Biomarkers For Diagnosis Research Articles

Breath Analysis: Identification of Potential Volatile Biomarkers for Non-Invasive Diagnosis of Chronic Kidney Disease (CKD).

Recently, volatile organic compound (VOC) determination in exhaled breath has seen growing interest due to its promising potential in early diagnosis of several pathological conditions, including chronic kidney disease (CKD). Therefore, this study aimed to identify the breath VOC pattern providing an accurate, reproducible and fast CKD diagnosis at early stages of disease. A cross-sectional observational study was carried out, enrolling a total of 30 subjects matched for age and gender. More specifically, the breath samples were collected from (a) 10 patients with end-stage kidney disease (ESKD) before undergoing hemodialysis treatment (DIAL); (b) 10 patients with mild-moderate CKD (G) including 3 patients in stage G2 with mild albuminuria, and 7 patients in stage G3 and (c) 10 healthy controls (CTRL). For each volunteer, an end-tidal exhaled breath sample and an ambient air sample (AA) were collected at the same time on two sorbent tubes by an automated sampling system and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry. A total of 110 VOCs were detected in breath samples but only 42 showed significatively different levels with respect to AA. Nonparametric tests, such as Wilcoxon/Kruskal-Wallis tests, allowed us to identify the most weighting variables able to discriminate between AA, DIAL, G and CTRL breath samples. A promising multivariate data mining approach incorporating only selected variables (showing p-values lower than 0.05), such as nonanal, pentane, acetophenone, pentanone, undecane, butanedione, ethyl hexanol and benzene, was developed and cross-validated, providing a prediction accuracy equal to 87% and 100% in identifying patients with both mild-moderate CKD (G) and ESKD (DIAL), respectively.

Unravelling the Role of Gut and Oral Microbiota in the Pediatric Population with Type 1 Diabetes Mellitus

Type 1 Diabetes Mellitus (T1DM) is a chronic autoimmune disease that results in the destruction of pancreatic β cells, leading to hyperglycaemia and the need for lifelong insulin therapy. Although genetic predisposition and environmental factors are considered key contributors to T1DM, the exact causes of the disease remain partially unclear. Recent evidence has focused on the relationship between the gut, the oral cavity, immune regulation, and systemic inflammation. In individuals with T1DM, changes in the gut and oral microbial composition are commonly observed, indicating that dysbiosis may contribute to immune dysregulation. Gut dysbiosis can influence the immune system through increased intestinal permeability, altered production of short chain fatty acids (SCFAs), and interactions with the mucosal immune system, potentially triggering the autoimmune response. Similarly, oral dysbiosis may contribute to the development of systemic inflammation and thus influence the progression of T1DM. A comprehensive understanding of these relationships is essential for the identification of biomarkers for early diagnosis and monitoring, as well as for the development of therapies aimed at restoring microbial balance. This review presents a synthesis of current research on the connection between T1DM and microbiome dysbiosis, with a focus on the gut and oral microbiomes in pediatric populations. It explores potential mechanisms by which microbial dysbiosis contributes to the pathogenesis of T1DM and examines the potential of microbiome-based therapies, including probiotics, prebiotics, synbiotics, and faecal microbiota transplantation (FMT). This complex relationship highlights the need for longitudinal studies to monitor microbiome changes over time, investigate causal relationships between specific microbial species and T1DM, and develop personalised medicine approaches.

Integrin subunit beta 6 is a potential diagnostic marker for acute kidney injury in patients with diabetic kidney disease: a single cell sequencing data analysis

Background Diabetic kidney disease (DKD), a prevalent complication of diabetes mellitus, is often associated with acute kidney injury (AKI). Thus, the development of preventive and therapeutic strategies is crucial for delaying the progression of AKI and DKD. Methods The GSE183276 dataset, comprising the data of 20 healthy controls and 12 patients with AKI, was downloaded from the Gene Expression Omnibus (GEO) database to analyze the AKI group. For analyzing the DKD group, the GSE131822 dataset, comprising the data of 3 healthy controls and 3 patients with DKD, was downloaded from the GEO database. The common differentially expressed genes (DEGs) in renal tubular epithelial cells (TECs) were subjected to enrichment analyses. Next, a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes database to analyze gene-related regulatory networks. Finally, the AKI animal models and the DKD and AKI cell models were established, and the reliability of the identified genes was validated using quantitative real-time polymerase chain reaction analysis. Results Functional analysis was performed with 40 common DEGs in TECs. Eight hub genes were identified using the PPI and gene-related networks. Finally, validation experiments with the in vivo animal model and the in vitro cellular model revealed the four common DEGs. Four DEGs that share molecular mechanisms in the pathogenesis of DKD and AKI were identified. In particular, the expression of Integrin Subunit Beta 6(ITGB6), a hub and commonly upregulated gene, was upregulated in the in vitro models. Conclusion ITGB6 may serve as a biomarker for early AKI diagnosis in patients with DKD and as a target for early intervention therapies.

B-095 The level of procalcitonin is a biomarker for differentiation between pathogenic and non-pathogenic bacteria

Abstract Background Sepsis is caused by bacteria infected in the bloodstream and the body’s inability to control inflammation. Procalcitonin (PCT) is a biomarker of infection in patients with sepsis, the severity of the infection, and whether the patient is responding to therapy. Thus, diagnostics and early treatment are very important for doctors and patients. In a hospital concerned with sepsis in patients, a doctor ordered a PCT level and hemoculture before treating an antibiotic on a patient whose doctor suspects sepsis. The objective of this study is to compare the PCT level in the blood and hemoculture results in patients. Methods In this study, we evaluated the patients who suspected sepsis disease and were admitted to the Uttaradit Hospital, Thailand, between October 2022 and October 2023. The patients were divided into two groups: those with positive hemoculture results (sepsis group) and those with negative hemoculture results (non-sepsis group). The PCT levels in the blood were measured on Alinity ci analyzers and compared between the two groups to determine if there was a significant difference. Additionally, clinical data such as age and gender were collected to assess their potential impact on PCT levels and hemoculture results. Results The results of the study showed that the sepsis group had significantly higher PCT levels compared to the non-sepsis group. And the difference in PCT levels between the sepsis and non-sepsis groups was considerably more marked for patients older than 60. Furthermore, there was no significant difference in PCT levels based on gender in either group. Conclusions This finding suggests that measuring PCT levels could potentially serve as a diagnostic tool for identifying sepsis in patients. A patient age may be a contributing factor to the severity of sepsis and could potentially be used as a marker for early detection and intervention in older patients. Further research is needed to validate these results and explore the clinical implications of using PCT as a biomarker for sepsis diagnosis.

B-241 Next-generation monitoring: M-inSightclinical application in multiple myeloma

Abstract Background The improvement of multiple myeloma (MM) therapy effectiveness has increased the need for precise, sensitive, and dynamic monitoring of minimal residual disease (MRD). Current MRD assessment methods that are based on bone marrow aspirates, pose limitations due to the invasiveness of the procedure and potential heterogeneous sampling. Recognizing the need for non-invasive, frequent testing, a targeted mass spectrometry, M-InSight®, (MS)-based MRD blood-test was developed. This innovative assay identifies and tracks clonotypic peptides from the patient's monoclonal immunoglobulin, offering a blood-based alternative with equivalent sensitivity to existing bone marrow reliant techniques.Here we employed M-InSight® to sequence and select clonotypic peptides, enabling highly specific, ultra-sensitive quantification of the M-protein. This study examines the correlation between M-protein quantification using M-InSight® and conventional serum protein electrophoresis (SPEP) values routinely obtained in clinical settings. Given the established role of M-protein as a biomarker for MM diagnosis and longitudinal monitoring, ensuring concordance between emerging methodologies and existing standards is vital. Methods Serum samples were obtained from the clinical laboratory at the University of Miami and characterized using SPEP (capillary electrophoresis, Sebia), immunotyping (Sebia), and free light chain quantitation (The binding site). Patients with M-protein concentrations ≥0.2 g/dL by SPEP were included. Longitudinal samples were collected. Subsequently, all samples were analyzed using M-InSight®. The initial sample for each patient underwent de novo sequencing of the M-protein by mass spectrometry and was also used to calibrate the concentration with the known value from SPEP. An in-house bioinformatics algorithm aligned, sequenced, and selected patient-specific clonotypic peptides. These peptides were targeted for the M-inSight® follow up assay allowing the quantification of M-protein. Results A variety of M-protein isotypes were successfully sequenced (IgGK, IgGL, IgAK, IgAL, IgMK, IgML, free K, free L, IgDL, biclonal) at concentrations ranging from 0.2 to 2.9 g/dL. M-protein was successfully sequenced and clonotypic peptides were identified in 57 out of 59 patients using the initial sample. A total of 109 serum samples collected during maintenance or post treatment were analyzed, 50 of them were follow-up samples and were used to correlate M-protein values from both techniques. M-protein quantification by M-Insight showed high concordance with the one obtained by SPEP, with a correlation value of 0.88 (intercept set to 0, slope 0.91. Pearson = 0.886, p-value <0.001). Out of 57 patients whose M-protein was sequenced, 55 were positive by SPEP and were successfully quantified by M-InSight®. Furthermore, M-InSight® detected and quantified M-protein on samples of the 2 remaining patients that showed M-protein values below SPEP detection limits, highlighting the higher sensitivity of M-InSight®. Conclusions M-inSight® assay successfully identified clonotypic peptides in 57 out of 59 patients which were used to quantify M-protein concentration and showed a strong concordance with SPEP and affirming the utility of M-InSight® as a reliable method for routine monitoring of MM. The ability to seamlessly integrate M-InSight® into routine monitoring protocols offers clinicians a valuable tool for MRD monitoring, tracking disease progression and response to therapy with enhanced sensitivity and specificity.

A-035 Profiling of circulating-microRNAs' signatures in Pre-diabetic versus Diabetic patients

Abstract Background MicroRNAs (miRs) are small non-coding RNAs involved in the process of gene regulation, and hence affect various diseases including metabolic pathways in diabetes. Given their key roles, they are promising candidates as potential biomarkers or better understanding of disease etiology. In this study, our aim is to identify miRs signatures of plasma circulating-miRs related to glycemic control, vitamin D deficiency and platelet activation in diabetic, pre-diabetic versus non-diabetic patients. Methods Plasma samples were collected from 24 patients (both sexes) in total (eight non-diabetic (ND) control, twelve diabetic (DM), and four pre-diabetic (PD)), classified based on their glycemic control (HbA1c levels). miRs were extracted using the miRNeasy Serum/Plasma Advanced kit of Qiagen, run and analyzed on Next Generation Sequencing (NGS). Results Differential expression analysis revealed miRs expression variations between diabetic to pre-diabetic and non-diabetic groups. Expression levels of 131 miRs varied significantly between DM to ND group (92 down- and 39 up- regulated), and 141 miRs in PD compared to ND (97 down- and 39 up- regulated). Although DM and PD had similar differential expression, three miRs (has-miR4776-5p, -6778-3p, and 5002-3p) were over-expressed in PD with very low p-value. Differential expression comparisons accounting sex were performed showing significant differences between males to females that will be presented here. Conclusions Our findings supports the important involvement of circulating miRs in the regulation of glucose homeostasis and pathogenesis of diabetes.Further investigation of candidate miRs could identify promising new biomarkers for the early diagnosis and prevention of Diabetes and related health complications.

Potential use of SCAT1, SCAT2, and SCAT8 as diagnostic and prognosis markers in colorectal cancer

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related deaths worldwide. Despite advancements, the underlying mechanisms controlling CRC's etiology remain unclear, and reliable biomarkers for diagnosis and treatment are still lacking. Long noncoding RNAs (lncRNAs) are increasingly recognized for their role in cancer progression, though many remain unidentified and their functions poorly understood. In this study, we investigated the expression of SCAT1, SCAT2, and SCAT8 lncRNAs in both cancerous and adjacent non-cancerous tissues from CRC patients. Using cDNA synthesized from total RNA extracted from 100 tissue samples, we performed Real-Time PCR to measure the expression levels of these lncRNAs. In addition, their diagnostic potential was evaluated through ROC curve analysis. Our results demonstrate that SCAT1, SCAT2, and SCAT8 are significantly upregulated in CRC tissues, with ROC analysis suggesting SCAT1 as a moderate biomarker and SCAT2 and SCAT8 as promising biomarkers for CRC diagnosis. Moreover, we found strong correlations between SCAT1 and SCAT8, as well as SCAT2 and SCAT8. Collectively, our findings indicate that SCAT1, SCAT2, and SCAT8 may act as oncogenes in CRC, offering potential as novel biomarkers for diagnosis and prognosis.

Propensity Score Matching in CKD: Evaluating Guca2b as a Biomarker for Diagnosis and Treatment Monitoring

Propensity Score Matching in CKD: Evaluating Guca2b as a Biomarker for Diagnosis and Treatment Monitoring

Deciphering the role of heat shock protein HSPA1L: biomarker discovery and prognostic insights in Parkinson's disease and glioma

BackgroundHeat shock proteins (HSPs) play a critical role in cellular stress responses and have been implicated in numerous diseases, including Parkinson's disease (PD) and various cancers. Understanding the differential expression and functional implications of HSPs in these conditions is crucial for identifying potential therapeutic targets and biomarkers for diagnosis and prognosis. MethodsWe utilized combined datasets (GSE6613 and GSE72267) to identify and analyze the heat shock-related genes differentially expressed in PD. Gene Set Variation Analysis (GSVA) was performed to explore functional profiles, while LASSO regression was employed to screen potential PD biomarkers. In glioma, prognostic value, immune infiltration, and pathway enrichment associated with HSPA1L gene expression were assessed via Kaplan-Meier plots, ssGSEA, and enrichment analyses. ResultsIn PD, we identified 17 differentially expressed HSPs. Enrichment analysis revealed significant pathways related to protein homeostasis and cellular stress responses. LASSO regression pinpointed 12 genes, including HSPA1L, as significant markers for PD, with nomogram and calibration plots indicating predictive accuracy. Stratification based on HSPA1L expression in PD highlighted differentially active biological processes, immune responses, and metabolic disruptions. In the pan-cancer analysis, HSPA1L showed variable expression across cancer types and a significant correlation with patient survival and immune infiltration. In glioma, low HSPA1L expression was associated with worse overall survival, distinct immune infiltration patterns, and altered pathway activities. ConclusionThis integrative study reveals the substantial role of HSPs, especially HSPA1L, in the pathogenesis and prognosis of PD and glioma. Our findings offer new perspectives on the molecular mechanisms underlying these diseases and propose HSPA1L as a potential prognostic biomarker and a target for therapeutic intervention.

Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies' Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders.

Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined and change between and among subjects, the current methods for overcoming the loss of response are insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that change in response to internal and external perturbations. Inter and intra-subject variability characterize the immune system, making it difficult to provide a single therapeutic regimen to all patients and even the same patients over time. The dynamicity of the immune variability is also a significant challenge for personalizing immunotherapies. The CDP-based second-generation artificial intelligence system is an outcome-based dynamic platform that incorporates personalized variability signatures into the therapeutic regimen and may provide methods for improving the response and overcoming the loss of response to treatments. The signatures of immune variability may also offer a method for identifying new biomarkers for early diagnosis, monitoring immune-related disorders, and evaluating the response to treatments.

ERBB2 is a potential diagnostic and prognostic biomarker in renal clear cell carcinoma

Renal clear cell carcinoma (ccRCC) is a common parenchymal tumor of the kidney, and the discovery of biomarkers for early and effective diagnosis of ccRCC can improve the early diagnosis of patients and thus improve long-term survival. Erb-b2 receptor tyrosine kinase 2 (ERBB2) mediates the processes of cell proliferation, differentiation, and apoptosis inhibition. The purpose of this study was to investigate the diagnostic and prognostic role of ERBB2 in ccRCC. We analyzed the expression levels of ERBB2 in various cancers from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. RNA-seq data were analyzed using R packages to identify differentially expressed genes between the high and low ERBB2 expression groups in the TCGA-KIRC dataset. Spearman correlation analysis was performed to determine the correlation between ERBB2 expression and immune cell infiltration, immune checkpoint expression, and PTEN expression. DNA methylation changes and genetic alterations in ERBB2 were assessed using the MethSurv and cBioPortal databases. Logistic regression analysis was performed to determine the correlation between ERBB2 expression and the clinicopathological characteristics of ccRCC patients. The diagnostic and prognostic value of ERBB2 was assessed using Kaplan‒Meier (K‒M) survival curves, diagnostic ROC curves, time-dependent ROC curves, nomogram models, and Cox regression models. The expression level of ERBB2 is lower in tumor tissues of ccRCC patients than in the corresponding control tissues. Differentially expressed genes associated with ERBB2 were significantly enriched in the pathways “BMP2WNT4FOXO1 pathway in primary endometrial stromal cell differentiation” and “AMAN pathway”. In ccRCC tissues, ERBB2 expression levels were associated with immune cell infiltration, immune checkpoints, and PTEN. The DNA methylation status of 10 CpG islands in the ERBB2 gene was associated with the prognosis of ccRCC. ERBB2 expression levels in ccRCC tissues were associated with race, sex, T stage, M stage, histological grade, and pathological stage. Cox regression analysis showed that ERBB2 was a potential independent predictor of overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) in ccRCC patients. ROC curve analysis showed that the expression level of ERBB2 could accurately distinguish between ccRCC tissue and adjacent normal renal tissue. Our study showed that ERBB2 expression in ccRCC tissues can be of clinical importance as a potential diagnostic and prognostic biomarker.

Serum metabolite biomarkers for the early diagnosis and monitoring of age-related macular degeneration

IntroductionAge-related macular degeneration (AMD) is a leading cause of irreversible blindness worldwide, with significant challenges for early diagnosis and treatment. ObjectivesTo identify new biomarkers that are important for the early diagnosis and monitoring of the severity/progression of AMD. MethodsWe investigated the diagnostic and monitoring potential of blood metabolites in a cohort of 547 individuals (167 healthy controls, 240 individuals with other eye diseases as eye disease controls, and 140 individuals with AMD) from 2 centers over three phases: discovery phase 1, discovery phase 2, and an external validation phase. The samples were analyzed via a mass spectrometry-based, widely targeted metabolomic workflow. In discovery phases 1 and 2, we built a machine learning algorithm to predict the probability of AMD. In the external validation phase, we further confirmed the performance of the biomarker panel identified by the algorithm. We subsequently evaluated the performance of the identified biomarker panel in monitoring the progression and severity of AMD. ResultsWe developed a clinically specific three-metabolite panel (hypoxanthine, 2-furoylglycine, and 1-hexadecyl-2-azelaoyl-sn-glycero-3-phosphocholine) via five machine learning models. The random forest model effectively discriminated patients with AMD from patents in the other two groups and showed acceptable calibration (area under the curve (AUC) = 1.0; accuracy = 1.0) in both discovery phases 1 and 2. An independent validation phase confirmed the diagnostic model’s efficacy (AUC = 0.962; accuracy = 0.88). The three-biomarker panel model demonstrated an AUC of 1.0 in differentiating the severity of AMD via RF machine learning, which was consistent across both the discovery and external validation phases. Additionally, the biomarker concentrations remained stable under repeated freeze–thaw cycles (P > 0.05). ConclusionsThis study reveals distinct metabolite variations in the serum of AMD patients, paving the way for the development of the first routine laboratory test for AMD.

Aminoadipic acid aggravates atherosclerotic vascular inflammation through ROS/TXNIP/NLRP3 pathway, a harmful microbial metabolite reduced by paeonol

AimOur previous study has found a differential microbial metabolite in atherosclerosis (AS) mice, aminoadipic acid (AAA), which was considered as a potential harmful metabolite. However, whether it can promote AS vascular inflammation and its mechanisms remain unclear. Paeonol (Pae) plays an anti-AS role by regulating the metabolic profile, but whether Pae exerts its antiatherogenic effect by reducing serum AAA levels is unknown. ResultsThe clinical trial results showed that the AS patients’ serum AAA levels were higher than those healthy people’. Besides, AAA supplementation could increase aortic plaque size, serum inflammatory cytokines levels and liver malondialdehyde, superoxide dismutase levels in AS mice. Moreover, after AAA stimulation, the ROS levels and ASC, TXNIP, NLRP3 and caspase-1 proteins levels were increased in HUVECs, which could be reversed by antioxidant NAC and NLRP3 inhibitor. Pae significantly reduced the plaque size in the aorta, improved blood lipid levels and decreased serum inflammation factor levels in AS mice. Simultaneously, Pae could reduce the serum AAA levels of AS mice through the gut microbiota transmission. Finally, Pae inhibited NLRP3 inflammasome activation in aortas of AS mice. Broad-spectrum antibiotics could weaken the inhibitory effect of Pae on NLRP3 inflammasome. ConclusionOur study clarified that AAA could promote AS vascular inflammation via activating the ROS/TXNIP/NLRP3 pathway. Pae could inhibit AS development by reducing serum AAA levels in a microbiota-dependent manner. Taken together, we proposed that AAA could be served as a potential biomarker for AS clinical diagnosis and provided a new treatment strategy for AS.

A dual-signal biosensor based on surface-enhanced Raman spectroscopy for high-sensitivity quantitative detection and imaging of circRNA in living cells

Circular RNAs (circRNAs) are non-coding RNAs that play key roles in the development and progression of cancer through various mechanisms of action, making them promising biomarkers for cancer diagnosis, prognosis, and treatment. In the present study, a biosensor based on surface-enhanced Raman spectroscopy (SERS) was developed for rapid, simple, and sensitive quantitative detection of intracellular circRNAs for the first time. A dual-signal SERS nanoprobe with a 4MBN and ROX signal molecule was fabricated, and the ROX signal intensity was used to determine the concentration of target circSATB2. 4MBN was used as an internal standard to calibrate the ROX signal, thereby achieving highly sensitive and reliable detection of the target circRNA with a limit of detection of 0.043 pM. Furthermore, the relatively high expression of circSATB2 in lung cancer cells compared to that in normal lung epithelial cells was successfully characterized by the proposed SERS imaging method, which is consistent with the results of standard reverse transcription-polymerase chain reaction (RT-PCR). Monitoring of specific circRNAs using this SERS-based biosensor is a promising method for cancer diagnosis and gene therapy.

Biomarkers for systemic lupus erythematosus: A scoping review.

In recent years, newly discovered potential biomarkers have great research potential in the diagnosis, disease activity prediction, and treatment of systemic lupus erythematosus (SLE). In this study, a scoping review of potential biomarkers for SLE over several years has identified the extent to which studies on biomarkers for SLE have been conducted, the specificity, sensitivity, and diagnostic value of potential biomarkers of SLE, the research potential of these biomarkers in disease diagnosis, and activity detection is discussed. In PubMed and Google Scholar databases, "SLE," "biomarkers," "predictor," "autoimmune diseases," "lupus nephritis," "neuropsychiatric SLE," "diagnosis," "monitoring," and "disease activity" were used as keywords to systematically search for SLE molecular biomarkers published from 2020 to 2024. Analyze and summarize the literature that can guide the article. Recent findings suggest that some potential biomarkers may have clinical application prospects. However, to date, many of these biomarkers have not been subjected to repeated clinical validation. And no single biomarker has sufficient sensitivity and specificity for SLE. It is not scientific to choose only one or several biomarkers to judge the complex disease of SLE. It may be a good direction to carry out a meta-analysis of various biomarkers to find SLE biomarkers suitable for clinical use, or to evaluate SLE by combining multiple biomarkers through mathematical models. At the same time, advanced computational methods are needed to analyze large data sets and discover new biomarkers, and strive to find biomarkers that are sensitive and specific enough to SLE and can be used in clinical practice, rather than only staying in experimental research and data analysis.

Identification of SUMOylation-related signature genes associated with immune infiltration in ulcerative colitis through bioinformatics analysis and experimental validation

ObjectiveUlcerative colitis (UC) is a chronic inflammatory disorder challenging to diagnose clinically. We focused on identifying and validating SUMOylation-related signature genes in UC and their association with immune infiltration. MethodsFive eligible gene expression profiles were selected from the Gene Expression Omnibus (GEO) database and merged into a single dataset comprising 260 UC patients and 76 healthy controls (HC). Differentially expressed genes (DEGs) were identified, and these were intersected with SUMOylation-related genes to obtain differentially expressed SUMOylation-related genes (DESRGs). Next, we identify the signature genes and validate them through comprehensive analyses employing GO, KEGG, GSVA, Lasso-cox regression, ROC curves, and clustering analysis. The infiltrating immune cells were analyzed using the CIBERSORT algorithm and Pearson correlation analysis. Finally, in vitro and in vivo experiments validated the identified signature genes. ResultsPALMD, THRB, MAGED1, PARP1, and SLC16A1 were identified. Next, an excellent predictive model for UC was established and distinct subgroups of patients associated with SUMOylation were identified. Moreover, the NF-κB signaling pathway likely plays a pivotal role in the regulation of SUMOylation in UC. Additionally, we validated that the alterations in PALMD, THRB, and MAGED1 expression in LPSinduced Caco-2 cells concurred with our bioinformatics findings, particularly demonstrating statistically significant differences in PALMD and THRB expression. Finally, in a DSS-induced mouse colitis model, we observed a significant upregulation of PALMD expression. Powered by Editorial Manager® and ProduXion Manager® from Aries Systems Corporation. ConclusionThis study comprehensively elucidates the biological roles of SUMOylation-related genes in UC, identifying PALMD, MAGED1, THRB, PARP1, and SLC16A1 as signature genes that represent promising biomarkers for UC diagnosis and prognosis.

A biosensor based on carbon dots-protein interactions for specific and sensitive detection of pepsin in saliva

Salivary pepsin has emerged as a promising biomarker for rapid screening and diagnosis of gastroesophageal reflux disease (GERD). Pepsin mainly exists in strongly acidic environments and exhibits its highest activity. However, the poor stability of most fluorescent sensors in strong acidic environments brings a significant challenge for pepsin detection. Herein, an innovative biosensor was developed for the highly specific and sensitive detection of pepsin on the basis of green-emitting ionic liquid-based carbon dots (G-IL-CDs) conjugated with whey proteins (WPs). The G-IL-CDs exhibited aggregation-induced fluorescence enhancement when interacting with WP, and the fluorescence intensity decreased after incubation with pepsin due to the disruption of the aggregation structure. This strategy is highly selective for pepsin due to the strongly acidic environment in which other proteases are inactivated. Under optimal experimental conditions, this biosensor successfully detected pepsin in real human saliva with a satisfying recovery. Furthermore, this study not only developed a CDs-based sensor for detecting pepsin but also laid a solid theoretical foundation for the future development of novel biosensors combining CDs and proteins.

Biomarkers of acute kidney injury: a concise review of current literature

Abstract Background Acute kidney injury (AKI), a medical condition associated with increased hospitalization rates which requires interdisciplinary management, is a major health concern because of the burden it places on the health systems of different countries. Biomarkers represent the focus of recent years in furthering the early diagnosis of AKI, providing new opportunities for correct prophylaxis or early therapeutic intervention so that the evolution of patients with this pathology is favorable and the risk of life-threatening complications is negligible. Methods We performed an extensive literature search on PubMed and ScienceDirect databases, using keywords related to bio-markers for AKI. We searched for acute kidney injury (AKI), cystatin C (CYS-C), galectin-3 (GAL-3), kidney injury molecule-1 (KIM-1), neutrophil-gelatinase-associated lipocalin (NGAL), interleukin-8 (IL-8), and liver-type fatty acid-binding protein (L-FABP). We included a high number of papers, with an emphasis on more recent publications. Results Studies that analyzed the biomarkers for AKI show that CYS-C, GAL-3, KIM-1, NGAL, IL-8, calprotectin, and proteinuria were noted as potential biomarkers for early diagnosis of AKI. Conclusions Biomarkers represent the focus of recent years in furthering an early diagnosis of AKI, providing new opportunities for correct prophylaxis or early therapeutic intervention.

Exploring the Gut Microbiota Landscape in Cow Milk Protein Allergy: Clinical Insights and Diagnostic Implications in Pediatric Patients

Cow milk protein allergy (CMPA) is a significant health concern characterized by adverse immune reactions to cow milk proteins. Biomarkers for the accurate diagnosis and prognosis of CMPA are lacking. This study analyzed the clinical features of CMPA, and 16S RNA sequencing was used to investigate potential biomarkers through fecal microbiota profiling. Children with CMPA exhibit a range of clinical symptoms, including gastrointestinal (83% of patients), skin (53% of patients), and respiratory manifestations (26% of patients), highlighting the complexity of this condition. Laboratory analysis revealed significant differences in red cell distribution width (RDW) and inflammatory markers between the CMPA and control groups, suggesting immune activation and inflammatory responses in CMPA. Microbial diversity analysis revealed higher specific diversity indices in the CMPA group compared with those in control group, with significant differences at the genus and species levels. Bacteroides were more abundant in the CMPA group, whereas Bifidobacterium, Ruminococcus, Faecalibacterium, and Parabacteroides were less abundant. The control group exhibited a balanced microbial profile, with a predominant presence of Bifidobacterium bifidum and Akkermansia muciniphila. The significant abundance of Bifidobacterium in the control group (23.19% vs 9.89% in CMPA) was associated with improved growth metrics such as height and weight, suggesting its potential as a probiotic to prevent CMPA and enhance gut health. Correlation analysis linked specific microbial taxa such as Coprococcus and Bifidobacterium to clinical parameters such as family allergy history, weight and height, providing insights into CMPA pathogenesis. Significant differences in bacterial abundance suggested diagnostic potential, with a panel of 6 bacteria achieving high predictive accuracy (area under curve (AUC) = 0.8708). This study emphasizes the complex relationship between the gut microbiota and CMPA, offering valuable insights into disease mechanisms and diagnostic strategies.

LINC00941 is a diagnostic biomarker for lung adenocarcinoma and promotes tumorigenesis through cell autophagy.

Non-small cell lung cancer (NSCLC) is a lethal malignancy. There is mounting evidence indicating that lncRNAs are crucial players with dual roles as both biomarkers and regulators across various cancers. It was reported that LINC00941 plays a cancer-promoting role in NSCLC. However, its impact on tumour autophagy remains poorly understood. In this study, we developed a risk assessment model and identified an autophagy-related lncRNA LINC00941, which has independent predictive and early diagnostic potential. Using RT-qPCR analysis, we confirmed the upregulation of LINC00941 in tumour tissues and cell lines of human lung adenocarcinoma (LUAD). Functional assays, such as CCK8, colony formation and xenograft models, demonstrated the cancer-promoting activity of LINC00941 both invitro and invivo. Further analysis using Western blotting analysis, mRFP-GFP-LC3 double fluorescence lentivirus vector and transmission electron microscopy (TEM) confirmed that the knockdown of LINC00941 triggered autophagy. These results indicate that knockdown of LINC00941 induces autophagy and impairs the proliferation of LUAD. Therefore, we propose LINC00941 as an independent biomarker for early diagnosis as well as a therapeutic target in LUAD.