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Zhang J, Wang Q, Qi S, Duan Y, Liu Z, Liu J, Zhang Z, Li C. An oncogenic enhancer promotes melanoma progression via regulating ETV4 expression. J Transl Med 2024; 22:547. [PMID: 38849954 PMCID: PMC11157841 DOI: 10.1186/s12967-024-05356-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2024] [Accepted: 05/29/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Enhancers are important gene regulatory elements that promote the expression of critical genes in development and disease. Aberrant enhancer can modulate cancer risk and activate oncogenes that lead to the occurrence of various cancers. However, the underlying mechanism of most enhancers in cancer remains unclear. Here, we aim to explore the function and mechanism of a crucial enhancer in melanoma. METHODS Multi-omics data were applied to identify an enhancer (enh17) involved in melanoma progression. To evaluate the function of enh17, CRISPR/Cas9 technology were applied to knockout enh17 in melanoma cell line A375. RNA-seq, ChIP-seq and Hi-C data analysis integrated with luciferase reporter assay were performed to identify the potential target gene of enh17. Functional experiments were conducted to further validate the function of the target gene ETV4. Multi-omics data integrated with CUT&Tag sequencing were performed to validate the binding profile of the inferred transcription factor STAT3. RESULTS An enhancer, named enh17 here, was found to be aberrantly activated and involved in melanoma progression. CRISPR/Cas9-mediated deletion of enh17 inhibited cell proliferation, migration, and tumor growth of melanoma both in vitro and in vivo. Mechanistically, we identified ETV4 as a target gene regulated by enh17, and functional experiments further support ETV4 as a target gene that is involved in cancer-associated phenotypes. In addition, STAT3 acts as a transcription factor binding with enh17 to regulate the transcription of ETV4. CONCLUSIONS Our findings revealed that enh17 plays an oncogenic role and promotes tumor progression in melanoma, and its transcriptional regulatory mechanisms were fully elucidated, which may open a promising window for melanoma prevention and treatment.
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Affiliation(s)
- Junyou Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Qilin Wang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Sihan Qi
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Yingying Duan
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Zhaoshuo Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Jiaxin Liu
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Ziyi Zhang
- School of Engineering Medicine, Beihang University, Beijing, 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Chunyan Li
- School of Engineering Medicine, Beihang University, Beijing, 100191, China.
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing, 100191, China.
- School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing, 100191, China.
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2
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Laskar RS, Qu C, Huyghe JR, Harrison T, Hayes RB, Cao Y, Campbell PT, Steinfelder R, Talukdar FR, Brenner H, Ogino S, Brendt S, Bishop DT, Buchanan DD, Chan AT, Cotterchio M, Gruber SB, Gsur A, van Guelpen B, Jenkins MA, Keku TO, Lynch BM, Le Marchand L, Martin RM, McCarthy K, Moreno V, Pearlman R, Song M, Tsilidis KK, Vodička P, Woods MO, Wu K, Hsu L, Gunter MJ, Peters U, Murphy N. Genome-wide association studies and Mendelian randomization analyses provide insights into the causes of early-onset colorectal cancer. Ann Oncol 2024; 35:523-536. [PMID: 38408508 PMCID: PMC11213623 DOI: 10.1016/j.annonc.2024.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 01/30/2024] [Accepted: 02/20/2024] [Indexed: 02/28/2024] Open
Abstract
BACKGROUND The incidence of early-onset colorectal cancer (EOCRC; diagnosed <50 years of age) is rising globally; however, the causes underlying this trend are largely unknown. CRC has strong genetic and environmental determinants, yet common genetic variants and causal modifiable risk factors underlying EOCRC are unknown. We conducted the first EOCRC-specific genome-wide association study (GWAS) and Mendelian randomization (MR) analyses to explore germline genetic and causal modifiable risk factors associated with EOCRC. PATIENTS AND METHODS We conducted a GWAS meta-analysis of 6176 EOCRC cases and 65 829 controls from the Genetics and Epidemiology of Colorectal Cancer Consortium (GECCO), the Colorectal Transdisciplinary Study (CORECT), the Colon Cancer Family Registry (CCFR), and the UK Biobank. We then used the EOCRC GWAS to investigate 28 modifiable risk factors using two-sample MR. RESULTS We found two novel risk loci for EOCRC at 1p34.1 and 4p15.33, which were not previously associated with CRC risk. We identified a deleterious coding variant (rs36053993, G396D) at polyposis-associated DNA repair gene MUTYH (odds ratio 1.80, 95% confidence interval 1.47-2.22) but show that most of the common genetic susceptibility was from noncoding signals enriched in epigenetic markers present in gastrointestinal tract cells. We identified new EOCRC-susceptibility genes, and in addition to pathways such as transforming growth factor (TGF) β, suppressor of Mothers Against Decapentaplegic (SMAD), bone morphogenetic protein (BMP) and phosphatidylinositol kinase (PI3K) signaling, our study highlights a role for insulin signaling and immune/infection-related pathways in EOCRC. In our MR analyses, we found novel evidence of probable causal associations for higher levels of body size and metabolic factors-such as body fat percentage, waist circumference, waist-to-hip ratio, basal metabolic rate, and fasting insulin-higher alcohol drinking, and lower education attainment with increased EOCRC risk. CONCLUSIONS Our novel findings indicate inherited susceptibility to EOCRC and suggest modifiable lifestyle and metabolic targets that could also be used to risk-stratify individuals for personalized screening strategies or other interventions.
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Affiliation(s)
- R S Laskar
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France; Early Cancer Institute, Department of Oncology, School of Clinical Medicine, University of Cambridge, Cambridge, UK.
| | - C Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle
| | - J R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle
| | - T Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle
| | - R B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York
| | - Y Cao
- Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine, St Louis; Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, St Louis; Alvin J. Siteman Cancer Center, St Louis
| | - P T Campbell
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, USA
| | - R Steinfelder
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle
| | - F R Talukdar
- Epigenomics and Mechanisms Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - H Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - S Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston; Program in Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston
| | - S Brendt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, USA
| | - D T Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds, UK
| | - D D Buchanan
- Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville; University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Melbourne; Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, Australia
| | - A T Chan
- Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston; Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, USA
| | - M Cotterchio
- Ontario Health (Cancer Care Ontario), Toronto; Dalla Lana School of Public Health, University of Toronto, Toronto, Canada
| | - S B Gruber
- Department of Medical Oncology & Therapeutics Research, City of Hope National Medical Center, Duarte, USA
| | - A Gsur
- Center for Cancer Research, Medical University of Vienna, Vienna, Austria
| | - B van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - M A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia
| | - T O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, USA
| | - B M Lynch
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Australia; Cancer Epidemiology Division, Cancer Council Victoria, Melbourne; Physical Activity Laboratory, Baker Heart and Diabetes Institute, Melbourne, Australia
| | | | - R M Martin
- Medical Research Council (MRC) Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol; National Institute for Health Research (NIHR) Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and the University of Bristol, Bristol
| | - K McCarthy
- Department of Colorectal Surgery, North Bristol NHS Trust, Bristol, UK
| | - V Moreno
- Cancer Prevention and Control Program, Catalan Institute of Oncology-IDIBELL, L'Hospitalet de Llobregat, Barcelona; CIBER de Epidemiología y Salud Pública (CIBERESP), Madrid; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona, Spain
| | - R Pearlman
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus
| | - M Song
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston; Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA
| | - K K Tsilidis
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK; Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece
| | - P Vodička
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, Prague; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, Pilsen, Czech Republic
| | - M O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, Canada
| | - K Wu
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, USA
| | - L Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle
| | - M J Gunter
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France; Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK
| | - U Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle; Department of Epidemiology, University of Washington, Seattle, USA
| | - N Murphy
- Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization, Lyon, France.
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3
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Robles J, Prakash A, Vizcaíno JA, Casal JI. Integrated meta-analysis of colorectal cancer public proteomic datasets for biomarker discovery and validation. PLoS Comput Biol 2024; 20:e1011828. [PMID: 38252632 PMCID: PMC10833860 DOI: 10.1371/journal.pcbi.1011828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 02/01/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024] Open
Abstract
The cancer biomarker field has been an object of thorough investigation in the last decades. Despite this, colorectal cancer (CRC) heterogeneity makes it challenging to identify and validate effective prognostic biomarkers for patient classification according to outcome and treatment response. Although a massive amount of proteomics data has been deposited in public data repositories, this rich source of information is vastly underused. Here, we attempted to reuse public proteomics datasets with two main objectives: i) to generate hypotheses (detection of biomarkers) for their posterior/downstream validation, and (ii) to validate, using an orthogonal approach, a previously described biomarker panel. Twelve CRC public proteomics datasets (mostly from the PRIDE database) were re-analysed and integrated to create a landscape of protein expression. Samples from both solid and liquid biopsies were included in the reanalysis. Integrating this data with survival annotation data, we have validated in silico a six-gene signature for CRC classification at the protein level, and identified five new blood-detectable biomarkers (CD14, PPIA, MRC2, PRDX1, and TXNDC5) associated with CRC prognosis. The prognostic value of these blood-derived proteins was confirmed using additional public datasets, supporting their potential clinical value. As a conclusion, this proof-of-the-concept study demonstrates the value of re-using public proteomics datasets as the basis to create a useful resource for biomarker discovery and validation. The protein expression data has been made available in the public resource Expression Atlas.
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Affiliation(s)
- Javier Robles
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
- Protein Alternatives SL, Tres Cantos, Madrid, Spain
| | - Ananth Prakash
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory—European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, United Kingdom
| | - J. Ignacio Casal
- Department of Molecular Biomedicine, Centro de Investigaciones Biológicas Margarita Salas, Consejo Superior de Investigaciones Científicas, Madrid, Spain
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4
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Michas A, Michas V, Anagnostou E, Galanopoulos M, Tolia M, Tsoukalas N. The Clinical Significance of MicroRNAs in Colorectal Cancer Signaling Pathways: A Review. Glob Med Genet 2023; 10:315-323. [PMID: 38025193 PMCID: PMC10665125 DOI: 10.1055/s-0043-1777094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2023] Open
Abstract
Colorectal carcinoma (colon and rectum) is currently considered among the most prevalent malignancies of Western societies. The pathogenesis and etiological mechanisms underlying colorectal cancer (CRC) development remain complex and heterogeneous. The homeostasis and function of normal human intestinal cells is highly regulated by microRNAs. Therefore, it is not surprising that mutations and inactivation of these molecules appear to be linked with progression of colorectal tumors. Recent studies have reported significant alterations of microRNA expression in adenomas and CRCs compared with adjacent normal tissues. This observed deviation has been proposed to correlate with the progression and survival of disease as well as with choice of optimal treatment and drug resistance. MicroRNAs can adopt either oncogenic or tumor-suppressive roles during regulation of pathways that drive carcinogenesis. Typically, oncogenic microRNAs termed oncomirs, target and silence endogenous tumor-suppressor genes. On the other hand, tumor-suppressive microRNAs are critical in downregulating genes associated with cell growth and malignant capabilities. By extensively evaluating robust studies, we have emphasized and distinguished a discrete set of microRNAs that can modulate tumor progression by silencing specific driver genes crucial in signaling pathways including Wnt/b-catenin, epidermal growth factor receptor, P53, mismatch repair DNA repair, and transforming-growth factor beta.
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Affiliation(s)
- Athanasios Michas
- Department of Oncology, 401 General Military Hospital of Athens, Athens, Greece
| | - Vasileios Michas
- Department of Radiology, Achepa General Hospital Thessaloniki, Thessaloniki, Greece
| | - Evangelos Anagnostou
- Department of Neurosurgery, Queen Elizabeth Hospital Birmingham, Birmingham, United Kingdom
| | | | - Maria Tolia
- Department of Oncology, 401 General Military Hospital of Athens, Athens, Greece
| | - Nikolaos Tsoukalas
- Department of Oncology, 401 General Military Hospital of Athens, Athens, Greece
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5
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Arnold M, Stengel KR. Emerging insights into enhancer biology and function. Transcription 2023; 14:68-87. [PMID: 37312570 PMCID: PMC10353330 DOI: 10.1080/21541264.2023.2222032] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
Cell type-specific gene expression is coordinated by DNA-encoded enhancers and the transcription factors (TFs) that bind to them in a sequence-specific manner. As such, these enhancers and TFs are critical mediators of normal development and altered enhancer or TF function is associated with the development of diseases such as cancer. While initially defined by their ability to activate gene transcription in reporter assays, putative enhancer elements are now frequently defined by their unique chromatin features including DNase hypersensitivity and transposase accessibility, bidirectional enhancer RNA (eRNA) transcription, CpG hypomethylation, high H3K27ac and H3K4me1, sequence-specific transcription factor binding, and co-factor recruitment. Identification of these chromatin features through sequencing-based assays has revolutionized our ability to identify enhancer elements on a genome-wide scale, and genome-wide functional assays are now capitalizing on this information to greatly expand our understanding of how enhancers function to provide spatiotemporal coordination of gene expression programs. Here, we highlight recent technological advances that are providing new insights into the molecular mechanisms by which these critical cis-regulatory elements function in gene control. We pay particular attention to advances in our understanding of enhancer transcription, enhancer-promoter syntax, 3D organization and biomolecular condensates, transcription factor and co-factor dependencies, and the development of genome-wide functional enhancer screens.
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Affiliation(s)
- Mirjam Arnold
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Kristy R. Stengel
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY, USA
- Montefiore Einstein Cancer Center, Albert Einstein College of Medicine-Montefiore Health System, Bronx, NY, USA
- Ruth L. and David S. Gottesman Institute for Stem Cell and Regenerative Medicine Research, Albert Einstein College of Medicine, Bronx, NY, USA
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6
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Wang Y, Wang R, Yuan S, Liu X. Genetic polymorphisms of CYP24A1 gene and cancer susceptibility: a meta-analysis including 40640 subjects. World J Surg Oncol 2023; 21:279. [PMID: 37670334 PMCID: PMC10478352 DOI: 10.1186/s12957-023-03156-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 08/19/2023] [Indexed: 09/07/2023] Open
Abstract
BACKGROUND Whether cytochrome P450 24A1 (CYP24A1) polymorphism is associated with cancer susceptibility, the individual study results are still controversial. Therefore, we performed a comprehensive study to identify the association of CYP24A1 polymorphisms (rs4809960, rs6068816, rs2296241, rs4809957, rs2762939) with cancer susceptibility. METHODS Electronic databases including Cochrane Library, PubMed, and Embase were systematically retrieved for relevant publications. Fixed or random-effect model was selected to calculate odds ratios (ORs) with their 95% confidence intervals (95%CI). RESULTS Eighteen published articles were identified. The results indicated that rs4809960 polymorphism was associated with a decreased cancer risk in Caucasian (TT vs. TC+CC: P=0.035; C vs. T: P=0.016) and Asian population (CC vs. TC+TT: OR P=0.044; TT vs. TC+CC: P=0.021; CC vs. TT: P=0.020; C vs. T: P=0.008) and breast cancer risk (TT vs. TC+CC: P = 0.007; TC vs. TT: P=0.004; C vs. T: P=0.033). A significant association was found between rs2296241 polymorphism and esophageal squamous cell carcinoma risk (AA vs. GG+AG: P = 0.023) and prostate cancer susceptibility (A vs. G: P=0.022). Furthermore, rs4809957 polymorphism was associated with prostate cancer susceptibility in Caucasian (GG vs. GA+AA: P=0.029; GA vs. GG: P=0.022) and breast cancer susceptibility (AA vs. GG+GA: P=0.012; AA vs. GG, P=0.010; A vs. G: P=0.024). Additionally, rs6068816 polymorphism significantly decreased the lung cancer (CC vs. CT+TT: P = 0.016; TT vs. CC: P = 0.044; CT vs. CC: P = 0.036; T vs. C: P = 0.016) and breast cancer risk (TT vs. CC+CT: P = 0.043; TT vs. CC: P = 0.039). No association was found for rs2762939 polymorphism with overall cancer risk. However, for rs2296241, rs4809957, and rs6068816 polymorphisms, there were no significant differences after the Bonferroni correction. CONCLUSION The meta-analysis suggested that rs4809960 was associated with cancer risk and might be a genetic marker for predicting cancer risk. More large-scale and large-sample studies are necessary to further confirm these results.
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Affiliation(s)
- Yubin Wang
- Department of Radiotherapy, Ruian People's Hospital, Ruian, 325200, Zhejiang, China
| | - Ruiwen Wang
- Gannan Medical University, Ganzhou, 341000, Jiangxi, China
| | - Shaofei Yuan
- Department of Oncology, Ruian People's Hospital, Ruian, 325200, Zhejiang, China.
| | - Xiaotang Liu
- Department of Urology, Ruian People's Hospital, Ruian, 325200, Zhejiang, China.
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7
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Bhoite R, Smith R, Bansal U, Dowla M, Bariana H, Sharma D. Exome-based new allele-specific PCR markers and transferability for sodicity tolerance in bread wheat ( Triticum aestivum L.). PLANT DIRECT 2023; 7:e520. [PMID: 37600239 PMCID: PMC10435944 DOI: 10.1002/pld3.520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/10/2023] [Accepted: 07/27/2023] [Indexed: 08/22/2023]
Abstract
Targeted exome-based genotype by sequencing (t-GBS), a sequencing technology that tags SNPs and haplotypes in gene-rich regions was used in previous genome-wide association studies (GWAS) for sodicity tolerance in bread wheat. Thirty-nine novel SNPs including 18 haplotypes for yield and yield-components were identified. The present study aimed at developing SNP-derived markers by precisely locating new SNPs on ~180 bp allelic sequence of t-GBS, marker validation, and SNP functional characterization based on its exonic location. We identified unknown locations of significant SNPs/haplotypes by aligning allelic sequences on to IWGSC RefSeqv1.0 on respective chromosomes. Eighteen out of the target 39 SNP locations fulfilled the criteria for producing PCR markers, among which only eight produced polymorphic signals. These eight markers associated with yield, plants m-2, heads m-2, and harvest index, including a pleiotropic marker for yield, harvest index, and grains/head were validated for its amplification efficiency and phenotypic effects in focused identification germplasm strategy (FIGS) wheat set and a doubled haploid (DH) population (Scepter/IG107116). The phenotypic variation explained by these markers are in the range of 4.1-37.6 in the FIGS population. High throughput PCR-based genotyping using new markers and association with phenotypes in FIGS wheat set and DH population validated the effect of functional SNP on closely associated genes-calcineurin B-like- and dirigent protein, basic helix-loop-helix (BHLH-), plant homeodomain (PHD-) and helix-turn-helix myeloblastosis (HTH myb) type -transcription factor. Further, genome-wide SNP annotation using SnpEff tool confirmed that these SNPs are in gene regulatory regions (upstream, 3'-UTR, and intron) modifying gene expression and protein-coding. This integrated approach of marker design for t-GBS alleles, SNP functional annotation, and high-throughput genotyping of functional SNP offers translation solutions across crops and complex traits in crop improvement programs.
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Affiliation(s)
- Roopali Bhoite
- Grains Genetic ImprovementDepartment of Primary Industries and Regional DevelopmentSouth PerthWestern AustraliaAustralia
- The UWA Institute of AgricultureThe University of Western AustraliaPerthWestern AustraliaAustralia
| | - Rosemary Smith
- Grains Genetic ImprovementDepartment of Primary Industries and Regional DevelopmentSouth PerthWestern AustraliaAustralia
| | - Urmil Bansal
- Plant Breeding Institute, School of Life Sciences, Faculty of ScienceThe University of SydneyCobbittyNew South WalesAustralia
| | - Mirza Dowla
- Grains Genetic ImprovementDepartment of Primary Industries and Regional DevelopmentSouth PerthWestern AustraliaAustralia
| | - Harbans Bariana
- School of ScienceWestern Sydney UniversityRichmondNew South WalesAustralia
| | - Darshan Sharma
- Grains Genetic ImprovementDepartment of Primary Industries and Regional DevelopmentSouth PerthWestern AustraliaAustralia
- College of Science, Health, Engineering and EducationMurdoch UniversityPerthWestern AustraliaAustralia
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8
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Rozowsky J, Gao J, Borsari B, Yang YT, Galeev T, Gürsoy G, Epstein CB, Xiong K, Xu J, Li T, Liu J, Yu K, Berthel A, Chen Z, Navarro F, Sun MS, Wright J, Chang J, Cameron CJF, Shoresh N, Gaskell E, Drenkow J, Adrian J, Aganezov S, Aguet F, Balderrama-Gutierrez G, Banskota S, Corona GB, Chee S, Chhetri SB, Cortez Martins GC, Danyko C, Davis CA, Farid D, Farrell NP, Gabdank I, Gofin Y, Gorkin DU, Gu M, Hecht V, Hitz BC, Issner R, Jiang Y, Kirsche M, Kong X, Lam BR, Li S, Li B, Li X, Lin KZ, Luo R, Mackiewicz M, Meng R, Moore JE, Mudge J, Nelson N, Nusbaum C, Popov I, Pratt HE, Qiu Y, Ramakrishnan S, Raymond J, Salichos L, Scavelli A, Schreiber JM, Sedlazeck FJ, See LH, Sherman RM, Shi X, Shi M, Sloan CA, Strattan JS, Tan Z, Tanaka FY, Vlasova A, Wang J, Werner J, Williams B, Xu M, Yan C, Yu L, Zaleski C, Zhang J, Ardlie K, Cherry JM, Mendenhall EM, Noble WS, Weng Z, Levine ME, Dobin A, Wold B, Mortazavi A, Ren B, Gillis J, Myers RM, Snyder MP, Choudhary J, Milosavljevic A, Schatz MC, Bernstein BE, Guigó R, Gingeras TR, Gerstein M. The EN-TEx resource of multi-tissue personal epigenomes & variant-impact models. Cell 2023; 186:1493-1511.e40. [PMID: 37001506 PMCID: PMC10074325 DOI: 10.1016/j.cell.2023.02.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 10/16/2022] [Accepted: 02/10/2023] [Indexed: 04/03/2023]
Abstract
Understanding how genetic variants impact molecular phenotypes is a key goal of functional genomics, currently hindered by reliance on a single haploid reference genome. Here, we present the EN-TEx resource of 1,635 open-access datasets from four donors (∼30 tissues × ∼15 assays). The datasets are mapped to matched, diploid genomes with long-read phasing and structural variants, instantiating a catalog of >1 million allele-specific loci. These loci exhibit coordinated activity along haplotypes and are less conserved than corresponding, non-allele-specific ones. Surprisingly, a deep-learning transformer model can predict the allele-specific activity based only on local nucleotide-sequence context, highlighting the importance of transcription-factor-binding motifs particularly sensitive to variants. Furthermore, combining EN-TEx with existing genome annotations reveals strong associations between allele-specific and GWAS loci. It also enables models for transferring known eQTLs to difficult-to-profile tissues (e.g., from skin to heart). Overall, EN-TEx provides rich data and generalizable models for more accurate personal functional genomics.
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Affiliation(s)
- Joel Rozowsky
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jiahao Gao
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Beatrice Borsari
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai 200433, China; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Timur Galeev
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Gamze Gürsoy
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Kun Xiong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jinrui Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Tianxiao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jason Liu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Keyang Yu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ana Berthel
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Zhanlin Chen
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
| | - Fabio Navarro
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Maxwell S Sun
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Justin Chang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Christopher J F Cameron
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Noam Shoresh
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Jorg Drenkow
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jessika Adrian
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Sergey Aganezov
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | | | | | | | | | - Sora Chee
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Surya B Chhetri
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Gabriel Conte Cortez Martins
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Cassidy Danyko
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Carrie A Davis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Daniel Farid
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | | | - Idan Gabdank
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Yoel Gofin
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - David U Gorkin
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Mengting Gu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Vivian Hecht
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin C Hitz
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Robbyn Issner
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunzhe Jiang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Melanie Kirsche
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xiangmeng Kong
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bonita R Lam
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Bian Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Xiqi Li
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Khine Zin Lin
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, CHN
| | - Mark Mackiewicz
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Ran Meng
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jill E Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Jonathan Mudge
- European Bioinformatics Institute, Cambridge, Cambridgeshire, GB
| | | | - Chad Nusbaum
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ioann Popov
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Henry E Pratt
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Yunjiang Qiu
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Srividya Ramakrishnan
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joe Raymond
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Biological and Chemical Sciences, New York Institute of Technology, Old Westbury, NY, USA
| | - Alexandra Scavelli
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jacob M Schreiber
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Fritz J Sedlazeck
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Lei Hoon See
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Rachel M Sherman
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Xu Shi
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Minyi Shi
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Cricket Alicia Sloan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - J Seth Strattan
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Zhen Tan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Forrest Y Tanaka
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | - Anna Vlasova
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Comparative Genomics Group, Life Science Programme, Barcelona Supercomputing Centre, Barcelona, Spain; Institute of Research in Biomedicine, Barcelona, Spain
| | - Jun Wang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Jonathan Werner
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Brian Williams
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Min Xu
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Chengfei Yan
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Lu Yu
- Institute of Cancer Research, London, UK
| | - Christopher Zaleski
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jing Zhang
- Department of Computer Science, University of California, Irvine, Irvine, CA, USA
| | | | - J Michael Cherry
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA, USA
| | - Morgan E Levine
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Alexander Dobin
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Barbara Wold
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Ali Mortazavi
- Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA
| | - Bing Ren
- Ludwig Institute for Cancer Research, University of California, San Diego, La Jolla, CA, USA
| | - Jesse Gillis
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA; Department of Physiology, University of Toronto, Toronto, ON, Canada
| | - Richard M Myers
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
| | - Michael P Snyder
- Department of Genetics, School of Medicine, Stanford University, Palo Alto, CA, USA
| | | | | | - Michael C Schatz
- Departments of Computer Science and Biology, Johns Hopkins University, Baltimore, MD, USA; Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Bradley E Bernstein
- Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Roderic Guigó
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Catalonia, Spain; Universitat Pompeu Fabra, Barcelona, Catalonia, Spain.
| | - Thomas R Gingeras
- Functional Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
| | - Mark Gerstein
- Section on Biomedical Informatics and Data Science, Yale University, New Haven, CT, USA; Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA; Department of Statistics and Data Science, Yale University, New Haven, CT, USA; Department of Computer Science, Yale University, New Haven, CT, USA.
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9
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Meng H, Nan M, Li Y, Ding Y, Yin Y, Zhang M. Application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer. Front Endocrinol (Lausanne) 2023; 14:1148412. [PMID: 37020597 PMCID: PMC10067930 DOI: 10.3389/fendo.2023.1148412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 03/07/2023] [Indexed: 04/07/2023] Open
Abstract
Colon cancer is the fourth leading cause of cancer death worldwide, and its progression is accompanied by a complex array of genetic variations. CRISPR/Cas9 can identify new drug-resistant or sensitive mutations in colon cancer, and can use gene editing technology to develop new therapeutic targets and provide personalized treatments, thereby significantly improving the treatment of colon cancer patients. CRISPR/Cas9 systems are driving advances in biotechnology. RNA-directed Cas enzymes have accelerated the pace of basic research and led to clinical breakthroughs. This article reviews the rapid development of CRISPR/Cas in colon cancer, from gene editing to transcription regulation, gene knockout, genome-wide CRISPR tools, therapeutic targets, stem cell genomics, immunotherapy, metabolism-related genes and inflammatory bowel disease. In addition, the limitations and future development of CRISPR/Cas9 in colon cancer studies are reviewed. In conclusion, this article reviews the application of CRISPR-Cas9 gene editing technology in basic research, diagnosis and treatment of colon cancer.
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Affiliation(s)
- Hui Meng
- Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- *Correspondence: Mingzhi Zhang, ; Hui Meng,
| | - Manman Nan
- Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yizhen Li
- Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yi Ding
- Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Yuhui Yin
- Department of Pathology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Mingzhi Zhang
- Department of Oncology, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
- *Correspondence: Mingzhi Zhang, ; Hui Meng,
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10
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Lulli M, Napoli C, Landini I, Mini E, Lapucci A. Role of Non-Coding RNAs in Colorectal Cancer: Focus on Long Non-Coding RNAs. Int J Mol Sci 2022; 23:13431. [PMID: 36362222 PMCID: PMC9654895 DOI: 10.3390/ijms232113431] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/17/2022] [Accepted: 10/31/2022] [Indexed: 07/30/2023] Open
Abstract
Colorectal cancer is one of the most common causes of cancer-related deaths worldwide. Despite the advances in the knowledge of pathogenetic molecular mechanisms and the implementation of more effective drug treatments in recent years, the overall survival rate of patients remains unsatisfactory. The high death rate is mainly due to metastasis of cancer in about half of the cancer patients and the emergence of drug-resistant populations of cancer cells. Improved understanding of cancer molecular biology has highlighted the role of non-coding RNAs (ncRNAs) in colorectal cancer development and evolution. ncRNAs regulate gene expression through various mechanisms, including epigenetic modifications and interactions of long non-coding RNAs (lncRNAs) with both microRNAs (miRNAs) and proteins, and through the action of lncRNAs as miRNA precursors or pseudogenes. LncRNAs can also be detected in the blood and circulating ncRNAs have become a new source of non-invasive cancer biomarkers for the diagnosis and prognosis of colorectal cancer, as well as for predicting the response to drug therapy. In this review, we focus on the role of lncRNAs in colorectal cancer development, progression, and chemoresistance, and as possible therapeutic targets.
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Affiliation(s)
- Matteo Lulli
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, Section of General Pathology, University of Florence, 50134 Florence, Italy
| | - Cristina Napoli
- Department of Health Sciences, Section of Clinical Pharmacology and Oncology, University of Florence, 50139 Florence, Italy
| | - Ida Landini
- Department of Health Sciences, Section of Clinical Pharmacology and Oncology, University of Florence, 50139 Florence, Italy
| | - Enrico Mini
- Department of Health Sciences, Section of Clinical Pharmacology and Oncology, University of Florence, 50139 Florence, Italy
| | - Andrea Lapucci
- Department of Health Sciences, Section of Clinical Pharmacology and Oncology, University of Florence, 50139 Florence, Italy
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11
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Shen T, Ni T, Chen J, Chen H, Ma X, Cao G, Wu T, Xie H, Zhou B, Wei G, Saiyin H, Shen S, Yu P, Xiao Q, Liu H, Gao Y, Long X, Yin J, Guo Y, Wu J, Wei GH, Hou J, Jiang DK. An enhancer variant at 16q22.1 predisposes to hepatocellular carcinoma via regulating PRMT7 expression. Nat Commun 2022; 13:1232. [PMID: 35264579 PMCID: PMC8907293 DOI: 10.1038/s41467-022-28861-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 02/16/2022] [Indexed: 12/24/2022] Open
Abstract
Most cancer causal variants are found in gene regulatory elements, e.g., enhancers. However, enhancer variants predisposing to hepatocellular carcinoma (HCC) remain unreported. Here we conduct a genome-wide survey of HCC-susceptible enhancer variants through a three-stage association study in 11,958 individuals and identify rs73613962 (T > G) within the intronic region of PRMT7 at 16q22.1 as a susceptibility locus of HCC (OR = 1.41, P = 6.02 × 10-10). An enhancer dual-luciferase assay indicates that the rs73613962-harboring region has allele-specific enhancer activity. CRISPR-Cas9/dCas9 experiments further support the enhancer activity of this region to regulate PRMT7 expression. Mechanistically, transcription factor HNF4A binds to this enhancer region, with preference to the risk allele G, to promote PRMT7 expression. PRMT7 upregulation contributes to in vitro, in vivo, and clinical HCC-associated phenotypes, possibly by affecting the p53 signaling pathway. This concept of HCC pathogenesis may open a promising window for HCC prevention/treatment.
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Affiliation(s)
- Ting Shen
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.,School of Life Sciences, Central South University, 510006, Changsha, China
| | - Ting Ni
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Jiaxuan Chen
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Haitao Chen
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.,School of Public Health (Shenzhen), Sun Yat-sen University, 528406, Shenzhen, China
| | - Xiaopin Ma
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Guangwen Cao
- Department of Epidemiology, Naval Medical University, 200433, Shanghai, China
| | - Tianzhi Wu
- Institute of Bioinformatics, School of Basic Medical Science, Southern Medical University, 510515, Guangzhou, China
| | - Haisheng Xie
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Bin Zhou
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - Gang Wei
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Hexige Saiyin
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Suqin Shen
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Peng Yu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Qianyi Xiao
- School of Public Health, Fudan University, 200032, Shanghai, China
| | - Hui Liu
- School of Basic Medical Sciences; The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan People's hospital, Guangzhou Medical University, 510182, Guangzhou, China
| | - Yuzheng Gao
- Department of Forensic Medicine, Medical College of Soochow University, 215123, Suzhou, Jiangsu Province, China
| | - Xidai Long
- Department of Pathology, Youjiang Medical College for Nationalities, 533000, Baise, Guangxi Province, China
| | - Jianhua Yin
- Department of Epidemiology, Naval Medical University, 200433, Shanghai, China
| | - Yanfang Guo
- Institute of Bioinformatics, School of Basic Medical Science, Southern Medical University, 510515, Guangzhou, China
| | - Jiaxue Wu
- State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, Human Phenome Institute, School of Life Sciences, Fudan University, 200438, Shanghai, China
| | - Gong-Hong Wei
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, 90014, Oulu, Finland.,School of Basic Medical Sciences, Fudan University, 200032, Shanghai, China
| | - Jinlin Hou
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China
| | - De-Ke Jiang
- State Key Laboratory of Organ Failure Research, Guangdong Key Laboratory of Viral Hepatitis Research, Guangdong Institute of Liver Diseases, Department of Infectious Diseases and Hepatology Unit, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China.
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12
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The association between single polymorphic positions and the risk of acute lymphoblastic leukemia. Meta Gene 2022. [DOI: 10.1016/j.mgene.2021.101006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
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13
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Sheng T, Ho SWT, Ooi WF, Xu C, Xing M, Padmanabhan N, Huang KK, Ma L, Ray M, Guo YA, Sim NL, Anene-Nzelu CG, Chang MM, Razavi-Mohseni M, Beer MA, Foo RSY, Sundar R, Chan YH, Tan ALK, Ong X, Skanderup AJ, White KP, Jha S, Tan P. Integrative epigenomic and high-throughput functional enhancer profiling reveals determinants of enhancer heterogeneity in gastric cancer. Genome Med 2021; 13:158. [PMID: 34635154 PMCID: PMC8504099 DOI: 10.1186/s13073-021-00970-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 09/15/2021] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Enhancers are distal cis-regulatory elements required for cell-specific gene expression and cell fate determination. In cancer, enhancer variation has been proposed as a major cause of inter-patient heterogeneity-however, most predicted enhancer regions remain to be functionally tested. METHODS We analyzed 132 epigenomic histone modification profiles of 18 primary gastric cancer (GC) samples, 18 normal gastric tissues, and 28 GC cell lines using Nano-ChIP-seq technology. We applied Capture-based Self-Transcribing Active Regulatory Region sequencing (CapSTARR-seq) to assess functional enhancer activity. An Activity-by-contact (ABC) model was employed to explore the effects of histone acetylation and CapSTARR-seq levels on enhancer-promoter interactions. RESULTS We report a comprehensive catalog of 75,730 recurrent predicted enhancers, the majority of which are GC-associated in vivo (> 50,000) and associated with lower somatic mutation rates inferred by whole-genome sequencing. Applying CapSTARR-seq to the enhancer catalog, we observed significant correlations between CapSTARR-seq functional activity and H3K27ac/H3K4me1 levels. Super-enhancer regions exhibited increased CapSTARR-seq signals compared to regular enhancers, even when decoupled from native chromatin contexture. We show that combining histone modification and CapSTARR-seq functional enhancer data improves the prediction of enhancer-promoter interactions and pinpointing of germline single nucleotide polymorphisms (SNPs), somatic copy number alterations (SCNAs), and trans-acting TFs involved in GC expression. We identified cancer-relevant genes (ING1, ARL4C) whose expression between patients is influenced by enhancer differences in genomic copy number and germline SNPs, and HNF4α as a master trans-acting factor associated with GC enhancer heterogeneity. CONCLUSIONS Our results indicate that combining histone modification and functional assay data may provide a more accurate metric to assess enhancer activity than either platform individually, providing insights into the relative contribution of genetic (cis) and regulatory (trans) mechanisms to GC enhancer functional heterogeneity.
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Affiliation(s)
- Taotao Sheng
- Department of Biochemistry, National University of Singapore, Singapore, 117596, Singapore
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Shamaine Wei Ting Ho
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
| | - Wen Fong Ooi
- Epigenetic and Epitranscriptomic Regulation, Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Chang Xu
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore
| | - Manjie Xing
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
- Epigenetic and Epitranscriptomic Regulation, Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Nisha Padmanabhan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Kie Kyon Huang
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Lijia Ma
- The Institute for Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, USA
| | - Mohana Ray
- The Institute for Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, USA
| | - Yu Amanda Guo
- Precision Medicine and Population Genomics (Somatic), Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Ngak Leng Sim
- Precision Medicine and Population Genomics (Somatic), Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Chukwuemeka George Anene-Nzelu
- Cardiovascular Research Institute, National University Health System, Singapore, 119074, Singapore
- Precision Medicine and Population Genomics (Germline), Genome Institute of Singapore, Singapore, Singapore
- Montreal Heart Institute, Montreal, Canada
- Department of Medicine, University of Montreal, Montreal, Canada
| | - Mei Mei Chang
- Precision Medicine and Population Genomics (Somatic), Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Milad Razavi-Mohseni
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, USA
| | - Michael A Beer
- Department of Biomedical Engineering and McKusick-Nathans Department of Genetic Medicine, Johns Hopkins University, Baltimore, USA
| | - Roger Sik Yin Foo
- Cardiovascular Research Institute, National University Health System, Singapore, 119074, Singapore
- Precision Medicine and Population Genomics (Germline), Genome Institute of Singapore, Singapore, Singapore
| | - Raghav Sundar
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
- Department of Haematology-Oncology, National University Cancer Institute Singapore, National University Hospital, Singapore, 119074, Singapore
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 119228, Singapore
| | - Angie Lay Keng Tan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Xuewen Ong
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Anders Jacobsen Skanderup
- Precision Medicine and Population Genomics (Somatic), Genome Institute of Singapore, Singapore, 138672, Singapore
| | - Kevin P White
- The Institute for Genomics and Systems Biology, The University of Chicago, Chicago, Illinois, USA.
- Tempus Labs, Chicago, USA.
| | - Sudhakar Jha
- Department of Biochemistry, National University of Singapore, Singapore, 117596, Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore.
- NUS Center for Cancer Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117599, Singapore.
- Department of Physiological Sciences, College of Veterinary Medicine, Oklahoma State University, Stillwater, OK, USA.
| | - Patrick Tan
- Cancer and Stem Cell Biology Program, Duke-NUS Medical School, Singapore, 169857, Singapore.
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, 117599, Singapore.
- Epigenetic and Epitranscriptomic Regulation, Genome Institute of Singapore, Singapore, 138672, Singapore.
- SingHealth/Duke-NUS Institute of Precision Medicine, National Heart Centre Singapore, Singapore, 168752, Singapore.
- Department of Physiology, National University of Singapore, Singapore, 117593, Singapore.
- Singapore Gastric Cancer Consortium, Singapore, 119228, Singapore.
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14
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Novel insights into the molecular mechanisms underlying risk of colorectal cancer from smoking and red/processed meat carcinogens by modeling exposure in normal colon organoids. Oncotarget 2021; 12:1863-1877. [PMID: 34548904 PMCID: PMC8448508 DOI: 10.18632/oncotarget.28058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 08/13/2021] [Indexed: 12/23/2022] Open
Abstract
Tobacco smoke and red/processed meats are well-known risk factors for colorectal cancer (CRC). Most research has focused on studies of normal colon biopsies in epidemiologic studies or treatment of CRC cell lines in vitro. These studies are often constrained by challenges with accuracy of self-report data or, in the case of CRC cell lines, small sample sizes and lack of relationship to normal tissue at risk. In an attempt to address some of these limitations, we performed a 24-hour treatment of a representative carcinogens cocktail in 37 independent organoid lines derived from normal colon biopsies. Machine learning algorithms were applied to bulk RNA-sequencing and revealed cellular composition changes in colon organoids. We identified 738 differentially expressed genes in response to carcinogens exposure. Network analysis identified significantly different modules of co-expression, that included genes related to MSI-H tumor biology, and genes previously implicated in CRC through genome-wide association studies. Our study helps to better define the molecular effects of representative carcinogens from smoking and red/processed meat in normal colon epithelial cells and in the etiology of the MSI-H subtype of CRC, and suggests an overlap between molecular mechanisms involved in inherited and environmental CRC risk.
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15
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Kikuchi Y, Tokita S, Hirama T, Kochin V, Nakatsugawa M, Shinkawa T, Hirohashi Y, Tsukahara T, Hata F, Takemasa I, Sato N, Kanaseki T, Torigoe T. CD8 + T-cell Immune Surveillance against a Tumor Antigen Encoded by the Oncogenic Long Noncoding RNA PVT1. Cancer Immunol Res 2021; 9:1342-1353. [PMID: 34433589 DOI: 10.1158/2326-6066.cir-20-0964] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 04/11/2021] [Accepted: 08/23/2021] [Indexed: 11/16/2022]
Abstract
CD8+ T cells recognize peptides displayed by HLA class I molecules on cell surfaces, monitoring pathologic conditions such as cancer. Advances in proteogenomic analysis of HLA ligandomes have demonstrated that cells present a subset of cryptic peptides derived from noncoding regions of the genome; however, the roles of cryptic HLA ligands in tumor immunity remain unknown. In the current study, we comprehensively and quantitatively investigated the HLA class I ligandome of a set of human colorectal cancer and matched normal tissues, showing that cryptic translation products accounted for approximately 5% of the HLA class I ligandome. We also found that a peptide encoded by the long noncoding RNA (lncRNA) PVT1 was predominantly enriched in multiple colorectal cancer tissues. The PVT1 gene is located downstream of the MYC gene in the genome and is aberrantly overexpressed across a variety of cancers, reflecting its oncogenic property. The PVT1 peptide was recognized by patient CD8+ tumor-infiltrating lymphocytes, as well as peripheral blood mononuclear cells, suggesting the presence of patient immune surveillance. Our findings show that peptides can be translated from lncRNAs and presented by HLA class I and that cancer patient T cells are capable of sensing aberrations in noncoding regions of the genome.
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Affiliation(s)
- Yasuhiro Kikuchi
- Department of Pathology, Sapporo Medical University, Sapporo, Japan
| | - Serina Tokita
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.,Sapporo Dohto Hospital, Sapporo, Japan
| | - Tomomi Hirama
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.,Sapporo Dohto Hospital, Sapporo, Japan
| | - Vitaly Kochin
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.,Department of Immunology, Nagoya University, Nagoya, Japan
| | - Munehide Nakatsugawa
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.,Department of Pathology, Tokyo Medical University Hachioji Medical Center, Hachioji, Tokyo, Japan
| | - Tomoyo Shinkawa
- Department of Pathology, Sapporo Medical University, Sapporo, Japan
| | | | | | | | - Ichiro Takemasa
- Department of Surgery, Surgical Oncology and Science, Sapporo Medical University, Sapporo, Japan
| | - Noriyuki Sato
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.,Sapporo Dohto Hospital, Sapporo, Japan
| | - Takayuki Kanaseki
- Department of Pathology, Sapporo Medical University, Sapporo, Japan.
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16
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Epigenetic alteration contributes to the transcriptional reprogramming in T-cell prolymphocytic leukemia. Sci Rep 2021; 11:8318. [PMID: 33859327 PMCID: PMC8050249 DOI: 10.1038/s41598-021-87890-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 04/05/2021] [Indexed: 12/12/2022] Open
Abstract
T cell prolymphocytic leukemia (T-PLL) is a rare disease with aggressive clinical course. Cytogenetic analysis, whole-exome and whole-genome sequencing have identified primary structural alterations in T-PLL, including inversion, translocation and copy number variation. Recurrent somatic mutations were also identified in genes encoding chromatin regulators and those in the JAK-STAT signaling pathway. Epigenetic alterations are the hallmark of many cancers. However, genome-wide epigenomic profiles have not been reported in T-PLL, limiting the mechanistic study of its carcinogenesis. We hypothesize epigenetic mechanisms also play a key role in T-PLL pathogenesis. To systematically test this hypothesis, we generated genome-wide maps of regulatory regions using H3K4me3 and H3K27ac ChIP-seq, as well as RNA-seq data in both T-PLL patients and healthy individuals. We found that genes down-regulated in T-PLL are mainly associated with defense response, immune system or adaptive immune response, while up-regulated genes are enriched in developmental process, as well as WNT signaling pathway with crucial roles in cell fate decision. In particular, our analysis revealed a global alteration of regulatory landscape in T-PLL, with differential peaks highly enriched for binding motifs of immune related transcription factors, supporting the epigenetic regulation of oncogenes and genes involved in DNA damage response and T-cell activation. Together, our work reveals a causal role of epigenetic dysregulation in T-PLL.
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17
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Wang X, Hayes JE, Xu X, Gao X, Mehta D, Lilja HG, Klein RJ. Validation of prostate cancer risk variants rs10993994 and rs7098889 by CRISPR/Cas9 mediated genome editing. Gene 2020; 768:145265. [PMID: 33122083 DOI: 10.1016/j.gene.2020.145265] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 09/10/2020] [Accepted: 10/20/2020] [Indexed: 12/20/2022]
Abstract
GWAS have identified numerous SNPs associated with prostate cancer risk. One such SNP is rs10993994. It is located in the β-microseminoprotein (MSMB) promoter region, mediates MSMB prostate secretion levels, and is linked to mRNA expression changes in both MSMB and the adjacent gene NCOA4. In addition, our previous work showed a second SNP, rs7098889, is in positive linkage disequilibrium with rs10993994 and associated with MSMB expression independent of rs10993994. Here, we generate a series of clones with single alleles removed by double guide RNA (gRNA) mediated CRISPR/Cas9 deletions, through which we demonstrate that each of these SNPs independently and greatly alters MSMB expression in an allele-specific manner. We further show that these SNPs have no substantial effect on the expression of NCOA4. These data demonstrate that a single SNP can have a large effect on gene expression and illustrate the importance of functional validation studies to deconvolute observed correlations. The method we have developed is generally applicable to test any SNP for which a relevant heterozygous cell line is available. AUTHOR SUMMARY: In pursuing the underlying biological mechanism of prostate cancer pathogenesis, scientists utilized the existence of common single nucleotide polymorphisms (SNPs) in the human genome as genetic markers to perform large scale genome wide association studies (GWAS) and have so far identified more than a hundred prostate cancer risk variants. Such variants provide an unbiased and systematic new venue to study the disease mechanism, and the next big challenge is to translate these genetic associations to the causal role of altered gene function in oncogenesis. The majority of these variants are waiting to be studied and lots of them may act in oncogenesis through gene expression regulation. To prove the concept, we took rs10993994 and its linked rs7098889 as an example and engineered single cell clones by allelic-specific CRISPR/Cas9 deletion to separate the effect of each allele. We observed that a single nucleotide difference would lead to surprisingly high level of MSMB gene expression change in a gene specific and cell-type specific manner. Our study strongly supports the notion that differential level of gene expression caused by risk variants and their associated genetic locus play a major role in oncogenesis and also highlights the importance of studying the function of MSMB encoded β-MSP in prostate cancer pathogenesis.
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Affiliation(s)
- Xing Wang
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - James E Hayes
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Program in Cancer Biology and Genetics and Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, United States; Graduate School of Biomedical Sciences, Weill Cornell Medical College, New York, NY, United States
| | - Xing Xu
- Program in Cancer Biology and Genetics and Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, United States; Graduate School of Biomedical Sciences, Weill Cornell Medical College, New York, NY, United States
| | - Xiaoni Gao
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Program in Cancer Biology and Genetics and Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Dipti Mehta
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Hans G Lilja
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Departments of Laboratory Medicine and Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, United States; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK and Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Robert J Klein
- Department of Genetics and Genomic Sciences and Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States; Program in Cancer Biology and Genetics and Department of Medicine, Memorial Sloan-Kettering Cancer Center, New York, NY, United States.
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18
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Kumar R, Lathwal A, Kumar V, Patiyal S, Raghav PK, Raghava GP. CancerEnD: A database of cancer associated enhancers. Genomics 2020; 112:3696-3702. [DOI: 10.1016/j.ygeno.2020.04.028] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 04/16/2020] [Accepted: 04/27/2020] [Indexed: 01/11/2023]
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19
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Bakhtiari S, Sulaimany S, Talebi M, Kalhor K. Computational Prediction of Probable Single Nucleotide Polymorphism-Cancer Relationships. Cancer Inform 2020; 19:1176935120942216. [PMID: 32728337 PMCID: PMC7364831 DOI: 10.1177/1176935120942216] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Accepted: 06/22/2020] [Indexed: 12/18/2022] Open
Abstract
Genetic variations such as single nucleotide polymorphisms (SNPs) can cause susceptibility to cancer. Although thousands of genetic variants have been identified to be associated with different cancers, the molecular mechanisms of cancer remain unknown. There is not a particular dataset of relationships between cancer and SNPs, as a bipartite network, for computational analysis and prediction. Link prediction as a computational graph analysis method can help us to gain new insight into the network. In this article, after creating a network between cancer and SNPs using SNPedia and Cancer Research UK databases, we evaluated the computational link prediction methods to foresee new SNP-Cancer relationships. Results show that among the popular scoring methods based on network topology, for relation prediction, the preferential attachment (PA) algorithm is the most robust method according to computational and experimental evidence, and some of its computational predictions are corroborated in recent publications. According to the PA predictions, rs1801394-Non-small cell lung cancer, rs4880-Non-small cell lung cancer, and rs1805794-Colorectal cancer are some of the best probable SNP-Cancer associations that have not yet been mentioned in any published article, and they are the most probable candidates for additional laboratory and validation studies. Also, it is feasible to improve the predicting algorithms to produce new predictions in the future.
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Affiliation(s)
- Shahab Bakhtiari
- Department of Biological Sciences, University of Kurdistan, Sanandaj, Iran
| | - Sadegh Sulaimany
- Department of Computer Engineering, University of Kurdistan, Sanandaj, Iran
| | - Mehrdad Talebi
- Department of Medical Genetics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
| | - Kabmiz Kalhor
- Department of Biological Sciences, University of Kurdistan, Sanandaj, Iran
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20
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Brandes N, Linial N, Linial M. PWAS: proteome-wide association study-linking genes and phenotypes by functional variation in proteins. Genome Biol 2020; 21:173. [PMID: 32665031 PMCID: PMC7386203 DOI: 10.1186/s13059-020-02089-x] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Accepted: 07/01/2020] [Indexed: 12/16/2022] Open
Abstract
We introduce Proteome-Wide Association Study (PWAS), a new method for detecting gene-phenotype associations mediated by protein function alterations. PWAS aggregates the signal of all variants jointly affecting a protein-coding gene and assesses their overall impact on the protein's function using machine learning and probabilistic models. Subsequently, it tests whether the gene exhibits functional variability between individuals that correlates with the phenotype of interest. PWAS can capture complex modes of heritability, including recessive inheritance. A comparison with GWAS and other existing methods proves its capacity to recover causal protein-coding genes and highlight new associations. PWAS is available as a command-line tool.
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Affiliation(s)
- Nadav Brandes
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Nathan Linial
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Michal Linial
- Department of Biological Chemistry, The Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
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21
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Hemerich D, Pei J, Harakalova M, van Setten J, Boymans S, Boukens BJ, Efimov IR, Michels M, van der Velden J, Vink A, Cheng C, van der Harst P, Moore JH, Mokry M, Tragante V, Asselbergs FW. Integrative Functional Annotation of 52 Genetic Loci Influencing Myocardial Mass Identifies Candidate Regulatory Variants and Target Genes. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 12:e002328. [PMID: 30681347 DOI: 10.1161/circgen.118.002328] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Regulatory elements may be involved in the mechanisms by which 52 loci influence myocardial mass, reflected by abnormal amplitude and duration of the QRS complex on the ECG. Functional annotation thus far did not take into account how these elements are affected in disease context. METHODS We generated maps of regulatory elements on hypertrophic cardiomyopathy patients (ChIP-seq N=14 and RNA-seq N=11) and nondiseased hearts (ChIP-seq N=4 and RNA-seq N=11). We tested enrichment of QRS-associated loci on elements differentially acetylated and directly regulating differentially expressed genes between hypertrophic cardiomyopathy patients and controls. We further performed functional annotation on QRS-associated loci using these maps of differentially active regulatory elements. RESULTS Regions differentially affected in disease showed a stronger enrichment ( P=8.6×10-5) for QRS-associated variants than those not showing differential activity ( P=0.01). Promoters of genes differentially regulated between hypertrophic cardiomyopathy patients and controls showed more enrichment ( P=0.001) than differentially acetylated enhancers ( P=0.8) and super-enhancers ( P=0.025). We also identified 74 potential causal variants overlapping these differential regulatory elements. Eighteen of the genes mapped confirmed previous findings, now also pinpointing the potentially affected regulatory elements and candidate causal variants. Fourteen new genes were also mapped. CONCLUSIONS Our results suggest differentially active regulatory elements between hypertrophic cardiomyopathy patients and controls can offer more insights into the mechanisms of QRS-associated loci than elements not affected by disease.
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Affiliation(s)
- Daiane Hemerich
- Department of Cardiology (D.H., M.H., J.v.S., V.T., F.W.A.), UMC Utrecht, Utrecht University, The Netherlands.,CAPES Foundation, Ministry of Education of Brazil, Brasília (D.H.)
| | - Jiayi Pei
- CAPES Foundation, Ministry of Education of Brazil, Brasília (D.H.).,Department of Nephrology and Hypertension (J.P., C.C.), UMC Utrecht
| | - Magdalena Harakalova
- Department of Cardiology (D.H., M.H., J.v.S., V.T., F.W.A.), UMC Utrecht, Utrecht University, The Netherlands
| | - Jessica van Setten
- Department of Cardiology (D.H., M.H., J.v.S., V.T., F.W.A.), UMC Utrecht, Utrecht University, The Netherlands
| | - Sander Boymans
- Department of Genetics, Center for Molecular Medicine, Cancer Genomics Netherlands (S.B.), UMC Utrecht
| | - Bas J Boukens
- Department of Medical Biology, Academic Medical Center, Amsterdam, The Netherlands (B.J.B.)
| | - Igor R Efimov
- Department of Biomedical Engineering, The George Washington University, Washington, DC (I.R.E.)
| | - Michelle Michels
- Department of Cardiology, Erasmus MC, Rotterdam, The Netherlands (M. Michels)
| | - Jolanda van der Velden
- Department of Physiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Center (J.v.d.V.)
| | - Aryan Vink
- Department of Pathology (A.V.), UMC Utrecht, Utrecht University, The Netherlands
| | - Caroline Cheng
- Department of Nephrology and Hypertension (J.P., C.C.), UMC Utrecht
| | | | - Jason H Moore
- Department of Biostatistics and Epidemiology, Institute for Biomedical Informatics, University of Pennsylvania, PA (J.H.M.)
| | - Michal Mokry
- Department of Pediatrics, Wilhelmina Children's Hospital, Utrecht (M. Mokry.)
| | - Vinicius Tragante
- Department of Cardiology (D.H., M.H., J.v.S., V.T., F.W.A.), UMC Utrecht, Utrecht University, The Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology (D.H., M.H., J.v.S., V.T., F.W.A.), UMC Utrecht, Utrecht University, The Netherlands.,Durrer Center for Cardiogenetic Research, ICINNetherlands Heart Institute, Utrecht (F.W.A.).,Institute of Cardiovascular Science, Faculty of Population Health Sciences (F.W.A.), University College London, United Kingdom.,Health Data Research UK London, Institute of Health Informatics F.W.A.), University College London, United Kingdom
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22
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Yang N, Tian J, Wang X, Mei S, Zou D, Peng X, Zhu Y, Yang Y, Gong Y, Ke J, Zhong R, Chang J, Miao X. A functional variant in TNXB promoter associates with the risk of esophageal squamous-cell carcinoma. Mol Carcinog 2020; 59:439-446. [PMID: 32056283 DOI: 10.1002/mc.23166] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 11/23/2019] [Accepted: 02/04/2020] [Indexed: 11/11/2022]
Abstract
Our previous study identified a tag single-nucleotide polymorphism (SNP) rs204900 in TNXB associated with risk of esophageal squamous-cell carcinoma (ESCC) in the Chinese population. However, the functional role of TNXB and causal variants had not been interrogated in that study. In the present study, we explored the effects of TNXB expression in the development of ESCC and searched for functional variants in this gene. We found TNXB was downregulated in ESCC tumors. Using small interfering RNAs and CRISPR-Cas9 methods, we identified that both knockdown and knockout of TNXB significantly promoted ESCC cell growth in vitro, suggesting a tumor suppressor role of this gene in ESCC. Through further fine-mapping analysis, we identified that a noncoding variant in the promoter of TNXB, rs411337, predisposed to ESCC risk (odds ratio = 1.36, 95% confidence interval: 1.22-1.51, P = 9.10 × 10-9 ). These findings revealed the functional mechanism of TNXB in the development of ESCC and may contribute to the prevention and treatment of this disease in the future.
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Affiliation(s)
- Nan Yang
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianbo Tian
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoyang Wang
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shufang Mei
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Danyi Zou
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiating Peng
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Yang
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yajie Gong
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Juntao Ke
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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23
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Martínez-Barriocanal Á, Arango D, Dopeso H. PVT1 Long Non-coding RNA in Gastrointestinal Cancer. Front Oncol 2020; 10:38. [PMID: 32083000 PMCID: PMC7005105 DOI: 10.3389/fonc.2020.00038] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Accepted: 01/09/2020] [Indexed: 12/24/2022] Open
Abstract
Whole genome and transcriptome sequencing technologies have led to the identification of many long non-coding RNAs (lncRNAs) and stimulated the research of their role in health and disease. LncRNAs participate in the regulation of critical signaling pathways including cell growth, motility, apoptosis, and differentiation; and their expression has been found dysregulated in human tumors. Thus, lncRNAs have emerged as new players in the initiation, maintenance and progression of tumorigenesis. PVT1 (plasmacytoma variant translocation 1) lncRNA is located on chromosomal 8q24.21, a large locus frequently amplified in human cancers and predictive of increased cancer risk in genome-wide association studies (GWAS). Combined, colorectal and gastric adenocarcinomas are the most frequent tumor malignancies and also the leading cause of cancer-related deaths worldwide. PVT1 expression is elevated in gastrointestinal tumors and correlates with poor patient prognosis. In this review, we discuss the mechanisms of action underlying PVT1 oncogenic role in colorectal and gastric cancer such as MYC upregulation, miRNA production, competitive endogenous RNA (ceRNA) function, protein stabilization, and epigenetic regulation. We also illustrate the potential role of PVT1 as prognostic biomarker and its relationship with resistance to current chemotherapeutic treatments.
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Affiliation(s)
- Águeda Martínez-Barriocanal
- Group of Biomedical Research in Digestive Tract Tumors, CIBBIM-Nanomedicine, Vall d'Hebron University Hospital, Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Group of Molecular Oncology, IRB Lleida, Lleida, Spain
| | - Diego Arango
- Group of Biomedical Research in Digestive Tract Tumors, CIBBIM-Nanomedicine, Vall d'Hebron University Hospital, Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain.,Group of Molecular Oncology, IRB Lleida, Lleida, Spain
| | - Higinio Dopeso
- Group of Biomedical Research in Digestive Tract Tumors, CIBBIM-Nanomedicine, Vall d'Hebron University Hospital, Research Institute (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain
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24
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Dayama G, Priya S, Niccum DE, Khoruts A, Blekhman R. Interactions between the gut microbiome and host gene regulation in cystic fibrosis. Genome Med 2020; 12:12. [PMID: 31992345 PMCID: PMC6988342 DOI: 10.1186/s13073-020-0710-2] [Citation(s) in RCA: 67] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Accepted: 01/05/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Cystic fibrosis is the most common autosomal recessive genetic disease in Caucasians. It is caused by mutations in the CFTR gene, leading to poor hydration of mucus and impairment of the respiratory, digestive, and reproductive organ functions. Advancements in medical care have led to markedly increased longevity of patients with cystic fibrosis, but new complications have emerged, such as early onset of colorectal cancer. Although the pathogenesis of colorectal cancer in cystic fibrosis remains unclear, altered host-microbe interactions might play a critical role. To investigate this, we characterized changes in the microbiome and host gene expression in the colonic mucosa of cystic fibrosis patients relative to healthy controls, and identified host gene-microbiome interactions in the colon of cystic fibrosis patients. METHODS We performed RNA-seq on colonic mucosa samples from cystic fibrosis patients and healthy controls to determine differentially expressed host genes. We also performed 16S rRNA sequencing to characterize the colonic mucosal microbiome and identify gut microbes that are differentially abundant between patients and healthy controls. Lastly, we modeled associations between relative abundances of specific bacterial taxa in the gut mucosa and host gene expression. RESULTS We find that 1543 genes, including CFTR, show differential expression in the colon of cystic fibrosis patients compared to healthy controls. These genes are enriched with functions related to gastrointestinal and colorectal cancer, such as metastasis of colorectal cancer, tumor suppression, p53, and mTOR signaling pathways. In addition, patients with cystic fibrosis show decreased gut microbial diversity, decreased abundance of butyrate producing bacteria, such as Ruminococcaceae and Butyricimonas, and increased abundance of other taxa, such as Actinobacteria and Clostridium. An integrative analysis identified colorectal cancer-related genes, including LCN2 and DUOX2, for which gene expression is correlated with the abundance of colorectal cancer-associated bacteria, such as Ruminococcaceae and Veillonella. CONCLUSIONS In addition to characterizing host gene expression and mucosal microbiome in cystic fibrosis patients, our study explored the potential role of host-microbe interactions in the etiology of colorectal cancer in cystic fibrosis. Our results provide biomarkers that may potentially serve as targets for stratifying risk of colorectal cancer in patients with cystic fibrosis.
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Affiliation(s)
- Gargi Dayama
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA
| | - Sambhawa Priya
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA
| | - David E Niccum
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA
| | - Alexander Khoruts
- Department of Medicine, University of Minnesota, Minneapolis, MN, USA.
- Center for Immunology, BioTechnology Institute, University of Minnesota, Minneapolis, MN, USA.
| | - Ran Blekhman
- Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, MN, USA.
- Department of Ecology, Evolution, and Behavior, University of Minnesota, Minneapolis, MN, USA.
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25
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Guo Z, Wang Y, Zhao Y, Jin Y, An L, Xu H, Liu Z, Chen X, Zhou H, Wang H, Zhang W. A functional variant in CHK1 contributes to increased risk of nasopharyngeal carcinoma in a Han Chinese population. J Cell Biochem 2020; 121:3248-3255. [PMID: 31904144 DOI: 10.1002/jcb.29592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 12/11/2019] [Indexed: 02/05/2023]
Abstract
DNA damage checkpoints act as a supervisor by preventing the course of cell cycle upon DNA damage and keeping the steadiness of genome. Checkpoint kinase 1 (CHK1) cannot be ignore in the etiology of numerous human cancers including nasopharyngeal cancer (NPC). To discuss genetic polymorphisms of CHK1 rs492510 in the occurrence of NPC was our objective. Rs492510 polymorphism of CHK1 was genotyped in 684 patients with NPC and 823 cancer-free controls. We utilize logistic regression models to appraise the correlation of rs492510 and susceptibility of NPC. Comparative expression level about CHK1 in nasopharyngeal carcinoma tissues were determined by real-time polymerase chain reaction. And we made use of Dual-Luciferase Reporter Assay to assess the transcriptional ability of CHK1 with different rs492510 allele. Adjusting multivariate logistic regression based on age, sex, body mass index, smoking, and drinking status showed that CHK1 rs492510 GA + GG genotype carriers presented prominent higher risk in NPC (odds ratio = 1.376, 95% confidence interval: 1.087-1.742; P = .008). As a consequence, we revealed that CHK1 relative expression levels in NPC tissues was higher than rhinitis tissues. Besides, the expressions of CHK1 in rs492510 GA genotype carriers were higher compared with people in AA genotype. The G allele of rs492510 generated remarkable higher transcription activity of CHK1 vs A allele by luciferase reporter assay. Our study considered that single nucleotide polymorphism rs492510 could increase transcription activity of CHK1 with the functionality, contributing to the susceptibility of NPC.
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Affiliation(s)
- Zhen Guo
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Youhong Wang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Yu Zhao
- Key Laboratory of Translational Radiation Oncology, Hunan Province, Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Yi Jin
- Key Laboratory of Translational Radiation Oncology, Hunan Province, Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Liang An
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Heng Xu
- Department of Laboratory Medicine, National Key Laboratory of Biotherapy/Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Zhaoqian Liu
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Xiaoping Chen
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Honghao Zhou
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
| | - Hui Wang
- Key Laboratory of Translational Radiation Oncology, Hunan Province, Department of Radiation Oncology, Hunan Cancer Hospital and The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha, China
| | - Wei Zhang
- Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha, China.,Institute of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, Central South University, Changsha, China.,Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, Changsha, China.,National Clinical Research Center for Geriatric Disorders, Changsha, China
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26
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Cai Y, Rosen Vollmar AK, Johnson CH. Analyzing Metabolomics Data for Environmental Health and Exposome Research. Methods Mol Biol 2020; 2104:447-467. [PMID: 31953830 DOI: 10.1007/978-1-0716-0239-3_22] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The exposome is the cumulative measure of environmental influences and associated biological responses across the life span, with critical relevance for understanding how exposures can impact human health. Metabolomics analysis of biological samples offers unique advantages for examining the exposome. Simultaneous analysis of external exposures, biological responses, and host susceptibility at a systems level can help establish links between external exposures and health outcomes. As metabolomics technologies continue to evolve for the study of the exposome, metabolomics ultimately will help provide valuable insights for exposure risk assessment, and disease prevention and management. Here, we discuss recent advances in metabolomics, and describe data processing protocols that can enable analysis of the exposome. This chapter focuses on using liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics for analysis of the exposome, including (1) preprocessing of untargeted metabolomics data, (2) identification of exposure chemicals and their metabolites, and (3) methods to establish associations between exposures and diseases.
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Affiliation(s)
- Yuping Cai
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Ana K Rosen Vollmar
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA
| | - Caroline Helen Johnson
- Department of Environmental Health Sciences, Yale School of Public Health, New Haven, CT, USA.
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27
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Li N, Fan X, Wang X, Zhang X, Zhang K, Han Q, Lv Y, Liu Z. Genetic association of polymorphisms at the intergenic region between PRDM1 and ATG5 with hepatitis B virus infection in Han Chinese patients. J Med Virol 2019; 92:1198-1205. [PMID: 31729038 DOI: 10.1002/jmv.25629] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Accepted: 11/12/2019] [Indexed: 12/21/2022]
Abstract
Chronic hepatitis B virus (HBV) infection is related to chronic hepatitis, cirrhosis, and hepatocellular carcinoma (HCC), and the interplay between the virus and host immune response leads to different outcomes of the infection. PR domain zinc finger protein 1 (PRDM1) and autophagy-related protein 5 (ATG5) are involved in immune response and HBV infection. An intergenic region between PRDM1 and ATG5 (PRDM1-ATG5 region) has been identified, and single-nucleotide polymorphisms (SNPs) in this region were shown to be involved in immune regulation. This study investigated the functionally relevant rs548234, rs6937876, and rs6568431 polymorphisms at the PRDM1-ATG5 region in a Han Chinese population (403 patients with chronic HBV infection [171 chronic hepatitis, 119 cirrhosis, and 113 HCC], 70 infection resolvers, and 196 healthy controls). The frequencies of the rs6568431 allele A in HBV patients (P = .005) and genotype CA in infection resolvers (P = .005) were significantly higher than in healthy controls. In the dominant model, HCC patients had significantly higher frequencies of rs548234 genotypes CC + TC than cirrhosis patients (P = .009). Rs548234 was an independent factor for HCC in comparison with either cirrhosis (P = .005) or all chronic HBV infection without HCC (P = .018). Functional annotation showed evidence of the role of the SNPs in gene regulation. In conclusion, through this study it is revealed for the first time that rs6568431 may be associated with susceptibility to HBV infection and that rs548234 may be associated with HCC risk in chronic HBV infection, supporting the presence of HBV-related disease-causing regulatory polymorphisms in the PRDM1-ATG5 intergenic region.
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Affiliation(s)
- Na Li
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiude Fan
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaoyun Wang
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Xiaoge Zhang
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Kun Zhang
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Qunying Han
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Yi Lv
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.,Institute of Advanced Surgical Technology and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
| | - Zhengwen Liu
- Department of Infectious Diseases, First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.,Institute of Advanced Surgical Technology and Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi, China
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28
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Li X, Luo J, Zhang C, Liu L, Ou S, Zhang G, Peng X. Alliin protects against inflammatory bowel disease by preserving the gene expression in colonic epithelial cells rather than altering gut microbiota. J Funct Foods 2019. [DOI: 10.1016/j.jff.2019.05.048] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
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29
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Qian Y, Zhang L, Cai M, Li H, Xu H, Yang H, Zhao Z, Rhie SK, Farnham PJ, Shi J, Lu W. The prostate cancer risk variant rs55958994 regulates multiple gene expression through extreme long-range chromatin interaction to control tumor progression. SCIENCE ADVANCES 2019; 5:eaaw6710. [PMID: 31328168 PMCID: PMC6636982 DOI: 10.1126/sciadv.aaw6710] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 06/11/2019] [Indexed: 05/15/2023]
Abstract
Genome-wide association studies identified single-nucleotide polymorphism (SNP) rs55958994 as a significant variant associated with increased susceptibility to prostate cancer. However, the mechanisms by which this SNP mediates increased risk to cancer are still unknown. In this study, we show that this variant is located in an enhancer active in prostate cancer cells. Deletion of this enhancer from prostate tumor cells resulted in decreased tumor initiation, tumor growth, and invasive migration, as well as a loss of stem-like cells. Using a combination of capture chromosome conformation capture (Capture-C) and RNA sequencing, we identified genes on the same and different chromosomes as targets regulated by the enhancer. Furthermore, we show that expression of individual candidate target genes in an enhancer-deleted cell line rescued different aspects of tumorigenesis. Our data suggest that the rs55958994-associated enhancer affects prostate cancer progression by influencing expression of multiple genes via long-range chromatin interactions.
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Affiliation(s)
- Yuyang Qian
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Lei Zhang
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Mingyang Cai
- Department of Stem Cell Biology and Regenerative Medicine, Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Hongxia Li
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Heming Xu
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Hongzhen Yang
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Zhongfang Zhao
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Suhn Kyong Rhie
- Department of Biochemistry and Molecular Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Peggy J. Farnham
- Department of Biochemistry and Molecular Medicine, Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jiandang Shi
- State Key Laboratory of Medicinal Chemical Biology and College of Life Sciences, Nankai University, 94 Weijin Road, 300071 Tianjin, China
| | - Wange Lu
- Department of Stem Cell Biology and Regenerative Medicine, Broad Center for Regenerative Medicine and Stem Cell Research, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
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30
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Zhang Y, Wu W, Qu H. Integrated Analysis of the Gene Expression Changes During Colorectal Cancer Progression by Bioinformatic Methods. J Comput Biol 2019; 26:1168-1176. [PMID: 31246501 DOI: 10.1089/cmb.2019.0056] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
We attempted to analyze the aberrant pathways and genes underlying the successive stages of colorectal cancer (CRC). The CRC related microarray data (GSE77953) were retrieved from Gene Expression Omnibus database, which included 17 colonic adenoma, 17 carcinoma, 11 CRC metastases, and 13 normal colonic epithelium samples. The differential expression patterns in colonic adenoma, carcinoma, and metastases were analyzed. Gene functional interaction (FI) and coexpressed network were constructed. Pathway enrichment analysis was performed to investigate the perturbed pathways, and disease-related genes were explored based on the Comparative Toxicogenomics Database. Total 438 genes were identified to be differentially expressed in colonic adenoma, 885 in carcinoma and 736 in metastases. The upregulated genes in adenoma were significantly related with ribosome, oxidative phosphorylation, and protein export related pathways. The downregulated genes in carcinoma and metastases were enriched in the same pathways, such as nitrogen metabolism, mineral absorption, and steroid hormone biosynthesis. FI network was constructed with 219 and 3914 edges, which were further divided to 12 modules. The genes in module 0 were closely related with ribosome, protein export, and RNA transport. Coexpressed genes were enriched in ribosome, protein export, and mineral absorption pathways. Total eight common upregulated genes were found to be the CRC-related genes such as RNF43 (ring finger protein 43), EIF3H (eukaryotic translation initiation factor 3 subunit H), and STRAP (serine/threonine kinase receptor associated protein). The common downregulated genes included ABCG2 (ATP binding cassette subfamily G member 2), GCG (glucagon), and SULT1A1 (sulfotransferase family 1A member 1). Oxidative phosphorylation, nitrogen metabolism, mineral absorption, and protein synthesis may significantly be perturbed in the progression of CRC. The overexpression of EIF3H may be the predictor for CRC formation.
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Affiliation(s)
- Yudong Zhang
- Department of General Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
| | - Wenxiang Wu
- Department of Obstetrics and Gynecology, Peking University First Hospital, Beijing, China
| | - Hao Qu
- Department of General Surgery, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China
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31
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Benton ML, Talipineni SC, Kostka D, Capra JA. Genome-wide enhancer annotations differ significantly in genomic distribution, evolution, and function. BMC Genomics 2019; 20:511. [PMID: 31221079 PMCID: PMC6585034 DOI: 10.1186/s12864-019-5779-x] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 05/07/2019] [Indexed: 12/28/2022] Open
Abstract
Background Non-coding gene regulatory enhancers are essential to transcription in mammalian cells. As a result, a large variety of experimental and computational strategies have been developed to identify cis-regulatory enhancer sequences. Given the differences in the biological signals assayed, some variation in the enhancers identified by different methods is expected; however, the concordance of enhancers identified by different methods has not been comprehensively evaluated. This is critically needed, since in practice, most studies consider enhancers identified by only a single method. Here, we compare enhancer sets from eleven representative strategies in four biological contexts. Results All sets we evaluated overlap significantly more than expected by chance; however, there is significant dissimilarity in their genomic, evolutionary, and functional characteristics, both at the element and base-pair level, within each context. The disagreement is sufficient to influence interpretation of candidate SNPs from GWAS studies, and to lead to disparate conclusions about enhancer and disease mechanisms. Most regions identified as enhancers are supported by only one method, and we find limited evidence that regions identified by multiple methods are better candidates than those identified by a single method. As a result, we cannot recommend the use of any single enhancer identification strategy in all settings. Conclusions Our results highlight the inherent complexity of enhancer biology and identify an important challenge to mapping the genetic architecture of complex disease. Greater appreciation of how the diverse enhancer identification strategies in use today relate to the dynamic activity of gene regulatory regions is needed to enable robust and reproducible results. Electronic supplementary material The online version of this article (10.1186/s12864-019-5779-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Mary Lauren Benton
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37235, USA
| | - Sai Charan Talipineni
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA
| | - Dennis Kostka
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA. .,Department of Computational & Systems Biology, Pittsburgh Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15201, USA.
| | - John A Capra
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, 37235, USA. .,Departments of Biological Sciences and Computer Science, Vanderbilt Genetics Institute, Center for Structural Biology, Vanderbilt University, Nashville, TN, 37235, USA.
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32
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Niu HM, Yang P, Chen HH, Hao RH, Dong SS, Yao S, Chen XF, Yan H, Zhang YJ, Chen YX, Jiang F, Yang TL, Guo Y. Comprehensive functional annotation of susceptibility SNPs prioritized 10 genes for schizophrenia. Transl Psychiatry 2019; 9:56. [PMID: 30705251 PMCID: PMC6355777 DOI: 10.1038/s41398-019-0398-5] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/27/2018] [Accepted: 01/10/2019] [Indexed: 12/18/2022] Open
Abstract
Nearly 95% of susceptibility SNPs identified by genome-wide association studies (GWASs) are located in non-coding regions, which causes a lot of difficulty in deciphering their biological functions on disease pathogenesis. Here, we aimed to conduct a comprehensive functional annotation for all the schizophrenia susceptibility loci obtained from GWASs. Considering varieties of epigenomic regulatory elements, we annotated all 22,688 acquired susceptibility SNPs according to their genomic positions to obtain functional SNPs. The comprehensive annotation indicated that these functional SNPs are broadly involved in diverse biological processes. Histone modification enrichment showed that H3K27ac, H3K36me3, H3K4me1, and H3K4me3 were related to the development of schizophrenia. Transcription factors (TFs) prediction, methylation quantitative trait loci (meQTL) analyses, expression quantitative trait loci (eQTL) analyses, and proteomic quantitative trait loci analyses (pQTL) identified 447 target protein-coding genes. Subsequently, differential expression analyses between schizophrenia cases and controls, nervous system phenotypes from mouse models, and protein-protein interaction with known schizophrenia-related pathways and genes were carried out with our target genes. We finaly prioritized 10 target genes for schizophrenia (CACNA1C, CLU, CSNK2B, GABBR1, GRIN2A, MAPK3, NOTCH4, SRR, TNF, and SYNGAP1). Our results may serve as an encyclopedia of schizophrenia susceptibility SNPs and offer holistic guides for post-GWAS functional experiments.
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Affiliation(s)
- Hui-Min Niu
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Ping Yang
- grid.489086.bDepartment of Psychiatry, Hunan Brain Hospital, Changsha, Hunan Province China
| | - Huan-Huan Chen
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Ruo-Han Hao
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Shan-Shan Dong
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Shi Yao
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Xiao-Feng Chen
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Han Yan
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Yu-Jie Zhang
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Yi-Xiao Chen
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Feng Jiang
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Tie-Lin Yang
- 0000 0001 0599 1243grid.43169.39Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, 710049 Xi’an, China
| | - Yan Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, 710049, Xi'an, China.
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Yuan Y, Weidhaas JB. Functional microRNA binding site variants. Mol Oncol 2019; 13:4-8. [PMID: 30536617 PMCID: PMC6322190 DOI: 10.1002/1878-0261.12421] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Revised: 11/15/2018] [Accepted: 11/26/2018] [Indexed: 12/19/2022] Open
Abstract
Germline single nucleotide polymorphisms are one of the most common genetic variations. Polymorphisms that cause nonsynonymous mutations in gene coding regions are known to cause serious deleterious downstream effects. However, even polymorphisms in noncoding regions can have profound functional consequences by disrupting essential regulatory sites. Specifically, polymorphisms that alter microRNA binding sites can disrupt the regulation of hallmark biological pathways implicated in tumorigenesis and tumor progression. Many of these microRNA-associated polymorphisms (miR-SNPs) have recently been shown to be important biomarkers of cancer risk, prognosis, and treatment outcomes. This review will summarize the functional impact of key miR-SNPs and define a subset of miR-SNPs that may be clinically useful prognostic or predictive biomarkers.
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Affiliation(s)
- Ye Yuan
- Department of Radiation OncologyUCLALos AngelesCAUSA
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Rhie SK, Schreiner S, Witt H, Armoskus C, Lay FD, Camarena A, Spitsyna VN, Guo Y, Berman BP, Evgrafov OV, Knowles JA, Farnham PJ. Using 3D epigenomic maps of primary olfactory neuronal cells from living individuals to understand gene regulation. SCIENCE ADVANCES 2018; 4:eaav8550. [PMID: 30555922 PMCID: PMC6292713 DOI: 10.1126/sciadv.aav8550] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Accepted: 11/16/2018] [Indexed: 05/20/2023]
Abstract
As part of PsychENCODE, we developed a three-dimensional (3D) epigenomic map of primary cultured neuronal cells derived from olfactory neuroepithelium (CNON). We mapped topologically associating domains and high-resolution chromatin interactions using Hi-C and identified regulatory elements using chromatin immunoprecipitation and nucleosome positioning assays. Using epigenomic datasets from biopsies of 63 living individuals, we found that epigenetic marks at distal regulatory elements are more variable than marks at proximal regulatory elements. By integrating genotype and metadata, we identified enhancers that have different levels corresponding to differences in genetic variation, gender, smoking, and schizophrenia. Motif searches revealed that many CNON enhancers are bound by neuronal-related transcription factors. Last, we combined 3D epigenomic maps and gene expression profiles to predict enhancer-target gene interactions on a genome-wide scale. This study not only provides a framework for understanding individual epigenetic variation using a primary cell model system but also contributes valuable data resources for epigenomic studies of neuronal epithelium.
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Affiliation(s)
- Suhn K. Rhie
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Shannon Schreiner
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Heather Witt
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chris Armoskus
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Fides D. Lay
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Adrian Camarena
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Valeria N. Spitsyna
- Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yu Guo
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Benjamin P. Berman
- Department of Biomedical Sciences, Bioinformatics and Computational Biology Research Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Oleg V. Evgrafov
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - James A. Knowles
- Department of Cell Biology, SUNY Downstate Medical Center, Brooklyn, NY, USA
| | - Peggy J. Farnham
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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35
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Merkulov VM, Leberfarb EY, Merkulova TI. Regulatory SNPs and their widespread effects on the transcriptome. J Biosci 2018; 43:1069-1075. [PMID: 30541964] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
Abstract
Currently, it is generally accepted that the cis-acting effects of noncoding variants on gene expression are a major factor for phenotypic variation in complex traits and disease susceptibility. Meanwhile, the protein products of many target genes for the identified cis-regulatory variants (rSNPs) are regulatory molecules themselves (transcription factors, effectors, components of signal transduction pathways, etc.), which implies dramatic downstream effects of these variations on complex gene networks. Here, we brief the results of recent most comprehensive studies on the role of rSNPs in transcriptional regulation across the genome.
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Affiliation(s)
- Vasily M Merkulov
- Laboratory of Gene Expression Regulation, Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
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36
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Lancho O, Herranz D. The MYC Enhancer-ome: Long-Range Transcriptional Regulation of MYC in Cancer. Trends Cancer 2018; 4:810-822. [PMID: 30470303 DOI: 10.1016/j.trecan.2018.10.003] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/04/2018] [Accepted: 10/05/2018] [Indexed: 12/20/2022]
Abstract
MYC is one of the most important oncogenes in cancer. Indeed, MYC is upregulated in 50-60% of all tumors. MYC overexpression can be achieved through a variety of mechanisms, including gene duplications, chromosomal translocations, or somatic mutations leading to increased MYC stability. However, recent studies have identified numerous tissue-specific noncoding enhancers of MYC that play major roles in cancer, highlighting long-range transcriptional regulation of MYC as a critical novel mechanism leading to MYC hyperactivation and as a potential target for new therapeutic strategies in the near future. Here we summarize the regions and mechanisms involved in the long-range transcriptional regulation of MYC, underscoring the relevance of MYC enhancers both in normal physiological development and in MYC-driven cancer initiation and progression.
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Affiliation(s)
- Olga Lancho
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA
| | - Daniel Herranz
- Rutgers Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ, USA; Department of Pharmacology, Robert Wood Johnson Medical School, Rutgers University, Piscataway, NJ, USA.
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37
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Song N, Kim K, Shin A, Park JW, Chang HJ, Shi J, Cai Q, Kim DY, Zheng W, Oh JH. Colorectal cancer susceptibility loci and influence on survival. Genes Chromosomes Cancer 2018; 57:630-637. [DOI: 10.1002/gcc.22674] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2018] [Revised: 08/07/2018] [Accepted: 08/07/2018] [Indexed: 12/14/2022] Open
Affiliation(s)
- Nan Song
- Cancer Research Institute; Seoul National University College of Medicine; Seoul South Korea
| | - Kyeezu Kim
- Division of Epidemiology and Biostatistics; University of Illinois at Chicago School of Public Health; Chicago Illinois
| | - Aesun Shin
- Cancer Research Institute; Seoul National University College of Medicine; Seoul South Korea
- Department of Preventive Medicine; Seoul National University College of Medicine; Seoul South Korea
- Molecular Epidemiology Branch, National Cancer Center; Goyang South Korea
| | - Ji Won Park
- Department of Surgery; Seoul National University College of Medicine and Hospital; Seoul South Korea
- Center for Colorectal Cancer, National Cancer Center; Goyang South Korea
| | - Hee Jin Chang
- Center for Colorectal Cancer, National Cancer Center; Goyang South Korea
| | - Jiajun Shi
- Division of Epidemiology, Department of Medicine; Vanderbilt University School of Medicine; Nashville Tennessee
| | - Qiuyin Cai
- Division of Epidemiology, Department of Medicine; Vanderbilt University School of Medicine; Nashville Tennessee
| | - Dae Yong Kim
- Center for Colorectal Cancer, National Cancer Center; Goyang South Korea
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine; Vanderbilt University School of Medicine; Nashville Tennessee
| | - Jae Hwan Oh
- Center for Colorectal Cancer, National Cancer Center; Goyang South Korea
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38
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Guo Y, Perez AA, Hazelett DJ, Coetzee GA, Rhie SK, Farnham PJ. CRISPR-mediated deletion of prostate cancer risk-associated CTCF loop anchors identifies repressive chromatin loops. Genome Biol 2018; 19:160. [PMID: 30296942 PMCID: PMC6176514 DOI: 10.1186/s13059-018-1531-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2018] [Accepted: 09/09/2018] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Recent genome-wide association studies (GWAS) have identified more than 100 loci associated with increased risk of prostate cancer, most of which are in non-coding regions of the genome. Understanding the function of these non-coding risk loci is critical to elucidate the genetic susceptibility to prostate cancer. RESULTS We generate genome-wide regulatory element maps and performed genome-wide chromosome confirmation capture assays (in situ Hi-C) in normal and tumorigenic prostate cells. Using this information, we annotate the regulatory potential of 2,181 fine-mapped prostate cancer risk-associated SNPs and predict a set of target genes that are regulated by prostate cancer risk-related H3K27Ac-mediated loops. We next identify prostate cancer risk-associated CTCF sites involved in long-range chromatin loops. We use CRISPR-mediated deletion to remove prostate cancer risk-associated CTCF anchor regions and the CTCF anchor regions looped to the prostate cancer risk-associated CTCF sites, and we observe up to 100-fold increases in expression of genes within the loops when the prostate cancer risk-associated CTCF anchor regions are deleted. CONCLUSIONS We identify GWAS risk loci involved in long-range loops that function to repress gene expression within chromatin loops. Our studies provide new insights into the genetic susceptibility to prostate cancer.
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Affiliation(s)
- Yu Guo
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA 90089-9601 USA
| | - Andrew A. Perez
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA 90089-9601 USA
| | - Dennis J. Hazelett
- Department of Biomedical Sciences and the Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048 USA
| | | | - Suhn Kyong Rhie
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA 90089-9601 USA
| | - Peggy J. Farnham
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT 6503, Los Angeles, CA 90089-9601 USA
- Department of Biochemistry and Molecular Medicine and the Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, 1450 Biggy Street, NRT G511B, Los Angeles, CA 90089-9601 USA
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39
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Merkulov VM, Leberfarb EY, Merkulova TI. Regulatory SNPs and their widespread effects on the transcriptome. J Biosci 2018. [DOI: 10.1007/s12038-018-9817-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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40
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Yang C, Stueve TR, Yan C, Rhie SK, Mullen DJ, Luo J, Zhou B, Borok Z, Marconett CN, Offringa IA. Positional integration of lung adenocarcinoma susceptibility loci with primary human alveolar epithelial cell epigenomes. Epigenomics 2018; 10:1167-1187. [PMID: 30212242 DOI: 10.2217/epi-2018-0003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
AIM To identify functional lung adenocarcinoma (LUAD) risk SNPs. MATERIALS & METHODS Eighteen validated LUAD risk SNPs (p ≤ 5 × 10-8) and 930 SNPs in high linkage disequilibrium (r2 > 0.5) were integrated with epigenomic information from primary human alveolar epithelial cells. Enhancer-associated SNPs likely affecting transcription factor-binding sites were predicted. Three SNPs were functionally investigated using luciferase assays, expression quantitative trait loci and cancer-specific expression. RESULTS Forty-seven SNPs mapped to putative enhancers; 11 located to open chromatin. Of these, seven altered predicted transcription factor-binding motifs. Rs6942067 showed allele-specific luciferase expression and expression quantitative trait loci analysis indicates that it influences expression of DCBLD1, a gene that encodes an unknown membrane protein and is overexpressed in LUAD. CONCLUSION Integration of candidate LUAD risk SNPS with epigenomic marks from normal alveolar epithelium identified numerous candidate functional LUAD risk SNPs including rs6942067, which appears to affect DCBLD1 expression. Data deposition: Data are provided in GEO record GSE84273.
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Affiliation(s)
- Chenchen Yang
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
| | - Theresa Ryan Stueve
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA.,Department of Preventive Medicine, University of Southern California, CA 90089, USA
| | - Chunli Yan
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
| | - Suhn K Rhie
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
| | - Daniel J Mullen
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
| | - Jiao Luo
- Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Department of Medicine, Division of Pulmonary & Critical Care & Sleep Medicine, University of Southern California, CA 90089, USA
| | - Beiyun Zhou
- Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA.,Department of Medicine, Division of Pulmonary & Critical Care & Sleep Medicine, University of Southern California, CA 90089, USA.,Hastings Center for Pulmonary Research, Keck School of Medicine, University of Southern California, CA 90089, USA
| | - Zea Borok
- Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA.,Department of Medicine, Division of Pulmonary & Critical Care & Sleep Medicine, University of Southern California, CA 90089, USA.,Hastings Center for Pulmonary Research, Keck School of Medicine, University of Southern California, CA 90089, USA
| | - Crystal N Marconett
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
| | - Ite A Offringa
- Department of Surgery, University of Southern California, CA 90089, USA.,Department of Biochemistry & Molecular Medicine, University of Southern California, CA 90089, USA.,Norris Comprehensive Cancer Center, University of Southern California, CA 90089, USA
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41
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Dong SS, Zhang YJ, Chen YX, Yao S, Hao RH, Rong Y, Niu HM, Chen JB, Guo Y, Yang TL. Comprehensive review and annotation of susceptibility SNPs associated with obesity-related traits. Obes Rev 2018. [PMID: 29527783 DOI: 10.1111/obr.12677] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We aimed to summarize the results of genetic association studies for obesity and provide a comprehensive annotation of all susceptibility single nucleotide polymorphisms (SNPs). A total of 72 studies were summarized, resulting in 90,361 susceptibility SNPs (738 index SNPs and 89,623 linkage disequilibrium SNPs). Over 90% of the susceptibility SNPs are located in non-coding regions, and it is challenging to understand their functional significance. Therefore, we annotated these SNPs by using various functional databases. We identified 24,623 functional SNPs, including 4 nonsense SNPs, 479 missense SNPs, 399 untranslated region SNPs which might affect microRNA binding, 262 promoter and 5,492 enhancer SNPs which might affect transcription factor binding, 7 splicing sites, 76 SNPs which might affect gene methylation levels, 1,839 SNPs under natural selection and 17,351 SNPs which might modify histone binding. Expression quantitative trait loci analyses for functional SNPs identified 98 target genes, including 69 protein coding genes, 27 long non-coding RNAs and 3 processed transcripts. The percentage of protein coding genes that could be correlated with obesity-related pathways directly or through gene-gene interaction is 75.36 (52/69). Our results may serve as an encyclopaedia of obesity susceptibility SNPs and offer guide for functional experiments.
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Affiliation(s)
- S-S Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y-J Zhang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y-X Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - S Yao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - R-H Hao
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y Rong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - H-M Niu
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - J-B Chen
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Y Guo
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - T-L Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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42
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Lin Y, Liu L, Sheng Y, Shen C, Zheng X, Zhou F, Yang S, Yin X, Zhang X. A catalog of potential putative functional variants in psoriasis genome-wide association regions. PLoS One 2018; 13:e0196635. [PMID: 29715312 PMCID: PMC5929547 DOI: 10.1371/journal.pone.0196635] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2018] [Accepted: 04/15/2018] [Indexed: 01/20/2023] Open
Abstract
Psoriasis is a common inflammatory skin disease, with considerable genetic contribution. Genome-wide association studies have successfully identified a number of genomic regions for the risk of psoriasis. However, it is challenging to pinpoint the functional causal variants and then further decipher the genetic mechanisms underlying each region. In order to prioritize potential functional causal variants within psoriasis susceptibility regions, we integrated the genetic association findings and functional genomic data publicly available, i.e. histone modifications in relevant immune cells. We characterized a pervasive enrichment pattern of psoriasis variants in five core histone marks across immune cells/tissues. We discovered that genetic alleles within psoriasis association regions might influence gene expression levels through significantly affecting the binding affinities of 17 transcription factors. We established a catalog of 654 potential functional causal variants for psoriasis and suggested that they significantly overlapped with causal variants for autoimmune diseases. We identified potential causal variant rs79824801 overlay with the peaks of five histone marks in primary CD4+ T cells. Its alternative allele affected the binding affinity of transcription factor IKZF1. This study highlights the complex genetic architecture and complicated mechanisms for psoriasis. The findings will inform the functional experiment design for psoriasis.
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Affiliation(s)
- Yan Lin
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Department of Dermatology, The Fourth Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Lu Liu
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Yujun Sheng
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Changbing Shen
- Department of Dermatology, China-Japan Friendship Hospital, Beijing, China
| | - Xiaodong Zheng
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Fusheng Zhou
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Sen Yang
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
| | - Xianyong Yin
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
- Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Xuejun Zhang
- Institute of Dermatology, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, China
- Key lab of Dermatology, Ministry of Education, Anhui Medical University and State Key lab of Dermatology Incubation, Hefei, Anhui, China
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43
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Abstract
Advances in omics technologies - such as genomics, transcriptomics, proteomics and metabolomics - have begun to enable personalized medicine at an extraordinarily detailed molecular level. Individually, these technologies have contributed medical advances that have begun to enter clinical practice. However, each technology individually cannot capture the entire biological complexity of most human diseases. Integration of multiple technologies has emerged as an approach to provide a more comprehensive view of biology and disease. In this Review, we discuss the potential for combining diverse types of data and the utility of this approach in human health and disease. We provide examples of data integration to understand, diagnose and inform treatment of diseases, including rare and common diseases as well as cancer and transplant biology. Finally, we discuss technical and other challenges to clinical implementation of integrative omics.
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Affiliation(s)
- Konrad J Karczewski
- Massachusetts General Hospital, Boston, MA, USA
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
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44
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CRISPR-based strategies for studying regulatory elements and chromatin structure in mammalian gene control. Mamm Genome 2018; 29:205-228. [PMID: 29196861 PMCID: PMC9881389 DOI: 10.1007/s00335-017-9727-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/27/2017] [Indexed: 01/31/2023]
Abstract
The development of high-throughput methods has enabled the genome-wide identification of putative regulatory elements in a wide variety of mammalian cells at an unprecedented resolution. Extensive genomic studies have revealed the important role of regulatory elements and genetic variation therein in disease formation and risk. In most cases, there is only correlative evidence for the roles of these elements and non-coding changes within these elements in pathogenesis. With the advent of genome- and epigenome-editing tools based on the CRISPR technology, it is now possible to test the functional relevance of the regulatory elements and alterations on a genomic scale. Here, we review the various CRISPR-based strategies that have been developed to functionally validate the candidate regulatory elements in mammals as well as the non-coding genetic variants found to be associated with human disease. We also discuss how these synthetic biology tools have helped to elucidate the role of three-dimensional nuclear architecture and higher-order chromatin organization in shaping functional genome and controlling gene expression.
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45
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Zhou D, Zhang D, Sun X, Li Z, Ni Y, Shan Z, Li H, Liu C, Zhang S, Liu Y, Zheng R, Pan F, Zhu Y, Shi Y, Lai M. A novel variant associated with HDL-C levels by modifying DAGLB expression levels: An annotation-based genome-wide association study. Eur J Hum Genet 2018; 26:838-847. [PMID: 29476167 DOI: 10.1038/s41431-018-0108-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2017] [Revised: 12/10/2017] [Accepted: 01/23/2018] [Indexed: 11/09/2022] Open
Abstract
Although numbers of genome-wide association studies (GWAS) have been performed for serum lipid levels, limited heritability has been explained. Studies showed that combining data from GWAS and expression quantitative trait loci (eQTLs) signals can both enhance the discovery of trait-associated SNPs and gain a better understanding of the mechanism. We performed an annotation-based, multistage genome-wide screening for serum-lipid-level-associated loci in totally 6863 Han Chinese. A serum high-density lipoprotein cholesterol (HDL-C) associated variant rs1880118 (hg19 chr7:g. 6435220G>C) was replicated (Pcombined = 1.4E-10). rs1880118 was associated with DAGLB (diacylglycerol lipase, beta) expression levels in subcutaneous adipose tissue (P = 5.9E-42) and explained 47.7% of the expression variance. After the replication, an active segment covering variants tagged by rs1880118 near 5' of DAGLB was annotated using histone modification and transcription factor binding signals. The luciferase report assay revealed that the segment containing the minor alleles showed increased transcriptional activity compared with segment contains the major alleles, which was consistent with the eQTL analyses. The expression-trait association tests indicated the association between the DAGLB and serum HDL-C levels using gene-based approaches called "TWAS" (P = 3.0E-8), "SMR" (P = 1.1E-4), and "Sherlock" (P = 1.6E-6). To summarize, we identified a novel HDL-C-associated variant which explained nearly half of the expression variance of DAGLB. Integrated analyses established a genotype-gene-phenotype three-way association and expanded our knowledge of DAGLB in lipid metabolism.
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Affiliation(s)
- Dan Zhou
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China.,Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Dandan Zhang
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Xiaohui Sun
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Zhiqiang Li
- The Affiliated Hospital of Qingdao University & The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266000, China.,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yaqin Ni
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Zhongyan Shan
- The Endocrine Institute and Liaoning Provincial Key Laboratory of Endocrine Diseases, Department of Endocrinology and Metabolism, The First Hospital of China Medical University, Shenyang, Liaoning, 110001, China
| | - Hong Li
- Department of Endocrinology, Sir Run Run Shaw Hospital Affiliated to School of Medicine, Zhejiang University, Hangzhou, Zhejiang, 310020, China
| | - Chengguo Liu
- Putuo District People's Hospital, Zhoushan, Zhejiang, 316100, China
| | - Shuai Zhang
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China.,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China
| | - Yi Liu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Ruizhi Zheng
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Feixia Pan
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China
| | - Yimin Zhu
- Department of Epidemiology & Biostatistics, Zhejiang University School of Public Health, Hangzhou, Zhejiang, 310058, China.
| | - Yongyong Shi
- The Affiliated Hospital of Qingdao University & The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, Qingdao, 266000, China. .,Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Collaborative Innovation Center for Brain Science, Shanghai Jiao Tong University, Shanghai, 200030, China. .,Department of Psychiatry, The First Teaching Hospital of Xinjiang Medical University, Urumqi, 830000, China.
| | - Maode Lai
- Department of Pathology, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310058, China. .,Key Laboratory of Disease Proteomics of Zhejiang Province, Hangzhou, Zhejiang, 310058, China.
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46
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Zhang ZX, Tong X, Zhang WN, Fu WN. Association between the HOTAIR polymorphisms and cancer risk: an updated meta-analysis. Oncotarget 2018; 8:4460-4470. [PMID: 27965458 PMCID: PMC5354846 DOI: 10.18632/oncotarget.13880] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2016] [Accepted: 12/01/2016] [Indexed: 12/17/2022] Open
Abstract
Purpose LncRNA HOTAIR plays an important role in many cancer. Several studies have shown that some HOTAIR SNPs might be associated with tumor risk in case-control studies, but the results are inconsistent and inconclusive. Therefore, it is necessary to better evaluate association between the HOTAIR SNPs and the risk of cancer. Results rs920778, rs7958904 and rs874945 but not rs4759314 and rs1899663 loci were significantly related to cancer risk, among of which rs920778 and rs874945 increased and rs7958904 decreased cancer risk, respectively. Moreover, rs920778 is significantly susceptible in both Asian population and digestive cancer risks. Materials and Methods Data were collected from PubMed, Embase and Web of Science. A total of 11 case-control studies were selected for the quantitative analysis. Software Stata (Version 12) was used to calculate Odds ratios (ORs) and 95% confidence intervals (CIs) to evaluate the strength of the associations. Subgroup analysis, sensitivity analysis, and publication bias were also performed. Five HOTAIR SNPs were finally enrolled in the study. Conclusions HOTAIR SNP rs920778, rs7958904 and rs874945 are susceptible to cancer risk. SNP rs920778 is also a useful risk factor in evaluation of Asian population and digestive cancer. In addition, the cancer risk SNP rs874945 is first reported in the meta-analysis.
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Affiliation(s)
- Zhao-Xiong Zhang
- Department of Medical Genetics, China Medical University, Shenyang, 110122, P.R. China
| | - Xue Tong
- Department of Medical Genetics, China Medical University, Shenyang, 110122, P.R. China
| | - Wan-Ni Zhang
- Department of Medical Genetics, China Medical University, Shenyang, 110122, P.R. China
| | - Wei-Neng Fu
- Department of Medical Genetics, China Medical University, Shenyang, 110122, P.R. China
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47
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Integrative expression quantitative trait locus-based analysis of colorectal cancer identified a functional polymorphism regulating SLC22A5 expression. Eur J Cancer 2018; 93:1-9. [PMID: 29428571 DOI: 10.1016/j.ejca.2018.01.065] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2017] [Revised: 01/03/2018] [Accepted: 01/06/2018] [Indexed: 01/03/2023]
Abstract
Multiple single nucleotide polymorphisms (SNPs) have been found to be highly correlated with colorectal cancer (CRC) risk. However, the variants identified thus far only explain a small part of the cases, suggesting the existence of many uncharacterised genetic determinants. In this study, using the multilevel 'omics' data provided in The Cancer Genome Atlas, we systematically performed expression quantitative trait locus (eQTL) analysis for CRC and identified nine SNPs with significant effects on mRNA expression (correlation |r| > 0.3 and FDR < 0.01). Then we conducted a two-stage case-control study consisting of 1528 cases and 1528 controls to examine the associations between candidate SNPs and CRC risk. We found that rs27437 in SLC22A5 was significantly correlated with CRC risk in both stages and the combined study (additive model, OR = 1.31, 95%CI = 1.17-1.47, P = 1.97 × 10-6). eQTL analysis showed that rs27437 GG and GA genotypes were associated with lower expression of SLC22A5 compared with the AA genotype. Dual-luciferase reporter assays confirmed that the G risk allele could decrease the expression of luciferase. SLC22A5 was significantly decreased in CRC tumour tissues compared with adjacent normal tissues, indicating that SLC22A5 may play important roles in CRC, and rs27437 in SLC22A5 might serve as a novel biomarker for early detection and prevention of CRC.
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48
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Catarino RR, Stark A. Assessing sufficiency and necessity of enhancer activities for gene expression and the mechanisms of transcription activation. Genes Dev 2018; 32:202-223. [PMID: 29491135 PMCID: PMC5859963 DOI: 10.1101/gad.310367.117] [Citation(s) in RCA: 124] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Enhancers are important genomic regulatory elements directing cell type-specific transcription. They assume a key role during development and disease, and their identification and functional characterization have long been the focus of scientific interest. The advent of next-generation sequencing and clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9-based genome editing has revolutionized the means by which we study enhancer biology. In this review, we cover recent developments in the prediction of enhancers based on chromatin characteristics and their identification by functional reporter assays and endogenous DNA perturbations. We discuss that the two latter approaches provide different and complementary insights, especially in assessing enhancer sufficiency and necessity for transcription activation. Furthermore, we discuss recent insights into mechanistic aspects of enhancer function, including findings about cofactor requirements and the role of post-translational histone modifications such as monomethylation of histone H3 Lys4 (H3K4me1). Finally, we survey how these approaches advance our understanding of transcription regulation with respect to promoter specificity and transcriptional bursting and provide an outlook covering open questions and promising developments.
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Affiliation(s)
- Rui R Catarino
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), 1030 Vienna, Austria
| | - Alexander Stark
- Research Institute of Molecular Pathology (IMP), Vienna Biocenter (VBC), 1030 Vienna, Austria
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49
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Ke J, Lou J, Chen X, Li J, Liu C, Gong Y, Yang Y, Zhu Y, Zhang Y, Tian J, Chang J, Zhong R, Gong J, Miao X. Identification of a functional variant for colorectal cancer risk mapping to chromosome 5q31.1. Oncotarget 2018; 7:35199-207. [PMID: 27177089 PMCID: PMC5085221 DOI: 10.18632/oncotarget.9298] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2016] [Accepted: 04/11/2016] [Indexed: 12/31/2022] Open
Abstract
Genome-wide association studies (GWASs) have established chromosome 5q31.1 as a risk locus for colorectal cancer (CRC). We previously identified a potentially regulatory single nucleotide polymorphism (SNP) rs17716310 within 5q31.1. Now, we extended our study with another independent Chinese population, functional assays and analyses of TCGA (The Cancer Genome Atlas) data. Significant associations between rs17716310 and CRC risk were found in Present Study including 1075 CRC cases and 1999 controls (additive model: OR = 1.149, 95% CI = 1.027–1.286, P = 0.016), and in Combined Study including 1766 cases and 2708 controls (additive model: OR = 1.145, 95% CI = 1.045–1.254, P = 0.004). Dual luciferase reporter gene assays indicated that the variant C allele obviously increased transcriptional activity. Using TCGA datasets, we indicated rs17716310 as a cis expression quantitative trait locus (eQTL) for the gene SMAD5, whose expression was significantly higher in CRC tissues. These findings suggested that the functional polymorphism rs17716310 A > C might be a genetic modifier for CRC, promoting the expression of SMAD5 that belonged to the transforming growth factor beta (TGF-β) signaling pathway.
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Affiliation(s)
- Juntao Ke
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiao Lou
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xueqin Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiaoyuan Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Cheng Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yajie Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yi Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jianbo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jiang Chang
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rong Zhong
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Gong
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,State Key Laboratory of Environment Health (Incubation), MOE (Ministry of Education) Key Laboratory of Environment and Health, Ministry of Environmental Protection Key Laboratory of Environment and Health (Wuhan) and School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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50
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Liu S, Liu Y, Zhang Q, Wu J, Liang J, Yu S, Wei GH, White KP, Wang X. Systematic identification of regulatory variants associated with cancer risk. Genome Biol 2017; 18:194. [PMID: 29061142 PMCID: PMC5651703 DOI: 10.1186/s13059-017-1322-z] [Citation(s) in RCA: 54] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 09/20/2017] [Indexed: 01/01/2023] Open
Abstract
Background Most cancer risk-associated single nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) are noncoding and it is challenging to assess their functional impacts. To systematically identify the SNPs that affect gene expression by modulating activities of distal regulatory elements, we adapt the self-transcribing active regulatory region sequencing (STARR-seq) strategy, a high-throughput technique to functionally quantify enhancer activities. Results From 10,673 SNPs linked with 996 cancer risk-associated SNPs identified in previous GWAS studies, we identify 575 SNPs in the fragments that positively regulate gene expression, and 758 SNPs in the fragments with negative regulatory activities. Among them, 70 variants are regulatory variants for which the two alleles confer different regulatory activities. We analyze in depth two regulatory variants—breast cancer risk SNP rs11055880 and leukemia risk-associated SNP rs12142375—and demonstrate their endogenous regulatory activities on expression of ATF7IP and PDE4B genes, respectively, using a CRISPR-Cas9 approach. Conclusions By identifying regulatory variants associated with cancer susceptibility and studying their molecular functions, we hope to help the interpretation of GWAS results and provide improved information for cancer risk assessment. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1322-z) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Song Liu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yuwen Liu
- Institute for Genomics and Systems Biology, and Department of Human Genetics, University of Chicago, Chicago, Illinois, 60637, USA
| | - Qin Zhang
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, FIN-90014, Oulu, Finland
| | - Jiayu Wu
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Junbo Liang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Shan Yu
- Institute for Genomics and Systems Biology, and Department of Human Genetics, University of Chicago, Chicago, Illinois, 60637, USA
| | - Gong-Hong Wei
- Biocenter Oulu, Faculty of Biochemistry and Molecular Medicine, University of Oulu, FIN-90014, Oulu, Finland
| | - Kevin P White
- Institute for Genomics and Systems Biology, and Department of Human Genetics, University of Chicago, Chicago, Illinois, 60637, USA. .,Tempus Labs, Inc., Chicago, Illinois, 60654, Finland.
| | - Xiaoyue Wang
- State Key Laboratory of Medical Molecular Biology, Department of Biochemistry and Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
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