·The clinicopathological data of gastric cancer patients were downloaded from the Cancer Genome Atlas (TCGA). There were 258 males and 139 females with a median age of 67 years. Three hundred and fifty-six of the 397 patients had complete clinicopathological data. The 397 patients were allocated into training cohort consisting of 278 patients and validation cohort consisting of 119 patients using the random sampling method, with a ratio of 7∶3. A miRNA sequencing dataset GSE93415 containing 20 pairs of gastric cancer and corresponding adjacent normal tissue was downloaded from Gene Expression Omnibus database. The candidate miRNAs were selected from differentially expressed miRNAs in gastric cancer and adjacent tissue. A prognosis associated miRNA prediction model was constructed upon survival-associated miRNAs which were selected from candidate miRNAs through LASSO-Cox regression analysis. The performance of prognosis-associated miRNA prediction model was validated in the training cohort and validation cohort. The reliability of the model was evaluated by using Log-Rank test, and the accuracy of the model was evaluated by using the area under curve (AUC) of the receiver operating characteristic curves. Gene expression profiles and algorithms in the pRRophetic package were utilized to predict patients' sensitivity to chemotherapy drugs. Calibration curves were used to verify the accuracy of the nomogram. Consistency index (C-index) was used to check the consistency of the nomogram and other factors. Decision curve analysis (DCA) was employed to predict the contribution of candidate factors to clinical decision making.

·① There was no significant difference in the baseline and overall survival between the training cohort and validation cohort (P>0.05). ② There were 111 differentially expressed miRNAs calculated from GSE93415 dataset, of which 20 were up-regulated in tumor tissue while 91 were down-regulated. Fifty-nine miRNAs were selected as candidate miRNAs after filtration. ③ Among the 59 candidate miRNAs, 5 survival-associated miRNAs were selected, including let-7i-5p, let-7f-5p, miR-708-5p, miR-135b-5p and miR-100-5p. The differential expression patterns of gastric cancer to adjacent tissue were all down-regulation, with the fold change of 2.55, 2.78, 2.17, 3.08 and 3.26. Risk score = (-0.049×let-7i-5p expression level -0.033 2×let-7f-5p expression level +0.202 9×miR-708-5p expression level -0.088 9×miR-135b-5p expression level +0.016 3×miR-100-5p expression level). ④ In the training cohort and the validation cohort, the overall survival rate of patients in the high-risk group was lower, and the difference was statistically significant (P<0.05) . The AUC of the prognosis-associated miRNA model for 1-, 3- and 5-year survival prediction was 0.640, 0.763 and 0.853, and was 0.631, 0.735 and 0.750 in validation cohort. ⑤Results of univariate analysis showed that age, tumor pathological stage, T stage, N stage, M stage and prognosis-associated miRNA model score were related factors for prognosis of gastric cancer patients (HR=1.017, 1.633, 1.353, 1.346, 2.652, 15.874; 95%CI 1.002?1.033, 1.333?2.001, 1.109?1.650, 1.169?1.548, 1.553?4.529, 5.729?43.985; P<0.05). Results of multivariate analysis showed that age, M stage and prognosis-associated miRNA model score were independent risk factors for prognosis of gastric cancer patients (HR=1.03, 2.27, 18.72; 95%CI 1.01?1.05, 1.09?4.70, 5.96?58.77; P<0.05). ⑥The AUC of the prognosis-associated miRNA model for 5-year survival prediction was 0.818 in 356 gastric cancer patients with complete clinicopathological data, higher than that of age, gender, tumor pathological stage, T stage, N stage, M stage and merged clinical factors. ⑦The results of Calibration curves, C-index, and DCA all indicated that patients with gastric cancer may get more net benefits from the prognostic nomogram than age, gender and tumor pathological stage.

·A prognosis-associated miRNA prediction model that can be used to predict the survival of gastric cancer patients is constructed based on 5 miRNAs, which has a certain predictive ability to distinguish the survival status and prognosis of gastric cancer patients and can provide reference for clinical treatment.