Comprehensive profiling identifies a novel signature with robust predictive value and reveals the potential drug resistance mechanism in glioma

Abstract

Background: Gliomas are the most common and malignant brain tumors. The standard therapy is surgery combined with radiotherapy, chemotherapy, and/or other comprehensive methods. However, the emergence of chemoresistance is the main obstacle in treatment and its mechanism is still unclear. Methods: We firstly developed a multi-gene signature by integrated analysis of cancer stem cell and drug resistance related genes. The Chinese Glioma Genome Atlas (CGGA, 325 samples) and The Cancer Genome Atlas (TCGA, 699 samples) datasets were then employed to verify the efficacy of the risk signature and investigate its significance in glioma prognosis. GraphPad Prism, SPSS and R language were used for statistical analysis and graphical work. Results: This signature could distinguish the prognosis of patients, and patients with high risk score exhibited short survival time. The Cox regression and Nomogram model indicated the independent prognostic performance and high prognostic accuracy of the signature for survival. Combined with a well-known chemotherapy impact factor-MGMT promoter methylation status, this risk signature could further subdivide patients with distinct survival. Functional analysis of associated genes revealed signature-related biological process of cell proliferation, immune response and cell stemness. These mechanisms were confirmed in patient samples. Conclusions: The signature was an independent and powerful prognostic biomarker in glioma, which would improve risk stratification and provide a more accurate assessment of personalized treatment.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

“…Due to the biological diversity of glioma cells, multimodal treatments remained unsatisfactory with surgical resection, radiotherapy, and temozolomide (36). Stimulated by the progress in immunotherapies on other tumor types, researchers have been actively examining the effect of immunotherapies in gliomas (6).…”

Different Calculation Strategies Are Congruent in Determining Chemotherapy Resistance of Brain Tumors In Vitro

“…However, in vitro screening approaches are the only realistic strategy to provide large-scale drug response analysis on living material within a rational time span and affordable budget requirement for wider application. Alternatively, tumor gene expression-based prediction of drug response emerges as a new avenue in personalized chemotherapy resistance for brain cancer patients [34,38,39] and shall be considered in the wake of AI technology developments. However, all computation-only-based measures remain in silico without real biologic/functional response measurement, sometimes leaving questions about their clinical relevance.…”

Prognostic signature of lung adenocarcinoma based on stem cell-related genes

“…Patients were divided into low-risk and high-risk groups based on the cutoff of risk score, which was calculated by formula as follows: HR 1 × gene 1 expression + HR 2 × gene 2 expression … + HR n × gene n expression 10 . In the TCGA and GEO cohorts, the risk curve was drawn to describe further the relationship between the patients' risk value and survival states and protein expression, the Kaplan–Meier curve and ROC curve were used to verify the reliability of the signature 11 .…”