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.

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Published in Human Genomics, v. 15, issue 1, article no. 23.

© The Author(s). 2021

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This study was supported by a fund (No. 31401123) from the National Natural Science Foundation of China and a fund (No. JJKH20190032KJ) from the Education Department of Jilin Province.

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Pre-processed data (Accession #: GSE85034) were downloaded from the GEO database (https://www.ncbi.nlm.nih.gov/geo/).

40246_2021_323_MOESM1_ESM.docx (127 kB)
Additional file 1: Supplementary File 1–Another application of the iCluster ensemble procedure on multiple sclerosis data, and separate analyses stratified by treatments for psoriasis data. Table S1—The clinical and demographic characteristics of psoriasis patients in the longitudinal microarray experiment. Table S2—Relevant lncRNAs identified by separate analyses. Table S3—Comparison between iCluster-ensemble and competing methods for the multiple sclerosis application.