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Zhou J, Tan B, Gao F. Prognostic values of combined ratios of white blood cells in glioma: a systematic review and meta-analysis. Neurosurg Rev 2024; 47:831. [PMID: 39477886 DOI: 10.1007/s10143-024-03064-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2024] [Revised: 10/09/2024] [Accepted: 10/18/2024] [Indexed: 11/15/2024]
Abstract
Gliomas, the most prevalent type of neurological tumor, pose a challenging prognosis for patients. Recent studies have underscored the importance of inflammatory markers such as the neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR), and monocyte/lymphocyte ratio (MLR) in predicting the prognosis of gliomas. We undertook a thorough meta-analysis to elucidate the role of these inflammatory markers in forecasting the prognosis of glioma patients. We extracted hazard ratios (HR) and their corresponding 95% confidence intervals (95% CI) from each study for analysis. To assess heterogeneity and identify influential studies, we conducted sensitivity analysis. Subgroup analysis was performed to investigate sources of heterogeneity, and we employed Egger's test to evaluate publication bias in the meta-analysis. Higher NLR levels were associated with shorter overall survival (HR = 1.46, 95% CI: 1.33-1.60) and progression-free survival (HR = 1.24, 95% CI: 1.04-1.48). There was no significant correlation between PLR levels and overall survival (HR = 1.01, 95% CI: 1.00-1.01) or progression-free survival (HR = 1.00, 95% CI: 0.98-1.02) in glioma patients. Elevated MLR levels were associated with decreased overall survival in glioma patients (HR = 1.78, 95% CI: 1.36-2.34). SII levels did not show any significant association with overall or progression-free survival in glioma patients (HR = 1.00, 95% CI: 0.99-1.01).In the sensitivity analysis, two studies potentially contributed to the instability. Subgroup analyses showed patient population and area were identified as potential sources of heterogeneity. Egger's test showed that there was publication bias in the relationship between NLR and PLR and overall survival (P < 0.05).All randomized controlled models, except for these, were not affected by publication bias. NLR and MLR are two reliable indicators of inflammation in the prognosis of glioma patients; PLR and SII do not have significant value in the prognosis of glioma patients.
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Affiliation(s)
- JiaNuo Zhou
- School of Medicine, LiShui University, LiShui, 323000, Zhejiang, China
| | - Botao Tan
- LiShui University, LiShui, 323000, Zhejiang, China.
| | - Feng Gao
- Department of Neurosurgery, the Affiliated People's Hospital of Ningbo University, Ningbo, 315040, Zhejiang, China.
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Joyce T, Tasci E, Jagasia S, Shephard J, Chappidi S, Zhuge Y, Zhang L, Cooley Zgela T, Sproull M, Mackey M, Camphausen K, Krauze AV. Serum CD133-Associated Proteins Identified by Machine Learning Are Connected to Neural Development, Cancer Pathways, and 12-Month Survival in Glioblastoma. Cancers (Basel) 2024; 16:2740. [PMID: 39123468 PMCID: PMC11311306 DOI: 10.3390/cancers16152740] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/24/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
Glioma is the most prevalent type of primary central nervous system cancer, while glioblastoma (GBM) is its most aggressive variant, with a median survival of only 15 months when treated with maximal surgical resection followed by chemoradiation therapy (CRT). CD133 is a potentially significant GBM biomarker. However, current clinical biomarker studies rely on invasive tissue samples. These make prolonged data acquisition impossible, resulting in increased interest in the use of liquid biopsies. Our study, analyzed 7289 serum proteins from 109 patients with pathology-proven GBM obtained prior to CRT using the aptamer-based SOMAScan® proteomic assay technology. We developed a novel methodology that identified 24 proteins linked to both serum CD133 and 12-month overall survival (OS) through a multi-step machine learning (ML) analysis. These identified proteins were subsequently subjected to survival and clustering evaluations, categorizing patients into five risk groups that accurately predicted 12-month OS based on their protein profiles. Most of these proteins are involved in brain function, neural development, and/or cancer biology signaling, highlighting their significance and potential predictive value. Identifying these proteins provides a valuable foundation for future serum investigations as validation of clinically applicable GBM biomarkers can unlock immense potential for diagnostics and treatment monitoring.
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Affiliation(s)
- Thomas Joyce
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Erdal Tasci
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Sarisha Jagasia
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Jason Shephard
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Shreya Chappidi
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
- Department of Computer Science and Technology, University of Cambridge, 15 JJ Thomson Ave, Cambridge CB3 0FD, UK
| | - Ying Zhuge
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Longze Zhang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Theresa Cooley Zgela
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Megan Mackey
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute NIH, 9000 Rockville Pike, Bethesda, MD 20892, USA; (T.J.); (S.J.); (J.S.); (S.C.); (Y.Z.); (L.Z.); (T.C.Z.); (M.S.); (M.M.); (K.C.)
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Wang Y, Xu C, Zhang Z. Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis. BMC Med 2023; 21:486. [PMID: 38053096 PMCID: PMC10696791 DOI: 10.1186/s12916-023-03199-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Accepted: 11/27/2023] [Indexed: 12/07/2023] Open
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.
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Affiliation(s)
- Yan Wang
- Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, The Fifth School of Clinical Medicine Zhejiang Chinese Medical University, Huzhou, 313000, Zhejiang, China
| | - Chu Xu
- Department of Neurosurgery, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, 401120, China
| | - Zongxin Zhang
- Clinical Laboratory, Huzhou Central Hospital, Affiliated Central Hospital of Huzhou University, The Fifth School of Clinical Medicine Zhejiang Chinese Medical University, Huzhou, 313000, Zhejiang, China.
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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; 23:432. [PMID: 37173662 PMCID: PMC10176909 DOI: 10.1186/s12885-023-10889-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
OBJECTIVE In recent years, an increasing number of studies have revealed that patients' preoperative inflammatory response, coagulation function, and nutritional status are all linked to the occurrence, development, angiogenesis, and metastasis of various malignant tumors. The goal of this study is to determine the relationship between preoperative peripheral blood neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), systemic immune-inflammatory index (SII), platelet to lymphocyte ratio (PLR), and platelet to fibrinogen ratio (FPR). Prognostic nutritional index (PNI) and the prognosis of glioblastoma multiforme (GBM) patients, as well as establish a forest prediction model that includes preoperative hematological markers to predict the individual GBM patient's 3-year survival status after treatment. METHODS The clinical and hematological data of 281 GBM patients were analyzed retrospectively; overall survival (OS) was the primary endpoint. X-Tile software was used to determine the best cut-off values for NLR, SII, and PLR, and the survival analysis was carried out by the Kaplan-Meier method as well as univariate and multivariate COX regression. Afterward, we created a random forest model that predicts the individual GBM patient's 3-year survival status after treatment, and the area under the curve (AUC) is used to validate the model's effectiveness. RESULTS The best cut-off values for NLR, SII, and PLR in GBM patients' preoperative peripheral blood were 2.12, 537.50, and 93.5 respectively. The Kaplan-Meier method revealed that preoperative GBM patients with high SII, high NLR, and high PLR had shorter overall survival, and the difference was statistically significant. In addition to clinical and pathological factors. Univariate Cox showed NLR (HR = 1.456, 95% CI: 1.286 ~ 1.649, P < 0.001) MLR (HR = 1.272, 95% CI: 1.120 ~ 1.649, P < 0.001), FPR (HR = 1.183,95% CI: 1.049 ~ 1.333, P < 0.001), SII (HR = 0.218,95% CI: 1.645 ~ 2.127, P < 0.001) is related to the prognosis and overall survival of GBM. Multivariate Cox proportional hazard regression showed that SII (HR = 1.641, 95% CI: 1.430 ~ 1.884, P < 0.001) is also related to the overall survival of patients with GBM. In the random forest prognostic model with preoperative hematologic markers, the AUC in the test set and the validation set was 0.907 and 0.900, respectively. CONCLUSION High levels of NLR, MLR, PLR, FPR, and SII before surgery are prognostic risk factors for GBM patients. A high preoperative SII level is an independent risk factor for GBM prognosis. The random forest model that includes preoperative hematological markers has the potential to predict the individual GBM patient's 3-year survival status after treatment,and assist the clinicians for making a good clinical decision.
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Affiliation(s)
- Xiaozong Duan
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Bo Yang
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
| | - Chengbin Zhao
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Boran Tie
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Lei Cao
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuyuan Gao
- The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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