HOCM is regarded as a primary risk factor of SCD caused by HCM. Therefore, exploration of the pathogenic mechanism is of utmost importance. In our recent study, two datasets were integrated from the GEO database on HOCM and associated genes of HCM in the GENE and OMIM databases to obtain the candidate gene set. Then, WGCNA method was used to identify the related modules of HOCM. The integration of high-throughput data, online databases and bioinformatic method for scale-free network have widened the disease spectrum and strengthened the evidence. BP analysis indicated that the candidate gene set and genes in most of the relevant modules were targeted to muscle system process, muscle contraction and heart process. Pathway analysis indicated that candidate gene set was mostly enriched in HCM, focal adhesion and dilated cardiomyopathy. These results showed correlation with HOCM, and so, the co-expressed 32 genes with the highest degree were chosen in the two modules as core genes.
The miRNAs, lncRNAs and TFs that interact with the co-expressed key genes were then screened to obtain a multi-factor regulatory network. To date, several studies have reported the features of ncRNAs in the HOCM (Ntelios et al., 2017; Salman et al., 2018). Nevertheless, the details of RNA crosstalk in HOCM have not been elucidated. In this study, an integrated lncRNA-miRNA-mRNA-TF regulatory network was constructed, expounding the views on gene regulation at the pre-transcriptional and post-transcriptional levels. Moreover, bioinformatics technology was applied to explore the key molecules that are involved in the process of HOCM, which might be considered as optimal candidate markers for future therapy. The first 7 regulatory factors were found as the core regulatory factors, which included the lncRNAs (XIST, MALAT1, H19), TFs (SPI1 and SP1) and miRNAs (hsa-miR-29b-39 and has-miR-29a-3p). XIST, which is named as lncRNA X-inactive specific transcript, has been identified as a necessary regulator of cardiac hypertrophy by regulating miR-101(Xiao et al., 2019) and miR-330(Chen et al., 2018). XIST also showed association with heart failure in females (Heidecker et al., 2010). In vivo experiments revealed that knockdown of XIST can inhibit the myocardial cell apoptosis in acute myocardial infarction rat model by regulating miR-449 (Zhang et al., 2019). Besides, H19 has been identified as a regulator that targets PPARα of cardiac hypertrophy (Liu et al., 2016; Zhou et al., 2019). The results showed that XIST, MALAT1 and H19 possibly regulated other miRNAs involved in cardiac hypertrophy, such as miR-15b(Ooi et al., 2014), miR-19b(Li et al., 2019) and miR-29b (Yang et al., 2020) in our research.
The miRNAs and TFs consistent with other researches were identified. MiR-29 is a regulator of cardiomyocyte hypertrophy through wnt and mTOR signaling pathways (Sassi et al., 2017; Shi et al., 2019). Moreover, SP1 can regulate cardiomyocyte hypertrophy by inducing lncRNA CTBP-AS2 (Luo et al.) and SP1/GATA4 signaling pathways (Sun et al., 2018). However, SPI1 has not been reported in cardiac hypertrophy so far.
So, the core genes were used to divide the HOCM into 4 clusters. Although no difference was observed in the by the expression of a single gene, the HOCM was divided into 4 clusters clearly by combining the 32 co-expressed key genes. Finally, the 4 genes COMP, FMOD, AEBP1 and SULF1 showed significantly different expression in the four clusters. From this point, we speculated that these 32 co-expressed key genes are of great significance for HOCM typing and the 4 genes are considered as important biomarkers due to different progressive stages or prognosis of HOCM. However, there are several limitations to this study. Firstly, our study focused on data mining and analysis without any experimental confirmation; and 2) due to lack of relevant prognostic information, clinical classification of HOCM related to key genes and survival analysis associated with key genes was not conducted.