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Results for “ASPN”

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PubMed2026

Screening of molecular biomarkers ASPN and LBH and construction of a prediction nomogram for the progression of esophagogastric junction adenocarcinoma.

BACKGROUND: Esophagogastric junction adenocarcinoma (EGJA) is an aggressive malignancy of the digestive system with poor prognosis. Early diagnosis and accurate prediction of tumor progression remain major clinical challenges. This study aimed to identify and validate molecular biomarkers and construct a precise diagnostic model, providing a scientific basis for individualized treatment. METHODS: Differentially expressed genes (DEGs) associated with EGJA were identified using The Cancer Genome Atlas (TCGA) database. Quantitative real-time polymerase chain reaction (qRT-PCR) was then performed for further screening. The protein expression levels of ASPN and LBH were validated by immunohistochemistry in both tumor and adjacent non-tumor tissues. A nomogram was constructed by integrating clinical and pathological features, and its performance and clinical utility were assessed using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). RESULTS: Immunohistochemistry demonstrated that the protein expression of ASPN was significantly upregulated in tumor tissues, with expression levels increasing with tumor stage. Conversely, LBH was downregulated in tumor tissues and decreased with advancing stages. The predictive model achieved an area under the curve (AUC) value of 0.977, indicating excellent diagnostic and prognostic performance. DCA confirmed the clinical net benefit of the model. CONCLUSIONS: ASPN and LBH are critical molecular biomarkers for EGJA. The nomogram combining these two markers enables accurate distinction between early and advanced-stage tumors, offering significant support for early diagnosis of EGJA.

ASPN↗

PubMed2025

Fetal-like reversion in the regenerating intestine is regulated by mesenchymal asporin.

Mesenchymal cells and the extracellular matrix (ECM) support epithelium during homeostasis and regeneration. However, the role of the mesenchyme in epithelial conversion into a fetal-like regenerative state after damage is not known. We modeled epithelial regeneration by culturing intestinal epithelium on decellularized small intestinal scaffolds (iECM) and identify asporin (Aspn), an ECM-bound proteoglycan, as a critical mediator of epithelial fetal-like reprogramming. After damage, transient increase in Aspn expression by the pericryptal fibroblasts induces epithelial transforming growth factor β (TGF-β)-signaling via CD44 and promotes timely epithelial reprogramming. Temporal control of Aspn is lost in old mice, and after damage, the persistently high level of Aspn stagnates epithelium in the regenerative state. Increase in Wnt signaling can resolve the stagnated regenerative program of the old epithelium, promoting restoration of tissue function. In summary, we establish a platform for modeling epithelial injury responses ex vivo and show that the mesenchymal Aspn-producing niche modulates tissue repair by regulating epithelial fetal-like reprogramming.

Animals↗

PubMed2026

Graph neural network-based risk stratification of prostate cancer using gene expression and SHAP interpretability.

Accurate risk stratification is essential for guiding treatment decisions and preventing over treatment of prostate cancer, which remains one of the most prevalent cancers among adult men. While the Gleason score, obtained from prostate biopsies, is routinely used to assess tumor aggressiveness, the biopsy procedure carries risks such as pain, infection, and, in some cases, serious complications such as sepsis. In this study, we proposed an artificial intelligence-based framework that integrates mRNA expression profiles with functional interaction networks to classify prostate cancer patients into low-, medium-, and high-risk groups defined by Gleason scores. The pipeline comprised five steps: (1) data collection from The Cancer Genome Atlas (TCGA), (2) preprocessing of gene expression data, (3) two-stage feature selection to identify informative biomarkers, (4) risk classification using a dual-branch graph neural network (GNN) that combines gene-gene interaction graphs with sample-level expression features, and (5) model interpretation using SHAP to quantify feature contributions. Differentially expressed genes were identified in the High (ASPN, GMNN, PEBP4, C2, KNCK17), Medium (C2, IGSF1, ASPN, CDKN3, AMH), and Low (TNMD, VWA5B2, ST6GALNAC5, CYP3A5, PHGR1) risk groups, underscoring the molecular heterogeneity of disease progression. On an independent held-out test set, the model achieved AUCs of 0.86, 0.88, and 0.95 for the low-, medium-, and high-risk groups, respectively, with an overall accuracy of 80%. These results suggest that combining GNN-based modeling with explainable AI can capture both global and local molecular patterns relevant to tumor aggressiveness. However, as the model was developed and evaluated solely on the TCGA cohort, the findings should be regarded as exploratory, and external validation will be required to establish generalizability. Within these limitations, the proposed framework highlights the potential of molecular profiling and graph-based deep learning to support more precise, potentially less invasive, risk assessment and individualized treatment planning in prostate cancer.

Prostatic Neoplasms↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗