Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with ES-SCLC between 2010 and 2018 were screened from the Surveillance, Epidemiology, and End Results (SEER) database. Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. The accuracy, sensitivity, specificity, AUROC, and AUPRC were compared among these models and traditional logistic regression (LR). The random forest (RF) model demonstrated the best performance and was chosen for further analysis. The AUROC and AUPRC were calculated and compared. The findings from the RF model were utilized to identify the risk factors linked to BMs in patients diagnosed with ES-SCLC. Examining 4,716 instances of ES-SCLC, the research conducted an analysis, with brain metastases arising in 1,900 cases. Through evaluation of the ROC curve and PRC concerning the RF Model, results depicted an AUROC of 0.896 (95% CI: 0.889–0.899) and AUPRC of 0.900 (95% CI: 0.895–0.904). Test accuracy measured at 0.810 (95% CI: 0.784–0.833), sensitivity at 0.797 (95% CI: 0.756–0.841), and specificity at 0.819 (95% CI: 0.754–0.879). Based on the SHAP analysis of the RF predictive model, the top 10 most relevant features were identified and ranked in order of relative importance: bone metastasis, liver metastasis, radiation, age, tumor size, primary tumor location, N-stage, race, T-stage, and chemotherapy. The research developed and validated a predictive RF model using clinical and pathological data to predict the risk of BMs in patients with ES-SCLC. This model may assist physicians in making clinical decisions that could delay the onset of BMs and improve patient survival rates.

Background: Long non-coding RNA (lncRNA) has been confirmed to exert a critical effect on the progression of tumors, including prostate cancer. Previous literature has demonstrated LINC01116 involves in activities of multiple cancers. However, the underlying role of LINC01116 in prostate cancer remains unclear.Methods: qRT-PCR measured the expression of LINC01116 in prostate cancer cells. EdU experiment was used to detect cell proliferation. Transwell assays detected cell migration and invasion. Immunofluorescence staining and western blot assays were utilized to measure EMT progress. The binding relationship between RNAs was confirmed by a series of mechanism assays. In addition, rescue experiments were conducted to verify the relationship among RNAs.Results: LINC01116 was found to be highly expressed in prostate cancer cells. Functional assays indicated that inhibition of LINC01116 could suppress cell proliferation, migration, invasion and EMT progress. Also, miR-744-5p was proven to bind with LINC01116. Moreover, UBE2L3 was verified as the target gene of miR-744-5p. In rescue assays, we discovered that inhibited miR-744-5p or overexpressed UBE2L3 could offset the suppressive influence of silencing LINC01116 on prostate cancer cells.Conclusion: Our study suggested that lncRNA LINC01116 acted as an oncogene in prostate cancer and accelerated prostate cancer cell growth through regulating miR-744-5p/UBE2L3 axis.