Prognostic significance of alterations in fibrinogen level and fibrinogen-to-lymphocyte ratio after radiotherapy on survival outcomes in glioblastoma

Introduction

Glioblastoma belongs to the family of intracranial primary tumours with the highest degree of malignancy and an extremely poor prognosis (1). Surgery followed by chemoradiotherapy remains the mainstay of current treatment for glioblastoma (2). Despite novel tumour treating fields (TTFields) therapy and immunotherapy (3,4), the survival of glioblastoma patients has not increased remarkably, and treatment options and drugs for glioblastoma are limited (5). In recent years, prognosis-related molecular markers have been increasingly investigated (6). Hematologic markers that are frequently employed in prognosis, such as the lymphocyte and neutrophil counts, can be easily and quickly obtained among clinical markers (7). It has been claimed that fibrinogen (FIB) has a significant role in carcinogenesis, development, and metastasis (8,9). In addition to its well-established function in coagulation, FIB has emerged as a key player in various aspects of cancer biology. A large number of studies have shown that FIB contributes to the complex interactions between tumor cells and the tumor microenvironment, affecting tumor progression and treatment response (10). FIB can promote tumor angiogenesis and provide a scaffold for tumor cell migration and invasion (11). Understanding the complex interactions between FIB and cancer is becoming increasingly important to elucidate tumor progression and develop new therapeutic strategies. At present, there is no research on the clinical significance of FIB in glioblastoma.

This study was conducted with an aim to analyze whether changes in FIB levels are associated with poor prognosis in glioblastoma and to explore whether the fibrinogen-to-lymphocyte ratio (FLR) value can serve as a novel hematological biomarker for prognostic assessment. Furthermore, we try to explore the impact of radiotherapy on FIB and FLR. We present this article in accordance with the REMARK reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2271/rc).

Methods

Clinical data

General information

From among 669 glioblastoma patients who underwent postoperative radiotherapy from February 2017 and February 2022, this retrospective study included 104 participants with glioblastoma who received postoperative concurrent and adjuvant chemo-radiotherapy. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital ethics committee (No. 2022-541-001) and individual consent for this retrospective analysis was waived.

Inclusion and exclusion criteria

Inclusion criteria: (I) age ≥18 years; (II) Eastern Cooperative Oncology Group (ECOG) score ≤2 points; (III) documented complete pathology report or glioblastoma diagnosis by pathologic consultation in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital [the histopathological diagnosis of glioblastoma was based on the 2016 World Health Organization (WHO) classification of tumors of the central nervous system (CNS)]; (IV) no previous surgery or postoperative chemotherapy and radiotherapy; (V) blood and biochemical indices that were less than two times the upper limit of normal before radiotherapy; and (VI) no antineoplastic drugs other than temozolomide (TMZ) were used during radiotherapy. Exclusion criteria: (I) occurrence of postoperative infection, or hyperthermia; (II) complications such as coagulative dysfunction, haemorrhagic disorder, autoimmune disease, or other severe comorbidities; and (III) incomplete radiotherapy, or insufficient radiation dose.

Follow-up

All participants were followed up after the completion of radiotherapy via outpatient visit, medical record review, or telephonic interview. Patient overall survival (OS) was the interval from diagnosis to death of any causes or the last follow-up, whereas progression-free survival (PFS) was the period from diagnosis to first radiographic progression or death.

Research methods

Hematologic data collection

The participants’ general data were collected from the electronic medical record system of the hospital. Data of peripheral blood sample analysis conducted within one week before and after radiotherapy were obtained. According to the CTCAE5.0, hypoalbuminemia is defined as albumin (ALB) value less than 35 g/L, the range of grade 1 is: 30 g/L—the lower limit of the normal value; grades 2, 3, and 4 are characterised by ALB levels of 20–30 g/L, <20 g/L, and life-threatening hypoalbuminemia necessitating urgent treatment, respectively. Anaemia was classified as grades 1, 2, 3, and 4 based on haemoglobin (HGB) 100 g/L—the lower limit of normal, 80–100 g/L, <80 g/L, and life-threatening anaemia necessitating emergency treatment, respectively. Grades 1, 2, 3, and 4 FIB elevations were 25% below baseline (g/L), 25–49% above baseline, 50–74% from baseline, and ≥75% from baseline. The FLR was defined as the FIB (g/L) to lymphocyte (10⁹/L) ratio.

Radiotherapy

The Eclipse treatment planning system (TPS) (Version 15.6) was used for treatment planning and dose calculation. Computed tomography (CT) was performed after 4-hour fasting. The participants were placed in a position based on their body size, and the head-neck and shoulders were fixed using a thermoplastic mask. After positioning in the Philips 16-detector row CT scanner (Philips, The Netherlands), 100 mL contrast agent was administered as intravenous bolus injections at 3 mL/s using a high-pressure injector. The whole brain was scanned with 3 mm layer thickness and 3 mm layer space. Images were transmitted to the Eclipse TPS and fused with the enhanced magnetic resonance imaging (MRI) image of the head before radiotherapy to outline the target area. Target regions including gross tumour volume (GTV), clinical target volume (CTV), and planning target volume (PTV), involved organs and brainstem were delineated. All participants underwent volumetric modulated arc therapy (VMAT). The prescribed radiation dose was 60 Gy administrated with conventional radiotherapy in 30 fractions within 6 weeks. Radiation dose parameters, including prescribed dose for PTV (Gy), PTV volume [cc (cubic centimeter)], mean dose for whole brain (Gy), whole-brain volume (cc), whole brain V10, V15, V20, V25, V30, V35, and V50 (%), mean dose for brainstem (Gy), brainstem volume (cc), brainstem V10, V15, V20, V25, V30, V35, and V50 (%), were collected from dose-volume histograms (DVH). Monitor units (MUs) were recorded.

Chemotherapy

All participants underwent concurrent TMZ-based chemotherapy during radiotherapy, followed by adjuvant TMZ-based chemotherapy (Stuup scheme) starting 4 weeks after the completion of the radiotherapy.

Statistical analysis

All data were collected and analysed with SPSS 26.0. Measurement data with normal distribution were obtained as mean ± standard deviation and compared using a two independent-sample t-test; otherwise, data were expressed by median (interquartile range) and compared with the Mann-Whitney U test. Numerical data were presented by frequency (percentage) and compared using the Chi-square test. Uni- and multivariate Cox regression analyses were employed to explore the influencing factors for PFS and OS outcomes. The Kaplan-Meier method was used to generate survival curves of PFS and OS. Spearman’s correlation analysis was conducted. P<0.05 was considered statistically significant.

Results

Characteristics of study cohort

In this cohort of 104 participants, including 64 men and 40 women, and the median age at diagnosis was 52 years; 20, 22, and 62 participants completed ≤2, 3–4, and >4 chemotherapy cycles, respectively. The time to follow-up ranged from 5 to 60 months (July 2017–August 2022), with a median duration of 16.6 months.

Optimal cut-off values of FIB and FLR

The best cut-off values of FIB and FLR for predicting PFS and OS were determined using receiver operating characteristic (ROC) curve analysis. For FIB, the best cut-off for predicting PFS was 2.44, with an area under the curve (AUC) value of 0.922 [95% confidence interval (CI): 0.869, 0.974] (P<0.001), specificity of 1.000, sensitivity of 0.851, and the Youden index of 0.851; while the greatest cut-off for predicting OS was 2.44, with an AUC value of 0.997 (95% CI: 0.990, 1.000) (P<0.001), specificity of 1.000, sensitivity of 0.976, and the Youden index of 0.976. For FLR, the best cut-off for predicting PFS was 1.92, with an AUC value of 0.796 (95% CI: 0.630, 0.962) (P=0.002), specificity of 0.800, sensitivity of 0.851, and the Youden index of 0.651; while the best cut-off for predicting OS was 1.92, with an AUC value of 0.849 (95% CI: 0.732, 0.965) (P<0.001), specificity of 0.773, sensitivity of 0.939, and the Youden index of 0.712.

Survival significance of FIB and FLR

According to the best cut-off of 2.44 determined by ROC curve, patients were divided into the high FIB (≥2.44) and low FIB (<2.44) groups. In the same way, patients were classified into the high FLR (FLR ≥1.92) and low FLR (FLR <1.92) groups according to the cut off of 1.92. The Kaplan-Meier method and the log rank (Mantel-Cox) test were used for the survival study. It was discovered that the mean PFS and OS of the high FIB and high FLR groups were considerably poorer than those of the corresponding low FIB and low FLR groups (P<0.05; Figures 1,2).

Figure 1 Survival curve for PFS. (A) Survival curve for PFS in high- and low-FIB groups; (B) survival curve for PFS in high- and low-FLR groups. FIB, fibrinogen; FLR, fibrinogen-to-lymphocyte ratio; PFS, progression-free survival.

Figure 2 Survival curve for OS. (A) Survival curve for OS in high- and low-FIB groups; (B) survival curve for OS in high- and low-FLR groups. FIB, fibrinogen; FLR, fibrinogen-to-lymphocyte ratio; OS, overall survival.

Comparison of clinical data between high and low FIB groups

According to the best cut-off of 2.44 determined by ROC curve, patients were divided into the high FIB (≥2.44) and low FIB (<2.44) groups. Following two independent-sample t-tests, the Chi-square test, and the Mann-Whitney U test, no distinct intergroup difference was found with regard to the dexamethasone dose during radiotherapy, body mass index (BMI) before and after radiotherapy, weight loss >3 kg within 3 months, HGB after radiotherapy, ALB after radiotherapy, proportion of O6-methylguanine-DNA methyl-transferase (MGMT) methylation, isocitrate dehydrogenase (IDH) mutation, tumour site, ECOG score, postoperative tumor volume, PTV dose, brain V10, brainstem V10, brainstem V15, brainstem V20, brainstem V25, brainstem V30, brainstem V35, and brainstem V50 (all P>0.05). The age, male ratio, PTV volume, mean brain dose, brain V15, brain V20, brain V25, brain V30, brain V35, brain V50, brain volume, mean brainstem dose, and brainstem volume in the high FIB group were significantly higher than those in the low FIB group (P<0.05), whereas the total MU was significantly lower (P<0.05, Table 1).

Comparison of clinical data between high and low FLR groups

According to the best cut-off of 1.92 determined by ROC curve, patients were divided into the high FLR (FLR ≥1.92) and low FLR (FLR <1.92) groups. Following two independent-sample t-test, Chi-square test, and Mann-Whitney U test, no distinct differences were found between the two groups in the dexamethasone dose during radiotherapy, BMI before and after radiotherapy, weight loss >3 kg within 3 months, HGB after radiotherapy, ALB after radiotherapy, proportion of MGMT methylation, IDH mutation, tumour site, ECOG score, postoperative tumor volume, PTV dose, brain V10, brain V15, brain V20, brain V25, brain V30, brain V35, brainstem V10, brainstem V15, brainstem V20, brainstem V25, brainstem V30, brainstem V35, brainstem V50, and total MU (all P>0.05). The age, male ratio, PTV volume, mean brain dose, brain V50, brain volume, mean brainstem dose, and brainstem volume in the high FLR group were significantly higher than those in the low FLR group (P<0.05) (please refer to Table 2).

Cox regression analysis for factors influencing PFS of patients

Univariate Cox regression analysis

In the univariate analysis, male gender, age, ECOG score, PTV volume, mean brain dose, brain V25, brain V30, brain V35, brain volume, mean brainstem dose, brainstem volume, FIB and FLR after radiotherapy were positively associated with the risk of poor PFS (Table 3).

Multivariate Cox regression analysis

The variables with statistical significance in univariate analysis were further included in a multivariate model wherein male sex, higher PTV volume, mean brain dose, and mean brainstem dose were independently prognostic factors for poor PFS (Table 4).

Cox regression analysis for factors influencing OS of patients

Univariate Cox regression analysis

In the univariate analysis, male sex, age, ECOG score, PTV volume, mean brain dose, brain V25, brain V30, brain V35, brain volume, mean brainstem dose, brainstem volume, FIB and FLR after radiotherapy were positively associated with the risk of poor OS of patients, whereas the total MU exhibited a negative association (Table 5).

Multivariate Cox regression analysis

The variables with statistical significance in univariate analysis were further included in a multivariate model. It was demonstrated that higher PTV volume, mean brain dose, and mean brainstem dose were independent prognostic factors for poor OS (Table 6).

Spearman correlation analysis

Association between FIB and clinical indicators

The PTV volume, mean brain dosage, mean brainstem dose, and brainstem volume were all inversely correlated with the changes in FIB from before to after radiotherapy. (P<0.05).

Association between FLR and clinical indicators

The PTV volume, mean brain dosage, mean brainstem dose, and brainstem volume were all inversely correlated with the changes in FLR before and after radiation. (P<0.05).

Discussion

Studies have increasingly proved that the inflammatory response is closely associated with multiple stages of tumour occurrence and development (12), and has significant implications for the body’s immune function, patient response to therapy, and prognosis, etc. (13-15). FIB is the most abundant plasma coagulation factor that is synthesised by the liver, and participates in tissue inflammation, infection, or tissue damage repair through conversion to fibrin in the presence of thrombin. Besides its role in coagulation, FIB is crucial for infiltration, metastasis, and inflammatory response in multiple tumours (16,17). A high level of FIB is an indicator of coagulation and fibrinolysis, and is a prognostic marker for progression of various tumours, such as head and neck tumours, oesophageal carcinoma, breast and colon cancer (18). Sheng et al. (19) retrospectively analysed the preoperative FIB levels of 110 laryngeal cancer patients who were scheduled for tumour resection and found that higher levels of preoperative plasm FIB (>4.00 g/L) were predictive of shorter OS and disease-free survival (DFS), and an advanced tumour stage in these patients. In a different retrospective analysis, 68 patients scheduled for radical oesophageal cancer resection and undergoing neoadjuvant therapy had significantly shorter DFS after surgery if they had preoperative hyperfibrinogenaemia and elevated plasma FIB during neoadjuvant therapy (20). In tumour patients with activation of the fibrinolytic system, it is frequently observed that the extent of activation is highly involved in the distant metastasis, progression, and prognosis of diverse malignancies. The clinical significance of FIB in glioblastoma has not been reported yet. The present study found that the mean PFS and OS of patients in the high FIB group after radiotherapy were 10.9 and 14.8 months, respectively, which was significantly lower than those in the low FIB group (P<0.05) and is consistent with the aforementioned studies. The mechanism underlying the association between FIB and tumour progression remains indescribable but there are three theories: (I) FIB binds with multiple growth factors, such as fibroblast growth factor (FGF), platelet-derived growth factor (PDGF), and neurotrophic factor, which are involved in various tumour pathophysiological processes (e.g., tumour development, inflammatory response, tumour microenvironment) via regulation of tumour cell growth and inhibition of the function of natural killer (NK) cells, thereby promoting proliferation, invasion, and migration of tumour cells (21); (II) FIB stimulates tumour angiogenesis through promoting vascular endothelial cell chemotaxis and enhancing the proangiogenic effects of vascular endothelial growth factor (VEGF) and FGF, which is conducive for tumour microenvironment remodelling (11); and (III) FIB can be synthesized by tumour cells and can augment the blockade of activated immune cells, which may help tumour cells escape from host immune surveillance, leading to more active proliferation, invasion, and migration in tumour cells; (IV) FIB can protect tumor cells from NK cell-mediated cytotoxicity by accumulating around them and forming a dense fibrin layer (22). FIB may therefore have a significant role in the development and spread of tumours. Furthermore, FIB-like proteins, which have structural similarities to FIB, can inhibit antigen-mediated T cell responses and evade immune surveillance (23). High expression has been detected in solid tumors such as liver cancer and lung cancer, and it has been shown that it can lead to poor therapeutic effects of immune checkpoint inhibitors and affect the development process of tumors. However, it plays a role in mediating tumor immune escape in the tumor microenvironment. The mechanism of action has not been clearly studied (10). Glioblastoma is more invasive and capable of neovascularization than tumours with lesser malignancy, and it has a higher blood vessel density than glioblastoma multiforme. Brain oedema is a secondary, multivariate, extremely complex pathophysiological condition that usually develops in glioblastoma patients following surgery. After brain cancer surgery, brain oedema is linked to chronic thrombin release, which increases FIB and fibrin synthesis and promotes the growth of tumours (24,25). No study has investigated radiotherapy-induced FIB changes, possibly attributable to the increasing rate and sensitivity of fibrin hydrolysis. In the present study, we found that the age, male ratio, PTV volume, mean brain dose, brain V15, brain V20, brain V25, brain V30, brain V35, brain V50, brain volume, mean brainstem dose, and brainstem volume were significantly higher in the high FIB group than those in the low FIB group (P<0.05), whereas the total MU was significantly lower (P<0.05). Furthermore, changes in FIB from before to after radiotherapy was negatively associated with the PTV volume, mean brain dose, mean brainstem dose, and brainstem volume (P<0.05). Since this study is a retrospective study, we adopted the 4th edition staging criteria from 2016. However, the 2021 WHO classification for CNS tumors no longer defines IDH-mutant astrocytomas as glioblastomas but rather as astrocytomas, IDH-mutant, WHO grade 4. Furthermore, low hemoglobin and anemia can directly affect tumor cell sensitivity to radiation, leading to decreased survival rates. Low ALB levels are a common manifestation of malnutrition in tumor patients and are associated with prognosis in various types of cancer (26). Considering that patients with glioblastoma require concurrent postoperative radiochemotherapy, their blood and biochemical indicators should not exceed more than twice the normal values before radiotherapy. Otherwise, they would not meet the criteria for receiving concurrent radiochemotherapy. Our study results indicate that no differences between the high and low FIB groups were detected for dexamethasone dose, BMI before and after radiotherapy, weight loss <3 kg within 3 months, HGB after radiotherapy, ALB after radiotherapy, proportion of MGMT methylation, IDH mutation, tumour site, ECOG score, and postoperative tumor volume. Thus, FIB changes caused by radiotherapy-associated dose-volume parameters (especially target region volume and dose volume of involved organs) correlated to a poor prognosis, though target region dose was not statistically significant for FIB changes, possibly because a standard dose was used in both groups. Furthermore, our study revealed a significantly lower total MU in the high FIB group, which might be attributable to the morphology of the single target region of radiotherapy for glioblastoma that facilitates planned dose optimisation, whereas the dose distribution in a large target volume does not necessarily need more complicated subfields and multi-leaf collimator (MLC) sequence.

Lymphocytes are a routine hematologic index of the immune status of patients during tumour-related treatment. Multiple traditional inflammatory markers, such as neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR), are known potential prognostic biomarkers for various tumours (27). For instance, Liu et al. (28) retrospectively analysed 139 patients with small cell lung cancer and reported that high NLR (>4.55) and high PLR (>148) were mortality predictors, and high NLR was associated with a poor prognosis. Zhang et al. (29) found that preoperative serum NLR could be used as a prognostic indicator of EGFR mutation-positive stage Ⅳ non-small cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors (EGFR-TKIs). Hirahara et al. (30) demonstrated that NLR/PLR was closely associated with tumour progression in patients with advanced gastric cancer, and could be used clinically as a novel hematologic predictor of tumour response to first-line therapy. Lymphocytes are sensitive to radiotherapy though they are not actively in mitosis (31). Besides directly damaging DNA in tumour cells, radiotherapy can lead to apoptosis by enhancing immunogenicity through promoting inflammation and releasing tumour antigens, or affect the host anti-tumour immune response via specifically recognizing and releasing a series of cytokines. Preoperative lymphocyte count reduction indicates an immunosuppressed status, whereas a low peripheral lymphocyte count indicates poor immune response that results in a decreased treatment effect. Nevertheless, the association between the decrease in lymphocytes and the reduction in tumour response to therapy is extremely complicated and needs validation in further experiments. Radiotherapy can directly kill lymphocytes to reduce their number, though different degrees of reduction may induce interindividual differences in anti-tumour immune functions. Differences in immune functions might mediate varying outcomes in tumour patients who maintain the same stage after identical treatment (32). Moreover, the target volume, radiation dose, segmentation mode (including conventional fractionated radiotherapy, hypofractionated radiotherapy, hyperfractionated radiotherapy, etc.), and the irradiated site, have significant implications for the decreased lymphocyte count after radiotherapy. A significant relationship between the irradiated tumour volume and the change of total lymphocyte count has been reported. In a study of 711 NSCLC patients receiving radiotherapy, a larger irradiated tumour volume was associated with a distinct decrease in the lymphocyte count after radiotherapy, possibly due to the exposure of more circulating cells to radiation and destruction of normal lymphocytes (26). In another study, 183 patients with high-grade glioma (HGG) were treated with radiotherapy + TMZ, 53 patients (29%) developed acute severe lymphopenia (ASL). Patients with ASL had significantly worse OS than those without (median: 12.5 vs. 20.2 months, respectively; P<0.001). Higher brain V25Gy are significant predictors of ASL during radiotherapy + TMZ therapy for HGG (33). Yovino et al. (34) showed that after a single dose of 2 Gy, approximately 5% of circulating blood cells will be exposed to 0.5 Gy. Although this dose is relatively low compared with the total dose received by the bulk tumor, it is still enough to cause a large number of lymphopenia: 62%, 92%, and 99% of circulating blood cells received at least 0.5 Gy after 10, 20, and 30 fractions of radiotherapy, respectively, which also suggests that lymphopenia is cumulative with radiotherapy dose-fractionation effects are closely related. FLR was first reported by Fan et al. (35), who found that oesophageal carcinoma patients with high preoperative FLR tended to have a low survival rate. Consistently, our study demonstrated that the mean PFS and OS of patients in the high FLR group after radiotherapy were significantly lower than those of patients in the low FLR group. Multiple studies have explored the role of FLR in the prognosis of lung, head and neck, and liver cancer (36-38). To our knowledge, FLR on prognosis in glioblastoma has not been reported yet. In the present study, FLR, which combines the FIB level and lymphocyte count, was evaluated as a prognostic indicator in glioblastoma patients receiving radiotherapy. We noted that the age, male ratio, PTV volume, mean brain dose, brain V50, brain volume, mean brainstem dose, and brainstem volume were significantly higher in the high FLR group than those in the low FLR group (P<0.05). Compared to the high FLR group, brain V15, 20, 25, 30, 35 were missing in the low FLR group, possibly due to the varying degrees of lymphocyte decrease after radiotherapy. However, we did not include the degree of lymphocyte decrease in the analysis, which is a limitation of the study. Moreover, the changes in FLR before and after radiotherapy were negatively associated with the PTV volume, mean brain dose, mean brainstem dose, and brainstem volume (P<0.05), which is consistent with the changing trend of FIB. Furthermore, larger PTV volume, mean brain and brainstem doses were independent prognostic factors for poor PFS and OS of patients. These findings imply that the FLR could be used as a clinical index for predicting the prognosis and sensitivity to radiotherapy in patients with glioblastoma.

Conclusions

In conclusion, the dose and volume parameters related to radiotherapy will affect the changes in FIB and FLR, thus affecting the prognosis. Shortening the irradiation time and optimizing the radiotherapy plan without changing the total irradiation dose and compromising the treatment effect to reduce the irradiated dose and volume of surrounding normal organs will remain an area of active research interest. The FLR, which combines the FIB level and lymphocyte count, is simple to measure and may have useful clinical applications. To develop more suitable, standardised cut-off values for FLR to direct clinical use in glioblastoma patient prognostication, however, large-scale, multi-centre prospective investigations are further warranted which can provide a basis for individualized treatment of patients.

Acknowledgments

Funding: This study was supported by the 2021 Science and Technology Development Plan Project of Henan Province (212102310663).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2271/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2271/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2271/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-23-2271/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study was approved by The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital ethics committee (No. 2022-541-001) and individual consent for this retrospective analysis was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Wirsching HG, Galanis E, Weller M. Glioblastoma. Handb Clin Neurol 2016;134:381-97. [Crossref] [PubMed]
2. Janjua TI, Rewatkar P, Ahmed-Cox A, et al. Frontiers in the treatment of glioblastoma: Past, present and emerging. Adv Drug Deliv Rev 2021;171:108-38. [Crossref] [PubMed]
3. Stupp R, Taillibert S, Kanner AA, et al. Maintenance Therapy With Tumor-Treating Fields Plus Temozolomide vs Temozolomide Alone for Glioblastoma: A Randomized Clinical Trial. JAMA 2015;314:2535-43. [Crossref] [PubMed]
4. Daubon T, Hemadou A, Romero Garmendia I, et al. Glioblastoma Immune Landscape and the Potential of New Immunotherapies. Front Immunol 2020;11:585616. [Crossref] [PubMed]
5. Glavatskyi OY, Zemskova OV, Khmelnytskyi HV, et al. Temozolomide in glioblastoma treatment: 15-year clinical experience and analysis of its efficacy. Exp Oncol 2020;42:148-56. [Crossref] [PubMed]
6. Sasmita AO, Wong YP, Ling APK. Biomarkers and therapeutic advances in glioblastoma multiforme. Asia Pac J Clin Oncol 2018;14:40-51. [Crossref] [PubMed]
7. Wang PF, Meng Z, Song HW, et al. Preoperative Changes in Hematological Markers and Predictors of Glioma Grade and Survival. Front Pharmacol 2018;9:886. [Crossref] [PubMed]
8. Dzikowski L, Mirzaei R, Sarkar S, et al. Fibrinogen in the glioblastoma microenvironment contributes to the invasiveness of brain tumor-initiating cells. Brain Pathol 2021;31:e12947. [Crossref] [PubMed]
9. Wach J, Apallas S, Schneider M, et al. Baseline Serum C-Reactive Protein and Plasma Fibrinogen-Based Score in the Prediction of Survival in Glioblastoma. Front Oncol 2021;11:653614. [Crossref] [PubMed]
10. Li JJ, Wang JH, Tian T, et al. The liver microenvironment orchestrates FGL1-mediated immune escape and progression of metastatic colorectal cancer. Nat Commun 2023;14:6690. [Crossref] [PubMed]
11. Wojtukiewicz MZ, Mysliwiec M, Matuszewska E, et al. Heterogeneous Expression of Proangiogenic and Coagulation Proteins in Gliomas of Different Histopathological Grade. Pathol Oncol Res 2021;27:605017. [Crossref] [PubMed]
12. Fridman WH, Zitvogel L, Sautès-Fridman C, et al. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 2017;14:717-34. [Crossref] [PubMed]
13. Qu X, Tang Y, Hua S. Immunological Approaches Towards Cancer and Inflammation: A Cross Talk. Front Immunol 2018;9:563. [Crossref] [PubMed]
14. Galdiero MR, Varricchi G, Loffredo S, et al. Roles of neutrophils in cancer growth and progression. J Leukoc Biol 2018;103:457-64. [Crossref] [PubMed]
15. Walczak H. Death receptor-ligand systems in cancer, cell death, and inflammation. Cold Spring Harb Perspect Biol 2013;5:a008698. [Crossref] [PubMed]
16. Selzer E, Grah A, Heiduschka G, et al. Pre-therapeutic fibrinogen levels are of prognostic significance in locally advanced head and neck cancer. Wien Klin Wochenschr 2016;128:320-8. [Crossref] [PubMed]
17. Zhu LR, Li J, Chen P, et al. Clinical significance of plasma fibrinogen and D-dimer in predicting the chemotherapy efficacy and prognosis for small cell lung cancer patients. Clin Transl Oncol 2016;18:178-88. [Crossref] [PubMed]
18. Kuwahara T, Takahashi H, Sano D, et al. Fibrinogen and Neutrophil-to-lymphocyte Ratio Predicts Survival in Patients with Advanced Hypopharyngeal Squamous Cell Carcinoma. Anticancer Res 2018;38:5321-30. [Crossref] [PubMed]
19. Sheng X, Zhang H, Ge P, et al. A Retrospective Study of The Prognostic Significance of Preoperative Plasma Fibrinogen, Mean Platelet Volume, and the Neutrophil-to-Lymphocyte Ratio in Patients with Laryngeal Squamous Cell Carcinoma. Med Sci Monit 2019;25:4527-34. [Crossref] [PubMed]
20. Matsuda S, Takeuchi H, Fukuda K, et al. Clinical significance of plasma fibrinogen level as a predictive marker for postoperative recurrence of esophageal squamous cell carcinoma in patients receiving neoadjuvant treatment. Dis Esophagus 2014;27:654-61. [Crossref] [PubMed]
21. Martino MM, Briquez PS, Ranga A, et al. Heparin-binding domain of fibrin(ogen) binds growth factors and promotes tissue repair when incorporated within a synthetic matrix. Proc Natl Acad Sci U S A 2013;110:4563-8. [Crossref] [PubMed]
22. Luo Y, Lin K, Zhang M, et al. Combination of the platelet-to-lymphocyte ratio and fibrinogen may predict 5-year overall survival of patient in non-small cell lung cancer treated with surgery. J Thorac Dis 2023;15:6967-75. [Crossref] [PubMed]
23. Zhang Y, Zhang K, Wen H, et al. FGL1 in plasma extracellular vesicles is correlated with clinical stage of lung adenocarcinoma and anti-PD-L1 response. Clin Exp Immunol 2024;216:68-79. [Crossref] [PubMed]
24. Ye F, Garton HJL, Hua Y, et al. The Role of Thrombin in Brain Injury After Hemorrhagic and Ischemic Stroke. Transl Stroke Res 2021;12:496-511. [Crossref] [PubMed]
25. Larsen JB, Hvas AM. Thrombin: A Pivotal Player in Hemostasis and Beyond. Semin Thromb Hemost 2021;47:759-74. [Crossref] [PubMed]
26. Tang C, Liao Z, Gomez D, et al. Lymphopenia association with gross tumor volume and lung V5 and its effects on non-small cell lung cancer patient outcomes. Int J Radiat Oncol Biol Phys 2014;89:1084-91. [Crossref] [PubMed]
27. Cupp MA, Cariolou M, Tzoulaki I, et al. Neutrophil to lymphocyte ratio and cancer prognosis: an umbrella review of systematic reviews and meta-analyses of observational studies. BMC Med 2020;18:360. [Crossref] [PubMed]
28. Liu D, Huang Y, Li L, et al. High neutrophil-to-lymphocyte ratios confer poor prognoses in patients with small cell lung cancer. BMC Cancer 2017;17:882. [Crossref] [PubMed]
29. Zhang Y, Feng YC, Zhu HG, et al. The peripheral blood neutrophil-to-lymphocyte ratio is a prognostic predictor for survival of EGFR-mutant nonsmall cell lung cancer patients treated with EGFR-TKIs. Medicine (Baltimore) 2018;97:e11648. [Crossref] [PubMed]
30. Hirahara T, Arigami T, Yanagita S, et al. Combined neutrophil-lymphocyte ratio and platelet-lymphocyte ratio predicts chemotherapy response and prognosis in patients with advanced gastric cancer. BMC Cancer 2019;19:672. [Crossref] [PubMed]
31. Deek MP, Benenati B, Kim S, et al. Thoracic Vertebral Body Irradiation Contributes to Acute Hematologic Toxicity During Chemoradiation Therapy for Non-Small Cell Lung Cancer. Int J Radiat Oncol Biol Phys 2016;94:147-54. [Crossref] [PubMed]
32. Venkatesulu BP, Mallick S, Lin SH, et al. A systematic review of the influence of radiation-induced lymphopenia on survival outcomes in solid tumors. Crit Rev Oncol Hematol 2018;123:42-51. [Crossref] [PubMed]
33. Huang J, DeWees TA, Badiyan SN, et al. Clinical and Dosimetric Predictors of Acute Severe Lymphopenia During Radiation Therapy and Concurrent Temozolomide for High-Grade Glioma. Int J Radiat Oncol Biol Phys 2015;92:1000-7. [Crossref] [PubMed]
34. Yovino S, Kleinberg L, Grossman SA, et al. The etiology of treatment-related lymphopenia in patients with malignant gliomas: modeling radiation dose to circulating lymphocytes explains clinical observations and suggests methods of modifying the impact of radiation on immune cells. Cancer Invest 2013;31:140-4. [Crossref] [PubMed]
35. Fan N, Chen D, Zheng J, et al. A novel preoperative plasma indicator to predict prognoses for patients with esophageal squamous cell carcinoma after radical esophagectomy: fibrinogen-to-lymphocyte ratio. Cancer Manag Res 2019;11:4719-28. [Crossref] [PubMed]
36. Li Y, Li Z, Deng K, et al. Fibrinogen/Lymphocyte Count Ratio Can Be Used as a New Indicator of Prognosis in Patients with Hepatocellular Carcinoma After Radical Resection. Cancer Manag Res 2020;12:9057-66. [Crossref] [PubMed]
37. Liu M, Yang J, Wan L, et al. Elevated Pretreatment Fibrinogen-to-Lymphocyte Percentage Ratio Predict Tumor Staging and Poor Survival in Non-Small Cell Lung Cancer Patients with Chemotherapy or Surgery Combined with Chemotherapy. Cancer Manag Res 2021;13:4921-33. [Crossref] [PubMed]
38. Brkic FF, Stoiber S, Friedl M, et al. The Potential Prognostic Value of a Novel Hematologic Marker Fibrinogen-to-Lymphocyte Ratio in Head and Neck Adenoid-Cystic Carcinoma. J Pers Med 2021;11:1228. [Crossref] [PubMed]