Spss statistical software version 19

For BMD-correlated EIs, the expression values of miR-194-5p and the bone mass parameter BMD were normalized into a 0 to 1 number before further model integration was performed as previously described^{32 (link)}. The Intelligent Problem Solver (IPS) tool in the software STATISTICA Neural Networks (SNN, Release 4.0E; Statsoft, Tulsa, OK, USA) was applied to construct a RBF-ANN model to investigate the effect of miRNA and EIs integration on the spine BMD association. The RBF-ANN model was named as ANN I in this study. The holdout cross-validation method was applied for preliminary validation of the model, as IPS randomly divided all the participants into three sets (training set, verification set, and testing set) in a 2:1:1 ratio. Thus, one-quarter of the participants was not included in model building and was used for model testing. The IPS calculated correlation coefficients for the training set (R_Tr) and the testing set (R_Te). The two correlation coefficients measured the correlation between model output and spine BMD. Similar values of R_Tr and R_Te indicate good generalization ability of the model. For model type comparison, the SPSS statistical software version 19.0 (IBM Corp., New York city, NY, USA) was applied to integrate miR-194-5p and BMD-correlated EIs into a MLR model.

Statistical analysis of all collected data was performed with SPSS statistical software version 19.0 (IBM Corporation, Armonk, NY, USA). Categorical variables are expressed using frequency. Numerical variables are expressed as means with 95% confidence interval. Statistical analysis was performed with Fisher's exact test and the chi-square test, where appropriate. A two-sided P value of <0.05 was considered statistically significant.

Statistical analysis was performed using SPSS statistical software version 19.0 (SPSS Inc., IBM Corporation, Chicago, IL, USA). Significant differences in patient demographics and tumor characteristics between the two groups were detected using the Pearson Chi-square test and the Wilcoxon rank sum test. Overall mortality was chosen as an endpoint. Survival was calculated using the Kaplan-Meier method and the log-rank test was used to compare survival curves. Univariate and multivariate models were established to evaluate correlations between various covariates and survival. Those variables that achieved a P-values < 0.1 were included in a multivariable Cox proportional hazard model. We reported our findings using hazard ratios (HRs), 95% confidence intervals (CIs), and P values. Two-sided P-values < 0.05 were considered statistically significant.

SPSS statistical software version 19.0 was used for data analysis (IBM Corp., Armonk, NY, USA). Chi-square and ANOVA tests were used to analyze the differences among the two groups. Statistical significance was set at a P < 0 .05. The RNA sequencing data has been deposited in the Gene Expression Omnibus (accession number: GSE135083).