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Results for “Biomarker stratification”

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PubMed2026

Circulating IGF2BP3 enables risk stratification and predicts treatment response in Ewing sarcoma.

Ewing sarcoma (EWS), the second most common pediatric bone tumor, presents with a markedly heterogeneous clinical spectrum and optimal risk stratification is therefore crucial for improving treatment outcomes. The RNA-binding protein IGF2BP3 is a critical oncogenic driver of EWS malignancy. This study evaluates the clinical utility of circulating IGF2BP3 as a biomarker to predict treatment response and risk of disease progression in patients with EWS. Plasma samples from 60 patients with EWS diagnosed and treated at the IRCCS Rizzoli Orthopedic Institute (Bologna, Italy) were collected at diagnosis before treatment initiation and/or after induction chemotherapy. For 51 of these patients, blood was collected at diagnosis, prior to any treatments. For 25 patients, blood samples were available at diagnosis and before surgical intervention, allowing longitudinal analysis in the same patient. For 9 patients, blood was collected only after preoperative chemotherapy, before surgical intervention. Circulating IGF2BP3 levels were quantified using a highly specific and sensitive ELISA assay. Plasma samples from healthy donors served as controls. IGF2BP3 plasma levels were correlated with IGF2BP3 tumor tissue expression, established clinical risk factors, and cumulative incidence of relapse using univariable and multivariable analyses. Plasma IGF2BP3 levels were significantly elevated in patients with EWS compared with healthy controls, with a subset of patients (22/51, 43.2%) exhibiting clinically relevant concentrations. Circulating IGF2BP3 levels reflected tumor expression of the molecule and provided additional prognostic information beyond standard clinicopathologic features. The prognostic impact of circulating IGF2BP3 was primarily observed in patients with localized disease, in whom elevated levels were identified as a significant adverse prognostic factor for disease-specific survival (hazard ratio, 10.63; 95% CI, 1.27-88.62; P = 0.029). Longitudinal monitoring demonstrated that persistence of IGF2BP3 in plasma after induction chemotherapy was a strong predictor of poor clinical outcomes. Circulating IGF2BP3 represents a valuable biomarker for early risk stratification in EWS, particularly in patients with localized disease. Although this is single-marker assay, the expression of the molecule may impact on the fate of many mRNAs. We present an accurate, simple, cost-effective and easy clinical applicable tool to support risk-adapted therapeutic interventions. The limited number of employed patients warrants the need of larger cohorts for validation.

Humans↗

PubMed2006

Methods of detecting atherosclerosis in non-cardiac surgical patients; the role of biochemical markers.

Atherosclerosis is a common condition in both the developed and developing world and is now recognised to be an inflammatory condition leading to the development of ischaemic heart disease, cerebrovascular disease and peripheral vascular disease. Ischaemic heart disease is a major risk factor in the pathogenesis of perioperative adverse cardiovascular events which lead to significant morbidity and mortality within the high risk surgical patient population. Current methods of evaluating the likelihood of postoperative cardiovascular complications depend largely on risk scoring systems, and the preoperative assessment of the functional status of the cardiovascular system. However, the possible role of inflammation in the generation of atherosclerosis has led to the identification of several biochemical markers such as acute phase proteins, cellular adhesion molecules and cytokines. An alternative approach therefore is the measurement of preoperative levels of these biomarkers with the aim of assessing pre-existing disease activity. This review summarises the pathophysiology of atherosclerosis and perioperative myocardial infarction, and discusses the possible future role of biomarkers in the risk stratification of patients undergoing non-cardiac surgery.

Biomarkers↗

PubMed2026

Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.

BACKGROUND: Risk stratification in colorectal cancer (CRC) plays an important role in treatment decision-making. As such, prognostic biomarkers that can augment risk stratification have clinical value. Quantitative histologic features from routine hematoxylin and eosin (H&E)-stained whole slide images (WSIs) provide a novel avenue for biomarker discovery. In this study, we explored the potential for a computational histology artificial intelligence (CHAI) platform to develop and validate a prognostic biomarker in CRC. METHODS: The Cancer Genome Atlas Colorectal Adenocarcinoma project was utilized for this study, with inclusion of all subjects (stage I-IV) with available digitized H&E specimens. The cohort was split into development and validation cohorts by a stratified random split. The previously developed CHAI platform was applied in the development cohort to construct a continuous risk score from histologic features associated with progression-free interval (PFI) that was dichotomized based on an optimized cutpoint for distinguishing PFI into a high risk CHAI (+) and lower risk CHAI (-). PFI was compared between CHAI (+) and CHAI (-) patients in the validation cohort in multivariable Cox proportional hazards models. Time-dependent area under the curve (tdAUC) and C-indices were also calculated for PFI. RESULTS: A total of 583 participants were included in the study, with 409 assigned to the validation cohort. The CHAI biomarker classified 229 participants (56%) as CHAI (+) and 180 (44%) as CHAI (-) in the validation set. CHAI (+) participants had worse PFI in a multivariable analysis adjusting for available clinicopathologic variables (hazard ratio (HR) = 2.65; 95% confidence interval (CI), 1.63-4.30). TdAUC for the CHAI biomarker was 0.60 (95% CI, 0.53-0.67) at 12 months, 0.62 (0.55-0.69) at 36 months, and 0.67 (0.55-0.79) at 60 months; the C-index was 0.62 (95% CI, 0.58-0.67). CONCLUSIONS: The CHAI platform was used to develop a prognostic digital pathology biomarker in CRC. This demonstrates the feasibility and potential to apply this artificial intelligence-based digital pathology biomarker platform for risk stratification in CRC and supports its further study.

Artificial intelligence↗

PubMed2005

DNA repair and cyclin D1 polymorphisms and styrene-induced genotoxicity and immunotoxicity.

1-SO-adenine DNA adducts, DNA single-strand breaks (SBs), chromosomal aberrations (CAs), mutant frequency (MF) at the HPRT gene, and immune parameters (hematological and of humoral immunity) were studied in styrene-exposed human subjects and controls. Results were correlated with genetic polymorphisms in DNA repair genes (XPD, exon 23, XPG, exon 15, XPC, exon 15, XRCC1, exon 10, XRCC3, exon 7) and cell cycle gene cyclin D1. Results for biomarkers of genotoxicity after stratification for the different DNA repair genetic polymorphisms showed that the polymorphism in exon 23 of the XPD gene modulates levels of chromosomal and DNA damage, HPRT MF, and moderately affects DNA adduct levels. The highest levels of biomarkers were associated with the wild-type homozygous AA genotype. The exposed individuals with the wild-type GG genotype for XRCC1 gene exhibited the lowest CA frequencies, compared to those with an A allele (P < 0.05). Cyclin D1 polymorphism seems to modulate the number of leukocytes and lymphocytes in the analyzed subjects. The number of eosinophiles was positively associated with XPD variant C allele and negatively with XRCC1 variant A allele (P < 0.05) and XPC variant C allele (P < 0.05). Immunoglobulin IgA was positively associated with an XRCC3 variant T allele (P < 0.01) and negatively with XPC variant C allele (P < 0.05). Both C3- and C4-complement components were lower in individuals with XRCC3 CT (P < 0.05) and TT genotypes (P < 0.01). Adhesion molecules sL-selectin and sICAM-1 were associated with XPC genotype (P < 0.05). Individual susceptibility may be reflected in genotoxic and immunotoxic responses to environmental and occupational exposures to xenobiotics.

Adult↗

PubMed2006

Short- and long-term risk stratification in acute coronary syndromes: the added value of quantitative ST-segment depression and multiple biomarkers.

OBJECTIVES: The purpose of this study was to develop 30-day and 1-year risk stratification models for non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients that incorporate quantitative ST-segment depression and novel biomarkers. BACKGROUND: Several novel biomarkers have changed the risk profile of ACS; thus, the reassessment of traditional indicators such as ST-segment depression in this new context is warranted. METHODS: Multivariable logistic regression was used to identify significant predictors of 30-day death and death/myocardial infarction (MI) and 1-year mortality in 7,800 NSTE-ACS patients enrolled in the GUSTO-IV (Global Utilization of Strategies to Open Occluded Arteries-IV ACS) trial between 1998 and 2000. RESULTS: Among all other predictors, the degree of ST-segment depression had the highest prognostic value for 30-day death, 30-day death/MI, and 1-year death. Troponin T (TnT), creatinine clearance, N-terminal pro-brain natriuretic peptide (NT-proBNP), heart rate, and age were also highly influential on adverse outcomes. Unlike TnT and NT-proBNP, C-reactive protein was only predictive of long-term death. In contrast to mortality, the contribution of TnT to predicting 30-day death/MI increased, whereas NT-proBNP's role was attenuated. The discriminatory power was excellent (c-index [adjusted for over-optimism]: 0.82 [30-day death]; 0.72 [30-day death/MI]; 0.81 [1-year]). CONCLUSIONS: In this large contemporary study of NSTE-ACS patients, novel insights into risk stratification were observed-in particular, the utility of quantitative ST-segment depression and multiple biomarkers. Collection of these indicators in future NSTE-ACS populations is recommended to evaluate generalizability and clinical application of these findings.

Aged↗

PubMed2025

Integrative metabolomic and proteomic analysis of diabetic kidney disease progression with younger-onset type 2 diabetes.

AIM: Younger-onset type 2 diabetes (YT2D) confers a disproportionately high risk of diabetic kidney disease (DKD), yet early biomarkers and underlying mechanisms remain poorly defined. We aimed to identify metabolites associated with DKD progression and integrate metabolomic and proteomic data to elucidate pathways involved in a multi-ethnic Asian cohort. MATERIALS AND METHODS: In this prospective study, 787 YT2D patients (diagnosed at ≤ age 40) were followed for a median of 5.7 years. DKD progression was defined as an annual decline in estimated glomerular filtration rate (eGFR) of ≥3 mL/min/1.73 m2 or ≥ 40% reduction in eGFR from baseline. Plasma metabolites were measured by nuclear magnetic resonance spectroscopy. Multivariable regression analysis was performed in a discovery (N = 550) and internal validation cohort (N = 237). Integrative metabolomic-proteomic analysis (N = 428) was performed using sparse partial least squares discriminant analysis (sPLS-DA). RESULTS: Ninety-eight metabolites were differentially expressed between DKD progressors and non-progressors, of which total branched-chain amino acids (BCAAs) (OR = 0.60, 95% CI 0.46-0.79), valine (OR = 0.62, 95% CI 0.48-0.81), and leucine (OR = 0.56, 95% CI 0.43-0.74) associated with DKD progression, independent of metabolic risk factors. Integrative analysis identified three components comprising 23 proteins and 30 metabolites, involved in the citrate cycle and apoptosis, which improved prediction of DKD progression beyond clinical risk factors (AUC 0.69-0.83). CONCLUSION: Lower plasma BCAA levels are independently associated with DKD progression in YT2D. Integrative multi-omics analysis highlights disruptions in metabolic and apoptotic pathways, providing insights into DKD pathophysiology and potential biomarkers for early risk stratification.

Humans↗

PubMed2026

Multi-level Transcriptomic and Machine-learning Analyses Identify MZT1 as a Proliferation-associated Prognostic Marker in Lung Adenocarcinoma.

BACKGROUND/AIM: Lung adenocarcinoma (LUAD) exhibits substantial molecular heterogeneity and variable clinical outcomes, highlighting the need for biomarkers that reflect core tumor biological processes. Centrosome-associated proteins regulate mitotic fidelity and genome stability, yet their roles in LUAD remain incompletely defined. In this study, we systematically characterized mitotic spindle organizing protein 1 (MOZART1; MZT1) and related family members in LUAD. MATERIALS AND METHODS: We performed integrated analyses combining bulk transcriptomic datasets, survival modeling, gene set enrichment, immune deconvolution, machine-learning based prognostic modeling, and single-cell RNA sequencing. Expression patterns and clinical associations of MZT family genes were evaluated across pan-cancer and LUAD cohorts. RESULTS: MZT family genes were consistently upregulated in tumor tissues, with MZT1 showing the most robust expression pattern. Elevated MZT1 expression was significantly associated with reduced overall survival. Functional analyses revealed coordinated activation of proliferative and genome maintenance pathways, including G2/M checkpoint regulation, E2F and MYC signaling, and DNA repair. A multivariable analysis indicated that the prognostic association of MZT1 was reduced after adjusting for canonical proliferation markers, suggesting partial overlap with established proliferation signals. The LASSO-based Cox model demonstrated stable time-dependent predictive performance at 1-, 3-, and 5-year survival. Immune analyses indicated associations between MZT1 expression and tumor microenvironmental features. Single-cell analysis showed that MZT1 expression was predominantly enriched in malignant epithelial cells and associated with proliferative cellular states. Protein-level validation supported concordance with transcriptomic findings. CONCLUSION: MZT1 is a proliferation-associated marker that integrates clinical risk, transcriptional programs, cellular heterogeneity, and predictive modeling in LUAD, providing a potential framework for biomarker development and risk stratification.

Humans↗

PubMed2026

Protein Biomarkers in Risk and Prognosis of Amyotrophic Lateral Sclerosis.

BACKGROUND: Plasma and cerebrospinal fluid (CSF) protein biomarkers in amyotrophic lateral sclerosis (ALS) may provide insight into disease mechanisms and yield clinically useful biomarkers. METHODS: Overall, 363 proteins in plasma and CSF from 198 patients with ALS and 125 matched controls were profiled using Olink assays. Associations with disease status, survival, and functional decline, as well as longitudinal biomarker stability across the disease course were assessed, together with network and enrichment analyses. ALS risk-associated biomarkers were externally validated in the UK Biobank (UKB). RESULTS: Overall, 125 proteins were significantly associated with at least one outcome (i.e., case status, risk, survival, or functional decline), and 21 were associated with three or more outcomes. NEFL was the most robust biomarker in plasma and CSF, alongside TNFRSF12A in plasma and CSF, EDA2R in plasma, and FABP4 in plasma and CSF. Most biomarkers remained stable longitudinally across the disease course. ALS risk-associated biomarkers were replicated in UKB, in which > 3000 plasma proteins were measured in 52,990 participants, including 298 with ALS. Network and enrichment analyses highlighted their roles in immune response and extracellular-matrix remodeling, and their enrichments in the brain and T-cell subsets. Construction of an ALS risk-prediction model achieved an ROC-AUC of 0.72 in the UKB validation cohort. CONCLUSIONS: These findings suggest candidate protein biomarkers for ALS risk stratification, early detection, and clinical therapeutic monitoring.

Humans↗

PubMed2026

Genetic variants in IGF2BP family genes are associated with glioma risk in Chinese children.

BACKGROUND: Glioma is a highly prevalent malignant tumor of the central nervous system in children and is driven by complex genetic and environmental factors. IGF2BP family genes (IGF2BP1, IGF2BP2, and IGF2BP3) encode critical RNA epigenetic "readers" that participate in posttranscriptional gene regulation and modulate various cellular processes. However, the contributions of these gene variants to glioma risk remain unclear. METHODS: A multicenter case-control study was conducted, enrolling 360 patients with glioma and 547 cancer-free controls. Genotyping of 11 potentially functional polymorphisms within IGF2BP family genes was performed using the TaqMan assay. Unconditional logistic regression models were employed to estimate odds ratios and 95% confidence intervals. Furthermore, expression quantitative trait loci (eQTL) and The Cancer Genome Atlas (TCGA) clinical database analyses were conducted to investigate the potential regulatory mechanisms and clinical significance of the identified variants. RESULTS: We found that the IGF2BP1 rs2270575 polymorphism was significantly associated with a decreased risk of glioma. Conversely, the IGF2BP2 rs17289925 and rs7646419 polymorphisms were linked to increased glioma risk. Stratification and cumulative effect analyses revealed that harboring multiple risk genotypes of IGF2BP1 or IGF2BP2 substantially increased glioma susceptibility. This cumulative risk was especially notable among males, younger children (< 60 months), and patients diagnosed with early-stage (I + II) tumors. Additionally, eQTL and TCGA analyses revealed that the rs2270575 and rs7646419 alleles correlated significantly with altered mRNA expression levels of CALCOCO2 and AC099661.1 (ENSG00000286086), respectively, which further correlated with favorable molecular subtypes (IDH mutation status) and WHO tumor grades. CONCLUSION: Genetic polymorphisms within IGF2BP family genes significantly modulate pediatric glioma susceptibility and have cumulative, subtype-specific, and age-dependent effects; thus, these genes may serve as promising noninvasive biomarkers for early risk stratification of childhood glioma.

Adolescent↗

PubMed2026

PIK3CA Polymorphisms in Cervical Cancer: Differential Impact of rs6443624 and rs141178472.

BACKGROUND: Cervical cancer remains a major cause of mortality in low- and middle-income settings. We assessed whether two PIK3CA single-nucleotide polymorphisms (SNPs) rs6443624 (A/C) and rs141178472 (C/T) are associated with disease risk, clinicopathological features, and survival. MATERIALS AND METHODS: In a prospective case control study at a tertiary center, 154 participants were enrolled (77 cases, 77 controls). Genomic DNA was isolated from FFPE cervical tumors (QIAamp DNA FFPE Tissue Kit) and genotyped using TaqMan allelic discrimination. Clinicopathological variables (FIGO stage, histology, grade, treatment, tumor-infiltrating lymphocytes [TILs]) were abstracted from records. Genotype distributions were compared by Pearson chi-square. Associations with clinicopathological features used chi-square/Fisher's exact as appropriate; ANOVA compared age across genotypes. Overall survival (OS) was estimated by Kaplan-Meier and compared with the log-rank test; mean OS with SE and 95% CI is reported. RESULTS: Cases were predominantly locally advanced at presentation and squamous histology; most were moderately differentiated. rs6443624 differed significantly between cases and controls (χ²=21.1, p<0.001), with CC over-represented in cases and CA less frequent. rs141178472 showed no significant case control difference (χ²=2.9, p=0.086). For OS, rs6443624 showed a significant genotype effect (log-rank χ²=23.45, p=0.001): AA had the poorest survival, CA the longest, CC intermediate. rs141178472 was not associated with OS (χ²=1.06, p=0.588). Genotype clinicopathological correlations for stage group, grade, TILs, and treatment were non-significant or inconsistent, with some comparisons limited by small sample sizes. CONCLUSION: The PIK3CA rs6443624 variant appears to influence both susceptibility and prognosis in cervical cancer, highlighting its potential as a biomarker for molecular risk stratification. Validation in larger, multi-center cohorts incorporating HPV/p16 assessment and extended follow-up is warranted to confirm its clinical relevance.

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

Plasma cell-CD8+ T cell co-enrichment distinguishes immunotherapy-responsive hepatocellular carcinoma subtypes.

BACKGROUND: Hepatocellular carcinoma (HCC) is characterised by significant racial disparities in incidence and outcomes, yet whether these reflect distinct tumour biology or differential distribution of molecular subtypes among immunotherapy patients remains unclear. METHODS: We characterised molecular heterogeneity among 46 patients with HCC of differing background population from the NCI-CLARITY cohort receiving immune checkpoint inhibitor therapy, using transcriptomic and genomic profiling, with validation across multiple independent cohorts. RESULTS: Differential expression analysis comparing African American versus non-African American patients identified 126 genes, of which 55 demonstrated tumour-specific expression across independent validation cohorts with paired tumour-normal samples. Consensus clustering revealed two molecular subtypes with no significant race association, indicating these clusters capture tumour-intrinsic biology rather than ancestry. The genomic landscape showed minimal differences between subtypes. A prognostic signature derived from these expression profiles demonstrated significant risk stratification in the NCI-CLARITY cohort and TCGA-LIHC, but not in Asian cohorts, suggesting population-specific applicability. Immune deconvolution revealed that the two subtypes represent distinct immune microenvironments: one subtype exhibited markedly elevated plasma cell infiltration with strong plasma cell-CD8+T cell correlation suggesting coordinated adaptive immunity, along with elevated tertiary lymphoid structure signatures. The other subtype showed regulatory T cell-macrophage correlation and enrichment for immune-excluded phenotypes. The immune-enriched subtype trended towards higher immunotherapy response rates. CONCLUSIONS: Molecular heterogeneity in HCC reveals distinct tumour-immune ecosystems that transcend racial classification. Tumour immune heterogeneity in HCC reflects distinct molecular patterns, with immune hot tumours characterised by elevated tertiary lymphoid structure signatures and enriched plasma cell and CD8+T cells. These patterns may serve as prognostic biomarkers for immunotherapy patient stratification and demonstrate the value of diverse cohort representation in identifying clinically relevant therapeutic targets.

Gastrointestinal Cancer↗

PubMed2026

Enhanced risk stratification in hypertrophic cardiomyopathy through the integration of extracellular volume fraction on cardiovascular magnetic resonance.

AIMS: This study investigated the incremental prognostic value of cardiovascular magnetic resonance (CMR)-derived extracellular volume fraction (ECV), a marker of diffuse interstitial fibrosis, beyond late gadolinium enhancement (LGE) in hypertrophic cardiomyopathy (HCM). METHODS AND RESULTS: We analysed 990 consecutive HCM patients (median age 58 years, male 68.3%) who underwent CMR between 2012 and 2024. LGE and global ECV were quantified, and their associations with the primary endpoint of HCM-related events-a composite of sudden cardiac death (SCD) events, heart failure (HF) events, and HCM-related death-were assessed. During a median follow-up of 3.2 years, 64 (6.5%) patients experienced the primary endpoint. While LGE (median 7.1%, IQR 2.3-16.9%) and ECV (median 29.0%, IQR 26.6-32.0%) were moderately correlated (R = 0.604, P < 0.001), both were significantly associated with increased risk of the primary endpoint and individual outcomes of SCD and HF events, and optimal cutoffs were determined as LGE ≥ 27% and ECV ≥ 35%. Patients with ECV ≥ 35% had more symptoms, a more severe phenotype with greater systolic and diastolic dysfunction, and more pathogenic gene variants. Notably, ECV remained a significant predictor of the primary endpoint (adjusted HR 1.08, 95% CI 1.02-1.15, per 1%) after adjustment for key disease variables, including left ventricular ejection fraction and LGE. Elevated ECV effectively identified high-risk individuals even among lower-risk subgroups, including those with low LGE burden. CONCLUSION: Increased ECV is an independent predictor of HCM-related outcomes. ECV may serve as a novel imaging biomarker to refine risk stratification in HCM patients who do not meet traditional LGE-based high-risk criteria.

Humans↗

PubMed2026

Papillary Thyroid Carcinoma with Terminal Immune Exhaustion Phenotype Correlates with Increased Risk of Lymph Node Metastasis: An Exploratory Study Combining Flow Cytometry and TCGA.

BACKGROUND: Papillary thyroid carcinoma (PTC) is the most common thyroid malignancy, with lymph node metastasis (LNM) being a key predictor of recurrence and poor prognosis. Preoperative detection of LNM remains challenging due to the limitations of imaging modalities, leading to inadequate surgical resection in 20-30% of patients. While immune checkpoint molecules have been implicated in PTC progression, the heterogeneity of CD8+ T cell exhaustion subsets and their specific association with LNM remain poorly defined. In this study, we aimed to perform an exploratory characterization of the distinct immune landscape of PTC prone to LNM, with a focus on terminal immune exhaustion, in order to generate hypotheses for improved risk stratification and therapeutic strategies. METHODS: Fresh PTC tissues from 40 patients (22 LNM-positive and 18 LNM-negative) were analyzed via flow cytometry (FCM) to quantify immune cell subsets, inflammatory cytokines, and chemokines. Immunohistochemistry (IHC) validated CD45+ immune cell infiltration. Transcriptomic and clinical data from 448 PTC patients in The Cancer Genome Atlas (TCGA-PTC) cohort were used for bioinformatic analysis consistent with the observed phenotype, including Gene Set Variation Analysis (GSVA) of terminal exhaustion gene signatures. RESULTS: LNM-positive PTC exhibited a unique inflammatory milieu with significantly elevated IL-6, IL-1ra, CCL5, and IL-9 levels (all p < 0.05) in tumor interstitial fluid. FCM analysis revealed that LNM-positive PTC had increased infiltration of total CD45+ immune cells, CD3+ T cells, and CD3+CD8+ T cells (all p < 0.05). Critically, terminally exhausted PD-1hiTIM-3+ CD8+ T cells were significantly enriched in LNM-positive PTC (p = 0.022) and positively correlated with extrathyroidal extension (p = 0.044). Additionally, LNM risk was associated with increased CD4+ regulatory T (Treg) cell frequency (p = 0.023) and elevated CTLA-4 expression on CD4+ T cells (p = 0.047). In TCGA-PTC validation, the terminal exhaustion gene signature was predominantly enriched in LNM-positive (p < 0.0001) and advanced-stage PTC (p < 0.001) and strongly correlated with BRAF mutation (predominantly V600E) (p < 0.0001)-the most common oncogenic driver in aggressive PTC. CONCLUSIONS: Our findings suggest a terminal immune exhaustion phenotype (characterized by PD-1hiTIM-3+ CD8+ T cells and Treg enrichment) as a potential key feature associated with LNM-prone PTC. This phenotype shows consistency across clinical samples and TCGA datasets, linking BRAF mutation (predominantly V600E) to immune suppression and metastatic potential. These insights provide a novel exploratory immune-based biomarker for LNM risk stratification and support the potential of combining anti-PD-1/TIM-3 therapy with BRAF inhibitors for high-risk PTC, which should be confirmed in future studies.

lymph node metastasis↗

PubMed2025

Uncovering the genetic architecture of ME/CFS: a precision approach reveals impact of rare monogenic variation.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling and heterogeneous disorder lacking validated biomarkers or targeted therapies. Clinical variability and elusive pathophysiology hinder progress toward effective diagnostics and treatment. Core symptoms include persistent fatigue, post-exertional malaise, unrefreshing sleep, cognitive dysfunction, and pain. We tested whether an individualized, “n-of-1” genomic and transcriptomic framework combined with comprehensive, participant-informed phenotyping could reveal molecular signatures unique to each patient. METHODS: Clinical-grade whole-genome sequencing was conducted in 31 affected individuals from 25 families, with RNA-seq performed on a subset (16 affected, 7 unaffected) using blood samples. Machine-learning assisted variant triage, transcript-aware damage prediction, and expert review identified pathogenic or likely pathogenic variants in 8 of 25 probands (32%) and 12 of 31 affected individuals (39%). RESULTS: Findings revealed marked genetic heterogeneity, including large-effect rare and more common variants. Implicated pathways included ATP generation, oxidative phosphorylation, fatty acid oxidation; regulation of glycolysis, amino acid and lipid turnover; ion and solute homeostasis; synaptic signaling, excitability, oxygen transport, and muscle integrity, resilience, and post-exertional recovery; previously implicated processes. Plausible modifiers influencing disease onset, severity, and relapsing–remitting patterns and possibly explaining intrafamilial variability and inconsistent findings across studies, were also identified. Despite gene-level diversity, downstream effects converged on impaired energy production, reduced stress resilience, and vulnerability to post-exertional metabolic failure; disruptions consistent with core ME/CFS symptoms of exertional intolerance, cognitive fog, and fatigue. CONCLUSIONS: Our findings support the hypothesis that at least a subset of ME/CFS cases represent distinct molecular disorders that converge on shared physiological pathways. Validation in larger, more diverse cohorts will be essential to test this hypothesis and establish generalizability, but increase size alone is unlikely to resolve causation in a disorder defined by rarity, heterogeneity, and molecular complexity. We suggest that progress will require experimental designs that integrate individual-level genomic data with deep, participant-informed deep phenotyping, capturing the combined effects of rare and common variants and environmental modifiers on disease expression and progression. We believe that an individualized precision medicine framework will uncover molecular drivers and modifiers of ME/CFS previously obscured by heterogeneity, enabling biologically informed stratification, improved trial design, biomarker discovery, and targeted interventions in this historically neglected condition.

Humans↗

PubMed2005

Chromosome 1p and 11q deletions and outcome in neuroblastoma.

BACKGROUND: Neuroblastoma is a childhood cancer with considerable morbidity and mortality. Tumor-derived biomarkers may improve risk stratification. METHODS: We screened 915 samples of neuroblastoma for loss of heterozygosity (LOH) at chromosome bands 1p36 and 11q23. Additional analyses identified a subgroup of cases of 11q23 LOH with unbalanced 11q LOH (unb11q LOH; defined as loss of 11q with retention of 11p). The associations of LOH with relapse and survival were determined. RESULTS: LOH at 1p36 was identified in 209 of 898 tumors (23 percent) and LOH at 11q23 in 307 of 913 (34 percent). Unb11q LOH was found in 151 of 307 tumors with 11q23 LOH (17 percent of the total cohort). There was a strong association of 1p36 LOH, 11q23 LOH, and unb11q LOH with most high-risk disease features (P<0.001). LOH at 1p36 was associated with amplification of the MYCN oncogene (P<0.001), but 11q23 LOH and unb11q LOH were not (P<0.001 and P=0.002, respectively). Cases with unb11q LOH were associated with three-year event-free and overall survival rates (+/-SE) of 50+/-5 percent and 66+/-5 percent, respectively, as compared with 74+/-2 percent and 83+/-2 percent among cases without unb11q LOH (P<0.001 for both comparisons). In a multivariate model, unb11q LOH was independently associated with decreased event-free survival (P=0.009) in the entire cohort, and both 1p36 LOH and unb11q LOH were independently associated with decreased progression-free survival in the subgroup of patients with features of low-risk and intermediate-risk disease (P=0.002 and P=0.02, respectively). CONCLUSIONS: Unb11q LOH and 1p36 LOH are independently associated with a worse outcome in patients with neuroblastoma.

Analysis of Variance↗

PubMed2025

Genetic predisposition to systemic inflammatory proteins is causally associated with inflammatory bowel disease: Insights from multi-omics association study and single-cell RNA-sequencing analysis.

Systemic inflammatory proteins have been reported to be related to inflammatory bowel disease (IBD) in previous observational research. However, their causal links remain obscure. Herein, we performed a Mendelian randomization (MR) analysis to analyze the causality between systemic inflammatory proteins and IBD. Genetic variants related to systemic inflammatory proteins were extracted from a meta-analysis of genome-wide association study (GWAS) data of 8293 European participants. Summary statistics of IBD diverse subtypes were obtained from the international IBD genetic consortium (IIBDGC). We conducted multi-omics method and MR study to detect the causal links through integrating GWAS and protein quantity trait loci (pQTL) data. Inverse variance weighted (IVW) approach was utilized as the dominated analysis method. Moreover, complementary approaches such as MR-Egger intercept test, Cochran Q test and leave-one-out analysis were utilized to validate pleiotropy and heterogeneity. Finally, single-cell RNA-sequencing analysis was performed to detect the expression of significant genes. For IBD, IVW estimates suggested that genetically predicted IL-10 and IL-13 were suggestively associated with an elevated risk of IBD (IL-10: OR: 1.12, 95% CI: 1.00-1.24, P = .04; IL-13: OR: 1.09, 95% CI: 1.01-1.18, P = .023), while CXCL10 was suggestively linked to a lower risk of IBD (CXCL10: OR: 0.90, 95% CI: 0.82-0.99, P = .037). For Crohn disease (CD), the IVW approach provided evidence to sustain that genetically determined IL-13 and CCL3 had a suggestive association with a higher risk of CD (IL-13: OR: 1.13, 95% CI: 1.02-1.26, P = .023; CCL3: OR: 1.22, 95% CI: 1.03-1.45, P = .018). Sensitivity analysis did not explore any heterogeneity and pleiotropy. Our findings supported the causal relationships between 4 specific inflammatory proteins (IL-10, IL-13, CXCL10, and CCL3) and the risk of IBD and CD, thereby providing promising biomarkers of various subtypes stratification and new insights for the prevention and therapeutic target of IBD.

Humans↗

PubMed2006

Phospho-proteomic immune analysis by flow cytometry: from mechanism to translational medicine at the single-cell level.

Understanding a molecular basis for cellular function is a common goal of biomedicine. The complex and dynamic cellular processes underlying physiological processes become subtly or grossly perturbed in human disease. A primary objective is to demystify this complexity by creating and establishing relevant model systems to study important aspects of human disease. Although significant technological advancements over the last decade in both genomic and proteomic arenas have enabled progress, accessing the complexity of cellular interactions that occur in vivo has been a difficult arena in which to make progress. Moreover, there are extensive challenges in translating research tools to clinical applications. Flow cytometry, over the course of the last 40 years, has revolutionized the field of immunology, in both the basic science and clinical settings, as well as having been instrumental to new and exciting areas of discovery such as stem cell biology. Multiparameter machinery and systems exist now to access the heterogeneity of cellular subsets and enable phenotypic characterization and functional assays to be performed on material from both animal models and humans. This review focuses primarily on the development and application of using activation-state readouts of intracellular activity for phospho-epitopes. We present recent work on how a flow cytometric platform is used to obtain mechanistic insight into cellular processes as well as highlight the clinical applications that our laboratory has explored. Furthermore, this review discusses the challenges faced with processing high-content multidimensional and multivariate data sets. Flow cytometry, as a platform that is well situated in both the research and clinical settings, can contribute to drug discovery as well as having utility for both biomarker and patient-stratification.

Diagnostic Techniques and Procedures↗