BackgroundPure germinomas account for 40% of pineal tumors and are characterized by the lack of appreciable tumor markers, thus requiring a tumor biopsy for diagnosis. MicroRNAs (miRNA) have emerged as potential non-invasive biomarkers for germ cell tu…
Malignancy manifests itself by deregulated growth and the ability to invade surrounding tissues or metastasize to other organs. These properties are due to genetic and/or epigenetic changes, most often mutations. Many aspects of carcinogenesis are know…
Cancer is a severe disease that results in death in all countries of the world. A nano-based drug delivery approach is the best alternative, directly targeting cancer tumor cells with improved drug cellular uptake. Different types of nanoparticle-based…
Tumors are genetic changes that develop in an organism as a result of many internal and external causes. They affect the biological behavior of cells, cause them to grow independently, and give rise to new, perpetually proliferating organisms. Recent r…
The tumor microenvironment is a complex ecosystem where various cellular and molecular interactions shape the course of cancer progression. Macrophage colony-stimulating factor (M-CSF) plays a pivotal role in this context. This study delves into the bi…
The Keap1-Nrf2 signaling pathway is a major regulator of the cytoprotective response, participating in endogenous and exogenous stress caused by ROS (reactive oxygen species). Nrf2 is the core of this pathway. We summarized the literature on Keap1-Nrf2…
BackgroundIn contemporary study, the death of esophageal squamous cell carcinoma (ESCC) patients need precise and expedient prognostic methodologies.ObjectiveTo 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.MethodsThree 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. The models were evaluated by concordance index (C-index) and time receiver operating characteristics (Time ROC) curves. We used the optimal model to evaluate the correlation between features and prognosis and divide patients into low- and high-risk groups by risk stratification. Its performance was analyzed by Kaplan-Meier curve and the comparison with AJCC8 stage. We further evaluate the comprehensive effectiveness of the model in ESCC subgroup by risk score and KDE (kernel density estimation) plotting.ResultsRSF’s C-index (0.746) and AUC (three-year AUC 0.761, five-year AUC 0.771) had slight advantage over GBM and the classical Cox method. Subsequently, 14 features such as N stage, T stage, surgical margin, tumor length, age, Dissected LN number, MCH, Na, FIB, DBIL, CL, treatment, vascular invasion, and tumor grade were selected to build the model. Based on these, we found significant difference for survival rate between low-(3-year OS 81.8%, 5-year OS 69.8%) and high-risk (3-year OS 25.1%, 5-year OS 11.5%) patients in training set, which was also verified in test set (all P
Lung cancer is the leading cause of cancer-related mortality worldwide, with non-small cell lung cancer (NSCLC) being the predominant type. The roles of autophagy and apoptosis in NSCLC present a dual and intricate nature. Additionally, autophagy and a…