Bioinformatics Analysis of the Expression of ATP Binding Cassette Subfamily C Member 3 (ABCC3) in Human Glioma

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.

Recognition of a Novel Gene Signature for Human Glioblastoma

Glioblastoma (GBM) is one of the most common malignant and incurable brain tumors. The identification of a gene signature for GBM may be helpful for its diagnosis, treatment, prediction of prognosis and even the development of treatments. In this study, we used the GSE108474 database to perform GSEA and machine learning analysis, and identified a 33-gene signature of GBM by examining astrocytoma or non-GBM glioma differential gene expression. The 33 identified signature genes included the overexpressed genes COL6A2, ABCC3, COL8A1, FAM20A, ADM, CTHRC1, PDPN, IBSP, MIR210HG, GPX8, MYL9 and PDLIM4, as well as the underexpressed genes CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C, SHANK2 and VIPR2. Protein functional analysis by CELLO2GO implied that these signature genes might be involved in regulating various aspects of biological function, including anatomical structure development, cell proliferation and adhesion, signaling transduction and many of the genes were annotated in response to stress. Of these 33 signature genes, 23 have previously been reported to be functionally correlated with GBM; the roles of the remaining 10 genes in glioma development remain unknown. Our results were the first to reveal that GBM exhibited the overexpressed GPX8 gene and underexpressed signature genes including CHST9, CSDC2, ENHO, FERMT1, IGFN1, LINC00836, MGAT4C and SHANK2, which might play crucial roles in the tumorigenesis of different gliomas.

Clinicopathological and Prognostic Significance of ABCC3 in Human Glioma

Glioma is the most common malignant primary brain tumor with an inferior survival period and unsatisfactory prognoses. Identification of novel biomarkers is important for the improvements of clinical outcomes of glioma patients. In recent years, more and more biomarkers were identified in many types of tumors. However, the sensitive markers for diagnoses and prognoses of patients with glioma remained unknown. In the present research, our team intended to explore the expression and clinical significance of ABCC3 in glioma patients. Sequential data filtration (survival analyses, independent prognosis analyses, ROC curve analyses, and clinical association analyses) was completed, which gave rise to the determination of the relationship between glioma and the ABCC3 gene. Clinical assays on the foundation of CGGA and TCGA datasets unveiled that ABCC3 expression was distinctly upregulated in glioma and predicted a shorter overall survival. In the multivariable Cox analysis, our team discovered that the expression of ABCC3 was an independent prognosis marker for both 5-year OS (HR = 1.118, 95% CI: 1.052-1.188; P < 0.001). Moreover, our team also studied the association between ABCC3 expression and clinical features of glioma patients, finding that differential expression of ABCC3 was remarkably related to age, 1p19q codeletion, PRS type, chemo status, grade, IDH mutation state, and histology. Overall, our findings suggested ABCC3 might be a novel prognosis marker in glioma.

In silico identification of the prognostic biomarkers and therapeutic targets associated with cancer stem cell characteristics of glioma

Glioma is the common histological subtype of malignancy in central nervous system, with a high morbidity and mortality. Cancer stem cells (CSCs) play an important role in regulating the tumorigenesis and progression of glioma; however, the prognostic biomarkers and therapeutic targets associated with CSC characteristics have not been fully acknowledged in glioma. In order to identify the prognostic stemness-related genes (SRGs) of glioma in silico, the RNA sequencing data of patients with glioma were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) was significantly associated with the glioma histologic grade, isocitrate dehydrogenase 1 (IDH1) mutation and overall survival of glioma patients by the nonparametric test and Kaplan–Meier survival analysis. A total of 340 SRGs were identified as the overlapped stemness-related differential expressed genes (DEGs) of different histologic grade screened by the univariate Cox analysis. Based on 11 prognostic SRGs, the predict nomogram was constructed with the AUC of 0.832. Moreover, the risk score of the nomogram was an independent prognostic factor, indicating its significant applicability. Besides other eight reported biomarkers of glioma, we found that F2RL2, CLCNKA and LOXL4 were first identified as prognostic biomarkers for glioma. In conclusion, this bioinformatics study demonstrates the mRNAsi as a reliable index for the IDH1 mutation, histologic grade and OS of glioma patients and provides a well-applied model for predicting the OS for patients with glioma based on prognostic SRGs. Additionally, this in silico study also identifies three novel prognostic biomarkers (F2RL2, CLCNKA and LOXL4) for glioma patients.

Cannabinoid Receptor Interacting Protein 1a (CRIP1a) in Health and Disease

Endocannabinoid signaling depends upon the CB₁ and CB₂ cannabinoid receptors, their endogenous ligands anandamide and 2-arachidonoylglycerol, and intracellular proteins that mediate responses via the C-terminal and other intracellular receptor domains. The CB₁ receptor regulates and is regulated by associated G proteins predominantly of the Gi/o subtypes, β-arrestins 1 and 2, and the cannabinoid receptor-interacting protein 1a (CRIP1a). Evidence for a physiological role for CRIP1a is emerging as data regarding the cellular localization and function of CRIP1a are generated. Here we summarize the neuronal distribution and role of CRIP1a in endocannabinoid signaling, as well as discuss investigations linking CRIP1a to development, vision and hearing sensory systems, hippocampus and seizure regulation, and psychiatric disorders including schizophrenia. We also examine the genetic and epigenetic association of CRIP1a within a variety of cancer subtypes. This review provides evidence upon which to base future investigations on the function of CRIP1a in health and disease.