Cancers (Basel). 2026 Feb 28;18(5):800. doi: 10.3390/cancers18050800.

ABSTRACT

BACKGROUND AND OBJECTIVE: Liquid biopsy has transformed the management of advanced prostate cancer, yet its clinical role in non-metastatic disease remains uncertain. Conventional biomarkers such as PSA, imaging, and pathology have limited ability to capture minimal residual disease and biological aggressiveness. The objective of this review was to critically evaluate the current evidence on circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in non-metastatic prostate cancer, focusing on feasibility, prognostic value, and potential clinical applications.

METHODS: A narrative review of PubMed-indexed original studies evaluating liquid biopsy in clinically localized or non-metastatic prostate cancer was performed. Eligible studies included patients treated with curative-intent local therapy or experiencing biochemical recurrence without radiologic metastases. Study designs were predominantly prospective or retrospective observational cohorts. Liquid biopsy analytes included CTCs and ctDNA assessed from peripheral blood plasma using EpCAM-based enrichment, targeted next-generation sequencing, whole-genome sequencing, or ultra-sensitive tumor-informed assays. Primary outcomes included detection rates, associations with clinicopathologic features, biochemical recurrence, metastasis-free survival, and overall survival. Key Findings and Limitations: Across 11 studies, CTC detection using EpCAM-based platforms was infrequent in localized disease and biochemical recurrence and showed limited prognostic value (10-11% in preoperative settings). In contrast, ctDNA was detectable in a minority of patients but consistently identified biologically aggressive disease and a higher risk of recurrence when present, particularly using tumor-informed ultra-sensitive assays. Limitations include low detection rates, heterogeneous methodologies, small sample sizes, and predominantly exploratory study designs.

CONCLUSIONS AND CLINICAL IMPLICATIONS: Currently, its most promising application is not broad screening, but as a selective, biology-driven tool for detecting minimal residual disease and refining risk assessment. CtDNA acts as a biological risk modifier, potentially guiding the escalation or de-escalation of adjuvant therapy. However, prospective biomarker-driven trials are required to validate these strategies before routine clinical implementation.

PMID:41827734 | PMC:PMC12984391 | DOI:10.3390/cancers18050800

Int J Mol Sci. 2026 Feb 28;27(5):2271. doi: 10.3390/ijms27052271.

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide, yet only a fraction of smokers develops the disease, suggesting protective mechanisms in resilient individuals. Identifying airway-localized molecular signatures may improve our understanding of disease pathomechanisms and support hypothesis generation for biomarker research. In this pilot study, induced sputum from smokers with COPD (n = 28) and smokers without COPD (n = 16; Global Initiative for Chronic Obstructive Lung Disease (GOLD)-defined pre-COPD) was analyzed by untargeted proteomics, metabolomics, and lipidomics. After quality control, 1180 proteins, 187 metabolites, and 1234 lipids were retained. Analyses included univariate models with false discovery rate adjustment and multivariate analyses (PCA, PLS-DA), followed by pathway enrichment and protein interaction network analysis. While few features remained significant after FDR correction, consistent cross-omics patterns were observed. COPD was characterized by ↑ glutathione, creatine, and L-arginine; ↓ CCDC88A and ↑ STAT3 and SYDE2; and broad lipid remodeling involving phosphatidylcholines, sphingolipids, and eicosanoids. Network analysis highlighted STAT3 as a highly connected node linking COPD-related genes. These findings suggest that the multi-omic profiling of induced sputum can capture coherent airway-localized molecular signatures such as oxidative stress, cytoskeletal remodeling, and Rho-family GTPase signaling. However, the results should be interpreted as exploratory and require validation in functional studies.

PMID:41828494 | PMC:PMC12984585 | DOI:10.3390/ijms27052271

Int J Mol Sci. 2026 Feb 28;27(5):2271. doi: 10.3390/ijms27052271.

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a leading cause of mortality worldwide, yet only a fraction of smokers develops the disease, suggesting protective mechanisms in resilient individuals. Identifying airway-localized molecular signatures may improve our understanding of disease pathomechanisms and support hypothesis generation for biomarker research. In this pilot study, induced sputum from smokers with COPD (n = 28) and smokers without COPD (n = 16; Global Initiative for Chronic Obstructive Lung Disease (GOLD)-defined pre-COPD) was analyzed by untargeted proteomics, metabolomics, and lipidomics. After quality control, 1180 proteins, 187 metabolites, and 1234 lipids were retained. Analyses included univariate models with false discovery rate adjustment and multivariate analyses (PCA, PLS-DA), followed by pathway enrichment and protein interaction network analysis. While few features remained significant after FDR correction, consistent cross-omics patterns were observed. COPD was characterized by ↑ glutathione, creatine, and L-arginine; ↓ CCDC88A and ↑ STAT3 and SYDE2; and broad lipid remodeling involving phosphatidylcholines, sphingolipids, and eicosanoids. Network analysis highlighted STAT3 as a highly connected node linking COPD-related genes. These findings suggest that the multi-omic profiling of induced sputum can capture coherent airway-localized molecular signatures such as oxidative stress, cytoskeletal remodeling, and Rho-family GTPase signaling. However, the results should be interpreted as exploratory and require validation in functional studies.

PMID:41828494 | PMC:PMC12984585 | DOI:10.3390/ijms27052271

Phys Med Biol. 2026 Mar 13. doi: 10.1088/1361-6560/ae5209. Online ahead of print.

ABSTRACT

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT).
ApproachWe retrospectively analyzed data from 121 patients with locally advanced non-small cell lung cancer (NSCLC) treated with curative-intent IMRT between 2015 and 2019, including pre-treatment CT and dose maps. Q and V maps were generated from CT with deep learning-based and supervoxel-based approaches, respectively. Regions of interest (ROIs) combined the planning target volume (PTV) with each of three functional lung regions-high functional lung (HFL), low functional lung (LFL), and whole lung (WL)-defined by thresholds on Q and V maps. Radiomic and dosiomic features were extracted from CT and dose distributions within each ROI. For each ROI, For each ROI, three methods-radiomics (R), dosiomics (D), and dual-omics (RD)-were constructed. 13 machine learning algorithms were trained and evaluated using 10-fold cross-validation, and model performance was assessed by the average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RP was defined as CTCAE grade ≥ 2.
Main resultsOf the 35 selected features, 20 were from HFL. In dual-omics models, using HFL features improved predictive performance for RP (AUC 0.879±0.105) compared to WL (AUC 0.778 ± 0.100). In HFL, the RD method outperformed both R (AUC 0.786± 0.076) and D (AUC 0.791 ± 0.107) methods. Decision curve analysis showed the dual-omics model based on HFL provided the highest net benefit across threshold probabilities.
SignificanceThis study is the first to systematically demonstrate that features extracted from CT-derived HFL capture important functional differences and provide strong predictive value for RP. Compared to conventional methods, integrating radiomics, dosiomics, and CT-based functional information further improves predictive performance.&#xD.

PMID:41825133 | DOI:10.1088/1361-6560/ae5209

Crit Care Explor. 2026 Mar 12;8(3):e1390. doi: 10.1097/CCE.0000000000001390. eCollection 2026 Mar 1.

ABSTRACT

IMPORTANCE: Inflammation, endothelial dysfunction, and complement activation are associated with COVID-19 acute lung injury (ALI) and acute respiratory distress syndrome (ARDS).

OBJECTIVES: We hypothesized that higher levels of inflammation, endothelial dysfunction, and complement activation implicated in more severe COVID-19 ALI/ARDS are associated with post-acute sequelae of COVID-19 (PASC) phenotypes in the 3 years after hospitalization.

DESIGN, SETTING, AND PARTICIPANTS: A single-center prospective cohort of 150 adult survivors of severe and critical COVID-19 from the first wave of the pandemic with sampling weighted to include 50% survivors of mechanical ventilation.

MAIN OUTCOMES AND MEASURES: Eleven serum biomarkers at hospital discharge, 4 months, 15 months, and 3 years, and symptoms and physical function at 15 months and 3 years. PASC presence was defined using the 12 symptoms and scoring from the Researching COVID to Enhance Recovery (RECOVER) definition. We tested associations of biomarkers with PASC and symptom phenotypes of post-exertional malaise, fatigue, and brain fog while adjusting for age, sex, body mass index, comorbidities, and days since COVID-19 diagnosis.

RESULTS: The mean (sd) age of the cohort was 56 years (13); 67% were Hispanic and 25% were Black. PASC was present in 26% of participants at both 15 months and 3 years. PASC and symptom phenotypes at 15 months and 3 years were consistently associated with higher frailty phenotype category, worse short physical performance battery scores, and shorter 6-minute walk distance. Biomarkers of inflammation, including interleukin-6 and soluble tumor necrosis factor receptor-1, endothelial function, including angiopoietin, and complement, including C2, C4b, and C5, were not associated with PASC or symptom phenotypes in cross-sectional or longitudinal analyses.

CONCLUSIONS AND RELEVANCE: PASC persists for 3 years after acute COVID-19 ALI/ARDS, is associated with frailty, but not associated with persistently higher levels of inflammatory, endothelial, and complement biomarkers implicated in worse short-term outcomes in acute COVID-19, non-COVID-19 ARDS, and sepsis. Future studies should employ multiomics to elucidate potential mechanisms of PASC.

PMID:41824803 | DOI:10.1097/CCE.0000000000001390

Phys Med Biol. 2026 Mar 13. doi: 10.1088/1361-6560/ae5209. Online ahead of print.

ABSTRACT

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and dosiomic features from functionally defined lung regions, using generated perfusion (Q) and ventilation (V) from pre-radiotherapy planning computed tomography (CT).
ApproachWe retrospectively analyzed data from 121 patients with locally advanced non-small cell lung cancer (NSCLC) treated with curative-intent IMRT between 2015 and 2019, including pre-treatment CT and dose maps. Q and V maps were generated from CT with deep learning-based and supervoxel-based approaches, respectively. Regions of interest (ROIs) combined the planning target volume (PTV) with each of three functional lung regions-high functional lung (HFL), low functional lung (LFL), and whole lung (WL)-defined by thresholds on Q and V maps. Radiomic and dosiomic features were extracted from CT and dose distributions within each ROI. For each ROI, For each ROI, three methods-radiomics (R), dosiomics (D), and dual-omics (RD)-were constructed. 13 machine learning algorithms were trained and evaluated using 10-fold cross-validation, and model performance was assessed by the average area under the receiver operating characteristic curve (AUC), accuracy, precision, recall, and F1 score. RP was defined as CTCAE grade ≥ 2.
Main resultsOf the 35 selected features, 20 were from HFL. In dual-omics models, using HFL features improved predictive performance for RP (AUC 0.879±0.105) compared to WL (AUC 0.778 ± 0.100). In HFL, the RD method outperformed both R (AUC 0.786± 0.076) and D (AUC 0.791 ± 0.107) methods. Decision curve analysis showed the dual-omics model based on HFL provided the highest net benefit across threshold probabilities.
SignificanceThis study is the first to systematically demonstrate that features extracted from CT-derived HFL capture important functional differences and provide strong predictive value for RP. Compared to conventional methods, integrating radiomics, dosiomics, and CT-based functional information further improves predictive performance.&#xD.

PMID:41825133 | DOI:10.1088/1361-6560/ae5209

Crit Care Explor. 2026 Mar 12;8(3):e1390. doi: 10.1097/CCE.0000000000001390. eCollection 2026 Mar 1.

ABSTRACT

IMPORTANCE: Inflammation, endothelial dysfunction, and complement activation are associated with COVID-19 acute lung injury (ALI) and acute respiratory distress syndrome (ARDS).

OBJECTIVES: We hypothesized that higher levels of inflammation, endothelial dysfunction, and complement activation implicated in more severe COVID-19 ALI/ARDS are associated with post-acute sequelae of COVID-19 (PASC) phenotypes in the 3 years after hospitalization.

DESIGN, SETTING, AND PARTICIPANTS: A single-center prospective cohort of 150 adult survivors of severe and critical COVID-19 from the first wave of the pandemic with sampling weighted to include 50% survivors of mechanical ventilation.

MAIN OUTCOMES AND MEASURES: Eleven serum biomarkers at hospital discharge, 4 months, 15 months, and 3 years, and symptoms and physical function at 15 months and 3 years. PASC presence was defined using the 12 symptoms and scoring from the Researching COVID to Enhance Recovery (RECOVER) definition. We tested associations of biomarkers with PASC and symptom phenotypes of post-exertional malaise, fatigue, and brain fog while adjusting for age, sex, body mass index, comorbidities, and days since COVID-19 diagnosis.

RESULTS: The mean (sd) age of the cohort was 56 years (13); 67% were Hispanic and 25% were Black. PASC was present in 26% of participants at both 15 months and 3 years. PASC and symptom phenotypes at 15 months and 3 years were consistently associated with higher frailty phenotype category, worse short physical performance battery scores, and shorter 6-minute walk distance. Biomarkers of inflammation, including interleukin-6 and soluble tumor necrosis factor receptor-1, endothelial function, including angiopoietin, and complement, including C2, C4b, and C5, were not associated with PASC or symptom phenotypes in cross-sectional or longitudinal analyses.

CONCLUSIONS AND RELEVANCE: PASC persists for 3 years after acute COVID-19 ALI/ARDS, is associated with frailty, but not associated with persistently higher levels of inflammatory, endothelial, and complement biomarkers implicated in worse short-term outcomes in acute COVID-19, non-COVID-19 ARDS, and sepsis. Future studies should employ multiomics to elucidate potential mechanisms of PASC.

PMID:41824803 | PMC:PMC12987405 | DOI:10.1097/CCE.0000000000001390

Nat Commun. 2026 Mar 12. doi: 10.1038/s41467-026-70376-5. Online ahead of print.

ABSTRACT

Myasthenia gravis (MG) is an autoimmune disorder characterized by impaired neuromuscular transmission and motor symptoms. Its genetic background remains unclear, particularly beyond specific subtypes reported in European populations. Here, we perform a genome-wide association study (GWAS) of 1,434 MG cases covering all disease subtypes and 42,913 controls of Japanese, which newly identify the TERT locus (odds ratio [OR] = 1.31, P = 1.7×10^-10). Subtype-stratified GWASs show stronger signals for generalized MG (gMG; OR = 1.38, P = 1.6×10^-12), anti AChR antibody-positive gMG (g-AChR-Ab(+)MG; OR= 1.49, P = 2.1×10^-15), and thymoma-associated gMG (g-TAMG; OR = 1.92, P = 1.1×10^-15). Fine-mapping of the major histocompatibility complex region reveal distinct associations of HLA-DRB1 with late onset gMG (g-LOMG) and HLA-A with early onset gMG (g-EOMG). The MG risk TERT lead variant rs2736099 is associated with poor treatment response, especially in g-AChR-Ab(+)MG and g-EOMG (P < 0.0042). The biobank-based phenome-wide association study identify pleiotropic effects on lung cancer, hematological traits, and telomere length. Single cell transcriptomics and immunohistochemistry identified immature lymphocyte-specific TERT expression in thymoma specimens. Full-length transcriptomics reveal allele-specific decreasing effect of rs2736099-A on TERT expression. Our study unveils genetics of MG distinctly across disease subtypes, and involvement of TERT in its pathogenesis.

PMID:41820352 | DOI:10.1038/s41467-026-70376-5