Gene expression assays

Identification of Significant Genes in Lung Cancer of Nonsmoking Women via Bioinformatics Analysis

Background

The aim of this study was to identify potential key genes, proteins, and associated interaction networks for the development of lung cancer in nonsmoking women through a bioinformatics approach.

Methods

We used the GSE19804 dataset, which includes 60 lung cancer and corresponding paracancerous tissue samples from nonsmoking women, to perform the work. The GSE19804 microarray was downloaded from the GEO database and differentially expressed genes were identified using the limma package analysis in R software, with the screening criteria of p value < 0.01 and ∣log₂ fold change (FC) | >2.

Results

A total of 169 DEGs including 130 upregulated genes and 39 downregulated were selected. Gene Ontology and KEGG pathway analysis were performed using the DAVID website, and protein-protein interaction (PPI) networks were constructed and the hub gene module was screened through STING and Cytoscape.

Conclusions

We obtained five key genes such as GREM1, MMP11, SPP1, FOSB, and IL33 which were strongly associated with lung cancer in nonsmoking women, which improved understanding and could serve as new therapeutic targets, but their functionality needs further experimental verification.

In Silico Identification of Crucial Genes and Specific Pathways in Hepatocellular Cancer

Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Patients suffering from HCC are usually diagnosed during an advanced stage, which limits the effectiveness of treatment. This phenomenon has led to an urgent need to discover promising HCC diagnostic biomarkers and to identify novel targets for HCC treatment. Materials and Methods: In this study, the gene expression profiles of the GSE45436 participants were downloaded from the Gene Expression Omnibus database. The HCC differentially expressed genes (HCC_DEGs) were identified through a comparison with healthy controls. The Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed by DAVID, a free website used for annotating genes. Next, we used STRING, an online website, to identify likely protein-protein interactions among the DEGs. Cytoscape software was utilized to construct a protein-protein interaction network. MCODE, a plug-in of the Cytoscape software, was used for a module analysis. Finally, we used the Gene Expression Profiling Interactive Analysis website to determine the module genes' effects on overall survival. Results: A total of 313 genes were identified as differentially expressed, which comprised 118 upregulated genes and 195 downregulated genes. We used these data to identify 67 module genes. These were further verified using The Cancer Genome Atlas database resulting in 57 that remained statistically significant. Foremost, we identified one significant gene, DEP domain-containing protein 1B (DEPDC1B), which should be investigated for its usefulness as a new biomarker for diagnoses and prognoses. Conclusion: To our knowledge, DEPDC1B has not previously been reported as being associated with HCC. These results suggest that in silico methods, such as those employed, can provide valuable and even unique candidate biomarkers for further evaluation.

Construction of a competitive endogenous RNA network and analysis of potential regulatory axis targets in glioblastoma

BACKGROUND

Glioblastoma is the most common primary malignant brain tumor. Because of the limited understanding of its pathogenesis, the prognosis of glioblastoma remains poor. This study was conducted to explore potential competing endogenous RNA (ceRNA) network chains and biomarkers in glioblastoma by performing integrated bioinformatics analysis.

METHODS

Transcriptome expression data from The Cancer Genome Atlas database and Gene Expression Omnibus were analyzed to identify differentially expressed genes between glioblastoma and normal tissues. Biological pathways potentially associated with the differentially expressed genes were explored by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, and a protein-protein interaction network was established using the STRING database and Cytoscape. Survival analysis using Gene Expression Profiling Interactive Analysis was based on the Kaplan-Meier curve method. A ceRNA network chain was established using the intersection method to align data from four databases (miRTarBase, miRcode, TargetScan, and lncBace2.0), and expression differences and correlations were verified by quantitative reverse-transcription polymerase chain reaction analysis and by determining the Pearson correlation coefficient. Additionally, an MTS assay and the wound-healing and transwell assays were performed to evaluate the effects of complement C1s (C1S) on the viability and migration and invasion abilities of glioblastoma cells, respectively.

RESULTS

We detected 2842 differentially expressed (DE) mRNAs, 2577 DE long non-coding RNAs (lncRNAs), and 309 DE microRNAs (miRNAs) that were dysregulated in glioblastoma. The final ceRNA network consisted of six specific lncRNAs, four miRNAs, and four mRNAs. Among them, four DE mRNAs and one DE lncRNA were correlated with overall survival (p < 0.05). C1S was significantly correlated with overall survival (p= 0.015). In functional assays, knockdown of C1S inhibited the proliferation and invasion of glioblastoma cell lines.

CONCLUSIONS

We established four ceRNA networks that may influence the occurrence and development of glioblastoma. Among them, the MIR155HG/has-miR-129-5p/C1S axis is a potential marker and therapeutic target for glioblastoma. Knockdown of C1S inhibited the proliferation, migration, and invasion of glioblastoma cells. These findings clarify the role of the ceRNA regulatory network in glioblastoma and provide a foundation for further research.

Identifying FBLN1 (Gene ID: 2192) as a Potential Melanoma Biomarker for Melanoma based on an Analysis of microRNA Expression Profiles in the GEO and TCGA Databases

Aims: We analyzed and compared the gene expression profiles (GSE92763) from normal melanocytes with malignant melanoma cell lines to identify genes that were differentially expressed that could serve as potential biomarkers for melanoma diagnosis. Materials and Methods: Gene expression profiles from the GSE92763 dataset were downloaded from the Gene Expression Omnibus (GEO) database. By comparing normal human melanocytes with multiple melanoma cell lines we identified 127 differentially expressed genes whose expression was altered. These data were used to identify hub genes associated with protein-protein interaction networks using Cytoscape software. To explore the biological functions of the aforementioned hub genes, we utilized the clusterProfiler package in R studio to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We then used the Gene Expression Profiling Interactive Analysis (GEPIA) website to determine the association of these hub genes with overall survival (OS). In addition, we utilized the Oncomine and Cancer Cell Line Encyclopedia (CCLE) databases to further analyze and compare the expression of these key genes associated with melanoma with other tumor types. Results: The hub genes included three upregulated and seven downregulated genes, which were linked with extracellular junctions, migration, paracrine and proliferation functions based on GO. In addition, we performed a confirmatory analysis of the hub genes using The Cancer Genome Atlas (TCGA) database. This analysis revealed that the expression of the Fibulin 1 (FBLN1; gene ID: 2192) gene was significantly downregulated in melanomas, and that its expression level in melanoma patients was significantly associated with OS with high expressors having better OS (log-rank p = 0.0034, hazard ratio = 1.5, p = 0.0036). We further analyzed the expression of FBLN1 in melanoma using the TCGA and Oncomine databases, and confirmed that FBLN1 is expressed at lower levels than in other cells (p = 2.03E^-15, t = -15.586). FBLN1 has extremely high DNA copy number and low messenger RNA expression in melanoma cell lines according to the CCLE analysis. Conclusion: These results suggest that FBLN1 expression may be utilized as a biomarker and essential prognostic factor for melanoma; as well as provide an important theoretical basis for the development of melanoma treatments.

Importance of Mitochondrial-Related Genes in Dilated Cardiomyopathy Based on Bioinformatics Analysis

We designed this study to identify potential key protein interaction networks, genes, and correlated pathways in dilated cardiomyopathy (DCM) via bioinformatics methods. We selected the GSE3586 microarray dataset, consisting of 15 dilated cardiomyopathic heart biopsy samples and 13 nonfailing heart biopsy samples. Initially, the GSE3586 dataset was downloaded and was analyzed with the limma package to identify differentially expressed genes (DEGs). A total of 172 DEGs consisting of 162 upregulated genes and ten downregulated genes in DCM were selected by the criterion of adjusted Pvalues less than 0.01 and the log2-fold change of 0.6 or greater. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to view the biological processes, cellular components, molecular function, and KEGG pathways of the DEGs. Next, protein-protein interactions were constructed, and the hub protein modules were identified. Then we selected the key genes DLD, UQCRC2, DLAT, SUCLA2, ATP5A1, PRDX3, FH, SDHD, and NDUFV1, which are involved in a wide range of biological activities, such as the citrate cycle, oxidation-reduction processes and cellular respiration, and energy derivation by oxidation of organic compounds in mitochondria. Finally, we found that currently there are no related gene-targeting drugs after exploring the predicted interactions between key genes and drugs, and transcription factors. In conclusion, our study provides greater understanding of the pathogenesis and underlying molecular mechanisms in DCM. This contributes to the exploration of potential gene therapy targets.

Identification of Potential Hub Genes and Signal Pathways Promoting the Distinct Biological Features of Cord Blood-Derived Endothelial Progenitor Cells Via Bioinformatics

Background: Numerous studies, ranging from the alleviation of tissue ischemia to the assessment of cancer prognosis, have demonstrated the fundamental biological differences between human umbilical cord blood-derived endothelial progenitor cells (CB-EPCs) and adult peripheral blood-derived endothelial progenitor cells (PB-EPCs). However, the underlying molecular mechanisms that produce these differences are not clear.The purpose of this study was to identify potential hub genes, key protein interactive networks, and correlated signal pathways unique to CB-EPC biology via bioinformatic methods. Materials and Methods: We selected the microarray dataset GSE39763 and identified the differentially expressed genes (DEGs) using the "limma" package in the RStudio software. These DEGs were annotated by gene ontology enrichment analyses and signal pathway analyses. A protein-protein interaction (PPI) analysis was then performed to construct PPI networks and identify a hub protein module. We further validated candidate DEGs from the selected module in the gene expression profiling interactive analysis (GEPIA) database because the DEGs were enriched in cancer pathways. Results: Setting an adjusted p-value <0.01 and |Log₂ fold change (FC)| ≥ 2 as cutoff criteria, a total of 346 DEGs, including 314 upregulated genes and 32 downregulated genes in CB-EPCs, were identified. Expression of the genes encoding the AT-Hook Containing Transcription Factor 1 (AHCTF1), the Cancer Susceptibility Candidate 5 (CASC5), the Centromere Protein C (CENPC), the Centromere Protein E (CENPE), the Centromere Protein F (CENPF), the NUF2 Component of NDC80 Kinetochore Complex (NUF2), the RAN-Binding Protein 2 (RANBP2), the Shugoshin-like 2 (SGOL2), the Structural Maintenance of Chromosomes 3 (SMC3), and the Spindle Apparatus Coiled-Coil Protein 1 (SPDL1) proteins were specifically associated with CB-EPCs. Except for CENPC, the other nine genes' expression are all associated with a poorer overall survival rate in cancers. The expression levels of the CENPF and NUF2 genes in tumor patients were significantly higher than those in the controls. Conclusion: The CB-EPCs express genes with greater potential for proliferation and increased migration compared to PB-EPCs; in this regard they are similar to cancer cells.

Identification of Potential Key Genes and Pathways in Early-Onset Colorectal Cancer Through Bioinformatics Analysis

This study was designed to identify the potential key protein interaction networks, genes, and correlated pathways in early-onset colorectal cancer (CRC) via bioinformatics methods. We selected microarray data GSE4107 consisting 12 patient's colonic mucosa and 10 healthy control mucosa; initially, the GSE4107 were downloaded and analyzed using limma package to identify differentially expressed genes (DEGs). A total of 131 DEGs consisting of 108 upregulated genes and 23 downregulated genes of patients in early-onset CRC were selected by the criteria of adjusted P values <.01 and |log2 fold change (FC)| ≥ 2. The gene ontology functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were accomplished to view the biological process, cellular components, molecular function, and the KEGG pathways of DEGs. Finally, protein-protein interactions (PPIs) were constructed, and the hub protein module was identified. Genes such as ACTA2, ACTG2, MYH11, CALD1, MYL9, TPM2, and LMOD1 were strongly implicated in CRC. In summary, in this study, we indicated that molecular mechanisms were involved in muscle contraction and vascular smooth muscle contraction signaling pathway, which improve our understanding of CRC and could be used as new therapeutic targets for CRC.

Transcriptome analysis of peripheral blood mononuclear cells from chronic hepatitis B and hepatocellular carcinoma patients: a network-based attitude

Aim: The aim of the study was constructing a protein–protein interaction network for chronic hepatitis B (CHB) and hepatocellular carcinoma (HCC) patients. Materials & methods: Comprehensive gene expression profile of peripheral blood mononuclear cells of CHB and HCC were obtained from Gene Expression Omnibus/NCBI database. Differentially expressed genes (DEGs) of samples were analyzed using GEO2R web application. Results: The majority of DEGs in both CHB and HCC has been enriched in immune system responses. However, there was a significant disparity between the enrichment of these genes (especially genes associated with Toll-like receptor-and-TNF) in CHB-HCC compared with normal-CHB. Conclusion: The transcriptome properties of peripheral blood mononuclear cells are changed in patients with HBV-HCC. The immune response genes are the most deregulated genes in HCC patients.

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Use of Reporter Genes in the Generation of Vaccinia Virus-Derived Vectors

Vaccinia virus (VACV) is one of the most extensively-studied viruses of the Poxviridae family. It is easy to genetically modify, so it has become a key tool for many applications. In this context, reporter genes facilitate the study of the role of foreign genes introduced into the genome of VACV. In this review, we describe the type of reporter genes that have been used to generate reporter-expressing VACV and the applications of the recombinant viruses obtained. Reporter-expressing VACV are currently employed in basic and immunology research, in the development of vaccines and cancer treatment.

Investigating cell death mechanisms in amyotrophic lateral sclerosis using transcriptomics

Amyotrophic lateral sclerosis (ALS) is a motor neuron disease characterized by degeneration and loss of upper and lower motor neurons from the motor cortex, brainstem and spinal cord although evidence is suggesting that there is further involvement of other cell types in the surrounding tissue. Transcriptomic analysis by gene expression profiling using microarray technology has enabled the determination of patterns of cell death in the degenerating tissues. This work has examined gene expression at the level of the tissue and individual cell types in both sporadic and familial forms of the disease. In addition, further studies have examined the differential vulnerability of neuronal cells in different regions of the central nervous system. Model systems have also provided further information to help unravel the mechanisms that lead to death of the motor neurons in disease and also provided novel insights. In this review we shall describe the methods that have been used in these investigations and describe how they have contributed to our knowledge of the cell death mechanisms in ALS.

Analysis of the transcriptome in molecular epidemiology studies

The human transcriptome is complex, comprising multiple transcript types, mostly in the form of non-coding RNA (ncRNA). The majority of ncRNA is of the long form (lncRNA, ≥ 200 bp), which plays an important role in gene regulation through multiple mechanisms including epigenetics, chromatin modification, control of transcription factor binding, and regulation of alternative splicing. Both mRNA and ncRNA exhibit additional variability in the form of alternative splicing and RNA editing. All aspects of the human transcriptome can potentially be dysregulated by environmental exposures. Next-generation RNA sequencing (RNA-Seq) is the best available methodology to measure this although it has limitations, including experimental bias. The third phase of the MicroArray Quality Control Consortium project (MAQC-III), also called Sequencing Quality Control (SeQC), aims to address these limitations through standardization of experimental and bioinformatic methodologies. A limited number of toxicogenomic studies have been conducted to date using RNA-Seq. This review describes the complexity of the human transcriptome, the application of transcriptomics by RNA-Seq or microarray in molecular epidemiology studies, and limitations of these approaches including the type of cell or tissue analyzed, experimental variation, and confounding. By using good study designs with precise, individual exposure measurements, sufficient power and incorporation of phenotypic anchors, studies in human populations can identify biomarkers of exposure and/or early effect and elucidate mechanisms of action underlying associated diseases, even at low doses. Analysis of datasets at the pathway level can compensate for some of the limitations of RNA-Seq and, as more datasets become available, will increasingly elucidate the exposure-disease continuum.

Effects of molecular size and surface hydrophobicity on oligonucleotide interfacial dynamics

Single-molecule total internal reflection fluorescence microscopy was used to observe the dynamic behavior of polycytosine single-stranded DNA (ssDNA) (1-50 nucleotides long) at the interface between aqueous solution and hydrophilic (oligoethylene glycol-modified fused silica, OEG) and hydrophobic (octadecyltriethoxysilane-modified fused silica, OTES) solid surfaces. High throughput molecular tracking was used to determine >75,000 molecular trajectories for each molecular length, which were then used to calculate surface residence time and squared displacement (i.e., "step-size") distributions. On hydrophilic OEG surfaces, the surface residence time increased systematically with ssDNA chain length, as expected due to increasing molecule-surface interactions. Interestingly, the residence time decreased with increasing ssDNA length on the hydrophobic OTES surface, particularly for longer chains. Similarly, the interfacial mobility of polynucleotides slowed with increasing chain length on OEG, but became faster on OTES. On OTES surfaces, the rates associated with desorption and surface diffusion exhibited the distinctive anomalous temperature dependence that is characteristic of hydrophobic interactions for short-chain species but not for longer chains. These combined observations suggest that long oligonucleotides adopt conformations minimizing hydrophobic interactions, e.g., by internal sequestration of hydrophobic nucleobases.

Cancer systems biology

Cancer is a complex and heterogeneous disease, not only at a genetic and biochemical level, but also at a tissue, organism, and population level. Multiple data streams, from reductionist biochemistry in vitro to high-throughput "-omics" from clinical material, have been generated with the hope that they encode useful information about phenotype and, ultimately, tumour behaviour in response to drugs. While these data stand alone in terms of the biology they represent, there is the enticing prospect that if incorporated into systems biology models, they can help understand complex systems behaviour and provide a predictive framework as an additional tool in understanding how tumours change and respond to treatment over time. Since these biological data are heterogeneous and frequently qualitative rather than quantitative, at the present time a single systems biology approach is unlikely to be effective; instead, different computational and mathematical approaches should be tailored to different types of data, and to each other, in order to test and re-test hypotheses. In time, these models might converge and result in usable tractable models which accurately represent human cancer. Likewise, biologists and clinicians need to understand what the requirements of systems biology are so that compatible data are produced for computational modelling. In this review, we describe some theoretical approaches (data-driven and process-driven) and experimental methodologies which are being used in cancer research and the clinical context where they might be applied.

Measuring consequences of protein misfolding and cellular stress using OMICS techniques

The ambition to measure all or at least a significant fraction of relevant molecules in a cell culture or tissue sample has reached possible realization with the development of the so-called OMICS technologies. We will here briefly review current technologies and give examples of their applications in investigations related to protein misfolding diseases. We will primarily cover the classical OMICS categories GENOMICS, TRANSCRIPTOMICS, METABOLOMICS, and with some more detail PROTEOMICS. These techniques are in most cases performed by dedicated core facilities or commercial services. We will give an assessment of uses as well as limitations of these technologies supported by examples of their application in research related to protein misfolding. We will further briefly discuss genome-wide RNA interference and finally touch on bioinformatics, because the huge amounts of data typically collected with OMICS techniques requires the application of specific software to handle and stratify the data sets. Today, most biologists using OMICS-techniques must, at least in part, be able to analyze their own data using user-friendly web-based tools.

Broad clinical phenotypes associated with TAR-DNA binding protein (TARDBP) mutations in amyotrophic lateral sclerosis

The finding of TDP-43 as a major component of ubiquitinated protein inclusions in amyotrophic lateral sclerosis (ALS) has led to the identification of 30 mutations in the transactive response-DNA binding protein (TARDBP) gene, encoding TDP-43. All but one are in exon 6, which encodes the glycine-rich domain. The aim of this study was to determine the frequency of TARDBP mutations in a large cohort of motor neurone disease patients from Northern England (42 non-superoxide dismutase 1 (SOD1) familial ALS (FALS), nine ALS-frontotemporal dementia, 474 sporadic ALS (SALS), 45 progressive muscular atrophy cases). We identified four mutations, two of which were novel, in two familial (FALS) and two sporadic (SALS) cases, giving a frequency of TARDBP mutations in non-SOD1 FALS of 5% and SALS of 0.4%. Analysis of clinical data identified that patients had typical ALS, with limb or bulbar onset, and showed considerable variation in age of onset and rapidity of disease course. However, all cases had an absence of clinically overt cognitive dysfunction.

NMDA receptor subunit expression in GABAergic interneurons in the prefrontal cortex: application of laser microdissection technique

The selective involvement of a subset of neurons in many psychiatric disorders, such as gamma-aminobutyric acid (GABA)-ergic interneurons in schizophrenia, creates a significant need for in-depth analysis of these cells. Here we introduce a combination of techniques to examine the relative gene expression of N-methyl-d-aspartic acid (NMDA) receptor subtypes in GABAergic interneurons from the rat prefrontal cortex. Neurons were identified by immunostaining, isolated by laser microdissection and RNA was prepared for reverse transcription polymerase chain reaction (RT-PCR) and real-time PCR. These experimental procedures have been described individually; however, we found that this combination of techniques is powerful for the analysis of gene expression in individual identified neurons. This approach provides the means to analyze relevant molecular mechanisms that are involved in the neuropathological process of a devastating brain disorder.

Integrative genomic data mining for discovery of potential blood-borne biomarkers for early diagnosis of cancer

Background

With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types.

Methodology/Principal Findings

In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function.

Conclusion/Significance

The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.

Current and future directions in genomics of amyotrophic lateral sclerosis

New knowledge of the structure and function of the human genome and novel genomic technologies are being applied to the study of sporadic amyotrophic lateral sclerosis (ALS). These studies can examine tens to hundreds of thousands of items at once, and depend on sophisticated computer processing. Current studies are focused on genetic susceptibility and gene expression and future studies will likely focus on structural variation, gene regulation and non-protein coding regions. The hope is that they will lead to deeper understanding of molecular aspects of the disease and to rational therapeutic targets.

Genetic predictors of renal failure

BACKGROUND

Both environmental and genetic factors contribute to the development and progression of chronic kidney disease. The completion of the human genome sequence and advances in genomic technology make possible identification of gene variants associated with renal failure.

OBJECTIVE

This review discusses the relevant genetic studies in chronic kidney disease, with particular emphasis on the most common causes of end stage renal failure, diabetic nephropathy and glomerulonephritis.

METHODS

Most of the studies presented were performed in recent years and employed association studies, both population-based and with candidate genes, as well as the genome-wide association and genome-wide scan approaches.

RESULTS/CONCLUSION

Increasing evidence supports an important role of genetic susceptibility in the development and progression of renal failure. Identification of disease genes will allow the identification of patients at high risk and the development of new strategies to prevent or delay the renal disease process.