Comprehensive Characterization of the Function of Metabolic Genes and Establishment of a Prediction Model in Breast Cancer

Abstract

Background. Breast cancer (BC) is a highly heterogeneous disease with high morbidity and mortality. Its subtypes may have distinctly different biological behaviors, clinical outcomes, and therapeutic responses. The metabolic status of BC tissue is closely related to its progress. Therefore, we comprehensively characterized the function of metabolic genes in BC and identified new biomarkers to predict BC patients’ prognoses. Methods. Metabolic genes were identified by intersecting genes obtained from two published pieces of literature. The function of metabolic genes in BC was determined by extracting differentially expressed genes (DEGs), performing functional enrichment analyses, analyzing the infiltrating proportion of immune cells, and conducting metabolic subgroup analyses. A risk score model was constructed to assess the prognoses of BC patients by performing the univariate Cox regression, LASSO algorithm, multivariate Cox regression, Kaplan-Meier survival analyses, and ROC curve analyses in the training set. The prognostic model was then validated on the testing dataset, external dataset, the whole TCGA-BC database, and our clinical specimens. Finally, a nomogram was constructed for clinical prognostic prediction based on the risk score model and other clinicopathological parameters. Results. 955 metabolic genes were obtained. Among these, 157 metabolic DEGs were identified between BC and normal tissues for subsequent GO and KEGG pathway enrichment analyses. 5 metabolic genes were negatively correlated with CD8+ T cells, while 49 genes were positively correlated with CD8+ T cells. Furthermore, 5 metabolic subgroups with varying proportions of PAM50 subtypes, TNM classification, and immune cell infiltration were obtained. Finally, a risk score model was constructed to predict the prognoses of BC patients, and a nomogram incorporating the risk score model was established for clinical application. Conclusion. In this study, we elucidated tumor heterogeneity from metabolite profiling of BC. The roles of metabolic genes in the occurrence of BC were comprehensively characterized, clarifying the relationship between the tumor microenvironment (TME) and metabolic genes. Meanwhile, a concise prediction model was also constructed based on metabolic genes, providing a convenient and precise method for the individualized diagnosis and treatment of BC patients.

Variation Analysis in Premenopausal and Postmenopausal Breast Cancer Cases

“…The integration of molecular markers into conventional BCa classification systems has allowed for more effective treatment guidance and the exploration of potential treatment targets [11]. It has been noted that besides traditional prognostic tools, the immunohistochemical molecular classification of BCa may help detect patients with varying recurrence risks and provide insights into cancer treatment [12]. Especially, the accurate classification of Luminal A and Luminal B subtypes is emphasized to be of extreme importance in determining treatment [13].…”