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Progress towards a biotypic biomarker profile for amyotrophic lateral sclerosis-frontotemporal spectrum disorders.

Determining the optimal timing of disease-modifying therapies for neurodegenerative disorders will necessitate identification of when the underlying pathobiological process becomes active, well in advance of the point at which clinical manifestions appear. Phenoconversion, the emergence of clinically manifest syndomes, may be preceded by years to decades of silent pathobiological activity that can only be mapped by an array of biomarkers. ALS and FTD, traditionally identified as distinct clinical syndromes, are increasingly recognized to exist along a spectrum of clinical syndromes with shared genetic risk and shared underlying pathology. This clinicopathological spectrum is underpinned by cytoplasmic aggregation of TAR DNA-binding protein 43 (TDP-43) as the common neuropathological hallmark. In contrast, the majority of neuropathologically-defined frontotemporal lobar degeneration (FTLD) is associated with alterations in either TDP-43 metabolism (FTLD-TDP) or of the microtubule associated protein tau (FTLD-tau), with a smaller percentage associated with either autosomal dominant genetic mutations or impairments in the ubiquitin proteasome system. As the field of neurodegenerative disorders increasingly shifts towards the frameworks of a pathobiological definition of disease, there is a growing imperative to develop biomarkers that reflect the varied pathobiologies that underly these disorders, and to determine the sensitivity of such biomarkers to detect the presence of these pathobiologies before phenoconversion. To that end, an international workshop was convened in London, Canada in 2025 to review the evidence for existing or evolving biomarkers suitable for (1) the detection of either ALS or FTD pathobiology prior to phenoconversion and/or (2) predict phenoconversion in at risk individuals. Such biomarkers might be conceptualized as "biotypic biomarkers", capturing their ability to describe an underlying pathophysiology whilst being agnostic to the emergent clinical manifestations. Whereas no single biotypic marker is yet able to predict the emergence of ALS, FTD or their intersection, a multimodal approach to developing a biotypic biomarker profile holds promise for the detection of relevant pathobiological processes. The strength of such an approach would be augmented by also addressing issues of resiliency/susceptibility both in terms of genetic risk susceptibility profiles and developing sensitive biomarkers of genomic and cellular aging. By including such nontraditional markers of disease, a more robust picture of not only the degenerative process but also of those factors that might potentially mitigate or drive a heightened probability of disease can be derived.

cryptic exons

EGFR-Mutant Non-Small Cell Lung Cancer With Small Cell Transformation: Clinicopathological Features, Treatment Landscape, and Biomarker Profiles.

INTRODUCTION: Transformed small-cell lung cancer (tSCLC) is a clinically important resistance mechanism to EGFR tyrosine kinase inhibitors in EGFR-mutant non-small cell lung cancer. This study characterizes clinical features, treatment outcomes, and biomarker profiles in patients with tSCLC. METHODS: Data from 45 patients with EGFR-mutant NSCLC who developed tSCLC between 2014 and 2023 were analyzed. Demographic characteristics, treatment histories, and delta-like ligand 3 (DLL3) and B7-H3 expression were collected. Objective response rate, progression-free survival (PFS), and posttransformation survival (PTS) were assessed. Spatial transcriptomic profiling was performed in selected cases. RESULTS: Most patients were women (60%) and never-smokers (75.6%). Exon 19 deletion was the predominant EGFR mutation (57.8%). Median PFS and PTS were 3.3 and 9.2 months, respectively. Etoposide plus platinum (EP) was the predominant first-line regimen (69.8%), with 23.2% of the patients receiving EP plus immune checkpoint or tyrosine kinase inhibitors. EP-based combination regimens yielded a numerically higher objective response rate and a significantly longer PFS than EP alone (7.5 versus 2.8 months, p = 0.002). PTS was longer with EP-based regimens than with other regimens (10.4 versus 6.4 months, p = 0.035). DLL3 and B7-H3 were expressed in 87.5% and 66.7% of tumors, respectively, without prognostic significance. Multivariable analysis identified brain metastasis and liver progression at transformation as adverse prognostic factors. Spatial transcriptomic analysis revealed neuroendocrine lineage reprogramming, stromal depletion, and immune exclusion. CONCLUSIONS: tSCLC remains an aggressive resistance phenotype with poor outcomes. EP-based combination strategies may provide clinical benefit, whereas frequent DLL3 expression supports further evaluation of targeted therapies.

Delta-like ligand 3

Molecular biomarker profiling in noninfectious uveitis: a chronological review of discovery.

PURPOSE OR REVIEW: Noninfectious uveitis (NIU) encompasses a heterogeneous group of immune-mediated intraocular inflammatory diseases whose complexity has driven systematic molecular biomarker discovery. This review presents NIU molecular biomarkers organized by biological category; autoantigens, human leukocyte antigens (HLA) and genetic markers, cellular immune subsets, cytokines, chemokines, and multiomics platforms including proteomics, microbiome metagenomics, metabolomics, and single-cell transcriptomics with each category presented in strict chronological order of landmark discovery. RECENT FINDINGS: We present a review organized along two nested timelines. Categories are presented in the order they historically emerged in the field, and within each category, landmark discoveries appear in chronological sequence. This allows the reader to trace how each biomarker category evolved: from foundational autoantigen identification in experimental uveitis models, through the genomic revolution of HLA association studies, into cellular immunophenotyping, cytokine profiling of aqueous humor, chemokine mapping of intraocular trafficking, and finally the emerging omics platforms that may potentially anchor precision medicine in NIU. Each biomarker is paired in line with its linked targeted therapeutic. SUMMARY: Biomarker research has transformed the understanding of NIU from a clinically defined syndrome into a group of molecularly distinct immune disorders. Advances spanning autoantigens, genetics, immune-cell profiling, cytokines, chemokines, and multiomics have revealed novel pathogenic mechanisms and therapeutic targets. Integration of these biomarkers with targeted therapies may accelerate the transition toward precision medicine in uveitis care.

cytokines

Characteristics of Protein Profiling and Biomarkers in Aortic Regurgitation With Heart Failure.

BACKGROUND: Valvular heart disease, particularly aortic valve disease including stenosis and regurgitation, is a common heart disease. This study aimed to explore the protein profiling and the biomarkers in severe aortic valve disease and to provide new insights into the therapeutic strategy. METHODS: Blood samples from 80 subjects were collected and analyzed by data independent acquisition technique in 3 comparisons (mild/moderate-control, severe-control, and severe-mild/moderate) and validated by ELISA. The diagnostic value of differentially expressed proteins associated with severe valvular heart disease was also evaluated by the receiver operating characteristic curve. RESULTS: A total of 9976 peptides and 451 proteins were identified through liquid chromatography-tandem mass spectrometry analysis. From these, 64 in mild/moderate-control, 50 in severe-control, and 50 in severe-mild/moderate comparisons were identified as differentially expressed proteins. IGFBP7 (insulin-like growth factor-binding protein 7; 5581.0&#xb1;697.0&#x2009;ng/mL), DSG1 (desmoglein-1; 21.0&#xb1;2.0 pg/mL), ADIPOQ (adiponectin; 26&#x2009;686.0&#xb1;3730&#x2009;ng/mL), and JUP (junction plakoglobin; 10.2&#xb1;0.6&#x2009;ng/mL) levels in the severe group were significantly higher than that in the mild/moderate (P<0.05) group. Additionally, ADIPOQ and JUP levels in the severe group were also higher than that in control (P<0.001). Receiver operating characteristic curve analysis showed that IGFBP7, DSG1, JUP, and ADIPOQ had strong potential value to be associated with severe aortic valve disease. CONCLUSIONS: By constructing proteomics profile to identify the protein characteristics this study found that increased IGFBP7, DSG1, JUP, and ADIPOQ are the characteristics of proteins in patients with severe valvular heart disease. These findings provide new insight into the diagnosis and pathogenesis of valvular heart disease, particularly aortic valve disease.

Humans

Protein Profiling Identifies Biomarkers for Predicting Disease Severity in Anti-NMDAR Encephalitis.

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a severe autoimmune neurological disorder characterized by pathogenic antibodies against the NMDAR. A systematic protein profiling approach is warranted to identify biomarkers capable of predicting disease status. An Olink proximity extension assay (PEA) profiled 91 inflammation-related proteins from anti-NMDAR encephalitis patients. Disease severity or prognosis were assessed by CASE score or mRS score at 6-month follow-up. Patients were stratified into distinct molecular clusters using unsupervised clustering. Logistic regression models incorporating selected biomarkers were developed to predict disease severity and prognosis, followed by absolute quantification using ELISA. Patients were classified into four consensus clusters. Clusters 1 and 2 corresponded to the mild group, while Cluster 3 represented the severe group, consistent with CASE score above 6. Cluster 4 showed heterogeneous clinical features. Elevated serum levels of IL-10, IL-6, and SIRT2, as well as increased CSF levels of CXCL10, CXCL11, and MMP10, were positively associated with severe disease. Conversely, several proteins including LTA and CCL11, CCL8, TGFB1, CXCL6 were associated with severe disease or unfavorable 6-month outcomes. A logistic regression model combining serum CXCL6 and CCL11 with CSF MMP10 achieved an area under the curve (AUC) of 0.95 for predicting disease severity. Serum CCL11 alone showed predictive value for 6-month prognosis, with an AUC of 0.79. These findings delineate distinct protein signatures associated with clinical heterogeneity of anti-NMDAR encephalitis. Prediction models incorporating multiple biomarkers may provide an approach for disease severity stratification and prognosis forecast.

Humans

Urinary porphyrin profiles as biomarkers of trace metal exposure and toxicity: studies on urinary porphyrin excretion patterns in rats during prolonged exposure to methyl mercury.

Studies were conducted to define the specific changes in the urinary porphyrin excretion pattern (porphyrin profile) and the time course of those changes in rats exposed to mercury as methyl mercury hydroxide (MMH) at 5 or 10 ppm in the drinking water for up to 30 weeks. The urinary porphyrin profile elicited by MMH is uniquely characterized by highly elevated levels of 4- and 5-carboxyl porphyrins, and of a third atypical porphyrin with as yet undetermined chemical characteristics. Changes in the porphyrin profile were observed as early as 1 or 2 weeks following initiation of exposure to MMH at 10 or 5 ppm, respectively, and were sustained as long as 40 weeks following cessation of MMH treatment. The magnitude of the urinary porphyrin profile at either MMH dose level increased progressively during the course of mercury treatment and was highly correlated with the renal mercury concentration. A subsequent decline in the magnitude of the urinary porphyrin profile in animals exposed to 10 ppm MMH for more than 10 weeks was associated with the accumulation of high levels of Hg2+ in kidney cells and loss of renal functional status. These findings demonstrate that mercury elicits a unique change in the urinary porphyrin excretion pattern which is related to the dose and duration of mercury treatment. The association of urinary porphyrin excretion rates with renal mercury content and functional status suggests that urinary porphyrin profiles may serve as a useful biomarker of mercury accumulation and nephrotoxicity during prolonged mercury exposure.

Animals

Stochastic epigenetic mutation profiles as biomarkers of clinical activity in juvenile idiopathic arthritis: a multi-omic machine learning approach for gene prioritization.

BACKGROUND: Juvenile idiopathic arthritis (JIA) is a rare autoimmune disease arising from a complex interplay between genetic and environmental factors. Epigenetic modifications such as DNA methylation (DNAm) have been described as potential mediators in gene-environment interactions, contributing to immune system dysregulation. Emerging evidence suggests that DNAm profiles also predict therapeutic responses in autoimmune diseases. This study aims to identify epigenetic biomarkers and epigenetic-driven gene expression changes associated with JIA clinical activity. METHODS: We reanalyzed a publicly available dataset of 44 JIA patients, with whole-genome DNAm and gene expression from CD4&#x2009;+&#x2009;T cells measured at two points: at anti-TNF therapy withdrawal (T0) and eight months later (Tend). At Tend, 30 patients maintained inactive disease (ID) while 14 did not (NO ID). We investigated differences between ID and NO ID patients in the epigenetic mutation load and various epigenetic clocks through linear regression models, and prioritized genomic regions with significantly higher number of epimutations in NO ID patients through machine learning. RESULTS: We found a higher mutation load in NO ID than ID patients, both at T0 and at Tend, with the differences at Tend reaching statistical significance (p&#x2009;=&#x2009;0.02). In contrast, we found no evidence of association between epigenetic clocks and JIA clinical activity. Using a multi-omic approach, we identified a List of candidate epigenetically-driven differentially expressed genes, 80 up-regulated and 77 down-regulated, in NO ID patients. Finally, comparing our candidate gene list with the Connectivity Map database, we identified new candidate potential therapeutic targets. Key findings were validated in independent datasets: DNAm profiles from CD4&#x2009;+&#x2009;T cells (56 JIA patients, 57 controls) and transcriptomic data from PBMCs of JIA patients with active or inactive disease, confirming dysregulation of pathways such as TNF-&#x3b1; signaling via NF-kB and TGF-&#x3b2; signaling among others. CONCLUSIONS: We described a significant association of epigenetic mutations with JIA clinical activity, indicating that epigenetic changes might precede clinical symptoms and may serve as biomarkers for early disease monitoring. Further, our results shed light on biomolecular mechanisms of JIA, supporting the development of more effective treatments.

Humans

CancerTrialMatch: a computational resource for the management of biomarker-based clinical trials at a community cancer center.

MOTIVATION: The widespread implementation of next-generation sequencing in cancer care has enabled routine use of molecular and biomarker profiling. At our cancer center, as with many others, biomarker-based clinical trials are increasingly available to oncologists as potential treatment options via molecular tumor boards. To better support this effort, we developed CancerTrialMatch, a systematic approach to capture structured clinical trial data and match patients to trials based on their disease characteristics and sequencing profiles. RESULTS: CancerTrialMatch is an open-source application designed to streamline clinical trial curation and patient trial matching, while also enabling an institution's curated trial portfolio to be distributed across the institution for easy access to providers, care teams and researchers. It facilitates curating, updating, and searching for trials through a semi-automated interface built using R Shiny, MongoDB, and Docker. While much of the trial data is retrieved via the clinicaltrials.gov Application Programming Interface, certain items like biomarkers and disease subtypes are entered manually. The user inputs disease type using the OncoTree classification, and provides relevant biomarker details, such as mutations, copy numbers, fusions, and other disease-specific markers. This resource reduces the time required for institutional trial management and helps to identify potential clinical trials for patients, ultimately supporting larger clinical trial enrollment and enhancing the clinical application of precision oncology. AVAILABILITY AND IMPLEMENTATION: CancerTrialMatch was implemented and tested on Windows 11 (64-bit, 32 GB RAM) using WSL2 with Ubuntu 22.04. Docker 27.0.3 and Docker Compose 2.28.1 were used to build images and containers. Users can build it by cloning the repo and following the README instructions and supplemental file (cancertrialmatchsupplemental.pdf) . The source code and example data are available in GitHub and Figshare at https://github.com/AveraSD/CancerTrialMatch and 10.6084/m9.figshare.28447367 respectively.

Humans

Organoids in translation: a bench-to-bedside framework for pancreatic cancer precision medicine.

INTRODUCTION: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with a 5-year survival rate of < 13%. Standard treatments such as FOLFIRINOX or gemcitabine/nab-paclitaxel yield modest response rates, underscoring the urgent need for precision oncology approaches. Patient-derived organoids (PDOs) preserve the genomic, phenotypic, and histopathological features of the source tumor and offer a promising platform for drug screening, biomarker development, and personalized therapy. However, a systematic evaluation of their translational capacities is lacking. METHODS: A systematic review was conducted according to the PRISMA 2020 guidelines (PROSPERO registration pending) using PubMed, EMBASE, and Cochrane CENTRAL (December 10, 2024) to identify English-language PDAC PDO studies that incorporated therapeutic testing. Ninety-five studies met the inclusion criteria. Data extraction captured >75 variables per study, including spanning culture methodology, therapeutic profiling, biomarker integration, and clinical correlation. A 13-domain weighted Translatability Scoring Framework adapted from Wehling et al. assessed predictive validity, biomarker strength, pharmacogenetics, and clinical trial alignment. Scores ranged from 0 to 5 and were categorized as good (>4.0), moderate (3.0-4.0), or low (<3.0) translational potential. RESULTS: Of the 95 studies, 70.5% have been published since 2021, reflecting the rapid growth in this field. The mean PDO generation success rate was 89.7%, with the primary tumor tissue being the predominant source (48.4%). Only 24.8% were directly linked to clinical trials and 5.3% incorporated multi-omic profiling. The median translatability score was 3.13 (range, 1.72-4.59): 45.3% of the studies had low translatability, 50.5% moderate, and only 4.2% had good translational potential. High-scoring studies consistently combine multi-omic biomarker platforms, in vivo validation, clinical outcome correlation, and prospective trial integration. Conversely, the weakest domains were pharmacogenetics, endpoint strategies, and biomarker validation, limiting their overall clinical relevance. CONCLUSIONS: PDOs have demonstrated strong feasibility and in vitro clinical correlation in PDAC; however, their clinical translation remains constrained by limited multi-omic integration, absence of pharmacogenomic modeling, and sparse clinical trial embedding. Standardization of protocols, adoption of harmonized and clinically relevant endpoints, and systematic incorporation of biomarker-driven co-clinical trial frameworks are urgently needed to transition PDOs from promising experimental surrogates to validating precision oncology tools capable of informing therapeutic decision-making in PDAC.

Humans

Plasma proteomic profiling characterizes candidate biomarkers of perimesencephalic non-aneurysmal subarachnoid hemorrhage.

OBJECT: This study aims to explore the plasma proteomic profiles of angiographically confirmed pmSAH and aSAH, and to identify candidate protein biomarkers for discriminating these subtypes on a biological level. METHODS: The differentially abundant proteins of plasma samples from patients with pmSAH (n&#xa0;=&#xa0;30) and aSAH (n&#xa0;=&#xa0;30) were analyzed by data-independent acquisition proteomics, and candidate biomarkers were screened. RESULTS: 291 candidate biomarkers were obtained that could be used to distinguish pmSAH patients from aSAH patients, among which 76 were upregulated and 215 were downregulated in pmSAH. Subsequently, the 10 candidate biomarkers were validated by enzyme-linked immunosorbent assay in a validation cohort of 72 subjects. ORM1, ORM2, HP and NMNAT1 were specifically down-regulated in the pmSAH group, while ANP32A was specifically up-regulated in the pmSAH group. FGL2 was specifically up-regulated in the aSAH group. The combined model of ORM2, HP and ANP32A had the best discriminative power (AUC&#xa0;=&#xa0;0.880). CONCLUSIONS: This study identified ORM2, HP, and ANP32A as candidate biomarkers reflecting biological differences between pmSAH and aSAH. SIGNIFICANCE: Although some proteomic studies have analyzed aneurysmal subarachnoid hemorrhage, to date, there have been no reports on the circulating proteomic analysis of pmSAH. Comparative analysis of the circulating proteomic differences between pmSAH and aSAH may not only help understand the causes of pmSAH, but also contribute to a deeper understanding of mechanisms showing how pmSAH differs from the formation and rupture mechanisms of intracranial aneurysms.

Humans

Integrative Multiomics and Drug Sensitivity Profiling Reveal Potential Biomarkers and Therapeutic Strategies in Pediatric Solid Tumors.

UNLABELLED: Cure rates for childhood malignancies using established therapy protocols have increased to an average of 80% but have reached a plateau. Moreover, survival rates are particularly low for some pediatric tumors-such as high-risk group 3 medulloblastomas, osteosarcomas, Ewing sarcomas, high-risk neuroblastomas, and high-grade gliomas-and dismal for patients with relapsed malignancies. A functional drug response profiling platform for pediatric solid and brain tumors has been established within the INFORM program to identify patient-specific vulnerabilities and biomarkers and to unravel molecular mechanisms associated with drug response profiles for clinical translation. In this study, we performed a multiomics analysis using drug sensitivity profiles, as well as genomic and transcriptomic data, of 81 pediatric solid tumor samples. The integrative analysis suggested two multiomics signatures associated with drug sensitivity. One signature distinguished neuroblastoma samples with sensitivity to navitoclax, a BCL2 family inhibitor. A second signature was specific to a subset of Wilms tumors harboring the SIX1 (Q177R) hotspot mutation that displayed high expression of MGAM, PTPN14, STAT4, and KDM2B and high sensitivity to MEK inhibitors. A patient-specific causal interaction network analysis suggested possible molecular interactions between MEK inhibitors and the SIX1 mutation in Wilms tumor samples. In conclusion, the integration of drug sensitivity profiling and multiomics data revealed potential biomarkers that may be associated with drug sensitivity in pediatric solid tumors. Patient-specific causal interaction network analysis further elucidated the interaction between inhibitors and signature biomarkers, providing insights that may inform clinical translation. SIGNIFICANCE: The combination of multiomics analysis and drug sensitivity profiling identified two signatures related to drug sensitivity in pediatric solid tumors, contributing to the advancement of functional precision medicine and personalized treatment strategies. This article is part of a special series: Driving Cancer Discoveries with Computational Research, Data Science, and Machine Learning/AI .

Humans

Gene expression profiling identifies potential biomarkers for vaso-occlusive episodes in sickle cell disease.

Vaso-occlusive episodes (VOEs) or acute pain events, involving complex interactions between sickle erythrocytes and other blood cells, are a hallmark of sickle cell disease (SCD). In this study, we analyzed changes in peripheral blood transcriptomes between steady state and VOEs in individuals with SCD. We followed a cohort of 174 individuals with SCD with or without chronic pain and collected peripheral blood at clinic visits (steady state) and during hospitalizations (VOEs). We performed RNA-Seq profiling of CD45+ leukocytes and CD71+ erythroid cells. Pathways linked to complement activation, coagulation, and IL-6/JAK/STAT3 signaling were enriched during VOEs in the CD45+ cells. Contrastingly, the CD71+ cells showed an enrichment of pathways related to the cell cycle, such as mTORC1 signaling and the G2M checkpoint during VOEs. We then analyzed the expression changes of genes in patients with longitudinal data to determine potential biomarkers for VOEs. Expression of 4 genes - FAM20A, IL1B, MS4A4A, and SERPINB2 - was elevated during VOEs compared with steady state in the majority of patients. Furthermore, our results indicate that patients experiencing chronic pain exhibited 44% increased enrichment of significant pathways during VOEs when compared with patients without chronic pain.

Humans

Baseline metabolomic profile as potential biomarker for weight change after Roux-en-Y gastric bypass (RYGB) surgery.

Metabolic and bariatric surgery (MBS) is the most effective intervention for sustained weight loss and cardiometabolic improvement in individuals with severe obesity. However, long-term outcomes vary, with many patients experiencing weight regain. The biological determinants of this variability remain incompletely understood. Given the integrative nature of the metabolome-capturing interactions among host genetics, diet, microbiota, and environmental exposures-we hypothesized that baseline circulating metabolites could stratify individuals into distinct long-term weight trajectory groups. We profiled untargeted fasting plasma metabolites in a nested case-control study within the Longitudinal Assessment of Bariatric Surgery (LABS-2) cohort. From these metabolites, a 13-metabolite risk score (MetRS) predictive of weight regain five years after Roux-en-Y gastric bypass was derived. The MetRS, which captures pathways including fatty acid oxidation, bile acid conjugation, and microbial-host co-metabolism, outperformed clinical variables in predicting long-term weight outcomes. Its performance was evaluated in two independent cohorts, including one assessed a median of seven years post-surgery. Genomic analyses identified common variants in loci including AGXT2 and SLC7A5 associated with key MetRS metabolites, suggesting a heritable component to the observed metabolic signature. Together, these findings lay the groundwork for a clinically actionable framework to identify individuals at risk for weight recidivism and support the integration of metabolic profiling into preoperative assessment for personalized obesity care.

Journal Article

Plasma Proteomic Profiling Identifies Candidate Biomarkers for Pancreatic Ductal Adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that is often diagnosed after curative treatment is no longer feasible. Existing biomarkers, particularly CA19-9, have limited sensitivity and specificity. Plasma proteins that capture tumor-associated biological alterations may therefore provide useful signals for earlier detection. METHODS: Plasma samples from 99 patients with PDAC and 30 healthy controls were analyzed using data-independent acquisition (DIA) proteomics. Differentially expressed proteins were identified using predefined statistical thresholds and further examined by functional enrichment analysis. Selected candidate biomarkers were validated by ELISA in an independent subset. RESULTS: Among 565 quantified plasma proteins, 52 were differentially expressed between PDAC and controls. These proteins were enriched in extracellular processes, cholesterol metabolism, complement and coagulation cascades, and pancreatic secretion pathways. ELISA validation confirmed higher plasma levels of Cathepsin S, CTRB2, MARCO, PIGR, PRDX6, REG1A, Trypsin-2, and PEP-FAP in patients with PDAC compared with healthy controls. ROC analyses showed moderate-to-good discriminatory performance for several candidates, and the MARCO&#x2009;+&#x2009;PEP-FAP model improved classification compared with either marker alone. CONCLUSION: These findings reveal circulating proteins linked to key PDAC-related biological processes and identify eight candidates for further evaluation in multi-protein diagnostic panels. Larger validation studies incorporating clinically relevant disease control groups are warranted to determine their diagnostic specificity and clinical utility.

Humans

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan&#x2013;Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans

Effects of 12 weeks of resistance and concurrent training with graded protein intakes on lipid profile, kidney and liver biomarkers in middle-aged to older women.

PURPOSE: To examine secondary lipid, kidney-related, and liver-enzyme responses to three protein intakes during resistance training (RT) alone or the same RT plus cycling (CT) in middle-aged to older women. METHODS: In this randomized 2&#xd7;3 factorial trial, 108 women aged 40-77 years were assigned to RT or CT and 0.8, 1.6, or 2.2 g kg-1 d-1 protein for 12 weeks. This complete-case secondary analysis included 83 participants. Linear mixed-effects models tested Time &#xd7; Training, Time &#xd7; Protein, and Time &#xd7; Training &#xd7; Protein effects, with false-discovery-rate-adjusted omnibus tests and Holm-adjusted contrasts. RESULTS: Triglycerides, total cholesterol, LDL-C, and apolipoprotein B decreased and HDL-C increased in all conditions. Lipid changes differed by protein condition, and several were more favorable with CT; however, CT comprised RT plus additional cycling and greater exercise exposure. Urea, blood urea nitrogen, creatinine, the blood urea nitrogen-to-creatinine ratio, and cystatin C increased, whereas three eGFR estimates decreased. Responses differed mainly between 0.8 and the two higher protein conditions, with little evidence of differences between 1.6 and 2.2 g kg-1 d-1. ALT, AST, and GGT differed by protein condition; AST and GGT also showed training-dependent responses. CONCLUSIONS: The dietary and exercise interventions modified lipid and clinical-chemistry responses. Because energy and food composition were not fully matched and CT added cycling to RT, the findings do not isolate protein dose or exercise modality. Changes in eGFR estimates and liver enzymes do not establish organ injury or long-term safety.

Humans

S100P as a Shared Biomarker in Inflammatory Bowel Disease, Colorectal Cancer, and Pancreatic Adenocarcinoma: An Integrated Transcriptomic Analysis.

Inflammatory bowel disease (IBD) is associated with an increased risk of colorectal cancer (CRC) and pancreatic adenocarcinoma (PAAD), yet the molecular features shared among these diseases remain incompletely understood. This study aimed to identify common genes and biological pathways associated with IBD, CRC, and PAAD through integrated transcriptomic analysis and experimental validation. Gene expression datasets for IBD, CRC, and PAAD were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Weighted gene co-expression network analysis and differential expression analysis were performed to identify disease-associated and shared genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (analyses were used to explore enriched biological functions and pathways. Immune cell infiltration was evaluated using Cell-type Identification by Estimating Relative Subsets of RNA Transcripts. Receiver operating characteristic analysis was performed to assess the diagnostic performance of common genes. Single-cell RNA sequencing analysis was conducted to examine the cellular distribution of S100P. In addition, the effects of S100P downregulation were evaluated in lipopolysaccharide (LPS)-stimulated colonic epithelial cells. A total of 162 disease-associated genes and four common genes were identified. Functional enrichment analyses indicated significant enrichment of immune- and inflammation-related pathways, including the interleukin-17 signaling pathway. Immune infiltration analysis revealed similar trends in several immune cell populations across IBD, CRC, and PAAD. Single-cell analysis showed elevated S100P expression in epithelial cells from all three diseases. Downregulation of S100P restored the proliferative capacity of LPS-stimulated colonic epithelial cells and reduced inflammatory cytokine expression. Integrated transcriptomic analysis identified S100P as a biomarker associated with IBD, CRC, and PAAD and highlighted shared immune-related features across these diseases.

Humans

Systematic Proteome Profiling of Maternal Plasma for Development of Preeclampsia Biomarkers.

Preeclampsia (PE) is a hypertensive disorder of pregnancy with various clinical symptoms. However, traditional markers for the disease including high blood pressure and proteinuria are poor indicators of the related adverse outcomes. Here, we performed systematic proteome profiling of plasma samples obtained from pregnant women with PE to identify clinically effective diagnostic biomarkers. Proteome profiling was performed using TMT-based liquid chromatography-mass spectrometry (LC-MS/MS) followed by subsequent verification by multiple reaction monitoring (MRM) analysis on normal and PE maternal plasma samples. Functional annotations of differentially expressed proteins (DEPs) in PE were predicted using bioinformatic tools. The diagnostic accuracies of the biomarkers for PE were estimated according to the area under the receiver-operating characteristics curve (AUC). A total of 1307 proteins were identified, and 870 proteins of them were quantified from plasma samples. Significant differences were evident in 138 DEPs, including 71 upregulated DEPs and 67 downregulated DEPs in the PE group, compared with those in the control group. Upregulated proteins were significantly associated with biological processes including platelet degranulation, proteolysis, lipoprotein metabolism, and cholesterol efflux. Biological processes including blood coagulation and acute-phase response were enriched for down-regulated proteins. Of these, 40 proteins were subsequently validated in an independent cohort of 26 PE patients and 29 healthy controls. APOM, LCN2, and QSOX1 showed high diagnostic accuracies for PE detection (AUC >0.9 and p&#xa0;<&#xa0;0.001, for all) as validated by MRM and ELISA. Our data demonstrate that three plasma biomarkers, identified by systematic proteomic profiling, present a possibility for the assessment of PE, independent of the clinical characteristics of pregnant women.

Humans