Cutaneous melanoma (CM) is a malignant neoplasia characterized by rapid progression, metastasis to regional lymph nodes as well as distant organs, and limited responsiveness to therapeutics (MacKie et al., 2009). It contributes to more than 80% death of skin cancer patients (Miller and Mihm, 2006), and its incidence is one of the most rapidly increasing cancers in the United States where it is estimated that there will be 91,270 new cases and 9,320 deaths due to this disease in 2018 (Siegel et al., 2018). Although significant progress has been made in both understanding CM biology and genetics, and in therapeutic approaches, the prognosis remains poor due to the high potential for CM invasion and metastasis. This malignancy still represents a major public health problem worldwide. Primary localized melanoma has a relatively high survival rate. The 5- year survival rate prognosis of patients with lymph node or distant metastases was only 15–20% (Siegel et al., 2018; Weiss et al., 2015). Assessment of patients prior to therapy may aid in the identification of individuals at high risk for recurrence and may guide the development of future targeted treatment strategies. For example, for patients who are at high operative risk, conservative therapy may be helpful in the operative decision-making process. Molecular biomarkers could provide additional prognostic information and insight into the mechanisms of melanoma progression, as well as guide treatment selection. Consequently, new strategies for the identification of effective prognostic biomarkers may improve the clinical management of melanoma by providing more accurate prognostic information.

Aberrant DNA methylation is an epigenetic hallmark of cancer and actively contributes to cancer development and progression by inactivating tumor suppressor genes (Egger et al., 2004). Some characteristics of methylation markers render them particularly attractive for development of clinically applicable biomarkers, including high stability in biologic samples, limited susceptibility to tumor environmental factors, and ease of detection (Keeley et al., 2013). A growing number of studies have demonstrated that aberrant DNA methylation plays important roles in tumorigenesis and progression, and DNA methylation has great potential to act as a biomarker for predicting the prognosis of patients with a variety of malignancies (Egger et al., 2004; Guo et al., 2004; Roh et al., 2016). For instance, GATA4 is epigenetically silenced in gastrointestinal cancers (Akiyama et al., 2003) and lung cancer (Guo et al., 2004); OPCML promoter methylation has been identified as a biomarker for predicting ovarian cancer prognosis (Zhou et al., 2014); PCDH19 methylation is associated with poor hepatocellular carcinoma prognosis (Zhang et al., 2018) and Sigalotti et al. identified a 17-gene methylation signature as a molecular marker of survival in stage IIIC melanoma (Sigalotti et al., 2012). Unfortunately, many recent melanoma studies have several limitations including relatively small sample cohorts, lack of subsequent biomarker validation, narrow focus on patient specimens with specific clinical features, or investigation of only one or a few genes. These studies lack the comprehensive and systematic approach of genome-wide methylation analysis. Because of this, the identified methylation signatures do not have universal prognostic power, and their capability is limited by the specimen type. The Cancer Genome Atlas (TCGA) database (Hudson et al., 2010), a large well-annotated database with nearly 500 CM samples collected worldwide is a helpful resource for developing promising biomarkers with prospective studies into a methylation signature which can be of clinical utility.

Consequently, the purpose of this study was to identify DNA methylation biomarkers so as to explore the utility of DNA methylation analysis for cancer prognosis. The whole genome methylation profiles of tumor tissues from patients with CM in the TCGA database were analyzed, and the potential clinical significance of methylation biomarkers serving as molecular prognostic predictors was examined using the Kaplan–Meier method and receiver operating characteristic (ROC) analyses. Furthermore, the prognostic capacity of the identified methylation biomarker was evaluated with an independent cohort of patients in the Gene Expression Omnibus (GEO) database. Also, we investigated the independence and reproducibility in various clinical groups, as well as the possible role of the methylation biomarker in immune-checkpoint blockade (ICB) immunotherapy.

CM is the deadliest form of skin cancer and lead to 60,712 deaths in 2018 (Miller and Mihm, 2006). In recent years, the importance of DNA methylation in the biology of CM has been increasingly acknowledged. For instance, hypermethylated estrogen receptor alpha (ER-α) is a significant factor in melanoma progression (Mori et al., 2006); methylation-dependent SOX9 expression mediates invasion in human melanoma cells and is a negative prognostic factor in advanced melanoma (Cheng et al., 2015), and MGMT gene promoter methylation in metastatic CM is associated with longer survival (Cesinaro et al., 2012). However, these studies usually concentrated on single gene methylation or patient samples with specific clinical characteristics, and combinations of DNA methylation as biomarkers could achieve higher sensitivity and specificity than individual DNA methylation (Dai et al., 2011). Moreover, a limited overlap between signatures published by various studies can be observed, possibly due to variation in disease causes, tissue heterogeneity, or stage of disease at the point of analysis (Tremante et al., 2012). The TCGA database provides a large number of samples with a variety of clinical characteristics. Based on a TCGA dataset that included 461 CM samples, the current study identified a prognostic DNA methylation signature with potential clinical applicability, validated it in an independent cohort, and investigated its high reproducibility and utility in various clinical groups.

CM has a high degree of heterogeneity in terms of clinical, dermatological, and histopathological presentation (Coricovac et al., 2018). Several parameters, such as age, sex, stage of disease, Breslow thickness, and ulceration status have significant influence on CM patient prognosis, in which Breslow thickness has been demonstrated as the most important clinicopathological characteristic for predicting prognosis. In order to be clinically useful, a DNA methylation signature must be independent of clinical factors. Here we adopted a grouping method not influenced by our subjective, that is based on the TCGA series number of patients, without adjusting for clinical parameters. If detection, treatment and surgical excision occur when the tumor burden is restricted to a primary site, a patient’s prognostic survival is enhanced, but once the disease has metastasized to distant organs such as the brain and liver, prognosis is poor (Balch et al., 2009). Since patients with primary tumors had shorter follow-ups, none more than 5 years, we analyzed the independence and applicability of our four-DNA methylation signature in samples obtained from distant metastasis, subcutaneous tissue, and regional lymph node metastasis. The results showed that our signature was independent of tumor metastatic sites. Moreover, as patients suffering from early stages of the disease exhibit higher potential for healing, a prognostic signature that can also efficiently risk-stratify these patients would have higher clinical application value (Segura et al., 2010). Our signature has been demonstrated to be not only independent of all clinical factors, but also to have higher predictive performance for patients with tumor thickness of less than 2 mm and patients in early stages (AUCs were 0.830 and 0.814, respectively). Therefore, our signature was an independent applicable prognostic biomarker, which may be of high clinical value.

Considering that an ideal prognostic marker is one that can also efficiently risk-stratify in other independent cohorts, we employed GEO dataset (GSE51547) to further evaluate the practicality of our four-DNA methylation signature. Although the predictive accuracy in the GEO dataset is not as high as in the validation dataset due to the low number of samples (N = 47), the four-DNA methylation signature performed well in distinguishing low- and high-risk groups (AUC = 0.708, p<0.05). Furthermore, it was demonstrated that in both the validation and independent cohorts, our signature outperformed other known prognostic biomarkers, including mRNA, lncRNA, and DNA methylation, and statistical comparison using Z-test revealed that it has significantly higher (p<0.05) predictive performance than almost all the other known biomarkers. When further samples become available it will be important to analyze this methylation signature in another validation dataset.

Targeting immune checkpoints such as PD-1, PD-L1, and CTLA-4 have achieved noteworthy benefit in multiple cancers by blocking immunoinhibitory signals and enabling patients to produce an effective antitumor response, especially in patients with CM (Riaz et al., 2017). However, a significant limitation of ICB is that less than one-third of patients respond to ICB treatment, and identification of ICB response biomarkers and resistance regulators is a critical challenge (Sharma et al., 2017). DNA methylation plays a critical role in cell lineage specification and may serve as a specific molecular marker for measurement of immune responses. Recently, Jeschke et al highlighted the power of MeTIL to evaluate local and functional TIL-based tumor immune responses and the ability of this approach to improve prognosis (Jeschke et al., 2017). However, the identification of MeTIL was focused on CpGs that are highly differentially methylated between T lymphocytes and epithelial cells. Lymphocytes only account for a small fraction of TME (Pretscher et al., 2009); thus, there may be bias when using MeTIL as a prognostic marker to predict survival outcomes. Intriguingly, the correlation analyses and the observed predictive performance suggested that our four-DNA methylation signature was significantly correlated with the ICB immunotherapy-related signature. Additionally, our signature displayed higher predictive performance than other known signatures, including PD-1, PD-L1, PD-L2, CTLA-4, and MeTIL. These results demonstrate that our four-DNA methylation signature, although developed for accurate prognosis, may also have potential as a guide for precision cancer ICB immunotherapy.

Furthermore, epigenetic changes have been shown to alter gene expression, and epigenetic inactivation of tumor suppressor genes has been implicated in tumorigenesis of various malignancies, including CM (Herman and Baylin, 2003). Here, the expression of GBP5 and KLHL21 were significantly (p<0.001) negatively correlated with their methylation levels, and the other two genes show significant positive correlation (p<0.001) between the expression and their methylation levels (Figure 1—figure supplement 3). We also found that expression of this four-gene can also be used as a prognostic biomarker (Figure 2—figure supplement 1), but the four-DNA methylation biomarker offer a better potential to fulfill much more sensitive and specific prognostic test. For our four-DNA methylation sites, researchers have revealed that their corresponding genes may be crucial in immunity and cancer development, including CM. For instance, GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals (Shenoy et al., 2012). GBP5 was induced by IFN-γ, could serve as a marker of IFN-γ-induced classically activated macrophages and the substitute indicator of IFN-γ, which can directly suppress tumorigenesis and infection and/or can modulate the immunological status in cancer cells (Chang et al., 2015; Yamamoto et al., 2012). Meanwhile, GBP5 expression in CM is associated with favorable prognosis (Wang et al., 2018). RAB37, as a tumor suppressor gene, promotes M1-like macrophage infiltration and suppresses tumor growth (Tzeng et al., 2018), and it was frequently down-regulated due to promoter hypermethylation in metastatic lung cancer, can serve as a potential predictive biomarker (Wu et al., 2009). RAB37-mediated SFRP1 secretion suppresses cancer stemness, and dysregulated RAB37-SFRP1 pathway confers cancer stemness via the activation of Wnt signaling (Cho et al., 2018). OCA2 is involved in the melanin biosynthetic process and mammalian pigmentation (Crawford et al., 2017), and the DNA variant in intron of OCA2 (rs4778138) has been found associated with CM risk (Law et al., 2015). The hypomethylation levels of cg18456782 (OCA2) was associated with lower expression of OCA2 and a lower risk. Meanwhile separating CM patients by median expression of OCA2, there is a significant differential survival (p<0.0001) with low expression favoring better survival. All these results suggest a risk pattern for OCA2 gene in CM. KLHL21 could affect cell migration and invasion, play an essential role in tumorigenesis and progression, and it might serve as a potential therapeutic target for cholangiocarcinoma (Chen et al., 2018) and hepatocellular carcinoma (Shi et al., 2016). Although the functional mechanism of these four genes in CM still needs further study, significant correlation between these four genes and OS or response to therapy of patients with CM, and DNA methylation might also be suitable as biomarkers for response to ICB therapy.

In addition, the results of the univariate Cox regression, Kaplan–Meier and ROC analyses for the four individual methylation sites show that each of the DNA methylation sites also could distinguish high- and low-risk patients, but the predictive performances were lower than the combination of these four DNA methylation sites in both the training and validation cohorts (Figure 2—figure supplements 2–3), indicating that single methylation sites are likely to play a role in the prognostic prediction, and a combination of methylation sites might offer better potential to fulfill much more sensitive and specific prognostic tests for patients with CM. To the best of our knowledge, the prognostic value of this multi-marker signature in melanoma has not been previously reported. Therefore, the current study provides new insight that a combination of epigenetic biomarkers could help to improve risk-stratification and prediction of survival in patients with melanoma.

In conclusion, we identified and verified a four-DNA methylation signature that was significantly associated with the OS of patients in TCGA and an independent cohort. The four-DNA methylation signature was not only independent of clinical factors including patient sex, age, stage, tumor location, and Breslow thickness, but also exhibited superior ability in predicting OS compared with known biomarkers. The four-DNA methylation signature was able to stratify patients with startling accuracy in survival differences, suggesting that it may be used to select patients for therapies, and help to determine whether patients may require more or less aggressive treatment. Furthermore, the four-DNA methylation signature was significantly correlated with the ICB immunotherapy-related signature. Therefore, though these exploratory findings are warranted to validate the potential role of this prognostic signature in clinical application and the functional characterization in CM development, these four-DNA methylation sites, or some of them, may participate in the progress of the cancer, and have great potential implications for both risk-stratification, adjuvant management and measures of response to ICB immunotherapy of patients with CM.

We downloaded the data from publicly available databases: The Cancer Genome Atlas database (https://cancergenome.nih.gov/) and GEO database (under accession code GSE51547).

1. 1. International Cancer Genome C
   2. Hudson TJ
   3. Anderson W

   (2010) The Cancer Genome Atlas

   International network of cancer genome projects.

   https://portal.gdc.cancer.gov/projects/TCGA-SKCM
2. 1. Lauss M

   (2015) NCBI Gene Expression Omnibus

   ID GSE51547. DNA methylation in melanoma is connected to gene expression phenotypes and MITF pathway expression.

   https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE51547

1. 1. Akiyama Y
   2. Watkins N
   3. Suzuki H
   4. Jair KW
   5. van Engeland M
   6. Esteller M
   7. Sakai H
   8. Ren CY
   9. Yuasa Y
   10. Herman JG
   11. Baylin SB

   (2003) GATA-4 and GATA-5 transcription factor genes and potential downstream antitumor target genes are epigenetically silenced in colorectal and gastric cancer

   Molecular and Cellular Biology 23:8429–8439.

   https://doi.org/10.1128/MCB.23.23.8429-8439.2003

   • PubMed
   • Google Scholar
2. 1. Balch CM
   2. Gershenwald JE
   3. Soong SJ
   4. Thompson JF
   5. Atkins MB
   6. Byrd DR
   7. Buzaid AC
   8. Cochran AJ
   9. Coit DG
   10. Ding S
   11. Eggermont AM
   12. Flaherty KT
   13. Gimotty PA
   14. Kirkwood JM
   15. McMasters KM
   16. Mihm MC
   17. Morton DL
   18. Ross MI
   19. Sober AJ
   20. Sondak VK

   (2009) Final version of 2009 AJCC melanoma staging and classification

   Journal of Clinical Oncology 27:6199–6206.

   https://doi.org/10.1200/JCO.2009.23.4799

   • PubMed
   • Google Scholar
3. 1. Cancer Genome Atlas Network

   (2015) Genomic classification of cutaneous melanoma

   Cell 161:1681–1696.

   https://doi.org/10.1016/j.cell.2015.05.044

   • PubMed
   • Google Scholar
4. 1. Cesinaro AM
   2. Sartori G
   3. Migaldi M
   4. Schirosi L
   5. Pellacani G
   6. Collina G
   7. Maiorana A

   (2012) Prognostic significance of MGMT gene promoter methylation in differently treated metastatic melanomas

   Pathology 44:313–317.

   https://doi.org/10.1097/PAT.0b013e328353a0ff

   • PubMed
   • Google Scholar
5. 1. Chang CH
   2. Qiu J
   3. O'Sullivan D
   4. Buck MD
   5. Noguchi T
   6. Curtis JD
   7. Chen Q
   8. Gindin M
   9. Gubin MM
   10. van der Windt GJ
   11. Tonc E
   12. Schreiber RD
   13. Pearce EJ
   14. Pearce EL

   (2015) Metabolic competition in the tumor microenvironment is a driver of cancer progression

   Cell 162:1229–1241.

   https://doi.org/10.1016/j.cell.2015.08.016

   • PubMed
   • Google Scholar
6. 1. Chen X
   2. Guo W
   3. Xu XJ
   4. Su F
   5. Wang Y
   6. Zhang Y
   7. Wang Q
   8. Zhu L

   (2017) Melanoma long non-coding RNA signature predicts prognostic survival and directs clinical risk-specific treatments

   Journal of Dermatological Science 85:226–234.

   https://doi.org/10.1016/j.jdermsci.2016.12.006

   • PubMed
   • Google Scholar
7. 1. Chen J
   2. Song W
   3. Du Y
   4. Li Z
   5. Xuan Z
   6. Zhao L
   7. Chen J
   8. Zhao Y
   9. Tuo B
   10. Zheng S
   11. Song P

   (2018) Inhibition of KLHL21 prevents cholangiocarcinoma progression through regulating cell proliferation and motility, arresting cell cycle and reducing erk activation

   Biochemical and Biophysical Research Communications 499:433–440.

   https://doi.org/10.1016/j.bbrc.2018.03.152

   • PubMed
   • Google Scholar
8. 1. Cheng PF
   2. Shakhova O
   3. Widmer DS
   4. Eichhoff OM
   5. Zingg D
   6. Frommel SC
   7. Belloni B
   8. Raaijmakers MI
   9. Goldinger SM
   10. Santoro R
   11. Hemmi S
   12. Sommer L
   13. Dummer R
   14. Levesque MP

   (2015) Methylation-dependent SOX9 expression mediates invasion in human melanoma cells and is a negative prognostic factor in advanced melanoma

   Genome Biology 16:42.

   https://doi.org/10.1186/s13059-015-0594-4

   • PubMed
   • Google Scholar
9. 1. Cho SH
   2. Kuo IY
   3. Lu PF
   4. Tzeng HT
   5. Lai WW
   6. Su WC
   7. Wang YC

   (2018) Rab37 mediates exocytosis of secreted frizzled-related protein 1 to inhibit wnt signaling and thus suppress lung cancer stemness

   Cell Death & Disease 9:868.

   https://doi.org/10.1038/s41419-018-0915-0

   • PubMed
   • Google Scholar
10. 1. Coricovac D
    2. Dehelean C
    3. Moaca E-A
    4. Pinzaru I
    5. Bratu T
    6. Navolan D
    7. Boruga O

    (2018) Cutaneous Melanoma—A Long Road from Experimental Models to Clinical Outcome: A Review

    International Journal of Molecular Sciences 19:1566.

    https://doi.org/10.3390/ijms19061566

    • Google Scholar
11. 1. Crawford NG
    2. Kelly DE
    3. Hansen MEB
    4. Beltrame MH
    5. Fan S
    6. Bowman SL
    7. Jewett E
    8. Ranciaro A
    9. Thompson S
    10. Lo Y
    11. Pfeifer SP
    12. Jensen JD
    13. Campbell MC
    14. Beggs W
    15. Hormozdiari F
    16. Mpoloka SW
    17. Mokone GG
    18. Nyambo T
    19. Meskel DW
    20. Belay G
    21. Haut J
    22. Rothschild H
    23. Zon L
    24. Zhou Y
    25. Kovacs MA
    26. Xu M
    27. Zhang T
    28. Bishop K
    29. Sinclair J
    30. Rivas C
    31. Elliot E
    32. Choi J
    33. Li SA
    34. Hicks B
    35. Burgess S
    36. Abnet C
    37. Watkins-Chow DE
    38. Oceana E
    39. Song YS
    40. Eskin E
    41. Brown KM
    42. Marks MS
    43. Loftus SK
    44. Pavan WJ
    45. Yeager M
    46. Chanock S
    47. Tishkoff SA
    48. NISC Comparative Sequencing Program

    (2017) Loci associated with skin pigmentation identified in african populations

    Science 358:eaan8433.

    https://doi.org/10.1126/science.aan8433

    • PubMed
    • Google Scholar
12. 1. Cristescu R
    2. Mogg R
    3. Ayers M
    4. Albright A
    5. Murphy E
    6. Yearley J
    7. Sher X
    8. Liu XQ
    9. Lu H
    10. Nebozhyn M
    11. Zhang C
    12. Lunceford JK
    13. Joe A
    14. Cheng J
    15. Webber AL
    16. Ibrahim N
    17. Plimack ER
    18. Ott PA
    19. Seiwert TY
    20. Ribas A
    21. McClanahan TK
    22. Tomassini JE
    23. Loboda A
    24. Kaufman D

    (2018) Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy

    Science 362:eaar3593.

    https://doi.org/10.1126/science.aar3593

    • PubMed
    • Google Scholar
13. 1. Dai W
    2. Teodoridis JM
    3. Zeller C
    4. Graham J
    5. Hersey J
    6. Flanagan JM
    7. Stronach E
    8. Millan DW
    9. Siddiqui N
    10. Paul J
    11. Brown R

    (2011) Systematic CpG islands methylation profiling of genes in the wnt pathway in epithelial ovarian cancer identifies biomarkers of progression-free survival

    Clinical Cancer Research 17:4052–4062.

    https://doi.org/10.1158/1078-0432.CCR-10-3021

    • PubMed
    • Google Scholar
14. 1. Egger G
    2. Liang G
    3. Aparicio A
    4. Jones PA

    (2004) Epigenetics in human disease and prospects for epigenetic therapy

    Nature 429:457–463.

    https://doi.org/10.1038/nature02625

    • PubMed
    • Google Scholar
15. 1. Ekmekcioglu S
    2. Davies MA
    3. Tanese K
    4. Roszik J
    5. Shin-Sim M
    6. Bassett RL
    7. Milton DR
    8. Woodman SE
    9. Prieto VG
    10. Gershenwald JE
    11. Morton DL
    12. Hoon DS
    13. Grimm EA

    (2016) Inflammatory marker testing identifies CD74 expression in melanoma tumor cells, and its expression associates with favorable survival for stage III melanoma

    Clinical Cancer Research 22:3016–3024.

    https://doi.org/10.1158/1078-0432.CCR-15-2226

    • PubMed
    • Google Scholar
16. 1. Garraway LA
    2. Widlund HR
    3. Rubin MA
    4. Getz G
    5. Berger AJ
    6. Ramaswamy S
    7. Beroukhim R
    8. Milner DA
    9. Granter SR
    10. Du J
    11. Lee C
    12. Wagner SN
    13. Li C
    14. Golub TR
    15. Rimm DL
    16. Meyerson ML
    17. Fisher DE
    18. Sellers WR

    (2005) Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma

    Nature 436:117–122.

    https://doi.org/10.1038/nature03664

    • PubMed
    • Google Scholar
17. 1. Goltz D
    2. Gevensleben H
    3. Vogt TJ
    4. Dietrich J
    5. Golletz C
    6. Bootz F
    7. Kristiansen G
    8. Landsberg J
    9. Dietrich D

    (2018) CTLA4 methylation predicts response to anti-PD-1 and anti-CTLA-4 immunotherapy in melanoma patients

    JCI Insight 3:.

    https://doi.org/10.1172/jci.insight.96793

    • PubMed
    • Google Scholar
18. 1. Guo M
    2. Akiyama Y
    3. House MG
    4. Hooker CM
    5. Heath E
    6. Gabrielson E
    7. Yang SC
    8. Han Y
    9. Baylin SB
    10. Herman JG
    11. Brock MV

    (2004) Hypermethylation of the GATA genes in lung cancer

    Clinical Cancer Research 10:7917–7924.

    https://doi.org/10.1158/1078-0432.CCR-04-1140

    • PubMed
    • Google Scholar
19. 1. Herman JG
    2. Baylin SB

    (2003) Gene silencing in cancer in association with promoter hypermethylation

    New England Journal of Medicine 349:2042–2054.

    https://doi.org/10.1056/NEJMra023075

    • PubMed
    • Google Scholar
20. 1. Hudson TJ
    2. Anderson W
    3. Artez A
    4. Barker AD
    5. Bell C
    6. Bernabé RR
    7. Bhan MK
    8. Calvo F
    9. Eerola I
    10. Gerhard DS
    11. Guttmacher A
    12. Guyer M
    13. Hemsley FM
    14. Jennings JL
    15. Kerr D
    16. Klatt P
    17. Kolar P
    18. Kusada J
    19. Lane DP
    20. Laplace F
    21. Youyong L
    22. Nettekoven G
    23. Ozenberger B
    24. Peterson J
    25. Rao TS
    26. Remacle J
    27. Schafer AJ
    28. Shibata T
    29. Stratton MR
    30. Vockley JG
    31. Watanabe K
    32. Yang H
    33. Yuen MM
    34. Knoppers BM
    35. Bobrow M
    36. Cambon-Thomsen A
    37. Dressler LG
    38. Dyke SO
    39. Joly Y
    40. Kato K
    41. Kennedy KL
    42. Nicolás P
    43. Parker MJ
    44. Rial-Sebbag E
    45. Romeo-Casabona CM
    46. Shaw KM
    47. Wallace S
    48. Wiesner GL
    49. Zeps N
    50. Lichter P
    51. Biankin AV
    52. Chabannon C
    53. Chin L
    54. Clément B
    55. de Alava E
    56. Degos F
    57. Ferguson ML
    58. Geary P
    59. Hayes DN
    60. Hudson TJ
    61. Johns AL
    62. Kasprzyk A
    63. Nakagawa H
    64. Penny R
    65. Piris MA
    66. Sarin R
    67. Scarpa A
    68. Shibata T
    69. van de Vijver M
    70. Futreal PA
    71. Aburatani H
    72. Bayés M
    73. Botwell DD
    74. Campbell PJ
    75. Estivill X
    76. Gerhard DS
    77. Grimmond SM
    78. Gut I
    79. Hirst M
    80. López-Otín C
    81. Majumder P
    82. Marra M
    83. McPherson JD
    84. Nakagawa H
    85. Ning Z
    86. Puente XS
    87. Ruan Y
    88. Shibata T
    89. Stratton MR
    90. Stunnenberg HG
    91. Swerdlow H
    92. Velculescu VE
    93. Wilson RK
    94. Xue HH
    95. Yang L
    96. Spellman PT
    97. Bader GD
    98. Boutros PC
    99. Campbell PJ
    100. Flicek P
    101. Getz G
    102. Guigó R
    103. Guo G
    104. Haussler D
    105. Heath S
    106. Hubbard TJ
    107. Jiang T
    108. Jones SM
    109. Li Q
    110. López-Bigas N
    111. Luo R
    112. Muthuswamy L
    113. Ouellette BF
    114. Pearson JV
    115. Puente XS
    116. Quesada V
    117. Raphael BJ
    118. Sander C
    119. Shibata T
    120. Speed TP
    121. Stein LD
    122. Stuart JM
    123. Teague JW
    124. Totoki Y
    125. Tsunoda T
    126. Valencia A
    127. Wheeler DA
    128. Wu H
    129. Zhao S
    130. Zhou G
    131. Stein LD
    132. Guigó R
    133. Hubbard TJ
    134. Joly Y
    135. Jones SM
    136. Kasprzyk A
    137. Lathrop M
    138. López-Bigas N
    139. Ouellette BF
    140. Spellman PT
    141. Teague JW
    142. Thomas G
    143. Valencia A
    144. Yoshida T
    145. Kennedy KL
    146. Axton M
    147. Dyke SO
    148. Futreal PA
    149. Gerhard DS
    150. Gunter C
    151. Guyer M
    152. Hudson TJ
    153. McPherson JD
    154. Miller LJ
    155. Ozenberger B
    156. Shaw KM
    157. Kasprzyk A
    158. Stein LD
    159. Zhang J
    160. Haider SA
    161. Wang J
    162. Yung CK
    163. Cros A
    164. Cross A
    165. Liang Y
    166. Gnaneshan S
    167. Guberman J
    168. Hsu J
    169. Bobrow M
    170. Chalmers DR
    171. Hasel KW
    172. Joly Y
    173. Kaan TS
    174. Kennedy KL
    175. Knoppers BM
    176. Lowrance WW
    177. Masui T
    178. Nicolás P
    179. Rial-Sebbag E
    180. Rodriguez LL
    181. Vergely C
    182. Yoshida T
    183. Grimmond SM
    184. Biankin AV
    185. Bowtell DD
    186. Cloonan N
    187. deFazio A
    188. Eshleman JR
    189. Etemadmoghadam D
    190. Gardiner BB
    191. Gardiner BA
    192. Kench JG
    193. Scarpa A
    194. Sutherland RL
    195. Tempero MA
    196. Waddell NJ
    197. Wilson PJ
    198. McPherson JD
    199. Gallinger S
    200. Tsao MS
    201. Shaw PA
    202. Petersen GM
    203. Mukhopadhyay D
    204. Chin L
    205. DePinho RA
    206. Thayer S
    207. Muthuswamy L
    208. Shazand K
    209. Beck T
    210. Sam M
    211. Timms L
    212. Ballin V
    213. Lu Y
    214. Ji J
    215. Zhang X
    216. Chen F
    217. Hu X
    218. Zhou G
    219. Yang Q
    220. Tian G
    221. Zhang L
    222. Xing X
    223. Li X
    224. Zhu Z
    225. Yu Y
    226. Yu J
    227. Yang H
    228. Lathrop M
    229. Tost J
    230. Brennan P
    231. Holcatova I
    232. Zaridze D
    233. Brazma A
    234. Egevard L
    235. Prokhortchouk E
    236. Banks RE
    237. Uhlén M
    238. Cambon-Thomsen A
    239. Viksna J
    240. Ponten F
    241. Skryabin K
    242. Stratton MR
    243. Futreal PA
    244. Birney E
    245. Borg A
    246. Børresen-Dale AL
    247. Caldas C
    248. Foekens JA
    249. Martin S
    250. Reis-Filho JS
    251. Richardson AL
    252. Sotiriou C
    253. Stunnenberg HG
    254. Thoms G
    255. van de Vijver M
    256. van't Veer L
    257. Calvo F
    258. Birnbaum D
    259. Blanche H
    260. Boucher P
    261. Boyault S
    262. Chabannon C
    263. Gut I
    264. Masson-Jacquemier JD
    265. Lathrop M
    266. Pauporté I
    267. Pivot X
    268. Vincent-Salomon A
    269. Tabone E
    270. Theillet C
    271. Thomas G
    272. Tost J
    273. Treilleux I
    274. Calvo F
    275. Bioulac-Sage P
    276. Clément B
    277. Decaens T
    278. Degos F
    279. Franco D
    280. Gut I
    281. Gut M
    282. Heath S
    283. Lathrop M
    284. Samuel D
    285. Thomas G
    286. Zucman-Rossi J
    287. Lichter P
    288. Eils R
    289. Brors B
    290. Korbel JO
    291. Korshunov A
    292. Landgraf P
    293. Lehrach H
    294. Pfister S
    295. Radlwimmer B
    296. Reifenberger G
    297. Taylor MD
    298. von Kalle C
    299. Majumder PP
    300. Sarin R
    301. Rao TS
    302. Bhan MK
    303. Scarpa A
    304. Pederzoli P
    305. Lawlor RA
    306. Delledonne M
    307. Bardelli A
    308. Biankin AV
    309. Grimmond SM
    310. Gress T
    311. Klimstra D
    312. Zamboni G
    313. Shibata T
    314. Nakamura Y
    315. Nakagawa H
    316. Kusada J
    317. Tsunoda T
    318. Miyano S
    319. Aburatani H
    320. Kato K
    321. Fujimoto A
    322. Yoshida T
    323. Campo E
    324. López-Otín C
    325. Estivill X
    326. Guigó R
    327. de Sanjosé S
    328. Piris MA
    329. Montserrat E
    330. González-Díaz M
    331. Puente XS
    332. Jares P
    333. Valencia A
    334. Himmelbauer H
    335. Himmelbaue H
    336. Quesada V
    337. Bea S
    338. Stratton MR
    339. Futreal PA
    340. Campbell PJ
    341. Vincent-Salomon A
    342. Richardson AL
    343. Reis-Filho JS
    344. van de Vijver M
    345. Thomas G
    346. Masson-Jacquemier JD
    347. Aparicio S
    348. Borg A
    349. Børresen-Dale AL
    350. Caldas C
    351. Foekens JA
    352. Stunnenberg HG
    353. van't Veer L
    354. Easton DF
    355. Spellman PT
    356. Martin S
    357. Barker AD
    358. Chin L
    359. Collins FS
    360. Compton CC
    361. Ferguson ML
    362. Gerhard DS
    363. Getz G
    364. Gunter C
    365. Guttmacher A
    366. Guyer M
    367. Hayes DN
    368. Lander ES
    369. Ozenberger B
    370. Penny R
    371. Peterson J
    372. Sander C
    373. Shaw KM
    374. Speed TP
    375. Spellman PT
    376. Vockley JG
    377. Wheeler DA
    378. Wilson RK
    379. Hudson TJ
    380. Chin L
    381. Knoppers BM
    382. Lander ES
    383. Lichter P
    384. Stein LD
    385. Stratton MR
    386. Anderson W
    387. Barker AD
    388. Bell C
    389. Bobrow M
    390. Burke W
    391. Collins FS
    392. Compton CC
    393. DePinho RA
    394. Easton DF
    395. Futreal PA
    396. Gerhard DS
    397. Green AR
    398. Guyer M
    399. Hamilton SR
    400. Hubbard TJ
    401. Kallioniemi OP
    402. Kennedy KL
    403. Ley TJ
    404. Liu ET
    405. Lu Y
    406. Majumder P
    407. Marra M
    408. Ozenberger B
    409. Peterson J
    410. Schafer AJ
    411. Spellman PT
    412. Stunnenberg HG
    413. Wainwright BJ
    414. Wilson RK
    415. Yang H
    416. International Cancer Genome Consortium

    (2010) International network of cancer genome projects

    Nature 464:993–998.

    https://doi.org/10.1038/nature08987

    • PubMed
    • Google Scholar
21. 1. Hugo W
    2. Zaretsky JM
    3. Sun L
    4. Song C
    5. Moreno BH
    6. Hu-Lieskovan S
    7. Berent-Maoz B
    8. Pang J
    9. Chmielowski B
    10. Cherry G
    11. Seja E
    12. Lomeli S
    13. Kong X
    14. Kelley MC
    15. Sosman JA
    16. Johnson DB
    17. Ribas A
    18. Lo RS

    (2016) Genomic and transcriptomic features of response to Anti-PD-1 therapy in metastatic melanoma

    Cell 165:35–44.

    https://doi.org/10.1016/j.cell.2016.02.065

    • PubMed
    • Google Scholar
22. 1. Jeschke J
    2. Bizet M
    3. Desmedt C
    4. Calonne E
    5. Dedeurwaerder S
    6. Garaud S
    7. Koch A
    8. Larsimont D
    9. Salgado R
    10. Van den Eynden G
    11. Willard Gallo K
    12. Bontempi G
    13. Defrance M
    14. Sotiriou C
    15. Fuks F

    (2017) DNA methylation-based immune response signature improves patient diagnosis in multiple cancers

    Journal of Clinical Investigation 127:3090–3102.

    https://doi.org/10.1172/JCI91095

    • PubMed
    • Google Scholar
23. 1. Keeley B
    2. Stark A
    3. Pisanic TR
    4. Kwak R
    5. Zhang Y
    6. Wrangle J
    7. Baylin S
    8. Herman J
    9. Ahuja N
    10. Brock MV
    11. Wang T-H

    (2013) Extraction and processing of circulating DNA from large sample volumes using methylation on beads for the detection of rare epigenetic events

    Clinica Chimica Acta 425:169–175.

    https://doi.org/10.1016/j.cca.2013.07.023

    • Google Scholar
24. 1. Law MH
    2. Bishop DT
    3. Lee JE
    4. Brossard M
    5. Martin NG
    6. Moses EK
    7. Song F
    8. Barrett JH
    9. Kumar R
    10. Easton DF
    11. Pharoah PDP
    12. Swerdlow AJ
    13. Kypreou KP
    14. Taylor JC
    15. Harland M
    16. Randerson-Moor J
    17. Akslen LA
    18. Andresen PA
    19. Avril MF
    20. Azizi E
    21. Scarrà GB
    22. Brown KM
    23. Dębniak T
    24. Duffy DL
    25. Elder DE
    26. Fang S
    27. Friedman E
    28. Galan P
    29. Ghiorzo P
    30. Gillanders EM
    31. Goldstein AM
    32. Gruis NA
    33. Hansson J
    34. Helsing P
    35. Hočevar M
    36. Höiom V
    37. Ingvar C
    38. Kanetsky PA
    39. Chen WV
    40. Landi MT
    41. Lang J
    42. Lathrop GM
    43. Lubiński J
    44. Mackie RM
    45. Mann GJ
    46. Molven A
    47. Montgomery GW
    48. Novaković S
    49. Olsson H
    50. Puig S
    51. Puig-Butille JA
    52. Qureshi AA
    53. Radford-Smith GL
    54. van der Stoep N
    55. van Doorn R
    56. Whiteman DC
    57. Craig JE
    58. Schadendorf D
    59. Simms LA
    60. Burdon KP
    61. Nyholt DR
    62. Pooley KA
    63. Orr N
    64. Stratigos AJ
    65. Cust AE
    66. Ward SV
    67. Hayward NK
    68. Han J
    69. Schulze HJ
    70. Dunning AM
    71. Bishop JAN
    72. Demenais F
    73. Amos CI
    74. MacGregor S
    75. Iles MM
    76. GenoMEL Consortium
    77. Essen-Heidelberg Investigators
    78. SDH Study Group
    79. Q-MEGA and QTWIN Investigators
    80. AMFS Investigators
    81. ATHENS Melanoma Study Group

    (2015) Genome-wide meta-analysis identifies five new susceptibility loci for cutaneous malignant melanoma

    Nature Genetics 47:987–995.

    https://doi.org/10.1038/ng.3373

    • PubMed
    • Google Scholar
25. 1. MacKie RM
    2. Hauschild A
    3. Eggermont AM

    (2009) Epidemiology of invasive cutaneous melanoma

    Annals of Oncology 20:vi1–vi7.

    https://doi.org/10.1093/annonc/mdp252

    • PubMed
    • Google Scholar
26. 1. Messeguer F
    2. Agustí-Mejías A
    3. Traves V
    4. Alegre V
    5. Oliver V
    6. Nagore E

    (2013) Risk factors for the development of locoregional cutaneous metastases as the sole form of recurrence in patients with melanoma

    Actas Dermo-Sifiliográficas 104:53–60.

    https://doi.org/10.1016/j.adengl.2012.11.006

    • Google Scholar
27. 1. Miller AJ
    2. Mihm MC

    (2006) Melanoma

    New England Journal of Medicine 355:51–65.

    https://doi.org/10.1056/NEJMra052166

    • PubMed
    • Google Scholar
28. 1. Mori T
    2. Martinez SR
    3. O'Day SJ
    4. Morton DL
    5. Umetani N
    6. Kitago M
    7. Tanemura A
    8. Nguyen SL
    9. Tran AN
    10. Wang HJ
    11. Hoon DS

    (2006) Estrogen receptor-alpha methylation predicts melanoma progression

    Cancer Research 66:6692–6698.

    https://doi.org/10.1158/0008-5472.CAN-06-0801

    • PubMed
    • Google Scholar
29. 1. Pretscher D
    2. Distel LV
    3. Grabenbauer GG
    4. Wittlinger M
    5. Buettner M
    6. Niedobitek G

    (2009) Distribution of immune cells in head and neck cancer: cd8+ T-cells and CD20+ B-cells in metastatic lymph nodes are associated with favourable outcome in patients with oro- and hypopharyngeal carcinoma

    BMC Cancer 9:292.

    https://doi.org/10.1186/1471-2407-9-292

    • PubMed
    • Google Scholar
30. 1. Riaz N
    2. Havel JJ
    3. Makarov V
    4. Desrichard A
    5. Urba WJ
    6. Sims JS
    7. Hodi FS
    8. Martín-Algarra S
    9. Mandal R
    10. Sharfman WH
    11. Bhatia S
    12. Hwu W-J
    13. Gajewski TF
    14. Slingluff CL
    15. Chowell D
    16. Kendall SM
    17. Chang H
    18. Shah R
    19. Kuo F
    20. Morris LGT
    21. Sidhom J-W
    22. Schneck JP
    23. Horak CE
    24. Weinhold N
    25. Chan TA

    (2017) Tumor and microenvironment evolution during immunotherapy with nivolumab

    Cell 171:934–949.

    https://doi.org/10.1016/j.cell.2017.09.028

    • Google Scholar
31. 1. Rizvi NA
    2. Hellmann MD
    3. Snyder A
    4. Kvistborg P
    5. Makarov V
    6. Havel JJ
    7. Lee W
    8. Yuan J
    9. Wong P
    10. Ho TS
    11. Miller ML
    12. Rekhtman N
    13. Moreira AL
    14. Ibrahim F
    15. Bruggeman C
    16. Gasmi B
    17. Zappasodi R
    18. Maeda Y
    19. Sander C
    20. Garon EB
    21. Merghoub T
    22. Wolchok JD
    23. Schumacher TN
    24. Chan TA

    (2015) Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer

    Science 348:124–128.

    https://doi.org/10.1126/science.aaa1348

    • PubMed
    • Google Scholar
32. 1. Robsahm TE
    2. Bergva G
    3. Hestvik UE
    4. Møller B

    (2013) Sex differences in rising trends of cutaneous malignant melanoma in Norway, 1954-2008

    Melanoma Research 23:70–78.

    https://doi.org/10.1097/CMR.0b013e32835c7e48

    • PubMed
    • Google Scholar
33. 1. Roh MR
    2. Gupta S
    3. Park KH
    4. Chung KY
    5. Lauss M
    6. Flaherty KT
    7. Jönsson G
    8. Rha SY
    9. Tsao H

    (2016) Promoter methylation of PTEN is a significant prognostic factor in melanoma survival

    Journal of Investigative Dermatology 136:1002–1011.

    https://doi.org/10.1016/j.jid.2016.01.024

    • PubMed
    • Google Scholar
34. 1. Segura MF
    2. Belitskaya-Lévy I
    3. Rose AE
    4. Zakrzewski J
    5. Gaziel A
    6. Hanniford D
    7. Darvishian F
    8. Berman RS
    9. Shapiro RL
    10. Pavlick AC
    11. Osman I
    12. Hernando E

    (2010) Melanoma MicroRNA signature predicts post-recurrence survival

    Clinical Cancer Research 16:1577–1586.

    https://doi.org/10.1158/1078-0432.CCR-09-2721

    • PubMed
    • Google Scholar
35. 1. Sharma P
    2. Hu-Lieskovan S
    3. Wargo JA
    4. Ribas A

    (2017) Primary, adaptive, and acquired resistance to cancer immunotherapy

    Cell 168:707–723.

    https://doi.org/10.1016/j.cell.2017.01.017

    • PubMed
    • Google Scholar
36. 1. Shenoy AR
    2. Wellington DA
    3. Kumar P
    4. Kassa H
    5. Booth CJ
    6. Cresswell P
    7. MacMicking JD

    (2012) GBP5 promotes NLRP3 inflammasome assembly and immunity in mammals

    Science 336:481–485.

    https://doi.org/10.1126/science.1217141

    • Google Scholar
37. 1. Shi L
    2. Zhang W
    3. Zou F
    4. Mei L
    5. Wu G
    6. Teng Y

    (2016) KLHL21, a novel gene that contributes to the progression of hepatocellular carcinoma

    BMC Cancer 16:815.

    https://doi.org/10.1186/s12885-016-2851-7

    • PubMed
    • Google Scholar
38. 1. Siegel RL
    2. Miller KD
    3. Jemal A

    (2018) Cancer statistics, 2018

    CA: A Cancer Journal for Clinicians 68:7–30.

    https://doi.org/10.3322/caac.21442

    • Google Scholar
39. 1. Sigalotti L
    2. Covre A
    3. Fratta E
    4. Parisi G
    5. Sonego P
    6. Colizzi F
    7. Coral S
    8. Massarut S
    9. Kirkwood JM
    10. Maio M

    (2012) Whole genome methylation profiles as independent markers of survival in stage IIIC melanoma patients

    Journal of Translational Medicine 10:185.

    https://doi.org/10.1186/1479-5876-10-185

    • PubMed
    • Google Scholar
40. 1. Tremante E
    2. Ginebri A
    3. Lo Monaco E
    4. Frascione P
    5. Di Filippo F
    6. Terrenato I
    7. Benevolo M
    8. Mottolese M
    9. Pescarmona E
    10. Visca P
    11. Natali PG
    12. Giacomini P

    (2012) Melanoma molecular classes and prognosis in the postgenomic era

    The Lancet Oncology 13:e205–e211.

    https://doi.org/10.1016/S1470-2045(12)70003-7

    • PubMed
    • Google Scholar
41. 1. Tzeng H-T
    2. Su C-C
    3. Chang C-P
    4. Lai W-W
    5. Su W-C
    6. Wang Y-C

    (2018) Rab37 in lung cancer mediates exocytosis of soluble ST2 and thus skews macrophages toward tumor-suppressing phenotype

    International Journal of Cancer 143:1753–1763.

    https://doi.org/10.1002/ijc.31569

    • Google Scholar
42. 1. Umezu D
    2. Okada N
    3. Sakoda Y
    4. Adachi K
    5. Ojima T
    6. Yamaue H
    7. Eto M
    8. Tamada K

    (2019) Inhibitory functions of PD-L1 and PD-L2 in the regulation of anti-tumor immunity in murine tumor microenvironment

    Cancer Immunology, Immunotherapy 68:201–211.

    https://doi.org/10.1007/s00262-018-2263-4

    • PubMed
    • Google Scholar
43. 1. Wang Q
    2. Wang X
    3. Liang Q
    4. Wang S
    5. Xiwen L
    6. Pan F
    7. Chen H
    8. Li D

    (2018) Distinct prognostic value of mRNA expression of guanylate-binding protein genes in skin cutaneous melanoma

    Oncology Letters 15:7914–7922.

    https://doi.org/10.3892/ol.2018.8306

    • PubMed
    • Google Scholar
44. 1. Weiss SA
    2. Hanniford D
    3. Hernando E
    4. Osman I

    (2015) Revisiting determinants of prognosis in cutaneous melanoma

    Cancer 121:4108–4123.

    https://doi.org/10.1002/cncr.29634

    • PubMed
    • Google Scholar
45. 1. Wouters J
    2. Vizoso M
    3. Martinez-Cardus A
    4. Carmona FJ
    5. Govaere O
    6. Laguna T
    7. Joseph J
    8. Dynoodt P
    9. Aura C
    10. Foth M
    11. Cloots R
    12. van den Hurk K
    13. Balint B
    14. Murphy IG
    15. McDermott EW
    16. Sheahan K
    17. Jirström K
    18. Nodin B
    19. Mallya-Udupi G
    20. van den Oord JJ
    21. Gallagher WM
    22. Esteller M

    (2017) Comprehensive DNA methylation study identifies novel progression-related and prognostic markers for cutaneous melanoma

    BMC Medicine 15:101.

    https://doi.org/10.1186/s12916-017-0851-3

    • Google Scholar
46. 1. Wu CY
    2. Tseng RC
    3. Hsu HS
    4. Wang YC
    5. Hsu MT

    (2009) Frequent down-regulation of hRAB37 in metastatic tumor by genetic and epigenetic mechanisms in lung cancer

    Lung Cancer 63:360–367.

    https://doi.org/10.1016/j.lungcan.2008.06.014

    • PubMed
    • Google Scholar
47. 1. Yamamoto M
    2. Okuyama M
    3. Ma JS
    4. Kimura T
    5. Kamiyama N
    6. Saiga H
    7. Ohshima J
    8. Sasai M
    9. Kayama H
    10. Okamoto T
    11. Huang DC
    12. Soldati-Favre D
    13. Horie K
    14. Takeda J
    15. Takeda K

    (2012) A cluster of interferon-γ-inducible p65 GTPases plays a critical role in host defense against toxoplasma gondii

    Immunity 37:302–313.

    https://doi.org/10.1016/j.immuni.2012.06.009

    • PubMed
    • Google Scholar
48. 1. Youden WJ

    (1950) Index for rating diagnostic tests

    Cancer 3:32–35.

    https://doi.org/10.1002/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3

    • PubMed
    • Google Scholar
49. 1. Zhang T
    2. Guan G
    3. Chen T
    4. Jin J
    5. Zhang L
    6. Yao M
    7. Qi X
    8. Zou J
    9. Chen J
    10. Lu F
    11. Chen X

    (2018) Methylation of PCDH19 predicts poor prognosis of hepatocellular carcinoma

    Asia-Pacific Journal of Clinical Oncology 14:e352–e358.

    https://doi.org/10.1111/ajco.12982

    • PubMed
    • Google Scholar
50. 1. Zhou F
    2. Tao G
    3. Chen X
    4. Xie W
    5. Liu M
    6. Cao X

    (2014) Methylation of OPCML promoter in ovarian cancer tissues predicts poor patient survival

    Clinical Chemistry and Laboratory Medicine 52:735–742.

    https://doi.org/10.1515/cclm-2013-0736

    • PubMed
    • Google Scholar

Author details

1. Wenna Guo

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Data curation, Software, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Contributed equally with

   Liucun Zhu

   Competing interests

   No competing interests declared

2. Liucun Zhu

   School of Life Sciences, Shanghai University, Shanghai, China

   Contribution

   Software, Formal analysis, Validation, Investigation, Methodology, Writing—review and editing

   Contributed equally with

   Wenna Guo

   Competing interests

   No competing interests declared

3. Rui Zhu

   School of Life Sciences, Shanghai University, Shanghai, China

   Contribution

   Validation, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. Qihan Chen

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Funding acquisition, Validation, Writing—review and editing

   Competing interests

   No competing interests declared

5. Qiang Wang

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Conceptualization, Software, Supervision, Funding acquisition, Validation, Project administration, Writing—review and editing

   For correspondence

   wangq@nju.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2907-9851

6. Jian-Qun Chen

   State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China

   Contribution

   Conceptualization, Supervision, Validation, Project administration, Writing—review and editing

   For correspondence

   chenjq@nju.edu.cn

   Competing interests

   No competing interests declared

• 3,378

  views

• 436

  downloads

• 53

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.