TY - JOUR
T1 - Artificial intelligence-driven pan-cancer analysis reveals miRNA signatures for cancer stage prediction
AU - Yerukala Sathipati, Srinivasulu
AU - Tsai, Ming Ju
AU - Shukla, Sanjay K.
AU - Ho, Shinn Ying
N1 - Publisher Copyright:
© 2023 The Author(s)
PY - 2023/7/13
Y1 - 2023/7/13
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Cancer diagnosis prediction
KW - Machine learning
KW - cancer early stage detection
KW - pan-cancer analysis
UR - http://www.scopus.com/inward/record.url?scp=85153486175&partnerID=8YFLogxK
U2 - 10.1016/j.xhgg.2023.100190
DO - 10.1016/j.xhgg.2023.100190
M3 - Article
AN - SCOPUS:85153486175
SN - 2666-2477
VL - 4
JO - Human Genetics and Genomics Advances
JF - Human Genetics and Genomics Advances
IS - 3
M1 - 100190
ER -