Analyzing the role of GNG4 molecular mechanism in neuroendocrine differentiation process through electrochemical sensors: biomarker diagnosis of prostate cancer
The application of electrochemical sensors in medical testing has indeed introduced innovative technical means for the diagnosis, prognosis assessment, and treatment of diseases, particularly in the context of prostate cancer. In prostate cancer research, electrochemical sensors can detect biomarkers such as GNG4, CHGA, and SYP, which are indicative of neuroendocrine differentiation. This capability is crucial for early detection and monitoring of disease progression, as neuroendocrine differentiation is often associated with aggressive, therapy-resistant phenotypes. The high sensitivity of electrochemical sensors allows for the detection of these biomarkers at ultra-low concentrations, even in complex biological samples like blood or tissue lysates. By combining single-cell sequencing with electrochemical detection, the study found that the GNG4 gene plays a key role in the neuroendocrine differentiation process. Its high expression is associated with a reduced risk of biochemical recurrence, but it is also consistent with an increased risk of death and sensitivity to platinum-based chemotherapy drugs. The prognostic model based on electrochemical detection demonstrated high predictive accuracy in multiple independent cohorts, with an average C-index value of more than 0.75, indicating that this model can effectively predict the biochemical recurrence risk of prostate cancer patients. Graphical abstract Fig. 1. Origin and development of NEPC. • Electrochemical sensors can detect biomarkers such as GNG4, CHGA, and SYP. • The high sensitivity of electrochemical sensors allows for the detection of these biomarkers at ultra-low concentrations. • The prognostic model based on electrochemical detection demonstrated high predictive accuracy in multiple independent cohorts.
Authors
- Yunfan Li (ORCID: https://orcid.org/0009-0002-6349-4688)
- Liangdong Song
- Shuai Su
- Jingke He
- Chengcheng Wei
- Kun Han
- Yu Luo
Institutions
- Army Medical University (CN)
- Xinqiao Hospital (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Microchemical Journal
- Published
- 2026-03-02
- DOI
- https://doi.org/10.1016/j.microc.2026.117511
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00