Identification of key candidate genes involved in melanoma metastasis

Chen,Jia; Wu,Fei; Shi,Yu; Yang,Degang; Xu,Mingyuan; Lai,Yongxian; Liu,Yeqiang

doi:10.3892/mmr.2019.10314

Identification of key candidate genes involved in melanoma metastasis

Authors:
- Jia Chen
- Fei Wu
- Yu Shi
- Degang Yang
- Mingyuan Xu
- Yongxian Lai
- Yeqiang Liu
View Affiliations

Affiliations: Department of Dermatopathology, Tongji University Affiliated Shanghai Skin Disease Hospital, Shanghai 200443, P.R. China, Department of Medical Cosmetology, Tongji University Affiliated Shanghai Skin Disease Hospital, Shanghai 200443, P.R. China, Department of Treatment, Tongji University Affiliated Shanghai Skin Disease Hospital, Shanghai 200443, P.R. China, Department of Dermatologic Surgery, Tongji University Affiliated Shanghai Skin Disease Hospital, Shanghai 200443, P.R. China
Published online on: May 30, 2019 https://doi.org/10.3892/mmr.2019.10314
Pages: 903-914
Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Metastasis is the most lethal stage of cancer progression. The present study aimed to investigate the underlying molecular mechanisms of melanoma metastasis using bioinformatics. Using the microarray dataset GSE8401 from the Gene Expression Omnibus database, which included 52 biopsy specimens from patients with melanoma metastasis and 31 biopsy specimens from patients with primary melanoma, differentially expressed genes (DEGs) were identified, subsequent to data preprocessing with the affy package, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A protein‑protein interaction (PPI) network was constructed. Mutated genes were analyzed with 80 mutated cases with melanoma from The Cancer Genome Atlas. The overall survival of key candidate DEGs, which were within a filtering of degree >30 criteria in the PPI network and involved three or more KEGG signaling pathways, and genes with a high mutation frequency were delineated. The expression analysis of key candidate DEGs, mutant genes and their associated genes were performed on UALCAN. Of the 1,187 DEGs obtained, 505 were upregulated and 682 were downregulated. ‘Extracellular exosome’ processes, the ‘amoebiasis’ pathway, the ‘ECM‑receptor interaction’ pathway and the ‘focal adhesion’ signaling pathway were significantly enriched and identified as important processes or signaling pathways. The overall survival analysis of phosphoinositide‑3‑kinase regulator subunit 3 (PIK3R3), centromere protein M (CENPM), aurora kinase A (AURKA), laminin subunit α 1 (LAMA1), proliferating cell nuclear antigen (PCNA), adenylate cyclase 1 (ADCY1), BUB1 mitotic checkpoint serine/threonine kinase (BUB1), NDC80 kinetochore complex component (NDC80) and protein kinase C α (PRKCA) in DEGs was statistically significant. Mutation gene analysis identified that BRCA1‑associated protein 1 (BAP1) had a higher mutation frequency and survival analysis, and its associated genes in the BAP1‑associated PPI network, including ASXL transcriptional regulator 1 (ASXL1), proteasome 26S subunit, non‑ATPase 3 (PSMD3), proteasome 26S subunit, non ATPase 11 (PSMD11) and ubiquitin C (UBC), were statistically significantly associated with the overall survival of patients with melanoma. The expression levels of PRKCA, BUB1, BAP1 and ASXL1 were significantly different between primary melanoma and metastatic melanoma. Based on the present study, ‘extracellular exosome’ processes, ‘amoebiasis’ pathways, ‘ECM‑receptor interaction’ pathways and ‘focal adhesion’ signaling pathways may be important in the formation of metastases from melanoma. The involved genes, including PIK3R3, CENPM, AURKA, LAMA1, PCNA, ADCY1, BUB1, NDC80 and PRKCA, and mutation associated genes, including BAP1, ASXL1, PSMD3, PSMD11 and UBC, may serve important roles in metastases of melanoma.

View Figures

View References

1	Palmieri G, Capone M, Ascierto ML, Gentilcore G, Stroncek DF, Casula M, Sini MC, Palla M, Mozzillo N and Ascierto PA: Main roads to melanoma. J Transl Med. 7:862009. View Article : Google Scholar : PubMed/NCBI
2	Pessina F, Navarria P, Tomatis S, Cozzi L, Franzese C, Di Guardo L, Ascolese AM, Reggiori G, Franceschini D, Del Vecchio M, et al: Outcome evaluation of patients with limited brain metastasis from malignant melanoma, treated with surgery, radiation therapy, and targeted therapy. World Neurosurg. 105:184–190. 2017. View Article : Google Scholar : PubMed/NCBI
3	Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA Jr and Kinzler KW: Cancer genome landscapes. Science. 339:1546–1558. 2013. View Article : Google Scholar : PubMed/NCBI
4	Guo Y, Bao Y, Ma M and Yang W: Identification of key candidate genes and pathways in colorectal cancer by integrated bioinformatical analysis. Int J Mol Sci. 18:E7222017. View Article : Google Scholar : PubMed/NCBI
5	Xu L, Shen SS, Hoshida Y, Subramanian A, Ross K, Brunet JP, Wagner SN, Ramaswamy S, Mesirov JP and Hynes RO: Gene expression changes in an animal melanoma model correlate with aggressiveness of human melanoma metastases. Mol Cancer Res. 6:760–769. 2008. View Article : Google Scholar : PubMed/NCBI
6	Edgar R, Domrachev M and Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30:207–210. 2002. View Article : Google Scholar : PubMed/NCBI
7	Gautier L, Cope L, Bolstad BM and Irizarry RA: affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
8	Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. https://doi.org/10.2202/1544-6115.1027
9	Hulsegge I, Kommadath A and Smits MA: Globaltest and GOEAST: Two different approaches for Gene Ontology analysis. BMC Proc. 3 (Suppl 4):S102009. View Article : Google Scholar : PubMed/NCBI
10	Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al The Gene Ontology Consortium, : Gene ontology: Tool for the unification of biology. Nat Genet. 25:25–29. 2000. View Article : Google Scholar : PubMed/NCBI
11	Kanehisa M and Goto S: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
12	Huang W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI
13	Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al: STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 43(D1): D447–D452. 2015. View Article : Google Scholar : PubMed/NCBI
14	Bindea G, Mlecnik B, Hackl H, Charoentong P, Tosolini M, Kirilovsky A, Fridman WH, Pagès F, Trajanoski Z and Galon J: ClueGO: A Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics. 25:1091–1093. 2009. View Article : Google Scholar : PubMed/NCBI
15	Bindea G, Galon J and Mlecnik B: CluePedia Cytoscape plugin: Pathway insights using integrated experimental and in silico data. Bioinformatics. 29:661–663. 2013. View Article : Google Scholar : PubMed/NCBI
16	Scardoni G, Petterlini M and Laudanna C: Analyzing biological network parameters with CentiScaPe. Bioinformatics. 25:2857–2859. 2009. View Article : Google Scholar : PubMed/NCBI
17	Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI
18	Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI
19	Guan J, Gupta R and Filipp FV: Cancer systems biology of TCGA SKCM: Efficient detection of genomic drivers in melanoma. Sci Rep. 5:78572015. View Article : Google Scholar : PubMed/NCBI
20	Tang Z, Li C, Kang B, Gao G, Li C and Zhang Z: GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res 45 (W1). W98–W102. 2017. View Article : Google Scholar
21	Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK and Varambally S: UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 19:649–658. 2017. View Article : Google Scholar : PubMed/NCBI
22	Chang X, Zhang H, Lian S and Zhu W: miR-137 suppresses tumor growth of malignant melanoma by targeting aurora kinase A. Biochem Biophys Res Commun. 475:251–256. 2016. View Article : Google Scholar : PubMed/NCBI
23	Krasagakis K, Lindschau C, Fimmel S, Eberle J, Quass P, Haller H and Orfanos CE: Proliferation of human melanoma cells is under tight control of protein kinase C alpha. J Cell Physiol. 199:381–387. 2004. View Article : Google Scholar : PubMed/NCBI
24	Lahn MM and Sundell KL: The role of protein kinase C-alpha (PKC-alpha) in melanoma. Melanoma Res. 14:85–89. 2004. View Article : Google Scholar : PubMed/NCBI
25	Kabbarah O, Nogueira C, Feng B, Nazarian RM, Bosenberg M, Wu M, Scott KL, Kwong LN, Xiao Y, Cordon-Cardo C, et al: Integrative genome comparison of primary and metastatic melanomas. PLoS One. 5:e107702010. View Article : Google Scholar : PubMed/NCBI
26	Hua Y, Ma X, Liu X, Yuan X, Qin H and Zhang X: Identification of the potential biomarkers for the metastasis of rectal adenocarcinoma. APMIS. 125:93–100. 2017. View Article : Google Scholar : PubMed/NCBI
27	Meng X, Chen X, Lu P, Ma W, Yue D, Song L and Fan Q: MicroRNA-202 inhibits tumor progression by targeting LAMA1 in esophageal squamous cell carcinoma. Biochem Biophys Res Commun. 473:821–827. 2016. View Article : Google Scholar : PubMed/NCBI
28	Fernandes H, D'Souza CR, Swethadri GK and Naik CN: Ameboma of the colon with amebic liver abscess mimicking metastatic colon cancer. Indian J Pathol Microbiol. 52:228–230. 2009. View Article : Google Scholar : PubMed/NCBI
29	Ayari H, Rebii S, Ghariani W, Daghfous A, Hasni R, Rehaiem R, Rezgui-Marhoul L and Zoghlami A: Colonic amoebiasis simulating a cecal tumor: Case report. Med Sante Trop. 23:274–275. 2013.(In French). PubMed/NCBI
30	Moorchung N, Singh V, Srinivas V, Jaiswal SS and Singh G: Caecal amebic colitis mimicking obstructing right sided colonic carcinoma with liver metastases: A rare case. J Cancer Res Ther. 10:440–442. 2014. View Article : Google Scholar : PubMed/NCBI
31	Grosse A: Diagnosis of colonic amebiasis and coexisting signet-ring cell carcinoma in intestinal biopsy. World J Gastroenterol. 22:8234–8241. 2016. View Article : Google Scholar : PubMed/NCBI
32	Slominski A, Kim TK, Brożyna AA, Janjetovic Z, Brooks DL, Schwab LP, Skobowiat C, Jóźwicki W and Seagroves TN: The role of melanogenesis in regulation of melanoma behavior: Melanogenesis leads to stimulation of HIF-1α expression and HIF-dependent attendant pathways. Arch Biochem Biophys. 563:79–93. 2014. View Article : Google Scholar : PubMed/NCBI
33	Lewis TB, Robison JE, Bastien R, Milash B, Boucher K, Samlowski WE, Leachman SA, Dirk Noyes R, Wittwer CT, Perreard L and Bernard PS: Molecular classification of melanoma using real-time quantitative reverse transcriptase-polymerase chain reaction. Cancer. 104:1678–1686. 2005. View Article : Google Scholar : PubMed/NCBI
34	Riker AI, Enkemann SA, Fodstad O, Liu S, Ren S, Morris C, Xi Y, Howell P, Metge B, Samant RS, et al: The gene expression profiles of primary and metastatic melanoma yields a transition point of tumor progression and metastasis. BMC Med Genomics. 1:132008. View Article : Google Scholar : PubMed/NCBI
35	Singh CK, George J, Nihal M, Sabat G, Kumar R and Ahmad N: Novel downstream molecular targets of SIRT1 in melanoma: A quantitative proteomics approach. Oncotarget. 5:1987–1999. 2014. View Article : Google Scholar : PubMed/NCBI
36	Puig-Butille JA, Vinyals A, Ferreres JR, Aguilera P, Cabré E, Tell-Martí G, Marcoval J, Mateo F, Palomero L, Badenas C, et al: AURKA Overexpression Is Driven by FOXM1 and MAPK/ERK Activation in Melanoma Cells Harboring BRAF or NRAS Mutations: Impact on Melanoma Prognosis and Therapy. J Invest Dermatol. 137:1297–1310. 2017. View Article : Google Scholar : PubMed/NCBI
37	Quan L, Shi J, Tian Y, Zhang Q, Zhang Y, Zhang Y, Hui Q and Tao K: Identification of potential therapeutic targets for melanoma using gene expression analysis. Neoplasma. 62:733–739. 2015. View Article : Google Scholar : PubMed/NCBI
38	Meng QC, Wang HC, Song ZL, Shan ZZ, Yuan Z, Zheng Q and Huang XY: Overexpression of NDC80 is correlated with prognosis of pancreatic cancer and regulates cell proliferation. Am J Cancer Res. 5:1730–1740. 2015.PubMed/NCBI
39	Qu Y, Li J, Cai Q and Liu B: Hec1/Ndc80 is overexpressed in human gastric cancer and regulates cell growth. J Gastroenterol. 49:408–418. 2014. View Article : Google Scholar : PubMed/NCBI
40	Chen ZH, Wang WT, Huang W, Fang K, Sun YM, Liu SR, Luo XQ and Chen YQ: The lncRNA HOTAIRM1 regulates the degradation of PML-RARA oncoprotein and myeloid cell differentiation by enhancing the autophagy pathway. Cell Death Differ. 24:212–224. 2016. View Article : Google Scholar : PubMed/NCBI
41	Yan X, Huang L, Liu L, Qin H and Song Z: Nuclear division cycle 80 promotes malignant progression and predicts clinical outcome in colorectal cancer. Cancer Med. 7:420–432. 2018. View Article : Google Scholar : PubMed/NCBI
42	Ahonen LJ, Kallio MJ, Daum JR, Bolton M, Manke IA, Yaffe MB, Stukenberg PT and Gorbsky GJ: Polo-like kinase 1 creates the tension-sensing 3F3/2 phosphoepitope and modulates the association of spindle-checkpoint proteins at kinetochores. Curr Biol. 15:1078–1089. 2005. View Article : Google Scholar : PubMed/NCBI
43	Zhang Z, Zhang G, Gao Z, Li S, Li Z, Bi J, Liu X, Li Z and Kong C: Comprehensive analysis of differentially expressed genes associated with PLK1 in bladder cancer. BMC Cancer. 17:8612017. View Article : Google Scholar : PubMed/NCBI
44	Yu T, Li J, Yan M, Liu L, Lin H, Zhao F, Sun L, Zhang Y, Cui Y, Zhang F, et al: MicroRNA-193a-3p and −5p suppress the metastasis of human non-small-cell lung cancer by downregulating the ERBB4/PIK3R3/mTOR/S6K2 signaling pathway. Oncogene. 34:413–423. 2015. View Article : Google Scholar : PubMed/NCBI
45	Cao G, Dong W, Meng X, Liu H, Liao H and Liu S: MiR-511 inhibits growth and metastasis of human hepatocellular carcinoma cells by targeting PIK3R3. Tumour Biol. 36:4453–4459. 2015. View Article : Google Scholar : PubMed/NCBI
46	Klahan S, Wu MS, Hsi E, Huang CC, Hou MF and Chang WC: Computational analysis of mRNA expression profiles identifies the ITG family and PIK3R3 as crucial genes for regulating triple negative breast cancer cell migration. BioMed Res Int. 2014:5365912014. View Article : Google Scholar : PubMed/NCBI
47	Liu K, Li X, Cao Y, Ge Y, Wang J and Shi B: MiR-132 inhibits cell proliferation, invasion and migration of hepatocellular carcinoma by targeting PIK3R3. Int J Oncol. 47:1585–1593. 2015. View Article : Google Scholar : PubMed/NCBI
48	Zhu Y, Zhao H, Rao M and Xu S: MicroRNA-365 inhibits proliferation, migration and invasion of glioma by targeting PIK3R3. Oncol Rep. 37:2185–2192. 2017. View Article : Google Scholar : PubMed/NCBI
49	Wang G, Yang X, Li C, Cao X, Luo X and Hu J: PIK3R3 induces epithelial-to-mesenchymal transition and promotes metastasis in colorectal cancer. Mol Cancer Ther. 13:1837–1847. 2014. View Article : Google Scholar : PubMed/NCBI
50	Calderón A, Ortiz-Espín A, Iglesias-Fernández R, Carbonero P, Pallardó FV, Sevilla F and Jiménez A: Thioredoxin (Trxo1) interacts with proliferating cell nuclear antigen (PCNA) and its overexpression affects the growth of tobacco cell culture. Redox Biol. 11:688–700. 2017. View Article : Google Scholar : PubMed/NCBI
51	Yang Q, Ou C, Liu M, Xiao W, Wen C and Sun F: NRAGE promotes cell proliferation by stabilizing PCNA in a ubiquitin-proteasome pathway in esophageal carcinomas. Carcinogenesis. 35:1643–1651. 2014. View Article : Google Scholar : PubMed/NCBI
52	Kadariya Y, Cheung M, Xu J, Pei J, Sementino E, Menges CW, Cai KQ, Rauscher FJ, Klein-Szanto AJ and Testa JR: Bap1 Is a bona fide tumor suppressor: Genetic evidence from mouse models carrying heterozygous germline Bap1 mutations. Cancer Res. 76:2836–2844. 2016. View Article : Google Scholar : PubMed/NCBI
53	Dey A, Seshasayee D, Noubade R, French DM, Liu J, Chaurushiya MS, Kirkpatrick DS, Pham VC, Lill JR, Bakalarski CE, et al: Loss of the tumor suppressor BAP1 causes myeloid transformation. Science. 337:1541–1546. 2012. View Article : Google Scholar : PubMed/NCBI
54	Field MG, Durante MA, Anbunathan H, Cai LZ, Decatur CL, Bowcock AM, Kurtenbach S and Harbour JW: Punctuated evolution of canonical genomic aberrations in uveal melanoma. Nat Commun. 9:1162018. View Article : Google Scholar : PubMed/NCBI
55	Smit KN, van Poppelen NM, Vaarwater J, Verdijk R, van Marion R, Kalirai H, Coupland SE, Thornton S, Farquhar N, Dubbink HJ, et al: Combined mutation and copy-number variation detection by targeted next-generation sequencing in uveal melanoma. Mod Pathol. 31:763–771. 2018. View Article : Google Scholar : PubMed/NCBI
56	Pitcovski J, Shahar E, Aizenshtein E and Gorodetsky R: Melanoma antigens and related immunological markers. Crit Rev Oncol Hematol. 115:36–49. 2017. View Article : Google Scholar : PubMed/NCBI
57	Garfield EM, Walton KE, Quan VL, VandenBoom T, Zhang B, Kong BY, Isales MC, Panah E, Kim G and Gerami P: Histomorphologic spectrum of germline-related and sporadic BAP1-inactivated melanocytic tumors. J Am Acad Dermatol. 79:525–534. 2018. View Article : Google Scholar : PubMed/NCBI
58	Tetzlaff MT, Torres-Cabala CA, Pattanaprichakul P, Rapini RP, Prieto VG and Curry JL: Emerging clinical applications of selected biomarkers in melanoma. Clin Cosmet Investig Dermatol. 8:35–46. 2015.PubMed/NCBI
59	Balasubramani A, Larjo A, Bassein JA, Chang X, Hastie RB, Togher SM, Lähdesmäki H and Rao A: Cancer-associated ASXL1 mutations may act as gain-of-function mutations of the ASXL1-BAP1 complex. Nat Commun. 6:73072015. View Article : Google Scholar : PubMed/NCBI
60	Scotto L, Narayan G, Nandula SV, Arias-Pulido H, Subramaniyam S, Schneider A, Kaufmann AM, Wright JD, Pothuri B, Mansukhani M and Murty VV: Identification of copy number gain and overexpressed genes on chromosome arm 20q by an integrative genomic approach in cervical cancer: Potential role in progression. Genes Chromosomes Cancer. 47:755–765. 2008. View Article : Google Scholar : PubMed/NCBI
61	Katoh M: Functional and cancer genomics of ASXL family members. Br J Cancer. 109:299–306. 2013. View Article : Google Scholar : PubMed/NCBI
62	Katoh M: Functional proteomics of the epigenetic regulators ASXL1, ASXL2 and ASXL3: A convergence of proteomics and epigenetics for translational medicine. Expert Rev Proteomics. 12:317–328. 2015. View Article : Google Scholar : PubMed/NCBI
63	Sahlberg KK, Hongisto V, Edgren H, Mäkelä R, Hellström K, Due EU, Moen Vollan HK, Sahlberg N, Wolf M, Børresen-Dale AL, et al: The HER2 amplicon includes several genes required for the growth and survival of HER2 positive breast cancer cells. Mol Oncol. 7:392–401. 2013. View Article : Google Scholar : PubMed/NCBI
64	Patel VN, Gokulrangan G, Chowdhury SA, Chen Y, Sloan AE, Koyutürk M, Barnholtz-Sloan J and Chance MR: Network signatures of survival in glioblastoma multiforme. PLOS Comput Biol. 9:e10032372013. View Article : Google Scholar : PubMed/NCBI
65	Deng S, Zhou H, Xiong R, Lu Y, Yan D, Xing T, Dong L, Tang E and Yang H: Over-expression of genes and proteins of ubiquitin specific peptidases (USPs) and proteasome subunits (PSs) in breast cancer tissue observed by the methods of RFDD-PCR and proteomics. Breast Cancer Res Treat. 104:21–30. 2007. View Article : Google Scholar : PubMed/NCBI
66	Qi T, Zhang W, Luan Y, Kong F, Xu D, Cheng G and Wang Y: Proteomic profiling identified multiple short-lived members of the central proteome as the direct targets of the addicted oncogenes in cancer cells. Mol Cell Proteomics. 13:49–62. 2014. View Article : Google Scholar : PubMed/NCBI