Bioinformatics Analysis Reveals an Association Between Cancer Cell Stemness, Gene Mutations, and the Immune Microenvironment in Stomach Adenocarcinoma

Abstract

Cancer stem cells (CSCs), characterized by infinite proliferation and self-renewal, greatly challenge tumor therapy. Research into their plasticity, dynamic instability, and immune microenvironment interactions may help overcome this obstacle. Data on the stemness indices (mRNAsi), gene mutations, copy number variations (CNV), tumor mutation burden (TMB), and corresponding clinical characteristics were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena Browser. Tumor purity and infiltrating immune cells in stomach adenocarcinoma (STAD) tissues were predicted using the ESTIMATE R package and CIBERSORT method, respectively. Differentially expressed genes (DEGs) between the high and low mRNAsi groups were used to construct prognostic models with weighted gene co-expression network analysis (WGCNA) and Lasso regression. The association between cancer stemness, gene mutations, and immune responses was evaluated in STAD. A total of 6,739 DEGs were identified between the high and low mRNAsi groups. DEGs in the brown (containing 19 genes) and blue (containing 209 genes) co-expression modules were used to perform survival analysis based on Cox regression. A nine-gene signature prognostic model (ARHGEF38-IT1, CCDC15, CPZ, DNASE1L2, NUDT10, PASK, PLCL1, PRR5-ARHGAP8, and SYCE2) was constructed from 178 survival-related DEGs that were significantly related to overall survival, clinical characteristics, tumor microenvironment immune cells, TMB, and cancer-related pathways in STAD. Gene correlation was significant across the prognostic model, CNVs, and drug sensitivity. Our findings provide a prognostic model and highlight potential mechanisms and associated factors (immune microenvironment and mutation status) useful for targeting CSCs.

Identification of Prognosis Biomarkers for High-Grade Serous Ovarian Cancer Based on Stemness

“…The more edges the gene connects, the more important the gene is in the PPI network. LCP2 has 28 sides and FCGR3A has 26 sides, so LCP2 and FCGR3A are the most critical proteins ( Guo et al, 2020 ; Ye et al, 2020 ). Indicating that they are closely related to cancer ( Figure 5B ).…”

“…In order to obtain the stemness-related key genes ( Figure 1C ), bulk RNA-Seq of ovarian cancer is analyzed first. The data is used for differential analysis and up-regulated differentially expressed genes are obtained ( Ye et al, 2020 ). Then, low-survival samples and up-regulated differentially expressed genes are used for WGCNA ( Langfelder and Horvath, 2008 ) analysis to obtain stemness-related key genes, and stemness-related key genes are analyzed for enrichment analysis and protein interaction analysis.…”

“…To our knowledge, this is an innovative analysis that the bulk sample and individual clinical characteristics of ovarian cancer are combined with stem cell characteristics, and the resulting attributes are migrated to HGSOC single-cell data to assist subsequent analysis of single-cell data. In the past, only cancer and cancer stem cells, gene mutations, and tumor microenvironment have been combined for analysis, but no analysis has been performed to transfer the characteristics of BULK RNA-Seq and cancer stem cells to single-cell data ( Ye et al, 2020 ). Thus, the final results are not only more accurate but also have more diagnostic significance.…”

Integrated Analysis of the Expression Characteristics, Prognostic Value, and Immune Characteristics of PPARG in Breast Cancer

“…To assess the differences in immune cell subtypes, we used the R package “CIBERSORT” ( Chang et al, 2020 ; Ye et al, 2020 ) and examined the proportion of 22 immune cell subtypes (naive B cells, memory B cells, plasma cells, CD8 T cells, naive CD4 T cells, resting memory CD4 T cells, activated memory CD4 T cells, follicular helper T cells, T cells regulatory, gamma delta T cells, resting NK cells, activated NK cells, monocytes, macrophages M0, macrophages M1, macrophages M2, resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils). Samples with a statistical significance difference of p < 0.05 were used for further analysis.…”

Identification of Biomarkers Related to Prognosis of Glioblastoma and Their Correlation with Immune Infiltration Cells

“…ECM is another key component of TME in addition to special types of cells. [11] . ECM not only participates in the supporting framework of TME cells, but also is an abundant pivotal growth factor [12] .…”