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PubMed2025

Unlocking Zeptomolar Single-Molecule Detection by Synergizing Digital Microfluidics and Digital CRISPR.

Accurate diagnosis relies on the highly sensitive and quantitative detection of multiple immune-related biomarkers. However, current detection methods still face significant limitations in sensitivity, specificity, and background signal control. Here, we introduce DDA (Dual-Digital immunoAssay), a fully automated, universal immunoassay platform that synergizes digital microfluidics with digital Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)-based amplification. This "dual-digital" strategy pushes the detection limit into the zeptomolar (zM) regime, enabling unprecedented sensitivity for single-molecule analysis. The DDA platform is built upon a digital microfluidic microwell array chip, integrating magnetic bead-based immunocapture with RNA-guided CRISPR/Cas13a signal amplification. This system enables a fully automated, "sample-in, answer-out" workflow. By systematically optimizing the entire process, DDA significantly reduces background noise and enhances detection sensitivity, achieving a limit of detection (LOD) down to 100 zM for key protein biomarkers. This represents a >100-fold improvement over leading commercial ultrasensitive assays. With single-molecule resolution and full automation, DDA provides a robust solution for the precise quantification of low-abundance immune biomarkers. As a proof-of-concept, we demonstrate its ability to accurately quantify key heart-failure-associated biomarkers, including NT-proBNP (LOD: 1 aM), IL-6 (LOD: 1.5 aM), and TNF-α (LOD: 2.5 aM), directly in complex serum samples. This platform holds great promise for automated multibiomarker screening and risk assessment, showcasing its powerful potential for the early diagnosis of major diseases such as cardiovascular diseases, cancers, neurodegenerative disorders, and infectious diseases.

Humans↗

PubMed2025

Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma.

BACKGROUND: One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and health of the patient. Nonetheless, it has been demonstrated that ubiquitination is crucial for the development and course of LSCC. Therefore, it is particularly important to identify biomarkers for ubiquitination-related genes (UbRGs) in LSCC. METHODS: Differentially expressed genes (DEGs) in the LSCC versus controls were obtained by differential expression analysis. Also, key modular genes associated with LSCC were obtained using weighted gene co-expression network analysis (WGCNA). Next, DEGs, key module genes, and UbRGs were taken to intersect to obtain candidate genes. And then machine algorithms were to screen potential biomarkers, further their diagnostic value were analyzed and validated. Then, therapeutic agents for biomarkers were predict. In addition, the regulatory networks of the biomarkers were mapped. The expression levels of biomarkers were detected in clinical samples using reverse transcription-quantitative PCR (RT-qPCR). RESULTS: A total of eight candidate genes were acquired by the overlap 1,911 DEGs, the key modular genes of WGCNA, and 1,393 UbRGs. A sum of four biomarkers (WDR54, KAT2B, NBEAL2 and LNX1) were identified by two machine learning, then these four biomarkers were validated in GSE127165 and the expression trend was consistent with TCGA-LSCC, they were recorded as biomarkers. Moreover, the accuracy of the biomarkers in predicting clinical aspects of LSCC was confirmed by the receiver operating characteristic (ROC) curves. Subsequently, cancers such as malignant neoplasms, colorectal cancers, tumors, and primary malignant neoplasms were significantly associated with the biomarkers, which further suggests that these four biomarkers were strongly associated with cancer. Meanwhile, the drugs garcinol, cocaine, and triazolam, among others, used for LSCC treatment were predicted. Finally, transcription factors (TFs) (BRD4, MYC, AR, and CTCF) were predicted to regulate the biomarkers. RT-qPCR assays illustrated that the expression trends of KAT2B, LNX1 and NBEAL2 remained consistent with the dataset. CONCLUSION: The identification of four biomarkers (WDR54, KAT2B, NBEAL2 and LNX1) associated with UbRGs could ultimately serve as a predictive clinical diagnosis of LSCC and provide insight into the molecular mechanisms of LSCC.

Humans↗

PubMed2025

Methylated ARHGAP40 in renal cell carcinoma associated with tumor necrosis and grade: a potential biomarker for non-invasive early detection.

This study investigated the expression, methylation patterns, and clinicopathological implications of ARHGAP40 in renal cell carcinoma (RCC), the most common urinary malignancy. A total of 60 clear cell renal cell carcinomas (ccRCC), 30 papillary renal cell carcinomas (pRCC), 30 chromophobe renal cell carcinomas (chRCC), and 13 other RCC subtypes were enrolled. ARHGAP40 expression was analyzed in both RCC tissues and matched paracancerous normal tissues using immunohistochemistry (IHC). The methylation status of the ARHGAP40 promoter region was assessed in both normal and tumor samples by bisulfite sequencing PCR (BSP). Circulating tumor DNA (ctDNA) extracted from peripheral blood samples of RCC patients (20), patients with benign renal tumors (1), and healthy controls (14) was quantitatively analyzed for methylation using quantitative methylation-specific PCR (qMSP). ARHGAP40 expression was significantly downregulated in RCC compared to matched normal tissues (P < 0.001). This reduced expression correlated with tumor necrosis (P = 0.009) but showed no significant association with age, gender, tumor location, tumor diameter, TNM stage, or vascular invasion. In the ccRCC subtype, ARHGAP40 expression exhibited a progressive decrease with larger tumor diameter (P = 0.045), advancing histological grade (P = 0.032), and tumor necrosis (P = 0.011). The methylation status of ARHGAP40 was consistent with its expression level in both tumor and adjacent normal tissues. Methylated ARHGAP40 DNA was detectable only in RCC patient ctDNA samples. ARHGAP40 is epigenetically silenced in RCC through methylation-mediated downregulation, which correlates with tumor necrosis and grade. The detection of methylated ARHGAP40 in ctDNA holds promise as a potential biomarker for early RCC diagnosis.

Humans↗

PubMed2024

Identification of a Proteomic Signature for Predicting Immunotherapy Response in Patients With Metastatic Non-Small Cell Lung Cancer.

Immunotherapy has improved survival rates in patients with cancer, but identifying those who will respond to treatment remains a challenge. Advances in proteomic technologies have enabled the identification and quantification of nearly all expressed proteins in a single experiment. Integrating mass spectrometry with high-throughput technologies has facilitated comprehensive analysis of the plasma proteome in cancer, facilitating early diagnosis and personalized treatment. In this context, our study aimed to investigate the predictive and prognostic value of plasma proteome analysis using the SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra) strategy in newly diagnosed patients with non-small cell lung cancer (NSCLC) receiving pembrolizumab therapy. We enrolled 64 newly diagnosed patients with advanced NSCLC treated with pembrolizumab. Blood samples were collected from all patients before and during therapy. A total of 171 blood samples were analyzed using the SWATH-MS strategy. Plasma protein expression in metastatic NSCLC patients prior to receiving pembrolizumab was analyzed. A first cohort (discovery cohort) was employed to identify a proteomic signature predicting immunotherapy response. Thus, 324 differentially expressed proteins between responder and non-responder patients were identified. In addition, we developed a predictive model and found a combination of seven proteins, including ATG9A, DCDC2, HPS5, FIL1L, LZTL1, PGTA, and SPTN2, with stronger predictive value than PD-L1 expression alone. Additionally, survival analyses showed an association between the levels of ATG9A, DCDC2, SPTN2 and HPS5 with progression-free survival (PFS) and/or overall survival (OS). Our findings highlight the potential of proteomic technologies to detect predictive biomarkers in blood samples from NSCLC patients, emphasizing the correlation between immunotherapy response and the idenfied protein set.

Humans↗

PubMed2026

Advances in Single-Molecule Immunoassay: From Counting Strategies to CRISPR-Enhanced Biosensing.

Single-molecule immunoassays (SMIs) overcome the sensitivity limitations of conventional bulk measurements by enabling a paradigm shift from analog to digital signal readouts, thereby facilitating highly sensitive quantification of ultra-low-abundance biomarkers for precision diagnostics. This review provides a systematic overview of recent advances in SMI technologies and the conceptual framework underlying their evolution. First, discretization strategies for single-molecule counting are classified into hard discretization, based on physical confinement, and soft discretization, based on spatiotemporal isolation, within heterogeneous and homogeneous assay systems, respectively. The fundamental mechanisms by which these strategies mitigate diffusion limitations and enhance signal-to-noise ratios are discussed. Second, the integration of SMIs with CRISPR-based diagnostic systems (CRISPR-dx) is examined, with particular emphasis on their complementary roles in target recognition and signal amplification. Finally, recent applications of SMIs in the diagnosis of oncological, neurological, infectious, and cardiovascular diseases are summarized, along with a critical discussion of current engineering challenges and future directions toward clinical translation.

Immunoassay↗

PubMed2025

Mutations in the transcriptional regulator MAB_2885 confer tedizolid and linezolid resistance through the MmpS-MmpL efflux pump MAB_2302-MAB_2303 in Mycobacterium abscessus.

Mycobacterium abscessus (MAB) is a clinically significant multidrug-resistant (MDR) pathogen, particularly implicated in pulmonary infections among cystic fibrosis (CF) patients. Tedizolid (TZD), an oxazolidinone-class antibacterial drug, has been recommended as an alternative treatment for MAB-infected patients who are intolerant to or whose isolate is resistant to first-line drugs including linezolid (LZD). To investigate the TZD resistance mechanisms in MAB, we isolated 23 TZD-resistant MAB mutants and performed whole-genome sequencing (WGS) to identify resistance-associated genes. Frequent mutations were identified in MAB_2885, encoding a putative TetR transcriptional regulator, and MAB_2303, encoding a putative mycobacterial membrane protein large (MmpL). Drug susceptibility testing confirmed that MAB_2885 mutations contribute to both TZD and LZD resistance in MAB. RNA-seq analysis revealed that restoring wild-type MAB_2885 in mutants downregulated the MAB_2302-MAB_2303. Electrophoretic mobility shift assay (EMSA) showed the MAB_2885 protein binds to its target sequence upstream of MAB_2302-MAB_2303, further confirming their regulatory relationship. The W91R mutation in the MAB_2885 protein was found to impair its DNA-binding activity compared to the wild-type. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis confirmed that MAB_2302-MAB_2303 functions as a TZD efflux pump. Additionally, overexpression of MAB_2885 in M. abscessus subsp. bolletii and M. abscessus subsp. massiliense also increased their TZD susceptibility and downregulated their respective MmpS-MmpL orthologs. Overall, our study demonstrates that mutations in MAB_ 2885 contribute to TZD and LZD resistance by disrupting the negative regulation of the downstream MAB_2302-MAB_2303, which functions as a direct efflux pump for TZD. These findings provide new insights into oxazolidinone resistance mechanisms in MAB and identify potential biomarkers for detecting drug resistance.

Mycobacterium abscessus↗

PubMed2026

A study on the mechanism of action of B7H4 in HER2-positive gastric cancer and its sensitivity to trastuzumab therapy.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2)-positive gastric cancer (GC) is characterized by high malignancy and a poor prognosis. Trastuzumab is the first-line targeted therapy for this disease, but the frequent development of primary and acquired resistance severely compromises treatment efficacy and impedes improvements in patient outcomes. B7H4 is a critical negative immune checkpoint molecule that has been demonstrated to contribute to tumor progression and targeted therapy resistance in multiple cancers. However, the specific mechanisms by which B7H4 regulates sensitivity to trastuzumab in HER2-positive GC remain unclear. This study aims to investigate the expression and biological functions of B7H4 in HER2-positive GC, elucidate the molecular mechanisms underlying B7H4-mediated trastuzumab resistance, and thereby provide a theoretical basis for targeted resistance intervention and therapeutic optimization for this malignancy. METHODS: The study began with an analysis of The Cancer Genome Atlas (TCGA) database and immunohistochemical staining of tissue sections to assess the expression levels of B7H4 and HER2, as well as the correlation between their expression. Furthermore, the correlation between B7H4 expression and various pathological parameters in patients with HER2-positive GC was analysed. Western blot analysis was used to screen for co-expressing cells, and short hairpin RNA (shRNA) was employed to knockdown B7H4. The effects of B7H4 knockdown on the proliferation, migration and invasion of HER2-positive GC cells were assessed using colony formation assays, Cell Counting Kit-8 (CCK-8) assays, cell scratch assays and Transwell assays, respectively. RNA sequencing (RNA-Seq) was utilised to analyse the biological processes and signalling pathways regulated by B7H4 and to detect relevant biomarkers. Colony formation assays and CCK-8 assays were employed to evaluate the therapeutic sensitivity of trastuzumab to HER2-positive GC cells following B7H4 knockdown. RESULTS: Analysis of the TCGA database and immunohistochemical staining of tissue sections revealed that B7H4 and HER2 were co-expressed in GC tissues, and their expression levels were positively correlated. Clinical correlation analysis revealed that B7H4 expression was significantly associated with tumor size, grade, depth of invasion, lymph node metastasis, distant metastasis, vascular invasion and nerve invasion in HER2-positive GC patients. Western blot analysis demonstrated co-expression of B7H4 and HER2 in NCI-N87 cells. Knockdown of B7H4 resulted in varying degrees of inhibition of proliferation, migration and invasion in NCI-N87 cells. RNA-seq results indicated that B7H4 knockdown affected biological processes such as cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT), and was significantly associated with the nuclear factor κB (NF-κB) signalling pathway. Knockdown of B7H4 significantly enhanced the sensitivity of HER2-positive GC cells to trastuzumab. CONCLUSIONS: B7H4 is highly expressed in HER2-positive GC and is associated with poor prognosis. B7H4 promotes tumor cell proliferation, migration and invasion by activating the NF-κB signalling pathway and driving the EMT process. B7H4 expression influences the sensitivity of HER2-positive GC cells to trastuzumab; inhibition of B7H4 significantly enhances the antitumor efficacy of trastuzumab.

B7H4↗

PubMed2026

Novel serum small extracellular vesicle miRNAs with multi-target RCA-CRISPR sensor for liver cancer detection.

BACKGROUND: Detecting liver cancer (LC) remains a significant challenge in clinical practice. Small extracellular vesicle (sEV) miRNAs show promise as non-invasive biomarkers for LC detection, yet their diagnostic potential remains largely unexplored. This study aimed to identify specific sEV miRNA signatures for LC detection and develop a novel synchronized multi-miRNA detection platform to enhance diagnostic efficiency and sensitivity. METHODS: High-throughput sequencing was conducted across four distinct cohorts: normal controls (NC), hepatitis B virus (HBV) patients, liver cirrhosis patients, and LC patients. This sequencing process identified miRNAs with differential expression, followed by RT-qPCR validation in serum sEV miRNAs from LC patients and NC. An innovative detection method, RCA-CRISPR, was introduced, combining rolling circle amplification (RCA) with CRISPR/Cas12a (RCA-CRISPR) for quick and sensitive miRNAs detection. RESULTS: Sequencing results showed a consistent elevation of hsa-miR-203b-5p, hsa-miR-4661-5p, and hsa-miR-219a-2-3p across all cohorts. RT-qPCR validations confirmed significant upregulation of these miRNAs in serum sEVs from LC patients, and the combined three-miRNA panel exhibited high diagnostic accuracy (p = 0.0003; AUC = 0.81). The RCA-CRISPR method demonstrated a detection limit of 3.12 pM for simultaneous multi-target miRNA detection, highlighting its exceptional sensitivity. CONCLUSIONS: Our study identifies hsa-miR-203b-5p, hsa-miR-4661-5p, and hsa-miR-219a-2-3p as promising sEV miRNA biomarkers for LC detection. The developed RCA-CRISPR sensor provides a robust tool for multi-miRNA analysis, potentially advancing non-invasive LC diagnostics. Future validation in larger, prospectively collected cohorts is essential to establish the clinical utility and performance of this biomarker panel and RCA-CRISPR sensor.

MicroRNAs↗

PubMed2026

Low-Cost Nucleic-Acid-Based Radial Flow Assay for the Detection of GSTP1 Promoter DNA Methylation in Prostate Cancer.

DNA methylation of the glutathione S-transferase pi 1 (GSTP1) promoter is a widely studied epigenetic biomarker for prostate cancer; however, its direct detection in genomic DNA remains analytically challenging without complex chemical or amplification-based workflows. Here, we report a nucleic acid-based radial flow assay (NABRFA) that enables visual and pattern-based detection of gene-specific DNA methylation using gold nanoparticle (AuNP)-conjugated oligonucleotide probes. Thiol-modified single-stranded DNA probes targeting the GSTP1 CpG island (5'ThG) were conjugated to AuNPs to form stable probe-nanoparticle constructs that retain colloidal stability under high ionic strength conditions (0.5 M NaCl). Upon hybridization with methylation-protected GSTP1 DNA, the resulting AuNP-DNA complexes exhibit hybridization-dependent modulation of transport and retention on a porous nylon membrane, generating characteristic concentric radial patterns. These patterns arise from spatial separation between retained hybridized complexes and outwardly transported unbound probe-functionalized nanoparticles, enabling direct visual discrimination of target presence. The assay demonstrated concentration-dependent pattern evolution, with visual detection achievable down to 1 ng of target DNA and an analytically determined limit of detection of approximately 32 ng, based on image-derived gray value analysis. The human prostate cancer cell line LNCaP, known for GSTP1 promoter hypermethylation, was used as the test model for assay validation. Comparative analysis using methyl-sensitive restriction enzyme-treated native genomic DNA from the human osteosarcoma MG-63 cell line (non-prostate cancer, GSTP1 methylation-negative control) and the human lung fibroblast WI-38 cell line (non-cancerous, GSTP1 methylation-negative control) confirmed assay specificity. By coupling sequence-specific hybridization with transport-mediated nanoparticle pattern formation, NABRFA provides a label-free and conversion-free analytical strategy for detection of methylation-protected GSTP1 DNA using minimal instrumentation. This work establishes a proof-of-concept membrane-based, transport-driven sensing approach for epigenetic biomarker detection and highlights its potential for integration into simplified molecular diagnostic workflows.

Humans↗

PubMed2026

Validation and refinement of a biomarker panel for frailty assessment and prediction of muscle weakness in older adults.

Frailty is a complex geriatric syndrome characterized by age-related declines in physiological function and cognitive reserve. To promote early prevention and intervention, minimally invasive and objective biomarkers that can detect frailty progression are required. We aimed to identify biomarkers associated with frailty progression and to elucidate their relevance to the Japanese version of the Cardiovascular Health Study (J-CHS) criteria, consist of five components (unintentional weight loss, self-reported exhaustion, muscle weakness, slow walking speed, and low physical activity). A total of 168 individuals (61 robust, 25 pre-frail, and 82 frail) enrolled in the NCGG (National Center for Geriatrics and Gerontology) Biobank were analyzed. Clinical information, blood-test data, aging-related factors, and gene-expression data were integrated for the analysis. First, linear regression identified one clinical factor, five aging-related factors, and 251 gene-expression factors associated with frailty. Subsequent logistic regression analyses examining each J-CHS components highlighted six candidate biomarkers. Cross-validation further suggested that three of these biomarkers-SMI, apelin, and GDF15-may represent potential biomarkers. Finally, retrospective and prospective analyses further demonstrated that those biomarkers were predictive of future muscle weakness, yielding a concordance index of 0.70. In conclusion, we validated and refined a biomarker panel consisting of SMI, apelin, and GDF15 that is associated with frailty, particularly muscle weakness (a major J-CHS component). These biomarkers may be useful for frailty assessment. Longitudinal analyses further suggested that they may be associated with the future development of muscle weakness in initially robust older adults, although validation in larger prospective cohorts is warranted.

Journal Article↗

PubMed2026

Translating single-cell RNA sequencing into monocyte direct leukocyte subpopulation-transcript abundance assay ratio-based biomarkers (IFI27/PSAP or IFI27/CTSS) for clinical detection of viral infection.

A rapid method for triaging febrile patients by aetiology (e.g., viral or bacterial infection) using gene expression in peripheral blood (PB) is an intensively researched area. However, gene expression in blood represents a composite sum of gene expression of all the component cell types present in the sample. As a result, numerous genes are measured in most proposed signatures. Herein, we propose a simple ratio-based biomarker (RBB) called direct leukocyte subpopulation-transcript abundance assay (DIRECT LS-TA) that recapitulates gene expressions of a single cell type in PB (i.e., monocytes). Based on single-cell RNA sequencing (scRNAseq) data and bulk expression data, IFI27 and SIGLEC1 are found as interferon-stimulated genes (ISGs) predominantly expressed by monocytes. The DIRECT LS-TA method can use a simple ratio of two genes measured in PB as an RBB to represent the target gene expression in monocytes without the need for monocyte purification. Both scRNAseq and bulk RNA sequencing datasets were used to evaluate the correlation between ISG expression in monocytes and PB, with a particular focus on monocyte expression of IFI27. An iceberg plot of bulk transcriptome data was used to identify genes that were predominantly expressed by monocytes in PB. DIRECT LS-TA RBBs of the three genes (IFI27, IFI44L and SIGLEC1) were evaluated by group-wise comparison, receiver operating characteristic and meta-analysis. In addition, the conventional interferon (IFN) score was evaluated for comparison of diagnostic performance. In viral infection datasets, DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) was most intensely activated (p value by t test <1e-9) and had the best area under the curve (0.94) among the three potential monocyte ISGs analysed. DIRECT LS-TA SIGLEC1 was also another monocyte biomarker but showed a lower activation (p<9e-5). IFI27/PSAP showed better diagnostic performance than the conventional IFN score. On the other hand, IFI44L was not a predominant monocyte expression gene. DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) measured in PB was the best biomarker of viral infection and IFN activation among ISGs predominantly expressed by monocytes. It performed even better than the conventional IFN score which required quantification of eight genes. The results suggest that DIRECT LS-TA of IFI27 is a monocyte-informative biomarker which is easy to determine in PB without the need for cell sorting.

Humans↗

PubMed2026

Potential evaluation of SULT1A3 as an early diagnostic marker for nasopharyngeal carcinoma: a study based on serum proteomics screening and ELISA validation.

BACKGROUND: Nasopharyngeal carcinoma (NPC) represents a highly prevalent and aggressive malignancy endemic to Southeast Asia. Early and accurate diagnosis is critical to improving survival outcomes; however, the absence of robust, stage-specific biomarkers remains a key obstacle to clinical implementation of early screening strategies. METHODS: We performed untargeted serum proteomic profiling using mass spectrometry in 15 treatment-naïve early-stage NPC patients and 15 VCA-IgA-positive healthy controls. Bioinformatics analyses were conducted to identify differentially expressed proteins (DEPs). Machine learning (random forest combined with recursive feature elimination) was employed to prioritize candidate biomarkers, which were subsequently verified using enzyme-linked immunosorbent assay (ELISA) in independent sample cohorts. RESULTS: In total, 1,428 serum proteins were identified, among which 1,410 were reliably quantified. We observed 31 upregulated and 189 downregulated proteins in NPC patients relative to controls. Spearman correlation analysis revealed significant associations: LTA4H (leukotriene A4 hydrolase) levels correlated with serum cell infiltration (r = 0.383, p = 0.032) and CD8 + T-cell abundance (r = 0.408, p = 0.021); both SULT1A3 (sulfotransferase family 1 A member 3) and FGL1 (fibrinogen-like protein 1) levels were positively associated with M1 macrophage infiltration (r = 0.510, p = 0.003 and r = 0.430, p = 0.015, respectively). In a preliminary validation cohort (n = 80), ELISA yielded AUC values of 0.631 (95% CI: 0.515-0.736, p = 0.04) for LTA4H, 0.787 (95% CI: 0.681-0.871, p < 0.001) for SULT1A3, and 0.688 (95% CI: 0.575-0.787, p = 0.002) for FGL1. In large-scale independent validation, SULT1A3 achieved an AUC of 0.826 (95% CI: 0.766-0.876; sensitivity = 78.89%, specificity = 75.47%) in cohort 1 (n = 196) and 0.796 (95% CI: 0.723-0.857; sensitivity = 76.67%, specificity = 76.67%) in cohort 2 (n = 150). CONCLUSIONS: Through an integrated workflow combining proteomic screening, machine learning prioritization, and multi-stage ELISA validation, we identified SULT1A3 as a candidate serum-based biomarker for early detection of NPC. Preliminary findings suggest that SULT1A3 may have potential utility in clinical screening, though further validation in independent, multi‑center cohorts is required.

Humans↗

PubMed2026

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 688 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

DNA methylation↗

PubMed2025

Machine Learning-Based Identification of Survival-Associated CpG Biomarkers in Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is an exceptionally aggressive cancer with a 5-year survival rate of less than 10%, driven by late-stage diagnosis, limited treatment options, and a lack of reliable biomarkers for early detection and prognosis. In this study, we integrated DNA methylation data from TCGA and ICGC cohorts, categorizing samples based on survival time, and identified 684 differentially methylated CpG sites, along with 224 CpG biomarkers significantly associated with patient survival through statistical and machine learning-based analyses. We developed a random forest model to predict patient survival, achieving 85.2% accuracy for short-survival patients and 70.0% for long-survival patients in the validation set. External dataset validation further confirmed the model's robustness and accuracy. De novo motif analysis of genomic regions surrounding the 224 CpG biomarkers identified TWIST1 and FOXA2 as key transcriptional regulators enriched in survival-associated CpG sites, linking their activity to patient survival outcomes. Collectively, our findings highlight valuable epigenetic biomarkers and provide a predictive model to assess PDAC risk levels post-surgery, offering the potential for improved patient stratification and personalized therapeutic strategies.

Journal Article↗

PubMed2025

Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with a largely unknown duration and pathophysiology of the pre-diagnostic phase, especially for the common non-monogenic form. METHODS: We leveraged the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with up to 30 years of follow-up to identify incident ALS cases across five European countries. Pre-diagnostic plasma samples from initially healthy participants underwent high-throughput proteomic profiling (7,285 protein markers, SomaScan). Cox proportional hazards models based on 4,567 participants (including 172 incident ALS cases) were used to identify protein biomarkers associated with future ALS diagnosis. Top results were indirectly validated in two independent case-control studies of prevalent ALS (n=417 ALS, 852 controls). Functional annotation included cross-disease comparisons, gene set and tissue enrichment testing, organ-specific proteomic clocks, and the application of large-language models (LLM). FINDINGS: Five proteins (SECTM1, CA3, THAP4, KLHL41, SLC26A7) were identified as significant pre-diagnostic ALS biomarkers (FDR=0.05), detectable approximately two decades before diagnosis. Of these, all except SECTM1 were indirectly validated in independent cohorts of prevalent ALS cases, supporting their clinical significance. Additionally, 22 nominally significant (p<0.05) pre-diagnostic biomarkers were FDR-significant in prevalent ALS with consistent effect directions. Cross-disease comparisons with pre-diagnostic Parkinson's and Alzheimer's disease suggested a largely specific pre-diagnostic ALS biomarker signature. Gene ontology and tissue enrichment highlighted early involvement of immune, muscle, metabolic, and digestive processes. Furthermore, analyses of proteomic clocks revealed accelerated aging in brain-cognition, immune, and muscle tissues before clinical diagnosis. Druggability and LLM analyses revealed possible therapeutic targets and novel strategies, emphasizing translational relevance. INTERPRETATION: Our study provides first evidence of ultra-early molecular changes in common ALS up to two decades prior to clinical onset, mainly affecting immune, muscle, metabolic, digestive, and cognitive systems. Our study nominates several compelling candidates for risk stratification studies and novel therapeutic targets for early intervention. FUNDING: Clinical Research in ALS and Related Disorders for Therapeutic Development (CreATe) Consortium, Cure Alzheimer's Fund, Michael J Fox Foundation, Interdisciplinary Centre for Clinical Research, University Münster.

Journal Article↗

PubMed2025

Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement.

Journal Article↗

PubMed2026

Clinical Validation of a Multiplex Urine Biomarker Assay for Surveillance of Recurrent Bladder Cancer.

PURPOSE: More than 50% of patients with non-muscle-invasive bladder cancer experience recurrence, requiring lifelong surveillance with repeated cystoscopy. Given the invasive nature and cost of cystoscopy, accurate noninvasive tools are needed to support risk-adapted monitoring. We evaluated the ability of Oncuria-Monitor, a multiplex urine biomarker assay, to detect recurrent bladder cancer during surveillance. PATIENTS AND METHODS: Between February 2017 and August 2020, six medical centers in the United States and Japan prospectively enrolled 300 patients with a history of bladder cancer, generating 1,248 serial urine samples. Participants were divided into training and validation cohorts. At each surveillance visit over 2 years, urine samples were analyzed in a blinded manner using Oncuria-Monitor and BladderChek, alongside urine cytology. Test performance was compared with cystoscopy and histopathology-confirmed recurrence. RESULTS: Recurrent bladder cancer was identified in 31% (93/300) of participants, with 143 total recurrences during follow-up, including 90 tumors in the validation cohort. In the validation cohort, Oncuria-Monitor achieved a sensitivity of 85.6% [95% confidence interval (CI), 78.1%-92.2%] and a negative predictive value (NPV) of 93% (95% CI, 89.2%-96.4%). In comparison, BladderChek demonstrated a sensitivity of 20.0% and an NPV of 88%, whereas urine cytology showed a sensitivity of 36.9% and an NPV of 91.6%. The number needed to evaluate to detect one recurrence was 3 for both cystoscopy and Oncuria-Monitor, compared with 15 for BladderChek and 8 for cytology. CONCLUSIONS: In this large prospective longitudinal study, Oncuria-Monitor demonstrated clinically actionable performance, enabling a rule-out strategy that could safely reduce cystoscopy in approximately 25% of surveillance visits. These findings support a paradigm shift toward biomarker-guided, risk-adapted surveillance in bladder cancer that reduces unnecessary invasive procedures while maintaining oncologic safety.

Humans↗

PubMed2026

ceRNA network of lncRNAs and mRNAs in OSF-to-OSCC progression: Diagnostic biomarkers and functional pathways.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic potentially malignant disorder that can progress to oral squamous cell carcinoma (OSCC). Although dysregulated non-coding RNAs have been implicated in oral carcinogenesis, the competing endogenous RNA (ceRNA)-mediated regulatory mechanisms underlying OSF-to-OSCC progression remain poorly understood. This study aimed to identify candidate regulatory molecules and construct a putative lncRNA-miRNA-mRNA network associated with malignant transformation. METHODS: Publicly available microarray datasets (GSE117973 and GSE125866) were analyzed to identify differentially expressed genes between OSF and OSCC. Differentially expressed transcripts were classified into mRNAs and lncRNAs based on public transcript annotations. Highly correlated lncRNA-mRNA pairs were identified using Pearson correlation analysis and integrated with multiMiR-supported miRNA-mRNA interactions obtained from public databases to construct a putative ceRNA regulatory network. Functional characterization focused on apoptosis, epithelial-mesenchymal transition (EMT), and immune checkpoint-related pathways. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance, and selected biomarkers were externally validated using The Cancer Genome Atlas (TCGA) OSCC cohort. RESULTS: Integrated transcriptomic analysis identified several dysregulated mRNAs and lncRNAs associated with OSF-to-OSCC progression. Network analysis highlighted TBC1D3B, RREB1, TEAD3, SREBF1, TMEM41B, FOXK2, and KIAA1958 as prominent hub genes within the putative regulatory network. Functional analyses demonstrated significant associations with apoptosis-, EMT-, and immune checkpoint-related genes, suggesting potential involvement in multiple biological processes contributing to malignant transformation. Several hub genes exhibited strong diagnostic performance, with ROC analysis yielding AUC values ranging from 0.891 to 1.000, indicating excellent discrimination between OSF and OSCC samples. External validation using TCGA further supported the relevance of the identified biomarkers in OSCC. CONCLUSIONS: This study provides a comprehensive transcriptomic framework describing putative lncRNA-miRNA-mRNA regulatory interactions associated with OSF progression to OSCC. The identified hub genes and regulatory networks represent candidate biomarkers for early detection and provide a foundation for future mechanistic and experimental validation. As the proposed ceRNA interactions are computationally inferred, further biological validation is required before clinical application.

RNA, Long Noncoding↗