Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
Background: Infectious agents have long been postulated to be disease triggers for systemic sclerosis (SSc), but\na definitive link has not been found. Metagenomic analyses of high-throughput data allows for the unbiased\nidentification of potential microbiome pathogens in skin biopsies of SSc patients and allows insight into the\nrelationship with host gene expression.\nMethods: We examined skin biopsies from a diverse cohort of 23 SSc patients (including lesional forearm and\nnon-lesional back samples) by RNA-seq. Metagenomic filtering and annotation was performed using the Integrated\nMetagenomic Sequencing Analysis (IMSA). Associations between microbiome composition and gene expression\nwere analyzed using single-sample gene set enrichment analysis (ssGSEA).\nResults: We find the skin of SSc patients exhibits substantial changes in microbial composition relative to controls,\ncharacterized by sharp decreases in lipophilic taxa, such as Propionibacterium, combined with increases in a wide\nrange of gram-negative taxa, including Burkholderia, Citrobacter, and Vibrio.\nConclusions: Microbiome dysbiosis is associated with disease duration and increased inflammatory gene\nexpression. These data provide a comprehensive portrait of the SSc skin microbiome and its association with\nlocal gene expression, which mirrors the molecular changes in lesional skin....
Metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is one of the most\nabundant, long non-coding RNAs (lncRNAs) in normal tissues. This lncRNA is highly conserved\namong mammalian species, and based on in vitro results, has been reported to regulate alternative\npre-mRNA splicing and gene expression. However, Malat1 knockout mice develop and grow\nnormally, and do not show alterations in alternative splicing. While MALAT1 was originally\ndescribed as a prognostic marker of lung cancer metastasis, emerging evidence has linked this\nlncRNA to other cancers, such as breast cancer, prostate cancer, pancreatic cancer, glioma, and\nleukemia. The role described for MALAT1 is dependent on the cancer types and the experimental\nmodel systems. Notably, different or opposite phenotypes resulting from different strategies for\ninactivating MALAT1 have been observed, which led to distinct models for MALAT1â??s functions\nand mechanisms of action in cancer and metastasis. In this review, we reflect on different\nexperimental strategies used to study MALAT1â??s functions, and discuss the current mechanistic\nmodels of this highly abundant and conserved lncRNA....
Early diagnosis of cirrhosis and hepatocellular carcinoma (HCC) due to chronic Hepatitis\nC (CHC) remain clinical priorities. In this pilot study, we assessed serum microRNA (miRNA)\nexpression to distinguish cirrhosis and HCC, alone and in combination with the aminotransferase-toplatelet\nratio (APRI), Fibrosis 4 (FIB-4), and alpha-fetoprotein (AFP). Sixty CHC patients were\nsubdivided into 3 cohorts: Mild disease (fibrosis stage F0-2; n = 20); cirrhosis (n = 20); and\ncirrhosis with HCC (n = 20). Circulating miRNA signatures were determined using a liver-specific\nreal-time quantitative reverse transcription PCR (qRT-PCR) microarray assessing 372 miRNAs\nsimultaneously. Differentially-expressed miRNA candidates were independently validated using\nqRT-PCR. Serum miRNA-409-3p was increased in cirrhosis versus mild disease. In HCC versus\ncirrhosis, miRNA-486-5p was increased, whereas miRNA-122-5p and miRNA-151a-5p were decreased.\nA logistic regression model-generated panel, consisting of miRNA-122-5p + miRNA-409-3p,\ndistinguished cirrhosis from mild disease (area under the curve, AUC = 0.80; sensitivity = 85%,\nspecificity = 70%; p < 0.001). When combined with FIB-4 or APRI, performance was improved\nwith AUC = 0.89 (p < 0.001) and 0.87 (p < 0.001), respectively. A panel consisting of miRNA-122-5p\n+ miRNA-486-5p + miRNA-142-3p distinguished HCC from cirrhosis (AUC = 0.94; sensitivity =\n80%, specificity = 95%; p < 0.001), outperforming AFP (AUC = 0.64, p = 0.065). Serum miRNAs\nare differentially expressed across the spectrum of disease severity in CHC. MicroRNAs have great\npotential as diagnostic biomarkers in CHC, particularly in HCC where they outperform the only\ncurrently-used biomarker, AFP...
Breast cancer is a heterogeneous disease. Although gene expression profiling has led to\nthe definition of several subtypes of breast cancer, the precise discovery of the subtypes remains\na challenge. Clinical data is another promising source. In this study, clinical variables are utilized\nand integrated to gene expressions for the stratification of breast cancer. We adopt two phases:\ngene selection and clustering, where the integration is in the gene selection phase; only genes\nwhose expressions are most relevant to each clinical variable and least redundant among themselves\nare selected for further clustering. In practice, we simply utilize maximum relevance minimum\nredundancy (mRMR) for gene selection and k-means for clustering. We compare the results of our\nmethod with those of two commonly used only expression-based breast cancer stratification methods:\nprediction analysis of microarray 50 (PAM50) and highest variability (HV). The result is that our\nmethod outperforms them in identifying subtypes significantly associated with five-year survival and\nrecurrence time. Specifically, our method identified recurrence-associated breast cancer subtypes that\nwere not identified by PAM50 and HV. Additionally, our analysis discovered three survival-associated\nluminal-A subgroups and two survival-associated luminal-B subgroups. The study indicates that\nscreening clinically relevant gene expressions yields improved breast cancer stratification....
An increasing number of research studies over recent years have focused on the function of microRNA (miRNA) molecules which\nhave unique characteristics in terms of structure and function.They represent a class of endogenous noncoding single-strand small\nmolecules. An abundance of miRNA clusters has been found in the genomes of various organisms often located in a polycistron.\nThe miR-17-92 family is among the most famous miRNAs and has been identified as an oncogene. The functions of this cluster,\ntogether with the seven individual molecules that it comprises, are most related to cancers, so it would not be surprising that they\nare considered to have involvement in the development of tumors. The miR-17-92 cluster is therefore expected not only to be a\ntumor marker, but also to perform an important role in the early diagnosis of those diseases and possibly also be a target for tumor\nbiotherapy. The miR-17-92 cluster affects the development of disease by regulating many related cellular processes and multiple\ntarget genes. Interestingly, it also has important roles that cannot be ignored in disease of the nervous system and circulation and\nmodulates the growth and development of bone. Therefore, it provides newopportunities for disease prevention, clinical diagnosis,\nprognosis, and targeted therapy. Here we review the role of the miR-17-92 cluster that has received little attention in relation to\nneurological diseases, cardiac diseases, and the development of bone and tumors....
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