Laboratory blood parameters and machine learning for the prognosis of esophageal squamous cell carcinoma.

Background: In contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.

Objective: To develop and validate a prognostic model tailored to ESCC patients, leveraging the power of machine learning (ML) techniques and drawing insights from comprehensive datasets of laboratory-derived blood parameters.

Methods: Three ML approaches, including Gradient Boosting Machine (GBM), Random Survival Forest (RSF), and the classical Cox method, were employed to develop models on a dataset of 2521 ESCC patients with 27 features.

Discovery and identification of the prognostic significance and potential mechanism of FMO2 in breast cancer.

Background: Flavin containing dimethylaniline monoxygenase 2 (FMO2), is downexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value and potential mechanism of FMO2 in breast cancer remain unclear.

Methods: The expression of FMO2 was analyzed and the relationship between FMO2 expression level and clinical indicators in breast cancer was analyzed.