Pretreatment Geriatric Assessments of Elderly Patients with Glioma: Development and Implications

Development of preoperative and postoperative models to predict recurrence in postoperative glioma patients: a longitudinal cohort study

BACKGROUND

Glioma recurrence, subsequent to maximal safe resection, remains a pivotal challenge. This study aimed to identify key clinical predictors influencing recurrence and develop predictive models to enhance neurological diagnostics and therapeutic strategies.

METHODS

This longitudinal cohort study with a substantial sample size (n = 2825) included patients with non-recurrent glioma who were pathologically diagnosed and had undergone initial surgical resection between 2010 and 2018. Logistic regression models and stratified Cox proportional hazards models were established with the top 15 clinical variables significantly influencing outcomes screened by the least absolute shrinkage and selection operator (LASSO) method. Preoperative and postoperative models predicting short-term (within 6 months) postoperative recurrence in glioma patients were developed to explore the risk factors associated with short- and long-term recurrence in glioma patients.

RESULTS

Preoperative and postoperative logistic models predicting short-term recurrence had accuracies of 0.78 and 0.87, respectively. A range of biological and early symptomatic characteristics linked to short- and long-term recurrence have been pinpointed. Age, headache, muscle weakness, tumor location and Karnofsky score represented significant odd ratios (t > 2.65, p < 0.01) in the preoperative model, while age, WHO grade 4 and chemotherapy or radiotherapy treatments (t > 4.12, p < 0.0001) were most significant in the postoperative period. Postoperative predictive models specifically targeting the glioblastoma and IDH wildtype subgroups were also performed, with an AUC of 0.76 and 0.80, respectively. The 50 combinations of distinct risk factors accommodate diverse recurrence risks among glioma patients, and the nomograms visualizes the results for clinical practice. A stratified Cox model identified many prognostic factors for long-term recurrence, thereby facilitating the enhanced formulation of perioperative care plans for patients, and glioblastoma patients displayed a median progression-free survival (PFS) of only 11 months.

CONCLUSION

The constructed preoperative and postoperative models reliably predicted short-term postoperative glioma recurrence in a substantial patient cohort. The combinations risk factors and nomograms enhance the operability of personalized therapeutic strategies and care regimens. Particular emphasis should be placed on patients with recurrence within six months post-surgery, and the corresponding treatment strategies require comprehensive clinical investigation.

Research progress of N1-methyladenosine RNA modification in cancer

N1-methyladenosine (m1A) is a post-transcriptionally modified RNA molecule that plays a pivotal role in the regulation of various biological functions and activities. Especially in cancer cell invasion, proliferation and cell cycle regulation. Over recent years, there has been a burgeoning interest in investigating the m1A modification of RNA. Most studies have focused on the regulation of m1A in cancer enrichment areas and different regions. This review provides a comprehensive overview of the methodologies employed for the detection of m1A modification. Furthermore, this review delves into the key players in m1A modification, known as the "writers," "erasers," and "readers." m1A modification is modified by the m1A methyltransferases, or writers, such as TRMT6, TRMT61A, TRMT61B, TRMT10C, NML, and, removed by the demethylases, or erasers, including FTO and ALKBH1, ALKBH3. It is recognized by m1A-binding proteins YTHDF1, TYHDF2, TYHDF3, and TYHDC1, also known as "readers". Additionally, we explore the intricate relationship between m1A modification and its regulators and their implications for the development and progression of specific types of cancer, we discuss how m1A modification can potentially facilitate the discovery of novel approaches for cancer diagnosis, treatment, and prognosis. Our summary of m1A methylated adenosine modification detection methods and regulatory mechanisms in various cancers provides useful insights for cancer diagnosis, treatment, and prognosis. Video Abstract.

IFN-γ Triggered IFITM2 Expression to Induce Malignant Phenotype in Elderly GBM

Advanced age is an important risk factor for the worse clinical presentation of gliomas, especially glioblastoma (GBM). The tumor microenvironment (TME) in elderly GBM (eGBM) patients is considerably different from that in young ones, which causes the inferior clinical outcome. Based on the data from the Chinese Glioma Genome Atlas RNA sequence (CGGA RNA-seq), the Cancer Genome Atlas RNA array (TCGA RNA-array), and gene set enrichment (GSE) 16011 array sets, the differential genes and function between eGBM (≥ 60 years old) and young GBM (yGBM, 20-60 years old) groups were explored. Immunohistochemistry (IHC) was utilized to depict the abundance of CD8+ cells (the main resource of IFN-γ) and IFITM2 protein expression in GBM samples. Furthermore, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting (WB) were performed to verify the link between IFN-γ and IFITM2. Moreover, the small-interfering RNA (siRNA) of IFITM2 was used to explore the function of IFITM2 in GBM. Characterized by inflammatory TME and higher IFITM2 expression, eGBM harbored a shorter survival time. Chemotaxis and inflammatory cytokine-related genes were enriched in the eGBM group, with more infiltrative CD8+ T cells. The IHC of CD8 and IFITM2-staining could demonstrate these results. In addition, the IFN-γ response pathway was activated in eGBM and resulted in a dismal outcome. Next, it was found that IFITM2 triggered by IFN-γ played a key role in IFN-γ-induced malignant phenotype in eGBM.

Characteristic analysis and identification of novel molecular biomarkers in elderly glioblastoma patients using the 2021 WHO Classification of Central Nervous System Tumors

Introduction

Elderly glioblastoma (GBM) patients is characterized by high incidence and poor prognosis. Currently, however, there is still a lack of adequate molecular characterization of elderly GBM patients. The fifth edition of the WHO Classification of Central Nervous System Tumors (WHO5) gives a new classification approach for GBM, and the molecular characteristics of elderly GBM patients need to be investigated under this new framework.

Methods

The clinical and radiological features of patients with different classifications and different ages were compared. Potential prognostic molecular markers in elderly GBM patients under the WHO5 classification were found using Univariate Cox regression and Kaplan-Meier survival analysis.

Results

A total of 226 patients were included in the study. The prognostic differences between younger and elderly GBM patients were more pronounced under the WHO5 classification. Neurological impairment was more common in elderly patients (p = 0.001), while intracranial hypertension (p = 0.034) and epilepsy (p = 0.038) were more common in younger patients. Elderly patients were more likely to have higher Ki-67(p = 0.013), and in elderly WHO5 GBM patients, KMT5B (p = 0.082), KRAS (p = 0.1) and PPM1D (p = 0.055) were each associated with overall survival (OS). Among them, KRAS and PPM1D were found to be prognostic features unique to WHO5 elderly GBM patients.

Conclusion

Our study demonstrates that WHO5 classification can better distinguish the prognosis of elderly and younger GBM. Furthermore, KRAS and PPM1D may be potential prognostic predictors in WHO5 elderly GBM patients. The specific mechanism of these two genes in elderly GBM remains to be further studied.

Aging microenvironment and antitumor immunity for geriatric oncology: the landscape and future implications

The tumor microenvironment (TME) has been extensively investigated; however, it is complex and remains unclear, especially in elderly patients. Senescence is a cellular response to a variety of stress signals, which is characterized by stable arrest of the cell cycle and major changes in cell morphology and physiology. To the best of our knowledge, senescence leads to consistent arrest of tumor cells and remodeling of the tumor-immune microenvironment (TIME) by activating a set of pleiotropic cytokines, chemokines, growth factors, and proteinases, which constitute the senescence-associated secretory phenotype (SASP). On the one hand, the SASP promotes antitumor immunity, which enhances treatment efficacy; on the other hand, the SASP increases immunosuppressive cell infiltration, including myeloid-derived suppressor cells (MDSCs), regulatory T cells (Tregs), M2 macrophages, and N2 neutrophils, contributing to TIME suppression. Therefore, a deeper understanding of the regulation of the SASP and components contributing to robust antitumor immunity in elderly individuals with different cancer types and the available therapies is necessary to control tumor cell senescence and provide greater clinical benefits to patients. In this review, we summarize the key biological functions mediated by cytokines and intercellular interactions and significant components of the TME landscape, which influence the immunotherapy response in geriatric oncology. Furthermore, we summarize recent advances in clinical practices targeting TME components and discuss potential senescent TME targets.

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma

Background: Gliomas are the most common and fatal malignant type of tumor of the central nervous system. RNA post-transcriptional modifications, as a frontier and hotspot in the field of epigenetics, have attracted increased attention in recent years. Among such modifications, methylation is most abundant, and encompasses N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1 methyladenosine (m1A), and 7-methylguanosine (m7G) methylation.

Methods: RNA-sequencing data from healthy tissue and low-grade glioma samples were downloaded from of The Cancer Genome Atlas database along with clinical information and mutation data from glioblastoma tumor samples. Forty-nine m6A/m5C/m1A/m7G-related genes were identified and an m6A/m5C/m1A/m7G-lncRNA signature of co-expressed long non-coding RNAs selected. Least absolute shrinkage and selection operator Cox regression analysis was used to identify 12 m6A/m5C/m1A/m7G-related lncRNAs associated with the prognostic characteristics of glioma and their correlation with immune function and drug sensitivity analyzed. Furthermore, the Chinese Glioma Genome Atlas dataset was used for model validation.

Results: A total of 12 m6A/m5C/m1A/m7G-related genes (AL080276.2, AC092111.1, SOX21-AS1, DNAJC9-AS1, AC025171.1, AL356019.2, AC017104.1, AC099850.3, UNC5B-AS1, AC006064.2, AC010319.4, and AC016822.1) were used to construct a survival and prognosis model, which had good independent prediction ability for patients with glioma. Patients were divided into low and high m6A/m5C/m1A/m7G-LS groups, the latter of which had poor prognosis. In addition, the m6A/m5C/m1A/m7G-LS enabled improved interpretation of the results of enrichment analysis, as well as informing immunotherapy response and drug sensitivity of patients with glioma in different subgroups.

Conclusion: In this study we constructed an m6A/m5C/m1A/m7G-LS and established a nomogram model, which can accurately predict the prognosis of patients with glioma and provides direction toward promising immunotherapy strategies for the future.

microRNA-27a-3p delivered by extracellular vesicles from glioblastoma cells induces M2 macrophage polarization via the EZH1/KDM3A/CTGF axis

Glioblastoma (GBM) cell-derived extracellular vesicles (EVs) have been demonstrated to modulate tumor microenvironment. In the present study, we attempted to discuss the role of hsa-microRNA-27a-3p (miR-27a-3p) delivered by GBM-EVs in M2 macrophage polarization. The isolated GBM-EVs were co-cultured with macrophages. After co-culture under normoxia/hypoxia, the effect of EV-derived hsa-miR-27a-3p on GBM cell biological processes was analyzed. Additionally, the target genes of hsa-miR-27a-3p were predicted. Moreover, the binding of enhancer of zeste homologue 1 (EZH1) to lysine-specific demethylase 3A (KDM3A) promoter region and the interaction between KDM3A and connective tissue growth factor (CTGF) were analyzed. GBM mouse models were established to verify the functions of EV-derived hsa-miR-27a-3p in vivo. We found increased hsa-miR-27a-3p in GBM tissues as well as GBM-EVs, which induced M2 polarization, thus promoting proliferative, migrative and invasive potentials of GBM cells. hsa-miR-27a-3p targeted EZH1 and promoted KDM3A expression to elevate the CTGF expression. GBM-EV-delivered hsa-miR-27a-3p promoted the KDM3A-upregulated CTGF by downregulating EZH1, thereby promoting M2 macrophage polarization and development of GBM in vivo. We demonstrated that EV-derived hsa-miR-27a-3p may promote M2 macrophage polarization to induce GBM.

Preoperative Contrast-Enhanced MRI in Differentiating Glioblastoma From Low-Grade Gliomas in The Cancer Imaging Archive Database: A Proof-of-Concept Study

Purpose

This study aimed to develop a repeatable MRI-based machine learning model to differentiate between low-grade gliomas (LGGs) and glioblastoma (GBM) and provide more clinical information to improve treatment decision-making.

Methods

Preoperative MRIs of gliomas from The Cancer Imaging Archive (TCIA)–GBM/LGG database were selected. The tumor on contrast-enhanced MRI was segmented. Quantitative image features were extracted from the segmentations. A random forest classification algorithm was used to establish a model in the training set. In the test phase, a random forest model was tested using an external test set. Three radiologists reviewed the images for the external test set. The area under the receiver operating characteristic curve (AUC) was calculated. The AUCs of the radiomics model and radiologists were compared.

Results

The random forest model was fitted using a training set consisting of 142 patients [mean age, 52 years ± 16 (standard deviation); 78 men] comprising 88 cases of GBM. The external test set included 25 patients (14 with GBM). Random forest analysis yielded an AUC of 1.00 [95% confidence interval (CI): 0.86–1.00]. The AUCs for the three readers were 0.92 (95% CI 0.74–0.99), 0.70 (95% CI 0.49–0.87), and 0.59 (95% CI 0.38–0.78). Statistical differences were only found between AUC and Reader 1 (1.00 vs. 0.92, respectively; p = 0.16).

Conclusion

An MRI radiomics-based random forest model was proven useful in differentiating GBM from LGG and showed better diagnostic performance than that of two inexperienced radiologists.

Treatment of Older Adult Patients with Glioblastoma: Moving towards the Inclusion of a Comprehensive Geriatric Assessment for Guiding Management

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, and over half of patients with newly diagnosed GBM are over the age of 65. Management of glioblastoma in older patients includes maximal safe resection followed by either radiation, chemotherapy, or combined modality treatment. Despite recent advances in the treatment of older patients with GBM, survival is still only approximately 9 months compared to approximately 15 months for the general adult population, suggesting that further research is required to optimize management in the older population. The Comprehensive Geriatric Assessment (CGA) has been shown to have a prognostic and predictive role in the management of older patients with other cancers, and domains of the CGA have demonstrated an association with outcomes in GBM in retrospective studies. Furthermore, the CGA and other geriatric assessment tools are now starting to be prospectively investigated in older GBM populations. This review aims to outline current treatment strategies for older patients with GBM, explore the rationale for inclusion of geriatric assessment in GBM management, and highlight recent data investigating its implementation into practice.

A functional artificial neural network for noninvasive pretreatment evaluation of glioblastoma patients

Background

Pretreatment assessments for glioblastoma (GBM) patients, especially elderly or frail patients, are critical for treatment planning. However, genetic profiling with intracranial biopsy carries a significant risk of permanent morbidity. We previously demonstrated that the CUL2 gene, encoding the scaffold cullin2 protein in the cullin2-RING E3 ligase (CRL2), can predict GBM radiosensitivity and prognosis. CUL2 expression levels are closely regulated with its copy number variations (CNVs). This study aims to develop artificial neural networks (ANNs) for pretreatment evaluation of GBM patients with inputs obtainable without intracranial surgical biopsies.

Methods

Public datasets including Ivy-GAP, The Cancer Genome Atlas Glioblastoma (TCGA-GBM), and the Chinese Glioma Genome Atlas (CGGA) were used for training and testing of the ANNs. T1 images from corresponding cases were studied using automated segmentation for features of heterogeneity and tumor edge contouring. A ratio comparing the surface area of tumor borders versus the total volume (SvV) was derived from the DICOM-SEG conversions of segmented tumors. The edges of these borders were detected using the canny edge detector. Packages including Keras, Pytorch, and TensorFlow were tested to build the ANNs. A 4-layered ANN (8-8-8-2) with a binary output was built with optimal performance after extensive testing.

Results

The 4-layered deep learning ANN can identify a GBM patient’s overall survival (OS) cohort with 80%–85% accuracy. The ANN requires 4 inputs, including CUL2 copy number, patients’ age at GBM diagnosis, Karnofsky Performance Scale (KPS), and SvV ratio.

Conclusion

Quantifiable image features can significantly improve the ability of ANNs to identify a GBM patients’ survival cohort. Features such as clinical measures, genetic data, and image data, can be integrated into a single ANN for GBM pretreatment evaluation.

Mutational profiling of low-grade gliomas identifies prognosis and immunotherapy-related biomarkers and tumour immune microenvironment characteristics

Low-grade glioma (LGG) is a heterogeneous tumour with the median survival rate less than 10 years. Therefore, it is urgent to develop efficient immunotherapy strategies of LGG. In this study, we analysed mutation profiles based on the data of 510 LGG patients from the Cancer Genome Atlas (TCGA) database and investigated the prognostic value of mutated genes and evaluate their immune infiltration. Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to indicate the characteristics of gliomas that respond to immune checkpoint blockade (ICB) therapy. Univariate and multivariate cox regression analysis was performed to identify indicators to construct the nomogram model. 485 (95.47%) of 508 LGG samples showed gene mutation, and 9 mutated genes were significantly related to overall survival (OS), among which 6 mutated genes were significantly correlated with OS between mutation and wildtypes. Immune infiltration and immune score analyses revealed that these six mutated genes were significantly associated with tumour immune microenvironment in LGG. The response of LGG with different characteristics to ICB was evaluated by TIDE algorithm. Finally, CIC gene was screened through both univariate and multivariate Cox regression analyses, and the nomogram model was established to determine the potential prognostic value of CIC in LGG. Our study provides comprehensive analysis of mutated genes in LGG, supporting modulation of mutated genes in the management of LGG.

TROAP regulates cell cycle and promotes tumor progression through Wnt/β-Catenin signaling pathway in glioma cells

AIMS

Experimental evidence demonstrated a crucial role of TROAP (Trophinin-associated protein) in regulating the cell proliferation of multiple tumors, while TROAP expression and function were largely unknown in glioma. We aimed to investigate the oncogenic role of TROAP and its potential mechanisms in gliomagenesis.

METHODS

Four gene expression databases (GEO, TCGA, GTEx and CCLE) were enrolled in our study and used for TROAP expression and survival analysis. TROAP expression was quantified by qRT-PCR, western blot and immunohistochemistry assays in glioma tissues and cell lines. TROAP knockdown and overexpression vector were constructed and transfected into glioma cells. CCK-8, colony formation, transwell, and wound healing assays were used to evaluate cell viability, migration and invasion, flow cytometry to determine cell cycle arrest. Gene set enrichment analysis (GSEA) was conducted to screen the pathway involved in TROAP-high phenotype. The expression of cell cycle and Wnt/β-Catenin signaling proteins were analyzed by immunofluorescence and western blot.

RESULTS

Based on the bioinformatic analysis and a series of functional assays, we found the TROAP was enriched in glioma tissues and cell lines, its overexpression was correlated with the clinicopathologic characteristics and poor prognosis. TROAP knockdown inhibited cell proliferation, migration, invasion, and G1/S cell cycle arrest compared with control group in glioma. Mechanism analysis revealed that TROAP activated Wnt/β-Catenin pathway and upregulated its downstream targets expression, while silencing β-Catenin or Axin2 could reverse the tumor-promoting effects caused by TROAP, confirming that TROAP-induced malignant phenotype and tumorigenesis via Wnt/β-Catenin signaling pathway.

CONCLUSION

The present study found that TROAP accelerated the progression of gliomagenesis through Wnt/β-Catenin pathway, and TROAP might be considered as a novel target for glioma therapy.

Application of sulfur-doped graphene quantum dots@gold-carbon nanosphere for electrical pulse-induced impedimetric detection of glioma cells

Glioma is the predominant brain tumor with high death rate. The successful development of biosensor to achieve an efficient detection of glioma cells at low concentration remains a great challenge for the personalized glioma therapy. Herein, an ultrasensitive pulse induced electrochemically impedimetric biosensor for glioma cells detection has been successfully fabricated. The 4-11 nm sulfur-doped graphene quantum dots (S-GQDs) are homogeneously deposited onto gold nanoparticles decorated carbon nanospheres (Au-CNS) by Au-thiol linkage to form S-GQDs@Au-CNS nanocomposite which acts as dual functional probe for enhancing the electrochemical activity as well as conjugating the angiopep-2 (Ang-2) for glioma cell detection. Moreover, the application of an externally electrical pulse at +0.6 V expend the surface of glioma cells to accelerate the attachment of glioma cells onto the Ang-2-conjugated S-GQDs@Au-CNS nanocomposite, resulting in the enhanced sensitivity toward glioma cell detection. An ultrasensitive impedimetric detection of glioma cells with a wide linear range of 100-100,000 cells mL^-1 and a limit of detection of 40 cells mL^-1 is observed. Moreover, the superior selectivity with long-term stability of the developed biosensor in human serum matrix corroborates the feasibility of using S-GQDs@Au-CNS based nanomaterials as the promising sensing probe for practical application to facilitate the ultrasensitive and highly selective detection of cancer cells.

Surgical Management of Intracranial Meningiomas in the Elderly: Early and Long-term Outcomes

Purpose

In view of the risk associated with the surgical treatment of intracranial meningiomas in the elderly population due to the physiology of aging and multiple comorbidities, an attempt was made to identify factors influencing outcomes and to define the subgroup of patients who should not be operated on due to poor results.

Patients and Methods

A retrospective analysis of 58 patients over 70 years old with assessment of short-term and long-term outcomes. Scores by previously described CRGS, SKALE, and GSS grading systems were also calculated for our patients.

Results

Neurological morbidity was only associated with a critical location according to the SKALE grading system (P=0.02). Six patients (10.3%) died. Mortality was associated with the Karnofsky Performance Scale score (KPS ≤60 vs KPS ≥70; P=0.0162), the American Society of Anesthesiologists scale status (ASA 1 or 2 vs ASA 3; P=0.0022) and the WHO grade of meningiomas (P=0.012). Risk factors for tumor recurrence (six patients) were WHO grade (P=0.00048) and Simpson grade of resection (P=0.0437). At follow-up, excluding patients who died due to surgery or recurrence (15.5%), most patients improved (50%) or remained unchanged (25.9%) in relation to the preoperative KPS status.

Conclusion

Postoperative neurological deterioration was only associated with a critical tumor location (skull base, eloquent area, large vessels involvement by the tumor). Due to a significantly higher risk of death, careful consideration should be taken for surgery in patients in a poor functional condition (KPS ≤60) or in a poor physical condition (ASA 3 status). An improvement or at least nonworsening of the neurological status in relation to the preoperative condition was observed in the majority of patients during follow-up.