to a limited understanding of the disease mechanisms. The aim of this study was to explore and identify the key genes and signaling pathways in ccRCC.

Methods

The GSE36895 gene expression profiles were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were then screened using software packages in R. After Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, a protein-protein interaction (PPI) network of DEGs was constructed with Cytoscape software, and submodules were subsequently analyzed using the MCODE plug-in.

Results

Twenty-nine ccRCC samples and 23 normal samples were incorporated into this study, and a total of 468 DEGs were filtered, consisting of 180 upregulated genes and 288 downregulated genes. The upregulated DEGs were significantly enriched in the immune response, response to wounding, inflammatory response, and response to hypoxia, whereas downregulated genes were mainly enriched in ion transport, anion transport, and monovalent inorganic cation transport biological processes (BPs). According to Molecular Complex Detection analysis in PPI, C1QA, C1QB, C1QC, CCND1 and EGF had higher degrees of connectivity and could participate in the majority of important pathways, such as cytokine-cytokine receptor interactions, the chemokine signaling pathway, and the complement and coagulation cascade pathways.

Conclusions

Our study suggests that C1QA, C1QB, C1QC, CCND1 and EGF may play key roles in the progression of ccRCC, which will be useful for future studies on the underlying mechanisms of ccRCC.

Discovery of Carboline Derivatives as Potent Antifungal Agents for the Treatment of Cryptococcal Meningitis

Clinical treatment of cryptococcal meningitis (CM) remains a significant challenge because of the lack of effective and safe drug therapies. Developing novel CM therapeutic agents with novel chemical scaffolds and new modes of action is of great importance. Herein, new β-hexahydrocarboline derivatives are shown to possess potent anticryptococcal activities. In particular, compound A4 showed potent in vitro and in vivo anticryptococcal activity with good metabolic stability and blood-brain barrier permeability. Compound A4 was orally active and could significantly reduce brain fungal burdens in a murine model of CM. Moreover, compound A4 could inhibit several virulence factors of Cryptococcus neoformans and might act by a new mode of action. Preliminary mechanistic studies revealed that compound A4 induced DNA double-stranded breaks and cell cycle arrest at the G2 phase by acting on the Cdc25c/CDK1/cyclin B pathway. Taken together, β-hexahydrocarboline A4 represents a promising lead compound for the development of next-generation CM therapeutic agents.

Identification of differentially expressed genes and enriched pathways in lung cancer using bioinformatics analysis

Lung cancer is the leading cause of cancer‑associated mortality worldwide. The aim of the present study was to identify the differentially expressed genes (DEGs) and enriched pathways in lung cancer by bioinformatics analysis, and to provide potential targets for diagnosis and treatment. Valid microarray data of 31 pairs of lung cancer tissues and matched normal samples (GSE19804) were obtained from the Gene Expression Omnibus database. Significance analysis of the gene expression profile was used to identify DEGs between cancer tissues and normal tissues, and a total of 1,970 DEGs, which were significantly enriched in biological processes, were screened. Through the Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, 77 KEGG pathways associated with lung cancer were identified, among which the Toll‑like receptor pathway was observed to be important. Protein‑protein interaction network analysis extracted 1,770 nodes and 10,667 edges, and identified 10 genes with key roles in lung cancer with highest degrees, hub centrality and betweenness. Additionally, the module analysis of protein‑protein interactions revealed that 'chemokine signaling pathway', 'cell cycle' and 'pathways in cancer' had a close association with lung cancer. In conclusion, the identified DEGs, particularly the hub genes, strengthen the understanding of the development and progression of lung cancer, and certain genes (including advanced glycosylation end‑product specific receptor and epidermal growth factor receptor) may be used as candidate target molecules to diagnose, monitor and treat lung cancer.

Up-regulation of fibroblast growth factor receptor 1 due to prenatal tobacco exposure can lead to developmental defects in new born

Introduction: Tobacco-smoking is one of the most important risk factor for preterm delivery, pregnancy loss, low birth weight, and fetal growth restriction. It is estimated that approximately 30% of growth-restricted neonates could be independently associated with maternal smoking.Methods: In this study, gene expression profile, GSE11798, was chosen from GEO database with an aim to perceive change in gene expression signature in new born due to maternal smoking. Enrichment analysis was performed to annotate differentially expressed genes (DEGs) through gene ontology and pathway analysis using DAVID. Protein-protein interactions and module detection of these DEGs were carried out using cytoscape v3.6.0. Thirty umbilical cord tissue samples from 15 smokers and 15 non-smokers pregnant women were included in this analysis.Results: Twenty-six differentially expressed genes (DEGs) between two groups were selected using GEO2R tool. The DEGs were observed to be participating in biological processes/pathways related to growth releasing hormone, angiogenesis, embryonic skeletal, and cardiac development. Fibroblast growth factor receptor-1 (FGFR1) was identified to be the hub node with 348 interacting partners, which regulates transcription, cell growth, differentiation, and apoptosis. The up-regulation of FGFR1 in umbilical cord tissue may lead to reproductive and developmental complications such as encephalocraniocutaneous lipomatosis, osteoglophonic dysplasia, and Pfeiffer syndrome in new-borns.Conclusion: The findings manifests the possibility of overcoming these adverse health effects in new born through FGFR1 modulating treatments during pregnancy.

Identification of the key genes and pathways in prostate cancer

Prostate cancer (PCa) is one of the most common malignancies in men globally. The aim of the present study was to identify the key genes and pathways involved in the occurrence of PCa. Gene expression profile (GSE55945) was downloaded from Gene Expression Omnibus, and the differentially expressed genes (DEGs) were identified. Subsequently, Gene ontology analysis, KEGG pathway analysis and protein-protein interaction (PPI) analysis of DEGs were performed. Finally, the identified key genes were confirmed by immunohistochemistry. The GO analysis results showed that the DEGs were mainly participated in cell cycle, cell division, cell development and cell junction. The KEGG pathway analysis showed that the DEGs were mainly enriched in proteoglycans in cancer, endocytosis, focal adhesion and hippo signaling pathway. The PPI analysis results showed that RPS21, FOXO1, BIRC5, POLR2H, RPL22L1 and NPM1 were the key genes involved in the occurrence of PCa, and the Module analysis indicated that the occurrence of PCa was associated with cell cycle, oocyte meiosis and ribosome biogenesis. IHC result showed that the expression of RPS21, BIRC5, POLR2H, RPL22L1 and NPM1 were significantly upregulated in PCa, while the expression of FOXO1 was significantly downregulated in PCa, matching with the bioinformatics analysis. Taken together, several key genes and pathways were identified involved in PCa, which might provide the potential biomarker for prognosis, diagnosis and drug targets.

Identification of key genes and pathways by bioinformatics analysis with TCGA RNA sequencing data in hepatocellular carcinoma

Improved insight into the molecular characteristics of hepatocellular carcinoma (HCC) is required to predict prognosis and to develop a new rationale for targeted therapeutic strategy. Bioinformatics methods, including functional enrichment and network analysis combined with survival analysis, are required to process a large volume of data to obtain further information on differentially expressed genes (DEGs). The RNA sequencing data related to HCC in The Cancer Genome Atlas (TCGA) database were analyzed to screen DEGs, which were separately submitted to perform gene enrichment analysis to identify gene sets and signaling pathways, and to construct a protein-protein interaction (PPI) network. Subsequently, hub genes were selected by the core level in the network, and the top hub genes were focused on gene expression analysis and survival analysis. A total of 610 DEGs were identified, including 444 upregulated and 166 downregulated genes. The upregulated DEGs were significantly enriched in the Gene Ontology analysis (GO): Cell division and in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway: Cell cycle, whereas the downregulated DEGs were enriched in GO: Negative regulation of growth and in the KEGG pathway: Retinol metabolism, with significant differences. Cyclin-dependent kinase (CDK)1 was selected as the top hub gene by the PPI network, which exhibited a similar expression trend with the data from the Gene Expression Omnibus (GEO) database. Survival analysis revealed a significantly negative correlation between CDK1 expression level and overall survival in the TCGA group (P<0.01) and the GEO group (P<0.01). Therefore, high-throughput TCGA data analysis appears to be an effective method for screening tumor molecular markers, and high expression of CDK1 is a prognostic factor for HCC.

Analysis of differentially expressed genes among human hair follicle-derived iPSCs, induced hepatocyte-like cells, and primary hepatocytes

Background

Differentiation of human induced pluripotent stem cells (hiPSCs) into hepatocytes has important clinical significance in providing a new stem cell source for cell therapy of terminal liver disease. The differential gene expression analysis of hiPSCs, induced hepatocyte-like cells (HLCs), and primary human hepatocytes (PHHs) provides valuable information for optimization of an induction scheme and exploration of differentiation mechanisms.

Methods

Human hair follicle-derived iPSCs (hHF-iPSCs) were induced in vitro by mimicking the environment of a developing liver for 19 days. Expression of specific proteins was determined by immunofluorescence staining; the function of HLCs in storage and metabolism was identified by detecting periodic acid–Schiff, indocyanine green, and low-density lipoprotein. Based on the transcriptomics data, the differential gene expression profiles of hHF-iPSCs, HLCs, and PHHs were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, FunRich, and network analysis methods.

Results

HLCs were able to express albumin (ALB), alpha-fetoprotein, CYP3A4, and CYP7A1, and exhibited matured liver cell functions such as glycogen synthesis and storage. Complement and coagulation cascades and metabolic pathways ranked top in the downregulated list of HLCs/PHHs, while the cell cycle ranked top in the upregulated list of HLCs/PHHs. In the protein–protein interaction network, according to the degree rankings, TOP2A, CDK1, etc. were the important upregulated differentially expressed genes (DEGs), while ALB, ACACB, etc. were the major downregulated DEGs in HLCs/PHHs; the module analysis indicated that CDCA8, AURKB, and AURKA were the top upregulated DEGs in HLCs/PHHs.

Conclusions

We presented the differences in gene expression among hHF-iPSCs, HLCs, and PHHs through transcriptome array data and provided new ideas for the optimization of induction.

Electronic supplementary material

The online version of this article (10.1186/s13287-018-0940-z) contains supplementary material, which is available to authorized users.

Identification of key pathways and biomarkers in sorafenib-resistant hepatocellular carcinoma using bioinformatics analysis

Hepatocellular carcinoma (HCC) is one of the most common malignant types of cancer, with a high mortality rate. Sorafenib is the sole approved oral clinical therapy against advanced HCC. However, individual patients exhibit varying responses to sorafenib and the development of sorafenib resistance has been a new challenge for its clinical efficacy. The current study identified gene biomarkers and key pathways in sorafenib-resistant HCC using bioinformatics analysis. Gene dataset GSE73571 was obtained from the Gene Expression Omnibus (GEO) database, including four sorafenib-acquired resistant and three sorafenib-sensitive HCC phenotypes. Differentially expressed genes (DEGs) were identified using the web tool GEO2R. Functional and pathway enrichment of DEGs were analyzed using the Database for Annotation, Visualization and Integrated Discovery and the protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape. A total of 1,319 DEGs were selected, which included 593 upregulated and 726 downregulated genes. Functional and pathway enrichment analysis revealed DEGs enriched in negative regulation of endopeptidase activity, cholesterol homeostasis, DNA replication and repair, coagulation cascades, insulin resistance, RNA transport, cell cycle and others. Eight hub genes, including kininogen 1, vascular cell adhesion molecule 1, apolipoprotein C3, alpha 2-HS glycoprotein, erb-b2 receptor tyrosine kinase 2, secreted protein acidic and cysteine rich, vitronectin and vimentin were identified from the PPI network. In conclusion, the present study identified DEGs and key genes in sorafenib-resistant HCC, which further the knowledge of potential mechanisms in the development of sorafenib resistance and may provide potential targets for early diagnosis and new treatments for sorafenib-resistant HCC.

Bioinformatical identification of key pathways and genes in human hepatocellular carcinoma after CSN5 depletion

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. It has been previously reported that CSN5 depletion is an effective method in human HCC. In the current study, we aimed to uncover gene signatures and key pathways during HCC. Gene expression profiles of GSE26485 were downloaded from GEO database. Totally, 101 differentially expressed genes (DEGs) were up-regulated and 146 ones were down-regulated. Biological processes (BP) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) analysis showed that the DEGs were mainly enriched in regulation of cell growth, oxidation-reduction process, mitotic cytokinesis, negative regulation of macroautophagy, endosome organization, lysosome, biosynthesis of antibiotics, small cell lung cancer and glutathione metabolism and so on (P < 0.05). Protein-protein interaction (PPI) network, Kaplan-Meier, log-rank method, western blot, immunohistochemistry and encyclopedia of DNA elements (ENCODE) analysis showed that CSN5 depletion took effects through down-regulation of SMAD5-related pathways which include EXO1, CENPA and NCAPG, resulting in the inactivation of H3K4me3 and H3K36me3. Those genes represent the promising targets for therapeutic intervention in HCC patients.

Analysis of potential genes and pathways associated with the colorectal normal mucosa-adenoma-carcinoma sequence

This study aimed to identify differentially expressed genes (DEGs) related to the colorectal normal mucosa-adenoma-carcinoma sequence using bioinformatics analysis. Raw data files were downloaded from Gene Expression Omnibus (GEO) and underwent quality assessment and preprocessing. DEGs were analyzed by the limma package in R software (R version 3.3.2). Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the DAVID online tool. In a comparison of colorectal adenoma (n = 20) and colorectal cancer (CRC) stage I (n = 31), II (n = 38), III (n = 45), and IV (n = 62) with normal colorectal mucosa (n = 19), we identified 336 common DEGs. Among them, seven DEGs were associated with patient prognosis. Five (HEPACAM2, ITLN1, LGALS2, MUC12, and NXPE1) of the seven genes presented a sequentially descending trend in expression with tumor progression. In contrast, TIMP1 showed a sequentially ascending trend. GCG was constantly downregulated compared with the gene expression level in normal mucosa. The significantly enriched GO terms included extracellular region, extracellular space, protein binding, and carbohydrate binding. The KEGG categories included HIF-1 signaling pathway, insulin secretion, and glucagon signaling pathway. We discovered seven DEGs in the normal colorectal mucosa-adenoma-carcinoma sequence that was associated with CRC patient prognosis. Monitoring changes in these gene expression levels may be a strategy to assess disease progression, evaluate treatment efficacy, and predict prognosis.

Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis

Rheumatoid arthritis (RA) and osteoarthritis (OA) comprise the most common forms of arthritis. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between RA and OA using a bioinformatics approach to elucidate their potential pathogenesis.The gene expression profiles of the GSE55457 datasets, originally produced through use of the high-throughput Affymetrix Human Genome U133A Array, were downloaded from the Gene Expression Omnibus (GEO) database. The GSE55457 dataset contains information from 33 samples, including 10 normal control (NC) samples, 13 RA samples, and 10 OA samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and associated molecular and biochemical pathways, respectively, for the identified DEGs, and a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software.GO and KEGG results suggested that several biological pathways (ie, "immune response," "inflammation," and "osteoclast differentiation") are commonly involved in the development of both RA and OA, whereas several other pathways (eg, "MAPK signaling pathway," and "ECM-receptor interaction") presented significant differences between these disorders.This study provides further insights into the underlying pathogenesis of RA and OA, which may facilitate the diagnosis and treatment of these diseases.

Integrated bioinformatics analysis reveals key candidate genes and pathways in breast cancer

Breast cancer (BC) is the leading malignancy in women worldwide, yet relatively little is known about the genes and signaling pathways involved in BC tumorigenesis and progression. The present study aimed to elucidate potential key candidate genes and pathways in BC. Five gene expression profile data sets (GSE22035, GSE3744, GSE5764, GSE21422 and GSE26910) were downloaded from the Gene Expression Omnibus (GEO) database, which included data from 113 tumorous and 38 adjacent non-tumorous tissue samples. Differentially expressed genes (DEGs) were identified using t-tests in the limma R package. These DEGs were subsequently investigated by pathway enrichment analysis and a protein-protein interaction (PPI) network was constructed. The most significant module from the PPI network was selected for pathway enrichment analysis. In total, 227 DEGs were identified, of which 82 were upregulated and 145 were downregulated. Pathway enrichment analysis results revealed that the upregulated DEGs were mainly enriched in ‘cell division’, the ‘proteinaceous extracellular matrix (ECM)’, ‘ECM structural constituents’ and ‘ECM-receptor interaction’, whereas downregulated genes were mainly enriched in ‘response to drugs’, ‘extracellular space’, ‘transcriptional activator activity’ and the ‘peroxisome proliferator-activated receptor signaling pathway’. The PPI network contained 174 nodes and 1,257 edges. DNA topoisomerase 2-a, baculoviral inhibitor of apoptosis repeat-containing protein 5, cyclin-dependent kinase 1, G2/mitotic-specific cyclin-B1 and kinetochore protein NDC80 homolog were identified as the top 5 hub genes. Furthermore, the genes in the most significant module were predominantly involved in ‘mitotic nuclear division’, ‘mid-body’, ‘protein binding’ and ‘cell cycle’. In conclusion, the DEGs, relative pathways and hub genes identified in the present study may aid in understanding of the molecular mechanisms underlying BC progression and provide potential molecular targets and biomarkers for BC.

Screening for implicated genes in colorectal cancer using whole‑genome gene expression profiling

To identify biologically relevant genes associated with the pathogenesis of colorectal cancer (CRC), genome wide expression profiles of 17 pairs of CRC tumor and adjacent tissues, previously published in a DNA microarray study, were analyzed. Cytoscape, String tools and DAVID tools were used to investigate the biological pathways encoded by the genes identified as being either upregulated or downregulated in CRC, to determine protein-protein interactions and to identify potential hub genes associated with CRC. As a result, a total of 3,264 genes were identified as being differentially expressed in CRC and adjacent tissues, including 1,594 downregulated and 1,670 upregulated genes. Furthermore, 306 genes were revealed to be clustered in a complex interaction network, and the top 20 hub genes in this network were determined by application of the Matthews Correlation Coefficient algorithm. In addition, the patterns of the expression levels of the 20 hub genes were investigated using reverse transcription-quantitative polymerase chain reaction. Gene Ontology analysis revealed that four of the 20 hub genes encoded small subunit processome components (UTP3 small subunit processome component; UTP14 small subunit processome component; UTP 18 small subunit processome component; and UTP20 small subunit processome component) and a further four encoded WD repeat domains (WD repeat-containing protein 3, WD repeat domain 12, WD repeat-containing protein 43 and WD repeat-containing protein 75). In conclusion, the present DNA microarray study identified genes involved in the pathogenesis of CRC. Furthermore, it was revealed that hub genes identified from among the total identified upregulated and downregulated genes in CRC encoding subunit processome components and WD repeat domains may represent novel target molecules for future treatments of CRC.

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids

It is hypothesized that there may be common characteristics between the genetic regulatory networks of different diseases. To identify these potential similarities, analysis of overlapping differentially expressed genes (DEGs) in several diseases, which are believed to be associated in traditional Chinese medicine (TCM) was performed in the present study. The gene expression profiles associated with depression, papillary thyroid carcinoma (PTC) and uterine fibroids (UF) were preliminarily analyzed using Gene Expression Omnibus 2R tools. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction network analysis of the overlapping DEGs in depression, PTC and UF was performed. The results indicated that multiple genes, including activating transcription factor 3 and WSC domain containing 2 and the phosphoinositide 3 kinase/protein kinase b signaling pathway and its downstream effectors may be common factors associated with depression, PTC and/or UF. The neuroendocrine functions of the hypothalamic-pituitary-ovarian axis and hypothalamic-pituitary-thyroid axis were also identified as being mutually associated with depression, PTC and/or UF. However, due to the limitations of DNA microassays, it is recommended that future studies take epigenetics into consideration. Further transcriptomic, methylomic and metabolomic analyses of depression, PTC and UF are also required to identify and elucidate the key associated biomarkers. In conclusion, the results of the current study shed light on the potential genetic interconnections between depression, PTC and UF, which may be beneficial for understanding their underlying coregulatory mechanisms and contributing to the development of homeotherapy based on bioinformatics prediction.

Exploring the key genes and pathways in enchondromas using a gene expression microarray

Enchondromas are the most common primary benign osseous neoplasms that occur in the medullary bone; they can undergo malignant transformation into chondrosarcoma. However, enchondromas are always undetected in patients, and the molecular mechanism is unclear. To identify key genes and pathways associated with the occurrence and development of enchondromas, we downloaded the gene expression dataset GSE22855 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in enchondromas. In total, 635 genes were identified as DEGs. Of these, 225 genes (35.43%) were up-regulated, and the remaining 410 genes (64.57%) were down-regulated. We identified the predominant gene ontology (GO) categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were significantly over-represented in the enchondromas samples compared with the control samples. Subsequently the top 10 core genes were identified from the protein-protein interaction (PPI) network. The enrichment analyses of the genes mainly involved in two significant modules showed that the DEGs were principally related to ribosomes, protein digestion and absorption, ECM-receptor interaction, focal adhesion, amoebiasis and the PI3K-Akt signaling pathway.Together, these data elucidate the molecular mechanisms underlying the occurrence and development of enchondromas and provide promising candidates for therapeutic intervention and prognostic evaluation. However, further experimental studies are needed to confirm these results.

Identification of key differentially expressed genes associated with non‑small cell lung cancer by bioinformatics analyses

Increasing evidence has indicated that the abnormal expressions of certain genes serve important roles in tumorigenesis, progression and metastasis. The aim of the present study was to explore the key differentially expressed genes (DEGs) between non-small cell lung cancer (NSCLC) and matched normal lung tissues by analyzing 4 different mRNA microarray datasets downloaded from the Gene Expression Omnibus (GEO) database. In improving the reliability of the bioinformatics analysis, the DEGs in each dataset that met the cut-off criteria (adjust P-value <0.05 and |log₂fold-change (FC)|>1) were intersected with each other, from which 195 were identified (consisting of 57 upregulated and 138 downregulated DEGs). The GO analysis results revealed that the upregulated DEGs were significantly enriched in various biological processes (BP), including cell cycle, mitosis and cell proliferation while the downregulated DEGs were significantly enriched in angiogenesis and response to drug and cell adhesion. The hub genes, including CCNB1, CCNA2, CEP55, PBK and HMMR, were identified based on the protein-protein interaction (PPI) network. The Kaplan-Meier survival analysis indicated that the high expression level of each of these hub genes correlates with poorer overall survival in all patients with NSCLC, which indicates that they may serve important roles in the progression of NSCLC. In conclusion, the DEGs and hub genes identified in the present study may contribute to the comprehensive understanding of the molecular mechanisms of NSCLC and may be used as diagnostic and prognostic biomarkers as well as molecular targets for the treatment of NSCLC.

Differential expression profiles of long noncoding RNA and mRNA in colorectal cancer tissues from patients with lung metastasis

Lungs are the most common extra-abdominal site of metastasis of colorectal cancer (CRC), in which long noncoding RNA (lncRNA) may serve a role. In the present study, a high-throughput microarray assay was performed to detect lncRNA expression and identify novel targets for further study of lung metastasis in CRC. In the CRC tissues from patients with lung metastasis, 7,632 lncRNA (3,574 upregulated and 4,058 downregulated) and 6,185 mRNA (3,394 upregulated and 2,791 downregulated) were detected to be differentially expressed with a fold change ≥2 and P<0.05 compared with the CRC tissues without metastasis. A total of six differentially regulated lncRNA were confirmed by reverse transcription-quantitative polymerase chain reaction in 20 pairs of CRC samples. Furthermore, gene ontology and pathway analysis were conducted to predict the possible roles of the identified mRNA. The upregulated mRNA were associated with cell division (biological processes), protein kinase B binding (molecular functions) and cellular components. The downregulated mRNA were associated with cell adhesion, platelet-derived growth factor binding and membrane components. Pathway analysis determined that the upregulated mRNA were associated with the Wnt signaling pathway in the CRC tissues from patients with lung metastasis, while the downregulated mRNA were associated with the phosphoinositide 3-kinase/Akt signaling pathway. The results of the present study suggested that differentially expressed lncRNA may be associated with lung metastasis and may provide insights into the biology and prevention of lung metastasis.

Focusing on long non-coding RNA dysregulation in newly diagnosed multiple myeloma

AIMS

Multiple myeloma (MM) is an incurable hematological cancer with a higher rate of relapse. Alterations in the function of long non-coding RNAs (lncRNAs) promote the progression and metastasis of cancer. We carry out this study to explore the expression profile of differently expressed lncRNAs in newly diagnosed MM.

MAIN METHODS

The Bone marrows we analyzed were obtained from five MM and five IDA patients (serving as controls). Arraystar Human LncRNA Array V4.0 was used to profile expression of lncRNAs and mRNAs. Gene ontology (GO) and pathway analysis were utilized to understand the biological roles of differently expressed genes, while Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for constructing the lncRNA-mRNA co-expression network. Quantitative polymerase chain reaction (qRT-PCR) was performed to confirm the expressions of dysregulated lncRNAs.

KEY FINDINGS

Bioinformatic analysis of the lncRNA expression identified >3000 dysregulated lncRNAs (difference ≥ 2-fold) in MM samples. GO and pathway analysis revealed that ECM-receptor and cell cycle pathway-related genes were significantly associated with MM. Four dysregulated lncRNAs were confirmed by qRT-PCR. Among them, the expression of ST3GAL6-AS1, LAMA5-AS1and RP11-175D17.3wereassociated with stage and risk status of MM. On the basis of GEO public database analysis, LAMA5-AS1 was related with an overall survival rate of MM patients.

SIGNIFICANCE

These results reveal the feasible functions of lncRNAs in pathogenesis of MM. Further studies are required to explore whether these lncRNAs could serve as candidate therapeutic targets and new molecular biomarkers for MM.

Investigation into the underlying molecular mechanisms of hypertensive nephrosclerosis using bioinformatics analyses

Hypertensive nephrosclerosis (HNS) is a major risk factor for end-stage renal disease. However, the underlying pathogenesis of HNS remains to be fully determined. The gene expression profile of GSE20602, which consists of 14 glomeruli samples from patients with HNS and 4 normal glomeruli control samples, was obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions and pathways of differentially expressed genes (DEGs). Pathway relation and co‑expression networks were constructed in order to identify key genes and signaling pathways involved in HNS. In total, 483 DEGs were identified to be associated with HNS, including 302 upregulated genes and 181 downregulated genes. Furthermore, GO analysis revealed that DEGs were significantly enriched in the small molecule metabolic process. In addition, pathway analysis also revealed that DEGs were predominantly involved in metabolic pathways. The tricarboxylic acid (TCA) cycle was identified as the hub pathway in the pathway relation network, whereas the sorbitol dehydrogenase (SORD) and cubulin (CUBN) genes were revealed to be the hub genes in the co‑expression network. The present study revealed that the SORD, CUBN and albumin genes as well as the TCA cycle and metabolic pathways are involved in the pathogenesis of HNS. The results of the present study may contribute to the determination of the molecular mechanisms underlying HNS, and provide insight into the exploration of novel targets for the diagnosis and treatment of HNS.

MicroRNA-125b-1-3p mediates intervertebral disc degeneration in rats by targeting teashirt zinc finger homeobox 3

The present study aimed to investigate the association between Teashirt zinc finger homeobox 3 (TSHZ3) and the nucleus pulposus (NP) of intervertebral discs in rats. TSHZ3 was identified from the differentially expressed micro (mi)RNAs in the expression profile of GSE63492 by identifying the overlapped target genes of microRNA (miR)-125b-1-3p across different databases. TSHZ3 small interfering RNA (siRNA) and an miR-125b-1-3p inhibitor were used for gene silencing and gene silencing efficiency was assessed by reverse transcription-polymerase chain reaction. Western blotting was performed to detect the cell cycle proteins cyclin D1 and B1 and the proteins associated with DNA damage in NP. The results revealed that in normal NPs, the expression of TSHZ3 increased following the inhibition of miR-125b-1-3p and in DNA damaged NPs, the expression of TSHZ3 was associated with the degree of DNA damage. The present study demonstrated that TSHZ3, as a target gene of miR-125b-1-3p, may serve a protective role in intervertebral disc degeneration and that this protective function may be inhibited by high levels of miR-125b-1-3p.

Identification of differential protein-coding gene expressions in early phase lung adenocarcinoma

BACKGROUND

The diagnosis of early phase lung adenocarcinoma (LADC) is associated with therapeutic strategy, effect, and survival time. However, the sensitive biomarkers of early phase LADC are still unclear. This study aimed to identify protein-coding genes that can be used as biomarkers of early stage LADC.

METHODS

Gene microarray analysis was performed to identify key hub genes that show different expression in lung adenocarcinoma compared to normal tissues. The microarray data of lung adenocarcinoma in stages IA, IB, IIA, IIB, and normal tissues (GSE10072) were downloaded from a free online database, Gene Expression Omnibus (GEO).

RESULTS

A total of 572 differentially expressed genes (DEGs) were identified between early phase lung adenocarcinoma and normal tissues using R software. Database for Annotation, Visualization and Integrated Discovery online tools were used to obtain Gene Ontology analysis and the Kyoto Encyclopedia of Genes and Genomes was used to analyze DEGs. Cytoscape software was used to express the protein-protein interaction network. We found that some cancer-related Gene Ontology terms and pathways (e.g. cell adhesion, cell surface receptor signaling pathway, PI3K-Akt signaling pathway) were significantly enriched in DEGs.

CONCLUSION

Protein-coding genes JUN, FYN, CAV1, and SFN may play vital roles in the progress of early-stage lung adenocarcinoma. Consequently, through bioinformatics analysis, the key genes could be established to provide more potential references for the therapeutic targets of lung adenocarcinoma.

Identification of Key Genes and Pathways in Tongue Squamous Cell Carcinoma Using Bioinformatics Analysis

BACKGROUND Tongue squamous cell carcinoma (TSCC) is a major type of oral cancers and has remained an intractable cancer over the past decades. The aim of this study was to identify differentially expressed genes (DEGs) during TSCC and reveal their potential mechanisms. MATERIAL AND METHODS The gene expression profiles of GSE13601 were downloaded from the GEO database. The GSE13601 dataset contains 57 samples, including 31 tongue SCC samples and 26 matched normal mucosa samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed; Cytoscape software was used for the protein-protein interaction (PPI) network and module analysis of the DEGs. RESULTS We identified a total of 1,050 upregulated DEGs (uDEGs) and 702 downregulated DEGs (dDEGs) of TSCC. The GO analysis results showed that uDEGs were significantly enriched in the following biological processes (BP): signal transduction, positive or negative regulation of cell proliferation, and negative regulation of cell proliferation. The dDEGs were significantly enriched in the following biological processes: signal transduction, cell adhesion, and apoptotic process. The KEGG pathway analysis showed that uDEGs were enriched in metabolic pathways, pathways in cancer, and PI3K-Akt signaling pathway, while the dDEGs were enriched in focal adhesion and ECM-receptor interaction. The top centrality hub genes RAC1, APP, EGFR, KNG1, AGT, and HRAS were identified from the PPI network. Module analysis revealed that TSCC was associated with significant pathways, including neuroactive ligand-receptor interaction, calcium signaling pathway, and chemokine signaling pathway. CONCLUSIONS The present study identified key genes and signal pathways, which deepen our understanding of the molecular mechanisms of carcinogenesis and development of the disease, and might be used as diagnostic and therapeutic molecular biomarkers for TSCC.

Microarray analysis of an synthetic α-synuclein induced cellular model reveals the expression profile of long non-coding RNA in Parkinson's disease

Long non-coding RNAs (lncRNAs) are a new research focus that are reported to influence the pathogenetic process of neurodegenerative disorders. To uncover new disease-associated genes and their relevant mechanisms, we carried out a gene microarray analysis based on a Parkinson's disease (PD) in vitro model induced by α-synuclein oligomers. This cellular model induced by 25 μmol/L α-synuclein oligomers has been confirmed to show the stable, transmissible neurotoxicity of α-synuclein, a typical PD pathological marker. And several differentially expressed lncRNAs and mRNAs were identified in this model, such as G046036, G030771, AC009365.4, RPS14P3, CTB-11I22.1, and G007549. Subsequent ceRNA analysis determined the potential relationships between these lncRNAs and their associated mRNAs and microRNAs. The results of the present study widen our horizon of PD susceptibility genes and provide new pathways towards efficient diagnostic biomarkers and therapeutic targets for PD.

Primary tumor resection in stage IV unresectable colorectal cancer: what has changed?

Most current guidelines do not recommend primary tumor resection in stage IV unresectable colorectal cancer. Rapid chemotherapy development over the last decade has substantially changed the decision making. However, results of recently published trials and meta-analyses suggest that primary tumor resection may in fact be beneficial, principally in terms of prolonged survival. Additional factors, such as use of minimally invasive approach or protocols of enhanced recovery after surgery, affect clinical outcomes as well, but are often neglected when discussing the state of the art in this area. There are still no randomized studies determining the legitimacy of upfront surgery in asymptomatic patients. Also, quality of life also plays an important role in choosing appropriate treatment. Having said that, there is no data that would prove whether primary tumor resection has an advantage on that issue. With all the uncertainty, currently decision making in unresectable stage IV colorectal cancer is primarily up to clinicians' knowledge, common sense and patients' preferences.

Prediction and Validation of Hub Genes Associated with Colorectal Cancer by Integrating PPI Network and Gene Expression Data

Although hundreds of colorectal cancer- (CRC-) related genes have been screened, the significant hub genes still need to be further identified. The aim of this study was to identify the hub genes based on protein-protein interaction network and uncover their clinical value. Firstly, 645 CRC patients' data from the Tumor Cancer Genome Atlas were downloaded and analyzed to screen the differential expression genes (DEGs). And then, the Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis was performed, and PPI network of the DEGs was constructed by Cytoscape software. Finally, four hub genes (CXCL3, ELF5, TIMP1, and PHLPP2) were obtained from four subnets and further validated in our clinical setting and TCGA dataset. The results showed that mRNA expression of CXCL3, ELF5, and TIMP1 was increased in CRC tissues, whereas PHLPP2 mRNA expression was decreased. More importantly, high expression of CXCL3, ELF5, and TIMP1 was significantly associated with lymphatic invasion, distance metastasis, and advanced tumor stage. In addition, a shorter overall survival was observed in patients with increased CXCL3, TIMP1, and ELF5 expression and decreased PHLPP2 expression. In conclusion, the four hub genes screened by our strategy could serve as novel biomarkers for prognosis prediction of CRC patients.

A transcriptome-based protein network that identifies new therapeutic targets in colorectal cancer

Background

Colon cancer occurrence is increasing worldwide, making it the third most frequent cancer. Although many therapeutic options are available and quite efficient at the early stages, survival is strongly decreased when the disease has spread to other organs. The identification of molecular markers of colon cancer is likely to help understanding its course and, eventually, to uncover novel genes to be targeted by drugs. In this study, we compared gene expression in a set of 95 human colon cancer samples to that in 19 normal colon mucosae, focusing on 401 genes from 5 selected pathways (Apoptosis, Cancer, Cholesterol metabolism and lipoprotein signaling, Drug metabolism, Wnt/beta-catenin). Deregulation of mRNA levels largely matched that of proteins, leading us to build in silico protein networks, starting from mRNA levels, to identify key proteins central to network activity.

Results

Among the analyzed genes, 10.5% (42) had no reported link with colon cancer, including the SFRP1, IGF1 and ADH1B (down), and MYC and IL8 (up), whose encoded proteins were most interacting with other proteins from the same or even distinct networks. Analyzing all pathways globally led us to uncover novel functional links between a priori unrelated or rather remotely connected pathways, such as the Drug metabolism and the Cancer pathways or, even more strikingly, between the Cholesterol metabolism and lipoprotein signaling and the Cancer pathways. In addition, we analyzed the responsiveness of some of the deregulated genes essential to network activities, to chemotherapeutic agents used alone or in presence of Lovastatin, a lipid-lowering drug. Some of these treatments could oppose the deregulations occurring in cancer samples, including those of the CHECK2, CYP51A1, HMGCS1, ITGA2, NME1 or VEGFA genes.

Conclusions

Our network-based approach allowed discovering genes not previously known to play regulatory roles in colon cancer. Our results also showed that selected drug treatments might revert the cancer-specific deregulation of genes playing prominent roles within the networks operating to maintain colon homeostasis. Among those genes, some could constitute novel testable targets to eliminate colon cancer cells, either directly or, potentially, through the use of lipid-lowering drugs such as statins, in association with selected anticancer drugs.

Electronic supplementary material

The online version of this article (10.1186/s12864-017-4139-y) contains supplementary material, which is available to authorized users.

Exploring the key genes and pathways of osteosarcoma with pulmonary metastasis using a gene expression microarray

Osteosarcoma is a common and highly malignant tumour in children and teenagers that is characterized by drug resistance and high metastatic potential. Patients often develop pulmonary metastasis and have a low survival rate. However, the mechanistic basis for pulmonary metastasis remains unclear. To identify key gene and pathways associated with pulmonary metastasis of osteosarcoma, the authors downloaded the gene expression dataset GSE85537 and obtained the differentially expressed genes (DEGs) by analyzing high-throughput gene expression in primary tumours and lung metastases. Subsequently, the authors performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses and a protein-protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In total, 2,493 genes were identified as DEGs. Of these, 485 genes (19.45%) were upregulated, and the remaining 2,008 genes (80.55%) were downregulated. The authors identified the predominant GO categories and KEGG pathways that were significantly over-represented in the metastatic OS samples compared with the non-metastatic OS samples. A PPI network was constructed, and the results indicated that ALB, EGFR, INS, IL6, CDH1, FYN, ERBB2, IL8, CXCL12 and RAC2 were the top 10 core genes. The enrichment analyses of the genes involved in the top three significant modules demonstrated that the DEGs were principally related to neuroactive ligand-receptor interaction, the Rap1 signaling pathway, and protein digestion and absorption. Together, these data elucidated the molecular mechanisms of OS patients with pulmonary metastasis and provide potential therapeutic targets. However, further experimental studies are needed to confirm these results.

Portal vein ligation alters coding and noncoding gene expression in rat livers

Portal vein occlusion increases the resectability of initially unresectable liver cancer by inducing hypertrophy in non-occluded liver lobes. However, the mechanisms of how portal vein occlusion induces hepatic hypertrophy remain unclear. A cDNA microarray was used to identify the gene expression signatures of ligated (LLLs) and nonligated liver lobes (NLLLs) at different time points after portal vein ligation (PVL). The results of a bioinformatics analysis revealed that LLLs and NLLLs displayed different gene expression profiles. Moreover, the expression levels of both coding and noncoding RNA were different between LLLs and NLLLs at different time points after PVL. A series test of cluster analysis revealed that the No. 22 and No. 5 expression patterns, which showed altered expression at 24 h and maintained this altered expression over the following 14 days, had the lowest P values and the highest number of differentially expressed genes in both the LLLs and NLLLs. The results of a GO analysis showed the activation of hypoxia pathways in LLLs and the activation of cell proliferation and cell-cycle pathways in NLLLs, suggesting the involvement of these pathways in PVL-induced hepatic hypertrophy and regeneration. These results provide insight into the molecular mechanisms underlying hepatic hypertrophy and regeneration induced by portal vein occlusion, and they identify potential targeting pathways that can promote the clinical application of PVL in liver cancer therapy.

Identification of Driver Genes and Key Pathways of Pediatric Brain Tumors and Comparison of Molecular Pathogenesis Based on Pathologic Types

OBJECTIVE

This study is to identify pediatric brain tumors (PBT) driver genes and key pathways to detect the expression of the driver genes and also to clarify the relationship between patients' prognosis and expression of driver genes.

METHODS

The gene expression profile of GSE50161 was analyzed to identify the differentially expressed genes (DEGs) between tumor tissue and the normal tissue. Gene ontology, Kyoto Encyclopedia of Genes and Genomes analysis, and protein-protein interaction network analysis were conducted to identify the enrichment functions, pathways, and hub genes. After hub genes were identified, quantitative reverse transcription polymerase chain reaction was used to confirm the differential expression of these hub genes. Survival data of 325 patients' were analyzed to clarify the relationship between prognosis and expression levels of the mutual hub genes.

RESULTS

Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis showed that there were 13 common functions and 3 common pathways which were upregulated or downregulated among the 4 groups. Mutual hub genes were somatostatin (SST), glutamate decarboxylase 2 (GAD2), and single copy human parvalbumin gene (PVALB). The expression of SST, GAD2, and PVALB in glioma cells significantly decreased compared with normal glial cells (P < 0.05). In addition, survival analysis showed a favorable progression-free and overall survival in patients with glioma with SST, GAD2, and PVALB high expression (P < 0.05).

CONCLUSIONS

SST, GAD2, and PVALB significantly decrease in glioma cells compared with normal glial cells. Survival analysis suggests that patients with high-expressed SST, GAD2, and PVALB have a longer overall and progression-free survival. The differential expressed genes identified in this study provide novel targets for diagnosis and treatment.

Expression profiling of colorectal cancer cells reveals inhibition of DNA replication licensing by extracellular calcium

Colorectal cancer is one of the most common cancers in industrialised societies. Epidemiological studies, animal experiments, and randomized clinical trials have shown that dietary factors can influence all stages of colorectal carcinogenesis, from initiation through promotion to progression. Calcium is one of the factors with a chemoprophylactic effect in colorectal cancer. The aim of this study was to understand the molecular mechanisms of the anti-tumorigenic effects of extracellular calcium ([Ca²⁺]_o) in colon cancer cells.

Gene expression microarray analysis of colon cancer cells treated for 1, 4, and 24 h with 2 mM [Ca²⁺]_o identified significant changes in expression of 1571 probe sets (ANOVA, p < 10^− 5). The main biological processes affected by [Ca²⁺]_o were DNA replication, cell division, and regulation of transcription. All factors involved in DNA replication-licensing were significantly downregulated by [Ca²⁺]_o. Furthermore, we show that the calcium-sensing receptor (CaSR), a G protein-coupled receptor is a mediator involved in this process. To test whether these results were physiologically relevant, we fed mice with a standard diet containing low (0.04%), intermediate (0.1%), or high (0.9%) levels of dietary calcium. The main molecules regulating replication licensing were inhibited also in vivo, in the colon of mice fed high calcium diet.

We show that among the mechanisms behind the chemopreventive effect of [Ca²⁺]_o is inhibition of replication licensing, a process often deregulated in neoplastic transformation. Our data suggest that dietary calcium is effective in preventing replicative stress, one of the main drivers of cancer and this process is mediated by the calcium-sensing receptor.

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Extracellular calcium inhibits cell division, DNA replication, in colon cancer cells.

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Extracellular calcium inhibits replication licensing in vitro and in vivo.

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The calcium-sensing receptor is a critical mediator of this process.