Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method

Wang,Lu; Wei,Chuan‑Yuan; Xu,Yuan‑Yuan; Deng,Xin‑Yi; Wang,Qiang; Ying,Jiang‑Hui; Zhang,Si‑Min; Yuan,Xin; Xuan,Tian‑Fan; Pan,Yu‑Yan; Gu,Jian‑Ying

doi:10.3892/ol.2019.10961

Prognostic genes of melanoma identified by weighted gene co‑expression network analysis and drug repositioning using a network‑based method

Authors:
- Lu Wang
- Chuan‑Yuan Wei
- Yuan‑Yuan Xu
- Xin‑Yi Deng
- Qiang Wang
- Jiang‑Hui Ying
- Si‑Min Zhang
- Xin Yuan
- Tian‑Fan Xuan
- Yu‑Yan Pan
- Jian‑Ying Gu
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Affiliations: Department of Plastic Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, P.R. China, Department of Surgery, The First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, P.R. China
Published online on: October 4, 2019 https://doi.org/10.3892/ol.2019.10961
Pages: 6066-6078
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Melanoma is one of the most malignant types of skin cancer. However, the efficacy and utility of available drug therapies for melanoma are limited. The objective of the present study was to identify potential genes associated with melanoma progression and to explore approved therapeutic drugs that target these genes. Weighted gene co‑expression network analysis was used to construct a gene co‑expression network, explore the associations between genes and clinical characteristics and identify potential biomarkers. Gene expression proﬁles of the GSE65904 dataset were obtained from the Gene Expression Omnibus database. RNA‑sequencing data and clinical information associated with melanoma obtained from The Cancer Genome Atlas were used for biomarker validation. A total of 15 modules were identiﬁed through average linkage hierarchical clustering. In the two signiﬁcant modules, three network hub genes associated with melanoma prognosis were identiﬁed: C‑X‑C motif chemokine receptor 4 (CXCR4), interleukin 7 receptor (IL7R) and phosphatidylinositol‑4,5‑bisphosphate 3‑kinase catalytic subunit γ (PIK3CG). The receiver operating characteristic curve indicated that the mRNA levels of these genes exhibited excellent prognostic efﬁciency for primary and metastatic tumor tissues. In addition, the proximity between candidate genes associated with melanoma progression and drug targets obtained from DrugBank was calculated in the protein interaction network, and the top 15 drugs that may be suitable for treating melanoma were identified. In summary, co‑expression network analysis led to the selection of CXCR4, IL7R and PIK3CG for further basic and clinical research on melanoma. Utilizing a network‑based method, 15 drugs that exhibited potential for the treatment of melanoma were identified.

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1	Colebatch AJ and Scolyer RA: Trajectories of premalignancy during the journey from melanocyte to melanoma. Pathology. 50:16–23. 2018. View Article : Google Scholar : PubMed/NCBI
2	Slominski A, Tobin DJ, Shibahara S and Wortsman J: Melanin pigmentation in mammalian skin and its hormonal regulation. Physiol Rev. 84:1155–1228. 2004. View Article : Google Scholar : PubMed/NCBI
3	Slominski A, Zmijewski MA and Pawelek J: L-tyrosine and L-dihydroxyphenylalanine as hormone-like regulators of melanocyte functions. Pigment Cell Melanoma Res. 25:14–27. 2012. View Article : Google Scholar : PubMed/NCBI
4	Slominski A, Kim TK, Brozyna 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
5	Slominski RM, Zmijewski MA and Slominski AT: The role of melanin pigment in melanoma. Exp Dermatol. 24:258–259. 2015. View Article : Google Scholar : PubMed/NCBI
6	Hyams DM, Cook RW and Buzaid AC: Identification of risk in cutaneous melanoma patients: Prognostic and predictive markers. J Surg Oncol. 119:175–186. 2019. View Article : Google Scholar : PubMed/NCBI
7	Ugurel S, Rohmel J, Ascierto PA, Flaherty KT, Grob JJ, Hauschild A, Larkin J, Long GV, Lorigan P, McArthur GA, et al: Survival of patients with advanced metastatic melanoma: The impact of novel therapies-update 2017. Eur J Cancer. 83:247–257. 2017. View Article : Google Scholar : PubMed/NCBI
8	Read J, Wadt KA and Hayward NK: Melanoma genetics. J Med Genet. 53:1–14. 2016. View Article : Google Scholar : PubMed/NCBI
9	Koelblinger P, Dornbierer J and Dummer R: A review of binimetinib for the treatment of mutant cutaneous melanoma. Future Oncol. 13:1755–1766. 2017. View Article : Google Scholar : PubMed/NCBI
10	Silva IP and Long GV: Systemic therapy in advanced melanoma: Integrating targeted therapy and immunotherapy into clinical practice. Curr Opin Oncol. 29:484–492. 2017. View Article : Google Scholar : PubMed/NCBI
11	da Silveira Nogueira Lima JP, Georgieva M, Haaland B and de Lima Lopes G: A systematic review and network meta-analysis of immunotherapy and targeted therapy for advanced melanoma. Cancer Med. 6:1143–1153. 2017. View Article : Google Scholar : PubMed/NCBI
12	Appenzeller S, Gesierich A, Thiem A, Hufnagel A, Jessen C, Kneitz H, Regensburger M, Schmidt C, Zirkenbach V, Bischler T, et al: The identification of patient-specific mutations reveals dual pathway activation in most patients with melanoma and activated receptor tyrosine kinases in BRAF/NRAS wild-type melanomas. Cancer. 125:586–600. 2019. View Article : Google Scholar : PubMed/NCBI
13	Winder M and Virós A: Mechanisms of drug resistance in melanoma. Handb Exp Pharmacol. 249:91–108. 2018. View Article : Google Scholar : PubMed/NCBI
14	Slominski AT and Carlson JA: Melanoma resistance: A bright future for academicians and a challenge for patient advocates. Mayo Clin Proc. 89:429–433. 2014. View Article : Google Scholar : PubMed/NCBI
15	Cirenajwis H, Ekedahl H, Lauss M, Harbst K, Carneiro A, Enoksson J, Rosengren F, Werner-Hartman L, Törngren T, Kvist A, et al: Molecular stratification of metastatic melanoma using gene expression profiling: Prediction of survival outcome and benefit from molecular targeted therapy. Oncotarget. 6:12297–12309. 2015. View Article : Google Scholar : PubMed/NCBI
16	Tang J, Kong D, Cui Q, Wang K, Zhang D, Gong Y and Wu G: Prognostic genes of breast cancer identified by gene Co-expression network analysis. Front Oncol. 8:3742018. View Article : Google Scholar : PubMed/NCBI
17	Rahmani B, Zimmermann MT, Grill DE, Kennedy RB, Oberg AL, White BC, Poland GA and McKinney BA: Recursive indirect-paths modularity (RIP-M) for detecting community structure in RNA-Seq Co-expression networks. Front Genet. 7:802016. View Article : Google Scholar : PubMed/NCBI
18	Gene Ontology Consortium: Gene ontology consortium: Going forward. Nucleic Acids Res 43 (Database Issue). D1049–D1056. 2015. View Article : Google Scholar
19	Kanehisa M, Sato Y, Kawashima M, Furumichi M and Tanabe M: KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 44:D457–D462. 2016. View Article : Google Scholar : PubMed/NCBI
20	Su G, Morris JH, Demchak B and Bader GD: Biological network exploration with Cytoscape 3. Curr Protoc Bioinformatics. 47:8.13.1–24. 2014. View Article : Google Scholar
21	Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, Barabási AL and Loscalzo J: Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun. 9:26912018. View Article : Google Scholar : PubMed/NCBI
22	Rajabi P, Bagheri M and Hani M: Expression of estrogen receptor alpha in malignant melanoma. Adv Biomed Res. 6:142017. View Article : Google Scholar : PubMed/NCBI
23	Testori A, Ribero S and Bataille V: Diagnosis and treatment of in-transit melanoma metastases. Eur J Surg Oncol. 43:544–560. 2017. View Article : Google Scholar : PubMed/NCBI
24	Balkwill F: Cancer and the chemokine network. Nat Rev Cancer. 4:540–550. 2004. View Article : Google Scholar : PubMed/NCBI
25	Nazari A, Khorramdelazad H and Hassanshahi G: Biological/pathological functions of the CXCL12/CXCR4/CXCR7 axes in the pathogenesis of bladder cancer. Int J Clin Oncol. 22:991–1000. 2017. View Article : Google Scholar : PubMed/NCBI
26	Ishikawa T, Nakashiro K, Hara S, Klosek SK, Li C, Shintani S and Hamakawa H: CXCR4 expression is associated with lymph-node metastasis of oral squamous cell carcinoma. Int J Oncol. 28:61–66. 2006.PubMed/NCBI
27	Wu J, Wu X, Liang W, Chen C, Zheng L and An H: Clinicopathological and prognostic significance of chemokine receptor CXCR4 overexpression in patients with esophageal cancer: A meta-analysis. Tumour Biol. 35:3709–3715. 2014. View Article : Google Scholar : PubMed/NCBI
28	Kodama J, Hasengaowa, Kusumoto T, Seki N, Matsuo T, Ojima Y, Nakamura K, Hongo A and Hiramatsu Y: Association of CXCR4 and CCR7 chemokine receptor expression and lymph node metastasis in human cervical cancer. Ann Oncol. 18:70–76. 2007. View Article : Google Scholar : PubMed/NCBI
29	Konoplev S, Rassidakis GZ, Estey E, Kantarjian H, Liakou CI, Huang X, Xiao L, Andreeff M, Konopleva M and Medeiros LJ: Overexpression of CXCR4 predicts adverse overall and event-free survival in patients with unmutated FLT3 acute myeloid leukemia with normal karyotype. Cancer. 109:1152–1156. 2007. View Article : Google Scholar : PubMed/NCBI
30	Mitchell B, Leone D, Feller K, Menon S, Bondzie P, Yang S, Park HY and Mahalingam M: Protein expression of the chemokine receptor CXCR4 and its ligand CXCL12 in primary cutaneous melanoma-biomarkers of potential utility? Hum Pathol. 45:2094–2100. 2014. View Article : Google Scholar : PubMed/NCBI
31	Balch CM, Gershenwald JE, Soong SJ, Thompson JF, Atkins MB, Byrd DR, Buzaid AC, Cochran AJ, Coit DG, Ding S, et al: Final version of 2009 AJCC melanoma staging and classification. J Clin Oncol. 27:6199–6206. 2009. View Article : Google Scholar : PubMed/NCBI
32	Leung GA, Cool T, Valencia CH, Worthington A, Beaudin AE and Forsberg EC: The lymphoid-associated interleukin 7 receptor (IL7R) regulates tissue-resident macrophage development. Development. 146(pii): dev1761802019. View Article : Google Scholar : PubMed/NCBI
33	Vitiello G, Losi GR, Amarante MK, Ceribelli JR, Carmelo E and Watanabe M: Interleukin 7 receptor alpha Thr244Ile genetic polymorphism is associated with susceptibility and prognostic markers in breast cancer subgroups. Cytokine. 103:121–126. 2018. View Article : Google Scholar : PubMed/NCBI
34	Kim MJ, Choi SK, Hong SH, Eun JW, Nam SW, Han JW and You JS: Oncogenic IL7R is downregulated by histone deacetylase inhibitor in esophageal squamous cell carcinoma via modulation of acetylated FOXO1. Int J Oncol. 53:395–403. 2018.PubMed/NCBI
35	Oliveira DM, Santamaria G, Laudanna C, Migliozzi S, Zoppoli P, Quist M, Grasso C, Mignogna C, Elia L, Faniello MC, et al: Identification of copy number alterations in colon cancer from analysis of amplicon-based next generation sequencing data. Oncotarget. 9:20409–20425. 2018. View Article : Google Scholar : PubMed/NCBI
36	Vivanco I and Sawyers CL: The phosphatidylinositol 3-Kinase AKT pathway in human cancer. Nat Rev Cancer. 2:489–501. 2002. View Article : Google Scholar : PubMed/NCBI
37	Thorpe LM, Yuzugullu H and Zhao JJ: PI3K in cancer: Divergent roles of isoforms, modes of activation and therapeutic targeting. Nat Rev Cancer. 15:7–24. 2015. View Article : Google Scholar : PubMed/NCBI
38	Pridham KJ, Varghese RT and Sheng Z: The role of Class IA phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunits in glioblastoma. Front Oncol. 7:3122017. View Article : Google Scholar : PubMed/NCBI
39	Semba S, Itoh N, Ito M, Youssef EM, Harada M, Moriya T, Kimura W and Yamakawa M: Down-regulation of PIK3CG, a catalytic subunit of phosphatidylinositol 3-OH kinase, by CpG hypermethylation in human colorectal carcinoma. Clin Cancer Res. 8:3824–3831. 2002.PubMed/NCBI
40	Gorlov I, Orlow I, Ringelberg C, Hernando E, Ernstoff MS, Cheng C, Her S, Parker JS, Thompson CL, Gerstenblith MR, et al: Identification of gene expression levels in primary melanoma associated with clinically meaningful characteristics. Melanoma Res. 28:380–389. 2018.PubMed/NCBI
41	Abou DI, Jabbour E, Short NJ and Ravandi F: Treatment of philadelphia chromosome-positive acute lymphoblastic leukemia. Curr Treat Options Oncol. 20:42019. View Article : Google Scholar : PubMed/NCBI
42	Zhao Y and Adjei AA: Targeting angiogenesis in cancer therapy: Moving beyond vascular endothelial growth factor. Oncologist. 20:660–673. 2015. View Article : Google Scholar : PubMed/NCBI
43	Deeks ED: Ibrutinib: A review in chronic lymphocytic leukaemia. Drugs. 77:225–236. 2017. View Article : Google Scholar : PubMed/NCBI
44	Shaguft a and Ahmad I: Tamoxifen a pioneering drug: An update on the therapeutic potential of tamoxifen derivatives. Eur J Med Chem. 143:515–531. 2018. View Article : Google Scholar : PubMed/NCBI
45	Guerrero-Garcia TA, Mogollon RJ and Castillo JJ: Bortezomib in plasmablastic lymphoma: A glimpse of hope for a hard-to-treat disease. Leuk Res. 62:12–16. 2017. View Article : Google Scholar : PubMed/NCBI
46	Rai R, Shenoy MM, Viswanath V, Sarma N, Majid I and Dogra S: Contact sensitivity in patients with venous leg ulcer: A multi-centric Indian study. Int Wound J. 15:618–622. 2018. View Article : Google Scholar : PubMed/NCBI
47	Vemuri P, Harris KE, Suh LA and Grammer LC: Preparation of benzylpenicilloyl-polylysine: A preliminary study. Allergy Asthma Proc. 25:165–168. 2004.PubMed/NCBI
48	Yazdanyar S, Zarchi K and Jemec GBE: Pain during topical photodynamic therapy-comparing methyl aminolevulinate (Metvix^®) to aminolaevulinic acid (Ameluz^®); an intra-individual clinical study. Photodiagnosis Photodyn Ther. 20:6–9. 2017. View Article : Google Scholar : PubMed/NCBI
49	Jour G, Ivan D and Aung PP: Angiogenesis in melanoma: An update with a focus on current targeted therapies. J Clin Pathol. 69:472–483. 2016. View Article : Google Scholar : PubMed/NCBI
50	Ribatti D: Tumor refractoriness to anti-VEGF therapy. Oncotarget. 7:46668–46677. 2016. View Article : Google Scholar : PubMed/NCBI
51	Ott PA, Hodi FS and Buchbinder EI: Inhibition of immune checkpoints and vascular endothelial growth factor as combination therapy for metastatic melanoma: An overview of rationale, preclinical evidence, and initial clinical data. Front Oncol. 5:2022015. View Article : Google Scholar : PubMed/NCBI
52	Decoster L, Vande BI, Neyns B, Majois F, Baurain JF, Rottey S, Rorive A, Anckaert E, De Mey J, De Brakeleer S and De Grève J: Biomarker analysis in a Phase II study of sunitinib in patients with advanced melanoma. Anticancer Res. 35:6893–6899. 2015.PubMed/NCBI
53	Urbonas V, Schadendorf D, Zimmer L, Danson S, Marshall E, Corrie P, Wheater M, Plummer E, Mauch C, Scudder C, et al: Paclitaxel with or without trametinib or pazopanib in advanced wild-type BRAF melanoma (PACMEL): A multicentre, open-label, randomised, controlled phase II trial. Ann Oncol. 30:317–324. 2019. View Article : Google Scholar : PubMed/NCBI
54	Fruehauf JP, El-Masry M, Osann K, Parmakhtiar B, Yamamoto M and Jakowatz JG: Phase II study of pazopanib in combination with paclitaxel in patients with metastatic melanoma. Cancer Chemother Pharmacol. 82:353–360. 2018. View Article : Google Scholar : PubMed/NCBI
55	Hong DS, Kurzrock R, Falchook GS, Andresen C, Kwak J, Ren M, Xu L, George GC, Kim KB, Nguyen LM, et al: Phase 1b study of lenvatinib (E7080) in combination with temozolomide for treatment of advanced melanoma. Oncotarget. 6:43127–43134. 2015. View Article : Google Scholar : PubMed/NCBI
56	Noguchi S, Shibutani S, Fukushima K, Mori T, Igase M and Mizuno T: Bosutinib, an SRC inhibitor, induces caspase-independent cell death associated with permeabilization of lysosomal membranes in melanoma cells. Vet Comp Oncol. 16:69–76. 2018. View Article : Google Scholar : PubMed/NCBI
57	Skoko J, Rožanc J, Charles EM, Alexopoulos LG and Rehm M: Post-treatment de-phosphorylation of p53 correlates with dasatinib responsiveness in malignant melanoma. BMC Cell Biol. 19:282018. View Article : Google Scholar : PubMed/NCBI
58	Archibald WJ, Meacham PJ, Williams AM, Baran AM, Victor AI, Barr PM, Sahasrahbudhe DM and Zent CS: Management of melanoma in patients with chronic lymphocytic leukemia. Leuk Res. 71:43–46. 2018. View Article : Google Scholar : PubMed/NCBI
59	Eisen T, Marais R, Affolter A, Lorigan P, Robert C, Corrie P, Ottensmeier C, Chevreau C, Chao D, Nathan PD, et al: Sorafenib and dacarbazine as first-line therapy for advanced melanoma: Phase I and open-label phase II studies. Br J Cancer. 105:353–359. 2011. View Article : Google Scholar : PubMed/NCBI
60	Rossi UA, Finocchiaro L and Glikin GC: Bortezomib enhances the antitumor effects of interferon-β gene transfer on melanoma cells. Anticancer Agents Med Chem. 17:754–761. 2017. View Article : Google Scholar : PubMed/NCBI
61	Ribeiro MP, Santos AE and Custódio JB: Rethinking tamoxifen in the management of melanoma: New answers for an old question. Eur J Pharmacol. 764:372–378. 2015. View Article : Google Scholar : PubMed/NCBI