Objective: In studies conducted on non-small cell lung cancer (NSCLC) patients, many factors such as age, stage, weight loss, lymph node, and pleural involvement have been shown to affect survival. On the other hand, systemic inflammation plays a critical role in proliferation, migration, invasion, and metastasis. Inflammation and nutrition-based prognostic scores are reported to be associated with survival in patients with NSCLC. The aim of our study is to show the effects of these scores on survival and disease progression in NSCLC patients. Subjects and Methods: Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), modified Glasgow prognostic score (mGPS), and prognostic nutritional index (PNI) values in 102 patients with stages 1, 2, and 3A NSCLC were analyzed retrospectively. Results: NLR (p < 0.001), PLR (p = 0.001), PNI (p < 0.001), and mGPS (p = 0.001) variables showed a statistically significant difference according to mortality groups. NLR and PLR values were higher in exitus patients. However, PNI values were higher in surviving patients. NLR (p < 0.001), PLR (p = 0.004), PNI (p = 0.001), and mGPS (p = 0.015) variables showed a statistically significant difference in terms of locoregional recurrence. PNI (p = 0.001) and mGPS (p = 0.001) in terms of distant metastasis development during follow-up and treatment showed a statistically significant difference. Conclusion: NLR, PLR, PNI, and mGPS are easily accessible noninvasive parameters and provide predictive information about survival and disease course. We showed the effect of these parameters on the prognosis.

Highlights of the Study

  • Stages 1, 2, and 3A non-small cell lung cancer patients were reviewed retrospectively.

  • Neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, prognostic nutritional index, and modified Glasgow prognostic score were calculated.

  • We found that these scores were closely related to overall survival, development of distant metastases, and recurrence.

  • Inflammation and nutrition-based scores can be used to predict disease progression and survival.

Lung cancer is one of the leading causes of cancer-related deaths worldwide [1]. Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancers [2]. Surgical resection has been accepted as the gold standard in first-line therapy for patients with early-stage NSCLC. However, most patients are diagnosed at a locally advanced stage, and surgical resection alone cannot cure the disease. Chemotherapy, radiotherapy, targeted therapy, and immunotherapy have been evaluated for patients with advanced NSCLC. In studies conducted on NSCLC patients, many factors such as age, stage, performance status, smoking, weight loss, body mass index (BMI), lymph node involvement, pleural involvement, and vascular invasion have been shown to affect survival [3‒9]. On the other hand, systemic inflammation plays a critical role in proliferation, migration, invasion, and metastasis. In recent years, simply calculated scores based on inflammation and nutrition have been reported as useful and objective prognostic predictors for many different malignancies [10, 11]. Inflammation and nutrition-based prognostic scores (NLR, PLR, PNI, and mGPS) have been reported to be associated with survival in patients with NSCLC [12, 13]. Apart from these, other parameters such as advanced lung cancer inflammation index and systemic immune-inflammation index have also been shown to affect survival in NSCLC patients [14, 15]. PNI is calculated using serum albumin level and absolute lymphocyte count, and mGPS is calculated by serum albumin level and C-reactive protein (CRP) levels [16, 17]. In addition, the effect of inflammation-based scores similar to those in our study, such as the lung immune prognostic index (LIPI) and lung immune-oncology prognostic score (LIPS), which were examined in previous studies in lung cancer patients, has been shown to affect disease prognosis [18, 19]. In this study, we examined the association of scores such as mGPS, PNI, NLR, and PLR with overall survival (OS), local and locoregional recurrences, death, and development of distant metastases during follow-up and treatment in patients with nonmetastatic NSCLC.

A total of 102 NSCLC patients who had the diagnosis between February 2017 and February 2021 were included in our study. All patients included in the study applied to our clinic without any treatment. The data of the patients were analyzed retrospectively. Patients who accepted the recommended treatments and came for regular follow-up visits, who were over the age of 18, and those who did not have a second primary malignancy were randomly included in the study without any bias. Since these parameters were previously investigated more in metastatic cancer patients, we conducted our study with nonmetastatic patients. Patients with more than one malignancy were not included in the study. The study was carried out in accordance with ethical rules. It has been approved by the Institutional Ethics Committee (protocol No. and date: E-48670771-020-213402531, 04/12/23) according to the principles set out in the “Helsinki Declaration.”

The disease stage was determined according to the 8-TNM clinical staging system. Variables such as gender, age, BMI, histological type, stage, operation status (whether to have or not to have surgery), chemotherapy regimens, Eastern Cooperative Oncology Group (ECOG) performance score, distant metastasis development during follow-up and treatment, local and locoregional disease recurrence, NLR, PLR, mGPS, and PNI were examined before starting any surgical or medical treatment. FDG PET/CT was performed in all patients at the time of diagnosis, and SUVmax of the primary mass was recorded. Patients who developed distant metastases or locoregional recurrence were detected with FDG PET/CT. Our aim of the study was to show that NLR-PLR PNI and mGPS scores can predict prognosis and mortality in nonmetastatic NSCLC patients and to mediate the more frequent use of these parameters in the future.

The treatments received by all patients were determined by the National Comprehensive Cancer Network (NCCN) guidelines. Of the 102 patients, 18 received adjuvant chemotherapy, 33 received neoadjuvant chemotherapy, and 9 patients were followed up without chemotherapy after surgery. 42 patients were considered inoperable by thoracic surgeons (due to reasons such as tumor location, size, invasion into nearby tissues, or comorbidity and age). The numbers of patients receiving neoadjuvant and adjuvant chemotherapy and the regimens they received are shown in Table 1. Inoperable patients were treated with definitive concurrent chemoradiotherapy. It was observed that 24 of these patients developed distant metastases during their follow-up. There were 14 patients with positive EGFR mutation analysis. 12 of them received adjuvant erlotinib and 2 received adjuvant gefitinib. There were a total of 10 patients with PDL-1 > %1. These patients received pembrolizumab treatments after adjuvant treatment and nivolumab treatments after metastatic disease development. In our country, nivolumab is licensed and indicated for neoadjuvant treatment in patients diagnosed with squamous cell carcinoma only in stage 3A patients, but it is not paid by government institutions. It is licensed and indicated for adenocarcinoma patients only in stage 3A if EGFR ALK ROS mutations are negative, but it is not paid by government institutions. Since the average financial income of the patients who applied to our clinic was generally not able to cover the treatment costs, we did not have any patients who received neoadjuvant nivolumab, although it is recommended in the NCCN Guidelines. According to the recommendation of the NCCN guideline, 4 of the patients in our study with stages 2A, 2B, and 3A and high-risk factors received atezolizumab after adjuvant chemotherapy.

Table 1.

Chemotherapy times and regimens received by patients

NeoadjuvantAdjuvant
Carboplatin + paclitaxela 20 
Cisplatin + gemcitabineb 
Cisplatin + pemetrexedc 
Cisplatin + vinorelbined 
NeoadjuvantAdjuvant
Carboplatin + paclitaxela 20 
Cisplatin + gemcitabineb 
Cisplatin + pemetrexedc 
Cisplatin + vinorelbined 

aPaclitaxel 80 mg/m2, carboplatin AUC (2), once a week.

bCisplatin 75 mg/m2 1 day, gemcitabine 1,000–1,250 mg/m2 for 1 and 8 days, every 3 weeks.

cCisplatin 75 mg/m2 1 day, pemetrexed 500 mg/m2 for 1 day, every 3 weeks.

dCisplatin 80 mg/m2 1 day, vinorelbine IV 25–30 mg/m2 for 1 and 8 days, every 3 weeks.

Pretreatment PNI was calculated with the formula of 10×serum albumin value (g/dL)+0.005×peripheral lymphocyte counts (per mm3). Limit values for mGPS calculation were accepted as 10 mg/L for CRP and 3.5 g/dL for albumin. If CRP was >10 mg/L or albumin <3.5 g/dL, 1 point was given to the score. Accordingly, mGPS was scored between 0 and 2. PNI, mGPS, NLR, and PLR were calculated before receiving any treatment. In our study, cut-offs for NLR-PLR and PNI were explored by receiver operating characteristic (ROC) curves. When determining cut-off levels in ROC curves, the values where sensitivity and specificity were closest to each other were selected for each score. During ROC curve analysis, Youden’s index was used to choose the optimal cut-off values. For determining cut-off values, the endpoint was mortality. OS was defined as the time from diagnosis to death or last follow-up. Locoregional recurrence is defined as a recurrence of cancer following curative treatment, at the original tumor site (local), and/or the lymph nodes and tissue near the original tumor site (regional) [20]. Distant metastasis sites included contralateral lung, bone, brain, liver, lymph nodes other than regional nodes, adrenal, and pleura.

Statistical Analysis

Statistical analysis was done using “IBM SPSS Statistics for Windows version 25.0 (Statistical Package for the Social Sciences, IBM Corp., Armonk, NY, USA).” Descriptive statistics were presented as n and % for categorical variables and mean ± SD or median (IQR) for continuous variables. When the data of the study were analyzed in terms of normality assumptions, the independent t-test, ANOVA test from parametric tests, Mann-Whitney U test, and Kruskal-Wallis test from nonparametric tests were used in comparisons according to mortality, metastasis, and recurrence groups. The Bonferroni test, one of the post hoc tests, was used for comparisons between the groups. The χ2 test or Fisher’s exact test was used to compare categorical variables. ROC curve analysis of PNI, NLR, and PLR scores for predicting mortality was given. Finally, the Kaplan-Meier method was used to compare survival times among various clinical factors. Univariate Cox regression results were calculated for the risk of death from various clinical factors. p < 0.05 was considered statistically significant.

A total of 102 patients, 78 of them being men (%76.5), were included in the study. The mean age of the patients was 60.7 ± 10 and the mean BMI was 25.5 ± 4. BMI levels of all patients were calculated before starting any treatment. The distribution of sociodemographic and clinical information of the patients is shown in Table 2. Locoregional recurrence was seen in 26 patients; 15 of them had isolated local recurrence. All patients who died were primary malignancy-related deaths, and those who died due to nonmalignant causes were not included in the study. We collected our data in this manner as mortality status and overall survival time could be negatively affected. We especially wanted to investigate cancer-related mortality. The mean follow-up period was calculated as 43.3 months.

Table 2.

Distribution of sociodemographic and clinical information of patients

N%
Gender 
 Male 78 76.5 
 Female 24 23.5 
Stage 
 Stage 1 18 17.6 
 Stage 2 29 28.4 
 Stage 3A 55 53.9 
Type 
 Adenocarcinoma 51 50.0 
 Squamous cell carcinoma 38 37.3 
 Other 13 12.7 
Metastasis 
 No 71 69.6 
 Yes 31 30.4 
Locoregional recurrence 
 No 76 74.5 
 Yes 26 26.5 
Mortality 
 Alive 75 73.5 
 Dead 27 26.5 
 Mean±SD Median (IQR) 
Age 60.77±10.92 62.00 (15.25) 
BMI 25.50±4.20 25.46 (4.94) 
N%
Gender 
 Male 78 76.5 
 Female 24 23.5 
Stage 
 Stage 1 18 17.6 
 Stage 2 29 28.4 
 Stage 3A 55 53.9 
Type 
 Adenocarcinoma 51 50.0 
 Squamous cell carcinoma 38 37.3 
 Other 13 12.7 
Metastasis 
 No 71 69.6 
 Yes 31 30.4 
Locoregional recurrence 
 No 76 74.5 
 Yes 26 26.5 
Mortality 
 Alive 75 73.5 
 Dead 27 26.5 
 Mean±SD Median (IQR) 
Age 60.77±10.92 62.00 (15.25) 
BMI 25.50±4.20 25.46 (4.94) 

NLR (p < 0.001), PLR (p = 0.001), PNI (p < 0.001), and mGPS (p = 0.001) variables showed a statistically significant difference according to mortality groups (Table 3). NLR and PLR values were higher in exitus patients. However, PNI values were higher in surviving patients. BMI, age, gender, and pathological type did not affect mortality statistically. PNI (p = 0.001), mGPS (p = 0.001), operation status (p < 0.001), and stage (p < 0.001) in terms of distant metastasis development during follow-up and treatment showed a statistically significant difference (Table 4). In addition, PNI values were higher in those who did not develop metastases. Sixteen of the patients who developed distant metastases also had locoregional recurrence, but we did not consider these patients as a different subset and did not perform separate statistics.

Table 3.

Comparison of various clinical variables by mortality

Mortalityp value
alive, n = 75dead, n = 27
Age, mean±SD 61.84±10.58 57.81±11.50 0.101a 
BMI, mean±SD 25.37±4.02 25.85±4.71 0.609a 
NLR, median (IQR) 4.06 (1.13) 4.68 (1.13) <0.001b 
PLR, median (IQR) 198.57 (74.28) 258.75 (69.60) 0.001b 
PNI, mean±SD 46.43±4.55 43.09±2.49 <0.001a 
SUVmax, median (IQR) 12.20 (7.00) 12.90 (8.00) 0.841b 
Gender, n (%) 
 Male 56 (74.7) 22 (81.5) 0.474c 
 Female 19 (25.3) 5 (18.5) 
mGPS, n (%) 
 0 39 (52) 5 (18.5) 0.001c 
 1 31 (41.3) 14 (51.9) 
 2 5 (6.7) 8 (29.6) 
Type, n (%) 
 Adenocarcinoma 34 (45.3) 17 (63) 0.059c 
 Squamous cell carcinoma 33 (44) 5 (18.5) 
 Other 8 (10.7) 5 (18.5) 
Mortalityp value
alive, n = 75dead, n = 27
Age, mean±SD 61.84±10.58 57.81±11.50 0.101a 
BMI, mean±SD 25.37±4.02 25.85±4.71 0.609a 
NLR, median (IQR) 4.06 (1.13) 4.68 (1.13) <0.001b 
PLR, median (IQR) 198.57 (74.28) 258.75 (69.60) 0.001b 
PNI, mean±SD 46.43±4.55 43.09±2.49 <0.001a 
SUVmax, median (IQR) 12.20 (7.00) 12.90 (8.00) 0.841b 
Gender, n (%) 
 Male 56 (74.7) 22 (81.5) 0.474c 
 Female 19 (25.3) 5 (18.5) 
mGPS, n (%) 
 0 39 (52) 5 (18.5) 0.001c 
 1 31 (41.3) 14 (51.9) 
 2 5 (6.7) 8 (29.6) 
Type, n (%) 
 Adenocarcinoma 34 (45.3) 17 (63) 0.059c 
 Squamous cell carcinoma 33 (44) 5 (18.5) 
 Other 8 (10.7) 5 (18.5) 

aIndependent t test.

bMann-Whitney U test.

cχ2 test.

p < 0.05 statistically significant.

Table 4.

Comparison of various clinical variables according to distant metastasis development during follow-up and treatment

Distant metastasis developmentp value
no, n = 71yes, n = 31
Age, mean±SD 60.84±9.99 60.61±12.99 0.922a 
BMI, mean±SD 25.70±4.26 25.04±4.08 0.471a 
NLR, median (IQR) 4.18 (1.29) 4.40 (1.67) 0.122b 
PLR, median (IQR) 200.00 (88.14) 233.00 (90.56) 0.302b 
PNI, mean±SD 46.45±3.95 43.46±4.58 0.001a 
SUVmax, median (IQR) 12.00 (8.00) 14.80 (8.30) 0.020b 
Gender, n (%) 
 Male 54 (76.1) 24 (77.4) 0.881c 
 Female 17 (23.9) 7 (22.6) 
mGPS, n (%) 
 0 39 (54.9) 5 (16.1) 0.001c 
 1 26 (36.6) 19 (61.3) 
 2 6 (8.5) 7 (22.6) 
Operation, n (%) 
 No 18 (25.4) 24 (77.4) <0.001c 
 Yes 53 (74.6) 7 (22.6) 
Stage, n (%) 
 Stage 1 18 (25.4) 0 (0) <0.001c 
 Stage 2 25 (35.2) 4 (12.9) 
 Stage 3A 28 (39.4) 27 (87.1) 
Type, n (%) 
 Adenocarcinoma 34 (47.9) 17 (54.8) 0.750c 
 Squamous cell carcinoma 27 (38) 11 (35.5) 
 Other 10 (14.1) 3 (9.7) 
Distant metastasis developmentp value
no, n = 71yes, n = 31
Age, mean±SD 60.84±9.99 60.61±12.99 0.922a 
BMI, mean±SD 25.70±4.26 25.04±4.08 0.471a 
NLR, median (IQR) 4.18 (1.29) 4.40 (1.67) 0.122b 
PLR, median (IQR) 200.00 (88.14) 233.00 (90.56) 0.302b 
PNI, mean±SD 46.45±3.95 43.46±4.58 0.001a 
SUVmax, median (IQR) 12.00 (8.00) 14.80 (8.30) 0.020b 
Gender, n (%) 
 Male 54 (76.1) 24 (77.4) 0.881c 
 Female 17 (23.9) 7 (22.6) 
mGPS, n (%) 
 0 39 (54.9) 5 (16.1) 0.001c 
 1 26 (36.6) 19 (61.3) 
 2 6 (8.5) 7 (22.6) 
Operation, n (%) 
 No 18 (25.4) 24 (77.4) <0.001c 
 Yes 53 (74.6) 7 (22.6) 
Stage, n (%) 
 Stage 1 18 (25.4) 0 (0) <0.001c 
 Stage 2 25 (35.2) 4 (12.9) 
 Stage 3A 28 (39.4) 27 (87.1) 
Type, n (%) 
 Adenocarcinoma 34 (47.9) 17 (54.8) 0.750c 
 Squamous cell carcinoma 27 (38) 11 (35.5) 
 Other 10 (14.1) 3 (9.7) 

aIndependent t test.

bMann-Whitney U test.

cχ2 test.

p < 0.05 statistically significant.

NLR (p < 0.001), PLR (p = 0.004), PNI (p = 0.001), mGPS (p = 0.015), and stage (p < 0.001) variables showed a statistically significant difference in terms of locoregional recurrence (Table 5). We evaluated the relationship between SUVmax, BMI, NLR, PLR, and PNI values. A statistically significant negative correlation was found between SUVmax values in the primary mass at the time of diagnosis and BMI (r = −0.206, p = 0.038) and PNI (r = −0.210, p = 0.034) values. Patients with an mGPS score of 0-1-2 were analyzed in 3 groups. SUVmax levels of the primary mass at the time of diagnosis in PET/CT showed a statistically significant difference in mGPS groups. SUVmax levels were higher in the group with mGPS = 2 (n = 13). A significant difference in SUVmax levels was between the group with mGPS = 0 (n = 44) and the group with mGPS = 2 (p = 0.029).

Table 5.

Comparison of various clinical variables by locoregional recurrence groups

Locoregional recurrencep value
no, n = 76yes, n = 26
Age, mean±SD 61.82±11.36 57.69±9.04 0.922a 
BMI, mean±SD 24.86±3.49 27.34±5.46 0.471a 
NLR, median (IQR) 4.06 (0.97) 4.95 (1.19) <0.001b 
PLR, median (IQR) 196.16 (79.21) 247.00 (82.49) 0.004b 
PNI, mean±SD 46.05±4.66 44.05±2.89 0.001a 
Gender, n (%) 
 Male 60 (78.9) 18 (69.2) 0.313c 
 Female 16 (21.1) 8 (30.8) 
mGPS, n (%) 
 0 39 (51.3) 5 (19.2) 0.015c 
 1 28 (36.8) 17 (65.4) 
 2 9 (11.8) 4 (15.4) 
Stage, n (%) 
 Stage 1 7 (9.2) 11 (42.3) <0.001c 
 Stage 2 18 (23.7) 11 (42.3) 
 Stage 3A 51 (67.1) 4 (15.4) 
Type, n (%) 
 Adenocarcinoma 33 (43.4) 18 (69.2) 0.076c 
 Squamous cell carcinoma 32 (42.1) 6 (23.1) 
 Other 11 (14.5) 2 (7.7) 
Locoregional recurrencep value
no, n = 76yes, n = 26
Age, mean±SD 61.82±11.36 57.69±9.04 0.922a 
BMI, mean±SD 24.86±3.49 27.34±5.46 0.471a 
NLR, median (IQR) 4.06 (0.97) 4.95 (1.19) <0.001b 
PLR, median (IQR) 196.16 (79.21) 247.00 (82.49) 0.004b 
PNI, mean±SD 46.05±4.66 44.05±2.89 0.001a 
Gender, n (%) 
 Male 60 (78.9) 18 (69.2) 0.313c 
 Female 16 (21.1) 8 (30.8) 
mGPS, n (%) 
 0 39 (51.3) 5 (19.2) 0.015c 
 1 28 (36.8) 17 (65.4) 
 2 9 (11.8) 4 (15.4) 
Stage, n (%) 
 Stage 1 7 (9.2) 11 (42.3) <0.001c 
 Stage 2 18 (23.7) 11 (42.3) 
 Stage 3A 51 (67.1) 4 (15.4) 
Type, n (%) 
 Adenocarcinoma 33 (43.4) 18 (69.2) 0.076c 
 Squamous cell carcinoma 32 (42.1) 6 (23.1) 
 Other 11 (14.5) 2 (7.7) 

aIndependent t test.

bMann-Whitney U test.

cχ2 test.

p < 0.05 statistically significant.

NLR, PLR, and PNI score parameters were found to be statistically significant in order to differentiate the presence of mortality (p < 0.001, p = 0.001, p < 0.025, respectively) (Table 6). In the ROC analysis designed for mortality discrimination of NLR values, the AUC was 0.730 (95% [CI], 0.633–0.826). The sensitivity of NLR values at a cut-off value of ≥4.35 in mortality discrimination was 66.7%, and the specificity was 65.3%. In the ROC analysis designed for mortality discrimination of PLR values, the AUC was 0.715 (95% [CI], 0.608–0.821). The sensitivity of PLR values ​​at a cut-off value of ≥226.20 in mortality discrimination is 66.7%, and the specificity is 66.7%. In the ROC analysis designed for mortality discrimination of PNI values, the AUC was 0.764 (95% [CI], 0.671–0.856). The sensitivity of PNI values ​​at a cut-off value of ≤44.25 in the diagnosis of mortality was 74.1%, and the specificity was 73.3%. These analyses show that NLR, PLR, and PNI levels are important risk indicators for mortality with a certain cut-off level. In a previous large-scale study conducted with 784 advanced NSCLC patients, the cut-off value for NLR was determined as 4 [19]. There may be some reasons for the higher cut-off value in our study, even though the patients were at an early stage. These can be listed as late diagnosis in our country, long time between diagnosis and initiation of treatment, increasing malnutrition due to socioeconomic reasons, relatively high prevalence of smoking, and comorbidities. Especially, the number of patients included in the study may have affected the cut-off values. In addition, different cut-off values were accepted in different previous studies. For example, in one study, the cut-off for NLR was determined as 5 and the cut-off for PLR was determined as 300 [9].

Table 6.

Analysis of predictive values of prognostic nutritional index and inflammatory scores in differentiating mortality

AUC95% CICut-offSensitivity, %Specificity, %p value
NLR 0.730 0.633–0.826 ≥4.35 66.7 65.3 <0.001 
PLR 0.715 0.608–0.821 ≥226.20 66.7 66.7 0.001 
PNI 0.764 0.671–0.856 ≤44.25 74.1 73.3 <0.001 
AUC95% CICut-offSensitivity, %Specificity, %p value
NLR 0.730 0.633–0.826 ≥4.35 66.7 65.3 <0.001 
PLR 0.715 0.608–0.821 ≥226.20 66.7 66.7 0.001 
PNI 0.764 0.671–0.856 ≤44.25 74.1 73.3 <0.001 

AUC, area under the curve; 95% CI, confidence interval.

Median OS times (months) were not statistically significant according to gender groups (p = 0.205). Moreover, median OS times by age groups were not statistically significant when viewed as ≤65-year-old group and >65-year-old group (p = 0.945). When BMI was considered as underweight, normal, overweight, and obese groups, the median OS times were not statistically significant compared to the BMI groups (p = 0.793).

In the Kaplan-Meier analysis, OS variation was given according to NLR, PNI, mGPS, and PLR levels. Median OS times were statistically significant according to mGPS groups (p < 0.001) (Fig. 1). In the group with mGPS = 2, the median OS duration was 29.00 months (95% CI: 18.60–39.39). In the group with mGPS = 0, 2-year survival was 100.0%; in the group with mGPS = 1, 2-year survival was 78.3%; but in the group with mGPS = 2, 2-year survival was 61.5%. Eight of thirteen patients with mGPS = 2 died at the 2-year follow-up. Median OS times were statistically significant according to PNI groups (p < 0.001) (Fig. 2). In the group with PNI ≤44.25, the median OS was 33.00 months (95% CI: 25, 20–40.79). In the group with >44.25, 2-year survival was 95.7%. In the group with ≤44.25, 2-year survival was 71.2%. Considering the data according to NLR groups, median OS times were found to be statistically significant (p = 0.003) (Fig. 3). The 2-year survival was 96.9% in the <4.35 group, while the 2-year survival was 72.4% in the ≥4.35 group. In the group with NLR ≥4.35, the median OS was 42.00 months (95% CI: 29, 86–54.13). When analyzed according to PLR groups, median OS times were found to be statistically significant (p < 0.001) (Fig. 4). In patients with PLR ≥226.20, the median OS was 42.00 months (95% CI: 35, 35–48.64). In the group <226.20, the 2-year survival was 95.2%. In the ≥226.20 group, the 2-year survival was 68.4%. Median OS times were not statistically significant according to the pathological tumor type groups (p = 0.129). Median OS could not be calculated since more than half of the patients were alive in the mGPS = 0 and mGPS = 1 group, the PNI >44.25 group, the NLR <4.35 group, and the PLR <226.20 group.

Fig. 1.

Overall change in survival according to the mGPS level in Kaplan-Meier analysis.

Fig. 1.

Overall change in survival according to the mGPS level in Kaplan-Meier analysis.

Close modal
Fig. 2.

Overall change in survival according to the PNI level in Kaplan-Meier analysis.

Fig. 2.

Overall change in survival according to the PNI level in Kaplan-Meier analysis.

Close modal
Fig. 3.

Overall change in survival according to the NLR level in Kaplan-Meier analysis.

Fig. 3.

Overall change in survival according to the NLR level in Kaplan-Meier analysis.

Close modal
Fig. 4.

Overall change in survival according to the PLR level in Kaplan-Meier analysis.

Fig. 4.

Overall change in survival according to the PLR level in Kaplan-Meier analysis.

Close modal

As shown in Table 7, the risk of death according to univariate analysis results was 3.23 times for NLR ≥4.35 level (95% CI: 1.43–7.30, p = 0.005), 4.53 times for PLR ≥226.20 level (95% CI: 1.95–10.52, p < 0.001), 5.32 times for PNI ≤44.25 (95% CI: 2.24–12.64, p < 0.001), 3.67 times for mGPS = 1 (95% CI: 1.31–10.28, p = 0.013), increased 9.41 times for mGPS = 2 (HR: 95% CI: 3.05–29.05, p < 0.001).

Table 7.

Univariate Cox regression results for clinical variables

OS variablesUnivariate Cox regression
hazard ratio (95% CI)p value
NLR (Ref:<4.35) 3.23 (1.43–7.30) 0.005 
PLR (Ref:<226.20) 4.53 (1.95–10.52) <0.001 
PNI (Ref:>44.25) 5.32 (2.24–12.64) <0.001 
mGPS (Ref:0)  <0.001 
 1 3.67 (1.31–10.28) 0.013 
 2 9.41 (3.05–29.05) <0.001 
OS variablesUnivariate Cox regression
hazard ratio (95% CI)p value
NLR (Ref:<4.35) 3.23 (1.43–7.30) 0.005 
PLR (Ref:<226.20) 4.53 (1.95–10.52) <0.001 
PNI (Ref:>44.25) 5.32 (2.24–12.64) <0.001 
mGPS (Ref:0)  <0.001 
 1 3.67 (1.31–10.28) 0.013 
 2 9.41 (3.05–29.05) <0.001 

In our study, mGPS, PNI, NLR, and PLR significantly affected survival. Previous studies have shown that mGPS has a significant relationship with survival in NSCLC patients. Some of these studies were conducted in patients who underwent surgery in the early stage [21], and some in advanced patients [22]. In many studies, mGPS was generally examined before treatment, but there are also studies investigating the efficacy of treatment with mGPS after treatment [23, 24]. It has been shown that mGPS has a positive effect on survival for patients who receive radiation therapy [25]. In addition, as in our study, the relationship of mGPS with recurrence in NSCLC patients has also been examined before [26]. Likewise, the relationship between PNI and survival, disease progression, and recurrence has also been investigated previously and similar results were obtained with our study. While many studies have been conducted with PNI in patients with gastrointestinal, pancreatic, and genitourinary malignancies, studies in patients with NSCLC are relatively few [27‒29]. The result found in a meta-analysis showed that low PNI correlates with unfavorable OS in lung cancer [30]. There is also another study showing that preoperative PNI level is an independent risk factor for disease recurrence in 141 stage 1 NSCLC patients [31]. In one study, the rate of PLR was an independent and important prognostic factor for survival over 2 years in patients operated on for NSCLC [32]. Similarly, in our study, we showed that the rate of PLR was significantly associated with 2-year survival. Studies showing the relationship between T stage and lymph node involvement and NLR-PLR ratios have also been conducted before, but in our study, we showed that PNI, SUVmax, and stage are significantly related to the development of distant metastasis during follow-up and treatment [33, 34]. In one study, survival analyses showed that high NLR (HR = 3.53, p = 0.0375) and high mGPS (HR = 23.2, p = 0.0038) after atezolizumab were independent prognostic factors for OS [35]. Another study showed that PLR and PNI were significantly associated with 5-year OS (p < 0.05) [36]. Similarly, in our study, high NLR (HR: 3.23, p = 0.005) and high mGPS (HR: 3.67, p = 0.013 for mGPS = 1, HR: 9.41, p <0.001 for mGPS = 2) were found to increase mortality. In another retrospective study with a similar number to our study, NLR, PLR, and PNI scores were each shown to be significantly associated with postoperative OS in NSCLC patients who underwent surgery and received adjuvant platinum-based therapy [37]. Previous studies have shown that LIPI and LIPS scores, which are linked to NLR, determine the prognosis in lung cancer patients, but in our study, we looked at mGPS and PNI levels because we wanted to include the albumin level of the patients as a nutritional parameter [38, 39] because the number of patients with malnutrition in our clinic is relatively high.

The relationship between SUVmax value and mGPS and PNI levels in our study has not been examined before in lung cancer patients in the literature. In a study, mGPS PNI and SUVmax were considered significant among the risk factors for early recurrence in resectable pancreatic cancer patients [40]. We see that scores such as mGPS and PNI deteriorate negatively in patients with larger and more aggressive tumors, and recurrence, metastasis, and mortality are higher in these patients. We could suggest that the higher the tumor growth, the higher the consequent immunological compromise. However, more studies are needed on this subject in the coming years.

The main limitations of our study are the sample size and the 5-year follow-up period of all patients has not yet expired. For this reason, 2-year survival calculations were made to give more realistic results. In addition, this can be considered a partial limitation, as all of the mGPS PNI NLR and PLR parameters are laboratory-dependent results. Since the follow-up period in our study was relatively short, it can be thought that the locoregional recurrence results may be more meaningful than the mortality results.

The incidence of lung cancer continues to increase. Many lung cancer patients are diagnosed at an advanced stage. It is the leading cause of cancer-related deaths in men. In women, it is the second most common cause of cancer-related death after breast cancer. Serum albumin is an objective biomarker for assessing long-standing nutritional status and decreases during the systematic inflammatory response. Malnutrition and chronic systemic inflammatory response suppress serum albumin synthesis. Lymphocytes inhibit the proliferation, invasion, and migration of tumor cells. Therefore, low lymphocyte count is an indicator of poor prognosis in malignancies. PNI, mGPS, NLR, and PLR provide information about the inflammatory and nutritional statuses of the patient and indirectly about the disease prognosis. In conclusion, inflammation and nutrition-based scores (NLR, PLR, PNI, and mGPS) are easily accessible, noninvasive, and cost-effective parameters and provide predictive information about survival and disease prognosis. In our study, we showed the effect of these parameters on the course of the disease.

The study was carried out under ethical rules. The institutional ethics committee has approved it according to the principles set out in the “Helsinki Declaration.”

The authors have no conflicts of interest to declare.

This study was not supported by any sponsor or funder.

Mert Erciyestepe designed the study outline. All authors were responsible for data acquisition. Mert Erciyestepe and Oğuzhan Selvi analyzed the data and wrote the original draft of the manuscript. Sezai Vatansever revised the manuscript after peer review. All authors read and approved the final manuscript.

The data underlying this article will be shared on reasonable request to the corresponding author.

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