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Guodong Guo

Publications and source records attributed to Guodong Guo.

2 recordsLinked to original sources

ITPRIPL1: A tumor immune-associated biomarker with prognostic and therapeutic implications in gastrointestinal cancer.

Inositol 1,4,5-trisphosphate receptor-interacting protein-like 1(ITPRIPL1) has recently been implicated in tumor-immune regulation, yet its tumor-type specificity and clinical relevance in gastrointestinal malignancies remain unclear. Here, we performed an integrative analysis of ITPRIPL1 across stomach adenocarcinoma (STAD), colon adenocarcinoma (COAD), rectal adenocarcinoma (READ), and esophageal carcinoma (ESCA) using bulk transcriptomics, immune pathway analyses, survival modeling, single-cell RNA sequencing, immunofluorescence validation, and therapeutic correlation analyses. Although ITPRIPL1 was upregulated across gastrointestinal cancers, its prognostic significance was highly tumor-specific, with elevated expression consistently predicting unfavorable survival only in STAD. In gastric cancer, ITPRIPL1 expression was closely associated with immune-related pathways and genomic instability features, and its prognostic association varied across immune contexts, particularly according to CD8⁺/CD4⁺ T-cell abundance, with an exploratory association also observed for zeta-chain-associated protein kinase 70 (ZAP70) expression. Single-cell and immunofluorescence analyses demonstrated preferential enrichment of ITPRIPL1 in T cells and tumor-adjacent immune structures. Notably, Exploratory analyses further showed that higher ITPRIPL1 expression was associated with favorable survival outcomes in selected external pretreatment immunotherapy cohorts and with lower IC50 values for several agents in cancer cell-line pharmacogenomic datasets. Collectively, these findings identify ITPRIPL1 as an immune-associated biomarker with primary clinical relevance in gastric cancer.

Humans↗

Learning from examples in the small sample case: face expression recognition.

Example-based learning for computer vision can be difficult when a large number of examples to represent each pattern or object class is not available. In such situations, learning from a small number of samples is of practical value. To study this issue, the task of face expression recognition with a small number of training images of each expression is considered. A new technique based on linear programming for both feature selection and classifier training is introduced. A pairwise framework for feature selection, instead of using all classes simultaneously, is presented. Experimental results compare the method with three others: a simplified Bayes classifier, support vector machine, and AdaBoost. Finally, each algorithm is analyzed and a new categorization of these algorithms is given, especially for learning from examples in the small sample case.

Algorithms↗