Abstract
The current tumor node metastasis (TNM) staging system is inadequate for identifying high‐risk gastric cancer (GC) patients. Using a systematic and comprehensive‐biomarker discovery and validation approach, we attempted to build a microRNA (miRNA)‐recurrence classifier (MRC) to improve the prognostic prediction of GC. We identified 312 differentially expressed miRNAs in 446 GC tissues compared to 45 normal controls by analyzing high‐throughput data from The Cancer Genome Atlas (TCGA). Using a Cox regression model, we developed an 11‐miRNA signature that could successfully discriminate high‐risk patients in the training set (n = 372; P < 0.0001). Quantitative real‐time polymerase chain reaction‐based validation in an independent clinical cohort (n = 88) of formalin‐fixed paraffin‐embedded clinical GC samples showed that MRC‐derived high‐risk patients succumb to significantly poor recurrence‐free survival in GC patients (P < 0.0001). Cox and stratification analysis indicated that the prognostic value of this signature was independent of clinicopathological risk factors. Time‐dependent receiver operating characteristic (ROC) analysis revealed that the area under the curve of this signature was significantly larger than that of TNM stage in the TCGA (0.733 vs. 0.589 at 3 years, P = 0.004; 0.802 vs. 0.635 at 5 years, P = 0.005) and validation cohort (0.835 vs. 0.689 at 3 years, P = 0.003). A nomogram was constructed for clinical use, which integrated both MRC and clinical‐related variables (depth of invasion, lymph node status and distance metastasis) and did well in the calibration plots. In conclusion, this novel miRNA‐based signature is superior to currently used clinicopathological features for identifying high‐risk GC patients. It can be readily translated into clinical practice with formalin‐fixed paraffin‐embedded specimens for specific decision‐making applications.
Highlights
Gastric cancer (GC) is the fourth most common malignancy and ranks as the second leading cause of cancer death worldwide (Siegel et al, 2017)
Based on the miRNA expression data from the The Cancer Genome Atlas (TCGA) dataset, we compared miRNA expression profiles between 446 gastric cancer (GC) and normal 45 control groups and found 312 miRNAs with an absolute fold-change differences of 2 and a false discovery rate (FDR) < 0.05 (Table S1). These significantly differentially expressed miRNAs were considered as candidate prognostic biomarkers for GC patients, among which 260 miRNAs were identified as upregulated and 52 as downregulated in GC compared to normal control (Fig. S1)
24 miRNAs were found to be significantly associated with the GC patient recurrence-free survival (RFS) (P < 0.05) (Table S2) and were subsequently entered into a multivariate Cox regression analysis
Summary
Gastric cancer (GC) is the fourth most common malignancy and ranks as the second leading cause of cancer death worldwide (Siegel et al, 2017). GC is a clinically heterogeneous disease and it is difficult to accurately predict outcomes even within the same stage. The identification of novel markers that could predict survival and relapse in GC would greatly optimize the treatment planning and benefit patients
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
