Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction

Srinivasulu Yerukala Sathipati*, Ming Ju Tsai, Sanjay K. Shukla, Shinn Ying Ho*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

The ability to detect cancer at an early stage in patients who would benefit from effective therapy is a key factor in increasing survivability. This work proposes an evolutionary supervised learning method called CancerSig to identify cancer stage-specific microRNA (miRNA) signatures for early cancer predictions. CancerSig established a compact panel of miRNA signatures as potential markers from 4,667 patients with 15 different types of cancers for the cancer stage prediction, and achieved a mean performance: 10-fold cross-validation accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 84.27% ± 6.31%, 0.81 ± 0.12, 0.80 ± 0.10, and 0.80 ± 0.06, respectively. The pan-cancer analysis of miRNA signatures suggested that three miRNAs, hsa-let-7i-3p, hsa-miR-362-3p, and hsa-miR-3651, contributed significantly toward stage prediction across 8 cancers, and each of the 67 miRNAs of the panel was a biomarker of stage prediction in more than one cancer. CancerSig may serve as the basis for cancer screening and therapeutic selection.

Original languageEnglish
Article number100190
JournalHuman Genetics and Genomics Advances
Volume4
Issue number3
DOIs
StatePublished - 13 Jul 2023

Keywords

  • Artificial Intelligence
  • Cancer diagnosis prediction
  • Machine learning
  • cancer early stage detection
  • pan-cancer analysis

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