Synthesis of β‑<i>C</i>‑GlcNAc Ser from β‑<i>C</i>‑Glc Ser

The glycosylation of proteins, specifically installation of <i>O</i>-GlcNAc on Ser/Thr residues, is a dynamic control element for transcription repression, protein degradation, and nutrient sensing. To provide homogeneous and stable structures with this motif, the synthesis of a C-linked mimic, <i>C</i>-GlcNAc Ser, has been prepared from the <i>C</i>-Glc Ser by a double inversion strategy using azide to insert the C-2 nitrogen functionality. The <i>C</i>-Glc Ser was available by a ring-closing metathesis and hydroalkoxylation route

Table_1_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.xlsx

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.</p

Image_2_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.tif

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.</p

Image_1_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.tif

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.</p

Table_3_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.xlsx

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.</p

Table_2_CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.xlsx

BackgroundDNA mismatch repair (MMR) deficiency has attracted considerable attention as a predictor of the immunotherapy efficacy of solid tumors, including gastric cancer. We aimed to develop and validate a computed tomography (CT)-based radiomic nomogram for the preoperative prediction of MMR deficiency in gastric cancer (GC).MethodsIn this retrospective analysis, 225 and 91 GC patients from two distinct hospital cohorts were included. Cohort 1 was randomly divided into a training cohort (n = 176) and an internal validation cohort (n = 76), whereas cohort 2 was considered an external validation cohort. Based on repeatable radiomic features, a radiomic signature was constructed using the least absolute shrinkage and selection operator (LASSO) regression analysis. We employed multivariable logistic regression analysis to build a radiomics-based model based on radiomic features and preoperative clinical characteristics. Furthermore, this prediction model was presented as a radiomic nomogram, which was evaluated in the training, internal validation, and external validation cohorts.ResultsThe radiomic signature composed of 15 robust features showed a significant association with MMR protein status in the training, internal validation, and external validation cohorts (both P-values ConclusionThe CT-based radiomic nomogram showed good performance for preoperative prediction of MMR protein status in GC. Furthermore, this model was a noninvasive tool to predict MMR protein status and guide neoadjuvant therapy.</p