Biomedical subjects

Rui Guo

Publications and source records attributed to Rui Guo.

3 recordsLinked to original sources

PubMed2026

Spatially confined electrochemical strategy with DNA-assembled nanogaps for SNP detection.

Accurate detection of low-abundance single nucleotide polymorphisms (SNPs) against a large excess of homologous wild-type sequences requires both selective molecular recognition and effective transduction of small sequence differences into measurable signals. Here, we report a spatially confined electrochemical strategy that couples sequence-selective recognition with size-dependent mass-transport gating. DNA-hybridization-driven self-assembly of gold nanoparticles (AuNPs) forms a three-dimensional self-assembled electrode (3D-SAE) with a DNA-defined interparticle architecture. Competitive probes (SP/WP) convert single-base recognition into distinct molecular-size states: the SNP-associated pathway preferentially triggers a hybridization chain reaction (HCR), generating bulky AuNP-anchored HCR/methylene blue complexes (Au@HCR/MB) with reduced electrochemical accessibility through the porous 3D-SAE, whereas the wild-type pathway does not trigger HCR and maintains a high-current response from more readily accessible MB-containing species. Thus, sequence recognition is translated into a molecular-size difference and subsequently into an electrochemical signal through differential mass transport. Under buffer conditions, the platform achieved a statistically estimated detection limit of ∼0.47 fM and a quantitative range of 1 fM-100 pM. It discriminated a 0.1% mutant abundance in a fragmented genomic-DNA background. The downstream signal-transduction chemistry is enzyme-free and isothermal. This work establishes a mechanistical recognition-size-conversion-mass-transport-gating architecture for electrochemical nucleic acid analysis.

Polymorphism, Single Nucleotide↗

PubMed2026

Stromal Hedgehog Signaling Drives Segment-Specific Malignant Transformation of Gastrointestinal Stem Cells by Producing Bone Morphogenetic Protein Antagonists.

BACKGROUND & AIMS: Hedgehog signaling plays a complex role in epithelial-stromal interactions, but its effects on gastrointestinal stem cells mediated by heterogeneous stromal cell populations remain incompletely defined. Here, we investigate how stromal Hedgehog signaling regulates gastric stem cells and tumorigenesis in a segment-specific manner. METHODS: We genetically activated Hedgehog signaling in distinct stromal cell lineages using Col1a2-, Pdgfra-, Gli1-, Acta2-, and Prrx1-CreERT mouse lines, combined with lineage tracing, RNA sequencing, chromatin immunoprecipitation-quantitative polymerase chain reaction, and pharmacologic interventions. Human gastric cancer data from The Cancer Genome Atlas were also analyzed. RESULTS: We show that genetic activation of Hedgehog signaling in stromal cells marked by Col1a2, Pdgfra, or Gli1, but not by Acta2, induces tumorigenesis in the stomach and gastroesophageal junction, but not in the small or large intestine. Hedgehog signaling increases the expression of multiple bone morphogenetic protein antagonists in gastric but not colonic stromal cells, via Gli1-mediated transcription. These bone morphogenetic protein antagonists, in turn, activate Wnt/β-catenin signaling in gastric stem cells, driving their proliferation and initiating gastric cancer expressing CD44 and Sox9, but not Lgr5. Activating bone morphogenetic protein or inhibiting Wnt signaling blocks tumor initiation. Analysis of patient data from The Cancer Genome Atlas reveals elevated Hedgehog signaling in gastric cancers, which correlates with suppressed bone morphogenetic protein signaling. CONCLUSIONS: These findings uncover a gastrointestinal segment-specific oncogenic role for Hedgehog signaling in Col1a2+Acta2- stromal cells, mediated through the bone morphogenetic protein-Wnt-β-catenin axis.

BMP Antagonists↗

PubMed2026

SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.

Bladder cancer survival outcomes exhibit significant heterogeneity, influenced by multifaceted factors. While digital pathology-based survival models leveraging artificial intelligence show promise, they often overlook complementary data sources. Conversely, imaging lacks cellular detail, and genomics/proteomics entail complexity and cost. To integrate multidimensional data for enhanced survival prediction, we propose SurvGRN, a multi-feature fusion framework. SurvGRN synergistically combines clinical variables, transcriptomics, and digital pathology slides using a gated residual network architecture. Pathological features are extracted via multiple instance learning, while clinical and transcriptomic data are processed as static inputs. These features are dynamically fused using a long short-term memory (LSTM) network for comprehensive survival risk assessment. Evaluated on 400 bladder cancer patients, SurvGRN significantly outperformed existing methods: improving the C-index by 12.6% over DeepMISL; 20.6% and 7.1% over graph-based models (DeepGraphConv and Patch-GCN); and 5.4% and 4.0% over attention-based approaches (Surformer and HVTSurv). Ablation studies confirmed the contributions of pathology features (extracted via ResNet-50 pre-trained on bladder tissue), clinical/transcriptomic data, and the LSTM fusion. SurvGRN also enabled significant stratification of patients into distinct risk cohorts. This work demonstrates that holistic integration of multi-source data through tailored fusion architectures substantially improves bladder cancer survival prediction.

bladder cancer↗