Serum beta2-microglobulin acts as a biomarker for severity and prognosis in glioma patients: a preliminary clinical study.

BACKGROUND: Gliomas are the deadliest malignant tumors of the adult central nervous system. We previously discovered that beta2-microglobulin (B2M) is abnormally upregulated in glioma tissues and that it exerts a range of oncogenic effects. Besides its tissue presence, serum B2M levels serve as biomarkers for various diseases. This study aimed to explore whether serum B2M levels can be used in the diagnosis and prognosis of gliomas. METHODS: Medical records from 246 glioma patients were retrospectively analyzed. The relationship between preoperative serum B2M levels and clinicopathological features was examined. Kaplan-Meier analysis, alongside uni- and multivariate Cox regression, assessed the association between B2M levels, systemic inflammatory markers, and glioma patient prognosis. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic significance of these biomarkers specifically for glioblastoma (GBM). RESULTS: Patients with malignant gliomas exhibited elevated preoperative serum B2M levels. Glioma patients with high serum B2M levels experienced shorter survival times. Multivariate Cox analysis determined the relationship between B2M levels (hazard ratio = 1.92, 95% confidence interval: 1.05-3.50, P = 0.034) and the overall survival of glioma patients. B2M demonstrated superior discriminatory power in distinguishing between GBM and non-GBM compared to inflammation indicators. Moreover, postoperative serum B2M levels were lower than preoperative levels in the majority of glioma patients. CONCLUSIONS: High preoperative serum B2M levels correlated with malignant glioma and a poor prognosis. Serum B2M shows promise as a novel biomarker for predicting patient prognosis and reflecting the therapeutic response.

Machine learning-based identification of a cell death-related signature associated with prognosis and immune infiltration in glioma.

Accumulating evidence suggests that a wide variety of cell deaths are deeply involved in cancer immunity. However, their roles in glioma have not been explored. We employed a logistic regression model with the shrinkage regularization operator (LASSO) Cox combined with seven machine learning algorithms to analyse the patterns of cell death (including cuproptosis, ferroptosis, pyroptosis, apoptosis and necrosis) in The Cancer Genome Atlas (TCGA) cohort. The performance of the nomogram was assessed through the use of receiver operating characteristic (ROC) curves and calibration curves. Cell-type identification was estimated by using the cell-type identification by estimating relative subsets of known RNA transcripts (CIBERSORT) and single sample gene set enrichment analysis methods. Hub genes associated with the prognostic model were screened through machine learning techniques. The expression pattern and clinical significance of MYD88 were investigated via immunohistochemistry (IHC). The cell death score represents an independent prognostic factor for poor outcomes in glioma patients and has a distinctly superior accuracy to that of 10 published signatures. The nomogram performed well in predicting outcomes according to time-dependent ROC and calibration plots. In addition, a high-risk score was significantly related to high expression of immune checkpoint molecules and dense infiltration of protumor cells, these findings were associated with a cell death-based prognostic model. Upregulated MYD88 expression was associated with malignant phenotypes and undesirable prognoses according to the IHC. Furthermore, high MYD88 expression was associated with poor clinical outcomes and was positively related to CD163, PD-L1 and vimentin expression in the in-horse cohort. The cell death score provides a precise stratification and immune status for glioma. MYD88 was found to be an outstanding representative that might play an important role in glioma.

The Father of Wisdom: "The Influence of Surgical Experience on Overall Survival in Patients with Malignant Gliomas".

BACKGROUND: The role of surgery in the management of malignant gliomas has been feverishly deliberated after the publication of the first expansive case series, the last two decades reinvigorating the discussion regarding the value of total removal in improving survivability. Despite numerous technologies being implemented to increase the resection rates of malignant gliomas, the role of surgical experience has been largely overlooked. This article aims to discuss the importance of a single surgeon's experience in treating high-grade gliomas over a period of 20 years. MATERIAL AND METHODS: In order to demonstrate the role of surgical experience, we divided the patients operated by a single neurosurgeon into two distinct intervals: between 2000 and 2009 and between 2012 and 2020, respectively. Only cases with subsequent adjuvant radio-chemotherapy were included. For objective reasons, no technologies that could assist the extent of resection (EOR) such as intraoperative MRI (iMRI) or 5-ALA could be used in the country of our study. Gross total resection was the main goal whenever possible, whereas subtotal removal was defined as a clear remnant on contrasted MRI or CT performed 24-48 h postoperatively. Using the Kaplan-Meier method, we analyzed the survival and disease-free interval of our patients according to age, pathology, and degree of resection. RESULTS: In the 20-year interval of our retrospective study, the main author (ISF) operated 1591 cases of gliomas in a total of 1878 surgeries, including recurrences. The number of high-grade glioma (HGG) patients was 909 (57.10%), 495 of which were male (54.5%) and 414 (45.5%) female. The mean age of the HGG population was 51.9 years. The most common type of HGG subtype were glioblastomas with a total number 620 cases (68.2%). Regarding overall survival (OS), average survival at 12 months was better by 1.6%, and 12.1% improved at 18 months and 17.8% longer at 24 months in the 2012-2020 interval. The mean OS in the earlier interval was 11.00 months compared to the second when it reached 13.441 months (CI, 12.642-14.24). CONCLUSION: Surgical treatment represents a critical step in the multimodal treatment of malignant gliomas. According to our results, surgical experience improves not only overall survival in a manner equivalent to adjuvant chemotherapy but also the quality of life. As such, a special qualification in neurooncology may prove necessary in offering these patients a second chance at life.

The effect of somatic mutations in mitochondrial DNA on the survival of patients with primary brain tumors.

AIM: To assess the presence of mitochondrial (mt) DNA somatic mutations, determine the relationship between clinicopathological characteristics and mutations, and assess the survival outcomes in Malay patients with primary brain tumors. METHODS: The study enrolled 54 patients with primary brain tumors. DNA extracted from paired tissue and blood samples was subjected to Sanger sequencing to identify alterations in the entire mtDNA. The associations between clinicopathological characteristics and mutations were evaluated. Cox-regression multivariate analysis was conducted to identify factors significantly associated with survival, and Kaplan-Meier analysis was used to compare the survival of patients with and without mutations. RESULTS: Overall, 29.6% of the patients harbored 19 somatic mutations distributed across 15 loci within the mtDNA. Notably, 36.8% of these mutations were not previously documented in MITOMAP. One newly identified mutation caused a frameshift in the ATPase6 gene, resulting in a premature stop codon. Three mutations were classified as deleterious in the MitImpact2 database. Overall, 1097 mtDNA polymorphisms were identified across 331 different locations. Patients with mutations exhibited significantly shorter survival than patients without mutations. CONCLUSIONS: mtDNA mutations negatively affected the survival outcomes of Malaysian patients with primary brain tumors. However, studies with larger samples are needed to confirm the association between mutation burden and survival rates.

Tumour growth rate predicts overall survival in patients with recurrent WHO grade 4 glioma.

PURPOSE: Accurate prognostication may aid in the selection of patients who will benefit from surgery at recurrent WHO grade 4 glioma. This study aimed to evaluate the role of serial tumour volumetric measurements for prognostication at first tumour recurrence. METHODS: We retrospectively analyzed patients with histologically-diagnosed WHO grade 4 glioma at initial and at first tumour recurrence at a tertiary hospital between May 2000 and September 2018. We performed auto-segmentation using ITK-SNAP software, followed by manual adjustment to measure serial contrast-enhanced T1W (CE-T1W) and T2W lesional volume changes on all MRI images performed between initial resection and repeat surgery. RESULTS: Thirty patients met inclusion criteria; the median overall survival using Kaplan-Meier analysis from second surgery was 10.5 months. Seventeen (56.7%) patients received treatment post second surgery. Univariate cox regression analysis showed that greater rate of increase in lesional volume on CE-T1W (HR = 2.57; 95% CI [1.18, 5.57]; p = 0.02) in the last 2 MRI scans leading up to the second surgery was associated with a higher mortality likelihood. Patients with higher Karnofsky Performance Score (KPS) (HR = 0.97; 95% CI [0.95, 0.99]; p = 0.01) and who received further treatment following second surgery (HR = 0.43; 95% CI [0.19, 0.98]; p = 0.04) were shown to have a better survival. CONCLUSION: Higher rate of CE-T1W lesional growth on the last 2 MRI images prior to surgery at recurrence was associated with increase mortality risk. A larger prospective study is required to determine and validate the threshold to distinguish rapidly progressive tumour with poor prognosis.

Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy.

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Indirect functional connectivity does not predict overall survival in glioblastoma.

BACKGROUND: Lesion network mapping (LNM) is a popular framework to assess clinical syndromes following brain injury. The classical approach involves embedding lesions from patients into a normative functional connectome and using the corresponding functional maps as proxies for disconnections. However, previous studies indicated limited predictive power of this approach in behavioral deficits. We hypothesized similarly low predictiveness for overall survival (OS) in glioblastoma (GBM). METHODS: A retrospective dataset of patients with GBM was included (n = 99). Lesion masks were registered in the normative space to compute disconnectivity maps. The brain functional normative connectome consisted in data from 173 healthy subjects obtained from the Human Connectome Project. A modified version of the LNM was then applied to core regions of GBM masks. Linear regression, classification, and principal component (PCA) analyses were conducted to explore the relationship between disconnectivity and OS. OS was considered both as continuous and categorical (low, intermediate, and high survival) variable. RESULTS: The results revealed no significant associations between OS and network disconnection strength when analyzed at both voxel-wise and classification levels. Moreover, patients stratified into different OS groups did not exhibit significant differences in network connectivity patterns. The spatial similarity among the first PCA of network maps for each OS group suggested a lack of distinctive network patterns associated with survival duration. CONCLUSIONS: Compared with indirect structural measures, functional indirect mapping does not provide significant predictive power for OS in patients with GBM. These findings are consistent with previous research that demonstrated the limitations of indirect functional measures in predicting clinical outcomes, underscoring the need for more comprehensive methodologies and a deeper understanding of the factors influencing clinical outcomes in this challenging disease.

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study.

OBJECTIVE: A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy. METHODS: Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years. RESULTS: The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential. CONCLUSIONS: For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

Profiling of Tumor-Infiltrating Immune Cells and Their Impact on Survival in Glioblastoma Patients Undergoing Immunotherapy with Dendritic Cells.

Glioblastomas (GBM) are the most common primary malignant brain tumors, comprising 2% of all cancers in adults. Their location and cellular and molecular heterogeneity, along with their highly infiltrative nature, make their treatment challenging. Recently, our research group reported promising results from a prospective phase II clinical trial involving allogeneic vaccination with dendritic cells (DCs). To date, six out of the thirty-seven reported cases remain alive without tumor recurrence. In this study, we focused on the characterization of infiltrating immune cells observed at the time of surgical resection. An analytical model employing a neural network-based predictive algorithm was used to ascertain the potential prognostic implications of immunological variables on patients' overall survival. Counterintuitively, immune phenotyping of tumor-associated macrophages (TAMs) has revealed the extracellular marker PD-L1 to be a positive predictor of overall survival. In contrast, the elevated expression of CD86 within this cellular subset emerged as a negative prognostic indicator. Fundamentally, the neural network algorithm outlined here allows a prediction of the responsiveness of patients undergoing dendritic cell vaccination in terms of overall survival based on clinical parameters and the profile of infiltrated TAMs observed at the time of tumor excision.

IL-6 significantly correlated with the prognosis in low-grade glioma and the mediating effect of immune microenvironment.

To screen immune-related prognostic biomarkers in low-grade glioma (LGG), and reveal the potential regulatory mechanism. The differential expressed genes (DEGs) between alive and dead patients were initially identified, then the key common genes between DEGs and immune-related genes were obtained. Regarding the key DEGs associated with the overall survival (OS), their clinical value was assessed by Kaplan-Meier, RCS, logistic regression, ROC, and decision curve analysis methods. We also assessed the role of immune infiltration on the association between key DEGs and OS. All the analyses were based on the TGCA-LGG data. Finally, we conducted the molecular docking analysis to explore the targeting binding of key DEGs with the therapeutic agents in LGG. Among 146 DEGs, only interleukin-6 (IL-6) was finally screened as an immune-related biomarker. High expression of IL-6 significantly correlated with poor OS time (all P < .05), showing a linear relationship. The combination of IL-6 with IDH1 mutation had the most favorable prediction performance on survival status and they achieved a good clinical net benefit. Next, we found a significant relationship between IL-6 and immune microenvironment score, and the immune microenvironment played a mediating effect on the association of IL-6 with survival (all P < .05). Detailly, IL-6 was positively related to M1 macrophage infiltration abundance and its biomarkers (all P < .05). Finally, we obtained 4 therapeutic agents in LGG targeting IL-6, and their targeting binding relationships were all verified. IL6, as an immune-related biomarker, was associated with the prognosis in LGG, and it can be a therapeutic target in LGG.

Artificial neural network identified a 20-gene panel in predicting immunotherapy response and survival benefits after anti-PD1/PD-L1 treatment in glioblastoma patients.

BACKGROUND: Immune checkpoint inhibitors (ICIs) are a promising immunotherapy approach, but glioblastoma clinical trials have not yielded satisfactory results. OBJECTIVE: To screen glioblastoma patients who may benefit from immunotherapy. METHODS: Eighty-one patients receiving anti-PD1/PD-L1 treatment from a large-scale clinical trial and 364 patients without immunotherapy from The Cancer Genome Atlas (TCGA) were included. Patients in the ICI-treated cohort were divided into responders and nonresponders according to overall survival (OS), and the most critical responder-relevant features were screened using random forest (RF). We constructed an artificial neural network (ANN) model and verified its predictive value with immunotherapy response and OS. RESULTS: We defined two groups of ICI-treated glioblastoma patients with large differences in survival benefits as nonresponders (OS ≤6 months, n = 18) and responders (OS ≥17 months, n = 8). No differentially mutated genes were observed between responders and nonresponders. We performed RF analysis to select the most critical responder-relevant features and developed an ANN with 20 input variables, five hidden neurons and one output neuron. Receiver operating characteristic analysis and the DeLong test demonstrated that the ANN had the best performance in predicting responders, with an AUC of 0.97. Survival analysis indicated that ANN-predicted responders had significantly better OS rates than nonresponders. CONCLUSION: The 20-gene panel developed by the ANN could be a promising biomarker for predicting immunotherapy response and prognostic benefits in ICI-treated GBM patients and may guide oncologists to accurately select potential responders for the preferential use of ICIs.

Revisiting prognostic factors of gliomatosis cerebri in adult-type diffuse gliomas.

PURPOSE: There is lack of comprehensive analysis evaluating the impact of clinical, molecular, imaging, and surgical data on survival of patients with gliomatosis cerebri (GC). This study aimed to investigate prognostic factors of GC in adult-type diffuse glioma patients. METHODS: Retrospective chart and imaging review was performed in 99 GC patients from adult-type diffuse glioma (among 1,211 patients; 6 oligodendroglioma, 16 IDH-mutant astrocytoma, and 77 IDH-wildtype glioblastoma) from a single institution between 2005 and 2021. Predictors of overall survival (OS) of entire patients and IDH-wildtype glioblastoma patients were determined. RESULTS: The median OS was 16.7 months (95% confidence interval [CI] 14.2-22.2) in entire patients and 14.3 months (95% CI 12.2-61.9) in IDH-wildtype glioblastoma patients. In entire patients, KPS (hazard ratio [HR] = 0.98, P = 0.004), no 1p/19q codeletion (HR = 10.75, P = 0.019), MGMTp methylation (HR = 0.54, P = 0.028), and hemorrhage (HR = 3.45, P = 0.001) were independent prognostic factors on multivariable analysis. In IDH-wildtype glioblastoma patients, KPS (HR = 2.24, P = 0.075) was the only independent prognostic factor on multivariable analysis. In subgroup of IDH-wildtype glioblastoma with CE tumors, total resection of CE tumor did not remain as a significant prognostic factor (HR = 1.13, P = 0.685). CONCLUSIONS: The prognosis of GC patients is determined by its underlying molecular type and patient performance status. Compared with diffuse glioma without GC, aggressive surgery of CE tumor in GC patients does not improve survival.

Identification and clinical validation of diverse cell-death patterns-associated prognostic features among low-grade gliomas.

Low-grade glioma (LGG) is heterogeneous at biological and transcriptomic levels, and it is still controversial for the definition and typing of LGG. Therefore, there is an urgent need for specific and practical molecular signatures for accurate diagnosis, individualized therapy, and prognostic evaluation of LGG. Cell death is essential for maintaining homeostasis, developing and preventing hyperproliferative malignancies. Based on diverse programmed cell death (PCD) related genes and prognostic characteristics of LGG, this study constructed a model to explore the mechanism and treatment strategies for LGG cell metastasis and invasion. We screened 1161 genes associated with PCD and divided 512 LGG samples into C1 and C2 subtypes by consistent cluster analysis. We analyzed the two subtypes' differentially expressed genes (DEGs) and performed functional enrichment analysis. Using R packages such as ESTIMATE, CIBERSOTR, and MCPcounter, we assessed immune cell scores for both subtypes. Compared with C1, the C2 subtype has a poor prognosis and a higher immune score, and patients in the C2 subtype are more strongly associated with tumor progression. LASSO and COX regression analysis screened four characteristic genes (CLU, FHL3, GIMAP2, and HVCN1). Using data sets from different platforms to validate the four-gene feature, we found that the expression and prognostic correlation of the four-gene feature had a high degree of stability, showing stable predictive effects. Besides, we found downregulation of CLU, FHL3, and GIMAP2 significantly impairs the growth, migration, and invasive potential of LGG cells. Take together, the four-gene feature constructed based on PCD-related genes provides valuable information for further study of the pathogenesis and clinical treatment of LGG.

mRNA markers for survival prediction in glioblastoma multiforme patients: a systematic review with bioinformatic analyses.

BACKGROUND: Glioblastoma multiforme (GBM) is a type of fast-growing brain glioma associated with a very poor prognosis. This study aims to identify key genes whose expression is associated with the overall survival (OS) in patients with GBM. METHODS: A systematic review was performed using PubMed, Scopus, Cochrane, and Web of Science up to Journey 2024. Two researchers independently extracted the data and assessed the study quality according to the New Castle Ottawa scale (NOS). The genes whose expression was found to be associated with survival were identified and considered in a subsequent bioinformatic study. The products of these genes were also analyzed considering protein-protein interaction (PPI) relationship analysis using STRING. Additionally, the most important genes associated with GBM patients' survival were also identified using the Cytoscape 3.9.0 software. For final validation, GEPIA and CGGA (mRNAseq_325 and mRNAseq_693) databases were used to conduct OS analyses. Gene set enrichment analysis was performed with GO Biological Process 2023. RESULTS: From an initial search of 4104 articles, 255 studies were included from 24 countries. Studies described 613 unique genes whose mRNAs were significantly associated with OS in GBM patients, of which 107 were described in 2 or more studies. Based on the NOS, 131 studies were of high quality, while 124 were considered as low-quality studies. According to the PPI network, 31 key target genes were identified. Pathway analysis revealed five hub genes (IL6, NOTCH1, TGFB1, EGFR, and KDR). However, in the validation study, only, the FN1 gene was significant in three cohorts. CONCLUSION: We successfully identified the most important 31 genes whose products may be considered as potential prognosis biomarkers as well as candidate target genes for innovative therapy of GBM tumors.

Association between elevated preoperative red cell distribution width and mortality after brain tumor craniotomy.

BACKGROUND: Red cell distribution width (RDW) has been recognized as a potential inflammatory biomarker, with elevated levels associated with adverse outcomes in various diseases. However, its role in predicting outcomes after brain tumor craniotomy remains unclear. We aimed to assess whether preoperative RDW influences mortality and postoperative complications in patients undergoing brain tumor craniotomy. METHODS: This retrospective cohort study analyzed serum RDW levels in patients undergoing brain tumor craniotomy at West China Hospital. RDW was evaluated in two forms: RDW-CV and RDW-SD, and was categorized into four quartiles for analysis by using logistic regression and multivariate analysis to adjust for confounding. RESULTS: The study encompassed 10,978 patients undergoing brain tumor craniotomy. our analysis revealed no significant difference in 30-day mortality across various RDW-CV levels. However, we observed a dose-response relationship with preoperative RDW-CV levels in assessing long-term mortality risks. Specifically, patients with RDW-CV levels of 12.6-13.2% (HR 1.04, 95% CI 1.01-1.18), 13.2-13.9% (HR 1.12, 95% CI 1.04-1.26), and > 13.9% (HR 1.34, 95% CI 1.18-1.51) exhibited a significantly higher hazard of long-term mortality compared to those with RDW-CV < 12.6%. When preoperative RDW-CV was analyzed as a continuous variable, for each 10% increase in RDW-CV, the adjusted OR of long-term mortality was 1.09 (95% CI 1.05-1.13). we also observed significant associations between preoperative higher RDW-CV levels and certain postoperative complications including acute kidney injury (OR 1.46, 95% CI: 1.10-1.94), pneumonia infection (OR 1.19 95% CI: 1.05-1.36), myocardial infarction (OR 1.32, 95% CI: 1.05-1.66), readmission (OR 1.15, 95% CI: 1.01-1.30), and a prolonged length of hospital stay (OR 1.11, 95% CI: 1.02-1.21). For RDW-SD levels, there was no significant correlation for short-term mortality, long-term mortality, and postoperative complications. CONCLUSIONS: Our study showed elevated preoperative RDW-CV is significantly associated with increased long-term mortality and multiple postoperative complications, but no such association is observed with RDW-SD. These findings show the prognostic importance of RDW-CV, reinforcing its potential as a valuable tool for risk stratification in the preoperative evaluation of brain tumor craniotomy patients.

Anamnestic radiological metastases outcome surgical score (ARMO-S). A purpose of a predictive surgical scoring system for brain metastases.

BACKGROUND: Several risk stratification scores have been suggested to aid prognostication and guide treatment strategies for brain metastases (BMs). However, the current scores do not focus on the specific neurosurgical population, therefore not predicting short-term mortality and postoperative performance status. METHODS: This retrospective observational study of 362 consecutive patients treated with surgery for BMs aims to identify the factors associated with post-surgical outcomes and propose a surgery-specific prognostic score for patients with BMs candidate for open surgery. RESULTS: Factors significantly associated with OS and performance status in multivariate analysis were age, KPS, surgical site, synchronous debut of BM, number, tumor volume, seizure, extra-cranial metastases, and deep-seated location. The variables were incorporated into the Anamnestic Radiological Metastases Outcome Surgical score (ARMO-S). The values range between 0 and 10. Patients were divided into two groups (low-risk and high-risk) based on each significant subgroup's median survival and performance status with an optimal cutoff value determined as 4. The two groups have significant differences in OS (9.6 versus 14 months, p = 0.0048) postoperative KPS (90 versus 70, p = 0.012) and KPS at last follow-up evaluation (75 versus 30, p < 0.001) CONCLUSION: ARMO-S is a simple and comprehensive score for BM patients selected for neurosurgery, as it incorporates the main factors of the most important prognostic scores, implementing them with more surgery-specific predictive elements such as tumor location and volume, presence of seizures at onset, and involvement of eloquent brain areas.

Predicting survival after brain metastases in patients with bladder cancer.

AIMS AND OBJECTIVES: Because of its rarity, limited data concerning brain metastasis (BM) from bladder cancer (BCa) are available, so this phenomenon remains unclear. We aimed to contribute to understanding this unique patient population's clinical behavior and outcomes. METHODS/MATERIALS: This retrospective cohort study included 27 BCa patients with BM treated at our Cancer Institute between April 2009 and December 2022. The time from initial diagnosis to BM and overall survival from BM diagnosis were calculated (Kaplan-Meier method). Cox regression was used to test key clinicopathologic associations. RESULTS: A total of 27 patients were included in the study (male/female = 23/4). The median patient age at BM diagnosis was 62.0 (47-79) years. The median interval from initial diagnosis to BM was 11.0 ± 2.59 (95 % CI, 5.91-16.08) months. Twenty (74.0 %) patients were diagnosed with BM by postsymptomatic imaging. The most common symptoms were headache-dizziness (n = 9, 33.3 %), seizure (n = 3, 11.1 %), hemiparesis (n = 2, 7.4 %), and vision defects (n = 2, 7.4 %). The most common sites of extracranial metastasis were the lung (n = 10, 52.6 %), bone (n = 7, 36.8 %), and lymph nodes (n = 6, 31.5 %). More than half of the patients (55.5 %) had multiple BMs. Eight (29.6 %) patients underwent surgery for BM. All of the patients received radiotherapy (RT) for BM (whole-brain radiotherapy (WBRT)/stereotactic radiotherapy (SRT) = 24/3), and eight patients received RT for the second time. Six patients were treated with systemic chemotherapy (CT) after BM. The median survival from BM was 3.0 ± 1.2 (95 % Cl, 0.4-5.5) months in the entire cohort. A low number of BMs (HR 0.270, 95 % CI 0.083-0.885; p = 0.031), surgery for BM (HR 0.174, 95 % CI 0.043-0.712; p = 0.015), CT after BM (HR 0.207, 95 % CI 0.057-0.755; p = 0.017), and better ECOG performance score (HR 0.248, 95 % CI 0.074-0.836; p = 0.025) were associated with better OS. CONCLUSIONS: Factors associated with improved survival in BCa patients with BM include a few brain lesions, intracranial resection, CT after BM, and better ECOG performance scores. Larger-scale prospective studies are needed to define the optimal management strategy further.

A cuproptosis-based prognostic model for predicting survival in low-grade glioma.

BACKGROUND: It is unknown what variables contribute to the formation and multiplication of low-grade gliomas (LGG). An emerging process of cell death is called cuproptosis. Our research aims to increase therapeutic options and gain a better understanding of the role that cuproptosis-related genes play in the physical characteristics of low-grade gliomas. METHODS: The TCGA database was utilized to find cuproptosis genes that may be used to develop LGG risk model. Cox analysis in three different formats: univariate, multivariate, and LASSO. The gene signature's independent predictive ability was assessed using ROC curves and Cox regression analysis based on overall survival. Use of CGGA data and nomogram model for external validation Immunohistochemistry, gene mutation, and functional enrichment analysis are also employed to clarify risk models' involvement. Next, we analyzed changes in the immunological microenvironment in the risk model and forecasted possible chemotherapeutic drugs to target each group. Finally, we validated the protein expression levels of cuproptosis-related genes using LGG and adjacent normal tissues in a small self-case-control study. RESULTS: This study developed a glioma predictive model based on five cuproptosis-associated genes. Compared to the high-risk group, the low-risk group's OS was significantly longer. The ROC curves showed high genetic signature performance in both groups. The signature-based categorisation was also linked to clinical characteristics and molecular subgroups. The prognosis of individuals with grade 2 or 3 glioma is also influenced by our risk model. Immunological testing revealed that the high-risk group had more immune cells and immunological function. The risk model also predicted immunotherapy and chemotherapy medication results. Also, this study confirmed that the expression of cuproptosis-related genes by Western blot. CONCLUSION: We developed a prediction model for LGG patients using genes associated with cuproptosis. With acceptable prediction performance, this risk model may effectively stratify the prognosis of glioma patients.

Epithelial ovarian cancer and brain metastases: might the BRCA status, PARP inhibitor administration, and surgical treatment impact the survival?

OBJECTIVE: To evaluate disease characteristics and survival according to BRCA status, administration of poly-(ADP-ribose) polymerase inhibitors (PARPi), and surgery in patients with ovarian cancer and brain metastases. METHODS: This is a monocentric retrospective cohort of patients with ovarian cancer and brain metastases treated between 2000 and 2021. Data were collected by a retrospective review of medical records and analyzed according to: (1) BRCA mutation; (2) PARPi before and after brain metastases; (3) surgery for brain metastases. RESULTS: Eighty-five patients with ovarian cancer and brain metastasis and known BRCA status (31 BRCA mutated (BRCAm), 54 BRCA wild-type (BRCAwt)) were analyzed. Twenty-two patients had received PARPi before brain metastases diagnosis (11 BRCAm, 11 BRCAwt) and 12 after (8 BRCAm, 4 BRCAwt). Brain metastases occurred >1 year later in patients who had received previous PARPi. Survival was longer in the BRCAm group (median post-brain metastasis survival: BRCAm 23 months vs BRCAwt 8 months, p=0.0015). No differences were found based on BRCA status analyzing the population who did not receive PARPi after brain metastasis (median post-brain metastasis survival: BRCAm 8 months vs BRCAwt 8 months, p=0.31). In the BRCAm group, survival was worse in patients who had received previous PARPi (median post-brain metastasis survival: PARPi before, 7 months vs no-PARPi before, 24 months, p=0.003). If PARPi was administered after brain metastases, survival of the overall population improved (median post-brain metastasis survival: PARPi after, 46 months vs no-PARPi after, 8 months, p=0.00038).In cases of surgery for brain metastases, the prognosis seemed better (median post-brain metastasis survival: surgery 13 months vs no-surgery 8 months, p=0.036). Three variables were significantly associated with prolonged survival at multivariate analysis: BRCA mutation, multimodal treatment, and ≤1 previous chemotherapy line. CONCLUSIONS: BRCA mutations might impact brain metastasis occurrence and lead to better outcomes. In a multimodal treatment, surgery seems to affect survival even in cases of extracranial disease. PARPi use should be considered as it seems to prolong survival if administered after brain metastasis.

Anti-epileptic drug use during adjuvant chemo-radiotherapy is associated with poorer survival in patients with glioblastoma: A nationwide population-based cohort study.

INTRODUCTION: There are emerging but inconsistent evidences about anti-epileptic drugs (AEDs) as radio- or chemo-sensitizers to improve survival in glioblastoma patients. We conducted a nationwide population-based study to evaluate the impact of concurrent AED during post-operative chemo-radiotherapy on outcome. MATERIAL AND METHODS: A total of 1057 glioblastoma patients were identified by National Health Insurance Research Database and Cancer Registry in 2008-2015. Eligible criteria included those receiving surgery, adjuvant radiotherapy and temozolomide, and without other cancer diagnoses. Survival between patients taking concurrent AED for 14 days or more during chemo-radiotherapy (AED group) and those who did not (non-AED group) were compared, and subgroup analyses for those with valproic acid (VPA), levetiracetam (LEV), or phenytoin were performed. Multivariate analyses were used to adjust for confounding factors. RESULTS: There were 642 patients in the AED group, whereas 415 in the non-AED group. The demographic data was balanced except trend of more patients in the AED group had previous drug history of AEDs (22.6% vs. 18%, P 0.078). Overall, the AED group had significantly increased risk of mortality (HR = 1.18, P 0.016) compared to the non-AED group. Besides, an adverse dose-dependent relationship on survival was also demonstrated in the AED group (HR = 1.118, P 0.0003). In subgroup analyses, the significant detrimental effect was demonstrated in VPA group (HR = 1.29,P 0.0002), but not in LEV (HR = 1.18, P 0.079) and phenytoin (HR = 0.98, P 0.862). CONCLUSIONS: Improved survival was not observed in patients with concurrent AEDs during chemo-radiotherapy. Our real-world data did not support prophylactic use of AEDs for glioblastoma patients.