Abstract
Objective To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice. Methods For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model. Results Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles. Conclusions We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.
Highlights
The landscape of the tumor microenvironment (TME) has been generally portrayed, of which the components are considered an essential composition of cancer immunity, with counterpart activities across the “immunoediting” process [1]
The infiltration patterns were analyzed with CIBERSORT deconvolution algorithms by quantifying the fractions of 22 immune cell types in TME of 1095 The Cancer Genome Atlas (TCGA)-BRCA patients
On the basis of the consensus clustering method, three robust TME clusters were retrieved, and the prognosis of patients from these curated subtypes was different with statistical significance (P = 0:023) (Figures 2(a) and 2(b))
Summary
The landscape of the tumor microenvironment (TME) has been generally portrayed, of which the components are considered an essential composition of cancer immunity, with counterpart activities across the “immunoediting” process [1]. In this tumor-related contexture, the density, activity, and organization of immunological infiltration is crucial and regarded as a promising indicator for both clinical response and prognosis of cancer patients [2]. We used the transcriptome mixture and clinicopathological information of 2620 individuals which were publicly retrieved on databases, BioMed Research International analyzed the potential immune-related mechanisms for divergent TME phenotypes, and constructed a prognostic model with good performance for patients with breast cancer
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