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The prognostic value of the preoperative inflammatory index on the survival of glioblastoma patients
OBJECTIVES: The growth and development of tumors are closely related to the initiation and amplification of the inflammatory response. Various inflammatory biomarkers had attained growing attention for nearly two decades and were discovered strongly associated with cancer patients’ prognosis, indica...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9126244/ https://www.ncbi.nlm.nih.gov/pubmed/35606674 http://dx.doi.org/10.1007/s10072-022-06158-w |
Sumario: | OBJECTIVES: The growth and development of tumors are closely related to the initiation and amplification of the inflammatory response. Various inflammatory biomarkers had attained growing attention for nearly two decades and were discovered strongly associated with cancer patients’ prognosis, indicating that systemic inflammatory response is possibly essential to cancer progression. However, little was known about the sensitive biomarkers associated with the detection, persistence, treatment, and prognosis of GBM. Hence, the retrospective research endeavored to evaluate the prognostic value of preoperative inflammatory biomarkers in patients with GBM who initially received standardized treatment. METHODS: The 232 glioblastoma patients eligible who were admitted to Qilu Hospitals in Shandong Province from January 2014 to January 2018 were collected for this analysis. Inflammatory markers, including the systemic immune-inflammation index (SII), systemic immune response index (SIRI), neutrophil–lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), monocyte-lymphocyte ratio (MLR), and albumin/globulin ratio (AGR), were designed. Progression-free survival (PFS) and overall survival (OS) were estimated using the Kaplan–Meier method, and we calculated the area under the ROC curve to determine the AUC value. Besides, we used the Cox proportional hazard model to estimate the relationship between variables and PFS and OS. The statistical differences between variables and PFS and OS were tested through the log-rank test. What is more, the LR method was used to perform Cox multiple regression analysis. The results were represented by hazard ratio (HR), 95% CI, any 2-tailed P < 0.01 was accepted as statistically different. RESULTS: The multivariate Cox proportional hazard model presented that SII ≥ 659.1 was an independent risk factor affecting OS (HR = 2.238, 95% CI = 1.471–3.406, P < 0.001) and postoperative PFS (HR = 2.000, 95% CI = 1.472–2.716, P < 0.001) in GBM patients. The 1-, 3-, and 5-year OS of the SII < 659.1 group was 70.8%, 26.9%, and 14.1%, respectively, while the 1- and 3-year OS of the SII ≥ 659.1 group was 37.5% and 11.5% (P < 0.001). The 1-, 3-, and 5-year PFS of the SII < 659.1 group was 36.3%, 19.6%, and 13%, respectively, while the 1-year PFS of the SII ≥ 659.1 group was 11.3% (P < 0.001). Results of patients’ clinical and pathological characteristics paraded that in comparison to the lower SII group, the higher SII group had significantly inferior Karnofsky Performance Scale (KPS) scores (P < 0.001) and more frequent cystic changes of the tumors (P < 0.001), whereas the values of SIRI, NLR, PLR, MLR, and AGR were low. CONCLUSIONS: SII is an independent inflammatory indicator for predicting the prognosis of GBM patients after receiving initially standardized treatments. |
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