Lung cancer is the second most frequently diagnosed human malignancy worldwide, ranking the first for cancer mortality among cancers (1). Despite advances in tobacco control and pre-cancer screening, the 5-year relative survival rate for lung cancer is only ~21–23% (2). Targeted therapies based on sequencing have dramatically improved the prognosis of lung cancer patients, however, drug resistance commonly occurs (3). Moreover, the therapeutical strategies for advanced lung cancer without gene mutation were extremely limited. The immune checkpoints inhibitors (ICIs) have shown promise in durable tumor control through taking advantage of host immunity (4). However, in addition to genetic background, various clinical factors were found to affect the actual efficacy of ICIs. For instance, our team previously had proved the correlation between concomitant medications (antibiotics, corticosteroids, proton pump inhibitor and β-blockers) and ICI efficacy (5–8). Therefore, further investigations into the impact of clinical factors on ICI efficacy is urgent and will contribute to more precise therapy decision.

Among lung cancer patients, ~70% are found to suffer from malnutrition or muscle loss, suggesting the crucial role of nutrition assessment during anti-cancer therapy (9). Traditional nutrition related markers such as body mass index (BMI) and albumin (ALB) level have been utilized to predict lung cancer risk or patient survival (10, 11). The prognostic nutritional index (PNI), calculated by serum albumin level and lymphocyte count, has recently been identified as a novel easy-to-perform and standardized tool for prognosis prediction in various cancers (12). In lung cancer, pretreatment PNI has been found to associate with the clinical efficacy of targeted therapy, chemotherapy and radiotherapy (13–15). With regard to ICI-based immunotherapy, in a small cohort of non-small cell lung cancer (NSCLC) patients treated with atezolizumab (n = 24), high PNI level was correlated with prolonged time to treatment failure and overall survival (OS) (16). The pre-treatment PNI was identified as an independent prognostic factor for OS instead of progression-free survival (PFS) in advanced non-small cell lung cancer (NSCLC) patients receiving anti-PD-1 therapy (17). A recent comprehensive study has investigated the prognostic value of PNI and inflammatory indexes in immunotherapy-treated lung cancer patients (18). The univariate analysis in this study demonstrated high PNI level was correlated with prolonged PFS instead of OS, and the multivariate analysis failed to identify it as an independent prognostic factor. Therefore, the prognostic impact of PNI in ICI-treated lung cancer patients remains inconclusive and more clinical validations are essential.

In this study, firstly, a systematic literature review and meta-analysis was performed to objectively assess the prognostic value of PNI in lung cancer patients receiving ICI therapy. Then, a retrospective cohort enrolling 123 lung cancer patients from two hospitals was utilized to confirm the prognostic value of PNI and establish a novel PNI-based prognostic nomogram. This study will not only contribute to the establishment of a non-invasive and low-cost predictive approach for ICI therapy, but also further highlight the crucial role of nutrition assessment and intervention in patient management.

The ICI-based immunotherapy has significantly improved the overall prognosis of advanced lung cancer patients through its unique sustained tumor control and tolerable adverse effects (34). Since cachexia is common before or during anti-cancer treatment, therefore nutritional assessment and intervention are vital in patient management (35). The PNI, an easily available nutritional marker, has been utilized to predict the clinical outcome of the lung cancer patients in numerous studies. For instance, the low preoperative PNI level was proved to be an independent unfavorable factor affecting the OS and recurrence-free survival of the patients with resectable NSCLC (36). The high pretreatment PNI level was also correlated with better OS and PFS in advanced lung cancer patients receiving anlotinib (37). In extensive-stage SCLC, patients with high PNI levels had a significant better OS and PFS than those with low PNI level (38). However, on the contrary, a recent study found high PNI was associated with worse 1-year OS and therapy response in ALK-positive NSCLC patients receiving crizotinib, suggesting its prognostic impact may depend on gene mutations (39). With regard to ICI-treated lung cancer patients, whether the PNI could function as a reliable prognostic indicator remains inconclusive (16, 18, 25). Therefore, for addressing this issue, we performed a meta-analysis based on current evidences, followed by an independent clinical cohort validation. These efforts will contribute to the clinical utilization of PNI in the precise management of lung cancer patients.

In this meta-analysis, firstly, we found the high PNI level was significantly correlated with better OS and PFS of the lung cancer patients receiving ICI based therapies. The following sensitivity and publication bias analysis confirmed the stability and reliability of the results. This pooled result was in accordance with some included studies. For instance, in NSCLC patients receiving PD-1 inhibitors, PNI ≥ 45 was an independent favorable factor affecting OS and PFS (24). The similar result was also observed in another study suggesting low PNI level also served as an independent predictor of early progression (27). A recent report suggested the nutritional status may be correlated with the proportion and function of immune cells (such as T and memory cells) in the tumor microenvironment, therefore affecting the clinical efficacy of the immunotherapies (40). In hepatocellular carcinoma, nutrition deprivation impaired the anti-cancer function of tumor-infiltrating CD8+T cells, partly explaining why low PNI was correlated with reduced ICI efficacy (41). On the other hand, it should be noted that the PNI was not correlated with patient prognosis or identified as an independent prognostic indicator in several included studies (16, 18, 25, 28, 29). These divergent results may be partly attributed to some uncontrollable factors of retrospective studies such as sample size, patient selection, therapy strategy and follow-up period. For minimizing the potential impact of these factors, the subgroup analysis was performed based on specific characteristics. As result, the positive correlation between PNI level and OS/PFS was significant regardless of region, age and PNI cut-off value. However, with regard to therapy type, we found the PNI level was not significantly correlated with PFS instead of OS in the patients receiving anti-PD-1 therapy. This result was in accordance with only one of the included study demonstrating that pretreatment PNI, BMI and presence of sarcopenia were not identified as independent predictors for PFS in the NSCLC patients receiving pembrolizumab as the first-line treatment (31). Considering the limited included studies, we suggested the correlation between pretreatment PNI level and the clinical efficacy of anti-PD-1 drugs needs to be clarified by more evidences.

For validating the result of the meta-analysis, an independent retrospective cohort from two hospitals was utilized. For the whole cohort, the high PNI level was identified as a favorable independent prognostic factor for both OS and PFS, which was subsequently confirmed by the subgroup analysis of NSCLC and other lung cancer types. In addition, the high PNI level was found to correlate with better therapy response in the lung cancer patients. These evidences collectively supported the speculation that the pretreatment PNI level may predict the efficacy of ICI therapy in the lung cancer patients. On the other hand, both the meta- and retrospective analysis further highlighted the crucial role of immunonutrition intervention in cancer immunotherapy (42). Although the nutritional support in cancer neoadjuvant and adjuvant therapies was widely acknowledged and routinely performed, its benefits in ICI-based therapy remains unclear and needs to be clarified by more clinical trials (43, 44). Previous studies have suggested BMI has the potential to be a prognostic indicator in ICI-treated lung cancer patients. For instance, a multicenter study has showed BMI variation was significantly correlated with OS and PFS in metastatic NSCLC patients receiving first-line pembrolizumab treatment (45). A real-world clinical study demonstrated BMI⩾25 kg/m² served as an independent favorable factor for the OS of ICI-treated NSCLC patients (46). Consistently, BMI > 25 kg/m² was significantly correlated with longer PFS in EGFR mutated NSCLC patients receiving ICI therapy (47). The mechanism investigations attributed this correlation partly to the activated leptin signaling in overweight patients, which increases PD-1 expression to induce tumors more responsive to ICI drugs (48). In our study, we found the high BMI level was correlated with better OS and PFS in NSCLC patients, but failed to independently predict the clinical outcome in the multivariate analysis. A recent study has found the association between BMI level and the survival of ICI-treated patients may be affected by some clinical confounders, which partly explains the inconsistent results in our study (49). Moreover, a narrative review has implied the unreliability of BMI as a clinical indicator for lung cancer patients and recommended novel radiological strategies to define obesity (50). The serum albumin is recently identified as a novel non-invasive biomarker for predicting the clinical efficacy of ICI therapy in numerous cancers (21, 51). In this study, the increased serum albumin was correlated with better OS but failed to be an independent predictor in the ICI-treated NSCLC patients. Then, the ROC analysis demonstrated the PNI performed better in stratifying OS than the BMI and serum albumin, suggesting PNI may be the priority for clinicians in predicting the clinical outcome. Finally, considering the increasing utility of nomogram models in cancer management, we integrated the PNI and other factors to establish a novel nomogram for predicting 1-year OS. Using the calibration curves, the predictive performance of the nomogram was well-validated, further supporting the application of PNI in clinical practice.

There are some mechanism investigations to support the role of PNI in predicting ICI efficacy. The PNI value was calculated based on the serum albumin level and lymphocyte count, both of which are closely linked to the clinical efficacy of ICI drugs. The ICI therapy is dependent on IgG-antibody drugs and their catabolism and recycling are regulated by neonatal Fc receptor (FcRn) mediated mechanisms (52). The abundance and efficiency of FcRn could be reflected by the serum albumin level (51). The lymphocyte count may be correlated with the apoptosis of tumor infiltrating lymphocytes, which is crucial for determining the individual response to ICI therapy (53). On the other hand, considering the complexity of tumor microenvironment, the roles of albumin and lymphocyte in cancer immunotherapy are expected to be investigated by more fundamental work based advanced techniques such as single-cell sequencing. In addition, whether increasing pretreatment PNI level (such as albumin supplement or other nutritional interventions) is actually beneficial for the ICI therapy still needs to be clarified by well-designed prospective studies in future.

There are several limitations in our study. First, in the meta-analysis, the therapy strategies varied among the included studies, which may result in study heterogeneity. Meanwhile, the included literatures were relatively limited and therefore the results of the subgroup analysis need further validations. Secondly, a multicenter study demonstrated the PNI level was not an independent prognostic factor for the OS of the patients with SCLC, which is inconsistent with our retrospective study (29). Considering the few relevant published reports and limited sample size of our study, more attention should be paid to the prognostic value of PNI in ICI-treated SCLC patients. Thirdly, since the PNI and other parameters (such as BMI and albumin) may vary with disease progression, we failed to investigate the clinical values of their dynamic changes during the therapy period. Finally, our study mainly focused on the prognostic significance of pretreatment PNI in ICI-treated lung cancer patients, how to develop precise nutritional support strategies and their potential benefits on ICI efficacy should be emphasized in our following work.

In conclusion, our findings collectively demonstrate the PNI level can be utilized as a reliable prognostic indicator for lung cancer patients treated with ICI therapy. These findings not only contribute to the clinical utility of PNI in cancer patient management, but also further highlight the crucial role of nutrition assessment and intervention in ICI therapy. Considering the study limitations, more clinical validations are needed in future. Moreover, relevant mechanism investigations may benefit developing novel intervention strategies to modulate PNI levels before or during ICI therapy.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

XY and JW wrote the main manuscript text. JM and YW collected the data and prepared all the figures. HQ analyzed the data and revised the manuscript text. XW and MY designed the study and revised the manuscript text. All authors reviewed the manuscript. All authors contributed to the article and approved the submitted version.