Construction of a Prognostic Gene Signature Associated with Immune Infiltration in Glioma: A Comprehensive Analysis Based on the CGGA

Abstract

Background. Tumor microenvironment (TME) is closely related to the progression of glioma and the therapeutic effect of drugs on this cancer. The aim of this study was to develop a signature associated with the tumor immune microenvironment using machine learning. Methods. We downloaded the transcriptomic and clinical data of glioma patients from the Chinese Glioma Genome Atlas (CGGA) databases (mRNAseq_693). The single-sample Gene Set Enrichment Analysis (ssGSEA) database was used to quantify the relative abundance of immune cells. We divided patients into two different infiltration groups via unsupervised clustering analysis of immune cells and then selected differentially expressed genes (DEGs) between the two groups. Survival-related genes were determined using Cox regression analysis. We next randomly divided patients into a training set and a testing set at a ratio of 7 : 3. By integrating the DEGs into least absolute shrinkage and selection operator (LASSO) regression analysis in the training set, we were able to construct a 15-gene signature, which was validated in the testing and total sets. We further validated the signature in the mRNAseq_325 dataset of CGGA. Results. We identified 74 DEGs associated with tumor immune infiltration, 70 of which were significantly associated with overall survival (OS). An immune-related gene signature was established, consisting of 15 key genes: adenosine triphosphate (ATP)-binding cassette subfamily C member 3 (ABCC3), collagen type IV alpha 1 chain (COL4A1), podoplanin (PDPN), annexin A1 (ANXA1), COL4A2, insulin-like growth factor binding protein 2 (IGFBP2), serpin family A member 3 (SERPINA3), CXXC-type zinc finger protein 11 (CXXC11), junctophilin 3 (JPH3), secretogranin III (SCG3), secreted protein acidic and rich in cysteine (SPARC)-related modular calcium-binding protein 1 (SMOC1), Cluster of Differentiation 14 (CD14), COL1A1, S100 calcium-binding protein A4 (S100A4), and transforming growth factor beta 1 (TGF-β1). The OS of patients in the high-risk group was worse than that of patients in the low-risk group. GSEA showed that interleukin-6 (IL-6)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT3) signaling, interferon gamma (IFN-γ) response, angiogenesis, and coagulation were more highly enriched in the high-risk group and that oxidative phosphorylation was more highly enriched in the low-risk group. Conclusion. We constructed a stable gene signature associated with immune infiltration to predict the survival rates of glioma patients.

[Retracted] Considering Computational Mathematics IGHG3 as Malignant Melanoma Is Associated with Immune Infiltration of Malignant Melanoma

“…In order to test the effect of Highg3 on the discovery of A375 cells, A375 cells, A375 negative cells, and A375 positive cells were subjected to traditional centrifugation and washing [ 32 ]. In order to dissolve the cell lysate, extract the total protein of each cell, and detect the appearance of catechin protein in a375 cells, a375-negative cells, and a375-positive cells; western blot was used.…”

Identification and Validation of a Gene Signature for Lower-Grade Gliomas Based on Pyroptosis-Related Genes to Predict Survival and Response to Immune Checkpoint Inhibitors

“…In the present study, an eight-gene signature utilized to predict the prognosis and response to ICIs was constructed based on 47 PRS genes identified by RSF, LASSO, and multivariate Cox regression analyses. Of these eight PRS genes, three (BMP5, TNFRSF11B, and IGFBP2) are immune-related genes, and their association with the prognosis of cancer patients has been previously reported [ 43 – 45 ]. EN1, EYA4, IGFBP2, and PTCRA were also discovered as predictive biomarkers in the prognosis and treatments of LGG patients [ 46 – 49 ].…”

PODNL1 Methylation Serves as a Prognostic Biomarker and Associates with Immune Cell Infiltration and Immune Checkpoint Blockade Response in Lower-Grade Glioma

“…Increased immune cell infiltration levels are linked to heightened tumor aggressiveness in a range of cancers including breast cancer, kidney renal clear cell carcinoma, uveal melanoma, pancreatic cancer, and osteosarcoma [41][42][43][44][45][46][47][48]. In gliomas, including LGG, increased immune cell infiltration levels showed poor prognosis and more aggressive phenotypes [21,22,[49][50][51][52][53][54][55][56][57]. Thus, we report that low PODNL1 methylation, specifically of CpG sites cg07425555, cg26969888, cg18547299, and cg24354933, may be a key factor in the modulation of immune infiltrates in the LGG tumor microenvironment, affecting its aggressiveness and prognosis.…”