The global change in protein abundance in colorectal cancer (CRC) and its contribution to tumorigenesis have not been comprehensively analyzed. In this study, we conducted a comprehensive proteomic analysis of paired tumors and adjacent tissues (AT) using high-resolution Fourier-transform mass spectrometry and a novel algorithm of quantitative pathway analysis. 12380 proteins were identified and 740 proteins that presented a 4-fold change were considered a CRC proteomic signature. A significant pattern of changes in protein abundance was uncovered which consisted of an imbalance in protein abundance of inhibitory and activating regulators in key signal pathways, a significant elevation of proteins in chromatin modification, gene expression and DNA replication and damage repair, and a decreased expression of proteins responsible for core extracellular matrix architectures. Specifically, based on the relative abundance, we identified a panel of 11 proteins to distinguish CRC from AT. The protein that showed the greatest degree of overexpression in CRC compared to AT was Dipeptidase 1 (DPEP1). Knockdown of DPEP1 in SW480 and HCT116 cells significantly increased cell apoptosis and attenuated cell proliferation and invasion. Together, our results show one of largest dataset in CRC proteomic research and provide a molecular link from genomic abnormalities to the tumor phenotype.

Objective: This study aimed to construct and validate a clinical prediction model for surgical site infection (SSI) risk 30 days after gastrointestinal surgery. Materials and methods: This multicentre study involving 57 units conducted a 30-day postoperative follow-up of 17 353 patients who underwent gastrointestinal surgery at the unit from 1 March 2021 to 28 February 2022. The authors collected a series of hospitalisation data, including demographic data, preoperative preparation, intraoperative procedures and postoperative care. The main outcome variable was SSI, defined according to the Centres for Disease Control and Prevention guidelines. This study used the least absolute shrinkage and selection operator (LASSO) algorithm to screen predictive variables and construct a prediction model. The receiver operating characteristic curve, calibration and clinical decision curves were used to evaluate the prediction performance of the prediction model. Results: Overall, 17 353 patients were included in this study, and the incidence of SSI was 1.6%. The univariate analysis combined with LASSO analysis showed that 20 variables, namely, chronic liver disease, chronic kidney disease, steroid use, smoking history, C-reactive protein, blood urea nitrogen, creatinine, albumin, blood glucose, bowel preparation, surgical antibiotic prophylaxis, appendix surgery, colon surgery, approach, incision type, colostomy/ileostomy at the start of the surgery, colostomy/ileostomy at the end of the surgery, length of incision, surgical duration and blood loss were identified as predictors of SSI occurrence (P<0.05). The area under the curve values of the model in the train and test groups were 0.7778 and 0.7868, respectively. The calibration curve and Hosmer–Lemeshow test results demonstrated that the model-predicted and actual risks were in good agreement, and the model forecast accuracy was high. Conclusions: The risk assessment system constructed in this study has good differentiation, calibration and clinical benefits and can be used as a reference tool for predicting SSI risk in patients.

Background. Gastrointestinal stromal tumor (GIST) originates from a pacemaker cell, the Cajal cell. However, little is known about the cancer neuroscience in GIST. In this study, we aimed to elucidate the clinical and biological roles of adrenoceptor beta 2 (ADRB2) in GIST. Methods. Immunohistochemistry was used to evaluate the expression of ADRB2 in GIST tissues. The biological effects of ADRB2 on GIST cell proliferation, migration, invasion, and apoptosis were explored using Cell Counting Kit 8, plate colony formation assay, transwell invasion assay, and flow cytometry. We also explored the growth and metastasis of xenograft tumors in nude mice. Western blotting was used to quantify protein expression and phosphorylation. Results. ADRB2 is generally highly expressed in GIST. High ADRB2 expression was significantly associated with risk level, tumor size, mitotic count, and metastasis. Overexpression of ADRB2 promoted GIST cell proliferation, migration, invasion, and apoptosis, while silencing ADRB2 expression showed the opposite effects. Furthermore, we found that silencing endogenous ADRB2 inhibited GIST progression and metastasis in nude mice. ADRB2-induced ETV1 upregulation enhanced the activation of c-KIT. Conclusion. ADRB2 plays an important role in the proliferation and metastasis of GIST and is expected to be a potential target for the treatment of GIST.