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MUC4/MUC16/Muc20high Signature As a Marker of Poor Prognostic for Pancreatic, Colon and Stomach Cancers
Jonckheere and Van Seuningen J Transl Med (2018) 16:259 https://doi.org/10.1186/s12967-018-1632-2 Journal of Translational Medicine RESEARCH Open Access Integrative analysis of the cancer genome atlas and cancer cell lines encyclopedia large‑scale genomic databases: MUC4/MUC16/ MUC20 signature is associated with poor survival in human carcinomas Nicolas Jonckheere* and Isabelle Van Seuningen* Abstract Background: MUC4 is a membrane-bound mucin that promotes carcinogenetic progression and is often proposed as a promising biomarker for various carcinomas. In this manuscript, we analyzed large scale genomic datasets in order to evaluate MUC4 expression, identify genes that are correlated with MUC4 and propose new signatures as a prognostic marker of epithelial cancers. Methods: Using cBioportal or SurvExpress tools, we studied MUC4 expression in large-scale genomic public datasets of human cancer (the cancer genome atlas, TCGA) and cancer cell line encyclopedia (CCLE). Results: We identifed 187 co-expressed genes for which the expression is correlated with MUC4 expression. Gene ontology analysis showed they are notably involved in cell adhesion, cell–cell junctions, glycosylation and cell signal- ing. In addition, we showed that MUC4 expression is correlated with MUC16 and MUC20, two other membrane-bound mucins. We showed that MUC4 expression is associated with a poorer overall survival in TCGA cancers with diferent localizations including pancreatic cancer, bladder cancer, colon cancer, lung adenocarcinoma, lung squamous adeno- carcinoma, skin cancer and stomach cancer. We showed that the combination of MUC4, MUC16 and MUC20 signature is associated with statistically signifcant reduced overall survival and increased hazard ratio in pancreatic, colon and stomach cancer. -
Supplementary Table 1: Adhesion Genes Data Set
Supplementary Table 1: Adhesion genes data set PROBE Entrez Gene ID Celera Gene ID Gene_Symbol Gene_Name 160832 1 hCG201364.3 A1BG alpha-1-B glycoprotein 223658 1 hCG201364.3 A1BG alpha-1-B glycoprotein 212988 102 hCG40040.3 ADAM10 ADAM metallopeptidase domain 10 133411 4185 hCG28232.2 ADAM11 ADAM metallopeptidase domain 11 110695 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 195222 8038 hCG40937.4 ADAM12 ADAM metallopeptidase domain 12 (meltrin alpha) 165344 8751 hCG20021.3 ADAM15 ADAM metallopeptidase domain 15 (metargidin) 189065 6868 null ADAM17 ADAM metallopeptidase domain 17 (tumor necrosis factor, alpha, converting enzyme) 108119 8728 hCG15398.4 ADAM19 ADAM metallopeptidase domain 19 (meltrin beta) 117763 8748 hCG20675.3 ADAM20 ADAM metallopeptidase domain 20 126448 8747 hCG1785634.2 ADAM21 ADAM metallopeptidase domain 21 208981 8747 hCG1785634.2|hCG2042897 ADAM21 ADAM metallopeptidase domain 21 180903 53616 hCG17212.4 ADAM22 ADAM metallopeptidase domain 22 177272 8745 hCG1811623.1 ADAM23 ADAM metallopeptidase domain 23 102384 10863 hCG1818505.1 ADAM28 ADAM metallopeptidase domain 28 119968 11086 hCG1786734.2 ADAM29 ADAM metallopeptidase domain 29 205542 11085 hCG1997196.1 ADAM30 ADAM metallopeptidase domain 30 148417 80332 hCG39255.4 ADAM33 ADAM metallopeptidase domain 33 140492 8756 hCG1789002.2 ADAM7 ADAM metallopeptidase domain 7 122603 101 hCG1816947.1 ADAM8 ADAM metallopeptidase domain 8 183965 8754 hCG1996391 ADAM9 ADAM metallopeptidase domain 9 (meltrin gamma) 129974 27299 hCG15447.3 ADAMDEC1 ADAM-like, -
Research2007herschkowitzetvolume Al
Open Access Research2007HerschkowitzetVolume al. 8, Issue 5, Article R76 Identification of conserved gene expression features between comment murine mammary carcinoma models and human breast tumors Jason I Herschkowitz¤*†, Karl Simin¤‡, Victor J Weigman§, Igor Mikaelian¶, Jerry Usary*¥, Zhiyuan Hu*¥, Karen E Rasmussen*¥, Laundette P Jones#, Shahin Assefnia#, Subhashini Chandrasekharan¥, Michael G Backlund†, Yuzhi Yin#, Andrey I Khramtsov**, Roy Bastein††, John Quackenbush††, Robert I Glazer#, Powel H Brown‡‡, Jeffrey E Green§§, Levy Kopelovich, reviews Priscilla A Furth#, Juan P Palazzo, Olufunmilayo I Olopade, Philip S Bernard††, Gary A Churchill¶, Terry Van Dyke*¥ and Charles M Perou*¥ Addresses: *Lineberger Comprehensive Cancer Center. †Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. ‡Department of Cancer Biology, University of Massachusetts Medical School, Worcester, MA 01605, USA. reports §Department of Biology and Program in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. ¶The Jackson Laboratory, Bar Harbor, ME 04609, USA. ¥Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. #Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC 20057, USA. **Department of Pathology, University of Chicago, Chicago, IL 60637, USA. ††Department of Pathology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA. ‡‡Baylor College of Medicine, Houston, TX 77030, USA. §§Transgenic Oncogenesis Group, Laboratory of Cancer Biology and Genetics. Chemoprevention Agent Development Research Group, National Cancer Institute, Bethesda, MD 20892, USA. Department of Pathology, Thomas Jefferson University, Philadelphia, PA 19107, USA. Section of Hematology/Oncology, Department of Medicine, Committees on Genetics and Cancer Biology, University of Chicago, Chicago, IL 60637, USA. -
NIH Public Access Author Manuscript Dev Biol
NIH Public Access Author Manuscript Dev Biol. Author manuscript; available in PMC 2013 January 1. NIH-PA Author ManuscriptPublished NIH-PA Author Manuscript in final edited NIH-PA Author Manuscript form as: Dev Biol. 2012 January 1; 361(1): 68±78. doi:10.1016/j.ydbio.2011.10.004. Regulation of intrahepatic biliary duct morphogenesis by Claudin 15-like b Isla D. Cheung1, Michel Bagnat1,2, Taylur P. Ma3, Anirban Datta4, Kimberley Evason1,5, John C. Moore6, Nathan Lawson6, Keith E. Mostov4, Cecilia B. Moens3, and Didier Y.R. Stainier1,* 1Department of Biochemistry and Biophysics; Programs in Developmental and Stem Cell Biology, Genetics and Human Genetics; Liver Center, Diabetes Center, and Institute for Regeneration Medicine; University of California, San Francisco, CA 94158, USA 3HHMI and Division of Basic Science, Fred Hutchinson Cancer Research Center, Seattle WA 98109, USA 4Department of Anatomy, University of California, San Francisco, CA 94143, USA 5Department of Pathology, University of California, San Francisco, CA 94110, USA 6Program in Gene Function and Expression, University of Massachusetts Medical School, Worcester, MA 01605, USA Abstract The intrahepatic biliary ducts transport bile produced by the hepatocytes out of the liver. Defects in biliary cell differentiation and biliary duct remodeling cause a variety of congenital diseases including Alagille Syndrome and polycystic liver disease. While the molecular pathways regulating biliary cell differentiation have received increasing attention (Lemaigre, 2010), less is known about the cellular behavior underlying biliary duct remodeling. Here, we have identified a novel gene, claudin 15-like b (cldn15lb), which exhibits a unique and dynamic expression pattern in the hepatocytes and biliary epithelial cells in zebrafish. -
Supplemental File
SUPPLEMENTARY MATERIALS AND METHODS ### reorder as minimun to maximum relative induction. allks2=cbind(allks,apply(allks,1,sum)) For the different analyses, the macros are as following: allks3=allks2[order(allks2[,length(colnames(allks2))]),] allks5=allks3[,-length(colnames(allks2))] -Heatmap and correlation analyses # check allks5 source("http://bioconductor.org/biocLite.R") biocLite() -Heatmap install.packages("gplots") library("gplots") #Set a color scale "zero centered" #Set Graph title (do one by one) breaks=c(seq(-(max(allks5)), max(allks5) , by = 0.05)) titre = "allkinases SASP " mycol <- colorpanel(n=length(breaks)- titre = "allkinases SASP + p16" 1,low="green",mid="black",high="red") titre = "allkinases SASP + p16 + NFkB" #Load Normalized RT-QPCR table (do one by one) # Heatmap, "symkey = T" allks<-read.table("allkS.txt", header = TRUE, sep = "\t", dec = ",", fill = TRUE, , row.names=1 ) dev.off() allks<-read.table("allkSp16.txt", header = TRUE, sep = heatmap.2(allks5, scale = "none", col = mycol, "\t", dec = ",", fill = TRUE, , row.names=1 ) breaks=breaks, symkey = F, allks<-read.table("allkSp16NFkB.txt", header = TRUE, trace = 'none', cexRow=0.8, cexCol = 1.6, srtCol sep = "\t", dec = ",", fill = TRUE, , row.names=1 ) = 0, keysize = 1.3, -Log2 relative fold changes induction calculation key.title = "", margins = c(8, 10), # matrix of minimun values main = paste("relative FC"," ", titre), min=apply(allks, 2, min ) Rowv=F mat<-matrix(min, length(row.names(allks)),length(colnames(allks)), -corrgram byrow=TRUE) # check mat install.packages("corrgram") -
Myc Targeted CDK18 Promotes ATR and Homologous Recombination to Mediate PARP Inhibitor Resistance in Glioblastoma
Myc targeted CDK18 promotes ATR and homologous recombination to mediate PARP inhibitor resistance in glioblastoma The MIT Faculty has made this article openly available. Please share how this access benefits you. Your story matters. Citation Ning, Jian-Fang et al. “Myc targeted CDK18 promotes ATR and homologous recombination to mediate PARP inhibitor resistance in glioblastoma.” Nature Communications 10 (2019): 2910 © 2019 The Author(s) As Published 10.1038/S41467-019-10993-5 Publisher Springer Science and Business Media LLC Version Final published version Citable link https://hdl.handle.net/1721.1/125202 Terms of Use Creative Commons Attribution 4.0 International license Detailed Terms https://creativecommons.org/licenses/by/4.0/ ARTICLE https://doi.org/10.1038/s41467-019-10993-5 OPEN Myc targeted CDK18 promotes ATR and homologous recombination to mediate PARP inhibitor resistance in glioblastoma Jian-Fang Ning1,2, Monica Stanciu3, Melissa R. Humphrey1, Joshua Gorham4, Hiroko Wakimoto 4, Reiko Nishihara5, Jacqueline Lees3, Lee Zou6,7, Robert L. Martuza1, Hiroaki Wakimoto 1,8 & Samuel D. Rabkin 1 1234567890():,; PARP inhibitors (PARPis) have clinical efficacy in BRCA-deficient cancers, but not BRCA- intact tumors, including glioblastoma (GBM). We show that MYC or MYCN amplification in patient-derived glioblastoma stem-like cells (GSCs) generates sensitivity to PARPi via Myc- mediated transcriptional repression of CDK18, while most tumors without amplification are not sensitive. In response to PARPi, CDK18 facilitates ATR activation by interacting with ATR and regulating ATR-Rad9/ATR-ETAA1 interactions; thereby promoting homologous recom- bination (HR) and PARPi resistance. CDK18 knockdown or ATR inhibition in GSCs suppressed HR and conferred PARPi sensitivity, with ATR inhibitors synergizing with PARPis or sensi- tizing GSCs. -
Combined Gene Expression Profiling and Rnai Screening in Clear Cell Renal Cell Carcinoma Identify PLK1 and Other Therapeutic Kinase Targets
Published OnlineFirst June 3, 2011; DOI: 10.1158/0008-5472.CAN-11-0076 Cancer Therapeutics, Targets, and Chemical Biology Research Combined Gene Expression Profiling and RNAi Screening in Clear Cell Renal Cell Carcinoma Identify PLK1 and Other Therapeutic Kinase Targets Yan Ding1, Dan Huang1, Zhongfa Zhang1, Josh Smith1, David Petillo1, Brendan D. Looyenga2, Kristin Feenstra3, Jeffrey P. MacKeigan2, Kyle A. Furge1,4, and Bin T. Teh1,5 Abstract In recent years, several molecularly targeted therapies have been approved for clear cell renal cell carcinoma (ccRCC), a highly aggressive cancer. Although these therapies significantly extend overall survival, nearly all patients with advanced ccRCC eventually succumb to the disease. To identify other molecular targets, we profiled gene expression in 90 ccRCC patient specimens for which tumor grade information was available. Gene set enrichment analysis indicated that cell-cycle–related genes, in particular, Polo-like kinase 1 (PLK1), were associated with disease aggressiveness. We also carried out RNAi screening to identify kinases and phosphatases that when inhibited could prevent cell proliferation. As expected, RNAi-mediated knockdown of PLK1 and other cell-cycle kinases was sufficient to suppress ccRCC cell proliferation. The association of PLK1 in both disease aggression and in vitro growth prompted us to examine the effects of a small-molecule inhibitor of PLK1, BI 2536, in ccRCC cell lines. BI 2536 inhibited the proliferation of ccRCC cell lines at concentrations required to inhibit PLK1 kinase activity, and sustained inhibition of PLK1 by BI 2536 led to dramatic regression of ccRCC xenograft tumors in vivo. Taken together, these findings highlight PLK1 as a rational therapeutic target for ccRCC. -
Table S3. RAE Analysis of Well-Differentiated Liposarcoma
Table S3. RAE analysis of well-differentiated liposarcoma Model Chromosome Region start Region end Size q value freqX0* # genes Genes Amp 1 145009467 145122002 112536 0.097 21.8 2 PRKAB2,PDIA3P Amp 1 145224467 146188434 963968 0.029 23.6 10 CHD1L,BCL9,ACP6,GJA5,GJA8,GPR89B,GPR89C,PDZK1P1,RP11-94I2.2,NBPF11 Amp 1 147475854 148412469 936616 0.034 23.6 20 PPIAL4A,FCGR1A,HIST2H2BF,HIST2H3D,HIST2H2AA4,HIST2H2AA3,HIST2H3A,HIST2H3C,HIST2H4B,HIST2H4A,HIST2H2BE, HIST2H2AC,HIST2H2AB,BOLA1,SV2A,SF3B4,MTMR11,OTUD7B,VPS45,PLEKHO1 Amp 1 148582896 153398462 4815567 1.5E-05 49.1 152 PRPF3,RPRD2,TARS2,ECM1,ADAMTSL4,MCL1,ENSA,GOLPH3L,HORMAD1,CTSS,CTSK,ARNT,SETDB1,LASS2,ANXA9, FAM63A,PRUNE,BNIPL,C1orf56,CDC42SE1,MLLT11,GABPB2,SEMA6C,TNFAIP8L2,LYSMD1,SCNM1,TMOD4,VPS72, PIP5K1A,PSMD4,ZNF687,PI4KB,RFX5,SELENBP1,PSMB4,POGZ,CGN,TUFT1,SNX27,TNRC4,MRPL9,OAZ3,TDRKH,LINGO4, RORC,THEM5,THEM4,S100A10,S100A11,TCHHL1,TCHH,RPTN,HRNR,FLG,FLG2,CRNN,LCE5A,CRCT1,LCE3E,LCE3D,LCE3C,LCE3B, LCE3A,LCE2D,LCE2C,LCE2B,LCE2A,LCE4A,KPRP,LCE1F,LCE1E,LCE1D,LCE1C,LCE1B,LCE1A,SMCP,IVL,SPRR4,SPRR1A,SPRR3, SPRR1B,SPRR2D,SPRR2A,SPRR2B,SPRR2E,SPRR2F,SPRR2C,SPRR2G,LELP1,LOR,PGLYRP3,PGLYRP4,S100A9,S100A12,S100A8, S100A7A,S100A7L2,S100A7,S100A6,S100A5,S100A4,S100A3,S100A2,S100A16,S100A14,S100A13,S100A1,C1orf77,SNAPIN,ILF2, NPR1,INTS3,SLC27A3,GATAD2B,DENND4B,CRTC2,SLC39A1,CREB3L4,JTB,RAB13,RPS27,NUP210L,TPM3,C1orf189,C1orf43,UBAP2L,HAX1, AQP10,ATP8B2,IL6R,SHE,TDRD10,UBE2Q1,CHRNB2,ADAR,KCNN3,PMVK,PBXIP1,PYGO2,SHC1,CKS1B,FLAD1,LENEP,ZBTB7B,DCST2, DCST1,ADAM15,EFNA4,EFNA3,EFNA1,RAG1AP1,DPM3 Amp 1 -
Quantitative Phosphoproteomic Analysis Reveals Vasopressin V2-Receptor–Dependent Signaling Pathways in Renal Collecting Duct Cells
Quantitative phosphoproteomic analysis reveals vasopressin V2-receptor–dependent signaling pathways in renal collecting duct cells Markus M. Rinschena,b, Ming-Jiun Yua, Guanghui Wangc, Emily S. Bojac, Jason D. Hofferta, Trairak Pisitkuna, and Mark A. Kneppera,1 aEpithelial Systems Biology Laboratory, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892; bDepartment of Internal Medicine D, University of Muenster, Muenster, Germany; and cProteomics Core Facility, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD 20892 Edited* by Peter Agre, Johns Hopkins Malaria Research Institute, Baltimore, MD, and approved December 22, 2009 (received for review September 16, 2009) Vasopressin’s actionin renal cells to regulate watertransport depends exhibits high levels of AQP2 expression, V2R-mediated trafficking on protein phosphorylation. Here we used mass spectrometry–based of AQP2 to the apical plasma membrane, and V2R-mediated quantitative phosphoproteomics to identify signaling pathways AQP2 phosphorylation resembling that seen in native collecting involved in theshort-term V2-receptor–mediated response in cultured duct cells (7). Here we apply the SILAC method to analysis of the collecting duct cells (mpkCCD) from mouse. Using Stable Isotope phosphoproteomic response of clone 11 mpkCCD cells to the Labeling by Amino acids in Cell culture (SILAC) with two treatment short-term action of the V2R-selective vasopressin analog dDAVP. groups (0.1 nM dDAVP or vehicle for 30 min), we carried out quanti- fication of 2884 phosphopeptides. The majority (82%) of quantified Results phosphopeptides did not change in abundance in response to dDAVP. Technical Controls. Incorporation of labeled amino acids was found Analysis of the 273 phosphopeptides increased by dDAVP showed a to be 98% complete after 16 days of growth of mpkCCD cells predominance of so-called “basophilic” motifs consistent with activa- (Table S1), providing a standard for further experimentation. -
Interpretable Deep Recommender System Model for Prediction of Kinase Inhibitor Efficacy Across Cancer Cell Lines
bioRxiv preprint doi: https://doi.org/10.1101/2021.01.26.428272; this version posted May 31, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Interpretable deep recommender system model for prediction of kinase inhibitor efficacy across cancer cell lines Krzysztof Koras1, Ewa Kizling1, Dilafruz Juraeva2, Eike Staub2, and Ewa Szczurek1, 1Faculty of Mathematics, Informatics and Mechanics, University of Warsaw 2Merck KGaA, Translational Medicine, Department of Bioinformatics Computational models for drug sensitivity prediction have the provide the basis for the discovery of molecular markers to potential to revolutionise personalized cancer medicine. Drug predict drug efficacy. To predict the response of a specific sensitivity assays, as well as profiling of cancer cell lines and cell line to a specific drug, there is a need of computational drugs becomes increasingly available for training such models. models that can leverage the abundance of information about Machine learning methods for drug sensitivity prediction must drugs and cancer cell lines. be optimized for: (i) leveraging the wealth of information about both cancer cell lines and drugs, (ii) predictive performance and Strongly parallelized assay formats provide a variety of data (iii) interpretability. Multiple methods were proposed for pre- that can be used to comprehensively describe the character- dicting drug sensitivity from cancer cell line features, some in istics of both cancer cell lines and drugs (2–6). Despite their a multi-task fashion. -
The Emerging Role of Cyclin-Dependent Kinases (Cdks) in Pancreatic Ductal Adenocarcinoma
International Journal of Molecular Sciences Review The Emerging Role of Cyclin-Dependent Kinases (CDKs) in Pancreatic Ductal Adenocarcinoma Balbina García-Reyes 1,†, Anna-Laura Kretz 1,†, Jan-Philipp Ruff 1, Silvia von Karstedt 2,3, Andreas Hillenbrand 1, Uwe Knippschild 1, Doris Henne-Bruns 1 and Johannes Lemke 1,* 1 Department of General and Visceral Surgery, Ulm University Hospital, Albert-Einstein-Allee 23, 89081 Ulm, Germany; [email protected] (B.G.-R.); [email protected] (A.-L.K.); [email protected] (J.-P.R.); [email protected] (A.H.); [email protected] (U.K.); [email protected] (D.H.-B.) 2 Department of Translational Genomics, University Hospital Cologne, Weyertal 115b, 50931 Cologne, Germany; [email protected] 3 Cologne Excellence Cluster on Cellular Stress Response in Aging-Associated Diseases (CECAD), University of Cologne, Joseph-Stelzmann-Straße 26, 50931 Cologne, Germany * Correspondence: [email protected]; Tel.: +49-731-500-53961 † These authors contributed equally to this work. Received: 31 August 2018; Accepted: 11 October 2018; Published: 18 October 2018 Abstract: The family of cyclin-dependent kinases (CDKs) has critical functions in cell cycle regulation and controlling of transcriptional elongation. Moreover, dysregulated CDKs have been linked to cancer initiation and progression. Pharmacological CDK inhibition has recently emerged as a novel and promising approach in cancer therapy. This idea is of particular interest to combat pancreatic ductal adenocarcinoma (PDAC), a cancer entity with a dismal prognosis which is owed mainly to PDAC’s resistance to conventional therapies. Here, we review the current knowledge of CDK biology, its role in cancer and the therapeutic potential to target CDKs as a novel treatment strategy for PDAC. -
Novel Compound Heterozygote Mutations of TJP2 in a Chinese
Ge et al. BMC Medical Genetics (2019) 20:18 https://doi.org/10.1186/s12881-019-0753-7 CASEREPORT Open Access Novel compound heterozygote mutations of TJP2 in a Chinese child with progressive cholestatic liver disease Ting Ge, Xinyue Zhang, Yongmei Xiao, Yizhong Wang* and Ting Zhang* Abstract Background: Progressive familial intrahepatic cholestasis (PFIC) is a group of genetic autosomal recessive disorders that predominantly affects young children and results in early-onset progressive liver damage. Several types of PFIC were defined based on different genetic aetiologies in last decades. Case presentation: Here, we report a Chinese young child diagnosed as PFIC with variants in tight junction protein 2 (TJP2). The patient was affected by a long history of jaundice, pruritus, and failure to thrive. Highly elevated level of serum total bile acid (TBA) and normal levels of gamma-glutamyltransferase (GGT) were observed at hospitalization. The patient’s clinical symptoms could be alleviated by administration of ursodeoxycholic acid. Genetic testing by next generation sequencing (NGS) found novel compound heterozygote mutations c.2448 + 1G > C/c.2639delC (p.T880Sfs*12) in TJP2, which were inherited from her mother and father, respectively. Both mutations were predicted to abolish TJP2 protein translation, and neither has previously been identified. Conclusion: We report a Chinese female PFIC child with novel compound heterozygous mutations of TJP2.Genetic testing by NGS is valuable in the clinical diagnosis of hereditary liver disease. Keywords: Autosomal recessive disorder, Child, Compound heterozygote mutations, Progressive cholestatic liver disease, TJP2 Background in adenosine triphosphate-binding cassette subfamily B Progressive familial intrahepatic cholestasis (PFIC), first de- member 4 (ABCB4), which encodes the multidrug scribed in 1969, is a group of genetic autosomal recessive resistance P-glycoprotein 3 protein (MDR3) leads to disorders that are characterized by fluctuating jaundice, the development of PFIC 3 [5].