Screening and Identification of Novel Potential Biomarkers for Breast Cancer Brain Metastases

Brain metastases represent a major cause of mortality among patients with breast cancer, and few effective targeted treatment options are currently available. Development of new biomarkers and therapeutic targets for breast cancer brain metastases (BCBM) is therefore urgently needed. In this study, we compared the gene expression profiles of the brain metastatic cell line MDA-MB-231-BR (231-BR) and its parental MDA-MB-231, and identified a total of 84 genes in the primary screening through a series of bioinformatic analyses, including construction of protein-protein interaction (PPI) networks by STRING database, identification of hub genes by applying of MCODE and Cytohubba algorithms, identification of leading-edge subsets of Gene Set Enrichment Analysis (GSEA), and identification of most up-regulated genes. Eight genes were identified as candidate genes due to their elevated expression in brain metastatic 231-BR cells and prognostic values in patients with BCBM. Then we knocked down the eight individual candidate genes in 231-BR cells and evaluated their impact on cell migration through a wound-healing assay, and four of them (KRT19, FKBP10, GSK3B and SPANXB1) were finally identified as key genes. Furthermore, the expression of individual key genes showed a correlation with the infiltration of major immune cells in the brain tumor microenvironment (TME) as analyzed by Tumor Immune Estimation Resource (TIMER) and Gene Expression Profiling Interactive Analysis (GEPIA), suggesting possible roles of them in regulation of the tumor immune response in TME. Therefore, the present work may provide new potential biomarkers for BCBM. Additionally, using GSEA, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, we determined the top enriched cellular functions or pathways in 231-BR cells, which may help better understand the biology governing the development and progression of BCBM.

Biomarker candidates for progression and clinical management of COVID-19 associated pneumonia at time of admission

COVID-19 pathophysiology is currently not fully understood, reliable prognostic factors remain elusive, and few specific therapeutic strategies have been proposed. In this scenario, availability of biomarkers is a priority. MS-based Proteomics techniques were used to profile the proteome of 81 plasma samples extracted in four consecutive days from 23 hospitalized COVID-19 associated pneumonia patients. Samples from 10 subjects that reached a critical condition during their hospital stay and 10 matched non-severe controls were drawn before the administration of any COVID-19 specific treatment and used to identify potential biomarkers of COVID-19 prognosis. Additionally, we compared the proteome of five patients before and after glucocorticoids and tocilizumab treatment, to assess the changes induced by the therapy on our selected candidates. Forty-two proteins were differentially expressed between patients' evolution groups at 10% FDR. Twelve proteins showed lower levels in critical patients (fold-changes 1.20–3.58), of which OAS3 and COG5 found their expression increased after COVID-19 specific therapy. Most of the 30 proteins over-expressed in critical patients (fold-changes 1.17–4.43) were linked to inflammation, coagulation, lipids metabolism, complement or immunoglobulins, and a third of them decreased their expression after treatment. We propose a set of candidate proteins for biomarkers of COVID-19 prognosis at the time of hospital admission. The study design employed is distinctive from previous works and aimed to optimize the chances of the candidates to be validated in confirmatory studies and, eventually, to play a useful role in the clinical practice.

Comparison of the Respiratory Tractmicrobiome in Hospitalized COVID-19 Patients with Different Disease Severity

Background: Little is known about the characteristics of respiratory tract microbiome in Coronavirus disease 2019 (COVID-19) inpatients with different severity. Methods: A cross-sectional study was conducted to characterize respiratory tract microbial communities of 69 COVID-19 inpatients from 64 nasopharyngeal swabs and 5 sputum specimens using 16S ribosomal RNA (rRNA) gene V3-V4 region sequencing. The bacterial profiles were used to find potential biomarkers by the two-step method, the combination of random forest model and the linear discriminant analysis effect size (LEfSe), and explore the connections with clinical characteristics by Spearman’s rank test.Results: Compared with mild COVID-19 patients, severe patients had significantly decreased bacterial diversity (Pvalues were less than 0.05 in the alpha and beta diversity) and relative lower abundance of opportunistic pathogens, including Actinomyces, Prevotella, Rothia, Streptococcus, Veillonella. Eight potential biomarkers including Treponema, Lachnoanaerobaculum, Parvimonas, Selenomonas, Alloprevotella, Porphyromonas, GemellaandStreptococcus were found to distinguish the mild COVID-19 patients from the severe COVID-19 patients. The genera of Actinomyces andPrevotella were negatively correlated with age and inpatient days. Intensive Care Unit (ICU) admission, neutrophil count (GRA) and lymphocyte count (LYMPH) were significantly correlated with different genera in the two groups. In addition, there were a positive correlation between Klebsiella and white blood cell count (WBC) in two groups.Conclusion: The respiratory tract microbiome had significant difference in COVID-19 patients with different severity. The value of the respiratory tract microbiome as predictive biomarkers for COVID-19 severity merits further exploration.

Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

NMR-Based Metabolomic Analysis of Sera in Mouse Models of CVB3-Induced Viral Myocarditis and Dilated Cardiomyopathy

Viral myocarditis (VMC) is an inflammatory heart condition which can induce dilated cardiomyopathy (DCM). However, molecular mechanisms underlying the progression of VMC into DCM remain exclusive. Here, we established mouse models of VMC and DCM by infecting male BALB/c mice with Coxsackievirus B3 (CVB3), and performed NMR-based metabonomic analyses of mouse sera. The mouse models covered three pathological stages including: acute VMC (aVMC), chronic VMC (cVMC) and DCM. We recorded 1D 1H-NMR spectra on serum samples and conducted multivariate statistical analysis on the NMR data. We found that metabolic profiles of these three pathological stages were distinct from their normal controls (CON), and identified significant metabolites primarily responsible for the metabolic distinctions. We identified significantly disturbed metabolic pathways in the aVMC, cVMC and DCM stages relative to CON, including: taurine and hypotaurine metabolism; pyruvate metabolism; glycine, serine and threonine metabolism; glycerolipid metabolism. Additionally, we identified potential biomarkers for discriminating a VMC, cVMC and DCM from CON including: taurine, valine and acetate for aVMC; glycerol, valine and leucine for cVMC; citrate, glycine and isoleucine for DCM. This work lays the basis for mechanistically understanding the progression from acute VMC to DCM, and is beneficial to exploitation of potential biomarkers for prognosis and diagnosis of heart diseases.

BSG (CD147) Serum Level and Genetic Variants Are Associated with Overall Survival in Acute Myeloid Leukaemia

Basigin (BSG, CD147) is a multifunctional protein involved in cancer cell survival, mostly by controlling lactate transport through its interaction with monocarboxylate transporters (MCTs) such as MCT1. Previous studies have found that single nucleotide polymorphisms (SNPs) in the gene coding for BSG and MCT1, as well as levels of the soluble form of BSG (sBSG), are potential biomarkers in various diseases. The goal of this study was to confirm BSG and MCT1 RNA overexpression in AML cell lines, as well as to analyse soluble BSG levels and selected BSG/MCT1 genetic variants as potential biomarkers in AML patients. We found that BSG and MCT1 were overexpressed in most AML cell lines. Soluble BSG was increased in AML patients compared to healthy controls, and correlated with various clinical parameters. High soluble BSG was associated with worse overall survival, higher bone marrow blast percentage, and higher white blood cell count. BSG SNPs rs4919859 and rs4682, as well as MCT1 SNP rs1049434, were also associated with overall survival of AML patients. In conclusion, this study confirms the importance of BSG/MCT1 in AML, and suggests that soluble BSG and BSG/MCT1 genetic variants may act as potential AML biomarkers.

Proteomics Analysis of Exosomes From Patients With Active Tuberculosis Reveals Infection Profiles and Potential Biomarkers

Although mycobacterial proteins in exosomes from peripheral serum of patients with tuberculosis (TB) have been identified, other exact compositions of exosomes remain unknown. In the present study, a comprehensive proteomics analysis of serum exosomes derived from patients with active TB (ATB) was performed. Exosomes from patients with ATB were characterized using nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), and western blotting analysis. Then identified protein components were quantified by label-free proteomics and were determined via bioinformatics analysis. A total of 123 differential proteins were identified in ATB serum exosomes and analyzed with Gene Ontology (GO) analysis. Among these proteins heat shock protein70 (HSP70), CD81, major histocompatibility complex-I (MHC-I ) and tumor susceptibility gene101 (TSG101) were present in exosomes of ATB and normal individuals confirmed via western blotting. In addition, among identified exosomal proteins lipopolysaccharide binding protein (LBP) increased significantly, but CD36 and MHC-I decreased significantly in ATB exosomes. Meanwhile, MHC-I was down-expressed in serum and peripheral blood mononuclear cells (PBMCs) of ATB, but interestingly CD36 was down-regulated in serum and up-expressed in PBMCs of ATB patients validated with ELISA and flow cytometry. CD36 was up-regulated by M. tuberculosis H37Ra infection in macrophages and suppressed in exosomes from H37Ra infected macrophages detected by western blotting. This study provided a comprehensive description of the exosome proteome in the serum of patients with ATB and revealed certain potential biomarkers associated with TB infection.

IL-17 Pathway Members as Potential Biomarkers of Effective Systemic Treatment and Cardiovascular Disease in Patients with Moderate-to-Severe Psoriasis

Psoriasis is a chronic inflammatory condition associated with atherosclerotic cardiovascular disease (CVD). Systemic anti-psoriatic treatments mainly include methotrexate and biological therapies targeting TNF, IL-12/23 and IL-17A. We profiled plasma proteins from patients with moderate-to-severe psoriasis to explore potential biomarkers of effective systemic treatment and their relationship to CVD. We found that systemically well-treated patients (PASI < 3.0, n = 36) had lower circulating levels of IL-17 pathway proteins compared to untreated patients (PASI > 10, n = 23). Notably, IL-17C and PI3 were decreased with all four examined systemic treatment types. Furthermore, in patients without CVD, we observed strong correlations among IL-17C/PI3/PASI (r ≥ 0.82, p ≤ 1.5 × 10−12) pairs or between IL-17A/PASI (r = 0.72, p = 9.3 × 10−8). In patients with CVD, the IL-17A/PASI correlation was abolished (r = 0.2, p = 0.24) and the other correlations were decreased, e.g., IL-17C/PI3 (r = 0.61, p = 4.5 × 10−5). Patients with moderate-to-severe psoriasis and CVD had lower levels of IL-17A compared to those without CVD (normalized protein expression [NPX] 2.02 vs. 2.55, p = 0.013), and lower IL-17A levels (NPX < 2.3) were associated with higher incidence of CVD (OR = 24.5, p = 0.0028, 95% CI 2.1–1425.1). As a result, in patients with moderate-to-severe psoriasis, we propose circulating IL-17C and PI3 as potential biomarkers of effective systemic anti-psoriatic treatment, and IL-17A as potential marker of CVD.