Construction and validation of a multiparameter diagnostic model based on conventional tumor markers and cytokines for lung cancer

Abstract

BACKGROUND

Early detection of lung cancer is urgently needed in clinical practice.

AIM

To evaluate the diagnostic value of conventional tumor markers and cytokines for lung cancer and construct a multiparameter diagnostic model lung cancer detection.

METHODS

A total of 152 healthy controls and 113 lung cancer patients were included in the model. In addition, 21 healthy controls and 36 lung cancer patients were separately included to validate the model. Three conventional tumor markers and 10 cytokines were detected. Four multiparameter joint analysis methods, binary logistic regression analysis, discriminant analysis, a classification tree and a neural network, were used to establish and compare multiparameter joint diagnosis models.

RESULTS

Six differentially expressed indicators [carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CY211), neuron-specific enolase, interleukin (IL)-8, monocyte chemoattractant protein-1, and tumor necrosis factor-alpha (TNF-α)] were screened out, among which IL-8 [area under the curve (AUC) = 0.957] and TNF-α (AUC = 0.936) had the optimal diagnostic efficacy. The binary logistic regression model was chosen as the optimal multiparameter combined auxiliary diagnostic model. When 152 healthy controls and 113 lung cancer s were differentiated via the model, the AUC was 0.980. After validation, when 21 healthy controls and 36 lung cancer patients were distinguished, the AUC was 0.922, indicating good stability and superior performance to that of CEA alone.

CONCLUSION

We constructed a multiparameter binary logistic regression diagnostic model that included CEA, CY211, IL-8 and TNF-α for the auxiliary detection of lung cancer. Compared with conventional CEA, it significantly improved diagnostic accuracy.

Key Words: Lung cancer; Tumor markers; Cytokines; Binary logistic regression; Detection; Carcinoembryonic antigen

Core Tip: We aimed to evaluate the diagnostic value of conventional tumor markers and cytokines and construct a multiparameter diagnostic model. After four multiparameter joint analysis methods analysis, a model that included carcinoembryonic antigen, cytokeratin 19 fragment, interleukin-8 and tumor necrosis factor-alpha for the detection of lung cancer was built and validated. It may provide more comprehensive theoretical support for the diagnosis and targeted therapy of lung cancer.

INTRODUCTION

Lung cancer continues to be the leading cause of cancer-associated deaths worldwide, with non-small cell lung cancer (NSCLC) representing approximately 85% of all diagnosed cases[1]. The clinical outcome of lung cancer patients is closely related to the disease stage at the time of diagnosis: Patients with stage I lung cancer who undergo surgical resection can achieve a 10-year survival rate of up to 88%, and the overall 3-year survival rate across all lung cancer patients remains below 35%, which is primarily attributed to delayed detection[2]. Low-dose computed tomography (LDCT) has made significant breakthroughs in lung cancer screening[3]. As evidenced by the national lung screening trial, compared with chest X-ray imaging, LDCT screening has been shown to lower lung cancer mortality by approximately 20% in high-risk populations. Nevertheless, LDCT is hampered by a high rate of false-positive results, which often leads to unnecessary invasive diagnostic procedures and increased health care expenditures[4]. To address this challenge, investigators have developed risk prediction models that integrate clinical parameters and biological biomarkers, aiming to increase the specificity of early lung cancer diagnosis[5].

Carcinoembryonic antigen (CEA) is among the most widely used traditional tumor markers for lung cancer diagnosis and belongs to a family of glycoproteins involved in cell adhesion[6]. Although CEA is not lung specific (elevated levels can be observed in colorectal cancer, gastrointestinal inflammation, and smoking-related conditions), its abnormal elevation is closely associated with advanced NSCLC, especially adenocarcinoma[7]. Squamous cell carcinoma (SCC) antigen is a glycoprotein biomarker that is specifically associated with lung SCC and accounts for approximately 30% of NSCLC cases. SCC antigen is secreted by malignant squamous epithelial cells and reflects the proliferation and invasion of tumor cells[8]. Cytokeratin 19 fragment (CY211) is a soluble fragment of cytokeratin 19, a structural protein of epithelial cells, and is a highly specific biomarker for NSCLC. It is particularly sensitive to lung SCC and adenocarcinoma, with elevated levels detected in the early stages of tumorigenesis[9].

Cytokines, which serve as key modulators of immune responses and the tumor microenvironment, play pivotal roles in the onset and progression of lung cancer[10]. Aberrant expression patterns of specific cytokines in peripheral blood or tissue samples have demonstrated considerable potential as noninvasive biomarkers for the early detection of lung cancer[11]. During the initial phases of lung cancer development, malignant cells and adjacent stromal cells secrete a spectrum of cytokines that facilitate tumor angiogenesis, immune evasion, and tissue invasion[12]. For example, proinflammatory cytokines, including interleukin (IL)-6 and tumor necrosis factor-alpha (TNF-α), are overexpressed in patients with early-stage NSCLC, contributing to the formation of a protumorigenic inflammatory microenvironment[13]. In contrast, anti-inflammatory or tumor-suppressive cytokines may exhibit reduced expression—this is exemplified by the decreased levels of IL-10 observed in early lung cancer patients compared with those in healthy individuals[14]. These cytokine perturbations occur prior to the appearance of overt clinical symptoms or the detection of tumor masses via imaging, rendering them promising indicators for early diagnosis[15]. Cytokines have been validated for their diagnostic utility in early lung cancer.

In our study, we evaluated the diagnostic value of ten cytokines: Granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon-γ (IFN-γ), IL-10, IL-1b, IL-2, IL-4, IL-6, IL-8, monocyte chemoattractant protein-1 (MCP-1), and TNF-α. Next, we evaluated the diagnostic value of individual biomarkers and their joint diagnostic value. We aimed to construct a diagnostic model based on these biomarkers to improve the diagnostic value of screening for lung cancer.

MATERIALS AND METHODS

Patients and healthy controls

This study was approved by the Ethics Committee with a waiver of informed consent. Patients with pathologically confirmed primary lung cancer were eligible for inclusion. The diagnosis was confirmed by histopathological or cytological examination of specimens obtained via bronchoscopy, computed tomography (CT)-guided biopsy, or surgical resection. Patients who had not received prior radiotherapy, chemotherapy, or other antitumor treatments were included. Eligible participants were ≥ 18 years of age, able to understand and sign the informed consent form, and willing to provide the required biological samples and clinical data. Patients with a history of other malignant tumors (except for adequately treated nonmelanoma skin cancer or in situ cervical cancer), metastatic cancer from other primary sites, or severe comorbidities that would preclude study participation (e.g., decompensated heart failure, end-stage renal disease, or severe coagulopathy) were excluded. Pregnant or lactating women were also excluded because of potential risks associated with sample collection and follow-up. Healthy controls were recruited from among health check-up participants. Eligible controls were ≥ 18 years of age.

Self-reported questionnaire: Excluding individuals with a history of malignant tumors, chronic lung disease, cardiovascular and cerebrovascular diseases, liver and kidney insufficiency, and other serious systemic diseases. Clinical physical examination: Normal results of routine physical examination (chest, abdomen, cardiovascular, etc.).

Examination: Normal chest LDCT (no pulmonary nodules, ground-glass opacity, or other abnormal lesions), normal serum tumor markers, and normal routine blood, liver and kidney function, and inflammatory indicators [C-reactive protein (CRP), white blood cell count]. Pregnant or lactating women were also excluded. A total of 152 healthy controls and 113 lung cancer patients were included in the diagnostic model. An independent cohort consisting of 21 healthy controls and 36 lung cancer patients was used for diagnostic model validation. The healthy control and lung cancer groups were age- and sex-matched, and the differences were not significant. For smoking history, the proportion of never smokers, former smokers, and current smokers in the healthy control group was 68.4% (104/152), 17.1% (26/152), and 14.5% (22/152), respectively; the corresponding proportions in the lung cancer group were 65.5% (74/113), 19.5% (22/113), and 15.0% (17/113), with no statistically significant difference between the two groups (P = 0.849). In terms of chronic lung disease history, only 3.3% (5/152) of the healthy control group and 4.4% (5/113) of the lung cancer group had a history of mild COPD (GOLD 1 stage), with no significant difference (P = 0.667).

Serum sample collection

Fasting venous blood (4 mL) was collected using vacuum blood collection tubes, with strict avoidance of hemolysis; the tubes were gently inverted to mix thoroughly and incubated at room temperature (25 °C) for 1 hour, following standard biosafety precautions for blood sample handling. After incubation, the blood samples were centrifuged at 3500 r/minute for 10 minutes at room temperature, and the separated serum was aspirated into labeled cryovials and immediately stored at -80 °C, with minimal freeze-thaw cycles to prevent peptide precipitation and loss. Serum samples were thawed on ice, transferred to centrifuge tubes, and centrifuged at 10000 r/minute for 10 minutes at 4 °C using a refrigerated centrifuge before subsequent experiments.

Biomarker detection

Serum levels of CEA, CY211, and neuron-specific enolase (NSE) were measured using a Roche Modular E170 electrochemiluminescence immunoassay analyzer, with all reagents supplied by Roche Diagnostics. Prior to sample analysis, calibration and quality control procedures were performed for each analyte, and standard curves were generated. Serum samples were then analyzed according to the manufacturer’s instructions. Cytokine detection was performed using a Luminex 200 Multiplex Analyzer based on liquid-phase bead array technology, where microspheres encoded with two fluorescent dyes at varying ratios are mixed with samples for suspension binding reactions, and two lasers identify bead types and quantify fluorescence intensity. For sample collection and storage, 3 mL of fasting venous blood was collected in the morning into BD Vacutainer tubes with a separation gel and centrifuged at 3500 rpm for 7 minutes, and the serum was stored at -80 °C in polypropylene tubes, with 25 μL of serum required per well and hemolytic/Lipemic samples avoided. For the assay, all the reagents were equilibrated to 20-25 °C; a 96-well plate loading scheme was designed, followed by the addition of 200 μL of assay buffer to each well, shaking for 10 minutes, and removing the buffer by vacuum aspiration. Afterward, 25 μL of assay buffer was added to the standard wells and sample wells, 25 μL of standards was added to the corresponding wells, and 25 μL of appropriate matrix diluent was added to the background, standard, and quality control wells, after which 25 μL of sample was added to the designated wells. After vortexing, 25 μL of mixed microspheres was added to each well, and the plate was sealed and incubated with shaking at 20-25 °C for 1 hour. The liquid was gently aspirated, the wells were washed twice with 200 μL of wash buffer, 25 μL of detection antibody was added at room temperature, and the plate was sealed and incubated with shaking for 30 minutes. Subsequently, 25 μL of streptavidin-PE was added, the mixture was incubated for 30 minutes at 20-25 °C with shaking, and the liquid was aspirated; the wells were washed twice again, 100 μL of shear fluid was added, and the plate was shaken for 5 minutes to resuspend the microspheres before the cytokine levels were measured on the Luminex instrument.

Diagnostic model construction and validation

We conducted comparative analyses of 3 routine clinical indicators and 10 cytokines between the healthy control group and the lung cancer group. Differentially expressed indicators were screened, and their potential as diagnostic markers was evaluated using area under the curve (AUC) and P value statistical metrics. Building on the differential diagnostic significance of indicators identified in the CRP vs lung cancer comparison, a set of multiparameter combined auxiliary diagnostic models was constructed. Four analytical approaches were employed for model development: Binary logistic regression analysis, discriminant analysis, classification tree, and an artificial neural network. For binary logistic regression, the forward-conditional selection criterion was adopted. Discriminant analysis was implemented using the Bayes discriminant approach, with stepwise discriminant analysis integrated into the model fitting process. The χ² automatic interaction detection (CHAID) algorithm was utilized for classification tree construction, and the resulting model was validated through cross-validation procedures. With respect to artificial neural networks, a multilayer perceptron architecture was employed for model establishment. The diagnostic models were compared, and the optimal multiparameter diagnostic model was selected. To validate the performance of the selected model, an independent cohort consisting of 21 healthy controls and 36 lung cancer patients was included. The diagnostic value of the validated model was then assessed.

Statistical analysis

All the statistical analyses were performed using SPSS 22.0 software. Continuous measurement data are presented as medians (interquartile ranges: 25^th and 75^th percentiles). For normally distributed data, intergroup comparisons were conducted using independent-samples t tests; for nonnormally distributed data, the Mann-Whitney U rank-sum test was applied. The diagnostic value of individual indicators was quantified using the AUC. Four multiparameter analytical methods (binary logistic regression, discriminant analysis, classification tree, and an artificial neural network) were utilized to construct combined diagnostic models, with the same parameter settings as described in the model development section. Univariate and multivariate logistic regression analyses were performed to calculate the odds ratio [Exp(B)] for each indicator. Differences in AUC values between different groups or models were compared using the Z test. A P value < 0.05 was considered to indicate statistical significance.

RESULTS

Differences in indicator levels between the healthy control and lung cancer groups

Because IFN-γ, IL-1b, IL-2, IL-4, and IL-6 levels in most of the samples were lower than the detection limit of Luminex 200, these indicators were not included in the following analysis. GM-CSF, IL-10, IL-8, MCP-1, and TNF-α were included. Differences in 9 indicators (including CEA, CY211, NSE, GM-CSF, IL-10, IL-8, MCP-1, and TNF-α) were analyzed between the two groups. As shown in Table 1. The median levels of CEA [3.40 (1.69, 8.43) ng/L vs 1.55 (0.98, 2.29) ng/L; P < 0.001], CY211 [2.55 (1.76, 3.13) ng/mL vs 1.65 (1.22, 2.21) ng/mL; P = 0.044], and NSE [10.79 (8.03, 12.48) vs 9.46 (8.25, 10.78); P = 0.009] in the lung cancer group were significantly greater than those in the healthy control group. The median level of GM-CSF in the lung cancer group [1.42 (0.82, 2.50) pg/mL] was greater than that in the healthy control group [0.72 (0.35, 1.75) pg/mL], but the difference was not statistically significant (P = 0.742); however, the median level of IL-10 in the lung cancer group [1.11 (0.81, 1.70) pg/mL] was lower than that in the healthy control group [1.98 (1.16, 3.05) pg/mL], but the difference was not statistically significant (P = 0.388). The median levels of IL-8 [315.75 (160.44, 523.19) pg/mL vs 17.12 (8.40, 38.69) pg/mL; P < 0.001]; MCP-1 [636.03 (506.75, 859.70) pg/mL vs 341.11 (258.94, 500.58) pg/mL; P < 0.001], and TNF-α [19.33 (12.77, 30.34) pg/mL vs 5.94 (4.62, 7.68) pg/mL; P < 0.001] in the lung cancer group were significantly greater than those in the healthy control group. After comparison, the levels of CEA, CY211, NSE, IL-8, MCP-1, and TNF-α, which significantly differed between the two groups, were further analyzed.

Table 1 Differences in indicator levels between healthy control group and lung cancer group.

Indicator	Healthy control group (n = 143)	Lung cancer group (n = 113)
CEA (ng/L)	1.55 (0.98, 2.29)	3.40 (1.69, 8.43)
CY211 (ng/mL)	1.65 (1.22, 2.21)	2.55 (1.76, 3.13)
NSE (ng/mL)	9.46 (8.25, 10.78)	10.79 (8.03, 12.48)
GM-CSF (pg/mL)	0.72 (0.35, 1.75)	1.42 (0.82, 2.50)
IL-10 (pg/mL)	1.98 (1.16, 3.05)	1.11 (0.81, 1.70)
IL-8 (pg/mL)	17.12 (8.40, 38.69)	315.75 (160.44, 523.19)
MCP-1 (pg/mL)	341.11 (258.94, 500.58)	636.03 (506.75, 859.70)
TNF-α (pg/mL)	5.94 (4.62, 7.68)	19.33 (12.77, 30.34)

Data are presented as medians (interquartile ranges: 25^th and 75^th percentiles). CEA: Carcinoembryonic antigen; CY211: Cytokeratin 19 fragment; NSE: Neuron-specific enolase; IL: Interleukin; MCP-1: Monocyte chemoattractant protein-1; TNF-α: Tumor necrosis factor-alpha.

Receiver operating characteristic curve analysis for indicators distinguishing the healthy control group from the lung cancer group

The diagnostic value of CEA, CY211, NSE, IL-8, MCP-1, and TNF-α for distinguishing the healthy control group from the lung cancer group was evaluated using receiver operating characteristic (ROC) curve analysis. The AUC, P value, 95%CI, threshold, sensitivity, and specificity were used to evaluate the diagnostic value. All AUC comparisons between the indicators were performed using the Z test in SPSS, with P < 0.05 considered to indicate statistical significance. As shown in Table 2 and Figure 1A, the results of the ROC curve analysis revealed that all six indicators (CEA, CY211, NSE, IL-8, MCP-1, and TNF-α) had a certain diagnostic value for distinguishing the healthy control group from the lung cancer group (all AUCs > 0.5, P < 0.05). Among them, IL-8 exhibited the highest diagnostic efficacy, with an AUC of 0.957 (95%CI: 0.935-0.978). TNF-α (AUC = 0.936; 95%CI: 0.907-0.965) and MCP-1 (AUC = 0.825; 95%CI: 0.772-0.877) had AUC values greater than 0.8, indicating excellent to good diagnostic performance. The AUC of IL-8 was significantly greater than that of the other indicators (all Z > 2.36; all P < 0.05). In contrast, NSE had the lowest diagnostic efficacy, with an AUC of 0.587 (95%CI: 0.514-0.661) and a P value of 0.017, suggesting relatively weak diagnostic value.

Figure 1 Receiver operating characteristic analysis. A: Receiver operating characteristic (ROC) analysis of carcinoembryonic antigen, cytokeratin 19 fragment, neuron-specific enolase, interleukin-8, monocyte chemoattractant protein-1, and tumor necrosis factor-alpha; B-E: ROC analysis of four multiparameter diagnostic models: Binary logistic regression model (B), bayes discriminant model (C), χ² automatic interaction detection classification tree model (D); and artificial neural network model (E); F: ROC analysis of binary logistic regression model for validation group. CEA: Carcinoembryonic antigen; CY211: Cytokeratin 19 fragment; NSE: Neuron-specific enolase; IL: Interleukin; MCP-1: Monocyte chemoattractant protein-1; TNF-α: Tumor necrosis factor-alpha.

Table 2 Diagnostic value of carcinoembryonic antigen, cytokeratin 19 fragment, neuron-specific enolase, interleukin-8, monocyte chemoattractant protein-1, and tumor necrosis factor-alpha.

Indicator	AUC (95%CI)	P value	Cut-off value	Sensitivity (%)	Specificity (%)
CEA	0.755 (0.694-0.816)	< 0.001	2.85	72.6	81.1
CY211	0.703 (0.639-0.767)	< 0.001	2.26	68.1	74.8
NSE	0.587 (0.514-0.661)	0.017	10.10	56.6	67.1
IL-8	0.957 (0.935-0.978)	< 0.001	126.25	93.8	94.4
MCP-1	0.825 (0.772-0.877)	< 0.001	427.64	86.7	88.1
TNF-α	0.936 (0.907-0.965)	< 0.001	9.85	89.4	90.9

AUC: Area under the curve; CEA: Carcinoembryonic antigen; CY211: Cytokeratin 19 fragment; NSE: Neuron-specific enolase; IL: Interleukin; MCP-1: Monocyte chemoattractant protein-1; TNF-α: Tumor necrosis factor-alpha.

Building and validation of multiparameter diagnostic models

On the basis of the 6 differentially expressed indicators (CEA, CY211, NSE, IL-8, MCP-1, and TNF-α), four multiparameter diagnostic models (Model 1: Binary logistic regression model; Model 2: Bayes discriminant model; Model 3: CHAID classification tree model; and Model 4: Artificial neural network model) were constructed. For Model 1, the forward-conditional selection criterion was used to screen the indicators. The results revealed that 4 indicators (CEA, CY211, IL-8, and TNF-α) were ultimately included in the model. The Exp(B) values of the 4 indicators are shown in Table 3. The regression equation was established as follows: Logit(P) = 1.000 - 0.471 × CEA - 0.654 × CY211 - 0.363 × IL-8 - 0.329 × TNF-α. As shown in Figure 1B, the AUC of model 1 was 0.980 (95%CI: 0.966-0.993). For model 2, stepwise discriminant analysis was integrated during model fitting. As shown in Figure 1C, the AUC of model 2 was 0.977 (95%CI: 0.961-0.993). For model 3, the CHAID algorithm was used. As shown in Figure 1D, the AUC of model 3 was 0.948 (95%CI: 0.924-0.972). The cross-validation accuracy of the model was 87.1%. For model 4, a multilayer perceptron architecture was adopted. The model was trained with 71% of the data and validated with 29%of the data. As shown in Figure 1E, the AUC of Model 4 was 0.97, the training accuracy was 90.7%, and the test accuracy was 93.2%. ROC curve analysis was performed to compare the diagnostic efficacy of the four multiparameter models. The results showed that model 1 had the highest AUC, followed by model 2, model 4, and model 3. The AUC of model 1 was not significantly greater than that of the other 3 models (P > 0.05). Considering the interpretability of the model, model 1 was selected as the optimal multiparameter diagnostic model. An independent validation cohort consisting of 21 healthy controls and 36 lung cancer patients was used for diagnostic model validation to validate the performance of model 1. As shown in Figure 1F, the AUC was 0.922 (95%CI: 0.834-1.000). The diagnostic efficacy of model 1 in the validation cohort was consistent with that in the training cohort (AUC difference: 0.004; Z = 0.36; P = 0.719), indicating good stability of the model. The AUC of model 1 was significantly greater than that of CEA (Z = 6.84, P < 0.001).

Table 3 Odds ration of the indicators in the binary analysis.

Indicator	β	SE	Wals	P value	Exp(B)	95%CI
						Lower	Upper
CEA	0.371	0.135	7.525	0.006	1.449	1.112	1.888
CY211	0.518	0.206	6.338	0.012	1.678	1.122	2.512
IL-8	0.014	0.003	25.746	< 0.001	1.014	1.009	1.020
TNF-α	0.100	0.040	6.125	0.013	1.105	1.021	1.196

CEA: Carcinoembryonic antigen; CY211: Cytokeratin 19 fragment; IL: Interleukin; TNF-α: Tumor necrosis factor-alpha.

DISCUSSION

Owing to the heterogeneity of lung cancer, single cytokine biomarkers often lack sufficient sensitivity and specificity[16]. Combinatorial cytokine panels have emerged as more effective tools for early diagnosis through the integration of multiple complementary biomarkers[17]. Combining cytokine biomarkers with serum biomarker screening can effectively improve the diagnostic value of individual biomarkers[18]. This synergy not only improves the accuracy of early diagnosis but also reduces unnecessary follow-up examinations and patient anxiety[19]. MCP-1, also referred to as C-C motif chemokine ligand 2, serves as a key chemokine responsible for mediating the recruitment of monocytes and macrophages to the tumor microenvironment[20]. In the early stages of lung cancer, malignant cells secrete substantial quantities of MCP-1 to recruit circulating monocytes, which subsequently differentiate into tumor associated macrophages that promote tumor angiogenesis and facilitate immune escape[21]. Clinical evidence has demonstrated that serum MCP-1 concentrations are markedly greater in patients with early-stage NSCLC than in those with benign lung diseases and healthy controls[20]. Furthermore, MCP-1 levels are correlated with the degree of immune cell infiltration in the tumor microenvironment[22]. In our study, we also demonstrated that MCP-1 may have potential diagnostic value for lung cancer.

As key proinflammatory cytokines, TNF-α and IL-8 have emerged as promising diagnostic biomarkers for lung cancer because of their abnormal expression patterns in tumor-related tissues and body fluids[23]. Accumulating evidence indicates that the levels of both cytokines are significantly greater in lung cancer patients than in healthy individuals and patients with benign pulmonary disease, suggesting that these cytokines are potential indicators for distinguishing malignant lung lesions from nonmalignant lung lesions[24]. TNF-α, a multifunctional cytokine, is involved in regulating the tumor microenvironment and immune response[25]. Clinical studies have demonstrated that serum TNF-α levels are notably elevated in lung cancer patients and that this elevation is closely associated with tumor progression[26]. For early-stage lung cancer, TNF-α shows moderate diagnostic performance. Moreover, combining TNF-α with traditional tumor markers, such as CEA and SCC antigen, improves its diagnostic efficacy, thereby enhancing the detection rates of early SCC and adenocarcinoma[27]. IL-8, a CXC chemokine, plays a critical role in promoting tumor angiogenesis and immune cell recruitment[28]. Its serum and pleural effusion levels are significantly greater in lung cancer patients, especially those with advanced-stage disease or distant metastases. Compared with several traditional markers, IL-8 has superior diagnostic accuracy for lung cancer[29]. This detection enhances the accuracy of early diagnosis and subtype differentiation, providing important clinical references for the screening and management of lung cancer[30]. In our study, IL-8 and TNF-α also had potential diagnostic value for lung cancer.

This study systematically evaluated the diagnostic value of 3 conventional tumor markers (CEA, CY211, and NSE) and 10 cytokines for lung cancer, ultimately identifying 6 differentially expressed indicators: CEA, CY211, NSE, IL-8, MCP-1, and TNF-α. ROC curve analysis revealed that the diagnostic efficacy of IL-8 (AUC = 0.957) and TNF-α (AUC = 0.936) was significantly superior to that of traditional tumor markers. Among the 4 multiparameter models constructed on the basis of these 6 indicators, the binary logistic regression model (model 1) was designated as the optimal model because it had the highest AUC (0.980) and good interpretability. Validation in an independent cohort demonstrated an AUC of 0.922, indicating favorable stability. Moreover, its diagnostic efficacy was significantly greater than that of CEA alone, providing a reliable combined biomarker strategy for the auxiliary diagnosis of lung cancer.

However, this study has several limitations. The sample size is small, and the model’s performance in distinguishing benign/malignant nodules needs to be verified in a large-sample multicenter study. The model is a single-center retrospective study, and prospective validation is needed to confirm its clinical application effect. The model has not been combined with other clinical factors (e.g., nodule size, morphology, density on LDCT) to construct a comprehensive diagnostic model, which is the focus of future research.

CONCLUSION

In conclusion, we constructed and validated an auxiliary diagnostic model for lung cancer detection. This diagnostic model may provide more comprehensive theoretical support for the diagnosis and targeted therapy of lung cancer.