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A three microRNA‐based prognostic signature for small cell lung cancer overall survival
Author(s) -
Yan Hao,
Xin Shaobin,
Ma Jing,
Wang Hui,
Zhang Heng,
Liu Jindong
Publication year - 2019
Publication title -
journal of cellular biochemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.028
H-Index - 165
eISSN - 1097-4644
pISSN - 0730-2312
DOI - 10.1002/jcb.28159
Subject(s) - microrna , univariate , proportional hazards model , oncology , multivariate statistics , medicine , lung cancer , survival analysis , multivariate analysis , cancer , biology , gene , computer science , genetics , machine learning
Background Small‐cell lung cancer (SCLC) is one of the most aggressive cancers with mechanisms far from understood. Objective We proposed to identify valuable prognostic signature for SCLC prognosis prediction. Methods microRNA (miRNA) expression profiles of 42 SCLC patients were acquired from the Gene Expression Omnibus. miRNAs that significantly associated with SCLC overall survival (OS‐relevant) were identified through univariate Cox regression analysis followed by random survival forest analysis for identification of more reliable miRNA signature. Results Eleven OS‐relevant miRNAs were obtained, and hsa‐miR‐194, hsa‐miR‐608, and hsa‐miR‐9 were further refined through RFS. A formula composed of the three miRNAs’ expression values weighted by their multivariate Cox regression coefficients was constructed, and based on which, SCLC patients with longer OS could be well distinguished from those with shorter OS. Conclusions This study should provide a valuable clue for SCLC prognosis evaluation.