Knowledge atlas of antibody-drug conjugates on CiteSpace and clinical trial visualization analysis

ObjectiveAntibody-drugs conjugates (ADCs) are novel drugs with highly targeted and tumor-killing abilities and developing rapidly. This study aimed to evaluate drug discovery and clinical trials of and explore the hotspots and frontiers from 2012 to 2022 using bibliometric methods.MethodsPublications on ADCs were retrieved between 2012 and 2022 from Web of Science (WoS) and analyzed with CiteSpace 6.1.R2 software for the time, region, journals, institutions, etc. Clinical trials were downloaded from clinical trial.org and visualized with Excel software.ResultsA total of 696 publications were obtained and 187 drug trials were retrieved. Since 2012, research on ADCs has increased year by year. Since 2020, ADC-related research has increased dramatically, with the number of relevant annual publications exceeding 100 for the first time. The United States is the most authoritative and superior country and region in the field of ADCs. The University of Texas MD Anderson Cancer Center is the most authoritative institution in this field. Research on ADCs includes two clinical trials and one review, which are the most influential references. Clinical trials of ADCs are currently focused on phase I and phase II. Comprehensive statistics and analysis of the published literature and clinical trials in the field of ADCs, have shown that the most studied drug is brentuximab vedotin (BV), the most popular target is human epidermal growth factor receptor 2 (HER2), and breast cancer may become the main trend and hotspot for ADCs indications in recent years.ConclusionAntibody-drug conjugates have become the focus of targeted therapies in the field of oncology. The innovation of technology and combination application strategy will become the main trend and hotspots in the future

Causal associations of Sjögren’s syndrome with cancers: a two-sample Mendelian randomization study

Abstract Background Several observational studies have explored the associations between Sjögren’s syndrome (SS) and certain cancers. Nevertheless, the causal relationships remain unclear. Mendelian randomization (MR) method was used to investigate the causality between SS and different types of cancers. Methods We conducted the two-sample Mendelian randomization with the public genome-wide association studies (GWASs) summary statistics in European population to evaluate the causality between SS and nine types of cancers. The sample size varies from 1080 to 372,373. The inverse variance weighted (IVW) method was used to estimate the causal effects. A Bonferroni-corrected threshold of P < 0.0031 was considered significant, and P value between 0.0031 and 0.05 was considered to be suggestive of an association. Sensitivity analysis was performed to validate the causality. Moreover, additional analysis was used to assess the associations between SS and well-accepted risk factors of cancers. Results After correcting the heterogeneity and horizontal pleiotropy, the results indicated that patients with SS were significantly associated with an increased risk of lymphomas (odds ratio [OR] = 1.0010, 95% confidence interval [CI]: 1.0005–1.0015, P = 0.0002) and reduced risks of prostate cancer (OR = 0.9972, 95% CI: 0.9960–0.9985, P = 2.45 × 10−5) and endometrial cancer (OR = 0.9414, 95% CI: 0.9158–0.9676, P = 1.65 × 10−5). Suggestive associations were found in liver and bile duct cancer (OR = 0.9999, 95% CI: 0.9997–1.0000, P = 0.0291) and cancer of urinary tract (OR = 0.9996, 95% CI: 0.9992–1.0000, P = 0.0281). No causal effect of SS on other cancer types was detected. Additional MR analysis indicated that causal effects between SS and cancers were not mediated by the well-accepted risk factors of cancers. No evidence of the causal relationship was observed for cancers on SS. Conclusions SS had significant causal relationships with lymphomas, prostate cancer, and endometrial cancer, and suggestive evidence of association was found in liver and bile duct cancer and cancer of urinary tract, indicating that SS may play a vital role in the incidence of these malignancies

Novel models by machine learning to predict prognosis of breast cancer brain metastases

Abstract Background Breast cancer brain metastases (BCBM) are the most fatal, with limited survival in all breast cancer distant metastases. These patients are deemed to be incurable. Thus, survival time is their foremost concern. However, there is a lack of accurate prediction models in the clinic. What’s more, primary surgery for BCBM patients is still controversial. Methods The data used for analysis in this study was obtained from the SEER database (2010–2019). We made a COX regression analysis to identify prognostic factors of BCBM patients. Through cross-validation, we constructed XGBoost models to predict survival in patients with BCBM. Meanwhile, a BCBM cohort from our hospital was used to validate our models. We also investigated the prognosis of patients treated with surgery or not, using propensity score matching and K–M survival analysis. Our results were further validated by subgroup COX analysis in patients with different molecular subtypes. Results The XGBoost models we created had high precision and correctness, and they were the most accurate models to predict the survival of BCBM patients (6-month AUC = 0.824, 1-year AUC = 0.813, 2-year AUC = 0.800 and 3-year survival AUC = 0.803). Moreover, the models still exhibited good performance in an externally independent dataset (6-month: AUC = 0.820; 1-year: AUC = 0.732; 2-year: AUC = 0.795; 3-year: AUC = 0.936). Then we used Shiny-Web tool to make our models be easily used from website. Interestingly, we found that the BCBM patients with an annual income of over USD$70,000 had better BCSS (HR = 0.523, 95$40,000. The results showed that in all distant metastasis sites, only lung metastasis was an independent poor prognostic factor for patients with BCBM (OS: HR = 1.606, 95%CI 1.157–2.230, P < 0.01; BCSS: HR = 1.698, 95%CI 1.219–2.365, P < 0.01), while bone, liver, distant lymph nodes and other metastases were not. We also found that surgical treatment significantly improved both OS and BCSS in BCBM patients with the HER2 + molecular subtypes and was beneficial to OS of the HR−/HER2− subtype. In contrast, surgery could not help BCBM patients with HR + /HER2− subtype improve their prognosis (OS: HR = 0.887, 95%CI 0.608–1.293, P = 0.510; BCSS: HR = 0.909, 95%CI 0.604–1.368, P = 0.630). Conclusion We analyzed the clinical features of BCBM patients and constructed 4 machine-learning prognostic models to predict their survival. Our validation results indicate that these models should be highly reproducible in patients with BCBM. We also identified potential prognostic factors for BCBM patients and suggested that primary surgery might improve the survival of BCBM patients with HER2 + and triple-negative subtypes