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Open Access 01.12.2023 | Research article

Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis

verfasst von: Yan Wang, Chu Xu, Zongxin Zhang

Erschienen in: BMC Medicine | Ausgabe 1/2023

Abstract

Background

Many studies have explored the prognostic role of the lymphocyte-to-monocyte ratio (LMR) in patients with glioma, but the results have been inconsistent. We therefore conducted the current meta-analysis to identify the accurate prognostic effect of LMR in glioma.

Methods

The electronic databases of PubMed, Web of Science, Embase, and Cochrane Library were thoroughly searched from inception to July 25, 2023. The pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated to estimate the prognostic role of LMR for glioma.

Results

A total of 16 studies comprising 3,407 patients were included in this meta-analysis. A low LMR was significantly associated with worse overall survival (OS) (HR = 1.35, 95% CI = 1.13–1.61, p = 0.001) in glioma. However, there was no significant correlation between LMR and progression-free survival (PFS) (HR = 1.20, 95% CI = 0.75–1.91, p = 0.442) in glioma patients. Subgroup analysis indicated that a low LMR was significantly associated with inferior OS and PFS in glioma when using a cutoff value of ≤ 3.7 or when patients received mixed treatment.

Conclusions

This meta-analysis demonstrated that a low LMR was significantly associated with poor OS in glioma. There was no significant correlation between LMR and PFS in glioma patients. The LMR could be a promising and cost-effective prognostic biomarker in patients with glioma in clinical practice.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Confidence interval

DFS

Disease-free survival

GBM

Glioblastoma

Hazard ratio

LMR

Lymphocyte-to-monocyte ratio

NLR

Neutrophil-to-lymphocyte ratio

NOS

Newcastle-Ottawa Scale

Overall survival

PFS

Progression-free survival

PLR

Platelet-to-lymphocyte ratio

PRISMA

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

SII

Systemic immune-inflammation index

TAMs

Tumor-associated macrophages

TME

Tumor microenvironment

TTR

Time to recurrence

WHO

World Health Organization

Background

Glioma is the most common primary malignant brain tumor, accounting for approximately 27% of all brain and central nervous system tumor [1, 2]. As gliomas are highly heterogeneous and proliferate invasively, therapeutic approaches can be challenging [3]. According to the latest 2021 World Health Organization (WHO) Central Nervous System (CNS) 5 classification [4, 5], adult-type diffuse gliomas are classified into 3 types: (1) astrocytoma, isocitrate dehydrogenase (IDH) mutant (WHO grades 2–4); (2) oligodendroglioma, IDH mutant and 1p/19q codeleted (WHO grades 2 and 3); and (3) glioblastoma (GBM), IDH wild type (WHO grade 4) [5]. GBM is the most common and aggressive type of primary brain tumor, which comprises up to 50% of all gliomas [6]. The survival outcomes of patients with glioma have not improved over the past several decades, despite treatment options, such as surgery, chemotherapy, and radiotherapy. The prognosis of GBM is poor, with a median overall survival (OS) of 15 months and a 5-year survival rate of only approximately 5% [7, 8]. Therefore, there is an urgent need to identify novel and effective prognostic markers for glioma.

Evidence suggests that the tumor microenvironment, notably the inflammatory response, may promote cancer development and progression and is associated with systemic inflammation [9]. A number of hematological indicators have been reported to be highly predictive of cancer patient prognosis in recent years, such as the neutrophil-to-lymphocyte ratio (NLR) [10], platelet-to-lymphocyte ratio (PLR) [11], systemic immune-inflammation index (SII) [12], and lymphocyte-to-monocyte ratio (LMR) [13]. For example, a review involving 11 studies showed that PLR could be a useful marker to aid in the prognosis of GBM [14]. Another important systematic review indicated that the NLR was a cost-effective and low-cost tool that was associated with tumor grading and overall survival (OS) in patients with glioma [15]. The LMR is derived by dividing the absolute lymphocyte count by the absolute monocyte count. Many studies have reported that LMR is a significant prognostic marker for various solid tumors, including thyroid cancer [16], renal cell carcinoma [17], small cell lung cancer [18], ovarian cancer [19], and cholangiocarcinoma [20]. Previous studies have also explored the prognostic effect of LMR in patients with glioma, but the results were controversial [21‐36]. For example, some researchers reported that a low LMR was significantly associated with poor survival in glioma [30, 31, 34, 35]. However, some other clinicians showed that there was no significant correlation between LMR and the prognosis of glioma patients [22‐24, 27]. Therefore, we performed a meta-analysis to identify the precise prognostic function of LMR in glioma.

Methods

Study guidelines

This meta-analysis was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [37].

Ethics statement

In our study, no human or animal experiments were conducted, and no primary personal information will be gathered. Therefore, no ethical approval or consent was needed.

Search strategy

The electronic databases of PubMed, Web of Science, Embase, and Cochrane Library were thoroughly searched from inception to July 25, 2023. The detailed search strategy was as follows: (LMR or lymphocyte-to-monocyte ratio or lymphocyte-monocyte ratio or lymphocyte-to-monocyte ratio) and (glioma or glioblastoma or glial tumor or astrocytoma or oligodendroglioma). Only publications in English were considered. Furthermore, references cited in these studies were also reviewed to identify additional published articles not indexed in the standard databases.

Eligibility criteria

The inclusion criteria were as follows: (1) the diagnosis of glioma was pathologically or histologically confirmed; (2) the association between LMR and survival outcomes in glioma was investigated; (3) the hazard ratios (HRs) and 95% confidence intervals (CIs) for survival outcomes were reported or could be calculated by given information; (4) a cutoff value to define low and high LMR was identified; and (5) the study was published in the English language. The exclusion criteria were as follows: (1) case reports, reviews, letters, conference abstracts, and comments; (2) animal studies; and (3) studies with overlapping patients.

Data extraction

Two investigators (Y.W. and C.X.) independently reviewed the eligible studies and extracted data from the included studies. All disagreements were resolved by discussion until consensus. The following information was extracted: first author’s name, year of publication, country, study period, sample size, age, sex, WHO grade, histology, treatment, cutoff value, methods to determine cutoff value, follow-up, survival outcomes, survival analysis type, and HRs with 95% CIs. The primary survival endpoint was OS, and the secondary survival endpoint was progression-free survival (PFS).

Quality assessment

The Newcastle‒Ottawa Scale (NOS) was used to evaluate the quality of each selected study by two independent reviewers (C.X. and Z.Z.) [38]. The NOS assesses the quality of studies in the following aspects: selection (4 points), comparability (2 points), and results and adequacy of follow-up (3 points). The NOS score ranges from 0 to 9, and studies with NOS scores ≥ 6 are considered high-quality.

Statistical analysis

The pooled HRs and 95% CIs were calculated to estimate the prognostic role of LMR for glioma patients. The heterogeneity among studies was evaluated by using Cochran’s Q test and Higgins I² statistic. A random-effects model was applied when significant heterogeneity was observed, as measured by an I² greater than 50% or a P value less than 0.1. Otherwise, a fixed-effects model was adopted. Subgroup analysis stratified by diverse factors and meta-regression analysis were conducted to explore the source of heterogeneity. Sensitivity analysis was conducted to evaluate the stability of the combined results. Begg’s test and Egger’s test were used to assess publication bias. Stata software version 12.0 was used to conduct all statistical analyses (Stata Corporation, College Station, TX, USA). P values < 0.05 were defined as statistically significant.

Results

Search results

As shown in Fig. 1, the initial literature search identified a total of 174 records, and 100 studies remained after the removal of duplicates. Through screening titles and abstracts, 61 studies were further discarded because they were irrelevant studies or animal experiments. Subsequently, the remaining 39 studies were examined by full-text reading. Then, 23 studies were eliminated for the following reasons: no survival data provided (n = 12), not concerning LMR (n = 7), and not concerning glioma (n = 4). Ultimately, a total of 16 studies comprising 3407 patients [21‐36] were included in this meta-analysis (Fig. 1).

Characteristics of included studies

The baseline characteristics of the included studies are shown in Table 1. They were published from 2016 to 2023. The sample sizes ranged from 22 to 592, and the median value was 193. Thirteen studies were performed in China [21‐25, 27, 29‐33, 35, 36], and one each in Australia [26], India [28], and Bulgaria [34]. Ten studies recruited patients with glioma [23‐27, 29‐32, 35], and six studies enrolled patients with GBM [21, 22, 28, 33, 34, 36]. The cutoff values of LMR ranged from 1.87 to 5, with a median value of 3.7. Twelve studies used the receiver operating characteristic (ROC) curve to determine the cutoff value [23, 24, 26, 27, 29‐36], and four studies adopted X-tile software [21, 22, 25, 28]. Fifteen studies included reporting on the prognostic role of LMR for OS [21‐27, 29‐36], and three studies presented the association between LMR and PFS [28, 29, 33]. Fourteen studies derived HRs and 95% CIs by using univariate analysis [21, 22, 24‐34, 36], and two studies applied multivariate analysis [23, 35]. The NOS scores of the included studies ranged from 7 to 9, with a median value of 8, which suggested the high quality of eligible studies (Table 1).

Table 1

Basic characteristics of included studies in this meta-analysis

Author	Year	Country	Sample size	Study period	Age (year) Median (range)	Gender (M/F)	Tumor grade	Histological type	Treatment	Cut-off value	Cut-off determination	Follow-up (month) Median (range)	Survival outcome	Survival analysis	NOS score
Zhou, X. W	2016	China	84	2013–2014	53 (43–62)	50/34	IV	GBM	Surgery	4.37	X-tile	1–40	OS	Univariate	8
Wang, P. F	2017	China	166	2009–2014	52 (18–80)	96/70	IV	GBM	Surgery	3.7	X-tile	1–45	OS	Univariate	7
Bao, Y	2018	China	219	2012–2017	≥ 50 years: 146 < 50 years: 73	124/95	I–IV	Glioma	Surgery	3.7	ROC curve	1–60	OS	Multivariate	8
He, Z. Q	2019	China	154	2001–2016	40	92/62	III	Glioma	Mixed	4.33	ROC curve	1–160	OS	Univariate	9
Zhang, Z. Y	2019	China	592	2011–2016	42	335/257	II–IV	Glioma	Surgery	2.94	X-tile	32	OS	Univariate	8
Chim, S. T	2021	Australia	64	1989–2018	51.5	44/20	II–IV	Glioma	Mixed	2.86	ROC curve	1–275	OS	Univariate	7
He, Q	2021	China	105	2013–2019	50 (18–79)	57/48	III–IV	Glioma	Surgery	5.0	ROC curve	1–80	OS	Univariate	8
Madhugiri, V. S	2021	India	408	2007–2017	55	280/128	IV	GBM	Surgery	1.87	X-tile	1–113	PFS	Univariate	8
Xie, T	2021	China	318	2001–2014	44 (5–78)	194/124	III–IV	Glioma	Mixed	3.86	ROC curve	1–180	OS, PFS	Univariate	9
Yan, P	2021	China	162	2012–2018	45 (7–82)	88/74	II–IV	Glioma	Mixed	4.26	ROC curve	1–96	OS	Univariate	7
Chen, X. Y	2022	China	199	2015–2020	< 60 years: 143 ≥ 60 years: 56	111/88	III–IV	Glioma	Mixed	4.47	ROC curve	1–30	OS	Univariate	8
Qi, Z	2022	China	214	2001–2013	41 (5–79)	131/83	II–III	Glioma	Mixed	4.81	ROC curve	1–144	OS	Univariate	7
Shi, X	2022	China	232	2014–2018	< 65 years: 193 ≥ 65 years: 39	127/105	IV	GBM	Mixed	2.78	ROC curve	1–70	OS, PFS	Univariate	8
Stoyanov, G. S	2022	Bulgaria	22	2018–2021	66 (50–86)	15/7	IV	GBM	Mixed	2.22	ROC curve	8 (1–26)	OS	Univariate	8
Yang, C	2022	China	187	2016–2019	50 (21–81)	111/76	II–IV	Glioma	Mixed	2.3	ROC curve	1–50	OS	Multivariate	7
Duan, X	2023	China	281	2015–2018	≤ 65 years: 223 > 65 years: 58	155/126	IV	GBM	Mixed	3.57	ROC curve	19 (3.5–63)	OS	Univariate	8

GBM glioblastoma, M male, F female, ROC, receiver operating characteristic, OS overall survival, PFS progression-free survival, NOS Newcastle–Ottawa Scale

LMR and OS

A total of 15 studies with 2999 patients [21‐27, 29‐36] provided the prognostic value of LMR for OS in glioma. As the heterogeneity was significant (I² = 64.9%, p < 0.001), a random-effects model was used. As shown in Table 2 and Fig. 2, the pooled results were HR = 1.35, 95% CI = 1.13–1.61, p = 0.001, indicating that a low LMR was significantly correlated with poor OS in patients with glioma. Subgroup analysis showed that the prognostic effect of LMR for OS was not influenced by country or histology (all p < 0.05; Table 2). Moreover, low LMR remained a significant prognostic indicator for poor OS in the following subgroups: sample size < 200 (p = 0.001), mixed treatment (p = 0.002), cutoff value of ≤ 3.7 (p < 0.001), cutoff determination by ROC curve (p = 0.002), and univariate analysis (p = 0.002) (Table 2). Meta-regression analysis showed that country, sample size, histological type, treatment, cutoff value, cutoff determination, and survival analysis were not the only factors that contributed to the significant heterogeneity (all p > 0.05; Table 2). The significant heterogeneity could be the result of multiple factors working together.

Table 2

Subgroup analysis of prognostic role of LMR for overall survival in patients with glioma

Subgroups	No. of studies	No. of patients	Effects model	HR (95%CI)	p	Heterogeneity		Mete-regression p
Subgroups	No. of studies	No. of patients	Effects model	HR (95%CI)	p	I² (%)	Ph	Mete-regression p
Total	15	2999	Random	1.35 (1.13–1.61)	0.001	64.9	< 0.001
Country								0.059
China	13	2913	Random	1.28 (1.08–1.51)	0.005	62.1	0.001
Others	2	86	Fixed	2.46 (1.47–4.14)	0.001	25.4	0.247
Sample size								0.156
< 200	9	1143	Random	1.56 (1.19–2.04)	0.001	61.4	0.008
≥ 200	6	1856	Random	1.16 (0.94–1.43)	0.166	62.0	0.022
Histological type								0.646
Glioma	10	2214	Random	1.42 (1.09–1.85)	0.009	72.2	< 0.001
GBM	5	785	Fixed	1.23 (1.07–1.42)	0.003	46.2	0.114
Treatment								0.241
Surgery	5	1166	Fixed	1.16 (0.96–1.39)	0.124	0	0.535
Mixed	10	1833	Random	1.49 (1.17–1.91)	0.002	75.0	< 0.001
Cut-off value								0.536
≤ 3.7	8	1763	Fixed	1.31 (1.15–1.48)	< 0.001	46.2	0.072
> 3.7	7	1236	Random	1.28 (0.92–1.77)	0.140	76.4	< 0.001
Cut-off determination								0.559
X-tile	3	842	Fixed	1.22 (0.98–1.53)	0.081	16.8	0.301
ROC curve	12	2157	Random	1.40 (1.13–1.74)	0.002	70.6	< 0.001
Survival analysis								0.960
Univariate	13	2593	Random	1.35 (1.12–1.63)	0.002	66.2	< 0.001
Multivariate	2	406	Random	1.45 (0.66–3.15)	0.352	76.9	0.037

GBM glioblastoma, ROC receiver operating characteristic

LMR and PFS

Three studies consisting of 958 patients [28, 29, 33] included reporting on the relationship between LMR and PFS in patients with glioma. The random-effects model was applied due to significant heterogeneity (I² = 81.7%, p = 0.004). The combined data showed that there was a nonsignificant association between LMR and PFS in glioma (HR = 1.20, 95% CI = 0.75–1.91, p = 0.442; Table 3 and Fig. 3). Subgroup analysis demonstrated that low LMR was a significant prognostic factor for inferior PFS in the following subgroups: studies in non-China countries (p = 0.013), histology of GBM (p = 0.011), surgery treatment (p = 0.013), cutoff value of ≤ 3.7 (p = 0.011), and cutoff value determination of X-tile software (p = 0.013) (Table 3).

Table 3

Subgroup analysis of prognostic role of LMR for progression-free survival in patients with glioma

Subgroups	No. of studies	No. of patients	Effects model	HR (95%CI)	p	Heterogeneity
Subgroups	No. of studies	No. of patients	Effects model	HR (95%CI)	p	I² (%)	Ph
Total	3	958	Random	1.20 (0.75–1.91)	0.442	81.7	0.004
Country
China	2	550	Random	1.00 (0.65–1.55)	0.997	78.2	0.032
Others	1	408	-	1.89 (1.14–3.11)	0.013	-	-
Histological type
Glioma	1	318	-	0.81 (0.64–1.03)	0.089	-	-
GBM	2	640	Fixed	1.43 (1.09–1.88)	0.011	40.9	0.194
Treatment
Surgery	1	408	-	1.89 (1.14–3.11)	0.013	-	-
Mixed	2	550	Random	1.00 (0.65–1.55)	0.997	78.2	0.032
Cut-off value
≤ 3.7	2	640	Fixed	1.43 (1.09–1.88)	0.011	40.9	0.194
> 3.7	1	318	-	0.81 (0.64–1.03)	0.089	-	-
Cut-off determination
X-tile	1	408	-	1.89 (1.14–3.11)	0.013	-	-
ROC curve	2	550	Random	1.00 (0.65–1.55)	0.997	78.2	0.032

GBM glioblastoma, ROC receiver operating characteristic

Sensitivity analysis

Using sensitivity analysis, it was shown that the results of the current meta-analysis were stable and reliable (Fig. 4). OS and PFS results were not significantly affected by any one of the included studies.

Publication bias

Potential publication bias was tested by using Begg’s test and Egger’s test. As shown in Fig. 5, the shapes of the funnel plots were symmetric. The results were as follows: for OS—Begg’s test, p = 0.092, Egger’s test, p = 0.150; for PFS—Begg’s test, p = 0.296, Egger’s test, p = 0.161. These results revealed that there was no significant publication bias in this meta-analysis.

Discussion

The LMR is calculated by dividing the absolute lymphocyte count by the absolute monocyte count. Therefore, a low LMR could be attributed to low lymphocyte counts and/or high monocyte counts. Although the precise mechanisms of the association between LMR and survival in glioma are not fully elucidated, they can be explained in the following aspects. First, lymphocytes play an important role in cellular antitumor responses. Lymphocytes facilitate the activation of the host immune response to cancer by inhibiting the growth and proliferation of cancer cells through direct cytotoxic cell death in immune surveillance [39]. Lymphocytopenia might be related to an inappropriate immune response during tumor growth [40]. Deficiencies in peripheral lymphocytes may result in tumor cell proliferation and metastasis when antitumor responses are impaired [39]. Moreover, cytokines impair T-lymphocytic function and cell-mediated immunity when pro-inflammatory status is present [41]. In contrast, monocytes can differentiate into tumor-associated macrophages (TAMs) and dendritic cells to promote tumorigenesis and suppress the immune response in the tumor microenvironment (TME) [42]. Angiogenesis may be promoted by TAMs, which produce growth factors and chemokines that contribute to malignant progression [43]. Moreover, in the TME, monocytes from the peripheral blood enter tumor sites constantly and release soluble inhibitory factors and inhibitory molecules that inhibit the immune system’s defenses against tumors [43, 44].

The prognostic value of LMR in patients with glioma was inconsistent according to previous studies. In the current meta-analysis, we retrieved the literature and synthesized the data from 16 studies with 3407 cases. Our meta-analysis indicated that a low LMR was a significant prognostic marker for poor OS in glioma. However, there was a nonsignificant correlation between LMR and PFS. Furthermore, a low LMR was significantly associated with inferior OS and PFS in glioma when using a cutoff value of ≤ 3.7 or when patients received mixed treatment. Sensitivity analysis and publication bias tests confirmed the reliability of our results. Taken together, this meta-analysis demonstrated that a low LMR was a significant prognostic biomarker for long-term survival in patients with glioma. To our knowledge, this is the first meta-analysis investigating the prognostic importance of LMR in glioma patients.

In recent years, many meta-analyses have also reported the prognostic role of LMR in various cancer types [45‐50]. Hamid et al. showed that a low LMR was associated with poorer OS and disease-free survival (DFS) in rectal cancer in a meta-analysis with 6683 patients [45]. Gao and colleagues revealed that a low LMR was associated with poor OS and reduced DFS/PFS in nasopharyngeal carcinoma through a meta-analysis involving 3773 patients [46]. Dotto-Vasquez et al. performed a meta-analysis including 19 studies and indicated that cholangiocarcinoma patients with low values of LMR were associated with worse OS and poor time to recurrence (TTR) [47]. In a recent meta-analysis comprising 8361 cases, it was reported that decreased pretreatment LMR was significantly correlated with reduced PFS and worse OS in lung cancer [48]. Another large-scale meta-analysis with 10,446 patients found that a low LMR was associated with inferior OS and PFS in lymphoma [49]. Cai and colleagues showed that a lower LMR was associated with poorer OS and PFS in ovarian cancer in their meta-analysis enrolling 2809 patients [50]. In the current meta-analysis, we identified the significant prognostic effect of LMR for OS in glioma, which was in line with findings in other solid tumors.

Notably, this meta-analysis showed that there was a nonsignificant correlation between LMR and PFS in patients with glioma (HR = 1.20, 95% CI = 0.75–1.91, p = 0.442). The negative results could be due to the following reasons. First, the sample size in the LMR and PFS analyses was small. Only three studies with 958 patients were included for analysis. Second, the survival duration for GBM patients was relatively short, with a median survival of 15 months [51]. Moreover, the median PFS after recurrence was only.

1.8 months in glioma patients [52]. Therefore, the follow-up in PFS was not long, so the prognostic role of LMR is nonsignificant. Third, the heterogeneity was significant, which could be a potential reason for this negative result.

The present meta-analysis has some limitations. First, all included studies were retrospective, and most of them were conducted in Asian countries. Therefore, selection bias may be introduced. Second, significant heterogeneity among studies was detected for the analysis of OS and PFS. We adopted a random-effects model or fixed-effects model according to the level of heterogeneity. Third, the cutoff values of LMR were not uniform in the included studies. Our meta-analysis showed that LMR ≤ 3.7 could be an optimal cutoff value for prognostication in glioma. A standard cutoff value of LMR in glioma prognosis needs to be established and validated in future studies. Therefore, due to several limitations, multicenter prospective trials are still needed to verify the results of our meta-analysis. Therefore, due to several limitations, multicenter prospective trials are still needed to verify the results of our meta-analysis.

Conclusions

In summary, this meta-analysis demonstrated that a low LMR was significantly associated with poor OS in glioma. LMR could be a promising and cost-effective prognostic biomarker in patients with glioma in clinical practice.

Acknowledgements

We would like to thank Jiang Liu for his assistance in statistical analysis. We would like to thank American Journal Experts (https://www.aje.com/) for English language editing.

Declarations

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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Wen PY, Huse JT. 2016 World Health Organization Classification of Central Nervous System Tumors. Continuum (Minneap Minn). 2017;23(6, Neuro-oncology):1531–47.PubMed

Ostrom QT, Bauchet L, Davis FG, Deltour I, Fisher JL, Langer CE, Pekmezci M, Schwartzbaum JA, Turner MC, Walsh KM, et al. The epidemiology of glioma in adults: a “state of the science” review. Neuro Oncol. 2014;16(7):896–913.PubMedPubMedCentralCrossRef

Hadjipanayis CG, Van Meir EG. Tumor initiating cells in malignant gliomas: biology and implications for therapy. J Mol Med (Berl). 2009;87(4):363–74.PubMedCrossRef

Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, Hawkins C, Ng HK, Pfister SM, Reifenberger G, et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol. 2021;23(8):1231–51.PubMedPubMedCentralCrossRef

Berger TR, Wen PY, Lang-Orsini M, Chukwueke UN. World Health Organization 2021 Classification of Central Nervous System Tumors and Implications for Therapy for Adult-Type Gliomas: A Review. JAMA Oncol. 2022;8(10):1493–501.PubMedCrossRef

Rong L, Li N, Zhang ZZ. Emerging therapies for glioblastoma: current state and future directions. J Exp Clin Cancer Res. 2022;41(1):142.PubMedPubMedCentralCrossRef

Tran B, Rosenthal MA. Survival comparison between glioblastoma multiforme and other incurable cancers. J Clin Neurosci. 2010;17(4):417–21.PubMedCrossRef

Ostrom QT, Gittleman H, Truitt G, Boscia A, Kruchko C, Barnholtz-Sloan JS. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2011–2015. Neuro Oncol. 2018;20(suppl_4):iv1–86.PubMedPubMedCentralCrossRef

Grivennikov SI, Greten FR, Karin M. Immunity, Inflammation, and Cancer. Cell. 2010;140(6):883–99.PubMedPubMedCentralCrossRef

10.

Yasumatsu R, Wakasaki T, Hashimoto K, Nakashima K, Manako T, Taura M, Matsuo M, Nakagawa T. Monitoring the neutrophil-to-lymphocyte ratio may be useful for predicting the anticancer effect of nivolumab in recurrent or metastatic head and neck cancer. Head Neck. 2019;41(8):2610–8.PubMedCrossRef

11.

Matsuda A, Yamada T, Matsumoto S, Shinji S, Ohta R, Sonoda H, Shinozuka E, Sekiguchi K, Suzuki H, Yoshida H. Prognostic Role of the Platelet-to-Lymphocyte Ratio for Patients With Metastatic Colorectal Cancer Treated With Aflibercept. In Vivo. 2020;34(5):2667–73.PubMedPubMedCentralCrossRef

12.

Li Q, Pu Y, Gong Z, Yu Y, Sun W, Cheng Z, Wang W, Zhao J. Preoperative systemic immune-inflammation index for predicting the prognosis of thymoma with radical resection. Thorac Cancer. 2023;14(13):1192–200.PubMedPubMedCentralCrossRef

13.

Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer. 2020;20(11):662–80.PubMedCrossRef

14.

Bispo RG, Bastos Siqueira IF, de Oliveira BFS, Moreira Fernandes CE, Figueiredo LA, Cintra LP, de Oliveira AJM. Prognostic Value of the Platelet-lymphocyte Ratio for Glioblastoma: A Systematic Review. World Neurosurg. 2023;175:137-141.e131.PubMedCrossRef

15.

Gomes Dos Santos A, de Carvalho RF, de Morais A, Silva TM, Baylão VMR, Azevedo M, de Oliveira AJM. Role of neutrophil-lymphocyte ratio as a predictive factor of glioma tumor grade: A systematic review. Crit Rev Oncol Hematol. 2021;163:103372.PubMedCrossRef

16.

Nakamoto S, Ikeda M, Kubo S, Yamamoto M, Yamashita T, Kuwahara C. Prognostic Value of Lymphocyte-to-Monocyte Ratio for Japanese Patients With Differentiated Thyroid Cancer Treated With Sorafenib Therapy. Cancer Diagn Progn. 2021;1(5):491–8.PubMedPubMedCentralCrossRef

17.

Zapała Ł, Kunc M, Sharma S, Biernat W, Radziszewski P. Low Lymphocyte-to-Monocyte Ratio Is the Potential Indicator of Worse Overall Survival in Patients with Renal Cell Carcinoma and Venous Tumor Thrombus. Diagnostics (Basel, Switzerland). 2021;11(11):2159.PubMedPubMedCentral

18.

Lang C, Egger F, Alireza Hoda M, Saeed Querner A, Ferencz B, Lungu V, Szegedi R, Bogyo L, Torok K, Oberndorfer F, et al. Lymphocyte-to-monocyte ratio is an independent prognostic factor in surgically treated small cell lung cancer: An international multicenter analysis. Lung cancer (Amsterdam, Netherlands). 2022;169:40–6.PubMedCrossRef

19.

Hu Q, Shen G, Li Y, Xie Y, Ma X, Jiang L, Lv Q. Lymphocyte-to-monocyte ratio after primary surgery is an independent prognostic factor for patients with epithelial ovarian cancer: A propensity score matching analysis. Front Oncol. 2023;13:1139929.PubMedPubMedCentralCrossRef

20.

Lv M, Wang K, Zhang Z, Zhang Z, Wan J. The predictive value of lymphocyte to monocyte ratio for overall survival in cholangiocarcinoma patients with hepatic resection. Cancer Med. 2023;12(8):9482–95.PubMedPubMedCentralCrossRef

21.

Zhou XW, Dong H, Yang Y, Luo JW, Wang X, Liu YH, Mao Q. Significance of the prognostic nutritional index in patients with glioblastoma: A retrospective study. Clin Neurol Neurosurg. 2016;151:86–91.PubMedCrossRef

22.

Wang PF, Song HW, Cai HQ, Kong LW, Yao K, Jiang T, Li SW, Yan CX. Preoperative inflammation markers and IDH mutation status predict glioblastoma patient survival. Oncotarget. 2017;8(30):50117–23.PubMedPubMedCentralCrossRef

23.

Bao Y, Yang M, Jin C, Hou S, Shi B, Shi J, Lin N. Preoperative Hematologic Inflammatory Markers as Prognostic Factors in Patients with Glioma. World Neurosurg. 2018;119:e710–6.PubMedCrossRef

24.

He ZQ, Duan H, Lin FH, Zhang J, Chen YS, Zhang GH, Guo CC, Ke C, Zhang XH, Chen ZH, et al. Pretreatment neutrophil-to-lymphocyte ratio plus albumin-to-gamma-glutamyl transferase ratio predict the diagnosis of grade III glioma. Ann Transl Med. 2019;7(22):623.PubMedPubMedCentralCrossRef

25.

Zhang ZY, Zhan YB, Zhang FJ, Yu B, Ji YC, Zhou JQ, Bai YH, Wang YM, Wang L, Jing Y, et al. Prognostic value of preoperative hematological markers combined with molecular pathology in patients with diffuse gliomas. Aging. 2019;11(16):6252–72.PubMedPubMedCentralCrossRef

26.

Chim ST, Sanfilippo P, O’Brien TJ, Drummond KJ, Monif M. Pretreatment neutrophil-to-lymphocyte/monocyte-to-lymphocyte ratio as prognostic biomarkers in glioma patients. J Neuroimmunol. 2021;361: 577754.PubMedCrossRef

27.

He Q, Li L, Ren Q. The Prognostic Value of Preoperative Systemic Inflammatory Response Index (SIRI) in Patients With High-Grade Glioma and the Establishment of a Nomogram. Front Oncol. 2021;11: 671811.PubMedPubMedCentralCrossRef

28.

Madhugiri VS, Subeikshanan V, Dutt A, Moiyadi A, Epari S, Shetty P, Gupta T, Jalali R, Dutt AK. Biomarkers of Systemic Inflammation in Patients with Glioblastoma: An Analysis of Correlation with Tumour-Related Factors and Survival. Neurol India. 2021;69(4):894–901.PubMedCrossRef

29.

Xie T, Guo X, Duan H, He Z, Mou Y. Prognostic value of modified systemic inflammatory score in patients with newly diagnosed high-grade gliomas. Clin Neurol Neurosurg. 2021;201: 106428.PubMedCrossRef

30.

Yan P, Li JW, Mo LG, Huang QR. A nomogram combining inflammatory markers and clinical factors predicts survival in patients with diffuse glioma. Medicine (Baltimore). 2021;100(47): e27972.PubMedCrossRef

31.

Chen XY, Pan DL, Xu JH, Chen Y, Xu WF, Chen JY, Wu ZY, Lin YX, You HH, Ding CY, et al. Serum Inflammatory Biomarkers Contribute to the Prognosis Prediction in High-Grade Glioma. Front Oncol. 2022;11:754920.

32.

Qi Z, Cai J, Meng X, Cai S, Tang C, Lang L. Prognostic value of preoperative inflammatory markers among different molecular subtypes of lower-grade glioma. J Clin Neurosci. 2022;96:180–6.PubMedCrossRef

33.

Shi X, Li H, Xu Y, Nyalali AMK, Li F. The prognostic value of the preoperative inflammatory index on the survival of glioblastoma patients. Neurol Sci. 2022;43(9):5523–31.PubMedPubMedCentralCrossRef

34.

Stoyanov GS, Lyutfi E, Georgieva R, Dzhenkov DL, Petkova L, Ivanov BD, Kaprelyan A, Ghenev P. The Role of Preoperative Neutrophil, Platelet, and Monocyte to Lymphocyte Ratios as Independent Prognostic Factors for Patient Survival in WHO 2021 Glioblastoma: A Single-Center Retrospective Study. Cureus. 2022;14(6): e25801.PubMedPubMedCentral

35.

Yang C, Lan T, Wang Y, Huang WH, Li SM, Li J, Li FP, Li YR, Wang ZF, Li ZQ. Cumulative Scoring Systems and Nomograms for Predicating Survival in Patients With Glioblastomas: A Study Based on Peripheral Inflammatory Markers. Front Oncol. 2022;12: 716295.PubMedPubMedCentralCrossRef

36.

Duan X, Yang B, Zhao C, Tie B, Cao L, Gao Y. Prognostic value of preoperative hematological markers in patients with glioblastoma multiforme and construction of random survival forest model. BMC Cancer. 2023;23(1):432.PubMedPubMedCentralCrossRef

37.

Moher D, Liberati A, Tetzlaff J, Altman DG, Grp P. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. J Clin Epidemiol. 2009;62(10):1006–12.PubMedCrossRef

38.

Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol. 2010;25(9):603–5.PubMedCrossRef

39.

Gooden MJ, de Bock GH, Leffers N, Daemen T, Nijman HW. The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br J Cancer. 2011;105(1):93–103.PubMedPubMedCentralCrossRef

40.

Hoffmann TK, Dworacki G, Tsukihiro T, Meidenbauer N, Gooding W, Johnson JT, Whiteside TL. Spontaneous apoptosis of circulating T lymphocytes in patients with head and neck cancer and its clinical importance. Clin Cancer Res. 2002;8(8):2553–62.PubMed

41.

Liu H, Wu Y, Wang Z, Yao Y, Chen F, Zhang H, Wang Y, Song Y. Pretreatment platelet-to-lymphocyte ratio (PLR) as a predictor of response to first-line platinum-based chemotherapy and prognosis for patients with non-small cell lung cancer. J Thorac Dis. 2013;5(6):783–9.PubMedPubMedCentral

42.

Olingy CE, Dinh HQ, Hedrick CC. Monocyte heterogeneity and functions in cancer. J Leukoc Biol. 2019;106(2):309–22.PubMedCrossRef

43.

Chanmee T, Ontong P, Konno K, Itano N. Tumor-associated macrophages as major players in the tumor microenvironment. Cancers (Basel). 2014;6(3):1670–90.PubMedCrossRef

44.

Peranzoni E, Zilio S, Marigo I, Dolcetti L, Zanovello P, Mandruzzato S, Bronte V. Myeloid-derived suppressor cell heterogeneity and subset definition. Curr Opin Immunol. 2010;22(2):238–44.PubMedCrossRef

45.

Hamid HKS, Emile SH, Davis GN. Prognostic Significance of Lymphocyte-to-Monocyte and Platelet-to-Lymphocyte Ratio in Rectal Cancer: A Systematic Review, Meta-analysis, and Meta-regression. Dis Colon Rectum. 2022;65(2):178–87.PubMedCrossRef

46.

Gao P, Peng W, Hu Y. Prognostic and clinicopathological significance of lymphocyte-to-monocyte ratio in patients with nasopharyngeal carcinoma. Head Neck. 2022;44(3):624–32.PubMedCrossRef

47.

Dotto-Vasquez G, Villacorta-Ampuero AK, Ulloque-Badaracco JR, Hernandez-Bustamante EA, Alarcón-Braga EA, Herrera-Añazco P, Benites-Zapata VA, Hernandez AV. Lymphocyte-to-Monocyte Ratio and Clinical Outcomes in Cholangiocarcinoma: A Systematic Review and Meta-Analysis. Diagnostics (Basel, Switzerland). 2022;12(11):2655.PubMedPubMedCentral

48.

Jin J, Yang L, Liu D, Li WM. Prognostic Value of Pretreatment Lymphocyte-to-Monocyte Ratio in Lung Cancer: A Systematic Review and Meta-Analysis. Technol Cancer Res Treat. 2021;20:1533033820983085.PubMedPubMedCentralCrossRef

49.

Gao F, Hu J, Zhang J, Xu Y. Prognostic Value of Peripheral Blood Lymphocyte/monocyte Ratio in Lymphoma. J Cancer. 2021;12(12):3407–17.PubMedPubMedCentralCrossRef

50.

Cai L, Song Y, Zhao X. Prognostic significance of lymphocyte monocyte ratio in patients with ovarian cancer. Medicine (Baltimore). 2020;99(14): e19638.PubMedCrossRef

51.

Zeng YF, Wei XY, Guo QH, Chen SY, Deng S, Liu ZZ, Gong ZC, Zeng WJ. The efficacy and safety of anti-PD-1/PD-L1 in treatment of glioma: a single-arm meta-analysis. Front Immunol. 2023;14:1168244.PubMedPubMedCentralCrossRef

52.

McKinnon C, Nandhabalan M, Murray SA, Plaha P. Glioblastoma: clinical presentation, diagnosis, and management. BMJ. 2021;374: n1560.PubMedCrossRef

Titel: Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis
verfasst von: Yan Wang
Chu Xu
Zongxin Zhang
Publikationsdatum: 01.12.2023
Verlag: BioMed Central
Erschienen in: BMC Medicine / Ausgabe 1/2023
Elektronische ISSN: 1741-7015
DOI: https://doi.org/10.1186/s12916-023-03199-6

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Abstract

Background

Methods

Results

Conclusions

Publisher’s Note

Background

Methods

Study guidelines

Ethics statement

Search strategy

Eligibility criteria

Data extraction

Quality assessment

Statistical analysis

Results

Search results

Characteristics of included studies

LMR and OS

LMR and PFS

Sensitivity analysis

Publication bias

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Conclusions

Acknowledgements

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