Survival estimation in patients with stomach and esophageal carcinoma using miRNA expression profiles

Srinivasulu Yerukala Sathipati*, Ming Ju Tsai, Tonia Carter, Patrick Allaire, Sanjay K. Shukla, Afshin Beheshti, Shinn Ying Ho

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Identifying a miRNA signature associated with survival will open a new window for developing miRNA-targeted treatment strategies in stomach and esophageal cancers (STEC). Here, using data from The Cancer Genome Atlas on 516 patients with STEC, we developed a Genetic Algorithm-based Survival Estimation method, GASE, to identify a miRNA signature that could estimate survival in patients with STEC. GASE identified 27 miRNAs as a survival miRNA signature and estimated the survival time with a mean squared correlation coefficient of 0.80 ± 0.01 and a mean absolute error of 0.44 ± 0.25 years between actual and estimated survival times, and showed a good estimation capability on an independent test cohort. The miRNAs of the signature were prioritized and analyzed to explore their roles in STEC. The diagnostic ability of the identified miRNA signature was analyzed, and identified some critical miRNAs in STEC. Further, miRNA-gene target enrichment analysis revealed the involvement of these miRNAs in various pathways, including the somatotrophic axis in mammals that involves the growth hormone and transforming growth factor beta signaling pathways, and gene ontology annotations. The identified miRNA signature provides evidence for survival-related miRNAs and their involvement in STEC, which would aid in developing miRNA-target based therapeutics.

Original languageEnglish
Pages (from-to)4490-4500
Number of pages11
JournalComputational and Structural Biotechnology Journal
StatePublished - Jan 2022


  • Machine learning
  • miRNA signature
  • Stomach and esophageal carcinoma
  • Survival estimation


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