A Prognosis Classifier for Breast Cancer Based on Conserved Gene Regulation between Mammary Gland Development and Tumorigenesis: A Multiscale Statistical Model

<div><p>Identification of novel cancer genes for molecular therapy and diagnosis is a current focus of breast cancer research. Although a few small gene sets were identified as prognosis classifiers, more powerful models are still needed for the definition of effective gene sets for the diagnosis and treatment guidance in breast cancer. In the present study, we have developed a novel statistical approach for systematic analysis of intrinsic correlations of gene expression between development and tumorigenesis in mammary gland. Based on this analysis, we constructed a predictive model for prognosis in breast cancer that may be useful for therapy decisions. We first defined developmentally associated genes from a mouse mammary gland epithelial gene expression database. Then, we found that the cancer modulated genes were enriched in this developmentally associated genes list. Furthermore, the developmentally associated genes had a specific expression profile, which associated with the molecular characteristics and histological grade of the tumor. These result suggested that the processes of mammary gland development and tumorigenesis share gene regulatory mechanisms. Then, the list of regulatory genes both on the developmental and tumorigenesis process was defined an 835-member prognosis classifier, which showed an exciting ability to predict clinical outcome of three groups of breast cancer patients (the predictive accuracy 64∼72%) with a robust prognosis prediction (hazard ratio 3.3∼3.8, higher than that of other clinical risk factors (around 2.0–2.8)). In conclusion, our results identified the conserved molecular mechanisms between mammary gland development and neoplasia, and provided a unique potential model for mining unknown cancer genes and predicting the clinical status of breast tumors. These findings also suggested that developmental roles of genes may be important criteria for selecting genes for prognosis prediction in breast cancer.</p> </div

The 835 prognosis classifier could predict clinical outcome in a large set of breast cancer patients.

<p>The intrinsic dataset was applied to 144 node positive and 151 node negative primary breast tumors. The accuracy of prediction (<b>A</b>) or the prognosis value (<b>B</b>) of 835 prognosis classifier and tumor risk factors was assessed by the same approach as described in the legend of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060131#pone-0060131-g005" target="_blank">Fig. 5B</a>.</p

Identification of genes associated with the developmental phases of growth, lactation, and involution among the mammary gland developmentally associated gene subset.

<p><b>A.</b> The developmentally associated genes were clustered into three groups by Principal Component Analysis. Expression profiles of genes in mammary pregnancy cycle are represented as dots in PC1 (1^st principal component axis) and PC2 (2^nd principal component axis). All probe sets were grouped into three groups: growth (PC1>0), involution (PC1<0&PC2>0) and lactation (PC1<0&PC2<0) based on the number of genes that have peak expression at a particular developmental time (showed in B). <b>B.</b> The time of peak expression for each developmentally associated gene was plotted on a histogram and classified according to the developmental phase (growth, yellow; lactation, blue; involution, purple). The column represents the number of genes that have peak expression at a particular developmental time. <b>C.</b> The frequency of a literature-based cancer modulated genes in the gene subsets associated with the three different stages of mammary gland development. The “growth” group contained more literature-based cancer modulated genes (20%) than the “lactation” (14.7%) and the involution (17%) groups (<i>p</i><0.05).</p

The enrichment of ontology in 835 intrinsic genes.

<p>EASE score<0.05.</p>#<p>genes with red word are cancer mutant gene identified in reference (Nat Rev Cancer,4(3):177).</p

Defining the 835 prognosis classifier from the developmentally associated genes based on their expression in tumors.

<p>For each developmentally associated gene, we first counted the number of the breast cancer datasets in which it was “altered” in expression. Based on this database number, all developmental genes were then grouped into six subsets (Sub0, Sub1, Sub2, Sub3, Sub4, and Sub5). The percentage of a literature-based cancer modulated genes in each subset is shown in table (<b>A</b>) and histogram (<b>B</b>). The results of non-developmental genes with same assay method are shown as a control. The details are described in the text.</p

Strong Biopolymer-Based Nanocomposite Hydrogel Adhesives with Removability and Reusability for Damaged Tissue Closure and Healing

Bioadhesives are widely used in a variety of medical settings due to their ease of use and efficient wound closure and repair. However, achieving both strong adhesion and removability/reusability is highly needed but challenging. Here, we reported an injectable mesoporous bioactive glass nanoparticle (MBGN)-incorporated biopolymer hydrogel bioadhesive that demonstrates a strong adhesion strength (up to 107.55 kPa) at physiological temperatures that is also removable and reusable. The incorporation of MBGNs in the biopolymer hydrogel significantly enhances the tissue adhesive strength due to an increased cohesive and adhesive property compared to the hydrogel adhesive alone. The detachment of bioadhesive results from temperature-induced weakening of interfacial adhesive strength. Moreover, the bioadhesive displays injectability, self-healing, and excellent biocompatibility. We demonstrate potential applications of the bioadhesive in vitro, ex vivo, and in vivo for hemostasis and intestinal leakage closure and accelerated skin wound healing compared to surgical wound closures. This work provides a novel design of strong and removable bioadhesives

One-Dimensional Ferroelectric Nanoarrays with Wireless Switchable Static and Dynamic Electrical Stimulation for Selective Regulating Osteogenesis and Antiosteosarcoma

Preventing local tumor recurrence and simultaneously improving bone-tissue regeneration are in great demand for osteosarcoma therapy. However, the current therapeutic implants fail to selectively suppress tumor growth and enhance osteogenesis, and antitumor therapy may compromise osseointegration of the bone implant. Here, based on the different responses of bone tumor cells and osteoblasts to different electric stimulations, we constructed ferroelectric BaTiO3 nanorod arrays (NBTO) on the surface of titanium implants with switchable dynamic and static electrical stimulation for selective bone-tumor therapy and bone tissue regeneration. Polarized NBTO (PNBTO) generated a sustained dynamic electrical stimulus in response to wireless ultrasonic irradiation (“switch-on”), which disrupted the orientation of the spindle filaments of the tumor cell, blocked the G2/M phase of mitosis, and ultimately led to tumor cell death, whereas it had almost no cytotoxic effect on normal bone cells. Under the switch-off state, PNBTO with a high surface potential provided static electrical stimulation, accelerating osteogenic differentiation of mesenchymal stem cells and enhancing the quality of bone regeneration both in vitro and in vivo. This study broadens the biomedical potential of electrical stimulation therapy and provides a comprehensive and clinically feasible strategy for the overall treatment and tissue regeneration in osteosarcoma

Inverse Spinel Cobalt–Iron Oxide and N‑Doped Graphene Composite as an Efficient and Durable Bifuctional Catalyst for Li–O₂ Batteries

Rational design of efficient bifunctional oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) electrocatalysts are critical for rechargeable Li–O₂ batteries. Here, we report inverse spinel Co­[Co,Fe]­O₄/nitrogen-doped graphene (NG) composite used as a promising catalyst for rechargeable Li–O₂ batteries. The cells with Co­[Co,Fe]­O₄/NG catalyst exhibit high initial capacity, remarkable cyclability, and good rate capability. Moreover, the overpotential of the Li–O₂ batteries is reduced significantly. The improved ORR/OER performances are attributed to the good property of Co­[Co,Fe]­O₄ with an inverse spinel structure toward ORR and the improved electronic conductivity of N-doped graphene. The density functional theory (DFT) calculation shows the rate limitation step for ORR on the inverse spinel surface is the growth of the Li₂O₂ cluster while the rate limitation step for the OER pathway is the oxidation of Li₂O₂. The inverse spinel surface in Co­[Co,Fe]­O₄/NG is more active than that of the normal spinel phases for the Li–O₂ battery reactions. This work not only provides a promising bifunctional catalyst for practical metal air batteries but also offers a general strategy to rationally design catalysts for various applications