Abstract: Objective:To construct a prognosis associated micro RNA（miRNA） prediction model based on bioinformatics analysis and evaluate its application value in pancreatic cancer patients.
Methods:The retrospective cohort study was conducted. The clinicopathological data of 171 pancreatic cancer patients from the Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov/) between establishment of database and September 2017 were collected. There were 93 males and 78 females, aged from 35 to 88 years, with a median age of 65 years. Of the 171 patients, 64 had complete clinicopathological data. Patients were allocated into training dataset consisting of 123 patients and validation dataset consisting of 48 patients using the random sampling method, with a ratio of 7〖DK〗∶3. The training dataset was used to construct a prediction model, and the validation dataset was used to evaluate performance of the prediction model. Nine pairs of miRNA sequencing data (GSE41372) of pancreatic cancer and adjacent tissues were downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in pancreatic cancer and adjacent tissues for LASSO-COX regression analysis based on the patients of training dataset. A prognosis associated miRNA prediction model was constructed upon survival associated miRNAs which were selected from candidate differentially expressed miRNAs. The performance of prognosis associated miRNA prediction model was validated in training dataset and validation dataset, the accuracy of model was evaluated using the area under curve (AUC) of the receiver operating characteristic curves and the efficiency was evaluated using the consistency index (C-index). Observation indicarors: (1) survival of patients; (2) screening results of differentially expressed miRNAs; (3) construction of prognosis associated miRNA model; (4) validation of prognosis associated miRNA model; (5) comparison of clinicopathological factors in pancreatic cancer patients; (6) analysis of factors for prognosis of pancreatic cancer patients; (7) comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging. Measurement data with normal distribution were represented as Mean±SD, comparison between groups was analyzed by the student-t test, and comparison between multiple groups was analyzed by the AVONA. Measurement data with skewed data were represented as M (range), and comparison between groups was analyzed using the Mann-Whitney U test. Count data were described as absolute numbers or percentages, and comparison between groups was conducted using the chi-square test. Ordinal data were analyzed using the rank sum test. Correlation analysis was conducted based on count data to mine the correlation between prognosis associated miRNA model and clinicopathological factors. COX univariate analysis and multivariate analysis were applied to evaluate correlation with the results described as hazard ratio (HR) and 95% confidence interval (CI). HR<1 indicated the factor as a protective factor, HR>1 indicated the factor as a risk factor, and HR equal to 1 indicated no influence on survival. The Kaplan-Meier method was used to draw survival curve and calculate survival rates, and the Log-rank test was used for survival analysis.
Results:(1) Survival of patients: 123 patients in the training dataset were followed up for 31-2 141 days, with a median follow-up time of 449 days. The 3- and 5-year survival rates were 16.67% and 8.06%. Forty-eight patients in the validation dataset were followed up for 41-2 182 days, with a median follow-up time of 457 days. The 3- and 5-year survival rates were 15.63% and 9.68%. There was no significant difference in the 3- or 5-year survival rates between the two groups (x²=0.017, 0.068, P>0.05). (2) Screening results of differentially expressed miRNAs. Results of bioinformatics analysis showed that 102 candidate differentially expressed miRNAs were selected, of which 63 were up-regulated in tumor tissues while 39 were down-regulated. (3) Construction of prognosis associated miRNA model: of the 102 candidate differentially expressed miRNAs, 5 survival associated miRNAs were selected, including miR-21, miR-125a-5p, miR-744, miR-374b, miR-664. The differential expression patterns of pancreatic cancer to adjacent tissues were up-regulation, up-regulation, down-regulation, up-regulation, and down-regulation, respectively, with the fold change of 4.00, 3.43, 3.85, 2.62, and 2.35. A prognostic expression equation constructed based on 5 survival associated miRNAs = 0.454×miR-21 expression level-0.492×miR-125a-5p expression level-0.49×miR-744 expression level-0.419×miR-374b expression level-0.036×miR-664 expression level. (4) Validation of prognosis associated miRNA model: The C-index of prognosis associated miRNA model was 0.643 and 0.642 for the training dataset and validation dataset,respectively. (5) Comparison of clinicopathological factors in pancreatic cancer patients: results of COX analysis showed that the prognosis associated miRNA model was highly related with pathological T stage and location of pancreatic cancer (Z=45.481, x²=10.176, P<0.05). (6) Analysis of factors for prognosis of pancreatic cancer patients: results of univariate analysis showed that pathological N stage, radiotherapy,molecular targeted therapy, score of prognosis associated miRNA model were related factors for prognosis pf pancreatic cancer patients (HR=2.471, 0.290, 0.172, 2.001, 95%CI: 1.012-6.032, 0.101-0.833, 0.082-0.364, 1.371-2.922, P<0.05). Results of multivariate analysis showed that molecular targeted therapy was an independent protective factor for prognosis of pancreatic cancer patients (HR=0.261, 95%CI: 0.116-0.588, P<0.05) and score of prognosis associated miRNA model≥1.16 was an independent risk factor for prognosis of pancreatic cancer patients (HR=1.608, 95%CI: 1.091-2.369, P<0.05). (7) Comparison of prediction performance between prognosis associated miRNA model and the eighth edition TNM staging: in the training dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging (Z= -1.671,-1.867, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging for 3- and 5-year survival prediction was 0.797, 0.935 and 0.737 , 0.703, with the 95%CI of 0.622-0.972, 0.828-1.042 and 0.571-0.904 , 0.456-0.951. The C-index was 0.643 and 0.534. In the validation dataset, there was a significant difference in the prediction probability for 3- and 5-year survival of pancreatic cancer patients between prognosis associated miRNA model and the eighth edition TNM staging (Z=-1.729, -1.923, P<0.05). The AUC of the prognosis associated miRNA model and the eight edition TNM staging was 0.750, 0.873 and 0.721 , 0.703, with the 95%CI of 0.553-0.948, 0.720-1.025 and 0.553-0.889, 0.456-0.950, respectively. The C-index was 0.642 and 0.544.
Conclusions:A prognosis associated miRNA prediction model can be constructed based on 5 survival associated miRNAs in pancreatic cancer patients, as a complementation to current TNM staging and other clinicopathological parameters, which provides individual and accurate prediction of survival for reference in the clinical treatment.