{"paper_id":"2a2b97fe-71df-45bb-b8dc-30c12a9376cc","body_text":"Prognostic analysis of immunomarkers for non-small cell lung cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic analysis of immunomarkers for non-small cell lung cancer Xinmei Wang, Zhiqiong Yu, Weihua Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7042926/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract NSCLC, as a major subtype of lung cancer, has a very poor prognosis for advanced patients, and although the application of immune checkpoint inhibitors has revolutionized the treatment paradigm, significant efficacy heterogeneity still exists. This study aimed to investigate the expression characteristics of TILs and PD-L1 in NSCLC and their prognostic value. By retrospectively analyzing the clinicopathological data of 50 surgically resected NSCLC patients from 2018–2023, IHC was used to detect the expression levels of CD4 + TILs, CD8 + TILs, CD68 + TAMs, and PD-L1 in the tumor tissues.The high expression rate of PD-L1 reached 70% (35/50), and the intensity of its expression was significantly correlated with the size of the tumor ( p = 0.021); the percentage of high infiltration of CD8 + TILs reached 80% (40/50), which was positively correlated with lymph node metastasis ( p = 0.004). Correlation analysis revealed that PD-L1 was positively correlated with CD8 + TILs infiltration (r = 0.327, p = 0.020) and negatively correlated with CD68 + TAMs (r=-0.369, p = 0.008). Survival analysis showed significantly longer median PFS in the CD4 + TILs low infiltration group (15 months vs. 6 months, p = 0.027), while OS was worse in the PD-L1 high expression group (HR = 2.850, p = 0.039). Multifactorial Cox regression confirmed PD-L1 high expression (HR = 3.093, p = 0.022) and lung adenocarcinoma pathologic type (HR = 2.898, p = 0.026) as independent risk factors for PFS. In NSCLC, high membrane-specific expression of PD-L1 was positively correlated with tumor load and was tissue-selective (tumor tissue vs. normal lung tissue); membrane-localized infiltration of CD8 + TILs was positively correlated with lymph node metastasis. high expression of PD-L1 and pathologic type (adenocarcinoma vs. squamous carcinoma) were independent risk factors for postoperative PFS; PD-L1 could be used as a positive predictive marker for OS. The combined PD-L1 expression and CD8 + TILs/CD68 + TAMs immune infiltration characteristics provide a theoretical basis for individualized immunotherapy. NSCLC tumor-infiltrating lymphocytes PD-L1 prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Lung cancer, as the leading cause of cancer-related deaths worldwide, presents a significant disease burden in our country. According to the statistics of 2022, lung cancer accounted for 24.7% (1,060,600 cases/4,292,000 cases) of new cases in the year, and 733,300 deaths, which accounted for 28.5% of all malignant tumor deaths, with a significantly higher disease burden indicator than that of other cancers [ 1 ][ 2 ].Between 1990–2019, China's standardized incidence rate of cancer showed a 2.7-fold increase (21.7/100,000 to 58.6/100,000), and this growth rate is closely related to population aging and environmental carcinogenic exposure [ 3 ]. Pathogenic risk factors include cigarette smoking, occupational carcinogens (asbestos, radon, etc.) and exposure characteristics specific to our country: occupational exposure to wood products/mining/petrochemical pollutants, and a high prevalence of adenocarcinoma in nonsmoking women due to synergistic effects of kitchen fumes and atmospheric fine particulate matter. Non-small cell lung cancer (NSCLC) accounts for 80–85% of lung cancers, with a predominance of adenocarcinomas (40–50%) and squamous carcinomas (20–30%), and its highly heterogeneous nature and late diagnostic features (5-year survival of about 20%) remain a major clinical challenge [ 4 ][ 7 ]. Treatment strategies have evolved from traditional surgery, radiotherapy and chemotherapy to precision medicine. Stage I-IIIa NSCLC is mainly treated with radical surgery, supplemented by radiotherapy and chemotherapy; unresectable stage III is treated with simultaneous radiotherapy and chemotherapy combined with immunotherapy. Molecular targeted therapy significantly improves patient survival by inhibiting driver genes such as EGFR, ALK, ROS1, etc., but drug resistance and limited benefits are still to be broken through. Immune checkpoint inhibitors (ICIs) have become key to the paradigm shift in NSCLC treatment by remodeling the anti-tumor immune response through blocking the PD-1/PD-L1 or CTLA-4 signaling axis. However, with a clinical response rate of less than 30% for ICIs and limited efficacy of PD-L1 as a single predictive marker, a multidimensional biomarker system is urgently needed[ 8 ]. Tumor-Infiltrating LymPhocytes (TILs) are mainly a class of highly heterogeneous lymphocyte populations present in the tumor microenvironment, whose spatial distribution can be classified into mesenchymal-infiltrating TILs, cancer-nesting-infiltrating TILs, and contain immune cell subpopulations such as CD4 + helper T cells, CD8 + cytotoxic T cells, regulatory T cells (Tregs) and natural killer cells (NK cells) and other immune cell subpopulations[ 9 ]. These subpopulations influence tumor progression and anticancer success by exerting pro- and anti-tumorigenic effects[ 10 ]. Regarding the prognostic value of TILs, they have been widely validated in triple-negative breast cancer, HER2-positive subtypes. The Jun Hou Team 2024 study confirmed that for every 10% increase in TILs, the risk of recurrence in patients with triple-negative breast cancer is reduced by 15%,and that high TILs loads are usually associated with better Pathological ComPlete Remission (PCR) rates and survival. Interestingly, however, the study also found that in some cases (e.g., Luminal B) high TILs may be associated with poor prognosis, which may involve differences in subtypes or treatment modalities [ 11 ]. Cross-cancer analysis showed that the prognostic value of TILs is also supported by clinical evidence in nasopharyngeal carcinoma, colorectal cancer, hepatocellular carcinoma, NSCLC, gastric cancer, and melanoma [ 12 ]. Regarding TILs and NSCLC, studies have shown that the spatial distribution of CD8 + TILs (e.g., immune infiltration score) is more prognostic than the number alone, and that it is positively correlated with the response to treatment with PD-1/PD-L1 blockers. ICB “reprograms” CD8 + TILs by targeting inhibitory receptors such as PD-1 to generate anti-tumor responses. TILs to generate anti-tumor responses, but the exact mechanism remains to be further elucidated[ 13 ].Federico1 and his team demonstrated that the prognosis of NSCLC is not absolutely quantitatively correlated with the functional status of TILs. Recent studies have confirmed that the immune checkpoint interaction network (e.g., PD-1/PD-L1 axis) in TME can remodel the function of TILs, but the molecular mechanisms and clinical applications still need to be explored in depth [ 14 ]. Therefore, integrating the multi-omics analysis of TILs subpopulation characteristics, spatial distribution and immune checkpoint expression is a key pathway to optimize the precision immunotherapy of NSCLC and provide a key biological basis for NSCLC precision immunotherapy. 2. Methods 2.1 Case selection Fifty patients who were diagnosed with primary NSCLC in the lung at the Affiliated Hospital of Yangtze University between 2018–2023 were retrospectively included. Inclusion criteria: (1) Patients were diagnosed with primary NSCLC in the lung and underwent surgical treatment by imaging and histopathological biopsy, and the pathological types of all tissue specimens were all confirmed to be NSCLC by postoperative pathology.(2) None of the patients had received radiotherapy, chemotherapy, neoadjuvant chemotherapy, targeted therapy, immunotherapy, and other antitumor treatments prior to surgery. (3) Eastern Cooperative Oncology Group (ECOG) score of 0–1. Exclusion criteria: (1) History of other malignant tumors. (2) The following diseases were combined at the time of lung cancer diagnosis: HIV carriers, autoimmune diseases, severe infectious diseases, severe chronic diseases, severe liver and renal insufficiency, severe anemia, infections, and febrile diseases. Clinicopathological parameters (including gender, age at diagnosis, smoking history, tumor pathological type, tumor size, TNM stage, clinical stage, recurrence or metastasis, etc.) were collected; paraffin-embedded tumor tissues and paracancerous normal lung tissue samples were collected, and immunohistochemistry (IHC) was used to detect PDL in the tumor tissues and paracancerous tissue samples of NSCLC. Immunohistochemistry (IHC) was used to detect the expression of PD-L1 and the infiltration degree of CD4 + TILs, CD8 + TILs and CD68 + TAMs in NSCLC tumor tissues and paraneoplastic tissues, and to analyze their relationship with clinicopathological features and prognosis. All tissue sections were cut from paraffin-embedded tissues before the start of the experiment. Pathologic type classification was performed according to the World Health Organization (WHO) classification criteria of 2021, and TNM staging was performed according to the International Association for the Study of Lung Cancer (IASLC) 2023 Ninth Edition classification criteria for TNM staging of lung cancer. The primary endpoint of follow-up was OS and the secondary endpoint was PFS from diagnosis follow-up to August 2024 by outpatient clinic or telephone.PFS was defined as the time from disease diagnosis to disease progression or recurrence.OS was defined as the time from the patient's diagnosis of NSCLC to death or the end of or the last follow-up visit. Time was measured in months. The samples were fixed with a mass fraction of 10% paraformaldehyde and paraffin-embedded, and finally 50 archived wax blocks of NSCLC patients' tissues were collected in this study for 3um tissue sectioning. The study was in accordance with the approval of the Ethics Committee of the First People's Hospital of Jingzhou City. 2.2 IHC IHC was performed using the kit from Beijing Bioss Biotechnology Co. The procedure was as follows: paraffin tissue sections were baked in an oven at 37°C for about 1 hour and then placed in three different containers of fresh xylene for dewaxing and then hydrolyzed in ethanol, the repair solution was repaired in a microwave oven, heated for 10 minutes to bring it to a boil and then cooled down to room temperature, washed with PBS and then incubated in 3% hydrogen peroxide for 10 minutes. hydrogen peroxide closed incubation for 10 minutes. Each section was evenly titrated with normal goat serum containment working solution and incubated at 37°C for 15–20 minutes in a thermostat and then the remaining liquid in the section was poured off, do not wash. Subsequently, each tissue section was uniformly added with a drop of configured primary antibody working solution, and the sections were placed in a wet box and incubated in a 37℃ thermostat for 1 hour, washed with PBS for 5 minutes, and repeated 3 times. After that, the sections were placed in the wet box and incubated in a constant temperature box at 37℃ for 15–20 minutes, washed with PBS for 5 minutes, and repeated three times.Finally, DAB staining, hematoxylin re-staining, and dehydration after hydrochloric acid alcohol differentiation were performed to seal the sections for microscopic examination. The concentration of primary antibody in the experiment was CD4 (1:500 recombinant rabbit monoclonal antibody), CD8 (1:500 recombinant rabbit monoclonal antibody), CD68 (1:300 recombinant murine monoclonal antibody), and PD-L1 (1:200 recombinant rabbit monoclonal antibody); and the secondary antibody was the Bowenson Ready-to-Use Assay Kit. 2.3 Image processing Leica microscope was used to observe the whole section under low magnification, and then 5 fields were randomly selected under high magnification (400×) to calculate the proportion of positive cells to all tumor cells in the section, and the average of the 5 fields was taken as the final result. Image Pro Plus assisted in the counting of positive cells, and for the sections with large gaps, the sections were read again by senior pathologists. A standardized quality control system was adopted for the experimental process, and positive and negative controls were set for the staining batches to ensure the accuracy and reproducibility of the experiments. 2.4 Statistical methods In this study, the relationship between PD-L1 and CD4 + TILs, CD8 + TILs, CD68 + TAMs and clinical characteristics was analyzed using the chi-square test or Fisher's exact test (for cells with expected frequency < 5).Spearman rank correlation analysis was used to assess the strength of correlation between the degree of infiltration of TILs and the expression of PD-L1 in NSCLC. KaPlan-Meier method was used to draw survival curves and compare the survival differences between different PD-L1 and TILs expression groups by log-rank test (log-rank test). A Cox proportional hazards regression model (Cox ProPortional hazards model) was established, and a one-way analysis was first performed to screen potential prognostic factors. Variables with P < 0.2 in the univariate analysis were included in the multivariate analysis. Hazard Ratio (HR) and its 95% confidence interval (95% CI) were used to quantify the risk association. All statistical tests were two-sided, and the significance threshold was set at a = 0.05. All statistical analyses were performed using SPSS version 26.0. 3. Results 3.1 Clinicopathologic features of the patient After screening, 50 NSCLC patients were finally included. Their basic clinical characteristics are shown in Table 1 Clinicopathologic characteristics of NSCLC patientsand briefly described as follows: 33 cases (66.0%) were male and 17 cases (34.0%) were female; the age range was 42–72 years old, with a median age of 61 years (mean 68 years). Grouped by 60 years of age: 21 cases (42.0%) in the < 60 years group and 29 cases (58.0%) in the ≥ 60 years group. Grouped by smoking history: 37 cases (74.0%) with smoking history and 13 cases (26.0%) without smoking history. According to IASLC 9th edition TNM staging T stage: 35 cases (70.0%) in T1-T2 stage and 15 cases (30.0%) in T3-T4 stage. Presence of lymph node metastasis was found in 30 cases (60.0%) and no lymph node metastasis in 20 cases (40.0%). Clinical staging (IASLC 9th edition): 13 cases (26.0%) in stage I, 15 cases (30.0%) in stage II, and 22 cases (44.0%) in stage III. Pathologic types: 33 cases (66.0%) of lung adenocarcinoma and 17 cases (34.0%) of lung squamous carcinoma. Follow-up results showed that PFS was 11 months (range 0–45, mean 13.96 months); OS was 36 months (range 5–72, mean 37.5 months). Table 1 Clinicopathologic characteristics of NSCLC patients clinical characteristic Number of persons (N = 50) % genders male 33 66% female 17 34% age < 60 21 42% ≥ 60 29 58% Smoking history yes 37 74% no 13 26% Tumor size T1-T2 35 70% T3-T4 15 30% lymphatic node transfer no 20 40% yes 30 60% Pathological type LUAD 33 66% LUSC 17 34% Clinical Stages Ⅰ-Ⅱ 28 56% Ⅲ 22 44% Table 2 TILs and PD-L1 immunohistochemistry IHC Number of persons (N = 50) % PD-L1 low expression(PD-L1<50%) 15 30% high expression(PD-L1 ≥ 50%) 35 70% CD4 low expression(0–1 point) 43 86% high expression(2–3 point) 7 14% CD8 low expression(0–1 point) 10 20% high expression(2–3 point) 40 80% CD68 low expression(0–1 point) 36 72% high expression(1–2 point) 14 28% 3.2 CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression in NSCLC tissues CD4 and CD8 molecules are mainly localized in the cell membrane of TILs, PD-L1 is mainly expressed in the membrane or cytoplasm of tumor cells, and positive staining is also seen in some mesenchyme, with positive signals of tan or brown particles.CD68 molecules are mainly localized in the cytoplasm of tumor-associated macrophages, with positive signals of tan particles.Among the 50 cases of NSCLC, there were 7 cases of high expression of CD4 ( 14%) and 43 (86%) with low expression, 40 (80%) with high expression and 10 (20%) with low expression of CD8, 14 (28%) with high expression and 36 (72%) with low expression of CD68, and 35 (70%) with high expression and 15 (30%) with low expression of PD-L1, as shown in Table 2. In normal lung tissues adjacent to the carcinoma, CD4 + TILs, CD8 + TILs, CD68 + TAMs infiltration degree was lower than that of tumor tissues, and PD-L1 expression was almost negative. For immunohistochemical staining of TILs, PD-L1 see Fig. 1. infiltration of TILs and expression of PD-L1 control staining Fig. 2. 3.3 Relationship between the degree of infiltration of CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression and clinicopathologic features The relationships between the degree of TILs infiltration, PD-L1 expression and clinicopathological features in the NSCLC tumor microenvironment were analyzed in Table 3 and Table 4, and the results are briefly described as follows: CD4 + TILs were not significantly correlated with gender ( p = 0.741), age ( p = 0.961), smoking history ( p = 0.868), tumor size ( p = 0.436), lymph node metastasis ( p = 0.498), clinical stage ( p = 0.948), and pathologic type ( p = 0.175) were not significantly correlated. The degree of infiltration of CD8 + TILs was not statistically significantly correlated with gender ( p = 0.058), age ( p = 0.568), smoking history ( p = 0.275), tumor size ( p = 1.000), clinical stage ( p = 0.775), and pathological type ( p = 0.279), but was significantly correlated with lymph node metastasis ( p = 0.004). The degree of infiltration of CD68 + TAMs was not significantly correlated (p > 0.05) with gender ( p = 0.610), age ( p = 0.230), smoking history ( p = 0.641), tumor size ( p = 0.224), lymph node metastasis ( p = 0.368), clinical stage ( p = 0.594), and pathological type ( p = 0.610). PD-L1 expression was not significantly correlated with gender ( p = 0.948), age ( p = 0.662), smoking history ( p = 0.994), lymph node metastasis ( p = 0.208), clinical stage ( p = 0.804), and pathological type ( p = 0.558), but was significantly correlated with tumor size ( p = 0.021). Table 3 Relationship between the degree of infiltration of CD4 + TILs and CD8 + TILs and clinical characteristics of NSCLC patients clinical characteristic Number of persons (N = 50) P (CD4) χ 2 (CD4) P (CD8) χ 2 (CD8) genders male 33 0.741 0.110 0.058 3.590 female 17 age < 60 21 0.961 0.002 0.568 0.325 ≥ 60 29 Smoking history yes 37 0.868 0.028 0.275 1.192 no 13 Tumor size T1-T2 35 0.436 0.607 1.000 0.000 T3-T4 15 lymphatic node transfer no 20 0.498 0.459 0.004 * 8.424 yes 30 Pathological type LUAD 33 0.175 1.840 0.279 1.170 LUSC 17 Clinical Stages Ⅰ-Ⅱ 28 0.948 0.004 0.775 0.082 Ⅲ 22 (Note:*: Differences are statistically significant, p < 0.05) Table 4 Relationship between the degree of infiltration of CD68 + TAMs and PD-L1 expression and clinical characteristics of NSCLC patients clinical characteristic Number of persons (N = 50) P (CD68) χ 2 (CD68) P (PD-L1) χ 2 (PD-L1) genders male 33 0.610 0.260 0.948 0.004 female 17 age < 60 21 0.230 1.439 0.662 0.192 ≥ 60 29 Smoking history yes 37 0.641 0.217 0.944 0.005 no 13 Tumor size T1-T2 35 0.224 1.477 0.021 * 5.331 T3-T4 15 lymphatic node transfer no 20 0.368 0.810 0.208 1.587 yes 30 Pathological type LUAD 33 0.610 0.260 0.558 0.344 LUSC 17 Clinical Stages Ⅰ-Ⅱ 28 0.594 0.284 0.804 0.062 Ⅲ 22 (Note:*: Differences are statistically significant, p < 0.05) 3.4 Correlation analysis of the degree of infiltration of CD4 + TILs, CD8 + TILs, CD68 + TAMs, and PD-L1 expression In this study, the correlation between the infiltration degree of TILs (CD4 + TILs, CD8 + TILs, CD68 + TAMs) and PD-L1 expression in NSCLC was analyzed by using the Spearman rank correlation test in the SPSS software, and the results were as follows: the expression of PD-L1 showed a significant positive correlation with that of CD8 + TILs (r = 0.327, p = 0.020), suggesting that the higher degree of infiltration of CD8 + TILs in NSCLC may be followed by an increase in the expression level of PD-L1. The expression of PD-L1 showed a significant negative correlation with CD68 + TAMs (r = -0.369, p = 0.008), suggesting that the enrichment of tumor-associated macrophages may inhibit the expression of PD-L1 or that high expression of PD-L1 may be be unfavorable to the infiltration of CD68 + TAMs. This result suggests that CD68 + TAMs and PD-L1 may have an antagonistic role in the immunoregulation of NSCLC, and there was no significant correlation between the degree of infiltration of CD68 + TAMs, CD4 + TILs, CD8 + TILs and CD68 + TAMs ( p > 0.05). See Table 5. Table 5 Correlation analysis between the degree of infiltration of TILs and PD-L1 expression CD4 + TILs CD8 + TILs CD68 + TAMs PD-L1 CD4 + TILs 1.000 CD8 + TILs 0.160 1.000 CD68 + TAMs 0.972 0.355 1.000 PD-L1 0.338 0.020 * 0.008 ** 1.000 (Note:*: Differences are statistically significant, p < 0.05) 3.5 Prognostic analysis of CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression and NSCLC This study has previously demonstrated significant lymphocyte heterogeneity in the NSCLC tumor microenvironment, with varying levels of immune cell infiltration. Further analysis showed that PD-L1 expression was significantly positively correlated with CD8 + TILs and negatively correlated with CD68 + TAMs, suggesting that TILs and PD-L1 expression may affect the prognosis of NSCLC patients. To test this hypothesis, in this study, survival analysis was performed on 50 NSCLC patients who underwent surgical treatment to assess the relationship between PD-L1 expression and TILs subtypes and prognosis. Survival curves are shown(Fig. 3,Fig. 4). The specific results were as follows: the median PFS in the CD4 + TILs high-infiltration group (7 cases) was 6 months, which was significantly lower than that in the low-infiltration group (43 cases) of 15 months ( p = 0.027), and the results suggested that the high infiltration of CD4 + TILs might affect the prognosis of NSCLC patients.The median OS in the CD4 + TILs high-infiltration group and the low-infiltration group were 35 months and 48 months, respectively ( p > 0.05), indicating that the degree of CD4 + TILs infiltration had no significant effect on the overall survival of NSCLC patients.The median PFS (14 months) and OS (48 months) of the CD8 + TILs high-infiltration group (40 cases) were slightly better than those of the low-expression group (10 cases, median PFS: 10 months, median OS: 36 months), but the difference was not statistically significant ( p > 0.05). The median PFS (15 months) and OS (48 months) of the CD68 + TAMs high infiltration group (14 cases) were not statistically significant ( p >0.05) compared with those of the low infiltration group (36 cases, median PFS: 14 months, median OS: 48 months), and the median PFS (12 months) and OS (47 months) of the PD-L1 high expression group (35 cases) were lower than those of the low expression group (The median PFS (12 months) and OS (47 months) were lower in the high PD-L1 expression group (35 cases) than in the low PD-L1 expression group (15 cases, median PFS: 30 months, median OS: 68 months). 3.6 Univariate analysis of various clinicopathologic features In 50 patients with surgically resected NSCLC, the median PFS was 12 months and 35 months ( p = 0.152) and the median OS was 40 months and 58 months ( p = 0.150) for men (33 patients) versus women (17 patients), respectively, and none of the differences were statistically significant. The age range was 42–72 years, with a median age of 61 years, and the median PFS was 12 versus 15 months ( p = 0.229) and the median OS was 48 versus 47 months ( p = 0.701) for patients < 60 years (21 patients) versus ≥ 60 years (29 patients), with no statistically significant difference. The median PFS for smokers (37 cases) and non-smokers (13 cases) was 12 months versus 35 months ( p = 0.090), and the median OS was 58 months versus 47 months ( p = 0.165), with none of the differences reaching statistical significance. According to the 9th edition of TNM staging of lung cancer, 50 patients with NSCLC treated by radical surgery had a median PFS of 14 months and 12 months ( p = 0.920) and a median OS of 48 months and 68 months ( p = 0.449) for patients with T1-T2 stage (35 patients) versus T3-T4 stage (15 patients), respectively. None of the differences were statistically significant. The median PFS of no postoperative lymph node metastasis (20 cases) and lymph node metastasis (30 cases) were 12 months and 15 months ( p = 0.629), and the median OS was 53 months and 48 months ( p = 0.523), respectively, and none of the differences were statistically significant. The median PFS was 18 months and 12 months ( p = 0.381), and the median OS was 53 months and 36 months ( p = 0.063) for patients with stage I-II (28 patients) versus stage III (22 patients) NSCLC, respectively. Although patients with stage I-II NSCLC had better median PFS and OS than stage III, none of the differences were statistically significant. The median PFS of lung adenocarcinoma (33 cases) and lung squamous carcinoma (17 cases) were 26 months and 10 months, respectively, and the difference was statistically significant ( p = 0.037). The median OS was 53 months and 19 months, respectively, and the difference was also statistically significant ( p = 0.043). Immunohistochemical staining was used to assess the degree of infiltration of CD4 + TILs in tumor tissues, and the median PFS of the low-infiltration group (43 cases) was significantly better than that of the high-infiltration group (7 cases) (15 months vs. 6 months, p = 0.042). However, there was no significant difference in OS between the two groups (48 months vs. 35 months, p = 0.207).There was no statistical significance in the difference in median PFS (10 months vs. 14 months, p = 0.879) as well as in OS (36 months vs. 48 months, p = 0.207) between the low infiltration group (10 cases) and the high infiltration group (40 cases) of CD8 + TAMs. CD68 + TAMs low infiltration group (36 cases) vs. high infiltration group (14 cases) were not statistically significant in terms of difference in median PFS (14 months vs. 15 months, p = 0.889), and OS (48 months vs. 48 months, p = 0.589).The PD-L1 low expression group (15 cases) were not statistically significant in terms of difference in median PFS (30 months vs. 12 months, p = 0.084), OS (68 months vs 47 months, p = 0.149), although significantly better than the high expression group (35 cases), the difference did not reach statistical significance. See Table 6. Table 6 Univariate analysis of NSCLC patients clinical characteristic PFS OS HR 95% CI P 值 HR 95% CI P 值 genders male vs female 1.897 0.791–4.520 0.152 1.897 0.794–4.532 0.150 age <60vs ≥ 60 0.668 0.311–1.432 0.299 1.166 0.533–2.552 0.701 Smoking history yes VS no 2.234 0.881–5.663 0.090 1.926 0.764–4.857 0.165 lymphatic node transfer yes VS no 0.820 0.367–1.834 0.629 1.299 0.582–2.897 0.523 Tumor size T3-4 vs T1-2 1.043 0.456–2.386 0.920 0.712 0.295–1.717 0.449 Clinical Stages Ⅰ-Ⅱ vs Ⅲ 1.405 0.657–3.004 0.381 2.070 0.962–4.456 0.063 Pathological type LUAD vsLUSC 2.247 1.05–4.809 0.037 * 2.191 1.025–4.685 0.043 * CD4 + TILs low vs high 3.192 1.042–9.779 0.042 * 2.018 0.678–6.013 0.207 CD8 + TILs low vs high 0.931 0.372–2.330 0.879 0.845 0.336–2.129 0.721 CD68 + TAMs low vs high 0.889 0.392–2.250 0.889 0.792 0.333–1.883 0.598 PD-L1 low vs high 2.153 0.901–5.145 0.084 1.973 0.784–4.963 0.149 (Note:*: Differences are statistically significant, p < 0.05) 3.7 Multifactorial analysis of various clinicopathologic features Based on the above univariate analysis to screen potential prognostic factors, a Cox proportional risk model was constructed for multivariate analysis, and variables with significance levels set at p < 0.2 in the univariate analysis were included in the multivariate model with a loose threshold (α = 0.2 rather than the traditional α = 0.05), aiming to reduce the risk of Type II errors (false negatives). See Table 7and Table 8 briefly described below: CD4 + TILs ( p = 0.042), pathology type ( p = 0.037), gender ( p = 0.152), smoking history ( p = 0.090), and PD-L1 ( p = 0.084) were included in the multifactorial analysis of PFS in NSCLC patients. Gender ( p = 0.150), smoking history ( p = 0.165), clinical stage ( p = 0.063), pathology type ( p = 0.043), and PD-L1 ( p = 0.149) were included in the OS multifactorial analysis. The results were as follows: pathologic type (HR = 2.898, 95% CI: 1.132–7.419, p = 0.026), and PD-L1 expression (HR = 3.093, 95% CI: 1.179–8.111, p = 0.022) were independent risk factors for PFS in patients with NSCLC. Other control variables: gender, smoking history, and CD4 + TILs did not independently predict PFS in NSCLC patients ( p >0.05).PD-L1 (HR = 2.850, 95% CI: 1.056–7.689, p = 0.039) was an independent predictor of OS in NSCLC patients as a risk factor. In NSCLC patients treated by surgical resection, high PD-L1 expression significantly increased the risk of disease progression. Gender, smoking history, pathologic type, and clinical stage were all non-significantly associated variables ( p > 0.05). Table 7 Multifactorial analysis of PFS in NSCLC patients clinical characteristic PFS HR 95% CI p genders male vs female 1.718 0.474–6.221 0.410 Smoking history yes VS no 0.714 0.227–2.244 0.565 Pathological type LUAD vs LUSC 2.898 1.132–7.419 0.026 * CD4 + TILs low vs high 3.401 0.983–11.770 0.053 PD-L1 low vs high 3.093 1.179–8.111 0.022 * (Note:*: Differences are statistically significant, p < 0.05) Table 8 Multifactorial analysis of OS in NSCLC patients clinical characteristic OS HR 95% CI p genders male vs female 2.297 0.59–8.948 0.231 Smoking history yes VS no 0.419 0.128–1.369 0.150 Pathological type LUAD vs LUSC 2.527 0.923–6.921 0.071 Clinical Stages Ⅰ-ⅡvsⅢ 1.660 0.707–3.896 0.244 PD-L1 low vs high 2.850 1.056–7.689 0.039 * (Note:*: Differences are statistically significant, p < 0.05) 4. Discussion 4.1 Association of TILs and PD-L1 expression with clinical features of NSCLC In the present study, high infiltration of CD8 + TILs was significantly associated with a reduced risk of NSCLC lymph node metastasis by IHC analysis ( p = 0.004), suggesting that it may influence the metastatic process by inhibiting tumor cell proliferation.Neither infiltration of CD4 + TILs nor CD68 + TAMs was significantly associated with clinical features.PD-L1 expression was significantly associated with tumor size ( p = 0.028), suggesting that tumor size may accompany the formation of an immunosuppressive microenvironment, which induces the upregulation of PD-L1, but TNM stage was not associated with gender, age, smoking history and lymph node metastasis ( p > 0.05). The available literature shows heterogeneity in the correlation between TILs and clinical features: Binnewies M et al. found a significantly higher infiltration of CD4 + Treg in metastatic lymph nodes ( p < 0.001) [ 15 ]. In contrast, studies in breast cancer have shown that CD4 + TILs are not associated with staging[ 16 ]. The present study confirmed that CD4 + TILs were not significantly associated with the basic clinical features of NSCLC.The findings for CD8 + TILs were divergent: Herbest's team noted that the degree of infiltration of CD8 + TILs correlated with the stage (early stage tumors (stage I-II), p = 0.006; while for advanced (stage III-IV) tumors, p = 0.02) [ 17 ]. Consistent with Donnem et al. who reported that high infiltration was associated with a lower rate of lymph node metastasis ( p = 0.02), the results of the present study are consistent with the latter[ 18 ]. The relationship between CD68 + TAMs and clinical characteristics of patients with lung cancer showed contradictory results: the CD68 + macrophages of B7-H4 in the peripheral blood of lung cancer were associated with tumor size, lymph node metastasis, and TNM stage.The team of Mantovani A found a positive correlation between the M2 isoform (CD163+) and TNM stage ( p = 0.008); the M2 type of TAMs promoted lymph node metastasis through the PD-L1 /IL-10 axis to promote lymph node metastasis [ 19 ].CD68, a pan-tumor-associated macrophage marker, was noted to have a significant positive correlation between its high expression and advanced TNM stage (p = 0.008), lymph node metastasis, and peritumoral lymphatic vessel density ( p < 0.001) in NSCLC, suggesting that the abundance of TAMs may reflect the invasive potential of the tumor[ 20 ]. In the present study, we found that CD68 + TAMs infiltration was not significantly associated with the clinical features of NSCLC patients, which might be related to the mutual functional counteractivity or spatial heterogeneity of M1/M2 subtypes, and it was negatively correlated with PD-L1 ( p = 0.008), suggesting a role of immune modulation. The PD-L1 expression controversy stems from multiple factors: a study by Garon et al. found a significantly higher proportion of patients with tumors ≥ 3 cm in diameter (T2-4) expressing PD-L1 ( p = 0.002). While KEYNOTE-001 showed no association with T-staging ( p = 0.15)[ 17 ]. However, no significant correlation between histologic subtypes and PD-L1 expression was found in KEYNOTE-024, which may be related to sample size or differences in assay platforms[ 21 ][ 22 ]. In this study, we confirmed that PD-L1 expression was correlated with tumor size ( p = 0.021), and there was no significant correlation with gender, age, smoking history, clinical stage, lymph node metastasis, or pathology type. The heterogeneity of the results may stem from: antibody and assay platforms, scoring criteria limitations, spatiotemporal heterogeneity, and class II errors due to sample size limitations. 4.2 Correlation analysis between the degree of infiltration of TILs and PD-L1 expression In this study, PD-L1 was significantly positively correlated with CD8 + TILs (r = 0.327, p = 0.020), suggesting that CD8 + TILs may induce PD-L1 upregulation through IFNγ signaling. On the contrary, PD-L1 and CD68 + TAMs showed a significant negative correlation (r =-0.369, p = 0.008), suggesting that they have an antagonistic effect in the immune microenvironment of NSCLC, and no significant correlation was found among CD68 + TAMs, CD4 + TILs, and CD8 + TILs ( p >0.05). When evaluated by the Tumor cell ProPortion Score (TPS), Immune Cell (IC), and Combined Positive Score (CPS) systems, a research team found that PD-L1 expression was associated with the CD4 + TILs, CD8 + TILs and M2-TAMs were significantly positively correlated. Notably, PD-L1 + lymphocytes had co-localization characteristics with CD4 + TILs and M1-TAMs, but showed negative spatial distribution with M1-TAMs in the mesenchymal region of the tumor[ 23 ]. At the mechanistic level, activated CD8 + TILs induce tumor PD-L1 expression by secreting IFNγ, while activation of the PD-L1/PD-L1 pathway can feedback inhibit the function of T cells, creating a vicious cycle of immune escape [ 24 ]. This biological process was clinically validated in the KEYNOTE-001 and CheckMate-816 studies: patients with high PD-L1 expression (TPS ≥ 50%) combined with high infiltration of CD8 + TILs had an objective remission rate (ORR) of 45%, which was significantly better than that of the single-indicator-positive group (34%-50% vs 34%) [ 25 ][ 26 ]. Significant heterogeneity exists between PD-L1 and CD4 + TILs[ 27 ][ 28 ].Herbst et al. found a positive association between Th1-type CD4 + TILs and PD-L1 ( p = 0.003), whereas Rizvi's team did not find a significant association in an immunotherapy cohort ( p = 0.21)[ 17 ][ 29 ]. These differences may be related to the following mechanisms: (1) dynamic balance of functional subpopulations of CD4 + TILs: functional antagonism of Th1, Tregs, and Th17 cells may mask the overall correlation; (2) spatiotemporal heterogeneity of PD-L1 expression: the tumor core region and invasive front showed significant expression differences; and (3) immunotherapy-mediated remodeling of the microenvironment. Regarding the mechanism of negative correlation between PD-L1 and CD68 + TAMs, available studies suggest that it may involve M1/M2 phenotypic transition.Hegde et al. found that enrichment of CD68 + TAMs in the mesenchymal region was associated with low expression of PD-L1 ( p = 0.004), whereas Mantovani's team demonstrated that M2-TAMs can upregulate PD-L1 through the IL-6/STAT3 pathway[ 30 ][ 33 ]. This contradiction may stem from (1) phenotypic heterogeneity of TAMs: the CD68 + population contains pro-inflammatory M1-type and immunosuppressive M2-type subpopulations, and (2) spatial distribution specificity: TAMs in the tumor parenchymal region mostly show M2 phenotype, with co-localization features with PD-L1 + lymphocytes[ 34 ][ 35 ]. The diversity of the current findings may be attributed to tumor heterogeneity and differences in immune scoring systems. In the future, the spatial and temporal dynamics of PD-L1-immune cell interactions need to be resolved by spatial transcriptomics, single-cell sequencing, and other high-dimensional technologies to provide a theoretical basis for precision immunotherapy. 4.3 Survival analysis of the extent of infiltration and PD-L1 expression in TILs In this study, we investigated the relationship between TILs subtypes and prognosis through survival analysis of 50 surgically treated NSCLC patients. The median PFS was found to be 6 months in the CD4 + TILs high infiltration group (n = 7), which was significantly lower than that of 15 months in the low infiltration group (n = 43) (Log-Rank p = 0.027), suggesting that CD4 + TILs may accelerate disease progression through regulatory T-cell (Treg)-mediated immunosuppressive effects. Univariate Cox analysis showed the prognostic value of CD4 + TILs (HR = 2.1, p = 0.042), but did not reach significance in multivariate analysis, which may be related to confounding factors interference (e.g., clinical staging, pathologic subtype) and sample size limitation. The prognostic value of CD4 + TILs showed significant heterogeneity: Galon's team confirmed that they were significantly associated with PFS[ 36 ], Bruni et al. found that Th1 subpopulation was associated with prolonged overall survival (OS) (HR = 0.71, p = 0.02) [ 37 ][ 38 ], whereas Plitas et al. reported that FoxP3 + Treg hyperinfiltration was significantly correlated with shortened OS (HR = 2.5, p = 0.004) [ 39 ][ 40 ]. This contradiction may stem from (1) the dynamic balance of Th1/Treg subpopulations and (2) the EGFR mutation status affecting Treg function [HR = 1.9, p = 0.01]. Regarding the prognostic value of CD8 + TILs, a meta-analysis including 2,559 cases showed that their high infiltration was significantly associated with improved OS (HR = 0.52), PFS (HR = 0.52) and objective remission rate (ORR) (all p < 0.001) [ 41 ]. However, Thommen's team found that PD-L1 + CD8 + TILs lost prognostic value due to the depletion phenotype (HR = 1.2, p = 0.32)[ 32 ], and Malaka A et al. confirmed the presence of histologic specificity in their prognostic effect[ 42 ]. The present study did not find a significant association between CD8 + TILs and prognosis, suggesting the need for precise assessment in combination with spatial distribution (tumor parenchyma/mesenchyme) and functional markers (PD-1/TIM-3). The prognostic value of CD68 + TAMs was modulated by M1/M2 polarization phenotype and spatial distribution: tumor parenchymal M1-TAMs (CD68 + iNOS+) were associated with prolonged OS (HR = 0.62, p = 0.03), whereas mesenchymal M2-TAMs (CD68 + CD163+) mediated immunosuppression via PD-L1/IL-10 signaling (HR = 1.89, p = 0.01)[ 43 ][ 44 ]. The association between overall infiltration of CD68 + TAMs and prognosis was not found in this study and may be related to (1) mixed M1/M2 phenotypic disturbances, (2) heterogeneity of tumor parenchymal-mesenchymal distribution, and (3) limitations of immunohistochemistry techniques in assessing functional status. The current study suggests that the prognostic value of immune cells depends on their functional phenotype, spatial topology and microenvironmental regulatory network. In the future, it is necessary to analyze the multidimensional features of the TME immune ecosystem through multiple immunofluorescence (e.g., CD68/CD163/PD-L1 co-staining) in combination with spatial transcriptome technology to provide a theoretical basis for precision immunotherapy. 4.4 Analysis of PD-L1 expression and NSCLC prognosis Survival analysis in this study showed that the median PFS and OS of the PD-L1 high expression group (n = 35) were 12 and 47 months, respectively, which showed a trend of survival disadvantage (Log-rank p > 0.05) compared to the PD-L1 low expression group (n = 15; PFS = 30 months, OS = 68 months).Cox multifactorial analysis confirmed that PD-L1 high expression was the most important factor in surgical resection of NSCLC patients with PFS (HR = 3.093, 95%CI:1.179–8.111, p = 0.022) and OS (HR = 2.850, 95%CI:1.056–7.689, p = 0.039) as an independent risk factor, with wide confidence intervals suggesting that extended cohort validation is needed. Pathologic type (lung adenocarcinoma vs. squamous carcinoma) was also an independent predictor of PFS (HR = 2.898, p = 0.026), which may be related to the biological characteristics of adenocarcinoma that is more likely to metastasize distantly[ 45 ]. The prognostic value of PD-L1 is significantly controversial: Garon et al. found that high PD-L1 expression was associated with a shortened OS in patients without immunotherapy (HR = 1.3, p = 0.02) [ 21 ], whereas a study enrolling 205 cases of squamous lung carcinoma showed that OS was significantly lower in those who were PD-L1 positive ( p < 0.01)[ 46 ]. In contrast, multivariate analysis of 544 cases showed that PD-L1 positivity combined with high infiltration of CD8 + TILs predicted prolonged OS ( p < 0.05)[ 47 ]. This contradiction may stem from (1) PD-L1 assay heterogeneity (antibody cloning/scoring system differences); (2) dynamic changes in the tumor immune microenvironment (e.g., IFNγ-induced adaptive resistance); and (3) differences in therapeutic contexts: the KEYNOTE-001 demonstrated that immunotherapy reversed the prognostic effect of PD-L1, transforming it into a predictive biomarker ( p < 0.001) [ 48 ]. 5. Conclusions PD-L1 was specifically highly expressed in the cell membranes of NSCLC tumor cells, and the degree of its expression was significantly correlated with tumor size, with almost no expression seen in normal lung tissues; the degree of membrane-localized infiltration of CD8 + TILs was significantly and positively correlated with lymph node metastasis. High PD-L1 expression and pathologic type (lung adenocarcinoma vs. lung squamous carcinoma) were independent risk factors for postoperative PFS, and PD-L1 could be used as a positive predictive marker for OS. The combined PD-L1 expression and the immune infiltration characteristics of CD8 + TILs/CD68 + TAMs provide a theoretical basis for individualized immunotherapy strategies. Declarations Acknowledgements We thank Dr Hu Weihua, Chief Physician of NSCLC, for his advice in the design of NSCLC immunohistochemistry experiments; and we thank the Hubei Provincial Clinical Research Centre for Individualised Tumour Diagnosis and Treatment for its special financial support. Authors ’ contributions Weihua Hu, Xinmei Wang and Zhiqiong Yu were involved in the experimental design and analysis of the results. Xinmei Wang was involved in specimen collection, immunohistochemistry experiments, data collection, statistical analysis, and writing the first draft of the manuscript. Wei-Hua Hu and Zhi-Qiong Yu contributed to conceptualisation, manuscript revision and overall project management. All authors read and approved the final manuscript. Funding Provided exclusively by Hubei Provincial Clinical Medical Research Centre for Individualized Cancer Diagnosis and Treatment. Data availability The data used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Ethics approval and consent to participate Written informed consent was obtained from all participating subjects at the time of tumour tissue collection. All study procedures, including patient recruitment, informed consent acquisition and specimen handling, were reviewed and approved by the Institutional Review Board of the Jingzhou First People's Hospital. All relevant ethical guidelines and regulations were followed throughout the study to ensure the integrity of the study. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Abu Rous, Fawzi et al.Lung Cancer Treatment Advances in 2022. Cancer investigation vol. 41,1 (2023): 12-24. Smolarz B , Ukasiewicz H , Samulak D , et al. Lung Cancer—Epidemiology, Pathogenesis,Treatment and Molecular Aspect (Review of Literature)[J].International Journal of Molecular Sciences, 2025, 26(5):2049. Zeng H, Zheng R, Sun K, et al. Cancer survival statistics in China 2019-2021:a multicenter, PoPulation-based study[J]. Journal of the National Cancer Center, 2024, 4(3): 203-213. De Polo A , Buja A , Pasello G ,et al.Non Small Cell Lung Cancer: Real-World Cost Consequence Analysis[J].European Journal of Public Health, 2021, 31.DOI:10.1093/eurpub/ckab164.840. Simiao C, Zhong C, Klaus P, et al. Estimates and Projections of the Global Economic Cost of 29 Cancers in 204 Countries and Territories From 2020 to 2050[J]. JAMA Oncology. 2023, 9(4): 465-472. Zhonghua Zhong Liu Za Zhi, et al. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2024 edition) [J]. Chinese Journal of Oncology, 2024, 46(9). 805-843. Allemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): analysis of individual records for 37 513 025 Patients diagnosed with one of 18 cancers from 322 PoPulation-based registries in 71 countries[J]. The Lancet, 2018, 391(10125): 1023-1075. Riely GJ, Wood DE, Ettinger DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2024 May;22(4):249-274. Genova C, DellePiane C, Carrega P, et al. TheraPeutic ImPlications of Tumor Microenvironment in Lung Cancer: Focus on Immune CheckPoint Blockade[J]. Frontiers in Immunology, 2022, 12: 799455-799455. Moreno V, Salazar R, Gruber S B, et al. The Prognostic value of TILs in stage III colon cancer must consider sidedness[J]. Annals of Oncology, 2022, 33(11): 1094-1096. Carsten Denkerta B C , Minckwitz G V , Darb-Esfahani S ,et al.Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy[J].The Lancet Oncology, 2018, 19(1):11.DOI:10.1016/S1470-2045(17)30904-X. Bai S, Yang X, Zhang N, et al. Function of tumor infiltrating lym P hocytes in solid tumors-a review[J]. Chinese journal of Biotechnology, 2019, 35(12): 2308-2325. Gueguen P , Metoikidou C, Du P ic T, et al. Contribution of resident and circulating Precursors to tumor-infiltrating CD8+ T cell Po P ulations in lung cancer[J]. Science Immunology, 2021, 6(55): eabd5778. Federico L, Mcgrail D J, Bentebibel S E, et al. Distinct tumor-infiltrating lym P hocyte landsca P es are associated with clinical outcomes in localized non-small-cell lung cancer [J]. Annals of Oncology, 2022, 33(1): 42-56. Binnewies M, P ollack J L, Rudol P h J, et al. Targeting TREM2 on tumor-associated macro P hages enhances immunothera P y[J]. Cell Re P orts, 2021, 37(3): 109869. Plitas G, Kono P acki C, Wu K, et al. Regulatory T cells exhibit distinct features in human breast cancer[J]. Immunity, 2016, 45(5): 1122-1134. Herbst R S, Soria J C, Kowanetz M, et al. P redictive correlates of res P onse to the anti- P D-L1 antibody M P DL3280A in cancer Patients[J]. Nature, 2014, 515(7528): 563-567. Donnem T, Kilvaer T K, Andersen S, et al. Strategies for clinical imPlementation of TNM-Immunoscore in resected nonsmall-cell lung cancer[J]. Annals of Oncology 2016, 27(2): 225-232. Mantovani A, Allavena P , Marchesi F, et al. MacroPhages as tools and targets in cancer thera P y[J]. Nature Reviews Drug Discovery, 2022, 21(11): 799-820. Jin M, Fang J, Peng J, et al. P D-1/ P D-L1 immune checkPoint blockade in breast cancer: research insights and sensitization strategies[J]. Molecular Cancer, 2024, 23(1): 1-22. Garon E B, Rizvi N A, Hui R, et al. Pembrolizumab for the treatment of non–small-cell lung cancer[J]. New England Journal of Medicine, 2015, 372(21): 2018-2028. Reck M, Rodríguez-Abreu D, Robinson A G, et al. Pembrolizumab versus chemothera P y for PD-L1–Positive non–small-cell lung cancer[J]. New England Journal of Medicine, 2016, 375(19): 1823-1833. Yan Q, Li S, He L, et al. P rognostic im P lications of tumor-infiltrating lym P hocytes in non-small cell lung cancer: a systematic review and meta-analysis[J]. Frontiers in Immunology, 2024, 15: 1476365. Latchman Y E, Liang S C, Wu Y, et al. P D-L1-deficient mice show that P D-L1 on T cells, antigen- P resenting cells, and host tissues negatively regulates T cells[J]. P roceedings of the National Academy of Sciences, 2004, 101(29): 10691-10696. To P alian S L, Drake C G, P ardoll D M. Immune check P oint blockade: a common denominator a PP roach to cancer thera P y[J]. Cancer Cell, 2015, 27(4): 450-461. Forde P M, S P icer J, Lu S, et al. Neoadjuvant nivolumab P lus chemothera P y in resectable lung cancer[J]. New England Journal of Medicine, 2022, 386(21): 1973-1985. Tamura H, Dong H, Zhu G, et al. B7-H1 costimulation Preferentially enhances CD28-inde P endent T-hel P er cell function[J]. Blood, The Journal of the American Society of Hematology, 2001, 97(6): 1809-1816. Gurevičienė G, Matulionė J, Poškienė L, et al. PD-L1+Lym P hocytes Are Associated with CD4+, FoxP3+CD4+, IL17+CD4+T Cells and SubtyPes of MacroPhages in Resected Early-Stage Non-Small Cell Lung Cancer[J]. International Journal of Molecular Sciences, 2024, 25(19): 10827. Kamada T, Togashi Y, Tay C, et al. PD-1+ regulatory T cells am P lified by PD-1 blockade Promote hyPerProgression of cancer[J]. Proceedings of the National Academy of Sciences, 2019, 116(20): 9999-10008. Hegde S, Krisnawan V E, Herzog B H, et al. Dendritic cell Paucity leads to dysfunctional immune surveillance in Pancreatic cancer[J]. Cancer Cell, 2020, 37(3): 289-307. Skoulidis F, Byers L A, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune Profiles, and theraPeutic vulnerabilities[J]. Cancer Discovery, 2015, 5(8): 860-877. Thommen D S, Koelzer V H, Herzig P , et al. A transcri P tionally and functionally distinct PD-1+ CD8+ T cell Pool with Predictive Potential in non-small-cell lung cancer treated with PD-1 blockade[J]. Nature Medicine, 2018, 24(7): 994-1004. Mantovani A, Marchesi F, Malesci A, et al. Tumour-associated macroPhages as treatment targets in oncology[J]. Nature Reviews Clinical Oncology, 2017, 14(7): 399-416. Gajewski T F, Woo S R, Zha Y, et al. Cancer immunotheraPy strategies based on overcoming barriers within the tumor microenvironment[J]. Current OPinion in Immunology, 2013, 25(2): 268-276. Gentles A J, Newman A M, Liu C L, et al. The Prognostic landscaPe of genes and infiltrating immune cells across human cancers[J]. Nature Medicine, 2015, 21(8): 938-945. Galon J, Mlecnik B, Bindea G, et al. Towards the introduction of the ‘Immunoscore’in the classification of malignant tumours[J]. The Journal of Pathology, 2014, 232(2): 199-209. Bruni D, Angell H K, Galon J. The immune contexture and Immunoscore in cancer Prognosis and theraPeutic efficacy[J]. Nature Reviews Cancer, 2020, 20(11): 662-680. Fridman W H, Zitvogel L, Sautès–Fridman C, et al. The immune contexture in cancer Prognosis and treatment[J]. Nature Reviews Clinical Oncology, 2017, 14(12): 717-734. Cheng C, Yi-Bei Z, Yu S, et al. Increase of circulating B7-H4-exPressing CD68+ macroPhage correlated with clinical stage of lung carcinomas[J]. Journal of ImmunotheraPy, 2012, 35(4): 354-358. Saito T, Nishikawa H, Wada H, et al. Two FOXP3+ CD4+ T cell subPoPulations distinctly control the Prognosis of colorectal cancers[J]. Nature Medicine, 2016, 22(6): 679-684. Cheng C, Yi-Bei Z, Yu S, et al. Increase of circulating B7-H4-ex P ressing CD68+ macro P hage correlated with clinical stage of lung carcinomas[J]. Journal of Immunothera P y, 2012, 35(4): 354-358. Alexandra G, Ioannis A, I P M, et al. Prognostic Relevance of the Relative Presence of CD4, CD8 and CD20 ExPressing Tumor Infiltrating LymPhocytes in OPerable Non-small Cell Lung Cancer Patients[J]. Anticancer Research, 2021, 41(8): 3989-3995. Guo Z, Song J, Hao J, et al. M2 macroPhages Promote NSCLC metastasis by u P regulating CRYAB[J]. Cell death & Disease, 2019, 10(6): 377. Yuan S, Dong Y, Peng L, et al. Tumorassociated macroPhages affect the biological behavior of lung adenocarcinoma A549 cells through the PI3K/AKT signaling P athway[J]. Oncology Letters, 2019, 18(2): 1840-1846. Sequist L V, Waltman B A, Dias-Santagata D, et al. GenotyPic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors[J]. Science translational Medicine, 2011, 3(75): 75ra26-75ra26. Takada K, Okamoto T, Shoji F, et al. Clinical significance of PD-L1 P rotein exPression in surgically resected P rimary lung adenocarcinoma[J]. Journal of Thoracic Oncology, 2016, 11(11): 1879-1890. Mu C Y, Huang J A, Chen Y, et al. High ex P ression of PD-L1 in lung cancer may contribute to Poor Prognosis and tumor cells immune escaPe through suPPressing tumor infiltrating dendritic cells maturation[J]. Medical Oncology, 2011, 28(3): 682-688. Brahmer J, ReckamP K L, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-cell non–small-cell lung cancer[J]. New England Journal of Medicine, 2015, 373(2): 123-135. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7042926\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":507923610,\"identity\":\"e8e2406c-b00f-4548-9523-73b0aebdcbc6\",\"order_by\":0,\"name\":\"Xinmei Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"The First Affiliated Hospital of Yangtze University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xinmei\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":507923612,\"identity\":\"45f9dc22-2931-4abc-8f84-2b23fcdd562f\",\"order_by\":1,\"name\":\"Zhiqiong Yu\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Yangtze University, Jingzhou City, Hubei Province, China.\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Zhiqiong\",\"middleName\":\"\",\"lastName\":\"Yu\",\"suffix\":\"\"},{\"id\":507923614,\"identity\":\"90118d72-a69f-4ff1-a63a-5f6eeae72867\",\"order_by\":2,\"name\":\"Weihua Hu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAxCR2MDAwI8sKkGUFskGECuBWC2MQOUGB4jVYi6RY/jg4Y7D8sY3kp89/PqjTs7gAPPB2zwMdnm4tFjOyDE2SDxz2HDbjTRzY5kENmODA2zJ1jwMycU4HXYjd5tEYtthxm03EsykJRJ4Ejcc4DGT5mE4kNiAW8v2H0At9ptnpH8DapGo33CA/xshLdsYgFoSN0jkmEl+SDBIMDjAw4Zfy5n3n4EOS0+eceZNmTRDWoLhzMNsxpZzDJJxazmelvjxZ5u1bX97+jbJHzZ18nzHmx/eeFNhh1MLAggkMDDzgBjMYKMIqgcC/gMMjD+IUTgKRsEoGAUjDgAA05lbQ0gJYbUAAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Yangtze University, Jingzhou City, Hubei Province, China.\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Weihua\",\"middleName\":\"\",\"lastName\":\"Hu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-07-04 04:38:08\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7042926/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7042926/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":90382751,\"identity\":\"2c552b05-00ca-4bcd-92dc-b8fe5fd4e208\",\"added_by\":\"auto\",\"created_at\":\"2025-09-02 06:54:28\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2711804,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ea: low infiltration of CD4+TILs; b: high infiltration of CD4+TILs; c: low infiltration of CD8+TILs; d: high infiltration of CD8+TILs; e: low infiltration of CD68+TAMs; f: high infiltration of CD68+TAMs; g: low expression of PD-L1 (\\u0026lt;50%); h: hig\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7042926/v1/e9bc1bf1e311b9b0d9f4a8d2.png\"},{\"id\":90383724,\"identity\":\"dc2f38d7-2930-4ced-a8bc-340a8a21b558\",\"added_by\":\"auto\",\"created_at\":\"2025-09-02 07:02:28\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1044347,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ea:CD4+ TILs in paraneoplastic lung tissues; b:CD8+ TILs in paraneoplastic lung tissues; c:CD68+ TAMs in paraneoplastic lung tissues; d:PD-L1 in paraneoplastic lung tissues\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7042926/v1/12cc89f9c056e8bb8d905a4e.png\"},{\"id\":90381010,\"identity\":\"84201121-bd07-4ca3-a5d9-6ee38e59265b\",\"added_by\":\"auto\",\"created_at\":\"2025-09-02 06:46:05\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":146384,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ea:CD4+ TILs correlate with PFS; b:CD8+ TILs correlate with PFS; c:CD68+ TAMs correlate with PFS; d:PD-L1 correlate with PFS\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7042926/v1/6906c6238563d4af07fa69bb.png\"},{\"id\":90381019,\"identity\":\"64cb7e5d-705f-4647-ba2f-c57675a47aa0\",\"added_by\":\"auto\",\"created_at\":\"2025-09-02 06:46:05\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":151051,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ea:CD4+ TILs correlated with OS; b:CD8+ TILs correlated with OS; c:CD68+ TAMs correlated with OS; d:PD-L1 correlated with OS\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7042926/v1/fdb39e74d9aed7e0db64fe6c.png\"},{\"id\":105034064,\"identity\":\"7d86b680-0247-408f-b6b1-94406a2421fa\",\"added_by\":\"auto\",\"created_at\":\"2026-03-20 07:22:34\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":6406912,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7042926/v1/ebd039a5-c8fd-4e0d-a8e0-44f7e7f11a34.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Prognostic analysis of immunomarkers for non-small cell lung cancer\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eLung cancer, as the leading cause of cancer-related deaths worldwide, presents a significant disease burden in our country. According to the statistics of 2022, lung cancer accounted for 24.7% (1,060,600 cases/4,292,000 cases) of new cases in the year, and 733,300 deaths, which accounted for 28.5% of all malignant tumor deaths, with a significantly higher disease burden indicator than that of other cancers [\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e].Between 1990\\u0026ndash;2019, China's standardized incidence rate of cancer showed a 2.7-fold increase (21.7/100,000 to 58.6/100,000), and this growth rate is closely related to population aging and environmental carcinogenic exposure [\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]. Pathogenic risk factors include cigarette smoking, occupational carcinogens (asbestos, radon, etc.) and exposure characteristics specific to our country: occupational exposure to wood products/mining/petrochemical pollutants, and a high prevalence of adenocarcinoma in nonsmoking women due to synergistic effects of kitchen fumes and atmospheric fine particulate matter. Non-small cell lung cancer (NSCLC) accounts for 80\\u0026ndash;85% of lung cancers, with a predominance of adenocarcinomas (40\\u0026ndash;50%) and squamous carcinomas (20\\u0026ndash;30%), and its highly heterogeneous nature and late diagnostic features (5-year survival of about 20%) remain a major clinical challenge [\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTreatment strategies have evolved from traditional surgery, radiotherapy and chemotherapy to precision medicine. Stage I-IIIa NSCLC is mainly treated with radical surgery, supplemented by radiotherapy and chemotherapy; unresectable stage III is treated with simultaneous radiotherapy and chemotherapy combined with immunotherapy. Molecular targeted therapy significantly improves patient survival by inhibiting driver genes such as EGFR, ALK, ROS1, etc., but drug resistance and limited benefits are still to be broken through. Immune checkpoint inhibitors (ICIs) have become key to the paradigm shift in NSCLC treatment by remodeling the anti-tumor immune response through blocking the PD-1/PD-L1 or CTLA-4 signaling axis. However, with a clinical response rate of less than 30% for ICIs and limited efficacy of PD-L1 as a single predictive marker, a multidimensional biomarker system is urgently needed[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eTumor-Infiltrating LymPhocytes (TILs) are mainly a class of highly heterogeneous lymphocyte populations present in the tumor microenvironment, whose spatial distribution can be classified into mesenchymal-infiltrating TILs, cancer-nesting-infiltrating TILs, and contain immune cell subpopulations such as CD4\\u0026thinsp;+\\u0026thinsp;helper T cells, CD8\\u0026thinsp;+\\u0026thinsp;cytotoxic T cells, regulatory T cells (Tregs) and natural killer cells (NK cells) and other immune cell subpopulations[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]. These subpopulations influence tumor progression and anticancer success by exerting pro- and anti-tumorigenic effects[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]. Regarding the prognostic value of TILs, they have been widely validated in triple-negative breast cancer, HER2-positive subtypes. The Jun Hou Team 2024 study confirmed that for every 10% increase in TILs, the risk of recurrence in patients with triple-negative breast cancer is reduced by 15%,and that high TILs loads are usually associated with better Pathological ComPlete Remission (PCR) rates and survival. Interestingly, however, the study also found that in some cases (e.g., Luminal B) high TILs may be associated with poor prognosis, which may involve differences in subtypes or treatment modalities [\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]. Cross-cancer analysis showed that the prognostic value of TILs is also supported by clinical evidence in nasopharyngeal carcinoma, colorectal cancer, hepatocellular carcinoma, NSCLC, gastric cancer, and melanoma [\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. Regarding TILs and NSCLC, studies have shown that the spatial distribution of CD8\\u0026thinsp;+\\u0026thinsp;TILs (e.g., immune infiltration score) is more prognostic than the number alone, and that it is positively correlated with the response to treatment with PD-1/PD-L1 blockers. ICB \\u0026ldquo;reprograms\\u0026rdquo; CD8\\u0026thinsp;+\\u0026thinsp;TILs by targeting inhibitory receptors such as PD-1 to generate anti-tumor responses. TILs to generate anti-tumor responses, but the exact mechanism remains to be further elucidated[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e].Federico1 and his team demonstrated that the prognosis of NSCLC is not absolutely quantitatively correlated with the functional status of TILs. Recent studies have confirmed that the immune checkpoint interaction network (e.g., PD-1/PD-L1 axis) in TME can remodel the function of TILs, but the molecular mechanisms and clinical applications still need to be explored in depth [\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]. Therefore, integrating the multi-omics analysis of TILs subpopulation characteristics, spatial distribution and immune checkpoint expression is a key pathway to optimize the precision immunotherapy of NSCLC and provide a key biological basis for NSCLC precision immunotherapy.\\u003c/p\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.1 Case selection\\u003c/h2\\u003e\\u003cp\\u003eFifty patients who were diagnosed with primary NSCLC in the lung at the Affiliated Hospital of Yangtze University between 2018\\u0026ndash;2023 were retrospectively included. Inclusion criteria: (1) Patients were diagnosed with primary NSCLC in the lung and underwent surgical treatment by imaging and histopathological biopsy, and the pathological types of all tissue specimens were all confirmed to be NSCLC by postoperative pathology.(2) None of the patients had received radiotherapy, chemotherapy, neoadjuvant chemotherapy, targeted therapy, immunotherapy, and other antitumor treatments prior to surgery. (3) Eastern Cooperative Oncology Group (ECOG) score of 0\\u0026ndash;1. Exclusion criteria: (1) History of other malignant tumors. (2) The following diseases were combined at the time of lung cancer diagnosis: HIV carriers, autoimmune diseases, severe infectious diseases, severe chronic diseases, severe liver and renal insufficiency, severe anemia, infections, and febrile diseases. Clinicopathological parameters (including gender, age at diagnosis, smoking history, tumor pathological type, tumor size, TNM stage, clinical stage, recurrence or metastasis, etc.) were collected; paraffin-embedded tumor tissues and paracancerous normal lung tissue samples were collected, and immunohistochemistry (IHC) was used to detect PDL in the tumor tissues and paracancerous tissue samples of NSCLC. Immunohistochemistry (IHC) was used to detect the expression of PD-L1 and the infiltration degree of CD4\\u0026thinsp;+\\u0026thinsp;TILs, CD8\\u0026thinsp;+\\u0026thinsp;TILs and CD68\\u0026thinsp;+\\u0026thinsp;TAMs in NSCLC tumor tissues and paraneoplastic tissues, and to analyze their relationship with clinicopathological features and prognosis. All tissue sections were cut from paraffin-embedded tissues before the start of the experiment. Pathologic type classification was performed according to the World Health Organization (WHO) classification criteria of 2021, and TNM staging was performed according to the International Association for the Study of Lung Cancer (IASLC) 2023 Ninth Edition classification criteria for TNM staging of lung cancer. The primary endpoint of follow-up was OS and the secondary endpoint was PFS from diagnosis follow-up to August 2024 by outpatient clinic or telephone.PFS was defined as the time from disease diagnosis to disease progression or recurrence.OS was defined as the time from the patient's diagnosis of NSCLC to death or the end of or the last follow-up visit. Time was measured in months. The samples were fixed with a mass fraction of 10% paraformaldehyde and paraffin-embedded, and finally 50 archived wax blocks of NSCLC patients' tissues were collected in this study for 3um tissue sectioning. The study was in accordance with the approval of the Ethics Committee of the First People's Hospital of Jingzhou City.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.2 IHC\\u003c/h2\\u003e\\u003cp\\u003eIHC was performed using the kit from Beijing Bioss Biotechnology Co. The procedure was as follows: paraffin tissue sections were baked in an oven at 37\\u0026deg;C for about 1 hour and then placed in three different containers of fresh xylene for dewaxing and then hydrolyzed in ethanol, the repair solution was repaired in a microwave oven, heated for 10 minutes to bring it to a boil and then cooled down to room temperature, washed with PBS and then incubated in 3% hydrogen peroxide for 10 minutes. hydrogen peroxide closed incubation for 10 minutes. Each section was evenly titrated with normal goat serum containment working solution and incubated at 37\\u0026deg;C for 15\\u0026ndash;20 minutes in a thermostat and then the remaining liquid in the section was poured off, do not wash. Subsequently, each tissue section was uniformly added with a drop of configured primary antibody working solution, and the sections were placed in a wet box and incubated in a 37℃ thermostat for 1 hour, washed with PBS for 5 minutes, and repeated 3 times. After that, the sections were placed in the wet box and incubated in a constant temperature box at 37℃ for 15\\u0026ndash;20 minutes, washed with PBS for 5 minutes, and repeated three times.Finally, DAB staining, hematoxylin re-staining, and dehydration after hydrochloric acid alcohol differentiation were performed to seal the sections for microscopic examination. The concentration of primary antibody in the experiment was CD4 (1:500 recombinant rabbit monoclonal antibody), CD8 (1:500 recombinant rabbit monoclonal antibody), CD68 (1:300 recombinant murine monoclonal antibody), and PD-L1 (1:200 recombinant rabbit monoclonal antibody); and the secondary antibody was the Bowenson Ready-to-Use Assay Kit.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.3 Image processing\\u003c/h2\\u003e\\u003cp\\u003eLeica microscope was used to observe the whole section under low magnification, and then 5 fields were randomly selected under high magnification (400\\u0026times;) to calculate the proportion of positive cells to all tumor cells in the section, and the average of the 5 fields was taken as the final result. Image Pro Plus assisted in the counting of positive cells, and for the sections with large gaps, the sections were read again by senior pathologists. A standardized quality control system was adopted for the experimental process, and positive and negative controls were set for the staining batches to ensure the accuracy and reproducibility of the experiments.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e2.4 Statistical methods\\u003c/h2\\u003e\\u003cp\\u003eIn this study, the relationship between PD-L1 and CD4\\u0026thinsp;+\\u0026thinsp;TILs, CD8\\u0026thinsp;+\\u0026thinsp;TILs, CD68\\u0026thinsp;+\\u0026thinsp;TAMs and clinical characteristics was analyzed using the chi-square test or Fisher's exact test (for cells with expected frequency\\u0026thinsp;\\u0026lt;\\u0026thinsp;5).Spearman rank correlation analysis was used to assess the strength of correlation between the degree of infiltration of TILs and the expression of PD-L1 in NSCLC. KaPlan-Meier method was used to draw survival curves and compare the survival differences between different PD-L1 and TILs expression groups by log-rank test (log-rank test). A Cox proportional hazards regression model (Cox ProPortional hazards model) was established, and a one-way analysis was first performed to screen potential prognostic factors. Variables with P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.2 in the univariate analysis were included in the multivariate analysis. Hazard Ratio (HR) and its 95% confidence interval (95% CI) were used to quantify the risk association. All statistical tests were two-sided, and the significance threshold was set at a\\u0026thinsp;=\\u0026thinsp;0.05. All statistical analyses were performed using SPSS version 26.0.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\"\\u003e\\n \\u003ch2\\u003e3.1 Clinicopathologic features of the patient\\u003c/h2\\u003e\\n \\u003cp\\u003eAfter screening, 50 NSCLC patients were finally included. Their basic clinical characteristics are shown in Table 1 Clinicopathologic characteristics of NSCLC patientsand briefly described as follows: 33 cases (66.0%) were male and 17 cases (34.0%) were female; the age range was 42–72 years old, with a median age of 61 years (mean 68 years). Grouped by 60 years of age: 21 cases (42.0%) in the \\u0026lt; 60 years group and 29 cases (58.0%) in the ≥ 60 years group. Grouped by smoking history: 37 cases (74.0%) with smoking history and 13 cases (26.0%) without smoking history. According to IASLC 9th edition TNM staging T stage: 35 cases (70.0%) in T1-T2 stage and 15 cases (30.0%) in T3-T4 stage. Presence of lymph node metastasis was found in 30 cases (60.0%) and no lymph node metastasis in 20 cases (40.0%). Clinical staging (IASLC 9th edition): 13 cases (26.0%) in stage I, 15 cases (30.0%) in stage II, and 22 cases (44.0%) in stage III. Pathologic types: 33 cases (66.0%) of lung adenocarcinoma and 17 cases (34.0%) of lung squamous carcinoma. Follow-up results showed that PFS was 11 months (range 0–45, mean 13.96 months); OS was 36 months (range 5–72, mean 37.5 months).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 1\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eClinicopathologic characteristics of NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of persons (N = 50)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e66%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003efemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e34%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e42%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e≥ 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e58%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e74%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e26%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTumor size\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT1-T2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e70%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3-T4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003elymphatic node transfer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e40%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e60%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e66%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e34%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eClinical Stages\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅠ-Ⅱ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e56%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e44%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 2\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eTILs and PD-L1 immunohistochemistry\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eIHC\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of persons (N = 50)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e%\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow expression(PD-L1\\u0026lt;50%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e30%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehigh expression(PD-L1 ≥ 50%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e70%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow expression(0–1 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e43\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e86%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehigh expression(2–3 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e7\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e14%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD8\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow expression(0–1 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e10\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e20%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehigh expression(2–3 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e40\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e80%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD68\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow expression(0–1 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e36\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e72%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ehigh expression(1–2 point)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e14\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e28%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec9\\\"\\u003e\\n \\u003ch2\\u003e3.2 CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression in NSCLC tissues\\u003c/h2\\u003e\\n \\u003cp\\u003eCD4 and CD8 molecules are mainly localized in the cell membrane of TILs, PD-L1 is mainly expressed in the membrane or cytoplasm of tumor cells, and positive staining is also seen in some mesenchyme, with positive signals of tan or brown particles.CD68 molecules are mainly localized in the cytoplasm of tumor-associated macrophages, with positive signals of tan particles.Among the 50 cases of NSCLC, there were 7 cases of high expression of CD4 ( 14%) and 43 (86%) with low expression, 40 (80%) with high expression and 10 (20%) with low expression of CD8, 14 (28%) with high expression and 36 (72%) with low expression of CD68, and 35 (70%) with high expression and 15 (30%) with low expression of PD-L1, as shown in Table 2. In normal lung tissues adjacent to the carcinoma, CD4 + TILs, CD8 + TILs, CD68 + TAMs infiltration degree was lower than that of tumor tissues, and PD-L1 expression was almost negative.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003eFor immunohistochemical staining of TILs, PD-L1 see Fig.\\u0026nbsp;1. infiltration of TILs and expression of PD-L1 control staining Fig.\\u0026nbsp;2.\\u003c/p\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3.3 Relationship between the degree of infiltration of CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression and clinicopathologic features\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eThe relationships between the degree of TILs infiltration, PD-L1 expression and clinicopathological features in the NSCLC tumor microenvironment were analyzed in Table 3 and Table 4, and the results are briefly described as follows:\\u003c/p\\u003e\\n \\u003cp\\u003eCD4 + TILs were not significantly correlated with gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.741), age (\\u003cem\\u003ep\\u003c/em\\u003e = 0.961), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.868), tumor size (\\u003cem\\u003ep\\u003c/em\\u003e = 0.436), lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e = 0.498), clinical stage (\\u003cem\\u003ep\\u003c/em\\u003e = 0.948), and pathologic type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.175) were not significantly correlated.\\u003c/p\\u003e\\n \\u003cp\\u003eThe degree of infiltration of CD8 + TILs was not statistically significantly correlated with gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.058), age (\\u003cem\\u003ep\\u003c/em\\u003e = 0.568), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.275), tumor size (\\u003cem\\u003ep\\u003c/em\\u003e = 1.000), clinical stage (\\u003cem\\u003ep\\u003c/em\\u003e = 0.775), and pathological type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.279), but was significantly correlated with lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e = 0.004).\\u003c/p\\u003e\\n \\u003cp\\u003eThe degree of infiltration of CD68 + TAMs was not significantly correlated (p \\u0026gt; 0.05) with gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.610), age (\\u003cem\\u003ep\\u003c/em\\u003e = 0.230), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.641), tumor size (\\u003cem\\u003ep\\u003c/em\\u003e = 0.224), lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e = 0.368), clinical stage (\\u003cem\\u003ep\\u003c/em\\u003e = 0.594), and pathological type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.610).\\u003c/p\\u003e\\n \\u003cp\\u003ePD-L1 expression was not significantly correlated with gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.948), age (\\u003cem\\u003ep\\u003c/em\\u003e = 0.662), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.994), lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e = 0.208), clinical stage (\\u003cem\\u003ep\\u003c/em\\u003e = 0.804), and pathological type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.558), but was significantly correlated with tumor size (\\u003cem\\u003ep\\u003c/em\\u003e = 0.021).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 3\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eRelationship between the degree of infiltration of CD4 + TILs and CD8 + TILs and clinical characteristics of NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of persons (N = 50)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e (CD4)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eχ\\u003csup\\u003e2\\u003c/sup\\u003e(CD4)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e (CD8)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eχ\\u003csup\\u003e2\\u003c/sup\\u003e(CD8)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.741\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.110\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.058\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.590\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003efemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.961\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.002\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.568\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.325\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e≥ 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.868\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.028\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.275\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.192\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTumor size\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT1-T2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.436\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.607\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3-T4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003elymphatic node transfer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.498\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.459\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e8.424\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.175\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.840\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.279\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.170\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eClinical Stages\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅠ-Ⅱ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.775\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.082\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 4\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eRelationship between the degree of infiltration of CD68 + TAMs and PD-L1 expression and clinical characteristics of NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eNumber of persons (N = 50)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e(CD68)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eχ\\u003csup\\u003e2\\u003c/sup\\u003e(CD68)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e (PD-L1)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eχ\\u003csup\\u003e2\\u003c/sup\\u003e(PD-L1)\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.610\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.260\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003efemale\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e21\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.230\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.439\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.662\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.192\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e≥ 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e29\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e37\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.641\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.217\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.944\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e13\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eTumor size\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT1-T2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e35\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.224\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.477\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.021\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e5.331\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3-T4\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e15\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003elymphatic node transfer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eno\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e20\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.368\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.810\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.208\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.587\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e30\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e33\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.610\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.260\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.558\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.344\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e17\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eClinical Stages\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅠ-Ⅱ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e28\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.594\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.284\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.804\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.062\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e22\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e3.4 Correlation analysis of the degree of infiltration of CD4 + TILs, CD8 + TILs, CD68 + TAMs, and PD-L1 expression\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003cp\\u003eIn this study, the correlation between the infiltration degree of TILs (CD4 + TILs, CD8 + TILs, CD68 + TAMs) and PD-L1 expression in NSCLC was analyzed by using the Spearman rank correlation test in the SPSS software, and the results were as follows: the expression of PD-L1 showed a significant positive correlation with that of CD8 + TILs (r = 0.327, \\u003cem\\u003ep\\u003c/em\\u003e = 0.020), suggesting that the higher degree of infiltration of CD8 + TILs in NSCLC may be followed by an increase in the expression level of PD-L1. The expression of PD-L1 showed a significant negative correlation with CD68 + TAMs (r = -0.369, \\u003cem\\u003ep\\u003c/em\\u003e = 0.008), suggesting that the enrichment of tumor-associated macrophages may inhibit the expression of PD-L1 or that high expression of PD-L1 may be be unfavorable to the infiltration of CD68 + TAMs. This result suggests that CD68 + TAMs and PD-L1 may have an antagonistic role in the immunoregulation of NSCLC, and there was no significant correlation between the degree of infiltration of CD68 + TAMs, CD4 + TILs, CD8 + TILs and CD68 + TAMs (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; 0.05). See Table 5.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 5\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eCorrelation analysis between the degree of infiltration of TILs and PD-L1 expression\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD4 + TILs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD8 + TILs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD68 + TAMs\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD4 + TILs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD8 + TILs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.160\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD68 + TAMs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.972\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.355\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.338\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.020\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.008\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.000\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec10\\\"\\u003e\\n \\u003ch2\\u003e3.5 Prognostic analysis of CD4 + TILs, CD8 + TILs, CD68 + TAMs and PD-L1 expression and NSCLC\\u003c/h2\\u003e\\n \\u003cp\\u003eThis study has previously demonstrated significant lymphocyte heterogeneity in the NSCLC tumor microenvironment, with varying levels of immune cell infiltration. Further analysis showed that PD-L1 expression was significantly positively correlated with CD8 + TILs and negatively correlated with CD68 + TAMs, suggesting that TILs and PD-L1 expression may affect the prognosis of NSCLC patients. To test this hypothesis, in this study, survival analysis was performed on 50 NSCLC patients who underwent surgical treatment to assess the relationship between PD-L1 expression and TILs subtypes and prognosis. Survival curves are shown(Fig.\\u0026nbsp;3,Fig.\\u0026nbsp;4). The specific results were as follows: the median PFS in the CD4 + TILs high-infiltration group (7 cases) was 6 months, which was significantly lower than that in the low-infiltration group (43 cases) of 15 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.027), and the results suggested that the high infiltration of CD4 + TILs might affect the prognosis of NSCLC patients.The median OS in the CD4 + TILs high-infiltration group and the low-infiltration group were 35 months and 48 months, respectively (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; 0.05), indicating that the degree of CD4 + TILs infiltration had no significant effect on the overall survival of NSCLC patients.The median PFS (14 months) and OS (48 months) of the CD8 + TILs high-infiltration group (40 cases) were slightly better than those of the low-expression group (10 cases, median PFS: 10 months, median OS: 36 months), but the difference was not statistically significant (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; 0.05). The median PFS (15 months) and OS (48 months) of the CD68 + TAMs high infiltration group (14 cases) were not statistically significant (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt;0.05) compared with those of the low infiltration group (36 cases, median PFS: 14 months, median OS: 48 months), and the median PFS (12 months) and OS (47 months) of the PD-L1 high expression group (35 cases) were lower than those of the low expression group (The median PFS (12 months) and OS (47 months) were lower in the high PD-L1 expression group (35 cases) than in the low PD-L1 expression group (15 cases, median PFS: 30 months, median OS: 68 months).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec11\\\"\\u003e\\n \\u003ch2\\u003e3.6 Univariate analysis of various clinicopathologic features\\u003c/h2\\u003e\\n \\u003cp\\u003eIn 50 patients with surgically resected NSCLC, the median PFS was 12 months and 35 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.152) and the median OS was 40 months and 58 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.150) for men (33 patients) versus women (17 patients), respectively, and none of the differences were statistically significant. The age range was 42–72 years, with a median age of 61 years, and the median PFS was 12 versus 15 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.229) and the median OS was 48 versus 47 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.701) for patients \\u0026lt; 60 years (21 patients) versus ≥ 60 years (29 patients), with no statistically significant difference. The median PFS for smokers (37 cases) and non-smokers (13 cases) was 12 months versus 35 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.090), and the median OS was 58 months versus 47 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.165), with none of the differences reaching statistical significance. According to the 9th edition of TNM staging of lung cancer, 50 patients with NSCLC treated by radical surgery had a median PFS of 14 months and 12 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.920) and a median OS of 48 months and 68 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.449) for patients with T1-T2 stage (35 patients) versus T3-T4 stage (15 patients), respectively. None of the differences were statistically significant. The median PFS of no postoperative lymph node metastasis (20 cases) and lymph node metastasis (30 cases) were 12 months and 15 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.629), and the median OS was 53 months and 48 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.523), respectively, and none of the differences were statistically significant. The median PFS was 18 months and 12 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.381), and the median OS was 53 months and 36 months (\\u003cem\\u003ep\\u003c/em\\u003e = 0.063) for patients with stage I-II (28 patients) versus stage III (22 patients) NSCLC, respectively. Although patients with stage I-II NSCLC had better median PFS and OS than stage III, none of the differences were statistically significant.\\u003c/p\\u003e\\n \\u003cp\\u003eThe median PFS of lung adenocarcinoma (33 cases) and lung squamous carcinoma (17 cases) were 26 months and 10 months, respectively, and the difference was statistically significant (\\u003cem\\u003ep\\u003c/em\\u003e = 0.037). The median OS was 53 months and 19 months, respectively, and the difference was also statistically significant (\\u003cem\\u003ep\\u003c/em\\u003e = 0.043). Immunohistochemical staining was used to assess the degree of infiltration of CD4 + TILs in tumor tissues, and the median PFS of the low-infiltration group (43 cases) was significantly better than that of the high-infiltration group (7 cases) (15 months vs. 6 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.042). However, there was no significant difference in OS between the two groups (48 months vs. 35 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.207).There was no statistical significance in the difference in median PFS (10 months vs. 14 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.879) as well as in OS (36 months vs. 48 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.207) between the low infiltration group (10 cases) and the high infiltration group (40 cases) of CD8 + TAMs. CD68 + TAMs low infiltration group (36 cases) vs. high infiltration group (14 cases) were not statistically significant in terms of difference in median PFS (14 months vs. 15 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.889), and OS (48 months vs. 48 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.589).The PD-L1 low expression group (15 cases) were not statistically significant in terms of difference in median PFS (30 months vs. 12 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.084), OS (68 months vs 47 months, \\u003cem\\u003ep\\u003c/em\\u003e = 0.149), although significantly better than the high expression group (35 cases), the difference did not reach statistical significance. See Table 6.\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 6\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eUnivariate analysis of NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003ePFS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eOS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e值\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e值\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale vs female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.791–4.520\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.152\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.794–4.532\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.150\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eage\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt;60vs ≥ 60\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.668\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.311–1.432\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.299\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.166\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.533–2.552\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.701\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes VS no\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.234\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.881–5.663\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.090\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.926\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.764–4.857\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.165\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elymphatic node transfer\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes VS no\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.820\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.367–1.834\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.629\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.299\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.582–2.897\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.523\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eTumor size\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eT3-4 vs T1-2\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.043\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.456–2.386\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.920\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.712\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.295–1.717\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.449\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClinical Stages\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅠ-Ⅱ vs Ⅲ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.405\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.657–3.004\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.381\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.070\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.962–4.456\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.063\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD vsLUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.247\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.05–4.809\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.037\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.191\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.025–4.685\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.043\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD4 + TILs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.192\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.042–9.779\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.042\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.018\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.678–6.013\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.207\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD8 + TILs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.931\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.372–2.330\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.879\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.845\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.336–2.129\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.721\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD68 + TAMs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.889\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.392–2.250\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.889\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.792\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.333–1.883\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.598\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.153\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.901–5.145\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.084\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.973\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.784–4.963\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.149\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"8\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec12\\\"\\u003e\\n \\u003ch2\\u003e3.7 Multifactorial analysis of various clinicopathologic features\\u003c/h2\\u003e\\n \\u003cp\\u003eBased on the above univariate analysis to screen potential prognostic factors, a Cox proportional risk model was constructed for multivariate analysis, and variables with significance levels set at \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.2 in the univariate analysis were included in the multivariate model with a loose threshold (α = 0.2 rather than the traditional α = 0.05), aiming to reduce the risk of Type II errors (false negatives). See Table 7and Table 8 briefly described below:\\u003c/p\\u003e\\n \\u003cp\\u003eCD4 + TILs (\\u003cem\\u003ep\\u003c/em\\u003e = 0.042), pathology type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.037), gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.152), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.090), and PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e = 0.084) were included in the multifactorial analysis of PFS in NSCLC patients. Gender (\\u003cem\\u003ep\\u003c/em\\u003e = 0.150), smoking history (\\u003cem\\u003ep\\u003c/em\\u003e = 0.165), clinical stage (\\u003cem\\u003ep\\u003c/em\\u003e = 0.063), pathology type (\\u003cem\\u003ep\\u003c/em\\u003e = 0.043), and PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e = 0.149) were included in the OS multifactorial analysis. The results were as follows: pathologic type (HR = 2.898, 95% CI: 1.132–7.419, \\u003cem\\u003ep\\u003c/em\\u003e = 0.026), and PD-L1 expression (HR = 3.093, 95% CI: 1.179–8.111, p = 0.022) were independent risk factors for PFS in patients with NSCLC. Other control variables: gender, smoking history, and CD4 + TILs did not independently predict PFS in NSCLC patients (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt;0.05).PD-L1 (HR = 2.850, 95% CI: 1.056–7.689, \\u003cem\\u003ep\\u003c/em\\u003e = 0.039) was an independent predictor of OS in NSCLC patients as a risk factor. In NSCLC patients treated by surgical resection, high PD-L1 expression significantly increased the risk of disease progression. Gender, smoking history, pathologic type, and clinical stage were all non-significantly associated variables (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt; 0.05).\\u003c/p\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 7\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eMultifactorial analysis of PFS in NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003ePFS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale vs female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.718\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.474–6.221\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.410\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes VS no\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.714\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.227–2.244\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.565\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD vs LUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.898\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.132–7.419\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.026\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eCD4 + TILs\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.401\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.983–11.770\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.053\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e3.093\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.179–8.111\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.022\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003ctable id=\\\"Tab8\\\" border=\\\"1\\\"\\u003e\\n \\u003ccaption language=\\\"En\\\"\\u003e\\n \\u003cdiv\\u003eTable 8\\u003c/div\\u003e\\n \\u003cdiv\\u003e\\n \\u003cp\\u003eMultifactorial analysis of OS in NSCLC patients\\u003c/p\\u003e\\n \\u003c/div\\u003e\\n \\u003c/caption\\u003e\\n \\u003cthead\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\" rowspan=\\\"2\\\"\\u003e\\n \\u003cp\\u003eclinical characteristic\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\" colspan=\\\"3\\\"\\u003e\\n \\u003cp\\u003eOS\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eHR\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e95% CI\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003cth align=\\\"left\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/th\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/thead\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003egenders\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003emale vs female\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.297\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.59–8.948\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.231\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eSmoking history\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eyes VS no\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.419\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.128–1.369\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.150\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePathological type\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eLUAD vs LUSC\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.527\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.923–6.921\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.071\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eClinical Stages\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003eⅠ-ⅡvsⅢ\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.660\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.707–3.896\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.244\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003ePD-L1\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"left\\\"\\u003e\\n \\u003cp\\u003elow vs high\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e2.850\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e1.056–7.689\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd align=\\\"char\\\"\\u003e\\n \\u003cp\\u003e0.039\\u003csup\\u003e*\\u003c/sup\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n \\u003ctfoot\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"5\\\"\\u003e(Note:*: Differences are statistically significant, \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; 0.05)\\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tfoot\\u003e\\n \\u003c/table\\u003e\\n \\u003c/div\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.1 Association of TILs and PD-L1 expression with clinical features of NSCLC\\u003c/h2\\u003e\\u003cp\\u003eIn the present study, high infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs was significantly associated with a reduced risk of NSCLC lymph node metastasis by IHC analysis (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), suggesting that it may influence the metastatic process by inhibiting tumor cell proliferation.Neither infiltration of CD4\\u0026thinsp;+\\u0026thinsp;TILs nor CD68\\u0026thinsp;+\\u0026thinsp;TAMs was significantly associated with clinical features.PD-L1 expression was significantly associated with tumor size (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.028), suggesting that tumor size may accompany the formation of an immunosuppressive microenvironment, which induces the upregulation of PD-L1, but TNM stage was not associated with gender, age, smoking history and lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05).\\u003c/p\\u003e\\u003cp\\u003eThe available literature shows heterogeneity in the correlation between TILs and clinical features: Binnewies M et al. found a significantly higher infiltration of CD4\\u0026thinsp;+\\u0026thinsp;Treg in metastatic lymph nodes (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) [\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. In contrast, studies in breast cancer have shown that CD4\\u0026thinsp;+\\u0026thinsp;TILs are not associated with staging[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e]. The present study confirmed that CD4\\u0026thinsp;+\\u0026thinsp;TILs were not significantly associated with the basic clinical features of NSCLC.The findings for CD8\\u0026thinsp;+\\u0026thinsp;TILs were divergent: Herbest's team noted that the degree of infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs correlated with the stage (early stage tumors (stage I-II), \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.006; while for advanced (stage III-IV) tumors, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.02) [\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. Consistent with Donnem et al. who reported that high infiltration was associated with a lower rate of lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.02), the results of the present study are consistent with the latter[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe relationship between CD68\\u0026thinsp;+\\u0026thinsp;TAMs and clinical characteristics of patients with lung cancer showed contradictory results: the CD68\\u0026thinsp;+\\u0026thinsp;macrophages of B7-H4 in the peripheral blood of lung cancer were associated with tumor size, lymph node metastasis, and TNM stage.The team of Mantovani A found a positive correlation between the M2 isoform (CD163+) and TNM stage (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008); the M2 type of TAMs promoted lymph node metastasis through the PD-L1 /IL-10 axis to promote lymph node metastasis [\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e].CD68, a pan-tumor-associated macrophage marker, was noted to have a significant positive correlation between its high expression and advanced TNM stage (p\\u0026thinsp;=\\u0026thinsp;0.008), lymph node metastasis, and peritumoral lymphatic vessel density (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) in NSCLC, suggesting that the abundance of TAMs may reflect the invasive potential of the tumor[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]. In the present study, we found that CD68\\u0026thinsp;+\\u0026thinsp;TAMs infiltration was not significantly associated with the clinical features of NSCLC patients, which might be related to the mutual functional counteractivity or spatial heterogeneity of M1/M2 subtypes, and it was negatively correlated with PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008), suggesting a role of immune modulation.\\u003c/p\\u003e\\u003cp\\u003eThe PD-L1 expression controversy stems from multiple factors: a study by Garon et al. found a significantly higher proportion of patients with tumors\\u0026thinsp;\\u0026ge;\\u0026thinsp;3 cm in diameter (T2-4) expressing PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.002). While KEYNOTE-001 showed no association with T-staging (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.15)[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]. However, no significant correlation between histologic subtypes and PD-L1 expression was found in KEYNOTE-024, which may be related to sample size or differences in assay platforms[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]. In this study, we confirmed that PD-L1 expression was correlated with tumor size (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.021), and there was no significant correlation with gender, age, smoking history, clinical stage, lymph node metastasis, or pathology type. The heterogeneity of the results may stem from: antibody and assay platforms, scoring criteria limitations, spatiotemporal heterogeneity, and class II errors due to sample size limitations.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.2 Correlation analysis between the degree of infiltration of TILs and PD-L1 expression\\u003c/h2\\u003e\\u003cp\\u003eIn this study, PD-L1 was significantly positively correlated with CD8\\u0026thinsp;+\\u0026thinsp;TILs (r\\u0026thinsp;=\\u0026thinsp;0.327, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.020), suggesting that CD8\\u0026thinsp;+\\u0026thinsp;TILs may induce PD-L1 upregulation through IFNγ signaling. On the contrary, PD-L1 and CD68\\u0026thinsp;+\\u0026thinsp;TAMs showed a significant negative correlation (r =-0.369, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008), suggesting that they have an antagonistic effect in the immune microenvironment of NSCLC, and no significant correlation was found among CD68\\u0026thinsp;+\\u0026thinsp;TAMs, CD4\\u0026thinsp;+\\u0026thinsp;TILs, and CD8\\u0026thinsp;+\\u0026thinsp;TILs (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026gt;0.05). When evaluated by the Tumor cell ProPortion Score (TPS), Immune Cell (IC), and Combined Positive Score (CPS) systems, a research team found that PD-L1 expression was associated with the CD4\\u0026thinsp;+\\u0026thinsp;TILs, CD8\\u0026thinsp;+\\u0026thinsp;TILs and M2-TAMs were significantly positively correlated. Notably, PD-L1\\u0026thinsp;+\\u0026thinsp;lymphocytes had co-localization characteristics with CD4\\u0026thinsp;+\\u0026thinsp;TILs and M1-TAMs, but showed negative spatial distribution with M1-TAMs in the mesenchymal region of the tumor[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. At the mechanistic level, activated CD8\\u0026thinsp;+\\u0026thinsp;TILs induce tumor PD-L1 expression by secreting IFNγ, while activation of the PD-L1/PD-L1 pathway can feedback inhibit the function of T cells, creating a vicious cycle of immune escape [\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. This biological process was clinically validated in the KEYNOTE-001 and CheckMate-816 studies: patients with high PD-L1 expression (TPS\\u0026thinsp;\\u0026ge;\\u0026thinsp;50%) combined with high infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs had an objective remission rate (ORR) of 45%, which was significantly better than that of the single-indicator-positive group (34%-50% vs 34%) [\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eSignificant heterogeneity exists between PD-L1 and CD4\\u0026thinsp;+\\u0026thinsp;TILs[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e].Herbst et al. found a positive association between Th1-type CD4\\u0026thinsp;+\\u0026thinsp;TILs and PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.003), whereas Rizvi's team did not find a significant association in an immunotherapy cohort (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.21)[\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]. These differences may be related to the following mechanisms: (1) dynamic balance of functional subpopulations of CD4\\u0026thinsp;+\\u0026thinsp;TILs: functional antagonism of Th1, Tregs, and Th17 cells may mask the overall correlation; (2) spatiotemporal heterogeneity of PD-L1 expression: the tumor core region and invasive front showed significant expression differences; and (3) immunotherapy-mediated remodeling of the microenvironment.\\u003c/p\\u003e\\u003cp\\u003eRegarding the mechanism of negative correlation between PD-L1 and CD68\\u0026thinsp;+\\u0026thinsp;TAMs, available studies suggest that it may involve M1/M2 phenotypic transition.Hegde et al. found that enrichment of CD68\\u0026thinsp;+\\u0026thinsp;TAMs in the mesenchymal region was associated with low expression of PD-L1 (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004), whereas Mantovani's team demonstrated that M2-TAMs can upregulate PD-L1 through the IL-6/STAT3 pathway[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]. This contradiction may stem from (1) phenotypic heterogeneity of TAMs: the CD68\\u0026thinsp;+\\u0026thinsp;population contains pro-inflammatory M1-type and immunosuppressive M2-type subpopulations, and (2) spatial distribution specificity: TAMs in the tumor parenchymal region mostly show M2 phenotype, with co-localization features with PD-L1\\u0026thinsp;+\\u0026thinsp;lymphocytes[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe diversity of the current findings may be attributed to tumor heterogeneity and differences in immune scoring systems. In the future, the spatial and temporal dynamics of PD-L1-immune cell interactions need to be resolved by spatial transcriptomics, single-cell sequencing, and other high-dimensional technologies to provide a theoretical basis for precision immunotherapy.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.3 Survival analysis of the extent of infiltration and PD-L1 expression in TILs\\u003c/h2\\u003e\\u003cp\\u003eIn this study, we investigated the relationship between TILs subtypes and prognosis through survival analysis of 50 surgically treated NSCLC patients. The median PFS was found to be 6 months in the CD4\\u0026thinsp;+\\u0026thinsp;TILs high infiltration group (n\\u0026thinsp;=\\u0026thinsp;7), which was significantly lower than that of 15 months in the low infiltration group (n\\u0026thinsp;=\\u0026thinsp;43) (Log-Rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.027), suggesting that CD4\\u0026thinsp;+\\u0026thinsp;TILs may accelerate disease progression through regulatory T-cell (Treg)-mediated immunosuppressive effects. Univariate Cox analysis showed the prognostic value of CD4\\u0026thinsp;+\\u0026thinsp;TILs (HR\\u0026thinsp;=\\u0026thinsp;2.1, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.042), but did not reach significance in multivariate analysis, which may be related to confounding factors interference (e.g., clinical staging, pathologic subtype) and sample size limitation.\\u003c/p\\u003e\\u003cp\\u003eThe prognostic value of CD4\\u0026thinsp;+\\u0026thinsp;TILs showed significant heterogeneity: Galon's team confirmed that they were significantly associated with PFS[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e], Bruni et al. found that Th1 subpopulation was associated with prolonged overall survival (OS) (HR\\u0026thinsp;=\\u0026thinsp;0.71, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.02) [\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e], whereas Plitas et al. reported that FoxP3\\u0026thinsp;+\\u0026thinsp;Treg hyperinfiltration was significantly correlated with shortened OS (HR\\u0026thinsp;=\\u0026thinsp;2.5, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004) [\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]. This contradiction may stem from (1) the dynamic balance of Th1/Treg subpopulations and (2) the EGFR mutation status affecting Treg function [HR\\u0026thinsp;=\\u0026thinsp;1.9, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.01].\\u003c/p\\u003e\\u003cp\\u003eRegarding the prognostic value of CD8\\u0026thinsp;+\\u0026thinsp;TILs, a meta-analysis including 2,559 cases showed that their high infiltration was significantly associated with improved OS (HR\\u0026thinsp;=\\u0026thinsp;0.52), PFS (HR\\u0026thinsp;=\\u0026thinsp;0.52) and objective remission rate (ORR) (all \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) [\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. However, Thommen's team found that PD-L1\\u0026thinsp;+\\u0026thinsp;CD8\\u0026thinsp;+\\u0026thinsp;TILs lost prognostic value due to the depletion phenotype (HR\\u0026thinsp;=\\u0026thinsp;1.2, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.32)[\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e], and Malaka A et al. confirmed the presence of histologic specificity in their prognostic effect[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]. The present study did not find a significant association between CD8\\u0026thinsp;+\\u0026thinsp;TILs and prognosis, suggesting the need for precise assessment in combination with spatial distribution (tumor parenchyma/mesenchyme) and functional markers (PD-1/TIM-3).\\u003c/p\\u003e\\u003cp\\u003eThe prognostic value of CD68\\u0026thinsp;+\\u0026thinsp;TAMs was modulated by M1/M2 polarization phenotype and spatial distribution: tumor parenchymal M1-TAMs (CD68\\u0026thinsp;+\\u0026thinsp;iNOS+) were associated with prolonged OS (HR\\u0026thinsp;=\\u0026thinsp;0.62, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.03), whereas mesenchymal M2-TAMs (CD68\\u0026thinsp;+\\u0026thinsp;CD163+) mediated immunosuppression via PD-L1/IL-10 signaling (HR\\u0026thinsp;=\\u0026thinsp;1.89, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.01)[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. The association between overall infiltration of CD68\\u0026thinsp;+\\u0026thinsp;TAMs and prognosis was not found in this study and may be related to (1) mixed M1/M2 phenotypic disturbances, (2) heterogeneity of tumor parenchymal-mesenchymal distribution, and (3) limitations of immunohistochemistry techniques in assessing functional status.\\u003c/p\\u003e\\u003cp\\u003eThe current study suggests that the prognostic value of immune cells depends on their functional phenotype, spatial topology and microenvironmental regulatory network. In the future, it is necessary to analyze the multidimensional features of the TME immune ecosystem through multiple immunofluorescence (e.g., CD68/CD163/PD-L1 co-staining) in combination with spatial transcriptome technology to provide a theoretical basis for precision immunotherapy.\\u003c/p\\u003e\\u003c/div\\u003e\\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003e4.4 Analysis of PD-L1 expression and NSCLC prognosis\\u003c/h2\\u003e\\u003cp\\u003eSurvival analysis in this study showed that the median PFS and OS of the PD-L1 high expression group (n\\u0026thinsp;=\\u0026thinsp;35) were 12 and 47 months, respectively, which showed a trend of survival disadvantage (Log-rank \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.05) compared to the PD-L1 low expression group (n\\u0026thinsp;=\\u0026thinsp;15; PFS\\u0026thinsp;=\\u0026thinsp;30 months, OS\\u0026thinsp;=\\u0026thinsp;68 months).Cox multifactorial analysis confirmed that PD-L1 high expression was the most important factor in surgical resection of NSCLC patients with PFS (HR\\u0026thinsp;=\\u0026thinsp;3.093, 95%CI:1.179\\u0026ndash;8.111, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022) and OS (HR\\u0026thinsp;=\\u0026thinsp;2.850, 95%CI:1.056\\u0026ndash;7.689, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.039) as an independent risk factor, with wide confidence intervals suggesting that extended cohort validation is needed. Pathologic type (lung adenocarcinoma vs. squamous carcinoma) was also an independent predictor of PFS (HR\\u0026thinsp;=\\u0026thinsp;2.898, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.026), which may be related to the biological characteristics of adenocarcinoma that is more likely to metastasize distantly[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e].\\u003c/p\\u003e\\u003cp\\u003eThe prognostic value of PD-L1 is significantly controversial: Garon et al. found that high PD-L1 expression was associated with a shortened OS in patients without immunotherapy (HR\\u0026thinsp;=\\u0026thinsp;1.3, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.02) [\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e], whereas a study enrolling 205 cases of squamous lung carcinoma showed that OS was significantly lower in those who were PD-L1 positive (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01)[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e]. In contrast, multivariate analysis of 544 cases showed that PD-L1 positivity combined with high infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs predicted prolonged OS (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05)[\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]. This contradiction may stem from (1) PD-L1 assay heterogeneity (antibody cloning/scoring system differences); (2) dynamic changes in the tumor immune microenvironment (e.g., IFNγ-induced adaptive resistance); and (3) differences in therapeutic contexts: the KEYNOTE-001 demonstrated that immunotherapy reversed the prognostic effect of PD-L1, transforming it into a predictive biomarker (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) [\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e].\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003ePD-L1 was specifically highly expressed in the cell membranes of NSCLC tumor cells, and the degree of its expression was significantly correlated with tumor size, with almost no expression seen in normal lung tissues; the degree of membrane-localized infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs was significantly and positively correlated with lymph node metastasis.\\u003c/p\\u003e\\u003cp\\u003eHigh PD-L1 expression and pathologic type (lung adenocarcinoma vs. lung squamous carcinoma) were independent risk factors for postoperative PFS, and PD-L1 could be used as a positive predictive marker for OS. The combined PD-L1 expression and the immune infiltration characteristics of CD8\\u0026thinsp;+\\u0026thinsp;TILs/CD68\\u0026thinsp;+\\u0026thinsp;TAMs provide a theoretical basis for individualized immunotherapy strategies.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe thank Dr Hu Weihua, Chief Physician of NSCLC, for his advice in the design of NSCLC immunohistochemistry experiments; and we thank the Hubei Provincial Clinical Research Centre for Individualised Tumour Diagnosis and Treatment for its special financial support.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026rsquo;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;contributions\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWeihua Hu, Xinmei Wang and Zhiqiong Yu were involved in the experimental design and analysis of the results. Xinmei Wang was involved in specimen collection, immunohistochemistry experiments, data collection, statistical analysis, and writing the first draft of the manuscript. Wei-Hua Hu and Zhi-Qiong Yu contributed to conceptualisation, manuscript revision and overall project management. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eProvided exclusively by Hubei Provincial Clinical Medical Research Centre for Individualized Cancer Diagnosis and Treatment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\u003cp\\u003eEthics approval and consent to participate\\u003c/p\\u003e\\n\\u003cp\\u003eWritten informed consent was obtained from all participating subjects at the time of tumour tissue collection. All study procedures, including patient recruitment, informed consent acquisition and specimen handling, were reviewed and approved by the Institutional Review Board of the Jingzhou First People\\u0026apos;s Hospital. All relevant ethical guidelines and regulations were followed throughout the study to ensure the integrity of the study.\\u003cp\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAbu Rous, Fawzi et al.Lung Cancer Treatment Advances in 2022. Cancer investigation vol. 41,1 (2023): 12-24.\\u003c/li\\u003e\\n\\u003cli\\u003eSmolarz B , Ukasiewicz H , Samulak D , et al. Lung Cancer\\u0026mdash;Epidemiology, Pathogenesis,Treatment and Molecular Aspect (Review of Literature)[J].International Journal of Molecular Sciences, 2025, 26(5):2049.\\u003c/li\\u003e\\n\\u003cli\\u003eZeng H, Zheng R, Sun K, et al. Cancer survival statistics in China 2019-2021:a multicenter, PoPulation-based study[J]. Journal of the National Cancer Center, 2024, 4(3): 203-213. \\u003c/li\\u003e\\n\\u003cli\\u003eDe Polo A , Buja A , Pasello G ,et al.Non Small Cell Lung Cancer: Real-World Cost Consequence Analysis[J].European Journal of Public Health, 2021, 31.DOI:10.1093/eurpub/ckab164.840.\\u003c/li\\u003e\\n\\u003cli\\u003eSimiao C, Zhong C, Klaus P, et al. Estimates and Projections of the Global Economic Cost of 29 Cancers in 204 Countries and Territories From 2020 to 2050[J]. JAMA Oncology. 2023, 9(4): 465-472.\\u003c/li\\u003e\\n\\u003cli\\u003eZhonghua Zhong Liu Za Zhi, et al. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2024 edition) [J]. Chinese Journal of Oncology, 2024, 46(9). 805-843. \\u003c/li\\u003e\\n\\u003cli\\u003eAllemani C, Matsuda T, Di Carlo V, et al. Global surveillance of trends in cancer survival 2000\\u0026ndash;14 (CONCORD-3): analysis of individual records for 37 513 025 Patients diagnosed with one of 18 cancers from 322 PoPulation-based registries in 71 countries[J]. The Lancet, 2018, 391(10125): 1023-1075. \\u003c/li\\u003e\\n\\u003cli\\u003eRiely GJ, Wood DE, Ettinger DS, et al. Non-Small Cell Lung Cancer, Version 4.2024, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw. 2024 May;22(4):249-274. \\u003c/li\\u003e\\n\\u003cli\\u003eGenova C, DellePiane C, Carrega P, et al. TheraPeutic ImPlications of Tumor Microenvironment in Lung Cancer: Focus on Immune CheckPoint Blockade[J]. Frontiers in Immunology, 2022, 12: 799455-799455.\\u003c/li\\u003e\\n\\u003cli\\u003eMoreno V, Salazar R, Gruber S B, et al. The Prognostic value of TILs in stage III colon cancer must consider sidedness[J]. Annals of Oncology, 2022, 33(11): 1094-1096. \\u003c/li\\u003e\\n\\u003cli\\u003eCarsten Denkerta B C , Minckwitz G V , Darb-Esfahani S ,et al.Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy[J].The Lancet Oncology, 2018, 19(1):11.DOI:10.1016/S1470-2045(17)30904-X.\\u003c/li\\u003e\\n\\u003cli\\u003eBai S, Yang X, Zhang N, et al. Function of tumor infiltrating lym\\u003cem\\u003eP\\u003c/em\\u003ehocytes in solid tumors-a review[J]. Chinese journal of Biotechnology, 2019, 35(12): 2308-2325. \\u003c/li\\u003e\\n\\u003cli\\u003eGueguen \\u003cem\\u003eP\\u003c/em\\u003e, Metoikidou C, Du\\u003cem\\u003eP\\u003c/em\\u003eic T, et al. Contribution of resident and circulating Precursors to tumor-infiltrating CD8+ T cell Po\\u003cem\\u003eP\\u003c/em\\u003eulations in lung cancer[J]. Science Immunology, 2021, 6(55): eabd5778.\\u003c/li\\u003e\\n\\u003cli\\u003eFederico L, Mcgrail D J, Bentebibel S E, et al. Distinct tumor-infiltrating lym\\u003cem\\u003eP\\u003c/em\\u003ehocyte landsca\\u003cem\\u003eP\\u003c/em\\u003ees are associated with clinical outcomes in localized non-small-cell lung cancer [J]. Annals of Oncology, 2022, 33(1): 42-56. \\u003c/li\\u003e\\n\\u003cli\\u003eBinnewies M, \\u003cem\\u003eP\\u003c/em\\u003eollack J L, Rudol\\u003cem\\u003eP\\u003c/em\\u003eh J, et al. Targeting TREM2 on tumor-associated macro\\u003cem\\u003eP\\u003c/em\\u003ehages enhances immunothera\\u003cem\\u003eP\\u003c/em\\u003ey[J]. Cell Re\\u003cem\\u003eP\\u003c/em\\u003eorts, 2021, 37(3): 109869.\\u003c/li\\u003e\\n\\u003cli\\u003ePlitas G, Kono\\u003cem\\u003eP\\u003c/em\\u003eacki C, Wu K, et al. Regulatory T cells exhibit distinct features in human breast cancer[J]. Immunity, 2016, 45(5): 1122-1134. \\u003c/li\\u003e\\n\\u003cli\\u003eHerbst R S, Soria J C, Kowanetz M, et al. \\u003cem\\u003eP\\u003c/em\\u003eredictive correlates of res\\u003cem\\u003eP\\u003c/em\\u003eonse to the anti-\\u003cem\\u003eP\\u003c/em\\u003eD-L1 antibody M\\u003cem\\u003eP\\u003c/em\\u003eDL3280A in cancer Patients[J]. Nature, 2014, 515(7528): 563-567.\\u003c/li\\u003e\\n\\u003cli\\u003eDonnem T, Kilvaer T K, Andersen S, et al. Strategies for clinical imPlementation of TNM-Immunoscore in resected nonsmall-cell lung cancer[J]. Annals of Oncology 2016, 27(2): 225-232. \\u003c/li\\u003e\\n\\u003cli\\u003eMantovani A, Allavena \\u003cem\\u003eP\\u003c/em\\u003e, Marchesi F, et al. MacroPhages as tools and targets in cancer thera\\u003cem\\u003eP\\u003c/em\\u003ey[J]. Nature Reviews Drug Discovery, 2022, 21(11): 799-820.\\u003c/li\\u003e\\n\\u003cli\\u003eJin M, Fang J, Peng J, et al. \\u003cem\\u003eP\\u003c/em\\u003eD-1/\\u003cem\\u003eP\\u003c/em\\u003eD-L1 immune checkPoint blockade in breast cancer: research insights and sensitization strategies[J]. Molecular Cancer, 2024, 23(1): 1-22.\\u003c/li\\u003e\\n\\u003cli\\u003eGaron E B, Rizvi N A, Hui R, et al. Pembrolizumab for the treatment of non\\u0026ndash;small-cell lung cancer[J]. New England Journal of Medicine, 2015, 372(21): 2018-2028.\\u003c/li\\u003e\\n\\u003cli\\u003eReck M, Rodr\\u0026iacute;guez-Abreu D, Robinson A G, et al. Pembrolizumab versus chemothera\\u003cem\\u003eP\\u003c/em\\u003ey for PD-L1\\u0026ndash;Positive non\\u0026ndash;small-cell lung cancer[J]. New England Journal of Medicine, 2016, 375(19): 1823-1833.\\u003c/li\\u003e\\n\\u003cli\\u003eYan Q, Li S, He L, et al. \\u003cem\\u003eP\\u003c/em\\u003erognostic im\\u003cem\\u003eP\\u003c/em\\u003elications of tumor-infiltrating lym\\u003cem\\u003eP\\u003c/em\\u003ehocytes in non-small cell lung cancer: a systematic review and meta-analysis[J]. Frontiers in Immunology, 2024, 15: 1476365. \\u003c/li\\u003e\\n\\u003cli\\u003eLatchman Y E, Liang S C, Wu Y, et al. \\u003cem\\u003eP\\u003c/em\\u003eD-L1-deficient mice show that \\u003cem\\u003eP\\u003c/em\\u003eD-L1 on T cells, antigen-\\u003cem\\u003eP\\u003c/em\\u003eresenting cells, and host tissues negatively regulates T cells[J]. \\u003cem\\u003eP\\u003c/em\\u003eroceedings of the National Academy of Sciences, 2004, 101(29): 10691-10696. \\u003c/li\\u003e\\n\\u003cli\\u003eTo\\u003cem\\u003eP\\u003c/em\\u003ealian S L, Drake C G, \\u003cem\\u003eP\\u003c/em\\u003eardoll D M. Immune check\\u003cem\\u003eP\\u003c/em\\u003eoint blockade: a common denominator a\\u003cem\\u003ePP\\u003c/em\\u003eroach to cancer thera\\u003cem\\u003eP\\u003c/em\\u003ey[J]. Cancer Cell, 2015, 27(4): 450-461.\\u003c/li\\u003e\\n\\u003cli\\u003eForde \\u003cem\\u003eP\\u003c/em\\u003e M, S\\u003cem\\u003eP\\u003c/em\\u003eicer J, Lu S, et al. Neoadjuvant nivolumab \\u003cem\\u003eP\\u003c/em\\u003elus chemothera\\u003cem\\u003eP\\u003c/em\\u003ey in resectable lung cancer[J]. New England Journal of Medicine, 2022, 386(21): 1973-1985.\\u003c/li\\u003e\\n\\u003cli\\u003eTamura H, Dong H, Zhu G, et al. B7-H1 costimulation Preferentially enhances CD28-inde\\u003cem\\u003eP\\u003c/em\\u003eendent T-hel\\u003cem\\u003eP\\u003c/em\\u003eer cell function[J]. Blood, The Journal of the American Society of Hematology, 2001, 97(6): 1809-1816.\\u003c/li\\u003e\\n\\u003cli\\u003eGurevičienė G, Matulionė J, Po\\u0026scaron;kienė L, et al. PD-L1+Lym\\u003cem\\u003eP\\u003c/em\\u003ehocytes Are Associated with CD4+, FoxP3+CD4+, IL17+CD4+T Cells and SubtyPes of MacroPhages in Resected Early-Stage Non-Small Cell Lung Cancer[J]. International Journal of Molecular Sciences, 2024, 25(19): 10827.\\u003c/li\\u003e\\n\\u003cli\\u003eKamada T, Togashi Y, Tay C, et al. PD-1+ regulatory T cells am\\u003cem\\u003eP\\u003c/em\\u003elified by PD-1 blockade Promote hyPerProgression of cancer[J]. Proceedings of the National Academy of Sciences, 2019, 116(20): 9999-10008.\\u003c/li\\u003e\\n\\u003cli\\u003eHegde S, Krisnawan V E, Herzog B H, et al. Dendritic cell Paucity leads to dysfunctional immune surveillance in Pancreatic cancer[J]. Cancer Cell, 2020, 37(3): 289-307.\\u003c/li\\u003e\\n\\u003cli\\u003eSkoulidis F, Byers L A, Diao L, et al. Co-occurring genomic alterations define major subsets of KRAS-mutant lung adenocarcinoma with distinct biology, immune Profiles, and theraPeutic vulnerabilities[J]. Cancer Discovery, 2015, 5(8): 860-877.\\u003c/li\\u003e\\n\\u003cli\\u003eThommen D S, Koelzer V H, Herzig \\u003cem\\u003eP\\u003c/em\\u003e, et al. A transcri\\u003cem\\u003eP\\u003c/em\\u003etionally and functionally distinct PD-1+ CD8+ T cell Pool with Predictive Potential in non-small-cell lung cancer treated with PD-1 blockade[J]. Nature Medicine, 2018, 24(7): 994-1004.\\u003c/li\\u003e\\n\\u003cli\\u003eMantovani A, Marchesi F, Malesci A, et al. Tumour-associated macroPhages as treatment targets in oncology[J]. Nature Reviews Clinical Oncology, 2017, 14(7): 399-416.\\u003c/li\\u003e\\n\\u003cli\\u003eGajewski T F, Woo S R, Zha Y, et al. Cancer immunotheraPy strategies based on overcoming barriers within the tumor microenvironment[J]. Current OPinion in Immunology, 2013, 25(2): 268-276.\\u003c/li\\u003e\\n\\u003cli\\u003eGentles A J, Newman A M, Liu C L, et al. The Prognostic landscaPe of genes and infiltrating immune cells across human cancers[J]. Nature Medicine, 2015, 21(8): 938-945.\\u003c/li\\u003e\\n\\u003cli\\u003eGalon J, Mlecnik B, Bindea G, et al. Towards the introduction of the \\u0026lsquo;Immunoscore\\u0026rsquo;in the classification of malignant tumours[J]. The Journal of Pathology, 2014, 232(2): 199-209.\\u003c/li\\u003e\\n\\u003cli\\u003eBruni D, Angell H K, Galon J. The immune contexture and Immunoscore in cancer Prognosis and theraPeutic efficacy[J]. Nature Reviews Cancer, 2020, 20(11): 662-680.\\u003c/li\\u003e\\n\\u003cli\\u003eFridman W H, Zitvogel L, Saut\\u0026egrave;s\\u0026ndash;Fridman C, et al. The immune contexture in cancer Prognosis and treatment[J]. Nature Reviews Clinical Oncology, 2017, 14(12): 717-734.\\u003c/li\\u003e\\n\\u003cli\\u003eCheng C, Yi-Bei Z, Yu S, et al. Increase of circulating B7-H4-exPressing CD68+ macroPhage correlated with clinical stage of lung carcinomas[J]. Journal of ImmunotheraPy, 2012, 35(4): 354-358. \\u003c/li\\u003e\\n\\u003cli\\u003eSaito T, Nishikawa H, Wada H, et al. Two FOXP3+ CD4+ T cell subPoPulations distinctly control the Prognosis of colorectal cancers[J]. Nature Medicine, 2016, 22(6): 679-684.\\u003c/li\\u003e\\n\\u003cli\\u003eCheng C, Yi-Bei Z, Yu S, et al. Increase of circulating B7-H4-ex\\u003cem\\u003eP\\u003c/em\\u003eressing CD68+ macro\\u003cem\\u003eP\\u003c/em\\u003ehage correlated with clinical stage of lung carcinomas[J]. Journal of Immunothera\\u003cem\\u003eP\\u003c/em\\u003ey, 2012, 35(4): 354-358. \\u003c/li\\u003e\\n\\u003cli\\u003eAlexandra G, Ioannis A, I \\u003cem\\u003eP\\u003c/em\\u003e M, et al. Prognostic Relevance of the Relative Presence of CD4, CD8 and CD20 ExPressing Tumor Infiltrating LymPhocytes in OPerable Non-small Cell Lung Cancer Patients[J]. Anticancer Research, 2021, 41(8): 3989-3995.\\u003c/li\\u003e\\n\\u003cli\\u003eGuo Z, Song J, Hao J, et al. M2 macroPhages Promote NSCLC metastasis by u\\u003cem\\u003eP\\u003c/em\\u003eregulating CRYAB[J]. Cell death \\u0026amp; Disease, 2019, 10(6): 377. \\u003c/li\\u003e\\n\\u003cli\\u003eYuan S, Dong Y, Peng L, et al. Tumorassociated macroPhages affect the biological behavior of lung adenocarcinoma A549 cells through the PI3K/AKT signaling \\u003cem\\u003eP\\u003c/em\\u003eathway[J]. Oncology Letters, 2019, 18(2): 1840-1846.\\u003c/li\\u003e\\n\\u003cli\\u003eSequist L V, Waltman B A, Dias-Santagata D, et al. GenotyPic and histological evolution of lung cancers acquiring resistance to EGFR inhibitors[J]. Science translational Medicine, 2011, 3(75): 75ra26-75ra26.\\u003c/li\\u003e\\n\\u003cli\\u003eTakada K, Okamoto T, Shoji F, et al. Clinical significance of PD-L1 \\u003cem\\u003eP\\u003c/em\\u003erotein exPression in surgically resected \\u003cem\\u003eP\\u003c/em\\u003erimary lung adenocarcinoma[J]. Journal of Thoracic Oncology, 2016, 11(11): 1879-1890.\\u003c/li\\u003e\\n\\u003cli\\u003eMu C Y, Huang J A, Chen Y, et al. High ex\\u003cem\\u003eP\\u003c/em\\u003eression of PD-L1 in lung cancer may contribute to Poor Prognosis and tumor cells immune escaPe through suPPressing tumor infiltrating dendritic cells maturation[J]. Medical Oncology, 2011, 28(3): 682-688. \\u003c/li\\u003e\\n\\u003cli\\u003eBrahmer J, ReckamP K L, Baas P, et al. Nivolumab versus docetaxel in advanced squamous-cell non\\u0026ndash;small-cell lung cancer[J]. New England Journal of Medicine, 2015, 373(2): 123-135.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"NSCLC, tumor-infiltrating lymphocytes, PD-L1, prognosis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7042926/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7042926/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eNSCLC, as a major subtype of lung cancer, has a very poor prognosis for advanced patients, and although the application of immune checkpoint inhibitors has revolutionized the treatment paradigm, significant efficacy heterogeneity still exists. This study aimed to investigate the expression characteristics of TILs and PD-L1 in NSCLC and their prognostic value. By retrospectively analyzing the clinicopathological data of 50 surgically resected NSCLC patients from 2018\\u0026ndash;2023, IHC was used to detect the expression levels of CD4\\u0026thinsp;+\\u0026thinsp;TILs, CD8\\u0026thinsp;+\\u0026thinsp;TILs, CD68\\u0026thinsp;+\\u0026thinsp;TAMs, and PD-L1 in the tumor tissues.The high expression rate of PD-L1 reached 70% (35/50), and the intensity of its expression was significantly correlated with the size of the tumor (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.021); the percentage of high infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs reached 80% (40/50), which was positively correlated with lymph node metastasis (\\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.004). Correlation analysis revealed that PD-L1 was positively correlated with CD8\\u0026thinsp;+\\u0026thinsp;TILs infiltration (r\\u0026thinsp;=\\u0026thinsp;0.327, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.020) and negatively correlated with CD68\\u0026thinsp;+\\u0026thinsp;TAMs (r=-0.369, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.008). Survival analysis showed significantly longer median PFS in the CD4\\u0026thinsp;+\\u0026thinsp;TILs low infiltration group (15 months vs. 6 months, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.027), while OS was worse in the PD-L1 high expression group (HR\\u0026thinsp;=\\u0026thinsp;2.850, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.039). Multifactorial Cox regression confirmed PD-L1 high expression (HR\\u0026thinsp;=\\u0026thinsp;3.093, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.022) and lung adenocarcinoma pathologic type (HR\\u0026thinsp;=\\u0026thinsp;2.898, \\u003cem\\u003ep\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;0.026) as independent risk factors for PFS. In NSCLC, high membrane-specific expression of PD-L1 was positively correlated with tumor load and was tissue-selective (tumor tissue vs. normal lung tissue); membrane-localized infiltration of CD8\\u0026thinsp;+\\u0026thinsp;TILs was positively correlated with lymph node metastasis. high expression of PD-L1 and pathologic type (adenocarcinoma vs. squamous carcinoma) were independent risk factors for postoperative PFS; PD-L1 could be used as a positive predictive marker for OS. The combined PD-L1 expression and CD8\\u0026thinsp;+\\u0026thinsp;TILs/CD68\\u0026thinsp;+\\u0026thinsp;TAMs immune infiltration characteristics provide a theoretical basis for individualized immunotherapy.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Prognostic analysis of immunomarkers for non-small cell lung cancer\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-09-02 06:46:00\",\"doi\":\"10.21203/rs.3.rs-7042926/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c774be39-e8ab-4f78-a783-9a869136d4cf\",\"owner\":[],\"postedDate\":\"September 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-18T07:58:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-09-02 06:46:00\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7042926\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7042926\",\"identity\":\"rs-7042926\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}