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• W4367837708 abstract "Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histologic alterations within nevi and surrounding tissue. Herein, a comprehensive analysis of publicly available gene expression data sets of melanoma, common or congenital nevi (CN), and dysplastic nevi (DN), assessed molecular and genetic pathways leading to early melanoma. The results demonstrate several pathways reflective of ongoing local structural tissue remodeling activity likely involved during the transition from benign to early-stage melanoma. These processes include the gene expression of cancer-associated fibroblasts, collagens, extracellular matrix, and integrins, which assist early melanoma development and the immune surveillance that plays a substantial role at this early stage. Furthermore, genes up-regulated in DN were also overexpressed in melanoma tissue, supporting the notion that DN may serve as a transitional phase toward oncogenesis. CN collected from healthy individuals exhibited different gene signatures compared with histologically benign nevi tissue located adjacent to melanoma (adjacent nevi). Finally, the expression profile of microdissected adjacent nevi tissue was more similar to melanoma compared with CN, revealing the melanoma influence on this annexed tissue. Early detection and treatment of melanoma, the most aggressive skin cancer, improves the median 5-year survival rate of patients from 25% to 99%. Melanoma development involves a stepwise process during which genetic changes drive histologic alterations within nevi and surrounding tissue. Herein, a comprehensive analysis of publicly available gene expression data sets of melanoma, common or congenital nevi (CN), and dysplastic nevi (DN), assessed molecular and genetic pathways leading to early melanoma. The results demonstrate several pathways reflective of ongoing local structural tissue remodeling activity likely involved during the transition from benign to early-stage melanoma. These processes include the gene expression of cancer-associated fibroblasts, collagens, extracellular matrix, and integrins, which assist early melanoma development and the immune surveillance that plays a substantial role at this early stage. Furthermore, genes up-regulated in DN were also overexpressed in melanoma tissue, supporting the notion that DN may serve as a transitional phase toward oncogenesis. CN collected from healthy individuals exhibited different gene signatures compared with histologically benign nevi tissue located adjacent to melanoma (adjacent nevi). Finally, the expression profile of microdissected adjacent nevi tissue was more similar to melanoma compared with CN, revealing the melanoma influence on this annexed tissue. Melanoma is the most aggressive form of skin cancer, which either develops de novo or from preexisting moles. Genome sequencing of benign and malignant nevi indicate that Ras/Raf mutations may act as drivers during the premelanoma stage.1Smith A.P. Hoek K. Becker D. Whole-genome expression profiling of the melanoma progression pathway reveals marked molecular differences between nevi/melanoma in situ and advanced-stage melanomas.Cancer Biol Ther. 2005; 4: 1018-1029Crossref PubMed Scopus (146) Google Scholar However, melanoma genesis is a multistep process involving the accumulation of a range of mutations associated with additional molecular pathways.2Davis E.J. Johnson D.B. Sosman J.A. Chandra S. Melanoma: what do all the mutations mean?.Cancer. 2018; 124: 3490-3499Crossref PubMed Scopus (92) Google Scholar, 3Pollock P.M. Harper U.L. Hansen K.S. Yudt L.M. Stark M. Robbins C.M. Moses T.Y. Hostetter G. Wagner U. Kakareka J. Salem G. Pohida T. Heenan P. Duray P. Kallioniemi O. Hayward N.K. Trent J.M. Meltzer P.S. High frequency of BRAF mutations in nevi.Nat Genet. 2003; 33: 19-20Crossref PubMed Scopus (1394) Google Scholar, 4Zhang T. Dutton-Regester K. Brown K.M. Hayward N.K. The genomic landscape of cutaneous melanoma.Pigment Cell Melanoma Res. 2016; 29: 266-283Crossref PubMed Scopus (114) Google Scholar During this initial process, tumor microfoci are formed, which under normal physiological conditions fail to progress to cancerous growth and are likely destroyed by the immune surveillance system. The early transition from benign to cancerous growth depends on crucial support from the extracellular matrix (ECM) and neoangiogenesis, providing early growth with space and nutrients while connecting the tumor to the circulatory system.5Ostrowski S.M. Fisher D.E. Biology of melanoma.Hematol Oncol Clin North Am. 2021; 35: 29-56Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar, 6Croce C.M. Oncogenes and cancer.N Engl J Med. 2008; 358: 502-511Crossref PubMed Scopus (792) Google Scholar, 7Iurlaro R. León-Annicchiarico C.L. Muñoz-Pinedo C. Regulation of cancer metabolism by oncogenes and tumor suppressors.Methods Enzymol. 2014; 542: 59-80Crossref PubMed Scopus (74) Google Scholar To date, gene expression analyses have been performed on various melanocytic specimens. These have focused primarily on the differences between melanoma and nonmelanoma tissues to better predict the prognosis or treatment outcomes, identify subclasses of melanoma, and characterize molecular changes occurring at the transition from melanoma in situ to primary melanoma and the immune pathway involved primarily with the later stages of melanoma development.1Smith A.P. Hoek K. Becker D. Whole-genome expression profiling of the melanoma progression pathway reveals marked molecular differences between nevi/melanoma in situ and advanced-stage melanomas.Cancer Biol Ther. 2005; 4: 1018-1029Crossref PubMed Scopus (146) Google Scholar,8Colebatch A.J. Ferguson P. Newell F. Kazakoff S.H. Witkowski T. Dobrovic A. Johansson P.A. Saw R.P.M. Stretch J.R. McArthur G.A. Long G.V. Thompson J.F. Pearson J.V. Mann G.J. Hayward N.K. Waddell N. Scolyer R.A. Wilmott J.S. Molecular genomic profiling of melanocytic nevi.J Invest Dermatol. 2019; 139: 1762-1768Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar, 9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar, 10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar, 11Talantov D. Mazumder A. Yu J.X. Briggs T. Jiang Y. Backus J. Atkins D. Wang Y. Novel genes associated with malignant melanoma but not benign melanocytic lesions.Clin Cancer Res. 2005; 11: 7234-7242Crossref PubMed Scopus (414) Google Scholar, 12Jiang J. Liu C. Xu G. Liang T. Yu C. Liao S. Zhang Z. Lu Z. Wang Z. Chen J. Chen T. Li H. Zhan X. Identification of hub genes associated with melanoma development by comprehensive bioinformatics analysis.Front Oncol. 2021; 11621430Google Scholar, 13Borden E.S. Adams A.C. Buetow K.H. Wilson M.A. Bauman J.E. Curiel-Lewandrowski C. Chow H.S. LaFleur B.J. Hastings K.T. Shared gene expression and immune pathway changes associated with progression from nevi to melanoma.Cancers (Basel). 2021; 14: 3Crossref PubMed Scopus (7) Google Scholar Melanoma tissue used in most of these studies was pooled from varying stages of melanoma, determined by histopathologic analyses. No studies, however, have attempted to elucidate the molecular signatures involved in the transition from common or congenital nevi (CN) to the early stage of melanoma. Gene expression profiling of CN, dysplastic nevi (DN), and early melanoma can help elucidate the transcriptional programs involved in the pathogenetic sequence for the transition from a benign state to malignant tumor.14Xiong D.D. Barriera-Silvestrini P. Knackstedt T.J. Delays in the surgical treatment of melanoma are associated with worsened overall and melanoma-specific mortality: a population-based analysis.J Am Acad Dermatol. 2022; 87: 807-814Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar Identifying molecular signatures at the earliest stages of tissue remodeling associated with melanoma development may also complement established histopathologic techniques to improve diagnosis and care. In the present study, a meta-analysis of publicly available data sets9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar, 10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar, 11Talantov D. Mazumder A. Yu J.X. Briggs T. Jiang Y. Backus J. Atkins D. Wang Y. Novel genes associated with malignant melanoma but not benign melanocytic lesions.Clin Cancer Res. 2005; 11: 7234-7242Crossref PubMed Scopus (414) Google Scholar,15Kabbarah O. Nogueira C. Feng B. Nazarian R.M. Bosenberg M. Wu M. Scott K.L. Kwong L.N. Xiao Y. Cordon-Cardo C. Granter S.R. Ramaswamy S. Golub T. Duncan L.M. Wagner S.N. Brennan C. Chin L. Integrative genome comparison of primary and metastatic melanomas.PLoS One. 2010; 5e10770Crossref PubMed Scopus (148) Google Scholar, 16Kunz M. Löffler-Wirth H. Dannemann M. Willscher E. Doose G. Kelso J. Kottek T. Nickel B. Hopp L. Landsberg J. Hoffmann S. Tüting T. Zigrino P. Mauch C. Utikal J. Ziemer M. Schulze H.J. Hölzel M. Roesch A. Kneitz S. Meierjohann S. Bosserhoff A. Binder H. Schartl M. RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.Oncogene. 2018; 37: 6136-6151Crossref PubMed Scopus (57) Google Scholar, 17Mitsui H. Kiecker F. Shemer A. Cannizzaro M.V. Wang C.Q.F. Gulati N. Ohmatsu H. Shah K.R. Gilleaudeau P. Sullivan-Whalen M. Cueto I. McNutt N.S. Suarez-Farinas M. Krueger J.G. Discrimination of dysplastic nevi from common melanocytic nevi by cellular and molecular criteria.J Invest Dermatol. 2016; 136: 2030-2040Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar, 18Shain A.H. Joseph N.M. Yu R. Benhamida J. Liu S. Prow T. Ruben B. North J. Pincus L. Yeh I. Judson R. Bastian B.C. Genomic and transcriptomic analysis reveals incremental disruption of key signaling pathways during melanoma evolution.Cancer Cell. 2018; 34: 45-55.e4Abstract Full Text Full Text PDF PubMed Scopus (116) Google Scholar containing CN, DN, and early stages of melanoma was used to identify molecular signatures that differentiate between CN and early-stage melanomas, implicating up-regulated gene sets and pathways at the earliest stage of melanoma development. A gene profiling comparison of CN and nevi tissue adjunct to melanoma [adjacent nevi (AN)] with melanoma tissue was also performed. This study entailed seven data sets following a comprehensive search for publicly available data sets of gene expression [RNA sequencing (RNAseq) or microarray] consisting of samples of both early melanoma and CN tissues or samples of both melanoma and DN tissues (Table 1). A summary of clinical information for data sets used in this study is available in Supplemental Table S1. For the three RNAseq data sets, the published gene-count tables were used after removing genes with a total coverage of <10 and >10,000 reads from the analysis.Table 1Data Sets Used in This StudyReference (data, GEO accession no.)∗GSE numbers refer to data set identification in GEO (http://www.ncbi.nlm.nih.gov/geo).CNDNEarly-stage melanomaIntermediate stageAdvanced stageReported mutationsPatient summaryBadal9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar (RNAseq, GSE98394)27–9TT ≤ 1.0271.0 < TT ≤ 4.014TT > 4.0 mmBRAF in 19 melanoma and 26 CN samples30 M, 20 FAll treatment naiveShain18Shain A.H. Joseph N.M. Yu R. Benhamida J. Liu S. Prow T. Ruben B. North J. Pincus L. Yeh I. Judson R. Bastian B.C. Genomic and transcriptomic analysis reveals incremental disruption of key signaling pathways during melanoma evolution.Cancer Cell. 2018; 34: 45-55.e4Abstract Full Text Full Text PDF PubMed Scopus (116) Google Scholar (RNAseq, NA†Raw sequencing data are available through dbGAP (https://www.ncbi.nlm.nih.gov/gap) with accession phs001550.v3.p1.)19–12 Stage T18 Stage T23 Stage T3 or T4BRAF in all and NRAS in 11 melanoma samples15 M, 17 F (aged 57 ± 18 years)Kunz16Kunz M. Löffler-Wirth H. Dannemann M. Willscher E. Doose G. Kelso J. Kottek T. Nickel B. Hopp L. Landsberg J. Hoffmann S. Tüting T. Zigrino P. Mauch C. Utikal J. Ziemer M. Schulze H.J. Hölzel M. Roesch A. Kneitz S. Meierjohann S. Bosserhoff A. Binder H. Schartl M. RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.Oncogene. 2018; 37: 6136-6151Crossref PubMed Scopus (57) Google Scholar (RNAseq, GSE112509)23–57 PrimaryBRAF V600 in 23 and NRAS in 19 melanoma samples29 M, 28 F (aged 68 ± 14 years)Talantov11Talantov D. Mazumder A. Yu J.X. Briggs T. Jiang Y. Backus J. Atkins D. Wang Y. Novel genes associated with malignant melanoma but not benign melanocytic lesions.Clin Cancer Res. 2005; 11: 7234-7242Crossref PubMed Scopus (414) Google Scholar (microarray, GSE3189)18–45 PrimaryNot reported23 M, 22 F (aged 66 ± 15 years)Kabbarah15Kabbarah O. Nogueira C. Feng B. Nazarian R.M. Bosenberg M. Wu M. Scott K.L. Kwong L.N. Xiao Y. Cordon-Cardo C. Granter S.R. Ramaswamy S. Golub T. Duncan L.M. Wagner S.N. Brennan C. Chin L. Integrative genome comparison of primary and metastatic melanomas.PLoS One. 2010; 5e10770Crossref PubMed Scopus (148) Google Scholar (microarray, GSE46517)9–31 PrimaryBRAF V600E in 13, wild type in 10 melanoma samples17 M, 14 F (aged 62 ± 15 years)Scatolini10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar (microarray, GSE12391)181123 PrimaryNot reported13 M, 10 F (aged 60 ± 15 years)Mitsui17Mitsui H. Kiecker F. Shemer A. Cannizzaro M.V. Wang C.Q.F. Gulati N. Ohmatsu H. Shah K.R. Gilleaudeau P. Sullivan-Whalen M. Cueto I. McNutt N.S. Suarez-Farinas M. Krueger J.G. Discrimination of dysplastic nevi from common melanocytic nevi by cellular and molecular criteria.J Invest Dermatol. 2016; 136: 2030-2040Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar (microarray, GSE53223)57–Not reported7 M, 5 F (aged 44 ± 16 years)Two data sets from studies (Badal9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar and Shain18Shain A.H. Joseph N.M. Yu R. Benhamida J. Liu S. Prow T. Ruben B. North J. Pincus L. Yeh I. Judson R. Bastian B.C. Genomic and transcriptomic analysis reveals incremental disruption of key signaling pathways during melanoma evolution.Cancer Cell. 2018; 34: 45-55.e4Abstract Full Text Full Text PDF PubMed Scopus (116) Google Scholar) are used for gene signatures of discrimination between early melanoma and CN, and other data sets are used to support the findings. In addition, the microarray data sets (Scatolini10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar and Mitsui17Mitsui H. Kiecker F. Shemer A. Cannizzaro M.V. Wang C.Q.F. Gulati N. Ohmatsu H. Shah K.R. Gilleaudeau P. Sullivan-Whalen M. Cueto I. McNutt N.S. Suarez-Farinas M. Krueger J.G. Discrimination of dysplastic nevi from common melanocytic nevi by cellular and molecular criteria.J Invest Dermatol. 2016; 136: 2030-2040Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar) are used to identify dysregulated genes common between DN and early melanoma (Figure 3). Gene-count table used in this study provided in courtesy by authors. Age format is mean ± SD.F, female; M, male; –, not applicable; BRAF: B-raf proto-oncogene, serine/threonine kinase; CN, benign common acquired nevus; DN, dysplastic nevus; GEO, Gene Expression Omnibus; NA, not available; NRAS, NRAS proto-oncogene, GTPase; RNAseq, RNA sequencing; TT, tumor thickness (in mm).∗ GSE numbers refer to data set identification in GEO (http://www.ncbi.nlm.nih.gov/geo).† Raw sequencing data are available through dbGAP (https://www.ncbi.nlm.nih.gov/gap) with accession phs001550.v3.p1. Open table in a new tab Two data sets from studies (Badal9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar and Shain18Shain A.H. Joseph N.M. Yu R. Benhamida J. Liu S. Prow T. Ruben B. North J. Pincus L. Yeh I. Judson R. Bastian B.C. Genomic and transcriptomic analysis reveals incremental disruption of key signaling pathways during melanoma evolution.Cancer Cell. 2018; 34: 45-55.e4Abstract Full Text Full Text PDF PubMed Scopus (116) Google Scholar) are used for gene signatures of discrimination between early melanoma and CN, and other data sets are used to support the findings. In addition, the microarray data sets (Scatolini10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar and Mitsui17Mitsui H. Kiecker F. Shemer A. Cannizzaro M.V. Wang C.Q.F. Gulati N. Ohmatsu H. Shah K.R. Gilleaudeau P. Sullivan-Whalen M. Cueto I. McNutt N.S. Suarez-Farinas M. Krueger J.G. Discrimination of dysplastic nevi from common melanocytic nevi by cellular and molecular criteria.J Invest Dermatol. 2016; 136: 2030-2040Abstract Full Text Full Text PDF PubMed Scopus (27) Google Scholar) are used to identify dysregulated genes common between DN and early melanoma (Figure 3). Gene-count table used in this study provided in courtesy by authors. Age format is mean ± SD. F, female; M, male; –, not applicable; BRAF: B-raf proto-oncogene, serine/threonine kinase; CN, benign common acquired nevus; DN, dysplastic nevus; GEO, Gene Expression Omnibus; NA, not available; NRAS, NRAS proto-oncogene, GTPase; RNAseq, RNA sequencing; TT, tumor thickness (in mm). Gene sets were downloaded from Molecular Signature Database,19Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (28593) Google Scholar and a set of gene lists involved in tissue remodeling was curated, including 80 laminin genes, 17 cancer-associated fibroblast (CAF) genes, 25 ECM genes, 59 collagen genes, 31 integrin genes, and 25 matrix metalloproteinase (MMP) genes (Supplemental Table S2).20Folkman J. 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Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (28593) Google Scholar the Molecular Signature Database19Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.Proc Natl Acad Sci U S A. 2005; 102: 15545-15550Crossref PubMed Scopus (28593) Google Scholar version v2022.1.HS, and WebGestalt36Wang J. Vasaikar S. Shi Z. Greer M. Zhang B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit.Nucleic Acids Res. 2017; 45: W130-W137Crossref PubMed Scopus (720) Google Scholar version April 28, 2019 were used to identify gene functional classifications and to perform gene set enrichment analysis (Figure 1). The data sets used in this study were previously published and are available in Gene Expression Omnibus with accession numbers provided in Table 1. A comprehensive literature search for publicly available data was conducted to identify data sets that included gene expression profiles of CN and DN with early or primary melanoma samples in the same study. Three RNAseq (referred throughout as Badal,9Badal B. Solovyov A. Di Cecilia S. Chan J.M. Chang L.W. Iqbal R. Aydin I.T. Rajan G.S. Chen C. Abbate F. Arora K.S. Tanne A. Gruber S.B. Johnson T.M. Fullen D.R. Raskin L. Phelps R. Bhardwaj N. Bernstein E. Ting D.T. Brunner G. Schadt E.E. Greenbaum B.D. Celebi J.T. Transcriptional dissection of melanoma identifies a high-risk subtype underlying TP53 family genes and epigenome deregulation.JCI Insight. 2017; 2e92102Crossref PubMed Google Scholar Shain,18Shain A.H. Joseph N.M. Yu R. Benhamida J. Liu S. Prow T. Ruben B. North J. Pincus L. Yeh I. Judson R. Bastian B.C. Genomic and transcriptomic analysis reveals incremental disruption of key signaling pathways during melanoma evolution.Cancer Cell. 2018; 34: 45-55.e4Abstract Full Text Full Text PDF PubMed Scopus (116) Google Scholar and Kunz16Kunz M. Löffler-Wirth H. Dannemann M. Willscher E. Doose G. Kelso J. Kottek T. Nickel B. Hopp L. Landsberg J. Hoffmann S. Tüting T. Zigrino P. Mauch C. Utikal J. Ziemer M. Schulze H.J. Hölzel M. Roesch A. Kneitz S. Meierjohann S. Bosserhoff A. Binder H. Schartl M. RNA-seq analysis identifies different transcriptomic types and developmental trajectories of primary melanomas.Oncogene. 2018; 37: 6136-6151Crossref PubMed Scopus (57) Google Scholar) and three microarray data sets (referred throughout as Talantov,11Talantov D. Mazumder A. Yu J.X. Briggs T. Jiang Y. Backus J. Atkins D. Wang Y. Novel genes associated with malignant melanoma but not benign melanocytic lesions.Clin Cancer Res. 2005; 11: 7234-7242Crossref PubMed Scopus (414) Google Scholar Kabbarah,15Kabbarah O. Nogueira C. Feng B. Nazarian R.M. Bosenberg M. Wu M. Scott K.L. Kwong L.N. Xiao Y. Cordon-Cardo C. Granter S.R. Ramaswamy S. Golub T. Duncan L.M. Wagner S.N. Brennan C. Chin L. Integrative genome comparison of primary and metastatic melanomas.PLoS One. 2010; 5e10770Crossref PubMed Scopus (148) Google Scholar and Scatolini10Scatolini M. Grand M.M. Grosso E. Venesio T. Pisacane A. Balsamo A. Sirovich R. Risio M. Chiorino G. Altered molecular pathways in melanocytic lesions.Int J Cancer. 2010; 126: 1869-1881Crossref PubMed Scopus (65) Google Scholar) were identified (see Table 1 and Materials and Methods for criteria of early melanoma; one microarray data set consisted only of CN and DN samples17Mitsui H. Kiecker F. Shemer A. Cannizzaro M.V. Wang C.Q.F. Gulati N. O" @default.
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• W4367837708 title "Transcriptome Analysis Identifies Oncogenic Tissue Remodeling during Progression from Common Nevi to Early Melanoma" @default.
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