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Recently, a transcriptome method using RNA in skin surface lipids (SSL-RNAs) which can be obtained non-invasively with an oil-blotting film was reported as a novel analytic method of sebum. Here we report transcriptome analyses using SSL-RNAs and the potential of these expression profiles with machine learning as diagnostic biomarkers for PD in double cohorts (PD [n\u2009=\u200915, 50], controls [n\u2009=\u200915, 50]). Differential expression analysis between the patients with PD and healthy controls identified more than 100 differentially expressed genes in the two cohorts. In each cohort, several genes related to oxidative phosphorylation were upregulated, and gene ontology analysis using differentially expressed genes revealed functional processes associated with PD. Furthermore, machine learning using the expression information obtained from the SSL-RNAs was able to efficiently discriminate patients with PD from healthy controls, with an area under the receiver operating characteristic curve of 0.806. 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