Baseline neutrophil-to-lymphocyte ratio as a prognostic biomarker in advanced non-small cell lung cancer patients treated with immune checkpoint inhibitors: a systematic review and meta-analysis

Introduction

Background

Lung cancer remains a major global health burden, ranking among the leading causes of cancer-related morbidity and mortality. According to GLOBOCAN 2024 data from the International Agency for Research on Cancer, lung cancer accounted for 11.72% of all new cancer cases and 20.45% of cancer-related deaths worldwide, underscoring its aggressive nature and poor prognosis (1). Non-small cell lung cancer (NSCLC) accounts for approximately 85% of all lung cancer diagnoses and comprises a heterogeneous group of histological subtypes (2). Multiple clinicopathological and molecular factors have been identified as prognostic markers for lung cancer, including clinical factors [Eastern Cooperative Oncology Group performance status (ECOG PS), tumor-node-metastasis (TNM) stage, smoking history and age], molecular biomarkers [programmed death-ligand 1 (PD-L1) expression, tumor mutational burden (TMB), epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) driver gene status] and inflammatory markers [platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII)] (3,4).

In recent years, immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) and PD-L1 have reshaped the therapeutic landscape of advanced NSCLC (5,6). Clinical trials have shown that ICIs can produce durable responses and survival benefits in a subset of patients, particularly those with high PD-L1 expression or other favorable immune profiles (7-9).

Rationale and knowledge gap

However, the proportion of patients who experience long-term benefit remains limited, with response rates hovering around 20–30% in unselected populations (10). This highlights an urgent need for accessible, cost-effective biomarkers to refine patient selection and optimize treatment strategies.

Systemic inflammation has emerged as a hallmark of cancer progression and immune evasion. Among inflammatory indices, the neutrophil-to-lymphocyte ratio (NLR) has drawn increasing attention for its prognostic relevance across a range of malignancies. Calculated from routine blood counts, the NLR reflects the balance between neutrophil-driven inflammation and lymphocyte-mediated immune surveillance. An elevated NLR has been associated with tumor-promoting processes, including angiogenesis, metastasis, and resistance to therapy (11). On the one hand, high levels of neutrophils can promote the recruitment of granulocytic myeloid-derived suppressor cells (g-MDSCs) in the tumor microenvironment (TME), secrete proinflammatory cytokines [interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α)] and matrix metalloproteinases (MMPs) to induce tumor angiogenesis, lymph node metastasis and distant organ colonization; on the other hand, reduced lymphocytes lead to impaired CD8⁺ T-cell immune surveillance, which further facilitates tumor immune escape and progression (12,13). For lung cancer, NLR is of particular importance for prognostication because it can be detected repeatedly and non-invasively in clinical practice, with extremely low cost based on routine blood tests, no need for invasive tumor tissue sampling, and wide applicability in primary medical institutions and resource-limited settings—advantages not possessed by expensive and technically demanding biomarkers such as PD-L1 and TMB (14,15). In addition, NLR can dynamically reflect changes in the patient’s systemic immune state during treatment, facilitating real-time adjustment of therapeutic strategies.

Several studies have explored the association between NLR and outcomes in patients with solid tumors, including gastric, hepatocellular, and breast cancers. In NSCLC, however, particularly among those receiving ICI-based regimens, findings have been inconsistent. This inconsistency may be due to variability in NLR cut-off values, study designs, and patient characteristics may contribute to the observed discrepancies (16-20). Furthermore, while PD-L1 expression and TMB are widely studied biomarkers, their predictive performance in real-world settings remains suboptimal.

Objective

In this context, we conducted a comprehensive meta-analysis to assess the prognostic significance of baseline NLR in patients with advanced NSCLC treated with ICIs. By synthesizing available evidence, we aimed to clarify whether NLR can serve as an independent marker of progression-free survival (PFS) and overall survival (OS), and to explore its potential utility in risk stratification and clinical decision-making in the era of immunotherapy. We present this article in accordance with the PRISMA reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2871/rc) (21).

Methods

Protocol registration

The protocol has been prospectively registered in the PROSPERO database (identifier: CRD420251085531).

Literature search strategy

We performed a comprehensive literature search in four electronic databases: PubMed, EMBASE, Web of Science, and the Cochrane Library. The search covered the period from database inception to June 2025. Both Medical Subject Headings (MeSH terms for PubMed/EMBASE) and free-text keywords were used to maximize search sensitivity and ensure coverage of relevant studies. The search was restricted to English-language publications and human studies (excluding animal or preclinical research).

The search strategy was constructed by combining three core thematic dimensions: NSCLC type, ICIs, and neutrophil-to-lymphocyte ratio (NLR). “OR” was used to expand coverage within each dimension, and “AND” to combine different dimensions. The detailed search strings for each database are provided in Appendix 1.

Inclusion and exclusion criteria

Studies were considered eligible if they met all the following criteria: (I) observational cohort studies (prospective or retrospective) published in English; (II) enrolled patients who were diagnosed with histologically confirmed advanced NSCLC; (III) patients who received ICI-based therapy (e.g., anti-PD-1, anti-PD-L1, or anti-CTLA-4 antibodies); (IV) reported baseline NLR prior to the initiation of ICIs; (V) reported hazard ratios (HRs) with 95% confidence intervals (CIs) for PFS and/or OS were calculated on the basis of stratified NLRs.

The following exclusion criteria were applied: (I) review articles, editorials, commentaries, conference abstracts, or case reports; (II) single-arm studies without comparator groups [high (H-NLR) vs. low NLR (L-NLR)]; (III) non-English publications; (IV) animal or preclinical studies; (V) studies lacking sufficient data to estimate HRs or not reporting PFS/OS; (VI) studies focusing on non-baseline NLR (e.g., post-treatment NLR); (VII) studies enrolling non-NSCLC patients [e.g., small-cell lung cancer (SCLC)].

Study selection and data extraction

All the search results were imported into EndNote 21 for duplicate removal. Two reviewers independently screened titles and abstracts for eligibility using Rayyan 2.10, and the full texts of potentially eligible studies were retrieved and assessed for final eligibility using Covidence 2.0. Discrepancies were resolved by discussion or consultation with a third reviewer.

Data extraction was performed via a standardized form, including: (I) study characteristics: first author, year, country, design, sample size; (II) patient characteristics: prior treatments, ICI modality (monotherapy vs. combination); (III) biomarker information: NLR cutoff value, timing of measurement (confirmed as baseline); (IV) outcomes: HRs with 95% CIs for PFS/OS (directly reported or extracted via survival curves using Engauge Digitizer 12.1 if not provided). When multiple NLR cutoffs were reported, the most commonly used (e.g., ≥5) or ROC-derived cutoff was selected.

Quality assessment

The methodological quality of each included study was assessed via the Newcastle-Ottawa Scale (NOS) for cohort studies (22) (https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp). The NOS evaluates three domains: selection of study groups (0–4 points), comparability of groups (0–2 points), and ascertainment of outcomes (0–3 points), with a maximum score of 9. Studies scoring ≥7 were considered high-quality, those scoring 5–6 were considered moderate-quality, and those scoring <5 were considered low-quality. Quality assessments were conducted independently by two reviewers, with disagreements resolved through consensus.

Statistical analysis

PFS was defined as the time from the initiation of ICI therapy to the first documentation of disease progression [per RECIST 1.1 (Response Evaluation Criteria in Solid Tumors) a criteria] or all-cause death, whichever occurred first. OS was defined as the time from the initiation of ICI therapy to all-cause death or the last follow-up, consistent with the definitions used in the included cohort studies.

Pooled HRs and 95% CIs were calculated for the H-NLR vs. L-NLR groups via the generic inverse variance method. Meta-analyses were conducted via Review Manager software (RevMan 5.4.1, Cochrane Collaboration). Heterogeneity was evaluated via the Cochran’s Q test (P<0.10 indicating significance) and the I² statistic, with I² values of 25%, 50%, and 75% representing low, moderate, and high heterogeneity, respectively. A fixed-effects model was used for low/moderate heterogeneity (I²<50%); a random-effects model was used for high heterogeneity (I²≥50%). Subgroup analyses were conducted as follows: (I) NLR cutoff values (≥5 vs. <5); (II) ICI treatment modality: monotherapy (anti-PD-1/PD-L1 alone) vs. combination therapy (ICI + chemotherapy/anti-CTLA-4). Publication bias was assessed visually via funnel plots for asymmetry. Subgroup analyses were predefined based on clinical practice relevance: NLR cutoff values (≥5 vs. <5) were selected because NLR ≥5 is the most commonly used threshold in clinical studies, and treatment modalities (monotherapy vs. combination therapy) reflect real-world therapeutic options for advanced NSCLC.

Results

Study selection

The initial database search identified 1,854 records, comprising 489 from PubMed, 1,233 from EMBASE, 100 from Web of Science, and 32 from the Cochrane Library. After removing 1,179 duplicate entries via EndNote 20, 675 unique records remained for title and abstract screening. Of these, 507 were excluded for reasons such as inclusion of non-NSCLC populations or analysis of non-baseline NLR values. A total of 168 full-text articles were assessed for eligibility in Covidence 2.0. Subsequently, 145 studies were excluded for the following reasons: full text not available (n=10); analysis based on non-baseline NLR (n=54); inclusion of SCLC patients (n=1); insufficient data to estimate HRs or outcomes not relevant to PFS or OS (n=80). Ultimately, 23 studies met the inclusion criteria and were included in the meta-analysis. The detailed selection process is illustrated in Figure 1. The detailed search strings for each database are provided in Appendix 1.

Figure 1 Flow diagram of study selection process. The diagram illustrates the number of records identified, screened, assessed for eligibility, and included in the final meta-analysis, following the PRISMA 2020 guidelines. HR, hazard ratio; NLR, neutrophil-to-lymphocyte ratio; SCLC, small cell lung cancer.

A total of 23 studies published from 2017 to 2025 were included in the meta-analysis, comprising 4,138 patients diagnosed with advanced NSCLC (23-45). The detailed search strings for each database are provided in Appendix 1. Among them, 22 studies were retrospective, while one (Möller M, 2022) was prospective. The sample sizes varied widely, ranging from 30 patients (Wasamoto S, 2025) to 845 patients (Descourt R, 2023).

These studies were conducted across multiple geographic regions, including Asia (China and Japan), Europe (Italy, France, Poland, Germany, Bulgaria, and Romania), North America (the United States and Canada), and Russia. All included patients received ICI-based therapies. Of the 23 studies, 16 evaluated ICIs as monotherapy, including anti-PD-1 agents (pembrolizumab, nivolumab) and anti-PD-L1 agents (atezolizumab, durvalumab). The remaining 7 studies investigated combination regimens, such as ICI plus chemotherapy (for example, pembrolizumab combined with platinum-pemetrexed) or dual ICI therapy (nivolumab plus ipilimumab).

The neutrophil-to-lymphocyte ratio (NLR) cutoff values used in the included studies ranged from 3.0 to 6.4. Specifically, 11 studies used a threshold of 5 or higher, while the remaining 12 used a cutoff below 5. Detailed characteristics of the included studies are presented in Table 1.

All included studies reported HRs for PFS and/or OS, comparing patients with high vs. low baseline NLR. For PFS, the median duration ranged from 1.4 to 12.8 months in the H-NLR groups, and from 2.6 to 27.7 months in the L-NLR groups. The pooled HRs consistently favored the L-NLR subgroup, with individual study estimates ranging from 1.13 to 4.47.

Regarding OS, patients in the H-NLR subgroup had a median survival of 3.7 and 41.6 months, whereas those in the L-NLR subgroup had a median survival ranging from 8.4 to 52.0 months. Reported HRs for OS ranged from 0.90 to 8.09, with most studies reporting statistically significant survival advantages for patients with lower baseline NLR values.

Detailed study-level data for PFS and OS outcomes are summarized in Tables 2,3, respectively.

Quality assessment

According to the NOS, 12 studies were assessed as high quality, with scores of 7 or higher. The remaining 11 studies were classified as moderate quality, with scores ranging from 5 to 6. No study was deemed low quality (score <5). A summary of the quality assessments is presented in Table 4.

Prognostic value of the NLR

PFS

All 23 included studies reported HRs for PFS stratified by baseline NLR. Pooled analysis revealed that patients with elevated NLRs had significantly shorter PFS than those with lower NLR levels. The combined HR was 1.91 (95% CI: 1.76–2.08; P<0.001), indicating a 91% greater risk of disease progression or death in the H-NLR group. The degree of between-study heterogeneity was low (I²=45%, P=0.01). Notably, I² of 45% suggests moderate heterogeneity, with approximately 45% of the total variation across studies attributed to actual differences rather than chance. This may be explained by minor discrepancies in baseline characteristics (e.g., tumor types, sample sizes) among studies, but the consistency of results supports the use of a fixed-effects model for pooled analysis. Individual study HRs ranged from 1.13 (Imai H, 2021) to 4.47 (Petrova MP, 2020), with 23/23 studies showing HR >1 (Figure 2).

Figure 2 Forest plot of the association between high baseline NLRs and PFS in patients with advanced NSCLC treated with ICIs. The size of each square indicates the weight of the corresponding study in the meta-analysis. The pooled effect estimate is shown as a diamond at the bottom of the plot. CI, confidence interval; H-NLR, high NLR; ICIs, immune checkpoint inhibitors; IV, inverse variance; L-NLR, low NLR; NLR, neutrophil-to-lymphocyte ratio; NSCLC, non-small cell lung cancer; PFS, progression-free survival; SE, standard error.

OS

All 23 included studies reported HRs for OS stratified by baseline NLR. The meta-analysis revealed that patients with elevated baseline NLRs had significantly worse OS compared to those with lower NLRs. The pooled HR was 2.28 (95% CI: 1.93–2.70; P<0.001), indicating that individuals in the H-NLR group had more than twice the risk of death compared with those in the L-NLR group.

Moderate heterogeneity was observed across studies (I²=61%, P<0.001), which may reflect variations in tumor histology, treatment regimens (such as ICI monotherapy vs. combination therapy), NLR assessment methods, and follow-up durations. Given this heterogeneity, a random-effects model was used to produce a more conservative, robust pooled estimate.

Individual study HRs for OS ranged from 0.90 (Imai H, 2021) to 8.09 (Petrova MP, 2020), with 22 of 23 studies reporting HRs >1, underscoring the adverse prognostic impact of elevated baseline NLR (Figure 3). Clinically, the pooled HR for OS (2.28) indicates that a high baseline NLR doubles the risk of death, comparable to the prognostic impact of PD-L1 negativity (HR ≈2.0 in previous studies). This suggests that NLR could be a complementary marker to PD-L1 for comprehensive risk assessment.

Figure 3 Forest plot of the associations between high baseline NLRs and OS in patients with advanced NSCLC treated with ICIs. Each square represents the HR for an individual study, with the size proportional to its statistical weight. The horizontal lines represent the 95% CI and the diamond at the bottom indicates the overall pooled HR. CI, confidence interval; H-NLR, high NLR; ICIs, immune checkpoint inhibitors; IV, inverse variance; L-NLR, low NLR; NLR, neutrophil-to-lymphocyte ratio; NSCLC, non-small cell lung cancer; OS, overall survival; SE, standard error.

Publication bias

To evaluate publication bias, funnel plots were generated for both PFS and OS outcomes (Figure 4A,4B). For PFS (Figure 4A), the funnel plot displays the standard error (SE) of the log HR on the y-axis and the HR on the x-axis. The distribution of data points appears asymmetric, with a greater concentration of studies on the left side of the pooled effect size (HR ≈2). This skewed pattern suggests the potential presence of publication bias, particularly due to the underrepresentation of smaller studies with non-significant or negative results.

Figure 4 Funnel plots for publication bias assessment of the included studies. (A) Funnel plot assessing publication bias for the meta-analysis of PFS. (B) Funnel plot assessing publication bias for the meta-analysis of OS. Each circle represents an individual study. The vertical dashed line indicates the pooled HR, and the diagonal lines represent pseudo-95% confidence limits. HR, hazard ratio; OS, overall survival; PFS, progression-free survival; SE, standard error.

Similarly, the funnel plot for OS (Figure 4B) shows an uneven distribution of points. The lack of symmetry and the clustering of studies in particular regions of the plot further raise concerns about selective reporting. The absence of studies in areas where they would typically be expected, especially among those with higher SEs, supports the likelihood of publication bias. These findings suggest that publication bias cannot be ruled out and should be considered when interpreting the overall results of this meta-analysis.

Subgroup analyses

By the NLR cutoff value

Subgroup analyses were conducted based on NLR cutoff (≥5 vs. <5). The pooled HRs for PFS were 1.91 (95% CI: 1.57–2.31, P<0.001) in studies using a cutoff of 5 or higher, and 2.06 (95% CI: 1.76–2.41, P<0.001) in studies using a cutoff lower than 5 (Figure 5A). Similarly, for OS, the pooled HRs were 2.14 (95% CI: 1.65–2.78, P<0.001) and 2.41 (95% CI: 1.92–3.02, P<0.001), respectively (Figure 5B). There were no statistically significant differences among these subgroups, indicating that the prognostic value of the NLR remains consistent across different cutoffs. This finding supports the NLR’s robustness as a reliable biomarker for survival outcomes in patients with advanced NSCLC receiving ICI therapy.

Figure 5 Subgroup analysis of NLR and clinical outcomes stratified by NLR cutoff values. (A) Subgroup analysis of the association between NLR and PFS based on NLR cutoff values (≥5 vs. <5). (B) Subgroup analysis of the association between NLR and OS based on NLR cutoff values (≥5 vs. <5). Each square represents an individual study’s effect size, and the size of the square reflects the study’s weight in the meta-analysis. The horizontal lines represent 95% CIs, and the diamonds represent pooled HRs for each subgroup and the overall population. CI, confidence interval; H-NLR, high NLR; HR, hazard ratio; IV, inverse variance; L-NLR, low NLR; NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival; SE, standard error.

By treatment modality

Subgroup analyses based on treatment strategy demonstrated consistent prognostic significance of the NLR across both monotherapy and combination therapy groups. For PFS, the pooled HR was 1.97 (95% CI: 1.68–2.30) in the ICI monotherapy group, and 2.04 (95% CI: 1.70–2.44) in the combination therapy group (Figure 6A). Similarly, for OS, the pooled HRs were 2.18 (95% CI: 1.76–2.71) and 2.47 (95% CI: 2.00–3.05), respectively (Figure 6B). No significant interaction effect was observed between subgroups, indicating that the prognostic impact of elevated baseline NLR is maintained across treatment modalities. These findings support the robustness of NLR as a predictive biomarker in diverse therapeutic settings for patients with advanced NSCLC.

Figure 6 Subgroup analysis of NLR and clinical outcomes stratified by treatment modality. (A) Subgroup analysis of the association between NLR and PFS based on treatment modality (monotherapy vs. combination therapy). (B) Subgroup analysis of the association between NLR and OS based on treatment modality (monotherapy vs. combination therapy). Each square represents the effect size of an individual study, with size proportional to its weight in the meta-analysis. The diamonds indicate the pooled effect estimates. CI, confidence interval; H-NLR, high NLR; ICIs, immune checkpoint inhibitors; IV, inverse variance; L-NLR, low NLR; NLR, neutrophil-to-lymphocyte ratio; OS, overall survival; PFS, progression-free survival; SE, standard error.

Discussion

Key findings

This meta-analysis targeted advanced NSCLC patients receiving ICIs, focusing on the prognostic value of baseline neutrophil-to-lymphocyte ratio (NLR) for long-term survival outcomes—PFS and OS. Pooling data from 23 studies involving 4,138 patients, the analysis confirmed a robust association between elevated baseline NLR and poorer survival, positioning NLR as a promising, accessible, and cost-effective prognostic biomarker in immunotherapy.

Patients with H-NLR exhibited significantly worse PFS (HR =1.91, 95% CI: 1.76–2.08) and OS (HR =2.28, 95% CI: 1.93–2.70) compared to those with L-NLR, and this association held regardless of the NLR cutoff value used. Most included studies reported HRs greater than 1 with confidence intervals that excluded the null, further validating the strong association between H-NLR and poor survival outcomes in this patient population.

Subgroup analyses provided additional clarity. Stratified by NLR cutoff values (≥5 vs. <5, Figure 5) and treatment modalities (monotherapy vs. combination therapy, Figure 6), both subgroups consistently showed unfavorable survival outcomes in H-NLR patients—confirming the stability of NLR’s prognostic value across clinical variables. These results’ reliability is supported by low-to-moderate heterogeneity (I²=45% for PFS; I²=61% for OS) and mild publication bias (Figure 4), the latter of which is deemed insufficient to undermine the core conclusions.

Strengths and limitations

Strengths

NLR’s inherent advantages—practicality and low cost, derived from routine peripheral blood tests—make it highly applicable in clinical settings, especially where advanced molecular profiling is unavailable. The meta-analysis further strengthens this value by integrating a broad dataset across diverse populations and study designs, significantly enhancing the generalizability of the findings beyond single-center limitations.

Methodologically, the consistent direction of results across subgroup analyses (cutoff values and treatment modalities) and low-to-moderate heterogeneity underscores the robustness of the association between H-NLR and poor survival. This consistency addresses the variability often seen in single-study analyses, reinforcing NLR’s reliability as a prognostic tool.

Limitations

Most included studies (22 out of 23) were retrospective, a design that may introduce selection bias and restrict the ability to draw causal inferences about NLR and ICI outcomes. Another key limitation is the considerable variability in NLR cutoff definitions—ranging from arbitrary thresholds such as 5.0 to values derived from receiver operating characteristic (ROC) analyses—which could affect the stability of effect size estimates. A critical limitation of this study is the inability to conduct subgroup analysis based on histopathological subtypes (adenocarcinoma vs. squamous cell carcinoma): only 8 of the 23 included studies clearly distinguished the two subtypes, and the sample size of squamous cell carcinoma in these studies was small (n<200 in total), leading to insufficient statistical power for valid subgroup comparison. This limits the further exploration of NLR’s prognostic value in different histological subtypes of NSCLC.

The analysis focused exclusively on baseline NLR, overlooking dynamic changes during treatment (e.g., at 4–8 weeks) that emerging evidence suggests may offer superior prognostic value. Additionally, NLR was not evaluated in combination with other systemic inflammation markers [e.g., platelet-lymphocyte ratio (PLR), C-reactive protein], a gap given that combined markers often improve prognostic accuracy.

Comparison with similar research

Our findings align with previous studies that have suggested a relationship between high NLR and poor outcomes in advanced NSCLC patients treated with ICIs. This meta-analysis definitively advances the field by integrating broad datasets, spanning diverse populations and study designs, to directly address and overcome the limited generalizability of earlier, smaller-scale research.

In contrast to previous studies, which were confined to single NLR cutoff values or specific treatment modalities, this analysis unequivocally demonstrates that NLR’s prognostic value remain robust regardless of cutoff (≥5 vs. <5) or therapy type (monotherapy vs. combination). This analysis decisively resolves previous inconsistencies inconsistencies in the literature, providing a unified understanding of NLR’s role in ICI-treated patients.

Unlike high-cost biomarkers such as PD-L1 expression or TMB, NLR serves as a practical alternative for resource-limited settings or patients who cannot undergo molecular profiling. The prognostic significance of NLR in ICI-treated solid tumors extends beyond NSCLC, with analogous patterns observed in other common malignancies. In melanoma, elevated baseline NLR independently predicts shorter PFS and OS in patients receiving anti-PD-1/CTLA-4 therapy, with a pooled HR for OS near 1.8 (46,47). Similarly, high NLR correlates with decreased ICI response and reduced survival in gastric cancer and hepatocellular carcinoma (48,49). A principal distinction among these tumors is the optimal NLR cutoff value: 5 for NSCLC, 3 for melanoma, and 4 for gastric cancer, potentially reflecting varying systemic immune characteristics by tumor type.

Compared with other clinicopathological prognostic factors for NSCLC treated with ICIs, NLR has unique clinical value and complementary advantages. First, compared with PD-L1 expression and TMB—the most well-recognized molecular biomarkers—NLR is based on routine blood tests at extremely low cost and requires no tumor tissue sampling, making it widely applicable in resource-limited settings where PD-L1/TMB detection is unavailable. In addition, NLR reflects the patient’s systemic immune-inflammatory state, while PD-L1/TMB reflects TME characteristics; together, they can improve risk stratification accuracy (50,51). Second, compared with other inflammatory markers such as PLR and SII, NLR offers more stable prognostic value in ICI treatment and is easier to detect (52,53). Third, NLR can be combined with clinical factors such as ECOG PS to form a comprehensive risk assessment system: for example, patients with ECOG PS ≥2 and H-NLR have the worst survival outcomes and may need more intensive therapy (54). Finally, NLR is superior to traditional clinical factors like TNM stage in dynamic prognostic assessment, as it can be repeatedly measured to track changes in the patient’s immune state during treatment (55).

While PD-L1 and TMB remain important, NLR offers an accessible alternative that addresses unmet needs for prognostic assessment in broader clinical contexts.

Explanations of findings

The biological basis of NLR’s prognostic significance lies in its ability to capture the balance between protumor inflammation and antitumor immunity—a dynamic central to ICI efficacy. Elevated neutrophils often reflect the expansion of granulocytic myeloid-derived suppressor cells (g-MDSCs), which suppress CD8⁺ T-cell activation via cytokines such as IL-6 and TNF-α, promoting an immunosuppressive TME (56).

Compounding this, reduced lymphocyte counts—particularly CD8⁺ effector T cells—signal immune exhaustion or impaired surveillance, directly limiting ICIs’ ability to trigger antitumor responses (57). This dual mechanism—immune suppression from g-MDSCs and cytotoxic T-cell deficiency—creates a TME hostile to ICI-mediated activation, explaining why H-NLR patients fare worse.

Neutrophils’ show phenotypic heterogeneity. Under chronic inflammation, tumor-promoting N2-type neutrophils become dominant in the TME. These cells drive angiogenesis and matrix remodeling using MMPs and arginase-1 (Arg-1). They also depleting L-arginine and generate nitric oxide, which impairs T-cell metabolism (58,59). In contrast, antitumor N1-type neutrophils-producers of reactive oxygen species and IL-12 (60)—are less prevalent in H-NLR patients. This imbalance increases treatment resistance (61). Persistent inflammation can cause lymphocyte depletion through Fas/FasL-mediated apoptosis (62), worsening immune exhaustion and limiting ICI efficacy.

Implications and actions needed

Clinical implications

Clinically, NLR is a practical tool for oncologists. It aids in risk stratification and treatment planning. NLR enables early identification of patients less likely to benefit from ICIs, guiding personalized care. H-NLR patients may need more intensive regimens or closer monitoring. L-NLR patients can receive ICI monotherapy, reducing unnecessary toxicity and healthcare costs.

In resource-limited settings or for patients ineligible for tumor molecular profiling, NLR is accessible for prognostic assessment. This addresses a key global health disparity. More patients receive personalized immunotherapy recommendations, regardless of access to advanced testing.

Future research directions

Prospective, multicenter studies should be prioritized to standardize NLR measurement timing and cutoff values—addressing current variability and strengthening causal inference. Integrating NLR into multidimensional predictive models (incorporating tumor genomic data, transcriptomic profiles, and single-cell immune profiling) could further enhance patient stratification and enable more precise therapeutic decision-making.

Translational studies exploring pharmacologic modulation of NLR-associated pathways (e.g., IL-6/STAT3 inhibition) are of substantial clinical interest, as they may unlock new strategies to improve ICI efficacy in H-NLR patients. Additionally, research into dynamic NLR changes during treatment (e.g., at 4–8 weeks) is needed to determine whether temporal shifts offer superior prognostic value compared to baseline measurements.

Finally, future analyses should evaluate NLR in combination with other systemic inflammation markers (e.g., PLR, C-reactive protein) to assess potential gains in prognostic accuracy—building on the current findings to develop more comprehensive and reliable predictive tools.

Conclusions

Elevated baseline NLR is an independent adverse prognostic factor for PFS and OS in advanced NSCLC patients receiving ICIs. Its simplicity and cost-effectiveness make it a valuable tool for clinical risk stratification. Future studies should validate these findings prospectively and explore strategies to modulate NLR-associated inflammation to improve immunotherapy outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the PRISMA reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2871/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2871/prf

Funding: This study was supported by the National Natural Science Foundation of China (No. 82204912), the Fundamental Research Funds for the Central Universities (No. 2024-JYB-JBZD-057), the Suzhou Municipal Science and Technology Bureau (No. SZZCZJ202335), and the Beijing Natural Science Foundation (No. 7252239).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2871/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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

References

1. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024;74:12-49. [Crossref] [PubMed]
2. Wang X, Romero-Gutierrez CW, Kothari J, et al. Prediagnosis Smoking Cessation and Overall Survival Among Patients With Non-Small Cell Lung Cancer. JAMA Netw Open 2023;6:e2311966. [Crossref] [PubMed]
3. Puderecki M, Szumiło J, Marzec-Kotarska B. Novel prognostic molecular markers in lung cancer. Oncol Lett 2020;20:9-18. [Crossref] [PubMed]
4. Ghosh DD, McDonald H, Dutta R, et al. Prognostic Indicators for Precision Treatment of Non-Small Cell Lung Carcinoma. Cells 2024;13:1785. [Crossref] [PubMed]
5. Jiang T, Zhou C. The past, present and future of immunotherapy against tumor. Transl Lung Cancer Res 2015;4:253-64. [PubMed]
6. Farkona S, Diamandis EP, Blasutig IM. Cancer immunotherapy: the beginning of the end of cancer? BMC Med 2016;14:73. [Crossref] [PubMed]
7. Liu J, Li S, Zhang S, et al. Systemic immune-inflammation index, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio can predict clinical outcomes in patients with metastatic non-small-cell lung cancer treated with nivolumab. J Clin Lab Anal 2019;33:e22964. [Crossref] [PubMed]
8. Guo L, Zhang H, Chen B. Nivolumab as Programmed Death-1 (PD-1) Inhibitor for Targeted Immunotherapy in Tumor. J Cancer 2017;8:410-6. [Crossref] [PubMed]
9. Huang Y, Hu Y. Impact of helicobacter pylori infection on the efficacy of ICIs in gastric cancer. BMC Cancer 2025;25:1101. [Crossref] [PubMed]
10. Raedler LA. Keytruda (Pembrolizumab): First PD-1 Inhibitor Approved for Previously Treated Unresectable or Metastatic Melanoma. Am Health Drug Benefits 2015;8:96-100. [PubMed]
11. Zhang Q, Wang M, Du H, et al. Predictive Role of Blood Cell-Derived Inflammatory Markers for the Risk of Asymptomatic Cerebral Infarction in Essential Hypertension: A Population-Based Cross-Sectional Study in Central China. J Inflamm Res 2025;18:3523-34. [Crossref] [PubMed]
12. Granot Z, Jablonska J. Distinct Functions of Neutrophil in Cancer and Its Regulation. Mediators Inflamm 2015;2015:701067. [Crossref] [PubMed]
13. Li R, Mukherjee MB, Lin J. Coordinated Regulation of Myeloid-Derived Suppressor Cells by Cytokines and Chemokines. Cancers (Basel) 2022;14:1236. [Crossref] [PubMed]
14. Song R, Liu F, Ping Y, et al. Potential non-invasive biomarkers in tumor immune checkpoint inhibitor therapy: response and prognosis prediction. Biomark Res 2023;11:57. [Crossref] [PubMed]
15. Lee JS, Kim NY, Na SH, et al. Reference values of neutrophil-lymphocyte ratio, lymphocyte-monocyte ratio, platelet-lymphocyte ratio, and mean platelet volume in healthy adults in South Korea. Medicine (Baltimore) 2018;97:e11138. [Crossref] [PubMed]
16. Guo W, Lu X, Liu Q, et al. Prognostic value of neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for breast cancer patients: An updated meta-analysis of 17079 individuals. Cancer Med 2019;8:4135-48. [Crossref] [PubMed]
17. Gao F, Li X, Geng M, et al. Pretreatment neutrophil-lymphocyte ratio: an independent predictor of survival in patients with hepatocellular carcinoma. Medicine (Baltimore) 2015;94:e639. [Crossref] [PubMed]
18. Gonda K, Shibata M, Sato Y, et al. Elevated neutrophil-to-lymphocyte ratio is associated with nutritional impairment, immune suppression, resistance to S-1 plus cisplatin, and poor prognosis in patients with stage IV gastric cancer. Mol Clin Oncol 2017;7:1073-8. [Crossref] [PubMed]
19. Zhang J, Cai L, Song Y, et al. Prognostic role of neutrophil lymphocyte ratio in patients with spontaneous intracerebral hemorrhage. Oncotarget 2017;8:77752-60. [Crossref] [PubMed]
20. Miyazaki T, Yamasaki N, Tsuchiya T, et al. Inflammation-based scoring is a useful prognostic predictor of pulmonary resection for elderly patients with clinical stage I non-small-cell lung cancer. Eur J Cardiothorac Surg 2015;47:e140-5. [Crossref] [PubMed]
21. Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. [Crossref] [PubMed]
22. Wells GA, Shea B, O'Connell D, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses [Internet]. Ottawa: Ottawa Hospital Research Institute; [cited 2026 Feb 13]. Available online: https://www.ohri.ca/programs/clinical_epidemiology/oxford.asp
23. Bagley SJ, Kothari S, Aggarwal C, et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung Cancer 2017;106:1-7. [Crossref] [PubMed]
24. Zer A, Sung MR, Walia P, et al. Correlation of Neutrophil to Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer 2018;19:426-434.e1. [Crossref] [PubMed]
25. Pavan A, Calvetti L, Dal Maso A, et al. Peripheral Blood Markers Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with Immune-Checkpoint Inhibitors. Oncologist 2019;24:1128-36. [Crossref] [PubMed]
26. Kartolo A, Holstead R, Khalid S, et al. Serum neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in prognosticating immunotherapy efficacy. Immunotherapy 2020;12:785-98. [Crossref] [PubMed]
27. Newman J, Preeshagul I, Kohn N, et al. Simple parameters to solve a complex issue: predicting response to checkpoint inhibitor therapy in lung cancer. Lung Cancer Manag 2020;10:LMT44. [Crossref] [PubMed]
28. Petrova MP, Eneva MI, Arabadjiev JI, et al. Neutrophil to lymphocyte ratio as a potential predictive marker for treatment with pembrolizumab as a second line treatment in patients with non-small cell lung cancer. Biosci Trends 2020;14:48-55. [Crossref] [PubMed]
29. Peng L, Wang Y, Liu F, et al. Peripheral blood markers predictive of outcome and immune-related adverse events in advanced non-small cell lung cancer treated with PD-1 inhibitors. Cancer Immunol Immunother 2020;69:1813-22. [Crossref] [PubMed]
30. Chen S, Li R, Zhang Z, et al. Prognostic value of baseline and change in neutrophil-to-lymphocyte ratio for survival in advanced non-small cell lung cancer patients with poor performance status receiving PD-1 inhibitors. Transl Lung Cancer Res 2021;10:1397-407. [Crossref] [PubMed]
31. Imai H, Kishikawa T, Minemura H, et al. Pretreatment Glasgow prognostic score predicts survival among patients with high PD-L1 expression administered first-line pembrolizumab monotherapy for non-small cell lung cancer. Cancer Med 2021;10:6971-84. [Crossref] [PubMed]
32. Pu D, Xu Q, Zhou LY, et al. Inflammation-nutritional markers of peripheral blood could predict survival in advanced non-small-cell lung cancer patients treated with PD-1 inhibitors. Thorac Cancer 2021;12:2914-23. [Crossref] [PubMed]
33. Ksienski D, Wai ES, Alex D, et al. Prognostic significance of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio for advanced non-small cell lung cancer patients with high PD-L1 tumor expression receiving pembrolizumab. Transl Lung Cancer Res 2021;10:355-67. [Crossref] [PubMed]
34. Descourt R, Greillier L, Perol M, et al. First-line single-agent pembrolizumab for PD-L1-positive (tumor proportion score ≥ 50%) advanced non-small cell lung cancer in the real world: impact in brain metastasis: a national French multicentric cohort (ESCKEYP GFPC study). Cancer Immunol Immunother 2023;72:91-9. [Crossref] [PubMed]
35. Lu X, Wan J, Shi H. Platelet-to-lymphocyte and neutrophil-to-lymphocyte ratios are associated with the efficacy of immunotherapy in stage III/IV non-small cell lung cancer. Oncol Lett 2022;24:266. [Crossref] [PubMed]
36. Möller M, Turzer S, Ganchev G, et al. Blood Immune Cell Biomarkers in Lung Cancer Patients Undergoing Treatment with a Combination of Chemotherapy and Immune Checkpoint Blockade. Cancers (Basel) 2022;14:3690. [Crossref] [PubMed]
37. Pirlog CF, Cotan HT, Parosanu A, et al. Correlation Between Pretreatment Neutrophil-to-Lymphocyte Ratio and Programmed Death-Ligand 1 Expression as Prognostic Markers in Non-Small Cell Lung Cancer. Cureus 2022;14:e26843. [Crossref] [PubMed]
38. Romano FJ, Ronga R, Ambrosio F, et al. Neutrophil-to-Lymphocyte Ratio Is a Major Prognostic Factor in Non-small Cell Lung Carcinoma Patients Undergoing First Line Immunotherapy With Pembrolizumab. Cancer Diagn Progn 2023;3:44-52. [Crossref] [PubMed]
39. Yuan Q, Xu C, Wang W, et al. Predictive Value of NLR and PLR in Driver-Gene-Negative Advanced Non-Small Cell Lung Cancer Treated with PD-1/PD-L1 Inhibitors: A Single Institutional Cohort Study. Technol Cancer Res Treat 2024;23:15330338241246651. [Crossref] [PubMed]
40. Matsumoto K, Yamamoto Y, Shiroyama T, et al. Risk Stratification According to Baseline and Early Change in Neutrophil-to-Lymphocyte Ratio in Advanced Non-Small Cell Lung Cancer Treated with Chemoimmunotherapy: A Multicenter Real-World Study. Target Oncol 2024;19:757-67. [Crossref] [PubMed]
41. Musaelyan AA, Moiseyenko FV, Emileva TE, et al. Clinical predictors of response to single‑agent immune checkpoint inhibitors in chemotherapy‑pretreated non‑small cell lung cancer. Mol Clin Oncol 2024;20:32. [Crossref] [PubMed]
42. Yoshimura A, Takeda T, Kataoka N, et al. Impact of the response to platinum-based chemotherapy on the second-line immune checkpoint inhibitor monotherapy in non-small cell lung cancer with PD-L1 expression ≤49%: a multicenter retrospective study. Front Oncol 2024;14:1303543. [Crossref] [PubMed]
43. Knetki-Wróblewska M, Grzywna A, Krawczyk P, et al. Prognostic significance of neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) in second-line immunotherapy for patients with non-small cell lung cancer. Transl Lung Cancer Res 2025;14:749-60. [Crossref] [PubMed]
44. Liu J, Liu J, Chen H, et al. Prognostic value of combined nutritional and inflammatory markers in NSCLC patients receiving ICIs. Discov Oncol 2025;16:571. [Crossref] [PubMed]
45. Wasamoto S, Imai H, Tsuda T, et al. Efficacy and Safety of First-line Pembrolizumab Plus Platinum and Pemetrexed in Elderly Patients with Non-squamous Non-small-cell Lung Cancer. Intern Med 2025;64:55-64. [Crossref] [PubMed]
46. Bartlett EK, Flynn JR, Panageas KS, et al. High neutrophil-to-lymphocyte ratio (NLR) is associated with treatment failure and death in patients who have melanoma treated with PD-1 inhibitor monotherapy. Cancer 2020;126:76-85. [Crossref] [PubMed]
47. Ferrucci PF, Gandini S, Battaglia A, et al. Baseline neutrophil-to-lymphocyte ratio is associated with outcome of ipilimumab-treated metastatic melanoma patients. Br J Cancer 2015;112:1904-10. [Crossref] [PubMed]
48. Wang ZF, Ma CH, Maswikiti EP, et al. The Black Hole of Immune Checkpoint Blocking Therapy for Gastric Cancer: Hyperprogressive Disease. Cancer Sci 2025;116:2630-9. [Crossref] [PubMed]
49. Xu C, Wu F, Du L, et al. Significant association between high neutrophil-lymphocyte ratio and poor prognosis in patients with hepatocellular carcinoma: a systematic review and meta-analysis. Front Immunol 2023;14:1211399. [Crossref] [PubMed]
50. Vornicu V, Negru AG, Vonica RC, et al. Inflammatory-Molecular Clusters as Predictors of Immunotherapy Response in Advanced Non-Small-Cell Lung Cancer. J Clin Med 2026;15:349. [Crossref] [PubMed]
51. Baek JM, Cha H, Moon Y, et al. A Systemic Immune Inflammation Index and PD-L1 (SP142) Expression as a Potential Combined Biomarker of the Clinical Benefit of Chemo-Immunotherapy in Extensive-Stage Small-Cell Lung Cancer. J Clin Med 2024;13:1521. [Crossref] [PubMed]
52. Zhong C, Yuan Y, Jiang Y, et al. Predictive role of inflammatory markers for the efficacy of first-line immunotherapy plus chemotherapy in advanced gastric cancer. Discov Oncol 2025;16:618. [Crossref] [PubMed]
53. Mehta A, Almel SV, Shaikh M. 118P Predictive role of neutrophil lymphocyte ratio (NLR) in metastatic solid tumour patients receiving immune checkpoint inhibitors (ICI). Ann Oncol 2022;33:S592. [Crossref]
54. Iannantuono GM, Giovagnoli T, Mastrantoni L, et al. Impact of ECOG performance status 2 participants on outcomes of pivotal cancer clinical trials: a meta-analysis and meta-regression. ESMO Open 2026;11:106065. [Crossref] [PubMed]
55. Lv H, Chen X, Chen X, et al. High Expression of NLR and SII in patients With Nasopharyngeal Carcinoma as Potential Prognostic Observations. Cancer Control 2024;31:10732748241288106. [Crossref] [PubMed]
56. Zhang M, Qin H, Wu Y, et al. Complex role of neutrophils in the tumor microenvironment: an avenue for novel immunotherapies. Cancer Biol Med 2024;21:849-63. [Crossref] [PubMed]
57. Zhong T, Sun S, Zhao M, et al. The mechanisms and clinical significance of CD8(+) T cell exhaustion in anti-tumor immunity. Cancer Biol Med 2025;22:460-80. [Crossref] [PubMed]
58. Chan L, Wood GA, Wootton SK, et al. Neutrophils in Dendritic Cell-Based Cancer Vaccination: The Potential Roles of Neutrophil Extracellular Trap Formation. Int J Mol Sci 2023;24:896. [Crossref] [PubMed]
59. Tong J, Tan Y, Ouyang W, et al. Targeting immune checkpoints in hepatocellular carcinoma therapy: toward combination strategies with curative potential. Exp Hematol Oncol 2025;14:65. [Crossref] [PubMed]
60. Guo Y, Xie Y, Luo Y. The Role of Long Non-Coding RNAs in the Tumor Immune Microenvironment. Front Immunol 2022;13:851004. [Crossref] [PubMed]
61. Ren D, Hua Y, Yu B, et al. Predictive biomarkers and mechanisms underlying resistance to PD1/PD-L1 blockade cancer immunotherapy. Mol Cancer 2020;19:19. [Crossref] [PubMed]
62. Bonam SR, Kotla NG, Bohara RA, et al. Potential immuno-nanomedicine strategies to fight COVID-19 like pulmonary infections. Nano Today 2021;36:101051. [Crossref] [PubMed]