RESUMO
Interferons are crucial for adaptive immunity and play an important role in the immune landscape of breast cancer. Using microarray-based gene expression analysis, we examined the subtype-specific prognostic significance of interferon-γ (IFN-γ) as a single gene as well as an IFN-γ signature covering the signaling pathway in 461 breast cancer patients. Prognostic significance of IFN-γ, as well as the IFN-γ signature for metastasis-free survival (MFS), were examined using Kaplan-Meier as well as univariate and multivariate Cox regression analyses in the whole cohort and in different molecular subtypes. The independent prognostic significance of IFN-γ as a single gene was limited to basal-like breast cancer (hazard ratio (HR) 2.779, 95% confidence interval (95% CI) 1.117-6.919, p = 0.028). In contrast, the IFN-γ-associated gene signature was an independent prognostic factor in the whole cohort (HR 2.287, 95% CI 1.410-3.633, p < 0.001) as well as in the basal-like (HR 3.458, 95% CI 1.154-10.359, p = 0.027) and luminal B (HR 2.690, 95% CI 1.416-5.112, p = 0.003) molecular subtypes. These results underline the subtype-dependent prognostic influence of the immune system in early breast cancer.
Assuntos
Biomarcadores Tumorais/genética , Neoplasias da Mama/genética , Interferon gama/genética , Idoso , Neoplasias da Mama/epidemiologia , Neoplasias da Mama/patologia , Intervalo Livre de Doença , Feminino , Regulação Neoplásica da Expressão Gênica/genética , Humanos , Estimativa de Kaplan-Meier , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Intervalo Livre de Progressão , Receptor ErbB-2/genética , Receptores de Estrogênio/genética , Receptores de Progesterona/genética , Transdução de Sinais/genéticaRESUMO
INTRODUCTION: Immunoglobulin κC (IGKC)-positive tumor-infiltrating plasma cells are associated with better prognosis in node-negative breast cancer patients without adjuvant systemic therapy. In the present study we evaluated the prognostic significance of IGKC in breast cancer patients treated with adjuvant chemotherapy ± tamoxifen. METHODS: IGKC expression was immunohistochemically analyzed in 193 breast cancer patients who were treated with adjuvant chemotherapy, either with cyclophosphamide, methotrexate, and 5-fluorouracil (CMF) or 5-fluorouracil, epirubicin, and cyclophosphamide (FEC) between 1993 and 2001 with a median follow-up of 11 years. The prognostic impact of IGKC expression was evaluated by Kaplan-Meier survival analyses as well as uni- and multivariate Cox regression. An interaction term was used to investigate a possible association between tamoxifen treatment and prognostic effect of IGKC expression. RESULTS: Kaplan-Meier analyses identified IGKC as a prognostic marker for metastasis-free survival (MFS): higher IGKC expression was associated with a better outcome (p = 0.02, log rank). Results of univariate Cox regression confirmed the prognostic impact of IGKC expression: patients with a strong IGKC expression had a longer MFS (hazard ratio [HR] 1.931; 95% confidence interval [CI] 1.087-3.431; p = 0.025). Multivariate Cox regression analysis showed the independent prognostic significance of IGKC expression (HR 2.070; 95% CI 1.088-3.938; p = 0.027). The interaction term confirmed a significant interaction between tamoxifen treatment and the prognostic impact of IGKC expression (pinteraction = 0.04). CONCLUSION: IGKC expression had an independent prognostic impact in early breast cancer patients who received adjuvant chemotherapy. There was a significant interaction with the use of tamoxifen.
RESUMO
Tumor-necrosis-factor-alpha-induced protein 3 (TNFAIP3), or A20, is a ubiquitin-modifying protein and negative regulator of canonical nuclear factor κB (NF-κB) signaling. Several single-nucleotide polymorphisms in TNFAIP3 are associated with autoimmune diseases, suggesting a role in tissue inflammation. While the role of A20 in peripheral immune cells has been well investigated, less is known about its role in the central nervous system (CNS). Here, we show that microglial A20 is crucial for maintaining brain homeostasis. Without microglial A20, CD8+ T cells spontaneously infiltrate the CNS and acquire a viral response signature. The combination of infiltrating CD8+ T cells and activated A20-deficient microglia leads to an increase in VGLUT1+ terminals and frequency of spontaneous excitatory currents. Ultimately, A20-deficient microglia upregulate genes associated with the antiviral response and neurodegenerative diseases. Together, our data suggest that microglial A20 acts as a sensor for viral infection and a master regulator of CNS homeostasis.
Assuntos
Encéfalo/imunologia , Encéfalo/patologia , Linfócitos T CD8-Positivos/imunologia , Microglia/metabolismo , Neuroproteção , Proteína 3 Induzida por Fator de Necrose Tumoral alfa/metabolismo , Animais , Regulação para Baixo , Potenciais Pós-Sinápticos Excitadores , Inflamação/patologia , Mediadores da Inflamação/metabolismo , Camundongos Endogâmicos C57BL , Camundongos Transgênicos , Neurônios/metabolismo , Fenótipo , Células Piramidais/metabolismo , Receptores de AMPA/metabolismo , Proteína 3 Induzida por Fator de Necrose Tumoral alfa/deficiência , Regulação para CimaRESUMO
There is an increasing accumulation of evidence supporting the existence of a hyperbolic geometry underlying the network representation of complex systems. In particular, it has been shown that the latent geometry of the human protein network (hPIN) captures biologically relevant information, leading to a meaningful visual representation of protein-protein interactions and translating challenging systems biology problems into measuring distances between proteins. Moreover, proteins can efficiently communicate with each other, without global knowledge of the hPIN structure, via a greedy routing (GR) process in which hyperbolic distances guide biological signals from source to target proteins. It is thanks to this effective information routing throughout the hPIN that the cell operates, communicates with other cells and reacts to environmental changes. As a result, the malfunction of one or a few members of this intricate system can disturb its dynamics and derive in disease phenotypes. In fact, it is known that the proteins associated with a single disease agglomerate non-randomly in the same region of the hPIN, forming one or several connected components known as the disease module (DM). Here, we present a geometric characterisation of DMs. First, we found that DM positions on the two-dimensional hyperbolic plane reflect their fragmentation and functional heterogeneity, rendering an informative picture of the cellular processes that the disease is affecting. Second, we used a distance-based dissimilarity measure to cluster DMs with shared clinical features. Finally, we took advantage of the GR strategy to study how defective proteins affect the transduction of signals throughout the hPIN.