Trend analysis and risk of gallbladder cancer mortality in China, 2013-2019

The footprint of gut microbiota in gallbladder cancer: a mechanistic review

Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system with the worst prognosis. Even after radical surgery, the majority of patients with GBC have difficulty achieving a clinical cure. The risk of tumor recurrence remains more than 65%, and the overall 5-year survival rate is less than 5%. The gut microbiota refers to a variety of microorganisms living in the human intestine, including bacteria, viruses and fungi, which profoundly affect the host state of general health, disease and even cancer. Over the past few decades, substantial evidence has supported that gut microbiota plays a critical role in promoting the progression of GBC. In this review, we summarize the functions, molecular mechanisms and recent advances of the intestinal microbiota in GBC. We focus on the driving role of bacteria in pivotal pathways, such as virulence factors, metabolites derived from intestinal bacteria, chronic inflammatory responses and ecological niche remodeling. Additionally, we emphasize the high level of correlation between viruses and fungi, especially EBV and Candida spp., with GBC. In general, this review not only provides a solid theoretical basis for the close relationship between gut microbiota and GBC but also highlights more potential research directions for further research in the future.

Parthenolide induces gallbladder cancer cell apoptosis via MAPK signalling

Objective

Parthenolide (PTL) has a wide range of clinical applications owing to its anti-inflammatory and antitumor effects. To date, the antitumor effect of PTL on gallbladder cancer (GBC) remains largely unknown. Therefore, we aimed to investigate the biological effects of PTL on GBC.

Methods

The cellular viability and proliferation of GBC-SD and NOZ cell lines after treatment with different concentrations of PTL were analyzed using the Cell Counting Kit-8 (CCK8)assay and colony formation assay. Apoptosis analysis was performed using flow cytometry. Hoechst staining was performed. RNA sequencing (RNA-seq) was performed to identify PTL-related genes and signalling pathways. Furthermore, we confirmed the involvement of these signalling pathways by qRT-PCR and western blotting. For the in-vivo experiments, a xenograft model was used to evaluate the effects of PTL on the proliferation of NOZ cells.

Results

PTL significantly inhibited GBC cell growth in vitro and induced apoptosis in the GBC-SD and NOZ cell lines in a dose-dependent manner. RNA sequencing data showed that the immune response and mitogen-activated protein kinase (MAPK) signalling pathways are closely associated with PTL-induced gallbladder cancer cell apoptosis. PTL upregulated BAX, cleaved PARP-1, cleaved caspase-3, cleaved caspase-9, P53 and decreased the expression of BCL-2, phosphorylated ERK, and phosphorylated MEK in vitro. Tumour volume and weight were also suppressed by PTL in vivo. Moreover, the effects of PTL on GBC cells might be mediated by the MAPK pathway.

Conclusion

PTL significantly inhibits gallbladder cancer cell proliferation and induces apoptosis through the MAPK pathway, which is a potential molecular reagent for treating GBC. However, further exploration is needed to verify the antitumor effects of PTL and its intracellular signalling mechanism.

A nomogram for predicting cancer-specific survival for elderly patients with gallbladder cancer

Background

Gallbladder cancer (GBC) is a highly aggressive malignancy in elderly patients. Our goal is aimed to construct a novel nomogram to predict cancer-specific survival (CSS) in elderly GBC patients.

Method

We extracted clinicopathological data of elderly GBC patients from the SEER database. We used univariate and multivariate Cox proportional hazard regression analysis to select the independent risk factors of elderly GBC patients. These risk factors were subsequently integrated to construct a predictive nomogram model. C-index, calibration curve, and area under the receiver operating curve (AUC) were used to validate the accuracy and discrimination of the predictive nomogram model. A decision analysis curve (DCA) was used to evaluate the clinical value of the nomogram.

Result

A total of 4241 elderly GBC patients were enrolled. We randomly divided patients from 2004 to 2015 into training cohort (n = 2237) and validation cohort (n = 1000), and patients from 2016 to 2018 as external validation cohort (n = 1004). Univariate and multivariate Cox proportional hazard regression analysis found that age, tumor histological grade, TNM stage, surgical method, chemotherapy, and tumor size were independent risk factors for the prognosis of elderly GBC patients. All independent risk factors selected were integrated into the nomogram to predict cancer-specific survival at 1-, 3-, and 5- years. In the training cohort, internal validation cohort, and external validation cohort, the C-index of the nomogram was 0.763, 0.756, and 0.786, respectively. The calibration curves suggested that the predicted value of the nomogram is highly consistent with the actual observed value. AUC also showed the high authenticity of the prediction model. DCA manifested that the nomogram model had better prediction ability than the conventional TNM staging system.

Conclusion

We constructed a predictive nomogram model to predict CSS in elderly GBC patients by integrating independent risk factors. With relatively high accuracy and reliability, the nomogram can help clinicians predict the prognosis of patients and make more rational clinical decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-022-02544-y.