Clin Epigenetics. 2026 May 26;18(1):90. doi: 10.1186/s13148-026-02084-2.
ABSTRACT
BACKGROUND: Non-invasive biomarkers offer potential to improve risk stratification and early diagnosis of lung cancer, complementing low-dose computed tomography (LDCT) screening. This study employed bibliometric analysis to identify global research trends, collaborative networks, and future directions in lung cancer biomarker research. Publications on lung cancer biomarkers for screening were retrieved from the Web of Science Core Collection (WoSCC). Data processing and visualisation were performed using Citespace, VOSviewer, KH Coder, Latent Dirichlet Allocation (LDA) topic modelling, and the online bibliometric analysis platform. Burst detection analysis was performed to predict emerging research trends.
RESULTS: Analysis of 3636 publications revealed exponential growth in research output since 2014. International collaboration demonstrated a dual-core structure centred on China and the United States, with Chinese institutions showing high publication volumes and American institutions demonstrating greater citation influence. Journal citation mapping revealed three evolutionary phases: basic mechanisms-clinical translation-intelligent integration. LDA topic modelling identified 22 topics grouped into five core research directions: imaging and pathological diagnostic techniques; molecular and omics marker research; liquid biopsy and new detection technologies; clinical and translational medicine research; and tumour biology and treatment mechanisms. Burst detection analysis predicted future four priority areas: epigenetic studies centred on DNA methylation for risk prediction; treatment resistance and invasion mechanisms; liquid biopsy technology development; and targeted therapy clinical trials.
CONCLUSIONS: Lung cancer biomarker research has evolved towards multimodal, intelligent screening approaches. Future research priorities include DNA methylation-based markers, circulating microRNA signatures, and artificial intelligence-assisted diagnostic platforms to improve early detection accuracy and complement LDCT screening.
PMID:42185923 | DOI:10.1186/s13148-026-02084-2
Nature, Published online: 08 April 2026; doi:10.1038/s41586-026-10342-9
A clinical phaseβ1 trial of a single infusion of CS-101, CD34+ cells modified using a transformer base editor to reactivate fetal haemoglobin production, led to early and enduring transfusion independence in patients with Ξ²-thalassaemia.
Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.
ABSTRACT
The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.
PMID:41914832 | DOI:10.1039/d5fo04958j
Food Funct. 2026 Mar 31. doi: 10.1039/d5fo04958j. Online ahead of print.
ABSTRACT
The probiotic yeast Saccharomyces boulardii is renowned for its clinical efficacy, which is intrinsically linked to its exceptional ability to survive the harsh gastrointestinal (GI) environment. However, a comprehensive understanding of the molecular mechanisms and regulatory pathways underlying the stress tolerance of S. boulardii remains limited. This study employed an integrated transcriptomic and proteomic approach to systematically map the dynamic responses of S. boulardii to simulated GI transit. Our analysis revealed that the intestinal phase posed a significantly greater challenge than the gastric phase, triggering extensive molecular reprogramming. A core adaptive strategy was the marked upregulation of the central carbon metabolism, particularly glycolysis, as evidenced by the concerted overexpression of key enzymes at both transcriptional and translational levels, indicating a heightened demand for energy to fuel stress defence mechanisms. Furthermore, significant enrichment was observed in the pathways related to nitrogen and fatty acid metabolism. Integration of the multi-omics datasets highlighted the complexity of the regulatory response, with frequent discordance between mRNA and protein abundance underscoring the importance of post-transcriptional regulation. This study provides a detailed molecular profile of the stress tolerance network in S. boulardii, elucidating the strategic metabolic rewiring and multi-layered regulation that underpin its probiotic resilience. The findings offer valuable insights and a foundational resource for the future development of enhanced probiotic therapies.
PMID:41914832 | DOI:10.1039/d5fo04958j
iScience. 2026 Mar 3;29(4):115203. doi: 10.1016/j.isci.2026.115203. eCollection 2026 Apr 17.
ABSTRACT
Mass spectrometry imaging (MSI)-based spatial metabolomics exhibits extensive missing values; yet, practical guidance on how imputation choices affect both imputation accuracy and downstream spatial analyses remains limited. In this study, we evaluated eight imputation methods, including both existing approaches and a graph convolutional network (GCN)-based method specifically designed for spatial metabolomics data, to identify suitable approaches for spatial metabolomics. To enable comprehensive assessment, we developed an evaluation framework focusing on two objective criteria: (a) imputation accuracy and (b) preservation of spatial cluster structure. We assembled six benchmark datasets spanning mouse brain and liver, human kidney and stomach, and plant seed sections, and conducted controlled dropout simulations of missing values. Across both evaluation dimensions, including imputation accuracy and preservation of spatial cluster structure, RF ranked first overall, and GCN ranked second in both dimensions. Overall, this systematic, dual-perspective benchmark study provides guidance for selecting imputation strategies in spatial metabolomics research.
PMID:41869568 | PMC:PMC12999350 | DOI:10.1016/j.isci.2026.115203
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