Background: This study aimed to identify potential stemness-related targets in gastric cancer (GC) in order to support the development of new treatment strategies and improve patient survival.
Methods: Using the edgeR package, we identified stemness-related differentially expressed genes (DEGs) using GSE112631 and the stemness-related signaling pathways in the Gene Set Enrichment Analysis (GSEA) database. Lasso-penalized Cox regression analysis and multivariate Cox regression analysis tested by Akaike Information Criterion (AIC) were used to screen out survival genes in order to construct a prognostic model. We verified the accuracy of our prognostic model using a nomogram and receiver operating characteristic (ROC) curve analysis. Patients were divided into two groups based on the median risk score, and functional enrichment analysis was used to explore the differences between the two groups.
Results: Eight genes were selected to establish a prognostic model of The Cancer Genome Atlas (TCGA) and a validation model of the GSE84437 dataset from the Genome Expression Omnibus (GEO). In both models, we found that the low risk score group had better overall survival (OS) than the high-risk score group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the two risk groups were totally different.
Conclusions: We used eight stemness-related genes to build a prognostic model. The high-risk score group had a worse prognosis compared to the low-risk score group.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798931 | PMC |
| http://dx.doi.org/10.21037/tcr-20-2622 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Background: Pivotal Alzheimer's Disease (AD) trials typically require thousands of participants, resulting in long enrollment timelines and substantial costs. We leverage deep learning predictive models to create prognostic scores (forecasted control outcome) of trial participants and in combination with a linear statistical model to increase statistical power in randomized clinical trials (RCT). This is a straightforward extension of the traditional RCT analysis, allowing for ease of use in any clinical program.
View Article and Find Full Text PDFBackground: In Alzheimer's Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI-generated digital twins - individualized predictions (or prognostic scores) of how each patient's clinical outcomes may change during a trial assuming they received placebo.
View Article and Find Full Text PDFDrug Development.
Alzheimers Dement
December 2024
Switch laboratory, VIB - KU Leuven Center for Brain & Disease Research, Leuven, Belgium.
Background: Pathological tau accumulation is the primary constituent of neurofibrillary tangles and other tau aggregates seen in various neurodegenerative diseases collectively known as tauopathies. Recently, immunotherapeutic strategies focused on tau have shown promise in reducing tauopathy in both cellular and animal models.
Method: We previously used humanized yeast models to purify recombinant hyper-phosphorylated human Tau for mouse immunizations and the isolation of a high-affinity anti-Tau monoclonal antibody (mAb) with enhanced diagnostic and prognostic capacities.
Technology and Dementia Preconference.
Alzheimers Dement
December 2024
University of Pennsylvania, Philadelphia, PA, USA.
Background: Structural and functional heterogeneity in the brains of patients with Alzheimer's disease (AD) leads to diagnostic and prognostic uncertainty and confounds clinical treatment planning. Normative modelling, where individual-level deviations in brain measures from a reference sample are computed to infer personalized effects of disease, allows parsing of disease heterogeneity. In this study, GAN based normative modelling technique quantifies individual level neuroanatomical abnormality thereby facilitating measurement of personalized disease related effects in AD patients.
View Article and Find Full Text PDFTechnology and Dementia Preconference.
Alzheimers Dement
December 2024
University of Melbourne, Parkville, VIC, Australia.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative condition, with considerable variation in disease progression from the mild cognitive impairment (MCI) stage. Predicting disease progression will support prognostic decisions and patient management. Here we designed a machine learning (ML) stack model, where a classifier was used to differentiate MCI progressors from non-progressors (i.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!