1. An ensemble of the iCluster method to analyze longitudinal lncRNA expression data for psoriasis patients
- Author
-
Suyan Tian and Chi Wang
- Subjects
Computational biology ,QH426-470 ,Biology ,Proteomics ,01 natural sciences ,Long non-coding RNAs (lncRNAs) ,010104 statistics & probability ,03 medical and health sciences ,Psoriasis ,Drug Discovery ,Gene expression ,Genetics ,medicine ,Humans ,Gene Regulatory Networks ,RNA, Messenger ,0101 mathematics ,KEGG ,Molecular Biology ,Skin ,030304 developmental biology ,Protein deubiquitination ,0303 health sciences ,Longitudinal data ,Gene Expression Profiling ,medicine.disease ,Human genetics ,Integrative clustering (iCluster) ,Cross-Sectional Studies ,Gene Expression Regulation ,Causal inference ,Proteasome inhibitor ,Medicine ,Molecular Medicine ,RNA, Long Noncoding ,Primary Research ,Transcriptome ,medicine.drug - Abstract
Background Psoriasis is an immune-mediated, inflammatory disorder of the skin with chronic inflammation and hyper-proliferation of the epidermis. Since psoriasis has genetic components and the diseased tissue of psoriasis is very easily accessible, it is natural to use high-throughput technologies to characterize psoriasis and thus seek targeted therapies. Transcriptional profiles change correspondingly after an intervention. Unlike cross-sectional gene expression data, longitudinal gene expression data can capture the dynamic changes and thus facilitate causal inference. Methods Using the iCluster method as a building block, an ensemble method was proposed and applied to a longitudinal gene expression dataset for psoriasis, with the objective of identifying key lncRNAs that can discriminate the responders from the non-responders to two immune treatments of psoriasis. Results Using support vector machine models, the leave-one-out predictive accuracy of the 20-lncRNA signature identified by this ensemble was estimated as 80%, which outperforms several competing methods. Furthermore, pathway enrichment analysis was performed on the target mRNAs of the identified lncRNAs. Of the enriched GO terms or KEGG pathways, proteasome, and protein deubiquitination is included. The ubiquitination-proteasome system is regarded as a key player in psoriasis, and a proteasome inhibitor to target ubiquitination pathway holds promises for treating psoriasis. Conclusions An integrative method such as iCluster for multiple data integration can be adopted directly to analyze longitudinal gene expression data, which offers more promising options for longitudinal big data analysis. A comprehensive evaluation and validation of the resulting 20-lncRNA signature is highly desirable.
- Published
- 2021