M1 Polarization Markers Are Upregulated in Basal-Like Breast Cancer Molecular Subtype and Associated With Favorable Patient Outcome

Background: Breast cancer heterogeneity is an essential element that plays a role in the therapy response variability and the patient’s outcome. This highlights the need for more precise subtyping methods that focus not only on tumor cells but also investigate the profile of stromal cells as well as immune cells. Objectives: To mine publicly available transcriptomic breast cancer datasets and reanalyze their transcriptomic profiling using unsupervised clustering in order to identify novel subsets in molecular subtypes of breast cancer, then explore the stromal and immune cells profile in each subset using bioinformatics and systems immunology approaches. Materials and Methods: Transcriptomic data from 1,084 breast cancer patients obtained from The Cancer Genome Atlas (TCGA) database were extracted and subjected to unsupervised clustering using a recently described, multi-step algorithm called Iterative Clustering and Guide-gene Selection (ICGS). For each cluster, the stromal and immune profile was investigated using ESTIMATE and CIBERSORT analytical tool. Clinical outcomes and differentially expressed genes of the characterized clusters were identified and validated in silico and in vitro in a cohort of 80 breast cancer samples by immunohistochemistry. Results: Seven unique sub-clusters showed distinct molecular and clinical profiles between the well-known breast cancer subtypes. Those unsupervised clusters identified more homogenous subgroups in each of the classical subtypes with a different prognostic profile. Immune profiling of the identified clusters showed that while the classically activated macrophages (M1) are correlated with the more aggressive basal-like breast cancer subtype, the alternatively activated macrophages (M2) showed a higher level of infiltration in luminal A and luminal B subtypes. Indeed, patients with higher levels of M1 expression showed less advanced disease and better patient outcomes presented as prolonged overall survival. Moreover, the M1 high basal-like breast cancer group showed a higher expression of interferon-gamma induced chemokines and guanylate-binding proteins (GBPs) involved in immunity against microbes. Conclusion: Adding immune profiling using transcriptomic data can add precision for diagnosis and prognosis and can cluster patients according to the available modalities of therapy in a more personalized approach

Identifying Asthma genetic signature patterns by mining Gene Expression BIG Datasets using Image Filtering Algorithms

Asthma is a treatable but incurable chronic inflammatory disease affecting more than 14% of the UAE population. Asthma is still a clinical dilemma as there is no proper clinical definition of asthma, unknown definitive underlying mechanisms, no objective prognostic tool nor bedside noninvasive diagnostic test to predict complication or exacerbation. Big Data in the form of publicly available transcriptomics can be a valuable source to decipher complex diseases like asthma. Such an approach is hindered by technical variations between different studies that may mask the real biological variations and meaningful, robust findings. A large number of datasets of gene expression microarray images need a powerful tool to properly translate the image intensities into truly differential expressed genes between conditioned examined from the noise. Here we used a novel bioinformatic method based on the coefficient of variance to filter nonvariant probes with stringent image analysis processing between asthmatic and healthy to increase the power of identifying accurate signals hidden within the heterogeneous nature of asthma. Our analysis identified important signaling pathways members, namely NFKB and TGFB pathways, to be differentially expressed between severe asthma and healthy controls. Those vital pathways represent potential targets for future asthma treatment and can serve as reliable biomarkers for asthma severity. Proper image analysis for the publicly available microarray transcriptomics data increased its usefulness to decipher asthma and identify genuine differentially expressed genes that can be validated across different datasets

An Integrative Phenotype-Genotype Approach Using Phenotypic Characteristics from the UAE National Diabetes Study Identifies HSD17B12 as a Candidate Gene for Obesity and Type 2 Diabetes

The United Arab Emirates National Diabetes and Lifestyle Study (UAEDIAB) has identified obesity, hypertension, obstructive sleep apnea, and dyslipidemia as common phenotypic characteristics correlated with diabetes mellitus status. As these phenotypes are usually linked with genetic variants, we hypothesized that these phenotypes share single nucleotide polymorphism (SNP)-clusters that can be used to identify causal genes for diabetes. We explored the National Human Genome Research Institute-European Bioinformatics Institute Catalog of Published Genome-Wide Association Studies (NHGRI-EBI GWAS) to list SNPs with documented association with the UAEDIAB-phenotypes as well as diabetes. The shared chromosomal regions affected by SNPs were identified, intersected, and searched for Enriched Ontology Clustering. The potential SNP-clusters were validated using targeted DNA next-generation sequencing (NGS) in two Emirati diabetic patients. RNA sequencing from human pancreatic islets was used to study the expression of identified genes in diabetic and non-diabetic donors. Eight chromosomal regions containing 46 SNPs were identified in at least four out of the five UAEDIAB-phenotypes. A list of 34 genes was shown to be affected by those SNPs. Targeted NGS from two Emirati patients confirmed that the identified genes have similar SNP-clusters. ASAH1, LRP4, FES, and HSD17B12 genes showed the highest SNPs rate among the identified genes. RNA-seq analysis revealed high expression levels of HSD17B12 in human islets and to be upregulated in type 2 diabetes (T2D) donors. Our integrative phenotype-genotype approach is a novel, simple, and powerful tool to identify clinically relevant potential biomarkers in diabetes. HSD17B12 is a novel candidate gene for pancreatic β-cell function

DKK3’s protective role in prostate cancer is partly due to the modulation of immune-related pathways

While it is considered one of the most common cancers and the leading cause of death in men worldwide, prognostic stratification and treatment modalities are still limited for patients with prostate cancer (PCa). Recently, the introduction of genomic profiling and the use of new techniques like next-generation sequencing (NGS) in many cancers provide novel tools for the discovery of new molecular targets that might improve our understanding of the genomic aberrations in PCa and the discovery of novel prognostic and therapeutic targets. In this study, we investigated the possible mechanisms through which Dickkopf-3 (DKK3) produces its possible protective role in PCa using NGS in both the DKK3 overexpression PCa cell line (PC3) model and our patient cohort consisting of nine PCa and five benign prostatic hyperplasia. Interestingly, our results have shown that DKK3 transfection-modulated genes are involved in the regulation of cell motility, senescence-associated secretory phenotype (SASP), and cytokine signaling in the immune system, as well as in the regulation of adaptive immune response. Further analysis of our NGS using our in vitro model revealed the presence of 36 differentially expressed genes (DEGs) between DKK3 transfected cells and PC3 empty vector. In addition, both CP and ACE2 genes were differentially expressed not only between the transfected and empty groups but also between the transfected and Mock cells. The top common DEGs between the DKK3 overexpression cell line and our patient cohort are the following: IL32, IRAK1, RIOK1, HIST1H2BB, SNORA31, AKR1B1, ACE2, and CP. The upregulated genes including IL32, HIST1H2BB, and SNORA31 showed tumor suppressor functions in various cancers including PCa. On the other hand, both IRAK1 and RIOK1 were downregulated and involved in tumor initiation, tumor progression, poor outcome, and radiotherapy resistance. Together, our results highlighted the possible role of the DKK3-related genes in protecting against PCa initiation and progression

Confounding patient factors affecting the proper interpretation of the periostin level as a biomarker in asthma development

Introduction: The proper use of serum periostin (POSTN) as a biomarker for asthma is hindered by inconsistent performance in different clinical settings. Objective: To explore patient’s factors that may affect POSTN expression locally and systematically and its utility as a biomarker for asthma development. Materials and Methods: Here we used bioinformatics analysis of publicly available transcriptomics data to confirm that POSTN is an asthma specific gene involved in core signaling pathways enriched in the bronchial epithelium during asthma. We then explored a large number of datasets to identify possible confounders that may affect the POSTN gene expression and consequently, its interpretation as a reliable biomarker for asthma. Plasma and saliva levels of POSTN were determined in locally recruited asthmatic patients (mild, moderate and severe) compared to healthy controls to confirm the bioinformatics findings. Results: Our bioinformatics results confirmed that POSTN was consistently upregulated in the bronchial epithelium in asthma, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) bronchial epithelium. In asthma, its mRNA expression was affected by gender, sample anatomical site and type, steroid therapy, and smoking. In our cohort, plasma POSTN was upregulated in severe and non-severe asthmatic patients. Saliva POSTN was significantly higher in non-severe asthmatic patients compared to healthy and severe asthmatic patients (specifically those who are not on Xolair (omalizumab)). Patients’ BMI, inhaled steroid use and Xolair treatment affected POSTN plasma levels. Conclusion: Up to our knowledge, this is the first study examining the level of POSTN in the saliva of asthmatic patients. Both plasma and saliva POSTN levels can aid in early diagnosis of asthma. Saliva POSTN level was more sensitive than plasma POSTN in differentiating between severe and non-severe asthmatics. Patients’ characteristics like BMI, the use of inhaled steroids, or Xolair treatment should be carefully reviewed before any meaningful interpretation of POSTN level in clinical practice

Wnt Signaling Is Deranged in Asthmatic Bronchial Epithelium and Fibroblasts

Both canonical and non-canonical Wnt signaling pathway alterations have been documented in pulmonary disease pathogenesis and progression; therefore, they can be an attractive target for pharmaceutical management of severe asthma. Wnt/β-catenin signaling was shown to link early embryonic lung development impairment to later in life asthmatic airway remodeling. Here we explored the changes in Wnt signaling associated with asthma initiation and progression in epithelial and fibroblasts using a comprehensive approach based on in silico analysis and followed by in vitro validation. In summary, the in silico analysis showed that the bronchial epithelium of severe asthmatic patients showed a deranged balance between Wnt enhancer and Wnt inhibitors. A Th2-high phenotype is associated with upregulated Wnt-negative regulators, while inflammatory and neutrophilic severe asthmatics showed higher canonical Wnt signaling member enrichment. Most of these genes are regulators of healthy lung development early in life and, if disturbed, can make people susceptible to developing asthma early in life and prone to developing a severe phenotype. Most of the Wnt members are secreted, and their effect can be in an autocrine fashion on the bronchial epithelium, paracrine on nearby adjacent structural cells like fibroblasts and smooth muscles, or systemic in blood. Our results showed that canonical Wnt signaling is needed for the proper response of cells to proliferative stimuli, which puts cells under stress. Cells in response to this proliferative stress will activate the senescence mechanism, which is also dependent on Wnt signaling. Inhibition of Wnt signaling using FH535 inhibits both proliferation and senescence markers in bronchial fibroblasts compared to DMSO-treated cells. In fibroblasts from asthmatic patients, inhibition of Wnt signaling did not show that effect as the Wnt signaling is deranged besides other pathways that might be non-functional

Derangement of cell cycle markers in peripheral blood mononuclear cells of asthmatic patients as a reliable biomarker for asthma control

In asthma, most of the identified biomarkers pertain to the Th2 phenotype and no known biomarkers have been verified for severe asthmatics. Therefore, identifying biomarkers using the integrative phenotype-genotype approach in severe asthma is needed. The study aims to identify novel biomarkers as genes or pathways representing the core drivers in asthma development, progression to the severe form, resistance to therapy, and tissue remodeling regardless of the sample cells or tissues examined. Comprehensive reanalysis of publicly available transcriptomic data that later was validated in vitro, and locally recruited patients were used to decipher the molecular basis of asthma. Our in-silicoanalysis revealed a total of 10 genes (GPRC5A, SFN, ABCA1, KRT8, TOP2A, SERPINE1, ANLN, MKI67, NEK2, and RRM2) related to cell cycle and proliferation to be deranged in the severe asthmatic bronchial epithelium and fibroblasts compared to their healthy counterparts. In vitro, RT qPCR results showed that (SERPINE1 and RRM2) were upregulated in severe asthmatic bronchial epithelium and fibroblasts, (SFN, ABCA1, TOP2A, SERPINE1, MKI67, and NEK2) were upregulated in asthmatic bronchial epithelium while (GPRC5A and KRT8) were upregulated only in asthmatic bronchial fibroblasts. Furthermore, MKI76, RRM2, and TOP2A were upregulated in Th2 high epithelium while GPRC5A, SFN, ABCA1 were upregulated in the blood of asthmatic patients. SFN, ABCA1 were higher, while MKI67 was lower in severe asthmatic with wheeze compared to nonasthmatics with wheezes. SERPINE1 and GPRC5A were downregulated in the blood of eosinophilic asthmatics, while RRM2 was upregulated in an acute attack of asthma. Validation of the gene expression in PBMC of locally recruited asthma patients showed that SERPINE1, GPRC5A, SFN, ABCA1, MKI67, and RRM2 were downregulated in severe uncontrolled asthma. We have identified a set of biologically crucial genes to the homeostasis of the lung and in asthma development and progression. This study can help us further understand the complex interplay between the transcriptomic data and the external factors which may deviate our understanding of asthma heterogeneity

Blood and Salivary Amphiregulin Levels as Biomarkers for Asthma

BACKGROUND: mphiregulin (AREG) expression in asthmatic airways and sputum was shown to increase and correlate with asthma. However, no studies were carried out to evaluate the AREG level in blood and saliva of asthmatic patients. OBJECTIVE: To measure circulating AREG mRNA and protein concentrations in blood, saliva, and bronchial biopsies samples from asthmatic patients. METHODS: Plasma and Saliva AREG protein concentrations were measured using ELISA while PBMCs, and Saliva mRNA expression was measured by RT qPCR in non-severe, and severe asthmatic patients compared to healthy controls. Primary asthmatic bronchial epithelial cells and fibroblasts were assessed for AREG mRNA expression and released soluble AREG in their conditioned media. Tissue expression of AREG was evaluated using immunohistochemistry of bronchial biopsies from asthmatic patients and healthy controls. Publicly available transcriptomic databases were explored for the global transcriptomic profile of bronchial epithelium, and PBMCs were explored for AREG expression in asthmatic vs. healthy controls. RESULTS: Asthmatic patients had higher AREG protein levels in blood and saliva compared to control subjects. Higher mRNA expression in saliva and primary bronchial epithelial cells plus higher AREG immunoreactivity in bronchial biopsies were also observed. Both blood and saliva AREG levels showed positive correlations with allergic rhinitis status, atopy status, eczema status, plasma periostin, neutrophilia, Montelukast sodium use, ACT score, FEV1, and FEV1/FVC. In silico analysis showed that severe asthmatic bronchial epithelium with high AREG gene expression is associated with higher neutrophils infiltration. CONCLUSION: AREG levels measured in a minimally invasive blood sample and a non-invasive saliva sample are higher in non-allergic severe asthma