ObjectiveTo investigate the prognostic significance of tumor necrosis factor receptor (TNFR),-associated factor 6 (TRAF6),-and ubiquitin in gastric cancer patients.MethodsBiopsies of the rectus abdominis muscle were obtained intra operatively from 102 gastric cancer patients and 29 subjects undergoing surgery for benign abdominal diseases, and muscle TRAF6 and ubiquitin mRNA expression and proteasome proteolytic activities were assessed.ResultsTRAF6 was significantly upregulated in muscle of gastric cancer compared with the control muscles. TRAF6 was upregulated in 67.65% (69/102) muscle of gastric cancer. Over expression of TRAF6 in muscles of gastric cancer were associated with TNM stage, level of serum albumin and percent of weight loss. Ubiquitin was significantly upregulated in muscle of gastric cancer compared with the control muscles. Ubiquitin was upregulated in 58.82% (60/102) muscles of gastric cancer. Over expression of ubiquitin in muscles of gastric cancer were associated with TNM (Tumor-Node-Metastasis) stage and weight loss. There was significant relation between TRAF6 and ubiquitin expression.ConclusionsWe found a positive correlation between TRAF6 and ubiquitin expression, suggesting that TRAF6 may up regulates ubiquitin activity in cancer cachexia. While more investigations are required to understand its mechanisms of TRAF6 and ubiquitin in skeletal muscle. Correct the catabolic-anabolic imbalance is essential for the effective treatment of cancer cachexia.

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Background: The early noninvasive screening of patients suitable for neoadjuvant chemotherapy (NCT) is essential for personalized treatment in locally advanced gastric cancer (LAGC). The aim of this study was to develop and visualized a radio-clinical biomarker from pretreatment oversampled CT images to predict the response and prognosis to NCT in LAGC patients.Methods: 1060 LAGC patients were retrospectively recruited from six hospitals from January 2008 to December 2021. The training (TC) and internal validation cohort (IVC) were randomly selected from center I. The external validation cohort (EVC) comprised 265 patients from 5 other centers. An SE-ResNet50-based chemotherapy response predicting system (DL signature) was developed from pretreatment CT images preprocessed with imaging oversampling method (i.e. DeepSMOTE). Then, DL signature and clinic-based features were fed into the deep learning radio-clinical signature (DLCS). The predictive performance was evaluated according to discrimination, calibration and clinical usefulness. Model for OS prediction were built to further explore the survival benefit of the proposed DL signatures and clinicopathological characteristic. Result: DLCS showed perfect performance in predicting the response to NCT in the IVC (AUC, 0.86) and EVC (AUC, 0.82), with good calibration in all cohorts (p > 0.05). In addition, the performance of DLCS was better than that of the clinical model (p＜0.05). Finally, we found that the DL signature could also serve as an independent factor for prognosis (HR, 0.828, p = 0.004). The C-index, iAUC, IBS for the OS model were 0.64, 1.24 and 0.71 in the test set.Conclusion: We proposed the DLCS that links the imaging features to clinical risk factors to generate high accuracy classification of tumor response and risk identification of OS in LAGC patients prior to NCT that then can be used for guiding personalized treatment plans with the help of the visualization of computerized tumor-level characterization.

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