Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer.

BACKGROUND: Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression. AIM: To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation. METHODS: We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR). RESULTS: Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR. CONCLUSION: We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

Divalent metal ions and intermolecular interactions facilitate DNA network formation.

The interactions between divalent metal ions and DNA are crucial for basic life processes. These interactions are also important in advanced technological products such as DNA-based ion sensors. Current polyelectrolyte theories cannot describe these interactions well and do not consider the corresponding dynamics. In this study, we report the single-molecule dynamics of the binding of divalent metal ions to a single DNA molecule and the morphology characterization of the complex. We found that most of the divalent metal ions (Mn2+, Zn2+, Co2+, Ni2+, and Cd2+), except Mg2+ and Ca2+, could cause monomolecular DNA condensation. For transition metal ions, different ionic strengths were required to induce the compaction, and different shortening speeds were displayed in the dynamics, indicating ionic specificity. Atomic force microscopy revealed that the morphologies of the metal ion-DNA complexes were affected by the ionic strength of the metal ion, DNA chain length, and DNA concentration. At low metal ion concentration, DNA tended to adopt a random coil conformation. Increasing the ionic strength led to network-like condensed structures, suggesting that divalent metal ions can induce attraction between DNA molecules. Furthermore, higher DNA concentration and longer chain length enhanced intermolecular interactions and consequently resulted in network structures with a higher degree of interconnectivity.

The adenosine A2A receptor antagonist SCH58261 reduces macrophage/microglia activation and protects against experimental autoimmune encephalomyelitis in mice.

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system (CNS) affecting more than 2.5 million individuals worldwide. In the present study, myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalomyelitis (EAE) mice were treated with adenosine receptor A2A antagonist SCH58261 at different periods of EAE development. The administration of SCH58261 at 11-28 days post-immunization (d.p.i.) with MOG improved the neurological deficits. This time window corresponds to the therapeutic time window for MS treatment. SCH58261 significantly reduced the CNS neuroinflammation including reduced local infiltration of inflammatory cells, demyelination, and the numbers of macrophage/microglia in the spinal cord. Importantly, SCH58261 ameliorated the EAE-induced neurobehavioral deficits. By contrast, the SCH58261 treatment was ineffective when administered at the beginning of the onset of EAE (i.e., 1-10 d.p.i). The identification of the effective therapeutic window of A2A receptor antagonist provide insight into the role of A2A receptor signaling in EAE, and support SCH58261 as a candidate for the treatment of MS in human.

2-BFI ameliorates EAE-induced mouse spinal cord damage: effective therapeutic time window and possible mechanisms.

Our previous studies showed that ligands to type 2 imidazoline receptors (I2R), including 2-(2-Benzofuranyl)-2-imidazoline (2-BFI) and Idazoxan, were effective in reducing spinal cord inflammation caused by experimental autoimmune encephalomyelitis (EAE). In the present study, we determined the effective therapeutic time window of 2-BFI and found that administration of 2-BFI in mice before the appearance of ascending flaccid paralysis (1-10 days post immunization), but not during the period when neurological deficits occurred (11-20 days post immunization), significantly ameliorated EAE-induced neurobehavioral deficits, reduced the infiltration of inflammatory cells into the spinal cord, and reduced the level of demyelination. More interestingly, giving 2-BFI during 1-10 days post immunization selectively suppressed IL-17 levels in the peripheral blood, which strongly suggests that IL-17 may be a good early marker to indicate EAE progression and that 2-BFI may target CD4⁺ T lymphocytes, especially Th17 cells to reduce IL-17 expression. Collectively, these studies led us to envisage that 2-BFI can be a useful drug to treat multiple sclerosis (MS) when used in combination with an early indicator of MS progression, such as IL-17.

[Analysis and recognition of CD4 cell microscopic image].

This paper introduces the technology of recognizing CD4 cell by a computer, which successfully segments the objects from the background through image processing and analyses the four features including geometric features, optical density, color and texture features. The method based on Principal Component Analysis algorithm is used for feature extraction, and a Back Propagation neural network classifier is constructed, from which the CD4 cell can be well recognized. CD4 cell, image segmentation, feature analysis, Principal Component Analysis (PCA), Back Propagation (BP) neural network classifier is constructed, from which the CD4 cell can be well recognized.