Classifying Lung Neuroendocrine Neoplasms through MicroRNA Sequence Data Mining
| Autor: | Paula S. Ginter, Xiaojing Yang, Yao-Tseng Chen, Jina Nanayakkara, Neil Renwick, Kathrin Tyryshkin, Justin J M Wong, Zier Zhou, Thomas Tuschl |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Cancer Research Small RNA markers Biology lcsh:RC254-282 Article 03 medical and health sciences 0302 clinical medicine microRNA medicine small RNA sequencing Lung lung neuroendocrine neoplasms External validation lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens 030104 developmental biology medicine.anatomical_structure Oncology classification 030220 oncology & carcinogenesis Cancer research Typical carcinoid Small Cell Lung Carcinoma Atypical carcinoid Sequence data mining |
| Zdroj: | Cancers, Vol 12, Iss 2653, p 2653 (2020) Cancers Volume 12 Issue 9 |
| ISSN: | 2072-6694 |
| Popis: | Lung neuroendocrine neoplasms (NENs) can be challenging to classify due to subtle histologic differences between pathological types. MicroRNAs (miRNAs) are small RNA molecules that are valuable markers in many neoplastic diseases. To evaluate miRNAs as classificatory markers for lung NENs, we generated comprehensive miRNA expression profiles from 14 typical carcinoid (TC), 15 atypical carcinoid (AC), 11 small cell lung carcinoma (SCLC), and 15 large cell neuroendocrine carcinoma (LCNEC) samples, through barcoded small RNA sequencing. Following sequence annotation and data preprocessing, we randomly assigned these profiles to discovery and validation sets. Through high expression analyses, we found that miR-21 and -375 are abundant in all lung NENs, and that miR-21/miR-375 expression ratios are significantly lower in carcinoids (TC and AC) than in neuroendocrine carcinomas (NECs SCLC and LCNEC). Subsequently, we ranked and selected miRNAs for use in miRNA-based classification, to discriminate carcinoids from NECs. Using miR-18a and -155 expression, our classifier discriminated these groups in discovery and validation sets, with 93% and 100% accuracy. We also identified miR-17, -103, and -127, and miR-301a, -106b, and -25, as candidate markers for discriminating TC from AC, and SCLC from LCNEC, respectively. However, these promising findings require external validation due to sample size. |
| Databáze: | OpenAIRE |
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