Potential Diagnostic Biomarkers Research Articles

The relationship between salivary cytokines and oral cancer and their diagnostic capability for oral cancer: a systematic review and network meta-analysis

BackgroundOral cancer (OC) is a common malignancy in clinical practice. Saliva testing is a convenient and noninvasive early diagnostic technique for OC. Several salivary cytokines have been identified as potential biomarkers for OC, including IL-8, IL-6, TNF-α, IL-1β, and IL-10. Nonetheless, the optimal cytokine for OC diagnosis remains inconclusive and highly contentious.MethodsPubMed, Embase, Web of Science, and Cochrane Library databases were comprehensively retrieved to collect all case–control studies on OC. A meta-analysis was performed to compare the levels of salivary IL-8, IL-6, IL-10, TNF-α, and IL-1β in OC patients and healthy controls. Network meta-analysis (NMA) was carried out to probe into the accuracy of these salivary cytokines in diagnosing OC.ResultsThis analysis included 40 studies, encompassing 1280 individuals with OC and 1254 healthy controls. Significantly higher levels of salivary IL-8, IL-6, TNF-α, IL-1β, and IL-10 were observed in patients with OC in comparison to healthy controls. The results of NMA showed that TNF-α had the highest diagnostic accuracy for OC, with a sensitivity of 79% and a specificity of 92%, followed by IL-6 (sensitivity: 75%, specificity: 86%) and IL-8 (sensitivity: 80%, specificity: 80%).ConclusionThis study suggests that IL-8, IL-6, IL-10, TNF-α, and IL-1β may be potential diagnostic biomarkers for OC. Among them, TNF-α, IL-6, and IL-8 are highly accurate in the diagnosis of OC. Nevertheless, further studies that eliminate other confounding factors are warranted, and more standardized procedures and large-scale studies are needed to support the clinical use of saliva testing.

Integrated transcriptomic analysis and machine learning for characterizing diagnostic biomarkers and immune cell infiltration in fetal growth restriction.

Fetal growth restriction (FGR) occurs in 10% of pregnancies worldwide. Placenta dysfunction, as one of the most common causes of FGR, is associated with various poor perinatal outcomes. The main objectives of this study were to screen potential diagnostic biomarkers for FGR and to evaluate the function of immune cell infiltration in the process of FGR. Firstly, differential expression genes (DEGs) were identified in two Gene Expression Omnibus (GEO) datasets, and gene set enrichment analysis was performed. Diagnosis-related key genes were identified by using three machine learning algorithms (least absolute shrinkage and selection operator, random forest, and support vector machine model), and the nomogram was then developed. The receiver operating characteristic curve, calibration curve, and decision curve analysis curve were used to verify the validity of the diagnostic model. Using cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), the characteristics of immune cell infiltration in placental tissue of FGR were evaluated and the candidate key immune cells of FGR were screened. In addition, this study also validated the diagnostic efficacy of TREM1 in the real world and explored associations between TREM1 and various clinical features. By overlapping the genes selected by three machine learning algorithms, four key genes were identified from 290 DEGs, and the diagnostic model based on the key genes showed good predictive performance (AUC = 0.971). The analysis of immune cell infiltration indicated that a variety of immune cells may be involved in the development of FGR, and nine candidate key immune cells of FGR were screened. Results from real-world data further validated TREM1 as an effective diagnostic biomarker (AUC = 0.894) and TREM1 expression was associated with increased uterine artery PI (UtA-PI) (p-value = 0.029). Four candidate hub genes (SCD, SPINK1, TREM1, and HIST1H2BB) were identified, and the nomogram was constructed for FGR diagnosis. TREM1 was not only associated with a variety of key immune cells but also correlated with increased UtA-PI. The results of this study could provide some new clues for future research on the prediction and treatment of FGR.

Anti-sulfatide antibodies in neurological disorders: should we test?

Neurological autoimmune peripheral and central nervous system disorders can be associated with anti-sulfatide antibodies. These antibodies are considered potential diagnostic biomarkers, although their additional diagnostic value in neurological fields has been increasingly questioned. Given the little evidence of anti-sulfatide antibodies' frequency and diagnostic value in neurology, we aimed to fill this knowledge gap by investigating 10years of data. This retrospective study analyzed the results of the anti-ganglioside dot kits (GA Generic Assays GmbH) from 1318 serum samples and 462 cerebrospinal fluid (CSF) samples for the frequency, sensitivity, and specificity of anti-sulfatide antibodies in neurological disorders. Although anti-sulfatide antibodies are rarely present in neurological autoimmune disorders (serum IgM 2.5%, IgG 4.6%), they are also present in non-autoimmune diseases (serum IgM 1.2%, IgG 2.5%) and lack sensitivity and specificity towards being a diagnostic marker. Furthermore, anti-sulfatide antibodies are rarely found in CSF (e.g., no positive results for IgM), and including so-called borderline results ((+)) increases sensitivity and the false-positive rate in serum and CSF. While anti-sulfatide antibodies appear more frequently in neurological autoimmune diseases, they are rare overall and provide very limited diagnostic value in determining specific neurological diseases and-more importantly-if a neurological disease has a potential autoimmune etiology.

Identification of potential diagnostic biomarkers and therapeutic targets in patients with hypoxia pulmonary hypertension

BackgroundPulmonary hypertension is a serious disease. Emerging studies have shown that M2 macrophages play an essential role in pulmonary hypertension; however, their mechanism of action is uncertain. MethodsFour GEO datasets were downloaded. The differentially expressed genes (DEGs) were obtained using the limma package. Simultaneously, the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) algorithm and weighted gene co-expression network analysis (WGCNA) were used to get the information about M2 macrophage–related modules. Potential key genes were obtained by intersecting DEGs with M2 macrophage–related module genes (M2MRGs), and finally the area under the curve (AUC) was calculated. Rats were exposed to hypoxia condition (10 % O2) for 4 weeks to induce PH. Subsequently, potential key genes with AUC>0.7 were analyzed by quantitative real-time polymerase chain reaction and Western blot using normoxia and hypoxia rat lungs. We knocked down EPHA3 in Raw264.7 cells and detected the protein expression of M2 macrophage markers including arginase 1 (ARG1) and interleukin 10 (IL-10), phospho-protein kinase B (P-Akt), and protein kinase B (Akt) to explore the downstream pathways of EPHA3. ResultsSeven potential hub genes were detected by intersecting M2MRGs and DEGs. Six genes with AUC values above 0.7 were used for further exploration. The expression of EPHA3 mRNA and protein was significantly more upregulated in rats with hypoxia than in rats with normoxia. The expression levels of IL10, ARG1, and P-Akt/Akt decreased after knocking down EPHA3. ConclusionsThis study suggested that the activation of the P-Akt/Akt signaling pathway promoted by EPHA3 played an essential role in the progression of pulmonary hypertension.

Unraveling Shared Diagnostic Biomarkers of Fibromyalgia in Ankylosing Spondylitis: Evidence from Comprehensive Bioinformatic Analysis and Experimental Validation.

Fibromyalgia (FM) is a commonly encountered disease featuring chronic generalized pain, sleep disorder, and physical fatigue. Ankylosing spondylitis (AS) causes chronic lumbodorsalgia involving the sacroiliac joint, often clinically complicated with FM. Nevertheless, the pathophysiology of FM secondary to AS is still lacking. Gene expression data of the whole blood in FM and AS patients were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were evaluated employing the "limma" package. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were implemented to explore common pathways. Weighted gene correlation network analysis (WGCNA) was adopted to screen key gene modules. Three machine learning algorithms were performed to refine the intersected genes. Single sample gene set enrichment analysis (ssGSEA) was applied to explore the relationships between hub genes and immune cells. The dependability of hub gene expressions in clinical blood specimens was verified by RT-PCR. Molecular docking was conducted to predict small molecular compounds targeting hub genes. DEG analysis screened 419 shared up-regulated and 179 shared down-regulated genes in FM and AS. A total of 143 common genes in positive modules of AS and FM were identified via WGCNA. Six key genes (CETN3, CACNA1E, OGT, QRFPR, SCOC, DIAPH1) were obtained by intersecting the WGCNA-derived shared genes and up-regulated DEGs. CETN3 and CACNA1E were refined as hub genes via three machine-learning algorithms and they showed excellent diagnostic value for FM and AS. However, ssGSEA exhibited different immune cell infiltration patterns in FM and AS. Gabapentin enacarbil was recognized as a potential therapeutic drug for AS-FM patients. This study reveals the shared hub genes in AS and FM. Meanwhile, these results were confirmed in clinical samples. CETN3 and CACNA1E may become potential diagnostic biomarkers and therapeutic targets for patients with AS complicated by FM.

Exploration of potential biomarkers for prurigo nodularis based on plasma-metabolome analysis.

Prurigo nodularis (PN) is a chronic and debilitating skin disease with severe itching that negatively impacts patients' quality of life and mental state. However, the treatment options for PN remain limited. Global metabolomics analysis can offer effective information on energy metabolism, pathogenesis and potential diagnostic biomarkers. No study on metabolomic analysis of PN has been reported. To further understand the mechanisms of PN and analyse the plasma metabolite profiles in patients with PN. Targeted-metabolome analysis of 306 metabolites in plasma from 18 patients with PN and 19 healthy controls was performed using Liquid Chromatography-tandem Mass Spectrometer analysis. We identified 31 differential metabolites. Most acylcarnitines, long-chain fatty acids, alpha-aminobutyric acid, hydroxybutyric acid and lactic acid among these metabolites were up-regulated in patients with PN; in contrast, glucaric acid, suberic acid, bile acid derivatives and most amino acids were down-regulated. Positive correlations exist between glucaric acid and itching severity and acylcarnitines and insomnia. Suberic acid and the Investigator's Global Assessment (IGA) scores correlate negatively. Metabolite variation reflects the dysregulation of energy metabolism and chronic systematic inflammation in PN. Several metabolites, such as glucaric acid, suberic acid and acylcarnitines, merit further study as potential biomarkers of disease severity in PN.

CircZMYM2 plays a pivotal role in osteosarcoma by regulating the translation of NACA and ARPC1B

Osteosarcoma (OS) is a primary malignant bone tumor in children and young adults because of its intertumoral heterogeneity and absence of molecular targets. This study aimed to elucidate the role of circular RNA in the pathogenesis of OS. Using bioinformatics analysis and OS clinical samples, we found that hsa_circ_0029634 formed during the splicing process of Zinc finger MYM-type containing 2 (circZMYM2) was highly expressed in OS tissues. Nuclear cap-binding protein subunit 2 regulated circZMYM2 bio-generation by binding to flanking sequences of ZMYM2 transcripts. In addition, knockdown circZMYM2 inhibited cell growth and metastasis in OS cell lines in vitro and inhibited tumor growth in vivo. Mechanistically, circZMYM2 competitively bound to FXR1 to regulate the translation of NACA and ARPC1B. CircZMYM2 may be a crucial regulator of OS tumorigenesis and a potential diagnostic and therapeutic biomarker for OS patients.

Construction of Prognostic ceRNA Network Landscape in Breast Cancer to Explore Impacting Genes on Drug Response by Integrative Bioinformatics Analysis

Background: Breast cancer accounts for 30% of all new female cancers yearly. Bioinformatics serves us to find new biomarkers and facilitate future experimental research. Exploring a distinct network of competing endogenous RNA (ceRNA) that includes potential prognostic, diagnostic, and therapeutic biomarkers is captivating. Methods: Differentially expressed lncRNAs, mRNAs, and miRNAs were collected using Gene Expression Omnibus datasets. DEGs were validated based on TCGA. Functional analysis and pathway activity were also done. Drug sensitivity analyses were done, and IC50 vs. gene expression plots were depicted. Results: A total of 696 mRNAs, 48 lncRNAs, and, 43 miRNAs were identified to have significant differential expression in cancerous breast tissue than normal breast tissue samples. Functional analysis showed significant pathway enrichments in cancer. We found that 13 individual genes, lncRNAs, and miRNAs, CDC6, ERBB2, EZR, HELLS, MAPK13, MCM2, MMP1, SLC7A5, TINCR, TRIP13, hsa-miR-376a, hsa-miR-21, hsa-miR-454 were significantly predictive of poor overall survival and AKAP12, CXCL12, FGF2, IRS2, LINC00342, LINC01140, MEG3, MIR250HG, NAV3, NDRG2, NEAT1, TGFBR3 and, hsa-miR-29c were associated with favorable overall survival. We reached a set of five genes (EGR1, NFIB, TGFBR3, SMARCA4, and MCM2) that exhibit altered expression patterns in breast cancer, resulting in increased susceptibility of cancer cells to certain drug treatments. Conclusion: We successfully made a unique ce-network, providing new clues to understand the regulatory functions of non-coding RNAs (miRNAs and lncRNAs) in the pathogenesis and prognosis of breast cancer and will facilitate further experimental studies to develop new biomarkers in the diagnosis, prognosis and, therapy of breast cancer.

88 - Circulating microRNAs as potential diagnostic and prognostic biomarkers in people with class 3 obesity and T2DM

88 - Circulating microRNAs as potential diagnostic and prognostic biomarkers in people with class 3 obesity and T2DM

Potential Diagnostic Role of Serum Fibroblast Growth Factor-19 in Hepatocellular Carcinoma.

A highly accurate diagnostic method is crucial to reduce mortality and increase hepatocellular carcinoma (HCC) survival. Current biomarkers have limited accuracy, and novel ones are needed. Fibroblast growth factor-19 (FGF-19) is overexpressed in HCC. This study aimed to assess FGF-19 as a potential novel diagnostic biomarker for HCC. This case-control study involved 114 individuals divided into three equal groups: HCC (n=38), Cirrhosis (n=38), and Control (n=38). HCC biomarkers included alpha-fetoprotein (AFP), Des-γ-carboxy prothrombin (DCP), and FGF-19. The three markers, FGF-19, DCP, and AFP, were significantly different between the three groups, except that DCP was comparable between HCC and Cirrhosis groups (p=1.000). All individuals in the control group had FGF-19 levels below the minimum level in the HCC group. Thus, FGF-19 had 100% sensitivity and specificity in differentiating HCC from healthy controls. FGF-19 can discriminate between HCC and Cirrhosis groups at a 140.8 pg/mL cutoff with sensitivity and specificity of 81.8% and 87.9%, respectively. The sensitivity of FGF-19 was higher than AFP, trending toward statistical significance (p=0.095). Combining FGF-19 with AFP, DCP, or both improved sensitivity but decreased specificity. FGF-19 is a possible noninvasive serum biomarker for HCC. Its combination with AFP or DCP improves the sensitivity for detecting HCC.

Diagnostic Values of Ki67, Cox2, CD31, E-cadherin, VEGF, MMP2, and MMP9 Protein Expression in Human Melanoma

Background: Cutaneous melanoma, the most severe form of skin cancer, has an unpredictable behavior and a high mortality rate. Recent research has identified new biomarkers and their associations with certain histopathological features, offering essential insights into diagnosis, prognosis, and therapeutic strategies. Material and method: In this meticulously designed and executed study, we analyzed 85 formalin-fixed, paraffin-embedded (FFPE) tissue samples using the gold standard technique of immunohistochemistry (IHC) to characterize the protein expression profiles. We used a comprehensive panel of Ki67, Cox2, CD31, VEGF, MMP2, MMP9, and E-cadherin antibodies. Clinical stages were meticulously determined according to TNM classification, the thickness of the Breslow depth, and Clark levels. We employed robust statistical methods such as one-way ANOVA and curve estimation regression to analyze the data. The STRING database, a trusted resource, was used to identify protein-protein interactions and related pathways. Results: The results of our study unveiled novel pairwise positive correlations between CD31 and Ki67 (r = 0.72), and between VEGF and Ki67 protein expression (r = 0.63). We also found a significant inverse relationship between the expression of E-cadherin and some biomarkers, such as Ki67, CD31, and VEGF (p < 0.001 for all). Additionally, our findings revealed a strong positive association between the extent of tumor invasion and the expression levels of Ki67, CD31, and VEGF. The overexpression of these proteins was significantly correlated with advanced clinical stages (p < 0.001 for all). Conclusion: Our study suggests that the biomarkers we examined could be reliable potential diagnostic and prognostic biomarkers for cutaneous melanoma. These findings have the potential to significantly impact the diagnosis, prognosis, and treatment of this deadly disease, offering new avenues for improved patient care.

Initial Evaluation of lncRNA A2M-AS1 Gene Expression in Multiple Sclerosis Patients

Background: Multiple sclerosis (MS) is one of the three leading neurodegenerative diseases worldwide. Gene expression profile studies play an important role in recognizing and preventing disease. Considering the inherent ability of biomarkers to diagnose and prognose the occurrence of a disease, with the aim of gene therapy and changing gene expression, it can be helped to treat it. In this study, by examining the gene interaction and expression of non-coding genes in patients with MS, using bioinformatics analyses, laboratory research and potential non-coding diagnostic biomarkers of MS were selected for further investigations. Materials and Methods: First, by using micro-array data analysis of the GEO database, the expression status of the long non-coding ribonucleic acid (RNA) (lncRNA) A2M-AS1 gene was investigated in patients with MS. lncRNA–mRNA interaction analysis was performed in the lncRRisearch database. After sample collection, the total RNA extracted using the RNA extraction kit from 20 patient samples and 20 healthy samples was synthesized into cDNA with the synthesis kit. The quantitative reverse transcriptase polymerase chain reaction experiment was performed for the final validation of expression change. Results: Based on bioinformatic and laboratory analysis, the expression of the A2M-AS1 gene in MS samples showed a significant decrease in expression compared to healthy samples. Also, based on the receiver operating characteristic analysis, lncRNA A2M-AS1 can be introduced as an acceptable diagnostic biomarker to distinguish MS samples from healthy samples. Conclusion: lncRNA A2M-AS1, by reducing its expression as an acceptable diagnostic biomarker, can increase the risk of developing MS.

Machine learning and single-cell analysis identify the mitophagy-associated gene TOMM22 as a potential diagnostic biomarker for intervertebral disc degeneration

BackgroundMitophagy selectively eliminates potentially cytotoxic and damaged mitochondria and effectively prevents excessive cytotoxicity from damaged mitochondria, thereby attenuating inflammatory and oxidative responses. However, the potential role of mitophagy in intervertebral disc degeneration remains to be elucidated. MethodsThe GSVA method, two machine learning methods (SVM-RFE algorithm and random forest), the CIBERSORT and MCPcounter methods, as well as the consensus clustering method and the WGCNA algorithm were used to analyze the involvement of mitophagy in intervertebral disc degeneration, the diagnostic value of mitophagy-associated genes in intervertebral disc degeneration, and the infiltration of immune cells, and identify the gene modules that were closely related to mitophagy. Single-cell analysis was used to detect mitophagy scores and TOMM22 expression, and pseudo-temporal analysis was used to explore the function of TOMM22 in nucleus pulposus cells. In addition, TOMM22 expression was compared between human normal and degenerated intervertebral disc tissue samples by immunohistochemistry and PCR. ResultsThis study identified that the mitophagy pathway score was elevated in intervertebral disc degeneration compared with the normal condition. A strong link was present between mitophagy genes and immune cells, which may be used to typify intervertebral disc degeneration. The single-cell level showed that mitophagy-associated gene TOMM22 was highly expressed in medullary cells of the disease group. Further investigations indicated the upregulation of TOMM22 expression in late-stage nucleus pulposus cells and its role in cellular communication. In addition, human intervertebral disc tissue samples established that TOMM22 levels were higher in disc degeneration samples than in normal samples. ConclusionsOur findings revealed that mitophagy may be used in the diagnosis of intervertebral disc degeneration and its typing, and TOMM22 is a molecule in this regard and may act as a potential diagnostic marker in intervertebral disc degeneration.

Mutual Influence Between Allergic Rhinitis and Sleep: Factors, Mechanisms, and interventions-A Narrative Review.

Patients with allergic rhinitis (AR) have a high incidence of sleep disorders, such as insomnia, which can easily exacerbate nasal symptoms. The aggravation of nasal symptoms further promotes the deterioration of sleep disorders, forming a vicious cycle. Severe cases may even trigger psychological and neurological issues, such as anxiety, depression, and cognitive impairment, causing significant distress to patients, making clinical diagnosis and treatment difficult, and increasing costs. Furthermore, satisfactory therapeutics remain lacking. As the pathogenesis of AR-associated sleep disorders is not clear and research is still insufficient, paying attention to and understanding AR-related sleep disorders is crucial in clinical practice. Multiple studies have shown that the most crucial issues in current research on AR and sleep are analyzing the relationship between AR and sleep disorders, searching for the influencing factors, and investigating potential targets for diagnosis and treatment. This review aimed to identify and summarize the results of relevant studies using "AR" and "sleep disorders" as search terms. In addition, we evaluated the correlation between AR and sleep disorders and examined their interaction and potential mechanisms, offering a foundation for additional screening of potential diagnostic biomarkers and therapeutic targets.

Comprehensive Proteomic Analysis Reveals Distinct Features and a Diagnostic Biomarker Panel for Early Pregnancy Loss in Histological Subtypes

Early pregnancy loss (EPL) is a common event in human reproduction and is classified into histological subtypes such as hydropic abortion (HA) and hydatidiform moles (HMs), including complete hydatidiform moles (CHMs) and partial hydatidiform moles (PHMs). However, accurate diagnosis and improved patient management remain challenging due to high rates of misdiagnosis and diverse prognostic risks. Therefore, diagnostic biomarkers for EPL are urgently needed. Our study aimed to identify biomarkers for EPL through comprehensive proteomic analysis. Ten CHMs, six PHMs, ten HAs and ten normal control (NC) products of conception (POC) were used to obtain a proteomic portrait. Parallel reaction monitoring (PRM)-targeted proteomic and regression analyses were used to verify and select the diagnostic signatures. Finally, 14 proteins were selected and a panel of diagnostic classifiers (DLK1, SPTB/COL21A1, and SAR1A) was built to represent the CHM, PHM, and NC groups (auROC=0.900, 0.804/0.885, and 0.991, respectively). This high diagnostic power was further validated in another independent cohort (n = 148) by immunohistochemistry (IHC) (n = 120) and western blot (WB) analyses (n = 28). The protein SPTB was selected for further biological behaviour experiments in vitro. Our data suggest that SPTB maintains trophoblast cell proliferation, angiogenesis, cell motility and the cytoskeleton network. This study provides a comprehensive proteomic portrait and identifies potential diagnostic biomarkers. These findings enhance our understanding of EPL pathogenesis and offer novel targets for diagnosis and therapeutic interventions.

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.

Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis – Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers

Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SignificanceOur exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.

Isolation and characterization of ssDNA aptamers against BipD antigen of Burkholderia pseudomallei

BackgroundMelioidosis is difficult to diagnose due to its wide range of clinical symptoms. The culture method is time-consuming and less sensitive, emphasizing the importance of rapid and accurate diagnostic tests for melioidosis. Burkholderia invasion protein D (BipD) of Burkholderia pseudomallei is a potential diagnostic biomarker. This study aimed to isolate and characterize single-stranded DNA aptamers that specifically target BipD. MethodsThe recombinant BipD protein was produced, followed by isolation of BipD-specific aptamers using Systematic Evolution of Ligands by EXponential enrichment. The binding affinity and specificity of the selected aptamers were evaluated using Enzyme-Linked Oligonucleotide Assay. ResultsThe fifth SELEX cycle showed a notable enrichment of recombinant BipD protein-specific aptamers. Sequencing analysis identified two clusters with a total of seventeen distinct aptamers. AptBipD1, AptBipD13, and AptBipD50 were chosen based on their frequency. Among them, AptBipD1 exhibited the highest binding affinity with a Kd value of 1.0 μM for the recombinant BipD protein. Furthermore, AptBipD1 showed significant specificity for B. pseudomallei compared to other tested bacteria. ConclusionAptBipD1 is a promising candidate for further development of reliable, affordable, and efficient point-of-care diagnostic tests for melioidosis.

Cell Polarity-related Gene PTK7, a Potential Diagnostic Biomarker in Pan-cancer.

PTK7 (Protein Tyrosine Kinase 7), a member of the receptor protein tyrosine kinase family, was originally discovered in colon cancer cells. It plays a pivotal role in numerous developmental and physiological processes, particularly in the regulation of cell polarity. Despite accumulating evidence of PTK7's significant influence on tumor development, a comprehensive pan-cancer analysis of PTK7 has yet to be conducted. We conducted a comprehensive analysis of PTK7's expression, prognostic value, and mutational patterns across various tumor types. We further explored the correlations between PTK7 expression and tumor stemness, immune-related genes, immune scores, and immune cell infiltration. Enrichment analysis revealed PTK7's critical involvement in pan-cancer functions and processes, including the WNT pathway, Epithelial-Mesenchymal Transition (EMT), and cell polarity regulation. Additionally, we validated PTK7's expression in gastric cancer via immunohistochemistry. Our study indicates that PTK7 holds promise as an ideal pan-cancer biomarker due to its involvement in tumor progression and tumor immunity.

Amino acid metabolism in glioma: in vivo MR-spectroscopic detection of alanine as a potential biomarker of poor survival in glioma patients.

Reprogramming of amino acid metabolism is relevant for initiating and fueling tumor formation and growth. Therefore, there has been growing interest in anticancer therapies targeting amino acid metabolism. While developing personalized therapeutic approaches to glioma, in vivo proton magnetic resonance spectroscopy (MRS) is a valuable tool for non-invasive monitoring of tumor metabolism. Here, we evaluated MRS-detected brain amino acids and myo-inositol as potential diagnostic and prognostic biomarkers in glioma. We measured alanine, glycine, glutamate, glutamine, and myo-inositol in 38 patients with MRI-suspected glioma using short and long echo-time single-voxel PRESS MRS sequences. The detectability of alanine, glycine, and myo-inositol and the (glutamate + glutamine)/total creatine ratio were evaluated against the patients' IDH mutation status, CNS WHO grade, and overall survival. While the detection of alanine and non-detection of myo-inositol significantly correlated with IDH wildtype (p = 0.0008, p = 0.007, respectively) and WHO grade 4 (p = 0.01, p = 0.04, respectively), glycine detection was not significantly associated with either. The ratio of (glutamate + glutamine)/total creatine was significantly higher in WHO grade 4 than in 2 and 3. We found that the overall survival was significantly shorter in glioma patients with alanine detection (p = 0.00002). Focusing on amino acids in MRS can improve its diagnostic and prognostic value in glioma. Alanine, which is visible at long TE even in the presence of lipids, could be a relevant indicator for overall survival.