Cancer Stem Cell Characteristics by Network Analysis of Transcriptome Data Stemness Indices in Breast Carcinoma

Zeng-Hong Wu; You-Jing Zhang; Chang-Li Jia

doi:10.1155/2020/8841622

Cancer Stem Cell Characteristics by Network Analysis of Transcriptome Data Stemness Indices in Breast Carcinoma

J Oncol. 2020 Oct 6:2020:8841622. doi: 10.1155/2020/8841622. eCollection 2020.

Authors

Zeng-Hong Wu^{1

2}, You-Jing Zhang³, Chang-Li Jia⁴

Affiliations

¹ Department of Infectious Diseases, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
² Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
³ School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
⁴ School of Health Science, Wuhan University, Wuhan, Hubei Province 430072, China.

Abstract

Objective: Breast cancer (BC) affects women all over the world. This study aimed at screening out potential biomarkers through performing an in-depth analysis of data from the previous research and database.

Design: This study made full use of RNA sequencing (RNA-seq) data from cancer genomic maps (TCGA) and screened key genes related to stemness by merging WGCNA with BC mRNAsi.

Results: The related mRNAsi data were downloaded, and the transcriptional levels of mRNAsi in cancers contrasted with normal samples. The results showed that there was a significantly higher mRNAsi expression in BC tissues (P=1.791e - 43). Seven modules were obtained following the investigation through cluster analysis. The turquoise module showed a relatively high positive correlation with mRNAsi at 0.79; this module was chosen as the most interesting and was used for subsequent analysis. By setting related cutoffs, 38 key genes were screened, and the coexpression of these genes was explored next. The results showed that the lowest correlation was between CDC20 and KIF11 (0.54), and the highest connection was between BUB1 and CKAP2L (0.86). Furthermore, ten hub genes with the most nodes were sorted using a histogram. Using other databases to explore the prognosis value of key genes, the results showed that lower expression of key genes was significantly connected with longer overall survival (OS), distant metastasis-free survival (DMFS), and relapse-free survival (RFS). The immune infiltration relationship between hub genes and six kinds of basic immune cells was investigated; it was revealed that partial ones were positively or negatively related.

Conclusion: This study is the first to show the important role of stemness-related genes in the prognosis of BC. However, future clinical trials are needed to confirm these results and promote the application of these key genes in prognosis evaluation.