Research ArticleClinical Studies
Open Access

Prognostic Superiority of the White Cell-to-Lymphocyte Ratio (WLR) in Predicting Overall Survival in Patients With Gliomas

XIAO ZHANG, GUILAN CAI, HEYANG ZHANG and YONGBO ZHANG
In Vivo March 2026, 40 (2) 1163-1173; DOI: https://doi.org/10.21873/invivo.14271

Abstract

Background/Aim: Systemic inflammation influences glioma progression. We evaluated the prognostic value of the white cell-to-lymphocyte ratio (WLR) and aiming to develop a practical survival prediction model.

Patients and Methods: Patients with glioma from the INSCOC database were retrospectively analyzed and randomly split into training and validation cohorts (7:3). Kaplan–Meier analysis, restricted cubic splines, and Cox regression assessed associations between WLR and overall survival (OS). A WLR-based nomogram was established and internally validated using calibration and decision curve analysis.

Results: The optimal WLR cutoff was 4.182. Patients with WLR ≥4.182 had significantly worse OS (p<0.01). Restricted cubic splines showed a positive dose–response relationship between WLR and mortality risk. In multivariable Cox analysis, age, smoking, drinking, and WLR group remained independent predictors of OS (all p<0.05). Sensitivity analyses confirmed robustness. The nomogram showed good discrimination and calibration for predicting 1-, 3-, and 5-year OS and provided greater net clinical benefit than individual clinical factors.

Conclusion: WLR is an independent, readily available prognostic marker in glioma. A WLR-based nomogram may aid individualized risk stratification and clinical decision-making.

Keywords:

Introduction

Primary gliomas represent one of the most aggressive and life-threatening malignancies worldwide, characterized by high recurrence rates and limited long-term survival despite advances in neurosurgical and adjuvant therapies (1-3). According to recent global cancer statistics, the incidence of central nervous system tumors has steadily increased, especially in aging populations and in regions with improved neuroimaging detection capabilities (4, 5). Although multimodal treatment strategies combining surgery, radiotherapy, and chemotherapy have prolonged survival in selected patients, the overall prognosis for malignant gliomas remains unsatisfactory (6-8). Accurate and individualized prognostic assessment remains a major challenge in neuro-oncology and is crucial for optimizing treatment planning and patient counseling.

Accumulating evidence suggests that the systemic inflammatory and immune status of the host plays a critical role in tumor initiation, progression, and therapeutic response (9-12). Chronic inflammation promotes tumorigenesis through cytokine activation, immunosuppression, and angiogenesis, whereas a decline in immune surveillance facilitates tumor invasion and metastasis (13-15). In recent years, several hematologic inflammation-related indices – such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII) – have emerged as cost-effective and reproducible biomarkers for predicting cancer outcomes (16-18). These composite parameters, derived from routine blood tests, capture the dynamic balance between proinflammatory and immune-modulatory pathways and have been associated with prognosis in multiple tumor types, including gliomas and meningiomas (19). However, their predictive stability and discriminative power in glioma remain inconsistent across studies.

To refine inflammatory prognostic assessment, we examined a novel marker, the white cell-to-lymphocyte ratio (WLR), calculated as the total white blood cell count divided by the absolute lymphocyte count. The WLR simultaneously reflects systemic inflammatory activity and host immune competence – two interdependent processes that shape the tumor microenvironment. A higher WLR indicates heightened inflammation and relative lymphocyte suppression, both of which may facilitate tumor progression and resistance to therapy. While WLR has been preliminarily explored in other malignancies, its prognostic value in gliomas has not been systematically evaluated.

In this study, we aimed to investigate the prognostic significance of the WLR in patients with glioma and to develop a predictive nomogram model integrating WLR with relevant clinical parameters. By combining hematologic and clinical information, we sought to construct a simple, interpretable, and individualized tool to estimate long-term survival, thereby improving prognostic accuracy and supporting risk-based clinical management in patients with gliomas.

Patients and Methods

Study design. This retrospective cohort study was conducted using data extracted from the INSCOC database (Clinical Trial Registration No. ChiCTR1800020329, http://www.chictr.org.cn). A total of 182 patients with gliomas were initially screened between May 2013 and December 2018. After excluding individuals with incomplete hematologic or clinical information, immune disorders, or evidence of acute infection, 170 patients were ultimately included for analysis. Participants were randomly assigned into a training cohort (70%) and a validation cohort (30%) using simple random sampling. All patients were≥18 years old, pathologically diagnosed with primary gliomas, and had been hospitalized for at least 48 hours. For patients with multiple admissions, only data from the first hospitalization were analyzed. This study was conducted in accordance with the ethical principles of the Declaration of Helsinki. Written informed consent was obtained from all participants before enrollment, and the study protocol was approved by the institutional review boards of all participating hospitals (Registration No. ChiCTR1800020329).

Data collection. Clinical and laboratory data were retrieved from the electronic medical record system. Baseline variables included age, gender, smoking status, drinking status, and comorbidities (hypertension, diabetes). Laboratory parameters collected within 24 hours of admission comprised white blood cell count (WBC), lymphocyte count (LYM), hemoglobin, and serum albumin levels. All samples were processed in a central laboratory using standardized assays to minimize inter-instrument variability. The white cell-to-lymphocyte ratio (WLR) was calculated as the ratio of WBC to LYM (Table S1). A cutoff value of 4.182 was adopted to categorize patients into low (<4.182) and high (≥4.182) groups based on survival discrimination analysis.

Study endpoints. The primary endpoint was overall survival (OS), defined as the time interval between hospital admission and death or last follow-up. Patients who remained alive at the final follow-up were censored. Survival time was recorded in months, with the last follow-up conducted on December 24, 2018.

Determination of the optimal WLR cutoff. The optimal cutoff value for the white cell-to-lymphocyte ratio (WLR) was determined using the maximally selected rank statistics method implemented via the surv_cutpoint() function in the R survminer package. This method identifies the value that best discriminates survival outcomes by evaluating potential cut points based on the log-rank test statistic. To ensure clinical applicability, a minimum group proportion of 10% (minprop=0.1) was applied, guaranteeing that each subgroup contained a meaningful number of patients. The resulting cutoff of 4.182 was used to categorize patients into low-WLR (<4.182) and high-WLR (≥4.182) groups for subsequent analyses.

Nomogram development and validation. The nomogram was developed to create a parsimonious and clinically applicable predictive tool. Variable selection followed a hybrid approach: i) Statistical significance: Variables with p<0.05 in the multivariable Cox regression (Table I, Model c) were prioritized for inclusion. ii) Clinical relevance and parsimony: Additional factors with strong prior evidence in oncology or established clinical relevance were considered to enhance the model’s utility, provided they did not excessively complicate the tool. Based on this rationale, the final nomogram incorporated four variables: age and WLR group (both statistically significant), alongside smoking status and drinking status. Although the latter two were not statistically significant in the fully adjusted model, they are well-established behavioral risk factors with plausible biological links to cancer prognosis. Gender, which showed a non-significant trend (p=0.078) in the multivariable model, was omitted to maintain model simplicity and focus on stronger predictors.

View this table:
Table I.

Univariate and multivariate survival analysis of clinicopathological characteristics in overall cohort.

Statistical analysis. All statistical analyses were performed using R software (version 4.4.2). Continuous variables were presented as mean±standard deviation or median (interquartile range, IQR) as appropriate, and categorical variables were expressed as counts and percentages. Group comparisons were conducted using Student’s t-test or Mann–Whitney U-test for continuous data, and Chi-square or Fisher’s exact tests for categorical data. Time-dependent receiver operating characteristic (ROC) analysis was applied to evaluate the discriminatory performance of WLR compared with other inflammation-based indices (SII, NLR, WHR, PLR). The prognostic relevance of each variable was examined using univariate and multivariate Cox proportional hazards regression in the training cohort. Variables with p<0.05 in univariate analysis were entered into multivariate modeling. A nomogram model was constructed based on independent prognostic factors to predict 1-, 3-, and 5-year overall survival, utilizing the rms package in R. Model performance was further compared against traditional clinical parameters by examining the concordance index (C-index) and area under the time-dependent ROC curve (AUC). A two-tailed p<0.05 was considered statistically significant.

Results

Study population. A total of 182 patients diagnosed with primary gliomas were initially screened from the INSCOC database. After excluding cases with incomplete hematologic records (n=12), 170 patients were finally included in the analysis (Figure S1). These patients were randomly assigned to the training (n=119) and validation (n=51) cohorts.

Baseline characteristics of both cohorts were comparable (Table S2). The median age was 50.0 years (IQR=36.0-56.5) in the training cohort and 52.0 years (IQR=45.5-59.0) in the validation cohort. Males accounted for 60.5% and 52.9% of patients, respectively. The proportions of smokers (42.0% vs. 29.4%) and drinkers (15.1% vs. 13.7%) were similar. Median values of major inflammatory markers in the training vs validation cohorts were as follows: WLR 4.36 (3.04-5.99) vs. 3.79 (2.95-5.19), NLR 2.68 (1.60-4.47) vs. 2.46 (1.68-4.53), PLR 131.94 (100.31-217.40) vs. 149.16 (121.79-206.36), WHR 0.04 (0.04-0.06) vs. 0.04 (0.03-0.06) and SII 485.00 (289.94-977.90) vs. 506.80 (325.70-996.46).

Discriminative performance of inflammatory indices. To compare the prognostic ability of the five inflammation-related indices, C-index and time-dependent AUC analyses were conducted in the entire cohort and in each subset (Table S3 and Figure S2). Among all indicators, WLR consistently showed the strongest discriminatory power. In the overall population, its C-index reached 0.596 (95% CI=0.532-0.661), outperforming NLR (0.579), WHR (0.555), PLR (0.541), and SII (0.444). A similar pattern was observed in the training and validation cohort, where WLR achieved a C-index of 0.630 (95% CI=0.557-0.702) and 0.559 (95% CI=0.428-0.691). Across the 20%-80% survival time grid, the time-dependent ROC curves confirmed that WLR maintained the highest AUC(t) throughout the observation period, indicating robust and stable discrimination across time (Figure S2).

Survival outcomes by WLR classification. Next, a cutoff of 4.182 was identified for WLR in the training set (Figure S3). Patients were then categorized into high- and low-WLR groups based on this threshold. Kaplan–Meier survival analysis revealed that patients with elevated WLR had markedly worse overall survival (OS) in both the training (p=0.0038) and entire cohorts (p=0.0072), with a consistent trend in the validation cohort (p=0.25) (Figure 1). Median OS was significantly shorter in the high-WLR group compared with the low-WLR group, suggesting that an increased WLR reflected a higher mortality risk.

Figure 1.
Figure 1.

Overall survival curves of different cohorts based on the white cell-to-lymphocyte ratio (WLR) index.

Association between WLR and overall survival. Multivariate analyses were performed to identify independent prognostic variables in the overall cohort. As shown in Table I, age was positively associated with mortality, and the risk remained significant after stepwise adjustment (HR=1.05, 95% CI=1.00-1.09, p=0.020). Female sex showed a favorable trend toward improved survival, while patients with a family history of tumors tended to have lower risk in the fully adjusted model. Other clinicopathologic factors, including smoking, drinking, chemotherapy, radiotherapy, diabetes, and hypertension, did not reach statistical significance. When WLR was incorporated into the model, patients with elevated WLR (≥4.182) consistently showed poorer outcomes compared with those with lower values, with a gradually attenuated yet stable effect across adjustment levels. Sensitivity analyses confirmed the robustness of this association (Table II). Treating WLR as a continuous variable, each standard-deviation increase corresponded to approximately a 30 % rise in death risk (all p<0.01), and the trend persisted when categorized by quartiles; individuals in the highest WLR quartile exhibited more than twofold higher hazard of death relative to the lowest group, with a significant linear trend.

View this table:
Table II.

The sensitivity analysis of the relationship between WLR index and survival.

Restricted cubic spline analyses demonstrated a monotonic and approximately linear association between WLR and all-cause mortality (Figure S4). The risk increased steadily as WLR rose, with no evidence of a plateau or U-shaped trend. This pattern was stable across the overall, training, and validation cohorts, underscoring the reliability of the dose–response relationship.

Subgroup analysis. To further evaluate the robustness and consistency of the prognostic value of WLR, subgroup analyses were performed across clinically relevant strata. As shown in Figure 2, a high WLR was consistently associated with an increased risk of mortality in most predefined subgroups, including sex, age group, smoking status, drinking status, surgical treatment, chemotherapy, and radiotherapy.

Figure 2.
Figure 2.

Subgroup analyses of the association between white cell-to-lymphocyte ratio (WLR) and overall survival. Forest plot showing hazard ratios (HRs) and 95% confidence intervals (CIs) for overall survival according to WLR across predefined clinical subgroups.

Although the magnitude of the hazard ratio varied among subgroups, the direction of the association remained generally consistent, with no evidence of a reversal effect. Notably, the adverse prognostic impact of elevated WLR was more pronounced in patients aged <65 years, those without comorbid hypertension, and patients who did not receive radiotherapy. No statistically significant interaction was observed between WLR and major clinical variables, suggesting that the prognostic effect of WLR was generally consistent across subgroups. These findings support the stability of WLR as a prognostic biomarker across diverse clinical contexts.

Prognostic nomogram and internal validation. Using variables retained in the multivariable Cox model, we assembled a parsimonious nomogram with four inputs – age, smoking, drinking, and WLR group (cutoff 4.182) – to estimate 1-, 3-, and 5-year overall survival (Figure 3a). Each covariate contributes a point score that sums to a total predicting survival probability on the lower scales; higher age, current smoking, drinking, and high WLR increase the total points and correspond to lower predicted survival. Internal validation showed tight agreement between predicted and observed survival on calibration plots at all three time horizons with only minor deviation from the 45° line (Figure S5), supporting good model calibration in both the training and validation cohorts. To facilitate clinical use, we also built a lightweight web calculator that mirrors the nomogram scales and automatically computes WLR from WBC and lymphocyte counts, returning individualized 1-/3-/5-year survival estimates (Figure 3b).

Figure 3.
Figure 3.

Nomogram and web calculator for predicting overall survival of glioma patients. (a) Nomogram results in training and validation cohorts. (b) White cell-to-lymphocyte ratio (WLR) calculator.

Discussion

Chronic inflammation and immune disorders are associated with the progression of malignant tumors, and hematological markers derived from systemic inflammatory responses have recently received increasing attention as available prognostic markers (20, 21). In this study, we comprehensively evaluated the prognostic value of five inflammation-related indices – WLR, NLR, WHR, PLR, and SII – in patients with glioma. Among them, WLR consistently demonstrated the strongest predictive performance across multiple analyses, including the highest C-index and time-dependent AUC values in both training and validation cohorts. Moreover, survival curves, sensitivity analyses, and spline modeling all confirmed that an elevated WLR was independently associated with unfavorable overall survival. These findings establish WLR as a robust and easily obtainable biomarker reflecting the inflammatory–immune balance in patients with gliomas.

Inflammation-driven hematologic markers provide insight into the interaction between tumor biology and host immunity (22, 23). The WLR integrates two essential systemic parameters: white cell count, representing the magnitude of the inflammatory response (24), and lymphocyte count, indicating immune surveillance capability (25). An increased WLR suggests an imbalance favoring systemic inflammation and relative lymphocyte depletion, which together contribute to tumor progression and impaired host defense. Potential biological mechanisms linking WLR to glioma progression include expansion of myeloid-derived suppressor cells, systemic neutrophilia, and lymphocyte exhaustion, which are not fully captured by other composite ratios such as NLR, PLR, or SII. Similar to the prognostic impact of NLR or PLR reported in other malignancies, our results indicate that WLR captures a broader spectrum of inflammatory burden and immune suppression, thereby offering superior discriminative ability.

The prognostic significance of WLR in this study was further validated by multivariate Cox models and sensitivity analyses. The consistent association across continuous, binary, and quartile analyses supports a dose–response effect, with mortality risk rising steadily as WLR increases. The restricted cubic spline confirmed this linear gradient without evidence of plateau, suggesting that the detrimental effect of elevated WLR operates in a continuous rather than threshold-limited manner. Mechanistically, a high WLR may reflect excessive myeloid activation and cytokine-mediated lymphocyte exhaustion, leading to compromised antitumor immunity and accelerated tumor progression (26-28). These biological pathways have been linked to the release of interleukins and growth factors that promote angiogenesis, tissue remodeling, and metastasis – hallmarks of cancer-related inflammation (29-31).

Building upon these findings, we developed a WLR-based prognostic nomogram integrating age, smoking, drinking, and WLR group to predict individualized survival probabilities. The model showed excellent calibration and discrimination, with predicted outcomes closely matching observed survival in both the training and validation cohorts. The corresponding web-based calculator further enhances clinical applicability by allowing automatic computation of WLR from routine blood parameters and providing immediate estimates of 1-, 3-, and 5-year survival probabilities. This easily interpretable tool facilitates risk stratification, postoperative follow-up planning, and patient counseling in daily clinical practice.

Study limitations. Despite the strengths of our study, including internal validation and multiple analytical approaches, several limitations should be acknowledged. Although the sample size was modest, internal validation suggested that the model performance was reasonably stable, and all participants were from a single database, which may limit generalizability. External validation in independent cohorts is warranted to further confirm these findings. Additionally, although our analyses support the prognostic role of WLR, the underlying biological mechanisms remain to be clarified through molecular and immunological studies. Future work should explore whether modifying inflammatory or immune pathways could alter outcomes among patients with elevated WLR.

In summary, this study identifies WLR as an independent and reliable predictor of overall survival in glioma patients. By reflecting the systemic inflammatory–immune equilibrium, WLR provides prognostic information beyond traditional clinical parameters. The proposed WLR-based nomogram demonstrates promising utility for personalized risk assessment and could help refine prognostic evaluation and treatment planning in clinical practice.

Acknowledgements

We sincerely thank all patients for their participation in this study and gratefully acknowledge the dedicated efforts of the clinical and research staff from all participating centers in data collection and patient follow-up.

Footnotes

  • Authors’ Contributions

    X.Z. conceived and designed the study framework. Methodological development and statistical analysis were performed by X.Z. and G.C. Data collection and validation were conducted by H.Z. and Y.Z. The first draft of the manuscript was written by X.Z., with critical revisions provided by G.C. and Y.Z. Project supervision and overall guidance were provided by H.Z. and Y.Z. All authors read and approved the final manuscript.

  • Data Availability

    Data supporting the findings of this study are available from the corresponding author upon reasonable request.

  • Conflicts of Interest

    The authors declare that they have no conflicts of interest to disclose.

  • Artificial Intelligence (AI) Disclosure

    No AI-assisted technologies were utilized at any stage of this study, including manuscript preparation, data analysis, or figure creation.

  • Received December 14, 2025.
  • Revision received January 8, 2026.
  • Accepted February 1, 2026.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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Prognostic Superiority of the White Cell-to-Lymphocyte Ratio (WLR) in Predicting Overall Survival in Patients With Gliomas
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