Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2024 Nov 1;103(44):e40182. doi: 10.1097/MD.0000000000040182

Pretreatment systemic immune-inflammation index and lymphocyte-to-monocyte ratio as prognostic factors in oral cavity cancer: A meta-analysis

Jianghan Xu a, Yanjun Lin a, Jingbo Yang b, Yifeng Xing a, Xiaojie Xing a,*
PMCID: PMC11537607  PMID: 39496022

Abstract

Background:

The predictive implications of the pretreatment systemic immune-inflammation index (SII) and lymphocyte-to-monocyte ratio (LMR) in oral cavity cancer have been investigated extensively, however, the findings are conflicting.

Methods:

To assess the predictive importance of SII and LMR in patients with oral cavity cancer, a comprehensive Meta-analysis of the literature was conducted using the databases from PubMed, Embase, and the Cochrane Library. To determine the link between SII and LMR and overall survival (OS) and disease-free survival (DFS), hazard ratio (HR) and 95% confidence interval (CI) were retrieved.

Results:

The analysis comprised a total of 18 papers, covering 19 trials (SII = 5, LMR = 12, SII + prognostic nutritional index (PNI) = 2). According to pooled data, increased SII predicted poor OS (HR: 1.61, 95% CI: 1.38–1.87, P < .001) and DFS (HR: 1.90, 95% CI: 1.11–3.27, P = .02) while high LMR was linked with improved OS (HR: 0.64, 95% CI: 0.54–0.77, P < .001) and DFS (HR: 0.69, 95% CI: 0.61–0.79, P < .001). In addition, subgroup analysis indicated that high SII and low LMR negatively correlated with OS regardless of country, cutoff value, sample size, or types of Cox regression analysis.

Conclusions:

High SII and low LMR may predict worse survival in patients with oral cavity cancer. SII and LMR may therefore represent effective indicators of prognosis in oral cavity cancer.

Keywords: lymphocyte-to-monocyte 2 3 ratio, meta-analysis, oral cavity cancer, prognosis, systemic immune-inflammation index

1. Introduction

The most frequent kind of cancer in the head and neck region, oral cavity cancer, is one of the main causes of cancer fatalities and a serious public health concern globally.[1,2] Oral cavity cancer is expected to be the eighteenth most often diagnosed cancer and the sixteenth main cause of cancer-related mortality globally, with more than 370,000 new cases and 170,000 deaths per year, according to global cancer data.[3] Despite advances in illness detection, staging, and therapy, 5-year overall survival (OS) has not grown appreciably during the last 2 decades.[4] Surgery, radiation, and chemotherapy have been the primary methods for managing oral cavity cancer, either individually or in combination, thus far. Additionally, each patient requires personalized treatment strategies based on factors like cancer type, stage, presence of metastasis or lymph node involvement, and overall health. Typically, early-stage cancers are treated with a single method such as surgery or radiotherapy, while advanced cases often require a combination of treatments like surgery with chemoradiotherapy or chemotherapy with immunotherapy to improve prognosis. Designing a tailored treatment plan based on patients’ predicted survival time can be advantageous in improving the cure rate of oral cavity cancer and enhancing the quality of patient survival. Currently, the tumor-node-metastasis staging approach is extensively used to estimate survival in patients with oral cavity cancer.[5] Despite this, individuals with equal tumor-node-metastasis stages frequently have varying outcomes despite getting similar therapy. As a result, finding novel prognostic biomarkers to predict therapy response or long-term survival in patients with oral cavity cancer is critical.

It has been demonstrated that host systemic inflammatory responses play a key role in carcinogenesis and disease development.[6] Meta-analyses of the predictive usefulness of peripheral blood inflammation-based markers such as neutrophil-to-lymphocyte ratio[7,8] and platelet-to-lymphocyte ratio[9] in oral cavity cancer have recently sparked great attention. Systemic immune-inflammation index (SII), a novel integrated score calculated by multiplying the platelet count by the neutrophil count then dividing by the lymphocyte count, is used to predict poor prognosis in cancers of the gastrointestinal tract,[10] urinary tract,[11,12] gynecology[13] and lung.[14,15] However, the use of SII to indicate prognosis in patients with oral cavity cancer remains inconsistent. Furthermore, SII, which is based on the counts of 3 different types of circulating immune cells, may more accurately reflect the balance of host immunological and inflammatory state than neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio, which are based on just 2 types. The SII represents more robust indicators of prognosis in oral cavity cancer than other inflammation-based markers. Similarly, another inflammation-based biomarker, lymphocyte-to-monocyte ratio (LMR), based on peripheral blood lymphocyte and monocyte count, has been observed as an independent prognostic factor for lung cancer,[16] gastrointestinal cancers,[17] urinary system cancers,[18,19] and gynecological cancers,[20] but its use in oral cavity cancer remains uncertain. We therefore performed a meta-analysis to evaluate the predictive impact of SII and LMR in patients with oral cavity cancer.

2. Methods

2.1. Selection criteria and search approach

The PubMed, Embase, and Cochrane Library databases were looked through for relevant papers published up to June 2023 that examined the association between SII or LMR and oral cavity cancer’s prognosis. “Systemic immune-inflammation index,” “SII,” “oral cavity cancer,” “oral squamous cell carcinoma,” “lymphocyte-to-monocyte ratio,” “LMR,” “prognosis,” and “prognostic” were the search keywords utilized. To locate more relevant research, references referenced in chosen papers were also collected.

All candidate articles were evaluated by 2 independent writers. Studies were deemed qualified if they satisfied the following requirements: included patients with histopathologically confirmed oral cavity cancer; reported hazard ratios (HRs) and 95% confidence intervals (CIs) for OS, or disease-free survival (DFS); or included sufficient data to calculate HR and 95% CI; and published in English. Among the exclusion criteria were: letters, reviews, and case reports; HR and 95% CI were not supplied or could not be computed; the cutoff value was not provided; and studies were not published in English.

2.2. Data extraction and quality evaluation

Two authors thoroughly considered the eligible studies to acquire the following details: the surname of the first author, the country of origin, the year of publication, the number of patients, the study design, the patient’s age, the cutoff value, the duration of follow-up, and the survival analysis. Each study’s HR from multivariate or univariate analysis was retrieved to produce a summary HR with 95% CI. Three investigators individually assessed the quality of all primary studies using the Newcastle–Ottawa Quality Assessment Scale (NOS).[21] NOS ratings of 6 are considered high-quality research, with a maximum value of 9.

2.3. Statistical investigation

HR and 95% CI data were gathered directly from individual papers or computed as previously stated[22] if not presented directly. We calculated the pooled HRs from each study in multivariate models where available. We used Cochran Q and I2 statistical methods to assess the statistical heterogeneity of combined results.[23] If I2 was less than 50% or the P value was greater than .10, we considered our findings to be not influenced by heterogeneity. In such cases, the combined results were obtained using a fixed-effects model, while a random-effects model was employed as an alternative. Subgroup analysis was also performed based on nation, sample size, cutoff value, and Cox regression analysis method. Egger’s test and a funnel plot were used to assess potential publication bias. A statistically significant P value of .05 was used. Furthermore, a sensitivity analysis was performed by successively eliminating each study to analyze the impact of each individual study on the aggregate results. Stata software version 12.0 was used for all analyses.

3. Results

3.1. Characteristics of chosen articles

Figure 1 depicts a flow chart of the selection method. The database search yielded 302 possibly relevant papers, of which 280 were eliminated after examining titles and abstracts. Four publications were removed after further screening of the entire text of the remaining 22 research due to a lack of accessible data. Finally, 18 articles with 19 studies representing a total of 6571 patients were selected for meta-analysis.[24–41] Table 1 shows the features of the 19 studies. Five studies investigated SII, 12 investigated LMR, and 2 investigated both SII and LMR. According to the NOS, all the included studies were of good quality. Twelve of the investigations were done in China, 5 in Japan, and 2 in Spain or Korea. The sample sizes varied from 101 to 993 patients, with a median follow-up period of 31 to 105.6 months. Eight of the included studies assessed OS, whereas 11 assessed both OS and DFS.

Figure 1.

Figure 1.

Open in a new tab

Diagram showing the meta-analysis process.

Table 1.

Characteristics of studies included in this meta-analysis.

Study, year Country Duration Study design Sample size Age Gender (M/F) Follow-up (mo) Prognostic markers Survival outcome Cutoff Survival analysis NOS
Ong 2017 China 2009–2013 R 133 median: 51.92 71/62 median: 52 LMR OS/DFS 5.3/3.2 M 8
Diao 2018 1 China 2006–2016 R 138 NR 81/57 median: 48 SII OS/DFS 484.5 M 8
Diao 2018 2 China 2004–2014 R 171 NR 90/81 median: 45 SII OS/DFS 484.5 M 8
Eltohami 2018 China 2005–2014 R 613 mean: 50.5 556/57 NR LMR OS/DFS 4.85 U 6
Furukawa 2019 Japan 2001–2015 R 103 median: 63 55/48 NR LMR OS 4.29 M 7
Jariod-Ferrer 2019 Spain 2011–2014 R 215 median: 67.5 145/70 median: 31 LMR OS 2.6 M 8
Watabe 2019 Japan 2004–2012 R 110 median: 68 61/49 median: 68 LMR OS/DFS 5 M 8
Hasegawa 2020 Japan 2001–2013 R 433 mean: 66.3 246/187 mean: 59.1 LMR OS 4.35 U 7
Hung 2020 China 2005–2012 R 993 median: 51 922/71 median: 105.6 SII OS 810.6 M 8
Lee 2020 Korea 2005–2018 R 291 median: 63 108/183 mean: 41 LMR OS/DFS 4.65/4.45 U 7
Lu 2020 China 2012–2017 R 120 median: 55 79/41 median: 37.5 LMR, SII OS/DFS 4.02, 569 U, M 8
Mikoshiba 2020 Japan 1991–2018 R 101 median: 59 63/38 median: 65 LMR OS/DFS 5.54 M 8
Chen 2021 China 2010–2017 R 651 NR 423/228 median: 31.1 LMR OS 3.18 M 7
Ding 2021 China 2012–2015 R 493 NR 261/232 NR LMR OS/DFS 3.4 M 7
Lin 2021 China 2008–2019 R 169 median: 57 93/76 median: 50 LMR OS 4.15 M 8
Wei 2021 China 2008–2019 R 172 median: 69 96/76 NR LMR, SII OS 3.4, 20.4 U 6
Wu 2021 China 2005–2012 R 890 median: 50.8 828/62 median: 72.7 LMR OS 4.21 U 7
Huang 2022 China 2011–2020 R 592 mean: 54.2 518/74 median: 100 SII OS/DFS 337.1/348.33 M 8
Kubota 2022 Japan 2005–2017 R 183 median: 66 103/80 NR SII OS/DFS 569 U/M 7
Open in a new tab

DFS = disease-free survival, M = multivariate, NOS = Newcastle-Ottawa Quality Assessment Scale, NR = not reported, OS = overall survival, R = retrospective, U = univariate.

3.2. SII’s predictive value in oral cavity cancer

Seven research investigated the impact of SII on OS. Patients with high SII had a shorter life expectancy than those with low SII (HR: 1.61, 95% CI: 1.38–1.87, P < .001). Because there was no significant heterogeneity (I2 = 43.3%, P = .102), the fixed-effect model was employed for analysis (Fig. 2A). Subgroup analysis revealed that high SII predicted poor overall survival in Chinese patients (HR: 1.58, 95% CI: 1.35–1.84, P < .001). After grouping by sample size, the pooled HR for sample size > 200 was 1.43 (95% CI: 1.21–1.70, P < .001) and 2.46 (95% CI: 1.77–3.41, P < .001) for sample size < 200. Moreover, when SII > 484.5, pooled HR for OS was 2.03 (95% CI: 1.20–3.44, P = .009) and when SII ≤ 484.5, pooled HR for OS was 1.79 (95% CI: 1.30–2.45, P < .001). In the analysis of type of Cox regression method, pooled HR for patients from multivariate analysis was 1.78 (95% CI: 1.30–2.45, P < .001) and 2.39 (95% CI: 1.26–4.53, P = .008) from univariate analysis. These results are shown in Table 2.

Figure 2.

Figure 2.

Open in a new tab

Forest plot of the relationship between high SII and OS or DFS. DFS = disease-free survival, OS = overall survival, SII = systemic immune-inflammation index.

Table 2.

Subgroup analyses of SII.

Subgroup No. of studies HR (95% CI) P Heterogeneity Model
I2 (%) Ph
Overall survival
 Country
  China 6 1.58 (1.35–1.84) <.001 39.6 0.142 Fixed
  Japan 1 3.28 (1.29–8.32) .012 – – –
 Sample size
  ＞200 2 1.43 (1.21–1.70) <.001 0 0.469 Fixed
  <200 5 2.46 (1.77–3.41) <.001 0 0.769 Fixed
 Cut-off
  ＞484.5 3 2.03 (1.20–3.44) .009 59.8 0.083 Random
  ≤484.5 4 1.79 (1.30–2.45) <.001 40.7 0.167 Fixed
 Analysis
  Multivariate 5 1.78 (1.30–2.45) <.001 51.1 0.085 Random
 Univariate 2 2.39 (1.26–4.53) .008 0 0.358 Fixed
Disease-free survival
 Country
  China 4 1.64 (0.95–2.82) .073 75.3 0.007 Random
  Japan 1 4.10 (1.63–10.32) .003 – – –
 Sample size
  ＞200 1 0.94 (0.66–1.33) .729 – – –
  <200 4 2.28 (1.65–3.15) <.001 19.7 0.291 Fixed
 Cut-off
  ＞484.5 2 2.30 (0.83–6.39) .109 69.7 0.069 Random
  ≤484.5 3 1.74 (0.84–3.62) .138 83.5 0.002 Random
 Analysis
  Multivariate 5 1.90 (1.11–3.27) .02 76.4 0.002 Random
  Univariate 0 – – – – –
Open in a new tab

HR = hazard ratio, SII = systemic immune-inflammation index.

Five studies examined the effect of SII on DFS. Patients with high SII had poorer DFS than those with low SII, according to the pooled data (HR: 1.90, 95% CI: 1.11–3.27, P = .02). Huge heterogeneity (I2 = 76.4%, P = .002) was found, and the random model was therefore used for analysis (Fig. 2B). Subgroup studies revealed that increased SII was unrelated to DFS in Chinese patients (HR: 1.64, 95% CI: 0.95–2.82, P = .073). After stratification by sample size, the pooled HR was 0.94 (95% CI: 0.66–1.33, P = .729) for sample size > 200 and 2.28 (95% CI: 1.65–3.15, P < .001) for sample size < 200. Moreover, when SII > 484.5, pooled HR for DFS was 2.30 (95% CI: 0.83–6.39, P = .109) and when SII ≤ 484.5, pooled HR for DFS was 1.74 (95% CI: 0.84–3.62, P = .138). All the pooled HR evaluated DFS were from multivariate analysis, the subgroup based on type of Cox regression method therefore not conducted. These results are also shown in Table 2.

3.3. LMR’s prognostic value in oral cavity cancer

Combined data suggested that increased LMR significantly correlated with good OS, with a pooled HR estimate of 0.64 (95% CI: 0.54–0.77, P < .001; Fig. 3A) with moderate heterogeneity (I2 = 46.2%, P = .030). Subgroup analyses showed that increased LMR predicted better OS among patients in China (HR: 0.71, 95% CI: 0.63–0.81, P < .001) and non-China countries (HR: 0.56, 95% CI: 0.34–092, P = .023). Following stratification by sample size, pooled HR was 0.75 (95% CI: 0.66–0.85, P < .001) for sample size > 200 and 0.52 (95% CI: 0.40–0.67, P < .001) for sample size < 200. Moreover, when LMR > 4.21, pooled HR for OS was 0.58 (95% CI: 0.43–0.79, P = .001) and when LMR ≤ 4.21, pooled HR for OS was 0.75 (95% CI: 0.65–0.87, P < .001). In addition, no matter pooled the multivariate or univariate Cox regression results, high LMR had significant prognostic value in patients (Multivariate: HR: 0.56, 95% CI: 0.45–0.68, P < .001; Univariate: HR: 0.77, 95% CI: 0.67–0.88, P < .001). These results are shown in Table 3.

Figure 3.

Figure 3.

Open in a new tab

Forest plots of studies evaluating the association between high LMR and OS or DFS. DFS = disease-free survival, LMR = lymphocyte-to-monocyte ratio, OS = overall survival.

Table 3.

Subgroup analyses of LMR.

Subgroup No. of studies HR (95% CI) P Heterogeneity Model
I2 (%) Ph
Overall survival
 Country
  China 8 0.71 (0.63–0.81) <.001 23.9 0.239 Fixed
 Non-China 6 0.56 (0.34–0.92) .023 65.4 0.013 Random
 Sample size
  ＞200 7 0.75 (0.66–0.85) <.001 31 0.192 Fixed
  <200 7 0.52 (0.40–0.67) <.001 34.7 0.163 Fixed
 Cut-off
  ＞4.21 7 0.58 (0.43–0.79) .001 55 0.038 Random
  ≤4.21 7 0.75 (0.65–0.87) <.001 28.9 0.208 Fixed
 Analysis
  Multivariate 8 0.56 (0.45–0.68) <.001 32.8 0.166 Fixed
  Univariate 6 0.77 (0.67–0.88) <.001 30.9 0.204 Fixed
Disease-free survival
 Country
  China 4 0.71 (0.62–0.82) <.001 0 0.869 Fixed
  Non-China 3 0.59 (0.43–0.83) .002 36.4 0.208 Fixed
 Sample size
  ＞200 3 0.68 (0.56–0.82) <.001 0 0.581 Fixed
  <200 4 0.71 (0.59–0.84) <.001 19.3 0.294 Fixed
 Cut-off
  ＞4.21 4 0.68 (0.56–0.84) <.001 30 0.232 Fixed
  ≤4.21 3 0.70 (0.59–0.83) <.001 0 0.753 Fixed
 Analysis
  Multivariate 4 0.69 (0.58–0.81) <.001 27.1 0.249 Fixed
  Univariate 3 0.70 (0.57–0.87) .001 0 0.687 Fixed
Open in a new tab

HR = hazard ratio, LMR = lymphocyte-to-monocyte ratio.

Similarly, high LMR indicated greater DFS, with a pooled HR estimate of 0.69 (95% CI: 0.61–0.79, P < .001; Fig. 3B) with no heterogeneity (I2 = 0.0%, P = .557). As shown in Table 3, subgroup analyses showed that increased LMR predicted better DFS among patients in China (HR: 0.71, 95% CI: 0.62–0.82, P < .001) and non-China countries (HR: 0.59, 95% CI: 0.43–0.83, P = .002). Following stratification by sample size, pooled HR was 0.68 (95% CI: 0.56–0.82, P < .001) for sample size > 200 and 0.71 (95% CI: 0.59–0.84, P < .001) for sample size < 200. Moreover, when LMR > 4.21, pooled HR for DFS was 0.68 (95% CI: 0.56–0.84, P < .001) and when PNI ≤ 4.21, pooled HR for DFS was 0.70 (95% CI: 0.59–0.83, P < .001). In the analysis of type of Cox regression method, pooled HR for patients from multivariate analysis was 0.69 (95% CI: 0.58–0.81, P < .001) and 0.70 (95% CI: 0.57–0.87, P = .001) from univariate analysis.

3.4. Analysis of sensitivity and publication bias

As shown in Figure 4A and B, publication bias was not significant in studies on SII and pooled OS (P = .055) or DFS (P = .102) based on Egger’s test and funnel plots. In addition, publication bias was existed in studies on LMR and pooled OS (P = .028; Fig. 5A) but not significant in studies on LMR and pooled DFS (P = .346; Fig. 5B). The possible influence of published studies on the pooled data was estimated using sensitivity analysis. As demonstrated in Figure 6, the pooled HR did not change appreciably when each study was eliminated in turn.

Figure 4.

Figure 4.

Open in a new tab

Publication bias funnel plots by Egger. High SII’s relationship to OS (A), and DFS (B). DFS = disease-free survival, OS = overall survival, SII = systemic immune-inflammation index.

Figure 5.

Figure 5.

Open in a new tab

Publication bias funnel plots by Egger. High LMR’s relationship to OS (A), and DFS (B). DFS = disease-free survival, LMR = lymphocyte-to-monocyte ratio, OS = overall survival.

Figure 6.

Figure 6.

Open in a new tab

Analyzing the meta-analysis’s sensitivity. High SII correlates with OS (A) and DFS (B); high LMR correlates with OS (C) and DFS (D). DFS = disease-free survival, LMR = lymphocyte-to-monocyte ratio, OS = overall survival, SII = systemic immune-inflammation index.

4. Discussion

This meta-analysis of 19 studies in 6571 patients investigated the relationship between SII and LMR with prognosis in patients with oral cavity cancer. Our results indicated that pretreatment SII and LMR may represent independent prognostic indicators in this patient population. Increased SII was associated with poor OS and DFS, and increased LMR was significantly correlated with better OS and DFS. A subgroup analysis demonstrated that high LMR were significantly correlated with increased OS or DFS in patients with oral cavity cancer, irrespective of the country, cutoff value of the LMR, sample size and the methods of survival analysis. In addition, the negative effect of high SII on the OS was detected in every subgroup with varying the country, sample size, cutoff value and methods of survival analysis. Due to the insufficient included studies, we did not find a correlation between SII and DFS in some subgroups. Further randomized controlled studies (RCTs) with large sample size were needed to discover their correlation.

The link between inflammation and cancer has received a lot of attention.[42] Tumour cells secrete pro-inflammatory chemicals, and systemic inflammation enhances tumor cell proliferation, migration, and invasion by blocking apoptosis and increasing angiogenesis.[43,44] SII, a marker of systemic inflammation, is calculated as platelet count × neutrophil count/lymphocyte count. Patients who have a high SII score often exhibit elevated levels of thrombocytosis, neutrophilia, or lymphopenia. Tumor-associated platelets play a role in promoting tumor growth and invasion by releasing growth factors and chemical molecules like ADP, serotonin, and thromboxane A2 (TXA2).[45] These platelets can infiltrate the tumor microenvironment through focal adhesion kinase (FAK) activation, contributing to the progression of the tumor.[45] Additionally, neutrophils can directly support tumor growth by secreting various chemokines and cytokines, and by recruiting other cells that promote tumor development to the tumor microenvironment.[46] The presence of neutrophils in the tumor microenvironment is stimulated by vascular endothelial growth factor and interleukin 8 released by cancer cells, which in turn trigger the production of platelet-derived growth factor, fibroblast growth factor, matrix metalloproteinase, and interleukin 6. These elements play a crucial role in the initiation and progression of cancer.[47] In contrast to platelets and neutrophils, lymphocytes operate as anti-tumor agents by triggering the p53 signaling pathway and secreting IL-17 to cause tumor cell death and stop tumor cell growth.[48] Besides, persistent activation of T cells can enhance the anti-tumor effects of tumor-infiltrating T lymphocytes, leading to the apoptosis of cancer cells by presenting tumor-associated antigens to lymphocytes. This interaction is essential for enhancing adjunctive therapies and preventing tumor recurrence.[49,50] Thus, high SII denotes high neutrophil and platelet counts, but low lymphocyte counts are often linked to tumor cell invasion and metastasis and result in poor survival. Likewise, the prognostic value of LMR, which reflects the immune status of the patient, is calculated from peripheral blood lymphocyte and monocyte count. Lymphocytes play a crucial anti-tumor role as described above. Furthermore, studies have shown that monocytes could promote tumor progression by secreting various cytokines, destroying extracellular matrix and promoting angiogenesis.[51] Monocytes have the ability to transform into tumor-associated macrophages (TAMs) and dendritic cells within the tumor microenvironment (TME), supporting tumor growth and inhibiting the immune response.[52] TAMs can stimulate angiogenesis by releasing growth factors and chemokines that aid in the progression of cancer.[51] Therefore, high LMR is related to high levels of lymphocytes and low levels of monocyte, which are associated with good survival.

The limitations of this meta-analysis were numerous. First, insufficient numbers of studies evaluating SII and DFS were available and publication bias was existed in studies on LMR and pooled OS. As a result, the lack of statistical power may have reduced the dependability of the combined data. Additionally, most of the studies that were included in the analysis were carried out in Asia, which creates doubt about the generalizability of our results to other regions. Third, there were differences in the cutoff values amongst the investigations. The cutoffs in the included research didn’t reach a predetermined level, and few publications indicate how they were determined. Although we pooled the results according to SII > 200 and LMR > 4.21 in the subgroup analysis, bias may exist because using diverse cutoff values potentially leads to a different conclusion in the same article, which may influence the pooled results of this analysis. In the fourth place, the majority of the articles included in the study were retrospective in nature rather than RCTs, thereby diminishing their persuasiveness. Retrospective studies are more prone to biases such as lost to follow-up and information bias. In addition, some adjuvant drugs such as steroids which could affect overall survival in patients with oral cavity cancer[53] may be used in the course of cancer. Furthermore, because people of different ages have different health conditions, age may also affect the prognosis of cancer and lead to selection bias in included studies. Moreover, factors like the levels of platelets, neutrophils, and lymphocytes in patients can be influenced by their condition, infection status, and other related variables. These confounding factors were not sufficiently accounted for in the initial articles.

In summary, our results showed that pretreatment SII and LMR represent independent predictive factors for OS and DFS, and that SII and LMR may be effective prognostic biomarkers in patients with oral cavity cancer.

Author contributions

Data curation: Jingbo Yang.

Project administration: Yifeng Xing.

Supervision: Xiaojie Xing.

Writing – original draft: Jianghan Xu.

Writing – review & editing: Yanjun Lin.

Abbreviations:

DFS
disease-free survival
HR
hazard ratio
LMR
lymphocyte-to-monocyte ratio
NOS
Newcastle-Ottawa Quality Assessment Scale
OS
overall survival
PNI
prognostic nutritional index
SII
systemic immune-inflammation index
TNM
tumor-node-metastasis

JX, YL, and JY contributed equally to this work. This work was financially supported by the Natural Science Foundation of Fujian Province (no. 2021J01792).

This research work constitutes a meta-analysis of published data and does not include any studies with human participants or animals performed by any of the authors. Hence, no informed consent was required to perform this study.

The authors have no conflicts of interest to disclose.

The data that support the findings of this study are available from a third party, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are available from the authors upon reasonable request and with permission of the third party.

How to cite this article: Xu J, Lin Y, Yang J, Xing Y, Xing X. Pretreatment systemic immune-inflammation index and lymphocyte-to-monocyte ratio as prognostic factors in oral cavity cancer: A meta-analysis. Medicine 2024;103:44(e40182).

Contributor Information

Jianghan Xu, Email: newsyi@163.com.

Yanjun Lin, Email: yanjunlin@fjmu.edu.cn.

Jingbo Yang, Email: doctoryangjingbo@163.com.

Yifeng Xing, Email: xyf2775185725@fjmu.edu.cn.

References

  • [1].Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70:7–30. [DOI] [PubMed] [Google Scholar]
  • [2].Yan L, Chen F, Chen L, et al. Dynamic evaluation of conditional survival in patients with oral squamous cell carcinoma after surgical resection: a large-scale prospective study. Oral Oncol. 2020;104:104639. [DOI] [PubMed] [Google Scholar]
  • [3].Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71:209–49. [DOI] [PubMed] [Google Scholar]
  • [4].Chen F, Lin L, Liu F, et al. Three prognostic indexes as predictors of response to adjuvant chemoradiotherapy in patients with oral squamous cell carcinoma after radical surgery: a large-scale prospective study. Head Neck. 2019;41:301–8. [DOI] [PubMed] [Google Scholar]
  • [5].Sridharan S, Thompson LDR, Purgina B, et al. Early squamous cell carcinoma of the oral tongue with histologically benign lymph nodes: a model predicting local control and vetting of the eighth edition of the American Joint Committee on Cancer pathologic T stage. Cancer. 2019;125:3198–207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74. [DOI] [PubMed] [Google Scholar]
  • [7].Wang Y, Wang P, Andrukhov O, et al. Meta-analysis of the prognostic value of the neutrophil-to-lymphocyte ratio in oral squamous cell carcinoma. J Oral Pathol Med. 2018;47:353–8. [DOI] [PubMed] [Google Scholar]
  • [8].Yang Y, Liu R, Ren F, Guo R, Zhang P. Prognostic and clinicopathological significance of neutrophil-to-lymphocyte ratio in patients with oral cancer. Biosci Rep. 2018;38:BSR20181550. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [9].Zhang Y, Zheng L, Quan L, Du L. Prognostic role of platelet-to-lymphocyte ratio in oral cancer: a meta-analysis. J Oral Pathol Med. 2021;50:274–9. [DOI] [PubMed] [Google Scholar]
  • [10].Gao H, Wusiman L, Cao BW, Wujieke A, Zhang W-B. The role of preoperative systemic immune-inflammation index in predicting the prognosis of patients with digestive tract cancers: a meta-analysis. Transpl Immunol. 2022;73:101613. [DOI] [PubMed] [Google Scholar]
  • [11].Li X, Gu L, Chen Y, et al. Systemic immune-inflammation index is a promising non-invasive biomarker for predicting the survival of urinary system cancers: a systematic review and meta-analysis. Ann Med. 2021;53:1827–38. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [12].Wang Q, Zhu SR, Huang XP, Liu X-Q, Liu J-B, Tian G. Prognostic value of systemic immune-inflammation index in patients with urinary system cancers: a meta-analysis. Eur Rev Med Pharmacol Sci. 2021;25:1302–10. [DOI] [PubMed] [Google Scholar]
  • [13].Ji Y, Wang H. Prognostic prediction of systemic immune-inflammation index for patients with gynecological and breast cancers: a meta-analysis. World J Surg Oncol. 2020;18:197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Huang W, Luo J, Wen J, Jiang M. The relationship between systemic immune inflammatory index and prognosis of patients with non-small cell lung cancer: a meta-analysis and systematic review. Front Surg. 2022;9:898304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Zhou Y, Dai M, Zhang Z. Prognostic significance of the systemic immune-inflammation index (SII) in patients with small cell Lung cancer: a meta-analysis. Front Oncol. 2022;12:814727. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [16].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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Zhang J, Chen L, Zhou R, Sun H, Liao Y, Liao W. Pretreatment lymphocyte monocyte ratio predicts long-term outcomes in patients with digestive system tumor: a meta-analysis. Gastroenterol Res Pract. 2016;2016:9801063. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [18].Li J, Cheng Y, Ji Z. Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with urologic tumors: a PRISMA-compliant meta-analysis. Medicine (Baltimore). 2019;98:e14091. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [19].Li M, Deng Q, Zhang L, He S, Rong J, Zheng F. The pretreatment lymphocyte to monocyte ratio predicts clinical outcome for patients with urological cancers: a meta-analysis. Pathol Res Pract. 2019;215:5–11. [DOI] [PubMed] [Google Scholar]
  • [20].Cai L, Song Y, Zhao X. Prognostic significance of lymphocyte monocyte ratio in patients with ovarian cancer. Medicine (Baltimore). 2020;99:e19638. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].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;25:603–5. [DOI] [PubMed] [Google Scholar]
  • [22].Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8:16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Higgins JP, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. 2002;21:1539–58. [DOI] [PubMed] [Google Scholar]
  • [24].Kubota K, Ito R, Narita N, et al. Utility of prognostic nutritional index and systemic immune-inflammation index in oral cancer treatment. BMC Cancer. 2022;22:368. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Ding M, Song Y, Jing J, et al. The ratio of preoperative serum biomarkers predicts prognosis in patients with oral squamous cell carcinoma. Front Oncol. 2021;11:719513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [26].Mikoshiba T, Ozawa H, Watanabe Y, et al. Prognostic value of the lymphocyte-to-monocyte ratio in patients with parotid gland carcinoma. Laryngoscope. 2021;131:E864–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Lee S, Kim DW, Kwon S, et al. Prognostic value of systemic inflammatory markers for oral cancer patients based on the 8th edition of AJCC staging system. Sci Rep. 2020;10:12111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Chen L, Qian J, Lin L, et al. Prognostic value of preoperative lymphocyte-to-monocyte ratio in oral cancer patients and establishment of a dynamic nomogram. Oral Dis. 2021;27:1127–36. [DOI] [PubMed] [Google Scholar]
  • [29].Hung SP, Chen PR, Ho TY, et al. Prognostic significance of the preoperative systemic immune-inflammation index in patients with oral cavity squamous cell carcinoma treated with curative surgery and adjuvant therapy. Cancer Med. 2021;10:649–58. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [30].Furukawa K, Kawasaki G, Naruse T, Umeda M. Prognostic significance of pretreatment lymphocyte-to-monocyte ratio in patients with tongue cancer. Anticancer Res. 2019;39:405–12. [DOI] [PubMed] [Google Scholar]
  • [31].Wei LF, Huang XC, Lin YW, et al. A prognostic model based on clinicopathological features and inflammation- and nutrition-related indicators predicts overall survival in surgical patients with tongue squamous cell carcinoma. Technol Cancer Res Treat. 2021;20:15330338211043048. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Diao P, Wu Y, Li J, et al. Preoperative systemic immune-inflammation index predicts prognosis of patients with oral squamous cell carcinoma after curative resection. J Transl Med. 2018;16:365. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Watabe Y, Aoki K, Ichikawa H, et al. A preoperative prognostic nutritional index is a prognostic indicator in oral squamous cell carcinoma patients undergoing radical surgery. Int J Oral Maxillofac Surg. 2021;50:1413–21. [DOI] [PubMed] [Google Scholar]
  • [34].Eltohami YI, Kao HK, Lao WW, et al. The prediction value of the systemic inflammation score for oral cavity squamous cell carcinoma. Otolaryngol Head Neck Surg. 2018;158:1042–50. [DOI] [PubMed] [Google Scholar]
  • [35].Lu Z, Yan W, Liang J, et al. Nomogram based on systemic immune-inflammation index to predict survival of tongue cancer patients who underwent cervical dissection. Front Oncol. 2020;10:341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [36].Lin YW, Kang WP, Huang BL, et al. Nomogram based on clinical characteristics and serological inflammation markers to predict overall survival of oral tongue squamous cell carcinoma patient after surgery. BMC Oral Health. 2021;21:667. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Hasegawa T, Iga T, Takeda D, et al. Neutrophil-lymphocyte ratio associated with poor prognosis in oral cancer: a retrospective study. BMC Cancer. 2020;20:568. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Ong HS, Gokavarapu S, Wang LZ, Tian Z, Zhang CP. Low pretreatment lymphocyte-monocyte ratio and high platelet-lymphocyte ratio indicate poor cancer outcome in early tongue cancer. J Oral Maxillofac Surg. 2017;75:1762–74. [DOI] [PubMed] [Google Scholar]
  • [39].Wu YY, Chang KP, Ho TY, et al. Comparative prognostic value of different preoperative complete blood count cell ratios in patients with oral cavity cancer treated with surgery and postoperative radiotherapy. Cancer Med. 2021;10:1975–88. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Huang CH, Lue KH, Chen PR, Hsieh T-C, Chou Y-F. Association between sarcopenia and immediate complications and mortality in patients with oral cavity squamous cell carcinoma undergoing surgery. Cancers (Basel). 2022;14:785. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Jariod-Ferrer M, Arbones-Mainar JM, Gavin-Clavero MA, et al. Are comorbidities associated with overall survival in patients with oral squamous cell carcinoma? J Oral Maxillofac Surg. 2019;77:1906–14. [DOI] [PubMed] [Google Scholar]
  • [42].Proctor MJ, Morrison DS, Talwar D, et al. A comparison of inflammation-based prognostic scores in patients with cancer. A Glasgow Inflammation Outcome Study. Eur J Cancer. 2011;47:2633–41. [DOI] [PubMed] [Google Scholar]
  • [43].Mimatsu K, Fukino N, Ogasawara Y, Saino Y, Oida T. Utility of inflammatory marker- and nutritional status-based prognostic factors for predicting the prognosis of stage IV gastric cancer patients undergoing non-curative surgery. Anticancer Res. 2017;37:4215–22. [DOI] [PubMed] [Google Scholar]
  • [44].Shaverdian N, Wang J, Levin-Epstein R, et al. Pro-inflammatory state portends poor outcomes with stereotactic radiosurgery for brain metastases. Anticancer Res. 2016;36:5333–7. [DOI] [PubMed] [Google Scholar]
  • [45].Haemmerle M, Stone RL, Menter DG, Afshar-Kharghan V, Sood AK. The platelet lifeline to cancer: challenges and opportunities. Cancer Cell. 2018;33:965–83. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [46].Shaul ME, Fridlender ZG. Cancer related circulating and tumor-associated neutrophils – subtypes, sources and function. FEBS J. 2018;285:4316–42. [DOI] [PubMed] [Google Scholar]
  • [47].Huang SH, Waldron JN, Milosevic M, et al. Prognostic value of pretreatment circulating neutrophils, monocytes, and lymphocytes in oropharyngeal cancer stratified by human papillomavirus status. Cancer. 2015;121:545–55. [DOI] [PubMed] [Google Scholar]
  • [48].Ostroumov D, Fekete-Drimusz N, Saborowski M, Kühnel F, Woller N. CD4 and CD8 T lymphocyte interplay in controlling tumor growth. Cell Mol Life Sci. 2018;75:689–713. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [49].Youn JI, Collazo M, Shalova IN, Biswas SK, Gabrilovich DI. Characterization of the nature of granulocytic myeloid-derived suppressor cells in tumor-bearing mice. J Leukoc Biol. 2012;91:167–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [50].Apetoh L, Tesniere A, Ghiringhelli F, Kroemer G, Zitvogel L. Molecular interactions between dying tumor cells and the innate immune system determine the efficacy of conventional anticancer therapies. Cancer Res. 2008;68:4026–30. [DOI] [PubMed] [Google Scholar]
  • [51].Chanmee T, Ontong P, Konno K, Itano N. Tumor-associated macrophages as major players in the tumor microenvironment. Cancers (Basel). 2014;6:1670–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [52].Olingy CE, Dinh HQ, Hedrick CC. Monocyte heterogeneity and functions in cancer. J Leukoc Biol. 2019;106:309–22. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [53].Zhang L, Wang S, Che X, Li X. Vitamin D and lung cancer risk: a comprehensive review and meta-analysis. Cell Physiol Biochem. 2015;36:299–305. [DOI] [PubMed] [Google Scholar]

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES