Preoperative fasting hyperglycemia is an independent prognostic factor for postoperative survival after gallbladder carcinoma radical surgery

Prognostic factors for resected cases with gallbladder carcinoma: a systematic review and meta-analysis

OBJECTIVE

Current meta-analysis was performed to systematically evaluate the potential prognostic factors for overall survival among resected cases with gallbladder carcinoma.

METHODS

PubMed, EMBASE, and the Cochrane Library were systematically retrieved and hazard ratio (HR) and its 95% confidence interval were directly extracted from the original study or roughly estimated via Tierney's method. Standard Parmar modifications were used to determine pooled HRs.

RESULTS

A total of 36 studies with 11 502 cases were identified. Pooled results of univariate analyses indicated that advanced age (HR=1.02, P =0.00020), concurrent gallstone disease (HR=1.22, P =0.00200), elevated preoperative CA199 level (HR=1.93, P <0.00001), advanced T stage (HR=3.09, P <0.00001), lymph node metastasis (HR=2.78, P <0.00001), peri-neural invasion (HR=2.20, P <0.00001), lymph-vascular invasion (HR=2.37, P <0.00001), vascular invasion (HR=2.28, P <0.00001), poorly differentiated tumor (HR=3.22, P <0.00001), hepatic side tumor (HR=1.85, P <0.00001), proximal tumor (neck/cystic duct) (HR=1.78, P <0.00001), combined bile duct resection (HR=1.45, P <0.00001), and positive surgical margin (HR=2.90, P <0.00001) were well-established prognostic factors. Pathological subtypes ( P =0.53000) and postoperative adjuvant chemotherapy ( P =0.70000) were not prognostic factors. Pooled results of multivariate analyses indicated that age, gallstone disease, preoperative CA199, T stage, lymph node metastasis, peri-neural invasion, lymph-vascular invasion, tumor differentiation status, tumor location (peritoneal side vs hepatic side), surgical margin, combined bile duct resection, and postoperative adjuvant chemotherapy were independent prognostic factors.

CONCLUSION

Various prognostic factors have been identified beyond the 8th AJCC staging system. By incorporating these factors into a prognostic model, a more individualized prognostication and treatment regime would be developed. Upcoming multinational studies are required for the further refine and validation.

Fasting-induced RNF152 resensitizes gallbladder cancer cells to gemcitabine by inhibiting mTORC1-mediated glycolysis

Abnormal mTORC1 activation by the lysosomal Ragulator complex has been implicated in cancer and glycolytic metabolism associated with drug resistance. Fasting upregulates RNF152 and mediates the metabolic status of cells. We report that RNF152 regulates mTORC1 signaling by targeting a Ragulator subunit, p18, and attenuates gemcitabine resistance in gallbladder cancer (GBC). We detected levels of RNF152 and p18 in tissues and undertook mechanistic studies using activators, inhibitors, and lentivirus transfections. RNF152 levels were significantly lower in GBC than in adjacent non-cancer tissues. Fasting impairs glycolysis, induces gemcitabine sensitivity, and upregulates RNF152 expression. RNF152 overexpression increases the sensitivity of GBC cells to gemcitabine, whereas silencing RNF152 has the opposite effect. Fasting-induced RNF152 ubiquitinates p18, resulting in proteasomal degradation. RNF152 deficiency increases the lysosomal localization of p18 and increases mTORC1 activity, to promote glycolysis and decrease gemcitabine sensitivity. RNF152 suppresses mTORC1 activity to inhibit glycolysis and enhance gemcitabine sensitivity in GBC.

Survival Prediction in Gallbladder Cancer Using CT Based Machine Learning

Objective

To establish a classifier for accurately predicting the overall survival of gallbladder cancer (GBC) patients by analyzing pre-treatment CT images using machine learning technology.

Methods

This retrospective study included 141 patients with pathologically confirmed GBC. After obtaining the pre-treatment CT images, manual segmentation of the tumor lesion was performed and LIFEx package was used to extract the tumor signature. Next, LASSO and Random Forest methods were used to optimize and model. Finally, the clinical information was combined to accurately predict the survival outcomes of GBC patients.

Results

Fifteen CT features were selected through LASSO and random forest. On the basis of relative importance GLZLM-HGZE, GLCM-homogeneity and NGLDM-coarseness were included in the final model. The hazard ratio of the CT-based model was 1.462(95% CI: 1.014–2.107). According to the median of risk score, all patients were divided into high and low risk groups, and survival analysis showed that high-risk groups had a poor survival outcome (P = 0.012). After inclusion of clinical factors, we used multivariate COX to classify patients with GBC. The AUC values in the test set and validation set for 3 years reached 0.79 and 0.73, respectively.

Conclusion

GBC survival outcomes could be predicted by radiomics based on LASSO and Random Forest.