Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Published: August 2021
AI Article Synopsis
Article Abstract
Background: The typical approach to identify blood-derived gene expression signatures as a biomarker for Alzheimer's disease (AD) have relied on training classification models using AD and healthy controls only. This may inadvertently result in the identification of markers for general illness rather than being disease-specific.
Objective: Investigate whether incorporating additional related disorders in the classification model development process can lead to the discovery of an AD-specific gene expression signature.
Methods: Two types of XGBoost classification models were developed. The first used 160 AD and 127 healthy controls and the second used the same 160 AD with 6,318 upsampled mixed controls consisting of Parkinson's disease, multiple sclerosis, amyotrophic lateral sclerosis, bipolar disorder, schizophrenia, coronary artery disease, rheumatoid arthritis, chronic obstructive pulmonary disease, and cognitively healthy subjects. Both classification models were evaluated in an independent cohort consisting of 127 AD and 687 mixed controls.
Results: The AD versus healthy control models resulted in an average 48.7% sensitivity (95% CI = 34.7-64.6), 41.9% specificity (95% CI = 26.8-54.3), 13.6% PPV (95% CI = 9.9-18.5), and 81.1% NPV (95% CI = 73.3-87.7). In contrast, the mixed control models resulted in an average of 40.8% sensitivity (95% CI = 27.5-52.0), 95.3% specificity (95% CI = 93.3-97.1), 61.4% PPV (95% CI = 53.8-69.6), and 89.7% NPV (95% CI = 87.8-91.4).
Conclusions: This early work demonstrates the value of incorporating additional related disorders into the classification model developmental process, which can result in models with improved ability to distinguish AD from a heterogeneous aging population. However, further improvement to the sensitivity of the test is still required.
Background: Previous research has demonstrated correlations between the complex types and functions of brain cells and the etiology of glioma. However, the causal relationship between gene expression regulation in specific brain cell types and glioma risk, along with its therapeutic implications, remains underexplored.
Methods: Utilizing brain cell type-specific cis-expression quantitative trait loci (cis-eQTLs) and glioma genome-wide association study (GWAS) datasets in conjunction with Mendelian randomization (MR) and colocalization analyses, we conducted a systematic investigation to determine whether an association exists between the gene expression of specific brain cell types and the susceptibility to glioma, including its subtypes.
Non-small cell lung cancer (NSCLC) is the most pervasive sort of lung cancer with deadly outcome. According to recent studies, a number of neoplastic disorders and ferroptosis are intimately connected. This study aims to identify the role of key ferroptosis-related gene (protein kinase AMP-activated catalytic subunit alpha 2, PRKAA2) and explore new directions for the diagnosis and treatment of NSCLC.
Background: Tumor recurrence or metastasis after surgery is a significant factor influencing bladder cancer (BC) prognosis. Novel molecular biomarkers are necessary to determine each patient's specific outcome because current biomarkers have limited power for predicting prognosis. The proto-oncogene MET encodes c-MET, a tyrosine kinase receptor.
Introduction: Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP.
Department of Hematology, The Affiliated Hospital of Qingdao University, Qingdao University, No. 16 Jiangsu Road, Qingdao, 266000, Shandong Province, China.
A poorer prognosis is thought to be associated with "double expressor lymphomas," which are a subtype of diffuse large B cell lymphomas (DLBCL) that co-express MYC and BCL2. While the role of ubiquitin-specific peptidase 37 (USP37) in lung cancer, where it mediates the deubiquitination and stabilization of c-myc, has been well-documented, its involvement in DLBCL remains unexplored. The use of RT-PCR, immunohistochemistry, or WB test allowed for the detection of elevated USP37 in DLBCL tissues and cells.
