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

Abstract

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]=2.472, P=0.003), the preoperative systemic inflammatory index (SII, HR=1.001, P<0.001), and depression severity (HR=2.398, 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 ROC curves (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.

Keywords: depression; esophageal cancer; immune-inflammatory biomarkers; predictive model; prognosis.

Figure 1

Inclusion and exclusion flow chart.

Figure 2

LASSO regression and multivariate Cox regression for prognostic factors in patients with esophageal cancer. (A) LASSO regression plot showing the relationship between the logarithm of the penalty parameter [Log (λ)] and the coefficients of selected prognostic variables. (B) Partial likelihood deviance plot from LASSO regression, illustrating the fit of the model as the Log (λ) changes. (C) Multivariate Cox regression analysis for relapse-free survival (RFS), showing the hazard ratios (HR) for clinical stage, the preoperative systemic inflammatory index (pre-SII), and depression severity. The HR values with 95% confidence intervals (CI) are provided for each factor.

Figure 3

Kaplan-Meier curves for prognostic factors in patients with esophageal cancer. (A) The distribution of the preoperative systemic inflammatory index (pre-SII) and the optimal cutoff value of 913.95 were determined using maximally selected rank statistics. (B) Kaplan-Meier curve illustrating the relapse-free survival (RFS) for patients with a preoperative SII above or below the established cutoff value. (C) Kaplan-Meier curve showing the RFS for patients stratified by clinical stage. (D) Kaplan-Meier curve showing the RFS for patients categorized by depression severity.

Figure 4

Nomogram and performance evaluation for prognosis prediction in patients with esophageal cancer. (A) Nomogram for predicting the 6-month and 12-month survival probabilities of patients with esophageal cancer. The total points are calculated based on the clinical stage, preoperative systemic inflammatory index (pre-SII), and depression severity. (B) Receiver operating characteristic (ROC) curve for the 6-month and 12-month survival predictive models in the training set. (C) Calibration curve for the 6-month and 12-month survival predictive models in the training set, showing the agreement between the predicted and observed survival probabilities. (D) Decision curve analysis (DCA) for the 6-month and 12-month survival models in the training set, evaluating the net clinical benefit of using the model at different threshold probabilities. (E) ROC curve for the 6-month and 12-month survival predictive models in the validation set. (F) Calibration curve for the 6-month and 12-month survival predictive models in the validation set, demonstrating model calibration. (G) DCA for the 6-month and 12-month survival models in the validation set was performed to assess the net clinical benefit of the model for decision-making.

Figure 5

LASSO regression analysis for postoperative depression in patients with esophageal cancer. (A) LASSO regression plot showing the relationship between the logarithm of the penalty parameter [Log (λ)] and the coefficients of the selected variables for depression. (B) Partial likelihood deviance plot from LASSO regression, illustrating the fit of the model as the Log (λ) changes.