Detection of Genetic Variations of Five MMAF-Related Genes and Their
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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. -
Protein Identities in Evs Isolated from U87-MG GBM Cells As Determined by NG LC-MS/MS
Protein identities in EVs isolated from U87-MG GBM cells as determined by NG LC-MS/MS. No. Accession Description Σ Coverage Σ# Proteins Σ# Unique Peptides Σ# Peptides Σ# PSMs # AAs MW [kDa] calc. pI 1 A8MS94 Putative golgin subfamily A member 2-like protein 5 OS=Homo sapiens PE=5 SV=2 - [GG2L5_HUMAN] 100 1 1 7 88 110 12,03704523 5,681152344 2 P60660 Myosin light polypeptide 6 OS=Homo sapiens GN=MYL6 PE=1 SV=2 - [MYL6_HUMAN] 100 3 5 17 173 151 16,91913397 4,652832031 3 Q6ZYL4 General transcription factor IIH subunit 5 OS=Homo sapiens GN=GTF2H5 PE=1 SV=1 - [TF2H5_HUMAN] 98,59 1 1 4 13 71 8,048185945 4,652832031 4 P60709 Actin, cytoplasmic 1 OS=Homo sapiens GN=ACTB PE=1 SV=1 - [ACTB_HUMAN] 97,6 5 5 35 917 375 41,70973209 5,478027344 5 P13489 Ribonuclease inhibitor OS=Homo sapiens GN=RNH1 PE=1 SV=2 - [RINI_HUMAN] 96,75 1 12 37 173 461 49,94108966 4,817871094 6 P09382 Galectin-1 OS=Homo sapiens GN=LGALS1 PE=1 SV=2 - [LEG1_HUMAN] 96,3 1 7 14 283 135 14,70620005 5,503417969 7 P60174 Triosephosphate isomerase OS=Homo sapiens GN=TPI1 PE=1 SV=3 - [TPIS_HUMAN] 95,1 3 16 25 375 286 30,77169764 5,922363281 8 P04406 Glyceraldehyde-3-phosphate dehydrogenase OS=Homo sapiens GN=GAPDH PE=1 SV=3 - [G3P_HUMAN] 94,63 2 13 31 509 335 36,03039959 8,455566406 9 Q15185 Prostaglandin E synthase 3 OS=Homo sapiens GN=PTGES3 PE=1 SV=1 - [TEBP_HUMAN] 93,13 1 5 12 74 160 18,68541938 4,538574219 10 P09417 Dihydropteridine reductase OS=Homo sapiens GN=QDPR PE=1 SV=2 - [DHPR_HUMAN] 93,03 1 1 17 69 244 25,77302971 7,371582031 11 P01911 HLA class II histocompatibility antigen, -
4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4). -
Genome-Wide DNA Methylation Analysis of KRAS Mutant Cell Lines Ben Yi Tew1,5, Joel K
www.nature.com/scientificreports OPEN Genome-wide DNA methylation analysis of KRAS mutant cell lines Ben Yi Tew1,5, Joel K. Durand2,5, Kirsten L. Bryant2, Tikvah K. Hayes2, Sen Peng3, Nhan L. Tran4, Gerald C. Gooden1, David N. Buckley1, Channing J. Der2, Albert S. Baldwin2 ✉ & Bodour Salhia1 ✉ Oncogenic RAS mutations are associated with DNA methylation changes that alter gene expression to drive cancer. Recent studies suggest that DNA methylation changes may be stochastic in nature, while other groups propose distinct signaling pathways responsible for aberrant methylation. Better understanding of DNA methylation events associated with oncogenic KRAS expression could enhance therapeutic approaches. Here we analyzed the basal CpG methylation of 11 KRAS-mutant and dependent pancreatic cancer cell lines and observed strikingly similar methylation patterns. KRAS knockdown resulted in unique methylation changes with limited overlap between each cell line. In KRAS-mutant Pa16C pancreatic cancer cells, while KRAS knockdown resulted in over 8,000 diferentially methylated (DM) CpGs, treatment with the ERK1/2-selective inhibitor SCH772984 showed less than 40 DM CpGs, suggesting that ERK is not a broadly active driver of KRAS-associated DNA methylation. KRAS G12V overexpression in an isogenic lung model reveals >50,600 DM CpGs compared to non-transformed controls. In lung and pancreatic cells, gene ontology analyses of DM promoters show an enrichment for genes involved in diferentiation and development. Taken all together, KRAS-mediated DNA methylation are stochastic and independent of canonical downstream efector signaling. These epigenetically altered genes associated with KRAS expression could represent potential therapeutic targets in KRAS-driven cancer. Activating KRAS mutations can be found in nearly 25 percent of all cancers1. -
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 -
Fig1-13Tab1-5.Pdf
Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13. -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
Article Reference
Article Comprehensive evaluation of coding region point mutations in microsatellite‐unstable colorectal cancer KONDELIN, Johanna, et al. Reference KONDELIN, Johanna, et al. Comprehensive evaluation of coding region point mutations in microsatellite‐unstable colorectal cancer. EMBO Molecular Medicine, 2018, vol. 10, no. 9, p. e8552 PMID : 30108113 DOI : 10.15252/emmm.201708552 Available at: http://archive-ouverte.unige.ch/unige:120174 Disclaimer: layout of this document may differ from the published version. 1 / 1 Published online: August 14, 2018 Report Comprehensive evaluation of coding region point mutations in microsatellite-unstable colorectal cancer Johanna Kondelin1,2, Kari Salokas3,4, Lilli Saarinen2, Kristian Ovaska2, Heli Rauanheimo1,2, Roosa-Maria Plaketti1,2, Jiri Hamberg1,2, Xiaonan Liu3,4 , Leena Yadav3,4, Alexandra E Gylfe1,2, Tatiana Cajuso1,2, Ulrika A Hänninen1,2, Kimmo Palin1,2, Heikki Ristolainen1,2, Riku Katainen1,2, Eevi Kaasinen1,2, Tomas Tanskanen1,2, Mervi Aavikko1,2, Minna Taipale5, Jussi Taipale1,2,6,7 , Laura Renkonen-Sinisalo8, Anna Lepistö8, Selja Koskensalo9, Jan Böhm10, Jukka-Pekka Mecklin11,12, Halit Ongen13,14,15, Emmanouil T Dermitzakis13,14,15, Outi Kilpivaara1,2, Pia Vahteristo1,2, Mikko Turunen2, Sampsa Hautaniemi2, Sari Tuupanen1,2, Auli Karhu1,2, Niko Välimäki1,2, Markku Varjosalo3,4, Esa Pitkänen1,2 & Lauri A Aaltonen1,2,* Abstract not been linked to CRC before. Two genes, SMARCB1 and STK38L, were also functionally scrutinized, providing evidence of Microsatellite instability (MSI) leads to accumulation of an a tumorigenic role, for SMARCB1 mutations in particular. excessive number of mutations in the genome, mostly small insertions and deletions. MSI colorectal cancers (CRCs), however, Keywords cancer genetics; colorectal cancer; microsatellite instability also contain more point mutations than microsatellite-stable Subject Categories Cancer; Chromatin, Epigenetics, Genomics & Functional (MSS) tumors, yet they have not been as comprehensively stud- Genomics; Systems Medicine ied. -
Insights Into MYC Biology Through Investigation of Synthetic Lethal Interactions with MYC Deregulation
Insights into MYC biology through investigation of synthetic lethal interactions with MYC deregulation Mai Sato Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2014 © 2014 Mai Sato All Rights Reserved ABSTRACT Insights into MYC biology through investigation of synthetic lethal interactions with MYC deregulation Mai Sato MYC (or c-myc) is a bona fide “cancer driver” oncogene that is deregulated in up to 70% of human tumors. In addition to its well-characterized role as a transcription factor that can directly promote tumorigenic growth and proliferation, MYC has transcription-independent functions in vital cellular processes including DNA replication and protein synthesis, contributing to its complex biology. MYC expression, activity, and stability are highly regulated through multiple mechanisms. MYC deregulation triggers genome instability and oncogene-induced DNA replication stress, which are thought to be critical in promoting cancer via mechanisms that are still unclear. Because regulated MYC activity is essential for normal cell viability and MYC is a difficult protein to target pharmacologically, targeting genes or pathways that are essential to survive MYC deregulation offer an attractive alternative as a means to combat tumor cells with MYC deregulation. To this end, we conducted a genome-wide synthetic lethal shRNA screen in MCF10A breast epithelial cells stably expressing an inducible MYCER transgene. We identified and validated FBXW7 as a high-confidence synthetic lethal (MYC-SL) candidate gene. FBXW7 is a component of an E3 ubiquitin ligase complex that degrades MYC. FBXW7 knockdown in MCF10A cells selectively induced cell death in MYC-deregulated cells compared to control. -
A Yeast Bifc-Seq Method for Genome-Wide Interactome Mapping
bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 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. 1 A yeast BiFC-seq method for genome-wide interactome mapping 2 3 Limin Shang1,a, Yuehui Zhang1,b, Yuchen Liu1,c, Chaozhi Jin1,d, Yanzhi Yuan1,e, 4 Chunyan Tian1,f, Ming Ni2,g, Xiaochen Bo2,h, Li Zhang3,i, Dong Li1,j, Fuchu He1,*,k & 5 Jian Wang1,*,l 6 1State Key Laboratory of Proteomics, Beijing Proteome Research Center, National 7 Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, 8 China; 2Department of Biotechnology, Beijing Institute of Radiation Medicine, 9 Beijing 100850, China; 3Department of Rehabilitation Medicine, Nan Lou; 10 Department of Key Laboratory of Wound Repair and Regeneration of PLA, College 11 of Life Sciences, Chinese PLA General Hospital, Beijing 100853, China; 12 Correspondence should be addressed to F.H. ([email protected]) and J.W. 13 ([email protected]). 14 * Corresponding authors. 15 E-mail:[email protected] (Fuchu He),[email protected] (Jian Wang) 16 a ORCID: 0000-0002-6371-1956. 17 b ORCID: 0000-0001-5257-1671 18 c ORCID: 0000-0003-4691-4951 19 d ORCID: 0000-0002-1477-0255 20 e ORCID: 0000-0002-6576-8112 21 f ORCID: 0000-0003-1589-293X 22 g ORCID: 0000-0001-9465-2787 23 h ORCID: 0000-0003-3490-5812 24 i ORCID: 0000-0002-3477-8860 25 j ORCID: 0000-0002-8680-0468 26 k ORCID: 0000-0002-8571-2351 27 l ORCID: 0000-0002-8116-7691 28 Total word counts:4398 (from “Introduction” to “Conclusions”) 29 Total Keywords: 5 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.16.154146; this version posted June 17, 2020. -
Characterization and Expression Profile of CMTM3/CKLFSF3
Journal of Biochemistry and Molecular Biology, Vol. 39, No. 5, September 2006, pp. 537-545 Characterization and Expression Profile of CMTM3/CKLFSF3 Ji Zhong, Yu Wang, Xiaoyan Qiu, Xiaoning Mo, Yanan Liu, Ting Li, Quansheng Song, Dalong Ma and Wenling Han* Peking University Center for Human Disease Genomics, 38 Xueyuan Road, Beijing 100083, China Received 22 February 2006, Accepted 10 May 2006 CMTM/CKLFSF is a novel family of proteins linking Introduction chemokines and TM4SF. In humans, these proteins are encoded by nine genes, CKLF and CMTM1-8/CKLFSF1-8. CKLFSF is a novel family of proteins linking chemokines and Here we report the characteristics and expression profile Transmembrane 4 Super Family (TM4SF), which are encoded of CMTM3/CKLFSF3. Human CMTM3/CKLFSF3 has a by nine genes, CKLF and CKLFSF1-8, in humans. CKLF, high sequence identity among various species and similar CKLFSF1, and CKLFSF2 are active during evolution, and characteristics as its mouse and rat homologues. Established CKLFSF2 has two orthologues in rat and mouse designated by results both of RT-PCR and Quantitative Real-time Cklfsf2a and Cklfsf2b. CKLFSF3-8 are evolutionarily conserved. PCR, the gene is highly transcribed in testis, leukocytes CKLFSF members form two gene clusters: CKLF and and spleen. For further verification, we generated a CKLFSF1-4 at chromosome 16q22.1, and CKLFSF6-8 at polyclonal antibody against human CMTM3/CKLFSF3 chromosome 3p23. Most CKLFSFs have different spliced and found that the protein is highly expressed in the testis forms, and the protein product of at least one spliced form of and some cells of PBMCs. -
RNA-Seq Transcriptome Analysis for the Study of Prostate Cancer Development and Evolution
MASTER THESIS RNA-Seq Transcriptome Analysis for the Study of Prostate Cancer Development and Evolution Esther Sauras Colón Tutors: María Jesús Álvarez Cubero, Eduardo Andrés León September 2019 MASTER IN TRANSLATIONAL RESEARCH AND PERSONALIZED MEDICINE Master Thesis RNA-Seq Transcriptome Analysis for the Study of Prostate Cancer Development and Evolution Esther Sauras Colón 1*, María Jesús Álvarez Cubero 2,3* and Eduardo Andrés León 4* 1 Master in Translational Research and Personalized Medicine. Faculty of Medicine, University of Granada. Granada, Spain. 2 GENYO. Centre for Genomics and Oncological Research. Pfizer / University of Granada / Andalusian Regional Government. Granada, Spain. 3 Department of Biochemistry and Molecular Biology III. Faculty of Medicine, University of Granada. Granada, Spain. 4 Bioinformatics Unit, Institute of Parasitology and Biomedicine “López-Neyra” (IPBLN). Spanish National Research Council (CSIC). Granada, Spain. * Correspondence: Esther Sauras Colón: [email protected]. María Jesús Álvarez Cubero: [email protected]. Eduardo Andrés León: [email protected]. September 2019 Abstract: Prostate cancer (PCa) is one of the most common cancers worldwide. Even though prostate specific antigen (PSA) test is the non-invasive routine blood test for the detection of asymptomatic disease, it can result in problems of diagnostic accuracy and overdiagnosis. Thus, it is necessary to continue investigating new efficient biomarkers for the prevention, diagnosis and prognosis of PCa. Here, we analyse the transcriptome of seven individuals by the use of next-generation sequencing (NGS) technique to identify differentially expressed genes, which can help to better understand PCa aggressiveness. Present analysis show that there are two upregulated genes in PCa regarding to controls: HP (Haptoglobin) and HLA-G (Human Leukocyte Antigen-G).