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Spatial and multi-omics transcriptomic dissects platinum resistance in lung adenocarcinoma: a five-gene predictive model with tumor microenvironment dynamics

Chem Biol Interact. 2026 Feb 7:111952. doi: 10.1016/j.cbi.2026.111952. Online ahead of print.

ABSTRACT

The scarcity of reliable biomarkers and predictive models for platinum resistance in lung adenocarcinoma (LUAD) poses a significant clinical challenge. This study endeavors to identify molecular subtypes related to platinum resistance and construct a robust predictive model through multi-omics techniques. We performed integrative analysis of public datasets using advanced bioinformatics strategies, including spatial transcriptome deconvolution and consensus clustering. Bulk RNA deconvolution analysis was conducted to characterize tumor microenvironment heterogeneity. Feature selection was performed using the Supervised Principal Component (SuperPC) algorithm, followed by diagnostic model construction validated through receiver operating characteristic (ROC) analysis. Functional validation was performed through cytological experiments measuring cisplatin IC50 alterations following gene manipulation in LUAD cell lines. Consensus clustering revealed distinct LUAD subtypes, with Cluster1 demonstrating significant platinum resistance. We first subtyped the patients in the bulk transcriptome data based on consistency clustering, and then analyzed the differences between different platinum-resistant subtypes (Cluster 1 and Cluster 2), so as to screen 333 isotype-specific differentially expressed genes and 15 platinum resistance-related (PRR) genes were selected through machine learning. A refined 5-gene signature (ANKRD29/CACNA2D2/DSP/HSD17B6/SPP1) achieved exceptional predictive performance (AUC=0.9639). Spatial transcriptomics demonstrated compartmentalized expression patterns: SPP1/DSP localized to tumor niches, HSD17B6/CACNA2D2 to epithelial regions, and ANKRD29 depletion in stromal areas. Cellular colocalization analysis revealed malignant epithelial PH proximity to myeloid and mast cells. Functional validation confirmed that ANKRD29/CACNA2D2 overexpression sensitized A549/DDP cells to cisplatin, while DSP/SPP1/HSD17B6 overexpression induced resistance. Experiments in nude mice have shown that these genes are closely related to cisplatin resistance in LUAD. This study identifies the Cluster1 subtype and malignant epithelial PH as crucial determinants of platinum resistance in LUAD. Our innovative 5-gene predictive model exhibits clinical-grade diagnostic accuracy, and spatial transcriptomic characterization offers mechanistic insights into the dynamics of the tumor microenvironment.

PMID:41662930 | DOI:10.1016/j.cbi.2026.111952

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Aetheria: A multimodal interpretable content safety framework based on multi-agent debate and collaboration

arXiv:2512.02530v1 Announce Type: new Abstract: The exponential growth of digital content presents significant challenges for content safety. Current moderation systems, often based on single models or fixed pipelines, exhibit limitations in identifying implicit risks and providing interpretable judgment processes. To address these issues, we propose Aetheria, a multimodal interpretable content safety framework based on multi-agent debate and collaboration.Employing a collaborative architecture of five core agents, Aetheria conducts in-depth analysis and adjudication of multimodal content through a dynamic, mutually persuasive debate mechanism, which is grounded by RAG-based knowledge retrieval.Comprehensive experiments on our proposed benchmark (AIR-Bench) validate that Aetheria not only generates detailed and traceable audit reports but also demonstrates significant advantages over baselines in overall content safety accuracy, especially in the identification of implicit risks. This framework establishes a transparent and interpretable paradigm, significantly advancing the field of trustworthy AI content moderation.
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Diagnostic Performance of Computed Tomography–Based Artificial Intelligence for Early Recurrence of Cholangiocarcinoma: Systematic Review and Meta-Analysis

Background: Despite artificial intelligence (AI) models demonstrating high predictive accuracy for early cholangiocarcinoma recurrence, their clinical application faces challenges, such as reproducibility, generalizability, hidden biases, and uncertain performance across diverse datasets and populations, raising concerns about their practical applicability. Objective: This meta-analysis aims to systematically assess the diagnostic performance of AI models using computed tomography (CT) imaging to predict early recurrence of cholangiocarcinoma. Methods: A systematic search was conducted in PubMed, Embase, and Web of Science for studies published up to May 2025. Studies were selected based on the Participants, Index test, Target condition, Reference standard, Outcomes, and Setting (PITROS) framework. Participants included patients diagnosed with cholangiocarcinoma (including intrahepatic and extrahepatic locations). The index test was AI techniques applied to CT imaging for early recurrence prediction (defined as within 1 year), while the target condition was early recurrence of cholangiocarcinoma (positive group: recurrence; negative group: no recurrence). The reference standard was pathological diagnosis or imaging follow-up confirming recurrence. Outcomes included sensitivity, specificity, diagnostic odds ratio (DOR), and area under the receiver operating characteristic curve (AUC), assessed in both internal and external validation cohorts. The setting comprised retrospective or prospective studies using hospital datasets. Methodological quality was assessed using an optimized version of the revised Quality Assessment of Diagnostic Accuracy Studies-2 tool. Heterogeneity was assessed using the I² statistic. Pooled sensitivity, specificity, DOR, and AUC were calculated using a bivariate random-effects model. Results: A total of 9 studies with 30 datasets involving 1537 patients were included. In internal validation cohorts, CT-based AI models showed a pooled sensitivity of 0.87 (95% CI 0.81-0.92), specificity of 0.85 (95% CI 0.79-0.89), DOR of 37.71 (95% CI 18.35-77.51), and AUC of 0.93 (95% CI 0.90-0.94). In external validation cohorts, pooled sensitivity was 0.87 (95% CI 0.81-0.91), specificity was 0.82 (95% CI 0.77-0.86), DOR was 30.81 (95% CI 18.79-50.52), and AUC was 0.85 (95% CI 0.82-0.88). The AUC was significantly lower in external validation cohorts compared to internal validation cohorts (P<.001). Conclusions: Our results show that CT-based AI models predict early cholangiocarcinoma recurrence with high performance in internal validation sets and moderate performance in external validation sets. However, the high heterogeneity observed may impact the robustness of these results. Future research should focus on prospective studies and establishing standardized gold standards to further validate the clinical applicability and generalizability of AI models.
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The emerging role of microbiota in lung cancer: a new perspective on lung cancer development and treatment

Cell Oncol (Dordr). 2025 Aug 26. doi: 10.1007/s13402-025-01103-3. Online ahead of print.

ABSTRACT

Lung cancer remains the leading cause of cancer-related mortality worldwide, with limited treatment efficacy and frequent resistance to conventional therapies. Recent advances have uncovered the critical influence of the human microbiota-complex communities of bacteria, viruses, fungi, and other microorganisms-on lung cancer pathogenesis and therapeutic responses. This review synthesizes current knowledge on the compositional and functional roles of microbiota across multiple body sites, including the gut, lung, tumor microenvironment, circulation, and oral cavity, highlighting their contributions to tumor initiation, progression, metastasis, and immune regulation. We emphasize the bidirectional communication between microbial metabolites and host immune pathways, particularly the gut-lung axis, which modulates systemic and local antitumor immunity. Importantly, microbiota composition has been linked to differential responses and toxicities in chemotherapy, radiotherapy, targeted therapy, and immune checkpoint blockade. Microbiota-targeted interventions, such as probiotics, fecal microbiota transplantation, and selective antibiotics, show promising potential to enhance treatment efficacy and mitigate adverse effects. However, challenges remain in clinical translation due to interindividual microbiome variability, mechanistic complexities, and limited longitudinal data. Future research integrating multi-omics, microbial functional profiling, and controlled clinical trials is essential to harness the microbiome as a precision medicine tool in lung cancer management. This review provides a comprehensive overview of the emerging role of microbiota in lung cancer development and therapy, offering new perspectives for innovative therapeutic strategies.

PMID:40856929 | DOI:10.1007/s13402-025-01103-3

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Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome

Nature Biotechnology, Published online: 30 November 2022; doi:10.1038/s41587-022-01616-4

Author Correction: m6A RNA modifications are measured at single-base resolution across the mammalian transcriptome
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The co-evolution of the genome and epigenome in colorectal cancer

Nature, Published online: 26 October 2022; doi:10.1038/s41586-022-05202-1

A study maps genetic and epigenetic heterogeneity of primary colorectal adenomas and cancers at single-clone resolution through spatial multi-omic profiling of individual glands and adjacent normal tissue.
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