Machine learning diagnosis by immunoglobulin N‐glycan signatures for precision diagnosis of urological diseases. Issue 7 (25th May 2022)

• Record Type: Journal Article
• Authors: Iwamura, Hiromichi; Mizuno, Kei; Akamatsu, Shusuke; Hatakeyama, Shingo; Tobisawa, Yuki; Narita, Shintaro; Narita, Takuma; Yamashita, Shinichi; Kawamura, Sadafumi; Sakurai, Toshihiko; Fujita, Naoki; Kodama, Hirotake; Noro, Daisuke; Kakizaki, Ikuko; Nakaji, Shigeyuki; Itoh, Ken; Tsuchiya, Norihiko; Ito, Akihiro; Habuchi, Tomonori; Ohyama, Chikara; Yoneyama, Tohru
• Is Part Of: Cancer science. Volume 113:Issue 7(2022)
• Page Start: 2434
• Page End: 2445
• Publication Date: 2022-05-25
• Subjects: biomarker -- glycosylation -- immunoglobulin -- machine learning -- urologic disease; Cancer -- Periodicals; Neoplasms -- Periodicals; Research -- Periodicals; Electronic journals; 616.994005
• DOI: 10.1111/cas.15395
• Languages: English
• ISSNs: 1347-9032

Abstract

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. Abstract : Early diagnosis of urological diseases is often challenging. We established a supervised‐ML urological disease‐specific scoring system by obtaining serum Ig N‐glycan signature in a large series of patients from multiple Japanese hospitals. This could be a promising approach for the diagnosis of urological diseases.