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

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..

Keywords: Artificial Intelligence; Cancer diagnosis prediction; Machine learning; cancer early stage detection; pan-cancer analysis.

Figure 1

Schematic diagram of the CancerSig method and analysis of the panel of miRNAs MicroRNA expression profiles of 15 cancer types along with cancer stage information are input in the workflow of the CancerSig method to identify miRNA signatures.

Figure 2

CancerSig prediction performance across cancers (A) Evaluating the prediction performance of CancerSig using receiver operating characteristic (ROC) across 15 cancers. CancerSig obtained a mean area under the curve (AUC) of 0.80 across all cancers. (B) Ranking of the relative miRNAs within the signature using MED analysis.

Figure 3

The predictive ability of miRNAs as a biomarker for cancer stage across cancer types (A) Three signature miRNAs and their contributions to stage prediction across eight cancer types. Each miRNA contributed to at least four cancers. The size of the line is proportional to the percent contribution toward the stage prediction. (B) Fourteen miRNAs contributed across cancers, and each miRNA contributed to at least three cancers. (C) Fifty miRNAs contributed to at least two cancers. (D) Heatmap showing 67 miRNAs and their ranks based on their predictive ability across 15 cancer types.