Diagnostic Biomarkers Research Articles

A novel electrochemical immunosensor based on biomaterials for detecting carcinoembryonic antigen biomarker in serum samples.

A new label-free electrochemical immunosensor was developed to detect carcinoembryonic antigen (CEA), a key diagnostic biomarker for various cancers. The surface of a glassy carbon electrode (GCE) was modified using a layer-by-layer assembly method incorporating sodium alginate (SA), gold nanoparticles (AuNPs), and gamma-manganese dioxide/chitosan (γ.MnO₂-CS). This modification enhanced the functional surface area and electrode conductivity, thereby improving the electrochemical response and sensitivity for CEA detection. Methods including cyclic voltammetry (CV) and differential pulse voltammetry (DPV) were used to evaluate the immunosensor`s performance.The sensor quantified CEA concentrations in serum samples by monitoring current variations at the oxidation peak, resulting from the formation of CEA antibody-CEA antigen complexes on the electrode surface. The sensor exhibited a linear response to CEA concentrations ranging from 10fg/mL to 0.1µg/mL, with a limit of detection (LOD) of 9.57fg/mL and a limit of quantification (LOQ) of 31.6fg/mL. The morphology and structural properties of the synthesized γ.MnO₂-CS nanocomposite and the modified electrode surface were characterized utilizing field-emission scanning electron microscopy (FE-SEM), Brunauer-Emmett-Teller (BET) and Barrett-Joyner-Halenda (BJH), X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), dynamic light scattering (DLS), zeta potential analysis, and Fourier transform infrared (FTIR) spectroscopy. The results highlight the potential clinical applicability of this electrochemical immunosensor for detecting CEA levels in human serum samples.

Clinical significance of lncRNA PAX8-AS1 and miR-96-5p in non-small cell lung cancer.

This study aims to examine the potential value of lncRNA PAX8-AS1 and miR-96-5p as diagnostic markers in non-small cell lung cancer (NSCLC). The goal is to provide a reference for improving adverse outcomes in NSCLC and developing effective early interventions in the clinic. A total of 112 NSCLC patients and 70 healthy volunteers were recruited as the study subjects. The expression levels of PAX8-AS1 and miR-96-5p were measured using fluorescence quantitative PCR. The expression levels of PAX8-AS1 and miR-96-5p were detected by fluorescence quantitative PCR. A dual luciferase reporter assay was used to detect the binding of PAX8-AS1 and miR-96-5p. ROC, Kaplan-Meier, and Cox regression analysis was used to assess the diagnostic and prognostic value of PAX8-AS1 and miR-96-5p in NSCLC. In NSCLC, PAX8-AS1 was found to be downregulated, whereas miR-96-5p was upregulated. These two molecules exhibited a negative correlation and a target-binding relationship. Receiver operating characteristic (ROC) curve analysis demonstrated that PAX8-AS1 and miR-96-5p had practical diagnostic values for distinguishing NSCLC patients. Mortality increased in NSCLC patients with low PAX8-AS1 levels and decreased in those with low miR-96-5p levels. Cox regression analysis showed that PAX8-AS1 and miR-96-5p could serve as independent predictors of prognostic survival in NSCLC patients. PAX8-AS1 and miR-96-5p can serve as potential diagnostic biomarkers for NSCLC patients.

Identification of Plasma rRNA‐Derived Small RNA Biomarkers for Diagnosis of Colorectal Cancer

ABSTRACTColorectal cancer (CRC) ranks among the most aggressive malignancies globally, with advanced‐stage patients exhibiting notably low survival rates. Consequently, there is an urgent imperative to identify novel biomarkers characterized by high sensitivity and specificity. Ribosomal RNA‐derived small RNAs (rsRNAs), which originate from ribosomal RNAs, represent the most prevalent small noncoding RNAs (sncRNAs) in CRC tissues and plasma. Thus, the development of a diagnostic panel comprising multiple rsRNAs holds considerable significance for CRC diagnosis. Utilizing PANDORA‐seq, we have, for the first time, delineated a novel sncRNA expression profile in CRC tissues and plasma, identifying rsRNAs as the predominant sncRNAs within these contexts. We identified six rsRNAs that were significantly upregulated in CRC plasma and subsequently constructed a co‐diagnostic panel. This panel demonstrated an area under the curve (AUC) value of 0.898, which increased to 0.942 when combined with clinically utilized tumor markers, indicating robust diagnostic efficacy. Our study is the first to establish that rsRNAs are the most abundantly expressed sncRNAs in CRC tissues and plasma. We have developed an rsRNA panel with substantial diagnostic efficacy in CRC plasma, presenting promising potential as diagnostic biomarkers.

Features of Diagnostics of Post-Exercise Malaise and Chronic Fatigue Syndrome in the Development of a Medical Rehabilitation Program for "Long Covid"

One of the important problems of modern medical rehabilitation is the recovery of patients with symptoms of "long COVID". The most effective methods of rehabilitation are various types of physical exercises. However, according to the literature, there is a phenotype of "long COVID" with chronic fatigue syndrome, which is characterized by deterioration after physical activity and exercise intolerance. In order to ensure the safety of medical rehabilitation, early diagnosis of chronic fatigue syndrome and the formation of an alternative rehabilitation program with limited physical activity in this category of patients are necessary. The search and selection of literary sources was carried out in the system of published research in scientific databases elibrary.ru, link.springer.com, frontiersin.org, pubmed.ncbi.nlm.nih.gov and others. "Long COVID" and chronic fatigue syndrome have no clear diagnostic criteria and specific biomarkers, which makes it necessary to thoroughly analyze the phenotypes of these diseases. The result of the literature analysis on this topic is a detailed description of the phenotype of chronic fatigue syndrome, as well as an attempt to create an optimal algorithm for diagnosing malaise after physical exertion using diagnostic tests, questionnaires and cardiopulmonary stress testing in patients with "long COVID".

Identification of diagnostic biomarkers and dissecting immune microenvironment with crosstalk genes in the POAG and COVID-19 nexus

An underlying association between primary open-angle glaucoma (POAG) and COVID-19 has been hypothesized, but the causal link and shared mechanisms remain unclear. This study integrates epidemiological and bioinformatics approaches to investigate their relationship, aiming to identify common molecular pathways and validate clinical correlations. Epidemiological data from 3,015 participants in the CHARLS database were analyzed using multivariate logistic regression and Cox proportional hazards models to assess the association between COVID-19 and POAG, with stratification by gender, smoking status, and alcohol consumption. Concurrently, gene expression datasets from GEO (POAG: GSE27276; COVID-19: GSE171110, GSE152418) were used to identify 57 crosstalk genes (CGs) via differential expression analysis. Machine learning algorithms (LASSO, SVM-RFE, Random Forest) were applied to screen POAG diagnostic biomarkers from CGs, followed by construction of transcription factor (TF)-microRNA (miRNA)-protein-compound regulatory networks and consensus clustering to characterize COVID-19 immune microenvironment subtypes. Epidemiological analyses revealed that COVID-19 was an independent risk factor for POAG, with adjusted odds ratios (ORs) of 12.775–15.688 and hazard ratios (HRs) of 4.893–5.060 (all P < 0.001), with stronger associations in males (OR = 15.054, HR = 5.645) and smokers (OR = 29.828, HR = 9.323). HDL levels were significantly higher in POAG patients (P = 0.028), while vb001_s1 positivity was negatively associated with POAG (P < 0.001). Bioinformatics identified CGs enriched in inflammatory response, oxidative stress, and hypoxia pathways. Five potential biomarkers (CA12, ECRG4, CEBPD, HBB, HBA2) showed high diagnostic efficacy (AUC > 0.9) but limited specificity (expressed in uveitis and diabetic retinopathy). COVID-19 patients were stratified into three clusters, with Cluster B exhibiting hyperactive immune microenvironments characterized by elevated HLA class I/II and immune checkpoint genes (CTLA4, PDCD1LG2) in antigen-presenting cells and T cells. This study provides epidemiological and molecular evidence linking POAG to COVID-19, highlighting inflammation and immune dysregulation as shared mechanisms. Gender and smoking modify this association, warranting targeted POAG screening in male COVID-19 survivors and smokers. While identified biomarkers require specificity improvement, findings underscore the need for mechanistic studies (e.g., ACE2-mediated ocular injury, T-cell neuroinflammation) to validate causality and inform clinical management.

Exploring total inflammatory vascular volume as a diagnostic and prognostic biomarkers in giant cell arteritis.

To investigate the role of total inflammation vascular volume (TIVV), a novel quantitative parameter obtained from FDG PET/CT, in assessing disease activity and predicting relapses and aortic dilatation in patients with large vessel (LV)-GCA. This retrospective analysis included PET/CT scans from the TOPAZIO study. Patients with active LV-GCA were enrolled and treated with tocilizumab monotherapy for 52 weeks, preceded by three boluses of intravenous methylprednisone. PET/CT scans were performed at baseline and at weeks 24, 52 and 78. TIVV was calculated using a semiautomatic approach, while total inflammation glycolytic volume (TIGV) was obtained by multiplying TIVV by SUVmean. Visual assessment was performed, and PET vascular activity score (PETVAS) and total vascular score (TVS) were calculated. 18 patients for a total of 61 PET scans were included. TIVV and TIGV were strongly associated with active disease, with odds ratios (OR) of 4.74 (95% CI 1.23-18.2) and 5.45 (95% CI 1.36-21.8), respectively, whereas PETVAS and TVS showed moderate associations (OR 2.16; 95% CI 0.87-5.33 and 1.86; 95% CI 0.76-4.52). Over a median follow-up of 117 weeks, 10 events occurred. Cox proportional hazard model showed strong associations of TIVV and TIGV with time to relapse or aortic dilatation, with hazard ratios (HRs) of 2.50 (95% CI 1.07-5.84) and 2.23 (95% CI 1.04-4.82), respectively. PETVAS and TVS exhibited a weaker association with prognosis. TIVV and TIGV showed superior diagnostic and prognostic performance compared with conventional PET parameters, underscoring the importance of inflammation volume in evaluating disease activity in LV-GCA.ClinicalTrials.gov ID NCT05394909.

Biomarkers selection and prediction of disease progression in tuberculosis patients based on transcriptome expression data

Objective: To use high-throughput transcriptome sequencing data to screen for a set of non-invasive diagnostic biomarkers for assessing the risk of pulmonary tuberculosis (PTB) and predicting disease progression stages. Methods: A total of 37 effective microarray transcriptome datasets were collected from the National Center for Biotechnology Information (NCBI) from January 2015 to December 2024, covering five groups: control group, PTB group, extrapulmonary TB (EPTB) group, latent TB infection (LTBI) group, and other diseases (OD) group. Background correction and quantile normalization were performed using the robust multi-array average (RMA) method. Differential expression analysis was conducted using the "limma" package in R language, and protein-protein interaction analysis was performed using the STRING database. Additionally, a random forest model was trained to predict different stages of TB progression, and a biomarker signature for predicting PTB progression was constructed using the "ssgsea" method in the "GSVA" package. Results: Differential expression analysis revealed gene expression differences between the PTB group and other groups, resulting in a set of genes with PTB-specific expression, including C1QA(Complement C1 subunit A), IRF7(Interferon Modulator 7), LGALS3BP(galectin 3-binding protein), SERPING1(Complement C1 inhibitors), and UBE2L6(ubiquitin ligase E2 L6). These genes exhibited rich interaction relationships and were closely associated with biological processes and pathways related to PTB. Using the random forest model, these five specific genes were identified as potential biomarkers for predicting PTB patient progression. In the test set, the model showed good diagnostic performance at different stages, with an area under the receiver operating characteristic curve (AUC)>0.8, especially performing best at 5 months (AUC=0.992). Conclusion: Using transcriptome data, a classification model was established to assess PTB risk and predict disease progression stages, and five important biomarkers were identified that provide crucial references for the early diagnosis and treatment of PTB.

LncRNA-Histone Modification Crosstalk: Orchestrating Cancer Pathobiology.

Long noncoding RNAs (lncRNAs) and histone modifications are key epigenetic regulators in cancer pathobiology. This review delves into the crosstalk between lncRNAs and histone modifications relevant to tumor initiation and development in a detailed manner. Histone modifications (e.g., methylation, acetylation, phosphorylation, and ubiquitination) play key roles in chromatin structure and gene expression, involved in such cellular processes as proliferation, metastasis, and drug resistance. These lncRNAs have a variety of mechanisms of action, such as signaling, scaffolding, decoying, and guiding to modify critical cellular pathways. They recruit histone methyltransferases, demethylases, acetyltransferases, and deacetylases to specific regions of genomic transcription regulation, thereby changing certain histone-modifying marks that affect gene expression. The abnormal expression and activities of lncRNAs such as HOTAIR, MALAT1, LINC00152, and SNHG1 are associated with tumor initiation, progression, and metastasis in various cancers. Therefore, these molecules are candidates for diagnostic and prognostic biomarkers, making non-invasive detection and prediction possible. LncRNA-related histone modifications also serve as an attractive candidate for future therapeutic targets in cancer. Antisense oligonucleotides, small molecule inhibitors, and RNA interference are some of the strategies currently being investigated to dislodge the interactions between lncRNAs and histone-modifying enzymes, and they represent novel strategies for cancer therapy. This review summarizes recent advances in understanding lncRNA-histone modification crosstalk and its translation to the clinic for precision oncotherapy.

Circulating Interleukin-6 and Janus Kinase 2 DNA Copies in Serum Cell-Free DNA Are Elevated in Patients With Systemic Sclerosis.

Recently, cell-free DNA (cfDNA) has gained attention as a diagnostic and prognostic biomarker for various conditions, including autoimmune and inflammatory skin diseases. However, reports of cfDNA use in patients with systemic sclerosis (SSc)are limited. The purpose of this study was to investigate the clinical significance of cfDNA in patients with SSc and assess the circulating DNA levels of key cytokines associated with SSc fibrosis. Serum samples were obtained from 64 patients with SSc, 9 with very early SSc, and 20 healthy controls (HC). Circulating DNA copies were quantified using droplet digital polymerase chain reaction analysis. The DNA copies of interleukin (IL)-6, IL-8, Janus kinase 2 (JAK2), and connective tissue growth factor were significantly higher in patients with SSc than in the HCs. Although not statistically significant, circulating IL-6 DNA copies were higher in patients with SSc than in patients with very early SSc. Receiver operating characteristic analysis indicated that IL-6 DNA copies could distinguish patients with SSc from both the HCs and patients with very early SSc (area under the curve > 0.7 in both cases). Disease type dcSSc and reduced %FVC were significant independent predictors of higher circulating IL-6 and JAK2 DNA copies in multivariate logistic regression analysis. cfDNA, including circulating IL-6 and JAK2 DNA in sera, may serve as valuable indicators for diagnosis and fibrosis progression in patients with SSc.

Construction of a glycolysis-related diagnostic model for osteoarthritis through integrated bioinformatics analysis and machine learning

BackgroundOsteoarthritis (OA) is a prevalent degenerative joint disease that significantly contributes to global disability. Glycolysis, a fundamental process in cellular energy metabolism, is particularly vital for chondrocytes in OA. This study aims to explore the intrinsic relationship between glycolysis-related genes (GRGs) and OA.MethodsWe incorporated three publicly available datasets from the Gene Expression Omnibus (GEO) database, which included 64 OA samples and 34 normal controls. By utilizing differential expression analysis, weighted gene co-expression network analysis, protein-protein interaction networks, and machine learning methods, we identified three diagnostic biomarkers of OA patients. The expression levels of these biomarkers were validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemical (IHC). Additionally, a competing endogenous RNA (ceRNA) network was constructed to explore potential regulatory interactions.ResultsThrough bioinformatics and machine learning approaches, three glycolysis-related biomarkers—HMGB2, SLC7A5, and ADM—were identified. The diagnostic model based on these GRGs demonstrated high predictive accuracy, with an AUC of 0.92 in the training set and 0.85 in the validation set. Subsequently, qRT-PCR and IHC confirmed the differential expression of hub genes in human cartilage samples. Furthermore, immunocyte infiltration analysis revealed distinct immune cell infiltration profiles between OA and HC groups. Notably, lncRNA XIST was found to regulate all three biomarkers, indicating its potential as a therapeutic target for OA.ConclusionThis study provides novel insights into the role of glycolysis in OA pathogenesis and highlights its potential as a target for diagnosis, prevention, and treatment strategies.

Clinical Pathways for Diagnosing Neurocognitive Disorders: Insights From Process Mining a Memory Clinic Cohort.

Clinical Pathways for Diagnosing Neurocognitive Disorders: Insights From Process Mining a Memory Clinic Cohort.

KFLC-index distinguishes multiple sclerosis from anti-myelin oligodendrocyte glycoprotein and aquaporin 4 diseases in a Chinese cohort.

Kappa free light chain (KFLC) index has emerged as a diagnostic biomarker for multiple sclerosis (MS). This study aims to evaluate the diagnostic accuracy of the KFLC-index in Chinese patients with MS, and its capacity to discriminate MS from myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) and neuromyelitis optica spectrum disorders with aquaporin-4 antibody (AQP4+NMOSD). 428 patients tested for KFLC-index were enrolled in the study, including 130 patients with MS, 41 with MOGAD, 25 with AQP4+NMOSD, 123 with other inflammatory or infectious neurological disorders (OIND) and 109 with non-inflammatory neurological disorders (NIND). Their oligoclonal band (OCB) results and clinical data were reviewed. KFLC-index was significantly higher in MS (20.1 (0.9-388.9)) compared with MOGAD (4.8 (0.8-56.1), p=0.003), AQP4+NMOSD (4.5 (1.5-46.4), p=0.011), OIND (2.9 (0.6-238.7), p<0.001) and NIND (1.8 (0.6-110.7), p<0.001). The optimal cut-off value for the KFLC-index to identify MS from the non-selective controls was 8.3, with an accuracy comparable to that of OCB (area under the curve 0.84 vs 0.81, p=0.249). The optimal cut-off values for differentiating MS from MOGAD and AQP4+NMOSD were 18.5 and 12.1, with performance similar to OCB (p=0.756 and 0.064). Combination of KFLC-index and OCB outperformed OCB alone in differentiating MS from non-selective controls and MOGAD (p<0.001 and p=0.044). Female (p=0.009) and higher cerebrospinal fluid leucocyte count (p<0.001) were associated with higher KFLC-index in MS. KFLC-index is a valuable diagnostic tool for differentiating MS from other inflammatory demyelinating diseases.

Exploration of the correlation between metabolic dysfunction-associated steatotic liver disease and the FAM19A5 factor: A multivariate linear regression analysis.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent metabolic disorder characterized by liver fat accumulation. Family with sequence similarity 19 member A5 (FAM19A5), a cytokine involved in metabolic regulation, may serve as a potential biomarker for MASLD diagnosis and treatment. A cross-sectional study was conducted on 37 participants from Quanzhou First Hospital between January and April 2024, including 18 MASLD patients and 19 healthy controls. Demographic data, anthropometric measurements, and clinical parameters were collected, and plasma FAM19A5 levels were quantified using Enzyme-Linked Immunosorbent Assay. After univariable analysis to screen variables associated with FAM19A5 levels, significant variables were included and potential confounders were adjusted for, followed by constructing multivariable linear regression models via stepwise variable selection. All statistical analyses were carried out using SPSS 26.0. Significant negative correlations were found between FAM19A5 levels and MASLD, body mass index, and total cholesterol. The expression of FAM19A5 was notably lower in MASLD patients, suggesting its potential role in the disease's pathophysiology. FAM19A5 could be a novel diagnostic biomarker for MASLD, with lower expression levels associated with the disease. Further research involving a broader patient population is needed to establish causality and explore its clinical implications.

LncRNA SDCBP2-AS1 is a putative biomarker for postmenopausal osteoporosis and promotes osteogenic differentiation of BMSCs by regulating miR-361-3p

BackgroundNumerous long noncoding RNAs (lncRNAs) have been proven to participate in osteogenesis and postmenopausal osteoporosis (PMOP). We measured serum SDCBP2-AS1 expression changes in patients with PMOP and investigated its effects on osteoblast differentiation in human bone marrow-derived mesenchymal stem cells (hBMSC) cells.MethodsRT-qPCR was used to measure SDCBP2-AS1 levels and the expression of osteogenic differentiation indicators. The diagnostic efficacy of SDCBP2-AS1 was assessed using a receiver operating characteristic (ROC) analysis. CCK-8 and flow cytometry methods were employed to investigate the functional impact of SDCBP2-AS1 on hBMSC cell proliferation and apoptosis during osteoblast differentiation. The bioinformatics, dual-luciferase reporter assay, and RNA Immunoprecipitation (RIP) assay were used to identify and confirm SDCBP2-AS1/miR-361-3p interaction.ResultsSerum SDCBP2-AS1 was decreased in patients with PMOP, especially in those with fractures. The SDCBP2-AS1 levels were positively correlated with patients’ T scores and BMDs. Decreased SDCBP2-AS1 had a certain high area under the ROC curve (AUC) value (AUC = 0.81) in distinguishing PMOP patients with fractures from those without fractures. SDCBP2-AS1 levels gradually increased after four weeks of treatment in PMOP patients and hBMSCs during cell differentiation. Enhanced SDCBP2-AS1 promoted cell proliferation and the levels of osteoblast differentiation markers, including ALP, OCN, RUNX2, and Collagen I, while decreasing cell apoptosis. miR-361-3p was a direct target of SDCBP2-AS1. The influence of SDCBP2-AS1 on cell activities and hBMSCs differentiation was diminished by miR-361-3p.ConclusionsSDCBP2-AS1 might be a diagnostic biomarker in predicting PMOP patients with fractures. By measuring the levels of SDCBP2-AS1 in patient samples, clinicians may be able to identify those who are more susceptible to bone fractures, enabling earlier and more targeted preventive measures. SDCBP2-AS1 targeting miR-361-3p regulates the osteogenic differentiation of hBMSCs, which might be a new target for the treatment of PMOP.

Integrative analysis identifies shared therapeutic pathways in thyroid eye disease and diabetes mellitus

Thyroid eye disease (TED) and diabetes mellitus (DM) cause vision loss, with DM aggravating TED. In this study, we aimed to explore the mechanisms of this interaction, and therefore, analyzed gene expression data from representative TED and DM datasets using bioinformatic tools. Following normalization and differential expression analysis, common differentially expressed genes (CDEGs) between the datasets were identified and subjected to Gene Set Enrichment Analysis (GSEA), as well as Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Interaction networks were constructed and the diagnostic potential of the CDEGs was assessed using receiver operating characteristic (ROC) curve analysis. The TED_Dataset and DM_Dataset contained 449 and 108 DEGs, respectively, with seven CDEGs. GO and KEGG analyses linked the CDEGs to biological processes including leukocyte adhesion and apoptosis. GSEA emphasized their roles in inflammation and fibrosis. Protein-protein interaction network analysis identified MFAP4 as a key hub gene. Constructed mRNA-transcription factor, mRNA-RNA binding protein, mRNA-microRNA, and mRNA-drug interaction networks revealed extensive regulatory relationships. ROC curve analysis demonstrated that CXCL12 and SFRP4 were potential diagnostic biomarkers for TED, and SFRP4, IL6, MFAP4, and CRISPLD2 were potential diagnostic biomarkers for DM. Altogether, our findings provide comprehensive molecular insights into DM and TED, identifying novel targets for therapeutic intervention and diagnostic biomarkers.

Integrated Analysis of Lactate-Related Genes Identifies S100A4 as a Novel Marker Promoting the Migration and Invasion of Endometrial Stromal Cell in Endometriosis.

Endometriosis is a prevalent gynecological disorder worldwide that significantly impairs the quality of life of patients and imposes a substantial economic burden. The limited efficacy and adverse effects of current pharmacological and surgical treatments highlight the urgent need for alternative diagnostic biomarkers and therapeutic targets. Recent evidence indicates that metabolic reprogramming, particularly lactate metabolism, plays a crucial role in the development and progression of endometriosis. In this study, we employed differential expression analysis to identify differentially expressed genes in endometriosis patients, and used Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) for feature selection and model construction. Five key genes (MRPS2, YARS2, SLC3A1, CALD1, and S100A4) were validated as highly accurate diagnostic markers. Furthermore, we conducted in-depth analysis of the regulatory activity of these key lactate metabolism-related genes and their association with immune infiltration. Additionally, we constructed a lncRNA-miRNA gene regulatory network, providing insights into the molecular mechanisms and potential therapeutic strategies. Among these key lactate metabolism-related genes, S100A4 was found to be significantly upregulated in the lesions of endometriosis and plays a pivotal role in regulating cellular invasion and metabolic reprogramming in endometriosis. Silencing of S100A4 significantly inhibited the migration and invasion ability of endometrial stromal cells, whereas overexpression of S100A4 reversed this inhibitory effect. This finding further underscores the importance of S100A4 as a potential therapeutic target. In conclusion, this study highlights the potential value of lactate-related genes as diagnostic biomarkers and therapeutic targets, especially targeting the S100A4 gene in endometrial stromal cells, which may serve as an effective therapeutic strategy to inhibit the progression of endometriosis, offering new directions for future clinical diagnosis and treatment.

Expression analysis of plasma extracellular vesicle associated candidate MiRNAs in endometriosis using integrative bioinformatics and experiential data

Laparoscopy is the gold standard for diagnosing endometriosis; however, it is an invasive and costly method. Recent studies offer a non-invasive approach based on extracellular vesicle miRNA. Despite this, no consensus diagnostic biomarker has been identified to date. For addressing this gap, we decided to investigate plasma derived extracellular vesicle associated candidate miRNAs. In order to identify candidate miRNAs, a comprehensive search was performed in PubMed database using the search terms “micro-RNA” and “endometriosis”. Then, bioinformatics analysis was performed utilizing the miRTarBase database, Enrichr, and relevant software. During the experimental phase, the presence of candidate miRNAs was assessed in blood samples of 13 women with severe endometriosis, confirmed through laparoscopy or doppler sonography, as well as in 11 endometriosis-free women, as control group. After literature review of 405 articles published between 2007 and 2023, followed by bioinformatics analysis, were identified five miRNAs (miR-451a, 148a, 23b, 100, and 154) as candidate miRNAs. Subsequently, the expression levels of miR-451a, 148a, 23b, and 100 found to exhibit differences between the case and control groups. Our study suggests to serve of these miRNAs as a potentially diagnostic biomarker panel for endometriosis, however it needs to be confirmed by future studies with large diagnostic validation.

Decoding the Transcriptional Complexity of the Human BRCA2 DNA Repair Gene Using Hybrid-seq.

BRCA2 plays a pivotal role in DNA repair and tumor suppression, with its dysregulation linked to breast and gynecological cancers. Despite the importance of BRCA2, its transcriptional complexity remains poorly understood due to the gene's size and intricate alternative splicing patterns. This study aims to comprehensively characterize the BRCA2 transcriptional landscape in breast, ovarian, and cervical cancers using a hybrid sequencing approach. A novel hybrid-seq method combining long-read nanopore sequencing and short-read NGS was applied to analyze BRCA2 transcripts from cancerous cell lines. Expression patterns were evaluated using the transcript-per-million (TPM) normalization method, and open reading frames (ORFs) of the identified transcripts were in silico characterized. Sequencing analysis led to the identification of 50 novel splice variants (BRCA2 sv.7-sv.56), expanding the known transcript repertoire of BRCA2 gene. Notably, transcript variants sv.9, sv.15, and sv.49 exhibited significant expression in breast and ovarian cancers, while others, such as sv.29 and sv.40, were specific to individual cancer types. Five cryptic exons (N1-N5) were unveiled, contributing to 10 unique splice variants. In silico analysis revealed that 19 novel transcripts retained coding potential, with some encoding BRCA2 isoforms harboring key functional domains. The identification of novel BRCA2 transcripts underscores the complexity of its regulation in cancer. These findings provide insights into the gene's potential role in tumorigenesis and highlight candidates for targeted therapies and diagnostic biomarkers.

MiR-3154: Novel Pathogenic and Therapeutic Target in Abdominal Aortic Aneurysm.

Abdominal aortic aneurysm (AAA) is a life-threatening condition with no effective pharmacological treatments currently available, likely due to our incomplete understanding of the molecular mechanisms underlying AAA pathogenesis and progression. In many cardiovascular diseases, circulatory microRNAs are potential diagnostic and prognostic biomarkers. Therefore, we investigated novel microRNAs with diagnostic and therapeutic potential in AAA. We performed microRNA expression analysis in aorta specimens isolated from mice with saline-induced or Ang II (angiotensin II)-induced AAA, wherein microRNA-3154 (miR-3154) was identified as a potential key molecule in AAA pathogenesis. We used RNA-sequencing analysis, cell migration assays, immunoblotting, protein interactome analysis, coimmunoprecipitation, molecular docking, and molecular dynamics simulation to determine the role of miR-3154 in AAA pathogenesis, clarify the phenotype, and elucidate the underlying molecular mechanisms. Using high-throughput sequencing, miR-3154 was identified in the aortic tissue of ApoE-/- mice with AAA as compared with control mice. Increased miR-3154 expression was confirmed in the early-stage Ang II-induced AA mouse model and the aortic tissues of patients with AAA. Patients with AAA had higher serum miR-3154 level, which positively correlated with computed tomography-estimated size of the aneurysm. Functionally, miR-3154 dose-dependently aggravated vascular smooth muscle cell phenotypic switching and AAA development, both in vivo and in vitro. In vascular smooth muscle cells, TNS1 (tensin-1) was identified as a direct target of miR-3154. Mechanistically, TNS1 interacts with the R1-R13 domain of TLN1 (talin-1), thereby suppressing Sp1 (specificity protein 1) phosphorylation at Thr739 and upregulating MEOX1 (mesenchyme homeobox 1) expression-a key transcription factor regulating vascular smooth muscle cell phenotypic switching. We uncovered a novel pathogenic role of miR-3154 in AAA, suggesting its potential as both a therapeutic target and prognostic biomarker.

Exosomal miRNA Profiling in Liquid Biopsy of Vitreous in Proliferative Diabetic Retinopathy

PurposeProliferative diabetic retinopathy (PDR) is a severe microvascular complication of diabetes mellitus. Although exosomes participate in various pathologic processes, their role in ocular pathologies is unclear. This study profiled exosomal microRNAs (exo-miRNAs) in the vitreous humor (VH) of patients with PDR to elucidate their regulatory roles in PDR pathogenesis.MethodsVH samples were obtained from 23 patients with PDR and 17 non-diabetic controls. Exosomes were isolated using ultracentrifugation and characterized via transmission electron microscopy, nanoparticle tracking analysis and Western blot. High-throughput sequencing identified differentially expressed miRNAs (DEMs), followed by target gene prediction and bioinformatic analyses. The top DEMs were validated using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsExosomes were isolated and characterized from VH samples. Sequencing analysis identified 843 unique miRNAs, with 60 showing significant differential expression between PDR and control groups (28 upregulated and 32 downregulated). The miR-451a and miR-486-5p were most upregulated; miR-204-5p and miR-211-5p were most downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed DEM target genes enriched in endocrine, metabolic, signaling transduction, and intercellular adhesion pathways, notably cGMP-PKG, mTOR, and cAMP pathways. Network analysis identified miR-486-5p and miR-451a as key hubs. Validation by qRT-PCR confirmed the differential expression in the sequencing analysis.ConclusionsThis study provides the comprehensive profile of vitreous exo-miRNAs in patients with PDR, identifying key DEMs and their potential regulatory networks. These findings indicate that vitreous exo-miRNAs, specifically miR-204-5p and miR-486-5p, represent promising diagnostic biomarkers and potential therapeutic targets for PDR.