Personalized differential expression analysis in triple-negative breast cancer

Hao Cai; Liangbo Chen; Shuxin Yang; Ronghong Jiang; You Guo; Ming He; Yun Luo; Guini Hong; Hongdong Li; Kai Song

doi:10.1093/bfgp/elad057

Personalized differential expression analysis in triple-negative breast cancer

Brief Funct Genomics. 2024 Jan 9:elad057. doi: 10.1093/bfgp/elad057. Online ahead of print.

Authors

Hao Cai¹, Liangbo Chen², Shuxin Yang², Ronghong Jiang³, You Guo¹, Ming He¹, Yun Luo¹, Guini Hong³, Hongdong Li³, Kai Song⁴

Affiliations

¹ Medical Big Data and Bioinformatics Research Centre, First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China.
² School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou, China.
³ School of Medical Information Engineering, Gannan Medical University, Ganzhou, China.
⁴ Department of Surgery, The Chinese University of Hong Kong, Shatin, Hong Kong SAR 999077, China.

PMID: 38197537
DOI: 10.1093/bfgp/elad057

Abstract

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Keywords: differentially expressed genes; individualized analysis; relative expression orderings; triple-negative breast cancer.

Abstract

Grants and funding