Pre-screening value of serum albumin and the glucose-lymphocyte ratio as the “transport-activation” effectors of immune checkpoint inhibitors in small cell lung cancer

Introduction

Small cell lung cancer (SCLC), a highly aggressive neuroendocrine malignancy, represents 13–15% of all lung cancers (1,2). Approximately 70% of SCLC patients are diagnosed at the extensive stage, which is characterized by distant metastasis (3,4). Platinum-based chemotherapy (carboplatin/cisplatin combined with etoposide) has been the first-line standard treatment for SCLC for over two decades (5,6); however, the clinical outcomes of SCLC patients remain dismal. The median overall survival (OS) for such patients is approximately 10 months, with a 5-year survival rate of less than 7% (1,7). The advent of immune checkpoint inhibitors (ICIs) targeting programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) has modestly improved patient outcomes, as evidenced by the landmark IMpower133, CASPIAN and ASTRUM-005 trials, which reported significant OS benefits with atezolizumab (12.3 vs. 10.3 months), durvalumab (12.9 vs. 10.5 months) and serplulimab (15.4 vs. 10.9 months) combined with chemotherapy compared with chemotherapy alone. However, subgroup analyses revealed heterogeneous responses, with a substantial proportion of patients deriving limited benefit from immunotherapy (8-10). This therapeutic heterogeneity underscores an urgent unmet need to develop predictive biomarkers to prospectively identify immunotherapy-responsive subpopulations to advance precision medicine in SCLC.

There is emerging evidence that serum albumin (ALB) is a pivotal modulator of ICI pharmacokinetics (11). Over 90% of circulating monoclonal antibody-based ICIs bind reversibly to ALB, which governs drug stability, tissue distribution, and catabolic clearance (12). Concurrently, lymphocyte functionality—a cornerstone of anti-tumor immunity—is intrinsically linked to glucose metabolism. The glucose-to-lymphocyte ratio (GLR), which reflects systemic inflammatory status and immune-metabolic crosstalk, has emerged as a prognostic indicator across malignancies (13). Notably, an elevated GLR is correlated with impaired cytotoxic T-cell activity and tumor-associated immunosuppression, and thus may have utility in predicting ICI responsiveness (14).

This study sought to address the gaps in the research by investigating the combined predictive value of ALB and the GLR in extensive-stage SCLC patients receiving ICIs. We hypothesized that the pre-treatment ALB level and GLR could be used to stratify patients into distinct metabolic-immunologic phenotypes with differential capacities of benefiting from immunotherapy. The objectives of the study were threefold. Specifically, this study sought to: (I) establish prognostic cut-off values for ALB and the GLR using survival-driven optimization; (II) validate the independent predictive utility of these biomarkers through multivariable modeling; and (III) develop a clinically deployable nomogram integrating these biomarkers with established prognostic variables. We present this article in accordance with the TRIPOD reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-825/rc).

Methods

Study design and participants

The retrospective cohort study evaluated consecutive SCLC patients treated with PD-1/PD-L1 inhibitors from The 900th Hospital of the Joint Logistic Support Force, People’s Liberation Army between January 2018 and October 2023. The study flowchart is schematically illustrated in Figure 1. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was approved by the Institutional Ethics Board of The 900th Hospital of the Joint Logistic Support Force, People’s Liberation Army (No. 2025-044). Individual consent for this retrospective analysis was waived.

Figure 1 Study flowchart. ALB, albumin; CR, complete response; DCR, disease control rate; GLR, glucose-to-lymphocyte ratio; ORR, objective response rate; OS, overall survival; PD, progressive disease; PD-1, programmed death-1; PD-L1, programmed death-ligand 1; PFS, progression-free survival; PR, partial response; SCLC, small cell lung cancer; SD, stable disease.

Data collection and endpoints

Demographic variables (i.e., sex, age, and smoking history) and clinical parameters [i.e., histological subtype, lymphocyte count (×10⁹/L), baseline serum ALB (g/L), the GLR, body mass index (BMI) (kg/m²), Veterans Administration Lung Study Group (VALG) stage, and treatment response] were extracted from electronic health records. The baseline laboratory values were obtained ≤3 weeks before treatment during routine clinical evaluation.

Primary endpoints

The primary endpoints of the study were OS, which was defined as the time from treatment initiation to death from any cause, and progression-free survival (PFS), which was defined as the time to radiographical progression or death as assessed by the immune Response Evaluation Criteria in Solid Tumours (iRECIST) (15).

Secondary endpoints

The secondary endpoints of the study were the objective response rate (ORR), which was defined as a complete/partial response as per the RECIST (version 1.1) (16), and the disease control rate (DCR), which was defined as stable disease and the ORR.

Inclusion/exclusion criteria

Inclusion criteria

Patients were included in the study if they met the following inclusion criteria:

• Were aged ≥18 years;
• Had histologically/cytologically confirmed SCLC;
• Had ≥1 measurable lesion (non-irradiated); and
• Had completed ≥2 cycles of PD-1/PD-L1 inhibitor therapy;
• Eastern Cooperative Oncology Group performance status ≤2 score.

Exclusion criteria

Patients were excluded from the study if they met any of the following exclusion criteria:

• Had other primary malignant tumors;
• Had an active infection requiring systemic antibiotics ≤3 weeks pre-/post-ICI therapy;
• Had used an immunomodulatory drug for >4 weeks; and/or
• Had insufficient follow-up data.

Biomarker quantification

The GLR was calculated as follows: GLR = fasting blood glucose (mmol/L)/lymphocyte count (×10⁹/L). The optimal prognostic thresholds for ALB and the GLR were determined using maximally selected rank statistics via the R package “survminer”, with cut-off values optimized to stratify patients based on maximal survival discrimination. The stability of these optimal cut-off values was further internally validated using bootstrap resampling with 500 repetitions to calculate 95% confidence interval (CI) via the R package “rms”.

Statistical analysis

The statistical analyses were performed using SPSS (version 26.0) and R statistical software (version 4.3.3). The survival curves were generated using the Kaplan-Meier method with log-rank testing. A multivariable Cox regression analysis was conducted to identify independent prognostic factors, reported as hazard ratios (HRs) with 95% CIs. The final multivariable Cox proportional hazards model included six variables. With 81 observed death events, the events-per-variable (EPV) ratio was 13.5, which exceeds the commonly recommended threshold of 10.

Propensity score matching (PSM)

The PSM analysis was conducted using nearest-neighbor matching at a ratio of 1:1 (caliper =0.1 standard deviation). The covariates were age, gender, BMI, clinical stage, brain metastasis, carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and diabetes. The missing data (<5% variables) were imputed multiple times using the “mice” package in R, and a chained equation algorithm was used to enhance the accuracy and robustness of the statistical analysis.

Nomogram development

Significant predictors (a P value <0.05 in the univariate analysis) were incorporated into a Cox model to predict 12-/24-month OS. Internally validated using a 500-repetition bootstrap resampling process to provide an unbiased estimate of model performance. The original queue is resampled to get a dataset of the same size. To assess external validity, model performance was assessed using Harrell’s concordance index (C-index) and, a time-dependent receiver operating characteristic (ROC) curve and calibration plot analysis. In addition, decision curve analysis (DCA) (using the “rmda” package in R) was used to evaluate the clinical utility of the model.

Results

Patient characteristics

Among 126 eligible patients, 93 (73.8%) presented with extensive-stage SCLC at initial diagnosis, while 33 (26.2%) had brain metastasis. Of the patients, 47 (37.3%) received therapeutic or prophylactic radiation targeting brain, pulmonary lesions, or lymph nodes. The baseline demographics and clinical characteristics of the patients are summarized in Table 1.

Optimal cut-off values for ALB and the GLR

The optimal threshold analysis identified the cut-off values for ALB and the GLR as 40.9 g/L and 5.02, respectively, in patients before receiving ICIs (Figure 2A,2B). Bootstrap internal validation (500 repetitions) confirmed the stability of these cut-offs, with 95% CIs of 39.8–42.1 g/L for ALB and 4.65–5.38 for the GLR. These cut-off values optimally stratified patients into distinct prognostic groups (Figure 2C,2D). Patients with ALB <40.9 g/L were allocated to the low ALB group, while those with ALB ≥40.9 g/L were allocated to the high ALB group. Similarly, patients with a GLR <5.02 were allocated to the low-GLR group, while those with a GLR ≥5.02 were allocated to the high GLR group. These patients respectively accounted for 56.3% (n=71), 43.7% (n=55), 72.2% (n=91), and 27.8% (n=35) of all the patients in the study. The differences between groups were statistically significant (P<0.001).

Figure 2 Distribution of the ALB levels and GLRs, and optimal cut-off values in the SCLC patients. (A,B) The upper panels of the histogram show the distribution of ALB (A) and the GLR (B), where the low-expression group (blue) and the high-expression group (red) have different density profiles. The lower panels show the distribution scatter plots of the maximally selected rank statistics of ALB and the GLR. The dashed line marks the optimal cut-off value (ALB cut-off value: 40.9 g/L; GLR cut-off value: 5.02). (C,D) The distribution of SCLC patients in different serum ALB and GLR groups. Boxes indicate median and quartiles, and lines show the overall distribution of the data. ALB, albumin; GLR, glucose-to-lymphocyte ratio; SCLC, small cell lung cancer.

Correlation analysis of ALB and the GLR, and the ORR

Lower GLR levels were associated with the ORR (P=0.03) and DCR (P=0.04), but no statistically significant correlation was observed between different ALB levels and the ORR (P=0.38) and DCR (P=0.29) (Table 2).

The objective efficacy analysis revealed statistically significant differences in the treatment outcomes between the low and high GLR groups. Notably, the low-GLR group had a significantly higher ORR (61.54% vs. 40.0%; P=0.03) and DCR (96.70% vs. 85.71%; P=0.04) than the high GLR group. Conversely, no statistically significant correlation was observed between the different ALB levels and the ORR (52.11% vs. 60.00%, P=0.38) and DCR (91.55% vs. 96.36%, P=0.29) (Table 2).

Correlation analysis of ALB and the GLR, and OS

The survival analysis stratified by the ALB level and GLR revealed distinct prognostic patterns. The median OS was substantially prolonged in subgroups with elevated biomarker levels (high ALB: 29.0 months vs. low ALB: 13.0 months; P<0.001; high GLR: 26.0 months vs. low GLR: 15.0 months; P=0.03) (Figure 3A,3B), while no significant correlation was observed in PFS (ALB: P=0.20; GLR: P=0.17) (Figure S1). The univariable analysis revealed that smoking history, clinical stage, CEA, NSE, ALB, and the GLR were associated with the efficacy of immunotherapy in SCLC patients. The multivariable analysis confirmed that clinical stage (HR =1.689, 95% CI: 0.922–3.097, P=0.09), ALB (HR =0.415, 95% CI: 0.247–0.696, P=0.001), and the GLR (HR =0.560, 95% CI: 0.315–0.994, P=0.048) were independent predictive factors (Figure 3C). Thus, both high ALB and high GLR could be used as potential predictors of immunotherapy efficacy in SCLC patients.

Figure 3 Correlation analysis of patient survival and the ALB level and GLR. Kaplan-Meier survival curves for the ALB level (A) and GLR (B) before ICI treatment; (C) the results of unifactorial and multifactorial risk analyses for OS. For categorical variables, the reference is the “No” or “Low” group. For continuous variables, the HR represents the risk associated with each 1-unit increase; (D,E) distribution of propensity score for the ALB (D) and GLR (E) subgroups; (F,G) Kaplan-Meier survival curves for different ALB and GLR subgroups after PSM. ALB, albumin; BMI, body mass index; CEA, carcinoembryonic antigen; CI, confidence interval; GLR, glucose-to-lymphocyte ratio; HR, hazard ratio; ICI, immune checkpoint inhibitor; NSE, neuron-specific enolase; OS, overall survival; PSM, propensity score matching.

Propensity score-matched validation

To mitigate potential confounding from baseline characteristics, 1:1 PSM was performed using MatchIt with the following covariates: age, sex, BMI, smoking history, chronic diseases, clinical stage, brain metastasis, radiotherapy history, and diabetes. The multivariable Cox regression generated propensity scores for ALB/GLR stratification. The post-PSM analysis retained 38 matched pairs in the ALB cohort and 28 pairs in the GLR cohort, achieving a balanced baseline characteristic (all P>0.05; Table S1). The survival analysis of the PSM-adjusted cohort confirmed a sustained OS advantage in the high ALB patients versus the low ALB patients (median OS: 29.0 vs. 13.0 months, P=0.002), while no such prognostic significance was found in relation to the GLR stratification (P=0.19). These findings validate ALB as an independent predictor of immunotherapy outcomes in SCLC (Figure 3D-3G).

Prognostic nomogram development

To predict OS in patients with SCLC, the following four predictive models were constructed: Model 1, which included CEA, NSE, and clinical stage; Model 2, which incorporated serum ALB into Model 1; Model 3, which integrated the GLR into Model 1; and Model 4, which combined Model 1 with both ALB and the GLR. The results of the multivariate Cox analysis for Model 4 were further visualized using nomograms (Figure 4A). The model results emphasized ALB and NSE as significant predictors of the prognosis of SCLC treated with ICIs (Figure 4A). The C-index value of the column-line graph was 0.752 (95% CI: 0.667–0.776, P<0.001), indicating that the model has moderate to good discriminatory ability. The C-index values of Models 1, 2, and 3 were 0.704 (95% CI: 0.648–0.759), 0.749 (95% CI: 0.696–0.802), and 0.700 (95% CI: 0.644–0.756), respectively, and the C-index value of Model 4 was 0.752 (95% CI: 0.694–0.811). The C-index value of Model 4 was better than the C-index values of the other models, indicating good discriminatory ability.

Figure 4 Joint prediction model construction and evaluation of OS prediction models. (A) Nomogram for predicting the survival probability of SCLC patients. This nomogram is used to predict the probabilities related to 12 and 36 months. For each patient, points are assigned based on the values of ALB, GLR, clinical stage, CEA, and NSE by locating their respective values on the corresponding scales. The total points are then calculated by summing these individual points. Finally, the probabilities associated with 12 and 36 months can be determined by finding the total points on their respective probability scales. (B) ROC curves of the four predictive models for 12-month survival probability. (C) ROC curves of the four predictive models for 24-month survival probability. (D) Kaplan-Meier survival curve for OS in the combined groups of ALB and GLR. Survival curves for four different combinations of high ALB (≥40.9 g/L) and a high GLR (≥5.02) (ALB+GLR+, red), high ALB and a low GLR (<5.02) (ALB+GLR−, blue), low ALB (<40.9 g/L) and a high GLR (ALB−GLR+, green), and low ALB and a low GLR (ALB−GLR−, orange). The horizontal axis indicates OS time (months) and the vertical axis indicates survival probability. Model 1 included CEA, NSE, and clinical stage; Model 2 incorporated serum ALB into Model 1; Model 3 integrated the GLR into Model 1; Model 4 combined Model 1 with both ALB and the GLR. ALB, albumin; AUC, area under the curve; CEA, carcinoembryonic antigen; GLR, glucose-to-lymphocyte ratio; NSE, neuron-specific enolase; OS, overall survival; ROC, receiver operating characteristic; SCLC, small cell lung cancer.

Time-dependent ROC curves were used to compare the predictive performance of each prognostic factor and prediction model. The results showed that the combined ALB and GLR model (i.e., Model 4) had higher predictability than any independent factor. The 12- and 24-month area under the curve (AUC) values for the OS prediction model were 0.804 and 0.781, respectively (Figure 4B,4C), which also suggested that the model had good discriminatory ability. The calibration curves for OS showed good agreement between prediction and observation (Figure S2A). DCA demonstrated that the nomogram provided a superior net benefit across a wide range of threshold probabilities (Figure S2B).

The Kaplan-Meier survival analysis of the different ALB and GLR cohorts showed a distinct difference in the survival rates between the four cohorts, such that the patients with high ALB and a high GLR exhibited significantly better OS than the other patients (P<0.001) (Figure 4D).

Discussion

SCLC is a highly aggressive malignancy with a dismal prognosis (1,17). While the integration of ICIs into first-line therapy has modestly improved the survival outcomes of such patients, the overall ORR of extensive-stage SCLC patients to ICI therapy is about 15–20%, with some patients exhibiting primary resistance or only short-term benefits from immunotherapy (18-20). The heterogeneous response of patients underscores the urgent need for robust biomarkers to identify those most likely to benefit from immunotherapy. In this study, we showed that ALB and the GLR were independent predictors of survival in SCLC patients receiving ICIs, and their combined use enhances prognostic stratification. Our findings provide novel insights into the interplay between drug transportation, immune inflammation activation, and therapeutic efficacy, which may guide personalized treatment strategies.

As a key plasma protein synthesized by the liver, ALB serves as both a nutritional indicator and a well-established prognostic biomarker in oncology. Emerging pan-cancer evidence and machine-learning models suggest that elevated ALB levels predict improved OS following ICIs therapy across various malignancies (21-24). Consistent with these reports, our study demonstrated that patients with high ALB levels (≥40.9 g/L) had significantly longer median OS compared to those with low ALB levels (median OS: 29 vs. 13 months, P<0.001), supporting its role as a robust predictor of survival in immunotherapy. Notably, we found no significant association between ALB levels and ORR, DCR, or PFS, aligning with previous observations in ICI-treated esophageal cancer (25). This discrepancy suggests that ALB may influence long-term survival through mechanisms beyond initial tumor response. As the most abundant transport protein in blood, ALB facilitates the delivery of antitumor agents and modulates the immune microenvironment by regulating inflammatory responses and immune cell function (21,26,27). Additionally, ALB shares homeostatic pathways with immunoglobulin G (IgG), and its serum level reflects IgG catabolism, directly influencing the pharmacokinetics and clearance of therapeutic monoclonal antibodies (28,29).

In relation to the setting of the optimal cut-off value, although previous studies have generally used 3.7 g/dL as the optimal cut-off value for ALB to assess the relevance of clinical efficacy (23,30), Zheng et al. (28) revealed a significant dose-dependent relationship between baseline ALB levels and the efficacy of ICI therapy by conducting an in-depth analysis of the cumulative effect of ALB on the therapeutic response. Using maximally selected rank statistics, we identified 40.9 g/L as the optimal serum ALB cutoff for OS prediction in ICI-treated patients.

The GLR reflects a critical interplay between systemic metabolic status and immune function. In cancer biology, abnormalities in glucose metabolism are one of the characteristics of cancer, and such changes may contribute to the development of immune evasion mechanisms and lead to resistance to multiple therapies, including ICIs (31). Lymphocytes, as one of the key immune cells in the systemic inflammatory response, are involved in the cell-mediated anti-tumor immune response, and ICIs enhance the body’s anti-tumor immune response by blocking the binding between the immune cells and the tumor cells, and enhancing the recognition of the tumor by the immune cells (32). This mechanism not only increases the functional activity of lymphocytes in the immune microenvironment, but also promotes the remodeling of the tumor microenvironment, which results in a decrease in the ability of tumor cells to escape from the immune system (33,34). While the GLR has emerged as a potential prognostic biomarker in multiple cancers, its predictive value for immunotherapy outcomes in SCLC remains underexplored.

By investigating the impact of GLR on immunotherapy outcomes in SCLC patients, we found that GLR expression levels were significantly associated with rapid tumor response (ORR: 61.5% vs. 40.0%, DCR: 96.7% vs. 85.71%), suggesting that GLR may serve as a potential predictor of short-term efficacy in immunotherapy. We observed a high GLR was associated with a longer OS benefit (median OS: 26 vs. 15 months, P=0.03). The results of our study contradicted those of previous studies, such as the results of studies associating high GLR values with adverse OS in patients with colorectal cancer, non-SCLC (35), and gallbladder cancer (36). Hyperglycemia may fuel tumor progression by enhancing glycolysis, lactate production, and the activation of oncogenic pathways (37,38). Conversely, lymphocytes can inhibit tumor progression by activating cell-mediated immune responses and stimulating the release of other cytokines, such as interferon (39,40). Some researchers (41,42) have suggested that a high-glucose environment may enhance anti-tumor immune responses by modulating the metabolic reprogramming of lymphocytes. Furthermore, studies indicate that the synergistic elevation of glucose and lactate levels enhances the efficacy of PD-1 blockade therapy. This enhancement occurs through the upregulation of nuclear factor of activated T cells (NFAT) in regulatory T cells, a process that promotes the induction of effector regulatory T cells and leads to increased PD-1 expression. Furthermore, lactate can enhance immune responses by activating the GPR31 receptor expressed on phagocytic dendritic cells (43,44). Therefore, we hypothesize that during immunotherapy, a high-glucose environment enhances the therapeutic efficacy of PD-1 inhibition and limits glucose-starvation-induced promotion of cancer cell metastasis (45), thereby correlating with improved OS. However, it must be cautiously emphasized that this study does not provide direct evidence supporting this speculative mechanism. Furthermore, while the prognostic significance of the GLR was no longer statistically significant after PSM (P=0.19), suggesting it may not function as a robust independent predictor, the Kaplan-Meier curves continued to suggest a favorable survival trend in the high GLR group. This result may be related to insufficient sample size after matching, making it impossible to draw statistically significant conclusions. It indicates that the GLR may still hold potential clinical value as a complementary biomarker. Since this study failed to demonstrate whether subsequent fluctuations in blood glucose levels affect the prediction of treatment efficacy, future studies should include more stable glycemic markers such as HbA1c. Therefore, the association between GLR and survival benefit in this study should be regarded as a preliminary, exploratory finding. Its potential biological mechanisms and general applicability in SCLC immunotherapy require urgent validation through prospective cohort studies incorporating multi-omics data.

Finally, our study constructed a comprehensive prognostic prediction model for SCLC immunotherapy by integrating multidimensional indicators such as ALB, the GLR, CEA, NSE, and clinical stage. Compared with prediction models that rely only on a single factor of ALB or GLR in combination with clinical stage and tumor markers, the comprehensive model in this study demonstrates good discrimination, calibration, and positive DCA results, indicating its potential for future translation into a clinical tool. Furthermore, compared to a predictive model from a real-world study integration of clinical and blood parameters, it also demonstrates superior discriminatory power (AUC: 0.804 vs. 0.760 at 12 months) (46). The synergistic prognostic value of ALB and the GLR is not only reflected in their independent ability to predict the immunotherapy response, but also in their complementary roles in regulating the tumor microenvironment and systemic immune status.

This study identified ALB and the GLR as novel prognostic biomarkers and developed a predictive model for SCLC patients receiving ICIs; however, it had several limitations. First, as a single-center retrospective study, our model lacks external validation. Its generalizability needs to be confirmed in future prospective, multi-center studies. Second, the PD-L1 expression data were incomplete, precluding any assessment of its synergy with ALB or the GLR. Third, as a biomarker for predicting the additional therapeutic effects of ICI for SCLC, the SCLC subtype has been found to be useful, and there are reports that the SCLC-I type and tumor-associated macrophages (TAM) within the tumor are promising (43,47). This point could not be confirmed in the present study. While our model achieved statistically significant risk stratification (C-index =0.752), its moderate accuracy (ACC =0.70) likely reflects both intrinsic biological features of SCLC and methodological constraints. SCLC’s hallmark metabolic plasticity and rapid clonal evolution may fundamentally limit the predictive ceiling of baseline biomarker-based models, as tumor-immune interactions dynamically shift and exogenous regulation dynamically evolve during treatment. Due to the availability of retrospective data, serial biomarker measurements to capture treatment-induced metabolic adaptation are lacking. Future studies should integrate multi-omics data to address these gaps.

In summary, our findings showed that the pre-treatment ALB level and GLR could serve as clinically accessible biomarkers for identifying the SCLC patients most likely to benefit from immunotherapy. The combined use of these markers significantly enhances long-term survival prediction, offering a practical tool for therapeutic decision making. These results not only provide a stratification strategy for precision immunotherapy but also underscore the intricate interplay between metabolic, nutritional, and immunological mechanisms underlying the ICI response in SCLC.

Conclusions

ALB and the GLR represent cost-effective, clinically accessible biomarkers that synergistically predict survival in SCLC patients undergoing immunotherapy. Their combined use provides complementary biological insights into drug pharmacokinetics, metabolic reprogramming, and immune dysregulation. Future research should explore therapeutic optimization strategies targeting these pathways to improve ICI outcomes.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-825/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-825/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-825/prf

Funding: This work was funded by grants from The 900th Hospital of the Joint Logistic Support Force of China: Youth Incubation Special Program (No. 2023QN04), and Fujian Medical University (No. 2022QH1333).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2025-825/coif). H.T. receives lecture fees from Daiichi Sankyo, AstraZeneca, Novartis Pharma, Chugai pharmaceutical, MSD, and Taiho Pharmaceutical. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study protocol was reviewed and approved by the Institutional Ethics Board of The 900th Hospital of the Joint Logistic Support Force, People’s Liberation Army (No. 2025-044). Individual consent for this retrospective analysis was waived.

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

References

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2024;74:229-63. [Crossref] [PubMed]
2. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin 2022;72:7-33. [Crossref] [PubMed]
3. Rudin CM, Brambilla E, Faivre-Finn C, et al. Small-cell lung cancer. Nat Rev Dis Primers 2021;7:3. [Crossref] [PubMed]
4. Giunta EF, Addeo A, Rizzo A, et al. First-Line Treatment for Advanced SCLC: What Is Left Behind and Beyond Chemoimmunotherapy. Front Med (Lausanne) 2022;9:924853. [Crossref] [PubMed]
5. 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;22:249-74. [Crossref] [PubMed]
6. Armstrong SA, Liu SV. Dashing Decades of Defeat: Long Anticipated Advances in the First-line Treatment of Extensive-Stage Small Cell Lung Cancer. Curr Oncol Rep 2020;22:20. [Crossref] [PubMed]
7. Foster NR, Renfro LA, Schild SE, et al. Multitrial Evaluation of Progression-Free Survival as a Surrogate End Point for Overall Survival in First-Line Extensive-Stage Small-Cell Lung Cancer. J Thorac Oncol 2015;10:1099-106. [Crossref] [PubMed]
8. Horn L, Mansfield AS, Szczęsna A, et al. First-Line Atezolizumab plus Chemotherapy in Extensive-Stage Small-Cell Lung Cancer. N Engl J Med 2018;379:2220-9. [Crossref] [PubMed]
9. Paz-Ares L, Chen Y, Reinmuth N, et al. Durvalumab, with or without tremelimumab, plus platinum-etoposide in first-line treatment of extensive-stage small-cell lung cancer: 3-year overall survival update from CASPIAN. ESMO Open 2022;7:100408. [Crossref] [PubMed]
10. Cheng Y, Han L, Wu L, et al. Effect of First-Line Serplulimab vs Placebo Added to Chemotherapy on Survival in Patients With Extensive-Stage Small Cell Lung Cancer: The ASTRUM-005 Randomized Clinical Trial. JAMA 2022;328:1223-32. [Crossref] [PubMed]
11. Centanni M, Moes DJAR, Trocóniz IF, et al. Clinical Pharmacokinetics and Pharmacodynamics of Immune Checkpoint Inhibitors. Clin Pharmacokinet 2019;58:835-57. [Crossref] [PubMed]
12. Sleep D. Albumin and its application in drug delivery. Expert Opin Drug Deliv 2015;12:793-812. [Crossref] [PubMed]
13. Liu R, Shen Y, Cui J, et al. Association between glucose to lymphocyte ratio and prognosis in patients with solid tumors. Front Immunol 2024;15:1454393. [Crossref] [PubMed]
14. Yang W, Chen X, Hu H. CD4(+) T-Cell Differentiation In Vitro. Methods Mol Biol 2020;2111:91-9. [Crossref] [PubMed]
15. Seymour L, Bogaerts J, Perrone A, et al. iRECIST: guidelines for response criteria for use in trials testing immunotherapeutics. Lancet Oncol 2017;18:e143-52. [Crossref] [PubMed]
16. Schwartz LH, Seymour L, Litière S, et al. RECIST 1.1 - Standardisation and disease-specific adaptations: Perspectives from the RECIST Working Group. Eur J Cancer 2016;62:138-45. [Crossref] [PubMed]
17. Wang S, Tang J, Sun T, et al. Survival changes in patients with small cell lung cancer and disparities between different sexes, socioeconomic statuses and ages. Sci Rep 2017;7:1339. [Crossref] [PubMed]
18. Chung HC, Piha-Paul SA, Lopez-Martin J, et al. Pembrolizumab After Two or More Lines of Previous Therapy in Patients With Recurrent or Metastatic SCLC: Results From the KEYNOTE-028 and KEYNOTE-158 Studies. J Thorac Oncol 2020;15:618-27.
19. Johnson ML, Cho BC, Luft A, et al. Durvalumab With or Without Tremelimumab in Combination With Chemotherapy as First-Line Therapy for Metastatic Non-Small-Cell Lung Cancer: The Phase III POSEIDON Study. J Clin Oncol 2023;41:1213-27. [Crossref] [PubMed]
20. Lee JH, Saxena A, Giaccone G. Advancements in small cell lung cancer. Semin Cancer Biol 2023;93:123-8. [Crossref] [PubMed]
21. Lu JF, Bruno R, Eppler S, et al. Clinical pharmacokinetics of bevacizumab in patients with solid tumors. Cancer Chemother Pharmacol 2008;62:779-86. [Crossref] [PubMed]
22. Saito Y, Kobayashi K, Fukuoka O, et al. Ultra-high combined positive score and high serum albumin are favorable prognostic biomarkers for immune checkpoint inhibitors in head and neck squamous cell carcinoma. Head Neck 2024;46:367-77. [Crossref] [PubMed]
23. Kuang Z, Miao J, Zhang X. Serum albumin and derived neutrophil-to-lymphocyte ratio are potential predictive biomarkers for immune checkpoint inhibitors in small cell lung cancer. Front Immunol 2024;15:1327449. [Crossref] [PubMed]
24. Chowell D, Yoo SK, Valero C, et al. Improved prediction of immune checkpoint blockade efficacy across multiple cancer types. Nat Biotechnol 2022;40:499-506. [Crossref] [PubMed]
25. Chen N, Yu Y, Shen W, et al. Nutritional status as prognostic factor of advanced oesophageal cancer patients treated with immune checkpoint inhibitors. Clin Nutr 2024;43:142-53. [Crossref] [PubMed]
26. Sheinenzon A, Shehadeh M, Michelis R, et al. Serum albumin levels and inflammation. Int J Biol Macromol 2021;184:857-62. [Crossref] [PubMed]
27. Rabi R, Alsaid RM, Matar AN, et al. The role of serum albumin in critical illness, predicting poor outcomes, and exploring the therapeutic potential of albumin supplementation. Sci Prog 2024;107:368504241274023. [Crossref] [PubMed]
28. Zheng M. Serum albumin: a pharmacokinetic marker for optimizing treatment outcome of immune checkpoint blockade. J Immunother Cancer 2022;10:e005670. [Crossref] [PubMed]
29. Nicholson JP, Wolmarans MR, Park GR. The role of albumin in critical illness. Br J Anaesth 2000;85:599-610. [Crossref] [PubMed]
30. Ott PA, Bang YJ, Piha-Paul SA, et al. T-Cell-Inflamed Gene-Expression Profile, Programmed Death Ligand 1 Expression, and Tumor Mutational Burden Predict Efficacy in Patients Treated With Pembrolizumab Across 20 Cancers: KEYNOTE-028. J Clin Oncol 2019;37:318-27. [Crossref] [PubMed]
31. Kazemi MH, Najafi A, Karami J, et al. Immune and metabolic checkpoints blockade: Dual wielding against tumors. Int Immunopharmacol 2021;94:107461. [Crossref] [PubMed]
32. Paijens ST, Vledder A, de Bruyn M, et al. Tumor-infiltrating lymphocytes in the immunotherapy era. Cell Mol Immunol 2021;18:842-59. [Crossref] [PubMed]
33. Cohen JT, Miner TJ, Vezeridis MP. Is the neutrophil-to-lymphocyte ratio a useful prognostic indicator in melanoma patients? Melanoma Manag 2020;7:MMT47. [Crossref] [PubMed]
34. Naimi A, Mohammed RN, Raji A, et al. Tumor immunotherapies by immune checkpoint inhibitors (ICIs); the pros and cons. Cell Commun Signal 2022;20:44. [Crossref] [PubMed]
35. Yang M, Zhang Q, Ge YZ, et al. Prognostic Roles of Glucose to Lymphocyte Ratio and Modified Glasgow Prognosis Score in Patients With Non-small Cell Lung Cancer. Front Nutr 2022;9:871301. [Crossref] [PubMed]
36. Navarro J, Kang I, Hwang HK, et al. Glucose to Lymphocyte Ratio as a Prognostic Marker in Patients With Resected pT2 Gallbladder Cancer. J Surg Res 2019;240:17-29. [Crossref] [PubMed]
37. Wahdan-Alaswad R, Fan Z, Edgerton SM, et al. Glucose promotes breast cancer aggression and reduces metformin efficacy. Cell Cycle 2013;12:3759-69. [Crossref] [PubMed]
38. Liu L, Zhang BB, Li YZ, et al. Preoperative glucose-to-lymphocyte ratio predicts survival in cancer. Front Endocrinol (Lausanne) 2024;15:1284152. [Crossref] [PubMed]
39. Greten FR, Grivennikov SI. Inflammation and Cancer: Triggers, Mechanisms, and Consequences. Immunity 2019;51:27-41. [Crossref] [PubMed]
40. Lin DZ, Qu N, Shi RL, et al. Risk prediction and clinical model building for lymph node metastasis in papillary thyroid microcarcinoma. Onco Targets Ther 2016;9:5307-16. [Crossref] [PubMed]
41. Wu L, Jin Y, Zhao X, et al. Tumor aerobic glycolysis confers immune evasion through modulating sensitivity to T cell-mediated bystander killing via TNF-α. Cell Metab 2023;35:1580-1596.e9. [Crossref] [PubMed]
42. Liu Y, Wang F, Peng D, et al. Activation and antitumor immunity of CD8(+) T cells are supported by the glucose transporter GLUT10 and disrupted by lactic acid. Sci Transl Med 2024;16:eadk7399. [Crossref] [PubMed]
43. Kumagai S, Koyama S, Itahashi K, et al. Lactic acid promotes PD-1 expression in regulatory T cells in highly glycolytic tumor microenvironments. Cancer Cell 2022;40:201-218.e9. [Crossref] [PubMed]
44. Honn KV, Guo Y, Cai Y, et al. 12-HETER1/GPR31, a high-affinity 12(S)-hydroxyeicosatetraenoic acid receptor, is significantly up-regulated in prostate cancer and plays a critical role in prostate cancer progression. FASEB J 2016;30:2360-9. [Crossref] [PubMed]
45. Xiong Z, Yang L, Zhang C, et al. MANF facilitates breast cancer cell survival under glucose-starvation conditions via PRKN-mediated mitophagy regulation. Autophagy 2025;21:80-101. [Crossref] [PubMed]
46. Li X, Tong L, Wang S, et al. Integration of clinical and blood parameters in risk prognostication for patients receiving immunochemotherapy for extensive stage small cell lung cancer: real-world data from two centers. BMC Med 2024;22:381. [Crossref] [PubMed]
47. Gay CM, Stewart CA, Park EM, et al. Patterns of transcription factor programs and immune pathway activation define four major subtypes of SCLC with distinct therapeutic vulnerabilities. Cancer Cell 2021;39:346-360.e7. [Crossref] [PubMed]