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"Triple negative breast cancer" (TNBC) is associated with a higher rate and earlier time of recurrence and worse prognosis after recurrence. In this study, we aimed to examine the crosstalk between fibroblasts and TNBC cells. The fibroblasts were isolated from TNBC patients' tissue in tumor burden zones, distal normal zones and interface zones. The fibroblasts were indicated as cancer-associated fibroblasts (CAFs), normal zone fibroblasts (NFs) and interface zone fibroblasts (INFs). Our study found that INFs grew significantly faster than NFs and CAFs in vitro. The epithelial BT20 cells cultured with the conditioned medium of INFs (INFs-CM) and CAFs (CAFs-CM) showed more spindle-like shape and cell scattering than cultured with the conditioned medium of NFs (NFs-CM). These results indicated that factors secreted by INFs-CM or CAFs-CM could induce the epithelial-mesenchymal transition (EMT) phenotype in BT20 cells. Using an in vitro co-culture model, INFs or CAFs induced EMT and promoted cancer cell migration in BT20 cells. Interestingly, we found that emodin inhibited INFs-CM or CAFs-CM-induced EMT programming and phenotype in BT20 cells. Previous studies reported that CAFs and INFs-secreted TGF-β promoted human breast cancer cell proliferation, here; our results indicated that TGF-β initiated EMT in BT20 cells. Pretreatment with emodin significantly suppressed the TGF-β-induced EMT and cell migration in BT20 cells. These results suggest that emodin may be used as a novel agent for the treatment of TNBC.

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called "supervised" methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative "unsupervised" approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.

Complex diseases are typically caused by combinations of molecular disturbances that vary widely among different patients. Endophenotypes, a combination of genetic factors associated with a disease, offer a simplified approach to dissect complex trait by reducing genetic heterogeneity. Because molecular dissimilarities often exist between patients with indistinguishable disease symptoms, these unique molecular features may reflect pathogenic heterogeneity. To detect molecular dissimilarities among patients and reduce the complexity of high-dimension data, we have explored an endophenotype-identification analytical procedure that combines non-negative matrix factorization (NMF) and adjusted rand index (ARI), a measure of the similarity of two clusterings of a data set. To evaluate this procedure, we compared it with a commonly used method, principal component analysis with k-means clustering (PCA-K). A simulation study with gene expression dataset and genotype information was conducted to examine the performance of our procedure and PCA-K. The results showed that NMF mostly outperformed PCA-K. Additionally, we applied our endophenotype-identification analytical procedure to a publicly available dataset containing data derived from patients with late-onset Alzheimer's disease (LOAD). NMF distilled information associated with 1,116 transcripts into three metagenes and three molecular subtypes (MS) for patients in the LOAD dataset: MS1 (n1=80), MS2 (n2=73), and MS3 (n3=23). ARI was then used to determine the most representative transcripts for each metagene; 123, 89, and 71 metagene-specific transcripts were identified for MS1, MS2, and MS3, respectively. These metagene-specific transcripts were identified as the endophenotypes. Our results showed that 14, 38, 0, and 28 candidate susceptibility genes listed in AlzGene database were found by all patients, MS1, MS2, and MS3, respectively. Moreover, we found that MS2 might be a normal-like subtype. Our proposed procedure provides an alternative approach to investigate the pathogenic mechanism of disease and better understand the relationship between phenotype and genotype.