Abstract
Breast cancer (BC) is the most commonly diagnosed malignancy accompanied by high invasion and metastasis features. Importantly, emerging studies have supported that aging is a key clue that participates in the immune state and development of BC. Nevertheless, there are no studies concerning the aging-related genes (AGs) in constructing the prognosis signature of BC. Here, to address this issue, we initially performed a systematic investigation of the associations between AGs and BC prognosis and accordingly constructed a prognosis risk model with 10 AGs including PLAU, JUND, IL2RG, PCMT1, PTK2, HSPA8, NFKBIA, GCLC, PIK3CA, and DGAT1 by using the least absolute shrinkage and selection operator (LASSO) regression and Cox regression analysis. Meanwhile, our analysis further confirmed that the nomogram possessed a robust performance signature for predicting prognosis compared to clinical characteristics of BC patients, including age, clinical stage, and TNM staging. Moreover, the risk score was confirmed as an independent prognostic index of BC patients and was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Furthermore, in the external clinical sample validation, AGs were expressed differentially in patients from different risk groups, and tumor-associated macrophage markers were elevated in high-risk BC tissues with more co-localization of AGs. In addition, the proliferation, transwell, and wound healing assays also confirmed the promoting effect of DGAT1 in BC cell proliferation and migration. Therefore, this well-established risk model could be used for predicting prognosis and immunotherapy in BC, thus providing a powerful instrument for combating BC.
Highlights
Breast cancer (BC) in women has outstripped lung cancer as the most commonly diagnosed malignancy, with an estimated 2.3 million new cases (11.7%) around the world [1]
Among them, nearly 73 differentially expressed AGs (DEAGs) were further identified between 1,066 BC and 112 normal samples, including 40 upregulated and 33 downregulated DEAGs [false discovery rate (FDR) 1] (Figure 1B)
The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) pathway analyses were performed to validate the potential function of those DEAGs
Summary
Breast cancer (BC) in women has outstripped lung cancer as the most commonly diagnosed malignancy, with an estimated 2.3 million new cases (11.7%) around the world [1]. The distant organ colonization caused by BC cells with high invasion and metastasis ability is the major threat to the management of advanced BC patients [2]. BC encompasses disparate entities accompanied by various biomarkers and genetic signatures, leading to differences in prognosis among different BC subtypes [3]. Reliable predictive biomarkers and the according predictive model are necessary for early precise diagnosis and individualized treatment for BC patients. Aging is a complex multifactorial phenomenon manifested by the changes of the progressive loss of function or degeneration at every level of the human organism [4]. There are many triggers in inducing aging, including telomere dysfunction, oxidase stress, DNA damage, and epigenetic changes (mouse models in modeling aging and cancer). Aging is a comorbidity of BC that intensively implies the change of aging-associated transcriptome in promoting BC progression [5]
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