Abstract
Lung cancer remains the leading cause of cancer-related deaths worldwide, with non-small cell lung cancer (NSCLC) accounting for the vast majority. In recent years, the interaction between inflammation and tumorigenesis has become the focus of attention, which has also confirmed the importance of inflammatory markers such as C-reactive protein (CRP) in prognosis. In this study, we explored the effects of CRP, systemic inflammatory immune index (SII), and monocyte/lymphocyte ratio (MLR) on the prognosis of patients with advanced NSCLC. We conducted a retrospective study of 274 patients suffering from stage III/IV NSCLC. Among them, 224 patients served as the training set and 50 patients served as the validation set. The independent factors affecting PFS (Progression-Free Survival) and OS (Overall Survival) in the patients were analyzed by Cox regression. Our results showed that CPR (HR=1.691, P=0.004), SlI (HR=1.960, P<0.001), MLR (HR=1.578, P=0.003), CEA (HR=1.845, P=0.006), NSE (HR=2.138, P=0.003) and adrenal metastasis (HR=2.896, P<0.003) were independent factors affecting the PFS of NSCLC patients. SII (HR=1.645, P=0.004), CEA (HR=2.021, P=0.002) and brain metastasis (HR=2.899, P<0.001) were independent factors affecting the OS of NLSCL patients. The DCA curve demonstrated that the prediction model provided better clinical net benefit in predicting patients’ 6-month PFS and 12-month OS under different threshold probabilities. DeLong test showed no significant difference between AUCs of SII and risk score (P>0.05). Compared with CEA, SII and risk score had higher predictive value for patients’ 6-month PFS and 12-month OS (P<0.05). In conclusion, the results of this study indicate that serum inflammatory factor SII can be used as an independent indicator to evaluate 6-month PFS and 12-month OS in patients with advanced NSCLC, and its predictive value is similar to that of the nomogram model.
Keywords: CRP, SII, MLR, advanced NSCLC, prognosis, nomogram
Introduction
Lung cancer (LC) is a leading malignant tumor worldwide, with rising incidence and mortality rates [1,2]. According to China’s National Cancer Registry Annual Report [3], there are about 780,000 new LC cases each year in China. Clinically, LC is categorized into small cell LC (SCLC) and non-small cell LC (NSCLC). NSCLC accounts for 85% of LC cases, making it the primary cause of cancer-related deaths globally [4].
NSCLC is a multifaceted disease that can be further categorized into squamous cell carcinoma, adenocarcinoma, and large cell carcinoma based on the specific sites of manifestation. Notably, the incidence of adenocarcinoma is on a steep rise, representing over 40% of NSCLC. In contrast, squamous cell carcinoma and large cell carcinoma account for 20%-30% and 10%-15%, respectively [5,6]. Given the relentless escalation in LC incidence coupled with its alarming mortality rate, it has evolved into a paramount health threat for the Chinese populace, imposing immense challenges and social burdens on the public health sector.
The prognosis for NSCLC remains grim, with a significant portion of patients being diagnosed at advanced stages, culminating in increased mortality [7]. As the tools like medical imaging, bronchoscopy, bronchial washing cytology, and histopathology have played an important role in NSCLC screening and early diagnosis, a profound comprehension of the biological characteristics and molecular mechanisms of NSCLC have catalyzed the evolution of innovative diagnostic tools and therapeutic interventions [8]. Yet, a consensus on clinical standards for NSCLC’s prevention, early diagnosis, and treatment remains elusive. Serum biomarkers, given their ease operation, non-invasiveness, efficiency, and dynamic monitoring capabilities, hold paramount importance in NSCLC management, offering invaluable insights for clinical decision-making.
In the intricate landscape of malignant tumor development, inflammation emerges as a pivotal player, fostering tumor cell proliferation and angiogenesis [9]. The quest for the optimal inflammatory biomarker for tumor prognosis prediction has spurred a plethora of studies in recent times. Blood routine parameters, such as leukocyte and subgroup counts, offer a glimpse into the body’s inflammatory status [10]. Comprehensive inflammatory indices rooted in these blood routine parameters have been linked with the prognosis of an array of malignant tumors [11]. The systemic immune-inflammation index (SII), a novel inflammatory index, incorporates three distinct inflammatory cells and is computed by multiplying the neutrophil count with the platelet count, followed by division by the lymphocyte count. This index, introduced by Hu et al. in 2014 [12], revealed that elevated SII values were indicative of an unfavorable prognosis in primary liver cancer patients. The monocyte to lymphocyte ratio (MLR) mirrors the equilibrium between tumor progression and inhibition within the body and has been associated with the prognosis of cancers like liver and breast cancer [13,14]. C-reactive protein (CRP), an acute phase reactant protein predominantly synthesized by hepatocytes, significantly elevates in a majority of cancer patients [15]. Research indicates that CRP levels might be augmented by the stimulation of proinflammatory factors within the tumor microenvironment, thereby triggering a cascade of events leading to excessive cell proliferation and DNA damage [16].
Despite the extensive exploration of the prognostic significance of serum inflammatory factors across various cancers, the amalgamated application of comprehensive inflammatory indices such as SII, MLR, and CRP in gauging the prognosis of patients with advanced NSCLC remains in its infancy. In this study, we juxtaposed and scrutinized the efficacy of SII, MLR, and CRP against conventional tumor markers like CEA and NSE in determining the prognosis of patients with advanced NSCLC. Our findings underscore that SII, MLR, and CRP can more adeptly forecast patients’ PFS and OS, paving the way for a novel prognostic evaluation paradigm for advanced NSCLC based on serum indicators.
Materials and methods
Ethical statement
The current study was conducted with the approval of the Binzhou Medical University Hospital Medical Ethics Committee (20214230).
Sample source
The data of 337 patients with advanced NSCLC who received treatment between January 2018 and January 2020 were retrospectively analyzed.
Inclusion and exclusion criteria
Inclusion criteria: 1) Patients with confirmed non-small cell lung cancer indicated by pathological and imaging examinations; 2) Patients with pathological type of adenocarcinoma or squamous cell carcinoma; 3) Patients that met AJCC 8th edition clinical stage III or IV [17]; 4) Patients unable to receive surgical treatment; 5) All patients received first-line chemotherapy with platinum-based doublet, the medication complied with guideline recommendations, and completed at least 2 cycles with an interval of no more than 1 month; 6) Patients with complete clinical data and follow-up imaging data that available for efficacy evaluation after chemotherapy.
Exclusion criteria: 1) Patients combined with other primary malignant tumors; 2) Patients with severe heart, liver, and kidney dysfunction; 3) Patients combined with various infectious diseases or trauma; 4) Patients with a history of medication use before chemotherapy that may affect blood routine, such as glucocorticoids; 5) Patients combined with hematologic diseases or autoimmune diseases.
Group definition
The treatment efficacy was evaluated based on RECIST version 1.1 [18]. Complete response: In addition to the disappearance of all target lesions, pathological lymph nodes (whether target or non-target) also shrink to normal size and the short axis value is less than 10 mm; Partial response: The sum of the diameters of all target lesions decreases by at least 30% from baseline; Stable disease: Neither partial response nor progression criteria are met; Progression: The sum of the diameters of all target lesions increases by at least 20% from baseline, and the absolute value of the increase is greater than 5 mm, or new lesions appear. We categorized the patients with complete response, partial response, and stable disease into control group and the patients with progression into progression group.
Sample screening
Patients were screened according to the inclusion and exclusion criteria. Among them, 32 patients who did not meet the diagnosis of adenocarcinoma or squamous cell carcinoma were excluded according to the pathologic type criteria; 16 patients with stage III or IV disease were not excluded according to the clinical staging criteria; 9 patients who did not receive platinum-based doublet first-line chemotherapy or received less than 2 cycles of chemotherapy were also excluded; moreover, 4 patients with incomplete information that could not be evaluated and 2 patients with a combination of malignancies, severe organ dysfunction, or autoimmune diseases were excluded according to the criteria for patients with comorbid diseases. After stratified screening, a total of 54 patients who did not meet this criterion were excluded, and a total of 274 NSCLC patients who met all the prespecified screening criteria were finally included in this study. We divided the patients into 224 patients in the training group and 50 patients in the validation group according to a ratio of 8:2 (training group:validation group) (Figure 1).
Figure 1.
Flow chart of sample screening and model construction.
Clinical data collection
According to the patients’ electronic medical records and outpatient follow-up records, we obtained the patients’ clinical data and laboratory examination indicators. The clinical data included gender, age, clinical stage, tumor location, T stage, N stage, pathological type, contralateral lung metastasis, pericardial metastasis, pleural metastasis, liver metastasis, brain metastasis, adrenal metastasis, bone metastasis, smoking history and chronic obstructive pulmonary disease (COPD). Laboratory data included pre-treatment WBC (white blood cell count), Hb (Hemoglobin), ALB (Albumin), FIB (Fibrinogen), CRP (C-Reactive Protein), SII (Systemic Immune-Inflammation Index), MLR (Monocyte-to-Lymphocyte Ratio), CEA (Carcinoembryonic Antigen) and NSE (Neuron Specific Enolase). The calculation formula for SII is N×P/L, and the calculation formula for MLR is M/L (Note: N: neutrophil, P: platelet, L: lymphocyte, M: monocyte). All the indicators were the test results on the first day of admission.
Follow-up
The last follow-up time was set as December 31, 2022. PFS (progression-free survival) is defined as the time from the start of a certain treatment to tumor progression (such as tumor enlargement or appearance of new metastatic lesions) or patient death (regardless of cause). OS (overall survival) is defined as the time from the start of a certain treatment to patient death (regardless of cause). The PFS and OS of patients were statistically analyzed. Follow-up was performed monthly for the first 3 months, then every 3 months thereafter, and every 4 months after the second year. All lung cancer patients underwent CT, MRI and other examinations of the corresponding parts after every 2 cycles of chemotherapy to evaluate the efficacy and disease progression. Based on the patient’s progress in the third month, the patients were divided into the progression group and the control group.
Statistical analysis
Data processing was conducted using SPSS 26.0 software. Dichotomous variables were represented as ratios (frequencies) and analyzed using Chi-square tests. Measurement data were presented as mean ± standard deviation, and independent samples t-test was applied. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of various indicators for patient prognosis. Cox proportional hazards regression model was employed to analyze factors influencing the prognosis of NSCLC patients. Regarding the nomogram and DCA (Decision Curve Analysis method), we used R language (4.2.2) for the analysis where ‘rms’ program package was used to create the nomogram and ‘rmda’ program package was used for DCA. c-index was calculated using the ‘rms’ program package to calculate it. X-Tile software was used to determine the optimal survival cutoff value for the measured data. All tests were two-sided, and a P-value less than 0.05 was considered statistically significant.
Results
General information
This study included a total of 224 patients with advanced NSCLC, including 185 males and 39 females. According to clinical staging, there were 86 patients with stage III and 138 patients with stage IV. They were divided into progression group (64 cases) and control group (160 cases). In terms of age, there were 111 cases ≥60 years old and 113 cases <60 years old. Tumor locations were mainly central type (142 cases), T stages were mainly T3-4 (157 cases), and N stages were mainly N2-3 (175 cases). Pathological types were mainly adenocarcinoma (125 cases), followed by squamous carcinoma (99 cases). There were 20 cases with contralateral lung metastasis, 27 cases with pericardial metastasis, 86 cases with pleural metastasis, 18 cases with liver metastasis, 25 cases with brain metastasis, 25 cases with adrenal metastasis, and 50 cases with bone metastasis. There were 60 cases with chronic obstructive pulmonary disease (COPD). Among them, 175 cases had a smoking history.
Comparison of clinical data between progression group and control group
Comparison of clinical data between the two groups found that the proportion of patients with stage III, adenocarcinoma, no pleural metastasis, liver metastasis, adrenal metastasis, bone metastasis and brain metastasis in the control group was significantly higher than that in the progression group (all P<0.05, Table 1). There were no significant differences in other clinical indicators (all P>0.05, Table 1).
Table 1.
Comparison of clinical data between progression group and control group
| Factors | Control group (n=160) | Progression group (n=64) | x2 value | P value |
|---|---|---|---|---|
| Gender | 1.503 | 0.220 | ||
| Male | 129 | 56 | ||
| Female | 31 | 8 | ||
| Age | 2.445 | 0.117 | ||
| ≥60 years | 74 | 37 | ||
| <60 years | 86 | 27 | ||
| Clinical staging | 25.397 | <0.001 | ||
| Stage III | 78 | 8 | ||
| Stage IV | 82 | 56 | ||
| Tumor site | 2.926 | 0.087 | ||
| Central | 107 | 35 | ||
| Peripheral | 53 | 29 | ||
| T-stage | 2.759 | 0.096 | ||
| T1-2 | 53 | 14 | ||
| T3-4 | 107 | 50 | ||
| N-stage | 0 | >0.999 | ||
| N0-1 | 35 | 14 | ||
| N2-3 | 125 | 50 | ||
| Pathologic type | 4.708 | 0.030 | ||
| Adenocarcinoma | 82 | 43 | ||
| Squamous cell carcinoma | 78 | 21 | ||
| Contralateral lung metastasis | 0.021 | 0.882 | ||
| Yes | 14 | 6 | ||
| No | 146 | 58 | ||
| Pericardial metastasis | 0.606 | 0.436 | ||
| Yes | 21 | 6 | ||
| No | 139 | 58 | ||
| Pleural metastasis | 10.058 | 0.002 | ||
| Yes | 51 | 35 | ||
| No | 109 | 29 | ||
| Liver metastasis | 13.918 | 0.001 | ||
| Yes | 6 | 12 | ||
| No | 154 | 52 | ||
| Brain metastasis | 21.436 | <0.001 | ||
| Yes | 8 | 17 | ||
| No | 152 | 47 | ||
| Adrenal metastasis | 21.436 | <0.001 | ||
| Yes | 8 | 17 | ||
| No | 152 | 47 | ||
| Bone metastasis | 14.483 | <0.001 | ||
| Yes | 25 | 25 | ||
| No | 135 | 39 | ||
| Smoking history | 2.048 | 0.152 | ||
| Yes | 121 | 54 | ||
| No | 39 | 10 | ||
| COPD | 3.826 | 0.050 | ||
| Yes | 37 | 23 | ||
| No | 123 | 41 |
Note: COPD, chronic obstructive pulmonary disease.
Relationship between CRP, SII, MLR, tumor markers, and treatment efficacy
Patients were classified based on the optimal cutoff value of X-tile. Subsequent analysis of the relationship between each index and treatment efficacy revealed a significantly higher number of patients in the progression group with high expression of CRP, SII, MLR, CEA, and NSE compared to the control group (all P<0.001, Table 2).
Table 2.
CRP, SII, MLR, and tumor markers with efficacy
| Factors | Control group (n=160) | Progression group (n=64) | x2 value | P value |
|---|---|---|---|---|
| CRP (mg/L) | 61.717 | <0.001 | ||
| ≥12.100 | 17 | 39 | ||
| <12.100 | 143 | 25 | ||
| SII | 82.024 | <0.001 | ||
| ≥789.700 | 31 | 54 | ||
| <789.700 | 129 | 10 | ||
| MLR | 20.036 | <0.001 | ||
| ≥0.510 | 34 | 33 | ||
| <0.510 | 126 | 31 | ||
| CEA (μg/L) | 31.376 | <0.001 | ||
| ≥41.400 | 8 | 21 | ||
| <41.400 | 152 | 43 | ||
| NSE (ng/mL) | 36.674 | <0.001 | ||
| ≥26.500 | 4 | 19 | ||
| <26.500 | 156 | 45 |
Note: CRP, C-reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase.
Analysis of prognostic factors for patients’ PFS
Univariate Cox analysis revealed that CRP (HR=2.315, P<0.001), SII (HR=2.796, P<0.001), MLR (HR=1.697, P<0.001), CEA (HR=2.849, P<0.001), NSE (HR=4.033, P<0.001), smoking history (HR=1.421, P=0.033), pathologic type (HR=1.536, P=0.002), liver metastasis (HR=1.921, P=0.008), brain metastasis (HR=3.281, P<0.001), adrenal metastasis (HR=2.449, P<0.001), and clinical staging (HR=0.679, P=0.005), significantly influenced patient PFS (Table 3). Furthermore, a multi-factor Cox regression analysis identified CRP (HR=1.691, P=0.004), SII (HR=1.960, P<0.001), MLR (HR=1.578, P=0.003), CEA (HR=1.845, P=0.006), NSE (HR=2.138, P=0.003), and adrenal metastasis (HR=2.896, P<0.001) as independent prognostic factors for patients’ PFS (Table 4). These findings provide valuable insights for clinical prognosis assessment and the development of treatment strategies.
Table 3.
Univariate Cox regression analysis of factors affecting PFS
| Factors | β | SE | χ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| WBC | -0.042 | 0.035 | 1.461 | 0.227 | 0.958 | 0.895 | 1.027 |
| Hb | 0.006 | 0.004 | 2.424 | 0.120 | 1.006 | 0.998 | 1.014 |
| ALB | 0.003 | 0.014 | 0.043 | 0.836 | 1.003 | 0.975 | 1.032 |
| FIB | -0.053 | 0.066 | 0.646 | 0.422 | 0.949 | 0.834 | 1.079 |
| CRP | 0.840 | 0.158 | 28.368 | <0.001 | 2.315 | 1.700 | 3.154 |
| SII | 1.028 | 0.146 | 49.319 | <0.001 | 2.796 | 2.098 | 3.724 |
| MLR | 0.529 | 0.147 | 12.965 | <0.001 | 1.697 | 1.272 | 2.262 |
| CEA | 1.047 | 0.207 | 25.659 | <0.001 | 2.849 | 1.900 | 4.273 |
| NSE | 1.395 | 0.235 | 35.192 | <0.001 | 4.033 | 2.544 | 6.394 |
| Gender | 0.257 | 0.178 | 2.102 | 0.147 | 1.293 | 0.913 | 1.832 |
| Age | -0.005 | 0.007 | 0.435 | 0.510 | 0.995 | 0.981 | 1.010 |
| Smoking history | 0.352 | 0.165 | 4.529 | 0.033 | 1.421 | 1.028 | 1.965 |
| Tumor Site | -0.138 | 0.140 | 0.971 | 0.324 | 0.871 | 0.662 | 1.146 |
| T-stage | -0.194 | 0.147 | 1.738 | 0.187 | 0.824 | 0.617 | 1.099 |
| N-stage | -0.184 | 0.164 | 1.258 | 0.262 | 0.832 | 0.604 | 1.147 |
| Pathologic type | 0.429 | 0.140 | 9.468 | 0.002 | 1.536 | 1.169 | 2.020 |
| Contralateral lung metastasis | -0.085 | 0.237 | 0.129 | 0.719 | 0.919 | 0.578 | 1.460 |
| Pericardial metastasis | -0.018 | 0.209 | 0.007 | 0.932 | 0.982 | 0.652 | 1.480 |
| Pleural metastasis | 0.187 | 0.138 | 1.838 | 0.175 | 1.206 | 0.920 | 1.580 |
| Liver metastasis | 0.653 | 0.248 | 6.953 | 0.008 | 1.921 | 1.182 | 3.120 |
| Brain metastasis | 1.188 | 0.222 | 28.743 | <0.001 | 3.281 | 2.125 | 5.065 |
| Adrenal metastasis | 0.896 | 0.217 | 17.089 | <0.001 | 2.449 | 1.602 | 3.744 |
| Bone metastasis | 0.285 | 0.161 | 3.127 | 0.077 | 1.330 | 0.970 | 1.824 |
| Clinical staging | -0.387 | 0.138 | 7.804 | 0.005 | 0.679 | 0.518 | 0.891 |
| COPD | 0.115 | 0.152 | 0.572 | 0.449 | 1.122 | 0.833 | 1.512 |
Note: WBC, white blood cell count; Hb, hemoglobin; ALB, albumin; FIB, fibrinogen; CRP, C-reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase; COPD, chronic obstructive pulmonary disease; OS, overall survival.
Table 4.
Multivariate Cox regression analysis of factors affecting PFS
| Factors | β | SE | χ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| CRP | 0.525 | 0.181 | 8.389 | 0.004 | 1.691 | 1.185 | 2.412 |
| SII | 0.673 | 0.171 | 15.413 | <0.001 | 1.960 | 1.401 | 2.743 |
| MLR | 0.456 | 0.153 | 8.901 | 0.003 | 1.578 | 1.170 | 2.130 |
| CEA | 0.612 | 0.222 | 7.594 | 0.006 | 1.845 | 1.193 | 2.851 |
| NSE | 0.760 | 0.260 | 8.552 | 0.003 | 2.138 | 1.285 | 3.557 |
| Smoking history | 0.080 | 0.174 | 0.209 | 0.648 | 1.083 | 0.770 | 1.523 |
| Pathologic type | 0.240 | 0.147 | 2.677 | 0.102 | 1.272 | 0.954 | 1.696 |
| Brain metastasis | 0.353 | 0.256 | 1.892 | 0.169 | 1.423 | 0.861 | 2.352 |
| Adrenal metastasis | 1.063 | 0.234 | 20.720 | <0.001 | 2.896 | 1.832 | 4.577 |
| Clinical staging | 0.448 | 0.233 | 3.696 | 0.055 | 1.565 | 0.991 | 2.470 |
Note: CRP, C-Reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte Ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase; PFS, progression-free survival.
Analysis of prognostic factors for patients’ OS
Univariate Cox analysis revealed that CRP (HR=1.820, P<0.001), SII (HR=2.273, P<0.001), MLR (HR=1.583, P=0.002), CEA (HR=2.670, P<0.001), NSE (HR=2.362, P<0.001), pathologic type (HR=1.542, P=0.002), liver metastasis (HR=1.903, P=0.009), brain metastasis (HR=3.257, P<0.001), adrenal metastasis (HR=2.063, P=0.001), and clinical staging (HR=0.743, P=0.031) significantly influenced patient OS (Table 5). Subsequently, multivariate Cox regression analysis demonstrated that SII (HR=1.645, P=0.004), CEA (HR=2.021, P=0.002), and brain metastasis (HR=2.899, P<0.001) independently affected patient OS (Table 6). These findings provide valuable insights for clinical prognosis assessment and treatment decisions.
Table 5.
Univariate Cox regression analysis of factors affecting OS
| Factors | β | SE | χ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| WBC | -0.045 | 0.035 | 1.631 | 0.202 | 0.956 | 0.892 | 1.025 |
| Hb | 0.007 | 0.004 | 2.886 | 0.089 | 1.007 | 0.999 | 1.015 |
| ALB | 0.001 | 0.014 | 0.004 | 0.949 | 1.001 | 0.973 | 1.030 |
| FIB | -0.055 | 0.066 | 0.692 | 0.406 | 0.947 | 0.832 | 1.077 |
| CRP | 0.599 | 0.157 | 14.575 | <0.001 | 1.820 | 1.338 | 2.475 |
| SII | 0.821 | 0.145 | 31.876 | <0.001 | 2.273 | 1.709 | 3.022 |
| MLR | 0.460 | 0.147 | 9.826 | 0.002 | 1.583 | 1.188 | 2.110 |
| CEA | 0.982 | 0.207 | 22.513 | <0.001 | 2.670 | 1.780 | 4.006 |
| NSE | 0.860 | 0.226 | 14.493 | <0.001 | 2.362 | 1.517 | 3.677 |
| Gender | 0.251 | 0.178 | 1.990 | 0.158 | 1.286 | 0.907 | 1.823 |
| Age | -0.001 | 0.007 | 0.025 | 0.875 | 0.999 | 0.985 | 1.013 |
| Smoking history | 0.307 | 0.165 | 3.462 | 0.063 | 1.359 | 0.984 | 1.879 |
| Tumor Site | -0.089 | 0.140 | 0.410 | 0.522 | 0.914 | 0.695 | 1.202 |
| T-stage | -0.139 | 0.146 | 0.905 | 0.341 | 0.870 | 0.653 | 1.159 |
| N-stage | -0.183 | 0.163 | 1.249 | 0.264 | 0.833 | 0.605 | 1.148 |
| Pathologic type | 0.433 | 0.141 | 9.471 | 0.002 | 1.542 | 1.170 | 2.033 |
| Contralateral lung metastasis | -0.149 | 0.236 | 0.397 | 0.529 | 0.862 | 0.542 | 1.369 |
| Pericardial metastasis | 0.032 | 0.206 | 0.024 | 0.876 | 1.033 | 0.689 | 1.548 |
| Pleural metastasis | 0.130 | 0.138 | 0.890 | 0.345 | 1.139 | 0.869 | 1.492 |
| Liver metastasis | 0.644 | 0.247 | 6.777 | 0.009 | 1.903 | 1.172 | 3.090 |
| Brain metastasis | 1.181 | 0.220 | 28.759 | <0.001 | 3.257 | 2.115 | 5.014 |
| Adrenal metastasis | 0.724 | 0.216 | 11.269 | 0.001 | 2.063 | 1.352 | 3.149 |
| Bone metastasis | 0.205 | 0.161 | 1.617 | 0.203 | 1.228 | 0.895 | 1.685 |
| Clinical staging | -0.297 | 0.138 | 4.646 | 0.031 | 0.743 | 0.567 | 0.973 |
| COPD | 0.001 | 0.153 | 0.000 | 0.997 | 1.001 | 0.742 | 1.350 |
Note: WBC, white blood cell count; Hb, hemoglobin; ALB, albumin; FIB, fibrinogen; CRP, C-reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase; COPD, chronic obstructive pulmonary disease; OS, overall survival.
Table 6.
Multivariate Cox regression analysis of factors affecting OS
| Factors | β | SE | χ2 | P | HR | 95% CI | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| CRP | 0.297 | 0.189 | 2.478 | 0.115 | 1.346 | 0.930 | 1.948 |
| SII | 0.498 | 0.171 | 8.453 | 0.004 | 1.645 | 1.176 | 2.301 |
| MLR | 0.296 | 0.153 | 3.738 | 0.053 | 1.345 | 0.996 | 1.816 |
| CEA | 0.704 | 0.223 | 9.992 | 0.002 | 2.021 | 1.307 | 3.127 |
| NSE | 0.019 | 0.262 | 0.005 | 0.942 | 1.019 | 0.610 | 1.704 |
| Pathologic type | 0.275 | 0.148 | 3.446 | 0.063 | 1.317 | 0.985 | 1.761 |
| Liver metastasis | 0.379 | 0.255 | 2.211 | 0.137 | 1.461 | 0.886 | 2.409 |
| Brain metastasis | 1.064 | 0.232 | 20.967 | <0.001 | 2.899 | 1.838 | 4.572 |
| Adrenal metastasis | 0.161 | 0.238 | 0.458 | 0.499 | 1.175 | 0.737 | 1.873 |
| Clinical staging | -0.116 | 0.147 | 0.618 | 0.432 | 0.891 | 0.667 | 1.189 |
Note: CRP, C-reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase; OS, overall survival.
The value of SII and CEA in predicting PFS and OS in NSCLC patients
To further explore the predictive value of SII and CEA for patients’ 6-month PFS and 12-month OS, we conducted ROC curve analysis. The results revealed that at an SII of 775.150, the sensitivity and specificity for 6-month PFS were 83.75% and 54.17%, respectively, with an AUC of 0.683 (95% CI: 0.611-0.755). Similarly, at a CEA level of 40.050 μg/L, the sensitivity and specificity for 6-month PFS were 92.50% and 23.61%, respectively, but the AUC was 0.529 (95% CI: 0.452-0.605), indicating a lower predictive value. Regarding the prediction of 12-month OS, at an SII of 780.150, the sensitivity and specificity were 74.75% and 52.00%, respectively, with an AUC of 0.609 (95% CI: 0.534-0.684). For CEA at 26.950 μg/L, both the sensitivity and specificity were 74.75%, but the specificity was 37.60% and the AUC was 0.529 (95% CI: 0.454-0.605) (Figure 2; Table 7).
Figure 2.
ROC curves of SII and CEA in predicting OS and PFS in advanced NSCLC patients. A. AUCs of SII and CEA in predicting 6-month PFS. B. AUCs of SII and CEA in predicting 12-month OS. Note: SII, systemic immune-inflammation index; CEA, carcinoembryonic antigen; PFS, progression-free survival; OS, overall survival; AUC, area under curve; ROC, receiver operating characteristic.
Table 7.
ROC curve analysis of SII and CEA in predicting PFS and OS
| Variables | Time (month) | Status | Cut-off | Sensitivity | Specificity | AUC | 95% CI | P value |
|---|---|---|---|---|---|---|---|---|
| SII | 6 | PFS | 775.150 | 83.75% | 54.17% | 0.683 | 0.611-0.755 | <0.001 |
| CEA (μg/L) | 40.050 | 92.50% | 23.61% | 0.529 | 0.452-0.605 | 0.479 | ||
| SII | 12 | OS | 780.150 | 74.75% | 52.00% | 0.609 | 0.534-0.684 | 0.005 |
| CEA (μg/L) | 26.950 | 74.75% | 37.60% | 0.529 | 0.454-0.605 | 0.426 |
Note: SII, Systemic Immune-Inflammation Index; CEA, Carcinoembryonic Antigen; PFS, Progression-Free Survival; OS, Overall Survival; AUC, area under curve; ROC, Receiver operating characteristic.
Nomogram prognostic model construction
Later, we developed a prediction model using a nomogram to forecast the 6-month PFS and 12-month OS of patients. This model was constructed based on independent prognostic factors identified through multivariable Cox regression analysis (Figure 3A, 3B). The PFS chart had a C index of 0.707 (0.687-0.727, P<0.0001), while the OS chart had a C index of 0.627 (0.606-0.648, P<0.0001). ROC curve analysis revealed that the AUC of risk score for predicting patient PFS was 0.715, and for predicting OS was 0.605 (Figure 3C; Table 8). The DCA curve demonstrated that the prediction model yielded favorable clinical net benefit across various threshold probabilities, confirming its effectiveness (Figure 3D).
Figure 3.
Nomogram in predicting 6-month PFS and 12-month OS. A, B. Nomogram predicting 6-month PFS and 12-month OS in patients with advanced NSCLC. C. ROC curve of nomogram model for predicting 6-month PFS and 12-month OS of patients. D. DCA curves for predictive prognostic modeling of line graph models. Note: CRP, C-reactive protein; SII, systemic immune-inflammation index; MLR, monocyte-to-lymphocyte ratio; CEA, carcinoembryonic antigen; NSE, neuron specific enolase; ROC, receiver operating characteristic; PFS, progression-free survival; OS, overall survival; DCA, decision curve analysis.
Table 8.
Validation group versus the training group risk model in predicting the ROC curve parameters of patient PFS and OS
| Variables | Time (month) | Status | Cut-off | Sensitivity | Specificity | AUC | 95% CI | P value |
|---|---|---|---|---|---|---|---|---|
| Training group | 6 | PFS | 1.426 | 95.00% | 50.69% | 0.715 | 0.650-0.780 | <0.001 |
| Validation group | 0.786 | 69.57% | 74.07% | 0.767 | 0.636-0.897 | 0.001 | ||
| Training group | 12 | OS | 0.488 | 71.72% | 55.20% | 0.605 | 0.534-0.676 | 0.007 |
| Validation group | 0.488 | 66.67% | 58.62% | 0.641 | 0.503-0.780 | 0.090 |
External model validation
To test the generality of our model, we included an additional group of 50 patients with advanced NSCLC as a validation group. We first compared the baseline data of the patients who experienced progression and those who did not among the 50 patients. The results revealed significant differences between the two groups in terms of pathologic data, including type, brain metastasis, adrenal metastasis, and bone metastasis (all P<0.05, Table 9). However, there were no differences in other clinical data (all P>0.05). When comparing the baseline data of patients in the training group and the validation group, we found no differences in clinical data between the two groups (all P>0.05, Table 10). We calculated the ROC curve of the validation group model based on the beta coefficients of 6-month PFS and 12-month OS. The risk formula for 6-month PFS is CRP*0.639860124 + SII*0.836133444 + MLR*0.413880205 + CEA*0.321500044 + NSE*0.320666118 + adrenal metastasis*0.374129155. The risk formula for 12-month OS is SII*0.661431908 + CEA*0.313806623 + adrenal metastasis*1.048705535. The calculation results showed that the risk model had an AUC of 0.767 for 6-month PFS in the validation group, and an AUC of 0.641 for pre-existing OS (Figure 4A, 4B). The C-index of the PFS plot was 0.711 (0.673-0.749, P<0.0001), while the C-index of the OS plot was 0.612 (0.568-0.656, P<0.0001). The DCA curve demonstrated that the prediction model provided better clinical net benefit in predicting patients’ 6-month PFS and 12-month OS under different threshold probabilities (Figure 4C, 4D), confirming its practicality. Finally, our comparison also revealed that comparing the C-index of the training set with that of the validation set for 6-month PFS by Delong’s test showed no difference in the C-index between the two models (Z=0.026, P=0.979), and comparing the C-index of the training set with that of the validation set for 12-month OS also showed no difference in the C-index between the two models (Z=0.956, P=0.342).
Table 9.
Training group baseline data
| Factors | Control group (n=35) | Progression group (n=15) | x2 value | P value |
|---|---|---|---|---|
| Gender | 1.023 | 0.312 | ||
| Male | 28 | 10 | ||
| Female | 7 | 5 | ||
| Age | 0.192 | 0.662 | ||
| ≥60 years | 14 | 7 | ||
| <60 years | 21 | 8 | ||
| Clinical staging | 0.599 | 0.439 | ||
| Stage III | 8 | 5 | ||
| Stage IV | 27 | 10 | ||
| Tumor site | 0.010 | 0.922 | ||
| Central | 18 | 9 | ||
| Peripheral | 17 | 9 | ||
| T-stage | 0.272 | 0.602 | ||
| Central | 7 | 4 | ||
| Peripheral | 28 | 11 | ||
| N-stage | 0.008 | 0.929 | ||
| N0-1 | 5 | 2 | ||
| N2-3 | 30 | 13 | ||
| Pathologic type | 4.667 | 0.030 | ||
| Adenocarcinoma | 21 | 4 | ||
| Squamous cell carcinoma | 14 | 11 | ||
| Contralateral lung metastasis | 0.577 | 0.447 | ||
| Yes | 5 | 1 | ||
| No | 30 | 14 | ||
| Pericardial metastasis | 0.017 | 0.897 | ||
| Yes | 2 | 1 | ||
| No | 33 | 14 | ||
| Pleural metastasis | 1.020 | 0.312 | ||
| Yes | 9 | 6 | ||
| No | 26 | 9 | ||
| Liver metastasis | 0.641 | 0.423 | ||
| Yes | 4 | 3 | ||
| No | 31 | 12 | ||
| Brain metastasis | 9.235 | 0.002 | ||
| Yes | 1 | 5 | ||
| No | 34 | 10 | ||
| Adrenal metastasis | 4.193 | 0.040 | ||
| Yes | 1 | 3 | ||
| No | 34 | 12 | ||
| Bone metastasis | 4.365 | 0.036 | ||
| Yes | 2 | 4 | ||
| No | 33 | 11 | ||
| Smoking history | 0.019 | 0.891 | ||
| Yes | 25 | 11 | ||
| No | 10 | 4 | ||
| COPD | 0.058 | 0.810 | ||
| Yes | 6 | 3 | ||
| No | 29 | 12 |
Table 10.
Comparison of the baseline data between the training group and the validation group
| Factors | Control group (n=160) | Progression group (n=50) | x2 value | P value |
|---|---|---|---|---|
| Gender | 1.172 | 0.279 | ||
| Male | 185 | 38 | ||
| Female | 39 | 12 | ||
| Age | 0.934 | 0.334 | ||
| ≥60 years | 111 | 21 | ||
| <60 years | 113 | 29 | ||
| Clinical staging | 2.720 | 0.099 | ||
| Stage III | 86 | 13 | ||
| Stage IV | 138 | 37 | ||
| Tumor site | 1.526 | 0.217 | ||
| Central | 142 | 27 | ||
| Peripheral | 82 | 23 | ||
| T-stage | 1.256 | 0.262 | ||
| T1-2 | 67 | 11 | ||
| T3-4 | 157 | 39 | ||
| N-stage | 1.559 | 0.212 | ||
| N0-1 | 49 | 7 | ||
| N2-3 | 175 | 43 | ||
| Pathologic type | 0.556 | 0.456 | ||
| Adenocarcinoma | 125 | 25 | ||
| Squamous cell carcinoma | 99 | 25 | ||
| Contralateral lung metastasis | 0.449 | 0.503 | ||
| Yes | 20 | 6 | ||
| No | 204 | 44 | ||
| Pericardial metastasis | 1.536 | 0.215 | ||
| Yes | 27 | 3 | ||
| No | 197 | 47 | ||
| Pleural metastasis | 1.237 | 0.266 | ||
| Yes | 86 | 15 | ||
| No | 138 | 35 | ||
| Liver metastasis | 1.754 | 0.185 | ||
| Yes | 18 | 7 | ||
| No | 206 | 43 | ||
| Brain metastasis | 0.029 | 0.865 | ||
| Yes | 25 | 6 | ||
| No | 199 | 44 | ||
| Adrenal metastasis | 0.056 | 0.812 | ||
| Yes | 25 | 5 | ||
| No | 199 | 45 | ||
| Bone metastasis | 1.718 | 0.190 | ||
| Yes | 50 | 7 | ||
| No | 174 | 43 | ||
| Smoking history | 0.866 | 0.352 | ||
| Yes | 175 | 36 | ||
| No | 49 | 14 | ||
| COPD | 1.675 | 0.196 | ||
| Yes | 60 | 9 | ||
| No | 164 | 41 | ||
| Progress | 0.040 | 0.840 | ||
| Yes | 64 | 15 | ||
| No | 160 | 35 |
Figure 4.
ROC curves and DCA curves assessing the efficacy of predictive models for patients’ 6-month PFS and 12-month OS. A, B. ROC curve for the prediction of patient 6-month PFS and 12-month OS. C, D. DCA curves for predictive prognostic modeling of line graph models. Note: PFS, progression-free survival; OS, overall survival; ROC, receiver operating characteristic; DCA, decision curve analysis.
Comparison of the value of individual indicators and risk models in predicting patient PFS and OS
At the end of the study, we conducted an analysis to determine the efficacy of individual indicators and risk models in predicting patients’ 6-month PFS and 12-month OS. The Delong test was employed for this purpose. The findings revealed that there was no significant difference between SII and risk score (P>0.05). However, both SII and risk score demonstrated higher predictive value for patients’ 6-month PFS and 12-month OS compared to CEA (Table 11, P<0.05).
Table 11.
The value of each index to predict PFS and OS in patients verified by Delong test
| The test results on | Time (month) | Status | Z value | P value | AUC difference | 95% CI | |
|---|---|---|---|---|---|---|---|
|
| |||||||
| Lower | Upper | ||||||
| Risk Score - SII | 6 | PFS | 0.976 | 0.329 | 0.032 | -0.033 | 0.097 |
| Risk Score - CEA | 5.113 | <0.001 | 0.186 | 0.115 | 0.258 | ||
| SII - CEA | 3.222 | 0.001 | 0.154 | 0.060 | 0.248 | ||
| Risk Score - SII | 12 | OS | -0.091 | 0.927 | -0.004 | -0.087 | 0.079 |
| Risk Score - CEA | 3.427 | 0.001 | 0.135 | 0.058 | 0.212 | ||
| SII - CEA | 3.068 | 0.002 | 0.139 | 0.050 | 0.227 | ||
Discussion
Although serum inflammatory markers have been extensively studied for their prognostic value in various cancers, their combined application in assessing the prognosis of patients with advanced NSCLC remains limited [19]. In this study, we investigated for the first time the relationships among routine laboratory indicators such as white blood cells, CRP, and lactate dehydrogenase, as well as the impact of short-term chemotherapy on patients with advanced non-small cell lung cancer. According to our research findings, there were significant differences in the expression of inflammatory and immune markers such as SII, MLR, CRP, and tumor markers between patients responsive to chemotherapy and those with disease progression. Further detailed analysis revealed significant differences in clinical staging, pleura, bone, brain, liver, and adrenal distant metastasis rates between the progression group and the control group. This suggests that the inflammatory status, clinical stage, and metastatic state of patients with advanced NSCLC may affect the short-term efficacy of chemotherapy. Therefore, we speculate that inflammatory and tumor markers such as SII, MLR, and CRP, as well as the disease progression status, may be the keys in predicting chemotherapy sensitivity in these patients.
To further understand the factors influencing patients’ OS and PFS, we employed Cox regression analysis to identify the prognostic factors affecting patient OS and PFS. The analysis revealed that CRP, SII, MLR, CEA, NSE, and Adrenal metastasis were independent prognostic factors affecting patient PFS. Meanwhile, SII, CEA, and Brain metastasis were independent prognostic factors affecting patient OS. A study by Hatanaka et al. [20] found that in patients with hepatocellular carcinoma treated with altizumab and bevacizumab, CRP≥1.0 mg/dL was significantly associated with PFS and OS. Additionally, Chen et al. [21] conducted a comprehensive retrospective analysis of 1,383 patients undergoing radical resection of colorectal cancer and found that patients with lower NLR, PLR, and SII levels had longer DFS and OS. In multivariate analysis, SII was identified as an independent prognostic factor for DFS and OS, even outperforming NLR and PLR in prognostic value. Ding et al. [22] also supported this, showing in a retrospective study of 30 patients with advanced cancer treated with immunotherapy that higher pre-treatment SII and PIN levels were associated with worse PFS and OS. These studies highlight the clinical significance of inflammatory markers in predicting tumor OS and PFS, consistent with our research findings.
In this study, we found that SII and CEA are common factors affecting OS and PFS. However, it remains unclear whether both are clinically valuable in predicting OS and PFS. We analyzed the predictive value of SII and CEA for 6-month PFS and 12-month OS in this study. The results revealed that the AUC of SII for predicting 6-month PFS was 0.683 and for 12-month OS was 0.529. In contrast, the AUC of CEA for predicting 6-month PFS was 0.609 and for 12-month OS was 0.529. This suggests that SII and CEA are not ideal for predicting patient survival. Recent studies have found that risk models have high clinical value for predicting and evaluating patient prognosis. For instance, Wang et al. [23] successfully predicted postoperative outcomes in patients with pulmonary metastasis of colorectal cancer by constructing a nomogram model. In this study, we constructed a nomogram based on Cox regression prognostic factors to predict 6-month PFS and 12-month OS in patients. Through validation, it was found that this model has some value in predicting patient survival. However, at the end of the study, we found through the Delong test that the risk score and SII had no difference in predicting 6-month PFS and 12-month OS for patients. This indicates that although our risk score model was constructed based on multiple prognostic factors, it did not offer predictive power beyond the known SII index. In other words, the SII index already covers most of the predictive information in our model, making their predictive capabilities comparable. This also suggests that for patient prognosis prediction, the SII index may be sufficient, eliminating the need for a complex risk score model. This is beneficial for clinical practice as using a simple index can more quickly and conveniently predict patient outcomes. Previous research by He et al. [24] found that SII is a simple yet potent prognostic predictor after radical surgery for stage I-II gastric cancer. Furthermore, a meta-analysis [25] suggested that a high SII might be a promising poor prognostic predictor in patients with gynecological and breast cancers, especially ovarian cancer and triple-negative breast cancer. In line with previous research, SII is a promising high-level prognostic marker with the potential for optimizing individualized treatment plans for non-small cell lung cancer [26-28]. Compared to traditional tumor markers, it provides superior predictive power, but this claim still requires validation in larger sample studies.
However, there are some limitations to this study. First, this is a retrospective analysis based on existing data, which might introduce selection bias. Secondly, although a considerable patient sample was included, it is still relatively limited compared to the entire NSCLC patient population. This might limit our exploration of some rare but significant factors. Additionally, the study mainly utilizes single-center data, potentially affecting the generalizability of the results. Hence, even though this study offers some insights into the prognosis prediction for patients with NSCLC, further extensive, multi-center validation studies are needed before applying the findings to clinical practice. We look forward to subsequent research further investigating the prognostic biomarkers for NSCLC patients and providing a basis for individualized treatment.
In summary, the results of this study indicate that serum inflammatory factor SII can be used as an independent indicator to evaluate 6-month PFS and 12-month OS in patients with advanced NSCLC, and its predictive value is similar to that of the nomogram model.
Acknowledgements
The Shandong Natural Science Foundation (ZR2017PH014).
Disclosure of conflict of interest
None.
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