Crit Rev Oncol Hematol. 2026 Sep;225:105483. doi: 10.1016/j.critrevonc.2026.105483. Epub 2026 Jul 10.

ABSTRACT

Low-dose CT (LDCT) lung cancer screening significantly reduces mortality but has dramatically increased the detection of pulmonary nodules. Most of these nodules are benign, leading to a high false-positive rate that triggers unnecessary invasive procedures and patient anxiety, underscoring the need for more precise noninvasive diagnostic tools. Critically, single-modality liquid biopsy biomarkers, including circulating tumor cells, cell-free DNA mutations, or individual microRNAs, have demonstrated insufficient sensitivity or specificity for independent clinical deployment when used in isolation. This necessitates a paradigm shift toward multimodal molecular integration, wherein complementary biomarker classes are combined to overcome the inherent limitations of any single analyte. Traditional clinical prediction models (Mayo, VA, Brock, Herder) assist in estimating malignancy risk, yet their accuracy remains modest. Emerging approaches harness radiomics and artificial intelligence (AI) to extract high-dimensional imaging features from chest CT scans, improving risk stratification beyond human assessment alone. In parallel, minimally invasive liquid biopsy biomarkers offer complementary avenues to detect occult malignancy signals. Additionally, bronchial airway gene expression classifiers leverage the "field-of-injury" effect in normal respiratory epithelium to help identify lung cancer even when the nodule itself cannot be directly sampled via biopsy. Integrating these radiologic and molecular data streams into a multi-omics framework has the potential to enhance diagnostic precision for indeterminate pulmonary nodules, enabling more confident discrimination between benign and malignant lesions. However, most of these emerging tools have not yet been validated in large prospective trials and face technological barriers as well as challenges in real-world implementation. This review focuses primarily on LDCT screening detected pulmonary nodules, while incorporating evidence from incidentally detected and other indeterminate nodule cohorts when relevant to broader CT based management. By synthesizing advances in radiomics, AI, liquid biopsy, airway classifiers, and multi-omics integration, we highlight the need for prospective validation and multidisciplinary collaboration to translate these approaches into clinically useful pathways that improve early lung cancer detection, reduce unnecessary interventions, and enhance patient outcomes.

PMID:42431477 | DOI:10.1016/j.critrevonc.2026.105483

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

Front Pediatr. 2026 Mar 16;14:1770578. doi: 10.3389/fped.2026.1770578. eCollection 2026.

ABSTRACT

The neonatal lung, once considered sterile, is now recognized to harbor a dynamic and complex microbiome that plays a critical role in respiratory health and disease. This review synthesizes current evidence on the composition, development, and functional impact of the lung microbiome in neonates, with a focus on its involvement in key respiratory disorders such as bronchopulmonary dysplasia, respiratory syncytial virus infection, neonatal acute respiratory distress syndrome, cystic fibrosis, and asthma predisposition. We place particular emphasis on the bidirectional communication along the gut-lung axis as a central mechanism, wherein intestinal microbiota and their metabolites modulate pulmonary immunity and inflammation. Emerging multi-omics studies that integrate microbial data with host metabolomic and immune profiles are highlighted for their role in deciphering disease-specific dysbiotic signatures and mechanistic pathways. Critically, this review advances the discussion beyond association by evaluating the translational potential of the microbiome as both a diagnostic biomarker and a therapeutic target. We provide a critical appraisal of innovative microbiome-targeted strategies-including probiotics, postbiotics, phage therapy, and bacterial lysates-and discuss the unique challenges and future directions for translating these approaches into safe, effective clinical interventions for vulnerable neonates. By bridging foundational science with clinical implications, this work aims to inform the development of novel, ecology-informed therapeutics to prevent and mitigate neonatal respiratory diseases.

PMID:41918694 | PMC:PMC13033698 | DOI:10.3389/fped.2026.1770578

Cell Rep Med. 2026 Mar 30:102708. doi: 10.1016/j.xcrm.2026.102708. Online ahead of print.

ABSTRACT

Intratumor heterogeneity (ITH) undermines transcriptome-based stratification in intrahepatic cholangiocarcinoma (iCCA). Here, we integrate multi-omics data from multi-region, single-region, and single-cell RNA sequencing cohorts to systematically characterize gene expression ITH. We uncover that immune and stromal heterogeneity are primary drivers of ITH, leading to misclassification of a median 27.8% of tumors by existing subtyping systems. To overcome this, we identify a low-intratumor-heterogeneity/high-intertumor-variability (LIHV) gene set and develop an ITH-insensitive classification system defining five subgroups: inflammatory (SI), metabolic (SII), atypical (SIII-1), immune-silent (SIII-2), and neurodegenerative (SIII-3). These subgroups exhibit distinct clinical outcomes, molecular features, immune landscapes, and therapeutic vulnerabilities. GPRC5A and VTCN1 serve as robust immunohistochemical biomarkers for SI and SIII tumors, while serum CEA and CA19-9 identify inflammatory iCCA. Therapeutically, HSP90 inhibition synergizes with anti-PD1 in inflammatory iCCA, whereas combined anti-PD1 and anti-TIM3 suppresses neurodegenerative iCCA. Collectively, our study provides a robust molecular framework and actionable therapeutic strategies for iCCA.

PMID:41916296 | DOI:10.1016/j.xcrm.2026.102708