With the advances in high throughput sequencing, an increasing number of snoRNAs have been identified and are emerging as important RNAs, thereby attracting the attention of researchers. Studies have shown that some snoRNAs play important roles in biological processes, and dysfunction of snoRNAs may lead to oncogenesis . It has also been reported that snoRNAs could serve as biomarkers in several diseases, including cancers . Concerning the host gene for snoRNAs, SNHG genes may have diverse regulatory effects on cellular processes in some cancers [24, 25]. In the present study, a total of 19 DE-snoRNAs and 9 DE-SNHGs were identified between EC samples and normal controls, among which SNORA14B, SNORA47, SNORA71C, SNORD12B, and SNORD14E were selected as the key members due to their topological characteristics in the snoRNA-mRNA co-expression network; Similarly, SNHG23, SNHG3, SNHG17, SNHG4, and SNHG8 was selected as the key members in the ceRNA regulation network. SNORA47 is involved in lung cancer tumorigenesis  and promotes tumorigenesis by regulating EMT markers in hepatocellular carcinoma . SNORA71C is overexpressed in breast cancer brain metastases . Furthermore, SNHG3 facilitates cell proliferation and migration in oral squamous cell carcinoma via targeting nuclear transcription factor Y subunit gamma . It has been also been reported to that SNHG3-related ceRNAs can be potential research targets to explore the molecular mechanisms of HCC . Moreover, SNHG17 is involved in gastric cancer, non-small-cell lung cancer, or melanoma through regulation of pathways, such as the phosphoinositide 3-kinase (PI3K)-AKT pathway [31–33]. However, until, little has been published of these snoRNAs or SNHGs in EC.
Oncogenesis is a complex process that is involved in the interaction of various genes and signaling pathways. Hence, the analysis of candidate snoRNAs or SNHGs and pathways related to EC could provide the cognitive basis for disease development. The present data reveal the key snoRNAs or SNHGs in co-expression and ceRNA networks mainly enriched in pathways, such as the NOD-like receptor signaling pathway and neuroactive ligand-receptor interaction, as well as calcium signaling pathway, cytokine-cytokine receptor interaction, and ECM-receptor interaction. Actually, cytokine-cytokine receptor interaction, NOD-like receptor signaling pathway, and ECM-receptor interaction are chief contributors to EC progression . Zeng et al.  reported that the calcium signaling pathway and the neuroactive ligand-receptor interaction are the most relevant pathways in EC diagnosis using miRNA-seq and RNA-seq data. In accordance with previous studies, our data suggest the involvement of snoRNAs or SNHGs in the progression of EC by the aforementioned pathways.
Recent studies have suggested that snoRNAs can be a novel prognostic biomarkers and therapeutic targets for cancers. For instance, Zhu et al.  reported that SNORD89 deleteriously affects the prognosis of ovarian cancer patients by regulating the Notch1-c-Myc pathway. Another study described that the inhibition of SNORA7B expression impaired cell growth, proliferation, migration, and invasion by inducing apoptosis . In this study, based on data from TCGA, patients with high expression of SNORA70D displayed shorter overall survival compared to those with low expression. However, the role of SNORA70D has not been reported in any disease, and it will be necessary to further validate the clinical prognostic value of SNORA70D EC. In addition to their host genes, snoRNAs can participate in the regulation of methylation. In EC, several genes with aberrant DNA methylation have been selected as potential disease biomarkers [38, 39] But until now, few studies have investigated the interaction between snoRNAs and methylation in EC. In our study, the SNORD11B and SNORD15A were positively correlated with the levels of methylation sites, such as cg16620283 and cg27122942. However, this study only analyzed the correlation between snoRNAs and methylation sites, and the detailed regulatory mechanism between them needs to be further clarified.
There were some limitations in this study. For example, all results in this study were predicted by the bioinformatics methods, and hence, further experiments and more samples are needed to validate these findings.