Impact of Postoperative Depression and Immune-inflammatory Biomarkers on the Prognosis of Patients with Esophageal Cancer Receiving Minimally Invasive Esophagectomy: A Retrospective Cohort Study Based on a Chinese Population

Pei-xin Tan¹Lin-xin Wu¹Shuang Ma²Shi-jing Wei¹Tai-hang Wang¹Bing-chen Wang³Bing-bing Fu¹Jia-shuo Yang¹Qing Zhao^4,5Li Sun^6*Yi Liu^7*Yan Tao^1*

• ¹Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
• ²School of Information Science and Engineering, Shenyang Ligong University, Shenyang, China
• ³Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
• ⁴Department of Diagnostic Radiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
• ⁵Department of Radiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China
• ⁶Department of Anesthesiology, Shenzhen Samii Medical Centre, The Fourth People’s Hospital of Shenzhen, Shenzhen, China
• ⁷Department of Anesthesiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, China

Background: Patients with esophageal cancer (EC) frequently experience depression following neoadjuvant therapy and surgery, a condition that may trigger systemic inflammation, suppress antitumor immunity, and alter immune-inflammatory pathways in the tumor microenvironment (TME), potentially contributing to residual tumor progression and theoretically worsening patient prognosis. This study aimed to investigate the interrelationship between depression and prognosis in patients with EC, with a focus on immune-inflammatory biomarkers. Methods: This single-center retrospective trial was conducted at the National Cancer Center/Cancer Hospital of the Chinese Academy of Medical Sciences. A total of 319 patients who underwent minimally invasive esophagectomy between November 2023 and December 2024 were enrolled. Least absolute shrinkage and selection operator (LASSO) regression in combination with multivariate Cox and logistic regression were employed to identify the main impact indicators of relapse-free survival (RFS) and depression. The developed predictive model was evaluated using calibration plots, receiver operating characteristic (ROC) curves, and decision curve analysis (DCA). Internal validation was carried out using a 7:3 data split. Results: LASSO and Cox regression identified clinical stage (hazard ratio [HR]=2.47, P=0.003), the preoperative systemic inflammatory index (SII, HR=1.001, P<0.001), and depression severity (HR=2.40, P=0.004) as independent predictors of RFS. Based on these variables, a predictive model for RFS was constructed utilizing multivariate logistic regression and visualized as a nomogram. The model demonstrated good discriminative ability, with the areas under the curve (AUCs) of 0.826 (6 months) and 0.773 (12 months) in the training set and 0.817 (6 months) and 0.789 (12 months) in the validation set. The incidence of postoperative depression in the study cohort was 28.2%, with chronic postsurgical pain identified as the sole independent risk factor for depression. Conclusion: This study revealed that preoperative immune-inflammatory biomarkers and postoperative depression significantly affect patient prognosis after minimally invasive esophagectomy. Our work has also provided new insight into the individualized and comprehensive management of patients with EC, underscoring the necessity for comprehensive psychosocial interventions alongside conventional anticancer therapies to optimize clinical endpoints.