Biomarkers For Early Detection Research Articles

Promising microRNAs in pre-diagnostic serum associated with lung cancer up to eight years before diagnosis: a HUNT study

IntroductionBlood biomarkers for early detection of lung cancer (LC) are in demand. There are few studies of the full microRNome in serum of asymptomatic subjects that later develop LC. Here we searched for novel microRNA biomarkers in blood from non-cancer, ever-smokers populations up to eight years before diagnosis.MethodsSerum samples from 98,737 subjects from two prospective population studies, HUNT2 and HUNT3, were considered initially. Inclusion criteria for cases were: ever-smokers; no known cancer at study entrance; 0–8 years from blood sampling to LC diagnosis. Each future LC case had one control matched to sex, age at study entrance, pack-years, smoking cessation time, and similar HUNT Lung Cancer Model risk score. A total of 240 and 72 serum samples were included in the discovery (HUNT2) and validation (HUNT3) datasets, respectively, and analysed by next-generation sequencing. The validated serum microRNAs were also tested in two pre-diagnostic plasma datasets from the prospective population studies NOWAC (n = 266) and NSHDS (n = 258). A new model adding clinical variables was also developed and validated.ResultsFifteen unique microRNAs were discovered and validated in the pre-diagnostic serum datasets when all cases were contrasted against all controls, all with AUC > 0.60. In combination as a 15-microRNAs signature, the AUC reached 0.708 (discovery) and 0.703 (validation). A non-small cell lung cancer signature of six microRNAs showed AUC 0.777 (discovery) and 0.806 (validation). Combined with clinical variables of the HUNT Lung Cancer Model (age, gender, pack-years, daily cough parts of the year, hours of indoor smoke exposure, quit time in years, number of cigarettes daily, body mass index (BMI)) the AUC reached 0.790 (discovery) and 0.833 (validation). These results could not be validated in the plasma samples.ConclusionThere were a few significantly differential expressed microRNAs in serum up to eight years before diagnosis. These promising microRNAs alone, in concert, or combined with clinical variables have the potential to serve as early diagnostic LC biomarkers. Plasma is not suitable for this analysis. Further validation in larger prospective serum datasets is needed.

Targeted proteomics of plasma extracellular vesicles uncovers MUC1 as combinatorial biomarker for the early detection of high-grade serous ovarian cancer

BackgroundThe five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.ResultsWe conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC.ConclusionsThis study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.

Extracellular Vesicles, Circadian Rhythms, and Cancer: A Comprehensive Review with Emphasis on Hepatocellular Carcinoma

This review comprehensively explores the complex interplay between extracellular vesicles (ECVs)/exosomes and circadian rhythms, with a focus on the role of this interaction in hepatocellular carcinoma (HCC). Exosomes are nanovesicles derived from cells that facilitate intercellular communication by transporting bioactive molecules such as proteins, lipids, and RNA/DNA species. ECVs are implicated in a range of diseases, where they play crucial roles in signaling between cells and their surrounding environment. In the setting of cancer, ECVs are known to influence cancer initiation and progression. The scope of this review extends to all cancer types, synthesizing existing knowledge on the various roles of ECVs. A unique aspect of this review is the emphasis on the circadian-controlled release and composition of exosomes, highlighting their potential as biomarkers for early cancer detection and monitoring metastasis. We also discuss how circadian rhythms affect multiple cancer-related pathways, proposing that disruptions in the circadian clock can alter tumor development and treatment response. Additionally, this review delves into the influence of circadian clock components on ECV biogenesis and their impact on reshaping the tumor microenvironment, a key component driving HCC progression. Finally, we address the potential clinical applications of ECVs, particularly their use as diagnostic tools and drug delivery vehicles, while considering the challenges associated with clinical implementation.

The Inflammasome Adaptor Protein ASC in Plasma as a Biomarker of Early Cognitive Changes

Dementia is a group of symptoms including memory loss, language difficulties, and other types of cognitive and functional impairments that affects 57 million people worldwide, with the incidence expected to double by 2040. Therefore, there is an unmet need to develop reliable biomarkers to diagnose early brain impairments so that emerging interventions can be applied before brain degeneration. Here, we performed biomarker analyses for apoptosis-associated speck-like protein containing a caspase recruitment domain (ASC), neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and amyloid-β 42/40 (Aβ42/40) ratio in the plasma of older adults. Participants had blood drawn at baseline and underwent two annual clinical and cognitive evaluations. The groups tested either cognitively normal on both evaluations (NN), cognitively normal year 1 but cognitively impaired year 2 (NI), or cognitively impaired on both evaluations (II). ASC was elevated in the plasma of the NI group compared to the NN and II groups. Additionally, Aβ42 was increased in the plasma in the NI and II groups compared to the NN group. Importantly, the area under the curve (AUC) for ASC in participants older than 70 years old in NN vs. NI groups was 0.81, indicating that ASC is a promising plasma biomarker for early detection of cognitive decline.

Associations between Serum Metabolites and Female Cancers: a Bidirectional Two-sample Mendelian Randomization Study

Female cancers, especially breast, ovarian, cervical and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers—breast, ovarian, cervical and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo’s studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane’s Q, Egger’s intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

The Landscape of microRNAs in Bone Tumor: A Comprehensive Review in Recent Studies.

Cancer, the second greatest cause of mortality worldwide, frequently causes bone me-tastases in patients with advanced-stage carcinomas such as prostate, breast, and lung cancer. The existence of these metastases contributes to the occurrence of skeletal-related events (SREs), which are defined by excessive pain, pathological fractures, hypercalcemia, and spinal cord com-pression. These injurious incidents leave uncomfortably large holes in each of the cancer patient's life quality. Primary bone cancers, including osteosarcoma (OS), chondrosarcoma (CS), and Ewing's sarcoma (ES), have unclear origins. MicroRNA (miRNA) expression patterns have been changed in primary bone cancers such as OS, CS, and ES, indicating a role in tumor development, invasion, metastasis, and treatment response. These miRNAs are persistent in circulation and ex-hibit distinct patterns in many forms of bone tumors, making them potential biomarkers for early detection and treatment of such diseases. Given their crucial regulatory functions in various bio-logical processes and conditions, including cancer, this study aims to look at miRNAs' activities and possible contributions to bone malignancies, focusing on OS, CS, and ES. In conclusion, miRNAs are valuable tools for diagnosing, monitoring, and predicting OS, CS, and ES outcomes. Further research is required to fully comprehend the intricate involvement of miRNAs in these bone cancers and to develop effective miRNA-based treatments.

Extraction and characterization of exosomes from the exhaled breath condensate and sputum of lung cancer patients and vulnerable tobacco consumers—potential noninvasive diagnostic biomarker source

Noninvasive sample sources of exosomes, such as exhaled breath and sputum, which are in close proximity to the tumor microenvironment and may contain biomarkers indicative of lung cancer, are far more permissive than invasive sample sources for biomarker screening. Standardized exosome extraction and characterization approaches for low-volume noninvasive samples are critically needed. We isolated and characterized exhaled breath condensate (EBC) and sputum exosomes from healthy nonsmokers (n = 30), tobacco smokers (n = 30), and lung cancer patients (n = 40) and correlated the findings with invasive sample sources. EBC samples were collected by using commercially available R-Tubes. To collect sputum samples the participants were directed to take deep breaths, hold their breath, and cough in a collection container. Dynamic light scattering, nanoparticle tracking analysis, and transmission electron microscopy were used to evaluate the exosome morphology. Protein isolation, western blotting, exosome quantification via EXOCET, and Fourier transform infrared spectroscopy were performed for molecular characterization. Exosomes were successfully isolated from EBC and sputum samples, and their yields were adequate and sufficiently pure for subsequent downstream processing and characterization. The exosomes were confirmed based on their size, shape, and surface marker expression. Remarkably, cancer exosomes were the largest in size not only in the plasma subgroups, but also in the EBC (p < 0.05) and sputum (p = 0.0036) subgroups, according to our findings. A significant difference in exosome concentrations were observed between the control sub-groups (p < 0.05). Our research confirmed that exosomes can be extracted from noninvasive sources, such as EBC and sputum, to investigate lung cancer diagnostic biomarkers for research, clinical, and early detection in smokers.

Combined detection of serum EFNA1 and MMP13 as diagnostic biomarker for gastric cancer

We previously identified that serum EFNA1 and MMP13 were potential biomarker for early detection of esophageal squamous cell carcinoma. In this study, our aim is to explore the diagnostic value of serum EFNA1 and MMP13 for gastric cancer. We used enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of serum EFNA1 and MMP13 in 210 GCs and 223 normal controls. The diagnostic value of EFNA1 and MMP13 was evaluated in an independent cohorts of GC patients and normal controls (n = 238 and 195, respectively). Receiver operating characteristics were used to calculate diagnostic accuracy. In training and validation cohorts, serum EFNA1 and MMP13 levels in the GC groups were significantly higher than those in the normal controls (P < 0.001). The area under the curve (AUC) of the combined detection of serum EFNA1 and MMP13 for GC was improved (0.794), compared with single biomarker used. Similar results were observed in the validation cohort. Importantly, the combined measurement of serum EFNA1 and MMP13 to detect early-stage GC also had acceptable diagnostic accuracy in training and validation cohort. Combined detection of serum EFNA1 and MMP13 could help identify early-stage GC, suggesting that it may be a promising tool for the early detection of GC.

Combined detection of serum EFNA1 and MMP13 as diagnostic biomarker for gastric cancer

We previously identified that serum EFNA1 and MMP13 were potential biomarker for early detection of esophageal squamous cell carcinoma. In this study, our aim is to explore the diagnostic value of serum EFNA1 and MMP13 for gastric cancer. We used enzyme-linked immunosorbent assay (ELISA) to detect the expression levels of serum EFNA1 and MMP13 in 210 GCs and 223 normal controls. The diagnostic value of EFNA1 and MMP13 was evaluated in an independent cohorts of GC patients and normal controls (n = 238 and 195, respectively). Receiver operating characteristics were used to calculate diagnostic accuracy. In training and validation cohorts, serum EFNA1 and MMP13 levels in the GC groups were significantly higher than those in the normal controls (P < 0.001). The area under the curve (AUC) of the combined detection of serum EFNA1 and MMP13 for GC was improved (0.794), compared with single biomarker used. Similar results were observed in the validation cohort. Importantly, the combined measurement of serum EFNA1 and MMP13 to detect early-stage GC also had acceptable diagnostic accuracy in training and validation cohort. Combined detection of serum EFNA1 and MMP13 could help identify early-stage GC, suggesting that it may be a promising tool for the early detection of GC.

Global Burden of Lung Cancer: Prognosis, Symptom Management, And Challenges with Pulmonary Fibrosis

Lung cancer represents a significant global health burden, accounting for the highest number of cancer-related deaths worldwide. Despite advancements in treatment modalities, the prognosis remains poor, with a low 5-year survival rate post-diagnosis and substantial mortality within the first year. Patients often experience debilitating symptoms such as fatigue, pain, dyspnea, and coughing, which severely impact their physical and psychological well-being. Pain management is particularly challenging and critical due to its potential leading to depression and anxiety. Lung cancer encompasses histological classifications such as adenocarcinoma, squamous cell carcinoma, large cell carcinoma and small cell lung cancer (SCLC), each with distinctive subtypes. Patients with idiopathic pulmonary fibrosis (IPF) face exacerbated challenges when they also develop lung cancer, impacting their prognosis significantly compared to those with lung cancer alone. The management of pulmonary fibrosis (PF) in lung cancer necessitates a multifaceted approach, incorporating supportive care, pharmacological interventions, and emerging therapies like molecular targeted therapies, gene therapy, and stem cell therapy. This comprehensive management requires collaboration among various healthcare professionals to optimize outcomes and enhance quality of life for affected patients. Despite considerable progress, managing fibrosis in lung cancer remains complex due to its heterogeneous nature and varied progression. Ongoing future research aims to identify biomarkers for early detection, refine treatment strategies, and explore novel therapies through clinical trials. This review highlights lung cancer's global burden, poor prognosis, and severe symptoms impacting patient well-being. It discusses histological types, compounded challenges with IPF, and the need for a multifaceted treatment approach. Emphasis is placed on ongoing research for early detection, refined treatments, and novel therapies to improve outcomes.

Global Burden of Lung Cancer: Prognosis, Symptom Management, And Challenges with Pulmonary Fibrosis

Lung cancer represents a significant global health burden, accounting for the highest number of cancer-related deaths worldwide. Despite advancements in treatment modalities, the prognosis remains poor, with a low 5-year survival rate post-diagnosis and substantial mortality within the first year. Patients often experience debilitating symptoms such as fatigue, pain, dyspnea, and coughing, which severely impact their physical and psychological well-being. Pain management is particularly challenging and critical due to its potential leading to depression and anxiety. Lung cancer encompasses histological classifications such as adenocarcinoma, squamous cell carcinoma, large cell carcinoma and small cell lung cancer (SCLC), each with distinctive subtypes. Patients with idiopathic pulmonary fibrosis (IPF) face exacerbated challenges when they also develop lung cancer, impacting their prognosis significantly compared to those with lung cancer alone. The management of pulmonary fibrosis (PF) in lung cancer necessitates a multifaceted approach, incorporating supportive care, pharmacological interventions, and emerging therapies like molecular targeted therapies, gene therapy, and stem cell therapy. This comprehensive management requires collaboration among various healthcare professionals to optimize outcomes and enhance quality of life for affected patients. Despite considerable progress, managing fibrosis in lung cancer remains complex due to its heterogeneous nature and varied progression. Ongoing future research aims to identify biomarkers for early detection, refine treatment strategies, and explore novel therapies through clinical trials. This review highlights lung cancer's global burden, poor prognosis, and severe symptoms impacting patient well-being. It discusses histological types, compounded challenges with IPF, and the need for a multifaceted treatment approach. Emphasis is placed on ongoing research for early detection, refined treatments, and novel therapies to improve outcomes.

Traditional to technological advancements in Ganoderma detection methods in oil palm.

Ganoderma sp., the fungal agent causing basal stem rot (BSR), poses a severe threat to global oil palm production. Alarming increases in BSR occurrences within oil palm growing zones are attributed to varying effectiveness in its current management strategies. Asymptomatic progression of the disease and the continuous monoculture of oil palm pose challenges for prompt and effective management. Therefore, the development of precise, early, and timely detection techniques is crucial for successful BSR management. Conventional methods such as visual assessments, culture-based assays, and biochemical and physiological approaches prove time-consuming and lack specificity. Serological-based diagnostic methods, unsuitable for fungal diagnostics due to low sensitivity, assay affinity, cross-contamination which further underscores the need for improved techniques. Molecular PCR-based assays, utilizing universal, genus-specific, and species-specific primers, along with functional primers, can overcome the limitations of conventional and serological methods in fungal diagnostics. Recent advancements, including real-time PCR, biosensors, and isothermal amplification methods, facilitate accurate, specific, and sensitive Ganoderma detection. Comparative whole genomic analysis enables high-resolution discrimination of Ganoderma at the strain level. Additionally, omics tools such as transcriptomics, proteomics, and metabolomics can identify potential biomarkers for early detection of Ganoderma infection. Innovative on-field diagnostic techniques, including remote methods like volatile organic compounds profiling, tomography, hyperspectral and multispectral imaging, terrestrial laser scanning, and Red-Green-Blue cameras, contribute to a comprehensive diagnostic approach. Ultimately, the development of point-of-care, early, and cost-effective diagnostic techniques accessible to farmers is vital for the timely management of BSR in oil palm plantations.

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer

ObjectivesThe study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection. MethodsDifferentially expressed genes (DEGs), Series test of cluster (STC) and protein–protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential. ResultsUpregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis. ConclusionsECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

Exploring Fibrosis Pathophysiology in Lean and Obese Metabolic-Associated Fatty Liver Disease: An In-Depth Comparison.

While obesity-related nonalcoholic fatty liver disease (NAFLD) is linked with metabolic dysfunctions such as insulin resistance and adipose tissue inflammation, lean NAFLD more often progresses to liver fibrosis even in the absence of metabolic syndrome. This review aims to summarize the current knowledge regarding the mechanisms of liver fibrosis in lean NAFLD. The most commonly used lean NAFLD models include a methionine/choline-deficient (MCD) diet, a high-fat diet with carbon tetrachloride (CCl4), and a high-fructose and high-cholesterol diet. The major pro-fibrogenic mechanisms in lean NAFLD models include increased activation of the extracellular signal-regulated kinase (ERK) pathway, elevated expression of α-smooth muscle actin (α-SMA), collagen type I, and TGF-β, and modulation of fibrogenic markers such as tenascin-X and metalloproteinase inhibitors. Additionally, activation of macrophage signaling pathways promoting hepatic stellate cell (HSC) activation further contributes to fibrosis development. Animal models cannot cover all clinical features that are evident in patients with lean or obese NAFLD, implicating the need for novel models, as well as for deeper comparisons of clinical and experimental studies. Having in mind the prevalence of fibrosis in lean NAFLD patients, by addressing specific pathways, clinical studies can reveal new targeted therapies along with novel biomarkers for early detection and enhancement of clinical management for lean NAFLD patients.

Development of an organ-on-chip model for the detection of volatile organic compounds as potential biomarkers of tumour progression

Early detection of tumours remains a significant challenge due to their invasive nature and the limitations of current monitoring techniques. Liquid biopsies have emerged as a minimally invasive diagnostic approach, wherein volatile organic compounds (VOCs) show potential as compelling candidates. However, distinguishing tumour-specific VOCs is difficult due to the presence of gases from non-tumour tissues and environmental factors. Therefore, it is essential to develop preclinical models that accurately mimic the intricate tumour microenvironment to induce cellular metabolic changes and secretion of tumour-associated VOCs. In this study, a microfluidic device was used to recreate the ischaemic environment within solid tumours for the detection of tumour-derived VOCs. The system represents a significant advance in understanding the role of VOCs as biomarkers for early tumour detection and holds the potential to improve patient prognosis; particularly for inaccessible and rapidly progressing tumours such as glioblastoma.

The association between systemic inflammation markers and paroxysmal atrial fibrillation

BackgroundSystemic inflammation markers have recently been identified as being associated with cardiac disorders. However, limited research has been conducted to estimate the pre-diagnostic associations between these markers and paroxysmal atrial fibrillation (PAF). Our aim is to identify potential biomarkers for early detection of PAF.Methods91 participants in the PAF group and 97 participants in the non-PAF group were included in this study. We investigated the correlations between three systemic inflammation markers, namely the systemic immune inflammation index (SII), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), and PAF.ResultsThe proportion of patients with PAF gradually increased with increasing logSII, logSIRI, and logAISI tertiles. Compared to those in the lowest tertiles, the PAF risks in the highest logSII and logSIRI tertiles were 3.2-fold and 2.9-fold, respectively. Conversely, there was no significant correlation observed between logAISI and PAF risk within the highest tertile of logAISI. The restricted cubic splines (RCS) analysis revealed a non-linear relationship between the elevation of systemic inflammation markers and PAF risk. Specifically, the incidence of PAF is respectively increased by 56%, 95%, and 150% for each standard deviation increase in these variables. The ROC curve analysis of logSII, logSIRI and logAISI showed that they had AUC of 0.6, 0.7 and 0.6, respectively. It also demonstrated favorable sensitivity and specificity of these systemic inflammation markers in detecting the presence of PAF.ConclusionsIn conclusion, our study reveals significant positive correlations between SII, SIRI, and AISI with the incidence of PAF.

Urinary Kidney Injury Molecule 1 and Urinary Monocyte Chemotactic Protein 1 as Novelbiomarkers for Early Detection of Sickle Cell Nephropathy

Abstract Background Sickle cell disease (SCD) is one of the most common genetic disorders, it not onlyhas hematological pathophysiological consequences, but also has systemic pathologicalmanifestations such as Sickle cell nephopathy(SN). There are no randomized control trials thatexamined the utility of screening test for renal disease in individuals with SCD. Aim of the Work To evaluate urinary monocyte chemoattractant protein-1 (MCP-1) and urinarykidney injury molecule 1 (KIM-1) as biomarkers for early detection of sickle cell nephropathy and correlate them with proteinuria and decline in estimate glomerular filtration rate ( eGFR). Patient and Methods This study was a cross sectional study, conducted at Pediatrics Hematology-Oncology Unit at Ain Shams University Children’s Hospital, where 45 children with sickle cell disease were enrolled aged 6 months - 16 years. Urine samples were collected to measure MCP-1and KIM-1 levels and compared to age and sex matched control. Results There was a statistically significant difference found between control group and patients’group regarding the level of MCP-1 in urine as it was found higher in patients than control group with median(IQR) values of 600 pg/ml (300–780) vs. 47.5 pg/ml (30–80, p &amp;lt; 0.001). Regarding the level of KIIM-1 in urine, it was found higher in patients than control group with median (IQR)values of 2.8 ng/ml (1.6–5.6) vs. 0.4 ng/ml (0.4–0.55, p&amp;lt;0.001) respectively. Conclusion Our data shows a distinct biomarker pattern in children with SCD, identifying KIM-1and MCP-1 as two potential biomarkers of early SN and its progression.

Unveiling diabetic nephropathy: a novel diagnostic model through single-cell sequencing and co-expression analysis.

Diabetic nephropathy (DN) is a severe complication of diabetes that affects the kidneys. Disulfidptosis, a newly defined type of programmed cell death, has emerged as a potential area of interest, yet its significance in DN remains unexplored. This study utilized single-cell sequencing data GSE131882 from GEO database combined with bulk transcriptome sequencing data GSE30122, GSE30528 and GSE30529 to investigate disulfidptosis in DN. Single-cell sequencing analysis was performed on samples from DN patients and healthy controls, focusing on cell heterogeneity and communication. Weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA) were employed to identify disulfidptosis-related gene sets and pathways. A diagnostic model was constructed using machine learning techniques based on identified genes, and immunocorrelation analysis was conducted to explore the relationship between key genes and immune cells. PCR validation was performed on blood samples from DN patients and healthy controls. The study revealed significant disulfidptosis heterogeneity and cell communication differences in DN. Specific targets related to disulfidptosis were identified, providing insights into the pathogenesis of DN. The diagnostic model demonstrated high accuracy in distinguishing DN from healthy samples across multiple datasets. Immunocorrelation analysis highlighted the complex interactions between immune cells and key disulfidptosis-related genes. PCR validation supported the differential expression of model genes VEGFA, MAGI2, THSD7A and ANKRD28 in DN. This research advances our understanding of DN by highlighting the role of disulfidptosis and identifying potential biomarkers for early detection and personalized treatment.

Advancements, Challenges, and clinical implications of integration of metabolomics technologies in diabetic nephropathy

BackgroundDiabetic nephropathy (DN), a severe complication of diabetes, involves a range of renal abnormalities driven by metabolic derangements. Metabolomics, revealing dynamic metabolic shifts in diseases like DN and offering insights into personalized treatment strategies, emerges as a promising tool for improved diagnostics and therapies. MethodsWe conducted an extensive literature review to examine how metabolomics contributes to the study of DN and the challenges associated with its implementation in clinical practice. We identified and assessed relevant studies that utilized metabolomics methods, including nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to assess their efficacy in diagnosing DN. ResultsMetabolomics unveils key pathways in DN progression, highlighting glucose metabolism, dyslipidemia, and mitochondrial dysfunction. Biomarkers like glycated albumin and free fatty acids offer insights into DN nuances, guiding potential treatments. Metabolomics detects small-molecule metabolites, revealing disease-specific patterns for personalized care. ConclusionMetabolomics offers valuable insights into the molecular mechanisms underlying DN progression and holds promise for personalized medicine approaches. Further research in this field is warranted to elucidate additional metabolic pathways and identify novel biomarkers for early detection and targeted therapeutic interventions in DN.

Rapid enzyme-free detection of miRNA-21 in human ovarian cancerous cells using a fluorescent nanobiosensor designed based on hairpin DNA-templated silver nanoclusters

BackgroundCancer is known as one of the main non-communicable diseases and the leading cause of death in the new era. Early diagnosis of cancer requires the identification of special biomarkers. Currently, microRNAs (miRNAs) have attracted the attention of researchers as useful biomarkers for cancer early detection. Hence, various methods have been recently developed for detecting and monitoring miRNAs. Among all miRNAs, detection of miRNA-21 (miR-21) is important because it is abnormally overexpressed in most cancers. Here, a new biosensor based on silver nanoclusters (AgNCs) is introduced for detecting miR-21. ResultsAs a fluorescent probe, a rationally designed hairpin sequence containing a poly-cytosine motif was used to facilitate the formation of AgNCs. A guanine-rich sequence was also employed to enhance the sensing signal. It was found that in the absence of miR-21, adding a guanine-rich sequence to the detecting probe caused only a slight change in the fluorescence emission intensity of AgNCs. While in the presence of miR-21, the emission signal enhanced. A direct correlation was observed between the increase in the fluorescence of AgNCs and the concentration of miR-21. The performance of the proposed biosensor was characterized thoroughly and confirmed. The biosensor detected miR-21 in an applicable linear range from 9 pM to 1.55 nM (LOD: 2 pM). SignificanceThe designed biosensor was successfully applied for detecting miR-21 in human plasma samples and also in human normal and lung and ovarian cancer cells. This biosensing strategy can be used as a model for detecting other miRNAs. The designed nanobiosensor can measure miR-21 without using any enzymes, with fewer experimental steps, and at a low cost compared to the reported biosensors in this field.