Research Paper Volume 16, Issue 10 pp 8611—8629
A novel signature incorporating genes related to lipid metabolism and immune for prognostic and functional prediction of breast cancer
- 1 Clinical Laboratory, People’s Hospital of Xinjin District, Chengdu 611430, China
- 2 Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital and Chongqing Cancer Institute and Chongqing Cancer Hospital, Chongqing 400030, China
- 3 Tumor and Hematology Department, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
Received: January 15, 2024 Accepted: April 10, 2024 Published: May 20, 2024
https://doi.org/10.18632/aging.205828How to Cite
Copyright: © 2024 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Purpose: Breast cancer prognosis and functioning have not been thoroughly examined in relation to immunological and lipid metabolism. However, there is a lack of prognostic and functional analyses of the relationship between lipid metabolism and immunity in breast cancer.
Methods: DEGs in breast cancer were obtained from UCSC database, and lipid metabolism and immune-related genes were obtained from GSEA and Immune databases. A predictive signature was constructed using univariate Cox and LASSO regression on lipid metabolism and immune-related DEGs. The signature’s prognostic significance was assessed using Kaplan-Meier, time-dependent ROC, and risk factor survival scores. Survival prognosis, therapeutic relevance, and functional enrichment were used to mine model gene biology. We selected IL18, which has never been reported in breast cancer before, in the signature to learn more about its function, potential to predict outcome, and immune system role. RT-PCR was performed to verify the true expression level of IL18.
Results: A total of 136 DEGs associated with breast cancer responses to both immunity and lipid metabolism. Nine key genes (CALR, CCL5, CEPT, FTT3, CXCL13, FLT3, IL12B, IL18, and IL24, p < 1.6e−2) of breast cancer were identified, and a prognostic was successfully constructed with a good predictive ability. IL18 in the model also had good clinical prognostic guidance value and immune regulation and therapeutic potential. Furthermore, the expression of IL18 was higher than that in paracancerous tissue.
Conclusions: A unique predictive signature model could effectively predict the prognosis of breast cancer, which can not only achieve survival prediction, but also screen out key genes with important functional mechanisms to guide clinical drug experiments.
Abbreviations
DEGs: Differentially expressed genes; LASSO: Least absolute shrinkage and selection operator; ROC: Receiver-operating characteristics; RT-PCR: Reverse transcription-polymerase chain reaction; UCSC: University of California Santa Cruz; GSEA: Gene set enrichment analyses; KEGG: Kyoto Encyclopedia of Genes and Genomes; GO: Gene ontology; OS: Overall survive; AUC: Areas under the curve; UALCAN: University of Alabama at Birmingham; HPA: The Human Protein Atlas; GEPIA: Gene expression profiling interactive analysis; TCGA: The cancer genome atlas; HA: Hazard ratio; TME: Tumor microenvironment; ROS: Reactive oxygen species; GSDC: Genomics of drug sensitivity in cancer; FASN: Fatty acid synthase.