## 5 Conclusions  
Our study established a unique top ten signature genes and predicted the overall survival of ESCC, which can help in clinical decision-making for individual treatment by quantifying the expression of HGsT10 using quantitative real-time PCR (qPCR) technique. Accordingly, medications have to provide for the ESCC patients to save their lives. Additionally, independent miRNA datasets acquired from miRNet, the identified let-7 family and other miRNAs, mir-21-5p, miR-21-5p, mir-16-5p, mir-19b-3p, and mir-19a-3p were identified as independent prognostic miRNAs for ESCC overexpression of the predicted miRNAs, which might increase expression of HGsT10 within the GSE161533 and GDC TCGA samples. Our findings could also be clinically valuable in predicting the prognosis of ESCC in the advanced tumor stage. However, further experiments are warranted to confirm the miRNA utility in the expression of HGsT10, which is involved in the ESCC progression and development. Furthermore, further validation, such as small molecule discovery and transdifferentiation, are needed to develop appropriate medications to treat ESCC cancer patients even at the advanced stage.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12031083/s1](https://www.mdpi.com/article/10.3390/app12031083/s1)]([https://www.mdpi.com/article/10.3390/app12031083/s1](https://www.mdpi.com/article/10.3390/app12031083/s1)), Table S1: The predicted top 250 DEGs of GSE161533 dataset from GEO Database; Table S2: The predicted down-regulated genes from the top 250 DEGs of GSE161533 dataset from GEO Database; Table S3: The predicted up-regulated genes from the top 250 DEGs of GSE161533 dataset from GEO Database; Table S4: FDR values for the enriched pathway terms; Table S5: The annotated GO function, Process and KEGG Pathway; Figure S1: Kaplan-Meier analysis of the relationship between overall survival and HGsT10 expression using GEPIA analysis included 91 normal and 91 tumor samples. The green trend line indicated the lower expression of HGsT10 and increased survivability. The red trend line indicates the overexpression of HGsT10 and decreased survivability in ESCA samples; Figure S2: The identification of somatic mutation for the HGsT10 and its co-relation with the overall survivability of the ESCA patients using the GDC TCGA dataset includes 160 samples (A-J). The Greyline indicates the overall survivability of the ESCA patients without the mutation. The blue line represents the overall survivability of the patients with mutations. Abbreviation: LRTS--Log-rank test statistics. The gene COL1A2 (I) and MMP9 (A) mutations were noted in 6 and 5 samples, respectively, with the decreased overall survivability.  
Conceptualization, B.M., B.S.S., P.K. and C.C.; methodology, B.M.; software, B.M.; validation, B.M., B.S.S. and C.C.; formal analysis, B.M.; investigation, B.M.; resources, C.C.; data curation, B.M.; writing--original draft preparation, B.M. and B.S.S.; writing--article and editing, B.M., B.S.S., P.K., S.T. and C.C.; supervision, C.C.; project administration, C.C. and B.S.S.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.