Identification of potential biomarkers for melanoma cancer (black tumor) using bioinformatics strategy: a study based on GEO and SRA datasets.

Melanoma, a highly invasive type of skin cancer that penetrates the entire dermis layer, is associated with increased mortality rates. Excessive exposure of the skin to sunlight, specifically ultraviolet radiation, is the underlying cause of this malignant condition. The appearance of unique skin moles represents a visible clue, referred to as the "ugly duckling" sign, indicating the presence of melanoma and its association with cellular DNA damage. This research aims to explore potential biomarkers derived from microarray data, employing bioinformatics techniques and methodologies, for a thorough investigation of melanoma skin cancer. The microarray dataset for melanoma skin cancer was obtained from the GEO database, and thorough data analysis and quality control measures were performed to identify differentially expressed genes (DEGs). The top 14 highly expressed DEGs were identified, and their gene information and protein sequences were retrieved from the NCBI gene and protein database. These proteins were further analyzed for domain identification and network analysis. Gene expression analysis was conducted to visualize the upregulated and downregulated genes. Additionally, gene metabolite network analysis was carried out to understand the interactions between highly interconnected genes and regulatory transcripts. Molecular docking was employed to investigate the ligand-binding sites and visualize the three-dimensional structure of proteins. Our research unveiled a collection of genes with varying expression levels, some elevated and others reduced, which could function as promising biomarkers closely linked to the development and advancement of melanoma skin cancer. Through molecular docking analysis of the GINS2 protein, we identified two natural compounds (PubChem-156021169 and PubChem-60700) with potential as inhibitors against melanoma. This research has implications for early detection, treatment, and understanding the molecular basis of melanoma.

Evaluation of Expression Profile of Patients with Acute Myeloid Leukemia in Response to Azacitidine with Biological System Approach.

BACKGROUND: Acute myeloid leukemia (AML) is a prevalent type of leukemia that is associated with high rates of chemoresistance, including resistance to Azacitidine (AZA). Understanding the molecular mechanisms of chemoresistance can lead to the development of novel therapeutic approaches. In this study, we aimed to identify dysregulated miRNAs and their target genes involved in chemoresistance to AZA in AML patients. METHODS: We analyzed expression profiles from two GEO datasets (GSE16625 and GSE77750) using the "Limma" package in R. We identified 29 differentially expressed miRNAs between AML patients treated with AZA and healthy individuals. MultiMiR package of R was used to predict target genes of identified miRNAs, and functional enrichment analysis was performed using FunRich software. Protein-protein interaction networks were constructed using STRING and visualized using Cytoscape. MiR-582 and miR- 597 were the most up- and down-regulated miRNAs, respectively. Functional enrichment analysis revealed that metal ion binding, regulation of translation, and proteoglycan syndecan-mediated signaling events were the most enriched pathways. The tumor necrosis factor (TNF) gene was identified as a hub gene in the protein-protein interaction network. DISCUSSION: Our study identified dysregulated miRNAs and their target genes in response to AZA treatment in AML patients. These findings provide insights into the molecular mechanisms of chemoresistance and suggest potential therapeutic targets for the treatment of AML. CONCLUSION: Further experimental validation of the identified miRNAs and their targets is warranted.

Development of a novel microarray data analysis tool without normalization for genotyping degraded forensic DNA.

Since the arrest of the Golden State Killer in the US in April 2018, forensic geneticists have been increasingly interested in the investigative genetic genealogy (IGG) method. While this method has already been in practical use as a powerful tool for criminal investigation, we have yet to know well the limitations and potential risks. In this current study, we performed an evaluation study focusing on degraded DNA using the Affymetrix Genome-Wide Human SNP Array 6.0 platform (Thermo Fisher Scientific). We revealed one of the potential problems that occur during SNP genotype determination using a microarray-based platform. Our analysis results indicated that the SNP profiles derived from degraded DNA contained many false heterozygous SNPs. In addition, it was confirmed that the total amount of probe signal intensity on microarray chips derived from degraded DNA decreased significantly. Because the conventional analysis algorithm performs normalization during genotype determination, we concluded that noise signals could be genotype-called. To address this issue, we proposed a novel microarray data analysis method without normalization (nMAP). Although the nMAP algorithm resulted in a low call rate, it substantially improved genotyping accuracy. Finally, we confirmed the usefulness of the nMAP algorithm for kinship inferences. These findings and the nMAP algorithm will make a contribution to the advance of the IGG method.

Promoter-motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model.

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database 'EcoPromDB' has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

CeDAR: incorporating cell type hierarchy improves cell type-specific differential analyses in bulk omics data.

Bulk high-throughput omics data contain signals from a mixture of cell types. Recent developments of deconvolution methods facilitate cell type-specific inferences from bulk data. Our real data exploration suggests that differential expression or methylation status is often correlated among cell types. Based on this observation, we develop a novel statistical method named CeDAR to incorporate the cell type hierarchy in cell type-specific differential analyses of bulk data. Extensive simulation and real data analyses demonstrate that this approach significantly improves the accuracy and power in detecting cell type-specific differential signals compared with existing methods, especially in low-abundance cell types.

Identification of common genes in obesity and cancer through network interaction and targeting those genes by virtual screening approach.

Obesity may have an effect on cancer outcomes, resulting in global inequalities in cancer survival and death. Microarray data analysis was done to identify differentially expressed genes (DEGs) in obese and cancer patients. Total 1977 differentially expressed genes among obesity and gastric cancer, breast cancer, pancreatic cancer, and colorectal cancer were used to build a gene interaction network, which was then analyzed by using Cytoscape software. It has been identified that JUN, CXCL12, and LEP genes show a higher degree and stress, and play an important role in obesity and cancer progression. Further, CXCL12 and LEP were taken for virtual screening study with coumarin and its derivatives to develop a drug against obesity and cancer. The interactions of CXCL12 and LEP with coumarins were studied by molecular docking and it shows good interaction as well as docking score as compared to the standard one. The ADME properties were predicted to check the drug-likeness activity of coumarins and the most of the drug-likeness activities are in admire range. The Binding free energy of the docked complex was calculated by performing MM-GBSA. The molecular docking, ADME properties prediction, and MM-GBSA was performed on Maestro 12.6. The top docked score compounds were further subjected to molecular dynamic simulation to check the stability by using GROMACS. The MM-PBSA study was performed to calculate the binding energy components as well as the energy contributions of specific amino acids. The resultant compounds could be a potent anti-obesity and anti-cancer drug.Communicated by Ramaswamy H. Sarma.

Mechanism of Polygonum capitatum in treatment of Helicobacter Pylori associated gastritis by network pharmacology, microarray data analysis and molecular docking / 中国药理学通报

Aim To explore the mechanism of Polygonum capitatum(PC)in the treatment of Helicobacter Pylori associated gastritis(HAG). Methods The databases were used to identify the target of PC active compounds and HAG-related genes,and the intersection was taken to obtain the potential targets of PC treatment of HAG. The interaction network diagram of “drug-active compound-target-disease” and the protein-protein interaction(PPI)network of potential target protein interaction in HAG treated by PC were constructed by software Cytoscape 3.6.0. The important nodes in the network were screened by several topological indexes,and the GO and KEGG enrichment were analyzed by STRING database to obtain the potential signaling pathway of PC in the treatment of HAG. The binding ability of PC active components with key target proteins was observed by molecular docking method. On this basis,the related targets of PC in the treatment of HAG were verified in vivo and in vitro experiments. Results The PC active compounds and targets were identified through the database,and the “drug-active compound-target-disease” network diagram and the PPI network of potential target proteins were constructed. Combined with several topological indexes,the PPI network of potential target-protein interaction was analyzed,and 52 hub genes were screened. Further bioinformatics analysis and high-throughput sequencing revealed that PC exerted an effect on HAG through the Akt/NF-κB/NLRP3 pathway. Based on this,it was found that PC could reduce IL-18 and IL-1β in HAG GES-1 cells and HAG SD rats,up-regulate Akt and its phosphorylation level and reduce NF-κB expression,inhibit the activation of NLRP3 inflammatory body,so as to improve HAG inflammatory response. Conclusions PC could exert a therapeutic effect on HAG by activating Akt and its phosphorylation level,and inhibiting the expression of NF-κB and NLRP3 inflammasome related factors. This study provides a theoretical basis for explaining the mechanism of PC in the treatment of HAG.

Identification of key candidate genes and biological pathways in neuropathic pain.

BACKGROUND: Neuropathic pain is a common chronic pain, characterized by spontaneous pain and mechanical allodynia. The incidence of neuropathic pain is on the rise due to infections, higher rates of diabetes and stroke, and increased use of chemotherapy drugs in cancer patients. At present, due to its pathophysiological process and molecular mechanism remaining unclear, there is a lack of effective treatment and prevention methods in clinical practice. Now, we use bioinformatics technology to integrate and filter hub genes that may be related to the pathogenesis of neuropathic pain, and explore their possible molecular mechanism by functional annotation and pathway enrichment analysis. METHODS: The expression profiles of GSE24982, GSE2884, GSE2636 and GSE30691 were downloaded from the Gene Expression Omnibus（GEO）database, and these datasets include 93 neuropathic pain Rattus norvegicus and 59 shame controls. After the four datasets were all standardized by quantiles, the differentially expressed genes (DEGs) between NPP Rattus norvegicus and the shame controls were finally identified by the robust rank aggregation (RRA) analysis method. In order to reveal the possible underlying biological function of DEGs, the Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway enrichment analysis of DEGs were performed. In addition, a Protein-protein Interaction (PPI) network was also established. At the end of our study, a high throughput sequencing dataset GSE117526 was used to corroborate our calculation results. RESULTS: Through RRA analysis of the above four datasets GSE24982, GSE2884, GSE2636, and GSE30691, we finally obtained 231 DEGs, including 183 up-regulated genes and 47 down-regulated genes. Arranging 231 DEGs in descending order according to |log2 fold change (FC)|, we found that the top 20 key genes include 14 up-regulated genes and 6 down-regulated genes. The most down-regulated hub gene abnormal expressed in NPP was Egf17 (P-value = 0.008), Camk2n2 (P-value = 0.002), and Lep (P-value = 0.02), and the most up-regulated hub gene abnormal expressed in NPP was Nefm (P-value = 1.08E-06), Prx (P-value = 2.68E-07), and Stip1 (P-value = 4.40E-07). In GO functional annotation analysis results, regulation of ion transmembrane transport (GO:0034765; P-value = 1.45E-09) was the most remarkable enriched for biological process, synaptic membrane (GO:0097060; P-value = 2.95E-08) was the most significantly enriched for cellular component, channel activity (GO:0015267; P-value = 2.44E-06) was the most prominent enriched for molecular function. In KEGG pathway enrichment analysis results, the top three notable enrichment pathways were Neuroactive ligand-receptor interaction (rno04080; P-value = 3.46E-08), Calcium signaling pathway (rno04020; P-value = 5.37E-05), and Osteoclast differentiation (rno04380; P-value = 0.000459927). Cav1 and Lep appeared in the top 20 genes in both RRA analysis and PPI analysis, while Nefm appeared in RRA analysis and datasets GSE117526 validation analysis, so we finally identified these three genes as hub genes. CONCLUSIONS: Our research identified the hub genes and signal pathways of neuropathic pain, enriched the pathophysiological mechanism of neuropathic pain to some extent, and provided a possible basis for the targeted therapy of neuropathic pain.

Exploring the pathogenesis of diabetic kidney disease by microarray data analysis.

Diabetic kidney disease (DKD) is a major complication of diabetes mellitus, and the leading contributor of end-stage renal disease. Hence, insights into the molecular pathogenesis of DKD are urgently needed. The purpose of this article is to reveal the molecular mechanisms underlying the pathogenesis of DKD. The microarray datasets of GSE30528 and GSE30529 were downloaded from the NCBI Gene Expression Omnibus (GEO) database to identify the common differentially expressed genes (DEGs) between the glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to analyze the function and pathways of the common DEGs. After constructing the protein-protein interaction (PPI) network and subnetwork analysis, three types of analyses were performed, namely, identification of hub genes, analysis of the coexpressed network, and exploration of transcription factors (TFs). Totally, 348 and 463 DEGs were identified in GDKD and TDKD, respectively. Then, 66 common DEGs (63 upregulated DEGs and three downregulated DEGs) were obtained in DKD patients. GO and KEGG pathway analyses revealed the importance of inflammation response, immune-related pathways, and extracellular matrix-related pathways, especially chemokines and cytokines, in DKD. Fifteen hub genes from the 66 common DEGs, namely, IL10RA, IRF8, LY86, C1QA, C1QB, CD53, CD1C, CTSS, CCR2, CD163, CCL5, CD48, RNASE6, CD52, and CD2 were identified. In summary, through the microarray data analysis, the common functions and hub genes greatly contribute to the elucidation of the molecular pathogenesis associated with DKD.

Upregulation of miR-142 in papillary thyroid carcinoma tissues: a report based on in silico and in vitro analysis.

Papillary thyroid carcinoma (PTC) accounts for approximately 80% of all human thyroid malignancies. Recently, there has been a dramatic rise in the prevalence of thyroid cancer all over the globe. Through analysis of the GEO database, GSE104005, the authors of the current research were able to determine the differential expression of microRNAs (DEMs) as well as their target genes. Real-time PCR was used on a total of 40 samples, 40 of which were from PTC samples and 40 from normal tissues, in order to validate the discovered DEMs and the genes. Gene Ontology (GO) categories were identified, and KEGG was used to conduct pathway enrichment analysis. The multiMiR R package was used to predict target genes of DEMs. Mir-142 was found to be overexpressed in PTC samples, as compared to normal tissues, and this was validated by the identification and validation. In addition, metal ion binding and the cellular response to metal ions were identified as essential pathways in the carcinogenesis of PTC. This demonstrates their significance in the development of this malignancy. The results of our research will serve as the foundation for further research in the area of miRNA-based cancer treatment.

Exploring the role of Tibetan medicinal formula Qishiwei Zhenzhu Pills (Ranasampel) against diabetes mellitus-linked cognitive impairment of db/db mice through serum pharmacochemistry and microarray data analysis.

Background: Diabetes cognitive impairment (DCI) is a common diabetic central nervous system disorder that severely affects the quality of life of patients. Qishiwei Zhenzhu Pills (Ranasampel) is a valuable Tibetan medicine formula with the ability to improve cerebral blood vessels, protect nerves and improve learning and memory, which has also been widely verified in clinical and basic research. Currently, the prevention and treatment of DCI are still in the exploratory research stage, and the use of Ranasampel will provide new ideas and insights for its treatment. Objective: This study is to explore the absorbed components in serum derived from Ranasampel using serum pharmacochemistry, then identify the potential mechanism of Ranasampel for the treatment of DCI through bioinformatics and microarray data validation. Methods: The UPLC-Q-Exactive MS/MS-based serum pharmacochemistry method was conducted to identify the main active components in serum containing Ranasampel. Then, these components were used to predict the possible biological targets of Ranasampel and explore the potential targets in treating DCI by overlapping with differentially expressed genes (DEGs) screened from Gene Expression Omnibus datasets. Afterward, the protein-protein interaction network, enrichment analyses, hub gene identification, and co-expression analysis were used to study the potential mechanism of Ranasampel. Particularly, the hub genes and co-expression transcription factors were further validated using hippocampal expression profiles of db/db mice treated with Ranasampel, while the Morris water-maze test and H&E staining were used to assess the spatial learning and memory behaviors and histopathological changes. Results: Totally, 40 compounds derived from Ranasampel had been identified by serum sample analysis, and 477 genes related to these identified compounds in Ranasampel, 110 overlapping genes were collected by the intersection of Ranasampel target genes and DEGs. Further comprehensive analysis and verification emphasized that the mechanism of Ranasampel treatment of DCI may be related to the improvement of learning and memory function as well as insulin resistance, hyperglycemia-induced neuronal damage, and neuroinflammation. Conclusion: This study provided useful strategies to explore the potential material basis for compound prescriptions such as Ranasampel. These hub genes and common pathways also provided new ideas for further study of therapeutic targets of DCI and the pharmacological mechanism of Ranasampel.

A Computational Systems Analyses to Identify Biomarkers and Mechanistic Link in Psoriasis and Cutaneous Squamous Cell Carcinoma.

Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.

Protein Microarray-Based Proteomics for Disease Analysis.

As we approach the twentieth anniversary of completing the international Human Genome Project, the next (and arguably most significant) frontier in biology consists of functionally understanding the proteins, which are encoded by the genome and play a crucial role in all of biology and medicine. To accomplish this challenge, different proteomics strategies must be devised to examine the activities of gene products (proteins) at scale. Among them, protein microarrays have been used to accomplish a wide variety of investigations such as examining the binding of proteins and proteoforms to DNA, small molecules, and other proteins; characterizing humoral immune responses in health and disease; evaluating allergenic proteins; and profiling protein patterns as candidate disease-specific biomarkers. In Protein Microarray for Disease Analysis: Methods and Protocols, expert researchers involved in the field of protein microarrays provide concise descriptions of the methodologies that they currently use to fabricate microarrays and how they apply them to analyze protein interactions and responses of proteins to dissect human disease.

Molecular differential analysis of uterine leiomyomas and leiomyosarcomas through weighted gene network and pathway tracing approaches.

Uterine smooth muscular neoplastic growths like benign leiomyomas (UL) and metastatic leiomyosarcomas (ULMS) share similar clinical symptoms, radiological and histological appearances making their clinical distinction a difficult task. Therefore, the objective of this study is to identify key genes and pathways involved in transformation of UL to ULMS through molecular differential analysis. Global gene expression profiles of 25 ULMS, 25 UL, and 29 myometrium (Myo) tissues generated on Affymetrix U133A 2.0 human genome microarrays were analyzed by deploying robust statistical, molecular interaction network, and pathway enrichment methods. The comparison of expression signals across Myo vs UL, Myo vs ULMS, and UL vs ULMS groups identified 249, 1037, and 716 significantly expressed genes, respectively (p ≤ 0.05). The analysis of 249 DEGs from Myo vs UL confirms multistage dysregulation of various key pathways in extracellular matrix, collagen, cell contact inhibition, and cytokine receptors transform normal myometrial cells to benign leiomyomas (p value ≤ 0.01). The 716 DEGs between UL vs ULMS were found to affect cell cycle, cell division related Rho GTPases and PI3K signaling pathways triggering uncontrolled growth and metastasis of tumor cells (p value ≤ 0.01). Integration of gene networking data, with additional parameters like estimation of mutation burden of tumors and cancer driver gene identification, has led to the finding of 4 hubs (JUN, VCAN, TOP2A, and COL1A1) and 8 bottleneck genes (PIK3R1, MYH11, KDR, ESR1, WT1, CCND1, EZH2, and CDKN2A), which showed a clear distinction in their distribution pattern among leiomyomas and leiomyosarcomas. This study provides vital clues for molecular distinction of UL and ULMS which could further assist in identification of specific diagnostic markers and therapeutic targets.Abbreviations UL: Uterine Leiomyomas; ULMS: Uterine Leiomyosarcoma; Myo: Myometrium; DEGs: Differential Expressed Genes; RMA: Robust Multiarray Average; DC: Degree of Centrality; BC: Betweenness of Centrality; CGC: Cancer Gene Census; FDR: False Discovery Rate; TCGA: Cancer Genome Atlas; BP: Biological Process; CC: Cellular Components; MF: Molecular Function; PPI: Protein-Protein Interaction.

Rebuilding Tendons: A Concise Review on the Potential of Dermal Fibroblasts.

Tendons are vital to joint movement by connecting muscles to bones. Along with an increasing incidence of tendon injuries, tendon disorders can burden the quality of life of patients or the career of athletes. Current treatments involve surgical reconstruction and conservative therapy. Especially in the elderly population, tendon recovery requires lengthy periods and it may result in unsatisfactory outcome. Cell-mediated tendon engineering is a rapidly progressing experimental and pre-clinical field, which holds great potential for an alternative approach to established medical treatments. The selection of an appropriate cell source is critical and remains under investigation. Dermal fibroblasts exhibit multiple similarities to tendon cells, suggesting they may be a promising cell source for tendon engineering. Hence, the purpose of this review article was in brief, to compare tendon to dermis tissues, and summarize in vitro studies on tenogenic differentiation of dermal fibroblasts. Furthermore, analysis of an open source Gene Expression Omnibus (GEO) data repository was carried out, revealing great overlap in the molecular profiles of both cell types. Lastly, a summary of in vivo studies employing dermal fibroblasts in tendon repair as well as pilot clinical studies in this area is included. Altogether, dermal fibroblasts hold therapeutic potential and are attractive cells for rebuilding injured tendons.

Computational analysis of microarray data of Arabidopsis thaliana challenged with Alternaria brassicicola for identification of key genes in Brassica.

BACKGROUND: Alternaria blight, a recalcitrant disease caused by Alternaria brassicae and Alternaria brassicicola, has been recognized for significant losses of oilseed crops especially rapeseed-mustard throughout the world. Till date, no resistance source is available against the disease; hence, plant breeding methods cannot be used to develop disease-resistant varieties. Therefore, in the present study, efforts have been made to identify resistance and defense-related genes as well as key components of JA-SA-ET-mediated pathway involved in resistance against Alternaria brasscicola through computational analysis of microarray data and network biology approach. Microarray profiling data from wild type and mutant Arabidopsis plants challenged with Alternaria brassicicola along with control plant were obtained from the Gene Expression Omnibus (GEO) database. The data analysis, including DEGs extraction, functional enrichment, annotation, and network analysis, was used to identify genes associated with disease resistance and defense response. RESULTS: A total of 2854 genes were differentially expressed in WT9C9; among them, 1327 genes were upregulated and 1527 genes were downregulated. A total of 1159 genes were differentially expressed in JAM9C9; among them, 809 were upregulated and 350 were downregulated. A total of 2516 genes were differentially expressed in SAM9C9; among them, 1355 were upregulated and 1161 were downregulated. A total of 1567 genes were differentially expressed in ETM9C9; among them, 917 were upregulated and 650 were downregulated. Besides, a total of 2965 genes were differentially expressed in contrast WT24C24; among them, 1510 genes were upregulated and 1455 genes were downregulated. A total of 4598 genes were differentially expressed in JAM24C24; among them, 2201 were upregulated and 2397 were downregulated. A total of 3803 genes were differentially expressed in SAM24C24; among them, 1819 were upregulated and 1984 were downregulated. A total of 4164 genes were differentially expressed in ETM24C24; among them, 1895 were upregulated and 2269 were downregulated. The upregulated genes of Arabidopsis thaliana were mapped and annotated with CDS sequences of Brassica rapa obtained from PlantGDB database. Additionally, PPI network of these genes were constructed to investigate the key components of hormone-mediated pathway involved in resistance during pathogenesis. CONCLUSION: The obtained information from present study can be used to engineer resistance to Alternaria blight caused by Alternaria brasscicola through molecular breeding or genetic manipulation-based approaches for improving Brassica oilseed productivity.

Microarray data analysis on gene and miRNA expression to identify biomarkers in non-small cell lung cancer.

BACKGROUND: The aim of this study was to gain further investigation of non-small cell lung cancer (NSCLC) tumorigenesis and identify biomarkers for clinical management of patients through comprehensive bioinformatics analysis. METHODS: miRNA and mRNA microarray datasets were downloaded from GEO (Gene Expression Omnibus) database under the accession number GSE102286 and GSE101929, respectively. Genes and miRNAs with differential expression were identified in NSCLC samples compared with controls, respectively. The interaction between differentially expressed genes (DEGs) and differentially expressed miRNAs (DEmiRs) was predicted, followed by functional enrichment analysis, and construction of miRNA-gene regulatory network, protein-protein interaction (PPI) network, and competing endogenous RNA (ceRNA) network. Through comprehensive bioinformatics analysis, we anticipate to find novel therapeutic targets and biomarkers for NSCLC. RESULTS: A total of 123 DEmiRs (5 up- and 118 down-regulated miRNAs) and 924 DEGs (309 up- and 615 down-regulated genes) were identified. These genes and miRNAs were significantly involved in different pathways including adherens junction, relaxin signaling pathway, and axon guidance. Furthermore, hsa-miR-9-5p, has-miR-196a-5p and hsa-miR-31-5p, as well as hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p were shown to have higher degree in the miRNA-gene regulatory network and ceRNA network, respectively. Furthermore, BIRC5 and FGF2, as well as RTKN2 and SLIT3 were hubs in the PPI network and ceRNA network, respectively. CONCLUSION: Several pathways (adherens junction, relaxin signaling pathway, and axon guidance) miRNAs (hsa-miR-9-5p, has-miR-196a-5p, hsa-miR-31-5p, hsa-miR-1, hsa-miR-218-5p and hsa-miR-135a-5p) and genes (BIRC5, FGF2, RTKN2 and SLIT3) may play important roles in the pathogenesis of NSCLC.

Survival genes expression analysis following ionizing radiation to LiCl treated KG1a cells.

Purpose: Lithium chloride (LiCl) is clinically used for manic disorders. Its role has been shown in improving cell survival by decreasing Bax and p53 expression and increasing Bcl-2 concentration in the cell. This potential of LiCl is responsible for reducing irradiated cell death. In this study, we have explored the role of LiCl as a radioprotectant affecting survival genes.Materials and methods: To find out the cellular response upon LiCl pretreatment to radiation-exposed KG1a cells; viability, clonogenic assay and microarray studies were performed. This was followed by the detection of transcription factor binding motif in coregulated genes. These results were confirmed by reverse transcription-polymerase chain reaction (RT-PCR) and chromatin immunoprecipitation (CHIP).Results: LiCl improved irradiated KG1a cell survival and its clonogenicity at 2 mM concentration (clinically used). Microarray data analysis showed differential expression of cell-protecting genes playing an important role in apoptosis, cell cycle, adhesion and inflammation, etc. The coregulation analysis revealed genes involved in bile acid biosynthesis were also affected by LiCl treatment, these genes are likely to be responsible for radiation-induced gastrointestinal (GI) syndrome through bile production.Conclusions: This is the first study with respect to global genetic expression upon LiCl treatment to radiation-exposed cells. Our results suggest considering repurposing of LiCl as a protective agent for radiation injury.

Identification of adriamycin resistance genes in breast cancer based on microarray data analysis.

BACKGROUND: Breast cancer is a common malignant tumor with increasing incidence worldwide. This study aimed to investigate the molecular mechanisms of the adriamycin (ADR) resistance in breast cancer. METHODS: The GSE76540 dataset downloaded from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database was adopted for analysis. Differentially expressed genes (DEGs) in chemo-sensitive cases and chemo-resistant cases were identified using the GEO2R online tool respectively. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs were carried out by using the DAVID online tool. The protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) and visualized with Cytoscape software. The impact of key tumor genes on the survival and prognosis were described. RESULTS: A total of 1,481 DEGs were excavated, including 549 up-regulated genes and 932 down-regulated genes. According to the GO analysis, the DEGs were significantly enriched in: extracellular matrix organization, positive regulation of transcription from RNA polymerase II promoter, lung development, positive regulation of gene expression, axon guidance and so on. The results of KEGG pathway enrichment analysis showed that the most enriched DEGs can be detected in: pathways in cancer, PI3K/AKT signaling pathway, focal adhesion, Ras signaling pathway and so on. In the PPI network analysis, hub genes of CDH1, ESR1, SOX2, AR, GATA3, FOXA1, KRT19, CLDN7, AGR2, ESRP1, RAB25, CLDN4, IGF1R, CLDN3 and IRS1 were detected. Finally, there is a correlation filter out these hub genes in resistance of ADR. CONCLUSIONS: Hub genes associated with ADR resistance were identified using bioinformatic techniques. The results of this study may contribute to the development of targeted therapy for breast cancer.

pwrEWAS: a user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS).

BACKGROUND: When designing an epigenome-wide association study (EWAS) to investigate the relationship between DNA methylation (DNAm) and some exposure(s) or phenotype(s), it is critically important to assess the sample size needed to detect a hypothesized difference with adequate statistical power. However, the complex and nuanced nature of DNAm data makes direct assessment of statistical power challenging. To circumvent these challenges and to address the outstanding need for a user-friendly interface for EWAS power evaluation, we have developed pwrEWAS. RESULTS: The current implementation of pwrEWAS accommodates power estimation for two-group comparisons of DNAm (e.g. case vs control, exposed vs non-exposed, etc.), where methylation assessment is carried out using the Illumina Human Methylation BeadChip technology. Power is calculated using a semi-parametric simulation-based approach in which DNAm data is randomly generated from beta-distributions using CpG-specific means and variances estimated from one of several different existing DNAm data sets, chosen to cover the most common tissue-types used in EWAS. In addition to specifying the tissue type to be used for DNAm profiling, users are required to specify the sample size, number of differentially methylated CpGs, effect size(s) (Δß), target false discovery rate (FDR) and the number of simulated data sets, and have the option of selecting from several different statistical methods to perform differential methylation analyses. pwrEWAS reports the marginal power, marginal type I error rate, marginal FDR, and false discovery cost (FDC). Here, we demonstrate how pwrEWAS can be applied in practice using a hypothetical EWAS. In addition, we report its computational efficiency across a variety of user settings. CONCLUSION: Both under- and overpowered studies unnecessarily deplete resources and even risk failure of a study. With pwrEWAS, we provide a user-friendly tool to help researchers circumvent these risks and to assist in the design and planning of EWAS. AVAILABILITY: The web interface is written in the R statistical programming language using Shiny (RStudio Inc., 2016) and is available at https://biostats-shinyr.kumc.edu/pwrEWAS/ . The R package for pwrEWAS is publicly available at GitHub ( https://github.com/stefangraw/pwrEWAS ).