Since the past 30 years, the prevalence of diabetes has more than doubled, making it an urgent challenge globally. We carried out systematic analysis with the public data of mRNA expression profiles in skeletal muscle to study the pathogenesis, since insulin resistance in the skeletal muscle is an early feature. We utilized three GEO datasets, containing total 60 cases and 63 normal samples. After the background removal, R package QC was utilized to finish the preprocessing of datasets. We obtained a dataset containing 2481 genes and 123 samples after the preprocessing. Quantitative quality control measures were calculated to represent the quality of these datasets. MetaDE package provides functions for conducting different systematic analysis methods for differential expression analysis. The GO term enrichment was carried out using PANTHER. Protein-protein interactions, drug-gene interactions, and genetic association of the identified differentially expressed genes were analyzed using STRING v10.0 online tool, DGIdb, and the Genetic Association Database, respectively. The datasets had good performances on IQC and EQC, which suggested that the datasets had good internal and external quality. Totally 96 differentially expressed genes were detected using 0.01 as cutoff of AW. The enriched GO terms were mainly associated with the response to glucocorticoid. There were seven genes involving in the gluconeogenesis were differentially expressed, which might be the potential treatment target for this disease. The closely connected networks and potential targets of existed drugs suggested that some of the drugs might be applied to the treatment of diabetes as well.