A Circulating Panel of circRNA Biomarkers for the Noninvasive and Early Detection of Pancreatic Ductal Adenocarcinoma.

BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies. Delayed manifestation of symptoms and lack of specific diagnostic markers lead patients being diagnosed with PDAC at advanced stages. This study aimed to develop a circular RNA (circRNA)-based biomarker panel to facilitate noninvasive and early detection of PDAC. METHODS: A systematic genome-wide discovery of circRNAs overexpressed in patients with PDAC was conducted. Subsequently, validation of the candidate markers in the primary tumors from patients with PDAC was performed, followed by their translation into a plasma-based liquid biopsy assay by analyzing 2 independent clinical cohorts of patients with PDAC and nondisease controls. The performance of the circRNA panel was assessed in conjunction with the plasma levels of cancer antigen 19-9 for the early detection of PDAC. RESULTS: Initially, a panel of 10 circRNA candidates was identified during the discovery phase. Subsequently, the panel was reduced to 5 circRNAs in the liquid biopsy-based assay, which robustly identified patients with PDAC and distinguished between early-stage (stage I/II) and late-stage (stage III/IV) disease. The areas under the curve of this diagnostic panel for the detection of early-stage PDAC were 0.83 and 0.81 in the training and validation cohorts, respectively. Moreover, when this panel was combined with cancer antigen 19-9 levels, the diagnostic performance for identifying patients with PDAC improved remarkably (area under the curve, 0.94) for patients in the validation cohort. Furthermore, the circRNA panel could also efficiently identify patients with PDAC (area under the curve, 0.85) who were otherwise deemed clinically cancer antigen 19-9-negative (<37 U/mL). CONCLUSIONS: A circRNA-based biomarker panel with a robust noninvasive diagnostic potential for identifying patients with early-stage PDAC was developed.

Plasma MERTK is a promising biomarker for the diagnosis and prognosis of HBV-related acute-on-chronic liver failure.

BACKGROUND: Hepatitis B-related acute-on-chronic liver failure (HBV-ACLF) has a high short-term mortality. This study aimed to determine the diagnostic and prognostic role of MER tyrosine kinase (MERTK) in HBV-ACLF patients. METHODS: Transcriptomics analysis evaluated MERTK expression and function during disease progression. The diagnostic and prognostic significance of MERTK for HBV-ACLF patients were verified by ELISA, the area under the receiver operating characteristic curve (AUROC) analysis, and immunohistochemistry (IHC) of liver tissues. RESULTS: MERTK mRNA was highly expressed in the HBV-ACLF compared to the liver cirrhosis (LC), chronic hepatitis B (CHB) and normal controls (NC) groups. Elevated MERTK mRNA predicted poor prognosis for HBV-ACLF at 28/90 days (AUROCs=0.814/0.731). Functional analysis showed MERTK was significantly associated with TLR and inflammatory signaling, and several key biological processes. External validation with 285 plasma subjects confirmed the high diagnostic accuracy of plasma MERTK for HBV-ACLF (AUROC=0.859) and potential prognostic value for 28/90-day mortality rates (AUROC=0.673 and 0.644, respectively). Risk stratification analysis indicated higher mortality risk for patients with plasma MERTK level above the cut-off value. Moreover, IHC staining showed increasing MERTK expression from NC, CHB and LC to HBV-ACLF patients. CONCLUSIONS: MERTK shows promise as a candidate biomarker for early diagnosis and prognosis of HBV-ACLF.

Identification of biomarkers for abdominal aortic aneurysm in Behçet's disease via mendelian randomization and integrated bioinformatics analyses.

Behçet's disease (BD) is a complex autoimmune disorder impacting several organ systems. Although the involvement of abdominal aortic aneurysm (AAA) in BD is rare, it can be associated with severe consequences. In the present study, we identified diagnostic biomarkers in patients with BD having AAA. Mendelian randomization (MR) analysis was initially used to explore the potential causal association between BD and AAA. The Limma package, WGCNA, PPI and machine learning algorithms were employed to identify potential diagnostic genes. A receiver operating characteristic curve (ROC) for the nomogram was constructed to ascertain the diagnostic value of AAA in patients with BD. Finally, immune cell infiltration analyses and single-sample gene set enrichment analysis (ssGSEA) were conducted. The MR analysis indicated a suggestive association between BD and the risk of AAA (odds ratio [OR]: 1.0384, 95% confidence interval [CI]: 1.0081-1.0696, p = 0.0126). Three hub genes (CD247, CD2 and CCR7) were identified using the integrated bioinformatics analyses, which were subsequently utilised to construct a nomogram (area under the curve [AUC]: 0.982, 95% CI: 0.944-1.000). Finally, the immune cell infiltration assay revealed that dysregulation immune cells were positively correlated with the three hub genes. Our MR analyses revealed a higher susceptibility of patients with BD to AAA. We used a systematic approach to identify three potential hub genes (CD247, CD2 and CCR7) and developed a nomogram to assist in the diagnosis of AAA among patients with BD. In addition, immune cell infiltration analysis indicated the dysregulation in immune cell proportions.

Lipid metabolism-related genes are involved in the occurrence of asthma and regulate the immune microenvironment.

BACKGROUND: Lipid metabolism plays a pivotal role in asthma pathogenesis. However, a comprehensive analysis of the importance of lipid metabolism-related genes (LMRGs) in regulating the immune microenvironment in asthma remains lacking. The transcriptome matrix was downloaded from the Gene Expression Omnibus (GEO) dataset. Differentially expressed analysis and weighted gene coexpression network analysis (WGCNA) were conducted on the GSE74986 dataset to select hub LMRGs, and gene set enrichment analysis (GSEA) was conducted to explore their biological functions. The CIBERSORT algorithm was used to determine immune infiltration in the asthma and control groups, and the correlation of diagnostic biomarkers and immune cells was performed via Spearman correlation analysis. Subsequently, a competitive endogenous RNA (ceRNA) network was constructed to investigate the hidden molecular mechanism of asthma. The expression levels of the hub genes were further validated in the GSE143192 dataset, and RT‒qPCR and immunofluorescence were performed to verify the reliability of the results in the OVA asthma model. Lastly, the ceRNA network was confirmed by qRT-PCR and RNAi experiments in the characteristic cytokine (IL-13)-induced asthma cellular model. RESULTS: ASAH1, ACER3 and SGPP1 were identified as hub LMRGs and were mainly involved in protein secretion, mTORC1 signaling, and fatty acid metabolism. We found more infiltration of CD8+ T cells, activated NK cells, and monocytes and less M0 macrophage infiltration in the asthma group than in the healthy control group. In addition, ASAH1, ACER3, and SGPP1 were negatively correlated with CD8+ T cells and activated NK cells, but positively correlated with M0 macrophages. Within the ceRNA network, SNHG9-hsa-miR-615-3p-ACER3, hsa-miR-212-5p and hsa-miR-5682 may play crucial roles in asthma pathogenesis. The low expression of ASAH1 and SGPP1 in asthma was also validated in the GSE74075 dataset. After SNHG9 knockdown, miR-615-3p expression was significantly upregulated, while that of ACER3 was significantly downregulated. CONCLUSION: ASAH1, ACER3 and SGPP1 might be diagnostic biomarkers for asthma, and are associated with increased immune system activation. In addition, SNHG9-hsa-miR-615-3p-ACER3 may be viewed as effective therapeutic targets for asthma. Our findings might provide a novel perspective for future research on asthma.

Multiplex plasma protein assays as a diagnostic tool for lung cancer.

Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer-associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno-Onc-I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit-link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early-stage LC detection.

A Liquid Biopsy Signature for the Early Detection of Gastric Cancer in Patients.

BACKGROUND & AIMS: Diagnosing gastric cancer (GC) while the disease remains eligible for surgical resection is challenging. In view of this clinical challenge, novel and robust biomarkers for early detection thus improving prognosis of GC are necessary. The present study is to develop a blood-based long noncoding RNA (LR) signature for the early-detection of GC. METHODS: The present 3-step study incorporated data from 2141 patients, including 888 with GC, 158 with chronic atrophic gastritis, 193 with intestinal metaplasia, 501 healthy donors, and 401 with other gastrointestinal cancers. The LR profile of stage I GC tissue samples were analyzed using transcriptomic profiling in discovery phase. The extracellular vesicle (EV)-derived LR signature was identified with a training cohort (n = 554) and validated with 2 external cohorts (n = 429 and n = 504) and a supplemental cohort (n = 69). RESULTS: In discovery phase, one LR (GClnc1) was found to be up-regulated in both tissue and circulating EV samples with an area under the curve (AUC) of 0.9369 (95% confidence interval [CI], 0.9073-0.9664) for early-stage GC (stage I/II). The diagnostic performance of this biomarker was further confirmed in 2 external validation cohorts (Xi'an cohort, AUC: 0.8839; 95% CI: 0.8336-0.9342; Beijing cohort, AUC: 0.9018; 95% CI: 0.8597-0.9439). Moreover, EV-derived GClnc1 robustly distinguished early-stage GC from precancerous lesions (chronic atrophic gastritis and intestinal metaplasia) and GC with negative traditional gastrointestinal biomarkers (CEA, CA72-4, and CA19-9). The low levels of this biomarker in postsurgery and other gastrointestinal tumor plasma samples indicated its GC specificity. CONCLUSIONS: EV-derived GClnc1 serves as a circulating biomarker for the early detection of GC, thus providing opportunities for curative surgery and improved survival outcomes.

Identification and exploration of aging-related subtypes and distinctive role of SERPINE1 in heart failure based on single-cell and bulk RNA sequencing data.

Aging is a major risk factor for heart failure (HF) and is the leading cause of death worldwide. Currently, the nature of the relationship between aging and HF is not entirely clear. Herein, this study aimed to explore new diagnostic biomarkers, molecular typing and therapeutic strategies for HF by investigating the biological significance of aging-related genes in HF. A total of 157 differentially expressed genes (DEGs) were screened totally between HF and normal samples, and functional enrichment analysis of DEGs revealed the strong association of HF progression with aging, immune processes and metabolism. Six HF-specific aging-related genes were further identified, and a diagnostic model was developed and validated for good diagnostic efficacy. In addition, we collected blood samples from 10 normal controls and 10 HF patients for RT-qPCR analysis to verify the bioinformation. We also identified two aging-associated subtypes with distinctly different immune infiltration and metabolic microenvironment. Further single-cell sequencing analysis conducted in the study identified SERPINE1 as a key gene in HF. The distinctive role of SERPINE1 fibroblasts was revealed, including three main findings: (I) fibroblasts had a higher proportion and expression of SERPINE1 levels in HF; (II) the ligand-receptor pair MDK-LRP1 made the most contributions in high interactions with other cell types in SERPINE1 fibroblasts; and (III) SERPINE1 fibroblasts were associated with the interaction of extracellular matrix and receptor and may be regulated by the transcription factor EGR1. In conclusion, this study highlights the importance of aging-related genes in diagnosing HF and regulating immune infiltration. We also identified different HF subtypes and a potentially crucial gene, which may provide a better understanding of the molecular-level mechanisms of aging-related HF and aid in developing effective therapeutic strategies.

Comprehensive pan-cancer analysis of ZNF337 as a potential diagnostic, immunological, and prognostic biomarker.

BACKGROUND: Zinc Finger Protein 337 (ZNF337) is a novel Zinc Finger (ZNF) protein family member. However, the roles of ZNF337 in human cancers have not yet been investigated. METHODS: In this study, with the aid of TCGA databases, GTEx databases, and online websites, we determined the expression levels of ZNF337 in pan-cancer and its potential value as a diagnostic and prognostic marker for pan-cancer and analyzed the relationship between ZNF337 expression and immune cell infiltration and immune checkpoint genes. We then focused our research on the potential of ZNF337 as a biomarker for diagnostic and prognostic in KIRC (kidney renal clear cell carcinoma) and validated in the E-MTAB-1980 database. Moreover, the expression of ZNF337 was detected through qRT-PCR and Western blotting (WB). CCK-8 experiment, colony formation experiment, and EDU experiment were performed to evaluate cell proliferation ability. Wound healing assay and transwell assay were used to analyze its migration ability. The qRT-PCR and WB were used to detect the expression of ZNF337 in tumor tissues and paracancerous tissues of KIRC patients. RESULTS: The pan-cancer analysis revealed that abnormal ZNF337 expression was found in multiple human cancer types. ZNF337 had a high diagnostic value in pan-cancer and a significant association with the prognosis of certain cancers, indicating that ZNF337 may be a valuable prognostic biomarker for multiple cancers. Further analysis demonstrated that the expression level of ZNF337 displayed significant correlations with cancer-associated fibroblasts, immune cell infiltration, and immune checkpoint genes in many tumors. Additionally, ZNF337 was observed to have a high expression in KIRC. Its expression was significantly associated with poor prognosis [overall survival (OS), disease-specific survival (DSS)], age, TNM stage, histologic grade, and pathologic stage. The high ZNF337 expression was associated with poor prognosis in the E-MTAB-1980 validation cohort. The in vitro experiments suggested that the expression of ZNF337 in KIRC tumor tissues was higher than in adjacent tissues, and ZNF337 knockdown inhibited the proliferation and migration of KIRC cells, whereas overexpression of ZNF337 had the opposite effects. CONCLUSIONS: ZNF337 might be an important prognostic and immunotherapeutic biomarker for pan-cancer, especially in KIRC.

Identification and diagnostic potential of hsa_circ_101303 in colorectal cancer: unraveling a regulatory network.

BACKGROUND: The role of novel circular RNAs (circRNAs) in colorectal cancer (CRC) remains to be determined. This study aimed to identify a novel circRNA involved in CRC pathogenesis, assess its diagnostic value, and construct a regulatory network. METHODS: Differential expression analysis was conducted using circRNA datasets to screen for differentially expressed circRNAs. The expression of selected circRNAs was validated in external datasets and clinical samples. Diagnostic value of plasma circRNA levels in CRC was assessed. A competing endogenous RNA (ceRNA) network was constructed for the circRNA using TCGA dataset. RESULTS: Analysis of datasets revealed that hsa_circ_101303 was significantly overexpressed in CRC tissues compared to normal tissues. The upregulation of hsa_circ_101303 in CRC tissues was further confirmed through the GSE138589 dataset and clinical samples. High expression of hsa_circ_101303 was associated with advanced N stage, M stage, and tumor stage in CRC. Plasma levels of hsa_circ_101303 were markedly elevated in CRC patients and exhibited moderate diagnostic ability for CRC (AUC = 0.738). The host gene of hsa_circ_101303 was also found to be related to the TNM stage of CRC. Nine miRNAs were identified as target miRNAs for hsa_circ_101303, and 27 genes were identified as targets of these miRNAs. Subsequently, a ceRNA network for hsa_circ_101303 was constructed to illustrate the interactions between the nine miRNAs and 27 genes. CONCLUSIONS: The study identifies hsa_circ_101303 as a highly expressed circRNA in CRC, which is associated with the progression of the disease. Plasma levels of hsa_circ_101303 show promising diagnostic potential for CRC. The ceRNA network for hsa_circ_101303 provides valuable insights into the regulatory mechanisms underlying CRC.

Alanine, a potential amino acid biomarker of pediatric sepsis: a pilot study in PICU.

Sepsis is characterized by a metabolic disorder of amino acid occurs in the early stage; however, the profile of serum amino acids and their alterations associated with the onset of sepsis remain unclear. Thus, our objective is to identify the specific kinds of amino acids as diagnostic biomarkers in pediatric patients with sepsis. Serum samples were collected from patients with sepsis admitted to the pediatric intensive care unit (PICU) between January 2019 and December 2019 on the 1st, 3rd and 7th day following admission. Demographic and laboratory variables were also retrieved from the medical records specified times. Serum amino acid concentrations were detected by UPLC-MS/MS system. PLS-DA (VIP > 1.0) and Kruskal-Wallis test (p < 0.05) were employed to identify potential biomarkers. Spearman's rank correlation analysis was conducted to find the potential association between amino acid levels and clinical features. The diagnostic utility for pediatric sepsis was assessed using receiver operating characteristic (ROC) curve analysis. Most of amino acid contents in serum were significantly decreased in patients with sepsis, but approached normal levels by the seventh day post-diagnosis. Threonine (THR), lysine (LYS), valine (VAL) and alanine (ALA) emerged as potential biomarkers related for sepsis occurrence, though they were not associated with PELOD/PELOD-2 scores. Moreover, alterations in serum THR, LYS and ALA were linked to complications of brain injury, and serum ALA levels were also related to sepsis-associated acute kidney injury. Further analysis revealed that ALA was significantly correlated with the Glasgow score, serum lactate and glucose levels, C-reactive protein (CRP), and other indicators for liver or kidney dysfunction. Notably, the area under the ROC curve (AUC) for ALA in distinguishing sepsis from healthy controls was 0.977 (95% CI: 0.925-1.000). The serum amino acid profile of children with sepsis is significantly altered compared to that of healthy controls. Notably, ALA shows promise as a potential biomarker for the early diagnosis in septic children.

Advancing gastrointestinal cancer diagnostics: a systematic review and meta-analysis of circulating microRNA-1246 as a non-invasive biomarker.

BACKGROUND: Despite numerous reports on the alterations of microRNA-1246 (miR-1246) expression level in digestive system cancers, its role in gastrointestinal cancers (GICs) remains unclear. This meta-analysis aimed to assess the diagnostic potential of circulating miR-1246 in GICs. METHODS: Meta-disc version 1.4 and Comprehensive Meta-Analysis (CMA) version 3.7 software were used to calculate pooled sensitivity, specificity, likelihood ratios, diagnostic odds ratio (DOR), area under the curve (AUC), Q*index and summary receiver-operating characteristic (SROC). Subgroup analyses were conducted for cancer type, sample type and geographical region. Publication bias was assessed using Begg's and Egger's tests. RESULTS: A total of 14 articles involving 18 studies and 1526 participants (972 cases and 554 controls) were included. The diagnostic accuracy of miRNA-1246 in GICs was as follows: pooled sensitivity: 0.81 (95% CI: 0.79 - 0.83), specificity: 0.74 (95% CI: 0.71 - 0.77), PLR: 3.315 (95% CI: 2.33 - 4.72), NLR: 0.221 (95% CI: 0.153 - 0.319), DOR: 16.87 (95% CI: 9.45 - 30.09), AUC: 0.891, and Q*-index: 0.807. No publication bias was found based on Begg's (p = 0.172) and Egger's (p = 0.113) tests. CONCLUSION: Circulating miR-1246 shows promise as a non-invasive biomarker for early detection of GICs.

Integrated Assessment of GFAP and UCH-L1 for their utility in severity assessment and outcome prediction in Traumatic Brain Injury.

OBJECTIVES: This study aimed to explore the potential of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) as biomarkers for diagnosis and prognosis in mild and severe TBI cases, including TBI-related deaths. METHODS: This prospective cohort study includes 40 cases each of mild, severe, fatal TBI cases, and 40 healthy controls. Serum samples were collected from live patients at 8 and 20 h post injury for UCH-L1 and GFAP respectively, and from deceased patients within 6 h of death. RESULTS: Elevated levels of both GFAP and UCH-L1 were observed in patients with severe and fatal TBI cases. These biomarkers exhibited promising potential for predicting various Glasgow Outcome Scale Extended (GOSE) categories. Combining GFAP and UCH-L1 yielded higher predictive accuracy both for diagnosis and prognosis in TBI cases. The study additionally established specific cut-off levels for GFAP and UCH-L1 stratified according to the severity and prognosis. CONCLUSION: GFAP and UCH-L1 individually demonstrated moderate to good discrimination capacity in predicting TBI severity and functional outcomes. However, combining these biomarkers is recommended for improved diagnostic and prognostic utility. This precision tool can enhance patient care, enabling tailored treatment plans, ultimately reducing morbidity and mortality rates in TBI cases.

TOX, TWIST1, STAT4, and SATB1 protein expressions in early-stage mycosis fungoides.

BACKGROUND: Diagnosis of early mycosis fungoides (eMF) is challenging and often delayed as many of its clinical and histopathologic features may mimic various benign inflammatory dermatoses (BIDs). The products of the thymocyte selection-associated high mobility group box (TOX), twist family BHLH transcription factor 1 (TWIST1), signal transducer and activator of transcription 4 (STAT4), and special AT-rich sequence-binding protein 1 (SATB1) genes function as transcription factors and are involved in the pathogenesis of MF. OBJECTIVES: We aim to determine the diagnostic value of TOX, TWIST1, STAT4, and SATB1 protein expressions in eMF. METHODS: This non-randomized, controlled, prospective analytic study was conducted by performing immunohistochemistry staining with TOX, TWIST1, STAT4, and SATB1 polyclonal antibodies in lesional skin biopsies of eMF and BID patients. Nuclear staining of lymphocytes was compared between eMF and BIDs, and the capacity of these antibodies to predict eMF was determined. RESULTS: Immunostainings with anti-TWIST1 showed an increase in protein expression (p = 0.003) and showed a decrease with anti-SATB1 antibodies in eMF compared to BIDs (p = 0.005) while anti-TOX and anti-STAT4 antibodies did not exhibit significant differences (p = 0.384; p = 0.150). Receiver operating characteristic analysis showed that immunohistochemical evaluations of TWIST1 and SATB1 protein expressions can differentiate eMF (area under the curve [AUC]: 0.728, 95% confidence interval [CI]: 0.605-0.851, p = 0.002; AUC: 0.686, 95% CI: 0.565-0.807, p = 0.013). CONCLUSIONS: TWIST1 and SATB1 are potential diagnostic markers for the histologic diagnosis of eMF.

Gamma auditory steady-state response as a promising electrophysiological biomarker for depression: an in vivo study with chronic unpredictable mild stress (CUS)-induced rats.

Gamma oscillations play a functional role in brain cognitions. Recently, auditory steady-state response (ASSR) has been reported abnormally in depression clinically, particularly in the low-gamma band. However, clinical electroencephalography research has challenges obtaining pure signals straight from the source level, making information isolation and precise localization difficult. Besides, the ASSR deficits pattern remains unclear. Herein, we focused on the origin of ASSR-primary auditory cortex (A1), the central node in the auditory pathway. We assessed the evoked-power and phase-synchronization using local field potentials (LFP) in depression (n = 21) and control (n = 22) rats. Subsequent processing of the received auditory information was examined using event-related potentials (AEPs). Results showed that depressed rats exhibited significant gamma ASSR impairments in peak-to-peak amplitude, inter-trial phase coherence, and signal-to-noise ratio. These deficits were more pronounced during 40-Hz auditory stimuli in right-A1, indicating severe gamma network abnormalities in the right auditory pathway. Besides, increased N2 and P3 amplitudes in depression group were found, indicating excessive inhibitory control and contextual processing. Taken together, these ASSR abnormalities have a high specificity of more than 90% and high sensitivity of more than 80% to distinguish depression under 40-Hz auditory stimuli. Our findings provided an abnormal gamma network in the auditory pathway, as a promising diagnostic biomarker in the future.

Untargeted metabolomics reveals potential plasma biomarkers for diagnosis of primary aldosteronism using liquid chromatography-mass spectrometry.

Metabolite profiling has the potential to comprehensively bridge phenotypes and complex heterogeneous physiological and pathological states. We performed a metabolomics study using parallel liquid chromatography-mass spectrometry (LC-MS) combined with multivariate data analysis to screen for biomarkers of primary aldosteronism (PA) from a cohort of 111 PA patients and 218 primary hypertension (PH) patients. Hydrophilic interaction chromatography and reversed-phase liquid chromatography separations were employed to obtain a global plasma metabolome of endogenous metabolites. The satisfactory classification between PA and PH patients was obtained using the MVDA model. A total of 35 differential metabolites were screened out and identified. A diagnostic biomarker panel was established using the least absolute shrinkage and selection operator (LASSO) binary logistic regression model and receiver operating characteristic analysis. Joint analysis with clinical indicators, including plasma supine aldosterone level, plasma orthostatic aldosterone level, body mass index, and blood potassium, revealed that the combination of metabolite biomarker panel and plasma supine aldosterone has the best clinical diagnostic efficacy.

Identification and Validation of the Diagnostic Markers for Inflammatory Bowel Disease by Bioinformatics Analysis and Machine Learning.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract which is mediated by the inappropriate immune responses. This study was aimed to identify novel diagnostic biomarkers for diagnosis of IBD and explore the relationship between the diagnostic biomarkers and infiltrated immune cells. GSE38713, GSE53306, and GSE75214 downloaded from the Gene Expression Omnibus (GEO) database were split into training and testing sets. Differentially expressed genes (DEGs) were screened using the "limma" package. Gene Ontology (GO) and KEGG pathway enrichment analysis of DEGs were performed by clusterProfiler package. The LASSO regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were conducted to identify novel diagnostic biomarkers. The receiver operating characteristic (ROC) curve was applied to evaluate the diagnostic value of the candidate biomarkers. The relationship of the candidate biomarkers and infiltrating immune cells in IBD were evaluated by CIBERSOTR. Quantitative Real-Time PCR (qRT-PCR) was applied to measure the expression level of the biomarkers in IBD. A total of 289 dysregulated genes were identified as DEGs in IBD. These DEGs were significantly enriched in chemokine signaling pathway and cytokine-cytokine receptor interaction. RHOU was identified as a critical diagnostic gene in IBD, which was confirmed using ROC curve and qRT-PCR assays. Immune cell infiltration analysis showed that RHOU was correlated with macrophages M2, dendritic cells resting, mast cells resting, T cells CD4 memory resting, macrophages M0, and mast cells activated. Our results imply that RHOU may be a potential diagnostic biomarker for IBD.

Comprehensive Pan-Cancer Analysis of Connexin 43 as a Potential Biomarker and Therapeutic Target in Human Kidney Renal Clear Cell Carcinoma (KIRC).

Background and Objectives: Connexin 43 (Cx43) is involved in the transfer of small signaling molecules between neighboring cells, thereby exerting a major influence on the initiation and progression of tumorigenesis. However, there is a lack of systematic research on Cx43 expression and its predictive role in clinical diagnosis and prognosis in pan-cancer. Materials and Methods: Several biological databases were used to evaluate the expression levels of GJA1 (encoding Cx43) and its diagnostic and prognostic significance in pan-cancer. We targeted kidney renal clear cell carcinoma (KIRC) and investigated the relationship between GJA1 expression and different clinical features of KIRC patients. Then, we performed cell-based experiments to partially confirm our results and predicted several proteins that were functionally related to Cx43. Results: The expression of GJA1 has a high level of accuracy in predicting KIRC. High GJA1 expression was remarkably correlated with a favorable prognosis, and this expression was reduced in groups with poor clinical features in KIRC. Cell experiments confirmed the inhibitory effects of increased GJA1 expression on the migratory capacity of human renal cancer (RCC) cell lines, and protein-protein interaction (PPI) analysis predicted that CDH1 and CTNNB1 were closely related to Cx43. Conclusions: GJA1 could be a promising independent favorable prognostic factor for KIRC, and upregulation of GJA1 expression could inhibit the migratory capacity of renal cancer cells.

Construct of qualitative diagnostic biomarkers specific for glioma by pairing serum microRNAs.

BACKGROUND: Serum microRNAs (miRNAs) are promising non-invasive biomarkers for diagnosing glioma. However, most reported predictive models are constructed without a large enough sample size, and quantitative expression levels of their constituent serum miRNAs are susceptible to batch effects, decreasing their clinical applicability. METHODS: We propose a general method for detecting qualitative serum predictive biomarkers using a large cohort of miRNA-profiled serum samples (n = 15,460) based on the within-sample relative expression orderings of miRNAs. RESULTS: Two panels of miRNA pairs (miRPairs) were developed. The first was composed of five serum miRPairs (5-miRPairs), reaching 100% diagnostic accuracy in three validation sets for distinguishing glioma and non-cancer controls (n = 436: glioma = 236, non-cancers = 200). An additional validation set without glioma samples (non-cancers = 2611) showed a predictive accuracy of 95.9%. The second panel included 32 serum miRPairs (32-miRPairs), reaching 100% diagnostic performance in training set on specifically discriminating glioma from other cancer types (sensitivity = 100%, specificity = 100%, accuracy = 100%), which was reproducible in five validation datasets (n = 3387: glioma = 236, non-glioma cancers = 3151, sensitivity> 97.9%, specificity> 99.5%, accuracy> 95.7%). In other brain diseases, the 5-miRPairs classified all non-neoplastic samples as non-cancer, including stroke (n = 165), Alzheimer's disease (n = 973), and healthy samples (n = 1820), and all neoplastic samples as cancer, including meningioma (n = 16), and primary central nervous system lymphoma samples (n = 39). The 32-miRPairs predicted 82.2 and 92.3% of the two kinds of neoplastic samples as positive, respectively. Based on the Human miRNA tissue atlas database, the glioma-specific 32-miRPairs were significantly enriched in the spinal cord (p = 0.013) and brain (p = 0.015). CONCLUSIONS: The identified 5-miRPairs and 32-miRPairs provide potential population screening and cancer-specific biomarkers for glioma clinical practice.

Constructing ferroptosis-related competing endogenous RNA networks and exploring potential biomarkers correlated with immune infiltration cells in asthma using combinative bioinformatics strategy.

BACKGROUND: Asthma is a common chronic respiratory disease worldwide. Recent studies have revealed the critical effects of the ceRNA network and ferroptosis on patients with asthma. Thus, this study aimed to explore the potential ferroptosis-related ceRNA network, investigate the immune cell infiltration level in asthma through integrated analysis of public asthma microarray datasets, and find suitable diagnostic biomarkers for asthma. METHODS: First, three asthma-related datasets which were downloaded from the Gene Expression Omnibus (GEO) database were integrated into one pooled dataset after correcting for batch effects. Next, we screened differentially expressed lncRNAs (DElncRNAs) between patients and healthy subjects, constructed a ceRNA network using the StarBase database and screened ferroptosis-related genes from the predicted target mRNAs for Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We also performed Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) on the batch effect-corrected mRNA expression profile. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to screen potential diagnostic biomarkers, and the diagnostic efficacy was assessed using a receiver operating characteristic (ROC) curve. Finally, we determined the proportion of 22 immune cells in patients with asthma using CIBERSORT and investigated the correlation between key RNAs and immune cells. RESULTS: We obtained 19 DElncRNAs, of which only LUCAT1 and MIR222HG had corresponding target miRNAs. The differentially expressed ferroptosis-related genes were involved in multiple programmed cell death-related pathways. We also found that the mRNA expression profile was primarily enriched in innate immune system responses. We screened seven candidate diagnostic biomarkers for asthma using LASSO regression (namely, BCL10, CD300E, IER2, MMP13, OAF, TBC1D3, and TMEM151A), among which the area under the curve (AUC) value for CD300E and IER2 were 0.722 and 0.856, respectively. Finally, we revealed the infiltration ratio of different immune cells in asthma and found a correlation between LUCAT1, MIR222HG, CD300E, and IER2 with some immune cells. CONCLUSION: This study explored a potential lncRNA-miRNA-mRNA regulatory network and its underlying diagnostic biomarkers (CD300E and IER2) in asthma and identified the immune cells most associated with them, providing possible diagnostic markers and immunotherapeutic targets for asthma.

Real-time quaking-induced conversion assay using a small-scale substrate production workflow for the diagnosis of Creutzfeldt-Jakob disease.

The lack of a dedicated surveillance program for prion disease, particularly in low- and middle-income countries (LMICs), has hindered the global effort to address this public health threat. Although cerebrospinal fluid (CSF) Real-time quaking-induced conversion (RT-QuIC) is considered the most reliable test for sporadic Creutzfeldt-Jakob disease (sCJD), its availability in LMICs is limited because of its cost and technical difficulty in generating the recombinant prion protein substrate (recPrP). This study aimed to evaluate the performance of RT-QuIC with recPrP produced in-house through a small-scale method-that is, the application of reusable prepacked chromatography columns and subsequent dialysis. Here, CSF specimens from patients suspected of having prion disease were consecutively collected and stored between October 2015 and January 2023. Electronic medical record data were reviewed to clinically classify participants as probable sCJD or non-sCJD. CSF RT-QuIC was performed using in-house recPrP. Its specificity and sensitivity for diagnosing probable sCJD were reported, along with details of other clinical data and investigations. We found that among 39 eligible participants, with a median (interquartile range) age of 64 (56-70) years and 16 (41%) female, 13 had probable sCJD and the remaining 26 unequivocally suffered from nonprion disorders. Magnetic resonance imaging and electroencephalogram were suggestive of sCJD in 100% (13/13) and 46.2% (6/13) of sCJD participants, respectively. RT-QuIC was positive in 12/13 sCJD participants (sensitivity 0.92, 95% confidence interval [CI] 0.67-0.99) and negative in all non-sCJD participants (specificity 1.00, 95% CI 0.87-1.00). CSF tau/p-tau ratio showed sensitivity and specificity of 0.62-1.0 and 0.85-1.0, respectively. In summary, RT-QuIC using recPrP generated through a small-scale workflow demonstrated great performance in detecting sCJD. Given its performance results along with its low cost, this technique could feasibly be implemented in LMICs and potentially be the first step toward establishing local prion disease surveillance programs.