Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte and platelet score in patients with lung cancer: a meta-analysis

Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte and platelet score in patients with 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 Article Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte and platelet score in patients with lung cancer: a meta-analysis Wenxia Wang, Bi Ren, Haocheng Gou, Wu Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5216062/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be associated with the progression of many types of tumors. Nevertheless, the connection between HALP score and individuals with lung cancer is still a subject of debate. We conducted a systematic evaluation to investigate the prognostic role of HALP score on survival outcomes in lung cancer patients. As of July 2024, we searched the PubMed, PubMed Central, Web of Science, and Embase databases to collect relevant articles evaluating the relationship between HALP and lung cancer prognosis. The pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-free survival (DFS) were analyzed. A total of 12 studies involving 7775 lung cancer patients were included. The combined results revealed that a low HALP score was associated with poorer OS (HR = 1.71, 95% CI, 1.28–2.30, p < 0.001), PFS (HR = 1.44, 95% CI, 1.15–1.80, p < 0.001), and DFS/RFS (HR = 2.78, 95% CI, 1.14–6.78, p < 0.001). Subgroup analysis further confirmed that pretreatment HALP was an independent predictor of OS in lung cancer patients. The decreased pretreatment HALP score was strongly associated with inferior prognosis in lung cancer patients. Our findings highlight that HALP score is a reliable biomarker of lung cancer prognosis. However, multicenter and prospective studies are needed to further validate its clinical utility. Biological sciences/Cancer/Lung cancer Biological sciences/Immunology Health sciences/Biomarkers HALP lung cancer prognosis survival Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Lung cancer is a prominent contributor to global cancer mortality [ 1 , 2 ] . Non-small cell lung cancer (NSCLC) is the predominant type of primary malignant tumor of the lungs, accounting for approximately 85% of all lung cancer cases [ 3 ] . Compared with NSCLC, small cell lung cancer (SCLC) is a less prevalent form of lung cancer, but it rapidly growth and has a poor prognosis [ 4 ] . Conventional therapy includes surgical removal, radiation therapy, and chemotherapy [ 5 , 6 ] . Immunotherapy is an innovative approach for treating cancer [ 7 ] . Despite the ongoing advancements in treatment modalities and pharmaceuticals, a significant number cases of lung cancer patients are being detected at advanced stages, resulting in detrimental effects on both early detection and therapy [ 8 ] . Furthermore, there is large variation in the survival rate of individuals with the same clinical stage who receive postoperative adjuvant chemotherapy [ 9 ] . Hence, there is a pressing demand for straightforward yet trustworthy biomarkers to estimate the survival rate of cancer patients and assist clinicians in categorizing patients and administering the most effective treatment. The involvement of the systemic inflammatory response in carcinogenesis, progression, and metastasis has been documented [ 10 , 11 ] . Extended inflammation in the body triggers the production of various inflammatory substances, resulting in the epithelial mesenchymal transition and tumor microenvironment formation, increasing damage to body tissues and organs, and ultimately increasing the susceptibility to cancer development [ 12 , 13 ] . Recently, studies have indicated that cancer can cause malnutrition through many metabolic mechanisms [ 14 , 15 ] . There is an increasing apprehension regarding the nutritional condition of patients and its impact on tumor outcomes. Multiple studies have demonstrated that several prognostic markers of inflammation, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR), can be used to predict survival outcomes in patients with malignancies [ 16 – 18 ] . A novel biomarker called the HALP score, which measures the levels of hemoglobin, albumin, lymphocyte, and platelet, has recently been found to have a substantial correlation with the prognosis of various types of malignancies, including gastric, esophageal, rectal, and pancreatic tumors [ 19 , 20 ] . Nevertheless, the predictive significance of the HALP score in individuals with lung cancer has yet to be examined. The aim of this study was to investigate the correlation between pretreatment HALP scores and survival outcomes in patients with lung cancer. Materials and methods 3.1 Search strategy The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement [ 21 ] was followed in the execution of this meta-analysis. As of July 2024, we were able to locate pertinent papers investigating the association between HALP and lung cancer survival outcomes by searching a number of databases, including PubMed, PubMed Central, Web of Science, and Embase. The literature search utilized the following keywords: "hemoglobin", "albumin", "lymphocyte", "platelet", "HALP", "lung cancer", and "lung tumor". Furthermore, a manual search was conducted to locate references listed in the chosen papers and relevant studies. The process was conducted autonomously by both authors. All discrepancies that emerged were settled through negotiation. 3.2 Inclusion and exclusion criteria We initially screened the literature based on the basis of titles and abstracts before conducting a full-text search for potentially relevant literature. The inclusion criteria were as follows: (1) Research that concentrates on the correlation between pretreatment HALP and the prognosis of lung cancer patients. (2) Studies reporting survival outcomes, including hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and/or disease-free survival (DFS), as well as their corresponding 95% confidence intervals (CIs) and specific data. (3) Accurate HALP thresholds must be provided for dividing patients into high and low HALP groups. The exclusion criteria were as follows: (1) case reports, reviews, commentaries, conference abstracts, and correspondences. (2) duplicate published studies. (3) Research with incomplete or unavailable data. (4) Research lacking precise HALP thresholds or without preoperative HALP data. 3.3 Data extraction and quality assessment Two authors autonomously extracted data from all the literature that was included and settled any disagreements through negotiation. We obtained the following information from each study: first author, year, country, tumor type, tumor stage, sample size, gender, age, treatment modality, HALP score cutoff value, median follow-up time, and HRs with corresponding 95% CIs. The Quality of Prognostic Studies (QUIPS) tool was used to assess the risk of bias of the included studies [ 22 ] . The tool covers six main areas, including the study population, study attrition, prognostic factor measurement, outcome measurement, study confounding, statistical analysis, and reporting. As described in the original studies, each study was rated as having a high, moderate, or low risk of bias. Two researchers independently conducted the quality assessment and all discrepancies were resolved through discussion. 3.4 Statistical analysis In our investigation, pooled HRs and the corresponding 95% CIs were used to evaluate the prognostic significance of HALP score in lung cancer patients. Patients with low HALP score had decreased survival rates (HRs > 1), whereas those with HRs 50% or p < 0.1 represented significant heterogeneity, and a random effects model was used for the combined analysis. Otherwise, a fixed-effects model was used. Subgroup analyses were then used to identify sources of heterogeneity. To ensure the credibility of the pooled data, sensitivity analyses were conducted iteratively, omitting individual investigations one at a time. Additionally, we used the Begg's test to evaluate publication bias. P values < 0.05 were considered statistically significant. All the statistical analyses were carried out using STATA software (version 15.0). Results 4.1 Search results and study characteristics The literature search process is outlined in Fig. 1 . Initially, 78 studies were found using keywords in the database search; of these, 37 were discarded as duplicates; additionally, 25 studies were removed from the analysis because full text articles were unavailable; and four more studies were eliminated after examining the full texts of the remaining articles. Finally, 12 studies with 7,775 patients who were published between 2019 and 2024 met the inclusion criteria for this meta-analysis [ 23 – 32 ] . Among these, 9 studies examined the relationship between HALP and OS [ 23 – 29 , 31 , 32 ] , 2 studies examined the relationship between HALP and PFS [ 28 , 32 ] , 2 studies examined the relationship between HALP and DFS [ 24 , 27 ] , and 1 study examined the relationship between HALP and RFS [ 30 ] . The basic characteristics of the included studies are presented in Table 1 . In this meta-analysis, the QUIPS tool was used to assess the quality of each study, and all studies were judged to be at medium risk of bias, as shown in Fig. 2 . Table 1 Baseline characteristics of reviewed studies Author (year) Country Cancer type Tumor stage Treatment Sample size Age (years) Gender (M/F) Smoking history Cut-off value Outcome Follow-up (months) Shen 2019 [ 29 ] China SCLC NA Chemotherapy 178 Mean 61.24 ± 9.27 142/36 NA 25.8 PFS NA Yang 2020 [ 16 ] China SCLC I-IV Chemotherapy 335 Median 61 254/81 243/92 18.6 OS Median 27.1 Zhai 2021 [ 18 ] China NSCLC IA-IV Surgery 238 Mean 62.3 ± 8.4 150/88 87/151 48 OS NA Güç 2022 [ 39 ] Turkey NSCLC NA Chemotherapy 401 Mean 63.47 ± 9.75 317/84 310/91 23.24 OS Median 18 Wei 2022 [45] China NSCLC I-IV Chemotherapy 362 NA 217/145 141/221 48.2 OS, DFS Median 64 Fang 2023 [45] China NSCLC IIIB-IV Immunotherapy 223 Mean 60.4 189/34 NA 39.33 OS, PFS Median 20.4 Mazzella 2023 [51] Italy NSCLC I-III Surgery 257 NA 149/108 NA 32.2 OS Median 40 Zhao 2023 [54] China NSCLC IA-IIIA Surgery 219 NA NA 87/132 29.31 RFS Median 24 Zhang 2023 China NSCLC IA-IIIB Surgery 52 Rang (43–79) 35/17 25/27 24.3 OS, DFS NA Cavdar 2024 Turkey NSCLC NA Chemotherapy 278 Median 63 (40–82) 260/18 157/121 26 OS Median 15.3 Gao 2024 China NSCLC IIIB-IV Chemotherapy 203 Mean 59.6 ± 9.7 140/63 92/111 28.02 OS, PFS Median 16 Taylor 2024 UK NSCLC I-III Surgery 5029 Mean 68.6 ± 9.1 2444/2585 4144/885 36.87 OS Median 33 SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; OS, overall survival; PFS, progression-free survival; DFS, disease-free survival; NA, not available. Begg = 0.283 trim p = 0.01 4.2 HALP and OS A total of 10 studies including 7,378 patients were available for OS analysis [ 23 – 29 , 31 , 32 ] . The pooled analysis demonstrated that a lower HALP score was significantly associated with worse OS (HR = 1.71; 95% CI, 1.28–2.30; P < 0.001; Fig. 3 ). The test for heterogeneity was significant (I 2 = 90.0%, P < 0.001), so a random-effects model was selected. We conducted subgroup analyses based on race, cancer type, sample size, treatment, cutoff value, and determination of cutoff value to investigate potential heterogeneity issues (Table 2 ). The results showed that patients with low pretreatment HALP score consistently had a poorer OS when stratified by the aforementioned factors. Meta-regression analyses were not performed because there was no significant heterogeneity among the studies. Table 2 Subgroup analyses of overall survival. Subgroup Variable No. of studies Model HR (95% CI) P Heterogeneity P* interaction I 2 (%) P Ethnic Asian 6 Random 1.66 (1.20, 2.29) < 0.01 69.70 < 0.01 0.862 Caucasian 4 Random 1.75 (1.03, 2.98) < 0.01 94.2 < 0.01 Cancer type NSCLC 9 Random 1.75 (1.27, 2.42) < 0.01 90.7 < 0.01 0.484 SCLC 1 - 1.47 (1.00, 2.15) 0.048 - - Treatment surgery 4 Random 1.70 (1.01, 2.30) < 0.01 86.4 < 0.01 0.957 adjuvant therapy 6 Random 1.73 (1.24, 2.41) < 0.01 80.2 < 0.01 Sample size ≥ 278 5 Random 1.54 (1.05, 2.26) < 0.01 92.2 < 0.01 0.415 < 278 5 Random 1.99 (0.49, 0.71) < 0.01 77.8 < 0.01 Cut-off value ≥ 28 6 Random 1.51 (1.09, 2.11) < 0.01 86.5 < 0.01 0.276 < 28 4 Random 2.04 (1.34, 3.11) < 0.01 74.4 < 0.01 Selection of Cut-off value ROC analysis 6 Random 1.90 (1.22, 2.95) < 0.01 93.5 < 0.01 0.052 X-tile software 3 Random 1.58 (1.10, 2.27) 0.013 40.4 0.19 median 1 - 1.00 (0.71, 1.41) 0.982 - - NSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; ROC, receiver-operating characteristics; HR, hazard ratio; CI, confidence interval; * P value for the test for subgroup difference. 4.3 HALP and PFS Three studies, including 604 patients, were included to explore the correlation between pretreatment HALP and PFS [ 28 , 32 ] , and the results revealed that a decreased HALP score was associated with reduced PFS (HR = 1.44; 95% CI: 1.15–1.80; P < 0.001; Fig. 4 A). The heterogeneity of the PFS analysis was not significant (I 2 = 24.1%, P = 0.268). 4.4 HALP and RFS/DFS The association between the pretreatment HALP score and RFS/DFS was reported in three studies [ 24 , 27 , 30 ] , involving 362 patients. The pooled results revealed that patients with reduced HALP score had worse RFS/DFS (HR = 2.78, 95% CI, 1.14–6.78, P < 0.001; Fig. 4 B). Significant heterogeneity was identified (I 2 = 75.9%, P = 0.016). 4.5 Sensitivity analysis When sensitivity analyses of OS were conducted, the pooled HRs and 95% CIs remained statistically significant for OS after any of the individual studies were excluded (Fig. 5 A). This suggests that the findings of our analysis are reliable. 4.6 Publication bias We evaluated the publication bias of OS using a funnel plot and the Begg test. As depicted in Fig. 5 B, the funnel plot revealed an asymmetrical distribution of the included studies, indicating the possibility of a publication bias for OS. We subsequently conducted further tests to determine the impact of expanding the number of studies on the results. We employed the cut-and-fill method and ultimately confirmed that there was still a substantial association between OS and the outcome ( HR = 1.63, P < 0.001; Fig. 5 C). Discussion According to previous reports, the ability of the HALP score to predict the prognosis of lung cancer patients remains controversial. In this study, we synthesized 12 articles involving 7,775 patients to determine the exact influence of the HALP score on the prediction of lung cancer prognosis. The results of the meta-analysis indicated that a reduced HALP score significantly predicted worse OS (pooled HR = 1.71) in lung cancer patients. Moreover, subgroup analyses also revealed a significant correlation between the HALP score and OS. The results of the publication bias and sensitivity analyses further confirmed the reliability and stability of this study. We also observed that a lower HALP score was significantly associated with PFS (pooled HR = 1.44) and RFS/DFS (pooled HR = 2.78) in patients with lung cancer. Therefore, the HALP score can serve as a cost-effective and reliable prognostic marker for lung cancer patients. To the best of our knowledge, this meta-analysis is the first to investigate the impact of the HALP score on predicting lung cancer prognosis. Although the HALP score has great potential for predicting cancer prognosis, the underlying mechanism remains unclear. Since HALP represents the ratio of hemoglobin, albumin, and lymphocytes to platelets, it is reasonable to speculate that the mechanism may be related to their function [ 20 ] . Reduced hemoglobin leads to anemia, which is associated with poor cancer prognosis [ 33 ] . Albumin is the major component of total serum protein and reflects the nutritional status of the host [ 34 ] . Studies have shown that hypoalbuminemia may reduce host immunity and lead to poor outcomes [ 35 ] . In addition, lymphocytes are a major component of the body's antitumor immune response [ 36 ] . They can play an important role in cancer defense by inducing cytotoxic cell death and inhibiting cancer cell proliferation and migration [ 19 ] . Platelets secrete many granules and exosomes, which are involved in tumor growth, infiltration, and metastasis, affecting the survival time of patients [ 37 ] . Therefore, elevated platelet counts and decreased hemoglobin, albumin and lymphocytes affect survival time in cancer patients. We can conclude that decreased HALP may reflect poor host nutritional status and decreased lymphocyte-mediated anti-tumor immune response or increased platelet-dependent inflammatory response, which creates an immune microenvironment conducive to tumor growth and leads to poor patient prognosis. There has been a continuous search for new cancer prognostic biomarkers by researchers. Studies have shown that inflammation and nutritional indicators are strongly associated with cancer survival outcomes [ 38 ] . Chronic inflammation promotes tumor development and invasion [ 39 ] . Indicators reflecting the degree of inflammation include lymphopenia and increases in neutrophils and platelets [ 37 , 40 , 41 ] . Previous meta-analyses have shown the association of the NLR [ 16 ] and PLR [ 42 ] , among other parameters, with survival outcomes in lung cancer patients. Consistent with these studies, our research indicates that the HALP score, as a novel immuno-nutritional marker, performs well in predicting OS, PFS, and RFS/DFS. In a word, our study highlights the potential of pretreatment HALP score as an important prognostic predictor for lung cancer patients. This information could be of guiding significance in formulating individualized treatment plans for lung cancer patients in clinical practice. There are several limitations in the current work. First, all the studies we included were retrospective, which may have led to some bias in the results. Second, compared with that of other prognostic markers, the study period for HALP as a novel cancer prognostic marker is relatively short, and the limited number of studies included in the subgroup analysis may affect the reliability of the results. Third, the differences in HALP cutoff values between the included studies may have introduced heterogeneity into the analysis and potentially influenced the overall outcomes. Therefore, standardizing this value in clinical applications would help improve the reliability of the results. To further validate the prognostic value of the HALP score in lung cancer, larger-scale, multicenter prospective studies are still needed. Conclusions The results of current meta-analysis revealed that patients with lower pretreatment HALP score had worse survival outcomes. This prospective prognostic marker helps in the clinical assessment of patient survival and recurrence, facilitates risk stratification of tumors, and better guides treatment decisions. Abbreviations HALP hemoglobin, albumin, lymphocyte, and platelet HR hazard ratio CI confidence intervals OS overall survival PFS progression free survival RFS recurrence free survival DFS disease free survival NSCLC non small cell lung cancer SCLC small cell lung cancer NLR neutrophil lymphocyte ratio PLR platelet lymphocyte ratio LMR lymphocyte monocyte ratio PRISMA Preferred Reporting Items for Systematic Reviews and Meta Analyses QUIPS Quality of Prognostic Studies. Declarations Author Contribution Conceptualization, W.W. and W.G.; methodology, W.W. ; formal analysis, W.W., B.R. and H.G.; investigation, W.W. and B.R.; data curation, W.W. and W.G.; writing—original draft preparation, W.W. and W.G.; writing—review and editing, B.R. and H.G.; All authors have read and agreed to the published version of the manuscript. Data Availability The data requirements mentioned in the article can be found in the article/Supplementary materials. For further details may directly contact the corresponding author through the provided contact information. References Ming-Yue, L., Li-Zhong, L. & Ming, D. J. M. C. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis. 20 (1). (2021). Saleem, J. et al. Review of current thermal ablation treatment for lung cancer and the potential of electrochemotherapy as a means for treatment of lung tumours. 39 (8). (2013). Romain, R. et al. 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Prognostic Value of Inflammatory Indexes in Patients with Resected Non-Small-Cell Lung Cancer. 15 (6). (2023). Faustino, M. J. T. I. Neutrophil Degranulation, Plasticity, and Cancer Metastasis. 40(3). (2019). Mandaliya, H. et al. Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). 8 (6):886–894. (2019). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-5216062","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379784367,"identity":"0a877a31-94e2-4d54-ad06-ab25a3f9dfa2","order_by":0,"name":"Wenxia Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLCCBAYJOX6GwweAzANEa7Ewlmw8lgDVwkyUporEDYfPGBCnxeB47zGJBzUSiRuOnfkm8aHmDoM5ez9+1xmcOZdskHBMwnjmmbPbJGcce8Zg2XMYvy1mN3IMHySwScj23Ti7TZqH7TCDwY1kAlruvzE4kPBPgrHh/ptn0n/+AbXcf0zIFh7DB4ltEooTDpxhk2ZsA9lCwPv2Z3KMDRL7JIwlG44ZW/b2HeYxOJNsgFeLZPsZM8kf3+pAUfnwxo9vh+UMjh98gN8aJMAiASR4iFYOAswfSFI+CkbBKBgFIwYAAFLaUo2OpfqYAAAAAElFTkSuQmCC","orcid":"","institution":"Fuling Hospital affiliated to Chongqing University","correspondingAuthor":true,"prefix":"","firstName":"Wenxia","middleName":"","lastName":"Wang","suffix":""},{"id":379784368,"identity":"7ed033cc-2853-47cc-b51b-d5d58b316372","order_by":1,"name":"Bi Ren","email":"","orcid":"","institution":"Beijing Anzhen Hospital Affiliated to Capital Medical University Nanchong Hospital, Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Bi","middleName":"","lastName":"Ren","suffix":""},{"id":379784369,"identity":"7bae59a5-a385-4bb5-8b30-ecb8564fce67","order_by":2,"name":"Haocheng Gou","email":"","orcid":"","institution":"Beijing Anzhen Hospital Affiliated to Capital Medical University Nanchong Hospital, Nanchong Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haocheng","middleName":"","lastName":"Gou","suffix":""},{"id":379784370,"identity":"165c36ee-a7ea-442e-aa13-c6d298bfbd07","order_by":3,"name":"Wu Guo","email":"","orcid":"","institution":"Fuling Hospital affiliated to Chongqing University","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2024-10-07 07:23:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5216062/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5216062/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70405803,"identity":"f8a2355e-bb38-4f5d-aeec-a3b9f7aef0ae","added_by":"auto","created_at":"2024-12-02 23:16:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":18034,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram of the literature retrieval strategy.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/278668744d8911fc852d7e20.png"},{"id":70406274,"identity":"09a64bab-4014-41d8-9035-d2a062a7dc7f","added_by":"auto","created_at":"2024-12-02 23:24:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382786,"visible":true,"origin":"","legend":"\u003cp\u003eRisk of bias assessment of the overall included studies.\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/1bb1f74b005de4a4a25b2486.png"},{"id":70405800,"identity":"ac3e61a5-4e29-45e7-9986-9e82cb314d28","added_by":"auto","created_at":"2024-12-02 23:16:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40016,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic impact of the HALP score on overall survival.\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/3e6e9ebe4669ca81968284f6.png"},{"id":70405802,"identity":"4bbd10f8-eebe-44ae-a76a-93410ccadf3a","added_by":"auto","created_at":"2024-12-02 23:16:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":166543,"visible":true,"origin":"","legend":"\u003cp\u003eThe prognostic impact of the HALP score on progression-free survival (A) and recurrence/disease-free survival (B).\u003c/p\u003e","description":"","filename":"OnlineFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/c20a74ff21fea95d21b2d6ac.png"},{"id":70405801,"identity":"2b3e7852-c35b-49fc-a249-95830da2a07a","added_by":"auto","created_at":"2024-12-02 23:16:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":38722,"visible":true,"origin":"","legend":"\u003cp\u003eSensitivity analysis and publication bias of the included studies. Sensitivity analysis of the association between HALP and overall survival (A), Begg’s funnel plot using data of overall survival to detect publication bias (B), the trim-and-fill analyses to adjust for potential publication bias in overall survival (C).\u003c/p\u003e","description":"","filename":"OnlineFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/980d1454cdfa193aff6442fe.png"},{"id":74019958,"identity":"a4ece78f-2bcc-48af-99b4-c236cd9674f5","added_by":"auto","created_at":"2025-01-17 04:53:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1589240,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5216062/v1/ad5d9104-4363-45a4-b1eb-e453c978cd69.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte and platelet score in patients with lung cancer: a meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLung cancer is a prominent contributor to global cancer mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. Non-small cell lung cancer (NSCLC) is the predominant type of primary malignant tumor of the lungs, accounting for approximately 85% of all lung cancer cases\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Compared with NSCLC, small cell lung cancer (SCLC) is a less prevalent form of lung cancer, but it rapidly growth and has a poor prognosis\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Conventional therapy includes surgical removal, radiation therapy, and chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Immunotherapy is an innovative approach for treating cancer\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Despite the ongoing advancements in treatment modalities and pharmaceuticals, a significant number cases of lung cancer patients are being detected at advanced stages, resulting in detrimental effects on both early detection and therapy\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. Furthermore, there is large variation in the survival rate of individuals with the same clinical stage who receive postoperative adjuvant chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Hence, there is a pressing demand for straightforward yet trustworthy biomarkers to estimate the survival rate of cancer patients and assist clinicians in categorizing patients and administering the most effective treatment.\u003c/p\u003e \u003cp\u003eThe involvement of the systemic inflammatory response in carcinogenesis, progression, and metastasis has been documented\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Extended inflammation in the body triggers the production of various inflammatory substances, resulting in the epithelial mesenchymal transition and tumor microenvironment formation, increasing damage to body tissues and organs, and ultimately increasing the susceptibility to cancer development\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Recently, studies have indicated that cancer can cause malnutrition through many metabolic mechanisms\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. There is an increasing apprehension regarding the nutritional condition of patients and its impact on tumor outcomes. Multiple studies have demonstrated that several prognostic markers of inflammation, such as the neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and lymphocyte-monocyte ratio (LMR), can be used to predict survival outcomes in patients with malignancies\u003csup\u003e[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA novel biomarker called the HALP score, which measures the levels of hemoglobin, albumin, lymphocyte, and platelet, has recently been found to have a substantial correlation with the prognosis of various types of malignancies, including gastric, esophageal, rectal, and pancreatic tumors\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Nevertheless, the predictive significance of the HALP score in individuals with lung cancer has yet to be examined. The aim of this study was to investigate the correlation between pretreatment HALP scores and survival outcomes in patients with lung cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Search strategy\u003c/h2\u003e \u003cp\u003eThe Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e was followed in the execution of this meta-analysis. As of July 2024, we were able to locate pertinent papers investigating the association between HALP and lung cancer survival outcomes by searching a number of databases, including PubMed, PubMed Central, Web of Science, and Embase. The literature search utilized the following keywords: \"hemoglobin\", \"albumin\", \"lymphocyte\", \"platelet\", \"HALP\", \"lung cancer\", and \"lung tumor\". Furthermore, a manual search was conducted to locate references listed in the chosen papers and relevant studies. The process was conducted autonomously by both authors. All discrepancies that emerged were settled through negotiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Inclusion and exclusion criteria\u003c/h2\u003e \u003cp\u003eWe initially screened the literature based on the basis of titles and abstracts before conducting a full-text search for potentially relevant literature. The inclusion criteria were as follows: (1) Research that concentrates on the correlation between pretreatment HALP and the prognosis of lung cancer patients. (2) Studies reporting survival outcomes, including hazard ratios (HRs) for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and/or disease-free survival (DFS), as well as their corresponding 95% confidence intervals (CIs) and specific data. (3) Accurate HALP thresholds must be provided for dividing patients into high and low HALP groups. The exclusion criteria were as follows: (1) case reports, reviews, commentaries, conference abstracts, and correspondences. (2) duplicate published studies. (3) Research with incomplete or unavailable data. (4) Research lacking precise HALP thresholds or without preoperative HALP data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Data extraction and quality assessment\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eTwo authors autonomously extracted data from all the literature that was included and settled any disagreements through negotiation. We obtained the following information from each study: first author, year, country, tumor type, tumor stage, sample size, gender, age, treatment modality, HALP score cutoff value, median follow-up time, and HRs with corresponding 95% CIs. The Quality of Prognostic Studies (QUIPS) tool was used to assess the risk of bias of the included studies\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The tool covers six main areas, including the study population, study attrition, prognostic factor measurement, outcome measurement, study confounding, statistical analysis, and reporting. As described in the original studies, each study was rated as having a high, moderate, or low risk of bias. Two researchers independently conducted the quality assessment and all discrepancies were resolved through discussion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn our investigation, pooled HRs and the corresponding 95% CIs were used to evaluate the prognostic significance of HALP score in lung cancer patients. Patients with low HALP score had decreased survival rates (HRs\u0026thinsp;\u0026gt;\u0026thinsp;1), whereas those with HRs\u0026thinsp;\u0026lt;\u0026thinsp;1 had a better prognosis. In addition, we examined heterogeneity via the Cochran\u0026rsquo;s Q-test and Higgins I \u003csup\u003e2\u003c/sup\u003e tics (I \u003csup\u003e2\u003c/sup\u003e). I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;50% or p\u0026thinsp;\u0026lt;\u0026thinsp;0.1 represented significant heterogeneity, and a random effects model was used for the combined analysis. Otherwise, a fixed-effects model was used. Subgroup analyses were then used to identify sources of heterogeneity. To ensure the credibility of the pooled data, sensitivity analyses were conducted iteratively, omitting individual investigations one at a time. Additionally, we used the Begg's test to evaluate publication bias. P values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. All the statistical analyses were carried out using STATA software (version 15.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Search results and study characteristics\u003c/h2\u003e \u003cp\u003eThe literature search process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Initially, 78 studies were found using keywords in the database search; of these, 37 were discarded as duplicates; additionally, 25 studies were removed from the analysis because full text articles were unavailable; and four more studies were eliminated after examining the full texts of the remaining articles. Finally, 12 studies with 7,775 patients who were published between 2019 and 2024 met the inclusion criteria for this meta-analysis\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28 CR29 CR30 CR31\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Among these, 9 studies examined the relationship between HALP and OS\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, 2 studies examined the relationship between HALP and PFS\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, 2 studies examined the relationship between HALP and DFS\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, and 1 study examined the relationship between HALP and RFS\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. The basic characteristics of the included studies are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In this meta-analysis, the QUIPS tool was used to assess the quality of each study, and all studies were judged to be at medium risk of bias, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\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\u003eBaseline characteristics of reviewed studies\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAuthor (year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCancer type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTumor stage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003cp\u003e(M/F)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eSmoking history\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eCut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eFollow-up\u003c/p\u003e \u003cp\u003e(months)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShen 2019\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e61.24\u0026thinsp;\u0026plusmn;\u0026thinsp;9.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e142/36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYang 2020\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e254/81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e243/92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 27.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhai 2021\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e62.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e150/88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87/151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG\u0026uuml;\u0026ccedil; 2022\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e63.47\u0026thinsp;\u0026plusmn;\u0026thinsp;9.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e317/84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e310/91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e23.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWei 2022\u003csup\u003e[45]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e217/145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e141/221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS, DFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFang 2023\u003csup\u003e[45]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIIB-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eImmunotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean 60.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e189/34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e39.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS, PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 20.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMazzella 2023\u003csup\u003e[51]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e149/108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e32.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhao 2023\u003csup\u003e[54]\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA-IIIA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e87/132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eRFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZhang 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIA-IIIB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRang (43\u0026ndash;79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35/17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e25/27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS, DFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCavdar 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian 63\u003c/p\u003e \u003cp\u003e(40\u0026ndash;82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e260/18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e157/121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 15.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGao 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIIIB-IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e59.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e140/63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e92/111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS, PFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaylor 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eI-III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003cp\u003e68.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2444/2585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4144/885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e36.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eMedian 33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eSCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; OS, overall survival; PFS, progression-free survival; DFS, disease-free survival; NA, not available.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eBegg\u0026thinsp;=\u0026thinsp;0.283 trim p\u0026thinsp;=\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.2 HALP and OS\u003c/h2\u003e \u003cp\u003eA total of 10 studies including 7,378 patients were available for OS analysis\u003csup\u003e[\u003cspan additionalcitationids=\"CR24 CR25 CR26 CR27 CR28\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The pooled analysis demonstrated that a lower HALP score was significantly associated with worse OS (HR\u0026thinsp;=\u0026thinsp;1.71; 95% CI, 1.28\u0026ndash;2.30; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The test for heterogeneity was significant (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;90.0%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), so a random-effects model was selected. We conducted subgroup analyses based on race, cancer type, sample size, treatment, cutoff value, and determination of cutoff value to investigate potential heterogeneity issues (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The results showed that patients with low pretreatment HALP score consistently had a poorer OS when stratified by the aforementioned factors. Meta-regression analyses were not performed because there was no significant heterogeneity among the studies.\u003c/p\u003e \u003cp\u003e \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\u003eSubgroup analyses of overall survival.\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSubgroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of\u003c/p\u003e \u003cp\u003estudies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHeterogeneity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP*\u003c/em\u003e interaction\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eP\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\u003e\u003cb\u003eEthnic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.66 (1.20, 2.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.03, 2.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \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\u003e\u003cb\u003eCancer type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.75 (1.27, 2.42)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.484\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSCLC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.47 (1.00, 2.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \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\u003e\u003cb\u003eTreatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.70 (1.01, 2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.957\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eadjuvant therapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.73 (1.24, 2.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e80.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \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\u003e\u003cb\u003eSample size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.54 (1.05, 2.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.99 (0.49, 0.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \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\u003e\u003cb\u003eCut-off value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.51 (1.09, 2.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e86.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.04 (1.34, 3.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \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\u003e\u003cb\u003eSelection of Cut-off value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eROC analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.90 (1.22, 2.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e93.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eX-tile software\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRandom\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.58 (1.10, 2.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emedian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (0.71, 1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNSCLC, non-small cell lung cancer; SCLC, small cell lung cancer; ROC, receiver-operating characteristics; HR, hazard ratio;\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eCI, confidence interval; * \u003cem\u003eP\u003c/em\u003e value for the test for subgroup difference.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.3 HALP and PFS\u003c/h2\u003e \u003cp\u003eThree studies, including 604 patients, were included to explore the correlation between pretreatment HALP and PFS\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e, and the results revealed that a decreased HALP score was associated with reduced PFS (HR\u0026thinsp;=\u0026thinsp;1.44; 95% CI: 1.15\u0026ndash;1.80; P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The heterogeneity of the PFS analysis was not significant (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;24.1%, P\u0026thinsp;=\u0026thinsp;0.268).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.4 HALP and RFS/DFS\u003c/h2\u003e \u003cp\u003eThe association between the pretreatment HALP score and RFS/DFS was reported in three studies\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e, involving 362 patients. The pooled results revealed that patients with reduced HALP score had worse RFS/DFS (HR\u0026thinsp;=\u0026thinsp;2.78, 95% CI, 1.14\u0026ndash;6.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Significant heterogeneity was identified (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;75.9%, P\u0026thinsp;=\u0026thinsp;0.016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eWhen sensitivity analyses of OS were conducted, the pooled HRs and 95% CIs remained statistically significant for OS after any of the individual studies were excluded (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). This suggests that the findings of our analysis are reliable.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Publication bias\u003c/h2\u003e \u003cp\u003eWe evaluated the publication bias of OS using a funnel plot and the Begg test. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, the funnel plot revealed an asymmetrical distribution of the included studies, indicating the possibility of a publication bias for OS. We subsequently conducted further tests to determine the impact of expanding the number of studies on the results. We employed the cut-and-fill method and ultimately confirmed that there was still a substantial association between OS and the outcome ( HR\u0026thinsp;=\u0026thinsp;1.63, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAccording to previous reports, the ability of the HALP score to predict the prognosis of lung cancer patients remains controversial. In this study, we synthesized 12 articles involving 7,775 patients to determine the exact influence of the HALP score on the prediction of lung cancer prognosis. The results of the meta-analysis indicated that a reduced HALP score significantly predicted worse OS (pooled HR = 1.71) in lung cancer patients. Moreover, subgroup analyses also revealed a significant correlation between the HALP score and OS. The results of the publication bias and sensitivity analyses further confirmed the reliability and stability of this study. We also observed that a lower HALP score was significantly associated with PFS (pooled HR = 1.44) and RFS/DFS (pooled HR = 2.78) in patients with lung cancer. Therefore, the HALP score can serve as a cost-effective and reliable prognostic marker for lung cancer patients. To the best of our knowledge, this meta-analysis is the first to investigate the impact of the HALP score on predicting lung cancer prognosis.\u003c/p\u003e \u003cp\u003eAlthough the HALP score has great potential for predicting cancer prognosis, the underlying mechanism remains unclear. Since HALP represents the ratio of hemoglobin, albumin, and lymphocytes to platelets, it is reasonable to speculate that the mechanism may be related to their function\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Reduced hemoglobin leads to anemia, which is associated with poor cancer prognosis\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. Albumin is the major component of total serum protein and reflects the nutritional status of the host\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. Studies have shown that hypoalbuminemia may reduce host immunity and lead to poor outcomes\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. In addition, lymphocytes are a major component of the body's antitumor immune response\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. They can play an important role in cancer defense by inducing cytotoxic cell death and inhibiting cancer cell proliferation and migration \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Platelets secrete many granules and exosomes, which are involved in tumor growth, infiltration, and metastasis, affecting the survival time of patients \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Therefore, elevated platelet counts and decreased hemoglobin, albumin and lymphocytes affect survival time in cancer patients. We can conclude that decreased HALP may reflect poor host nutritional status and decreased lymphocyte-mediated anti-tumor immune response or increased platelet-dependent inflammatory response, which creates an immune microenvironment conducive to tumor growth and leads to poor patient prognosis.\u003c/p\u003e \u003cp\u003eThere has been a continuous search for new cancer prognostic biomarkers by researchers. Studies have shown that inflammation and nutritional indicators are strongly associated with cancer survival outcomes\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Chronic inflammation promotes tumor development and invasion \u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Indicators reflecting the degree of inflammation include lymphopenia and increases in neutrophils and platelets \u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. Previous meta-analyses have shown the association of the NLR\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e and PLR\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e, among other parameters, with survival outcomes in lung cancer patients. Consistent with these studies, our research indicates that the HALP score, as a novel immuno-nutritional marker, performs well in predicting OS, PFS, and RFS/DFS. In a word, our study highlights the potential of pretreatment HALP score as an important prognostic predictor for lung cancer patients. This information could be of guiding significance in formulating individualized treatment plans for lung cancer patients in clinical practice.\u003c/p\u003e \u003cp\u003eThere are several limitations in the current work. First, all the studies we included were retrospective, which may have led to some bias in the results. Second, compared with that of other prognostic markers, the study period for HALP as a novel cancer prognostic marker is relatively short, and the limited number of studies included in the subgroup analysis may affect the reliability of the results. Third, the differences in HALP cutoff values between the included studies may have introduced heterogeneity into the analysis and potentially influenced the overall outcomes. Therefore, standardizing this value in clinical applications would help improve the reliability of the results. To further validate the prognostic value of the HALP score in lung cancer, larger-scale, multicenter prospective studies are still needed.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThe results of current meta-analysis revealed that patients with lower pretreatment HALP score had worse survival outcomes. This prospective prognostic marker helps in the clinical assessment of patient survival and recurrence, facilitates risk stratification of tumors, and better guides treatment decisions.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHALP hemoglobin, albumin, lymphocyte, and platelet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHR hazard ratio\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI confidence intervals\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOS overall survival\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFS progression\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efree survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFS recurrence\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efree survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDFS disease\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003efree survival\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSCLC non\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esmall cell lung cancer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCLC small cell lung cancer\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNLR neutrophil\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLR platelet\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003elymphocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLMR lymphocyte\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emonocyte ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePRISMA Preferred Reporting Items for Systematic Reviews and Meta\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalyses\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eQUIPS Quality of Prognostic Studies.\u003c/div\u003e \u003cdiv class=\"Description\"\u003e\u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, W.W. and W.G.; methodology, W.W. ; formal analysis, W.W., B.R. and H.G.; investigation, W.W. and B.R.; data curation, W.W. and W.G.; writing\u0026mdash;original draft preparation, W.W. and W.G.; writing\u0026mdash;review and editing, B.R. and H.G.; All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data requirements mentioned in the article can be found in the article/Supplementary materials. For further details may directly contact the corresponding author through the provided contact information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMing-Yue, L., Li-Zhong, L. \u0026amp; Ming, D. J. M. C. Progress on pivotal role and application of exosome in lung cancer carcinogenesis, diagnosis, therapy and prognosis. \u003cb\u003e20\u003c/b\u003e(1). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaleem, J. et al. Review of current thermal ablation treatment for lung cancer and the potential of electrochemotherapy as a means for treatment of lung tumours. \u003cb\u003e39\u003c/b\u003e(8). (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRomain, R. et al. The non-small cell lung cancer immune contexture. \u003cem\u003emajor determinant tumor characteristics patient outcome\u003c/em\u003e \u003cb\u003e191\u003c/b\u003e(4). 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(2019).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"HALP, lung cancer, prognosis, survival","lastPublishedDoi":"10.21203/rs.3.rs-5216062/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5216062/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be associated with the progression of many types of tumors. Nevertheless, the connection between HALP score and individuals with lung cancer is still a subject of debate. We conducted a systematic evaluation to investigate the prognostic role of HALP score on survival outcomes in lung cancer patients. As of July 2024, we searched the PubMed, PubMed Central, Web of Science, and Embase databases to collect relevant articles evaluating the relationship between HALP and lung cancer prognosis. The pooled hazard ratios (HRs) and corresponding 95% confidence intervals (CIs) for overall survival (OS), progression-free survival (PFS), recurrence-free survival (RFS), and disease-free survival (DFS) were analyzed. A total of 12 studies involving 7775 lung cancer patients were included. The combined results revealed that a low HALP score was associated with poorer OS (HR\u0026thinsp;=\u0026thinsp;1.71, 95% CI, 1.28\u0026ndash;2.30, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PFS (HR\u0026thinsp;=\u0026thinsp;1.44, 95% CI, 1.15\u0026ndash;1.80, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and DFS/RFS (HR\u0026thinsp;=\u0026thinsp;2.78, 95% CI, 1.14\u0026ndash;6.78, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Subgroup analysis further confirmed that pretreatment HALP was an independent predictor of OS in lung cancer patients. The decreased pretreatment HALP score was strongly associated with inferior prognosis in lung cancer patients. Our findings highlight that HALP score is a reliable biomarker of lung cancer prognosis. However, multicenter and prospective studies are needed to further validate its clinical utility.\u003c/p\u003e","manuscriptTitle":"Revealing the prognostic landscape of hemoglobin, albumin, lymphocyte and platelet score in patients with lung cancer: a meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-02 23:16:10","doi":"10.21203/rs.3.rs-5216062/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"22e576a7-187e-4368-8e26-5c1cac40a025","owner":[],"postedDate":"December 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40442312,"name":"Biological sciences/Cancer/Lung cancer"},{"id":40442313,"name":"Biological sciences/Immunology"},{"id":40442314,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-01-17T04:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-02 23:16:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5216062","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5216062","identity":"rs-5216062","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}