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KMID : 0620920210530020223
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Experimental & Molecular Medicine
2021 Volume.53 No. 2 p.223 ~ p.234
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Practical prediction model of the clinical response to programmed death-ligand 1 inhibitors in advanced gastric cancer
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Noh Myung-Giun
Yoon Young-Min
Kim Gi-Hyeon
Kim Hyun
Lee Eul-Gi
Kim Yeong-Min
Park Chang-Ho
Lee Kyung-Hwa
Park Han-Soo
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Abstract
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The identification of predictive biomarkers or models is necessary for the selection of patients who might benefit the most from immunotherapy. Seven histological features (signet ring cell [SRC], fibrous stroma, myxoid stroma, tumor-infiltrating lymphocytes [TILs], necrosis, tertiary lymphoid follicles, and ulceration) detected in surgically resected tissues (N?=?44) were used to train a model. The presence of SRC became an optimal decision parameter for pathology alone (AUC?=?0.78). Analysis of differentially expressed genes (DEGs) for the prediction of genomic markers showed that C-X-C motif chemokine ligand 11 (CXCL11) was high in responders (P?
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KEYWORD
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Predictive markers, Predictive medicine, Translational research
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