Biologic and small-molecule therapy for treating moderate to severe atopic dermatitis: Mechanistic considerations.

Atopic dermatitis (AD) is a complex and heterogeneous skin disease for which achieving complete clinical clearance for most patients has proven challenging through single cytokine inhibition. Current studies integrate biomarkers and evaluate their role in AD, aiming to advance our understanding of the diverse molecular profiles implicated. Although traditionally characterized as a TH2-driven disease, extensive research has recently revealed the involvement of TH1, TH17, and TH22 immune pathways as well as the interplay of pivotal immune molecules, such as OX40, OX40 ligand (OX40L), thymic stromal lymphopoietin, and IL-33. This review explores the mechanistic effects of treatments for AD, focusing on mAbs and Janus kinase inhibitors. It describes how these treatments modulate immune pathways and examines their impact on key inflammatory and barrier biomarkers.

Screening of potential hub genes involved in Kidney Wilms tumor via bioinformatics analysis and experimental validation.

BACKGROUND: Wilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys. METHODS: Differential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes' functions in WiT-49 cells. RESULTS: The enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients' prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells. CONCLUSIONS: EMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

Using SWATH-MS to identify new molecular biomarkers in gingival crevicular fluid for detecting periodontitis and its response to treatment.

AIM: To identify new biomarkers to detect untreated and treated periodontitis in gingival crevicular fluid (GCF) using sequential window acquisition of all theoretical mass spectra (SWATH-MS). MATERIALS AND METHODS: GCF samples were collected from 44 periodontally healthy subjects and 40 with periodontitis (Stages III-IV). In the latter, 25 improved clinically 2 months after treatment. Samples were analysed using SWATH-MS, and proteins were identified by the UniProt human-specific database. The diagnostic capability of the proteins was determined with generalized additive models to distinguish the three clinical conditions. RESULTS: In the untreated periodontitis vs. periodontal health modelling, five proteins showed excellent or good bias-corrected (bc)-sensitivity/bc-specificity values of >80%. These were GAPDH, ZG16B, carbonic anhydrase 1, plasma protease inhibitor C1 and haemoglobin subunit beta. GAPDH with MMP-9, MMP-8, zinc-α-2-glycoprotein and neutrophil gelatinase-associated lipocalin and ZG16B with cornulin provided increased bc-sensitivity/bc-specificity of >95%. For distinguishing treated periodontitis vs. periodontal health, most of these proteins and their combinations revealed a predictive ability similar to previous modelling. No model obtained relevant results to differentiate between periodontitis conditions. CONCLUSIONS: New single and dual GCF protein biomarkers showed outstanding results in discriminating untreated and treated periodontitis from periodontal health. Periodontitis conditions were indistinguishable. Future research must validate these findings.

The potential molecular markers of inflammatory response in KOA with AD based on single-cell transcriptome sequencing analysis and identification of ligands by virtual screening.

Alzheimer's disease (AD) and osteoarthritis (OA) are both senile degenerative diseases. Clinical studies have found that OA patients have a significantly increased risk of AD in their later life. This study hypothesized that chronic aseptic inflammation might lead to AD in KOA patients. However, current research has not yet clarified the potential mechanism between AD and KOA. Therefore, this study intends to use KOA transcriptional profiling and single-cell sequencing analysis technology to explore the molecular mechanism of KOA affecting AD development, and screen potential molecular biomarkers and drugs for the prediction, diagnosis, and prognosis of AD in KOA patients. It was found that the higher the expression of TXNIP, MMP3, and MMP13, the higher the risk coefficient of AD was. In addition, the AUC of TXNIP, MMP3, and MMP13 were all greater than 0.70, which had good diagnostic significance for AD. Finally, through the virtual screening of core proteins in FDA drugs and molecular dynamics simulation, it was found that compound Cobicistat could be targeted to TXNIP, Itc could be targeted to MMP3, and Isavuconazonium could be targeted to MMP13. To sum up, TXNIP, MMP3, and MMP13 are prospective molecular markers in KOA with AD, which could be used to predict, diagnose, and prognosis.

Applications of Mass Spectrometry in the Characterization, Screening, Diagnosis, and Prognosis of COVID-19.

Mass spectrometry (MS) is a powerful analytical technique that plays a central role in modern protein analysis and the study of proteostasis. In the field of advanced molecular technologies, MS-based proteomics has become a cornerstone that is making a significant impact in the post-genomic era and as precision medicine moves from the research laboratory to clinical practice. The global dissemination of COVID-19 has spurred collective efforts to develop effective diagnostics, vaccines, and therapeutic interventions. This chapter highlights how MS seamlessly integrates with established methods such as RT-PCR and ELISA to improve viral identification and disease progression assessment. In particular, matrix-assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF-MS) takes the center stage, unraveling intricate details of SARS-CoV-2 proteins, revealing modifications such as glycosylation, and providing insights critical to formulating therapies and assessing prognosis. However, high-throughput analysis of MALDI data presents challenges in manual interpretation, which has driven the development of programmatic pipelines and specialized packages such as MALDIquant. As we move forward, it becomes clear that integrating proteomic data with various omic findings is an effective strategy to gain a comprehensive understanding of the intricate biology of COVID-19 and ultimately develop targeted therapeutic paradigms.

Unveiling the Role of Exosomes in the Pathophysiology of Sepsis: Insights into Organ Dysfunction and Potential Biomarkers.

Extracellular vesicles (EVs) are tools for intercellular communication, mediating molecular transport processes. Emerging studies have revealed that EVs are significantly involved in immune processes, including sepsis. Sepsis, a dysregulated immune response to infection, triggers systemic inflammation and multi-organ dysfunction, posing a life-threatening condition. Although extensive research has been conducted on animals, the complex inflammatory mechanisms that cause sepsis-induced organ failure in humans are still not fully understood. Recent studies have focused on secreted exosomes, which are small extracellular vesicles from various body cells, and have shed light on their involvement in the pathophysiology of sepsis. During sepsis, exosomes undergo changes in content, concentration, and function, which significantly affect the metabolism of endothelia, cardiovascular functions, and coagulation. Investigating the role of exosome content in the pathogenesis of sepsis shows promise for understanding the molecular basis of human sepsis. This review explores the contributions of activated immune cells and diverse body cells' secreted exosomes to vital organ dysfunction in sepsis, providing insights into potential molecular biomarkers for predicting organ failure in septic shock.

Microbial biomarkers of tree water status for next-generation biomonitoring of forest ecosystems.

Next-generation biomonitoring proposes to combine machine-learning algorithms with environmental DNA data to automate the monitoring of the Earth's major ecosystems. In the present study, we searched for molecular biomarkers of tree water status to develop next-generation biomonitoring of forest ecosystems. Because phyllosphere microbial communities respond to both tree physiology and climate change, we investigated whether environmental DNA data from tree phyllosphere could be used as molecular biomarkers of tree water status in forest ecosystems. Using an amplicon sequencing approach, we analysed phyllosphere microbial communities of four tree species (Quercus ilex, Quercus robur, Pinus pinaster and Betula pendula) in a forest experiment composed of irrigated and non-irrigated plots. We used these microbial community data to train a machine-learning algorithm (Random Forest) to classify irrigated and non-irrigated trees. The Random Forest algorithm detected tree water status from phyllosphere microbial community composition with more than 90% accuracy for oak species, and more than 75% for pine and birch. Phyllosphere fungal communities were more informative than phyllosphere bacterial communities in all tree species. Seven fungal amplicon sequence variants were identified as candidates for the development of molecular biomarkers of water status in oak trees. Altogether, our results show that microbial community data from tree phyllosphere provides information on tree water status in forest ecosystems and could be included in next-generation biomonitoring programmes that would use in situ, real-time sequencing of environmental DNA to help monitor the health of European temperate forest ecosystems.

Urine supernatant reveals a signature that predicts survival in clear-cell renal cell carcinoma.

OBJECTIVE: To profile the cell-free urine supernatant and plasma of a small cohort of clear-cell renal cell carcinoma (ccRCC) patients by measuring the relative concentrations of 92 proteins related to inflammation. Using The Cancer Genome Atlas (TCGA), we then performed a targeted mRNA analysis of genes encoding the above proteins and defined their effects on overall survival (OS). SUBJECTS/PATIENTS AND METHODS: Samples were collected prospectively from ccRCC patients. A multiplex proximity extension assay was used to measure the concentrations of 92 inflammation-related proteins in cell-free urine supernatants and plasma. Transcriptomic and clinical information from ccRCC patients was obtained from TCGA. Unsupervised clustering and differential protein expression analyses were performed on protein concentration data. Targeted mRNA analysis on genes encoding significant differentially expressed proteins was performed using TCGA. Backward stepwise regression analyses were used to build a nomogram. The performance of the nomogram and clinical benefit was assessed by discrimination and calibration, and a decision curve analysis, respectively. RESULTS: Unsupervised clustering analysis revealed inflammatory signatures in the cell-free urine supernatant of ccRCC patients. Backward stepwise regressions using TCGA data identified transcriptomic risk factors and risk groups associated with OS. A nomogram to predict 2-year and 5-year OS was developed using these risk factors. The decision curve analysis showed that our model was associated with a net benefit improvement compared to the treat-all/none strategies. CONCLUSION: We defined four novel biomarkers using proteomic and transcriptomic data that distinguish severity of prognosis in ccRCC. We showed that these biomarkers can be used in a model to predict 2-year and 5-year OS in ccRCC across different tumour stages. This type of analysis, if validated in the future, provides non-invasive prognostic information that could inform either management or surveillance strategies for patients.

Endogenous and viral microRNAs in nasal secretions of water buffaloes (Bubalus bubalis) after Bubaline alphaherpesvirus 1 (BuHV-1) challenge infection.

Bubaline alphaherpesvirus 1 (BuHV-1) is a pathogen of water buffaloes responsible for economic loss worldwide. MicroRNAs (miRNAs) regulate gene expression produced by alphaherpesviruses and hosts. This study aimed at (a) unravelling the ability of BuHV-1 to produce miRNAs, including hv1-miR-B6, hv1-miR-B8, hv1-miR-B9; (b) measuring the host immune-related miRNAs associated to herpesvirus infection, including miR-210-3p, miR-490-3p, miR-17-5p, miR-148a-3p, miR-338-3p, miR-370-3p, by RT-qPCR; (c) identifying candidate markers of infection by receiver-operating characteristic (ROC) curves; (d) exploiting the biological functions by pathway enrichment analyses. Five water buffaloes BuHV-1 and Bovine alphaherpesvirus 1 (BoHV-1) free were immunized against Infectious Bovine Rhinotracheitis (IBR). Five additional water buffaloes served as negative controls. All animals were challenged with a virulent wild-type (wt) BuHV-1 via the intranasal route 120 days after the first vaccination. Nasal swabs were obtained at days (d) 0, 2, 4, 7, 10, 15, 30, and 63 post-challenge (pc). The animals of both groups shed wt BuHV-1 up to d7 pc. Results demonstrated that (a) miRNAs produced by the host and BuHV-1 could be efficiently quantified in the nasal secretion up to d63 and d15 pc, respectively; b) the levels of host and BuHV-1 miRNAs are different between vaccinated and control buffaloes; c) miR-370-3p discriminated vaccinated and control animals; d) host immune-related miRNAs may modulate genes involved in the cell adhesion pathway of the neuronal and immune system. Overall, the present study provides evidence that miRNAs can be detected in nasal secretions of water buffaloes and that their expression is modulated by BuHV-1.

An Update on Potential Molecular Biomarkers of Dietary Phytochemicals Targeting Lung Cancer Interception and Prevention.

Since ancient times, dietary phytochemicals are known for their medicinal properties. They are broadly classified into polyphenols, terpenoids, alkaloids, phytosterols, and organosulfur compounds. Currently, there is considerable interest in their potential health effects against various diseases, including lung cancer. Lung cancer is the leading cause of cancer deaths with an average of five-year survival rate of lung cancer patients limited to just 14%. Identifying potential early molecular biomarkers of pre-malignant lung cancer cells may provide a strong basis to develop early cancer detection and interception methods. In this review, we will discuss molecular changes, including genetic alterations, inflammation, signal transduction pathways, redox imbalance, epigenetic and proteomic signatures associated with initiation and progression of lung carcinoma. We will also highlight molecular targets of phytochemicals during lung cancer development. These targets mainly consist of cellular signaling pathways, epigenetic regulators and metabolic reprogramming. With growing interest in natural products research, translation of these compounds into new cancer prevention approaches to medical care will be urgently needed. In this context, we will also discuss the overall pharmacokinetic challenges of phytochemicals in translating to humans. Lastly, we will discuss clinical trials of phytochemicals in lung cancer patients.

Accuracy of periodontitis diagnosis obtained using multiple molecular biomarkers in oral fluids: A systematic review and meta-analysis.

AIM: To determine the accuracy of biomarker combinations in gingival crevicular fluid (GCF) and saliva through meta-analysis to diagnose periodontitis in systemically healthy subjects. METHODS: Studies on combining two or more biomarkers providing a binary classification table, sensitivity/specificity values or group sizes in subjects diagnosed with periodontitis were included. The search was performed in August 2022 through PUBMED, EMBASE, Cochrane, LILACS, SCOPUS and Web of Science. The methodological quality of the articles selected was evaluated using the QUADAS-2 checklist. Hierarchical summary receiver operating characteristic modelling was employed to perform the meta-analyses (CRD42020175021). RESULTS: Twenty-one combinations in GCF and 47 in saliva were evaluated. Meta-analyses were possible for six salivary combinations (median sensitivity/specificity values): IL-6 with MMP-8 (86.2%/80.5%); IL-1ß with IL-6 (83.0%/83.7%); IL-1ß with MMP-8 (82.7%/80.8%); MIP-1α with MMP-8 (71.0%/75.6%); IL-1ß, IL-6 and MMP-8 (81.8%/84.3%); and IL-1ß, IL-6, MIP-1α and MMP-8 (76.6%/79.7%). CONCLUSIONS: Two-biomarker combinations in oral fluids show high diagnostic accuracy for periodontitis, which is not substantially improved by incorporating more biomarkers. In saliva, the dual combinations of IL-1ß, IL-6 and MMP-8 have an excellent ability to detect periodontitis and a good capacity to detect non-periodontitis. Because of the limited number of biomarker combinations evaluated, further research is required to corroborate these observations.

Biomarkers for diagnosis of stage III, grade C with molar incisor pattern periodontitis in children and young adults: a systematic review and meta-analysis.

AIM: To explore the existing salivary, gingival crevicular fluid (GCF), blood, and serum biomarkers associated with grade C molar-incisor pattern (C/MIP) periodontitis in systemically healthy children and young adults. MATERIALS AND METHODS: Cross-sectional, case-control, and cohort studies on stage III grade C periodontitis or former equivalent diagnosis with analysis of molecular biomarkers in saliva, GCF, blood, or serum were retrieved from six databases and screened based on the eligibility criteria. The risk of bias in included studies was evaluated. Meta-analysis was planned for biomarkers assessed using the same detection methods and sample type in at least two papers. RESULTS: Out of 5621 studies identified at initial screening, 28 papers were included in the qualitative analysis of which 2 were eligible for meta-analysis for IgG in serum samples. Eighty-seven biomarkers were assessed with the majority being higher in cases than in controls. Only the meta-analysis of total serum IgG with low heterogeneity value revealed a significant increase in its levels in C/MIPs compared to controls (standardised mean difference: 1.08; 95% CI: 0.76, 1.40). CONCLUSION: There is a paucity of data on biomarkers associated with molar-incisor pattern periodontitis. Although serum IgG levels are raised, other more specific biomarkers in saliva, GCF, and blood/serum may be promising but require further investigation.

Assessing the Feasibility of a Multimodal Approach to Pain Evaluation in Early Stages after Spinal Cord Injury.

This research evaluates the feasibility of a multimodal pain assessment protocol during rehabilitation following spinal cord injury (SCI). The protocol amalgamates clinical workup (CW), quantitative sensory testing (QST), and psychosocial factors (PSF) administered at 4 (T1), 12 (T2), and 24 (T3) weeks post injury and at discharge (T4). Molecular blood biomarkers (BB) were evaluated via gene expression and proteomic assays at T1 and T4. Different pain trajectories and temporal changes were identified using QST, with inflammation and pain-related biomarkers recorded. Higher concentrations of osteopontin and cystatin-C were found in SCI patients compared to healthy controls, indicating their potential as biomarkers. We observed altered inflammatory responses and a slight increase in ICAM-1 and CCL3 were noted, pointing towards changes in cellular adhesion linked with spinal injury and a possible connection with neuropathic pain. Despite a small patient sample hindering the correlation of feasibility data, descriptive statistical analyses were conducted on stress, depression, anxiety, quality of life, and pain interferences. The SCI Pain Instrument (SCIPI) was efficient in distinguishing between nociceptive and neuropathic pain, showing a progressive increase in severity over time. The findings emphasize the need for the careful consideration of recruitment setting and protocol adjustments to enhance the feasibility of multimodal pain evaluation studies post SCI. They also shed light on potential early adaptive mechanisms in SCI pathophysiology, warranting the further exploration of prognostic and preventive strategies for chronic pain in the SCI population.

Endocrine Disrupting Chemicals Influence Hub Genes Associated with Aggressive Prostate Cancer.

Prostate cancer (PCa) is one of the most frequently diagnosed cancers among men in the world. Its prevention has been limited because of an incomplete understanding of how environmental exposures to chemicals contribute to the molecular pathogenesis of aggressive PCa. Environmental exposures to endocrine-disrupting chemicals (EDCs) may mimic hormones involved in PCa development. This research aims to identify EDCs associated with PCa hub genes and/or transcription factors (TF) of these hub genes in addition to their protein-protein interaction (PPI) network. We are expanding upon the scope of our previous work, using six PCa microarray datasets, namely, GSE46602, GSE38241, GSE69223, GSE32571, GSE55945, and GSE26126, from the NCBI/GEO, to select differentially expressed genes based on |log2FC| (fold change) ≥ 1 and an adjusted p-value < 0.05. An integrated bioinformatics analysis was used for enrichment analysis (using DAVID.6.8, GO, KEGG, STRING, MCODE, CytoHubba, and GeneMANIA). Next, we validated the association of these PCa hub genes in RNA-seq PCa cases and controls from TCGA. The influence of environmental chemical exposures, including EDCs, was extrapolated using the chemical toxicogenomic database (CTD). A total of 369 overlapping DEGs were identified associated with biological processes, such as cancer pathways, cell division, response to estradiol, peptide hormone processing, and the p53 signaling pathway. Enrichment analysis revealed five up-regulated (NCAPG, MKI67, TPX2, CCNA2, CCNB1) and seven down-regulated (CDK1, CCNB2, AURKA, UBE2C, BUB1B, CENPF, RRM2) hub gene expressions. Expression levels of these hub genes were significant in PCa tissues with high Gleason scores ≥ 7. These identified hub genes influenced disease-free survival and overall survival of patients 60-80 years of age. The CTD studies showed 17 recognized EDCs that affect TFs (NFY, CETS1P54, OLF1, SRF, COMP1) that are known to bind to our PCa hub genes, namely, NCAPG, MKI67, CCNA2, CDK1, UBE2C, and CENPF. These validated differentially expressed hub genes can be potentially developed as molecular biomarkers with a systems perspective for risk assessment of a wide-ranging list of EDCs that may play overlapping and important role(s) in the prognosis of aggressive PCa.

Emerging RNA-Based Therapeutic and Diagnostic Options: Recent Advances and Future Challenges in Genitourinary Cancers.

Renal cell carcinoma, bladder cancer, and prostate cancer are the most widespread genitourinary tumors. Their treatment and diagnosis have significantly evolved over recent years, due to an increasing understanding of oncogenic factors and the molecular mechanisms involved. Using sophisticated genome sequencing technologies, the non-coding RNAs, such as microRNAs, long non-coding RNAs, and circular RNAs, have all been implicated in the occurrence and progression of genitourinary cancers. Interestingly, DNA, protein, and RNA interactions with lncRNAs and other biological macromolecules drive some of these cancer phenotypes. Studies on the molecular mechanisms of lncRNAs have identified new functional markers that could be potentially useful as biomarkers for effective diagnosis and/or as targets for therapeutic intervention. This review focuses on the mechanisms underlying abnormal lncRNA expression in genitourinary tumors and discusses their role in diagnostics, prognosis, and treatment.

Non-invasive molecular biomarkers for predicting outcomes of micro-TESE in patients with idiopathic non-obstructive azoospermia.

Non-obstructive azoospermia (NOA), the most severe type of male infertility, affects approximately 1% of men worldwide. However, the aetiology of most NOA cases is not definite, that is defined as idiopathic NOA (INOA), posing a clinical conundrum worldwide. Most of these patients must receive donor sperm treatment until the emergence of microdissection testicular sperm extraction (micro-TESE). Although this procedure has recently become a promising treatment for INOA, the low sperm retrieval rate and testicular trauma have prompted us to explore appropriate non-invasive molecular biomarkers to predict the outcomes of sperm recovery preoperatively. Previous studies have identified a spectrum of biomarkers to address this challenging issue at various levels in different tissues, such as DNAs, RNAs, protein and steroid levels in the blood and seminal fluid. To better understand and assess the predictive values of diverse molecular biomarkers from different tissues on the outcome of sperm retrieval by micro-TESE in patients with INOA, we summarised recent findings and discussed the potential applications of these methods. The ultimate goal of this study was to provide references for further studies and clinical management.

Molecular biomarkers correlate with brain grey and white matter changes in patients with mitochondrial m.3243A > G mutation.

INTRODUCTION: The mitochondrial DNA (mtDNA) m.3243A > G mutation in the MT-TL1 gene results in a multi-systemic disease, that is commonly associated with neurodegenerative changes in the brain. METHODS: Seventeen patients harboring the m3243A > G mutation were enrolled (age 43.1 ± 11.4 years, 10 M/7F). A panel of plasma biomarkers including lactate acid, alanine, L-arginine, fibroblast growth factor 21 (FGF-21), growth/differentiation factor 15 (GDF-15) and circulating cell free -mtDNA (ccf-mtDNA), as well as blood, urine and muscle mtDNA heteroplasmy were evaluated. Patients also underwent a brain standardized MR protocol that included volumetric T1-weighted images and diffusion-weighted MRI. Twenty sex- and age-matched healthy controls were included. Voxel-wise analysis was performed on T1-weighted and diffusion imaging, respectively with VBM (voxel-based morphometry) and TBSS (Tract-based Spatial Statistics). Ventricular lactate was also evaluated by 1H-MR spectroscopy. RESULTS: A widespread cortical gray matter (GM) loss was observed, more severe (p < 0.001) in the bilateral calcarine, insular, frontal and parietal cortex, along with infratentorial cerebellar cortex. High urine mtDNA mutation load, high levels of plasma lactate and alanine, low levels of plasma arginine, high levels of serum FGF-21 and ventricular lactate accumulation significantly (p < 0.05) correlated with the reduced brain GM density. Widespread microstructural alterations were highlighted in the white matter, significantly (p < 0.05) correlated with plasma alanine and arginine levels, with mtDNA mutation load in urine, with high level of serum GDF-15 and with high content of plasma ccf-mtDNA. CONCLUSIONS: Our results suggest that the synergy of two pathogenic mechanisms, mtDNA-related mitochondrial respiratory deficiency and defective nitric oxide metabolism, contributes to the brain neurodegeneration in m.3243A > G patients.

Microfluidic systems for the analysis of blood-derived molecular biomarkers.

Biomarkers are relevant indicators of the physiological state of an individual. Although biomarkers can be found in diseased tissue and different biofluids, sampling from blood plasma is relatively easy and less invasive. Among the molecular biomarkers that can be found circulating in plasma are proteins, metabolites, nucleic acids, and exosomes. Some of these plasma-circulating biomarkers are now employed for patient stratification in a broad range of diseases with high sensitivity and specificity and are useful in early diagnosis, initial risk assessment, and therapy selection. However, there is a pressing need to develop novel approaches for biomarker analysis that can be translated into clinical or other settings without complex methodologies or instrumentation. Microfluidics has been touted as a promising technology to carry out this task because it offers high-throughput, automation, multiplexed detection, and portability, possibly overcoming the bottleneck that prevent the translation of novel biomarkers to the point-of-care (POC). Here, we provide a review of the microfluidic systems that have been engineered to detect circulating molecular biomarkers in blood plasma. We also review the different microfluidic approaches for plasma enrichment, which are now being integrated with microfluidic-based biomarker analyzers. Such integration should lead to cost-effective solutions in in vitro diagnostics, with special relevance to POC platforms.

Identification of a key glioblastoma candidate gene, FUBP3, based on weighted gene co-expression network analysis.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common aggressive malignant brain tumor. However, the molecular mechanism of glioblastoma formation is still poorly understood. To identify candidate genes that may be connected to glioma growth and development, weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network between gene sets and clinical characteristics. We also explored the function of the key candidate gene. METHODS: Two GBM datasets were selected from GEO Datasets. The R language was used to identify differentially expressed genes. WGCNA was performed to construct a gene co-expression network in the GEO glioblastoma samples. A custom Venn diagram website was used to find the intersecting genes. The GEPIA website was applied for survival analysis to determine the significant gene, FUBP3. OS, DSS, and PFI analyses, based on the UCSC Cancer Genomics Browser, were performed to verify the significance of FUBP3. Immunohistochemistry was performed to evaluate the expression of FUBP3 in glioblastoma and adjacent normal tissue. KEGG and GO enrichment analyses were used to reveal possible functions of FUBP3. Microenvironment analysis was used to explore the relationship between FUBP3 and immune infiltration. Immunohistochemistry was performed to verify the results of the microenvironment analysis. RESULTS: GSE70231 and GSE108474 were selected from GEO Datasets, then 715 and 694 differentially expressed genes (DEGs) from GSE70231 and GSE108474, respectively, were identified. We then performed weighted gene co-expression network analysis (WGCNA) and identified the most downregulated gene modules of GSE70231 and GSE108474, and 659 and 3915 module genes from GSE70231 and GSE108474, respectively, were selected. Five intersection genes (FUBP3, DAD1, CLIC1, ABR, and DNM1) were calculated by Venn diagram. FUBP3 was then identified as the only significant gene by survival analysis using the GEPIA website. OS, DSS, and PFI analyses verified the significance of FUBP3. Immunohistochemical analysis revealed FUBP3 expression in GBM and adjacent normal tissue. KEGG and GO analyses uncovered the possible function of FUBP3 in GBM. Tumor microenvironment analysis showed that FUBP3 may be connected to immune infiltration, and immunohistochemistry identified a positive correlation between immune cells (CD4 + T cells, CD8 + T cells, and macrophages) and FUBP3. CONCLUSION: FUBP3 is associated with immune surveillance in GBM, indicating that it has a great impact on GBM development and progression. Therefore, interventions involving FUBP3 and its regulatory pathway may be a new approach for GBM treatment.

Data analysis methods for defining biomarkers from omics data.

Omics mainly includes genomics, epigenomics, transcriptomics, proteomics and metabolomics. The rapid development of omics technology has opened up new ways to study disease diagnosis and prognosis and to define prospective information of complex diseases. Since omics data are usually large and complex, the method used to analyze the data and to define important information is crucial in omics study. In this review, we focus on advances in biomarker discovery methods based on omics data in the last decade, and categorize them as individual feature analysis, combinatorial feature analysis and network analysis. We also discuss the challenges and perspectives in this field.