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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jan 15, 2024; 16(1): 61-78
Published online Jan 15, 2024. doi: 10.4251/wjgo.v16.i1.61
C-reactive protein to albumin ratio predict responses to programmed cell death-1 inhibitors in hepatocellular carcinoma patients
Bai-Bei Li, Lei-Jie Chen, Shi-Liu Lu, Biao Lei, Gui-Lin Yu, Shui-Ping Yu
Bai-Bei Li, Shi-Liu Lu, Biao Lei, Shui-Ping Yu, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Bai-Bei Li, Shi-Liu Lu, Biao Lei, Shui-Ping Yu, Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Bai-Bei Li, Shi-Liu Lu, Biao Lei, Shui-Ping Yu, Guangxi Key Laboratory of Immunology and Metabolism for Liver Diseases, Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Lei-Jie Chen, Department of Gastroenterology, The Second Xiangya Hospital of Central South University, Nanning 410011, Guangxi Zhuang Autonomous Region, China
Gui-Lin Yu, Department of Emergency, The First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi Zhuang Autonomous Region, China
Co-first authors: Bai-Bei Li and Lei-Jie Chen.
Author contributions: Li BB, Chen LJ and Yu SP designed the study; Lu SL, Lei B and Yu GL collected the data; Li BB, Lu SL and Yu SP assembled the data; Li BB, Chen LJ and Yu SP analysed, interpreted the data and wrote the article; all authors read and approved the final manuscript. Bai-Bei Li and Lei-Jie Chen contributed efforts of equal substance throughout the research process. The choice of these researchers as co-first authors acknowledges and respects this equal contribution, while recognizing the spirit of teamwork and collaboration of this study.
Supported by the Key Laboratory of Early Prevention and Treatment for Regional High Frequency Tumor (Guangxi Medical University), Ministry of Education, No. GKE-ZZ202117 and No. GKE-ZZ202334.
Institutional review board statement: This study was approved by the Ethics Committee of the First Affiliated Hospital of Guangxi Medical University (Approval No.2023-E332-01).
Informed consent statement: All participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Shui-Ping Yu, MD, Chief Physician, Department of Hepatobiliary Surgery, The First Affiliated Hospital of Guangxi Medical University, No. 22 Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, China.
478517575@qq.com
Received: August 19, 2023
Peer-review started: August 19, 2023
First decision: October 9, 2023
Revised: October 26, 2023
Accepted: December 11, 2023
Article in press: December 11, 2023
Published online: January 15, 2024
Processing time: 144 Days and 18.9 Hours
BACKGROUND
Over the years, programmed cell death-1 (PD-1) inhibitors have been routinely used for hepatocellular carcinoma (HCC) treatment and yielded improved survival outcomes. Nonetheless, significant heterogeneity surrounds the outcomes of most studies. Therefore, it is critical to search for biomarkers that predict the efficacy of PD-1 inhibitors in patients with HCC.
AIM
To investigate the role of the C-reactive protein to albumin ratio (CAR) in evaluating the efficacy of PD-1 inhibitors for HCC.
METHODS
The clinical data of 160 patients with HCC treated with PD-1 inhibitors from January 2018 to November 2022 at the First Affiliated Hospital of Guangxi Medical University were retrospectively analyzed.
RESULTS
The optimal cut-off value for CAR based on progression-free survival (PFS) was determined to be 1.20 using x-tile software. Cox proportional risk model was used to determine the factors affecting prognosis. Eastern Cooperative Oncology Group performance status [hazard ratio (HR) = 1.754, 95% confidence interval (95%CI) = 1.045-2.944, P = 0.033], CAR (HR = 2.118, 95%CI = 1.057-4.243, P = 0.034) and tumor number (HR = 2.932, 95%CI = 1.246-6.897, P = 0.014) were independent prognostic factors for overall survival. CAR (HR = 2.730, 95%CI = 1.502-4.961, P = 0.001), tumor number (HR = 1.584, 95%CI = 1.003-2.500, P = 0.048) and neutrophil to lymphocyte ratio (HR = 1.120, 95%CI = 1.022-1.228, P = 0.015) were independent prognostic factors for PFS. Two nomograms were constructed based on independent prognostic factors. The C-index index and calibration plots confirmed that the nomogram is a reliable risk prediction tool. The ROC curve and decision curve analysis confirmed that the nomogram has a good predictive effect as well as a net clinical benefit.
CONCLUSION
Overall, we reveal that the CAR is a potential predictor of short- and long-term prognosis in patients with HCC treated with PD-1 inhibitors. If further verified, CAR-based nomogram may increase the number of markers that predict individualized prognosis.
Core Tip: This study suggests that the ratio of C-reactive protein to albumin (CAR), already studied as prognosticator in other malignancies may also have a useful role to predict the outcome of hepatocellular carcinoma (HCC). Our study found that higher CAR levels are associated with poor prognosis in patients with HCC treated with programmed cell death-1 inhibitors. Nomogram based on CAR can also be used for prognostic risk stratification.
