Molecular Subtypes Based on the Stemness Index Predict Prognosis in Glioma Patients

Abstract

Glioma is the common histological subtype of malignancy in the central nervous system, with high morbidity and mortality. Glioma cancer stem cells (CSCs) play essential roles in tumor recurrence and treatment resistance. Thus, exploring the stem cell-related genes and subtypes in glioma is important. In this study, we collected the RNA-sequencing (RNA-seq) data and clinical information of glioma patients from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. With the differentially expressed genes (DEGs) and weighted gene correlation network analysis (WGCNA), we identified 86 mRNA expression-based stemness index (mRNAsi)-related genes in 583 samples from TCGA RNA-seq dataset. Furthermore, these samples from TCGA database could be divided into two significantly different subtypes with different prognoses based on the mRNAsi corresponding gene, which could also be validated in the CGGA database. The clinical characteristics and immune cell infiltrate distribution of the two stemness subtypes are different. Then, functional enrichment analyses were performed to identify the different gene ontology (GO) terms and pathways in the two different subtypes. Moreover, we constructed a stemness subtype-related risk score model and nomogram to predict the prognosis of glioma patients. Finally, we selected one gene (ETV2) from the risk score model for experimental validation. The results showed that ETV2 can contribute to the invasion, migration, and epithelial-mesenchymal transition (EMT) process of glioma. In conclusion, we identified two distinct molecular subtypes and potential therapeutic targets of glioma, which could provide new insights for the development of precision diagnosis and prognostic prediction for glioma patients.

A Novel Cancer Stemness-Related Signature for Predicting Prognosis in Patients with Colon Adenocarcinoma

“…Shiokawa et al also demonstrated that the slow-cycling subpopulation of CSCs was rather significant in COAD development and chemoresistance [13]. Recently, a novel caner stemness index (named mRNAsi), which was developed by deep learning methods [14], has been extensively researched in various types of cancer, including bladder cancer [15], gastric carcinoma [16], lung cancer [17], and glioma [18]. However, there was not an integrated investigation of COAD stemness features using high-throughput data.…”

Stemness inhibition by (+)-JQ1 in canine and human mammary cancer cells revealed by machine learning

“…In addition, stemness phenotype can exhibit a particular molecular signature. Malta et al (2018) took advantage of this profile and proposed the OCLR algorithm that can be applied to determine a stemness index in different cells and tissues in addition to being able to reveal the stemness levels in different human cancers such as lung cancer (Liao et al, 2020;Zhang et al, 2020), bladder cancer (Pan et al, 2019), medulloblastoma (Lian et al, 2019), breast cancer (Pei et al, 2020), and glioma (Tan et al, 2021). In this study, we showed that mRNAsi can also be determined in canine species correlating to stemness features.…”

Metabolic Signature-Based Subtypes May Pave Novel Ways for Low-Grade Glioma Prognosis and Therapy

“…MC1 showed the highest proliferation score, followed by MC2 and MC3 ( Supplementary Figure S3D ). Studies have demonstrated that LGG patients with higher mRNAsi indices have a better prognosis than those with low mRNAsi indices ( Tan et al, 2021 ) ( Supplementary Figure S3E ). We obtained and analyzed mRNAsi differences in LGG patients from our data, and found that the mRNAsi indices of MC2 and MC3 were significantly higher than that of MC1.…”