The relationship between blood biomarkers and prognosis of pathologic stage IA pure-solid non-small cell lung cancer patients

Introduction

Lung cancer is the most common malignant tumor worldwide, of which 85% patients’ pathological type is non-small cell lung cancer (NSCLC) (1). Currently, surgery is the most important treatment for NSCLC, while the 5-year survival rate of patients undergoing radical resection of lung cancer is between 36% and 82% (2). So far, the prognosis of NSCLC is mainly based on the tumor-node-metastasis (TNM) staging system. Research has shown that more than 10% of pathologic stage IA (pIA) NSCLC patients recur after receiving radical resection (3), which indicates that patients with the same TNM stage may have different prognoses.

Tumor markers are specific substances existing in malignant tumor cells or being produced abnormally by malignant tumor cells, which may reflect the occurrence and development of tumors. Nowadays, as a simple and easily accessible test method, a variety of tumor markers have been widely used in the clinical auxiliary diagnosis of cancer (4). NSCLC-related tumor markers mainly include serum squamous cell carcinoma antigen (SCCA), serum carcinoembryonic antigen (CEA), and serum cytokeratin 19 fragments (CYFRA21-1) (5). However, it still remains unclear about the influence of tumor markers on the prognosis of early NSCLC patients undergoing radical surgery.

Inflammatory cells are one of the important parts of the tumor microenvironment. Inflammatory response plays a crucial role in the progression of cancer, and chronic inflammation has been proven to increase the risk of metastasis of numerous malignant tumors (6,7). Inflammatory markers such as neutrophil to lymphocyte ratio (NLR) and monocyte to lymphocyte ratio (MLR) have been reported as prognostic factors for solid malignant tumors (8). Moreover, several studies have proven that MLR and NLR are associated with a poor prognosis of NSCLC (9,10), and that systemic inflammatory response index (SIRI), as an inflammatory marker, is also related to the prognosis of malignant tumors (11).

Inflammatory markers and tumor markers can be obtained by blood test, which have the advantages of easy accessibility, less trauma, and low cost. Therefore, in this study, we aimed to find effective biomarkers that could predict the prognosis of pIA NSCLC by analyzing preoperative tumor markers CEA, SCCA, and CYFRA21-1 and preoperative inflammatory markers NLR, MLR, and SIRI, to guide more accurate stratification and individualized treatment for pIA pure-solid NSCLC patients. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2098/rc).

Methods

Patients

This retrospective study reviewed data from NSCLC patients who underwent radical surgery in our center from January 2014 to December 2016. To compare the impact of blood markers and metastatic lymph nodes on the prognosis of NSCLC patients with the same tumor diameter, we not only included stage pIA NSCLC patients but also screened NSCLC patients with pathological stage T1N1–2M0 (pT1N1–2M0). The inclusion criteria were as follows: (I) pathologic stage pIA and pT1N1–2M0 NSCLC confirmed by histopathology; (II) preoperative chest computed tomography (CT) showed pure-solid nodules of lung lesions; (III) blood tests taken one week before surgery; (IV) surgical method was lobectomy with systematic lymph node dissection; (V) complete clinical, laboratory, imaging, and follow-up data. Patients were excluded with the following conditions: (I) preoperative chemotherapy and radiotherapy; (II) history of malignant tumor; (III) hematopoietic system or autoimmune disease; (IV) taking glucocorticoid or drugs that stimulate bone marrow hematopoietic system within one week before surgery. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of Harbin Medical University Cancer Hospital (No. KY2021-33) and informed consent was obtained from all individual participants.

Clinical data collection and follow-up

Clinical data included patients’ age, gender, preoperative blood cell count, and tumor marker test results, smoking history, tumor location, histopathology, and TNM classification. These data were collected from retrospective electronic medical records. Tumor staging was based on the 8^th edition of TNM classification. The survival information was assembled by medical records of postoperative follow-up or telephone interviews. After surgical resection, routine check-ups were performed at 3-month intervals for the first 2 years, 6-month intervals for 2–5 years, and 1-year intervals after 5 years. Follow-up was performed until the death of patients or the last time point, March 31^st, 2022, in the follow-up period. In addition, tumor markers and imaging examinations (including computed tomography, magnetic resonance imaging, and ultrasound) were used to evaluate patients as clinically required.

Overall survival (OS) was defined as the time between surgery and death of any cause or the last follow-up. Disease-free survival (DFS) was defined as the time between surgery and recurrence or death due to any cause. NLR = absolute neutrophil count/absolute lymphocyte count; MLR = absolute monocyte count/absolute lymphocyte count; SIRI = absolute neutrophil count × absolute monocyte count/absolute lymphocyte count.

Statistical analysis

The optimal cut-off values for pIA pure-solid NSCLC patients’ biomarkers were determined using receiver operating characteristic (ROC) curve analysis with recurrence as the outcome variable. To address potential multicollinearity issues, we will calculate variance inflation factors (VIFs) for factors such as NLR, MLR, SIRI, etc. Univariate and multivariate analyses were performed by using the Cox proportional hazards model to identify pIA pure-solid NSCLC patients’ independent predictors. The chi-squared test and Fisher’s exact test were used to analyze the clinicopathological characteristics of pIA pure-solid NSCLC patients’ independent predictors. Kaplan-Meier method was used to plot the survival curve of pIA pure-solid NSCLC patients’ independent predictors, and a log-rank test was used for comparison. Kaplan-Meier method was used to plot pT1N1–2M0 NSCLC patients’ survival curve and to compare with pIA pure-solid NSCLC patients. SPSS statistics package (SPSS statistics 26.0) and GraphPad Prism software were used for all statistical analysis, and P<0.05 was considered statistically significant.

Results

A total of 255 NSCLC patients were enrolled in this study, with 205 stage pIA and 50 stage pT1N1–2M0 NSCLC patients.

Results of pIA pure-solid NSCLC patients

Patient characteristics

Among 205 pIA pure-solid NSCLC patients, the mean age was 57.9±7.8 years (range, 32–75 years), and the mean tumor diameter was 20.3±6.5 mm (range, 3–30 mm). The clinicopathological characteristics and biomarker levels of patients are shown in Table 1.

Cut-off value of biomarkers

We performed ROC curve analysis to evaluate the predictive capability of these biomarkers for recurrence in pIA pure-solid NSCLC patients after surgery (Table 2). The optimal cut-off values of CYFRA21-1, CEA, SCCA, SIRI, NLR, and MLR before surgery were 2.251 ng/mL, 3.78 ng/mL, 0.65 ng/mL, 0.981, 1.492, and 0.164, respectively, based on the maximum principle of the Youden index. Therefore, we defined SIRI >0.981, NLR >1.492, MLR >0.164, CEA >3.78 ng/mL, SCCA >0.65 ng/mL, and CYFRA21-1 >2.251 ng/mL as high-value groups, and SIRI ≤0.981, NLR ≤1.492, MLR ≤0.164, CEA ≤3.78 ng/mL, SCCA ≤0.65 ng/mL, and CYFRA21-1 ≤2.251 ng/mL as low-value groups.

Prognostic analysis

The median follow-up time was 71 months (range, 16–98 months). The 5-year OS and 5-year DFS rates were 91.2% and 83.9%, respectively. During the follow-up period, 37 patients (18%) had tumor recurrence, the median disease-free time was 70 months (range, 3–94 months). Besides, 26 patients (12.7%) died in follow-up, and the median survival time was 71 months (range, 16–83 months).

The VIF values for NLR, MLR, and SIRI, which were 1.484, 1.303, and 1.422, respectively, confirm that multicollinearity is not a significant problem. According to the results of univariate analysis, we found that the factors significantly related to DFS were CEA (P=0.005), SIRI (P=0.01), NLR (P=0.005), and MLR (P=0.02) (Table 3). Factors with P<0.1 in univariate analysis were included in multivariate analysis, and the results showed that CEA [P=0.007, hazard ratio (HR) =2.506, 95% confidence interval (CI): 1.281–4.901] and NLR (P=0.005, HR =3.869, 95% CI: 1.499–9.986) were independent predictors of DFS (Table 3). There was a significant difference between OS and the factors CEA (P=0.003), SCCA (P=0.002), SIRI (P=0.005), and NLR (P=0.04), respectively (Table 3). Factors with P<0.1 in univariate analysis were included in multivariate analysis, with the results showing that CEA (P=0.002, HR =3.691, 95% CI: 1.646–8.280), SCCA (P=0.004, HR =4.323, 95% CI: 1.606–11.640) and SIRI (P=0.03, HR =2.501, 95% CI: 1.110–5.636) were independent predictors of OS (Table 3).

Kaplan-Meier survival curves were plotted to illustrate DFS and OS differences between the high and the low value groups of preoperative biomarkers. Patients in low-value groups of NLR (P=0.001) and CEA (P=0.004) had significantly better DFS than those in high-value groups (Figure 1A, A1,A2). Similarly, the low-value groups of SIRI (P=0.003), CEA (P=0.002), and SCCA (P<0.001) all showed a better OS (Figure 1A, A3-A5). The subgroup analysis was conducted according to the pathological types. Due to the smaller number of ‘others’ of pathological types, only the patients with adenocarcinoma (n=151) and squamous cell carcinoma (n=49) were analyzed. In adenocarcinoma patients, the low-value group of CEA showed a better DFS with a statistical significance (P=0.003), but there was no significant difference in DFS between NLR’s low-value group and high-value group (P=0.07) (Figure 1B, B1,B2). The low-value groups of SIRI (P=0.005), CEA (P<0.001), and SCCA (P=0.03) in adenocarcinoma patients all had a better OS (Figures 1B, B3-B5). In patients with squamous cell carcinoma, only the low-value group of NLR (P=0.004) showed a better DFS than NLR’s high-value group (Figure 1C, C1,C2), and only the low-value group of SCCA (P=0.04) showed a better OS than SCCA’s high-value group (Figure 1C, C3-C5).

Figure 1 Kaplan-Meier survival curves for pIA pure-solid NSCLC and subgroup patients’ DFS and OS. (A) pIA pure-solid NSCLC patients: (A1) NLR >1.492 group had a lower DFS (P=0.001); (A2) CEA >3.78 ng/mL group had a lower DFS (P=0.004); (A3) SIRI >0.981 group had a lower OS (P=0.003); (A4) CEA >3.78 ng/mL group had a lower OS (P=0.002); (A5) SCCA >0.65 ng/mL group had a lower OS (P<0.001). (B) pIA adenocarcinoma patients: (B1) no significant difference in DFS between NLR >1.492 group and NLR ≤1.492 group (P=0.07); (B2) CEA >3.78 ng/mL group had a lower DFS (P=0.003); (B3) SIRI >0.981 group had a lower OS (P=0.005); (B4) CEA >3.78 ng/mL group had a lower OS (P<0.001); (B5) SCCA >0.65 ng/mL group had a lower OS (P=0.03). (C) pIA squamous cell carcinoma patients: (C1) NLR >1.492 group had a lower DFS (P=0.004); (C2) no significant difference in DFS between CEA >3.78 ng/mL group and CEA ≤3.78 ng/mL group (P=0.36); (C3) no significant difference in OS between SIRI >0.981 group and SIRI ≤0.981 group (P=0.37); (C4) no significant difference in OS between CEA >3.78 ng/mL group and CEA ≤3.78 ng/mL group (P=0.80); (C5) SCCA >0.65 ng/mL group had a lower OS (P=0.04). pIA, pathologic stage IA; NSCLC, non-small cell lung cancer; DFS, disease-free survival; OS, overall survival; NLR, neutrophil to lymphocyte ratio; CEA, carcinoembryonic antigen; SIRI, systemic inflammatory response index; SCCA, squamous cell carcinoma antigen.

Relationship between biomarkers and clinicopathological characteristics

The relationship between biomarkers (independent predictors of DFS and OS) and clinicopathological characteristics was evaluated. The results showed that SIRI was correlated with gender, smoking history, and pathological type (Table 4). Moreover, SCCA had a significant association with gender, smoking history, intralobular location, and pathological types (Table 4). However, there was only a significant difference between NLR and gender (P=0.01), and so was CEA and smoking history (P=0.004) (Table 4).

Kaplan-Meier survival curve analysis of biomarkers and metastatic lymph nodes on the prognosis of pT1 NSCLC

Fifty pT1N1–2M0 NSCLC patients with a mean age of 57.0±7.8 years (range, 40–71 years), and a mean tumor diameter of 22.5±5.0 mm (range, 10–30 mm) were analyzed. The median follow-up time was 62 months (range, 17–98 months). During the follow-up period, 31 patients (62%) had tumor recurrence, the median disease-free time was 22 months (range, 3–62 months). Besides, 19 patients (38%) died in follow-up, and the median survival time was 41 months (range, 17–68 months).

According to Kaplan-Meier survival curve, DFS and OS were the lowest in the pN2 group and the highest in the low-value groups. Patients’ DFS in the high-value groups of CEA and NLR were between the low-value groups and the pN1 group (Figure 2A,2B). Patients’ OS of CEA, SCCA, and SIRI in the high-value groups were between the low-value groups and the pN1 group (Figure 2C-2E). High-value groups of CEA and SIRI had similar 3- and 5-year OS with the pN1 group, while the high-value group of SCCA had a similar 3-year OS with the pN1 group (Table 5).

Figure 2 Kaplan-Meier survival curves for pIA and pT1N1–2M0 stage NSCLC patients’ DFS and OS. (A) DFS in NLR ≤1.492, NLR >1.492, pN1 and pN2 groups; (B) DFS in CEA ≤3.78 ng/mL, CEA >3.78 ng/mL, pN1 and pN2 groups; (C) OS in SIRI ≤0.981, SIRI >0.981, pN1 and pN2 groups; (D) OS in CEA ≤3.78 ng/mL, CEA >3.78 ng/mL, pN1 and pN2 groups; (E) OS in SCCA ≤0.65 ng/mL, SCCA >0.65 ng/mL, pN1 and pN2 groups. Comparing the high value groups (NLR >1.492, CEA >3.78 ng/mL, SIRI >0.981, SCCA >0.65 ng/mL) and pN1 groups in the above graphs separately, there is no significant statistical difference (P≥0.05). pIA, pathologic stage IA; NSCLC, non-small cell lung cancer; DFS, disease-free survival; OS, overall survival; NLR, neutrophil to lymphocyte ratio; CEA, carcinoembryonic antigen; SIRI, systemic inflammatory response index; SCCA, squamous cell carcinoma antigen.

Comparing the Kaplan Meier survival curves of the pN1 group and the high-value biomarker group separately, there was no statistically significant difference in DFS between patients in the CEA and NLR high-value groups and patients in the pN1 group (P values of 0.31 and 0.08, respectively) (Figure 2A,2B). Also, no statistically significant differences had been shown in OS between the high CEA, SCCA, and SIRI groups and the pN1 group (P values of 0.25, 0.11, and 0.16, respectively) (Figure 2C-2E).

Discussion

It is a consensus that pathologic stage IA (pT1N0M0) NSCLC patients have an early pathological stage and do not need adjuvant treatment after surgery. However, some patients still have adverse prognoses such as recurrence and metastasis (2,3). Our results indicated that in pIA NSCLC patients, preoperative NLR and CEA may be independent predictors of DFS, and preoperative SIRI, CEA, and SCCA may be independent predictors of OS. These findings might add supportive evidence for predicting the survival of pIA pure-solid NSCLC patients.

SIRI and NLR, as inflammatory markers measured by peripheral blood tests, reflect the relative level of neutrophils, monocytes, and lymphocytes in peripheral blood. These simple blood tests have been used to evaluate the condition of lung cancer patients through the state of white blood cells, neutrophils, and lymphocytes (12). The potential mechanism may involve neutrophils participating in the formation of tumor cell microenvironment by secreting various cytokines and chemokines, releasing reactive oxygen species (ROS), forming neutrophil extracellular traps (NETs), and promoting tumor cell differentiation, metastasis, invasion, angiogenesis and immunosuppression (13,14). In addition, lymphocytes play a key role in tumor defense by inducing cytotoxic cell death and inhibiting tumor cell proliferation and migration, and they also promote cytokine apoptosis and inhibit tumor cell differentiation (7,15). Compared with the decrease of lymphocytes, the increase of neutrophils and monocytes might better reflect the loss of anti-tumor T cell activity in the inflammatory and tumor microenvironment (16). Therefore, the increasing number of neutrophils and monocytes and the reduction of lymphocytes might reflect a state of relatively promotes tumor progression in the human body. A higher pre-treatment NLR is a predictor of poor prognosis in NSCLC patients receiving immunotherapy and targeted therapy (17,18). Some previous studies have shown the relationship between the increase in NLR and the poor postoperative prognosis of NSCLC patients (19,20). Our study has yielded similar results. SIRI >1.9 was an independent predictor of poor prognosis of advanced NSCLC (21). Moreover, our study also confirmed that the group of SIRI is an independent predictor of poor prognosis of pIA pure-solid NSCLC after surgery. Bayir et al. suggest a correlation between MLR and the prognosis of advanced NSCLC (22). Similarly, Shoj et al. showed that preoperative high MLR is related to the poor postoperative prognosis of NSCLC (23). On the contrary, Huang et al. found that MLR is not an independent predictor of postoperative survival in NSCLC patients (24). In this study, the statistical results of MLR were not found to be correlated with DFS and OS in pIA pure-solid NSCLC patients. In addition, the high NLR group and the high SIRI group were adverse prognostic factors for DFS and OS in the adenocarcinoma subgroup, respectively, which did not exist in the squamous cell carcinoma subgroup. Therefore, this result did not rule out that there might be fewer patients in the subgroup of squamous cell carcinoma.

CEA, SCCA, and CYFRA21-1 are serum tumor markers for the early diagnosis of lung cancer. CEA is a high molecular glycoprotein, which is highly expressed in cancer and embryonic tissues. Many studies have shown that the expression of CEA, SCCA, and CYFRA21-1 in the serum of NSCLC patients is correlated with the severity and prognosis of the disease (25-27). However, our results only showed that pIA pure-solid NSCLC in the high SCCA group had a worse OS, while no correlation was found between DFS and OS of CYFRA21-1 and pIA pure-solid NSCLC. In addition, for patients with adenocarcinoma, we found a worse DFS in the high CEA group and a worse OS in the high SCCA group. While for patients with squamous cell carcinoma at stage pIA, only a worse OS had been seen in the high SCCA group. The phenomenon of inconsistent results of tumor markers in overall and subgroup studies may be related to a higher proportion of a subgroup in the overall study, and more data is needed to verify this issue.

Our results indicated that the prognosis of high SIRI, NLR, CEA, and SCCA groups in pIA pure-solid NSCLC patients was worse than that in patients with low SIRI, NLR, CEA, and SCCA. To explore the degree of poor prognosis in the groups with high biomarkers, we divided additionally the patients in the pIA (pT1N0M0) phase into two groups with high and low biomarkers and compared the prognosis with the patients in the pT1N1M0 phase (pN1 group) and pT1N2M0 phase (pN2 group) by drawing K-M curves. The results showed that DFS and OS were the lowest in patients with pN2 and the best in patients with low biomarkers. DFS and OS of high biomarker groups are basically between the low biomarker groups and the pN1 group. Kaplan-Meier survival curve analysis showed that there were no statistically significant differences in DFS and OS between high-value groups and the pN1 group. Compared with the pN1 group, SIRI >0.981, CEA >3.78 ng/mL, and SCCA>0.65 ng/mL groups had similar 3-year OS after surgery (89.7%, 94.0%, 91.1% vs. 92.9%, respectively). The 5-year OS after surgery in SIRI >0.981 and CEA >3.78 ng/mL groups were similar to that in the pN1 group (82.4%, 82.0% vs. 78.6%). All patients in the pN1 group received postoperative adjuvant therapy, and we did not include high-risk factors such as spread through air spaces (STAS), vascular invasion, and poorly differentiated tumors. In this study, the baselines of patients with NSCLC in stages pIA and pT1N1–2M0 were different, so it cannot be simply assumed that the DFS and OS of the high biomarker groups were consistent with those of the pN1 group. Compared with the low biomarker groups, our results showed that pIA pure-solid NSCLC patients in the high biomarker groups had worse DFS and OS after radical surgery. Thus, more observation or treatment should be offered to patients with pIA pure-solid NSCLC in the high-value group of biomarkers (such as SIRI, NLR, CEA, and SCCA) after radical surgery.

In this study, we found that SIRI, CEA, and SCCA were all related to smoking history. It could be suspected that smoking history might change the chronic inflammatory status of patients. Yet our research results suggested no correlation between smoking history on DFS and OS of pIA pure-solid NSCLC patients. There were 49 male smokers in our data, accounting for 56.3% (49/87) of male patients and 68% (49/72) of the total smokers. Moreover, our study showed that SIRI, NLR, and SCCA were all related to gender, which could be caused by more smokers in male patients.

One limitation of the present study was the retrospective nature and its performance at a single medical center, the analysis results of subgroups with fewer cases only provide ideas for further research and do not serve as the final conclusion. Therefore, further multicenter randomized trials and even a prospective study are needed to confirm the existence of a relationship between the predictive biomarkers and the clinical outcome in NSCLC. On the other hand, we plan to conduct more research to investigate the related genes and pathways involved in the predictive role of these biomarkers for NSCLC at a molecular level. As such, the mechanism underlying the effect of predictors on the prognosis of NSCLC can be better clarified, which might provide solid support to improve the deepened classification of TNM staging and individualized treatment strategies for NSCLC patients. Future studies might include longitudinal biomarker analysis and biological follow-up for resected NSCLC patients to detect significant changes over time and to provide more comprehensive insights into the prognostic value of these biomarkers.

Conclusions

In this study, we conclude that SIRI, NLR, CEA, and SCCA may be used as clinical biomarkers to predict DFS and OS in pIA pure-solid NSCLC. Specifically, for NSCLC patients with pIA pure-solid, higher NLR, CEA, SIRI, and SCCA levels predict poor survival and early recurrence, while higher CEA, SIRI, and SCCA levels predict a similar survival to the pT1N1M0 stage NSCLC patients receiving postoperative adjuvant therapy. Careful post-operative follow-up should be considered in this population.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2098/rc

Data Sharing Statement: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2098/dss

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2098/prf

Funding: The present study was supported by Individualized and Precise Treatment of Lung Cancer (No. Nn10py2017-04).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2024-2098/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by institutional review board of Harbin Medical University Cancer Hospital (No. KY2021-33) and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Cancer.Net. Lung Cancer—Non-Small Cell: Statistics: ASCO; 2020. Available online: https://www.cancer.net/cancer-types/lung-cancer-non-small-cell/statistics
3. Okami J, Shintani Y, Okumura M, et al. Demographics, Safety and Quality, and Prognostic Information in Both the Seventh and Eighth Editions of the TNM Classification in 18,973 Surgical Cases of the Japanese Joint Committee of Lung Cancer Registry Database in 2010. J Thorac Oncol 2019;14:212-22.
4. Tamura M, Ohta Y, Nakamura H, et al. Diagnostic value of plasma vascular endothelial growth factor as a tumor marker in patients with non-small cell lung cancer. Int J Biol Markers 2002;17:275-9. [Crossref] [PubMed]
5. Zhang Y, Zhang Y, Yin Y, et al. Detection of circulating exosomal miR-17-5p serves as a novel non-invasive diagnostic marker for non-small cell lung cancer patients. Pathol Res Pract 2019;215:152466. [Crossref] [PubMed]
6. Comen EA, Bowman RL, Kleppe M. Underlying Causes and Therapeutic Targeting of the Inflammatory Tumor Microenvironment. Front Cell Dev Biol 2018;6:56. [Crossref] [PubMed]
7. Mantovani A, Allavena P, Sica A, et al. Cancer-related inflammation. Nature 2008;454:436-44. [Crossref] [PubMed]
8. Dolan RD, Lim J, McSorley ST, et al. The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: Systematic review and meta-analysis. Sci Rep 2017;7:16717. [Crossref] [PubMed]
9. Yuan C, Li N, Mao X, et al. Elevated pretreatment neutrophil/white blood cell ratio and monocyte/lymphocyte ratio predict poor survival in patients with curatively resected non-small cell lung cancer: Results from a large cohort. Thorac Cancer 2017;8:350-8. [Crossref] [PubMed]
10. Weng J, Huang J, Yu W, et al. Combination of albumin concentration and neutrophil-to-lymphocyte ratio for predicting overall survival of patients with non-small cell lung cancer. J Thorac Dis 2021;13:5508-16. [Crossref] [PubMed]
11. Li S, Lan X, Gao H, et al. Systemic Inflammation Response Index (SIRI), cancer stem cells and survival of localised gastric adenocarcinoma after curative resection. J Cancer Res Clin Oncol 2017;143:2455-68. [Crossref] [PubMed]
12. Şahin F, Aslan AF. Relationship between Inflammatory and Biological Markers and Lung Cancer. J Clin Med 2018;7:160. [Crossref] [PubMed]
13. Masucci MT, Minopoli M, Carriero MV. Tumor Associated Neutrophils. Their Role in Tumorigenesis, Metastasis, Prognosis and Therapy. Front Oncol 2019;9:1146. [Crossref] [PubMed]
14. Wu L, Saxena S, Singh RK. Neutrophils in the Tumor Microenvironment. Adv Exp Med Biol 2020;1224:1-20. [Crossref] [PubMed]
15. Mlecnik B, Bifulco C, Bindea G, et al. Multicenter International Society for Immunotherapy of Cancer Study of the Consensus Immunoscore for the Prediction of Survival and Response to Chemotherapy in Stage III Colon Cancer. J Clin Oncol 2020;38:3638-51. [Crossref] [PubMed]
16. Ray-Coquard I, Cropet C, Van Glabbeke M, et al. Lymphopenia as a prognostic factor for overall survival in advanced carcinomas, sarcomas, and lymphomas. Cancer Res 2009;69:5383-91. [Crossref] [PubMed]
17. Diem S, Schmid S, Krapf M, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung Cancer 2017;111:176-81. [Crossref] [PubMed]
18. Xie X, Li X, Tang W, et al. Prognostic value of the neutrophil-to-lymphocyte ratio and primary tumor location in epidermal growth factor receptor-mutated metastatic non-small cell lung cancer. J Cancer Res Ther 2021;17:1618-25. [Crossref] [PubMed]
19. Zhang H, Xia H, Zhang L, et al. Clinical significance of preoperative neutrophil-lymphocyte vs platelet-lymphocyte ratio in primary operable patients with non-small cell lung cancer. Am J Surg 2015;210:526-35. [Crossref] [PubMed]
20. Mizuguchi S, Izumi N, Tsukioka T, et al. Neutrophil-lymphocyte ratio predicts recurrence in patients with resected stage 1 non-small cell lung cancer. J Cardiothorac Surg 2018;13:78. [Crossref] [PubMed]
21. Topkan E, Selek U, Kucuk A, et al. Prechemoradiotherapy Systemic Inflammation Response Index Stratifies Stage IIIB/C Non-Small-Cell Lung Cancer Patients into Three Prognostic Groups: A Propensity Score-Matching Analysis. J Oncol 2021;2021:6688138. [Crossref] [PubMed]
22. Bayir D, Seber S, Yetisyigit T. Prognostic values of various hematological variables as markers of systemic inflammation in metastatic lung cancer. J Cancer Res Ther 2020;16:731-6. [Crossref] [PubMed]
23. Shoji F, Kozuma Y, Toyokawa G, et al. Complete Blood Cell Count-Derived Inflammatory Biomarkers in Early-Stage Non-Small-Cell Lung Cancer. Ann Thorac Cardiovasc Surg 2020;26:248-55. [Crossref] [PubMed]
24. Huang Q, Diao P, Li CL, et al. Preoperative platelet-lymphocyte ratio is a superior prognostic biomarker to other systemic inflammatory response markers in non-small cell lung cancer. Medicine (Baltimore) 2020;99:e18607. [Crossref] [PubMed]
25. Kataoka Y, Hirano K, Narabayashi T, et al. Carcinoembryonic Antigen as a Predictive Biomarker of Response to Nivolumab in Non-small Cell Lung Cancer. Anticancer Res 2018;38:559-63. [Crossref] [PubMed]
26. Tomita M, Ayabe T, Maeda R, et al. The Inflammatory Prognostic Index Predicts Cancer-Specific Outcomes of Patients with Resected Non-Small Cell Lung Cancer. Asian Pac J Cancer Prev 2018;19:2867-70. [Crossref] [PubMed]
27. Qu T, Zhang J, Xu N, et al. Diagnostic value analysis of combined detection of Trx, CYFRA21-1 and SCCA in lung cancer. Oncol Lett 2019;17:4293-8. [Crossref] [PubMed]