Non cancer causes of death after gallbladder cancer diagnosis: a population-based analysis

Cardiovascular Disease and Other Competing Causes of Death in Older Kidney Cancer Patients

Background

To study the risk of cardiovascular disease (CVD) and other competing causes of death in older kidney cancer patients.

Methods

Data on older patients (aged 65 and above) diagnosed with kidney cancer between 1975 and 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. We delved into the distribution of CVD and other competing causes of death across the entire cohort and in various patient subgroups. The competing risk analysis was used to produce cumulative mortality curves based on cumulative mortality for the primary outcomes by follow-up period. Utilizing the standardized mortality ratios (SMRs) and absolute excess risks (AERs), we contrasted the risk of CVD and other competing causes of death in older kidney cancer patients to that observed in the general population.

Results

The analysis included 29,349 older kidney cancer patients, of which included 4563 CVD deaths. As survival time extended, the proportion of non-cancer deaths increased in older kidney cancer patients, with CVD accounting for the largest share of non-cancer deaths. At 10-15 years after diagnosis, cumulative non-cancer mortality exceeded primary kidney cancer as the predominant cause of death, and cumulative CVD mortality is higher among all non-cancer causes. Older kidney cancer patients exhibited a greater risk of CVD and other non-cancer deaths than their counterparts in the general older population did (SMR: 1.38-2.81; AER: 1.1-143.69).

Conclusions

As survival time increases, the risk of non-cancer death in older kidney cancer patients gradually surpassed that of primary cancer, and CVD death accounted for the majority of non-cancer deaths. Among older kidney cancer patients, the risk of CVD mortality was higher than in the general population. Managing non-cancer deaths, especially CVD deaths, should be a focus in the care of older kidney cancer patients.

TRIM47 is a prognostic biomarker for gallbladder cancer and promotes tumor progression through regulating K63-linked ubiquitination of PARP1

BACKGROUND

Gallbladder cancer (GBC) is one of the most lethal malignancies worldwide with an extremely poor prognosis. Previous studies have suggested that tripartite motif containing 47 (TRIM47) is involved in the progression of numerous cancers. However, the molecular mechanism and function of TRIM47 in GBC remain unclear.

METHODS

The clinical significance of TRIM47 was evaluated using immunohistochemistry. Functional assays were performed in vitro and in vivo to determine the role of TRIM47 in GBC. Mass spectrometric analysis, western blotting, and immunoprecipitation assays were performed to investigate the molecular mechanisms involved.

RESULTS

In this study, TRIM47 was upregulated in GBC tissues and associated with shorter overall survival rates and TRIM47 was involved in GBC cell proliferation, migration, and apoptosis. Mechanistically, TRIM47 interacts with PARP1 and mediates the K63-linked polyubiquitination of PARP1, thereby stabilizing its expression. Furthermore, TRIM47 activated the AKT signaling pathway via PARP1.

CONCLUSION

The present study revealed that TRIM47 contributes to the progression of GBC and is therefore an important biomarker for predicting the prognosis of GBC and for therapeutic intervention.

Interplay of Cardiometabolic Syndrome and Biliary Tract Cancer: A Comprehensive Analysis with Gender-Specific Insights

BTC overall incidence is globally increasing. CCA, including its subtypes, is a form of BTC. MetS, obesity, MASLD, and diabetes are all linked to CCA in interconnected ways. The link between obesity and CCA is less well-defined in Eastern countries as compared to Western. Although more research is needed to determine the relationship between MASLD and extrahepatic CCA (eCCA), MASLD may be a concurrent risk factor for intrahepatic CCA, particularly in populations with established or unidentified underlying liver disease. Interestingly, the risk of biliary tract cancer (BTC) seemed to be higher in patients with shorter diabetes durations who were not treated with insulin. Therefore, early detection and prevention of chronic liver disease, as well as additional intervention studies, will undoubtedly be required to determine whether improvements to MetS, weight loss, and diabetes therapy can reduce the risk and progression of BTC. However, further studies are needed to understand how reproductive hormones are involved in causing BTC and to develop consistent treatment for patients. Finally, it is critical to carefully assess the cardiological risk in BTC patients due to their increased intrinsic cardiovascular risk, putting them at risk for thrombotic complications, cardiovascular death, cardiac metastasis, and nonbacterial thrombotic endocarditis. This review aimed to provide an updated summary of the relation between the abovementioned cardio-metabolic conditions and BTC.

Machine learning-based short-term DFS-associated characteristic factor screening and model construction for patients with gallbladder cancer after radical surgery

Gallbladder cancer (GBC) is a malignancy with a bleak prognosis, and radical surgery remains the primary treatment option. However, the high postoperative recurrence rate and the lack of individualized risk assessment tools limit the effectiveness of current treatment strategies. This study aims to identify risk factors affecting the short-term disease-free survival (DFS) of GBC patients using machine learning methods and to build a prediction model. A retrospective analysis was conducted on the clinical data from 328 GBC patients treated at the First Affiliated Hospital of Huzhou University from 2008 to 2021. Patients were randomly divided into a training set (n=230) and a validation set (n=98). Clinical data, laboratory indexes, and follow-up data were collected. Univariate Cox regression analysis identified age, tumor T-staging, lymph node metastasis, differentiation degree, and CA199 level as prognostic factors affecting DFS (all P<0.05). A prediction model constructed using the LASSO regression achieved AUCs of 0.827 and 0.801 for predicting 1-year and 3-year DFS, respectively. Notably, the XGBoost regression model showed higher prediction accuracy with AUCs of 0.922 and 0.947, respectively. The Delong test confirmed that the XGBoost model had significantly higher AUC values compared to the LASSO model (all P<0.001). In the validation set, the XGBoost model demonstrated AUCs of 0.764 and 0.761 for predicting 1-year and 3-year DFS, respectively. Overall, the XGBoost regression model demonstrates high accuracy and clinical value in predicting short-term DFS in GBC patients after radical surgery, offering a valuable tool for personalized treatment.

Establishment and characterization of the gemcitabine-resistant human gallbladder cancer cell line NOZ GemR

BACKGROUND

Patients with gallbladder cancer (GBC) generally receive gemcitabine as the standard treatment; however, its efficacy is often limited owing to the development of resistance.

METHODS

To identify the mechanisms underlying gemcitabine resistance in GBC, a gemcitabine-resistant GBC cell line (NOZ GemR) was established by exposing the parental NOZ cell line to increasing concentrations of gemcitabine. Morphological changes, growth rates, and migratory and invasive capabilities were evaluated. Protein expression was detected using western blotting.

RESULTS

The results demonstrated that the IC50 of NOZ and NOZ GemR was 0.011 and 4.464 μM, respectively, and that the resistance index ratio was 405.8. In comparison, NOZ GemR cells grew slower and had significantly lower migration and invasion abilities than NOZ cells. There were altered levels of epithelial-mesenchymal transformation markers in NOZ GemR cells, as well as increased levels of the Akt/mTOR pathway protein.

CONCLUSION

The NOZ GemR cell line could be used as an effective in vitro model to improve our understanding of gemcitabine resistance in GBC.