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Retrospective Study
Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Stem Cells. Aug 26, 2026; 18(8): 122836
Published online Aug 26, 2026. doi: 10.4252/wjsc.122836
Pretransplant T-cell immune imbalance predicts slow engraftment after autologous hematopoietic stem cell transplantation in lymphoma
Xi Quan, Huai-Bin Zhang, Long-Rong Ran, Zhi-Ming Luo, Nan Zhang, Xiao Hu, Jian-Chuan Deng, Yao Liu, Shi-Feng Lou
Xi Quan, Huai-Bin Zhang, Zhi-Ming Luo, Nan Zhang, Xiao Hu, Jian-Chuan Deng, Shi-Feng Lou, Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing 400010, China
Long-Rong Ran, Yao Liu, Department of Hematology-Oncology, Chongqing University Cancer Hospital, Hematologic Oncology Intelligent Diagnosis and Treatment Engineering Research Center of Chongqing Education Commission of China, Chongqing 400030, China
Co-first authors: Xi Quan and Huai-Bin Zhang.
Co-corresponding authors: Yao Liu and Shi-Feng Lou.
Author contributions: Quan X and Zhang HB contributed equally to this work and share co-first authorship, based on their substantial contributions to data collection, statistical analysis, interpretation of results, and manuscript drafting. Liu Y and Lou SF contributed equally to this work and share co-corresponding authorship, based on their contributions to study conception and design, manuscript revision, and overall supervision. Quan X, Liu Y, and Lou SF contributed to study conception and design; Quan X, Zhang HB, Ran LR, and Hu X contributed to data collection; Zhang HB, Zhang N, and Deng JC contributed to statistical analysis; Quan X and Luo ZM contributed to analysis and interpretation of results; Quan X, Zhang HB, Ran LR, Luo ZM, Zhang N, and Hu X contributed to draft manuscript; Deng JC, Liu Y, and Lou SF contributed to manuscript revision; and all authors reviewed the results and approved the final version of the manuscript.
AI contribution statement: The authors declare that no AI tools were used in the preparation of this manuscript.
Supported by the Joint Project of Pinnacle Disciplinary Group, the Second Affiliated Hospital of Chongqing Medical University, No. JJCSQN-202510.
Institutional review board statement: This investigation was approved by the Institutional Ethics Committee of the Second Affiliated Hospital of Chongqing Medical University, approval No. 2025(227).
Informed consent statement: The need for patient consent was waived due to the retrospective nature of the study.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
Corresponding author: Shi-Feng Lou, Department of Hematology, The Second Affiliated Hospital of Chongqing Medical University, No. 288 Tianwen Avenue, Nan’an District, Chongqing 400010, China. loushifeng@hospital.cqmu.edu.cn
Received: April 30, 2026
Revised: June 16, 2026
Accepted: July 17, 2026
Published online: August 26, 2026
Processing time: 113 Days and 16.6 Hours
Abstract
BACKGROUND

Autologous hematopoietic stem cell transplantation (auto-HSCT) is a cornerstone therapeutic strategy for lymphoma. Slow engraftment after auto-HSCT increases the risks of infection and hemorrhage, prolongs hospital stay, and is therefore a critical determinant of transplant safety and clinical outcomes. To date, clinical evidence on risk factors for slow engraftment after auto-HSCT in patients with lymphoma remains insufficient, particularly regarding the pretransplant immune microenvironment and lymphocyte subsets.

AIM

To explore clinical and immune risk factors and construct a prediction model for slow engraftment after auto-HSCT in patients with lymphoma.

METHODS

We retrospectively enrolled 166 patients with lymphoma who underwent auto-HSCT at two Chongqing centers from July 2022 to June 2025. Patients were divided into slow engraftment (neutrophil engraftment > 10 days or platelet engraftment > 12 days, based on the median times) and early engraftment groups. Baseline clinical features, transplantation parameters, pretransplant lymphocyte subsets, and inflammatory cytokines were collected. Univariate and multivariate logistic regression analyses were used to identify independent risk factors, construct a combined predictive model, and assess its performance.

RESULTS

Multivariate analysis identified advanced age, reinfused CD34+ cell dose < 3.5 × 106/kg, a higher proportion of CD8+ T cells, a lower absolute CD4+ T-cell count, and prolonged peritransplant fever as independent risk factors for slow engraftment after auto-HSCT in lymphoma patients. Patients receiving a high CD34+ cell dose (≥ 3.5 × 106/kg) achieved faster neutrophil and platelet engraftment than those in the low-dose group. Compared with patients with early engraftment, those with slow engraftment showed pretransplant T-cell subset imbalance and elevated interleukin-2 and interferon-γ levels. A prediction model integrating these variables demonstrated good predictive performance and outperformed individual indicators, achieving an area under the curve (AUC) of 0.780 for identifying patients at risk of slow engraftment after auto-HSCT.

CONCLUSION

Slow engraftment after auto-HSCT is associated with clinical and immune factors. Pretransplant T-cell imbalance predicts delayed engraftment, and a combined model improves risk prediction (AUC = 0.780).

Keywords: Autologous hematopoietic stem cell transplantation; Slow engraftment; T lymphocyte subsets; T-cell immune imbalance; Risk factor; Lymphoma

Core Tip: This retrospective study enrolled 166 lymphoma patients undergoing autologous hematopoietic stem cell transplantation. Advanced age, low CD34+ cell dose, prolonged peritransplant fever, and pretransplant Tcell immune imbalance were independent risk factors for slow engraftment. A combined predictive model (area under the curve = 0.780) showed good performance. Pretransplant immune evaluation helps identify high-risk patients and improve transplant safety.

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