Top

BMC Cancer

Published in:

Open Access 01-12-2021 | Actinic Keratosis | Research article

Seven key hub genes identified by gene co-expression network in cutaneous squamous cell carcinoma

Authors: Huizhen Chen, Jiankang Yang, Wenjuan Wu

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Cutaneous squamous cell carcinoma (cSCC) often follows actinic keratosis (AK) and is the second most common skin cancer worldwide. To reduce metastasis risk, it is important to diagnose and treat cSCC early. This study aimed to identify hub genes associated with cSCC and AK.

Methods

This study used three datasets GSE45216, GSE98774, and GSE108008. We combined samples from the GSE45216 and GSE98774 datasets into the new dataset GSE45216–98774. We applied a weighted gene co-expression network analysis (WGCNA) to investigate key modules and hub genes associated with cSCC and AK. We considered the hub genes found in both the GSE45216–98774 and GSE108008 datasets as validated hub genes. We tested whether the expression of hub genes could predict patient survival outcomes in other cancers using TCGA pan-cancer data.

Results

We identified modules most relevant to cSCC and AK. Additionally, we identified and validated seven hub genes of cSCC: GATM, ARHGEF26, PTHLH, MMP1, POU2F3, MMP10 and GATA3. We did not find validated hub genes for AK. Each hub gene was significantly associated with the survival of various cancer types. Only GATA3 was significantly associated with melanoma survival.

Conclusions

We applied WGCNA to find seven hub genes that play important roles in cSCC tumorigenesis. These results provide new insights that help explain the pathogenesis of cSCC. These hub genes may become biomarkers or therapeutic targets for accurate diagnosis and treatment of cSCC in the future.

Available only for authorised users

Feller L, Khammissa RAG, Kramer B, Altini M, Lemmer J. Basal cell carcinoma, squamous cell carcinoma and melanoma of the head and face. Head Face Med. 2016;12(1):11. https://doi.org/10.1186/s13005-016-0106-0.CrossRefPubMedPubMedCentral

Asgari MM, Wang W, Ioannidis NM, Itnyre J, Hoffmann T, Jorgenson E, et al. Identification of susceptibility loci for cutaneous squamous cell carcinoma. J Invest Dermatol. 2016;136(5):930–7. https://doi.org/10.1016/j.jid.2016.01.013.CrossRefPubMedPubMedCentral

Chahal HS, Lin Y, Ransohoff KJ, Hinds DA, Wu W, Dai HJ, et al. Genome-wide association study identifies novel susceptibility loci for cutaneous squamous cell carcinoma. Nat Commun. 2016;7(1):12048. https://doi.org/10.1038/ncomms12048.CrossRefPubMedPubMedCentral

Das Mahapatra K, Pasquali L, Sondergaard JN, Lapins J, Nemeth IB, Baltas E, et al. A comprehensive analysis of coding and non-coding transcriptomic changes in cutaneous squamous cell carcinoma. Sci Rep. 2020;10(1):3637. https://doi.org/10.1038/s41598-020-59660-6.CrossRefPubMedPubMedCentral

Liu H, Chen D, Liu P, Xu S, Lin X, Zeng R. Secondary analysis of existing microarray data reveals potential gene drivers of cutaneous squamous cell carcinoma. J Cell Physiol. 2019;234(9):15270–8. https://doi.org/10.1002/jcp.28172.CrossRef

Zhang L, Qin H, Wu Z, Chen W, Zhang G. Pathogenic genes related to the progression of actinic keratoses to cutaneous squamous cell carcinoma. Int J Dermatol. 2018;57(10):1208–17. https://doi.org/10.1111/ijd.14131.CrossRefPubMed

Bao L, Guo T, Wang J, Zhang K, Bao M. Prognostic genes of triple-negative breast cancer identified by weighted gene co-expression network analysis. Oncol Lett. 2020;19(1):127–38. https://doi.org/10.3892/ol.2019.11079.CrossRefPubMed

Tang J, Kong D, Cui Q, Wang K, Zhang D, Gong Y, et al. Prognostic genes of breast cancer identified by gene co-expression network analysis. Front Oncol. 2018;8:374. https://doi.org/10.3389/fonc.2018.00374.CrossRefPubMedPubMedCentral

Yang L, Xu Y, Yan Y, Luo P, Chen S, Zheng B, et al. Common nevus and skin cutaneous melanoma: prognostic genes identified by gene co-expression network analysis. Genes (Basel). 2019;10(10):747. https://doi.org/10.3390/genes10100747.CrossRef

10.

Zhou Z, Cheng Y, Jiang Y, Liu S, Zhang M, Liu J, et al. Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis. Int J Biol Sci. 2018;14(2):124–36. https://doi.org/10.7150/ijbs.22619.CrossRefPubMedPubMedCentral

11.

Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8(1):118–27. https://doi.org/10.1093/biostatistics/kxj037.CrossRefPubMed

12.

Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9(1):559. https://doi.org/10.1186/1471-2105-9-559.CrossRefPubMedPubMedCentral

13.

Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol. 2005;4:17.CrossRef

14.

Gene Ontology C. Gene ontology consortium: going forward. Nucleic Acids Res. 2015;43(Database issue):D1049–56.CrossRef

15.

Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D457–62. https://doi.org/10.1093/nar/gkv1070.CrossRefPubMed

16.

Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16(5):284–7. https://doi.org/10.1089/omi.2011.0118.CrossRefPubMedPubMedCentral

17.

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.CrossRef

18.

Chen L, Yuan L, Wang Y, Wang G, Zhu Y, Cao R, et al. Co-expression network analysis identified FCER1G in association with progression and prognosis in human clear cell renal cell carcinoma. Int J Biol Sci. 2017;13(11):1361–72. https://doi.org/10.7150/ijbs.21657.CrossRefPubMedPubMedCentral

19.

Zhang X, Klamer B, Li J, Fernandez S, Li L. A pan-cancer study of class-3 semaphorins as therapeutic targets in cancer. BMC Med Genet. 2020;13(S5):45. https://doi.org/10.1186/s12920-020-0682-5.CrossRef

20.

Pflueger D, Mittmann C, Dehler S, Rubin MA, Moch H, Schraml P. Functional characterization of BC039389-GATM and KLK4-KRSP1 chimeric read-through transcripts which are up-regulated in renal cell cancer. BMC Genomics. 2015;16(1):247. https://doi.org/10.1186/s12864-015-1446-z.CrossRefPubMedPubMedCentral

21.

Boguslawska J, Poplawski P, Alseekh S, Koblowska M, Iwanicka-Nowicka R, Rybicka B, et al. MicroRNA-mediated metabolic reprograming in renal cancer. Cancers (Basel). 2019;11(12):1825. https://doi.org/10.3390/cancers11121825.CrossRef

22.

Ensign SP, Roos A, Mathews IT, Dhruv HD, Tuncali S, Sarkaria JN, et al. SGEF is regulated via TWEAK/Fn14/NF-kappaB signaling and promotes survival by modulation of the DNA repair response to temozolomide. Mol Cancer Res. 2016;14(3):302–12. https://doi.org/10.1158/1541-7786.MCR-15-0183.CrossRefPubMed

23.

Goicoechea SM, Zinn A, Awadia SS, Snyder K, Garcia-Mata R. A RhoG-mediated signaling pathway that modulates invadopodia dynamics in breast cancer cells. J Cell Sci. 2017;130(6):1064–77. https://doi.org/10.1242/jcs.195552.CrossRefPubMedPubMedCentral

24.

Lv Z, Wu X, Cao W, Shen Z, Wang L, Xie F, et al. Parathyroid hormone-related protein serves as a prognostic indicator in oral squamous cell carcinoma. J Exp Clin Cancer Res. 2014;33(1):100. https://doi.org/10.1186/s13046-014-0100-y.CrossRefPubMedPubMedCentral

25.

Urosevic J, Garcia-Albeniz X, Planet E, Real S, Cespedes MV, Guiu M, et al. Colon cancer cells colonize the lung from established liver metastases through p38 MAPK signalling and PTHLH. Nat Cell Biol. 2014;16(7):685–94. https://doi.org/10.1038/ncb2977.CrossRefPubMed

26.

Ni W, Zhang S, Jiang B, Ni R, Xiao M, Lu C, et al. Identification of cancer-related gene network in hepatocellular carcinoma by combined bioinformatic approach and experimental validation. Pathol Res Pract. 2019;215(6):152428. https://doi.org/10.1016/j.prp.2019.04.020.CrossRefPubMed

27.

Gobin E, Bagwell K, Wagner J, Mysona D, Sandirasegarane S, Smith N, et al. A pan-cancer perspective of matrix metalloproteases (MMP) gene expression profile and their diagnostic/prognostic potential. BMC Cancer. 2019;19(1):581. https://doi.org/10.1186/s12885-019-5768-0.CrossRefPubMedPubMedCentral

28.

Uhlirova M, Bohmann D. JNK- and Fos-regulated Mmp1 expression cooperates with Ras to induce invasive tumors in drosophila. EMBO J. 2006;25(22):5294–304. https://doi.org/10.1038/sj.emboj.7601401.CrossRefPubMedPubMedCentral

29.

Klupp F, Neumann L, Kahlert C, Diers J, Halama N, Franz C, et al. Serum MMP7, MMP10 and MMP12 level as negative prognostic markers in colon cancer patients. BMC Cancer. 2016;16(1):494. https://doi.org/10.1186/s12885-016-2515-7.CrossRefPubMedPubMedCentral

30.

Justilien V, Regala RP, Tseng IC, Walsh MP, Batra J, Radisky ES, et al. Matrix metalloproteinase-10 is required for lung cancer stem cell maintenance, tumor initiation and metastatic potential. PLoS One. 2012;7(4):e35040. https://doi.org/10.1371/journal.pone.0035040.CrossRefPubMedPubMedCentral

31.

Rudin CM, Poirier JT, Byers LA, Dive C, Dowlati A, George J, et al. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat Rev Cancer. 2019;19(5):289–97. https://doi.org/10.1038/s41568-019-0133-9.CrossRefPubMedPubMedCentral

32.

Wang XD, Hu R, Ding Q, Savage TK, Huffman KE, Williams N, et al. Subtype-specific secretomic characterization of pulmonary neuroendocrine tumor cells. Nat Commun. 2019;10(1):3201. https://doi.org/10.1038/s41467-019-11153-5.CrossRefPubMedPubMedCentral

33.

Miettinen M, McCue PA, Sarlomo-Rikala M, Rys J, Czapiewski P, Wazny K, et al. GATA3: a multispecific but potentially useful marker in surgical pathology: a systematic analysis of 2500 epithelial and nonepithelial tumors. Am J Surg Pathol. 2014;38(1):13–22. https://doi.org/10.1097/PAS.0b013e3182a0218f.CrossRefPubMedPubMedCentral

34.

Takaku M, Grimm SA, Wade PA. GATA3 in breast cancer: tumor suppressor or oncogene? Gene Expr. 2015;16(4):163–8. https://doi.org/10.3727/105221615X14399878166113.CrossRefPubMed

35.

Mertens RB, de Peralta-Venturina MN, Balzer BL, Frishberg DP. GATA3 expression in normal skin and in benign and malignant epidermal and cutaneous adnexal neoplasms. Am J Dermatopathol. 2015;37(12):885–91. https://doi.org/10.1097/DAD.0000000000000306.CrossRefPubMedPubMedCentral

36.

Prasad NB, Fischer AC, Chuang AY, Wright JM, Yang T, Tsai HL, et al. Differential expression of degradome components in cutaneous squamous cell carcinomas. Mod Pathol. 2014;27(7):945–57. https://doi.org/10.1038/modpathol.2013.217.CrossRefPubMed

37.

Solus JF, Hassan K, Lee SJ, Hsi AC, Rosman IS, Dehmeri S, et al. Cutaneous squamous cell carcinoma progression is associated with decreased GATA-3 immunohistochemical staining. J Cutan Pathol. 2016;43(4):347–53. https://doi.org/10.1111/cup.12667.CrossRefPubMed

Title: Seven key hub genes identified by gene co-expression network in cutaneous squamous cell carcinoma
Authors: Huizhen Chen
Jiankang Yang
Wenjuan Wu
Publication date: 01-12-2021
Publisher: BioMed Central
Keywords: Actinic Keratosis
Cutaneous Squamous Cell Carcinoma
Cutaneous Squamous Cell Carcinoma
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08604-y

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Comprehensive analysis of ceRNA network related to lincRNA in glioblastoma and prediction of clinical prognosis

Clinical outcomes of bulky parotid gland cancers: need for self-examination and screening program for early diagnosis of parotid tumors

Outcomes of renal cell carcinoma with associated venous tumor thrombus: experience from a large cohort and short time span in a single center

The ideal approach for treatment of cT1N+ and cT2Nany esophageal cancer.: a NCDB analysis

MiR-3168, miR-6125, and miR-4718 as potential predictors of cisplatin-induced nephrotoxicity in patients with head and neck cancer

The efficiency of 18F-FDG-PET/CT in the assessment of tumor response to preoperative chemoradiation therapy for locally recurrent rectal cancer

Keynote webinar | Spotlight on antibody–drug conjugates in cancer