Novel Nomograms Based on Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio Predict Prognosis of Hepatocellular Carcinoma Patients After Hepatectomy

Cheng Ma; Yin Cao; Guang Zhang; Jiannan Qiu; Yan Zhou; Peng Wang; Shuo Wang; Dongliang Yan; Ding Ma; Chunping Jiang; Zhongxia Wang

doi:10.2147/JHC.S391755

Novel Nomograms Based on Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio Predict Prognosis of Hepatocellular Carcinoma Patients After Hepatectomy

J Hepatocell Carcinoma. 2023 Feb 9:10:217-230. doi: 10.2147/JHC.S391755. eCollection 2023.

Authors

Cheng Ma^#^{1

2

3

4}, Yin Cao^#¹, Guang Zhang^#^{1

2

3}, Jiannan Qiu², Yan Zhou¹, Peng Wang², Shuo Wang², Dongliang Yan¹, Ding Ma², Chunping Jiang^{1

2

3}, Zhongxia Wang^{1

2

3}

Affiliations

¹ Department of Hepatobiliary Surgery, Drum Tower Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.
² Department of Hepatobiliary Surgery, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing, People's Republic of China.
³ Department of Tissue Engineering, Jinan Microecological Biomedicine Shandong Laboratory, Jinan, People's Republic of China.
⁴ Department of Gastrointestinal Surgery, Xuzhou Central Hospital, Xuzhou, People's Republic of China.

^# Contributed equally.

Abstract

Background: The prediction of prognosis of hepatocellular carcinoma (HCC) is of great significance in improving disease outcome and optimizing clinical management, while reliable prognostic indicators are lacking. This study was conducted to develop readily-to-use nomograms for prognosis prediction of HCC after hepatectomy.

Materials and methods: Data of eligible patients were collected and analyzed retrospectively. Independent prognostic factors were identified by Cox regression, and nomograms for the prediction of disease-free survival (DFS) and overall survival (OS) were developed. The performance of the nomograms was evaluated by receiver operating characteristics (ROC) curves, C-indexes and calibration curves and was verified by the validation cohort. The predictive value of the nomograms was also compared with the 8th edition of American Joint Committee on Cancer (AJCC) Tumor-Node-Metastasis (TNM) and the Barcelona Clinic Liver Cancer (BCLC) staging systems.

Results: In total, 599 patients were enrolled in the analysis: 420 in the training cohort and 179 in the validation cohort. The optimal cut-off value of Gamma-Glutamyl Transpeptidase-to-Lymphocyte Ratio (GLR) was 19.5. GLR contributed significantly to the nomograms with good predictive power. In ROC analyses, the areas under curve (AUCs) of the nomograms for 1-, 3- and 5-year DFS and OS prediction were 0.758, 0.756, 0.734 and 0.810, 0.799, 0.758, respectively. The C-indexes of the DFS nomogram were 0.697 (95% CI 0.665-0.729) in the training cohort and 0.710 (95% CI 0.664-0.756) in the validation cohort. For OS prediction, the C-indexes were 0.741 (95% CI 0.704-0.778) and 0.758 (95% CI 0.705-0.811) in the training and validation cohorts, respectively. The calibration curves demonstrated satisfactory agreement between nomogram predictions and actual observations. The nomograms demonstrated superior predictive performance to the TNM and the BCLC staging systems.

Conclusion: Our novel nomograms showed adequate performance in the prediction of HCC prognosis after hepatectomy, which may facilitate the risk stratification and individualized management of HCC patients.

Keywords: gamma-glutamyl transferase; hepatectomy; hepatocellular carcinoma; lymphocyte; nomogram.

Grants and funding

This research was supported by Research Project of Jinan Microecological Biomedicine Shandong Laboratory (JNL202204A, JNL202219B); the National Natural Science Foundation of China (No. 81572393 and 81602093); the Natural Science Foundation of Jiangsu Province (No. BK20160118); the Key Project supported by the Medical Science and Technology Development Foundation, Nanjing Municipality Health Bureau (No. ZKX15020 and ZKX17022); the Fundamental Research Funds for the Central Universities (No. 021414380215, 021414380242 and 021414380258) and the Chen Xiao-Ping Foundation for the Development of Science and Technology of Hubei Province (No. CXPJJH12000001-2020318).