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Beneficial effects of metformin on cancer risk and mortality have been proved by epidemiological and clinical studies, thus attracting research interest in elucidating the underlying mechanisms. Recently, tumour‐associated macrophages (TAMs) appeared to be implicated in metformin‐induced antitumour activities. However, how metformin inhibits TAMs‐induced tumour progression remains ill‐defined. Here, we report that metformin‐induced antitumour and anti‐angiogenic activities were not or only partially contributed by its direct inhibition of functions of tumour and endothelial cells. By skewing TAM polarization from M2‐ to M1‐like phenotype, metformin inhibited both tumour growth and angiogenesis. Depletion of TAMs by clodronate liposomes eliminated M2‐TAMs‐induced angiogenic promotion, while also abrogating M1‐TAMs‐mediated anti‐angiogenesis, thus promoting angiogenesis in tumours from metformin treatment mice. Further in vitro experiments using TAMs‐conditioned medium and a coculture system were performed, which demonstrated an inhibitory effect of metformin on endothelial sprouting and tumour cell proliferation promoted by M2‐polarized RAW264.7 macrophages. Based on these results, metformin‐induced inhibition of tumour growth and angiogenesis is greatly contributed by skewing of TAMs polarization in microenvironment, thus offering therapeutic opportunities for metformin in cancer treatment.

Background and Objectives To determine whether radical resection can benefit patients with advanced gallbladder adenocarcinoma using a Bayesian network (BN) with clinical data. Methods In total, 362 patients who had undergone surgical treatment of gallbladder adenocarcinoma at a tertiary institute were evaluated to establish two BN models using a tree-augmented naive Bayes algorithm. We then chose 250 patients with T3-4N0-2M0 stage gallbladder adenocarcinoma to test the posterior probability after the surgical type was taken into account. Results In total, 170 patients (≤7 months) and 137 patients (>7 months) were correctly classified in the median survival time model (accuracy, 84.81%), and 204 patients (≤12 months), 15 patients (12-36 months), 17 patients (36-60 months), and 34 patients (>60 months) were correctly classified in the 1-, 3-, and 5-year survival model (accuracy, 74.59%), respectively. Every posterior probability in the two models upregulated the ratio of the longer survival time and suggested a better prognosis for gallbladder adenocarcinoma that can be improved by R0 resection. Conclusions These BN models indicate that stages T4 and N2 gallbladder adenocarcinoma are not contraindications for surgery and that R0 resection can improve survival in patients with advanced gallbladder adenocarcinoma.

The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was employed to establish and test a BN model using BayesiaLab software. A tree-augmented naïve Bayes method was also used to mine relationships between factors. Composite importance measures were applied to rank the influence of factors on survival time. The accuracy of BN model was 81.15%. For patients with long survival time (>6 months), the true-positive rate of the model was 77.78% and the false-positive rate was 15.25%. According to the built BN model, the sex, age, and pathological type were independent factors for survival of GBC patients. The N stage, liver infiltration, T stage, M stage, and surgical type were dependent variables for survival time prediction. Surgical type and TNM stages were identified as the most significant factors for the prognosis of GBC based on the analysis results of importance measures.