Lnc2Cancer 3.0: an updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data

An updated Lnc2Cancer 3.0 (http://www.bio-bigdata.net/lnc2cancer or http://bio-bigdata.hrbmu.edu.cn/lnc2cancer) database, which includes comprehensive data on experimentally supported long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) associated with human cancers. In addition, web tools for analyzing lncRNA expression by high-throughput RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) are described. Lnc2Cancer 3.0 was updated with several new features, including (i) Increased cancer-associated lncRNA entries over the previous version. The current release includes 9254 lncRNA-cancer associations, with 2659 lncRNAs and 216 cancer subtypes. (ii) Newly adding 1049 experimentally supported circRNA-cancer associations, with 743 circRNAs and 70 cancer subtypes. (iii) Experimentally supported regulatory mechanisms of cancer-related lncRNAs and circRNAs, involving microRNAs, transcription factors (TF), genetic variants, methylation and enhancers were included. (iv) Appending experimentally supported biological functions of cancer-related lncRNAs and circRNAs including cell growth, apoptosis, autophagy, epithelial mesenchymal transformation (EMT), immunity and coding ability. (v) Experimentally supported clinical relevance of cancer-related lncRNAs and circRNAs in metastasis, recurrence, circulation, drug resistance, and prognosis was included. Additionally, two flexible online tools, including RNA-seq and scRNA-seq web tools, were developed to enable fast and customizable analysis and visualization of lncRNAs in cancers. Lnc2Cancer 3.0 is a valuable resource for elucidating the associations between lncRNA, circRNA and cancer.

LnCeCell: a comprehensive database of predicted lncRNA-associated ceRNA networks at single-cell resolution

Within the tumour microenvironment, cells exhibit different behaviours driven by fine-tuning of gene regulation. Identification of cellular-specific gene regulatory networks will deepen the understanding of disease pathology at single-cell resolution and contribute to the development of precision medicine. Here, we describe a database, LnCeCell (http://www.bio-bigdata.net/LnCeCell/ or http://bio-bigdata.hrbmu.edu.cn/LnCeCell/), which aims to document cellular-specific long non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) networks for personalised characterisation of diseases based on the ‘One Cell, One World’ theory. LnCeCell is curated with cellular-specific ceRNA regulations from >94 000 cells across 25 types of cancers and provides >9000 experimentally supported lncRNA biomarkers, associated with tumour metastasis, recurrence, prognosis, circulation, drug resistance, etc. For each cell, LnCeCell illustrates a global map of ceRNA sub-cellular locations, which have been manually curated from the literature and related data sources, and portrays a functional state atlas for a single cancer cell. LnCeCell also provides several flexible tools to infer ceRNA functions based on a specific cellular background. LnCeCell serves as an important resource for investigating the gene regulatory networks within a single cell and can help researchers understand the regulatory mechanisms underlying complex microbial ecosystems and individual phenotypes.

Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers

Lnc2Cancer 2.0 (http://www.bio-bigdata.net/lnc2cancer) is an updated database that provides comprehensive experimentally supported associations between lncRNAs and human cancers. In Lnc2Cancer 2.0, we have updated the database with more data and several new features, including (i) exceeding a 4-fold increase over the previous version, recruiting 4989 lncRNA-cancer associations between 1614 lncRNAs and 165 cancer subtypes. (ii) newly adding about 800 experimentally supported circulating, drug-resistant and prognostic-related lncRNAs in various cancers. (iii) appending the regulatory mechanism of lncRNA in cancer, including microRNA (miRNA), transcription factor (TF), variant and methylation regulation. (iv) increasing more than 70 high-throughput experiments (microarray and next-generation sequencing) of lncRNAs in cancers. (v) Scoring the associations between lncRNA and cancer to evaluate the correlations. (vi) updating the annotation information of lncRNAs (version 28) and containing more detailed descriptions for lncRNAs and cancers. Moreover, a newly designed, user-friendly interface was also developed to provide a convenient platform for users. In particular, the functions of browsing data by cancer primary organ, biomarker type and regulatory mechanism, advanced search following several features and filtering the data by LncRNA-Cancer score were enhanced. Lnc2Cancer 2.0 will be a useful resource platform for further understanding the associations between lncRNA and human cancer.

LnCeVar: a comprehensive database of genomic variations that disturb ceRNA network regulation

LnCeVar (http://www.bio-bigdata.net/LnCeVar/) is a comprehensive database that aims to provide genomic variations that disturb lncRNA-associated competing endogenous RNA (ceRNA) network regulation curated from the published literature and high-throughput data sets. LnCeVar curated 119 501 variation–ceRNA events from thousands of samples and cell lines, including: (i) more than 2000 experimentally supported circulating, drug-resistant and prognosis-related lncRNA biomarkers; (ii) 11 418 somatic mutation–ceRNA events from TCGA and COSMIC; (iii) 112 674 CNV–ceRNA events from TCGA; (iv) 67 066 SNP–ceRNA events from the 1000 Genomes Project. LnCeVar provides a user-friendly searching and browsing interface. In addition, as an important supplement of the database, several flexible tools have been developed to aid retrieval and analysis of the data. The LnCeVar–BLAST interface is a convenient way for users to search ceRNAs by interesting sequences. LnCeVar–Function is a tool for performing functional enrichment analysis. LnCeVar–Hallmark identifies dysregulated cancer hallmarks of variation–ceRNA events. LnCeVar–Survival performs COX regression analyses and produces survival curves for variation–ceRNA events. LnCeVar–Network identifies and creates a visualization of dysregulated variation–ceRNA networks. Collectively, LnCeVar will serve as an important resource for investigating the functions and mechanisms of personalized genomic variations that disturb ceRNA network regulation in human diseases.

CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer

Many biological indicators related to chronological age have been proposed. Recent studies found that epigenetic clock or DNA methylation age is highly correlated with chronological age. In particular, a significant difference between DNA methylation age (m-age) and chronological age was observed in cancers. However, the prediction and characterization of m-age in pan-cancer remains an explored area. In this study, 1,631 age-related methylation sites in normal tissues were discovered and analyzed. A comprehensive computational model named CancerClock was constructed to predict the m-age for normal samples based on methylation levels of the extracted methylation sites. LASSO linear regression model was used to screen and train the CancerClock model in normal tissues. The accuracy of CancerClock has proved to be 81%, and the correlation value between chronological age and m-age was 0.939 (P < 0.01). Next, CancerClock was used to evaluate the difference between m-age and chronological age for 33 cancer types from TCGA. There were significant differences between predicted m-age and chronological age in large number of cancer samples. These cancer samples were defined as “age-related cancer samples” and they have some differential methylation sites. The differences between predicted m-age and chronological age may contribute to cancer development. Some of these differential methylation sites were associated with cancer survival. CancerClock provided assistance in estimating the m-age in normal and cancer samples. The changes between m-age and chronological age may improve the diagnosis and prognosis of cancers.

A novel 13-plex STR typing system for individual identification and parentage testing of donkeys (Equus asinus)

BACKGROUND

Previous studies investigating donkey parentage and genetic diversity used horse-specific multiplex systems. However, several mis-allele and null-allele issues were found with some of the horse primers when used in donkeys. In 2017, the International Society for Animal Genetics (ISAG) recommended 13 dinucleotide short tandem repeats (STRs) (AHT4, ASB23, HMS2, HMS3, HMS6, HMS7, HMS18, HTG7, HTG10, TKY297, TKY312, TKY337 and TKY343) as a core panel that should be used to identify individuals and to test for parentage in donkeys. To date, no single multiplex STR typing system containing all 13 donkey STRs recommended by the ISAG has been reported.

OBJECTIVES

To establish a novel and donkey-specific multiplex STR typing system containing all 13 recommended STRs.

STUDY DESIGN

Assay development and validation in field population.

METHODS

Primers for seven of the STRs were redesigned and conditions for polymerase chain reaction (PCR) were optimised. We analysed the allele sequences, sensitivity, species-specificity and stutter ratios of this new system.

RESULTS

A 13-plex STR typing system for donkey was established. A full profile could be generated from a single PCR reaction using as little as 5 ng of DNA template with the 13 pairs of primers labelled with fluorescent dyes. An allele ladder, containing 101 alleles from the 13 STRs, was generated. No full genotype profile was generated with these primers if DNA from humans, or 11 other commonly encountered animals, was used. Genotypes could be generated for the horse and horse-donkey hybrids (mule and hinny). Stutter ratios and population genetic parameters were calculated based on samples from 150 donkeys. The combined probabilities of paternity exclusion for this system were 0.988907326 (CPEduo) and 0.999665018 (CPEtrio).

MAIN LIMITATIONS

This system cannot detect sex.

CONCLUSIONS

Our results indicate that our donkey-specific 13-plex STR typing system is sensitive, species-specific and robust for individual identification, paternity testing and population genetic analysis in donkeys, and has potential forensic applications.

Validation of Zero TE-MRA in the Characterization of Cerebrovascular Diseases: A Feasibility Study

BACKGROUND AND PURPOSE

Zero TE-MRA is less sensitive to field heterogeneity, complex flow, and acquisition noise. This study aimed to prospectively validate the feasibility of zero TE-MRA for cerebrovascular diseases assessment, compared with TOF-MRA.

MATERIALS AND METHODS

Seventy patients suspected of having cerebrovascular disorders were recruited. Sound levels were estimated for each MRA subjectively and objectively in different modes. MRA image quality was estimated by 2 neuroradiologists. The degree of stenosis (grades 0-4) and the z-diameter of aneurysms (tiny group ≤3 mm and large group >3 mm) were measured for further quantitative analysis. CTA was used as the criterion standard.

RESULTS

Zero TE-MRA achieved significantly lower subjective perception and objective noise reduction (37.53%). Zero TE-MRA images showed higher signal homogeneity (3.29 ± 0.59 versus 3.04 ± 0.43) and quality of venous signal suppression (3.67 ± 0.47 versus 2.75 ± 0.46). The intermodality agreement was higher for zero TE-MRA than for TOF-MRA (zero TE, 0.90; TOF, 0.81) in the grading of stenosis. Zero TE-MRA had a higher correlation than TOF-MRA (zero TE, 0.84; TOF, 0.74) in the tiny group and a higher consistency with CTA (intraclass correlation coefficient, 0.83; intercept, -0.5084-1.1794; slope -0.4952 to -0.2093) than TOF-MRA (intraclass correlation coefficient, 0.64; intercept, 0.7000-2.6133; slope -1.0344 to -0.1923). Zero TE-MRA and TOF-MRA were comparable in the large group. Zero TE-MRA had more accurate details than TOF-MRA of AVM and Moyamoya lesions.

CONCLUSIONS

Compared with TOF-MRA, zero TE-MRA achieved more robust performance in depicting cerebrovascular diseases. Therefore, zero TE-MRA was shown to be a promising MRA technique for further routine application in the clinic in patients with cerebrovascular diseases.

Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer

Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments.

[Clinical significance of exosomal miR-1231 in pancreatic cancer]

Objective: To investigate the expression and clinical significance of exosomal miR-1231 in plasma of pancreatic cancer (PC) patients and pancreatic cancer cells. Methods: A total of 16 patients who were diagnosed with pancreatic cancer in Hunan Cancer Hospital were collected from April 2016 to August 2017. Meanwhile, 16 healthy volunteers were recruited as the healthy control group at the same period. The plasma exosomes were extracted, and the levels of miR-1231 were detected by qRT-PCR in PC and healthy control groups. Moreover, the clinicopathological significance of exosomal miR-1231 expression was analyzed. Furthermore, the expression of exosomal miR-1231 was detected in several pancreatic cancer cells (MIA PaCa-2, PANC-1, SW1990, AsPC-1 and BxPc-3) and two normal pancreatic epithelial cells (HPDE and human primary pancreatic epithelial cell). Results: qRT-PCR results showed that the expression level of miR-1231 in plasma exosomes of pancreatic cancer patients (1.06±0.46) was significantly lower than that in healthy controls (2.30±0.99; P<0.05). The levels of exosomal miR-1231 in patients with stage Ⅰ-Ⅱ (1.515±0.531), no distant metastasis (1.236±0.461) and no lymph node metastasis (1.337±0.522) were significantly higher than those with stage Ⅲ-Ⅳ (0.848±0.224), distant metastasis (0.757±0.278) and lymph node metastasis (0.838±0.261), respectively (P<0.05 for all). In addition, there were no correlation between exosomal miR-1231 expression and age, sex, smoking history, CA19-9 levels and tumor sites (P>0.05). Furthermore, the expression level of exosomal miR-1231 in pancreatic cancer cell lines (0.142±0.135) was significantly lower than that in normal epithelial cells (1.127±0.179; P<0.05). Conclusions: The downregulation of exosomal miR-1231 in plasma of pancreatic cancer patients and pancreatic cancer cells suggests that it is related to the initiation and development of PC. It may be a new diagnostic and prognostic marker for PC.

Short communication: Genotyping and single nucleotide polymorphism analysis of bovine leukemia virus in Chinese dairy cattle

Bovine leukemia virus (BLV) causes enzootic leucosis in cattle and is classified into 10 genotypes with a worldwide distribution, except for several European countries, Australia, and New Zealand. Although BLV is widespread in Chinese cows with the positive rate of 49.1% at the individual level, very little is known about the BLV genotype in dairy cattle in China. To determine BLV genetic variability in cows in China, 112 BLV-positive samples from 5 cities in China were used for BLV molecular characterization in this study. Phylogenetic analysis using the neighbor-joining method on partial env sequence encoding gp51 obtained from 5 Chinese cities and those available in GenBank (n = 53, representing BLV genotype 1-10) revealed the Chinese strains belonged to genotype 6. Seven unique SNP were identified among Yancheng, Shanghai, and Bengbu strains out of the total 12 SNP identified in Chinese strains. The genotyping coupled with SNP analysis of BLV can serve as a useful molecular epidemiological tool for tracing the source of pathogens. This study highlights the importance of genetic analysis of geographically diverse BLV strains to understand BLV global genetic diversity.

Integrated analysis of the prognostic role of the lymph node ratio in node-positive gastric cancer: A meta-analysis

BACKGROUND

The lymph node ratio (LNR) as a prognostic parameter for gastric cancer has yet to be fully validated in the current tumor node metastasis staging system. We assessed the prognostic role of LNR in lymph node-positive gastric cancer through a meta-analysis.

MATERIALS AND METHODS

PubMed and EMBASE were searched for relevant studies up until December 2016. The effect measure for meta-analysis of primary outcomes was the hazard ratio (HR) for overall survival. Pooled HRs and 95% confidence intervals were calculated using random effects models. The I² statistic was used to measure heterogeneity. Subgroup analysis and meta-regression were chosen to illustrate the potential heterogeneity of the risk factors of outcomes. Publication bias was assessed using Egger's test and Begg's funnel plots. Sensitivity analysis was applied to evaluate the origin of the heterogeneity.

RESULTS

We included 27 studies in this meta-analysis. Higher LNRs were significantly associated with a shorter overall survival (OS). High heterogeneity among the studies was identified (I² = 85.6), and the publication bias was moderate. Subgroup analysis showed similar results, and elevated LNR was associated with late-stage gastric cancer and indicative of a worse prognosis. Univariate meta-regression analysis of OS indicated that both treatment type and ethnicity may be causes of heterogeneity in patients with gastric cancer (p values were 0.005 and 0.008, respectively).

CONCLUSION

LNR was associated with a significantly poorer OS and LNR was an independent predictor of survival in patients with gastric cancer. LNR should be added as one of the parameters to be used in future tumor staging classification systems.

Exploration of drug-response mechanism by integrating genetics and epigenetics across cancers

Aim: To discover CpG island methylator phenotype (CIMP) as a predictor for cancer drug-response mechanism. Materials & methods: CIMP classification of 966 cancer cell lines was determined according to identified copy number alteration and differential methylation by DNA methylation profiles. CIMP-related drugs were analyzed by analysis of variance. Tissue–cell–drug networks were developed to predict drug response of individual samples. Results & conclusion: One hundred and thirty-six copy number gain and 142 copy number loss cell lines were classified into CIMP-high and CIMP-low groups, meanwhile 9 and 24 CIMP-associated drugs were identified, respectively. Specially, breast invasive carcinoma samples primarily composed by HCC1419 were predicted to be sensitive to GSK690693. The study provides guidance for drug response in cancer therapy through genome-wide DNA methylation.

Expressions and clinical significance of factors related to acute coronary syndrome

Acute coronary syndrome (ACS) is the acute stage of coronary artery disease, which remains a major cause of mortality and morbidity. It is essential to explore the role of matrix metalloproteinase -2 (MMP-2) and interleukin (IL)-18 and their association with disease in patients with severe ACS. Circulating MMP-2 and IL-18 levels were measured using enzyme linked immunosorbent assay (ELISA) in 94 subjects with acute coronary syndrome (ACS, n=38), stable angina pectoris (SAP, n=27) and healthy individuals (control group, n=29). We examined the correlations between the levels of MMP-2 and IL-18 and cardiac risk factors in ACS. Logistic regression analysis was performed to screen for factors that predict ACS. Both MMP-2 and IL-18 concentrations were increased in the ACS group compared to the SAP group or control group (P less than 0.01). Especially, MMP-2 and IL-18 were highly expressed in the patients with ST elevated myocardial infarction (STEMI). Both serum levels of MMP-2 and IL-18 in the single-, double- and triple-vessel lesion group were higher compared to the control group (P less than 0.01). MMP-2 levels were positively correlated with IL-18 (r=0.639, P less than 0.01), CK-MB (r=0.47, P=0.003) and hs-CRP levels (r=0.583, P less than 0.01). The logistic regression analysis showed that increases in MMP-2 levels may be a powerful predictor of ACS. Thus, the changes in levels of serum MMP-2 and IL-18 may be useful in the diagnosis of ACS and prediction of its prognosis.

DNA methylation dynamics: identification and functional annotation

DNA methylation is an epigenetic modification of cytosines that undergoes dynamic changes in a temporal, spatial and cell-type-specific manner. Recent advances in technology have permitted the profiling of high-throughput methylomes in large numbers of biological samples. Various computational tools have been developed to identify and analyze DNA methylation dynamics in a variety of critical biological processes. As DNA methylation is becoming increasingly viewed as a dynamic process, the mechanisms governing DNA methylation dynamics and its roles in the transcriptional regulatory network are of great interest. It has been reported that DNA methylation dynamics plays essential roles in multiple biological processes, including development and cancer. As a functional event, the dynamics of DNA methylation have become increasingly relevant to many researchers. Here, we review state-of-the-art advances at three levels (genome-wide identification, regulatory mechanism investigation and the functional annotation) in the field of DNA methylation dynamics, as well as the future perspective of DNA methylation dynamics.

Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes

DNA methylation is a key epigenetic mark that is critical for gene regulation in multicellular eukaryotes. Although various human cell types may have the same genome, these cells have different methylomes. The systematic identification and characterization of methylation marks across cell types are crucial to understand the complex regulatory network for cell fate determination. In this study, we proposed an entropy-based framework termed SMART to integrate the whole genome bisulfite sequencing methylomes across 42 human tissues/cells and identified 757 887 genome segments. Nearly 75% of the segments showed uniform methylation across all cell types. From the remaining 25% of the segments, we identified cell type-specific hypo/hypermethylation marks that were specifically hypo/hypermethylated in a minority of cell types using a statistical approach and presented an atlas of the human methylation marks. Further analysis revealed that the cell type-specific hypomethylation marks were enriched through H3K27ac and transcription factor binding sites in cell type-specific manner. In particular, we observed that the cell type-specific hypomethylation marks are associated with the cell type-specific super-enhancers that drive the expression of cell identity genes. This framework provides a complementary, functional annotation of the human genome and helps to elucidate the critical features and functions of cell type-specific hypomethylation.

SEA: a super-enhancer archive

Super-enhancers are large clusters of transcriptional enhancers regarded as having essential roles in driving the expression of genes that control cell identity during development and tumorigenesis. The construction of a genome-wide super-enhancer database is urgently needed to better understand super-enhancer-directed gene expression regulation for a given biology process. Here, we present a specifically designed web-accessible database, Super-Enhancer Archive (SEA, http://sea.edbc.org). SEA focuses on integrating super-enhancers in multiple species and annotating their potential roles in the regulation of cell identity gene expression. The current release of SEA incorporates 83 996 super-enhancers computationally or experimentally identified in 134 cell types/tissues/diseases, including human (75 439, three of which were experimentally identified), mouse (5879, five of which were experimentally identified), Drosophila melanogaster (1774) and Caenorhabditis elegans (904). To facilitate data extraction, SEA supports multiple search options, including species, genome location, gene name, cell type/tissue and super-enhancer name. The response provides detailed (epi)genetic information, incorporating cell type specificity, nearby genes, transcriptional factor binding sites, CRISPR/Cas9 target sites, evolutionary conservation, SNPs, H3K27ac, DNA methylation, gene expression and TF ChIP-seq data. Moreover, analytical tools and a genome browser were developed for users to explore super-enhancers and their roles in defining cell identity and disease processes in depth.

Abstract P4-05-12: MultiOmyx™ screening for prognostic indicators of breast cancer

Over the last three decades, the incidence of early-stage breast cancer has doubled while the incidence of late stage breast cancer has slightly decreased; however, the standard treatment for both has not changed significantly in the last 10 years. Of those diagnosed with early-stage breast cancer, 30% will develop recurrent disease, though there is currently no diagnostic available to identify these cases. Conversely, identifying late stage patients with a low- risk for recurrence could spare them from post-surgical chemotherapy. We have developed a technology that allows in situ staining and analysis of up to 60 proteins on a single tissue slide. In this exploratory study we evaluated 25 known biomarkers on a cohort of more than 800 breast cancer cases assembled onto three tissue arrays. The cohort spans clinical grades and breast cancer subtypes, and is thus useful in novel associations of biomarker expression across a variety of clinical features. Twenty-five markers were chosen based on literature reports for marker association with disease recurrence and represent a range of biological features including signaling pathways and cell phenotypes. The slides were sequentially stained with antibodies that were directly conjugated to Cy3 and Cy5 dyes, imaged, evaluated for quality and subject inclusion, and subsequently analyzed for statistical correlations of expression values to clinical features. The images were then processed using a single cell analysis algorithm which allows for quantification of individual tumor cells and signal intensities for the markers to be extracted from sub-cellular domains (nucleus, cytoplasm, and membrane of each cell) allowing unique patterns of protein expression to be determined. Univariate Cox proportional hazards analysis was first applied to determine those features with strongest association with ‘death due to disease’. Of the 25 markers analyzed, several markers (e.g. API3, AMPH2, beta-catenin, CD44, CEACAM5, CK15, CK19, cMET, Her2, Ki67, TRIM29) were found to have strong association with poor outcome (FDR &lt; 0.05). After adjusting for clinical variables (stage, number of positive nodes, grade), a number of markers (e.g. CD44, CEACAM5, cMET, TRIM29) still show a strong association with poor clinical outcome (FDR &lt; 0.05, Cox proportional hazards model). CEACAM5 is part of the MammostratTM panel of markers used for determining prognosis in ER positive breast cancers after Tamoxifen® treatment. CD44, a cancer stem cell marker, and TRIM 29, a transcriptional regulatory factor involved in carcinogenesis, have been associated with basal-like breast cancers which have the worst prognosis of all the breast cancer subtypes. High levels of cMET have been associated with poor prognosis for all breast cancer subtypes and cMET is being explored as a therapeutic target. The MultiomyxTM methodology enabled us to verify a subset of biomarkers that are clinically relevant to breast cancer outcome. Using this technology could lead to identification of novel biosignatures that stratify patients and enable precision medicine that results in better treatment decisions and prognosis for patients.

Citation Information: Cancer Res 2013;73(24 Suppl): Abstract nr P4-05-12.

Temporal expression of metalloproteinase-8 and -13 and their relationships with extracellular matrix metalloproteinase inducer in the development of ligature-induced periodontitis in rats

BACKGROUND AND OBJECTIVE

Matrix metalloproteinases (MMPs) play important roles in extracellular matrix degradation and may be regulated by extracellular matrix metalloproteinase inducer (EMMPRIN). The aim of this study was to investigate the temporal expression and localization of MMP-8 and MMP-13 during the development of ligature-induced periodontitis in rats, and to analyze the correlations of EMMPRIN with MMP-8 and MMP-13 in periodontitis.

MATERIAL AND METHODS

Periodontitis was simulated in rats by ligaturing the cervix of the lower first molars, as described in our previous method. The rats were killed 0, 3, 5, 7, 11, 15 and 21 d after ligation. Micro-computed tomography examinations were performed to detect alveolar bone loss. Semiquantitative western blotting was used to assess the temporal changes in the levels of MMP-8, MMP-13 and EMMPRIN proteins in gingival tissue. Immunohistochemistry was applied to detect the expression and locations of MMP-8 and MMP-13 in gingival tissue and alveolar bone.

RESULTS

Alveolar bone loss showed an exponential increase from days 3 to 11, followed by a slower rate of loss at subsequent study time points. MMP-8 showed a rapid increase of expression from baseline to a peak on day 3, a gradual decrease from days 5 to 7 and then stabilized thereafter. MMP-8 was predominantly located in neutrophil-like cells. Statistically, the expression of MMP-8 was not correlated with the expression of EMMPRIN. The expression of MMP-13 and of EMMRPIN increased from days 3 to 7, and showed a moderate decrease thereafter. The immunoreactivity of MMP-13 was mainly detected in monocytes/macrophages, on the alveolar bone surface, in osteoclasts and in gingival epithelial cells. Statistically, MMP-13 had a strong, positive correlation with EMMPRIN (r = 0.855, p < 0.01).

CONCLUSION

The levels of expression of MMP-8 and MMP-13 are temporally varied at different periods during the development of experimental periodontitis. The level of expression of EMMPRIN is closely associated with the expression of MMP-13, but not with the expression of MMP-8. In addition, MMP-13 might be involved in alveolar bone destruction, as well as in physiological bone remodeling.