ABSTRACT
Cuproptosis is characterized by the aggregation of lipoylated enzymes of the tricarboxylic acid cycle and subsequent loss of iron-sulfur cluster proteins as a unique copper-dependent form of regulated cell death. As dysregulation of copper homeostasis can induce cuproptosis, there is emerging interest in exploiting cuproptosis for cancer therapy. However, the molecular drivers of cancer cell evasion of cuproptosis were previously undefined. Here, we found that cuproptosis activates the Wnt/ß-catenin pathway. Mechanistically, copper binds PDK1 and promotes its interaction with AKT, resulting in activation of the Wnt/ß-catenin pathway and cancer stem cell (CSC) properties. Notably, aberrant activation of Wnt/ß-catenin signaling conferred resistance of CSCs to cuproptosis. Further studies showed the ß-catenin/TCF4 transcriptional complex directly binds the ATP7B promoter, inducing its expression. ATP7B effluxes copper ions, reducing intracellular copper and inhibiting cuproptosis. Knockdown of TCF4 or pharmacological Wnt/ß-catenin blockade increased the sensitivity of CSCs to elesclomol-Cu-induced cuproptosis. These findings reveal a link between copper homeostasis regulated by the Wnt/ß-catenin pathway and cuproptosis sensitivity, and suggest a precision medicine strategy for cancer treatment through selective cuproptosis induction.
ABSTRACT
This article presents a dataset comprising signal data collected from Inertial Measurement Unit (IMU) sensors during the administration of the Time Up and Go (TUG) test for assessing fall risk in older adults. The dataset is divided into two main sections. The first section contains personal, behavioral, and health-related data from 34 participants. The second section contains signal data from tri-axial acceleration and tri-axial gyroscope sensors embedded in an IMU sensor, which was affixed to the participants' waist area to capture signal data while they walked. The chosen assessment method for fall risk analysis is the TUG test, requiring participants to walk a 3-meter distance back and forth. To prepare the dataset for subsequent analysis, the raw signal data underwent processing to extract only the walking periods during the TUG test. Additionally, a low-pass filter technique was employed to reduce noise interference. This dataset holds the potential for the development of effective models for fall risk detection based on insights garnered from questionnaires administered to specialists who observed the experiments. The dataset also contains anonymized participant information that can be explored to investigate fall risk, along with other health-related conditions or behaviors that could influence the risk of falling. This information is invaluable for devising tailored treatment or rehabilitation plans for individual older adults. The complete dataset is accessible through the Mendeley repository."
