Breast cancer is the major common malignancy worldwide among women. Previous studies reported that cancer-associated fibroblasts (CAFs) showed pivotal roles in regulating tumor progression via exosome-mediated cellular communication. However, the detailed mechanism underlying the exosomal circRNA from CAFs in breast cancer progression remains ambiguous. Here, exosomal circRNA profiling of breast cancer-derived CAFs and normal fibroblasts (NFs) was detected by high-throughput sequencing, and upregulated circTBPL1 expression was identified in CAF exosomes. The exosomal circTBPL1 from CAFs could be transferred to breast cancer cells and promoted cell proliferation, migration, and invasion. Consistently, circTBPL1 knockdown in CAFs attenuated their tumor-promoting ability. Further exploration identified miR-653-5p as an inhibitory target of circTBPL1, and ectopic expression of miR-653-5p could partially reverse the malignant phenotypes induced by circTBPL1 overexpression in breast cancer. Additionally, TPBG was selected as a downstream target gene, and circTBPL1 could protect TPBG from miR-653-5p-mediated degradation, leading to enhanced breast cancer progression. Significantly, the accelerated tumor progression triggered by exosomal circTBPL1 from CAFs was confirmed in xenograft models. Taken together, these results revealed that exosomal circTBPL1 derived from CAFs contributed to cancer progression via miR-653-5p/TPBG pathway, indicating the potential of exosomal circTBPL1 as a biomarker and novel therapeutic target for breast cancer.
Background: This study used machine learning to develop a 3-year lung cancer risk prediction model with large real-world data in a mostly younger population. Methods: Over 4.7 million individuals, aged 45-65 years with no history of any cancer or lung cancer screening, diagnostic, or treatment procedures, with an outpatient visit in 2013 were identified in the Optum® De-identified Electronic Health Record (EHR) Dataset. A Least Absolute Shrinkage and Selection Operator model was fit using all available data in the 365 days prior. Temporal validation was assessed with recent data. External validation was assessed with data from Mercy Health Systems EHR and Optum® De-Identified Clinformatics Data Mart. Racial inequities in model discrimination were assessed with xAUCs. Results: The model AUC was 0.76. Top predictors included age, smoking, race, ethnicity, and diagnosis of chronic obstructive pulmonary disease. The model identified a high-risk group with lung cancer incidence 9 times the average cohort incidence, representing 10% of lung cancer patients. Model performed well temporally and externally, while performance was reduced for Asians and Hispanics. Conclusions: A high-dimensional model trained using big data identified a subset of patients with high lung cancer risk. The model demonstrated transportability to EHR and claims data, while underscoring the need to assess racial disparities when using machine learning methods. Impact: This internally and externally validated real-world data-based lung cancer prediction model is available on an open-source platform for broad sharing and application. Model integration into an EHR system could minimize physician burden by automating identification of high-risk patients.
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