Four specific biomarkers associated with the progression of glioblastoma multiforme in older adults identified using weighted gene co-expression network analysis

Abstract

Glioblastoma multiforme (GBM) is the most common primary intracranial malignancy in adults. Owing to individual tolerance and tumor heterogeneity, the therapy methods for young adults do not apply to older adults. The present study aimed to identify specific biomarkers for GBM in older adults using weighted gene co-expression network analysis (WGCNA). Gene expression profiles of older adults with GBM were downloaded from The Cancer Genome Atlas (TCGA) and set as a discovery cohort to construct WGCNA. Core genes of clinically significant modules were used to perform functional enrichment, protein-protein interaction, and Pearson correlation analyses. Gene expression profiles of young in TCGA and older GBM patients from our research group were set as verification cohorts for hub gene expression and diagnostic value. Four significant gene modules associated clinically with older adults with GBM were identified, whereas 251 genes were core genes with module membership>0.8 and gene significance>0.2. Ermin (ERMN), myelinassociated oligodendrocyte basic protein (MOBP), proteolipid protein 1 (PLP1), and oligodendrocytic myelin paranodal and inner loop protein (OPALIN) genes had significant relationships with the Karnofsky score (KPS) in older GBM patients. ERMN, MOBP, PLP1, and OPALIN had no relationship with KPS in young GBM patients. These genes were upregulated in GBM tissues from older patients with low but not high KPS and had high diagnostic value. In conclusion, ERMN, MOBP, PLP1, and OPALIN may serve as specific biomarkers for the progression of GBM in older adults.

Nucleotide‐Binding Oligomerization Domain (NOD)-Like Receptor Subfamily C (NLRC) as a Prognostic Biomarker for Glioblastoma Multiforme Linked to Tumor Microenvironment: A Bioinformatics, Immunohistochemistry, and Machine Learning-Based Study

“… 29 OPALIN refers to the oligodendrocytic myelin paranodal and inner loop protein gene, which was found to be significantly correlated with the KPS score of elderly GBM patients. 30 Moreover, downregulated GRIN1, KCNJ9, GABRA1, ATP2B3 , and SLC17A7 in Module 1 were also enriched in the GSEA pathway terms that were associated with impaired neurological function; the low expression of the abovementioned genes highly correlated with worse OS in patients with low-grade glioma. 31 In addition, D’Urso et al reported that downregulation of miR-155 expression restored GABRA1 expression, which makes GBM cells more vulnerable to GABRA1-mediated anti-proliferation signals.…”

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle

“…To differentiate between cell types in the GBM microenvironment, immune and non-immune clusters were classified based on marker genes identified in previous studies ( 41 – 58 ). Genes such as C1QB ( 41 ), CD68 ( 42 ), CSF1R ( 43 ), PTPRC ( 44 ), C1QC ( 45 ), P2RY12 ( 46 ), CX3CR1 ( 47 ), CD163 ( 48 ), PTGS2 ( 49 ) and CD86 ( 50 ) were used to identify immune clusters; whereas genes AQP4 ( 51 ), PTPRZ1 ( 52 ), CLDN5 ( 53 ), CD34 ( 54 ), GFAP ( 55 ), FDGFRA ( 56 ), OLIG2 ( 57 ) and PLP1 ( 58 ) were used to identify non-immune clusters. Clusters 0, 2, 6, 14 and 15 were identified as immune cells, whereas clusters 1, 3, 4, 5, 7, 8, 9, 10, 11, 12 and 13 were identified as non-immune cells based on the average expression of PTPRC, as demonstrated in Fig.…”

“…To differentiate between cell types in the GBM microenvironment, immune and non-immune clusters were classified based on marker genes identified in previous studies (41)(42)(43)(44)(45)(46)(47)(48)(49)(50)(51)(52)(53)(54)(55)(56)(57)(58). Genes such as C1QB (41), CD68 (42), CSF1R (43), PTPRC (44), C1QC (45), P2RY12 (46), CX3CR1 (47), CD163 (48), PTGS2 (49) and CD86 (50) were used to identify immune clusters; whereas genes AQP4 (51), PTPRZ1 (52), CLDN5 (53), CD34 (54), GFAP (55), FDGFRA (56), OLIG2 (57) and PLP1 (58) were used to identify non-immune clusters. Clusters 0, 2, 6, 14 and 15 were identified as immune cells, whereas clusters 1, 3, 4, 5, 7, 8, 9, 10, 11, 12 and 13 were identified as non-immune cells based on the average expression of PTPRC, as demonstrated in Fig.…”

Study of molecular patterns associated with ferroptosis in Parkinson’s disease and its immune signature

“…The essence of Gene Significance (GS) is the correlation between the genes within the module where it is located and the phenotype (our phenotype was selected as whether or not we had PD) [48]. Reviewing a large amount of literature, we found that, generally, a GS threshold between 0.2 and 0.5 is reasonable [53][54][55][56][57][58][59][60]. At the same time, we wanted to include as many genes as possible to see the relationship with ferroptosis, so for the GS threshold, we chose 0.2.…”