LAUSR

Recently, we read the article published in frontiers in Pharmacology on 23 September 2021 written by Yu et al. (2021) with great interest: “Dapagliflozin Mediates Plin5/PPARα Signaling Axis to Attenuate Cardiac Hypertrophy.” In this article, the authors explored the molecular mechanism of dapagliflozin (DAPA) to relieve cardiac hypertrophy by conducting gene microarray analysis to identify differentially expressed genes (DEGs) and subsequently performing signaling pathway enrichment analysis. The study is of vital significance. However, we believe bioinformatics analysis has some weak points in the original article. In the original article, microarray analysis was used to detect differentially expressed genes (DEGs) among the abdominal aortic constriction (AAC) group, AAC + DAPA group, and the sham group. The authors applied “fold change >1.5 or fold change <−1.5” as the threshold for significant differential expression. In the “Data Availability Statement” section of the original article, we noticed that the microarray GSE183120 that the authors analyzed was publicly available at the GEO database (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE183120). On the GEO website, we could see only one sample in each of the groups depicted as sham, AAC, and AAC + DAPA, which means the p-value could not be generated with only one sample per group. Thus, the identification of DEGs may not be statistically significant. What is more, it is noteworthy that the platform shown in the GEO database of GSE183120 is “GPL23038 [Clariom_S_Mouse] Affymetrix Clariom S Assay, Mouse (Includes Pico Assay)” rather than what the authors mentioned in the section of “Microarray Profiling,” “Affymetrix GeneChip PrimeviewTM.” Herein, we re-analyzed the microarray GSE183120 using “Transcriptome Analysis Console” (TAC software, version 4.0, Thermo Fisher Scientific) and produced different results. TAC4.0 is a software developed for researchers with no or little experience in bioinformatics, and it is suitable for Affymetrix microarray transcriptome analysis. Consistent with the original article, we also applied “fold change >1.5 or fold change <−1.5” as the threshold for significant differential expression, and other settings were default. We found that 2021 genes changed significantly, with 945 genes upregulated and 1,076 genes downregulated in the AAC + DAPA group compared with the AAC group, which was similar to the results of the original article. On the contrary, we were failed to reproduce fold change values of more than half of the representative genes, as shown in Table 1. In the results of our analysis (Table 2), representative genes showed the opposite direction in sham/AAC (sham vs. AAC comparison). For example, STAT1 was upregulated in the AAC Edited by: Ebru Arioglu Inan, Ankara University, Turkey