Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Li, Qiang; Aishwarya, S.; Li, Jiping; Pan, Dongxiao; Shi, Jiapei

doi:10.3389/fgene.2022.832742

Cited by 6 publications

(6 citation statements)

References 74 publications

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“…Более высокая экспрессия VIM связана с прогрессированием ГБ и снижением показателей выживаемости у пациентов. Активация этого белка наблюдалась в классическом и мезенхимальном подтипах ГБ, а переходы от классической к мезенхимальной ГБ коррелировали с более высокой экспрессией VIM и повышенной пролиферацией и миграцией клеток опухоли [22]. Подавление экспрессии VIM снижало инвазию и миграцию опухолевых клеток, действуя как регулятор AXL рецептора тирозинкиназы [23].…”

Section: Discussionunclassified

Identification of predictive markers in the cerebrospinal fluid of patients with glioblastoma

et al. 2023

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Introduction. Glioblastoma (GB) is not yet curable despite recent advances in the treatment of other malignant solid tumors. The management of GB is based solely on histopathological features, imaging of the tumor and its genomic analysis (somatic mutations in the isocitrate dehydrogenase genes, methylation status of the O6-methylguanine-DNA methyltransferase gene promoter). To adapt the treatment to the most recent tumor evolution, molecular information should be received regularly throughout the course of therapy. However, tumor tissue is often not available for diagnosis as the disease progresses. In this regard, the development of less invasive methods, such as analysis of the proteome of biological fluids of patients, is of particular interest. Cerebrospinal fluid (CSF) is an important source disease biomarkers to monitor the presence and progression of the disease.Aim. To identify proteomic predictive biomarkers in the CSF of patients with GB.Materials and methods. During the study, samples of patients’ CSF samples, high-resolution proteomic mass spectrometry, modern biochemical methods and bioinformatic technologies were used.Results. For the first time, the analysis of proteomes of CSF samples of patients with GB obtained before and 7 days after the removal of the primary tumor was carried out. Potential biomarkers of GB have been identified. After their validation using open databases, 11 proteomic predictive markers of GB (S100A9, S100A8, PLA2G15, PPIB, LTBP2, VIM, LAMB1, STC1, NRP1, COL6A1, HSPA5) were selected and their role in the molecular mechanisms of gliomagenesis was assessed. Conclusion. The proposed panel of proteomic predictive CSF biomarkers in GB patients can be further used in the development of test systems for assessing the effectiveness of therapy and early detection of disease relapses.

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Section: Discussionunclassified

Identification of predictive markers in the cerebrospinal fluid of patients with glioblastoma

et al. 2023

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“…GRN is a growth factor and secreted glycosylated peptide, which is involved in the invasion of colorectal cancer and is a marker of survival in patients ( 33 ). DDN has been identified as underexpressed in glioblastoma and is a biomarker candidate for prognosis ( 34 ). FPR1 participates in cancer immune surveillance and is regarded as a potential immunopharmacological target for neuroblastoma ( 35 , 36 ).…”

Section: Discussionmentioning

confidence: 99%

Characterization of osteosarcoma subtypes mediated by macrophage-related genes and creation and validation of a risk score system to quantitatively assess the prognosis of osteosarcoma and reflect the tumor microenvironment

Liu¹,

Zhang²,

Zhong³

2022

Ann Transl Med

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Background: Macrophages are the main immune components in the microenvironment of osteosarcoma.The treatment strategy centered on macrophages has become a hot topic to improve cancer treatment.However, the research on the role of macrophages in the treatment of osteosarcoma is still in its infancy. Methods:The data of osteosarcoma samples were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and GSE21257 datasets, and the macrophage enrichment fraction of osteosarcoma samples in TARGET was calculated by single-sample gene set enrichment analysis (ssGSEA) method to screen macrophage-related genes for consensus clustering.Differential expression analysis, univariable Cox, and least absolute shrinkage and selection operator (LASSO) regression were conducted to select reliable predictors and create a risk score system. The GSE21257 dataset was used as a verification set to verify the accuracy of risk score system. Results:We identified 2 osteosarcoma clusters mediated by 22 macrophage score-related genes, namely cluster 1 (C1) and cluster 2 (C2). Compared with C2, C1 had a significant advantage in prognosis, and the degree of immune cell infiltration in tumor microenvironment (TME) was significantly higher, the expression of immune checkpoint molecules was significantly enhanced, and the Tumor Immune Dysfunction and Exclusion (TIDE) score was also significantly down-regulated. A robust risk score system was presented and validated, which demonstrated accuracy and independence in assessing the risk of death of osteosarcoma. The risk score system could also monitor TME infiltration in osteosarcoma samples and showed a close relationship with osteosarcoma biology, including metastasis and immunity. Conclusions:We identified 2 types of clusters mediated by macrophage-related genes and helped to analyze the cluster suitable for immunotherapy. A new prognostic risk score system was created to quantitatively evaluate the prognosis and TME of osteosarcoma, and to provide a new entry point for the design of personalized treatment.

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“…The genes and proteins that regulate these changes have become potential targets for novel treatments [ 12 ]. Changes in gene expression have also been shown to lead to tumour progression [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of Novel Molecular and Clinical Biomarkers of Survival in Glioblastoma Multiforme Patients: A Study Based on The Cancer Genome Atlas Data

Esteves,

Caramelo,

Roda

et al. 2024

BioMed Research International

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Glioblastoma multiforme (GBM) is the most prevalent type of brain tumour; although advancements in treatment have been made, the median survival time for GBM patients has persisted at 15 months. This study is aimed at investigating the genetic alterations and clinical features of GBM patients to find predictors of survival. GBM patients’ methylation and gene expression data along with clinical information from TCGA were retrieved. The most overrepresented pathways were identified independently for each omics dataset. From the genes found in at least 30% of these pathways, one gene that was identified in both sets was further examined using the Kaplan-Meier method for survival analysis. Additionally, three groups of patients who started radio and chemotherapy at different times were identified, and the influence of these variations in treatment modality on patient survival was evaluated. Four pathways that seemed to negatively impact survival and two with the opposite effect were identified. The methylation status of PRKCB was highlighted as a potential novel biomarker for patient survival. The study also found that treatment with chemotherapy prior to radiotherapy can have a significant impact on patient survival, which could lead to improvements in clinical management and therapeutic approaches for GBM patients.

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Gene Expression Profiling of Glioblastoma to Recognize Potential Biomarker Candidates

Cited by 6 publications

References 74 publications

Identification of predictive markers in the cerebrospinal fluid of patients with glioblastoma

Identification of predictive markers in the cerebrospinal fluid of patients with glioblastoma

Characterization of osteosarcoma subtypes mediated by macrophage-related genes and creation and validation of a risk score system to quantitatively assess the prognosis of osteosarcoma and reflect the tumor microenvironment

Identification of Novel Molecular and Clinical Biomarkers of Survival in Glioblastoma Multiforme Patients: A Study Based on The Cancer Genome Atlas Data

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