Construction of a fatty acid metabolism-related gene signature for predicting prognosis and immune response in breast cancer - Qian, Li; Liu, Yi-Fei; Lu, Shu-Min; Yang, Juan-Juan; Miao, Hua-Jie; He, Xin; Huang, Hua; Zhang, Jian-Guo (Frontiers in Genetics, 2023)
Breast cancer has the highest incidence among malignant tumors in women, and its prevalence ranks first in global cancer morbidity.This study aimed to explore the feasibility of a prognostic model for patients with breast cancer based on the differential expression of genes related to fatty acid metabolism.The mRNA expression matrix of breast cancer and paracancer tissues was downloaded from The Cancer Genome Atlas database. The differentially expressed genes related to fatty acid metabolism were screened in R language. The TRRUST database was used to predict transcriptional regulators related to hub genes and construct an mRNA–transcription factor interaction network. A consensus clustering approach was used to identify different fatty acid regulatory patterns. In combination with patient survival data, Lasso and multivariate Cox proportional risk regression models were used to establish polygenic prognostic models based on fatty acid metabolism. The median risk score was used to categorize patients into high- and low-risk groups. Kaplan–Meier survival curves were used to analyze the survival differences between both groups. The Cox regression analysis included risk score and clinicopathological factors to determine whether risk score was an independent risk factor. Models based on genes associated with fatty acid metabolism were evaluated using receiver operating characteristic curves. A comparison was made between risk score levels and the fatty acid metabolism-associated genes in different subtypes of breast cancer. The differential gene sets of the Kyoto Encyclopedia of Genes and Genomes for screening high- and low-risk populations were compared using a gene set enrichment analysis. Furthermore, we utilized CIBERSORT to examine the abundance of immune cells in breast cancer in different clustering models.High expression levels of ACADL and UBE2L6 prevented breast cancer, whereas high RDH16 expression levels increased its risk. Our comprehensive assessment of the association between prognostic risk scoring models and tumor microenvironment characteristics showed significant differences in the abundance of various immune cells between high- and low-risk breast cancer patients. By assessing fatty acid metabolism patterns, we gained a better understanding of the infiltration characteristics of the tumor microenvironment. Our findings are valuable for predicting the prognosis and treating breast cancer patients based on their clinicopathological characteristics.