This systematic review and meta-analysis were conducted in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (24), focusing on evaluating the survival synergy of comorbid chronic obstructive pulmonary disease (COPD) in lung cancer patients undergoing ICIs therapy. To ensure methodological rigor, two independent investigators systematically searched PubMed, Embase, CNKI, and the Cochrane Library for studies published from database inception to March 31, 2025. The search strategy employed the following key concepts and their variants:”Lung Cancer”or”Lung Neoplasms”or”Non-Small Cell Lung Cancer”or “NSCLC”or”Small Cell Lung Cancer”or “Pulmonary Disease, Chronic Obstructive”or”Chronic Obstructive Pulmonary Diseases”or”COPD”or”COAD”or”Chronic Obstructive Airway Disease”or”Chronic Airflow Obstructions”and”Immune Checkpoint Inhibitors”or”PD-1 Inhibitors”or”PD-L1 Inhibitors”or”CTLA-4 Inhibitors”or “ICI Therapy”.

Inclusion Criteria: (1)Lung cancer patients confirmed by histopathological diagnosis with comorbid COPD (diagnosed according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines (25) or pulmonary function tests); (2)Receiving ICIs monotherapy or combination regimens (e.g., ICIs combined with chemotherapy, radiotherapy); (3) Prospective or retrospective clinical studies investigating the impact of COPD on survival outcomes in patients treated with ICIs; (4)Direct or indirect reporting of survival endpoints, including but not limited to OS, PFS, hazard ratios (HR), and 95% confidence intervals (CI), or accessible Kaplan-Meier curve data for HR extraction.

Exclusion Criteria: (1)Studies that did not stratify lung cancer patients by COPD status or reported only systemic inflammatory markers (e.g., CRP, IL-6) without COPD-specific data; (2)Studies including patients receiving non-ICIs therapies (e.g., targeted therapy, chemotherapy/radiotherapy alone) without subgroup analysis for ICIs-treated cohorts; (3)Case reports, conference abstracts, preclinical studies (e.g., animal experiments, cell models), reviews, or commentary articles; (4)Studies lacking sufficient data to extract HR and 95% CI for survival outcomes (even after contacting authors) or duplicate publications (retaining the most recent or largest cohort).

Data extraction was performed independently by two researchers, who extracted the following information from the included studies: first author, publication year, country, study design (retrospective/prospective), histological type of lung cancer (NSCLC/SCLC), diagnostic criteria for COPD (GOLD staging or pulmonary function indices), sample size (COPD group vs. non-COPD group), baseline patient characteristics (median age, gender distribution), immunotherapy regimens (PD-1/PD-L1 monotherapy or combination therapy), follow-up period (months), survival outcomes (OS, PFS), and their corresponding hazard ratios (HR) with 95% confidence intervals (CI). Study quality was assessed using the Newcastle-Ottawa Scale (NOS) for observational studies, focusing on three domains:(1)Selection of participants (0–4 points): Whether COPD (e.g., confirmed by pulmonary function tests) and lung cancer histology were clearly defined, and whether confounding factors (e.g., smoking history, COPD severity) were controlled via matching or multivariate analysis.(2)Comparability between groups (0–2 points): Balance in prognostic factors (e.g., age, cancer stage, PD-L1 expression) between COPD and non-COPD groups.(3)Outcome assessment (0–3 points): Blinding in survival endpoint adjudication and follow-up completeness (lost-to-follow-up rate <20%). The total score ranges from 0 to 9. Studies scoring above 6 points were classified as high quality (26). Any discrepancies were resolved through discussion or consultation with a third researcher.

This meta-analysis employed Stata SE (version 16.0; StataCorp, Texas, USA) to statistically evaluate the survival synergy between chronic obstructive pulmonary disease (COPD)-lung cancer immunoaxis interactions and clinical outcomes in patients receiving ICIs. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated through stratification. Heterogeneity across studies was assessed using Cochran’s Q-test and I² statistics. A random-effects model was applied for pooled analysis when significant heterogeneity existed (I² > 50% or Q-test p-value < 0.10), while a fixed-effects model was used otherwise. Publication bias was preliminarily evaluated via funnel plot symmetry testing, further validated by Egger’s regression analysis and Begg’s rank correlation test (p-values < 0.05 indicated potential bias). To enhance methodological rigor, sensitivity analyses (iteratively excluding individual studies) were performed to test the stability of the pooled results, thereby confirming the robustness of the survival synergy between dynamic changes in the COPD-lung cancer immunoaxis and ICIs therapeutic efficacy.

The literature selection protocol is detailed in Figure 1. An initial search across multiple academic databases yielded 1,210 records. Following the removal of 262 duplicates, 948 unique articles were retained for preliminary evaluation. Titles and abstracts of these records were systematically reviewed using predefined eligibility criteria, leading to the exclusion of 936 entries deemed irrelevant or non-compliant. Subsequently, full-text assessments were conducted on the remaining 12 articles, of which 2 were excluded due to insufficient data availability. In the final phase, 10 high-quality studies were incorporated into the meta-analysis (14, 20, 21, 27–33). A comprehensive overview of the selected studies is provided in Figure 1. The included studies, spanning publication years from 2017 to 2025, were conducted across diverse geographic regions, including the United States, Japan, China, France, Korea, and Canada. Cohort sizes exhibited substantial variation, ranging from 30 participants (Dong 2024) to 2,326 patients (Chan 2025), collectively representing multiple cancer subtypes, predominantly non-small cell lung cancer (NSCLC). Among these investigations, six studies evaluated survival outcomes (OS and/or PFS) in relation to PD-1/PD-L1 inhibitors, while three analyses incorporated combination therapies involving CTLA-4 inhibitors. Temporal follow-up periods ranged from a median of 18 months to over 80 months, with five studies reporting incomplete follow-up data (marked as NR). Detailed demographic characteristics, including age distributions (mean ± SD: 50–83 years) and gender disparities (male predominance: 53.9%–98.5% across cohorts), are systematically cataloged in Table 1. As shown in Table 2, the methodological quality assessed using the Newcastle-Ottawa Scale (NOS) was consistently high, with eight of the included studies scoring 7/9 and one study obtaining the maximum score of 8.

Heterogeneity test results indicated that neither OS (Figure 2A: I² = 29.6%, p = 0.182) nor PFS (Figure 2B: I² = 0.0%, p = 0.957) analyses reached significant heterogeneity thresholds (I² < 50%, p > 0.1), supporting the use of a fixed-effects model for data integration. The pooled results from 9 studies (OS) and 7 studies (PFS) demonstrated significant differences in survival outcomes between lung cancer patients with COPD and those without COPD following PD-1/PD-L1 inhibitor therapy. For OS analysis (Figure 2A), the hazard ratio (HR) for the COPD group was 0.90 (95% CI: 0.85–0.96, p < 0.001), indicating a statistically significant improvement in OS compared to the non-COPD group. Individual study HR distributions revealed that Shin 2019 (HR = 0.51, 95% CI: 0.28–0.90) and Greib 2025 (HR = 0.85, 95% CI: 0.75–0.97) contributed notably to the overall effect, accounting for 1.09% and 22.45% of the weight, respectively. Notably, the confidence intervals for Biton 2018 (HR = 0.62, 95% CI: 0.28–1.37) and Takayama 2021 (HR = 0.57, 95% CI: 0.27–1.20) crossed the null value (HR = 1), suggesting potential limitations in sample size or follow-up duration (>80 months). For PFS analysis (Figure 2B), the COPD group exhibited a pooled HR of 0.54 (95% CI: 0.44–0.87, p < 0.001), further supporting the association between COPD and superior PFS. Among these, Dong 2024 (HR = 0.32, 95% CI: 0.11–0.93) and Takayama 2021 (HR = 0.49, 95% CI: 0.28–0.84) demonstrated the most pronounced effect sizes, contributing 4.02% and 15.05% of the weight, respectively. The exclusion of the null value (HR = 1) in all confidence intervals indicated consistent results across studies. We performed subgroup analyses based on region (Asia vs. others), sample size of the COPD-positive group (≤70 vs. >70), treatment type (PD-1 inhibitor, PD-1/PD-L1 inhibitors, or ICIs broadly), and statistical method (univariate vs. multivariate analysis) to further evaluate the impact of comorbid COPD on survival outcomes among lung cancer patients treated with immune checkpoint inhibitors. As shown in Table 3, subgroup analysis of overall survival (OS) indicated that patients with COPD showed particularly improved outcomes in the following subgroups: those from non-Asian regions (HR = 0.91, p = 0.004), those in studies with larger sample sizes (COPD+ >70, HR = 0.92, p = 0.007), those receiving PD-1 inhibitor monotherapy (HR = 0.58, p = 0.004), and those analyzed using univariate methods (HR = 0.83, p = 0.001).Similarly, Table 4 shows that progression-free survival (PFS) was significantly improved among COPD patients across all subgroups, with especially pronounced benefits observed in Asian populations (HR = 0.54, p < 0.001), studies with smaller sample sizes (COPD+ ≤70, HR = 0.56, p < 0.001), patients treated with PD-1 inhibitors (HR = 0.52, p = 0.001), and those analyzed using multivariate methods (HR = 0.56, p < 0.001).

Publication bias was rigorously assessed using funnel plots, Egger’s test, and Begg’s test. The funnel plots for both OS and PFS demonstrated high symmetry, indicating that the robustness of the pooled results was not significantly affected by bias. For OS (Figure 3A), the funnel plot showed a symmetric distribution of effect sizes around the pooled estimates, suggesting no substantial publication bias. Similarly, the PFS funnel plot (Figure 3B) exhibited balanced dispersion of data points, further supporting the absence of significant bias.

The Begg’s rank correlation test demonstrated no significant publication bias in the OS analysis of lung cancer patients with comorbid COPD receiving ICIs (p = 0.118 > 0.05; Figure 4A). However, the Begg’s test results for PFS revealed significant publication bias in the PFS analysis (p= 0.016 < 0.05; Figure 4B). For OS, the results of Egger’s regression test aligned with Begg’s rank correlation test (p=0.504>0.05), further supporting the absence of significant publication bias (Figure 5A). In contrast, for PFS, Egger’s test corroborated Begg’s test findings (p=0.003<0.05), confirming the presence of statistically significant publication bias (Figure 5B).

The sensitivity analysis revealed that omitting specific studies (e.g., Chan 2025 or Greib 2025 with HR = 0.98) might significantly reduce the pooled HR for OS, suggesting a dilution effect of these studies on the overall results and potentially weakening the survival benefit conclusion of ICIs in the current meta-analysis (Figure 6A). In contrast, for PFS, even after excluding outliers (e.g., Mark 2017 with HR = 0.42), the pooled HR remained stable within the 0.44–0.70 range without crossing the null threshold (HR = 1). All studies consistently demonstrated significant PFS improvement, validating the robust and uniform efficacy of ICIs in delaying disease progression (Figure 6B).