DOCSLIB.ORG

Original Article Lowered Levels of Microrna-129 and Potential

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text

Load more

Int J Clin Exp Pathol 2017;10(7):7511-7527 www.ijcep.com /ISSN:1936-2625/IJCEP0052925 Original Article Lowered levels of microRNA-129 and potential signaling pathways in papillary thyroid carcinoma: a determination of microRNA sequencing in 507 patients and bioinformatics analysis Liang Liang1#, Yihuan Luo1#, Xia Yang3, Rui Zhang3, Hanlin Wang3, Hong Yang2, Yun He2, Gang Chen3, Wei Ma3*, Junqiang Chen1* Departments of 1Gastrointestinal Surgery, 2Ultrasonography, 3Pathology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, P. R. China. #Equal contributors and co-first authors. *Equal contributors. Received March 14, 2017; Accepted May 26, 2017; Epub July 1, 2017; Published July 15, 2017 Abstract: Papillary thyroid carcinoma (PTC) is one of the most common endocrine system malignancies. However, the mechanism of tumor development is unclear. microRNA-129-5p is a microRNA that plays an important role in the development of tumors. The main purpose of our article is to find the potential target genes of microRNA-129 and their pathways based on gene array, sequencing and bioinformatics studies. We obtained microRNA-129 expres- sion and clinical associations in the TCGA database. In addition, we found a microRNA-129-related chip GSE19933, which is overexpressing microR-129-5p in thyroid cancer cell lines. The down-regulated gene is considered to be a potential target gene for microRNA-129. The target genes were predicted through 12 online tools. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of all down-regulated and predicted target genes. Furthermore, protein-protein interactions (PPI) were also analyzed for all potential genes. Finally, with intersecting down-regulated genes by overexpressed microRNA-129 and predicted target genes, the 889 genes are mainly enriched in the calcium signaling pathway, cGMP-PKG signaling pathway, ErbB signaling pathway and Proteoglycans in cancer, etc. The role of ten hub genes is particularly prominent in PPI analysis. These genes are differentially expressed in the thyroid by immunohistochemistry. We confirmed that microRNA-129 may play a major role in PTC through the above pathways, but more experiments are still needed to prove our results. Keywords: microRNA-129, papillary thyroid carcinoma, TCGA, target gene, signaling pathways Introduction importance in clinical settings [5-8]. Recently, a class of non-coding single-stranded RNA mole- Thyroid cancer is a common disease and its cule coded by an endogenous gene, microRNAs diagnosis remains challenging. The symptoms (miRNAs) has been reported to be evidently of a palpation, examination by ultrasound, related to the incidence and progress of many detection of endocrine hormone, fine needle cancers, including PTC [9-13]. Hence, miRNAs aspiration (FNA) and pathological observation possess the potential to be trustworthy bio- play crucial parts in the diagnosis of thyroid car- markers in PTC. However, only a small number cinomas. However, the specificity and sensitivi- of miRNAs have been studied in PTC. ty are all required to be improved [1-4]. Papillary thyroid carcinoma (PTC) is the most common MicroRNA-129 is one of the miRNAs whose cancer originating from the thyroid. Although clinical role and function remain largely the prognosis of PTC is commonly good with a unknown in PTC. To the best of our knowledge, high 5-year survival rate, some patients have only two research groups have performed rele- poorer prognosis. An early diagnosis is of great vant studies on microRNA-129 in thyroid can- Down-regulated microRNA-129 in PTC Figure 1. Clinical value of microRNA-129 in papillary thyroid carcinoma based on microRNA sequencing data from TCGA. Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B). Receiver Operating Characteristic (ROCs) curve of microRNA-129-1 (C) and microRNA-129-2 (D). NT: non-tumorous tissue, T: tumor. AUC: area under the ROC curve. cer. Brest et al. [14] found that histone deacety- type. Besides, only one target gene, RET, was lase inhibitors (HDACi)s, trichostatin A and identified. vorinostat, could enhance microRNA-129-5p expression in cultured cell lines of BCPAP, TPC- Therefore, in the present study, the expression 1, 8505C, and CAL62, as well as in primary cul- level of microRNA-129 was analyzed based on tures of PTC cells. Moreover, microRNA-129 the microRNA sequencing data from the Cancer was adequate to induce cell death and accen- Genome Atlas (TCGA) Research Network data- tuate the anti-proliferative effects of other can- bases, which has recently published a molecu- cer drugs. However, no clinical value or molecu- lar signature based on of 507 PTC and 59 lar target genes of microRNA-129 in PTC were matched non-cancerous adjacent tissues with identified in the study [14]. Duan et al. [15] regard to genomic, transcriptomic and pro- found that microRNA-129-5p was apparently teomic characteristics, as well as clinicopatho- down-regulated in medullary thyroid carcino- logical features including survival status [16, mas. And microRNA-129-5p could suppress 17]. Subsequently, the potential target genes of the RET proto-oncogene expression by directly microRNA-129 were gathered by both microar- binding its 3’-untranslated regions. However, ray data and predicting platforms. Finally, a the study by Duan et al. solely focused on med- comprehensive functional annotation and vali- ullary subtype of thyroid carcinoma, without dation of the proteins were also performed mentioning the more frequent papillary sub- using different in silico tools. 7512 Int J Clin Exp Pathol 2017;10(7):7511-7527 Down-regulated microRNA-129 in PTC cally by TCGA data portal (https://gdc-portal.nci.nih. gov/). The expressions of microRNAs were log2 trans- formed and records were considered as censored wh- en the expression level was less than one. Student’s t test was performed examine the difference of microR- NA-129-1 and microRNA- 129-2 between cancerous tissues and their non-can- cerous counterparts. Rec- eiver operating characteris- tic (ROC) was drawn to evalu- ate the diagnostic values of microRNA-129-1 and microR- NA-129-2. Kaplan-Meier an- alysis and univariate Cox proportional hazards regres- sion model were used to assess the prognostic value of microRNA. Overall survival (OS), relapse free survival (RFS) and metastasis free survival data were obtained from PROGmiRV2-Pan Can- cer miRNA Prognostics Da- tabase (http://xvm145.jef- ferson.edu/progmir/index. php). SPSS 22.0 (SPSS Inc., Chicago, IL, USA) was used for the statistics and P<0.05 was regarded as being significant. Potential target genes of microRNA-129 Figure 2. Correlation between microRNA-129 level and overall survival in pap- illary thyroid carcinoma based on microRNA sequencing data from TCGA. Ex- To collect the potential target pression levels of microRNA-129-1 (A) and microRNA-129-2 (B) were divided according to the median level. The analysis was performed by “PROGmiR” genes of microRNA-129, we (http://www.compbio.iupui.edu/progmir). combined two parts of genes together. Materials and methods The first part was the differentially expressed genes (DEGs) post pre-microRNA-129 overex- Clinical significance of microRNA-129 in papil- pression from Gene Expression Omnibus (GEO) lary thyroid carcinoma based on microRNA database. After searching for “miR-129 OR sequencing data microRNA-129” in thyroid cancer in GEO, we The expression levels of microRNA-129-1 and obtained the database of GSE19933. In the microRNA-129-2, as well as the clinical data of study [14], three independent experiments papillary thyroid carcinoma patients and non- were performed in dye-swap with TCP1 PTC tumorous thyroid tissues were provided publi- cells: miRNA-129-5p versus miR-Neg. The 7513 Int J Clin Exp Pathol 2017;10(7):7511-7527 Down-regulated microRNA-129 in PTC The second part was the pre- dicted target genes achieved from 12 different predicting tools, including Targetscan miRWalk, miRMap, Microt4, miRanda, mirbridge, miRDB, miRNAMap, PITA, Pictar2, RNA22, and RNAhybrid. The genes co-predicted by at le- ast six databases were se- lected. Finally, the overlapped genes from the two parts were con- sidered as potential target genes of microRNA-129 in PTC. Functional annotation of the target genes of microR- NA-129 To evaluate the function of microRNA-129 target genes in PTC, we analyzed the Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation throu- gh DAVID (http://david.abcc. ncifcrf.gov/). The GO terms with a modified Fisher Exact P-value less than 0.01 and the KEGG pathways with P-value less than 0.05 were Figure 3. Correlation between microRNA-129 level and relapse free survival in chosen for next analysis. The papillary thyroid carcinoma based on microRNA sequencing data from TCGA. GO enrichment analysis was Expression levels of microRNA-129-1 (A) and microRNA-129-2 (B) were divid- visualized by software Cy- ed according to the median level. The analysis was performed by “PROGmiR” (http://www.compbio.iupui.edu/progmir). toscape v3.5.0. Further, the protein-protein interactions network of genes was con- experiments included a negative pre-miRNA as ducted through STRING v10.0 (http://string. control and a synthetic pre-miRNA-129-5p as embl.de/). experimental group. TCP1 papillary thyroid car- cinoma cells were transfected with 10 nM of a Validation of the protein expression level of synthetic pre-miRNA-129-5p or a negative pre- potential targets miRNA using Lipofectamine RNAiMAX reagent. RNA samples were harvested at 24 and 48 To further confirm the relationship between hours post-transfection. The results of three microRNA-129 and some of the potential target experiments were merged and the down- genes. The hub genes from the most signifi- expressed genes with a log(FC) value less than cantly enriched pathway in KEGG analysis were -0.5 were gathered as the genes which were selected randomly.
Recommended publications
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus

    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
  • Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous

    Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous

    ONCOLOGY LETTERS 21: 58, 2021 Circular RNA hsa_circ_0005114‑miR‑142‑3p/miR‑590‑5p‑ adenomatous polyposis coli protein axis as a potential target for treatment of glioma BO WEI1*, LE WANG2* and JINGWEI ZHAO1 1Department of Neurosurgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033; 2Department of Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, P.R. China Received September 12, 2019; Accepted October 22, 2020 DOI: 10.3892/ol.2020.12320 Abstract. Glioma is the most common type of brain tumor APC expression with a good overall survival rate. UALCAN and is associated with a high mortality rate. Despite recent analysis using TCGA data of glioblastoma multiforme and the advances in treatment options, the overall prognosis in patients GSE25632 and GSE103229 microarray datasets showed that with glioma remains poor. Studies have suggested that circular hsa‑miR‑142‑3p/hsa‑miR‑590‑5p was upregulated and APC (circ)RNAs serve important roles in the development and was downregulated. Thus, hsa‑miR‑142‑3p/hsa‑miR‑590‑5p‑ progression of glioma and may have potential as therapeutic APC‑related circ/ceRNA axes may be important in glioma, targets. However, the expression profiles of circRNAs and their and hsa_circ_0005114 interacted with both of these miRNAs. functions in glioma have rarely been studied. The present study Functional analysis showed that hsa_circ_0005114 was aimed to screen differentially expressed circRNAs (DECs) involved in insulin secretion, while APC was associated with between glioma and normal brain tissues using sequencing the Wnt signaling pathway. In conclusion, hsa_circ_0005114‑ data collected from the Gene Expression Omnibus database miR‑142‑3p/miR‑590‑5p‑APC ceRNA axes may be potential (GSE86202 and GSE92322 datasets) and explain their mecha‑ targets for the treatment of glioma.
  • Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis

    Figure S1. Representative Report Generated by the Ion Torrent System Server for Each of the KCC71 Panel Analysis and Pcafusion Analysis

    Figure S1. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. A Figure S1. Continued. Representative report generated by the Ion Torrent system server for each of the KCC71 panel analysis and PCaFusion analysis. (A) Details of the run summary report followed by the alignment summary report for the KCC71 panel analysis sequencing. (B) Details of the run summary report for the PCaFusion panel analysis. B Figure S2. Comparative analysis of the variant frequency found by the KCC71 panel and calculated from publicly available cBioPortal datasets. For each of the 71 genes in the KCC71 panel, the frequency of variants was calculated as the variant number found in the examined cases. Datasets marked with different colors and sample numbers of prostate cancer are presented in the upper right. *Significantly high in the present study. Figure S3. Seven subnetworks extracted from each of seven public prostate cancer gene networks in TCNG (Table SVI). Blue dots represent genes that include initial seed genes (parent nodes), and parent‑child and child‑grandchild genes in the network. Graphical representation of node‑to‑node associations and subnetwork structures that differed among and were unique to each of the seven subnetworks. TCNG, The Cancer Network Galaxy. Figure S4. REVIGO tree map showing the predicted biological processes of prostate cancer in the Japanese. Each rectangle represents a biological function in terms of a Gene Ontology (GO) term, with the size adjusted to represent the P‑value of the GO term in the underlying GO term database.
  • Supplemental Materials ZNF281 Enhances Cardiac Reprogramming

    Supplemental Materials ZNF281 Enhances Cardiac Reprogramming

    Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7
  • MTGR1 (CBFA2T2) (NM 001039709) Human Tagged ORF Clone Product Data

    MTGR1 (CBFA2T2) (NM 001039709) Human Tagged ORF Clone Product Data

    OriGene Technologies, Inc. 9620 Medical Center Drive, Ste 200 Rockville, MD 20850, US Phone: +1-888-267-4436 [email protected] EU: [email protected] CN: [email protected] Product datasheet for RG202013 MTGR1 (CBFA2T2) (NM_001039709) Human Tagged ORF Clone Product data: Product Type: Expression Plasmids Product Name: MTGR1 (CBFA2T2) (NM_001039709) Human Tagged ORF Clone Tag: TurboGFP Symbol: CBFA2T2 Synonyms: EHT; MTGR1; p85; ZMYND3 Vector: pCMV6-AC-GFP (PS100010) E. coli Selection: Ampicillin (100 ug/mL) Cell Selection: Neomycin This product is to be used for laboratory only. Not for diagnostic or therapeutic use. View online » ©2021 OriGene Technologies, Inc., 9620 Medical Center Drive, Ste 200, Rockville, MD 20850, US 1 / 4 MTGR1 (CBFA2T2) (NM_001039709) Human Tagged ORF Clone – RG202013 ORF Nucleotide >RG202013 representing NM_001039709 Sequence: Red=Cloning site Blue=ORF Green=Tags(s) TTTTGTAATACGACTCACTATAGGGCGGCCGGGAATTCGTCGACTGGATCCGGTACCGAGGAGATCTGCC GCCGCGATCGCC ATGGGGTTTCACCATGTTGGCCAGGCTCGTCTTGAACTCCTGACCTCAGGTGATCTGCCTGCATTGGCCT CCCAACGTGCTGGGATTACAGTTGGTCCTGAGAAAAGGGTGCCAGCGATGCCTGGATCGCCTGTGGAAGT GAAGATACAGTCCAGATCCTCACCTCCCACCATGCCACCCCTCCCACCAATAAATCCTGGAGGACCGAGG CCAGTGTCCTTCACTCCTACTGCATTAAGCAATGGCATCAACCATTCTCCTCCTACCCTGAATGGTGCCC CATCACCGCCACAGAGATTCAGCAATGGTCCTGCCTCCTCCACATCATCTGCACTCACAAATCAGCAATT GCCAGCCACTTGTGGTGCTCGACAACTCAGCAAGTTGAAACGCTTTCTTACCACTCTGCAACAGTTTGGC AATGACATCTCCCCTGAGATTGGGGAGAAGGTGCGGACTCTTGTTCTTGCACTGGTGAACTCAACAGTGA CAATTGAGGAATTCCACTGTAAGCTCCAAGAAGCCACAAACTTTCCCCTTCGTCCTTTTGTGATTCCATT
  • Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses

    Investigation of the Underlying Hub Genes and Molexular Pathogensis in Gastric Cancer by Integrated Bioinformatic Analyses

    bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Investigation of the underlying hub genes and molexular pathogensis in gastric cancer by integrated bioinformatic analyses Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2020.12.20.423656; this version posted December 22, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract The high mortality rate of gastric cancer (GC) is in part due to the absence of initial disclosure of its biomarkers. The recognition of important genes associated in GC is therefore recommended to advance clinical prognosis, diagnosis and and treatment outcomes. The current investigation used the microarray dataset GSE113255 RNA seq data from the Gene Expression Omnibus database to diagnose differentially expressed genes (DEGs). Pathway and gene ontology enrichment analyses were performed, and a proteinprotein interaction network, modules, target genes - miRNA regulatory network and target genes - TF regulatory network were constructed and analyzed. Finally, validation of hub genes was performed. The 1008 DEGs identified consisted of 505 up regulated genes and 503 down regulated genes.
  • B-Cell Malignancies in Microrna Eμ-Mir-17∼92 Transgenic Mice

    B-Cell Malignancies in Microrna Eμ-Mir-17∼92 Transgenic Mice

    B-cell malignancies in microRNA Eμ-miR-17∼92 transgenic mice Sukhinder K. Sandhua, Matteo Fassana,b, Stefano Voliniaa,c, Francesca Lovata, Veronica Balattia, Yuri Pekarskya, and Carlo M. Crocea,1 aDepartment of Molecular Virology, Immunology and Medical Genetics, The Ohio State University Wexner Medical Center, Columbus, OH 43210; bARC-NET Research Centre, University of Verona, VR 37134, Verona, Italy; cDepartment of Morphology, Surgery and Experimental Medicine, University of Ferrara, FE 44121 Ferrara, Italy Contributed by Carlo M. Croce, September 22, 2013 (sent for review July 12, 2013) miR-17∼92 is a polycistronic microRNA (miR) cluster (consisting of cluster, but not of its paralogs, has shown that miR-17∼92 plays miR-17, miR-18a, miR-19a, miR-19b, miR-20a, and miR-92a) which an important role in B-cell development, and the KO mice die frequently is overexpressed in several solid and lymphoid malig- shortly after birth from lung hypoplasia and ventricular septal nancies. Loss- and gain-of-function studies have revealed the role defects (8). Further examination of the role of individual miRs in of miR-17∼92 in heart, lung, and B-cell development and in Myc- B-cell lymphomas showed that miR-19a and miR19b are required induced B-cell lymphomas, respectively. Recent studies indicate and sufficient for the proliferative activities of the cluster (9). that overexpression of this locus leads to lymphoproliferation, To understand better the role of the miR-17∼92 cluster in but no experimental proof that dysregulation of this cluster causes B-cell neoplastic progression, we generated miR-17∼92 B-cell– B-cell lymphomas or leukemias is available.
  • Coactivation of NF-Kb and Notch Signaling Is Sufficient to Induce B

    Coactivation of NF-Kb and Notch Signaling Is Sufficient to Induce B

    Regular Article LYMPHOID NEOPLASIA Coactivation of NF-kB and Notch signaling is sufficient to induce B-cell transformation and enables B-myeloid conversion Downloaded from https://ashpublications.org/blood/article-pdf/135/2/108/1550992/bloodbld2019001438.pdf by UNIV OF IOWA LIBRARIES user on 20 February 2020 Yan Xiu,1,* Qianze Dong,1,2,* Lin Fu,1,2 Aaron Bossler,1 Xiaobing Tang,1,2 Brendan Boyce,3 Nicholas Borcherding,1 Mariah Leidinger,1 Jose´ Luis Sardina,4,5 Hai-hui Xue,6 Qingchang Li,2 Andrew Feldman,7 Iannis Aifantis,8 Francesco Boccalatte,8 Lili Wang,9 Meiling Jin,9 Joseph Khoury,10 Wei Wang,10 Shimin Hu,10 Youzhong Yuan,11 Endi Wang,12 Ji Yuan,13 Siegfried Janz,14 John Colgan,15 Hasem Habelhah,1 Thomas Waldschmidt,1 Markus Muschen,¨ 9 Adam Bagg,16 Benjamin Darbro,17 and Chen Zhao1,18 1Department of Pathology, Carver College of Medicine, University of Iowa, Iowa City, IA; 2Department of Pathology, China Medical University, Shenyang, China; 3Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, NY; 4Gene Regulation, Stem Cells and Cancer Program, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain; 5Josep Carreras Leukaemia Research Institute, Campus ICO-Germans Trias i Pujol, Barcelona, Spain; 6Department of Microbiology and Immunology, Carver College of Medicine, University of Iowa, Iowa City, IA; 7Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, MN; 8Department of Pathology, NYU School of Medicine,
  • The Expression Patterns and the Prognostic Roles of PTPN Family Members in Digestive Tract Cancers

    The Expression Patterns and the Prognostic Roles of PTPN Family Members in Digestive Tract Cancers

    Preprint: Please note that this article has not completed peer review. The expression patterns and the prognostic roles of PTPN family members in digestive tract cancers CURRENT STATUS: UNDER REVIEW Jing Chen The First Affiliated Hospital of China Medical University Xu Zhao Liaoning Vocational College of Medicine Yuan Yuan The First Affiliated Hospital of China Medical University Jing-jing Jing The First Affiliated Hospital of China Medical University [email protected] Author ORCiD: https://orcid.org/0000-0002-9807-8089 DOI: 10.21203/rs.3.rs-19689/v1 SUBJECT AREAS Cancer Biology KEYWORDS PTPN family members, digestive tract cancers, expression, prognosis, clinical features 1 Abstract Background Non-receptor protein tyrosine phosphatases (PTPNs) are a set of enzymes involved in the tyrosyl phosphorylation. The present study intended to clarify the associations between the expression patterns of PTPN family members and the prognosis of digestive tract cancers. Method Expression profiling of PTPN family genes in digestive tract cancers were analyzed through ONCOMINE and UALCAN. Gene ontology enrichment analysis was conducted using the DAVID database. The gene–gene interaction network was performed by GeneMANIA and the protein–protein interaction (PPI) network was built using STRING portal couple with Cytoscape. Data from The Cancer Genome Atlas (TCGA) were downloaded for validation and to explore the relationship of the PTPN expression with clinicopathological parameters and survival of digestive tract cancers. Results Most PTPN family members were associated with digestive tract cancers according to Oncomine, Ualcan and TCGA data. For esophageal carcinoma (ESCA), expression of PTPN1, PTPN4 and PTPN12 were upregulated; expression of PTPN20 was associated with poor prognosis.
  • The Regulatory Roles of Phosphatases in Cancer

    The Regulatory Roles of Phosphatases in Cancer

    Oncogene (2014) 33, 939–953 & 2014 Macmillan Publishers Limited All rights reserved 0950-9232/14 www.nature.com/onc REVIEW The regulatory roles of phosphatases in cancer J Stebbing1, LC Lit1, H Zhang, RS Darrington, O Melaiu, B Rudraraju and G Giamas The relevance of potentially reversible post-translational modifications required for controlling cellular processes in cancer is one of the most thriving arenas of cellular and molecular biology. Any alteration in the balanced equilibrium between kinases and phosphatases may result in development and progression of various diseases, including different types of cancer, though phosphatases are relatively under-studied. Loss of phosphatases such as PTEN (phosphatase and tensin homologue deleted on chromosome 10), a known tumour suppressor, across tumour types lends credence to the development of phosphatidylinositol 3--kinase inhibitors alongside the use of phosphatase expression as a biomarker, though phase 3 trial data are lacking. In this review, we give an updated report on phosphatase dysregulation linked to organ-specific malignancies. Oncogene (2014) 33, 939–953; doi:10.1038/onc.2013.80; published online 18 March 2013 Keywords: cancer; phosphatases; solid tumours GASTROINTESTINAL MALIGNANCIES abs in sera were significantly associated with poor survival in Oesophageal cancer advanced ESCC, suggesting that they may have a clinical utility in Loss of PTEN (phosphatase and tensin homologue deleted on ESCC screening and diagnosis.5 chromosome 10) expression in oesophageal cancer is frequent, Cao et al.6 investigated the role of protein tyrosine phosphatase, among other gene alterations characterizing this disease. Zhou non-receptor type 12 (PTPN12) in ESCC and showed that PTPN12 et al.1 found that overexpression of PTEN suppresses growth and protein expression is higher in normal para-cancerous tissues than induces apoptosis in oesophageal cancer cell lines, through in 20 ESCC tissues.
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA Co-Expressed Gene List in LUAD

    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
  • 1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor

    1714 Gene Comprehensive Cancer Panel Enriched for Clinically Actionable Genes with Additional Biologically Relevant Genes 400-500X Average Coverage on Tumor

    xO GENE PANEL 1714 gene comprehensive cancer panel enriched for clinically actionable genes with additional biologically relevant genes 400-500x average coverage on tumor Genes A-C Genes D-F Genes G-I Genes J-L AATK ATAD2B BTG1 CDH7 CREM DACH1 EPHA1 FES G6PC3 HGF IL18RAP JADE1 LMO1 ABCA1 ATF1 BTG2 CDK1 CRHR1 DACH2 EPHA2 FEV G6PD HIF1A IL1R1 JAK1 LMO2 ABCB1 ATM BTG3 CDK10 CRK DAXX EPHA3 FGF1 GAB1 HIF1AN IL1R2 JAK2 LMO7 ABCB11 ATR BTK CDK11A CRKL DBH EPHA4 FGF10 GAB2 HIST1H1E IL1RAP JAK3 LMTK2 ABCB4 ATRX BTRC CDK11B CRLF2 DCC EPHA5 FGF11 GABPA HIST1H3B IL20RA JARID2 LMTK3 ABCC1 AURKA BUB1 CDK12 CRTC1 DCUN1D1 EPHA6 FGF12 GALNT12 HIST1H4E IL20RB JAZF1 LPHN2 ABCC2 AURKB BUB1B CDK13 CRTC2 DCUN1D2 EPHA7 FGF13 GATA1 HLA-A IL21R JMJD1C LPHN3 ABCG1 AURKC BUB3 CDK14 CRTC3 DDB2 EPHA8 FGF14 GATA2 HLA-B IL22RA1 JMJD4 LPP ABCG2 AXIN1 C11orf30 CDK15 CSF1 DDIT3 EPHB1 FGF16 GATA3 HLF IL22RA2 JMJD6 LRP1B ABI1 AXIN2 CACNA1C CDK16 CSF1R DDR1 EPHB2 FGF17 GATA5 HLTF IL23R JMJD7 LRP5 ABL1 AXL CACNA1S CDK17 CSF2RA DDR2 EPHB3 FGF18 GATA6 HMGA1 IL2RA JMJD8 LRP6 ABL2 B2M CACNB2 CDK18 CSF2RB DDX3X EPHB4 FGF19 GDNF HMGA2 IL2RB JUN LRRK2 ACE BABAM1 CADM2 CDK19 CSF3R DDX5 EPHB6 FGF2 GFI1 HMGCR IL2RG JUNB LSM1 ACSL6 BACH1 CALR CDK2 CSK DDX6 EPOR FGF20 GFI1B HNF1A IL3 JUND LTK ACTA2 BACH2 CAMTA1 CDK20 CSNK1D DEK ERBB2 FGF21 GFRA4 HNF1B IL3RA JUP LYL1 ACTC1 BAG4 CAPRIN2 CDK3 CSNK1E DHFR ERBB3 FGF22 GGCX HNRNPA3 IL4R KAT2A LYN ACVR1 BAI3 CARD10 CDK4 CTCF DHH ERBB4 FGF23 GHR HOXA10 IL5RA KAT2B LZTR1 ACVR1B BAP1 CARD11 CDK5 CTCFL DIAPH1 ERCC1 FGF3 GID4 HOXA11 IL6R KAT5 ACVR2A