Machine learning diagnosis by immunoglobulin N-glycan signatures for precision diagnosis of urological diseases.

Abstract

Early diagnosis of urological diseases is often difficult due to the lack of specific biomarkers. More powerful and less invasive biomarkers that can be used simultaneously to identify urological diseases could improve patient outcomes. The aim of this study was to evaluate a urological disease-specific scoring system established with a machine learning (ML) approach using Ig N-glycan signatures. Immunoglobulin N-glycan signatures were analyzed by capillary electrophoresis from 1312 serum subjects with hormone-sensitive prostate cancer (n = 234), castration-resistant prostate cancer (n = 94), renal cell carcinoma (n = 100), upper urinary tract urothelial cancer (n = 105), bladder cancer (n = 176), germ cell tumors (n = 73), benign prostatic hyperplasia (n = 95), urosepsis (n = 145), and urinary tract infection (n = 21) as well as healthy volunteers (n = 269). Immunoglobulin N-glycan signature data were used in a supervised-ML model to establish a scoring system that gave the probability of the presence of a urological disease. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUC). The supervised-ML urologic disease-specific scores clearly discriminated the urological diseases (AUC 0.78-1.00) and found a distinct N-glycan pattern that contributed to detect each disease. Limitations included the retrospective and limited pathological information regarding urological diseases. The supervised-ML urological disease-specific scoring system based on Ig N-glycan signatures showed excellent diagnostic ability for nine urological diseases using a one-time serum collection and could be a promising approach for the diagnosis of urological diseases.

• Authors: Hiromichi Iwamura, Kei Mizuno, Shusuke Akamatsu, Shingo Hatakeyama, Yuki Tobisawa, Shintaro Narita, Takuma Narita, Shinichi Yamashita, Sadafumi Kawamura, Toshihiko Sakurai, Naoki Fujita, Hirotake Kodama, Daisuke Noro, Ikuko Kakizaki, Shigeyuki Nakaji, Ken Itoh, Norihiko Tsuchiya, Akihiro Ito, Tomonori Habuchi, Chikara Ohyama, Tohru Yoneyama
• Journal: Cancer science (Cancer Sci) Vol. 113 Issue 7 Pg. 2434-2445 (Jul 2022) ISSN: 1349-7006 [Electronic] England
• PMID: 35524940 (Publication Type: Journal Article)
• Copyright: © 2022 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association.

Chemical References

• Biomarkers, Tumor
• Immunoglobulins
• Polysaccharides

Topics

• Biomarkers, Tumor
• Humans
• Immunoglobulins
• Kidney Neoplasms
• Machine Learning
• Male
• Polysaccharides
• Prostatic Neoplasms
• Retrospective Studies
• Urinary Bladder Neoplasms (pathology)