Correlation analysis of depressive symptoms and immune function indicators in patients with malignant melanoma

Abstract

BACKGROUND

Malignant melanoma carries the highest mortality among skin cancers and is frequently complicate by depression, which may worsen prognosis. Emerging psychoneuroimmunology evidence links depressive states to suppressed cellular immunity and chronic inflammation, but large-scale studies specifically in melanoma are lacking.

AIM

To investigate the correlation between depressive symptoms and immune function indicators in patients with malignant melanoma, identify independent risk factors affecting depressive symptoms, and provide scientific evidence for clinical psychological intervention and immunomodulatory therapy.

METHODS

Clinical data of 202 patients with malignant melanoma from January 2019 to March 2025 were retrospectively analyzed. Patient Health Questionnaire-9, Pittsburgh Sleep Quality Index, Fatigue Severity Scale, and Perceived Stress Scale-10 were used to assess depressive symptoms. Cellular immune indicators [CD3+, CD4+, CD8+, natural killer (NK) cell counts and percentages], humoral immune indicators (immunoglobulins, complement C3, C4), inflammatory factors [interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), IL-10, interferon gamma, C-reactive protein, erythrocyte sedimentation rate], and routine blood biochemical indicators were detected. Pearson and Spearman correlation analyses were used to explore the relationship between depressive symptoms and immune indicators, multiple linear regression analysis was used to analyze independent influencing factors, and receiver operating characteristic curves were used to evaluate predictive efficacy.

RESULTS

Among 202 patients, 113 (55.9%) developed depressive symptoms. Patients in the depression group had significantly lower CD3+ cell count, CD4+ cell count, NK cell count, and CD4+/CD8+ ratio (all P < 0.05); complement C3 and C4 levels were decreased (all P < 0.05); IL-6, TNF-α, C-reactive protein, erythrocyte sedimentation rate, and neutrophil-to-lymphocyte ratio (NLR) levels were elevated, while IL-10 and interferon gamma were decreased (all P < 0.05). Patient Health Questionnaire-9 scores were positively correlated with IL-6 (r = 0.456), TNF-α (r = 0.398), and NLR (r = 0.418), and negatively correlated with CD4+ cell count (r = -0.367) and NK cell count (r = -0.298) (all P < 0.001). Multiple regression analysis showed that IL-6 (β = 0.365), tumor-node-metastasis stage (β = 0.178), and NLR (β = 0.198) were positive predictors of depressive symptoms, while CD4+ cell count (β = -0.234) and albumin (β = -0.124) were negative predictors (all P < 0.05), with model R² = 0.524. The area under the curve of IL-6 for predicting depressive symptoms was 0.782, and the combined multi-indicator model area under the curve was 0.834.

CONCLUSION

Depressive symptoms in patients with malignant melanoma are closely associated with cellular immune function suppression and chronic inflammatory responses, and immune indicators such as IL-6 can serve as effective predictors for assessing depression risk.

Key Words: Malignant melanoma; Depressive symptoms; Immune function; Cellular immunity; Inflammatory factors

Core Tip: This retrospective study revealed that over half of malignant melanoma patients experienced depressive symptoms, which were closely associated with suppressed cellular immunity and elevated inflammatory markers such as interleukin-6 (IL-6) and neutrophil-to-lymphocyte ratio. CD4+ T cell count and serum albumin were found to be protective factors, while IL-6 and advanced tumor-node-metastasis stage were risk predictors. A multi-indicator model combining IL-6, CD4+, and neutrophil-to-lymphocyte ratio achieved good predictive performance (area under the curve = 0.834). These findings highlight the need for integrated psychosocial and immunological assessment in melanoma patients to improve mental health and potentially affect prognosis.

INTRODUCTION

Malignant melanoma is a highly malignant tumor originating from melanocytes, with an incidence rate showing an annual upward trend globally, becoming the skin malignancy with the highest mortality rate[1]. This disease not only has biological characteristics of strong invasiveness and obvious metastatic tendency, but patients also face enormous psychological stress and survival pressure. Epidemiological surveys show that the incidence of depressive symptoms in malignant melanoma patients is significantly higher than in the general population, with approximately 30%-50% of patients experiencing varying degrees of depressive symptoms during the disease course[2]. These psychological symptoms not only seriously affect patients’ quality of life but may also have negative impacts on disease prognosis.

In recent years, the development of psychoneuroimmunology has provided new perspectives for understanding the relationship between depressive symptoms and the body’s immune function. Research indicates that depressive states can lead to immune system dysfunction through activation of the hypothalamic-pituitary-adrenal (HPA) axis and sympathetic nervous system, manifesting as cellular immune function suppression, inflammatory response imbalance, and cytokine network abnormalities[3]. Specifically, depressed patients often exhibit T lymphocyte subset ratio imbalances, decreased natural killer (NK) cell activity, elevated pro-inflammatory factors such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), while anti-inflammatory factors such as IL-10 levels are reduced[4].

For malignant tumor patients, immune function status is closely related to tumor occurrence, development, and prognosis. Defects in immune surveillance function may promote tumor cell proliferation and metastasis, while chronic inflammatory states provide favorable conditions for the tumor microenvironment[5]. However, current research on the specific correlations between depressive symptoms and immune function indicators in malignant melanoma patients remains relatively limited, lacking large-sample systematic analyses. In-depth exploration of the correlations between these two factors not only helps reveal potential mechanisms by which psychological factors affect tumor prognosis but also provides scientific evidence for developing comprehensive treatment strategies[6].

Therefore, this study retrospectively analyzed clinical data from 202 malignant melanoma patients, systematically assessed the severity of patients’ depressive symptoms, and detected multiple immune function-related indicators, aiming to elucidate the correlations between depressive symptoms and immune function indicators, identify independent risk factors affecting depressive symptoms, and provide theoretical support for psychological intervention and immunomodulatory therapy in clinical practice for malignant melanoma patients.

MATERIALS AND METHODS

Study design

This study was a single-center retrospective study. Clinical data of 202 malignant melanoma patients treated at our hospital from January 2019 to March 2025 were retrospectively collected to analyze the correlation between patients’ depressive symptoms and immune function indicators.

Inclusion and exclusion criteria

Inclusion criteria: (1) Adult patients aged ≥ 18 years; (2) Pathologically and histologically confirmed malignant melanoma[7]; (3) Completion of Depressive Symptom Assessment Scales during hospitalization; (4) Completion of immune function-related indicator testing during hospitalization; and (5) Complete clinical data, including complete medical history, physical examination, laboratory tests, and imaging data.

Exclusion criteria: (1) Patients with other malignant tumors; (2) Patients with previous history of psychiatric diseases (including depression, anxiety disorders, bipolar affective disorder, etc.) or currently taking antidepressant medications; (3) Patients with severe heart, liver, or kidney dysfunction (heart function New York Heart Association class III-IV, liver function Child-Pugh class B and C, kidney function estimated glomerular filtration rate < 30 mL/minutes/1.73 m²)[8]; (4) Patients with autoimmune diseases (such as systemic lupus erythematosus, rheumatoid arthritis, inflammatory bowel disease, etc.); (5) Patients who received conventional immunosuppressive therapy within the past 3 months (such as corticosteroids, cyclosporine, tacrolimus, etc., excluding immune checkpoint inhibitors); and (6) Clinical data incomplete or > 20% missing. A sensitivity analysis using 15% or 25% cut-offs altered the main regression coefficients by < 5%, indicating robustness.

Observation indicators

Baseline characteristics: Patients’ basic clinical data were collected, including: (1) Demographic characteristics: Age, gender, education level, marital status, and employment status; (2) Disease characteristics: Pathological type, tumor-node-metastasis (TNM) stage, Breslow thickness, ulceration status, and lymph node metastasis status; and (3) General condition: Body mass index, Eastern Cooperative Oncology Group performance status score, comorbidity status.

Depressive symptom assessment indicators: Multiple standardized scales were used to assess patients’ depressive symptoms and related psychological states: (1) Patient Health Questionnaire-9 (PHQ-9): Contains 9 items assessing patients’ depressive symptoms over the past 2 weeks. Each item uses a 4-point Likert scale: Not at all (0 points), several days (1 point), more than half the days (2 points), nearly every day (3 points). Total score ranges from 0-27 points, with higher scores indicating more severe depressive symptoms. Scoring criteria: 0-4 points no depressive symptoms, 5-9 points mild depression, 10-14 points moderate depression, 15-19 points moderately severe depression, 20-27 points severe depression; (2) Pittsburgh Sleep Quality Index (PSQI): Contains 18 self-assessment items evaluating sleep quality over the past month, including 7 dimensions: Subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medications, and daytime dysfunction. Each dimension is scored 0-3 points, with total scores ranging from 0-21 points. Higher scores indicate poorer sleep quality, with total scores > 7 points suggesting poor sleep quality and > 10 points suggesting very poor sleep quality; (3) Fatigue Severity Scale (FSS): Contains 9 items assessing the impact of fatigue on patients’ daily life and function, using a 7-point Likert scale (1-7 points). 1 point indicates “strongly disagree” and 7 points indicates “strongly agree”. The total score is the average of the 9 items, ranging from 1-7 points. Higher scores indicate more severe fatigue, with average scores ≥ 4 points suggesting significant fatigue and ≥ 5 points suggesting severe fatigue; and (4) Perceived Stress Scale-10 (PSS-10): Contains 10 items assessing individuals’ perception of unpredictability, uncontrollability, and overload of life events over the past month, using a 5-point Likert scale (0-4 points). 0 points indicates “never” and 4 points indicates “very often”. Items 4, 5, 7, and 8 are reverse-scored. Total scores range from 0-40 points, with higher scores indicating higher perceived stress levels: 0-13 points low stress level, 14-26 points moderate stress level, 27-40 points high stress level.

Immune function-related indicators: Patients’ cellular immunity, humoral immunity, and inflammation-related indicators were detected: (1) Cellular immune indicators: Flow cytometry was used to detect peripheral blood lymphocyte subsets, including total T lymphocyte count (CD3+) and percentage, helper T lymphocyte (CD3+CD4+) absolute count and percentage, cytotoxic T lymphocyte (CD3+CD8+) absolute count and percentage, CD4+/CD8+ ratio, NK cell (CD3-CD16+CD56+) absolute count and percentage, B lymphocyte (CD3-CD19+) absolute count and percentage; (2) Humoral immune indicators: Immune turbidimetric assay was used to detect serum immunoglobulin levels, including immunoglobulin G, immunoglobulin A, immunoglobulin M, complement C3, complement C4; and (3) Inflammatory and immune-related cytokines: Enzyme-linked immunosorbent assay was used to detect serum cytokine levels, including IL-6, TNF-α, IL-10, interferon-γ (IFN-γ), along with detection of serum C-reactive protein (CRP) levels and erythrocyte sedimentation rate (ESR).

Routine blood and biochemical indicators: Peripheral blood white blood cell count, lymphocyte count, neutrophil count, hemoglobin, and platelet count were detected, and neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were calculated. Serum albumin, prealbumin, and total protein levels were detected.

Data collection time points: All observation indicators were completed during patients’ hospitalization. Depressive symptom assessment was completed within 24 hours to 48 hours after admission, immune function indicator testing was completed within 3 days after admission with fasting venous blood sampling, avoiding treatment effects on test results. All tests were uniformly completed by our hospital’s Laboratory Department, strictly following standard operating procedures to ensure accuracy and comparability of results. All observations were completed during hospitalisation. Depressive symptom evaluation was performed 24 hours to 48 hours after admission; immune indicators were measured in fasting morning blood within 3 days of admission, preferably outside 7 days post immune-checkpoint-inhibitor (ICI) infusion to minimise acute therapy effects.

Statistical analysis

SPSS 26.0 statistical software was used for data analysis. Continuous variables were first tested for normality (Shapiro-Wilk test). Data conforming to normal distribution were expressed as mean ± SD, with between-group comparisons using independent samples t-test. Data not conforming to normal distribution were expressed as median (interquartile range) [M (Q1, Q3)], with between-group comparisons using Mann-Whitney U test. Categorical variables were expressed as n (%), with between-group comparisons using χ² test or Fisher’s exact test.

Univariate analysis used the following methods: Continuous variables were analyzed using independent samples t-test or Mann-Whitney U test according to data distribution characteristics; categorical variables used χ² test or Fisher’s exact test; correlation analysis used Pearson product-moment correlation analysis (for normally distributed data) or Spearman rank correlation analysis (for non-normally distributed data) to analyze correlations between Depressive Symptom Assessment Scale scores and immune function indicators. Patients were divided into non-depression group (0-4 points) and depression group (≥ 5 points) based on PHQ-9 scores, comparing differences in immune function indicators between the two groups.

Multiple linear regression analysis was used to explore independent risk factors affecting depressive symptoms. Variables with P < 0.10 in univariate analysis were included in the multiple regression model, using forward selection method for variable screening. Collinearity diagnosis used variance inflation factor, with variance inflation factor > 10 indicating multicollinearity. Receiver operating characteristic curves were used to evaluate the predictive efficacy of key immune indicators for depressive symptoms, calculating area under the curve (AUC) and 95% confidence intervals, using Youden index to determine optimal cutoff values. All statistical tests used two-sided testing, with P < 0.05 considered statistically significant.

Ethical considerations

This study was approved by our hospital’s medical ethics committee and complies with the ethical requirements of the Declaration of Helsinki. As this was a retrospective study, the ethics committee approved exemption from patient informed consent after review, but patient privacy was strictly protected, all data were de-identified, and used only for scientific research purposes. The research process strictly followed medical research ethical principles, ensuring patient information security and compliance of data use.

RESULTS

Patient baseline characteristics

This study included 202 patients with malignant melanoma, including 89 patients in the non-depression group and 113 patients in the depression group. There were no statistically significant differences between the two groups in age (P = 0.188), gender (P = 0.410), or body mass index (P = 0.264). Patients in the depression group had lower education levels (P = 0.038), higher proportion of unemployment/joblessness (P = 0.034), later TNM stage (P = 0.048), greater Breslow thickness (P = 0.005), higher ulceration incidence (P = 0.027), more lymph node metastasis (P = 0.003), and poorer Eastern Cooperative Oncology Group scores (P = 0.002) (Table 1).

Table 1 Comparison of patient baseline characteristics, n (%)/mean ± SD.

Characteristic	Total (n = 202)	Non-depression group (n = 89)	Depression group (n = 113)	χ2/t value	P value
Demographic characteristics
Age (years)	57.3 ± 13.8	55.8 ± 14.2	58.4 ± 13.5	-1.32	0.188
Gender
Male	117 (57.9)	49 (55.1)	68 (60.2)	0.68	0.410
Female	85 (42.1)	40 (44.9)	45 (39.8)
Education level
Middle school and below	76 (37.6)	27 (30.3)	49 (43.4)	8.42	0.038
High school/technical school	89 (44.1)	42 (47.2)	47 (41.6)
College and above	37 (18.3)	20 (22.5)	17 (15.0)
Marital status
Married	154 (76.2)	72 (80.9)	82 (72.6)	2.15	0.143
Single/divorced/widowed	48 (23.8)	17 (19.1)	31 (27.4)
Employment status
Employed	98 (48.5)	50 (56.2)	48 (42.5)	6.78	0.034
Retired	67 (33.2)	28 (31.5)	39 (34.5)
Unemployed/jobless	37 (18.3)	11 (12.4)	26 (23.0)
Disease characteristics
Pathological type
Superficial spreading	126 (62.4)	59 (66.3)	67 (59.3)	4.89	0.180
Nodular	51 (25.2)	19 (21.3)	32 (28.3)
Acral lentiginous	18 (8.9)	8 (9.0)	10 (8.8)
Others	7 (3.5)	3 (3.4)	4 (3.5)
TNM stage
Stage I	45 (22.3)	26 (29.2)	19 (16.8)	7.89	0.048
Stage II	78 (38.6)	36 (40.4)	42 (37.2)
Stage III	58 (28.7)	21 (23.6)	37 (32.7)
Stage IV	21 (10.4)	6 (6.7)	15 (13.3)
Breslow thickness (mm)	3.8 ± 2.6	3.2 ± 2.3	4.3 ± 2.8	-2.86	0.005
Ulceration
Present	89 (44.1)	32 (36.0)	57 (50.4)	4.92	0.027
Absent	113 (55.9)	57 (64.0)	56 (49.6)
Lymph node metastasis
Present	79 (39.1)	27 (30.3)	52 (46.0)	8.95	0.003
Absent	123 (60.9)	62 (69.7)	61 (54.0)
General condition
BMI (kg/m2)	23.7 ± 4.2	24.1 ± 3.8	23.4 ± 4.5	1.12	0.264
ECOG score
0 points	78 (38.6)	43 (48.3)	35 (31.0)	12.86	0.002
1 point	89 (44.1)	38 (42.7)	51 (45.1)
2 points	35 (17.3)	8 (9.0)	27 (23.9)
Comorbidities
Hypertension	68 (33.7)	26 (29.2)	42 (37.2)	3.68	0.159
Diabetes	32 (15.8)	12 (13.5)	20 (17.7)
Coronary heart disease	21 (10.4)	7 (7.9)	14 (12.4)
Others	15 (7.4)	6 (6.7)	9 (8.0)
None	66 (32.7)	38 (42.7)	28 (24.8)

TNM: Tumor-node-metastasis; BMI: Body mass index; ECOG: Eastern Cooperative Oncology Group.

Depressive symptom assessment results

Patients in the depression group scored significantly higher on all psychological scales compared to the non-depression group (all P < 0.001). The depression group had a median PHQ-9 score of 11.0 points, median PSQI score of 11.0 points, FSS score of 5.1 ± 1.6 points, and PSS-10 score of 22.1 ± 6.9 points (Table 2).

Table 2 Comparison of Depressive Symptom Assessment Scale total scores, mean ± SD.

Scale	Total (n = 202)	Non-depression group (n = 89)	Depression group (n = 113)	Z/t value	P value
PHQ-9 total score [M (Q1, Q3)]	7.0 (3.0, 12.0)	2.0 (1.0, 4.0)	11.0 (8.0, 15.0)	-12.84	< 0.001
PSQI total score [M (Q1, Q3)]	8.5 (5.0, 12.0)	6.0 (4.0, 8.0)	11.0 (8.0, 14.0)	-8.67	< 0.001
FSS total score	4.2 ± 1.8	3.1 ± 1.4	5.1 ± 1.6	-9.15	< 0.001
PSS-10 total score	18.6 ± 7.3	14.2 ± 5.8	22.1 ± 6.9	-8.42	< 0.001

PHQ-9: Patient Health Questionnaire-9; PSQI: Pittsburgh Sleep Quality Index; FSS: Fatigue Severity Scale; PSS: Perceived Stress Scale.

Immune function indicator test results

Patients in the depression group showed significantly impaired cellular immune function, with significantly lower CD3+ cell count (P < 0.001), CD4+ cell count (P < 0.001), CD8+ cell count (P = 0.038), NK cell count (P = 0.001), B cell count (P = 0.024), and decreased CD4+/CD8+ ratio (P = 0.004) (Table 3). Patients in the depression group had significantly lower complement C3 (P = 0.030) and C4 (P = 0.017) levels, while immunoglobulin levels showed a downward trend but no statistically significant differences (P > 0.05) (Table 4). Patients in the depression group had significantly higher levels of pro-inflammatory factors IL-6 (P < 0.001), TNF-α (P < 0.001), CRP (P < 0.001), ESR (P < 0.001), and significantly lower levels of anti-inflammatory factors IL-10 (P = 0.001) and IFN-γ (P = 0.001) (Table 5). Patients in the depression group had significantly lower lymphocyte count (P < 0.001), higher neutrophil count (P = 0.002), significantly higher NLR (P < 0.001) and PLR (P = 0.001), and lower levels of hemoglobin (P = 0.034), albumin (P = 0.002), prealbumin (P = 0.006), and total protein (P = 0.033) (Table 6).

Table 3 Comparison of cellular immune indicators, mean ± SD.

Indicator	Non-depression group (n = 89)	Depression group (n = 113)	t/Z value	P value
CD3+ cell count (cells/μL)	1486 ± 428	1267 ± 396	3.68	< 0.001
CD3+ cell percentage (%)	68.2 ± 9.8	64.7 ± 11.2	2.31	0.022
CD4+ cell count (cells/μL)	894 ± 287	736 ± 253	4.02	< 0.001
CD4+ cell percentage (%)	40.8 ± 7.6	37.2 ± 8.4	3.14	0.002
CD8+ cell count (cells/μL)	542 ± 198	486 ± 176	2.09	0.038
CD8+ cell percentage (%)	24.7 ± 6.2	25.8 ± 7.1	-1.15	0.252
CD4+/CD8+ ratio	1.72 ± 0.48	1.51 ± 0.52	2.94	0.004
NK cell count (cells/μL)	387 ± 142	325 ± 128	3.24	0.001
NK cell percentage (%)	17.8 ± 6.4	15.2 ± 5.9	2.98	0.003
B cell count (cells/μL)	248 ± 89	221 ± 76	2.27	0.024
B cell percentage (%)	11.3 ± 4.2	10.6 ± 3.8	1.23	0.220

NK: Natural killer.

Table 4 Comparison of humoral immune indicators, mean ± SD.

Indicator	Non-depression group (n = 89)	Depression group (n = 113)	t value	P value
IgG (g/L)	12.8 ± 3.2	11.9 ± 3.6	1.83	0.069
IgA (g/L)	2.4 ± 0.8	2.2 ± 0.9	1.65	0.101
IgM (g/L)	1.3 ± 0.4	1.2 ± 0.5	1.48	0.141
Complement C3 (g/L)	1.18 ± 0.26	1.09 ± 0.31	2.18	0.030
Complement C4 (g/L)	0.28 ± 0.09	0.25 ± 0.08	2.41	0.017

IgG: Immunoglobulin G; IgA: Immunoglobulin A; IgM: Immunoglobulin M.

Table 5 Comparison of inflammatory and immune-related cytokines, mean ± SD.

Indicator	Non-depression group (n = 89)	Depression group (n = 113)	t/Z value	P value
IL-6 (pg/mL), M (Q1, Q3)	4.2 (2.8, 7.1)	8.6 (5.3, 14.2)	-6.74	< 0.001
TNF-α (pg/mL), M (Q1, Q3)	12.4 (8.7, 18.3)	19.8 (14.2, 28.6)	-5.89	< 0.001
IL-10 (pg/mL)	8.3 ± 3.6	6.7 ± 3.2	3.26	0.001
IFN-γ (pg/mL)	15.8 ± 7.2	12.4 ± 6.8	3.42	0.001
CRP (mg/L), M (Q1, Q3)	5.8 (2.1, 12.4)	12.7 (6.8, 23.5)	-4.96	< 0.001
ESR (mm/hour), M (Q1, Q3)	28 (18, 42)	38 (25, 58)	-3.84	< 0.001

IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-α; IL-10: Interleukin-10; IFN-γ: Interferon-γ; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate.

Table 6 Comparison of routine blood and biochemical indicators, mean ± SD.

Indicator	Non-depression group (n = 89)	Depression group (n = 113)	t/Z value	P value
White blood cell count (× 109/L)	6.8 ± 2.1	7.4 ± 2.5	-1.83	0.069
Lymphocyte count (× 109/L)	1.9 ± 0.6	1.6 ± 0.5	3.89	< 0.001
Neutrophil count (× 109/L)	4.2 ± 1.8	5.1 ± 2.2	-3.15	0.002
NLR [M (Q1, Q3)]	2.1 (1.5, 3.2)	3.1 (2.1, 4.8)	-4.67	< 0.001
PLR [M (Q1, Q3)]	142 (108, 189)	168 (128, 235)	-3.21	0.001
Hemoglobin (g/L)	125 ± 18	119 ± 21	2.14	0.034
Platelet count (× 109/L)	268 ± 82	279 ± 96	-0.87	0.385
Albumin (g/L)	38.6 ± 5.2	36.1 ± 6.1	3.08	0.002
Prealbumin (g/L)	0.24 ± 0.08	0.21 ± 0.07	2.79	0.006
Total protein (g/L)	69.8 ± 7.3	67.4 ± 8.6	2.15	0.033

NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio.

Correlation analysis of depressive symptoms and immune function indicators

PHQ-9 scores showed significant correlations with multiple immune function indicators (all P < 0.001). IL-6, TNF-α, CRP, ESR, and NLR were positively correlated with depressive symptoms, with correlation coefficients of 0.456, 0.398, 0.372, 0.335, and 0.418, respectively; CD3+ and CD4+ cell counts, NK cell count, complement components, and anti-inflammatory factors were negatively correlated with depressive symptoms (Table 7 and Figure 1).

Figure 1 Correlation between Patient Health Questionnaire-9 scores and immune function indicators. The bar chart displays Pearson correlation coefficients (r) between Patient Health Questionnaire-9 depression scores and various immune function markers. Orange bars indicate positive correlations, showing immune indicators that increase with higher depression scores (interleukin-6, neutrophil-to-lymphocyte ratio, tumor necrosis factor-α, C-reactive protein, and erythrocyte sedimentation rate). Green bars represent negative correlations, indicating immune markers that decrease as depression severity increases (CD3+CD4+, lymphocyte count, interferon-γ, natural killer cell count, CD3+CD8+, CD3+CD56+, interleukin-10, and complement components C4 and C3). Correlation coefficients range from approximately +0.45 to -0.30, suggesting moderate associations between depressive symptoms and immune dysregulation. IL-6: Interleukin-6; NLR: Neutrophil-to-lymphocyte ratio; TNF-α: Tumor necrosis factor-α; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; IFN-γ: Interferon-γ; NK: Natural killer; IL-10: Interleukin-10.

Table 7 Correlation analysis of Patient Health Questionnaire-9 scores and immune function indicators.

Indicator	Correlation coefficient (r)	P value	Correlation type
CD3+ cell count	-0.312	< 0.001	Spearman
CD4+ cell count	-0.367	< 0.001	Spearman
CD4+/CD8+ ratio	-0.289	< 0.001	Pearson
NK cell count	-0.298	< 0.001	Pearson
Complement C3	-0.245	0.001	Pearson
Complement C4	-0.263	< 0.001	Pearson
IL-6	0.456	< 0.001	Spearman
TNF-α	0.398	< 0.001	Spearman
IL-10	-0.287	< 0.001	Pearson
IFN-γ	-0.324	< 0.001	Pearson
CRP	0.372	< 0.001	Spearman
ESR	0.335	< 0.001	Spearman
NLR	0.418	< 0.001	Spearman
Lymphocyte count	-0.356	< 0.001	Pearson
Albumin	-0.294	< 0.001	Pearson

NK: Natural killer; IL-6: Interleukin-6; TNF-α: Tumor necrosis factor-α; IL-10: Interleukin-10; IFN-γ: Interferon-γ; CRP: C-reactive protein; ESR: Erythrocyte sedimentation rate; NLR: Neutrophil-to-lymphocyte ratio.

Other psychological scale scores also showed significant correlations with immune indicators: PSQI scores were positively correlated with IL-6 (r = 0.387, P < 0.001) and TNF-α (r = 0.342, P < 0.001), and negatively correlated with CD4+ cell count (r = -0.298, P < 0.001); FSS scores were positively correlated with NLR (r = 0.365, P < 0.001) and CRP (r = 0.312, P < 0.001); PSS-10 scores were correlated with IL-6 (r = 0.334, P < 0.001) and CD3+ cell count (r = -0.267, P < 0.001).

Multiple linear regression analysis of factors affecting depressive symptoms

Multiple linear regression analysis showed that IL-6 (P < 0.001), TNM stage (P = 0.001), and NLR (P = 0.001) were positive predictors of depressive symptoms, while CD4+ cell count (P < 0.001) and albumin (P = 0.047) were negative predictors. The model fit was good (R² = 0.524, P < 0.001) (Table 8 and Figure 2).

Figure 2 Multiple linear regression analysis of independent predictors for depression symptoms. The bar chart presents standardized regression coefficients (β) from multivariable analysis identifying significant independent predictors of Patient Health Questionnaire-9 depression scores. Blue bars represent positive predictors: Interleukin-6 (ln transformed) shows the strongest positive association (β approximately 0.36), followed by tumor-node-metastasis stage (β approximately 0.18) and neutrophil-to-lymphocyte ratio (ln transformed) (β approximately 0.20), indicating that higher values of these variables are associated with increased depression severity. Purple bars indicate negative predictors: CD4+ cell count (β approximately -0.24) and albumin (β approximately -0.12) show inverse associations, suggesting that lower levels of these markers are linked to greater depressive symptoms. All displayed variables represent statistically significant independent predictors after controlling for potential confounders in the regression model. IL-6: Interleukin-6; TNM: Tumor-node-metastasis; NLR: Neutrophil-to-lymphocyte ratio.

Table 8 Multiple linear regression analysis of factors affecting depressive symptoms.

Variable	B	Standard error	β	t value	P value	95%CI
Constant	15.867	3.245	-	4.89	< 0.001	9.468-22.266
IL-6 (ln transformed)	2.186	0.324	0.365	6.75	< 0.001	1.547-2.825
CD4+ cell count	-0.008	0.002	-0.234	-4.12	< 0.001	-0.012 to -0.004
TNM stage	1.247	0.386	0.178	3.23	0.001	0.486-2.008
NLR (ln transformed)	1.834	0.567	0.198	3.24	0.001	0.716-2.952
Albumin	-0.156	0.078	-0.124	-2.00	0.047	-0.310 to -0.002

Model goodness of fit: R² = 0.524, adjusted R² = 0.512, F = 43.02, P < 0.001; variance inflation factor all < 3.0, no multicollinearity. IL-6: Interleukin-6; TNM: Tumor-node-metastasis; NLR: Neutrophil-to-lymphocyte ratio; CI: Confidence interval.

Predictive efficacy analysis of immune indicators for depressive symptoms

IL-6 had the highest predictive efficacy for depressive symptoms (AUC = 0.782, P < 0.001), with an optimal cutoff value of 6.35 pg/mL. The multi-indicator combined prediction model (IL-6 + CD4+ cell count + NLR) had an AUC of 0.834 (95% confidence interval: 0.781-0.887), significantly higher than single indicators (P < 0.05) (Table 9). The multi-indicator combined prediction model (IL-6 + CD4+ cell count + NLR) had an AUC of 0.834 (95% confidence interval: 0.781-0.887), significantly higher than single indicators (P < 0.05) (Figure 3).

Figure 3 Receiver operating characteristic curve analysis for predicting depression symptoms using immune indicators. Receiver operating characteristic curves demonstrate the diagnostic performance of various immune markers and predictive models for identifying depression in the study population. The combined model (orange line) incorporating multiple immune parameters shows the highest discriminative ability [area under the curve (AUC) = 0.834], followed by individual immune markers: Interleukin-6 (AUC = 0.782), CD4+ cell count (AUC = 0.731), neutrophil-to-lymphocyte ratio (AUC = 0.688), C-reactive protein (AUC = 0.682), and tumor necrosis factor-α (AUC = 0.671). The reference line (gray dashed diagonal, AUC = 0.5) represents no discriminative ability. All models demonstrate better-than-chance performance, with the combined model showing good overall accuracy for predicting depression symptoms, suggesting that integrating multiple immune biomarkers improves diagnostic utility compared to single markers alone. IL-6: Interleukin-6; NLR: Neutrophil-to-lymphocyte ratio; TNF-α: Tumor necrosis factor-α; CRP: C-reactive protein; AUC: Area under the curve.

Table 9 Receiver operating characteristic curve analysis of immune indicators for predicting depressive symptoms.

Indicator	AUC	95%CI	P value	Optimal cutoff value	Sensitivity	Specificity	Youden index
IL-6 (pg/mL)	0.782	0.719-0.844	< 0.001	6.35	71.7	75.3	0.470
CD4+ cell count (cells/μL)	0.731	0.664-0.798	< 0.001	798	69.0	70.8	0.398
NLR	0.698	0.627-0.769	< 0.001	2.65	66.4	67.4	0.338
TNF-α (pg/mL)	0.682	0.609-0.754	< 0.001	16.8	63.7	68.5	0.322
CRP (mg/L)	0.671	0.598-0.744	< 0.001	8.9	61.9	66.3	0.282

AUC: Area under the curve; CI: Confidence interval; IL-6: Interleukin-6; NLR: Neutrophil-to-lymphocyte ratio; TNF-α: Tumor necrosis factor-α; CRP: C-reactive protein.

Subgroup analysis

Stratified analysis was performed according to TNM stage, with 123 patients in early stage (stages I-II) and 79 patients in advanced stage (stages III-IV). Advanced-stage (III-IV) patients showed higher depression prevalence (65.8% vs 49.6%, P = 0.021), significantly elevated IL-6 and NLR, and lower CD4+ counts. They also received ICIs more frequently (48.1% vs 24.4%, P < 0.001). After adjusting for ICI exposure, the correlation coefficients between depression and immune markers decreased by < 8%, suggesting that the observed associations are not driven solely by treatment.

Advanced-stage patients had significantly higher IL-6 levels [12.4 (7.8, 19.6) pg/mL vs 6.8 (3.2, 11.5) pg/mL, P < 0.001] and NLR [3.8 (2.6, 5.4) vs 2.4 (1.7, 3.6), P < 0.001] compared to early-stage patients, and significantly lower CD4+ cell count (689 ± 241 cells/μL vs 821 ± 276 cells/μL, P < 0.001). The correlation between depressive symptoms and immune function indicators remained consistent across different disease stages, but the correlation was stronger in advanced-stage patients (IL-6 and PHQ-9 scores: Advanced stage r = 0.521 vs early-stage r = 0.386, P < 0.001).

DISCUSSION

This study systematically analyzed the correlation between depressive symptoms and immune function indicators in 202 patients with malignant melanoma, finding that depressive symptoms had an incidence rate of 55.9% in malignant melanoma patients, significantly higher than in the general population, and were closely associated with multiple immune function indicators. This finding provides important clinical evidence for understanding the impact of psychological factors on the tumor immune microenvironment.

The 55.9% depression prevalence exceeds previous 30%-50% estimates. Apart from instrument differences, our cohort had more advanced disease, greater Breslow thickness, and was enrolled during the coronavirus disease 2019 pandemic when isolation and delayed care could further elevate PHQ-9 scores. ICIs rapidly increase IL-6 and NLR and may induce immune-related adverse events, potentially confounding the depression-immunity link. In a sensitivity analysis, 68 patients (33.7%) with documented ICI use were flagged; inclusion of an ICI (yes/no) variable and interaction terms changed the main regression coefficients by < 10% and all interactions were P > 0.10, indicating relative robustness. Nevertheless, complete adjustment was impossible because timing, duration, and interval between infusion and blood draw were not fully recorded; our findings should therefore be regarded as exploratory.

The most critical limitation is the single-center, cross-sectional design, which precludes causal inference and full control of time-varying confounders such as ICIs or targeted therapy. Future prospective cohorts with complete treatment trajectories and marginal-structural models are needed to validate our observations[9]. This difference may be related to differences in study population characteristics, assessment tools, and diagnostic criteria. We found that patients in the depression group had later TNM stage, greater Breslow thickness, higher ulceration incidence, and lymph node metastasis rates, suggesting that disease severity is an important factor affecting patients’ psychological state[10]. Additionally, lower education levels and unemployment status were also associated with the occurrence of depressive symptoms, consistent with previous socioeconomic research results on cancer patients’ mental health[11].

This investigation demonstrated that participants with depression exhibited markedly compromised cellular immune capabilities, characterized by reduced counts of T lymphocyte subsets, diminished CD4+/CD8+ ratios, and attenuated NK cell functionality. These findings align closely with psychoneuroimmunology principles[12]. Depression can trigger HPA axis activation, resulting in elevated glucocorticoid secretion including cortisol, which subsequently inhibits T lymphocyte proliferation and differentiation[13]. As central regulatory components in immune reactions, the numerical reduction of CD4+ T cells directly impairs the organism’s anti-tumor immune monitoring capacity[14]. The diminished functionality of NK cells, which serve as crucial elements of innate immunity, may compromise the organism’s tumor cell elimination capability[15].

Of particular interest, we documented a substantially reduced CD4+/CD8+ ratio among individuals with depression, a finding that holds considerable relevance in tumor immune evasion pathways[16]. While CD8+ T cell populations also declined in the depressed cohort, this decrease was less pronounced than the CD4+ T cell reduction, creating ratio disruption that may indicate immune system functional abnormalities. Our investigation identified strong connections between depressive manifestations and persistent inflammatory conditions. The depression cohort demonstrated markedly increased serum concentrations of IL-6 and TNF-α, whereas anti-inflammatory mediators IL-10 and IFN-γ exhibited decreased levels, displaying a characteristic pro-inflammatory/anti-inflammatory dysregulation profile[17]. As a key pro-inflammatory mediator, IL-6 not only contributes to depression pathophysiology but can additionally facilitate tumor neovascularization and dissemination[18]. Excessive TNF-α expression can trigger invasive tumor cell proliferation while simultaneously suppressing T cell activity[19].

The increased inflammatory biomarkers CRP and ESR provided additional validation of systemic inflammatory activity in individuals with depression. Persistent inflammatory conditions can establish microenvironments conducive to tumor expansion through mechanisms including neovascularization promotion, apoptosis inhibition, and enhancement of tumor cell invasive properties, thereby expediting disease advancement[20]. This mechanism may represent a critical pathway linking depressive manifestations with unfavorable outcomes.

NLR and PLR, as convenient inflammatory markers, showed good clinical value in this study. Patients in the depression group had significantly elevated NLR and PLR, reflecting enhanced neutrophil-mediated inflammatory responses and suppressed lymphocyte-mediated immune function[21]. These indicators not only correlate with the severity of depressive symptoms but may also serve as convenient tools for monitoring changes in immune status.

Nutritional status indicators such as albumin, prealbumin, and total protein were generally decreased in the depression group, suggesting that depressive symptoms may further impair immune function by affecting appetite and nutritional intake[22]. Malnutrition and immune dysfunction form a vicious cycle that may worsen patients’ overall health status. Multiple linear regression analysis identified IL-6, CD4+ cell count, TNM stage, NLR, and albumin as independent predictors of depressive symptoms, with good model fit (R² = 0.524). Particularly, IL-6 alone predicted depressive symptoms with an AUC of 0.782, showing good diagnostic efficacy[23]. The multi-indicator combined prediction model further improved the AUC to 0.834, providing an effective tool for early identification of patients at risk of depression in clinical practice.

The establishment of this prediction model has important clinical significance. Through routine immune function testing, patient depression risk can be assessed, helping clinicians timely detect and intervene in psychological problems, thereby improving patients’ overall treatment outcomes[24]. Subgroup analysis showed that advanced-stage patients not only had higher depression incidence but also stronger correlations between depressive symptoms and immune function indicators. This suggests that with disease progression, the impact of psychological factors on the immune system may be amplified[25]. Advanced-stage patients face greater survival pressure and treatment burden, with more intense psychological stress responses, leading to more significant negative impacts on immune function.

This finding emphasizes the importance of implementing psychological interventions in advanced malignant melanoma patients. Timely psychological support and antidepressant treatment may not only improve patients’ quality of life but also help maintain immune function, potentially affecting disease prognosis[26]. The results of this study support the core theory of psychoneuroimmunology, namely that complex interaction networks exist among psychological states, neuroendocrine systems, and immune systems[27]. Depressive symptoms may affect immune function through the following pathways: (1) Activation of the HPA axis leading to increased glucocorticoid secretion, directly suppressing immune cell function; (2) Activation of the sympathetic nervous system, releasing catecholamines that regulate immune cell activity; and (3) Affecting sleep quality and lifestyle, indirectly impairing immune function[28]. The interaction of these mechanisms may form a vicious cycle of “depression-immune suppression-disease progression-depression worsening”, providing a theoretical basis for developing comprehensive treatment strategies[29].

The results of this study have important guiding significance for clinical practice. First, it is recommended to incorporate psychological state assessment into routine management of malignant melanoma patients, particularly for advanced-stage patients who should receive more attention. Second, immune function indicators, especially IL-6 levels, can serve as auxiliary tools for screening depression risk. Finally, for patients with depressive symptoms, comprehensive treatment plans including psychotherapy, pharmacological intervention, and immune regulation should be considered[30].

Antidepressant treatment can not only improve patients’ psychological states but may also have positive impacts on tumor prognosis by regulating immune function. Some studies indicate that selective serotonin reuptake inhibitors and other antidepressants have certain anti-inflammatory and immunomodulatory effects, potentially providing additional benefits for tumor treatment[31].

This study has some limitations that need consideration. First, as a single-center retrospective study, the generalizability of results may be limited. Second, the cross-sectional design cannot determine causal relationships between depressive symptoms and immune function indicators. Additionally, although patients with known psychiatric disease history were excluded, some unidentified confounding factors may still exist. Finally, although immune function indicator testing was standardized, it may be affected by multiple factors, including blood collection time and storage conditions.

Future research should adopt prospective cohort designs to long-term track dynamic changes in patients’ depressive symptoms and immune function, exploring causal relationships between them. Meanwhile, multi-center studies are needed to improve result representativeness. In mechanistic research, further exploration of the roles of neuroendocrine mediators and epigenetic changes in depression-immune interactions is needed. In clinical applications, randomized controlled trials of psychological interventions’ effects on immune function and disease prognosis should be conducted to provide high-quality evidence for evidence-based medical practice.

CONCLUSION

In conclusion, this study is the first to systematically reveal the correlation between depressive symptoms and immune function indicators in malignant melanoma patients, providing important insights for understanding the role of psychological factors in tumor development and laying a scientific foundation for developing individualized comprehensive treatment strategies.