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Early detection plays a critical role in mitigating mortality rates linked to gastric cancer. However, current clinical screening methods exhibit suboptimal efficacy. Methylation alterations identified from cell‐free DNA (cfDNA) present a promising biomarker for early cancer detection. Our study focused on identifying gastric cancer‐specific markers from cfDNA methylation to facilitate early detection. We enrolled 150 gastric cancer patients and 100 healthy controls in this study, and undertook genome‐wide methylation profiling of cfDNA using cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing. We identified 21 differentially methylated regions (DMRs) between the gastric tumor and nontumor groups using multiple algorithms. Subsequently, using the 21 DMRs, we developed a gastric cancer detection model by random forest algorithm in the discovery set, and validated the model in an independent set. The model was able to accurately discriminate gastric cancer with a sensitivity and specificity of 93.90% and 95.15% in the discovery set, respectively, and 88.38% and 94.23% in the validation set, respectively. These results underscore the efficacy and accuracy of cfDNA‐derived methylation markers in distinguishing early stage gastric cancer. This study highlighted the significance of cfDNA methylation alterations in early gastric cancer detection. Early detection plays a critical role in mitigating mortality rates linked to gastric cancer. In this study, we undertook genome‐wide methylation profiling using cell‐free methylated DNA immunoprecipitation and high‐throughput sequencing to investigate the efficacy and accuracy of cell‐free DNA (cfDNA)‐derived methylation markers in distinguishing early stage gastric cancer. Our findings emphasize the importance of cfDNA methylation alterations in the early detection of gastric cancer.

Metastatic colorectal cancer is commonly treated with oxaliplatin. However, patients may develop resistance to treatment over time, and currently, there are no validated predictive biomarkers for this resistance. A differential analysis of the transcriptome and DNA methylome of colorectal cancer cell lines, classified by their varying IC50 values for oxaliplatin, revealed that genes associated with resistance were enriched for interferon regulatory factor pathways. In contrast, sensitive genes showed enrichment for transcription, amino acid metabolism, development, and binding motifs of c-MyC:Max, AP1 and others. In univariate Cox’s proportional hazard model analysis, it was found that UBE2H expression is linked to shorter survival time in the TCGA dataset and was further validated across five GEO datasets of colorectal cancer. The transcription of UBE2H, along with its gene body methylation, and copy number variation was found to be higher in resistant cell lines compared to sensitive ones. Additionally, UBE2H levels were higher in cancer samples than in control samples, while the promoter methylation is lower in cancer samples than in control samples. In groups with high UBE2H, there was an increased infiltration of eight cell types, including CD8 + T cells and type 2 T helper cells. Conversely, only T helper 17 cells showed reduced infiltration. Moreover, UBE2H expression was positively correlated with checkpoint inhibitors, CTLA4 and PDCD1, along with immune regulatory genes, such as FOXP3, IL10, IGFB1, CD274, and LAG3, etc. Analysis of single cell RNA-sequencing data revealed that UBE2H expression is elevated in undifferentiated and proliferative cells located at the base of intestinal crypts in normal colon tissue. Our findings suggest that UBE2H could serve as a resistance marker to oxaliplatin, as it is associated with methylation, the presence of proliferative and undifferentiated cancer cells, and immune exhaustion. The proposed analytical pipeline may also be applicable to other cancers and diseases.

ObjectiveThe purpose of the article is to establish a quick enrichment and detection method using immunomagnetic beads and flow cytometry to analyze circulating tumor cells (CTCs) in the peripheral blood.ResultsAfter incubation with CD326-PE and CD45-APC antibodies, more than 60% MCF7 cells in M-Buffer could be detected while less than 10% of the same cells could be detected by flow cytometry (FCM) if spiked into blood. However, in combination with CD326 and CD45 immunomagnetic beads, detection rate of MCF7 cells in blood reached 57%. For circulating tumor cells, enrichment by CD326 and CD45 immunomagnetic beads improve the detection rate from nearly undetectable to more than 24.14%.ConclusionsLive CTCs in peripheral blood can be effectively and sensitively detected by using a combination of immunomagnetic beads (CD45 and CD326) and flow cytometry.

Mitogen-activated protein kinase （MAPK） cascades play important roles in regulating plant innate immune responses. In a genetic screen to search for mutants with constitutive defense responses, we identified multiple alleles of mpk4 and mekkl that exhibit cell death and constitutive defense responses. Bimolecular fluorescence complemen- tation （BiFC） analysis showed that both MPK4 and MEKK1 interact with MKK1 and MKK2, two closely related MAPK kinases, mkkl and mkk2 single mutant plants do not have obvious mutant phenotypes. To test whether MKK1 and MKK2 function redundantly, mkkl mkk2 double mutants were generated. The mkkl mkk2 double mutant plants die at seedling stage and the seedling-lethality phenotype is temperature-dependent. Similar to the mpk4 and mekkl mutants, the mkkl mkk2 double mutant seedlings accumulate high levels of H202, display spontaneous cell death, constitutively express Pathogenesis Related （PR） genes and exhibit pathogen resistance. In addition, activation of MPK4 by fig22 is impaired in the mkkl mkk2 double mutants, suggesting that MKK1 and MKK2 function together with MPK4 and MEKK1 in a MAP kinase cascade to negatively regulate innate immune responses in plants.

Tumor-associated microbial antigens represent promising immunotherapy targets, yet systematic identification methods remain underdeveloped. We developed MimicNeoAI, a computational pipeline integrating BiLSTM networks to identify microbial epitopes, mutation-derived neoepitopes, and their microbial mimics from sequencing data. Training on validated epitope datasets yielded 0.90 AUC with 91% accuracy on experimental validation sets. Application to colorectal cancer revealed that microbial epitopes, despite originating from a nine-fold smaller peptide pool, generated twice the immunogenic candidates (153 vs 75) compared to mutation-derived neoepitopes. These microbial epitopes exhibited exclusive tumor-specificity with no overlap in normal tissues. Single-cell TCR sequencing confirmed clonal expansion against 75% of predicted highly immunogenic epitopes, with molecular dynamics simulations demonstrating positive correlation between predicted immunogenicity and HLA-epitope-TCR binding stability. Collectively, our pipeline systematically unveils abundant, tumor-specific, and highly immunogenic microbial epitopes, providing a computational framework for developing broadly applicable cancer immunotherapies that leverage the tumor microbiome as an untapped source of therapeutic targets.

The therapeutic promise of tumor microbial peptides faces a critical safety paradox: tumors and peritumor tissues harbor compositionally similar microbiomes, yet safe immunotherapy demands tumor-specific targeting. We performed comprehensive transcriptomic analysis of microbial communities from 1,868 tumor and peritumor samples across 11 cancer types. Although 88.5% of microbial species are present in both tissue types, tumor and peritumor tissues exhibit profound transcriptional divergence: only 34.1% of microbial genes are expressed in both tissue types, and merely 18.2% of predicted HLA-binding peptides overlap. This transcriptional divergence represents microbial transcriptional reprogramming (MTR) in tumor tissues, through which microbes activate distinct genomic regions while adapting to tumor microenvironmental pressures, thereby generating tumor-specific peptide repertoires. Using Microbial Transcriptional Reprogramming Index (MiTRI) to quantify MTR, we found that higher MiTRI values correlate with expanded immunogenic peptide repertoires. Mass spectrometry-based immunopeptidomics confirmed that 25% of naturally presented microbial peptides originate from MTR regions. Crucially, in microsatellite-stable colorectal cancer, neo-adjuvant therapy responders exhibited significantly higher MiTRI values. T cell receptors (TCRs) recognized MTR-derived epitopes at 2.10-fold higher rates than genome-wide epitopes, accounting for 79.1% of clonally expanded TCRs. These findings establish MTR as the principle that ensures the tumor-specificity and safety of micorbiome-directed immunotherapy.

Stereoscopy harnesses two spatially offset cameras to mimic human vision for depth perception, enabling 3D optical imaging for various remote sensing applications. However, its depth precision and accuracy are limited by insufficient spatial resolving power. Achieving high precision alongside extensive measurable ranges and high-speed measuring capabilities has long been a challenge in 3D imaging. To address this, we introduce time-domain stereoscopy, a concept inspired by space-time duality in optics. Specifically, it employs two temporally offset optical gating cameras to capture time-domain parallax signals, enabling rapid and precise time-of-flight measurements for depth retrieval. Leveraging two advanced technologies—femtosecond electro-optical comb synthesis and nonlinear optical sampling—this method achieves sub-100-nanometer depth precision across multimeter-scale imaging ranges and supports millisecond-scale displacement and velocity measurements for 47 million spatial points simultaneously. As such, it provides a versatile tool for applications in surface metrology, mechanical dynamics, and precision manufacturing. Stereoscopic imaging mimics human vision by using two offset cameras to perceive depth. Here, the authors propose time-domain stereoscopy, leveraging space-time duality for high-resolution, real-time 3D imaging via temporally offset optical gating.

Background Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder for which no effective therapy is currently available. Given that various attempts to target beta-amyloid (Aβ) have been unsuccessful in clinical trials, other potential pathogenic factors such as brain energy metabolism (EM) have attracted increasing attention. Traditional Chinese medicines, including danggui-shaoyao-san (DSS), play a notable role in AD. However, it remains unclear whether DSS exerts therapeutic effects on AD through EM regulation. Methods In this study, we conducted behavioural tests, Nissl staining, haematoxylin and eosin staining, and thioflavin S staining, in APP/PS1 mice to assess the pharmacodynamic effect of DSS on AD. Subsequently, we integrated the drug target network of herbal ingredients in DSS and evaluated their absorption, distribution, metabolism, excretion, and toxicity properties to identify the core ingredients. We used proteomic and metabolomic approaches to explore the potential mechanisms of action of DSS against AD. Consequently, we verified the mechanism underlying EM using qPCR, western blotting, and ELISA. Results In vivo experimental results revealed that DSS ameliorated cognitive impairment in APP/PS1 mice, attenuated neuronal apoptosis, and reduced Aβ burden. Furthermore, the drug-target network comprised 6,514 drug-target interactions involving 1,118 herbal ingredients and 218 AD genes, of which 253 were identified as the core ingredients in DSS. The proteomic results implied that DSS could act on EM to alleviate AD, and targeted energy metabolomics suggested that DSS regulated 47 metabolites associated with EM. Mechanistically, we found that DSS could regulate the GSK3β/PGC1α signalling pathway to improve brain glucose uptake and mitigate mitochondrial dysfunction and oxidative stress, ultimately promoting EM to treat AD. Conclusion Our study is the first to integrate multi-omics approaches to reveal that DSS could regulate the GSK3β/PGC1α signalling pathway to exert therapeutic effects in AD through the promotion of EM, thereby providing new insights into the mechanism of action of DSS against AD. Graphical abstract

Alzheimer’s disease (AD) is one of the most common progressive neurodegenerative diseases, accompanied by global alterations in metabolic profiles. In the past 10 years, over hundreds of metabolomics studies have been conducted to unravel metabolic changes in AD, which provides insight into the identification of potential biomarkers for diagnosis, treatment, and prognostic assessment. However, since different species may lead to systemic abnormalities in metabolomic profiles, it is urgently needed to perform a comparative metabolomics analysis between AD animal models and human patients. In this study, we integrated 78 metabolic profiles from public literatures, including 11 metabolomics studies in different AD mouse models and 67 metabolomics studies from AD patients. Metabolites and enrichment analysis were further conducted to reveal key metabolic pathways and metabolites in AD. We totally identified 14 key metabolites and 16 pathways that are both differentially significant in AD mouse models and patients. Moreover, we built a metabolite-target network to predict potential protein markers in AD. Finally, we validated HER2 and NDF2 as key protein markers in APP/PS1 mice. Overall, this study provides a comprehensive strategy for AD metabolomics research, contributing to understanding the pathological mechanism of AD.