Abstract
PurposeGlioma is one of the most common tumors of the central nervous system, and many patients suffer from recurrence even after standard comprehensive treatment. However, little is known about the molecular markers that predict the recurrence patterns of glioma. This study aimed to demonstrate the correlations between molecular markers and glioma recurrence patterns, which included local/nonlocal recurrence and paraventricular/nonparaventricular recurrence.MethodsImmunohistochemical techniques were used to assess the molecular markers of 88 glioma tissues following surgical resection. The recurrence patterns were divided into local recurrence, marginal recurrence, distant recurrence, multirecurrence, and subarachniod recurrence, with the last four recurrence patterns being collectively called nonlocal recurrence. According to whether the recurrence invaded ventricles, the nonlocal recurrence patterns were divided into paraventricular and nonparaventricular recurrence. Then, we compared the different recurrence patterns and their clinical characteristics, focusing on the expression of molecular markers.ResultsMore patients in the nonlocal recurrence group received combined radiotherapy and chemotherapy than patients in the local recurrence group (p=0.019). Sex, age, extent of surgery, time to recurrence, tumor location, size, and WHO grade were not different in the defined groups (P>0.05). Recurrent tumor volume and WHO grade were significantly different between the paraventricular and nonparaventricular recurrence groups (p=0.046 and 0.033). The expression of Ki-67, P53, and PCNA in the nonlocal recurrence group was significantly higher than that in the local recurrence group (p=0.015, 0.009, and 0.037), while the expression of S-100 in the nonlocal recurrence group was significantly lower than that in the local recurrence group (p=0.015). Cox regression indicated hazard ratio (HR) for high expression level of PCNA associated with non-local recurrence was 3.43 (95% CI, 1.15, 10.24), and HR for high expression level of MGMT associated with paraventricular recurrence was 2.64 (95% CI, 1.15,6.08).ConclusionsKi-67, P53, PCNA, and MGMT might be important clinical markers for nonlocal recurrence and paraventricular recurrence.
Highlights
As the most common type of primary intracranial tumor, gliomas frequently lead to severe neurological deficits, with some subtypes showing very pessimistic outcomes, and treatment for gliomas is still a great challenge [1]
This study aimed to demonstrate the correlations between molecular markers and glioma recurrence patterns, which included local/nonlocal recurrence and paraventricular/nonparaventricular recurrence
Cox regression indicated hazard ratio (HR) for high expression level of PCNA associated with non-local recurrence was 3.43, and HR for high expression level of MGMT associated with paraventricular recurrence was 2.64
Summary
As the most common type of primary intracranial tumor, gliomas frequently lead to severe neurological deficits, with some subtypes showing very pessimistic outcomes, and treatment for gliomas is still a great challenge [1]. For high-grade gliomas (HGGs), such as WHO grade III anaplastic gliomas and glioblastoma (GBM), surgical treatment combined radiotherapy and chemotherapy is the standard strategy for most patients [3]. Among the factors leading to the short survival of glioma patients, a high incidence of recurrence is the greatest challenge [5]; analyzing the patterns of recurrence and its relevant factors may be of great significance for the prognosis of patients. Previous studies have focused on the patterns of recurrence for GBM after surgical resection, temozolomide (TMZ)-based chemotherapy, and radiotherapy with different protocols, as well as bevacizumab treatment, all revealing central recurrence to be the main pattern [6, 7]. For patients with LGGs undergoing radiotherapy, recurrence uniformly occurred within the treatment volume, suggesting that dose escalation for radiotherapy within conformal fields could improve the treatment outcome [9]
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