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PubMed2026

ADAM10's combined influence on the diagnostic usefulness of IL 22, IL 10, IL-17 A, and IL-17D in autism spectrum disorders: Predicted role on gut leakiness as co-morbidity.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with increasing global prevalence but a lack of reliable diagnostic biomarkers. Emerging evidence suggests that immune dysregulation, gut-brain axis dysfunction, and increased intestinal permeability play key roles in ASD pathophysiology. This study investigated the combined diagnostic value of ADAM10 and cytokines (IL-10, IL-22, IL-17 A, and IL-17D). Multivariable logistic regression produces an improved ROC curve that improves diagnostic accuracy over individual markers by combining numerous predictors into a single risk score (linear predictor). The technique, which frequently raises individual marker AUCs, entails modelling a binary result, calculating the probability, and visualizing ROC based on the projected probabilities. In this case-control study, plasma levels of ADAM10, IL-10, IL-22, IL-17 A, and IL-17D were measured in 37 male children with ASD and 37 age-matched controls. Group comparisons, correlation analyses, and receiver operating characteristic (ROC) curve analyses, including combined ROC models, were performed. ADAM10, IL-22, and IL-17 A levels were significantly reduced in children with ASD compared to controls, whereas IL-10 and IL-17D showed no significant differences. ADAM10, IL-17 A, and IL-22 demonstrated good diagnostic performance, with AUC values of 0.886, 0.855, and 0.812, respectively. In contrast, IL-10 and IL-17D showed poor discriminatory ability, with AUC values of 0.524 and 0.599, respectively. Combined ROC analysis markedly improved diagnostic accuracy, with all panels including ADAM10 achieving AUC values above 0.90, and some reaching as high as 0.988, with high sensitivity and specificity. The combination of ADAM10 with selected cytokines significantly enhances diagnostic performance compared to individual markers, supporting a link between immune dysregulation, barrier dysfunction, and gut permeability in ASD.

Humans↗

PubMed2026

From prediction to mechanism: Explainable AI uncovers plasma and CSF proteomic signatures of Alzheimer's disease.

Alzheimer's disease (AD) plasma and cerebrospinal fluid (CSF) proteomics can distinguish AD from cognitively normal controls, but the generalizability of machine learning performance and the recurrence of biological signals across datasets require cautious interpretation. We developed an explainable artificial intelligence framework spanning two fluids and four ADNI proteomic datasets, covering 2082 modality specific samples, all analysed internally within ADNI. Phase 1 analysed plasma using a 119 analyte NULISA and targeted UPENN panel (n = 727; 216 CE, 511 controls). Phase 2 extended the analysis to CSF using SOMAscan7k, TMT-MS and targeted SET2, with Elecsys Aβ42, Aβ40, total tau and p-tau181 as anchor biomarkers. Only SOMAscan was subject-independent relative to Phase 1 plasma; TMT-MS and SET2 overlapped with Phase 1 for 96.0% and 97.7% of subjects and therefore are not independent replication cohorts. Under subject-level splits with fold internal preprocessing, we compared Elastic Net, Explainable Boosting Machines and gradient boosted trees with SHAP-based explanations. Among the candidate pipelines, we selected the pipeline with the highest held-out test ROC AUC for each platform; the selected values were 0.927 in plasma and 0.954-0.973 across the three CSF datasets. Because the same held out test performance was used for pipeline selection and headline reporting, these are optimistically selected single-holdout estimates, not unbiased estimates of generalizable or clinical performance. Explanations identified five recurring biological axes within ADNI: cholinergic (ACHE), tau/14-3-3 (YWHAG, YWHAZ, YWHAB, YWHAE), neuro-axonal (NEFL, NEFH), microglial/complement (CHIT1, SMOC1, CHI3L1, C7, CFH) and synaptic (NPTXR, NPTX2, DLG4, SYT5, VSNL1, ELAVL2). CSF analyses showed synaptic vesicle-cycle enrichment (q = 2 × 10-6), and CSF YWHAG correlated strongly with total tau (ρ = 0.87). Cross-fluid directional concordance was modest overall (54-57%) but increased to 73-80% among mapped analyte/protein rows reaching q < 0.05 in CSF. These findings provide hypothesis-generating, internally supported evidence within ADNI. Independent external cohorts with locked pipelines are required to evaluate generalizable performance and biological reproducibility; the overlapping TMT-MS and SET2 analyses should not be interpreted as independent replication.

Alzheimer Disease↗

PubMed2026

Soluble CD163 as a Non-Invasive Biomarker in Autoimmune Nephrological and Rheumatological Diseases.

Autoimmune nephrological and rheumatological diseases involve macrophage-driven inflammation, yet disease activity is often assessed using invasive or non-specific measures. Soluble CD163 (sCD163), released from activated monocytes and macrophages, is emerging as a biomarker of macrophage-mediated inflammation in these conditions. This narrative review summarizes current evidence on the diagnostic, prognostic, and disease-monitoring potential of sCD163 measured in blood, urine, and synovial fluid in autoimmune nephrological and rheumatological diseases. This review is based on a narrative analysis of selected publications investigating the clinical utility of sCD163 in autoimmune kidney and rheumatic diseases, with emphasis on correlations with disease activity, histopathological findings, and clinical outcomes. Urinary sCD163 shows excellent diagnostic accuracy for active lupus nephritis (area under the receiver operating characteristic [AUROC] 0.89-0.998), correlates with histological activity index (but not chronicity), and distinguishes ongoing inflammation from chronic damage during treatment. In IgA nephropathy, it predicts remission failure and greater benefit from corticosteroids. In ANCA-associated vasculitis, it identifies active renal involvement (AUROC 0.95 in multicenter cohorts). In rheumatoid arthritis (RA), serum sCD163 correlates with early disease activity, predicts radiographic progression, and detects subclinical macrophage activation in remission. In spondylarthritis, synovial fluid sCD163 reflects a disease-specific M2-polarized macrophage phenotype distinct from RA. Utility is compartmentalized: urinary levels indicate intrarenal macrophage activation, synovial fluid local joint inflammation, and serum systemic activation. sCD163 is a promising macrophage-specific biomarker across autoimmune diseases, but its compartmentalized nature requires context-specific measurement. Before clinical implementation, assay standardization, multicenter validation, and interventional trials showing the benefit of sCD163-guided management are needed.

Humans↗

PubMed2026

Characteristics of p53 and Smad4 immunohistochemistry in pancreatic ductal adenocarcinoma and validation by next-generation sequencing.

BACKGROUND: Mutations in four major driver genes -KRAS, CDKN2A, TP53, and SMAD4- are central to the pathogenesis of pancreatic ductal adenocarcinoma (PDAC) and critically inform diagnosis, therapeutic decision-making, and prognostic assessment. Although next-generation sequencing (NGS) is widely regarded as the gold standard for detecting these mutations, its clinical application is often limited by suboptimal analytical efficiency and substantial economic cost. Among these genes, immunohistochemical (IHC) staining for the proteins encoded by TP53 and SMAD4 has been extensively adopted in routine pathology practice. However, standardized IHC pattern classification schemes and rigorous validation of their predictive accuracy for underlying genomic alterations remain lacking in PDAC. METHODS: We retrospectively enrolled 63 PDAC patients and systematically characterized the typical IHC expression patterns of p53 and Smad4. Targeted NGS was subsequently performed on all available tumor specimens, and the resulting mutational profiles were correlated with corresponding IHC findings. Diagnostic performance including sensitivity, specificity and accuracy of p53 IHC for predicting TP53 mutations and of Smad4 IHC for predicting SMAD4 mutations was rigorously evaluated. RESULTS: Among the four canonical driver genes, co-occurring double- or triple-gene mutations were prevalent; within TP53 and SMAD4, missense mutations constituted the most frequent variant type. Using NGS as the reference standard, we validated the diagnostic utility of a three-tiered p53 IHC classification system, particularly in fine-needle biopsy (FNB) specimens. Furthermore, we proposed a novel, refined Smad4 IHC pattern classification that incorporates an "intermediate" category, thereby expanding upon conventional binary interpretation. This new scheme achieved markedly improved mutation prediction accuracy (0.76) compared with traditional approaches (0.57). CONCLUSION: Our study highlights the complementary diagnostic value of p53 and Smad4 IHC relative to molecular testing in PDAC, especially when tissue is limited, as commonly encountered in FNB specimens. The newly established Smad4 IHC classification system, which integrates an intermediate expression category into the conventional two-tier framework, demonstrates superior clinical utility and enhances predictive accuracy for SMAD4 genomic alterations.

Humans↗

PubMed2026

Exploratory proteomic and metabolomic profiling of pleural effusions identifies histone H4 and alanine as promising complementary markers for pleural tuberculosis.

The diagnosis of pleural tuberculosis (Pl-TB) remains challenging. Histopathological analysis and pathogen detection in pleural biopsies are informative but limited. We investigated differentially expressed proteins and metabolites in pleural effusions from patients with Pl-TB, malignancies, and other pathologies. A proteomic analysis of pooled pleural effusions identified 45 proteins exclusively detected or upregulated in Pl-TB samples, many linked to infectious processes. Conversely, 18 proteins were uniquely found or upregulated in malignant pleural effusions, mainly associated with detoxification and hemostasis. To validate these findings, we employed targeted proteomics in individual samples. Eight proteins were validated: S100-A9, histone H4, insulin-like growth factor-binding protein 2, fibrinogen beta chain, ficolin-3, immunoglobulin heavy constant alpha 1, sulfhydryl oxidase 1, and histidine-rich glycoprotein. Additionally, NMR-based metabolomics identified 13 metabolites with differential abundance between Pl-TB and non-TB samples. Notably, N-acetyl-glycoprotein and the branched-chain amino acids, alanine and lysine differed between groups. Proteomic and metabolomic analyses revealed distinct molecular profiles between Pl-TB and non-TB patients, despite intra-group variability. To address this, we applied classification models. Histone H4 and alanine consistently emerged as discriminative features. Overall, this study provides novel insights into the molecular landscape of Pl-TB. The combined quantification of proteins and metabolites may improve differential diagnosis, although should be further validated in larger, independent cohorts before clinical application.

Humans↗

PubMed2026

Multi-omic biomarkers in cardiovascular disease: Discovery to clinical translation.

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, necessitating improved risk stratification and early detection strategies. Multiomics approaches that integrate genomics, transcriptomics, proteomics, metabolomics, and epigenomics offer unprecedented opportunities for biomarker discovery and precision medicine in cardiovascular care. This narrative review examines the current landscape of multiomics biomarkers for CVD, tracing their evolution from discovery to clinical translation. We synthesize evidence from recent studies evaluating the clinical utility of integrated omics approaches across diverse cardiovascular conditions, including atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation. High-throughput proteomics has identified novel protein signatures that enhance cardiovascular risk prediction beyond traditional risk factors. Metabolomics has revealed pathway-specific biomarkers, including trimethylamine N-oxide and lipid species, associated with atherogenesis. Polygenic risk scores derived from genomic data demonstrate incremental value when combined with clinical risk scores. Multiomics biomarkers represent a transformative approach to cardiovascular risk assessment and disease management.

Humans↗

PubMed2026

A machine learning-derived and functionally validated circadian rhythm signature predicts clinical outcomes and in silico drug sensitivity in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) displays considerable heterogeneity in clinical outcomes, highlighting the need for reliable prognostic biomarkers. While the aberrant expression of circadian rhythm-related genes has been implicated in cancer pathogenesis, its comprehensive role in CRC progression and predicted therapeutic vulnerabilities remains inadequately characterized. METHODS: Bulk and single-cell RNA-sequencing data were integrated from multiple CRC cohorts. A circadian rhythm signature (CRS) was developed through machine learning algorithms and validated for prognostic value. Comprehensive analyses of tumor microenvironment, genomic alterations, and drug sensitivity were performed. Furthermore, the biological function of the core gene, BHLHE40, was validated in CRC cell lines through CCK-8, EdU, and wound healing assays. RESULTS: Single-cell analysis demonstrated an elevated expression signature of circadian rhythm-related genes in dendritic cells. The optimized CRS, comprising 14 circadian rhythm-related genes, successfully categorized patients into high- and low-risk groups. Patients with a high CRS showed markedly poorer overall survival and computationally inferred immunosuppressive features, including reduced CD8+ T cell infiltration and increased M2 macrophage polarization. Genomic analysis revealed enhanced mutation burden in TP53 and alterations in RTK-RAS/WNT pathways. Notably, in vitro assays confirmed that BHLHE40 is significantly overexpressed in CRC cells. Knockdown of BHLHE40 markedly inhibited tumor cell proliferation and migration. Drug sensitivity profiling identified bexarotene and SMER-3 as potential therapeutic options for high-CRS patients. A nomogram integrating CRS with clinical parameters demonstrated superior predictive accuracy for 1-, 3-, and 5-year survival. CONCLUSIONS: The CRS represents a promising prognostic biomarker that reflects tumor immune status and genomic features, providing valuable insights for personalized treatment strategies in CRC.

Circadian rhythm↗

PubMed2026

Prognostic Value of Circulating Tumor DNA-Based Minimal Residual Disease for Recurrence-Free Survival in Resectable Gastric Cancer: A Systematic Review and Meta-Analysis with Serial Monitoring Analysis.

BACKGROUND: Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) is an emerging biomarker, but its utility in resectable gastric cancer remains incompletely characterized. METHODS: We conducted a systematic review and meta-analysis of eight studies (520 patients) to evaluate the prognostic value of ctDNA-based MRD for recurrence-free survival (RFS) and overall survival (OS) in resectable gastric cancer. RESULTS: In localized resectable gastric cancer (Stage I-III), the setting in which postoperative ctDNA most coherently represents true molecular residual disease after curative-intent surgery, postoperative ctDNA positivity was associated with diminished recurrence-free survival (RFS: HR 12.26, 95% CI 3.30-45.52) and overall survival (OS: HR 8.57, 95% CI 3.06-23.98). The test for subgroup differences between localized and mixed-stage cohorts was not statistically significant (P = 0.57), and the numerically higher HR in the localized subgroup should therefore not be interpreted as evidence of a quantitatively stronger prognostic effect. Postoperative ctDNA detection demonstrated substantially stronger prognostic value (overall RFS: HR 10.00, 95% CI 4.53-22.10) compared to preoperative assessment (HR 2.17, 95% CI 1.10-4.28). Both tumor-informed and tumor-agnostic strategies effectively stratified high-risk patients. However, these effect sizes should be interpreted cautiously given the small number of studies and substantial heterogeneity (I2 = 65-72%). Results from mixed-stage cohorts including Stage IV disease are supportive but should not be considered equivalent to localized-disease findings, as ctDNA in metastatic disease reflects persistent systemic burden rather than minimal residual disease in the postoperative sense. CONCLUSIONS: Postoperative ctDNA-based MRD shows a consistent adverse prognostic association in resectable gastric cancer, with localized disease (Stage I-III) representing the most biologically and clinically coherent setting for interpretation. However, the large pooled hazard ratios (HR 10.00-12.26) should be interpreted as a directionally consistent signal rather than precise quantitative estimates, given the small number of studies, wide confidence intervals, and substantial heterogeneity (I2 = 65-73%). This heterogeneity is largely driven by substantial variation in postoperative sampling timing (4 days to 16 weeks) and ctDNA assay characteristics (platform, sensitivity, coverage, variant filtering, and positivity thresholds), which require standardization in future studies. While ctDNA is prognostically valuable, its clinical utility remains unestablished. Prospective randomized trials are needed to determine whether ctDNA-guided strategies improve patient outcomes before routine clinical implementation can be recommended.

Humans↗

PubMed2026

PPRC1 is a prognostic biomarker and key regulator of mitochondrial oxidative phosphorylation in multiple myeloma.

BACKGROUND: Multiple myeloma (MM) remains an incurable haematological malignancy, underscoring the need for novel prognostic biomarkers and therapeutic targets. This study aimed to investigate the clinical and biological significance of peroxisome proliferator-activated receptor gamma coactivator-related protein 1 (PPRC1) in MM. METHODS: Expression and clinical data were obtained from public databases and an independent local cohort. Kaplan-Meier and Cox regression analyses were performed to evaluate prognostic value. Differential expression analysis, pathway enrichment analysis and single-cell RNA-seq data analysis were used to explore biological functions. PPRC1 was silenced in MM cell lines using siRNA to assess its effects on cell survival and oxidative phosphorylation. RESULTS: PPRC1 was significantly upregulated in MM and was associated with advanced disease stage and poor overall survival. Multivariate Cox analysis identified PPRC1 as an independent prognostic factor. A nomogram incorporating PPRC1 and revised-ISS improved survival prediction. Functional analyses revealed that PPRC1 was positively correlated with oxidative phosphorylation and oncogenic signalling pathways. A potential connection between PPRC1 expression and immune cell infiltration was observed. PPRC1 knockdown inhibited cell proliferation, induced cell cycle arrest and apoptosis and impaired oxidative phosphorylation in MM. CONCLUSIONS: PPRC1 acts as a prognostic biomarker and metabolic regulator in MM by sustaining mitochondrial oxidative phosphorylation. These findings highlight PPRC1 as a potential therapeutic target in MM.

Humans↗

PubMed2026

Effect of Semaglutide on the Inflammatory Biomarker High-Sensitivity CRP in Patients With Established Cardiovascular Disease and Overweight or Obesity in SELECT: A Prespecified Secondary Analysis.

BACKGROUND: In SELECT (Semaglutide Effects on Heart Disease and Stroke in Patients With Overweight or Obesity), among 17 604 patients with known atherosclerotic cardiovascular disease and overweight or obesity, but not diabetes, randomization to the glucagon-like peptide-1 receptor agonist semaglutide significantly reduced the primary outcome of major adverse cardiovascular events (MACE; cardiovascular death, nonfatal myocardial infarction, or nonfatal stroke) compared with placebo (mean follow-up, 39.8 months). Inflammation, as indicated by plasma hsCRP (high-sensitivity C-reactive protein) level, is implicated as a biomarker predicting cardiovascular risk in obesity and atherosclerotic cardiovascular disease. SELECT provides a unique opportunity to study the relationship among hsCRP, obesity, weight loss, and MACE outcomes in semaglutide versus placebo groups. METHODS: In this prespecified SELECT substudy, we evaluated whether baseline hsCRP levels predicted MACE risk and examined the relationships between changes in hsCRP levels and time to first MACE, baseline body weight, weight loss, and other clinical measures among treatment groups over time (104, 208 weeks) using multiple approaches, including Cox modeling. RESULTS: Baseline hsCRP level, which was similar in the semaglutide (geometric mean 1.96 mg/L) and placebo (geometric mean 1.91 mg/L) groups, was prognostic of future MACE. The risk of MACE increased across baseline hsCRP level <2, 2-<10, and ≥10 mg/L subgroups, including significant associations with cardiovascular and all-cause death. Semaglutide reduced hsCRP levels (-37.8% [104 weeks]) and risk of MACE across all hsCRP subgroups. Greater reductions in ratio-to-baseline hsCRP with semaglutide were associated with greater weight loss, but preceded major weight loss, evident by 4 and 8 weeks, and occurred among those without weight loss. Semaglutide-associated changes in hsCRP were independent of low-density lipoprotein cholesterol levels, statin use, and atherosclerotic cardiovascular disease entry criteria. hsCRP reductions were found to be prognostic of decreased risk of MACE. Modeling suggests decreased inflammation as contributing in part to the benefits seen with semaglutide in SELECT. CONCLUSIONS: In SELECT, hsCRP data at baseline and in response to treatment with semaglutide support inflammation as a potential prognostic factor associated with cardiovascular risk in these generally well-treated patients with atherosclerotic cardiovascular disease and overweight or obesity but not diabetes. These findings suggest that the MACE reduction observed with semaglutide versus placebo in SELECT may have partially involved a decrease in inflammation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03574597.

Humans↗

PubMed2026

Secretory Phospholipase A2 in Patients With Sickle Cell Disease Hospitalized for Vaso-Occlusive Pain Episodes.

BACKGROUND: Secretory phospholipase A2 (sPLA2) is an inflammatory mediator linked to acute chest syndrome (ACS) in sickle cell disease (SCD), a serious complication that can develop during an acute vaso-occlusive pain episode (VOE). Plasma sPLA2 levels have been proposed as a potential biomarker for predicting ACS onset. OBJECTIVE: To assess serial plasma sPLA2 levels in 105 pediatric patients hospitalized for SCD-VOE and determine the effects of arginine therapy compared to placebo. PROCEDURES: This is a pharmacokinetics/pharmacodynamics and randomized controlled trial of intravenous arginine therapy. Statistical methods included t-tests, chi-square, and correlation analyses. RESULTS: Mean age was 12.7 ± 3.7 years, 48% were male, 67% had Hb-SS, and 70% were prescribed hydroxyurea. Using a previously established SCD-specific cutoff of 48 ng/mL, presenting sPLA2 levels were elevated in 33% of patients (mean sPLA2 level 85.7 ± 32.9 ng/mL). SPLA2 elevation in the emergency department was more common in patients with ACS compared to those without ACS (64% vs. 30%; p = 0.02; negative predictive value of 94%). Peak sPLA2 levels were significantly higher in febrile (n = 34) versus afebrile patients (n = 71;101.0 ± 45.3 vs. 48.7 ± 35.4 ng/mL; p < 0.0001). Among subjects with elevated baseline sPLA2, arginine therapy resulted in a significant reduction in sPLA2 levels by discharge compared to placebo (-27.8 ± 38.1 ng/mL; p = 0.002; n = 23 vs. -15.0 ± 41.2 ng/mL; p = 0.23; n = 12). CONCLUSIONS: SPLA2 is an underutilized biomarker of ACS given accumulating evidence of its role. In particular, low levels may identify patients at low risk for ACS. Arginine therapy may modulate inflammation in patients with SCD during VOE and/or ACS. TRIAL REGISTRATION: ClinicalTrials.gov identifiers: NCT02447874; NCT02536170.

Humans↗

PubMed2026

Candidate biomarkers for early Giardia duodenalis infection revealed by time-resolved secretome proteomics.

Giardia duodenalis is a zoonotic protozoan parasite that causes giardiasis in humans and other mammals. Early diagnosis remains challenging because current diagnostic methods, including microscopy and enzyme-linked immunosorbent assays (ELISAs), primarily detect established infections. Consequently, a critical diagnostic gap exists during the early stage of infection within the first 2-48 h following exposure. To address this limitation, we characterized the proteins released by in vitro-cultured G. duodenalis trophozoites under serum-free conditions and evaluated their potential as early diagnostic biomarkers. Proteomic analysis of culture supernatants collected during early trophozoite incubation identified 31,773 peptides corresponding to 2504 quantifiable proteins. Temporal profiling showed distinct secretion patterns, including proteins that peaked during the early stage, progressively accumulated over time, or remained persistently abundant throughout the incubation period. Based on their secretion characteristics and predicted immunogenic properties, five candidate biomarkers were selected for further evaluation. Polyclonal antibodies raised against selected candidates successfully detected the corresponding proteins in serum-free culture supernatants, providing preliminary evidence for their potential utility as early-stage diagnostic targets. These findings identify stage-associated candidate proteins that may serve as a resource for future early giardiasis diagnostic development, provide a valuable resource for investigating host-parasite interactions, and establish a foundation for future diagnostic assay development. However, further validation in clinical and biological samples is required to confirm their diagnostic applicability. SIGNIFICANCE: Giardiasis, caused by Giardia duodenalis, is a major diarrheal disease worldwide. Although enzyme-linked immunosorbent assays (ELISAs) provide rapid detection, their diagnostic utility is limited by the lack of biomarkers capable of identifying infection during its earliest stages, creating a critical gap in the detection of active infection within 2-48 h following exposure. Using data-independent acquisition proteomics, this study provides a time-resolved characterization of proteins released by G. duodenalis trophozoites into serum-free culture supernatants. Our findings reveal temporal secretion dynamics of protein secretion and identify candidate biomarkers with potential utility for the development of early-stage diagnostic assays pending rigorous biological and clinical validation. In addition, this proteomic resource provides a foundation for investigating host-parasite interactions and may facilitate the development of future point-of-care diagnostic strategies.

Giardiasis↗

PubMed2026

Peripheral immune markers and choroid plexus volumes as predictors of change in depressive symptoms: Insights from the EMBARC study.

Changes in choroid plexus (ChP) volume and peripheral inflammation have been associated with Major Depressive Disorder (MDD), yet their individual and combined impact on depressive symptoms is unclear. This study investigated whether baseline immune markers and ChP volumes predict changes in depressive symptoms during the 8-week treatment period among Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study participants who received either sertraline or placebo. Adults (n = 222) with MDD with peripheral blood samples were included. Circulating chemokines and cytokines were examined using a 40-plex assay. Depressive symptoms were assessed over 8 weeks using the Hamilton Depression Rating Scale (HAMD-17). Principal component analysis (PCA) was used for dimension reduction. Mixed-effects models were used to examine whether immune profiles and ChP volumes, and their interaction predicted HAMD-17, adjusting for demographic/clinical covariates and baseline depression severity. PCA identified three immune profiles. One profile, characterized by higher levels of cytokines and chemokines including IL-6, TNF-α, and IL-1β, was associated with greater depression severity, higher BMI, age, and CRP at baseline. Higher levels of these immune markers were associated with less improvement in depressive symptoms at 8 weeks (estimate = 1.211, p = 0.018) in models adjusting for right and left ChP volume (right ChP model: estimate = 1.034, p = 0.005; left ChP model: estimate = 0.993, p = 0.007). Interactions between immune markers and ChP volumes were not significant. Future investigations are warranted to examine the relationships between immune markers and ChP volume beyond structural changes in the context of depression symptoms.

Adult↗

PubMed2026

Mobile health apps improve Health-Related Quality of Life in Type 2 Diabetes Mellitus by enhancing medication adherence: A multicentre randomised controlled trial with mediation analysis.

AIMS: This study evaluated whether a gamified mHealth application (CareAide®) improves Health-Related Quality of Life (HRQoL) in Type 2 Diabetes Mellitus (T2DM) and whether this effect is mediated by medication adherence. METHODS: Prespecified secondary analysis of the T2DM cohort from a 6-month multicentre RCT (NCT06068309; N = 663; three Malaysian hospitals). Participants were randomised 1:1 to standard care or CareAide®. Adherence (MMAS-8), EQ-5D-5L utility (Malaysian value set), and AQoL-6D were assessed at baseline and 6 months. Simple mediation analysis (PROCESS Model 4; 5000 bootstraps) adjusted for baseline HRQoL. RESULTS: CareAide® significantly predicted higher MMAS-8 scores (mean difference +1.756; d = 1.638; p < 0.001). Higher MMAS-8 scores significantly predicted improved AQoL-6D utility (b = 0.024; p < 0.001). The direct effect on AQoL-6D was non-significant (p = 0.248). Bootstrapped indirect effect confirmed full mediation via AQoL-6D (0.042; 95% CI [0.024, 0.060]). A sensitivity analysis adjusting for baseline HbA1c confirmed full mediation (indirect = 0.034; 95% CI [0.015, 0.052]; n = 563). EQ-5D-5L utility showed a significant direct between-group difference at 6 months (p = 0.012) but did not operate as a mediation outcome. CONCLUSIONS: Medication adherence fully mediates the AQoL-6D HRQoL benefit of a gamified mHealth intervention in T2DM, as confirmed by both the primary and HbA1c-adjusted sensitivity analyses. These findings support integration of behaviourally informed digital adjuncts into routine primary diabetes care.

Humans↗

PubMed2026

Manual, digital, and AI tumour-infiltrating lymphocyte scoring: a secondary analysis of the APHINITY randomised trial.

BACKGROUND: Stromal tumour-infiltrating lymphocytes (sTILs) are prognostic in early-stage HER2-positive breast cancer, but their role in the context of dual HER2 blockade remains undefined. We evaluated manual, digital, and artificial intelligence (AI)-based sTIL quantification, together with AI-derived spatial metrics, for prognostic and treatment-benefit stratification using tumour samples from the phase 3 APHINITY trial. METHODS: In the APHINITY trial, 4805 patients were randomly assigned to receive chemotherapy plus trastuzumab with pertuzumab or chemotherapy plus trastuzumab with placebo. Median follow-up was 74·1 months (IQR 68·3-75·4). We analysed 4262 haematoxylin and eosin-stained images using manual assessment, an automated digital approach, AI-based lymphocyte quantification (AI percentage lymphocytes), and two AI-derived spatial features (AI-TIL and immune hotspot). Interobserver reproducibility was assessed in 262 randomly chosen tumour samples scored independently by five pathologists. Multivariable Cox models were used to assess associations between TIL levels and invasive disease-free survival (primary outcome in APHINITY), distant recurrence-free interval, and overall survival. The heterogeneity of pertuzumab benefit was evaluated using subgroup analyses, subpopulation treatment effect pattern plot analyses, and nested Cox models with treatment-by-biomarker interaction terms. FINDINGS: Manual scoring showed high interobserver reproducibility (intraclass correlation coefficient 0·84 [95% CI 0·79-0·88]). Concordance between manual and automated methods was modest. AI-based scoring (AI percentage lymphocytes) reclassified 120 (11·6%) of 1035 node-positive tumours from immune-low (by manual scoring) to immune-high; this subgroup of patients showed greater separation of 5-year invasive disease-free survival curves between pertuzumab and placebo groups compared with patients whose tumours were concordantly classified as immune-low by both manual and AI-based approaches. Higher levels of TILs were associated with improved invasive disease-free survival for all sTIL measurement approaches and spatial measurements (hazard ratios [HRs] 0·41-0·93). Pertuzumab was associated with improved invasive disease-free survival at higher sTIL levels across all measurement approaches (HRs 0·36-0·48), but was not associated with higher values of spatial measures. The largest 6-year absolute improvements with pertuzumab were observed in patients with node-positive disease whose tumours scored in the highest level of immune infiltration of manual sTIL scoring (≥70·0%; mean absolute improvement 12·1 percentage points [SD 2·8]). In nested prognostic and predictive models, AI-based immune hotspot scores provided the most consistent additional information when combined with any sTIL measurement (all p<0·010). INTERPRETATION: Standardised manual sTIL scoring was reproducible, and digital and AI-based methods showed consistent prognostic stratification and potential for treatment-benefit stratification despite only modest correlation between platforms. AI spatial metrics provided complementary information beyond sTIL density and could support more scalable immune assessment. Future studies are needed to validate these approaches in independent cohorts and to clarify their clinical utility for stratifying contemporary HER2-directed therapies. FUNDING: None.

Humans↗

PubMed2026

To Treat or Not to Treat: Navigating Early-Stage CLL in the Era of Targeted Therapy.

Chronic lymphocytic leukemia (CLL) is most frequently diagnosed at early, asymptomatic stages (Rai 0/Binet A), in which a watch-and-wait strategy remains the standard of care, based on historical trials demonstrating no overall survival benefit from early treatment. Over the past two decades, however, substantial advances in genomic profiling-including immunoglobulin heavy-chain variable region (IGHV) mutational status, TP53 disruption, recurrent gene mutations, and complex karyotype-have uncovered marked biological heterogeneity among early-stage patients and substantially improved prediction of disease progression. In parallel, targeted therapies such as Bruton tyrosine kinase (BTK) inhibitors and venetoclax-based combinations have transformed the management of symptomatic CLL, raising renewed interest in whether early intervention might favorably alter the natural history of biologically high-risk disease. In this review, we critically examine the evolution of prognostication in early-stage CLL, integrate contemporary molecular and clinical risk models, and summarize evidence from both historical chemotherapy-era studies and modern early-intervention trials. We discuss key unresolved controversies, including reliance on surrogate endpoints, the risks of overtreatment, and the persistent absence of an overall survival benefit across all early-treatment strategies. Finally, we outline future research priorities, including refined genomic stratification, minimal residual disease-driven (MRD)-driven approaches, and combination targeted therapies currently under investigation. Despite renewed interest in preemptive treatment, available evidence supports continued observation for asymptomatic patients outside clinical trials.

Humans↗

PubMed2026

Antibody-drug conjugates against multidrug-resistant cancers: Biomarker-guided patient selection, payload engineering, linker chemistry, and bystander effects.

Antibody-drug conjugates (ADCs) are one of the most significant advancements in modern cancer therapeutics. Combining the target selectivity of monoclonal antibodies with the cytotoxic potential of payloads, ADCs effectively kill cancer cells and offer hope to patients with even refractory cancer types. Beyond simply increasing the number of therapeutic options available for cancer patients, ADCs have become a powerful frontline agent in overcoming multidrug resistance (MDR). As one of the most challenging obstacles to effective cancer care, MDR is mediated by ATP-binding cassette (ABC) transporter-mediated drug efflux, target-based mutations, and dysregulated apoptosis. The clinical success of ADCs specifically engineered to overcome MDR, including in heterogeneous tumors and cancer cells that exhibit bypass signaling, is well established. This is especially evident with trastuzumab deruxtecan (T-DXd) in HER2-low, HER2-positive, and HER2-mutant cancers; sacituzumab govitecan (SG) in TROP2-expressing triple-negative breast cancer (TNBC) and urothelial carcinoma; and enfortumab vedotin in Nectin-4-positive bladder cancer. By overcoming MDR, ADCs have enabled more effective treatment algorithms across multiple malignancies. Most importantly, the clinical application of ADCs has become inextricably linked to cancer genomics. HER2 testing has evolved from a two-tiered system to a continuous spectrum including HER2-ultralow, HER2-low, HER2-positive, and ERBB2-mutant categories. Each of these categories exhibits different eligibility guidelines for ADC patient selection. As cancer cells continue to evolve and develop resistance to even ADCs through mutations and variants, researchers and clinicians have used pharmacogenomics to predict ADC response and resistance. To define the genomic architecture of ADC-resistant tumor subpopulations, single-cell transcriptomic studies and liquid biopsy approaches are being used to enable real-time examination of the tumor genome during ADC therapy, thereby optimizing treatment and circumventing resistance driven by emerging mutations and variants. This review provides a comprehensive analysis of the molecular structure of ADCs, the pharmacological principles underlying their potent cytotoxic activity against MDR cancer cells, the genomic and transcriptomic biomarkers that guide ADC patient selection, and the emerging resistance mechanisms that will shape the next generation of promising ADC development.

Humans↗

PubMed2026

PGR expression as a pharmacogenomic companion biomarker to GENE70-derived genomic risk in ER-positive/HER2-negative breast cancer.

BACKGROUND: The biology of the estrogen receptor-positive (ER+) and human epidermal growth factor receptor 2-negative (HER2-) breast cancers is heterogeneous even when they are categorized by their risk via genomics. Transcriptomic PGR expression reflects endocrine pathway activity and may provide complementary biological information within established GENE70-derived genomic-risk categories. Whether this molecular marker improves the biological interpretation of genomic-risk stratification beyond conventional clinicopathological assessment remains uncertain. OBJECTIVES: The aim of this study was to determine whether transcriptomic PGR expression provides complementary biological and prognostic information within reconstructed GENE70-derived genomic-risk categories and refines the characterization of endocrine-related tumour biology in ER-positive/HER2-negative breast cancer. METHODS: This study analysed publicly available transcriptomic and clinical data from three cohorts: METABRIC (discovery cohort), GSE96058/SCAN-B cohort (validation cohort) and TCGA-BRCA cohort (molecular validation cohort). The GENE70-derived genomic-risk score was reconstructed for each cohort using matched genes. Cox regression, Kaplan-Meier analysis and subgroup comparisons were used to assess relationships between PGR expression, clinicopathologic variables, molecular features and survival outcomes. RESULTS: Across the three independent cohorts, low transcriptomic PGR expression was consistently associated with higher GENE70-derived genomic risk, increased MKI67 expression, reduced ESR1 expression and enrichment of the Luminal B subtype. Survival findings differed between cohorts. In the discovery METABRIC cohort, transcriptomic PGR expression showed heterogeneous associations with survival, particularly within GENE70-derived high-risk subgroups, whereas the external GSE96058/SCAN-B validation cohort demonstrated consistent associations between low PGR expression and poorer overall survival in both the overall ER-positive/HER2-negative population and GENE70-derived high-risk subgroups. CONCLUSION: These findings suggest that transcriptomic PGR provides complementary biological and prognostic information within GENE70-derived genomic-risk categories. However, because treatment response was not evaluated in the present study, the findings should not be interpreted as evidence of predictive or pharmacogenomic utility and prospective studies incorporating treatment-response analyses are required before such applications can be established.

Humans↗