Candidate Biomarkers Research Articles

Causality-driven candidate identification for reliable DNA methylation biomarker discovery

Despite vast data support in DNA methylation (DNAm) biomarker discovery to facilitate health-care research, this field faces huge resource barriers due to preliminary unreliable candidates and the consequent compensations using expensive experiments. The underlying challenges lie in the confounding factors, especially measurement noise and individual characteristics. To achieve reliable identification of a candidate pool for DNAm biomarker discovery, we propose a Causality-driven Deep Regularization framework to reinforce correlations that are suggestive of causality with disease. It integrates causal thinking, deep learning, and biological priors to handle non-causal confounding factors, through a contrastive scheme and a spatial-relation regularization that reduces interferences from individual characteristics and noises, respectively. The comprehensive reliability of the proposed method was verified by simulations and applications involving various human diseases, sample origins, and sequencing technologies, highlighting its universal biomedical significance. Overall, this study offers a causal-deep-learning-based perspective with a compatible tool to identify reliable DNAm biomarker candidates, promoting resource-efficient biomarker discovery.

Identification of IGFBP3 and LGALS1 as potential secreted biomarkers for clear cell renal cell carcinoma based on bioinformatics analysis and machine learning.

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC). Due to the lack of symptoms until advanced stages, early diagnosis of ccRCC is challenging. Therefore, the identification of novel secreted biomarkers for the early detection of ccRCC is urgently needed. This study aimed to identify novel secreted biomarkers for diagnosing ccRCC using bioinformatics and machine learning techniques based on transcriptomics data. Differentially expressed genes (DEGs) in ccRCC compared to normal kidney tissues were identified using 3 transcriptomics datasets (GSE53757, GSE40435 and GSE11151) from the Gene Expression Omnibus (GEO). Potential secreted biomarkers were examined within these common DEGs using a list of human secretome proteins from The Human Protein Atlas. The recursive feature elimination (RFE) technique was used to determine the optimal number of features for building classification machine learning models. The expression levels and clinical associations of candidate biomarkers identified with RFE were validated using transcriptomics data from The Cancer Genome Atlas (TCGA). Classification models were then developed based on the expression levels of these candidate biomarkers. The performance of the models was evaluated based on accuracy, evaluation metrics, confusion matrices, and ROC-AUC (receiver operating characteristic-area under the ROC curve) curves. We identified 44 DEGs that encode potential secreted proteins from 274 common DEGs found across all datasets. Among these, insulin-like growth factor binding protein 3 (IGFBP3) and lectin, galactoside-binding, soluble, 1 (LGALS1) were selected for further analysis using the RFE technique. Both IGFBP3 and LGALS1 showed significant upregulation in ccRCC tissues compared to normal tissues in the GEO and TCGA datasets. The results of the survival analysis indicated that patients with higher expression levels of these genes exhibited shorter overall and disease-free survival times (OS and DFS). Decision tree and random forest models based on IGFBP3 and LGALS1 levels achieved an accuracy of 98.04% and an AUC of 0.98. This study identified IGFBP3 and LGALS1 as promising novel secreted biomarkers for ccRCC diagnosis.

Glycogen synthase GYS1 overactivation contributes to glycogen insolubility and malto-oligoglucan-associated neurodegenerative disease

Abstract Polyglucosans are glycogen molecules with overlong chains, which are hyperphosphorylated in the neurodegenerative Lafora disease (LD). Brain polyglucosan bodies (PBs) cause fatal neurodegenerative diseases including Lafora disease and adult polyglucosan body disease (ABPD), for which treatments, biomarkers, and good understanding of their pathogenesis are currently missing. Mutations in the genes for the phosphatase laforin or the E3 ubiquitin ligase malin can cause LD. By depleting PTG, an activator of the glycogen chain-elongating enzyme glycogen synthase (GYS1), in laforin- and malin-deficient LD mice, we show that abnormal glycogen chain lengths and not hyperphosphorylation underlie polyglucosan formation, and that polyglucosan bodies induce neuroinflammation. We provide evidence indicating that a small pool of overactive GYS1 contributes to glycogen insolubility in LD and APBD. In contrast to previous findings, metabolomics experiments using in situ-fixed brains reveal only modest metabolic changes in laforin-deficient mice. These changes are not replicated in malin-deficient or APBD mice, and are not normalized in rescued LD mice. Finally, we identify a pool of metabolically volatile malto-oligoglucans as a polyglucosan body- and neuroinflammation-associated brain energy source, and promising candidate biomarkers for LD and APBD, including malto-oligoglucans and the neurodegeneration marker CHI3L1/YKL40.

Screening of obstructive sleep apnea and diabetes mellitus -related biomarkers based on integrated bioinformatics analysis and machine learning

BackgroundThe pathophysiology of obstructive sleep apnea (OSA) and diabetes mellitus (DM) is still unknown, despite clinical reports linking the two conditions. After investigating potential roles for DM-related genes in the pathophysiology of OSA, our goal is to investigate the molecular significance of the condition. Machine learning is a useful approach to understanding complex gene expression data to find biomarkers for the diagnosis of OSA.MethodsDifferentially expressed analysis for OSA and DM data sets obtained from GEO were carried out firstly. Then four machine algorithms were used to screen candidate biomarkers. The diagnostic model was constructed based on key genes, and the accuracy was verified by ROC curve, calibration curve and decision curve. Finally, the CIBERSORT algorithm was used to explore immune cell infiltration in OSA.ResultsThere were 32 important genes that were considered to be related both in OSA and DM datasets by differentially expressed analysis. Through enrichment analysis, the majority of these genes are enriched in immunological regulation, oxidative stress response, and nervous system control. When consensus characteristics from all four approaches were used to predict OSA diagnosis, STK17A was thought to have a high degree of accuracy. In addition, the diagnostic model demonstrated strong performance and predictive value. Finally, we explored the immune cells signatures of OSA, and STK17A was strongly linked to invasive immune cells.ConclusionSTK17A has been discovered as a gene that can differentiate between individuals with OSA and DM based on four machine learning methods. In addition to offering possible treatment targets for DM-induced OSA, this diagnostic approach can identify high-risk DM patients who also have OSA.

Smoking-induced shifts in salivary exosomal cytokines and amino acid profiles as potential early biomarkers for oral cancer.

Chronic smoking is an established risk factor for oral cancer (OC). The role of tobacco in oral squamous cell cancer (OSCC) emphasizes the need for non-invasive diagnostic approaches to identify early molecular alterations and improve patient outcomes. Salivary exosomes, which contain proteins, lipids, and nucleic acids, accessible and rich in biological content, making them interesting candidate biomarkers. Saliva samples were collected from non-smokers (n=20), smokers (n=20), and patients with oral cancer (n=20). Salivary exosomes were isolated and characterized using various techniques, including estimation of protein concentrations and amino acid profiling using High-Performance Liquid Chromatography (HPLC). Fourier Transform Infrared (FTIR) spectroscopy analyzed protein and amino acid peaks, while enzyme-linked immunosorbent assay (ELISA) measured pro-inflammatory cytokines IL-6 and IL-8. Elevated levels of salivary exosomal proteins (p=0.017), IL-6 (p=0.008), and IL-8 (p=0.0004), along with significant alterations in amino acid profiles, were observed in smokers compared with non-smokers. Additionally, protein (p=0.005) and IL-6 (p=0.004) levels were significantly elevated in oral cancer compared to non-smoker group. FTIR spectroscopy revealed distinct molecular fingerprints in exosomes, highlighting changes in protein and amino acid concentrations and indicating altered metabolism. This comparative cross-sectional study demonstrated that chronic smoking induces significant biochemical changes in salivary exosomes, establishing them as promising non-invasive biomarkers for early oral cancer detection. Elevated pro-inflammatory cytokines IL-6 and IL-8, along with altered amino acid profiles, may create pre-cancerous conditions. Notably, along with altered amino acid profiles, IL-6 levels progressively increase from smoking to oral cancer, highlighting its potential role in cancer progression.

Multivariable Predictive Model of Postoperative Delirium in Cardiac Surgery Patients: Proteomic and Demographic Contributions.

Delirium after cardiac surgery is common, morbid, and costly, but may be prevented with risk stratification and targeted intervention. In this study, we aimed to identify protein biomarkers and develop a predictive model for postoperative delirium in older patients undergoing cardiac surgery. SomaScan analysis of 1305 proteins in the plasma from 57 older adults undergoing cardiac surgery requiring cardiopulmonary bypass was conducted to define delirium-specific protein signatures at baseline (preoperative baseline timepoint [PREOP]) and postoperative day 2 (POD2). Selected proteins were validated in 115 patients using the Enzyme-Linked Lectin Assay (ELLA) multiplex immunoassay platform. Proteins were combined with clinical and demographic variables to build multivariable models that estimate the risk of postoperative delirium and bring light to the underlying pathophysiology. Of the 115 patients, 21 (18.3%) developed delirium after surgery. The SomaScan proteome screening evidenced differential expression of 115 and 85 proteins in delirious patients compared to nondelirious preoperatively and at POD2, respectively ( P < .05). Following biological and methodological criteria, 12 biomarker candidates (Tukey's fold change [|tFC|] >1.4, Benjamini-Hochberg [BH]- P < .01) were selected for ELLA multiplex validation. Statistical analyses of model fit resulted in the combination of age, sex, and 3 proteins (angiopoietin-2; C-C motif chemokine 5; and metalloproteinase inhibitor 1; area under the curve [AUC] = 0.829) as the best performing predictive model for delirium. Analyses of pathways showed that delirium-associated proteins are involved in inflammation, glial dysfunction, vascularization, and hemostasis. Our results support the identification of patients at higher risk of developing delirium after cardiac surgery using a multivariable model that combines demographic and physiological features, also bringing light to the role of immune and vascular dysregulation as underlying mechanisms.

Integrated bioinformatics analysis and experimental validation of exosome-related gene signature in steroid-induced osteonecrosis of the femoral head

BackgroundSteroid-induced osteonecrosis of the femoral head (SIONFH) is a universal hip articular disease and is very hard to perceive at an early stage. The understanding of the pathogenesis of SIONFH is still limited, and the identification of efficient diagnostic biomarkers is insufficient. This research aims to recognize and validate the latent exosome-related molecular signature in SIONFH diagnosis by employing bioinformatics to investigate exosome-related mechanisms in SIONFH.MethodThe GSE123568 and GSE74089 datasets were employed to conduct differentially expressed genes (DEGs) analysis, and the GSE123568 dataset was subjected to perform weighted genes co-expression network analysis (WGCNA). The exosome-related genes (ERGs) were retrieved from the GeneCards database. We identified differentially expressed exosome-related genes (DEERGs) between healthy controls (HC) and SIONFH patients, and a consensus clustering analysis was then implemented to group the SIONFH patients. The CIBERSORT was implemented to calculate the immune cell infiltration. Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted to investigate latent enriched pathways. In addition, machine-learning algorithms were applied to refine the DEERGs. Ultimately, we verified the diagnostic significance and expression of the hub genes using the SIONFH datasets and performing quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis.ResultsThis study identified twenty DEERGs from the peripheral serum and hip articular cartilage samples of SIONFH patients and HC. Two SIONFH subtypes related to ERGs were identified, and distinctions in pathways and immune cell infiltration patterns were compared. SIONFH's high-risk subpopulation exhibited enriched immune-related pathways and high immune cell infiltration, such as M0 macrophages, resting mast cells, and neutrophils. Three machine-learning algorithms then determined LCP1, PNP, UBE2V1, and ZFP36 as four exosome-related hub genes (ERHGs). Compared to HC samples, these ERHGs showed excellent diagnostic efficiency (overall AUC for ERHGs is in the range of 0.923 to 0.970 in GSE123568) in SIONFH samples. LCP1, PNP, UBE2V1, and ZFP36 expressions were validated in the GSE123568 and GSE74089 datasets and finally detected in peripheral serum samples with accordant expression by RT-qPCR.ConclusionTwenty potential exosome-related genes involved in SIONFH were identified through bioinformatics analysis. LCP1, PNP, UBE2V1, and ZFP36 might become candidate biomarkers and therapeutic targets because they have an intimate relationship with exosomes. These findings shed light on the exosome-related acquaintance of SIONFH and might contribute to the diagnosis and prognosis of SIONFH.

Protein alterations in patients with delirium after cardiac surgery: An exploratory case-control sub-study of the VISION Cardiac Surgery Biobank.

Delirium is an acute state of confusion associated with adverse postoperative outcomes. Delirium is diagnosed clinically using screening tools; most cases go undetected. Identifying a delirium biomarker would allow for accurate diagnosis, application of therapies, and insight into causal pathways. To agnostically discover novel biomarkers of delirium, we conducted a case-control sub-study using the VISION-Cardiac Surgery biobank. Our objective was to identify candidate biomarkers to investigate in future studies. We obtained a convenience sample of 30 patients with delirium on postoperative day 1 matched to 30 matched controls by age, sex, ethnicity, center and cardiopulmonary bypass time. The Olink Explore 3K platform was used to identify blood protein alterations on postoperative day 3. Protein concentrations were expressed as normalized protein expression (NPx) units (log2 fold scale). We compared protein expression between cases and controls using a paired t-test and reported significantly different biomarkers based on a False Discovery Rate (FDR)-adjusted p-value<0.05. Of 2,865 unique serum proteins, 26 (0.9%) were significantly associated with delirium status; all were elevated in cases versus controls at an FDR<0.05. Pathway analysis identified "calcium-release channel activity" (Padj=0.02) and "Guanosine 5' triphosphate (GTP)-binding" (Padj=0.005) functions as characteristic of proteins associated with delirium. The top three differentially expressed biomarkers were FKBP1B (Padj=0.003), C2CD2L (Padj=0.004), and RAB6B (Padj=0.004). The inflammatory biomarker IL-8 (CXCL8) (mean difference = 2.36; P=3.6x10-4) was also associated with delirium. We identified 26 biomarkers significantly associated with delirium; all are novel except for IL-8. We did not identify an association between delirium and recognized neuro-inflammatory proteins and markers of brain injury, which supports using biomarkers to differentiate between delirium and other neurological conditions. While exploratory, our findings support using biomarkers to diagnose postoperative delirium and validate using agnostic screens to identify potential delirium biomarkers.

Effect of magnetic field strength and segmentation variability on the reproducibility and repeatability of radiomic texture features in cardiovascular magnetic resonance parametric mapping.

Our study aims to assess the robustness of myocardial radiomic texture features (RTF) to segmentation variability and variations across scanners with different field strengths, addressing concerns about reliability in clinical practices. We conducted a retrospective analysis on 45 pairs of CMR T1 maps from 15 healthy volunteers using 1.5T and 3T Siemens scanners. Manual left ventricular myocardium segmentation and a deep learning-based model with Monte Carlo Dropout generated masks with different levels of variability and 1023 RTFs extracted from each region of interest (ROI). Reproducibility: the extent to which RTFs extracted from 1.5T and 3T images are consistent, and repeatability: the extent to which RTFs extracted from multiple segmentation runs at the same field strength agree with each other, were measured by the intraclass correlation coefficient (ICC). We categorized ICC values as excellent, good, moderate, and poor. We reported the proportion of RTFs that fell in each category. The proportion of RTFs with excellent repeatability decreased as the proportion of ROI pixels in congruence across segmentation runs decreased. Up to 31% of RTFs showed excellent repeatability, while 35% showed good repeatability across segmentation runs from the manually generated masks. Across scanners (i.e., 1.5T vs 3T), only 7% exhibited good reproducibility. While our results demonstrate RTF sensitivity to differences in field strength and segmentation variability, we identified certain preprocessing filters and feature classes that are less sensitive to these variations and, as such, may be good candidates for imaging biomarkers or building machine-learning models.

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. Through analyzing the classification of differential lipid metabolites and the association between differential lipid metabolites and clinical indexes, ensemble machine learning, data preprocessing, statistical test pre-screening, ensemble learning method secondary screening, biomarkers verification and evaluation, and diagnostic panel model construction and verification were performed on the data of untargeted lipidomics. Results indicated that different lipid metabolites not only differ between groups but also have close effects on different corresponding clinical indexes. PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. In addition, the correlation of candidate biomarkers was low, indicating that the overlap between the selected biomarkers was low, and the combination of panels was more optimized. When the AUC value of the test set of the constructed diagnostic panel model was 0.815, the model's accuracy in the test set was 0.74, specificity was 0.88, and sensitivity was 0.7. This study demonstrated the applicability and robustness of machine learning algorithms to analyze lipid metabolism data for efficient and reliable biomarker screening. PI (18:0/20:3)-H and PE (18:1p/22:6)-H showed great potential in diagnosing PCOS.

Proteomics Reveals Age as Major Modifier of Inflammatory CSF Signatures in Multiple Sclerosis.

Multiple sclerosis (MS) can start as relapsing or progressive. While their clinical features and treatment responses are distinct, it has remained uncertain whether their pathomechanisms differ. A notable age-related effect on MS phenotype and response to immunotherapies is well acknowledged, but the underlying pathophysiologic reasons are yet to be fully elucidated. We aimed to identify disease-specific and age-related proteomic signatures using a comprehensive targeted proteomic analysis. In our retrospective cohort study, we analyzed the CSF and serum proteome of age-matched individuals with treatment-naïve relapsing-remitting and primary progressive MS, neurologic controls (NC), and individuals with neuroborreliosis using targeted proteomics and validated findings in an independent cohort. Proteomic results were integrated with clinical and laboratory covariates. Among 2,500 proteins, 47 CSF proteins were distinct between individuals with MS (n = 60) and NC (n = 20), with a subset also differing from those with neuroborreliosis (n = 8). We identified MS-associated proteins, including novel candidate biomarkers such as LY9 and JCHAIN, and putative treatment targets, such as SLAMF7, BCMA, and IL5RA, for which drugs are already licensed in other indications. The CSF proteome differences between relapsing and progressive MS were minimal, but major changes were noted in individuals older than 50 years, indicating a shift from MS-associated inflammatory to age-related protein signature. NEFL was the only serum protein that differed between individuals with MS and controls. This study unveils a unique CSF proteomic signature in MS, providing new pathophysiologic insights and identifying novel biomarker candidates and potential therapeutic targets. Our findings highlight similar immunologic mechanisms in relapsing and progressive MS and underscore aging's profound effect on the intrathecal immune response. This aligns with the observed lower efficacy of immunotherapies in the elderly, thus emphasizing the necessity for alternative therapeutic approaches in treating individuals with MS beyond the age of 50.

A switchable magnetic resonance imaging nanoplatform for in situ microRNA imaging.

Aberrant microRNA (miRNA) expression is associated with various types of carcinogenesis, making miRNA a promising candidate for diagnostic and therapeutic biomarkers. However, in situ miRNA diagnostics remains a significant challenge owing to the various biological barriers. Herein, we report a novel miRNA imaging probe consisting of PEG-polylysine-PNIPAM polymer matrix-modified small Fe3O4 (PAA-Fe3O4-DNA@PPP) nanoparticles with an improved circulatory half-life, efficient tissue permeability, and enhanced tumor accumulation, for in situ miRNA magnetic resonance imaging (MRI). In this strategy, we employed large size PAA-Fe3O4-DNA@PPP to improve circulatory time and utilized PEG-polylysine-PNIPAM as a GSH-responsive moiety to dissociate PAA-Fe3O4-DNA@PPP and release small size PAA-Fe3O4-DNA for enhanced tumor permeability. Specifically, the target miRNA acts as a cross-linker for PAA-Fe3O4-DNA, forming larger assemblies that not only amplify the MRI signal for detection but also enhance retention for prolonged observation. Both the in vitro and in vivo results validate that the imaging probe exhibits an enhanced MRI signal with 3.69-fold amplification for tumor interior miRNA detection, allowing the dynamic changes in miRNA to be monitored by the probe. Given its long circulation, efficient penetration, and enhanced tumor accumulation, the PAA-Fe3O4-DNA@PPP probe holds great promise for in situ miRNA imaging and spatial genomics analysis in situ.

Differentiation of the chronic lymphocytic leukemia response to ibrutinib and acalabrutinib treatment by single-cell MALDI-TOF MS imaging.

Ibrutinib and acalabrutinib, Bruton's tyrosine kinase inhibitors (BTKi) used for chronic lymphocytic leukemia (CLL) treatment, aim the same target but their off-target effects are different. The aim of this study was to use single-cell MALDI TOF mass spectrometry imaging to compare the CD19+lymphocytes' mass spectra in untreated and ibrutinib- or acalabrutinib-treated subjects in order to better understand the therapeutic effect of BTKi. 180 cells from 9 male subjects divided in 3 groups (untreated, ibrutinib-treated and acalabrutinib-treated) were analyzed using MALDI-TOF mass spectrometry analyzer. Mass spectra were acquired in the 300-600 Da mass range. Partial least squares discriminant analysis (PLS-DA) was used for evaluation of mass spectra, while Volcano plots and Venn diagram were used for a review of significantly altered m/z values. Cross-validated PLS-DA classification accuracy of cells was 85 %. Ibrutinib-treated cells overlap with the untreated cell population, while acalabrutinib-treated cells form a separate class. 13 m/z signals were specific for each BTKi group. In conclusion, single-cell MALDI-TOF mass spectrometry imaging detects differences in the mass spectra of CD19+ lymphocytes treated with two different BTKi. Drug-specific m/z signal clusters can be identified as a chemical fingerprint of the BTKi effect and represent candidates for the therapeutic response biomarkers.

KLRD1 (CD94): A Prognostic Biomarker and Therapeutic Candidate in Head and Neck Squamous Cell Carcinoma.

Background: Killer Cell Lectin Like Receptor D1 (KLRD1) plays a crucial role in antitumor immunity. However, its expression patterns across various cancers, its relationship with patient prognosis, and its potential as an immunotherapy target remain inadequately understood. Methods: We analyzed KLRD1 expression across various cancer types using multi-omics data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases, correlating it with patient prognosis. Single-cell RNA sequencing data were employed to further explore KLRD1 expression in natural killer (NK) cells and exhausted CD8+ T cells (CD8Tex). Functional enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) identified the biological processes and pathways associated with KLRD1. Immune infiltration analysis, conducted via CIBERSORT, assessed the relationship between KLRD1 expression and immune cell infiltration within the tumor microenvironment. Furthermore, the Tracking Tumor Immunophenotype (TIP) meta-server and Easier tool were employed to assess the role of KLRD1 in the cancer immunity cycle and to predict immunotherapy responses. Drug sensitivity was predicted using tools like CellMiner and the Genomics of Drug Sensitivity in Cancer (GDSC) database to explore the link between KLRD1 expression and responsiveness to various anticancer drugs. Results: KLRD1 exhibits significant differential expression and strong prognostic value across cancers, particularly as an independent prognostic factor in head and neck squamous cell carcinoma (HNSC). Single-cell analysis revealed high expression of KLRD1 in NK and CD8Tex cells, indicating its critical role in antitumor immune responses. Functional enrichment analyses showed that KLRD1 is involved in several immune-related signaling pathways, including NK cell-mediated cytotoxicity and T cell receptor pathways. Immune infiltration analysis further confirmed a positive correlation between KLRD1 expression and the infiltration of various immune cells. Moreover, higher KLRD1 expression in HNSC is associated with enhanced immune pathway activity, increased sensitivity to cell division inhibitors, and the identification of arachidonyltrifluoromethane as a potential compound to counteract its oncogenic effects. Conclusion: In HNSC, KLRD1 is a key prognostic marker and potential target for personalized immunotherapy.

Platelet Indices and the Causal Relationship with Myeloid Leukemia: a Mendelian Randomization Study with Dual Samples.

Platelets are correlated with myeloid leukemia (ML), but to date, there have been no studies confirming the causal relationship between them. Platelet count (PLT), mean platelet volume (MPV), plateletcrit (PCT), and platelet distribution width (PDW) data were obtained from the GWAS catalog database as exposure factors. Acute myeloid leukemia (AML) and chronic myeloid leukemia (CML) data were obtained from the FinnGen database as outcome indicators. The causal relationship between exposure and outcome was explored using the inverse variance weighted, MR-Egger, weighted median, and simple mode methods of dual-sample Mendelian randomization (MR). The stability and reliability of the results were assessed using Cochran's test, MR-Egger regression, and MR-PRESSO methods. An elevated PCT is positively associated with the risk of CML [ORMR-Egger = 2.591, 95% CI (1.089 - 6.166), p = 0.032; ORSimple mode = 9.873, 95% CI (1.112 - 87.646), p = 0.040]. There was no evidence of heterogeneity or plei-otropy at the gene level. However, there were no causal associations between other indices and CML, and none of the four platelet indices were causally associated with AML. An increase in PCT significantly increases the risk of developing CML, making it a candidate biomarker for clinical screening of CML.

Differentiating multiple sclerosis with predominant spinal cord and optic nerve involvement from other autoimmune demyelinating diseases using B cell immunophenotyping and gene expression profiling.

Multiple sclerosis (MS) may present with predominant involvement of the spinal cord and optic nerve (MS/w-SCON) and mimic other autoimmune inflammatory demyelinating disorders (AIDD) such as neuromyelitis optica spectrum disorder (NMOSD), and relapsing inflammatory optic neuritis (RION). Thus, biomarkers are required for effective differential diagnosis of AIDD. Patients with MS/w-SCON (n = 20), MS without involvement of SCON (MS/wo-SCON) (n = 22), NMOSD (n = 16), RION (n = 15) and healthy individuals (n = 21) were included. Peripheral blood cell immunophenotypes were analyzed by flow cytometry and gene expression levels of isolated B cells were assessed by microarray analysis and quantitative real-time PCR. Significantly increased Breg, plasmablast, and plasma cell ratios distinguished MS/w-SCON from other groups. Most differentially expressed B cell genes were subjected to protein-protein interaction yielding a network of 13 immune mediators (IL18, IL18R1, P2RY12, CSF3R, TNFSF4, TNFRSF13C, TNFRSF1A, CCL20, CCL5, CCR4, CCL4L2, CXCL5, CXCL10). CCL4L2 and CCR4 expression levels distinguished MS/w-SCON. IL18/IL18R1 and CCL5 were distinguishing factors for NMOSD and RION, respectively. Peripheral blood B cell expression levels of CCL4L2, CCR4, IL18, IL18R1 and CCL5 are candidate biomarkers for differential diagnosis of AIDD of CNS. Our results substantiate the significance of B cells in the pathogenesis of these disorders.

Domain-Specific Prediction of Clinical Progression in Parkinson's Disease Using the Mosaic Approach.

Due to the highly individualized clinical manifestation of Parkinson's disease (PD), personalized patient care may require domain-specific assessment of neurological disability. Evidence from magnetic resonance imaging (MRI) studies has proposed that heterogenous clinical manifestation corresponds to heterogeneous cortical disease burden, suggesting customized, high-resolution assessment of cortical pathology as a candidate biomarker for domain-specific assessment. Herein, we investigate the potential of the recently proposed Mosaic Approach (MAP), a normative framework for quantifying individual cortical disease burden with respect to a population-representative cohort, in predicting domain-specific clinical progression. Using MRI and clinical data from 135 recently diagnosed PD patients from the Parkinson's Progression Markers Initiative, we first defined an extremity-specific motor score. We then identified cortical regions corresponding to "extremity functions" and restricted MAP, respectively, and contrasted the explanatory power of the extremity-specific MAP to unrestricted MAP. As control conditions, domain-related but less specific general motor function and nondomain-specific cognitive scores were considered. We also tested the predictive power of the restricted MAP in predicting disease progression over 1 and 3 years using support vector machines. The restricted, extremity-specific MAP yielded higher explanatory power for extremity-specific motor function at baseline as opposed to the unrestricted, whole-brain MAP. On the contrary, for general motor function, the unrestricted, whole-brain MAP yielded higher power. No associations were found for cognitive function. The extremity-specific MAP predicted extremity-specific motor progression over 1 and 3 years above chance level. The MAP framework allows for domain-specific prediction of customized PD disease progression, which can inform machine learning, thereby contributing to personalized PD patient care.

Serum Periostin as a Potential Biomarker in the Evaluation of Allergic Rhinitis: A Pilot Study.

Although periostin has recently emerged as a new mediator in chronic allergic diseases, particularly in upper airway disease, its significance as a biomarker for allergic rhinitis (AR) is still unclear. Therefore, we aimed to assess the potential of periostin as a novel candidate biomarker for diagnosing and assessing the severity of AR. A total of 40 patients with AR and 22healthy controls, all aged over 18 years, were recruited for the study. Participants underwent examinations to assess serum levels of total IgE (tIgE), specific IgE (sIgE), periostin, and remodeling-related factors, as well as fractional exhaled nitric oxide (FeNO) and fractional nasal nitric oxide (FnNO). Additionally, clinical characteristics questionnaire and nasal function assessments were completed by AR patients. The levels of serum periostin were significantly higher in patients with AR compared to healthy controls (Z=-3.605, p<0.001). There was a notable positive correlation between serum periostin and FeNO (r=0.398, p=0.012), FnNO (r=0.379, p=0.017), as well as the visual analogue scale (VAS) score for ocular tearing (r=0.351, p=0.026) in AR patients. Furthermore, the serum periostin levels were higher in moderate-to-severe AR compared to mild AR cases (Z=-2.007, p=0.045). The level of serum periostin in AR patients showed a sequential increase corresponding to shortness of breath scores from 0 to 3 (Z=10.137, p=0.017). The predicted probability of serum periostin demonstrated moderate diagnostic accuracy in detecting AR (AUC=0.773, p<0.001). Serum periostin shows potential as a candidate biomarker for detecting AR and can serve as a surrogate biomarker for assessing airway inflammation in AR patients.

Diagnosis of preeclampsia using metabolomic biomarkers

ABSTRACT The diagnostic criteria for preeclampsia do not accurately reflect the pathophysiological characteristics of patients with preeclampsia. Conventional biomarkers and diagnostic approaches have proven insufficient to fully comprehend the intricacies of preeclampsia. This study aimed to screen differentially abundant metabolites as candidate biomarkers for preeclampsia. A propensity score matching method was used to perform a 1:1 match between preeclampsia patients (n = 70) and healthy control individuals (n = 70). Based on univariate and multivariate statistical analysis methods, the different characteristic metabolites were screened and identified. Least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently used to further screen for differentially abundant metabolites. A receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic efficacy of the metabolites. A total of 1,630 metabolites were identified and quantified in maternal serum samples. Fifty-three metabolites were significantly increased, and two were significantly decreased in preeclampsia patients. The area under the curve (AUC) of the model composed of isobutyryl-L-carnitine and acetyl-leucine was 0.878, and the sensitivity and specificity in detecting preeclampsia were 81.4% and 87.1%, respectively. There are significant differences in metabolism between preeclampsia patients and healthy pregnant women, and a range of novel biomarkers have been identified. These findings lay the foundation for the use of metabolomic biomarkers for the diagnosis of preeclampsia.

Integration of miRNA expression analysis of purified leukocytes and whole blood reveals blood-borne candidate biomarkers for lung cancer

ABSTRACT Changes in leukocyte populations may confound the disease-associated miRNA signals in the blood of cancer patients. We aimed to develop a method to detect differentially expressed miRNAs from lung cancer whole blood samples that are not influenced by variations in leukocyte proportions. The Ref-miREO method identifies differential miRNAs unaffected by changes in leukocyte populations by comparing the within-sample relative expression orderings (REOs) of miRNAs from healthy leukocyte subtypes and those from lung cancer blood samples. Over 77% of the differential miRNAs observed between lung cancer and healthy blood samples overlapped with those between myeloid-derived and lymphoid-derived leukocytes, suggesting the potential impact of changes in leukocyte populations on miRNA profile. Ref-miREO identified 16 differential miRNAs that target 19 lung adenocarcinoma-related genes previously linked to leukocytes. These miRNAs showed enrichment in cancer-related pathways and demonstrated high potential as diagnostic biomarkers, with the LASSO regression models effectively distinguishing between healthy and lung cancer blood or serum samples (all AUC > 0.85). Additionally, 12 of these miRNAs exhibited significant prognostic correlations. The Ref-miREO method offers valuable candidates for circulating biomarker detection in cancer that are not affected by changes in leukocyte populations.