Front. Immunol. Frontiers in Immunology Front. Immunol. 1664-3224 Frontiers Media S.A. 10.3389/fimmu.2024.1483855 Immunology Systematic Review Prognostic significance of hemoglobin, albumin, lymphocyte and platelet score in solid tumors: a pooled study Li Jinze 1 * † Zheng Jing 2 † Wang Puze 1 Lv Dong 1 * 1 Department of Urology, People’s Hospital of Deyang City, Chengdu University of Traditional Chinese Medicine, Deyang, China 2 Department of Anesthesia & Operating room, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China

Edited by: Pengpeng Zhang, Nanjing Medical University, China

Reviewed by: Adolfo Martinez, General Hospital of Mexico, Mexico

Jianping Xiong, Peking University Third Hospital, China

Bicheng Ye, Yangzhou Polytechnic Institute, China

*Correspondence: Jinze Li, Dr_lijinze@163.com; Dong Lv, LV800919@163.com

†These authors have contributed equally to this work

 18 12 2024 2024 15 1483855 20 08 2024 15 11 2024 Copyright © 2024 Li, Zheng, Wang and Lv 2024 Li, Zheng, Wang and Lv

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 Objective

The high hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been reported to be a good prognostic indicator for several malignancies. However, more evidence is needed before it can be introduced into clinical practice. Here, we systematically evaluated the predictive value of HALP for survival outcomes in patients with solid tumors.

 Methods

This study was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and Assessing the Methodological Quality of Systematic Reviews (AMSTAR) Guidelines. In March 2024, an electronic literature search was performed for articles regarding the prognostic role of HALP in solid tumors. Data from studies with reported risk ratios (HRs) and 95% confidence intervals (CIs) were pooled in a meta-analysis. Study bias was assessed using the QUIPS tool.

 Results

Of the 729 articles reviewed, 45 cohorts including data from 17,049 patients with cancer were included in the pooled analysis. The pooled results demonstrated that elevated HALP score was significantly associated with favorable overall survival (HR = 0.60, 95% CI 0.54-0.67, p < 0.01), cancer-specific survival (HR = 0.53, 95% CI 0.44- 0.64, p < 0.01), progression-free survival (HR = 0.62, 95% CI 0.54-0.72, p < 0.01), recurrence-free survival (HR = 0.48, 95% CI 0.30-0.77, p < 0.01), and disease-free survival (HR = 0.72, 95% CI 0.57-0.82, p < 0.01). Subgroup analyses based on various confounding factors further revealed the consistent prognostic impact of HALP on overall survival in patients with solid tumors.

 Conclusions

Our findings suggest that high HALP is associated with better survival outcomes in patients. The HALP score is a potential prognostic biomarker in solid tumors, but it needs to be further studied whether it can improve the established prognostic model.

 solid tumors HALP biological marker prognosis survival section-in-acceptance Cancer Immunity and Immunotherapy Introduction

Cancer is a major public health problem worldwide, placing a heavy burden on human health. According to data from the International Agency for Research on Cancer (IARC) in 2020, an estimated 19.3 million new cancer cases and nearly 10 million cancer deaths occurred worldwide (1). Despite significant advances in current cancer treatment, such as the use of immune checkpoint inhibitors and oncogene-targeted drugs, overall cancer-related mortality remains high (2). In addition, cancer treatment varies greatly among individuals, making the prognosis of different individuals significantly different (3). Therefore, there is a need for a reliable biomarker to predict survival in patients with cancer so that therapeutic strategies can be tailored to improve outcomes (4).

Tumor progression and metastasis are not only dependent on the type of tumor cells, but also inflammatory response and nutritional status play important roles in these processes (5, 6). Substantial evidence suggests that parameters reflecting nutritional and inflammatory status, including albumin and hemoglobin levels and lymphocyte and platelet counts, are critical for cancer survival (7–10). The downside of these metrics, however, is that each captures only one aspect of inflammation or nutrition (11). Further studies discovered that a combination of these parameters, including platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutrition index (PNI), could accurately predict patient outcome more than any single index (12–14). In addition to these well-known markers, a novel inflammatory index combining hemoglobin, albumin, lymphocyte, and platelet (HALP) has been shown to be strongly associated with the prognosis of several malignancies (15–18).

Although a series of studies have attempted to explore the use of HALP as a prognostic marker in human cancer, the results of these findings have been inconsistent (15, 17, 19–22). The advantage of meta-analyses is that they allow pooled effect sizes to be derived from the results of previous studies and thus allow for more robust conclusions to be drawn using data from a large number of patients (23). The purpose of this study was to investigate whether HALP could be a new prognostic indicator for solid tumors using meta-analysis.

 Materials and methods

This meta-analysis was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline (24) and A MeaSurement Tool to Assess systematic Reviews 2 (AMSTAR 2) guideline (25). This study was also registered with PROSPERO (CRD42022334548).

Search strategy

An electronic literature search was conducted on PubMed, Ovid-Embase, Web of Science, and Cochrane Library in March 2024 for articles regarding the prognostic role of HALP in solid tumors. We used the following search terms: “hemoglobin, albumin, lymphocyte, and platelet”, “HALP”, “neoplasm”, “neoplasia”, “cancer”, “tumor”, “carcinoma” and “malignancy”. We also manually searched the literature reference list to further investigate potentially relevant studies. Discrepancies were addressed through discussion or ultimately by third-party adjudication.

 Selection criteria

The criteria for inclusion of studies were as follows: (1) prospective or retrospective clinical studies; (2) studies investigating the association of pretreatment HALP with prognosis in any histologically confirmed solid tumor; (3) patients were adults 18 years of age or older; (4)cut-off values for pre-treatment HALP have been determined and divided into high and low groups; and (5) sufficient data were obtained to assess the hazard ratio (HR) and corresponding 95% confidence interval (CI) between pretreatment HALP and survival outcomes including overall survival (OS), cancer-specific survival (CSS), progression-free survival (PFS), recurrence-free survival (RFS), and/or disease-free survival (DFS). Exclusion criteria were studies categorized as reviews, conference abstracts, letters, and expert opinions. Additionally, unpublished studies, duplicate published studies, studies with insufficient survival data, and studies focusing only on hematological malignancies were excluded.

 Data extraction

Two authors separately collected the following variables from the included studies: first author’s name, year of publication, country, ethnicity, study type, tumor type, tumor stage, treatment strategy, sample size, age of subjects, HALP cut-off value, analysis of survival, survival outcomes (HRs with corresponding 95% CIs for OS, CSS, PFS, RFS, and DFS), and follow-up period. Data were extracted from a multivariate analysis when survival data from a study were analyzed in two ways (univariate and multivariate analyses). Moreover, if relevant data for the article were missing, the corresponding author was contacted. If no response was received or data were not available, the article was excluded.

 Methodological quality

Risk of bias assessment for included studies using the Quality In Prognosis Studies (QUIPS) tool (26). This tool covers six main domains, including study population, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis and reporting. Each study was rated as high, moderate, or low risk of bias based on the description in the original study. Two reviewers independently conducted the quality assessment and all disagreements were resolved through discussion or adjudicated by a third party.

 Statistical analyses

We used software R 3.6.3 and Stata 14.0 for statistical analysis. A pooled HR with 95% CI was utilized to assess the association between pre-treatment HALP and survival outcomes. Heterogeneity between studies was estimated using Cochran’s Q test and Higgin’s I2 test, and I2 > 50% or p < 0.10 demonstrated significant heterogeneity. A random effects model was employed for the combined analysis in this meta-analysis. Moreover, any potential publication bias was evaluated by Begg’s test. We performed subgroup analyses to investigate potential sources of heterogeneity. Meta-regression analysis was conducted to assess the effect of the HALP cutoff value on the HR for OS. Subsequently, sensitivity analyses were also conducted to assess the robustness and reliability of the pooled results. Two-sided p < 0.05 was considered statistically significant.

 Results Study characteristics

The search initially identified 729 articles, leaving 406 articles after eliminating duplicate publications. By reading the titles and abstracts, 339 articles that did not fit the main idea were excluded. The full text of 67 studies was then reviewed, and 25 studies (including 4 studies that did not provide the HR with corresponding 95% CI data, 5 studies with missing survival outcome data, and 16 studies involving patients with non-solid tumors) were excluded. Finally, 42 studies containing 17,049 patients were included in this meta-analysis (11, 15–22, 27–59). The flowchart of the study screening process is presented in Figure 1 .

PRISMA flowchart depicting the search strategy used for this study.

Of these 42 studies, three studies had two cohorts (training and validation cohorts) (15, 21, 27), resulting in a total of 45 cohorts included in this meta-analysis. The 29 cohorts were from China (11, 15, 16, 18–21, 27–30, 32–34, 38, 45–49, 53–58), seven from Turkey (17, 31, 35–37, 39, 59), four from Japan (22, 41, 50, 52), three from European and American countries (42, 43, 51), and one study from Thailand (40). In the included cohorts, the most common tumor type was hepatobiliary and pancreatic cancer (n = 8) (21, 30, 31, 48, 50, 57, 58), followed by gastrointestinal cancer (n = 7) (15, 17, 27, 36, 43). Notably, only 4 cohorts were prospectively designed (17, 42, 51, 52), the rest were retrospective (11, 15, 16, 18–22, 27–41, 43–50, 53–59). Of the included cohorts, 31 cohorts underwent curative resection (11, 15, 17–19, 21, 27, 28, 30–36, 38, 41–43, 45, 47–51, 54, 56–58), 9 cohorts received adjuvant therapy (e.g., chemotherapy, radiotherapy, chemoradiotherapy, and immunotherapy) (16, 20, 22, 29, 39, 40, 53, 55, 59), and 2 cohorts received mixed treatment (37, 46). The number of patients included in the individual cohorts ranged from 39 to 1360. The cut-off value of HALP ranged from 0.277 to 56.8. thirty-seven cohorts reported associations between HALP and OS (15–22, 27, 30, 31, 33–37, 39–43, 45, 46, 48–53, 55, 57, 58), 6 cohorts investigated associations between HALP and CSS (11, 27, 32, 42, 46), 8 cohorts examined associations between HALP and PFS (20, 28, 29, 33, 40, 52, 53, 59), 7 cohorts investigated associations between HALP and RFS (38, 42, 47, 48), and 4 cohorts reported associations between HALP and DFS (43, 45, 49, 50, 54, 56, 58). The basic characteristics of the enrolled cohorts are shown in Table 1 .

Baseline characteristics of reviewed studies.

Author Year Country Study design Tumor type Tumor stage Treatment strategy Sample size Age(years) HALPCut-off value Analysis ofsurvival Survivaloutcome Follow-up(months)
Chen (15)(Training) 2015 China Retrospective GC IA-IV Curative resection 888 Mean57.3 ± 11.8 56.8 Multivariate OS Median 65.6
Chen (15)(Validation) 2015 China Retrospective GC IA-IV Curative resection 444 Mean56.8 ± 11.5 56.8 Multivariate OS Median 66
Jiang (26)(Training) 2016 China Retrospective CRC II-III Curative resection 684 Median 62(21-92) 26.5 Multivariate OS, CSS Median 67
Jiang (26) (Validation) 2016 China Retrospective CRC II-III Curative resection 136 Median 58(32-86) 26.5 Multivariate OS, CSS Median 68
Cong (20) 2017 China Retrospective EC II-IVA Chemoradiotherapy 39 Median 60(45 - 71) 48.34 Multivariate OS, PFS Median 27.2
Peng (19) 2018 China Retrospective BC NA Curative resection 516 Median 66(57–73) 22.2 Multivariate OS Median 37(20 - 56)
Peng (11) 2018 China Retrospective RCC NA Curative resection 1360 Median 55(46 - 65) 31.2 Multivariate CSS Median 67(36 - 74)
Guo (28) 2019 China Retrospective Prostatecancer NA Curative resection 82 Median 69(63-73) 32.4 Multivariate PSA-PFS 17.47
Shen (29) 2019 China Retrospective SCLC NA Chemotherapy 178 Mean61.24 ± 9.27 25.8 Multivariate PFS NA
Xu (30) 2020 China Retrospective Pancreaticcancer IA-III Curative resection 582 Median 61(29 - 82) 44.56 Multivariate OS Median 20.9
Yang (16) 2020 China Retrospective SCLC I-IV Chemotherapy 335 NA 18.6 Multivariate OS Median 27.1(0.5-46.2)
Arikan (31) 2021 Turkey Retrospective PAC NA Curative resection 129 Mean64.69 25 Multivariate OS NA
Dagmura (17) 2021 Turkey Prospective CRC Mixed Curative resection 139 Mean72.82 15.5 Multivariate OS NA
Feng (32) 2021 China Retrospective EC I-III Curative resection 355 Median 59(36 - 80) 31.8 Multivariate CSS Median 34(4 - 94)
Gao (33) 2021 China Retrospective UTUC NA Curative resection 533 Mean66.71 ± 10.4 28.67 Multivariate OS, PFS Median 39.6 (21.6 - 65)
Hu (34) 2021 China Retrospective EC I-III Curative resection 834 Median 60(55 - 65) 38.8 Multivariate OS NA
Sun (21)(Training) 2021 China Retrospective BTC I-III Curative resection 287 NA 42.68 Multivariate OS Median 19(9 - 37)
Sun (21)(Validation) 2021 China Retrospective BTC I-III Curative resection 131 NA 42.68 Multivariate OS Median 18(10 - 38)
Topal (35) 2021 Turkey Retrospective EC I-IV Curative resection 44 27-86 43 Multivariate OS NA
Yalav (36) 2021 Turkey Retrospective CRC I-IIIC Curative resection 279 Mean61.54 15.7 Multivariate OS NA
Zhai (18) 2021 China Retrospective NSCLC IA-IV Curative resection 238 Mean62.3 ± 8.4 48 Multivariate OS NA
Ekinci (37) 2022 Turkey Retrospective RCC NA Mixed 123 Median 64(21 - 81) 0.277 Multivariate OS NA
Güç (39) 2022 Turkey Retrospective NSCLC NA Chemotherapy 401 Mean63.47 ± 9.75 23.24 Multivariate OS Median 18(1 - 80)
Jiang (38) 2022 China Retrospective CC I-IIA Curative resection 1054 48.1 ± 9.2 39.5 Multivariate RFS Median 53(9 - 96)
Kurashina (22) 2022 Japan Retrospective UC NA Immunotherapy 54 70 ± 6.8 30.5 Univariate OS NA
Leetanaporn (40) 2022 Thailand Retrospective CC I-IVA Radiation therapy 1112 Median 52(44 - 61) 22.2 Multivariate OS, PFS Median 2.96
Matsui (41) 2022 Japan Retrospective RPS NA Curative resection 113 Median 59.7(17 - 82) 3 Univariate OS Median 43.8(1.8 - 43.8)
Njoku (42) 2022 UK Prospective Endometrial cancer I-IV Curative resection 439 Median 67(58 - 74) 24 Multivariate OS, CSS, RFS Median 42(27 – 59)
Ruiz (43) 2022 Mexico Retrospective CRC I-III Curative resection 640 NA 15 Multivariate OS, DFS Median 46.4
Vlatka (44) 2022 Croatia Retrospective large B-cell lymphoma I-IV Chemotherapy 153 Median 64(54 - 72) 20.8 Multivariate OS Median 40
Wei (45) 2022 China Retrospective NSCLC I-IV Chemotherapy 362 NA 48.2 Multivariate OS, DFS Median 64
Wu (46) 2022 China Retrospective Pharyngeal cancer I-IV Mixed 319 Mean57.1 ± 11.5 44 Multivariate OS, CSS Median 26.4(15.6 – 51.6)
Zhao (47) 2022 China Retrospective GIST NA Curative resection 458 Mean56.8 ± 12.1 31.5 Multivariate RFS Median 56(4 – 138)
Zhang (48) 2022 China Retrospective ICC I-IV Curative resection 162 NA 43.6 Univariate OS, RFS NA
Fang (49) 2023 China Retrospective Oral cavity cancer I-IV Curative resection 350 Median 60(52 - 67) 35.4 Multivariate OS, DFS Median 43
Mazzella (51) 2023 Italy Prospective NSCLC I-III Curative resection 257 NA 32.2 Multivariate OS Median 40(33 – 46)
Nishio (52) 2023 Japan Prospective Endometrial cancer I-IV Chemotherapy 712 Median 55(28 -74) 35.52 Multivariate OS, PFS NA
Shi (53) 2023 China Retrospective EC II-IVA Chemoradiotherapy 150 Median 65(37 - 79) 23.1 Multivariate OS, PFS Median 27.5
Toshida (50) 2023 Japan Retrospective HCC NA Curative resection 332 Median 69(28 - 87) 45.6 Univariate OS, DFS NA
Zhao B (54) 2023 China Retrospective NSCLC IA-IIIA Curative resection 219 NA 29.31 Multivariate RFS Median 24
Zhao R (55) 2023 China Retrospective Nasopharynx cancer III-IV Chemotherapy 400 Median 48(40 - 55) 46.61 Multivariate OS Median 50(2-126)
Zhao Z (56) 2023 China Retrospective Breast cancer I-III Curative resection 411 Median 54.52(28 - 98) 23.6 Multivariate RFS Median 54
Zhou (57) 2023 China Retrospective HCC I-IV Curative resection 273 53.99 ± 10.74 54.13 Multivariate OS 56.69 ± 1.25
Huang (58) 2024 China Retrospective ICC NA Curative resection 227 NA 37.1 Univariate OS, RFS Median 15
Mutlu (59) 2024 Turkey Retrospective malignant mesothelioma I-IV Chemotherapy 115 Median 64(24 - 83) 25.9 Multivariate OS, PFS NA

GC, gastric carcinoma; BC, bladder cancer; BTC, biliary tract cancer; CRC, colorectal cancer; EC, esophageal cancer; SCLC, small cell lung cancer; NSCLC, non-small cell lung cancer; RCC, renal cell carcinoma; PAC, periampullary carcinoma; UC, urothelial carcinoma; UTUC, upper tract urothelial carcinoma; RPS, retroperitoneal soft tissue sarcoma; CC, cervical cancer; GIST, gastrointestinal stromal tumor; ICC, intrahepatic cholangiocarcinoma; HCC, hepatocellular carcinoma; HALP, hemoglobin, albumin, lymphocyte, and platelet; OS, overall survival; CSS, cancer-specific survival; RFS, recurrence-free survival; PFS, progression-free survival; NA, not available.

 Quality of the studies

The study quality of each study was assessed using the QUIPS tool. QUIPS domains most commonly evaluated as low risk of bias were the prognostic factor measurement and outcome measurement, while the QUIPS domain most commonly evaluated as a moderate risk of bias was attrition. The majority of studies were judged to be moderate risk of bias and 2 studies were judged to be high risk, as illustrated in Figure 2 .

Risk of bias assessment of included studies.

 Association of HALP with survival outcomes Overall survival

Thirty-four studies comprising 37 cohorts investigated the association of HALP with OS in patients with cancer (15–22, 27, 30, 31, 33–37, 39–43, 45, 46, 48–53, 55, 57, 58). The results demonstrated that OS was significantly longer in patients with increased pretreatment HALP (HR = 0.60, 95% CI 0.54-0.67, p < 0.01), with significant heterogeneity among studies (I2 = 77%, p < 0.01) ( Figure 3 ).

Forest plot showing hazard ratio for overall survival for HALP greater than or less than the cutoff value. HALP, hemoglobin, albumin, lymphocyte and platelet.

Given the significant heterogeneity between studies, we performed subgroup analyses of OS based on study ethnicity, tumor type, treatment strategy, sample size, study design, analysis mode, cut-off value, and cut-off selection method ( Table 2 ). High pre-treatment HALP was found to be consistently associated with better OS regardless of ethnicity, tumor type, treatment strategy, sample size, cut-off value, or cut-off selection method (all p < 0.01). On subgroup analysis stratified by analysis mode, the multivariate analysis subgroup was significantly associated with longer OS (p < 0.01), while the univariate analysis subgroup was not associated with OS (p = 0.08). Furthermore, Meta-regression analysis revealed no significant association between the HALP cutoff value and the HR for OS (p = 0.401, Supplementary Data Sheet 1 ).

Subgroup analyses of overall survival.

Subgroup Variable No. ofcohorts Model HR (95% CI) P Heterogeneity
I2 (%) P
Ethnic Asian 27 Random 0.62 (0.57, 0.67) < 0.01 20.0 0.18
Caucasian 10 Random 0.57 (0.43, 0.75) < 0.01 85.0 < 0.01
Tumor type gastrointestinal cancer 7 Random 0.71 (0.59, 0.86) < 0.01 78.0 < 0.01
esophageal cancer 4 Random 0.62 (0.53, 0.74) < 0.01 0.0 0.46
hepatobiliary and pancreatic cancer 8 Random 0.63 (0.51, 0.79) < 0.01 54.0 0.03
genitourinary cancer 4 Random 0.53 (0.38, 0.73) < 0.01 35.0 0.20
lung cancer 5 Random 0.54 (0.40, 0.72) < 0.01 64.6 0.02
gynecologic cancer 3 Random 0.59 (0.44, 0.79) < 0.01 27.0 0.25
others 6 Random 0.54 (0.44, 0.66) < 0.01 0.0 0.65
Treatment strategy curative resection 25 Random 0.64 (0.57, 0.72) < 0.01 76 < 0.01
adjuvant therapy 10 Random 0.51 (0.43, 0.62) < 0.01 25.0 0.21
mixed 2 Random 0.36 (0.09, 1.44) 0.15 70 0.07
Sample size > 300 18 Random 0.61 (0.55, 0.68) < 0.01 43.0 0.03
≤ 300 19 Random 0.59 (0.49, 0.71) < 0.01 75.0 < 0.01
Analysis mode multivariate 32 Random 0.59 (0.52, 0.67) < 0.01 78.0 < 0.01
univariate 5 Random 0.69 (0.45, 1.05) 0.08 71.0 < 0.01
Cut-off value for HALP > 26.5 22 Random 0.64 (0.57, 0.71) < 0.01 39.0 0.03
≤ 26.5 15 Random 0.57 (0.47, 0.70) < 0.01 84.0 < 0.01
Selection of Cut-off value ROC analysis 21 Random 0.60 (0.51, 0.71) < 0.01 81.0 < 0.01
X-tile software 12 Random 0.59 (0.52, 0.67) < 0.01 29.0 0.16
median/mean 3 Random 0.72 (0.54, 0.96) < 0.01 0.0 0.97
Cutoff Finder 1 – 0.66 (0.45, 0.96) 0.03 – –

HALP, hemoglobin, albumin, lymphocyte and platelet; ROC, receiver-operating characteristics; HR, hazard ratio; CI, confidence interval.

 Cancer-specific survival

Five studies comprising 6 cohorts explored the association of HALP with CSS in patients with cancer (11, 27, 32, 42, 46). The results indicated that higher pretreatment HALP was associated with longer CSS in patients (HR = 0.53, 95% CI 0.44 - 0.64, p < 0.01), and there was low heterogeneity among studies (I2 = 24%, p = 0.25) ( Figure 4 ).

Forest plot showing hazard ratio for cancer-specific survival (A) and progression-free survival (B) for HALP greater than or less than the cutoff value. HALP, hemoglobin, albumin, lymphocyte and platelet.

 Progression-free survival

Eight studies reported the relationship between HALP and PFS in patients with cancer (20, 28, 29, 33, 40, 52, 53, 59). The results showed that patients with elevated pretreatment HALP had better PFS (HR = 0.62, 95% CI 0.54 - 0.72, p < 0.01), with low heterogeneity between studies (I2 = 1%, p = 0.42) ( Figure 4 ).

 Recurrence-free survival

Seven studies reported the relationship between HALP and RFS in patients with cancer (43, 45, 49, 50, 54, 56, 58). The results revealed that patients with elevated pretreatment HALP had favorable RFS in patients with solid tumors (HR = 0.48, 95% CI 0.30 - 0.77, p < 0.01), with significant heterogeneity among studies (I2 = 82%, p < 0.01) ( Figure 5 ).

Forest plot showing hazard ratio for recurrence-free survival (A) and disease-free survival (B) for HALP greater than or less than the cutoff value. HALP, hemoglobin, albumin, lymphocyte and platelet.

 Disease-free survival

Four studies reported the relationship between HALP and DFS in patients with cancer (43, 45, 49, 50). The results demonstrated that patients with elevated pretreatment HALP had better DFS (HR = 0.72, 95% CI 0.57 - 0.92, p < 0.01), with lower significant heterogeneity among studies (I2 = 45%, p = 0.14) ( Figure 5 ).

 Sensitivity analysis

We performed sensitivity analyses to assess the reliability of pooled HRs for OS ( Supplementary Data Sheet 1 ). The exclusion of individual studies had no significant effect on the combined HR, confirming that the results of this meta-analysis are relatively robust and reliable.

 Publication bias

The Begg’s test demonstrated that the results were not statistically significant (OS: p = 0.824), but the Begg’s funnel plots showed asymmetry between the left and right sides, which increases the likelihood of potential publication bias ( Supplementary Data Sheet 1 ).

 Discussion

To date, cancer remains the leading cause of death and a significant barrier to increasing life expectancy in all countries of the world (60). Due to the higher cost of cancer management, the establishment of reliable prognostic biomarkers is essential for predicting therapeutic outcomes and determining the patients most likely to benefit from treatment. HALP is a new score based on a combination of inflammatory and nutritional deficiency concepts that was first discovered in 2015 to predict the prognosis of patients with gastric cancer (15). Over the past few years, HALP has been successively used to evaluate survival outcomes in various malignancies. Although a recent systematic review has revealed that low pre-treatment HALP predicts a worse overall prognosis for cancer patients (61), however, there is great heterogeneity in studies investigating HALP in terms of cancer type, outcome, HALP threshold, and population of interest. Here, we conducted an updated meta-analysis based on the available literature to investigate the prognostic impact of HALP. In addition, subgroup analyses were performed to explore the influence of factors such as ethnicity, tumor type, and treatment strategy on the study results.

Evidence from the inclusion of 45 cohorts suggested that an elevated HALP was associated with better OS, CSS, PFS, and DFS in patients with solid tumors. When stratified by ethnicity, disease type, treatment strategy, sample size, and study design higher HALP was consistently an independent factor for favorable OS. Of interest, the included studies reported different HALP cut-off values for different disease types and used different methods to select HALP cut-off values. However, we observed that the prognostic impact of HALP on OS was retained across subgroups. Moreover, in subgroup analyses stratified by analysis mode, HALP scores in the multivariate analysis subgroup were independently associated with OS ( Table 2 ). Although no significant difference in OS was observed in the univariate subgroup, it is unlikely to affect the interpretation of our results given the small number of studies included in the analysis. Notably, in this meta-analysis, we included a substantial number of retrospective studies. The subgroup analysis based on study design showed no significant difference between the data from retrospective studies and the overall results ( Table 2 ). To some extent, this indicates that data from retrospective studies are consistent with those from other types of studies and did not introduce noticeable bias into the final comprehensive conclusion.

Furthermore, due to the heterogeneity of the studies themselves, we were unable to comprehensively assess the relationship between HALP and age or gender. As age increases, the prognosis of elderly cancer patients is generally worse. However, we observed that almost all studies accounted for patient age when performing multivariate regression or constructing nomograms. Therefore, age does not appear to influence the HALP score. Further research is needed to study HALP scores in healthy populations to accurately evaluate the correlation between HALP and age. Additionally, some studies have reported differences in baseline HALP scores between males and females, but after adjusting for gender, the HALP score remained significant (11, 15, 19, 34). Thus, based on current results, gender does not significantly affect the utility of HALP as a biomarker. In general, a more refined search method and more stringent inclusion criteria were used than in the previous systematic review (61), which dramatically improved the quality and credibility of the study.

The mechanism of the association between high HALP and favorable outcomes in cancer patients remains unclear. One potential mechanism for the prognostic impact of HALP could be the association of high HALP with inflammation and nutrition. Anemia is a well-documented cancer-related phenomenon. In chronic anemia, CD3 T lymphocytes and macrophages release pro-inflammatory cytokines such as IL-6 (62). IL-6 mediates the release of hepcidin from the liver, which inhibits iron absorption and iron release to prevent cancer cells from utilizing iron, thereby reducing erythropoiesis (63). Previous studies also have demonstrated that low hemoglobin levels were associated with adverse clinical outcomes in cancer patients, including impaired quality of life and reduced survival (64, 65). Serum albumin is a reliable indicator for assessing nutritional status and visceral protein function. Studies have reported that in the later stages of the disease, malnutrition and inflammation inhibit albumin synthesis, resulting in lower serum albumin concentrations (66). The reason for this may be due to the production of cytokines, such as IL-6, which regulate albumin production by hepatocytes (67). Furthermore, tumor necrosis factor may increase microvascular permeability, thereby increasing the passage of albumin through capillaries (68, 69). Therefore, mild or no hypoalbuminemia in the early stages of cancer, but a significant decrease in albumin levels as the disease progresses could be a good indicator of cancer prognosis (7).

Abundant evidence indicates that the inflammatory microenvironment is an important component of carcinogenesis. As the basic components of the systemic inflammatory response, platelets and lymphocytes are involved in the continuous inflammation of the tumor microenvironment (70, 71). Platelets have been reported to promote tumor growth and angiogenesis by secreting a mixture of major proangiogenic cytokines in the microcirculation of potentially prothrombotic tumors (72, 73). In addition, platelets also enhance tumor metastasis by covering circulating tumor cells to protect tumor cells from physical factors such as shear stress and host immune responses (72, 74). On the other hand, the importance of lymphocytes has been highlighted in earlier studies. It is an important component of anti-tumor immunity and can inhibit tumor proliferation and migration through cytotoxicity (70). These findings suggest that serum hemoglobin, albumin, and lymphocytes can be considered favorable factors for tumor prognosis, while platelets may be an unfavorable factor.

Over the past decade, energy and resources have been invested in developing biomarkers to help personalize treatment plans for cancer patients. The HALP score combines malnutrition factors (hemoglobin and albumin) with inflammatory response factors (lymphocyte and platelet counts). It may help identify more patients with a poor prognosis than a single index because abnormalities in any single indicator do not truly reflect the patient’s condition. In addition, HALP has even been shown to have the potential to distinguishing between benign and malignant processes (75). Therefore, we reasoned that HALP could serve as a more practical and comprehensive prognostic marker for human cancers, including gastrointestinal, lung, genitourinary tract, gynecological, among others.

Strengths and limitations

The strength of this study is that it followed international guidelines and a rigorous systematic search and bias assessment protocol were developed in advance. Additionally, this study is the up-to-date systematic review and meta-analysis on this topic and represents the available evidence. Nevertheless, some limitations should be acknowledged. First, this study analyzed aggregated data rather than individual patient data. Second, the majority of the included studies are retrospective, which increases the risk of bias. Future research should prioritize prospective study designs, especially randomized controlled trials, to confirm our conclusions with a higher level of evidence. Third, although stable results were shown in subgroup analyses stratified by treatment strategy, there was a greater heterogeneity in the treatment strategies of patients with different tumors, which could have some potential impact on the study results. Fourth, lymphocyte and platelet counts are non-specific parameters and may be affected by factors such as infection and inflammation (13). Despite most of the included studies have tried to control for these factors, the confounding effects of concurrent inflammatory conditions cannot be completely excluded. Finally, cutoff values for HALP were measured in different ways, and although we did not find a difference between the method of measurement and OS in our subgroup analysis, it is important to establish the optimal HALP cutoff value.

 Conclusions

This study found that an elevated HALP was correlated with better survival in patients with solid tumors, and HALP could be used as a cost-effective prognostic biomarker. The prognostic model based on HALP deserves further investigation.

 Data availability statement

The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.

 Author contributions

JL: Data curation, Methodology, Resources, Software, Writing – original draft. JZ: Data curation, Formal analysis, Methodology, Software, Writing – original draft. PW: Formal analysis, Methodology, Validation, Writing – review & editing. DL: Conceptualization, Formal analysis, Investigation, Writing – review & editing.

 Funding

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

 Conflict of interest

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