BackgrooundLung adenocarcinoma is one of the most common malignant lung cancers. Although platinum-based chemotherapy is the first-line adjuvant treatment for middle and late stage lung adenocarcinoma, the response of chemotherapy varies between patients. Moreover, there are no effective biomarkers that could predict chemotherapy response in clinical practice. MiRNAs that are stable in all types of body fluid have demonstrated their diagnostic and prognostic capacity in variety of cancers. Here, we utilized three different machine learning algorithms to identify miRNA signatures specific to chemotherapy response in lung cancer. MethodsThrough a public dataset, a panel of miRNAs for response to chemotherapy was identified by Machine Learning. The predictive capacity was determined by the receiver operation curve. A cohort involving 30 patients with lung adenocarcinoma was utilized for validate the miRNA panel. ResultsMachine Learnings identified five chemotherapy response featured miRNAs (miR-196b, miR-34c-5p, miR-181b, miR-27b and miR-26a). The putative targets of these miRNA signatures are enriched in the biosynthesis. Two of these miRNA signatures (miR-196b and miR-34c-5p) were validated for their chemotherapy response prediction in our 30 serum samples of lung adenocarcinoma with the accuracy of 0.90 and 0.93, respectively. ConclusionsOur study demonstrates circulating miRNA could potentially be predictive biomarker for chemotherapy response in lung adenocarcinoma.