The Diagnostic Significance of Cellular Immune Inflammation Markers in Assessing Different Malignancy Grades Gliomas

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Sklyar, Anastasiia S. Nechaeva, Alexei Yu. Ulitin, Marina V. Matsko, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6234246/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose. The purpose of this study was to evaluate the diagnostic relevance of inflammatory markers in gliomas, taking into account different histological subtypes and malignancy levels. Methods. This prospective study included 139 adult glioma patients. Patients were stratified by tumour grade and genetic mutation, yielding 25 cases of diffuse astrocytoma grade 2, 25 cases of glioma grade 3 or 4 with IDH1/2-mutations and 89 cases with glioblastoma. IDH1/2-mutations were detected in 50 patients, 15 of which had co-deletion at 1p19q. The pre-operative neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-lymphocyte ratio (PLR) were calculated. Results. The LMR in the glioma grade 2 group was higher than that in the glioma grade 3, 4 and glioblastoma groups (3,71 vs 3,09 vs 3; p < 0,05) with areas under the curve (AUCs) of 0,6552 (0,4930-0,8174) and 0,6586 (0,5583-0,7590) respectively. LMR was higher in patients with IDH1/2-mutation gliomas (3.44 vs 3.0; p = 0.039). No differences in LMR were observed between patients with oligodendroglioma and astrocytoma (3.43 vs 3.19; p = 0.76). LMR in all cohorts was not affected by use of corticosteroids. The NLR was higher in glioblastoma patients than in patients with glioma grade 2 (2.9 vs 1.96, p < 0.05). Increases in neutrophils and NLR in glioblastoma patients were correlated with the corticosteroids (3.7 vs. 8.0, p < 0.05 and 1.95 vs. 3.79, p < 0.05, respectively). Conclusion. LMR is a reliable, non-corticosteroid independent biomarker for diagnosing diffuse adult gliomas, with lower levels indicating higher tumor malignancy. Conversely, NLR is an unreliable biomarker due to its elevation, which often results from glucocorticoid therapy. brain tumors glioma inflammatory blood markers lymphocyte-monocyte ratio diagnostic indicator Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction According to global epidemiological data, approximately 320,000 new cases of primary central nervous system (CNS) tumors are diagnosed every year, with an estimated mortality of about 250,000 patients affected by these neoplasms [ 1 ]. Among primary intracerebral tumors, diffuse gliomas are the most common subtype [ 2 ]. In the latest classification of CNS tumors (5th edition) categorizes this group to include astrocytoma of grades 2, 3, and 4, oligodendrogliomas of grades 2 and 2, as well as glioblastomas classified as grade 4 [ 3 ]. Notably, all gliomas grade 2 are classified as "benign" tumors; conversely, diffuse astrocytoma grade 3 and 4, together with glioblastomas, are associated with a significantly poor prognosis and are classified as malignant neoplasms. Treatment of diffuse glioma patients includes neurosurgical procedures, adjuvant radiation therapy and systemic anti-tumour therapies, including chemotherapy and targeted therapy [ 4 , 5 ]. It is important to note that the specific anti-tumour regimen is not only adapted to the histological subtype of diffuse glioma but is also strongly influenced by the extent of resection [ 4 ]. The degree of tumor resection can substantially impact the necessity for subsequent anti-tumor therapy, particularly in cases of benign diffuse astrocytoma. For histological classifications such as astrocytomas grade 3 and 4, oligodendrogliomas grade 3 and glioblastomas, radiation and chemotherapy are required regardless of surgical outcome. Therefore, there is an urgent need for pre-operative confirmation of histological diagnosis to facilitate optimal neurosurgical planning and comprehensive treatment of the patient. Advanced imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have been developed in recent years and have made it easier to improve the accuracy of pre-operative diagnostics [ 6 , 7 ]. Liquid biopsy has proven to be a very promising diagnostic approach in general oncology and neuro-oncology [ 8 ]. Circulating nucleic acids have been identified as having significant diagnostic potential in gliomas of different malignancies [ 9 ]. However, the high costs associated with these techniques limit their availability in many health care settings. Therefore, there is still a critical need for a cost-effective, rapid and unambiguous method of differential diagnosis of brain gliomas. Research suggests that systemic immune inflammation plays a key role in the oncogenic process [ 10 – 12 ]. The primary focus of the study was to assess the local immune response within tumours. Reprogrammed immune cells have been shown to contribute approximately 30 percent of the cellular composition of malignant gliomas, facilitating oncogenesis [13 In addition, intracellular inflammatory signaling pathways were clarified and key cytokines were characterised. At the same time, research into systemic immune inflammation in gliomas is still in its early stages and should be further explored. Numerous studies have been carried out to evaluate the diagnostic and prognostic relevance of cellular inflammatory markers in patients with glioma [ 14 – 18 ]. It should be stressed that most of these studies did not stratify tumours according to histological classification and grade of malignancy. Moreover, the existing literature largely ignored the effect of glucocorticosteroid (GCS) therapy on patients, despite its significant impact on the dynamics of the immune system. The prognostic effects of inflammatory biomarkers in patients with glioblastoma have been previously assessed, including the effects of GCS [ 19 ]. The purpose of this study was to evaluate the diagnostic relevance of cellular inflammatory markers, specifically neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR), in patients with glioma of different histological subtypes and different degrees of malignancy, while considering the effect of GCS therapy. Materials and methods The study included a cohort of 139 adults aged 18 years and over who presented with newly diagnosed supratentorial glial neoplasms. Patients underwent surgery in the Department of Brain and Spinal Cord Tumour Surgery at the National Research Medical Centre of Almaz in the period from 2021 to 2024. Before being included in the study, informed consent was obtained from each of the participants. The exclusion criteria included diagnosis of immunodeficiency, autoimmune disease or neoplasms outside the central nervous system. In addition, patients who have received radiation therapy, chemotherapy or immunotherapy at any time in their medical history were also excluded from the study. The presence of acute inflammatory conditions, use of antibacterial agents at the time of enrolment and continued treatment with anticonvulsants were also considered disqualifying factors. Venous blood samples were collected from patients three days prior to surgical intervention in the morning, followed by a comprehensive clinical haematological examination and the quantification of C-reactive protein (CRP). Patients with CRP levels above 5 ng/ml were excluded from the study cohort. Clinical haematological analysis, including extended leukocyte differentiation, was performed with the use of the Sysmex XN-550 haematological analyser, using Sysmex reagents and control materials sourced from Japan. Inflammatory markers were derived from absolute lymphocyte, neutrophil, monocyte and platelet counts, specifically the neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-lymphocyte ratio (PLR) to assess the inflammatory microenvironment in neurooncologic diseases. Histopathological diagnosis was made by analysing surgical tumour samples according to standardised criteria described in the Fifth Edition of the World Health Organisation Central Nervous System Tumour Classification [ 3 ]. Histological sections were stained with haematoxylin and eosin, in addition to immunohistochemistry (IHC) panels including anti-GFAP (poly, DakoCytomation), anti-ATRX (Abcam), anti-EGFR (Abcam), anti-MGMT (NovusBiologics), anti-Ki-67 (Dako), anti-IDH1R132H (Dianova), and for differential diagnosis, Syn (DakoCytomation) and NB (Leica). Mutational analysis of the IDH1 (exon 4) and IDH2 (exon 4) genes was performed via high-resolution melting analysis (HRMA) of PCR products, followed by subsequent DNA sequencing to elucidate genetic alterations. Statistical analysis was performed using thePrism GraphPad 10 program (GraphPad Software, USA). The normality test was performed using Kolmogorov-Smirnov, Shapiro-Wilk tests. We used the mean ± standard deviations for normally distributed data, and median (rank) for non-normally distributed data. Group comparisons of nonparametric data were performed using the Mann-Whitney U test. The diagnostic performance of preoperative inflammatory markers in receivers was assessed by calculating the area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve. The differences were considered statistically significant at р<0,05. The resulting graphs were performed using the Prism GraphPad 10 program (GraphPad Software, USA). Results Patients were stratified into three cohorts based on histopathological classification: glioma grade 2 (n = 25), glioma grade 3,4 with IDH1 mutation (n = 25), and glioblastoma (n = 89) (Table 1 ). The glioma grade 2 and glioma grade 3,4 (IDH1-mt) cohorts also encompassed individuals diagnosed with oligodendroglioma (7 and 8 patients, respectively). Notably, patients exhibiting IDH1-positive tumor status across glioma grades 2, 3, and 4 were significantly younger than those diagnosed with glioblastoma (refer to Table 1 ). The mean age of patients with glioma grade 2 was 42.5 ± 12.5 years, comprising 14 males (56%) and 11 females (44%). Conversely, the mean age of patients with glioma grade 3,4 (IDH1-mt) was 46 ± 11 years, with 7 males (28%) and 18 females (72%). In the glioblastoma cohort, the mean age was 60 ± 12.5 years, with 51 males (57.31%) and 38 females (42.69%). Table 1 Preoperative characteristics of patients with cerebral gliomas (n = 139) Parameters glioma grade 2 glioma grade 3,4 (IDH-mt) glioblastoma No. of patients 25 25 89 Age 42,5 ± 12,5 b 46 ± 11 b 60 ± 12,5 Male ( n , %) 14 (56%) 7 (28%) 51 (57,31%) Female ( n , %) 11 (44%) 18 (72%) 38 (42,69%) Taking dexamethasone ( n , %) yes no 6 (24%) 19 (76%) 11 (44%) 14 (56%) 66 (74,15%) 23 (25,85%) Neutrophils (x10 9 /L) 4,01 (1,54 − 15,0) b 5,30 (1,53 − 16,80) 7,42 (1,85 − 24) Lymphocytes (x10 9 /L) 2,11 (1,20 − 4,0) 2,0 (1,01–2,7) 2,07 (0,66 − 4,82) Monocytes (x10 9 /L) 0,52 (0,2 − 1,50) b 0,60 (0,01–2,1) 0,81 (0,2–2,0) Platelets (x10 9 /L) 226 (158–401) 268 (108–408) 245 (117–487) NLR 1,96 (0,64 − 8,30) b 2,0 (0,76 − 16,63) 2,9 (0,62 − 34,25) LMR 3,71 (2,4–9,230) a−b 3,09 (0,99 − 8,33) 3,0 (0,53 − 8,48) PLR 104 (49,58–199,2) 114 (70,13–348,5) 117 (32,05-358,9) mt -mutant a p < 0,05 vs grade 3,4 (IDH1-mt) b p < 0,05 vs glioblastoma A significant proportion of patients diagnosed with grade 2 and grade 3 gliomas did not receive glucocorticoid therapy (dexamethasone) prior to surgical intervention, with rates of 76% and 56%, respectively. In contrast, within the glioblastoma cohort, only 25.85% of cases were not administered glucocorticoids, as detailed in Table 1 . Comparison of preoperative inflammatory blood markers for glioma of different subtypes There were no statistically significant differences observed in lymphocyte, platelet, or Platelet-Lymphocyte ratio (PLR) levels across the three studied cohorts (Fig. 1 b, 1 d, and 1 g). Notably, the counts of neutrophils and monocytes in the glioma grade 2 cohort [4.01 (1.54-15.0) and 0.52 (0.2–1.50), respectively] were significantly lower compared to those in the glioblastoma cohort [37.42 (1.85-24) and 0.81 (0.2-2.0), respectively] (Fig. 1 a, 1 c). The analysis revealed that the Neutrophil-Lymphocyte Ratio (NLR) and Lymphocyte-Monocyte Ratio (LMR) exhibited no significant differences between glioma grade 3,4 (IDH-mutant) and glioblastoma groups. Notably, the LMR was found to be statistically elevated in the glioma grade 2 group with a mean value of 3.71 (range: 2.4–9.230), in contrast to the biomarker values observed in glioma grade 3,4 (IDH-mutant) group at 3.09 (range: 0.99–8.33) and glioblastoma group at 3.0 (range: 0.53–8.48) (refer to Fig. 1 f). Conversely, the highest NLR values were recorded in glioblastoma patients, averaging 2.9 (range: 0.62–34.25), which demonstrated a statistically significant difference when compared to the NLR in low-grade glioma patients, which was 1.96 (range: 0.64–8.30) (Fig. 1 e). The analysis of hematological parameters, specifically neutrophils, lymphocytes, monocytes, NLR and LMR, was conducted in relation to the administration of GCS prior to surgical intervention (Table 2 ). In patients receiving GCS, a statistically significant elevation in neutrophil counts was observed within the glioma grade 3, 4 and glioblastoma cohorts (Fig. 2 a). Furthermore, NLR values were markedly increased in individuals with malignant gliomas undergoing treatment with dexamethasone (Fig. 2 d). The administration of dexamethasone was found to influence monocyte levels exclusively in the glioblastoma patient group (Fig. 2 c). Notably, the LMR index exhibited no significant variation between patients receiving dexamethasone and those who were not across all three patient categories (Fig. 2 e). Table 2 Preoperative inflammatory markers and dexamethasone intake in glioma patients Parameters grade 2 grade 3,4 (IDH-mt) glioblastoma without Dex (n = 19) with Dex (n = 6) without Dex (n = 14) with Dex (n = 11) without Dex (n = 23) with Dex (n = 66) Neutrophils (x10 9 /L) 3,43 (1,54 − 11,69) 5,20 (4,40 − 9,6) 4,0 (1,53 − 5,52) 10,0 (3,05–16,80) a 3,70 (1,85 − 7,55) 8,0 (2,14–24) b Lymphocytes (x10 9 /L) 2,0 (1,20 − 3,25) 3,48 (2,06 − 4,0) 2,0 (1,20 − 2,5) 2,20 (1,01–2,7) 1,76 (1,10 − 4,70) 2,18 (0,66 − 4,82) Monocytes (x10 9 /L) 0,50 (0,20 − 1,0) 0,57 (0,39 − 1,50) 0,52 (0,30 − 2,10) 0,72 (0,01–1,21) 0,59 (0,20 − 1,0) 0,82 (0,23 − 2,0) b NLR 1,86 (0,64 − 3,56) 2,1 (1,37 − 2,59) 1,86 (0,76 − 4) 5 (1,15–16,63) a 1,95 (0,62 − 5,0) 3,79 (0,80 − 34,35) b LMR 3,75 (2,4–9,23) 3,74 (2,47 − 8,33) 3,1 (0,99 − 6,67) 2,5 (1,0–4,58) 3,6 (1,3–8,2) 2,88 (0,53 − 8,48) Dex - dexamethasone a p < 0,05 vs grade 3,4 (IDH1-mt) without Dex b p < 0,05 vs glioblastoma without Dex Diagnostic value of inflammatory blood markers in glioma diagnosis and glioma grading Two biomarkers demonstrated significant diagnostic value: NLR and LMR (Fig. 3 ). In the comparative analysis of patients diagnosed with glioma grade 2 versus those with glioblastoma, NLR demonstrated a superior Area Under the Curve (AUC) value of 0.7157 (95% CI: 0.6087–0.8227), in contrast to LMR, which yielded an AUC of 0.6586 (95% CI: 0.5583–0.7590). Notably, the NLR levels were influenced by the administration of GCS (Table 2 ), whereas LMR remained unaffected by this therapeutic intervention, thereby establishing LMR as a more robust diagnostic marker. Furthermore, when evaluating the diagnostic efficacy of LMR in the context of glioma grade 2against more aggressive gliomas (grade 3, 4 with IDH1 mutation and glioblastoma), LMR maintained its diagnostic relevance with an AUC of 0.6579 (95% CI: 0.5635–0.7522). However, in the direct comparison between grade 2 glioma and grade 3.4 glioma (IDH-mt), the AUC for LMR was comparatively lower at 0.6552 (95% CI: 0.4930–0.8174) than in other evaluated cohorts. The investigation demonstrated that the LMR, in comparison to other cellular biomarkers, holds substantial diagnostic relevance for patients diagnosed with low-grade gliomas. Table 3 Diagnostic value of various inflammatory markers in glioma diagnosis Markers grade 2 vs grade 3,4 (IDH-mt) grade 2 vs glioblastoma grade 2 vs grade 3,4 (IDH-mt) + glioblastoma AUC (95%) p-value AUC (95%) p-value AUC (95%) p-value NLR 0,5888 (0,4292-0,7484) 0,28 0,7157 (0,6087-0,8227) 0,001 0,6876 (0,5798-0,7954) 0,003 LMR 0,6552 (0,4930-0,8174) 0,059 0,6586 (0,5583-0,7590) 0,01 0,6579 (0,5635-0,7522) 0,001 PLR 0,6232 (0,4670-0,7794) 0,13 0,5676 (0,4546-0,6807) 0,30 0,5798 (0,4708-0,6888) 0,20 Comparison of LMR for glioma of different IDH1-mutation and 1p/19q-codeleted status The LMR index was evaluated when all patients (n = 139) were divided into groups depending on the presence of the IDH1 gene mutation and the presence of 1p/19q-codeletion (Table 4 ). No significant differences in the value of LMR were found in the group of patients with oligodendroglioma and astrocytoma (Fig. 4 a). At the same time, in patients with glioma with a mutation in the IDH1 gene, the LMR value was significantly higher than in patients in the IDH-wt glioma group (Fig. 4 b). The LMR was assessed in a cohort of 139 patients stratified based on the presence of the IDH1/2 mutation and the occurrence of 1p/19q codeletion (Table 4 ). No statistically significant differences in LMR values were observed between the oligodendroglioma and astrocytoma patient groups (Fig. 4 a). Conversely, patients harboring the IDH1/2 mutation exhibited a markedly elevated LMR compared to those with IDH-wildtype gliomas (Fig. 4 b). Table 4 Preoperative LMR in glioma patients Glioma patients n = 139 LMR p-value 1p/19q-codeletion status Oligodendroglioma (n = 15) 3,43 (0,99 − 6,67) 0,7690 Astrocytoma (n = 124) 3,19 (0,53 − 9,23) IDH1/2-mutation status Glioma IDH-mt (n = 50) 3,44 (0,99 − 9,23) 0,0392 Glioma IDH-wt (n = 89) 3.0 (0,53 − 8,48) wt - wild type mt -mutant Discussion The hypothesis that there is a relationship between inflammatory responses and oncogenesis was first proposed by Rudolf Virchow in the 19th century, who elucidated the infiltration of leukocytes into neoplastic tissue [10; 11]. Recent decades have produced considerable evidence that inflammation is a key factor in the development and etiology of neoplastic diseases. Inflammatory processes are mediated by active involvement and regulation of immune cells, including neutrophils, lymphocytes and monocytes, in addition to platelets. Numerous studies have highlighted the important diagnostic and prognostic implications of these blood markers in different malignancies, in isolation or in combination [ 20 – 24 ]. The re-evaluation of immunological deficits associated with the central nervous system, together with the role of the immune system in oncogenesis, has stimulated research into the inflammatory mechanisms underlying intracerebral neoplasms, in particular diffuse gliomas [13; 25; 26; 27]. At the same time, there has been a worldwide interest in the local immune microenvironment to understand its complexity and its implications for tumour behavior [13; 28; 29]. Recent research has shown that the tumour microenvironment in gliomas has features similar to chronic inflammation, suggesting that there is a significant interaction between tumour biology and the immune response [ 29 – 31 ]. Gliomas are known to express chemokines that facilitate the recruitment of immune cells that differentiate into tumour-associated macrophages, neutrophils, and myeloid suppressor cells, which are also affected by cytokines. These immune components contribute to tumigenesis while simultaneously impairing the effector lymphocytes' ability to function. In view of these findings, it is reasonable to assume that systemic inflammatory markers such as neutrophil- lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-plasma ratio (PLR) can serve as reliable and sensitive biomarkers for the diagnosis and prognostic evaluation of glioma. Numerous clinical studies have been conducted to elucidate the diagnostic and prognostic relevance of NLR and LMR in glioma [14–18; 32–34]. In addition, consensus research has identified NLR as the most relevant and reliable biomarker in this context [14; 15; 17; 18; 34]. NLR serves as an indicator of both a non-specific neutrophil-mediated immune response and a specific adaptive immune response against cancer, mediated by lymphocytes. NLR has already been shown to be of diagnostic value in glioma patients compared to neoplasms such as meningioma, schwannoma and adenoma [17; 18; 35]. In preoperative diagnostics of cerebral gliomas of varying degrees of malignancy, a positive correlation has been established between NLR and tumor grade [14; 18; 34; 35]. However, these studies did not account for the administration of GCS by patients, despite evidence indicating that dexamethasone, commonly prescribed to reduce peritumoral edema, affects immune system functioning [ 36 ]. In our study, statistically significant differences in median NLR values were observed when comparing patient groups with grade 2 glioma and glioblastoma (1.96 vs 2.9; p < 0.05) (Table 1 , Fig. 1 ). However, it should be noted that in patients with glioblastoma, as well as in the group with grade 3 and 4 gliomas receiving GCS, there was an observed increase in neutrophils and NLR compared to patients who were not administered this therapy (8.0 vs 3.7, p < 0.05; 3.79 vs 1.95, p < 0.05; 10.0 vs 4.0, p < 0.05; 5.0 vs 1.86, p < 0.05, respectively) (Table 2 , Fig. 2 ). The NLR median for patients not receiving dexamethasone in the groups with glioma grade 2, 3 and 4 and glioblastoma was almost identical (1.86 vs 1.86 vs 1.95; p > 0.05). Therefore, according to the results of our study, NLR is a biomarker that is dependent on GCS, which significantly reduces its diagnostic value (Tables 1 and 3 ). Currently, LMR is recognized as a biomarker for anti-cancer immunological activation in general oncology. The increase in its value is related either to the high number of effector lymphocytes, which leads to an adequate immune response, or to the reduction in monocytes. Monocytes play a dual role in the pathology of cancer. After differentiation, these cells can either play a protective function by helping to destroy the neoplasm or reprogramme themselves to support the growth of the tumour. Our data reveal no correlation between blood lymphocyte levels in patients and glioma grade (Table 1 , Fig. 1 , Table 3 , Fig. 3 ). As expected, glucocorticoids were not found to affect lymphocyte levels (p > 0.05) (Table 2 , Fig. 2 ). However, absolute monocyte counts increased with tumour malignancy (0.52 vs 0.60 vs 0.81) (Table 1 , Fig. 1 , Table 3 , Fig. 3 ). Statistical significance was reached only when comparing values for benign glioma (grade 2) and glioblastoma (0.52 vs 0.81; p = 0.0044) (Table 1 , Fig. 1 ). Notably, dexamethasone increased monocytes, but only in the glioblastoma patient’s group (0.59 vs 0.82; p = 0.0152) (Table 3 , Fig. 3 ). The relevance of LMR in glioma patients remains controversial in the neurooncology community. Although some studies have questioned its predictive effects, others have demonstrated its relevance [15; 17; 18; 33; 35]. A key observation is that many studies evaluating LMR did not stratify patients with glioma by histological subtype or grade of malignancy or considered the effect of dexamethasone. Our previous cohort study in glioblastoma patients identified LMR as a key biomarker predictive of the risk of early relapse, independent of dexamethasone therapy [ 19 ]. In this analysis, a correlation between the increasing degree of malignancy and the decreasing LMR was observed. In particular, the median LMR for glioma grade 2 was 3.71, while glioma grade 3 and 4 had a median LMR of 3.09 and glioblastoma patients had a median LMR of 3.05, respectively (p < 0.05). In addition, it should be noted that LMR levels remained unchanged with corticosteroid treatment in all patient groups. The findings suggest that there is no correlation between the number of lymphocytes and tumor malignancy; however, there is a correlation with increased monocytes. This suggests that the LMR is primarily driven by absolute monocyte counts. Notably, in the glioblastoma cohort, monocytes were significantly increased in patients receiving corticosteroids, although the LMR was not affected. These results highlight the complex mechanisms and interactions between the immune cell populations and support the use of composite biomarkers for diagnostic evaluation rather than relying on absolute monocytes and lymphocytes alone. In addition, the LMR was assessed in cohorts of patients stratified by presence or absence of IDH/2 gene mutations and 1p/19q codeletion (Table 4 and Fig. 4 ). According to the fifth edition of the WHO classification system, these genetic alterations are critical to the definitive histological diagnosis. No statistically significant differences in LMR values were observed between patients with and without oligodendroglioma (3.43 vs 3.19; p = 0.76). Conversely, in a cohort of patients without IDH1 and IDH2 mutations, LMR was found to be significantly lower (3.44 vs 3.0; p = 0.039). These findings highlight the differential immune responses associated with benign and malignant gliomas. In neurooncology, it has been well documented that platelets activate neoplastic cells through cytokine signaling, which is important for thrombosis, angiogenesis, cell migration and dissemination of cancer. In addition, platelets contribute to evasion of immunological surveillance and are involved in inflammatory responses [ 37 ]. Consequently, the presence of thrombosis and increased platelet counts in patients with oncological diseases correlate with poor prognosis. However, the specific role of platelets in glioma progression and clinical course remains unclear. Our findings suggest that there is no significant correlation between the number of platelets and the histological grade of gliomas (226 vs 268 vs 245; p > 0.05) (Table 1 , Fig. 1 ). The PLR Inflammatory Index is recognized as a reliable independent biomarker indicating the progression of oncological diseases. Its diagnostic and prognostic usefulness has been demonstrated in various malignancies, including esophageal and gastric cancer, lung cancer and colorectal cancer [20; 23; 24; 37]. In a previous study involving a cohort of patients with glioblastoma, PLR was shown to be of significant prognostic importance [ 19 ]. However, its diagnostic effectiveness in distinguishing gliomas of different malignancies is still limited. Although an increasing trend in PLR values was observed with increasing grade of glioma, statistical significance was not reached (104 vs 114 vs 117; p > 0.05) (Table 1 , Fig. 1 ). The immune system plays a key role in oncogenesis and both exhibits anti-tumour properties and facilitates the progression of the disease. Inflammatory cell markers are key agents in these mechanisms and their prognostic and diagnostic relevance warrants investigation. Our findings suggest that the LMR is a reliable independent diagnostic marker in glioma patients regardless of corticosteroid therapy. Conversely, the NLR is not recommended for clinical use because it is correlated to corticosteroid use, thereby reducing its diagnostic utility. In addition, the PLR showed no significant correlation with the degree of malignancy. Thus, the increased LMR levels observed in low grade gliomas, as opposed to in malignant gliomas, therefore indicate an increased activation of the immune response. This may explain the favorable and prolonged clinical course often observed with benign glioma, which may reflect underlying immunological mechanisms, and require further investigation. Declarations Acknowledgements Authors and Affiliations The Research Laboratory of Neuro-Oncology, Almazov National Medical Research Centre, Saint Petersburg, Russia Sofia S. Sklyar, Anastasiia S. Nechaeva, Alexei Yu. Ulitin, Victor E. Olyushin, Konstantin A. Samochernykh Department of Neurosurgery named after Professor A.L. Polenov , Federal State Budgetary Educational Institution of Higher Education "Northwestern State Medical University named after I.I. Mechnikov" of the Ministry of Health of the Russian Federation, Saint Petersburg, Russia Alexei Yu. Ulitin Department of Oncology, State Budgetary Healthcare Institution "Saint Petersburg Clinical Scientific and Practical Center for Specialized Types of Medical Care (Oncology) named after N. P. Napalkov", Saint Petersburg, Russia. Marina V. Matsko Department of Oncology , Private Educational Institution of Higher Education "Saint Petersburg Medical and Social Institute" Saint Petersburg, Russia. Marina V. Matsko Contributions All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Sofia S. Sklyar and Anastasiia S. Nechaeva. The first draft of the manuscript was written by Sofia S. Sklyar, Anastasiia S. Nechaeva, Alexei Yu. Ulitin, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Corresponding author Correspondence to Sofia S. Sklyar Ethics declarations Consent for publication Not applicable. Competing interests The authors declare no competing interests. Ethics approval This prospective cohort study was approved by the Institutional Ethics Committee of Almazov National Medical Research Centre Consent to participate Informed consent was obtained from each participant prior to their inclusion in the study. 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Nat Rev Immunol 21(3):151–61. https://doi.org/10.1038/s41577-020-0406-2 Sklyar SS, Trashkov AP, Matsko MV, Safarov BI, Vasilieva AV (2022) Immune response to primary glioblastoma (Rus). Pediatrician 13(2): 49–60. https://doi.org/10.17816/PED13249-60 Yang Y, Hu F, Wu S, Huang Z, Wei K, Ma Y, Ou-Yang Q (2023) Blood-based biomarkers: diagnostic value in brain tumors (focus on gliomas). Front. Neurol 14:1297835. https://doi.org/10.3389/fneur.2023.1297835 Wang PF, Song HW, Cai HQ, Kong LW, Yan K, Jiang T, Li SW, Yan CX (2017) Preoperative inflammation markers and IDH mutation status predict glioblastoma patient survival. Oncotarget 8:50117–50123. https://doi.org/10.18632/oncotarget.15235 Yersal O, Odabasi E, Ozdemir O, Kemal Y (2018) Prognostic significance of pre-treatment neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with glioblastoma. Mol Clin Oncol 9:453–458. https://doi.org/10.3892/mco.2018.1695 Wang DP, Kang K, Lin Q, Hai J (2020) Prognostic Significance of Preoperative Systemic Cellular Inflammatory Markers in Gliomas: A Systematic Review and Meta-Analysis. Clin Transl Sci 13:179–188. https://doi.org/10.1111/cts.12700 Chen F, Chao M, Huang T, Guo S, Zhai Y, Wang Y, Wang N, Xie X, Wang L, Ji P (2022) The role of preoperative inflammatory markers in patients with central nervous system tumors, focus on glioma. Front Oncol 12:1055783. https://doi.org/10.3389/fonc.2022.1055783 Sklyar SS, Ulitin Ayu, Matsko MV, Zorina EYu, Konova AM, Baknina AK, Olyushin VE (2024) Cellular Inflammatory Markers are New Prognostic Factors For Patients with Glioblastoma (Rus). Problems in Oncology 70(6):1086-1095. https://doi.org/10.37469/0507-3758-2024-70-6-1086-1095 Mandaliya H, Jones M, Oldmeadow C, Nordman II (2019) Prognostic biomarkers in stage IV non-small cell lung cancer (NSCLC): neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), platelet to lymphocyte ratio (PLR) and advanced lung cancer inflammation index (ALI). Transl Lung Cancer Res 8(6):886–894. https://doi.org/10.21037/tlcr.2019.11.16 Trinh H, Dzul SP, Hyder J, Jang H, Kim S, Flowers J, Vaishampayan N, Chen J, Winer I, Miller S (2020) Prognostic value of changes in neutrophil-to-Lymphocyte ratio (Nlr), platelet-to-Lymphocyte ratio (Plr) and lymphocyte-to-Monocyte ratio (Lmr) for patients with cervical cancer undergoing definitive chemoradiotherapy (Dcrt). Clinica chimica acta 510:711–716. https://doi.org/10.1016/j.cca.2020.09.008 Novik AV, Danilova AB, Nekhaeva TL et al. (2021) Assessment of the dynamics of immunological parameters at the beginning of the therapy as prognostic and predictive factors in patients with melanoma (Rus). Pharmateca7:118-126. https://doi.org/10.18565/pharmateca.2021.7.118–126 Yamamoto T, Kawada K, Obama K (2021) Inflammation-related biomarkers for the prediction of prognosis in colorectal cancer patients. Int J Mol Sci 22 (15):8002. https://doi.org/10.3390/ijms22158002 Zheng J, Peng L, Zhang S, Liao H, Hao J, Wu S, Shen H (2023) Preoperative systemic immune-inflammation index as a prognostic indicator for patients with urothelial carcinoma. Front. Immunol 14:1275033. https://doi.org/10.3389/fimmu.2023.1275033 Louveau A, Smirnov I, Keyes TJ, Eccles JD, Rouhani SJ, Peske JD, Derecki NC, Castle D, Mandell JW, Lee KS (2015) Structural and functional features of central nervous system lymphatic vessels. Nature 523:337–341. https://doi.org/10.1038/nature14432 Majc B, Novak M, Kopitar-Jerala N, Jewett A, Breznik B (2021). Immunotherapy of Glioblastoma: Current Strategies and Challenges in Tumor Model Development. Cells 10(265):2–22. https://doi.org/10.3390/cells10020265 Sklyar SS, Sitovskaya DA, Iu.V. Mirolyubova IuV, Kushnirova VS, Safarov BI, Samochernykh KA (2023) Immune system dysfunction in patients with glioblastoma. Literature review. Clinical cases (Rus). Russian Neurosurgical Journal named after Professor A. L. Polenov:15(4):200–208. https://doi.org/10.56618/2071–2693_2023_15_4_200 Antunes ARP, Scheyltjens I, Duerinck J, Neyns B, Movahedi K, Ginderachter JAV (2020). Understanding the glioblastoma immune microenvironment as basis for the development of new immunotherapeutic strategies. Elife 9:e52176. https://doi.org/10.7554/eLife.52176 Regmi M, Wang Y, Liu W, Dai Y, Liu S, Ma K, Lin G, Yang J, Liu H, Wu J, Yang C (2024) From glioma gloom to immune bloom: unveiling novel immunotherapeutic paradigms-a review. J Exp Clin Cancer Res 43(1):47. https://doi.org/10.1186/s13046-024-02973-5 Friedmann-Morvinski D, Hambardzumyan D (2023) Monocyte-neutrophil entanglement in glioblastoma. Citation Information: J Clin Invest 133(1):e163451. https://doi.org/10.1172/JCI163451 Li B, Gao B, Zhu H, Luwor RB, Lu J, Zhang L, Kong B (2024) The Prognostic Value of Preoperative Inflammatory Markers for Pathological Grading of Glioma Patients. Technol Cancer Res Treat 23:15330338241273160. https://doi.org/10.1177/15330338241273160 Topkan E, Kucuk A, Selek U (2022) Pretreatment Pan-Immune-Inflammation Value Efficiently Predicts Survival Outcomes in Glioblastoma Multiforme Patients Receiving Radiotherapy and Temozolomide. J Immunol Res 2022:1346094. https://doi.org/10.1155/2022/1346094. Wang Y, Xu C, Zhang Z (2023) Prognostic value of pretreatment lymphocyte-to-monocyte ratio in patients with glioma: a meta-analysis. BMC Medicine. 21:486. https://doi.org/10.1186/s12916-023-03199-6 Duan X, Yang B, Zhao C, Tie B, Cao L, Gao Y (2023) Prognostic value of preoperative hematological markers in patients with glioblastoma multiforme and construction of random survival forest model. BMC Cancer 23(1):432. https://doi.org/10.1186/s12885-023-10889-0 Sharma G, Jain SK, Sinha VD (2021) Peripheral Inflammatory Blood Markers in Diagnosis of Glioma and IDH Status. J Neurosci Rural Pract 12(1):88-94. https://doi.org/10.1055/s-0040-1721166 Barden A, Phillips M, Hill LM, Fletcher EM, Mas E, Loh PS, French MA, Ho KM, Mori TA, Corcoran TB (2018) Antiemetic doses of dexamethasone and their effects on immune cell populations and plasma mediators of inflammation resolution in healthy volunteers, Prostaglandins, Leukotrienes and Essential Fatty Acids 139:31-39.ISSN 0952-3278. https://doi.org/10.1016/j.plefa.2018.11.004 Schlesinger M (2018) Role of platelets and platelet receptors in cancer metastasis. J Hematol Oncol 11:125. https://doi.org/10.1186/s13045-018-0669-2 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6234246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":432105307,"identity":"9bad3476-833a-4fb7-b463-a917f53d1a04","order_by":0,"name":"Sofia S. Sklyar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIie2PsWrDMBCGJQ7iRTSrTYe+whVPGZq+ig5Dt7RDwVAyRMYgLyFz+hZ5BAVBtbies7Uh0Dljhg6VIaVDqEi3Dvrgjrvh4/5jLBL5nwB7930IvDns+92co0jfs6ZW2fJPCrZOXYpzlCuV7JD0+IFtSOU3enx/4eYIT/p3BY3IJeliVC1JFRNdPGbti+SvIYUJMKQBISVlJxpotSkMrwJKH8wrMxykVNUjPaPV21YFFf9sH8yiEOsauLb+CoSvoBU5ys5hmlSazztHz+2dXKsuEKxpdum+nOKtTT7YoZzSwrXXW1UGgoEvPjgu34MJCEc+T4ZIJBKJ/PAFmnNXL7zezsYAAAAASUVORK5CYII=","orcid":"","institution":"Almazov National Medical Research Centre","correspondingAuthor":true,"prefix":"","firstName":"Sofia","middleName":"S.","lastName":"Sklyar","suffix":""},{"id":432105308,"identity":"2dc08e5a-71e3-4bf9-b9a6-d40743ff6100","order_by":1,"name":"Anastasiia S. Nechaeva","email":"","orcid":"","institution":"Almazov National Medical Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Anastasiia","middleName":"S.","lastName":"Nechaeva","suffix":""},{"id":432105309,"identity":"fe810111-ad60-446b-bc6f-66b111d03e67","order_by":2,"name":"Alexei Yu. Ulitin","email":"","orcid":"","institution":"Almazov National Medical Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Alexei","middleName":"Yu.","lastName":"Ulitin","suffix":""},{"id":432105310,"identity":"b86f2eb7-60bb-4b2b-9074-2441934fdc77","order_by":3,"name":"Marina V. Matsko","email":"","orcid":"","institution":"State Budgetary Healthcare Institution \"Saint Petersburg Clinical Scientific and Practical Center for Specialized Types of Medical Care (Oncology) named after N. P. Napalkov\", Saint Petersburg","correspondingAuthor":false,"prefix":"","firstName":"Marina","middleName":"V.","lastName":"Matsko","suffix":""},{"id":432105311,"identity":"4ecb6cc9-d844-4a38-9619-59d3ff998548","order_by":4,"name":"Victor E. Olyushin","email":"","orcid":"","institution":"Almazov National Medical Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Victor","middleName":"E.","lastName":"Olyushin","suffix":""},{"id":432105312,"identity":"05c5f9bb-7afb-4309-823c-472b3dfa0214","order_by":5,"name":"Konstantin A. Samochernykh","email":"","orcid":"","institution":"Almazov National Medical Research Centre","correspondingAuthor":false,"prefix":"","firstName":"Konstantin","middleName":"A.","lastName":"Samochernykh","suffix":""}],"badges":[],"createdAt":"2025-03-15 17:53:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6234246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6234246/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79258727,"identity":"1493d63a-a87c-4498-844a-af49f0249520","added_by":"auto","created_at":"2025-03-26 09:11:58","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":424329,"visible":true,"origin":"","legend":"\u003cp\u003eViolin diagram showing comparative results of preoperative inflammatory markers in the glioma grade 2, grade 3,4 (IDH1-mt), glioblastoma groups. (A) Neutrophils, (B) Lymphocytes, (C) Monocytes, (D) Platelets, (E) NLR, (F) LMR, (G) PLR. The dashed line in the middle represents the median and the dashed lines on both sides represent the interquartile range\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6234246/v1/e7ba43bf50bd4b201c623f85.jpeg"},{"id":79258732,"identity":"fc7102f3-6515-444c-9de7-d9d0f17471ab","added_by":"auto","created_at":"2025-03-26 09:11:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":296925,"visible":true,"origin":"","legend":"\u003cp\u003eViolin diagram showing comparative results of preoperative inflammatory markers in different grades of glioma groups and dexamethasone intake. (A) Neutrophils, (B) Lymphocytes, (C) Monocytes, (D) NLR, (E) LMR. The dashed line in the middle represents the median and the dashed lines on both sides represent the interquartile range\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6234246/v1/336571e5db8789a8fe31f966.png"},{"id":79258729,"identity":"dfbe6bf4-1dd5-4cc9-9c57-78c5c5875ce1","added_by":"auto","created_at":"2025-03-26 09:11:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":227033,"visible":true,"origin":"","legend":"\u003cp\u003eThe diagnostic value of preoperative inflammatory markers in glioma diagnosis. Glioma grade 2 vs glioma grade 3,4 (IDH-mt), glioma grade 2 vs glioblastoma, glioma grade 2 vs glioma grade 3,4 (IDH-mt) + glioblastoma\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6234246/v1/c3d246097f22c31fbcd56fb1.png"},{"id":79259334,"identity":"5e467349-2e2c-4a0f-9387-4f40317fda1f","added_by":"auto","created_at":"2025-03-26 09:19:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":105308,"visible":true,"origin":"","legend":"\u003cp\u003ePreoperative LMR in glioma patients. Violin diagram showing comparative results of LMR in oligodendroglioma vs astrocytoma (A); glioma IDH1/2-mt vs glioma IDH-wt (B). The dashed line in the middle represents the median and the dashed lines on both sides represent the interquartile range. wt - wild type, mt -mutant, ns - nonspecific\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6234246/v1/acc005734e33f4aeb8be1954.png"},{"id":80899559,"identity":"b357da39-75b3-4057-9462-883e4975bad3","added_by":"auto","created_at":"2025-04-18 13:01:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2189660,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6234246/v1/49e40c95-3d84-4571-bc90-ff274b2d3e82.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Diagnostic Significance of Cellular Immune Inflammation Markers in Assessing Different Malignancy Grades Gliomas\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to global epidemiological data, approximately 320,000 new cases of primary central nervous system (CNS) tumors are diagnosed every year, with an estimated mortality of about 250,000 patients affected by these neoplasms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among primary intracerebral tumors, diffuse gliomas are the most common subtype [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the latest classification of CNS tumors (5th edition) categorizes this group to include astrocytoma of grades 2, 3, and 4, oligodendrogliomas of grades 2 and 2, as well as glioblastomas classified as grade 4 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Notably, all gliomas grade 2 are classified as \"benign\" tumors; conversely, diffuse astrocytoma grade 3 and 4, together with glioblastomas, are associated with a significantly poor prognosis and are classified as malignant neoplasms.\u003c/p\u003e \u003cp\u003eTreatment of diffuse glioma patients includes neurosurgical procedures, adjuvant radiation therapy and systemic anti-tumour therapies, including chemotherapy and targeted therapy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is important to note that the specific anti-tumour regimen is not only adapted to the histological subtype of diffuse glioma but is also strongly influenced by the extent of resection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The degree of tumor resection can substantially impact the necessity for subsequent anti-tumor therapy, particularly in cases of benign diffuse astrocytoma. For histological classifications such as astrocytomas grade 3 and 4, oligodendrogliomas grade 3 and glioblastomas, radiation and chemotherapy are required regardless of surgical outcome. Therefore, there is an urgent need for pre-operative confirmation of histological diagnosis to facilitate optimal neurosurgical planning and comprehensive treatment of the patient.\u003c/p\u003e \u003cp\u003eAdvanced imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have been developed in recent years and have made it easier to improve the accuracy of pre-operative diagnostics [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Liquid biopsy has proven to be a very promising diagnostic approach in general oncology and neuro-oncology [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Circulating nucleic acids have been identified as having significant diagnostic potential in gliomas of different malignancies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, the high costs associated with these techniques limit their availability in many health care settings. Therefore, there is still a critical need for a cost-effective, rapid and unambiguous method of differential diagnosis of brain gliomas.\u003c/p\u003e \u003cp\u003eResearch suggests that systemic immune inflammation plays a key role in the oncogenic process [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The primary focus of the study was to assess the local immune response within tumours. Reprogrammed immune cells have been shown to contribute approximately 30 percent of the cellular composition of malignant gliomas, facilitating oncogenesis [13 In addition, intracellular inflammatory signaling pathways were clarified and key cytokines were characterised. At the same time, research into systemic immune inflammation in gliomas is still in its early stages and should be further explored.\u003c/p\u003e \u003cp\u003eNumerous studies have been carried out to evaluate the diagnostic and prognostic relevance of cellular inflammatory markers in patients with glioma [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It should be stressed that most of these studies did not stratify tumours according to histological classification and grade of malignancy. Moreover, the existing literature largely ignored the effect of glucocorticosteroid (GCS) therapy on patients, despite its significant impact on the dynamics of the immune system. The prognostic effects of inflammatory biomarkers in patients with glioblastoma have been previously assessed, including the effects of GCS [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe purpose of this study was to evaluate the diagnostic relevance of cellular inflammatory markers, specifically neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR) and lymphocyte-monocyte ratio (LMR), in patients with glioma of different histological subtypes and different degrees of malignancy, while considering the effect of GCS therapy.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe study included a cohort of 139 adults aged 18 years and over who presented with newly diagnosed supratentorial glial neoplasms. Patients underwent surgery in the Department of Brain and Spinal Cord Tumour Surgery at the National Research Medical Centre of Almaz in the period from 2021 to 2024.\u003c/p\u003e \u003cp\u003e Before being included in the study, informed consent was obtained from each of the participants. The exclusion criteria included diagnosis of immunodeficiency, autoimmune disease or neoplasms outside the central nervous system. In addition, patients who have received radiation therapy, chemotherapy or immunotherapy at any time in their medical history were also excluded from the study. The presence of acute inflammatory conditions, use of antibacterial agents at the time of enrolment and continued treatment with anticonvulsants were also considered disqualifying factors.\u003c/p\u003e \u003cp\u003eVenous blood samples were collected from patients three days prior to surgical intervention in the morning, followed by a comprehensive clinical haematological examination and the quantification of C-reactive protein (CRP). Patients with CRP levels above 5 ng/ml were excluded from the study cohort. Clinical haematological analysis, including extended leukocyte differentiation, was performed with the use of the Sysmex XN-550 haematological analyser, using Sysmex reagents and control materials sourced from Japan. Inflammatory markers were derived from absolute lymphocyte, neutrophil, monocyte and platelet counts, specifically the neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-lymphocyte ratio (PLR) to assess the inflammatory microenvironment in neurooncologic diseases.\u003c/p\u003e \u003cp\u003eHistopathological diagnosis was made by analysing surgical tumour samples according to standardised criteria described in the Fifth Edition of the World Health Organisation Central Nervous System Tumour Classification [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Histological sections were stained with haematoxylin and eosin, in addition to immunohistochemistry (IHC) panels including anti-GFAP (poly, DakoCytomation), anti-ATRX (Abcam), anti-EGFR (Abcam), anti-MGMT (NovusBiologics), anti-Ki-67 (Dako), anti-IDH1R132H (Dianova), and for differential diagnosis, Syn (DakoCytomation) and NB (Leica). Mutational analysis of the IDH1 (exon 4) and IDH2 (exon 4) genes was performed via high-resolution melting analysis (HRMA) of PCR products, followed by subsequent DNA sequencing to elucidate genetic alterations.\u003c/p\u003e \u003cp\u003eStatistical analysis was performed using thePrism GraphPad 10 program (GraphPad Software, USA). The normality test was performed using Kolmogorov-Smirnov, Shapiro-Wilk tests. We used the mean\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;standard deviations for normally distributed data, and median (rank) for non-normally distributed data. Group comparisons of nonparametric data were performed using the Mann-Whitney U test. The diagnostic performance of preoperative inflammatory markers in receivers was assessed by calculating the area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve. The differences were considered statistically significant at р\u0026lt;0,05. The resulting graphs were performed using the Prism GraphPad 10 program (GraphPad Software, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003ePatients were stratified into three cohorts based on histopathological classification: glioma grade 2 (n\u0026thinsp;=\u0026thinsp;25), glioma grade 3,4 with IDH1 mutation (n\u0026thinsp;=\u0026thinsp;25), and glioblastoma (n\u0026thinsp;=\u0026thinsp;89) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The glioma grade 2 and glioma grade 3,4 (IDH1-mt) cohorts also encompassed individuals diagnosed with oligodendroglioma (7 and 8 patients, respectively). Notably, patients exhibiting IDH1-positive tumor status across glioma grades 2, 3, and 4 were significantly younger than those diagnosed with glioblastoma (refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The mean age of patients with glioma grade 2 was 42.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5 years, comprising 14 males (56%) and 11 females (44%). Conversely, the mean age of patients with glioma grade 3,4 (IDH1-mt) was 46\u0026thinsp;\u0026plusmn;\u0026thinsp;11 years, with 7 males (28%) and 18 females (72%). In the glioblastoma cohort, the mean age was 60\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5 years, with 51 males (57.31%) and 38 females (42.69%).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreoperative characteristics of patients with cerebral gliomas (n\u0026thinsp;=\u0026thinsp;139)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eglioma grade 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eglioma grade 3,4\u003c/p\u003e \u003cp\u003e(IDH-mt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eglioblastoma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e42,5\u0026thinsp;\u0026plusmn;\u0026thinsp;12,5\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e46\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60\u0026thinsp;\u0026plusmn;\u0026thinsp;12,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (28%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51 (57,31%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (44%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (72%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38 (42,69%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaking dexamethasone (\u003cem\u003en\u003c/em\u003e, %)\u003c/p\u003e \u003cp\u003eyes\u003c/p\u003e \u003cp\u003eno\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (24%)\u003c/p\u003e \u003cp\u003e19 (76%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (44%)\u003c/p\u003e \u003cp\u003e14 (56%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e66 (74,15%)\u003c/p\u003e \u003cp\u003e23 (25,85%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4,01 (1,54\u0026thinsp;\u0026minus;\u0026thinsp;15,0)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,30 (1,53\u0026thinsp;\u0026minus;\u0026thinsp;16,80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7,42 (1,85\u0026thinsp;\u0026minus;\u0026thinsp;24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,11 (1,20\u0026thinsp;\u0026minus;\u0026thinsp;4,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,0 (1,01\u0026ndash;2,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,07 (0,66\u0026thinsp;\u0026minus;\u0026thinsp;4,82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0,52 (0,2\u0026thinsp;\u0026minus;\u0026thinsp;1,50)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,60 (0,01\u0026ndash;2,1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,81 (0,2\u0026ndash;2,0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (158\u0026ndash;401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e268 (108\u0026ndash;408)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245 (117\u0026ndash;487)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1,96 (0,64\u0026thinsp;\u0026minus;\u0026thinsp;8,30)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,0 (0,76\u0026thinsp;\u0026minus;\u0026thinsp;16,63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,9 (0,62\u0026thinsp;\u0026minus;\u0026thinsp;34,25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3,71 (2,4\u0026ndash;9,230)\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u0026minus;b\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,09 (0,99\u0026thinsp;\u0026minus;\u0026thinsp;8,33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,0 (0,53\u0026thinsp;\u0026minus;\u0026thinsp;8,48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104 (49,58\u0026ndash;199,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114 (70,13\u0026ndash;348,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117 (32,05-358,9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003emt -mutant\u003c/p\u003e \u003cp\u003ea p\u0026thinsp;\u0026lt;\u0026thinsp;0,05 vs grade 3,4 (IDH1-mt)\u003c/p\u003e \u003cp\u003eb p\u0026thinsp;\u0026lt;\u0026thinsp;0,05 vs glioblastoma\u003c/p\u003e \u003cp\u003eA significant proportion of patients diagnosed with grade 2 and grade 3 gliomas did not receive glucocorticoid therapy (dexamethasone) prior to surgical intervention, with rates of 76% and 56%, respectively. In contrast, within the glioblastoma cohort, only 25.85% of cases were not administered glucocorticoids, as detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003ch3\u003eComparison of preoperative inflammatory blood markers for glioma of different subtypes\u003c/h3\u003e\n\u003cp\u003eThere were no statistically significant differences observed in lymphocyte, platelet, or Platelet-Lymphocyte ratio (PLR) levels across the three studied cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Notably, the counts of neutrophils and monocytes in the glioma grade 2 cohort [4.01 (1.54-15.0) and 0.52 (0.2\u0026ndash;1.50), respectively] were significantly lower compared to those in the glioblastoma cohort [37.42 (1.85-24) and 0.81 (0.2-2.0), respectively] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eThe analysis revealed that the Neutrophil-Lymphocyte Ratio (NLR) and Lymphocyte-Monocyte Ratio (LMR) exhibited no significant differences between glioma grade 3,4 (IDH-mutant) and glioblastoma groups. Notably, the LMR was found to be statistically elevated in the glioma grade 2 group with a mean value of 3.71 (range: 2.4\u0026ndash;9.230), in contrast to the biomarker values observed in glioma grade 3,4 (IDH-mutant) group at 3.09 (range: 0.99\u0026ndash;8.33) and glioblastoma group at 3.0 (range: 0.53\u0026ndash;8.48) (refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Conversely, the highest NLR values were recorded in glioblastoma patients, averaging 2.9 (range: 0.62\u0026ndash;34.25), which demonstrated a statistically significant difference when compared to the NLR in low-grade glioma patients, which was 1.96 (range: 0.64\u0026ndash;8.30) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of hematological parameters, specifically neutrophils, lymphocytes, monocytes, NLR and LMR, was conducted in relation to the administration of GCS prior to surgical intervention (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In patients receiving GCS, a statistically significant elevation in neutrophil counts was observed within the glioma grade 3, 4 and glioblastoma cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Furthermore, NLR values were markedly increased in individuals with malignant gliomas undergoing treatment with dexamethasone (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). The administration of dexamethasone was found to influence monocyte levels exclusively in the glioblastoma patient group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Notably, the LMR index exhibited no significant variation between patients receiving dexamethasone and those who were not across all three patient categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreoperative inflammatory markers and dexamethasone intake in glioma patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003egrade 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003egrade 3,4\u003c/p\u003e \u003cp\u003e(IDH-mt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eglioblastoma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ewithout Dex (n\u0026thinsp;=\u0026thinsp;19)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ewith Dex (n\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ewithout Dex (n\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ewith Dex (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ewithout Dex (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ewith Dex (n\u0026thinsp;=\u0026thinsp;66)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,43 (1,54\u0026thinsp;\u0026minus;\u0026thinsp;11,69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5,20 (4,40\u0026thinsp;\u0026minus;\u0026thinsp;9,6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e4,0 (1,53\u0026thinsp;\u0026minus;\u0026thinsp;5,52)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e10,0 (3,05\u0026ndash;16,80)\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3,70 (1,85\u0026thinsp;\u0026minus;\u0026thinsp;7,55)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e8,0 (2,14\u0026ndash;24)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,0 (1,20\u0026thinsp;\u0026minus;\u0026thinsp;3,25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,48 (2,06\u0026thinsp;\u0026minus;\u0026thinsp;4,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,0 (1,20\u0026thinsp;\u0026minus;\u0026thinsp;2,5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,20 (1,01\u0026ndash;2,7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1,76 (1,10\u0026thinsp;\u0026minus;\u0026thinsp;4,70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,18 (0,66\u0026thinsp;\u0026minus;\u0026thinsp;4,82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (x10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0,50 (0,20\u0026thinsp;\u0026minus;\u0026thinsp;1,0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0,57 (0,39\u0026thinsp;\u0026minus;\u0026thinsp;1,50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0,52 (0,30\u0026thinsp;\u0026minus;\u0026thinsp;2,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0,72 (0,01\u0026ndash;1,21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,59 (0,20\u0026thinsp;\u0026minus;\u0026thinsp;1,0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,82 (0,23\u0026thinsp;\u0026minus;\u0026thinsp;2,0)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,86\u003c/p\u003e \u003cp\u003e(0,64\u0026thinsp;\u0026minus;\u0026thinsp;3,56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,1\u003c/p\u003e \u003cp\u003e(1,37\u0026thinsp;\u0026minus;\u0026thinsp;2,59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,86\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(0,76\u0026thinsp;\u0026minus;\u0026thinsp;4)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e(1,15\u0026ndash;16,63)\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e1,95 (0,62\u0026thinsp;\u0026minus;\u0026thinsp;5,0)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3,79 (0,80\u0026thinsp;\u0026minus;\u0026thinsp;34,35)\u003c/b\u003e \u003csup\u003e\u003cb\u003eb\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,75\u003c/p\u003e \u003cp\u003e(2,4\u0026ndash;9,23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3,74\u003c/p\u003e \u003cp\u003e(2,47\u0026thinsp;\u0026minus;\u0026thinsp;8,33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3,1 (0,99\u0026thinsp;\u0026minus;\u0026thinsp;6,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,5 (1,0\u0026ndash;4,58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,6 (1,3\u0026ndash;8,2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2,88 (0,53\u0026thinsp;\u0026minus;\u0026thinsp;8,48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDex - dexamethasone\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003ea p\u0026thinsp;\u0026lt;\u0026thinsp;0,05 vs grade 3,4 (IDH1-mt) without Dex\u003c/p\u003e \u003cp\u003eb p\u0026thinsp;\u0026lt;\u0026thinsp;0,05 vs glioblastoma without Dex\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eDiagnostic value of inflammatory blood markers in glioma diagnosis and glioma grading\u003c/h3\u003e\n\u003cp\u003eTwo biomarkers demonstrated significant diagnostic value: NLR and LMR (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the comparative analysis of patients diagnosed with glioma grade 2 versus those with glioblastoma, NLR demonstrated a superior Area Under the Curve (AUC) value of 0.7157 (95% CI: 0.6087\u0026ndash;0.8227), in contrast to LMR, which yielded an AUC of 0.6586 (95% CI: 0.5583\u0026ndash;0.7590). Notably, the NLR levels were influenced by the administration of GCS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), whereas LMR remained unaffected by this therapeutic intervention, thereby establishing LMR as a more robust diagnostic marker. Furthermore, when evaluating the diagnostic efficacy of LMR in the context of glioma grade 2against more aggressive gliomas (grade 3, 4 with IDH1 mutation and glioblastoma), LMR maintained its diagnostic relevance with an AUC of 0.6579 (95% CI: 0.5635\u0026ndash;0.7522). However, in the direct comparison between grade 2 glioma and grade 3.4 glioma (IDH-mt), the AUC for LMR was comparatively lower at 0.6552 (95% CI: 0.4930\u0026ndash;0.8174) than in other evaluated cohorts.\u003c/p\u003e \u003cp\u003eThe investigation demonstrated that the LMR, in comparison to other cellular biomarkers, holds substantial diagnostic relevance for patients diagnosed with low-grade gliomas.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDiagnostic value of various inflammatory markers in glioma diagnosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMarkers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003egrade 2 vs grade 3,4 (IDH-mt)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003egrade 2 vs glioblastoma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003egrade 2 vs grade 3,4 (IDH-mt)\u0026nbsp;+ glioblastoma\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAUC (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAUC (95%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0,5888 (0,4292-0,7484)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0,7157 (0,6087-0,8227)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0,6876 (0,5798-0,7954)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0,6552 (0,4930-0,8174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0,6586 (0,5583-0,7590)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0,6579 (0,5635-0,7522)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c2\"\u003e \u003cp\u003e0,6232 (0,4670-0,7794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0,13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e0,5676 (0,4546-0,6807)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c6\"\u003e \u003cp\u003e0,5798 (0,4708-0,6888)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eComparison of LMR for glioma of different IDH1-mutation and 1p/19q-codeleted status\u003c/h3\u003e\n\u003cp\u003eThe LMR index was evaluated when all patients (n\u0026thinsp;=\u0026thinsp;139) were divided into groups depending on the presence of the IDH1 gene mutation and the presence of 1p/19q-codeletion (Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No significant differences in the value of LMR were found in the group of patients with oligodendroglioma and astrocytoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). At the same time, in patients with glioma with a mutation in the IDH1 gene, the LMR value was significantly higher than in patients in the IDH-wt glioma group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003eThe LMR was assessed in a cohort of 139 patients stratified based on the presence of the IDH1/2 mutation and the occurrence of 1p/19q codeletion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). No statistically significant differences in LMR values were observed between the oligodendroglioma and astrocytoma patient groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Conversely, patients harboring the IDH1/2 mutation exhibited a markedly elevated LMR compared to those with IDH-wildtype gliomas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreoperative LMR in glioma patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioma patients n\u0026thinsp;=\u0026thinsp;139\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1p/19q-codeletion status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOligodendroglioma (n\u0026thinsp;=\u0026thinsp;15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,43 (0,99\u0026thinsp;\u0026minus;\u0026thinsp;6,67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,7690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAstrocytoma (n\u0026thinsp;=\u0026thinsp;124)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,19 (0,53\u0026thinsp;\u0026minus;\u0026thinsp;9,23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIDH1/2-mutation status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioma IDH-mt (n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,44 (0,99\u0026thinsp;\u0026minus;\u0026thinsp;9,23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlioma IDH-wt (n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.0 (0,53\u0026thinsp;\u0026minus;\u0026thinsp;8,48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ewt - wild type\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003emt -mutant\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe hypothesis that there is a relationship between inflammatory responses and oncogenesis was first proposed by Rudolf Virchow in the 19th century, who elucidated the infiltration of leukocytes into neoplastic tissue [10; 11]. Recent decades have produced considerable evidence that inflammation is a key factor in the development and etiology of neoplastic diseases. Inflammatory processes are mediated by active involvement and regulation of immune cells, including neutrophils, lymphocytes and monocytes, in addition to platelets. Numerous studies have highlighted the important diagnostic and prognostic implications of these blood markers in different malignancies, in isolation or in combination [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe re-evaluation of immunological deficits associated with the central nervous system, together with the role of the immune system in oncogenesis, has stimulated research into the inflammatory mechanisms underlying intracerebral neoplasms, in particular diffuse gliomas [13; 25; 26; 27]. At the same time, there has been a worldwide interest in the local immune microenvironment to understand its complexity and its implications for tumour behavior [13; 28; 29]. Recent research has shown that the tumour microenvironment in gliomas has features similar to chronic inflammation, suggesting that there is a significant interaction between tumour biology and the immune response [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGliomas are known to express chemokines that facilitate the recruitment of immune cells that differentiate into tumour-associated macrophages, neutrophils, and myeloid suppressor cells, which are also affected by cytokines. These immune components contribute to tumigenesis while simultaneously impairing the effector lymphocytes' ability to function. In view of these findings, it is reasonable to assume that systemic inflammatory markers such as neutrophil- lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-plasma ratio (PLR) can serve as reliable and sensitive biomarkers for the diagnosis and prognostic evaluation of glioma.\u003c/p\u003e \u003cp\u003eNumerous clinical studies have been conducted to elucidate the diagnostic and prognostic relevance of NLR and LMR in glioma [14\u0026ndash;18; 32\u0026ndash;34]. In addition, consensus research has identified NLR as the most relevant and reliable biomarker in this context [14; 15; 17; 18; 34]. NLR serves as an indicator of both a non-specific neutrophil-mediated immune response and a specific adaptive immune response against cancer, mediated by lymphocytes. NLR has already been shown to be of diagnostic value in glioma patients compared to neoplasms such as meningioma, schwannoma and adenoma [17; 18; 35].\u003c/p\u003e \u003cp\u003eIn preoperative diagnostics of cerebral gliomas of varying degrees of malignancy, a positive correlation has been established between NLR and tumor grade [14; 18; 34; 35]. However, these studies did not account for the administration of GCS by patients, despite evidence indicating that dexamethasone, commonly prescribed to reduce peritumoral edema, affects immune system functioning [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In our study, statistically significant differences in median NLR values were observed when comparing patient groups with grade 2 glioma and glioblastoma (1.96 vs 2.9; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). However, it should be noted that in patients with glioblastoma, as well as in the group with grade 3 and 4 gliomas receiving GCS, there was an observed increase in neutrophils and NLR compared to patients who were not administered this therapy (8.0 vs 3.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 3.79 vs 1.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 10.0 vs 4.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; 5.0 vs 1.86, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The NLR median for patients not receiving dexamethasone in the groups with glioma grade 2, 3 and 4 and glioblastoma was almost identical (1.86 vs 1.86 vs 1.95; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Therefore, according to the results of our study, NLR is a biomarker that is dependent on GCS, which significantly reduces its diagnostic value (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, LMR is recognized as a biomarker for anti-cancer immunological activation in general oncology. The increase in its value is related either to the high number of effector lymphocytes, which leads to an adequate immune response, or to the reduction in monocytes. Monocytes play a dual role in the pathology of cancer. After differentiation, these cells can either play a protective function by helping to destroy the neoplasm or reprogramme themselves to support the growth of the tumour. Our data reveal no correlation between blood lymphocyte levels in patients and glioma grade (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As expected, glucocorticoids were not found to affect lymphocyte levels (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, absolute monocyte counts increased with tumour malignancy (0.52 vs 0.60 vs 0.81) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Statistical significance was reached only when comparing values for benign glioma (grade 2) and glioblastoma (0.52 vs 0.81; p\u0026thinsp;=\u0026thinsp;0.0044) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Notably, dexamethasone increased monocytes, but only in the glioblastoma patient\u0026rsquo;s group (0.59 vs 0.82; p\u0026thinsp;=\u0026thinsp;0.0152) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe relevance of LMR in glioma patients remains controversial in the neurooncology community. Although some studies have questioned its predictive effects, others have demonstrated its relevance [15; 17; 18; 33; 35]. A key observation is that many studies evaluating LMR did not stratify patients with glioma by histological subtype or grade of malignancy or considered the effect of dexamethasone. Our previous cohort study in glioblastoma patients identified LMR as a key biomarker predictive of the risk of early relapse, independent of dexamethasone therapy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In this analysis, a correlation between the increasing degree of malignancy and the decreasing LMR was observed. In particular, the median LMR for glioma grade 2 was 3.71, while glioma grade 3 and 4 had a median LMR of 3.09 and glioblastoma patients had a median LMR of 3.05, respectively (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In addition, it should be noted that LMR levels remained unchanged with corticosteroid treatment in all patient groups.\u003c/p\u003e \u003cp\u003eThe findings suggest that there is no correlation between the number of lymphocytes and tumor malignancy; however, there is a correlation with increased monocytes. This suggests that the LMR is primarily driven by absolute monocyte counts. Notably, in the glioblastoma cohort, monocytes were significantly increased in patients receiving corticosteroids, although the LMR was not affected. These results highlight the complex mechanisms and interactions between the immune cell populations and support the use of composite biomarkers for diagnostic evaluation rather than relying on absolute monocytes and lymphocytes alone.\u003c/p\u003e \u003cp\u003eIn addition, the LMR was assessed in cohorts of patients stratified by presence or absence of IDH/2 gene mutations and 1p/19q codeletion (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). According to the fifth edition of the WHO classification system, these genetic alterations are critical to the definitive histological diagnosis. No statistically significant differences in LMR values were observed between patients with and without oligodendroglioma (3.43 vs 3.19; p\u0026thinsp;=\u0026thinsp;0.76). Conversely, in a cohort of patients without IDH1 and IDH2 mutations, LMR was found to be significantly lower (3.44 vs 3.0; p\u0026thinsp;=\u0026thinsp;0.039). These findings highlight the differential immune responses associated with benign and malignant gliomas.\u003c/p\u003e \u003cp\u003eIn neurooncology, it has been well documented that platelets activate neoplastic cells through cytokine signaling, which is important for thrombosis, angiogenesis, cell migration and dissemination of cancer. In addition, platelets contribute to evasion of immunological surveillance and are involved in inflammatory responses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Consequently, the presence of thrombosis and increased platelet counts in patients with oncological diseases correlate with poor prognosis. However, the specific role of platelets in glioma progression and clinical course remains unclear. Our findings suggest that there is no significant correlation between the number of platelets and the histological grade of gliomas (226 vs 268 vs 245; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PLR Inflammatory Index is recognized as a reliable independent biomarker indicating the progression of oncological diseases. Its diagnostic and prognostic usefulness has been demonstrated in various malignancies, including esophageal and gastric cancer, lung cancer and colorectal cancer [20; 23; 24; 37]. In a previous study involving a cohort of patients with glioblastoma, PLR was shown to be of significant prognostic importance [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, its diagnostic effectiveness in distinguishing gliomas of different malignancies is still limited. Although an increasing trend in PLR values was observed with increasing grade of glioma, statistical significance was not reached (104 vs 114 vs 117; p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe immune system plays a key role in oncogenesis and both exhibits anti-tumour properties and facilitates the progression of the disease. Inflammatory cell markers are key agents in these mechanisms and their prognostic and diagnostic relevance warrants investigation. Our findings suggest that the LMR is a reliable independent diagnostic marker in glioma patients regardless of corticosteroid therapy. Conversely, the NLR is not recommended for clinical use because it is correlated to corticosteroid use, thereby reducing its diagnostic utility. In addition, the PLR showed no significant correlation with the degree of malignancy.\u003c/p\u003e \u003cp\u003eThus, the increased LMR levels observed in low grade gliomas, as opposed to in malignant gliomas, therefore indicate an increased activation of the immune response. This may explain the favorable and prolonged clinical course often observed with benign glioma, which may reflect underlying immunological mechanisms, and require further investigation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eThe Research Laboratory of Neuro-Oncology, Almazov National Medical Research Centre, Saint Petersburg, Russia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSofia S. Sklyar, Anastasiia S. Nechaeva, Alexei Yu. Ulitin, Victor E. Olyushin, Konstantin A.\u0026nbsp;Samochernykh\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Neurosurgery named after Professor A.L. Polenov\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFederal State Budgetary Educational Institution of Higher Education \"Northwestern State Medical University named after I.I. Mechnikov\" of the Ministry of Health of the Russian Federation, Saint Petersburg, Russia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlexei Yu.\u0026nbsp;Ulitin\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Oncology, State Budgetary Healthcare Institution \"Saint Petersburg Clinical Scientific and Practical Center for Specialized Types of Medical Care (Oncology) named after N. P. Napalkov\", Saint Petersburg, Russia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarina V.\u0026nbsp;Matsko\u003cstrong\u003e\u0026nbsp;Department of Oncology\u003c/strong\u003e\u003cstrong\u003e, Private Educational Institution of Higher Education \"Saint Petersburg Medical and Social Institute\" Saint Petersburg, Russia.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMarina V.\u0026nbsp;Matsko\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Sofia S. Sklyar and Anastasiia S. Nechaeva. The first draft of the manuscript was written by Sofia S. Sklyar, Anastasiia S. Nechaeva, Alexei Yu. Ulitin, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch3\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eCorrespondence to Sofia S. Sklyar\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective cohort study was approved by the Institutional\u0026nbsp;Ethics Committee of\u0026nbsp;Almazov National Medical Research Centre\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from each participant prior to their inclusion in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray F, Laversanne M, Sung H, Ferlay J, Siengel RL, Soerjomataram I, Jemal A (2024) Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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J Hematol Oncol 11:125. https://doi.org/10.1186/s13045-018-0669-2 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"brain tumors, glioma, inflammatory blood markers, lymphocyte-monocyte ratio, diagnostic indicator","lastPublishedDoi":"10.21203/rs.3.rs-6234246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6234246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003ePurpose.\u003c/b\u003e The purpose of this study was to evaluate the diagnostic relevance of inflammatory markers in gliomas, taking into account different histological subtypes and malignancy levels.\u003c/p\u003e \u003cp\u003eMethods.\u003c/p\u003e \u003cp\u003eThis prospective study included 139 adult glioma patients. Patients were stratified by tumour grade and genetic mutation, yielding 25 cases of diffuse astrocytoma grade 2, 25 cases of glioma grade 3 or 4 with IDH1/2-mutations and 89 cases with glioblastoma. IDH1/2-mutations were detected in 50 patients, 15 of which had co-deletion at 1p19q. The pre-operative neutrophil-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR) and platelet-lymphocyte ratio (PLR) were calculated.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults.\u003c/b\u003e The LMR in the glioma grade 2 group was higher than that in the glioma grade 3, 4 and glioblastoma groups (3,71 vs 3,09 vs 3; p\u0026thinsp;\u0026lt;\u0026thinsp;0,05) with areas under the curve (AUCs) of 0,6552 (0,4930-0,8174) and 0,6586 (0,5583-0,7590) respectively. LMR was higher in patients with IDH1/2-mutation gliomas (3.44 vs 3.0; p\u0026thinsp;=\u0026thinsp;0.039). No differences in LMR were observed between patients with oligodendroglioma and astrocytoma (3.43 vs 3.19; p\u0026thinsp;=\u0026thinsp;0.76). LMR in all cohorts was not affected by use of corticosteroids. The NLR was higher in glioblastoma patients than in patients with glioma grade 2 (2.9 vs 1.96, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Increases in neutrophils and NLR in glioblastoma patients were correlated with the corticosteroids (3.7 vs. 8.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and 1.95 vs. 3.79, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively).\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion.\u003c/b\u003e LMR is a reliable, non-corticosteroid independent biomarker for diagnosing diffuse adult gliomas, with lower levels indicating higher tumor malignancy. Conversely, NLR is an unreliable biomarker due to its elevation, which often results from glucocorticoid therapy.\u003c/p\u003e","manuscriptTitle":"The Diagnostic Significance of Cellular Immune Inflammation Markers in Assessing Different Malignancy Grades Gliomas","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-26 09:11:53","doi":"10.21203/rs.3.rs-6234246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1298b70f-eca8-42c8-96d5-ca35622e09d0","owner":[],"postedDate":"March 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-18T12:53:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-26 09:11:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6234246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6234246","identity":"rs-6234246","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}