Search Results (171)

The reliability of molecular diagnostic and prognostic tools is contingent on the quality of biospecimens, which are often collected during surgical procedures. This study investigated the impact of surgical manipulation on gene expression in the urinary bladder mucosa during radical cystectomy. Seventeen patients with urinary bladder cancer were enrolled, and paired pre- and post-surgery biopsies were analyzed. Pre-surgical biopsies were obtained in situ under anesthesia, while post-surgical biopsies were collected ex vivo following bladder removal. Total RNA was extracted, and gene expression was assessed using qPCR arrays, measuring the expression of 374 inflammation-related genes. The findings from the exploratory phase were further validated by analyzing key genes in an independent patient cohort using TaqMan^® gene-specific assays. Exploratory analysis revealed significant differential expression in 27 genes, with key genes such as IL6, FOS, and PTGS2 being upregulated post-surgery. Validation of five selected genes in an independent cohort confirmed these findings. This study reinforces the necessity of accounting for surgery-induced alterations in gene expression when analyzing tissue samples collected intraoperatively. By elucidating the molecular impact of surgical interventions, this work provides critical insights for refining experimental methodologies and enhancing the interpretability of gene expression studies in clinical and research settings. Full article

Purpose: Preclinical studies suggest that interleukin-1β (IL-1β) influences tumor behavior in non-small cell lung cancer (NSCLC). While the CANTOS trial demonstrated reduced lung cancer incidence with IL-1β inhibition, the CANOPY trials failed to show survival benefit when combined with chemoimmunotherapy. The role of IL-1β in NSCLC with oncogenic mutations remains unclear. We evaluated the prognostic and predictive significance of IL-1β expression across NSCLC subtypes. Methods: We analyzed 21,698 NSCLC tumors profiled by Caris Life Sciences using DNA and RNA next-generation sequencing. IL-1β expression was stratified into quartiles (Q1: lowest 25%, Q4: highest 25%). Real-world overall survival (OS) and time on treatment (TOT) were obtained from insurance claims. Statistical comparisons used Chi-square, Fisher’s exact, or Mann–Whitney U tests. Survival outcomes were assessed with Cox models. Results: Across unselected NSCLC patients, low IL-1β expression (Q1) was associated with modestly longer OS versus high expression (Q4) (median OS 19.5 vs. 17.4 months; HR 0.94; p < 0.0001). This effect was more pronounced in EGFR-mutant adenocarcinoma (36.7 vs. 27.2 months; HR 0.76; p < 0.001) and ALK fusion-positive NSCLC (53.0 vs. 35.2 months; HR 0.62; p = 0.002). In NSCLC without targetable mutations, IL-1β expression was not prognostic. In KRAS-mutant adenocarcinoma, high IL-1β expression was associated with modestly longer TOT on immunotherapy (7.4 vs. 6.4 months; HR 1.15; p = 0.041), but not OS. High IL-1β expression correlated positively with TP53 mutation, TMB-high, and PD-L1 expression and inversely with EGFR, KRAS, BRAF, ERBB2, KEAP1, and STK11 mutations. Conclusions: IL-1β expression is a potential prognostic and predictive biomarker in NSCLC, associated with survival outcomes in defined molecular subsets. These findings suggest that IL-1β-targeted strategies may be particularly relevant in EGFR- or ALK-altered tumors. Full article

Introduction/aim: N-acetylgalactoseamine-conjugated small interfering RNAs (GalNAc-siRNAs) are an emerging class of drugs possessing an extensive clinical potential because of their high target specificity to the asialoglycoprotein receptor (ASGPR) in hepatocytes. Overall, GalNAc-sRNAs are well-tolerated across species but differences in pharmacokinetic (PK) and pharmacodynamic (PD) properties have been observed. Furthermore, despite GalNAc-siRNA’s high liver specificity, distribution into off-target organs does occur. Through whole-body physiologically based pharmacokinetic (PBPK) modeling, this study seeks to mechanistically address species differences, establish clinical PK-PD relationships, and characterize off-target organ accumulation, ultimately expediting the preclinical-to-clinical translation of GalNAc-sRNAs in drug development. Materials/Methods: For model development, validation, and establishment of species’ translations, three in-house GalNAc-siRNAs with PK data from different biospecimens, as well as downstream effects on mRNA and target proteins in mouse, monkey, and human, were leveraged. A WB-PBPK-PD legacy model, developed as an extension to the generic model for large molecules in the platform Open Systems Pharmacology Suite, was further validated and applied to address the specific aims of this study. Results: The model successfully quantified the PK-PD relationships across species and characterized accumulation in off-target organs. The model further sheds light on species-specific differences, such as liver permeability, subcutaneous absorption rate, as well as PD-related mechanisms. Moreover, the model confirmed previously established compound-specific pharmacokinetic differences and similarities. Conclusions: This PBPK-PD can serve as a framework for future investigations of novel GalNAc-siRNAs across species. Full article

Background: Self-directed lifestyle modifications are essential for managing symptoms in individuals diagnosed with irritable bowel syndrome (IBS). This study incorporated longitudinal multi-omics profiling to estimate the mechanisms underlying responses to a nurse-led person-centered self-management intervention in young adults with IBS. Methods: This pre-post study was nested within a 12-week parent randomized controlled trial (NCT03332537). Biospecimens (stool and blood) and clinical outcomes were collected at baseline and post-intervention. Symptoms were assessed using the Brief Pain Inventory and PROMIS^® short forms. Host transcriptomic profiling was performed using RNA sequencing, and gut microbial composition was analyzed via 16S rRNA sequencing. Host transcriptomic co-expression and microbial co-abundance modules were identified via weighted gene co-expression network analysis. Associations between multi-omics modules and symptoms were evaluated using linear mixed-effect models. Results: Among the 20 participants, most were non-Hispanic (75%), White (75%), and female (65%). The intervention significantly reduced self-reported pain severity (p = 0.019) and pain interference (p = 0.013). Decreased associations were observed between pain phenotypes and a microbial module enriched in core metabolic pathways (interference: β = −4.7, p < 0.001; severity: β = −2.4, p = 0.02). Anxiety strengthened associations with host transcriptomic cellular energy metabolism pathways post-intervention (p < 0.05). The intervention attenuated associations between fatigue, sleep disturbance, and immune–inflammatory transcriptomic and microbial adaptation modules (p < 0.05). Conclusions: Findings suggest that the IBS self-management intervention induces symptom-specific biological responses, implicating distinct host–microbe pathways. Larger longitudinal studies are warranted to validate these omics-based symptom signatures. Full article

Background/Objectives: Acute kidney injury (AKI) is a common and severe complication in patients with acute myocardial infarction (AMI). Very elderly patients are at a heightened risk of developing AKI. Fibrinogen and albumin are well-known biomarkers of inflammation and nutrition, which are highly related to AKI. We aim to explore the predictive value of the fibrinogen-to-albumin ratio (FAR) for AKI in very elderly patients with AMI. Methods: A retrospective cohort of AMI patients ≥ 75 years old hospitalized at the First Affiliated Hospital of Xi’an Jiaotong University between January 2018 and December 2022 was established. Clinical data and medication information were collected through the biospecimen information resource center at the hospital. Univariate and multivariable logistic regression models were used to analyze the association between FAR and the risk of AKI in patients with AMI. FAR was calculated as the ratio of fibrinogen (FIB) to serum albumin (ALB) level (FAR = FIB/ALB). The primary outcome is acute kidney injury, which was diagnosed based on KDIGO 2012 criteria. Results: Among 1236 patients enrolled, 66.8% of them were male, the median age was 80.00 years (77.00–83.00), and acute kidney injury occurred in 18.8% (n = 232) of the cohort. Comparative analysis revealed significant disparities in clinical characteristics between patients with or without AKI. Patients with AKI exhibited a markedly higher prevalence of arrhythmia (51.9% vs. 28.1%, p < 0.001) and lower average systolic blood pressure (115.77 ± 25.96 vs. 122.64 ± 22.65 mmHg, p = 0.013). In addition, after adjusting for age, sex, history of hypertension, left ventricular ejection fraction (LVEF), and other factors, FAR remained an independent risk factor for acute kidney injury (OR = 1.47, 95%CI: 1.36–1.58). ROC analysis shows that FAR predicted stage 2–3 AKI with superior accuracy (AUC 0.94, NPV 98.6%) versus any AKI (AUC 0.79, NPV 93.0%), enabling risk-stratified management. Conclusions: FAR serves as both a high-sensitivity screening tool for any AKI and a high-specificity sentinel for severe AKI, with NPV-driven thresholds guiding resource allocation in the fragile elderly. Full article

Heat stress in dairy cows, caused by high temperature and humidity during summer, has led to significant declines in milk production and severe economic losses for farms. Exosomes—extracellular vesicles carrying bioactive molecules—are critical for intercellular communication and immunity but remain understudied in heat-stressed Holstein cows. In this study, we extracted exosomes from three heat-stressed (HS) cows and three non-heat-stressed (Ctr) cows and employed proteomics to analyze plasma exosomes. We identified a total of 28 upregulated and 18 downregulated proteins in the HS group compared to the control group. Notably, we observed a significant upregulation of key protein groups, including cytoskeletal regulators, signaling mediators, and coagulation factors, alongside the downregulation of HP-25_1. These differentially expressed proteins demonstrate strong potential as heat stress biomarkers. GO and KEGG analyses linked the differentially expressed proteins to actin cytoskeleton regulation and endoplasmic reticulum pathways. Additionally, protein–protein interaction (PPI) analysis revealed the PI3K-Akt signaling pathway as a central node in the cellular response to heat stress. These findings establish plasma exosomes as valuable biospecimens, provide valuable insights into the molecular mechanisms of heat stress response, and may contribute to the development of precision breeding strategies for enhanced thermal resilience in dairy herds. Full article

Hair, as a non-invasive biospecimen, retains metabolic deposits from sebaceous glands and capillaries, reflecting substances from the peripheral circulation, and provides valuable biochemical information linked to phenotypes, yet its application in animal disease research remains limited. This work applied ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) to compare the hair metabolomic characteristics of healthy forest musk deer (FMD, Moschus berezovskii) and those diagnosed with hemorrhagic pneumonia (HP), phytobezoar disease (PD), and abscess disease (AD). A total of 2119 metabolites were identified in the FMD hair samples, comprising 1084 metabolites in positive ion mode and 1035 metabolites in negative ion mode. Differential compounds analysis was conducted utilizing the orthogonal partial least squares–discriminant analysis (OPLS-DA) model. In comparison to the healthy control group, the HP group displayed 85 upregulated and 92 downregulated metabolites, the PD group presented 124 upregulated and 106 downregulated metabolites, and the AD group exhibited 63 upregulated and 62 downregulated metabolites. Functional annotation using the Kyoto Encyclopedia of Genes and Genomes (KEGG) indicated that the differential metabolites exhibited significant enrichment in pathways associated with cancer, parasitism, energy metabolism, and stress. Receiver operating characteristic (ROC) analysis revealed that both the individual and combined panels of differential metabolites exhibited area under the curve (AUC) values exceeding 0.7, demonstrating good sample discrimination capability. This research indicates that hair metabolomics can yield diverse biochemical insights and facilitate the development of non-invasive early diagnostic techniques for diseases in captive FMD. Full article

A target specimen profile (TSP) corresponds to the required characteristics of the specimen panels needed to demonstrate that a diagnostic kit meets the target product profile (TPP). TSPs can guide biobanks in the prospective collection of sample panels to support the development and validation of diagnostics. Full article

Background/Objectives: Metabolic dysregulation underlies a myriad of chronic diseases, including metabolic dysfunction-associated steatotic liver disease (MASLD) and obesity, and bile acids emerge as an important mediator in their etiology. Weight control by improving diet quality is the standard of care in prevention and control of these metabolic diseases. Inclusion of pulses, such as common bean, is an affordable yet neglected approach to improving diet quality and metabolic outcomes. Thus, this study evaluated the possibility that common bean alters bile acid metabolism in a health-beneficial manner. Methods: Using biospecimens from several similarly designed studies, cecal content, feces, liver tissue, and plasma samples from C57BL/6 mice fed an obesogenic diet lacking (control) or containing cooked common bean were subjected to total bile acid analysis and untargeted metabolomics. RNA-seq, qPCR, and Western blot assays of liver tissue complemented the bile acid analyses. Microbial composition and predicted function in the cecal contents were evaluated using 16S rRNA gene amplicon and shotgun metagenomic sequencing. Results: Bean-fed mice had increased cecal bile acid content and excreted more bile acids per gram of feces. Consistent with these effects, increased synthesis of bile acids in the liver was observed. Microbial composition and capacity to metabolize bile acids were markedly altered by bean, with greater prominence of secondary bile acid metabolites in bean-fed mice, i.e., microbial metabolites of chenodeoxycholate/lithocholate increased while metabolites of hyocholate were reduced. Conclusions: In rendering mice resistant to obesogenic diet-induced MASLD and obesity, cooked bean consumption sequesters bile acids, increasing their hepatic synthesis and enhancing their diversity through microbial metabolism. Bean-induced changes in bile acid metabolism have potential to improve dyslipidemia. Full article

Arsenic (As) in soil, such as mining waste, is a concern for communities with legacy contamination. While the chronic health effects of As exposure through drinking water are well documented, the association between As in soil and population-wide health impacts is complex, involving factors like soil accessibility, soil properties, and exposure modes. This review summarizes evidence of associations between As in soil and human health, as well as biomarker and bioaccessibility evidence of exposure pathways. Fourteen studies were included in the final analysis. Reviewed studies reported associations between As in soil and birth outcomes, neurological effects, DNA damage, and cancer. Some of these health outcomes are not known to be linked to As in drinking water and were reported over a range of soil concentrations, indicating inconsistencies. Higher soil As concentrations are associated with higher As in human biospecimens, suggesting direct and indirect soil ingestion as primary exposure pathways. The subpopulations more likely to be exposed include younger children and those involved in soil-based activities. Future research should focus on standardized epidemiological studies, longitudinal studies, soil exposure and mitigating factors, combined exposure biomarker studies, the behavior of the different As species, soil dose related to bioavailability/bioaccessibility, and effects with other elements. Full article

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better survival outcomes compared to males. While sex differences in GBM have been observed, the underlying biological mechanisms remain poorly understood. Feature selection can lead to the identification of discriminative key biomarkers by reducing dimensionality from high-dimensional medical datasets to improve machine learning model performance, explainability, and interpretability. Feature selection can uncover unique sex-specific biomarkers, determinants, and molecular profiles in patients with GBM. We analyzed high-dimensional proteomic and metabolomic profiles from serum biospecimens obtained from 109 patients with pathology-proven glioblastoma (GBM) on NIH IRB-approved protocols with full clinical annotation (local dataset). Serum proteomic analysis was performed using Somalogic aptamer-based technology (measuring 7289 proteins) and serum metabolome analysis using the University of Florida’s SECIM (Southeast Center for Integrated Metabolomics) platform (measuring 6015 metabolites). Machine learning-based feature selection was employed to identify proteins and metabolites associated with male and female labels in high-dimensional datasets. Results were compared to publicly available proteomic and metabolomic datasets (CPTAC and TCGA) using the same methodology and TCGA data previously structured for glioma grading. Employing a machine learning-based and hybrid feature selection approach, utilizing both LASSO and mRMR, in conjunction with a rank-based weighting method (i.e., GLIO-Select), we linked proteomic and metabolomic data to clinical data for the purposes of feature reduction to identify molecular biomarkers associated with biological sex in patients with GBM and used a separate TCGA set to explore possible linkages between biological sex and mutations associated with tumor grading. Serum proteomic and metabolomic data identified several hundred features that were associated with the male/female class label in the GBM datasets. Using the local serum-based dataset of 109 patients, 17 features (100% ACC) and 16 features (92% ACC) were identified for the proteomic and metabolomic datasets, respectively. Using the CPTAC tissue-based dataset (8828 proteomic and 59 metabolomic features), 5 features (99% ACC) and 13 features (80% ACC) were identified for the proteomic and metabolomic datasets, respectively. The proteomic data serum or tissue (CPTAC) achieved the highest accuracy rates (100% and 99%, respectively), followed by serum metabolome and tissue metabolome. The local serum data yielded several clinically known features (PSA, PZP, HCG, and FSH) which were distinct from CPTAC tissue data (RPS4Y1 and DDX3Y), both providing methodological validation, with PZP and defensins (DEFA3 and DEFB4A) representing shared proteomic features between serum and tissue. Metabolomic features shared between serum and tissue were homocysteine and pantothenic acid. Several signals emerged that are known to be associated with glioma or GBM but not previously known to be associated with biological sex, requiring further research, as well as several novel signals that were previously not linked to either biological sex or glioma. EGFR, FAT4, and BCOR were the three features associated with 64% ACC using the TCGA glioma grading set. GLIO-Select shows remarkable results in reducing feature dimensionality when different types of datasets (e.g., serum and tissue-based) were used for our analyses. The proposed approach successfully reduced relevant features to less than twenty biomarkers for each GBM dataset. Serum biospecimens appear to be highly effective for identifying biologically relevant sex differences in GBM. These findings suggest that serum-based noninvasive biospecimen-based analyses may provide more accurate and clinically detailed insights into sex as a biological variable (SABV) as compared to other biospecimens, with several signals linking sex differences and glioma pathology via immune response, amino acid metabolism, and cancer hallmark signals requiring further research. Our results underscore the importance of biospecimen choice and feature selection in enhancing the interpretation of omics data for understanding sex-based differences in GBM. This discovery holds significant potential for enhancing personalized treatment plans and patient outcomes. Full article

The 2024 Nobel Prize in Physiology or Medicine was awarded to the pioneers who reported that microRNAs (miRNAs) regulate and direct the switch between physiological and pathological pathways via their over- or underexpression. The discovery changed the medical landscape and there are many completed and on-going clinical studies based on miRNAs. MiRNAs occur at the femtomolar level in biological fluids and are typically quantified using amplification-based techniques. Experimental nanopores have illustrated potential for trace analysis including amplification-free miRNA quantification. We repurposed the MinION, the only commercially available nanopore array device, and developed unique probes and protocols to detect and measure miRNA copies in blood and urine. Here, we report that miRNA copies are proportional to the total RNA isolated from the biospecimen, and that three known miRNA cancer biomarkers, i.e., miR-21, miR-375, and miR-141, were more than 1.5-fold overexpressed in blood samples from breast, ovarian, prostate, pancreatic, lung, and colorectal cancer patients compared to healthy patients. In these cancer samples, miR-15b was not overexpressed, in agreement with earlier studies. In contrast to literature reports, sample variability was undetectable in this study. The potential and limitations of this ready-to-use MinION/Yenos platform for multiple-cancer early detection (MCED) using blood or urine are discussed. Full article

Toxic chemicals and epigenetic biomarkers associated with cancer have been used successfully in clinical diagnostic screening of feces and urine from individuals, but they have been underutilized in a global setting. We analyzed peer-reviewed literature to achieve the following: (i) compile epigenetic biomarkers of disease, (ii) explore whether research locations are geographically aligned with disease hotspots, and (iii) determine the potential for tracking disease-associated epigenetic biomarkers. Studies (n = 1145) of epigenetic biomarkers (n = 146) in urine and feces from individuals have established notable diagnostic potential for detecting and tracking primarily gastric and urinary cancers. Panels with the highest sensitivity and specificity reported more than once were SEPT9 (78% and 93%, respectively) and the binary biomarker combinations GDF15, TMEFF2, and VIM (93% and 95%), NDRG4 and BMP3 (98% and 90%), and TWIST1 and NID2 (76% and 79%). Screening for epigenetic biomarkers has focused on biospecimens from the U.S., Europe, and East Asia, whereas data are limited in regions with similar/higher disease incidence rates (i.e., data for New Zealand, Japan, and Australia for colorectal cancer). The epigenetic markers discussed here may aid in the future monitoring of multiple cancers from individual- to population-level scales by leveraging the emerging science of wastewater-based epidemiology (WBE). Full article

Objectives: This scoping review aimed to provide both researchers and practitioners with an overview of how machine learning (ML) methods are applied to infrared spectroscopy for the diagnosis and prognosis of head and neck precancer and cancer. Methods: A subject headings and keywords search was conducted in MEDLINE, Embase, and Scopus on 14 January 2024, using predefined search algorithms targeting studies that integrated infrared spectroscopy and ML methods in head and neck precancer/cancer research. The results were managed through the COVIDENCE systematic review platform. Results: Fourteen studies met the eligibility criteria, which were defined by IR spectroscopy techniques, ML methodology, and a focus on head and neck precancer/cancer research involving human subjects. The IR spectroscopy techniques used in these studies included Fourier transform infrared (FTIR) spectroscopy and imaging, attenuated total reflection-FTIR, near-infrared spectroscopy, and synchrotron-based infrared microspectroscopy. The investigated human biospecimens included tissues, exfoliated cells, saliva, plasma, and urine samples. ML methods applied in the studies included linear discriminant analysis (LDA), principal component analysis with LDA, partial least squares discriminant analysis, orthogonal partial least squares discriminant analysis, support vector machine, extreme gradient boosting, canonical variate analysis, and deep reinforcement neural network. For oral cancer diagnosis applications, the highest sensitivity and specificity were reported to be 100%, the highest accuracy was reported to be 95–96%, and the highest area under the curve score was reported to be 0.99. For oral precancer prognosis applications, the highest sensitivity and specificity were reported to be 84% and 79%, respectively. Conclusions: This review highlights the promising potential of integrating infrared spectroscopy with ML methods for diagnosing and prognosticating head and neck precancer and cancer. However, the limited sample sizes in existing studies restrict generalizability of the study findings. Future research should prioritize larger datasets and the development of advanced ML models to enhance reliability and robustness of these tools. Full article

Cancer is one of the leading causes of death globally, and is ranked second in the United States. Early detection is crucial for more effective treatment and a higher chance of survival rates, reducing burdens on individuals and societies. Genitourinary cancers, in particular, face significant challenges in early detection. Finding new and cost-effective diagnostic methods is of clinical need. Metabolomic-based approaches, notably volatile organic compound (VOC) analysis, have shown promise in detecting cancer. VOCs are small organic metabolites involved in biological processes and disease development. They can be detected in urine, breath, and blood samples, making them potential candidates for sensitive and non-invasive alternatives for early cancer detection. However, developing robust VOC detection methods remains a hurdle. This review outlines the current landscape of major genitourinary cancers (kidney, prostate, bladder, and testicular), including epidemiology, risk factors, and current diagnostic tools. Furthermore, it explores the applications of using VOCs as cancer biomarkers, various analytical techniques, and comparisons of extraction and detection methods across different biospecimens. The potential use of VOCs in detection, monitoring disease progression, and treatment responses in the field of genitourinary oncology is examined. Full article