Unsupervised machine learning reveals prognostic value of dynamic carbohydrate antigen 125 trajectory in gastric cancer

Carbohydrate Antigen 125 (CA125), a tumor-associated glycoprotein, is an established prognostic marker in gastric cancer. However, the trajectory of CA125 levels incorporating dynamic changes over time have not been investigated. CA125 levels were collected during the first year of treatment to form individual trajectories and grouped using the unsupervised K-means algorithm. Cox proportional hazards model assessed the association between trajectory groups and overall survival (primary outcome). A total of 1,015 patients were included, covering stages I-IV. 7,347 CA125 tests were conducted, including 1,638 preoperatively and 5,709 postoperatively. The median CA125 trajectory exhibited a "rise-decline" pattern, with preoperative levels of 11.0 U/ml, followed by a temporary peak of 22.98 U/ml in the first quarter post-surgery, and subsequently declines to 12.21 U/ml in the second quarter and 10.90 U/ml at six months post-surgery. The unsupervised K-means machine learning identified 4 latent patterns of trajectories ranging at different levels. Trajectories in higher-level groups were significantly associated with worse survival outcomes: Low (n = 309, reference), Medium (n = 390, HR = 1.75, p = 0.006), High (n = 238, HR = 4.66, p < 0.001), and Ultra High (n = 78, HR = 17.9, p < 0.001). Subgroup analysis revealed that across all stages, the survival risks in the High and Ultra High trajectory groups were consistently higher compared to the Low group. Multivariable Cox proportional hazards model confirmed trajectory as an independent prognostic factor. In comparative Cox analysis, models with trajectory (c-index 0.74–0.81) showed superior efficacy than models with static values (c-index 0.59–0.66). We identified the distinct latent patterns of CA125 trajectory dynamics during the initial years of disease course. These patterns exhibited robust independent prognostic value. 1. First comprehensive study on CA125 trajectories in gastric cancer. 2. Large cohort, 1,015 patients, 7,347 CA125 tests, reliable results. 3. Novel unsupervised clustering algorithm, eliminating potential biases introduced by human judgment. 4. Robust prognostic value beyond tumor stage.

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Publication Details

Journal
BMC Cancer
Published
2025-11-28
DOI
https://doi.org/10.1186/s12885-025-15268-5
Citations
1
Primary Topic
Gastric Cancer Management and Outcomes
Type
article
Field-Weighted Citation Impact
1.52

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article

Unsupervised machine learning reveals prognostic value of dynamic carbohydrate antigen 125 trajectory in gastric cancer

Yonghe Chen, Lei Lian, Qian Sun, Junsheng Peng et al.
1 citations
BMC Cancer
Gastric Cancer Management and Outcomes
1.52
article

Unsupervised machine learning reveals prognostic value of dynamic carbohydrate antigen 125 trajectory in gastric cancer

Yonghe Chen, Lei Lian, Qian Sun, Junsheng Peng, Yi Lin, Dan Liu, Tao Luo, Kaiyu Sun, Xiaohan Jiang, Jun-Jie Liu
article en
1 citations

Abstract

Carbohydrate Antigen 125 (CA125), a tumor-associated glycoprotein, is an established prognostic marker in gastric cancer. However, the trajectory of CA125 levels incorporating dynamic changes over time have not been investigated. CA125 levels were collected during the first year of treatment to form individual trajectories and grouped using the unsupervised K-means algorithm. Cox proportional hazards model assessed the association between trajectory groups and overall survival (primary outcome). A total of 1,015 patients were included, covering stages I-IV. 7,347 CA125 tests were conducted, including 1,638 preoperatively and 5,709 postoperatively. The median CA125 trajectory exhibited a "rise-decline" pattern, with preoperative levels of 11.0 U/ml, followed by a temporary peak of 22.98 U/ml in the first quarter post-surgery, and subsequently declines to 12.21 U/ml in the second quarter and 10.90 U/ml at six months post-surgery. The unsupervised K-means machine learning identified 4 latent patterns of trajectories ranging at different levels. Trajectories in higher-level groups were significantly associated with worse survival outcomes: Low (n = 309, reference), Medium (n = 390, HR = 1.75, p = 0.006), High (n = 238, HR = 4.66, p < 0.001), and Ultra High (n = 78, HR = 17.9, p < 0.001). Subgroup analysis revealed that across all stages, the survival risks in the High and Ultra High trajectory groups were consistently higher compared to the Low group. Multivariable Cox proportional hazards model confirmed trajectory as an independent prognostic factor. In comparative Cox analysis, models with trajectory (c-index 0.74–0.81) showed superior efficacy than models with static values (c-index 0.59–0.66). We identified the distinct latent patterns of CA125 trajectory dynamics during the initial years of disease course. These patterns exhibited robust independent prognostic value. 1. First comprehensive study on CA125 trajectories in gastric cancer. 2. Large cohort, 1,015 patients, 7,347 CA125 tests, reliable results. 3. Novel unsupervised clustering algorithm, eliminating potential biases introduced by human judgment. 4. Robust prognostic value beyond tumor stage.

BMC CancerVol. 25(1)
Guangzhou University of Chinese Medicine (CN), Sun Yat-sen University (CN), Sixth Affiliated Hospital of Sun Yat-sen University (CN), The First Affiliated Hospital, Sun Yat-sen University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 12%
Gastric Cancer Management and Outcomes
1.52
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