Identification of Immunological Characteristics and Immune Subtypes Based on Single-Sample Gene Set Enrichment Analysis Algorithm in Lower-Grade Glioma

Abstract

Few breakthroughs have been achieved in the treatment of lower-grade glioma (LGG) in recent decades. Apart from the conventional pathological and histological classifications, subtypes based on immunogenomics would provide reference for individualized treatment and prognosis prediction. Our study identified four immunotypes of lower-grade glioma (clusters A, B, C, and D) by bioinformatics methods in TCGA-LGG and two CGGA datasets. Cluster A was an “immune-cold” phenotype with the lowest immune infiltration and longest survival expectation, whereas cluster D was an “immune-rich” subtype with the highest immune infiltration and poor survival expectation. The expression of immune checkpoints increased along with immune infiltration degrees among the clusters. It was notable that immune clusters correlated with a variety of clinical and immunogenomic factors such as age, WHO grades, IDH1/2 mutation, PTEN, EGFR, ATRX, and TP53 status. In addition, LGGs in cluster D were sensitive to cisplatin, gemcitabine, and immune checkpoint PD-1 inhibitors. RTK-RAS and TP53 pathways were affected in cluster D. Functional pathways such as cytokine–cytokine receptor interaction, antigen processing and presentation, cell adhesion molecules (CAMs), and ECM–receptor interaction were also enriched in cluster D. Hub genes were selected by the Matthews correlation coefficient (MCC) algorithm in the blue module of a gene co-expression network. Our studies might provide an immunogenomics subtyping reference for immunotherapy in LGG.

A Nomogram Based on SEER Database for Predicting Prognosis in Patients with Mucinous Ovarian Cancer: A Real-World Study

“…Recently, with the development of science and technology, more and more techniques have been used to predict the prognosis of patients with tumors. Recent research has revealed that computed tomography-based radiogenomic biomarkers, 15 long non-coding RNAs (lncRNAs) 16 and single-sample gene set enrichment analysis algorithm 17 have been used to predict overall survival in tumors. Nomogram, as a classic model, is a commonly used model for predicting prognosis, especially in oncology.…”

Exploration of Potential Biomarkers and Immune Landscape for Hepatoblastoma: Evidence from Machine Learning Algorithm

“…We used the single-sample gene-set enrichment analysis (ssGSEA) to quantify the tumor immune microenvironment (TIME) associated pathways in each tissue sample. The “IOBR” package [ 22 ] was used to download and analyze the gene sets of “TME-associated pathways.” In addition, xCell, EPIC, MCPcounter, and timer algorithms were used to analyze the immune infiltration levels of cells in TME. The heatmap diagram was used to compare the differences between immune cells and TME-associated pathways in different tissues.…”

“…e "IOBR" package [22] was used to download and analyze the gene sets of "TME-associated pathways." In addition, xCell, EPIC, MCPcounter, and timer algorithms were used to analyze the immune in ltration levels of cells in TME.…”

Integrative Analysis From Multicenter Studies Identifies a WGCNA-Derived Cancer-Associated Fibroblast Signature for Ovarian Cancer

“…In the least absolute shrinkage and selection operator (LASSO) regression analysis (33), 1,000 iterations were performed to reduce the genes, and subsequently, the above genes were subjected to multivariate Cox regression analysis to obtain the coefficients. CAF risk score was derived using the same formula as in our previous study (34,35). The OC patients in each cohort were divided into high-risk and lowrisk groups, and the cutoff value for each cohort was used as the threshold.…”