Prognostic Role Of Naples Prognostic Score In Lung Cancer: A Meta-Analysis

Prognostic Role Of Naples Prognostic Score In Lung Cancer: A Meta-Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Prognostic Role Of Naples Prognostic Score In Lung Cancer: A Meta-Analysis Zhaohui Han, Zengming Wang, Chunyan Zhao, Xiaopeng Zhang, Zhancong Niu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4816566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 12 Apr, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose: The prognostic value of the Naples prognostic score in lung cancer remains controversial. Therefore, we performed a meta-analysis of relevant published studies to determine the prognostic value of the Naples prognostic score in patients with lung cancer. Methods: We conducted a systematic search of relevant studies in PubMed, Ovid, the Cochrane Library, and Web of Science databases. Data and characteristics of each study were extracted and hazard ratios (HRs) at 95% confidence intervals (CIs) were calculated to estimate effects. A meta-regression analysis was used to assess the prognostic value of the Naples Prognostic Score in patients with lung cancer. Results: A total of 1691 patients from six studies were included in this meta-analysis, with a combined HR of 3.357 (95% CI: 1.964-5.738, p=0.000); the results suggest that a high Naples Prognostic Score predicts a shorter overall survival (OS) for patients. Conclusion: This meta-analysis suggests that a high Naples Prognostic Score may be a predictor of poor prognosis in lung cancer patients. Further large cohort studies are needed to confirm these findings. Naples Prognostic Score lung cancer prognosis meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lung cancer is one of the most common malignancies worldwide and the leading cause of cancer-related death worldwide. 1 Relevant research shows that, 2 in 2020, there were 2.2 million new lung cancer cases and 1.8 million deaths worldwide, accounting for about one in ten (11.4%) cancer diagnoses and one in five (18.0%) cancer-related deaths. In recent years, advances in our understanding of lung cancer risk, development, immune control, and treatment options have enabled lung cancer patients to increasingly receive precise, individualized diagnosis and treatment. However, the overall prognosis of lung cancer remains unsatisfactory due to local tumor recurrence and distant metastasis. 3 Therefore, it becomes critical to identify reliable and valid markers for better prognosis. In recent years, the Naples Prognostic Score (NPS) has entered the public eye as a new inflammatory nutrition scoring system, calculated from the patient's preoperative serum albumin(ALB), total cholesterol(TC), NLR and LMR, reflecting the patient's systemic inflammatory response and nutritional status. 4 The NPS was identified as a prognostic indicator for various cancers, such as breast cancer, 5 esophageal cancer, 6 gastric cancer, 7 ampullary carcinoma, 8 pancreatic cancer 9 and colorectal cancer 10 . The prognostic value of the NPS in lung cancer was also investigated. 11–16 However, these studies reported inconsistent results due to differences in study design and sample size. Therefore, it is unclear whether the NPS is a valid prognostic indicator for lung cancer. In this study, we searched existing studies and performed a meta-analysis to assess the prognostic role of the Naples Prognostic Score in lung cancer. Materials and Methods Search strategy We conducted a comprehensive literature search of the articles through the following databases, with no date limits: PubMed, Ovid, the Cochrane Library, and the Web of Science databases. The search was updated to November 2022. The main search terms included: (NPS or Naples prognostic score) and (lung cancer or lung carcinoma or NSCLC or SCLC). Relevant articles in the list of references were also examined. Data extraction All candidate articles were evaluated and extracted by two independent researchers (Zhaohui Han and Zengming Wang). The articles that cannot be classified by title and abstract only were full-text reviewed by retrieval. If disagreement occurred, the two researchers will discuss and reach a consensus with the third investigators (Duan Guochen). For each study, the following items were recorded: first author’s name, year of publication, country, sample size, gender, follow-ups, treatment strategy, cancer type and HRs with 95%CIs. Given that all studies were retrospective studies, both researchers used the Newcastle-Ottawa Scale (NOS) to assess the quality of each included study. The NOS consists of three parts: selection (0–4 points), comparability (0–2 points), and outcome assessment (0–3 points). NOS score ≥ 6 is considered to be a high-quality study. Inclusion and exclusion criteria Inclusion criteria for this meta-analysis: ( 1 ) patients with lung cancer in the study were diagnosed by pathological examination; ( 2 ) all the evaluation indicators were obtained from albumin (ALB), total cholesterol (TC), NLR, and LMR in the patient's preoperative serum; ( 3 ) the association of NPS with OS and / or PFS and / or DFS in lung cancer patients was reported; ( 4 ) full text articles in English.( 1 ) abstract, letters, reviews, expert opinions, case reports, or non-clinical studies; ( 2 ) studies with sufficient data to estimate HR and 95%CI were not available; ( 3 ) the study had repeated data or repeated analyses; ( 4 ) the study was not written in English. Statistical analysis We directly obtained HR and 95%CI from each literature or estimated these data from each study according to the method illustrated by Parmar et al. 17 The association of the NPS score and the prognosis of lung cancer patients was evaluated by these data. A HR > 1 indicated a worse prognosis in lung cancer patients with high NPS. We conducted a heterogeneity test on included studies using the I 2 test. The fixed effects model was applied to conduct the meta-analysis if no remarkable inter-study heterogeneity (P > 0.1 or I 2 < 50.0%). If significant heterogeneity (P < 0.1 or I 2 ≥ 50.0%) was found, the random effects model was applied for the meta-analysis. If there is considerable heterogeneity, the meta-regression analysis and the subgroup analysis of the restricted maximum likelihood (REML) method will be performed. Publication bias was estimated by Begg’s rank correlation test and Egger’s regression asymmetry test. 18 To assess the stability of the results, we performed a sensitivity analysis to test it. Statistical analyses were performed using STATA MP statistical software version 17.0. P < 0.05 was considered statistically significant. Results Study characteristics The process of study selection has been described in the flow chart (Fig. 1 ). The initial search strategies retrieved a total of 65 articles. After a careful examination of these articles, 6 studies with a total of 1,691 patients published between 2021 and 2022 were eventually enrolled in our meta-analysis. In 6 articles, the correlation between NPS and OS was studied. Among them, 3 articles studied the correlation between NPS and PFS; another 3 studies the correlation between NPS and DFS. HRs and 95%CIs were reported directly in all the studies. 2 of these studies enrolled ≤ 200 patients and 4 studies had > 200 patients. All studies were retrospective cohort studies. All of the studies were obtained from China. The characteristics of the included studies were shown in Table 1 . Table 1 Main characteristics of all the studies included in the meta-analysis Author Year Region No (M/F) Follow-up (months) (median and range) Treatment Age (years) (median and range) NPS Outcome Stage Type HR NOS Ren et al 11 2022 China 319(162/157) 32 Surgery Chemotherapy radiotherapy TKI treatment 61 (interquartile range, IQR: 13) 0, 1, 2 OS/DFS I/II/III NSCLC R (U/M) 6 Li et al 12 2021 China 457(283/174) 50 (12–66) Surgery 63 (IQR = 59–69) 0, 1, 2 OS/DFS I/II NSCLC R (U/M) 7 Chen et al 13 2022 China 128(97/37) 12.3 Surgery Chemotherapy radiotherapy 65 (14–82) 0, 1, 2 OS/PFS Limited stage Extensive stage SCLC R (U/M) 6 Guo et al 14 2021 China 206(120/86) 37 (13–59) Chemotherapy radiotherapy 62 (36–84) 0, 1, 2 OS/PFS III NSCLC R (U/M) 6 Peng et al 15 2022 China 395(252/143) 32 (1–60) Surgery Chemotherapy radiotherapy TKI treatment (24–86) 0, 1, 2 OS/PFS I/II/III/IV NSCLC R (U/M) 7 Xuan et al 16 2022 China 186(85/101) 32 (6–55) Radiotherapy 57 (39–72) 0, 1, 2 OS/DFS IV NSCLC R (U/M) 6 Abbreviations : M, male; F, female; NPS: Naples Prognostic Score; HR, hazard ratio; NOS, Newcastle–Ottawa Scale; OS, overall survival; DFS, disease-free survival; NSCLC, non-small-cell lung cancer; R, reporting; U, univariate analysis; M, multivariate; PFS, progression-free survival; SCLC, non-small-cell lung cancer; TKI, tyrosine kinase inhibitor. The relationship between NPS and OS of lung cancer All 6 studies reported a correlation between NPS and OS in lung cancer patients. Because of significant heterogeneity (I 2 = 69.8%, Ph = 0.005), Therefore, a random effects model was applied. The results show that high NPS predict worse OS outcomes (HR: 3.357, 95%CI༚1.964–5.738, P = 0.000). (Fig. 2 ) The relationship between NPS and PFS and DFS in lung cancer Three studies reported the relationship between NPS and PFS in lung cancer patients, and our meta-analysis showed that patients with high NPS were associated with shorter PFS (random effects model obtained by HR: 3.094, 95% CI : 1.344–7.126, P = 0.008 ; Fig. 3 A), with heterogeneity (I 2 = 66.4%, Ph = 0.051). Three other studies assessed the relationship between NPS and DFS, and our results show that high NPS predict worse DFS outcomes (obtained by the random effects model HR: 3.455, 95% CI: 1.518–7.862, P = 0.003; Fig. 3 B), with heterogeneity (I 2 = 72.4%, Ph = 0.027). Subgroup analysis We further explored potential causes of heterogeneity. For OS, subgroup analyses were performed by mode of treatment (treatment with surgery and treatment without surgery), type (NSCLC and SCLC), sample size (> 200 and ≤ 200), stage (advanced stage: III/IV and stages I to IV: I/II/III/IV). Most subgroup analyses did not significantly alter the prognostic role of NPS scores in OS (Table 2 ). However, for subgroup analyses with a sample size ≤ 200, patients with high NPS scores were not significantly associated with shorter OS, with a combined HR of 3.671(Random-effects model, 95% CI : 0.625–21.558, P = 0.150). Table 2 Summary of the meta-analysis results Analysis N References Random-effects model Fixed-effects model Heterogeneity HR(95%CI) P HR(95%CI) P I 2 Ph OS 6 11, 12, 13, 14, 15, 16 3.357(1.964–5.738) 0.000 2.586 (2.007–3.332) 0.000 69.8% 0.005 Subgroup 1:Treatment methods including surgery 4 11, 12, 13, 15 5.494 (2.596–11.626) 0.000 4.728 (3.018–7.406) 0.000 52.7% 0.096 reatment methods not including surgery 2 14, 16 1.949 (1.433–2.650) 0.000 0.0% 0.883 Subgroup 2:sample size>200 4 11, 12, 14, 15 3.760 (1.843–7.671) 0.000 2.845 (2.099–3.855) 0.000 75.7% 0.006 sample size ≤ 200 2 13, 16 3.671 (0.625–21.558) 0.150 2.077 (1.311–3.292) 0.002 66.4% 0.085 Subgroup 3: stage: I/II/III/IV 11, 12, 13, 15 5.494 (2.596–11.626) 0.000 4.728 (3.018–7.406) 0.000 52.7% 0.096 stage: III/IV 14, 16 2.065 (1.523−2.800) 0.000 0.0% 0.781 Subgroup 4: NSCLC 5 11, 12, 14, 15, 16 3.097 (1.816–5.282) 0.000 2.526 (1.957–3.261) 0.000 72.1% 0.006 DFS 3 11, 12, 16 3.455 (1.518–7.862) 0.003 2.716 (1.871–3.945) 0.000 72.4% 0.027 PFS 3 13, 14, 15 3.094 (1.344–7.126) 0.008 2.286 (1.599–3.267) 0.000 66.4% 0.051 Abbreviations : N, number; HR, hazard ratio; CI, confidence interval; OS, overall survival; NSCLC, non-small-cell lung cancer; DFS, disease-free survival; PFS, progression-free survival. Sensitivity analysis Sensitivity analysis was performed by eliminating one study at a time and analyzing the remaining studies. The results were not substantially changed, showing the reliability and stability of our results. (Fig. 4 ) Meanwhile, subgroup analysis was also conducted to explore the potential factors that are responsible for heterogeneity in OS. The results showed that the above factors could partly explain the heterogeneity but did not reach statistical significance Table 2 . Publication bias In order to estimate the publication bias, the Begg's funnel plot and Egger's linear regression test were applied. No publication bias was detected for OS in Begg's test(Pr > |z| = 0.133; Fig. 5 a)and Egger's test༈P> |t | = 0.056; Fig. 5 b༉. Discussion Our meta-analysis, which combined outcomes from 1691 lung cancer patients from 6 studies, showed that a high Naples prognostic score (NPS) significantly predicted poorer OS in lung cancer patients (HR: 3.357, 95%CI: 1.964–5.738, P = 0.000, Fig. 2 ). Despite heterogeneity, subgroup analyses suggest that high NPS are a valid prognostic factor for poor OS in lung cancer patients who have received various treatments, including surgical resection and combination therapy. Furthermore, our findings suggest that high NPS are associated with DFS (HR: 3.455, 95% CI: 1.518–7.862, P = 0.003) and PFS (HR: 3.094, 95% CI: 1.344–7.126, P = 0.008) in lung cancer patients. Taking all of these factors into account, NPS scores may be an important prognostic marker for lung cancer. There is ample evidence that, 19–23 Many assessment tools based on conventional systemic inflammation and nutritional biomarkers have reliable clinical significance in various types of cancer, 24–26 Although their importance in cancer etiology has been demonstrated, the exact mechanism between them and tumors has not been determined. As a new type of risk scoring system, NPS includes four parts: serum albumin (ALB), total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR), which fully considers the patient's inflammatory response, immune capacity and nutritional status, and its prognostic effect on lung cancer has also been revealed in the past two years. Neutrophils, as a widely existing inflammatory cell in the human body, can secrete related cytokines and chemokines during the occurrence and development of cancer, promote the proliferation and metastasis of tumor cells, and also promote the formation of new blood vessels in tumor tissue. And can increase the infiltration degree of blood vessels in tumor tissue, can also inhibit the immune activity of T lymphocytes and natural killer cells (natural killer ce11, NK), forming an immunosuppressive environment conducive to the survival of tumor cells, thereby promoting the development of malignant tumors. 27–29 Core solo cells can differentiate into tumor-associated macrophages during cancer development, stimulate tumor angiogenesis by secreting oncostatin-M and VEGF, enhance the fluidity and invasiveness of tumor cells, thereby accelerating tumor cell progression and dissemination. 30, 31 Different from the former two, lymphocytes, as an important part of the human immune system, kill cancer cells through tumor immune monitoring and cytotoxic activity in the tumor microenvironment, so as to inhibit the spread and migration of tumor cells. 32, 33 NLR and LMR objectively reflect the inflammatory and immune status of the host. Increased NLR and decreased LMR are usually accompanied by an increase in neutrophils or a significant decrease in peripheral blood lymphocytes. Elevated NLR and decreased LMR are often associated with higher mortality and poorer prognosis in various malignancies. 34, 35 Nutrition is closely related to tumor growth and progression, and malnutrition is often one of the reasons for poor prognosis. ALB is a marker for assessing nutritional status, and lower serum albumin concentrations have been shown to predict an adverse outcome in many nutrition scoring systems. 36, 37 Cholesterol is an important part of cell membranes, and low cholesterol levels reduce the fluidity of cell membranes, while also impairing the ability of cell surface receptors to transmit transmembrane signals, so its reduction may indicate a poor prognosis in patients. 38 Therefore, the NPS fully reflects the body's inflammatory response, immune capacity, and nutritional status, and is an effective prognostic marker that can help optimize clinical decision-making for lung cancer treatment. However, there are only a few studies evaluating the prognostic value of the NPS in lung cancer patients, and systematic analysis is lacking. Therefore, meta-analysis is the most effective method to gain insight into the prognostic impact of NPS on lung cancer patients. However, this meta-analysis had several limitations. First, the studies included in this study were retrospective in nature, which could easily lead to some biases such as information bias, misclassification bias, and selection bias. Second, all studies were from China, so publication bias is almost inevitable. Third, only a limited number of studies were selected for analysis. Further large-scale studies and randomized controlled trials are needed in the future. Conclusion In conclusion, this meta-analysis shows that high NPS are significantly associated with poorer prognosis in lung cancer patients. The NPS appears to be a convenient, reproducible, widely available and reliable method for predicting the survival status of lung cancer patients. In the future, large-scale, more well-designed prospective studies are needed to further validate the conclusion of the association between NPS and lung cancer prognosis. Declarations Conflict of interest The authors declare that there are no conflicts of interest regarding the publication of this article. Funding This research was funded by Key Research and Development Program of Hebei Province (22377790D). Author Contribution All candidate articles were evaluated and extracted by two independent researchers (ZH H and ZM W). The articles that cannot be classified by title and abstract only were full-text reviewed by retrieval. If disagreement occurred, the two researchers will discuss and reach a consensus with the third investigators (GC D).CY Z and XP Z produced the drawings and tables. ZC N and QT Z reviewed the article. References Xia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. Chin Med J (Engl) . 2022;135(5):584-590. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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Cite Share Download PDF Status: Published Journal Publication published 12 Apr, 2026 Read the published version in Journal of Cardiothoracic Surgery → Version 1 posted Editorial decision: Revision requested 30 Jun, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 13 Feb, 2025 Reviewers agreed at journal 22 Jan, 2025 Reviewers agreed at journal 19 Oct, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 30 Jul, 2024 Submission checks completed at journal 30 Jul, 2024 First submitted to journal 28 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4816566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":344858379,"identity":"a079e7e2-46c6-415b-8c40-0a38558d85fc","order_by":0,"name":"Zhaohui Han","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhaohui","middleName":"","lastName":"Han","suffix":""},{"id":344858380,"identity":"353ddb3f-c52c-493b-9bc0-9bd6de52aef2","order_by":1,"name":"Zengming Wang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zengming","middleName":"","lastName":"Wang","suffix":""},{"id":344858381,"identity":"6b56b2f1-e42f-422f-a6c3-adf0987fc67e","order_by":2,"name":"Chunyan Zhao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunyan","middleName":"","lastName":"Zhao","suffix":""},{"id":344858382,"identity":"0b694b91-499b-45a8-80fb-8f51666a4741","order_by":3,"name":"Xiaopeng Zhang","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaopeng","middleName":"","lastName":"Zhang","suffix":""},{"id":344858383,"identity":"e189339c-03eb-477e-a1e4-9624a74aa8c6","order_by":4,"name":"Zhancong Niu","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhancong","middleName":"","lastName":"Niu","suffix":""},{"id":344858384,"identity":"6c663549-fd58-48de-8877-2ce7582d3959","order_by":5,"name":"Qingtao Zhao","email":"","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qingtao","middleName":"","lastName":"Zhao","suffix":""},{"id":344858385,"identity":"f8098ffa-e7f0-44a6-b218-e0d3d60782a7","order_by":6,"name":"Guochen Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDCCA1Can5n54APStEi2syUbkKbF4DyPmQBROvhuJD97+LXNRt74MIMZA0ONTTRBLZI30syNZdvSDLcdZkh7wHAsLbeBkBaDGwlm0pJthxPMDjMcN2BsOEyMlvRvYC3GzYxtEkRqyTGT/AjUYsDMzEacFskzb8qkGc6lGc44zMZskECMX/iOp2+T/FFmI8/ff/7jgw81NoS1gAAzD4yVQIxyEGD8QazKUTAKRsEoGJkAAC9DPqKA9eoqAAAAAElFTkSuQmCC","orcid":"","institution":"Hebei General Hospital","correspondingAuthor":true,"prefix":"","firstName":"Guochen","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2024-07-28 12:05:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4816566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4816566/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s13019-026-04011-1","type":"published","date":"2026-04-12T15:57:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63832629,"identity":"0d7da70d-37a3-4ea3-95a9-0d09a69a98a8","added_by":"auto","created_at":"2024-09-02 19:19:18","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318860,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the included studies.\u003c/p\u003e","description":"","filename":"F1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/e01337c1c5a2459a9b070fc5.jpg"},{"id":63831682,"identity":"a25dbe4b-9e20-4cde-a3ec-2e7a44815855","added_by":"auto","created_at":"2024-09-02 19:11:18","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":874091,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analysis of the association between NPS and OS of lung cancer.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eNote：\u003c/strong\u003eWeights are from random-effects analysis.)\u003c/p\u003e","description":"","filename":"F2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/633387afef1d9c2774c42111.jpg"},{"id":63832627,"identity":"b47e1ad5-0a0e-483c-94b8-e2a277ab83b5","added_by":"auto","created_at":"2024-09-02 19:19:18","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":106352,"visible":true,"origin":"","legend":"\u003cp\u003e(A) Meta-analysis of the association between NPS and PFS of lung cancer. (B) Meta-analysis of the association between NPS and DFS of lung cancer.\u003c/p\u003e\n\u003cp\u003e(\u003cstrong\u003eNote：\u003c/strong\u003eWeights are from random-effects analysis.)\u003c/p\u003e","description":"","filename":"F3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/34b593eae430eaf7a69ce8a2.jpg"},{"id":63831680,"identity":"f50ced85-6208-429a-ac71-d994fcafbd1b","added_by":"auto","created_at":"2024-09-02 19:11:18","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":135307,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis of OS in this meta-analysis.\u003c/p\u003e","description":"","filename":"F4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/d6c2270006db8dd5209669fd.jpg"},{"id":63833238,"identity":"e2a18830-9dca-438c-80f4-864b0b69073e","added_by":"auto","created_at":"2024-09-02 19:27:18","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":90156,"visible":true,"origin":"","legend":"\u003cp\u003eBegg's funnel plot and Egger's funnel plot in the meta-analysis of lung cancer. (A) OS for NPS in Begg's funnel plot in the meta-analysis of lung cancer. (B) OS for NPS in Egger's funnel plot in the meta-analysis of lung cancer.\u003c/p\u003e","description":"","filename":"F5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/9836ea407971c6ada1d69a64.jpg"},{"id":106808946,"identity":"448a7f76-7eaf-43e1-b7c4-f6bd9c716366","added_by":"auto","created_at":"2026-04-13 16:05:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2303819,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4816566/v1/cc3c724e-8e00-49f5-9663-a2d5b76a506b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic Role Of Naples Prognostic Score In Lung Cancer: A Meta-Analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is one of the most common malignancies worldwide and the leading cause of cancer-related death worldwide.\u003csup\u003e1\u003c/sup\u003e Relevant research shows that,\u003csup\u003e2\u003c/sup\u003e in 2020, there were 2.2\u0026nbsp;million new lung cancer cases and 1.8\u0026nbsp;million deaths worldwide, accounting for about one in ten (11.4%) cancer diagnoses and one in five (18.0%) cancer-related deaths. In recent years, advances in our understanding of lung cancer risk, development, immune control, and treatment options have enabled lung cancer patients to increasingly receive precise, individualized diagnosis and treatment. However, the overall prognosis of lung cancer remains unsatisfactory due to local tumor recurrence and distant metastasis.\u003csup\u003e3\u003c/sup\u003e Therefore, it becomes critical to identify reliable and valid markers for better prognosis.\u003c/p\u003e \u003cp\u003eIn recent years, the Naples Prognostic Score (NPS) has entered the public eye as a new inflammatory nutrition scoring system, calculated from the patient's preoperative serum albumin(ALB), total cholesterol(TC), NLR and LMR, reflecting the patient's systemic inflammatory response and nutritional status.\u003csup\u003e4\u003c/sup\u003e The NPS was identified as a prognostic indicator for various cancers, such as breast cancer,\u003csup\u003e5\u003c/sup\u003e esophageal cancer,\u003csup\u003e6\u003c/sup\u003e gastric cancer,\u003csup\u003e7\u003c/sup\u003e ampullary carcinoma,\u003csup\u003e8\u003c/sup\u003e pancreatic cancer\u003csup\u003e9\u003c/sup\u003e and colorectal cancer\u003csup\u003e10\u003c/sup\u003e. The prognostic value of the NPS in lung cancer was also investigated.\u003csup\u003e11\u0026ndash;16\u003c/sup\u003e However, these studies reported inconsistent results due to differences in study design and sample size. Therefore, it is unclear whether the NPS is a valid prognostic indicator for lung cancer. In this study, we searched existing studies and performed a meta-analysis to assess the prognostic role of the Naples Prognostic Score in lung cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSearch strategy\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive literature search of the articles through the following databases, with no date limits: PubMed, Ovid, the Cochrane Library, and the Web of Science databases. The search was updated to November 2022. The main search terms included: (NPS or Naples prognostic score) and (lung cancer or lung carcinoma or NSCLC or SCLC). Relevant articles in the list of references were also examined.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData extraction\u003c/h2\u003e \u003cp\u003eAll candidate articles were evaluated and extracted by two independent researchers (Zhaohui Han and Zengming Wang). The articles that cannot be classified by title and abstract only were full-text reviewed by retrieval. If disagreement occurred, the two researchers will discuss and reach a consensus with the third investigators (Duan Guochen).\u003c/p\u003e \u003cp\u003eFor each study, the following items were recorded: first author\u0026rsquo;s name, year of publication, country, sample size, gender, follow-ups, treatment strategy, cancer type and HRs with 95%CIs. Given that all studies were retrospective studies, both researchers used the Newcastle-Ottawa Scale (NOS) to assess the quality of each included study. The NOS consists of three parts: selection (0\u0026ndash;4 points), comparability (0\u0026ndash;2 points), and outcome assessment (0\u0026ndash;3 points). NOS score\u0026thinsp;\u0026ge;\u0026thinsp;6 is considered to be a high-quality study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eInclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria for this meta-analysis: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) patients with lung cancer in the study were diagnosed by pathological examination; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) all the evaluation indicators were obtained from albumin (ALB), total cholesterol (TC), NLR, and LMR in the patient's preoperative serum; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the association of NPS with OS and / or PFS and / or DFS in lung cancer patients was reported; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) full text articles in English.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) abstract, letters, reviews, expert opinions, case reports, or non-clinical studies; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) studies with sufficient data to estimate HR and 95%CI were not available; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the study had repeated data or repeated analyses; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) the study was not written in English.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eWe directly obtained HR and 95%CI from each literature or estimated these data from each study according to the method illustrated by Parmar et al.\u003csup\u003e17\u003c/sup\u003e The association of the NPS score and the prognosis of lung cancer patients was evaluated by these data. A HR\u0026thinsp;\u0026gt;\u0026thinsp;1 indicated a worse prognosis in lung cancer patients with high NPS. We conducted a heterogeneity test on included studies using the I\u003csup\u003e2\u003c/sup\u003e test. The fixed effects model was applied to conduct the meta-analysis if no remarkable inter-study heterogeneity (P\u0026thinsp;\u0026gt;\u0026thinsp;0.1 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;50.0%). If significant heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.1 or I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;50.0%) was found, the random effects model was applied for the meta-analysis. If there is considerable heterogeneity, the meta-regression analysis and the subgroup analysis of the restricted maximum likelihood (REML) method will be performed. Publication bias was estimated by Begg\u0026rsquo;s rank correlation test and Egger\u0026rsquo;s regression asymmetry test.\u003csup\u003e18\u003c/sup\u003e To assess the stability of the results, we performed a sensitivity analysis to test it. Statistical analyses were performed using STATA MP statistical software version 17.0. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy characteristics\u003c/h2\u003e \u003cp\u003eThe process of study selection has been described in the flow chart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The initial search strategies retrieved a total of 65 articles. After a careful examination of these articles, 6 studies with a total of 1,691 patients published between 2021 and 2022 were eventually enrolled in our meta-analysis. In 6 articles, the correlation between NPS and OS was studied. Among them, 3 articles studied the correlation between NPS and PFS; another 3 studies the correlation between NPS and DFS. HRs and 95%CIs were reported directly in all the studies. 2 of these studies enrolled\u0026thinsp;\u0026le;\u0026thinsp;200 patients and 4 studies had\u0026thinsp;\u0026gt;\u0026thinsp;200 patients. All studies were retrospective cohort studies. All of the studies were obtained from China. The characteristics of the included studies were shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMain characteristics of all the studies included in the meta-analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo (M/F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFollow-up (months) (median and range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAge (years) (median and range)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNPS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\"\u003e \u003cp\u003eNOS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRen et al\u003csup\u003e11\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e319(162/157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eChemotherapy radiotherapy\u003c/p\u003e \u003cp\u003eTKI treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e61\u003c/p\u003e \u003cp\u003e(interquartile range, IQR: 13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/DFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eI/II/III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLi et al\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e457(283/174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50 (12\u0026ndash;66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63\u003c/p\u003e \u003cp\u003e(IQR\u0026thinsp;=\u0026thinsp;59\u0026ndash;69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/DFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eI/II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al\u003csup\u003e13\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e128(97/37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eChemotherapy radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e65\u003c/p\u003e \u003cp\u003e(14\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eLimited stage Extensive stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuo et al\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e206(120/86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37 (13\u0026ndash;59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChemotherapy radiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62\u003c/p\u003e \u003cp\u003e(36\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeng et al\u003csup\u003e15\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e395(252/143)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (1\u0026ndash;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003cp\u003eChemotherapy radiotherapy\u003c/p\u003e \u003cp\u003eTKI treatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(24\u0026ndash;86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eI/II/III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXuan et al\u003csup\u003e16\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186(85/101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32 (6\u0026ndash;55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadiotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e57\u003c/p\u003e \u003cp\u003e(39\u0026ndash;72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0, 1, 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOS/DFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eR\u003c/p\u003e \u003cp\u003e(U/M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e:\u0026nbsp;M, male; F, female; NPS: Naples Prognostic Score; HR, hazard ratio; NOS, Newcastle\u0026ndash;Ottawa Scale; OS, overall survival; DFS, disease-free survival; NSCLC, non-small-cell lung cancer; R, reporting; U, univariate analysis; M, multivariate; PFS, progression-free survival; SCLC, non-small-cell lung cancer; TKI, tyrosine kinase inhibitor.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eThe relationship between NPS and OS of lung cancer\u003c/h2\u003e \u003cp\u003eAll 6 studies reported a correlation between NPS and OS in lung cancer patients. Because of significant heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;69.8%, Ph\u0026thinsp;=\u0026thinsp;0.005), Therefore, a random effects model was applied. The results show that high NPS predict worse OS outcomes (HR: 3.357, 95%CI༚1.964\u0026ndash;5.738, P\u0026thinsp;=\u0026thinsp;0.000). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between NPS and PFS and DFS in lung cancer\u003c/h2\u003e \u003cp\u003eThree studies reported the relationship between NPS and PFS in lung cancer patients, and our meta-analysis showed that patients with high NPS were associated with shorter PFS (random effects model obtained by HR: 3.094, 95% CI : 1.344\u0026ndash;7.126, P\u0026thinsp;=\u0026thinsp;0.008 ; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;66.4%, Ph\u0026thinsp;=\u0026thinsp;0.051). Three other studies assessed the relationship between NPS and DFS, and our results show that high NPS predict worse DFS outcomes (obtained by the random effects model HR: 3.455, 95% CI: 1.518\u0026ndash;7.862, P\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), with heterogeneity (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;72.4%, Ph\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSubgroup analysis\u003c/h2\u003e \u003cp\u003eWe further explored potential causes of heterogeneity. For OS, subgroup analyses were performed by mode of treatment (treatment with surgery and treatment without surgery), type (NSCLC and SCLC), sample size (\u0026gt;\u0026thinsp;200 and \u0026le;\u0026thinsp;200), stage (advanced stage: III/IV and stages I to IV: I/II/III/IV). Most subgroup analyses did not significantly alter the prognostic role of NPS scores in OS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, for subgroup analyses with a sample size\u0026thinsp;\u0026le;\u0026thinsp;200, patients with high NPS scores were not significantly associated with shorter OS, with a combined HR of 3.671(Random-effects model, 95% CI : 0.625\u0026ndash;21.558, P\u0026thinsp;=\u0026thinsp;0.150).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of the meta-analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnalysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRandom-effects model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eFixed-effects model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eI\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ePh\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 13, 14, 15, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.357(1.964\u0026ndash;5.738)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.586\u003c/p\u003e \u003cp\u003e(2.007\u0026ndash;3.332)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup 1:Treatment methods including surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 13, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.494\u003c/p\u003e \u003cp\u003e(2.596\u0026ndash;11.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.728\u003c/p\u003e \u003cp\u003e(3.018\u0026ndash;7.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ereatment methods not including surgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.949\u003c/p\u003e \u003cp\u003e(1.433\u0026ndash;2.650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.883\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup 2:sample size\u0026gt;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 14, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.760\u003c/p\u003e \u003cp\u003e(1.843\u0026ndash;7.671)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.845\u003c/p\u003e \u003cp\u003e(2.099\u0026ndash;3.855)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esample size\u0026thinsp;\u0026le;\u0026thinsp;200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.671\u003c/p\u003e \u003cp\u003e(0.625\u0026ndash;21.558)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.077\u003c/p\u003e \u003cp\u003e(1.311\u0026ndash;3.292)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup 3:\u003c/p\u003e \u003cp\u003estage: I/II/III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 13, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.494\u003c/p\u003e \u003cp\u003e(2.596\u0026ndash;11.626)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.728\u003c/p\u003e \u003cp\u003e(3.018\u0026ndash;7.406)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.7%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003estage: III/IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.065\u003c/p\u003e \u003cp\u003e(1.523\u0026minus;2.800)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.0%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.781\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroup 4:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 14, 15, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.097\u003c/p\u003e \u003cp\u003e(1.816\u0026ndash;5.282)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.526\u003c/p\u003e \u003cp\u003e(1.957\u0026ndash;3.261)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.1%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11, 12, 16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.455\u003c/p\u003e \u003cp\u003e(1.518\u0026ndash;7.862)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.716\u003c/p\u003e \u003cp\u003e(1.871\u0026ndash;3.945)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e72.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13, 14, 15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.094\u003c/p\u003e \u003cp\u003e(1.344\u0026ndash;7.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.286\u003c/p\u003e \u003cp\u003e(1.599\u0026ndash;3.267)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: N, number; HR, hazard ratio; CI, confidence interval; OS, overall survival; NSCLC, non-small-cell lung cancer; DFS, disease-free survival; PFS, progression-free survival.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis was performed by eliminating one study at a time and analyzing the remaining studies. The results were not substantially changed, showing the reliability and stability of our results. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) Meanwhile, subgroup analysis was also conducted to explore the potential factors that are responsible for heterogeneity in OS. The results showed that the above factors could partly explain the heterogeneity but did not reach statistical significance Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePublication bias\u003c/h2\u003e \u003cp\u003eIn order to estimate the publication bias, the Begg's funnel plot and Egger's linear regression test were applied. No publication bias was detected for OS in Begg's test(Pr \u0026gt; |z| = 0.133; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea)and Egger's test༈P\u0026gt; |t | = 0.056; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb༉.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur meta-analysis, which combined outcomes from 1691 lung cancer patients from 6 studies, showed that a high Naples prognostic score (NPS) significantly predicted poorer OS in lung cancer patients (HR: 3.357, 95%CI: 1.964\u0026ndash;5.738, P\u0026thinsp;=\u0026thinsp;0.000, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite heterogeneity, subgroup analyses suggest that high NPS are a valid prognostic factor for poor OS in lung cancer patients who have received various treatments, including surgical resection and combination therapy. Furthermore, our findings suggest that high NPS are associated with DFS (HR: 3.455, 95% CI: 1.518\u0026ndash;7.862, P\u0026thinsp;=\u0026thinsp;0.003) and PFS (HR: 3.094, 95% CI: 1.344\u0026ndash;7.126, P\u0026thinsp;=\u0026thinsp;0.008) in lung cancer patients. Taking all of these factors into account, NPS scores may be an important prognostic marker for lung cancer.\u003c/p\u003e \u003cp\u003eThere is ample evidence that,\u003csup\u003e19\u0026ndash;23\u003c/sup\u003e Many assessment tools based on conventional systemic inflammation and nutritional biomarkers have reliable clinical significance in various types of cancer,\u003csup\u003e24\u0026ndash;26\u003c/sup\u003e Although their importance in cancer etiology has been demonstrated, the exact mechanism between them and tumors has not been determined.\u003c/p\u003e \u003cp\u003eAs a new type of risk scoring system, NPS includes four parts: serum albumin (ALB), total cholesterol (TC), neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR), which fully considers the patient's inflammatory response, immune capacity and nutritional status, and its prognostic effect on lung cancer has also been revealed in the past two years. Neutrophils, as a widely existing inflammatory cell in the human body, can secrete related cytokines and chemokines during the occurrence and development of cancer, promote the proliferation and metastasis of tumor cells, and also promote the formation of new blood vessels in tumor tissue. And can increase the infiltration degree of blood vessels in tumor tissue, can also inhibit the immune activity of T lymphocytes and natural killer cells (natural killer ce11, NK), forming an immunosuppressive environment conducive to the survival of tumor cells, thereby promoting the development of malignant tumors.\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e Core solo cells can differentiate into tumor-associated macrophages during cancer development, stimulate tumor angiogenesis by secreting oncostatin-M and VEGF, enhance the fluidity and invasiveness of tumor cells, thereby accelerating tumor cell progression and dissemination.\u003csup\u003e30, 31\u003c/sup\u003e Different from the former two, lymphocytes, as an important part of the human immune system, kill cancer cells through tumor immune monitoring and cytotoxic activity in the tumor microenvironment, so as to inhibit the spread and migration of tumor cells.\u003csup\u003e32, 33\u003c/sup\u003e NLR and LMR objectively reflect the inflammatory and immune status of the host. Increased NLR and decreased LMR are usually accompanied by an increase in neutrophils or a significant decrease in peripheral blood lymphocytes. Elevated NLR and decreased LMR are often associated with higher mortality and poorer prognosis in various malignancies.\u003csup\u003e34, 35\u003c/sup\u003e Nutrition is closely related to tumor growth and progression, and malnutrition is often one of the reasons for poor prognosis. ALB is a marker for assessing nutritional status, and lower serum albumin concentrations have been shown to predict an adverse outcome in many nutrition scoring systems.\u003csup\u003e36, 37\u003c/sup\u003e Cholesterol is an important part of cell membranes, and low cholesterol levels reduce the fluidity of cell membranes, while also impairing the ability of cell surface receptors to transmit transmembrane signals, so its reduction may indicate a poor prognosis in patients.\u003csup\u003e38\u003c/sup\u003e Therefore, the NPS fully reflects the body's inflammatory response, immune capacity, and nutritional status, and is an effective prognostic marker that can help optimize clinical decision-making for lung cancer treatment. However, there are only a few studies evaluating the prognostic value of the NPS in lung cancer patients, and systematic analysis is lacking. Therefore, meta-analysis is the most effective method to gain insight into the prognostic impact of NPS on lung cancer patients.\u003c/p\u003e \u003cp\u003eHowever, this meta-analysis had several limitations. First, the studies included in this study were retrospective in nature, which could easily lead to some biases such as information bias, misclassification bias, and selection bias. Second, all studies were from China, so publication bias is almost inevitable. Third, only a limited number of studies were selected for analysis. Further large-scale studies and randomized controlled trials are needed in the future.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this meta-analysis shows that high NPS are significantly associated with poorer prognosis in lung cancer patients. The NPS appears to be a convenient, reproducible, widely available and reliable method for predicting the survival status of lung cancer patients. In the future, large-scale, more well-designed prospective studies are needed to further validate the conclusion of the association between NPS and lung cancer prognosis.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that there are no conflicts of interest regarding the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was funded by Key Research and Development Program of Hebei Province (22377790D).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll candidate articles were evaluated and extracted by two independent researchers (ZH H and ZM W). The articles that cannot be classified by title and abstract only were full-text reviewed by retrieval. If disagreement occurred, the two researchers will discuss and reach a consensus with the third investigators (GC D).CY Z and XP Z produced the drawings and tables. ZC N and QT Z reviewed the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXia C, Dong X, Li H, et al. Cancer statistics in China and United States, 2022: profiles, trends, and determinants. \u003cem\u003eChin Med J (Engl)\u003c/em\u003e. 2022;135(5):584-590.\u003c/li\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin.\u003c/em\u003e 2021;71(3):209-249.\u003c/li\u003e\n\u003cli\u003eBade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. \u003cem\u003eClin Chest Med\u003c/em\u003e. 2020;41(1):1-24.\u003c/li\u003e\n\u003cli\u003eGalizia G, Lieto E, Auricchio A, et al. 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Prognostic value of lymphocyte-to-monocyte ratio in patients with solid tumors: a systematic review and meta-analysis. \u003cem\u003eCancer Treat Rev.\u003c/em\u003e 2015;41(10):971\u0026ndash;978. \u003c/li\u003e\n\u003cli\u003eTokunaga R, Sakamoto Y, Nakagawa S, et al. CONUT: a novel independent predictive score for colorectal cancer patients undergoing potentially curative resection. \u003cem\u003eInt J Colorectal Dis.\u003c/em\u003e 2017;32(1):99\u0026ndash;106. \u003c/li\u003e\n\u003cli\u003eMorhij R, Mahendra A, Jane M, McMillan DC. The modified Glasgow prognostic score in patients undergoing surgery for bone and soft tissue sarcoma. \u003cem\u003eJ Plast Reconstr Aesthet Surg.\u003c/em\u003e 2017;70(5):618\u0026ndash;624.\u003c/li\u003e\n\u003cli\u003eZhang G, Zhang D, Wu J, et al. Low serum levels of pre-surgical total cholesterol are associated with unfavorable overall survival in patients with operable non-small cell lung cancer. \u003cem\u003eClin Lab.\u003c/em\u003e 2018;64(3):321\u0026ndash;327.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Naples Prognostic Score, lung cancer, prognosis, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-4816566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4816566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e The prognostic value of the Naples prognostic score in lung cancer remains controversial. Therefore, we performed a meta-analysis of relevant published studies to determine the prognostic value of the Naples prognostic score in patients with lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We conducted a systematic search of relevant studies in PubMed, Ovid, the Cochrane Library, and Web of Science databases. Data and characteristics of each study were extracted and hazard ratios (HRs) at 95% confidence intervals (CIs) were calculated to estimate effects. A meta-regression analysis was used to assess the prognostic value of the Naples Prognostic Score in patients with lung cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 1691 patients from six studies were included in this meta-analysis, with a combined HR of 3.357 (95% CI: 1.964-5.738, p=0.000); the results suggest that a high Naples Prognostic Score predicts a shorter overall survival (OS) for patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis meta-analysis suggests that a high Naples Prognostic Score may be a predictor of poor prognosis in lung cancer patients. Further large cohort studies are needed to confirm these findings.\u003c/p\u003e","manuscriptTitle":"Prognostic Role Of Naples Prognostic Score In Lung Cancer: A Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-02 19:11:14","doi":"10.21203/rs.3.rs-4816566/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-30T17:33:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-29T01:53:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293493936137836462974643831318779605878","date":"2025-04-17T01:17:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186084161143785178535844724324476103539","date":"2025-02-13T21:05:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"12984626114422150025006412319683072552","date":"2025-01-22T13:42:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"144078481307209179944077742393519953649","date":"2024-10-19T13:27:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-22T15:58:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T06:33:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-30T06:33:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Cardiothoracic Surgery","date":"2024-07-28T12:04:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-cardiothoracic-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jcts","sideBox":"Learn more about [Journal of Cardiothoracic Surgery](http://cardiothoracicsurgery.biomedcentral.com)","snPcode":"13019","submissionUrl":"https://submission.nature.com/new-submission/13019/3","title":"Journal of Cardiothoracic Surgery","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b51bc908-b87a-4927-a294-2e82f1eb788a","owner":[],"postedDate":"September 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-13T16:01:59+00:00","versionOfRecord":{"articleIdentity":"rs-4816566","link":"https://doi.org/10.1186/s13019-026-04011-1","journal":{"identity":"journal-of-cardiothoracic-surgery","isVorOnly":false,"title":"Journal of Cardiothoracic Surgery"},"publishedOn":"2026-04-12 15:57:43","publishedOnDateReadable":"April 12th, 2026"},"versionCreatedAt":"2024-09-02 19:11:14","video":"","vorDoi":"10.1186/s13019-026-04011-1","vorDoiUrl":"https://doi.org/10.1186/s13019-026-04011-1","workflowStages":[]},"version":"v1","identity":"rs-4816566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4816566","identity":"rs-4816566","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}