Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples

Shang Chen; Shoubin Long; Yaru Liu; Shenglong Wang; Qian Hu; Li Fu; Dixian Luo

doi:10.3389/fonc.2024.1432869

Evaluation of a three-gene methylation model for correlating lymph node metastasis in postoperative early gastric cancer adjacent samples

Front Oncol. 2024 Oct 17:14:1432869. doi: 10.3389/fonc.2024.1432869. eCollection 2024.

Authors

Shang Chen^{1

2

3}, Shoubin Long², Yaru Liu^{2

4}, Shenglong Wang^{2

4}, Qian Hu^{2

5}, Li Fu⁶, Dixian Luo^{2

4

7}

Affiliations

¹ Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China.
² Laboratory Medicine Centre, Shenzhen Nanshan People's Hospital, Shenzhen University, Shenzhen, China.
³ Hunan Provincial University Key Laboratory of the Fundamental and Clinical Research on Functional Nucleic Acid, Hunan Provincial Key Laboratory of the Traditional Chinese Medicine Agricultural Biogenomics, Changsha Medical University, Changsha, China.
⁴ School of the First Clinical Medical, Ningxia Medical University, Yinchuan, China.
⁵ Institute of Pharmacy and Pharmacology, School of Pharmaceutical Science, Hengyang Medical School, University of South China, Hengyang, China.
⁶ Guangdong Provincial Key Laboratory of Regional Immunity and Diseases, Department of Pharmacology and International Cancer Center, Shenzhen University Health Science Center, Shenzhen, China.
⁷ Department of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China.

Abstract

Background: Lymph node metastasis (LNM) has a profound impact on the treatment and prognosis of early gastric cancer (EGC), yet the existing evaluation methods lack accuracy. Recent research has underscored the role of precancerous lesions in tumor progression and metastasis. The objective of this study was to utilize the previously developed EGC LNM prediction model to further validate and extend the analysis in paired adjacent tissue samples.

Methods: We evaluated the model in a monocentric study using Methylight, a methylation-specific PCR technique, on postoperative fresh-frozen EGC samples (n = 129) and paired adjacent tissue samples (n = 129).

Results: The three-gene methylation model demonstrated remarkable efficacy in both EGC and adjacent tissues. The model demonstrated excellent performance, with areas under the curve (AUC) of 0.85 and 0.82, specificities of 85.1% and 80.5%, sensitivities of 83.3% and 73.8%, and accuracies of 84.5% and 78.3%, respectively. It is noteworthy that the model demonstrated superior performance compared to computed tomography (CT) imaging in the adjacent tissue group, with an area under the curve (AUC) of 0.86 compared to 0.64 (p < 0.001). Furthermore, the model demonstrated superior diagnostic capability in these adjacent tissues (AUC = 0.82) compared to traditional clinicopathological features, including ulceration (AUC = 0.65), invasional depth (AUC = 0.66), and lymphovascular invasion (AUC = 0.69). Additionally, it surpassed traditional models based on these features (AUC = 0.77).

Conclusion: The three-gene methylation prediction model for EGC LNM is highly effective in both cancerous and adjacent tissue samples in a postoperative setting, providing reliable diagnostic information. This extends its clinical utility, particularly when tumor samples are scarce, making it a valuable tool for evaluating LNM status and assisting in treatment planning.

Keywords: DNA methylation; early gastric cancer; lymph node metastasis; paracancerous tissue; predictive model.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study was financially supported by the Guangdong Basic and Applied Basic Research Fund Enterprise Joint Fund (No. 2022A1515220042), Shenzhen Science and Technology Program (Nos. JCYJ20220530141609021, JCYJ20230807115826055 and JSGG20210802153410031), Major Project of Nanshan Health Committee (Nos. NS2021016, NS2023020 and NSZD2023038), Newly introduced discipline leader fund project in Nanshan District of Shenzhen City (No. NSZD2023020). Shenzhen University Medical-Engineering Interdisciplinary Research Fund Project (No. 2023YG022) and Project of Nanshan People’s Hospital (Nos. YN2022009 and YN2021002).