ABSTRACT
To explore extracellular vesicle microRNAs (EV miRNAs) and their target mRNAs in relation to diabetic kidney disease (DKD), we performed paired plasma and urinary EV small RNA sequencing (n = 18) in patients with type 2 diabetes and DKD (n = 5) and healthy subjects (n = 4) and metabolic network analyses using our own miRNA and public mRNA datasets. We found 13 common differentially expressed EV miRNAs in both fluids and 17 target mRNAs, including RRM2, NT5E, and UGDH. Because succinate dehydrogenase B was suggested to interact with proteins encoded by these three genes, we measured urinary succinate and adenosine in a validation study (n = 194). These two urinary metabolite concentrations were associated with DKD progression. In addition, renal expressions of NT5E and UGDH proteins were increased in db/db mice with DKD compared to control mice. In conclusion, we profiled DKD-related EV miRNAs in plasma and urine samples and found their relevant target pathways.
Subject(s)
Diabetes Mellitus, Type 2 , Diabetic Nephropathies , Extracellular Vesicles , MicroRNAs , Animals , Biomarkers/metabolism , Diabetes Mellitus, Type 2/genetics , Diabetic Nephropathies/genetics , Diabetic Nephropathies/metabolism , Extracellular Vesicles/metabolism , Humans , Mice , MicroRNAs/metabolism , RNA, Messenger/metabolismABSTRACT
BACKGROUND/AIMS: Diabetic nephropathy (DN), a major diabetic microvascular complication, has a long and growing list of biomarkers, including microRNA biomarkers, which have not been consistent across preclinical and clinical studies. This meta-analysis aims to identify significant blood- and urine-incident microRNAs as diagnostic/prognostic biomarker candidates for DN. METHODS: PubMed, Web of Science, and Cochrane Library were searched from their earliest records through 12th Dec 2016. Relevant publications for the meta-analysis included (1) human participants; (2) microRNAs in blood and urine; (3) DN studies; and (4) English language. Four reviewers, including two physicians, independently and blindly extracted published data regarding microRNA profiles in blood and/or urine from subjects with diabetic nephropathy. A random-effect model was used to pool the data. Statistical associations between diabetic nephropathy and urinary or blood microRNA expression levels were assessed. RESULTS: Fourteen out of 327 studies (n=2,747 patients) were selected. Blood or urinary microRNA expression data of diabetic nephropathy were pooled for this analysis. The hsa-miR-126 family was significantly (OR: 0.57; 95% CI: 0.44-0.74; p-value < 0.0001) downregulated in blood from patients with diabetic kidney disease, while its urinary level was upregulated (OR: 2931.12; 95% CI: 9.96-862623.21; p-value = 0.0059). The hsa-miR-770 family microRNA were significantly (OR: 10.24; 95% CI: 2.37-44.25; p-value = 0.0018) upregulated in both blood and urine from patients with diabetic nephropathy. CONCLUSIONS: Our meta-analysis suggests that hsa-miR-126 and hsa-miR-770 family microRNA may have important diagnostic and pathogenetic implications for DN, which warrants further systematic clinical studies.
Subject(s)
Biomarkers/analysis , Diabetic Nephropathies/diagnosis , MicroRNAs/analysis , Biomarkers/blood , Biomarkers/urine , Databases, Factual , Diabetic Nephropathies/genetics , Humans , MicroRNAs/blood , MicroRNAs/urine , Odds Ratio , Up-RegulationABSTRACT
Poorly differentiated thyroid carcinoma (PDTC) is a subtype of thyroid cancer that has a high rate of metastasis or recurrence and a relatively poor prognosis. However, there are few studies that have been conducted on PDTC at the whole protein-coding gene scale. Here, we performed genomic profiling of 15 patients with PDTC originated from follicular thyroid carcinoma using whole exome sequencing and also performed gene functional enrichment analysis of differentially expressed genes (DEGs) for three patients. Further, we investigated genetic variants associated with PDTC progression and the characteristics of clinical pathology. We revealed somatic genomic alterations in the RAF1, MAP2K2, and AKT2 genes that were not reported in previous studies. We confirmed frequent occurrences in the RAS gene in patients with PDTC; the genetic alterations were associated with the RAS-RAF-MEK-ERK/JNK, PI3K-AKT-mTOR signaling pathways, and the cell cycle. DEG analysis showed that immune response was lower in cancer tissues than in normal tissues. Through the association analysis of somatic mutations and the characteristics of clinical pathology from patients with PDTC, the somatic mutations of ABCA12, CLIP1, and ATP13A3 were significantly associated with a vascular invasion phenotype. By providing molecular genetic insight on PDTC, this study may contribute to the discovery of novel therapeutic target candidates.
Subject(s)
Adenocarcinoma, Follicular , Thyroid Neoplasms , Humans , Phosphatidylinositol 3-Kinases/metabolism , Thyroid Neoplasms/pathology , Genomics , Mutation , Adenosine Triphosphatases/metabolism , Membrane Transport Proteins/geneticsABSTRACT
Acral melanoma (AM), a rare subtype of cutaneous melanoma, shows higher incidence in Asians, including Koreans, than in Caucasians. However, the genetic modification associated with AM in Koreans is not well known and has not been comprehensively investigated in terms of oncogenic signaling, and hallmarks of cancer. We performed whole-exome and RNA sequencing for Korean patients with AM and acquired the genetic alterations and gene expression profiles. KIT alterations (previously known to be recurrent alterations in AM) and CDK4/CCND1 copy number amplifications were identified in the patients. Genetic and transcriptomic alterations in patients with AM were functionally converge to the hallmarks of cancer and oncogenic pathways, including 'proliferative signal persistence', 'apoptotic resistance', and 'activation of invasion and metastasis', despite the heterogeneous somatic mutation profiles of Korean patients with AM. This study may provide a molecular understanding for therapeutic strategy for AM.
Subject(s)
Melanoma , Skin Neoplasms , Humans , Melanoma/genetics , Melanoma/metabolism , Skin Neoplasms/genetics , Mutation/genetics , Signal Transduction/genetics , Republic of Korea , Melanoma, Cutaneous MalignantABSTRACT
Gastric cancer (GC) has the highest occurrence and fourth-highest mortality rate of all cancers in Korea. Although survival rates are improving with the development of diagnosis and treatment methods, the five-year survival rate for stage 4 GC in Korea remains <10%. Therefore, it is important to identify candidate prognostic factors for predicting poor prognosis. PRNP is a gene encoding the prion protein PrP, which has been noted for its role in the nervous system and is known to be upregulated in various cancers and associated with both cell proliferation and metastasis. However, the value of PRNP as a prognostic factor for Korean GC patients remains unclear. Here, we analyzed the relationship between PRNP expression and survival in three independent datasets for Korean patients with GC as well as the TCGA-STAD dataset. Survival analysis indicates that high levels of PRNP expression are associated with poor overall survival of patients with GC. Gene set enrichment analysis showed that PRNP is associated with epithelial mesenchymal transition and Hedgehog signaling. In addition, proliferation of GC cell lines was inhibited after siRNA-mediated knockdown of PRNP. In conclusion, our study suggests a potential role for PRNP as a candidate prognostic factor for patients with GC.
ABSTRACT
microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, "miR2Diabetes," equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases.
Subject(s)
Diabetes Complications/genetics , MicroRNAs/genetics , Microvessels/metabolism , Animals , Data Curation/methods , Databases, Nucleic Acid , Diabetes Mellitus/genetics , Disease Models, Animal , Gene Expression/genetics , Humans , Mice , Rats , Software , Transcriptome/genetics , User-Computer InterfaceABSTRACT
The "guardian of the genome," TP53, is one of the most frequently mutated genes of all cancers. Despite the important biological roles of TP53, the clinical relevance of TP53 mutations, in gastric cancer (GC), remains largely unknown. Here, we systematically assessed clinical relevance, in terms of TP53 mutation positions, finding substantial variability. Thus, we hypothesized that the position of the TP53 mutation might affect clinical outcomes in GC. We systematically inspected missense mutations in TP53, from a TCGA (The Cancer Genome Atlas) GC dataset in UCSC Xena repository. Specifically, we examined five aspects of each mutational position: (1) the whole gene body; (2) known hot-spots; (3) the DNA-binding domain; (4) the secondary structure of the domain; and (5) individual mutation positions. We then analyzed the clinical outcomes for each aspect. These results showed that, in terms of secondary structure, patients with mutations in turn regions showed poor prognosis, compared to those with mutations in beta strand regions (log rank ${\text{p}}= {{0.043}}$p=0.043). Also, in terms of individual mutation positions, patients having mutations at R248 showed poorer survival than other patients having mutations at different TP53 positions (log rank ${\text{p}}= {{0.035}}$p=0.035).