Potential Blood-based Biomarkers Research Articles

Understanding the Immune System and Biospecimen-Based Response in Glioblastoma: A Practical Guide to Utilizing Signal Redundancy for Biomarker and Immune Signature Discovery.

Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15-20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM.

Screening and Studying of Blood miRNAs as Potential Diagnostic Markers for Papillary Thyroid Carcinoma.

MiRNAs play a pivotal role in tumorigenesis and development by exerting negative regulation on the expression of target genes. In this study, bioinformatics techniques and online database were employed to investigate the specific miRNA-target gene regulatory network in PTC, which was subsequently validated using human blood samples and compared to existing tumor markers. The miRNA (GSE50901) and Gene Expression (GSE113629) chip screening data of human PTC tissues were retrieved from GEO database. A comparative analysis was conducted using the GEO2R to identify differentially expressed miRNAs and target genes of the patients with PTC. Prediction of the miRNA-target gene regulatory network, related signal transduction pathways, biological effects and their relationship to prognosis was performed based on GO, KEGG, qRT-PCR detection of human blood samples, analysis of correlation on the existing pathological tumor markers, and ROC. Compared to the corresponding normal thyroid tissues, a total of 2116 miRNAs were found to be differentially expressed in PTC patients, including 1968 up-regulated and 148 down-regulated genes. The abnormally expressed genes primarily participated in signal pathways associated with tumorigenesis and abnormal gene transcription. By utilizing data from the GEO database, five miRNAs closely linked to PTC prognosis were identified, which were miR-221-3p, miR-222-3p, miR-182-5p, miR-135a-5p, and miR-34a-5p, with elucidating the target genes. Experimental validation, correlation analysis with tumor markers along with bioinformatics analysis revealed a significant increase in expression levels of miR-182-5p in PTC patients which positively correlated with poor prognosis. These molecules could play crucial roles in both initiation and progression of PTC. This study identified potential novel blood-based miRNA biomarkers for PTC through bioinformatics analysis combined with the detection of human blood samples, thereby offering new possibilities for significant biomarkers associated with diagnosis and prognosis of PTC.

C-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease.

C-Triadem, our novel developed deep neural network, accurately predicts moderate cognitive impairment (MCI) and Alzheimer's disease (AD) while identifying potential blood biomarkers for AD. Current diagnostic methods have limitations, emphasizing the critical need for early AD detection. Our model integrates genetic, gene expression, and clinical data to differentiate among cognitively normal individuals, MCI, and AD cases. Training and validation using Alzheimer's Disease Neuroimaging Initiative (ADNI) data demonstrate superior performance, with a 97% AUC and 89% accuracy, surpassing previous models. SHapley Additive exPlanations (SHAP) analysis highlights key clinical features (e.g., MMSE scores, brain volume) and genes (e.g., CASP9, LCK, SDC3), revealing potential genetic markers and pathways in blood associated with AD. By incorporating Reactome pathways, our approach enhances interpretability, providing insight into the biological context of predictions. In summary, c-Triadem represents a significant advancement in AD diagnostics, enabling earlier and more accurate diagnoses for improved treatment strategies.

Ancestral retrovirus envelope protein ERVWE1 upregulates circ_0001810, a potential biomarker for schizophrenia, and induces neuronal mitochondrial dysfunction via activating AK2

BackgroundIncreasingly studies highlight the crucial role of the ancestral retrovirus envelope protein ERVWE1 in the pathogenic mechanisms of schizophrenia, a severe psychiatric disorder affecting approximately 1% of the global population. Recent studies also underscore the significance of circular RNAs (circRNAs), crucial for neurogenesis and synaptogenesis, in maintaining neuronal functions. However, the precise relationship between ERVWE1 and circRNAs in the etiology of schizophrenia remains elusive.ResultsThis study observed elevated levels of hsa_circ_0001810 (circ_0001810) in the blood samples of schizophrenia patients, displaying a significant positive correlation with ERVWE1 expression. Interestingly, in vivo studies demonstrated that ERVWE1 upregulated circ_0001810 in neuronal cells. Circ_0001810, acting as a competing endogenous RNA (ceRNA), bound to miR-1197 and facilitated the release of adenylate kinase 2 (AK2). The bioinformatics analysis of the schizophrenia datasets revealed increased levels of AK2 and enrichment of mitochondrial dynamics. Notably, miR-1197 was reduced in schizophrenia patients, while AK2 levels were increased. Additionally, AK2 showed positive correlations with ERVWE1 and circ_0001810. Further studies demonstrated that AK2 led to mitochondrial dysfunction, characterized by loss of intracellular ATP, mitochondrial depolarization, and disruption of mitochondrial dynamics. Our comprehensive investigation suggested that ERVWE1 influenced ATP levels, promoted mitochondrial depolarization, and disrupted mitochondrial dynamics through the circ_0001810/AK2 pathway.ConclusionsCirc_0001810 and AK2 were increased in schizophrenia and positively correlated with ERVWE1. Importantly, ERVWE1 triggered mitochondrial dysfunction through circ_0001810/miR-1197/AK2 pathway. Recent focus on the impact of mitochondrial dynamics on schizophrenia development had led to our discovery of a novel mechanism by which ERVWE1 contributed to the etiology of schizophrenia, particularly through mitochondrial dynamics. Moreover, these findings collectively proposed that circ_0001810 might serve as a potential blood-based biomarker for schizophrenia. Consistent with our previous theories, ERVWE1 is increasingly recognized as a promising therapeutic target for schizophrenia.

Diagnostic role of the neutrophil‑to‑lymphocyte ratio and the platelet‑to‑lymphocyte ratio in breast cancer: A systematic review and meta‑analysis.

Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and lymphocyte-to-monocyte ratio (LMR) may be indicative of breast cancer (BC); however, this remains inconclusive. With the aim to assess the current literature to evaluate the diagnostic roles of NLR PLR and LMR in BC, a systematic literature search was performed using the PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, VIP database and China Biology Medicine disc databases up to August 29, 2023. The standardized mean deviation and 95% confidence intervals (CI) for each outcome was reported, and heterogeneity and publication bias were assessed. Overall, 39 studies were included in the present study. Pooled analysis with the random-effects model demonstrated that patients with BC had significantly higher NLR and PLR, and a lower LMR, compared with non-BC subjects. The pooled sensitivities of the NLR and PLR were 0.68 (95% CI, 0.59-0.75) and 0.55 (95% CI, 0.36-0.72), respectively, and the pooled specificities of the NLR and PLR were 0.75 (95% CI, 0.68-0.81) and 0.80 (95% CI, 0.51-0.94), respectively. However, the limited number of studies included hindered the evaluation of the diagnostic role of LMR. In summary, a higher NLR and PLR and lower LMR were associated with the presence of BC. NLR and PLR may be potential blood-based biomarkers for the differentiation of BC. Despite these findings, further studies are needed to validate their clinical applicability and practicality. International Prospective Register of Systematic Reviews registration no. CRD42024522226.

The early diagnosis of Alzheimer's disease: Blood-based panel biomarker discovery by proteomics and metabolomics.

Diagnosis and prediction of Alzheimer's disease (AD) are increasingly pressing in the early stage of the disease because the biomarker-targeted therapies may be most effective. Diagnosis of AD largely depends on the clinical symptoms of AD. Currently, cerebrospinal fluid biomarkers and neuroimaging techniques are considered for clinical detection and diagnosis. However, these clinical diagnosis results could provide indications of the middle and/or late stages of AD rather than the early stage, and another limitation is the complexity attached to limited access, cost, and perceived invasiveness. Therefore, the prediction of AD still poses immense challenges, and the development of novel biomarkers is needed for early diagnosis and urgent intervention before the onset of obvious phenotypes of AD. Blood-based biomarkers may enable earlier diagnose and aid detection and prognosis for AD because various substances in the blood are vulnerable to AD pathophysiology. The application of a systematic biological paradigm based on high-throughput techniques has demonstrated accurate alterations of molecular levels during AD onset processes, such as protein levels and metabolite levels, which may facilitate the identification of AD at an early stage. Notably, proteomics and metabolomics have been used to identify candidate biomarkers in blood for AD diagnosis. This review summarizes data on potential blood-based biomarkers identified by proteomics and metabolomics that are closest to clinical implementation and discusses the current challenges and the future work of blood-based candidates to achieve the aim of early screening for AD. We also provide an overview of early diagnosis, drug target discovery and even promising therapeutic approaches for AD.

Elevated Circulating Sclerostin Levels in Frail Older Adults: Implications beyond Bone Health.

Sclerostin, initially recognized for its pivotal role in bone metabolism, has gained attention for its multifaceted impact on overall human health. However, its influence on frailty-a condition that best reflects biological age-has not been thoroughly investigated. We collected blood samples from 244 older adults who underwent comprehensive geriatric assessments. Sclerostin levels were quantified using an enzyme-linked immunosorbent assay. Frailty was assessed using two validated approaches: the phenotypic model by Fried and the deficit accumulation frailty index (FI) by Rockwood. After controlling for sex, age, and body mass index, we found that serum sclerostin levels were significantly elevated in frail individuals compared to their robust counterparts (P<0.001). There was a positive correlation between serum sclerostin concentrations and the FI (P<0.001). Each standard deviation increase in serum sclerostin was associated with an odds ratio of 1.87 for frailty (P=0.003). Moreover, participants in the highest quartile of sclerostin levels had a significantly higher FI and a 9.91-fold increased odds of frailty compared to those in the lowest quartile (P=0.003 and P=0.039, respectively). These findings, which for the first time explore the association between circulating sclerostin levels and frailty, have significant clinical implications, positioning sclerostin as one of potential blood-based biomarkers for frailty that captures the comprehensive physical, mental, and social aspects of the elderly, extending beyond its traditional role in bone metabolism.

Unveiling therapeutic biomarkers and druggable targets in ALS: An integrative microarray analysis, molecular docking, and structural dynamic studies

Amyotrophic lateral sclerosis (ALS), commonly known as Lou Gehrig's disease, is a debilitating neurodegenerative disorder characterized by the progressive degeneration of nerve cells in the brain and spinal cord. Despite extensive research, its precise etiology remains elusive, and early diagnosis is challenging due to the absence of specific tests. This study aimed to identify potential blood-based biomarkers for early ALS detection and monitoring using datasets from whole blood samples (GSE112680) and oligodendrocytes, astrocytes, and fibroblasts (GSE87385) obtained from the NCBI-GEO repository. Through bioinformatics analysis, including protein-protein interactions and molecular pathway analyses, we identified differentially expressed genes (DEGs) associated with ALS. Notably, ALS2, ADH7, ALDH8A1, ALDH3B1, ABHD2, ABHD17B, ABHD12, ABHD13, PGAM2, AURKB, ANAPC11, VAPA, UNC45B, and TNNT2 emerged as top-ranked DEGs, implicated in drug metabolism, protein depalmytilation, and the AKT/mTOR signaling pathways. Among these, AurKB established as a potential therapeutic biomarker with relevance to various neurological conditions. Consequently, AurKB was selected for identifying potential therapeutic molecules and utilized for in silico structural characterization studies. Exploration of the IMPATT database led to the discovery of a lead compound similar to Fostamatinib, currently used for AurKB. Initial molecular docking and MMGBSA-based binding energy analysis were followed by molecular dynamics simulation (MDS) and free energy landscape (FEL) analysis to validate the ligand's binding efficacy and understand dynamic processes within the biological system. The identified potential biomarkers and lead molecule provide novel insights into the correlation between blood cell transcripts and ALS pathology, paving the way for blood-based diagnostic tools for early ALS detection and ongoing disease monitoring.

Machine learning approach to predict blood-secretory proteins and potential biomarkers for liver cancer using omics data

Identifying non-invasive blood-based biomarkers is crucial for early detection and monitoring of liver cancer (LC), thereby improving patient outcomes. This study leveraged computational approaches to predict potential blood-based biomarkers for LC. Machine learning (ML) models were developed using selected features from blood-secretory proteins collected from the curated databases. The logistic regression (LR) model demonstrated the optimal performance. Transcriptome analysis across 7 LC cohorts revealed 231 common differentially expressed genes (DEGs). The encoded proteins of these DEGs were compared with the ML dataset, revealing 29 proteins overlapping with the blood-secretory dataset. The LR model also predicted 29 additional proteins as blood-secretory with the remaining protein-coding genes. As a result, 58 potential blood-secretory proteins were obtained. Among the top 20 genes, 13 common hub genes were identified. Further, area under the receiver operating characteristic curve (ROC AUC) analysis was performed to assess the genes as potential diagnostic blood biomarkers. Six genes, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6, exhibited an AUC value higher than 0.85 and were predicted as blood-secretory. This study highlights the potential of an integrative computational approach for discovering non-invasive blood-based biomarkers in LC, facilitating for further validation and clinical translation. SignificanceLiver cancer is one of the leading causes of premature death worldwide, with its prevalence and mortality rates projected to increase. Although current diagnostic methods are highly sensitive, they are invasive and unsuitable for repeated testing. Blood biomarkers offer a promising non-invasive alternative, but their wide dynamic range of protein concentration poses experimental challenges. Therefore, utilizing available omics data to develop a diagnostic model could provide a potential solution for accurate diagnosis. This study developed a computational method integrating machine learning and bioinformatics analysis to identify potential blood biomarkers. As a result, ESM1, FCN2, MDK, GPC3, CTHRC1 and COL6A6 biomarkers were identified, holding significant promise for improving diagnosis and understanding of liver cancer. The integrated method can be applied to other cancers, offering a possible solution for early detection and improved patient outcomes.

Higher serum uric acid as a risk factor for frailty in older adults: A nationwide population-based study.

Uric acid (UA), the terminal breakdown product of purine metabolism, possesses contradictory roles, functioning both as an inflammatory mediator and as an antioxidant. Its clinical relevance, particularly in geriatric populations, remains a topic of ongoing debate. Aiming to elucidate whether circulating UA is detrimental or beneficial to human health, we investigate the association between serum UA concentrations and the frailty index-a comprehensive measure of biological aging in a nationally representative cohort of community-dwelling older adults. We conducted a population-based, cross-sectional study utilizing data from the Korea National Health and Nutrition Examination Survey. The sample included 4268 participants aged 65years and above. A deficit accumulation frailty index (FI) was constructed using 38 items that assess physical, cognitive, psychological, and social domains. Based on the FI, participants were categorized into non-frail (FI≤0.15), pre-frail (0.15<FI≤0.25), or frail (FI>0.25). Serum UA levels were quantified through a colorimetric enzymatic assay. After controlling for confounders such as age, sex, socioeconomic status (including income and education level), lifestyle factors (smoking status), and medical history (hypertension, diabetes, dyslipidemia, stroke, cardiovascular diseases), and body mass index, serum UA levels were observed to be significantly higher in frail participants compared with their non-frail counterparts (P<0.001). Furthermore, serum UA concentrations demonstrated a positive correlation with the FI (P<0.001), and the odds ratio for frailty per 1mg/dL increase in serum UA was 1.22 (P<0.001). Additionally, older adults in the highest quartile of UA levels exhibited a significantly higher FI and 1.66-fold increased odds of frailty compared with those in the lowest quartile (P=0.011 and P=0.005, respectively). These findings suggest that elevated circulating UA levels may act as a pro-aging factor rather than an anti-aging one in older adults, highlighting its potential role in accelerating biological aging. The data further support the utility of serum UA as a potential blood-based biomarker for frailty in this demographic, contributing to the expanding evidence on its significance in geriatric health assessments.

Novel diagnostic biomarkers for pancreatic cancer: assessing methylation status with epigenetic-specific peptide nucleic acid and KRAS mutation in cell-free DNA.

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive tumor with a poor prognosis that poses challenges for diagnosis using traditional tissue-based techniques. DNA methylation alterations have emerged as potential and promising biomarkers for PDAC. In this study, we aimed to assess the diagnostic potential of a novel DNA methylation assay based on epigenetic-specific peptide nucleic acid (Epi-sPNA) in both tissue and plasma samples for detecting PDAC. The study involved 46 patients with PDAC who underwent surgical resection. Epi-TOP pancreatic assay was used to detect PDAC-specific epigenetic biomarkers. The Epi-sPNA allowed accurate and rapid methylation analysis without bisulfite sample processing. Genomic DNA extracted from paired normal pancreatic and PDAC tissues was used to assess the diagnostic efficacy of epigenetic biomarkers for PDAC. Subsequent validation was conducted on cell-free DNA (cfDNA) extracted from plasma samples, with 10 individuals represented in each group: PDAC, benign pancreatic cystic neoplasm, and healthy control. The combination of seven epigenetic biomarkers (HOXA9, TWIST, WT1, RPRM, BMP3, NPTX2, and BNC1) achieved 93.5% sensitivity and 96.7% specificity in discerning normal pancreatic from PDAC tissues. Plasma cfDNA, analyzed using these markers and KRAS mutations, exhibited a substantial 90.0% sensitivity, 95.0% specificity, and an overall 93.3% accuracy for discriminating PDAC. Notably, cancer antigen 19-9 and carcinoembryonic antigen both had an accuracy of 90.0%. Our study suggests that analyzing seven differentially methylated genes with KRAS mutations in cfDNA using the novel Epi-TOP pancreatic assay is a potential blood-based biomarker for the diagnosis of PDAC.

Long non-coding RNA TUG1 is down-regulated in Friedreich’s ataxia

Abstract Friedreich's Ataxia (FRDA) is a neurodegenerative disorder caused by reduced frataxin (FXN) levels. It leads to motor and sensory impairments and has a median life expectancy of around 35 years. As the most common inherited form of ataxia, FRDA lacks reliable, non-invasive biomarkers, prolonging and inflating the cost of clinical trials. This study proposes TUG1, a long non-coding RNA, as a promising blood-based biomarker for FRDA, which is known to regulate various cellular processes. In a previous study using a frataxin knockdown mouse model, we observed several hallmark FRDA symptoms. Building on this, we hypothesized that a dual-source approach—comparing the data from peripheral blood samples from FRDA patients with tissue samples from affected areas in FRDA knockdown mice, tissues usually unattainable from patients—would effectively identify robust biomarkers. A comprehensive reanalysis was conducted on gene expression data from 183 age- and sex-matched peripheral blood samples of FRDA patients, carriers, and controls, and 192 tissue datasets from FRDA knockdown mice. Blood and tissue samples underwent RNA isolation and quantitative reverse transcription PCR, and frataxin knockdown was confirmed through ELISA. Tug1 RNA interaction was explored via RNA pull-down assays. Validation was performed in serum samples on an independent set of 45 controls and 45 FRDA patients, and in blood samples from 66 heterozygous carriers and 72 FRDA patients. Tug1 and Slc40a1 emerged as potential blood-based biomarkers, confirmed in the FRDA knockdown mouse model (One-way ANOVA, p ≤ 0.05). Tug1 was consistently downregulated after Fxn knockdown and correlated strongly with Fxn levels (R2 = 0.71 during depletion, R2 = 0.74 during rescue). Slc40a1 showed a similar but tissue-specific pattern. Further validation of Tug1's downstream targets strengthened its biomarker candidacy. In additional human samples, TUG1 levels were significantly downregulated in both whole blood and serum of FRDA patients compared to controls (Wilcoxon signed-rank test, p &amp;lt; 0.05). Regression analyses revealed a negative correlation between TUG1 fold-change and disease onset (p &amp;lt; 0.0037), and positive correlations with disease duration and Functional Disability Stage score (p &amp;lt; 0.04). This suggests that elevated TUG1 levels correlate with earlier onset and more severe cases. This study identifies TUG1 as a potential blood-based biomarker for FRDA, showing consistent expression variance in human and mouse tissues related to disease severity and key FRDA pathways. It correlates with frataxin levels, indicating its promise as an early, non-invasive marker. TUG1 holds potential for FRDA monitoring and therapeutic development, meriting additional research.

Hypermethylation of the Gene Body in SRCIN1 Is Involved in Breast Cancer Cell Proliferation and Is a Potential Blood-Based Biomarker for Early Detection and a Poor Prognosis.

Breast cancer is a leading cause of cancer mortality in women worldwide. Using the Infinium MethylationEPIC BeadChip, we analyzed plasma sample methylation to identify the SRCIN1 gene in breast cancer patients. We assessed SRCIN1-related roles and pathways for their biomarker potential. To verify the methylation status, quantitative methylation-specific PCR (qMSP) was performed on genomic DNA and circulating cell-free DNA samples, and mRNA expression analysis was performed using RT‒qPCR. The results were validated in a Western population; for this analysis, the samples included plasma samples from breast cancer patients from the USA and from The Cancer Genome Atlas (TCGA) cohort. To study the SRCIN1 pathway, we conducted cell viability assays, gene manipulation and RNA sequencing. SRCIN1 hypermethylation was identified in 61.8% of breast cancer tissues from Taiwanese patients, exhibiting specificity to this malignancy. Furthermore, its presence correlated significantly with unfavorable 5-year overall survival outcomes. The levels of methylated SRCIN1 in the blood of patients from Taiwan and the USA correlated with the stage of breast cancer. The proportion of patients with high methylation levels increased from 0% in healthy individuals to 63.6% in Stage 0, 80% in Stage I and 82.6% in Stage II, with a sensitivity of 78.5%, an accuracy of 90.3% and a specificity of 100%. SRCIN1 hypermethylation was significantly correlated with increased SRCIN1 mRNA expression (p < 0.001). Knockdown of SRCIN1 decreased the viability of breast cancer cells. SRCIN1 silencing resulted in the downregulation of ESR1, BCL2 and various cyclin protein expressions. SRCIN1 hypermethylation in the blood may serve as a noninvasive biomarker, facilitating early detection and prognosis evaluation, and SRCIN1-targeted therapies could be used in combination regimens for breast cancer patients.

Identification of clinical potential diagnostic blood-based biomarkers associated with prostate cancer by LC-MS/MS approach

Identification of clinical potential diagnostic blood-based biomarkers associated with prostate cancer by LC-MS/MS approach

Alzheimer Disease Using Machine Learning

The successful development of amyloid-based biomarkers and tests for Alzheimer’s disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) &gt; 80%, specificity (SP) &gt; 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using non-amyloid based biomarkers.

Plasma glial fibrillary acidic protein in the visual and language variants of Alzheimer's disease.

Glial fibrillary acidic protein (GFAP) in plasma is a proxy for astrocytic activity and is elevated in amyloid-β (Aβ)-positive individuals, making GFAP a potential blood-based biomarker for Alzheimer's disease (AD). We assessed plasma GFAP in 72 Aβ-positive participants diagnosed with the visual or language variant of AD who underwent Aβ- and tau-PET. Fifty-nine participants had follow-up imaging. Linear regression was applied on GFAP and imaging quantities. GFAP did not correlate with Aβ- or tau-PET cross-sectionally. There was a limited positive correlation between GFAP and rates of tau accumulation, particularly in the language variant of AD, although associations were weaker after removing one outlier patient with the highest GFAP level. Among Aβ-positive AD participants with atypical presentations, plasma GFAP did not correlate with levels of AD pathology on PET, suggesting that the associations between GFAP and AD pathology might plateau during the advanced phase of the disease.

Quantification of COX-2 Level in Alzheimer's Disease Patients to Develop Potential Blood-Based Biomarker for Early Diagnosis and Therapeutic Target.

Alzheimer's disease (AD) is a progressive neurodegenerative disease and symptoms develop gradually over many years. The current direction for medication development in AD is focused on neuro-inflammation and oxidative stress. Amyloid-β (Aβ) deposition activates microglia leading to neuro-inflammation and neurodegeneration induced by activation of COX-2 via NFκB p50 in glioblastoma cells. The study aimed to evaluate the concentration of COX-2 and NFκB p50 in serum of AD, mild cognitive impairment (MCI), and geriatric control (GC) and to establish a blood-based biomarker for early diagnosis and its therapeutic implications. Proteins and their mRNA level in blood of study groups were measured by surface plasmon resonance (SPR) and quantitative polymerase chain reaction (qPCR), respectively. The level of protein was further validated by western blot. The binding study of designed peptide against COX-2 by molecular docking was verified by SPR. The rescue of neurotoxicity by peptide was also checked by MTT assay on SH-SY5Y cells (neuroblastoma cell line). Proteins and mRNA were highly expressed in AD and MCI compared to GC. However, COX-2 decreases with disease duration. The peptide showed binding affinity with COX-2 with low dissociation constant in SPR and rescued the neurotoxicity of SH-SY5Y cells by decreasing the level of Aβ, tau, and pTau proteins. It can be concluded that COX-2 protein can serve as a potential blood-based biomarker for early detection and can be a good platform for therapeutic intervention for AD.

Identification of Potential Blood-Based Biomarkers for Frailty by Using an Integrative Approach

Introduction: Although frailty is a geriatric syndrome that is associated with disability, hospitalization, and mortality, it can be reversible and preventable with the appropriate interventions. Additionally, as the current diagnostic criteria for frailty include only physical, psychological, cognitive, and social measurements, there is a need for promising blood-based molecular biomarkers to aid in the diagnosis of frailty. Methods: To identify candidate blood-based biomarkers that can enhance current diagnosis of frailty, we conducted a comprehensive analysis of clinical data, messenger RNA-sequencing (RNA-seq), and aging-related factors using a total of 104 older adults aged 65–90 years (61 frail subjects and 43 robust subjects) in a cross-sectional case-control study. Results: We identified two candidate biomarkers of frailty from the clinical data analysis, nine from the RNA-seq analysis, and six from the aging-related factors analysis. By using combinations of the candidate biomarkers and clinical information, we constructed risk prediction models. The best models used combinations that included skeletal muscle mass index measured by dual-energy X-ray absorptiometry (adjusted p = 0.026), GDF15 (adjusted p = 1.46E-03), adiponectin (adjusted p = 0.012), CXCL9 (adjusted p = 0.011), or apelin (adjusted p = 0.020) as the biomarker. These models achieved a high area under the curve of 0.95 in an independent validation cohort (95% confidence interval: 0.79–0.97). Our risk prediction models showed significantly higher areas under the curve than did models constructed using only basic clinical information (Welch’s t test p < 0.001). Conclusion: All five biomarkers showed statistically significant correlations with components of the frailty diagnostic criteria. We discovered several potential biomarkers for the diagnosis of frailty. Further refinement may lead to their future clinical use.

Transcriptomic Signatures of Neuronally Derived Extracellular Vesicles Reveal the Presence of Olfactory Receptors in Clinical Samples from Traumatic Brain Injury Patients.

Traumatic brain injury (TBI) is defined as an injury to the brain by external forces which can lead to cellular damage and the disruption of normal central nervous system functions. The recently approved blood-based biomarkers GFAP and UCH-L1 can only detect injuries which are detectable on CT, and are not sensitive enough to diagnose milder injuries or concussion. Exosomes are small microvesicles which are released from the cell as a part of extracellular communication in normal as well as diseased states. The objective of this study was to identify the messenger RNA content of the exosomes released by injured neurons to identify new potential blood-based biomarkers for TBI. Human severe traumatic brain injury samples were used for this study. RNA was isolated from neuronal exosomes and total transcriptomic sequencing was performed. RNA sequencing data from neuronal exosomes isolated from serum showed mRNA transcripts of several neuronal genes. In particular, mRNAs of several olfactory receptor genes were present at elevated concentrations in the neuronal exosomes. Some of these genes were OR10A6, OR14A2, OR6F1, OR1B1, and OR1L1. RNA sequencing data from exosomes isolated from CSF showed a similar elevation of these olfactory receptors. We further validated the expression of these samples in serum samples of mild TBI patients, and a similar up-regulation of these olfactory receptors was observed. The data from these experiments suggest that damage to the neurons in the olfactory neuroepithelium as well as in the brain following a TBI may cause the release of mRNA from these receptors in the exosomes. Hence, olfactory receptors can be further explored as biomarkers for the diagnosis of TBI.

P426 Hematological Composite Scores in Patients with Inflammatory Bowel Disease

Abstract Background Ulcerative colitis (CU) and Crohn’s disease (CD) are described as inflammatory bowel diseases (IBD). Emerging as potential blood-based inflammatory biomarkers in various chronic diseases are the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammatory index (SIRI, calculated as neutrophils × monocytes/lymphocytes). In this work we aim to analyze if these hematological composite scores differ between IBD patients and healthy controls, and whether they are related to disease activity. Methods A total of 197 IBD patients, 130 with CD and 67 with CU, and 208 age- and sex-matched healthy controls were recruited. NLR, MLR, PLR, and SIRI were calculated. Multivariable linear regression analysis was performed to study whether these scores differ between patients and controls and how they related to IBD activity scores. Results After multivariable analysis adjustment, NLR and PLR, but not SIRI and MLR, were significantly higher in IBD patients compared to controls (table 1). C-reactive protein and SIRI and NLR correlated in patients with IBD. Nevertheless, fecal calprotectin was not related to none of these blood scores. Besides, disease activity parameters were not associated with any of the composite blood-based scores in both patients with CD and CU. Conclusion NLR and PLR, independently, are heightened in IBD patients in contrast to controls. However, SIRI and MLR do not share this distinction. Surprisingly, none of the four hematological scores displayed correlations with disease activity in either CD or UC patients. Table 1. Multivariable analysis of the differences between patients and controls in hematological count cells and scores