Microarray Gene Expression Datasets Research Articles

Inhibiting the glycerophosphodiesterase EDI3 in ER-HER2+ breast cancer cells resistant to HER2-targeted therapy reduces viability and tumour growth

BackgroundIntrinsic or acquired resistance to HER2-targeted therapy is often a problem when small molecule tyrosine kinase inhibitors or antibodies are used to treat patients with HER2 positive breast cancer. Therefore, the identification of new targets and therapies for this patient group is warranted. Activated choline metabolism, characterized by elevated levels of choline-containing compounds, has been previously reported in breast cancer. The glycerophosphodiesterase EDI3 (GPCPD1), which hydrolyses glycerophosphocholine to choline and glycerol-3-phosphate, directly influences choline and phospholipid metabolism, and has been linked to cancer-relevant phenotypes in vitro. While the importance of choline metabolism has been addressed in breast cancer, the role of EDI3 in this cancer type has not been explored.MethodsEDI3 mRNA and protein expression in human breast cancer tissue were investigated using publicly-available Affymetrix gene expression microarray datasets (n = 540) and with immunohistochemistry on a tissue microarray (n = 265), respectively. A panel of breast cancer cell lines of different molecular subtypes were used to investigate expression and activity of EDI3 in vitro. To determine whether EDI3 expression is regulated by HER2 signalling, the effect of pharmacological inhibition and siRNA silencing of HER2, as well as the influence of inhibiting key components of signalling cascades downstream of HER2 were studied. Finally, the influence of silencing and pharmacologically inhibiting EDI3 on viability was investigated in vitro and on tumour growth in vivo.ResultsIn the present study, we show that EDI3 expression is highest in ER-HER2 + human breast tumours, and both expression and activity were also highest in ER-HER2 + breast cancer cell lines. Silencing HER2 using siRNA, as well as inhibiting HER2 signalling with lapatinib decreased EDI3 expression. Pathways downstream of PI3K/Akt/mTOR and GSK3β, and transcription factors, including HIF1α, CREB and STAT3 were identified as relevant in regulating EDI3 expression. Silencing EDI3 preferentially decreased cell viability in the ER-HER2 + cells. Furthermore, silencing or pharmacologically inhibiting EDI3 using dipyridamole in ER-HER2 + cells resistant to HER2-targeted therapy decreased cell viability in vitro and tumour growth in vivo.ConclusionsOur results indicate that EDI3 may be a potential novel therapeutic target in patients with HER2-targeted therapy-resistant ER-HER2 + breast cancer that should be further explored.

CXCR4 as possible druggable target linking inflammatory bowel disease and Parkinson's disease.

Parkinson's disease (PD) is a chronic, progressive, and second most prevalent neurological disorder affecting the motor system. It has been found that people suffering with inflammatory bowel disease (IBD) are at 22% more risk for PD. In the current study, we have established a molecular link between gut and brain. The microarray gene expression datasets of Homo sapiens were obtained from Gene Expression Omnibus Database. Major genes involved in gut-brain connection were found to be CXCR4, LRRK2, APOE, SNCA, IL6, HIF-1α, ABCA1 etc. The common biological pathways linking both the pathologies were found to be HIF-signaling, cytokines interactions, JAK-STAT pathway, cholesterol metabolism, apoptosis and CXCR4 signaling which modulates the synaptic function and neuronal survival in the mature brain. It is known that flavonoid-rich foods throughout life hold the potential to limit the inflammation, neurodegeneration and, to prevent the age-dependent cognitive impairment. Therefore, the potential receptor, CXCR4 was used further for docking with twenty-seven phytochemicals from 5 different classes of Flavonoids found in several dietary items. Docking studies of the top scoring compounds were compared with a known inhibitor (BPRCX807) of receptor CXCR4 (IC50 = 40.4 ± 8.0nM). The study indicates that Flavan-3-ol families of flavonoids are the best fit and finest dietary supplements for improving brain health. Hence the food items like Pistachio nuts, hazelnuts, Green Tea, walnuts, etc. should be incorporated more in the diet of healthy people as well as in IBD and PD patients to prevent inflammation in gut and brain damage from oxidative stress.

Transcriptional changes in human palate and skin healing.

Most human tissue injuries lead to the formation of a fibrous scar and result in the loss of functional tissue. One adult tissue that exhibits a more regenerative response to injury with minimal scarring is the oral mucosa. We generated a microarray gene expression dataset to examine the response to injury in human palate and skin excisional biopsies spanning the first 7 days after wounding. Differential expression analyses were performed in each tissue to identify genes overexpressed or underexpressed over time when compared to baseline unwounded tissue gene expression levels. To attribute biological processes of interest to these gene expression changes, gene set enrichment analysis was used to identify core gene sets that are enriched over the time-course of the wound healing process with respect to unwounded tissue. This analysis identified gene sets uniquely enriched in either palate or skin wounds and gene sets that are enriched in both tissues in at least one time point after injury. Finally, a cell type enrichment analysis was performed to better understand the cell type distribution in these tissues and how it changes over the time course of wound healing. This work provides a source of human wound gene expression data that includes two tissue types with distinct regenerative and scarring phenotypes.

Identifying breast cancer molecular class using integrated feature selection and deep learning model

The extraction of molecular subcategory is one such valuable evidence concerning breast cancer in determining its cure and prognosis. This manuscript has framed a model for molecular subtype-based feature selection known as CFS-BFS followed by classification using deep learning. The proposed model captures significant genes by utilising pre-processing ladder along with the combination of filter and wrapper-based technique CFS-BFS. The obtained genes are assessed via numerous machine learning methodologies where it is remarked that carefully chosen significant genes are more profitable in explaining this molecular problem using deep learning. The study has attained the maximum precision and beats brilliantly in terms of recall, F-score, TP_Rate, fallout, and MCC. Hence, proposed paradigm is recognised as one of the best effective technique determining the outstanding recital with all the chosen micro-array gene expression datasets for significant obtained genes. The genes identified by integrated model are also validated using Kaplan-Meier survival graph to show their credibility in breast cancer prognosis. Survival analysis show that selected genes using integrated approach can separate luminal, non-luminal subcategory utilising various factors including age, disease free survival, and relapse free survival.

Identification of hub genes associated with human cystic fibrosis: A Meta-analysis approach

Cystic fibrosis (CF) is a genetic disease affecting multiple organs caused by mutations in the epithelial chloride channel-cystic fibrosis transmembrane conductance regulator (CFTR). A study is being conducted to identify potential genes contributing to CF progression and prognosis. The present study examined upregulated and downregulated genes in cystic fibrosis based on three microarray gene expression datasets (GSE70442, GSE2395, and GSE10406). Functional enrichment and pathway analysis were conducted for DEGs revealed their involvement in IL-17 signaling pathway, Mineral absorption, Gastric acid secretion pathways. The hub genes (MYC, EZR, S100A9, S100A8, TF, TIA1, KYNU, KLF6, CSTA, and LRRFIP1) related to this disease were identified from a constructed protein-protein interaction network. Hub genes were further analyzed for their interactions with transcription factors and miRNAs. To conclude, identifying the above hub genes may contribute to a better understanding cystic fibrosis pathophysiology. The functional analysis must be conducted for a computational study to be physiologically relevant.

Robust identification of common genomic biomarkers from multiple gene expression profiles for the prognosis, diagnosis, and therapies of pancreatic cancer

Pancreatic cancer (PC) is one of the leading causes of cancer-related death globally. So, identification of potential molecular signatures is required for diagnosis, prognosis, and therapies of PC. In this study, we detected 71 common differentially expressed genes (cDEGs) between PC and control samples from four microarray gene-expression datasets (GSE15471, GSE16515, GSE71989, and GSE22780) by using robust statistical and machine learning approaches, since microarray gene-expression datasets are often contaminated by outliers due to several steps involved in the data generating processes. Then we detected 8 cDEGs (ADAM10, COL1A2, FN1, P4HB, ITGB1, ITGB5, ANXA2, and MYOF) as the PC-causing key genes (KGs) by the protein-protein interaction (PPI) network analysis. We validated the expression patterns of KGs between case and control samples by box plot analysis with the TCGA and GTEx databases. The proposed KGs showed high prognostic power with the random forest (RF) based prediction model and Kaplan-Meier-based survival probability curve. The KGs regulatory network analysis detected few transcriptional and post-transcriptional regulators for KGs. The cDEGs-set enrichment analysis revealed some crucial PC-causing molecular functions, biological processes, cellular components, and pathways that are associated with KGs. Finally, we suggested KGs-guided five repurposable drug molecules (Linsitinib, CX5461, Irinotecan, Timosaponin AIII, and Olaparib) and a new molecule (NVP-BHG712) against PC by molecular docking. The stability of the top three protein-ligand complexes was confirmed by molecular dynamic (MD) simulation studies. The cross-validation and some literature reviews also supported our findings. Therefore, the finding of this study might be useful resources to the researchers and medical doctors for diagnosis, prognosis and therapies of PC by the wet-lab validation.

A Human iPSC Model Reveals Specific Upregulation of Linker Histone H1-0 in ETV6::RUNX1+ Preleukemia and B Cell Precursor Acute Lymphoblastic Leukemia

The ETV6::RUNX1 translocation is the most common chromosomal rearrangement associated with pediatric B cell precursor acute lymphoblastic leukemia (BCP-ALL). As previously shown, ETV6::RUNX1 leads to a distinct clinically silent preleukemic state in utero that manifests in impaired early B cell differentiation. Acquisition of secondary oncogenic mutations gives rise to overt leukemia in 0.2% of children carrying ETV6::RUNX1. In-depth characterization of ETV6::RUNX1+ preleukemia is essential to gain an understanding of leukemic transformation and develop preventive strategies. Here, we identify dysregulation of the epigenetic regulator H1-0 in ETV6::RUNX1+ preleukemia and BCP-ALL. Utilizing CRISPR/Cas9 genome editing, we generated monoclonal ETV6::RUNX1+ human induced pluripotent stem cell (hiPSC) lines with stable ETV6::RUNX1 expression driven by the endogenous ETV6 promoter. To assess the preleukemic setting mediated by ETV6::RUNX1, we performed RNA sequencing (RNA-seq) analysis of ETV6::RUNX1+ and matching wild-type (WT) hiPSCs of two donors. We identified 20 differentially expressed genes in ETV6::RUNX1+ hiPSCs of both donors compared to the respective WT hiPSCs (up: 15, down: 5, absolute fold change ≥ 2, p ≤ 0.05). Interestingly, our RNA-seq analysis revealed consistent upregulation of H1-0 (mean: 2.43-fold), a linker histone involved in chromatin remodeling, cell proliferation and differentiation. Elevated H1-0 levels in the ETV6::RUNX1+ hiPSC lines were confirmed by quantitative real-time PCR and immunoblot (Figure 1A). To determine H1-0 levels in ETV6::RUNX1+ B cell precursors, we analyzed published gene expression microarray and RNA-seq data sets. Both ETV6::RUNX1+ CD34+ cells (n = 10) derived from umbilical cord blood (Polak et al. Haematologica 2019) and ETV6::RUNX1+ CD34+CD19+ cells (n = 4) generated by hematopoietic differentiation of hiPSCs (Böiers et al. Developmental Cell 2018) showed significant upregulation of H1-0 compared to the respective control cells lacking ETV6::RUNX1 (unpaired t-test, p = 0.0087 and p = 0.0076, respectively). Additionally, we differentiated ETV6::RUNX1+ and WT hiPSCs into CD34+CD45+ hematopoietic progenitor cells and performed single-cell RNA-seq analysis. We identified coexpression of H1-0 with cyclin-dependent kinase inhibitor genes CDKN1A and CDKN1B, indicating a slow or non-cycling phenotype of H1-0+ cells. Next, we examined H1-0 mRNA levels in gene expression data sets of pediatric and adult leukemia patients (St. Jude PeCan database (n = 876), Jerchel et al. Haematologica 2018 (n = 654), MILE study (n = 1299)). H1-0 expression was found to be significantly elevated in ETV6::RUNX1+ BCP-ALL patient samples compared to other leukemia entities, especially TCF3::PBX1+ ALL (ordinary one-way ANOVA, p ≤ 0.0001), KMT2A rearranged ALL (p ≤ 0.0001) and chronic lymphoblastic leukemia (CLL, p ≤ 0.0001). Strikingly, H1-0 expression was 22.37-fold lower in CLL compared to ETV6::RUNX1+ BCP-ALL (Figure 1B). This observation prompted us to investigate H1-0 expression in B cell developmental stages using published data sets. Interestingly, we found that H1-0 levels decrease during early B cell development in both normal bone marrow (Black et al. Nucleic Acids Research 2018) and umbilical cord blood-derived cells (Novershtern et al. Cell 2011). Additionally, analysis of single-cell RNA-seq data of early B cell precursors derived from healthy bone marrow (Mehtonen et al. Genome Medicine 2020) and fetal liver (Developmental Cell Atlas Newcastle University) revealed that relative numbers of H1-0 expressing cells per differentiation state decreased along the pseudotime B cell development trajectory. Taken together, our study identifies H1-0 upregulation as a specific alteration present already at the ETV6::RUNX1+ preleukemic state. High H1-0 expression was associated with slower proliferation and increased stemness. This phenotype might explain the persistence of in utero generated preleukemic ETV6::RUNX1+ clones residing in the bone marrow of approximately 5% of healthy children for years. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Towards a potential pan-cancer prognostic signature for gene expression based on probesets and ensemble machine learning

Cancer is one of the leading causes of death worldwide and can be caused by environmental aspects (for example, exposure to asbestos), by human behavior (such as smoking), or by genetic factors. To understand which genes might be involved in patients’ survival, researchers have invented prognostic genetic signatures: lists of genes that can be used in scientific analyses to predict if a patient will survive or not. In this study, we joined together five different prognostic signatures, each of them related to a specific cancer type, to generate a unique pan-cancer prognostic signature, that contains 207 unique probesets related to 187 unique gene symbols, with one particular probeset present in two cancer type-specific signatures (203072_at related to the MYO1E gene). We applied our proposed pan-cancer signature with the Random Forests machine learning method to 57 microarray gene expression datasets of 12 different cancer types, and analyzed the results. We also compared the performance of our pan-cancer signature with the performances of two alternative prognostic signatures, and with the performances of each cancer type-specific signature on their corresponding cancer type-specific datasets. Our results confirmed the effectiveness of our prognostic pan-cancer signature. Moreover, we performed a pathway enrichment analysis, which indicated an association between the signature genes and a protein-protein interaction analysis, that highlighted PIK3R2 and FN1 as key genes having a fundamental relevance in our signature, suggesting an important role in pan-cancer prognosis for both of them.

Differential expression of immune-regulatory proteins C5AR1, CLEC4A and NLRP3 on peripheral blood mononuclear cells in early-stage non-small cell lung cancer patients

Changes in gene expression profiling of peripheral blood mononuclear cells (PBMC) appear to represent the host’s response to the cancer cells via paracrine signaling. We speculated that protein expression on circulating T-lymphocytes represent T-lymphocyte trafficking before infiltration into the tumor microenvironment. The possibility of using protein expression on circulating T-lymphocytes as a biomarker to discriminate early-stage non-small cell lung cancer (NSCLC) was explored. Four independent PBMC gene expression microarray datasets (GSE12771, GSE13255, GSE20189 and GSE3934) were analyzed. We selected C5AR1, CLEC4A and NLRP3 based on their significant protein expression in tumor-infiltrating lymphocytes, but not in normal lymphoid tissue. A validation study using automated flow cytometry was conducted in 141 study participants including 76 treatment-naive early-stage non-small cell lung cancer patients (NSCLC), 12 individuals with non-malignant pulmonary diseases, and 53 healthy individuals. Median ratios of C5AR1, CLEC4A and NLRP3 specific antibody staining to CD3 positive cells in early-stage NSCLC patients compared to healthy controls were 0.014 [0–0.37] vs. 0.01 [0–0.07, p = 0.13], 0.03 [0–0.87] vs. 0.02 [0–0.13, p = 0.10] and 0.19 [0–0.60] vs. 0.09 [0.02–0.31, p < 0.0001], respectively. Median fluorescence intensity (MFI) of CD3+C5AR1+, CD3+CLEC4A+ and CD3+NLRP3+ expression in early-stage NSCLC patients compared to healthy volunteers was 185 [64.2–4801] vs. 107.5 [27–229, p < 0.0001], 91.2 [42.4–2355] vs. 71.25 [46.2–103, p = 0.0005], and 1585 [478–5224] vs. 758.5 [318–1976, p < 0.0001], respectively. NLRP3:CD3 ratio, CD3+C5AR1+, CD3+CLEC4A+ and CD3+NLRP3+ MFI were significantly higher in early-stage NSCLC than healthy volunteers with an area under the ROC curve of 0.69–0.76. The CD3+NLRP3+ MFI provided the most distinguishable expression at 71.5% sensitivity and 70% specificity. Furthermore, CD3+NLRP3+ MFI potentially discriminated between early-stage NSCLC from malignant-mimic inflammation and infection pulmonary disease. Further validation in various pulmonary inflammatory disease might be warranted. Our proof-of-principle findings strengthen the hypothesis that malignancies generate distinctive protein expression fingerprints on circulating T-lymphocytes.

IL2RA+VSIG4+ tumor-associated macrophage is a key subpopulation of the immunosuppressive microenvironment in anaplastic thyroid cancer

Extensive infiltration of tumor-associated macrophages was correlated poor prognosis in anaplastic thyroid cancer (ATC). However, the heterogeneity and characteristics of the ATC-associated macrophages (ATAMs) in ATC remain far from clear. We combined single-cell RNA-sequencing analysis and gene expression microarray datasets to assess the molecular signature of ATAMs. Compared with normal thyroid-associated macrophages (NTAMs), 778 differentially expressed genes (DEGs) significantly changed in ATAMs compared with NTAMs. These DEGs were correlated with oxidative phosphorylation (M2 phenotype) and phagocytosis (M1 phenotype). Moreover, ATAMs highly expressed pro-tumor genes associated with angiogenesis, fibrosis, metalloprotease activity, and metastasis. Notably, we identified one ATC-specific subset, IL2RA+ VSIG4+ ATAMs, co-expressed M1 and M2 markers. The infiltration of IL2RA+ VSIG4+ ATAMs showed strong correlation with BRAF and RAS signaling, and its high infiltration was associated with favorable prognosis in thyroid-cancer patients. IL2RA+ VSIG4+ ATAMs were associated with increased tumor-infiltrating lymphocytes (B cells, CD8+ T cells, Tregs). IL2RA+ VSIG4+ ATAMs interacted with CD8+ T cells and Tregs through immune checkpoints (such as LGALS9_HAVCR2), cytokines (such as CXCL10_CXCR3), and receptors (such as CSF1R_CSF1), thereby forming an immunosuppressive microenvironment. Multiplex immunohistochemistry staining and coculture experiment confirmed that ATC cancer cells were able to induce the polarization of IL2RA+ VSIG4+ ATAMs. Besides, we identified several novel ATC-specific immune checkpoint genes including the immunosuppressive molecule VSIG4, LAIR1, and LILRB2. Expression of VSIG4 was also significantly correlated with tumor-infiltrating lymphocytes (B cells, CD8+ T cells, Tregs). In conclusion, our study revealed an ATC-specific ATAM subset with bifunctional phenotype, which provided a comprehensive insight to delineate the molecular characteristics of ATC-associated macrophages.

Integrative weighted molecular network construction from transcriptomics and genome wide association data to identify shared genetic biomarkers for COPD and lung cancer.

Chronic obstructive pulmonary disease (COPD) is a multifactorial progressive airflow obstruction in the lungs, accounting for high morbidity and mortality across the world. This study aims to identify potential COPD blood-based biomarkers by analyzing the dysregulated gene expression patterns in blood and lung tissues with the help of robust computational approaches. The microarray gene expression datasets from blood (136 COPD and 6 controls) and lung tissues (16 COPD and 19 controls) were analyzed to detect shared differentially expressed genes (DEGs). Then these DEGs were used to construct COPD protein network-clusters and functionally enrich them against gene ontology annotation terms. The hub genes in the COPD network clusters were then queried in GWAS catalog and in several cancer expression databases to explore their pathogenic roles in lung cancers. The comparison of blood and lung tissue datasets revealed 63 shared DEGs. Of these DEGs, 12 COPD hub gene-network clusters (SREK1, TMEM67, IRAK2, MECOM, ASB4, C1QTNF2, CDC42BPA, DPF3, DET1, CCDC74B, KHK, and DDX3Y) connected to dysregulations of protein degradation, inflammatory cytokine production, airway remodeling, and immune cell activity were prioritized with the help of protein interactome and functional enrichment analysis. Interestingly, IRAK2 and MECOM hub genes from these COPD network clusters are known for their involvement in different pulmonary diseases. Additional COPD hub genes like SREK1, TMEM67, CDC42BPA, DPF3, and ASB4 were identified as prognostic markers in lung cancer, which is reported in 1% of COPD patients. This study identified 12 gene network- clusters as potential blood based genetic biomarkers for COPD diagnosis and prognosis.

Construction of a neural network diagnostic model and investigation of immune infiltration characteristics for Crohn's disease.

Objective: Crohn’s disease (CD), a chronic recurrent illness, is a type of inflammatory bowel disease whose incidence and prevalence rates are gradually increasing. However, there is no universally accepted criterion for CD diagnosis. The aim of this study was to create a diagnostic prediction model for CD and identify immune cell infiltration features in CD. Methods: In this study, gene expression microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. Then, we identified differentially expressed genes (DEGs) between 178 CD and 38 control cases. Enrichment analysis of DEGs was also performed to explore the biological role of DEGs. Moreover, the “randomForest” package was applied to select core genes that were used to create a neural network model. Finally, in the training cohort, we used CIBERSORT to evaluate the immune landscape between the CD and normal groups. Results: The results of enrichment analysis revealed that these DEGs may be involved in biological processes associated with immunity and inflammatory responses. Moreover, the top 3 hub genes in the protein-protein interaction network were IL-1β, CCL2, and CXCR2. The diagnostic model allowed significant discrimination with an area under the ROC curve of 0.984 [95% confidence interval: 0.971–0.993]. A validation cohort (GSE36807) was utilized to ensure the reliability and applicability of the model. In addition, the immune infiltration analysis indicated nine different immune cell types were significantly different between the CD and healthy control groups. Conclusion: In summary, this study offers a novel insight into the diagnosis of CD and provides potential biomarkers for the precise treatment of CD.

Bioinformatic Data Mining for Candidate Drugs Affecting Risk of Bisphosphonate-Related Osteonecrosis of the Jaw (BRONJ) in Cancer Patients.

Background Bisphosphonate-related osteonecrosis of the jaw (BRONJ) leads to significant morbidity. Other coadministered drugs may modulate the risk for BRONJ. The present study aimed to leverage bioinformatic data mining to identify drugs that potentially modulate the risk of BRONJ in cancer. Methods A GEO gene expression dataset of peripheral blood mononuclear cells related to BRONJ in multiple myeloma patients was downloaded, and differentially expressed genes (DEGs) in patients with BRONJ versus those without BRONJ were identified. A protein-protein interaction network of the DEGs was constructed using experimentally validated interactions in the STRING database. Overrepresented Gene Ontology (GO) molecular function terms and KEGG pathways in the network were analysed. Network topology was determined, and ‘hub genes' with degree ≥2 in the network were identified. Known drug targets of the hub genes were mined from the ‘drug gene interaction database' (DGIdb) and labelled as candidate drugs affecting the risk of BRONJ. Results 751 annotated DEGs (log FC ≥ 1.5, p < 0.05) were obtained from the microarray gene expression dataset GSE7116. A PPI network with 633 nodes and 168 edges was constructed. Data mining for drugs interacting with 49 gene nodes was performed. 37 drug interactions were found for 9 of the hub genes including TBP, TAF1, PPP2CA, PRPF31, CASP8, UQCRB, ACTR2, CFLAR, and FAS. Interactions were found for several established and novel anticancer chemotherapeutic, kinase inhibitor, caspase inhibitor, antiangiogenic, and immunomodulatory agents. Aspirin, metformin, atrovastatin, thrombin, androgen and antiandrogen drugs, progesterone, Vitamin D, and Ginsengoside 20(S)-Protopanaxadiol were also documented. Conclusions A bioinformatic data mining strategy identified several anticancer, immunomodulator, and other candidate drugs that may affect the risk of BRONJ in cancer patients.

Ranking of Classification Algorithm in Breast Cancer Based On Estrogen Receptor Using MCDM Technique

Classification algorithm selection is an important concern for breast cancer diagnosis. The traditional routine of adopting a unique performance metric for evaluating classifiers is not adequate in the case of micro-array gene expression dataset. This paper introduces an MCDM technique to evaluate classification algorithms in breast cancer forecasting by seeing different performance measure along with feature space. An empirical study is designed to support an overall assessment of classifiers on micro-array datasets using well-known MCDM technique. TOPSIS is used to rank 11 prominent assessment criteria of different classifiers. First, the sequence order of 20 classifiers along with 11 assessment criteria is generated. Further topmost classifiers are grounded on their performances highlighting the role of feature selection in the overall process supporting the genuine assessment of classifiers over any solitary performance criteria. Result indicates that AdaBoostM1 and Iterative Classifier Optimizer are graded as topmost classifiers without and with feature selection, respectively, grounded on their performances on different measures. Furthermore, the proposed MCDM-based model can reconcile distinct or even inconsistent evaluation performance to grasp a group agreement in a complicated decision-making environment.

Identification of cystic fibrosis transmembrane conductance regulator as a prognostic marker for juvenile myelomonocytic leukemia via the whole-genome bisulfite sequencing of monozygotic twins and data mining.

BackgroundLinked deoxyribonucleic acid (DNA) hypermethylation investigations of promoter methylation levels of candidate genes may help to increase the progressiveness and mortality rates of juvenile myelomonocytic leukemia (JMML), which is a unique myelodysplastic/myeloproliferative neoplasm caused by excessive monocyte and granulocyte proliferation in infancy/early childhood. However, the roles of hypermethylation in this malignant disease are uncertain.MethodsBone marrow samples from a JMML patient and peripheral blood samples from a healthy monozygotic twin and an unrelated healthy donor were collected with the informed consent of the participant’s parents. Whole-genome bisulfite sequencing (WGBS) was then performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to analyze specific differentially methylated region (DMG) related genes. The target genes were screened with Cytoscape and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), which are gene/protein interaction databases. A data mining platform was used to examine the expression level data of the healthy control and JMML patient tissues in Gene Expression Omnibus data sets, and a survival analysis was performed for all the JMML patients.ResultsThe STRING analysis revealed that the red node [i.e., the cystic fibrosis transmembrane conductance regulator (CFTR)] was the gene of interest. The gene-expression microarray data set analysis suggested that the CFTR expression levels did not differ significantly between the JMML patients and healthy controls (P=0.81). A statistically significant difference was observed in the CFTR promoter methylation level but not in the CFTR gene body methylation level. The overall survival analysis demonstrated that a high level of CFTR expression was associated with a worse survival rate in patients with JMML (P=0.039).ConclusionsCFTR promoter hypermethylation may be a novel biomarker for the diagnosis, monitoring of disease progression, and prognosis of JMML.

Analysis of microarray data by genetic algorithm

Microarray gene expression data is used to understand the actions of thousands of genes. Just a few genes out of thousands have a significant impact in any cancer process. Finding these defective genes using experimental data is impractical. To locate the relevant genes, computational techniques are required. A method to identifying cancer candidate genes from microarray data is created. Clustering of similar genes is necessary to find co-expressed genes in different biological conditions. It is important to develop methods to find the few candidate genes for cancer. An optimization process is used for such purpose. A genetic algorithm employs the principles of evolution: selection, recombination, and mutation to solve an optimization problem. Mutual information is used to find the dependency between genes. The two genes are similar if their expression levels are comparable. The similarity as well as positive and negative correlations between genes is considered while clustering them. Interdependence measure tells how the genes are correlated. The genes responsible for a sick state have higher interdependence measures. These genes are defective genes having cancer diagnostic information. Here microarray gene expression datasets from gastric cancer and colon cancer from the public domain are considered.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer.

Simple SummaryBladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Construction of Immune-Related ceRNA Network in Dilated Cardiomyopathy: Based on Sex Differences.

Background: Immune targeted therapy has become an attractive therapeutic approach for patients with dilated cardiomyopathy (DCM) recently. Genetic predisposition and gender play a critical role in immune-related responses of DCM. This study aimed to perform a bioinformatics analysis of molecular differences between male and female samples and identify immune-related ceRNA network in DCM. Methods: The gene expression microarray and clinical features dataset of GSE19303 was downloaded from the GEO. The raw data were preprocessed, followed by identification of differentially expressed genes (DEGs) between male and female DCM samples. Crucial functions and pathway enrichment analysis of DEGs were investigated through GO analysis and KEGG pathway analysis, respectively. A lncRNA–miRNA–mRNA network was constructed and a central module was extracted from the ceRNA network. Results: Compared with the female group, the male group benefits more from IA/IgG immunotherapy. Male patients of DCM had a significant positive correlation with the abundance of inflammatory cells (B cells, memory B cells, CD8+ Tem cells, and NK cells). Sex difference DEGs had a widespread impact on the signaling transduction, transcriptional regulation, and metabolism in DCM. Subsequently, we constructed an immune-related ceRNA network based on sex differences in DCM, including five lncRNAs, six miRNAs, and 29 mRNAs. Furthermore, we extracted a central module from the ceRNA network, including two lncRNAs (XIST and LINC00632), three miRNAs (miR-1-3p, miR-17-5p, and miR-22-3p), and six mRNAs (CBL, CXCL12, ESR1, IGF1R, IL6ST, and STC1). Among these DEGs, CBL, CXCL12, and IL6ST expression was considered to be associated with inflammatory cell infiltration in DCM. Conclusions: The identified ceRNA network and their enriched pathways may provide genetic insights into the phenotypic diversity of female and male patients with DCM and may provide a basis for development of sex-related individualization of immunotherapy.

Recognition of cancer mediating biomarkers using rough approximations enabled intuitionistic fuzzy soft sets based similarity measure

In recent years, gene expression analysis has become crucial in studying microarray and provides several techniques related to computational biology and bioinformatics. This article introduces a novel Intuitionistic fuzzy set under rough multigranulation approximation based feature extraction from microarray gene expression dataset. The proposed model identifies cancer mediating human biomarkers using an Intuitionistic fuzzy soft set based similarity measure. Firstly, Intuitionistic fuzzy rough reduct is devised in the microarray datasets preceded by an entropy based pre-processing step. Intuitionistic fuzzy rough reduct produces the feature sets from the microarray datasets through roughness and accuracy measures that are differentially expressed in a cancerous state. Rough multigranulation approximations reduce the dimensions of the microarray datasets. Thereafter, a weighted similarity measure is designed using the intuitionistic fuzzy soft set for the classification of biomarkers having significant expression patterns from the normal state to the carcinogenic state. The proposed method is demonstrated on six microarray datasets and is validated with different performance metrics resulting in better effectiveness than the state-of-the-art methods.

Identification of hub genes and role of CDKN2A as a biomarker in cervical cancer: An in-silico approach

Cervical cancer is one of the most common gynecological cancers in women and its molecular pathogenesis and disease progression are yet fully understood. Finding new biomarkers is important to detect early diagnosis of cervical cancer to reduce its incidence and mortality rates among women. We have selected 3 microarray gene expression datasets (GSE67522, GSE138080, and GSE75132) and then analysed up-regulated and down-regulated genes in cervical cancer. The hub genes related to this disease were identified from constructed protein-protein interaction network. The statistical significance of key genes was validated with experimental data obtained from TCGA cervical cancer patients. We identified 18 differentially expressed genes from 3 microarray datasets. These genes were highly associated with DNA replication and cell proliferation pathways. Network analysis revealed that CDKN2A as a biomarker for cervical cancer prognosis. The expression and interactions of this gene were analysed with bioinformatic tools. Results of this study showed that CDKN2A has significant interactions with transcription factors, signalling molecules, and miRNAs. In-silico analysis of microarray data can pave the way to predict CDKN2A as a gene target for the diagnosis of cervical cancer. Early diagnosis of this disease would decrease morbidity and mortality among females worldwide.