Abstract
MicroRNA (miRNA, miR) has been reported to be highly implicated in a wide range of biological processes in lung cancer (LC), and identification of differentially expressed miRNAs between normal and LC samples has been widely used in the discovery of prognostic factors for overall survival (OS) and response to therapy. The present study was designed to develop and evaluate a miRNA-based signature with prognostic value for the OS of lung adenocarcinoma (LUAD), a common histologic subtype of LC. In brief, the miRNA expression profiles and clinicopathological factors of 499 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. Kaplan–Meier (K-M) survival analysis showed significant correlations between differentially expressed miRNAs and LUAD survival outcomes. Afterward, 1,000 resample LUAD training matrices based on the training set was applied to identify the potential prognostic miRNAs. The least absolute shrinkage and selection operator (LASSO) cox regression analysis was used to constructed a six-miRNA based prognostic signature for LUAD patients. Samples with different risk scores displayed distinct OS in K-M analysis, indicating considerable predictive accuracy of this signature in both training and validation sets. Furthermore, time-dependent receiver operating characteristic (ROC) analysis demonstrated the nomogram achieved higher predictive accuracy than any other clinical variables after incorporating the clinical information (age, sex, stage, and recurrence). In the stratification analysis, the prognostic value of this classifier in LUAD patients was validated to be independent of other clinicopathological variables, such as age, gender, tumor recurrence, and early stage. Gene set annotation analyses were also conducted through the Hallmark gene set and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, indicating target genes of the six miRNAs were positively related to various molecular pathways of cancer, such as hallmark UV response, Wnt signaling pathway and mTOR signaling pathway. In addition, fresh cancer tissue samples and matched adjacent tissue samples from 12 LUAD patients were collected to verify the expression of miR-582’s target genes in the model, further revealing the potential relationship between SOX9, RASA1, CEP55, MAP4K4 and LUAD tumorigenesis, and validating the predictive value of the model. Taken together, the present study identified a robust signature for the OS prediction of LUAD patients, which could potentially aid in the individualized selection of therapeutic approaches for LUAD patients.
Highlights
Lung cancer (LC) is the leading cause of malignancy-related mortality, with approximately one million cases of death annually, which is a great burden for public health worldwide (Torre et al, 2015)
The preprocessed Lung adenocarcinoma (LUAD) mature miRNA expression profiles in the Cancer Genome Atlas (TCGA) database displayed as log2 converted reads per million (log2(RPM + 1)) were downloaded, along with the clinical information of related samples including survival follow-up data, age, gender, tumor node metastasis (TNM) stage, and tumor recurrence
The miRNA expression profile and clinical information of 518 LUAD cases were downloaded from the The Cancer Genome Atlas (TCGA) databases
Summary
Lung cancer (LC) is the leading cause of malignancy-related mortality, with approximately one million cases of death annually, which is a great burden for public health worldwide (Torre et al, 2015). Lung adenocarcinoma (LUAD) is the most common histologic subtype of LC (Brambilla et al, 2001; Wakelee et al, 2007), accounting for 50% of LC cases. 40% of stage IB and 66% of stage II patients are facing a high recurrence rate within 5 years, which is associated with a poor prognosis (Chansky et al, 2010). Recent studies have shown that adjuvant chemotherapy confers a 4–15% survival benefit for stage II-III patients receiving tumor resection rather than patients with stage I (Reck et al, 2013; Pignon et al, 2008). It is imperative to identify early diagnostic markers and establish effective predictive systems for prognosis to improve the survival of LUAD patients
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