Construction of a melanoma prognostic model based on Parkinson’s disease-related genes: A 7-gene signature and validation of its efficacy for prognostic assessment and treatment response prediction

XIE Luoyingzi; HE Zhiqiang; ZHANG Weinan; ZHENG Hong

doi:10.16016/j.2097-0927.202511006

您当前的位置：

首页 >

文章列表页 >

Construction of a melanoma prognostic model based on Parkinson’s disease-related genes: A 7-gene signature and validation of its efficacy for prognostic assessment and treatment response prediction

Clinical Medicine | 更新时间：2026-03-30

- Construction of a melanoma prognostic model based on Parkinson’s disease-related genes: A 7-gene signature and validation of its efficacy for prognostic assessment and treatment response prediction
- Journal of Army Medical University Vol. 48, Issue 6, Pages: 793-808(2026)
- 作者机构：
  
  1.重庆大学附属人民医院中心实验室，重庆
  2.陆军军医大学（第三军医大学）第一附属医院江北院区（中国人民解放军第 958 医院）皮肤科，重庆
  3.重庆医科大学附属第二医院泌尿肾病中心，重庆
  4.陆军军医大学（第三军医大学）第二附属医院胸外科，重庆
- 作者简介：
  
  ZHENG Hong，E-mail：ziecoe@tmmu.edu.cn
- 基金信息：
  
  the Top Young Medical Talent Project of Chongqing(YXQN202489);the National Natural Science Foundation for Young Scholars of China(82504240)
- DOI：10.16016/j.2097-0927.202511006
  CLC： R739.5;R730.7;R394-3
- Received：03 November 2025，
  
  Revised：2026-01-24，
  
  Published：30 March 2026
- 稿件说明：
移动端阅览
谢罗樱子，何志强，张伟楠，等.基于帕金森病相关基因构建黑色素瘤预后模型：七基因特征及其对预后评估与疗效预测的效能验证［J］.陆军军医大学学报，2026，48（06）：793-808.DOI：10.16016/j.2097-0927.202511006.

XIE L Y Z，HE Z Q，ZHANG W N，et al.Construction of a melanoma prognostic model based on Parkinson’s disease-related genes： a 7-gene signature and validation of its efficacy for prognostic assessment and treatment response prediction［J］.J Army Med Univ，2026，48（06）：793-808.DOI：10.16016/j.2097-0927.202511006.
谢罗樱子，何志强，张伟楠，等.基于帕金森病相关基因构建黑色素瘤预后模型：七基因特征及其对预后评估与疗效预测的效能验证［J］.陆军军医大学学报，2026，48（06）：793-808.DOI：10.16016/j.2097-0927.202511006. DOI：

XIE L Y Z，HE Z Q，ZHANG W N，et al.Construction of a melanoma prognostic model based on Parkinson’s disease-related genes： a 7-gene signature and validation of its efficacy for prognostic assessment and treatment response prediction［J］.J Army Med Univ，2026，48（06）：793-808.DOI：10.16016/j.2097-0927.202511006. DOI：

摘要

目的

探索帕金森病家族基因的遗传特征，构建帕金森家族基因特征模型，以指导皮肤黑色素瘤（cutaneous melanoma， CMM）患者的预后和治疗。

方法

①通过分析TCGA和GTEx数据库，比较帕金森病相关家族基因在黑色素瘤组织和正常组织中的表达差异，通过GEPIA2数据库分析相关基因在不同TNM分期和Clark分级中的表达差异。②基于TCGA数据库，利用预后森林图、LASSO回归模型和风险因素分析，建立包含7个关键基因的特征模型。对该模型进行内部和外部验证，并对模型基因进行GO富集和KEGG通路富集分析。③基于TIMER数据库，分析预后模型中基因突变对肿瘤微环境（tumor microenvironment， TME）免疫细胞浸润的影响，利用ssGSEA热图分析高风险组与低风险组间免疫细胞亚群的差异。通过PD-1免疫检查点抑制剂治疗反应分析、外部数据集验证以及不同药物的半数抑制浓度（IC50）值，研究高风险组与低风险组在药物治疗效果上可能存在的差异。④体外培养PIG1（正常人黑色素细胞系）和A375（人黑色素瘤细胞系）细胞，通过Western Blot 和RT-qPCR检测该预后模型中基因在黑色素细胞和人黑色素瘤细胞中的蛋白表达及mRNA相对表达差异。

结果

①分析TCGA和GTEx数据库发现，除

PARK14

（

PLA2G6

）外，所有帕金森病家族基因在黑色素瘤组织和正常组织中的表达均存在显著差异。分析HPA数据库发现，与正常组织相比，帕金森家族基因在黑色素瘤样本中蛋白表达发生上调或下调。在GEPIA2数据库分析帕金森家族基因在不同TNM分期及Clark分级的表达，均发现部分基因表达存在差异。②基于TCGA数据库选择COX回归分析发现其中13个基因表达水平可预测黑色素瘤患者总生存期，对该数据 LASSO回归分析构建包含7个帕金森家族基因（

LRRK2、UCHL1、SNCA、VPS13C、SPR、EIF4G1、FBXO7

）的黑色素瘤预后模型。采用风险因子列联图计算所有患者预后风险评分，将患者分为高、低风险组（中位数=-0.145），结果显示高风险组生存率显著较差，而风险评分越低，生存时间越长。随后对该模型在TCGA队列中进行内部验证：3年和5年的列线图校准曲线显示，黑色素瘤患者预后的预测曲线与实际曲线非常接近。在GEO队列中进行外部验证：在GEO数据库中选择3个包含完整临床数据和基因表达矩阵的数据集（GSE19234、GSE22153 和 GSE53118），基于预后模型的风险评分在ROC曲线上的敏感性和特异性优于TNM分期。GO富集和KEGG通路富集发现7个基因主要富集在肿瘤发生和肿瘤免疫的通路中，如磷酸化、程序性细胞死亡、细胞周期等，这表明预后模型与肿瘤发生和免疫相关。③GSVA评分发现，这7个基因在与肿瘤发生和抗肿瘤免疫相关的通路中富集，能抑制肿瘤生长的CD8

T细胞、细胞毒性细胞、Th1细胞则在低风险组中浸润更多。同时，低风险组患者肿瘤细胞对尼洛替尼（nilotinib）、BIBW2992、博来霉素（bleomycin）、硼替佐米（bortezomib）、达沙替尼（dasatinib）和MK-2206的IC50值较低（

0.05）。④通过RT-qPCR和Western blot分析，以在转录和翻译水平上验证A375（人恶性黑色素瘤细胞系）和PIG1细胞（正常皮肤黑色素细胞系）中这七个基因的相对表达差异。实验结果显示，

LRRK2

和

UCHL1

基因在A375细胞和PIG1细胞中几乎不表达，同时，与正常黑色素细胞相比，黑色素瘤细胞中

SNCA、VPS13C、SPR、EIF4G1

和

FBXO7

基因的mRNA表达显著上调（

0.05）；SPR、eIF4G1和FBXO7的蛋白表达显著上调（

0.05）。

结论

本研究建立的七基因特征预后模型能够有效评估CMM患者预后及其药物治疗疗效。

Abstract

Objective

To explore the genetic characteristics of familial Parkinson’s disease (PD)-related genes and construct a model of the gene signatures to guide the prognosis and treatment of patients with skin cutaneous melanoma (CMM).

Methods

① By analyzing the TCGA and GTEx databases

the expression differences of familial PD-related genes were compared between melanoma and normal tissues. The GEPIA2 database was used to analyze the expression differences of related genes across different TNM stages and Clark levels. ② Based on the TCGA database

a signature model containing 7 key genes was established using prognostic forest plot

LASSO regression model

and risk factor analysis. Internal and external validation of this model was performed

and GO enrichment and KEGG pathway enrichment analyses were conducted for these genes. ③ Based on the TIMER database

the impacts of gene mutations in the prognostic model on immune cell infiltration in the tumor microenvironment (TME) were analyzed. ssGSEA heatmaps were employed to analyze differences in immune cell subsets between high-risk and low-risk groups. Through PD-1 immune checkpoint inhibitor treatment response analysis

external dataset validation

and half-maximal inhibitory concentration (IC50) values of different drugs

possible differences in drug treatment efficacy between high-risk and low-risk groups were investigated. ④ Western blotting and RT-qPCR were utilized to detect the protein expression and relative mRNA expression differences of the genes in this prognostic model between normal human melanocyte cell line PIG1 and human melanoma cell line A375.

Results

① Analysis of the TCGA and GTEx databases revealed that

except for

PARK14

(

PLA2G6

)

all familial PD-related genes showed significant expression differences between melanoma tissues and normal tissues. Analysis of the HPA database found that the protein expression of familial PD-related genes was either up-regulated or down-regulated in melanoma samples compared to normal tissues. Analysis of the GEPIA2 database for the expression of these genes across different TNM stages and Clark levels also found differential expression in some genes. ② COX regression analysis based on TCGA database identified that expression levels of 13 genes could predict overall survival in melanoma patients. LASSO regression analysis of this data constructed a melanoma prognostic model containin

g 7 familial PD-related genes (

LRRK2

UCHL1

SNCA

VPS13C

SPR

EIF4G1

FBXO7

). The prognostic risk scores for all patients were calculated using a risk factor contingency plot

and then the patients were divided into high-risk and low-risk groups based on the median risk score (-0.145). The results showed that the high-risk group had a significantly worse survival rate

while the low-risk group exhibited a longer survival time. Internal validation of the model in the TCGA cohort: the calibration curves for the 3-year and 5-year nomograms showed that the predicted curves for melanoma patient prognosis were very close to the actual curves. External validation in GEO cohorts: 3 datasets from the GEO database containing complete clinical data and gene expression matrices (GSE19234

GSE22153

and GSE53118) were selected. Based on the prognostic model's risk score

the sensitivity and specificity on the ROC curve were better than TNM staging. GO and KEGG pathway enrichment analyses found that the 7 genes were mainly enriched in pathways related to tumorigenesis and tumor immunity

such as phosphorylation

programmed cell death

and cell cycle

suggesting the prognostic model is associated with tumorigenesis and immunity. ③ GSVA scoring revealed that these 7 genes were enriched in pathways related to tumorigenesis and anti-tumor immunity. CD8

T cells

cytotoxic cells

and Th1 cells

which can inhibit tumor growth

were more infiltrated in the low-risk group. Meanwhile

the IC

values for nilotinib

BIBW2992

bleomycin

bortezomib

dasatinib

and MK-2206 were lower in tumor cells from low-risk group patients (

0.05). ④ RT-qPCR and Western blotting analysis were used to verify the relative expression differences of these 7 genes at the transcriptional and translational levels in A375 and PIG1 cells. The experimental results showed that

LRRK2

UCHL1

were almost not expressed in both A375 and PIG1 cells. Meanwhile

compared to normal melanocytes

the mRNA expression of

SNCA

VPS13C

SPR

eIF4G1

and

FBXO7

genes was significantly up-regulated in melanoma cells (

0.05). The protein expression of SPR

EIF4G1

and FBXO7 was significantly upregulated (

0.05).

Conclusion

The 7-gene signature prognostic model developed in this study can effectively assess prognosis and treatment efficacy in CMM patients.

关键词

Keywords

references

CALNE D . A definition of Parkinson’s disease ［J］. Parkinsonism Relat Disord ， 2005 ， 11 （ Suppl 1 ）： S39 - S40 . DOI： 10.1016/j.parkreldis.2005.01.008 http://dx.doi.org/10.1016/j.parkreldis.2005.01.008 .

BLOEM B R ， OKUN M S ， KLEIN C . Parkinson’s disease ［J］. Lancet ， 2021 ， 397 （ 10291 ）： 2284 - 2303 . DOI： 10.1016/S0140-6736（21）00218-X http://dx.doi.org/10.1016/S0140-6736（21）00218-X .

WAKABAYASHI K ， TANJI K ， ODAGIRI S ， et al . The Lewy body in Parkinson’s disease and related neurodegenerative disorders ［J］. Mol Neurobiol ， 2013 ， 47 （ 2 ）： 495 - 508 . DOI： 10.1007/s12035-012-8280-y http://dx.doi.org/10.1007/s12035-012-8280-y .

KALIA L V ， LANG A E . Parkinson’s disease ［J］. Lancet ， 2015 ， 386 （ 9996 ）： 896 - 912 . DOI： 10.1016/S0140-6736（14）61393-3 http://dx.doi.org/10.1016/S0140-6736（14）61393-3 .

OWENS B . Melanoma ［J］. Nature ， 2014 ， 515 （ 7527 ）： S109 . DOI： 10.1038/515s109a http://dx.doi.org/10.1038/515s109a .

TAN B B J ， MAI A S ， LEE S J ， et al . Tamoxifen in breast cancer and risk of Parkinson’s disease： a meta-analysis ［J］. Am J Med Sci ， 2024 ， 367 （ 4 ）： 251 - 258 . DOI： 10.1016/j.amjms.2023.11.003 http://dx.doi.org/10.1016/j.amjms.2023.11.003 .

RUGBJERG K ， FRIIS S ， LASSEN C F ， et al . Malignant melanoma， breast cancer and other cancers in patients with Parkinson’s disease ［J］. Int J Cancer ， 2012 ， 131 （ 8 ）： 1904 - 1911 . DOI： 10.1002/ijc.27443 http://dx.doi.org/10.1002/ijc.27443 .

PAN T H ， LI X Q ， JANKOVIC J . The association between Parkinson’s disease and melanoma ［J］. Int J Cancer ， 2011 ， 128 （ 10 ）： 2251 - 2260 . DOI： 10.1002/ijc.25912 http://dx.doi.org/10.1002/ijc.25912 .

OLSEN J H ， TANGERUD K ， WERMUTH L ， et al . Treatment with levodopa and risk for malignant melanoma ［J］. Mov Disord ， 2007 ， 22 （ 9 ）： 1252 - 1257 . DOI： 10.1002/mds.21397 http://dx.doi.org/10.1002/mds.21397 .

DUBE U ， IBANEZ L ， BUDDE J P ， et al . Overlapping genetic architecture between Parkinson disease and melanoma ［J］. Acta Neuropathol ， 2020 ， 139 （ 2 ）： 347 - 364 . DOI： 10.1007/s00401-019-02110-z http://dx.doi.org/10.1007/s00401-019-02110-z .

CHANG J Y ， YI H S ， KIM H W ， et al . Dysregulation of mitophagy in carcinogenesis and tumor progression ［J］. Biochim Biophys Acta BBA Bioenerg ， 2017 ， 1858 （ 8 ）： 633 - 640 . DOI： 10.1016/j.bbabio.2016.12.008 http://dx.doi.org/10.1016/j.bbabio.2016.12.008 .

SOUTAR M P M ， KEMPTHORNE L ， ANNUARIO E ， et al . FBS/BSA media concentration determines CCCP’s ability to depolarize mitochondria and activate PINK1-PRKN mitophagy ［J］. Autophagy ， 2019 ， 15 （ 11 ）： 2002 - 2011 . DOI： 10.1080/15548627.2019.1603549 http://dx.doi.org/10.1080/15548627.2019.1603549 .

NIU K F ， FANG H B ， CHEN Z X ， et al . USP33 deubiquitinates PRKN/parkin and antagonizes its role in mitophagy ［J］. Autophagy ， 2020 ， 16 （ 4 ）： 724 - 734 . DOI： 10.1080/15548627.2019.1656957 http://dx.doi.org/10.1080/15548627.2019.1656957 .

LIN M T ， BEAL M F . Mitochondrial dysfunction and oxidative stress in neurodegenerative diseases ［J］. Nature ， 2006 ， 443 （ 7113 ）： 787 - 795 . DOI： 10.1038/nature05292 http://dx.doi.org/10.1038/nature05292 .

DELGADO-CAMPRUBI M ， ESTERAS N ， SOUTAR M P ， et al . Deficiency of Parkinson’s disease-related gene Fbxo7 is associated with impaired mitochondrial metabolism by PARP activation ［J］. Cell Death Differ ， 2017 ， 24 （ 1 ）： 120 - 131 . DOI： 10.1038/cdd.2016.104 http://dx.doi.org/10.1038/cdd.2016.104 .

YAO J Y ， WANG J B ， XU Y ， et al . CDK9 inhibition blocks the initiation of PINK1-PRKN-mediated mitophagy by regulating the SIRT1-FOXO 3 - BNIP3 axis and enhances the therapeutic effects involving mitochondrial dysfunction in hepatocellular carcinoma ［J］. Autophagy ， 2022 ， 18 （ 8 ）： 1879 - 1897 . DOI： 10.1080/15548627.2021.2007027 http://dx.doi.org/10.1080/15548627.2021.2007027 .

AGNIHOTRI S ， GOLBOURN B ， HUANG X ， et al . PINK1 is a negative regulator of growth and the Warburg effect in glioblastoma ［J］. Cancer Res ， 2016 ， 76 （ 16 ）： 4708 - 4719 . DOI： 10.1158/0008-5472.CAN-15-3079 http://dx.doi.org/10.1158/0008-5472.CAN-15-3079 .

STOTT S R ， RANDLE S J ， AL RAWI S ， et al . Loss of FBXO7 results in a Parkinson’s-like dopaminergic degeneration via an RPL23-MDM2-TP53 pathway ［J］. J Pathol ， 2019 ， 249 （ 2 ）： 241 - 254 . DOI： 10.1002/path.5312 http://dx.doi.org/10.1002/path.5312 .

HAM S J ， LEE D ， XU W J ， et al . Loss of UCHL1 rescues the defects related to Parkinson’s disease by suppressing glycolysis ［J］. Sci Adv ， 2021 ， 7 （ 28 ）： eabg4574 . DOI： 10.1126/sciadv.abg4574 http://dx.doi.org/10.1126/sciadv.abg4574 .

LIU S J ， GONZÁLEZ-PRIETO R ， ZHANG M D ， et al . Deubiquitinase activity profiling identifies UCHL1 as a candidate oncoprotein that promotes TGFβ-induced breast cancer metastasis ［J］. Clin Cancer Res ， 2020 ， 26 （ 6 ）： 1460 - 1473 . DOI： 10.1158/1078-0432.ccr-19-1373 http://dx.doi.org/10.1158/1078-0432.ccr-19-1373 .

CHEN W T ， YANG H B ， KE T W ， et al . Serum DJ-1 is a biomarker of colorectal cancer and DJ-1 activates mitophagy to promote colorectal cancer progression ［J］. Cancers ， 2021 ， 13 （ 16 ）： 4151 . DOI： 10.3390/cancers13164151 http://dx.doi.org/10.3390/cancers13164151 .

CHEN Q ， ZHONG L ， ZHOU C ， et al . Knockdown of Parkinson’s disease-related gene ATP13A2 reduces tumorigenesis via blocking autophagic flux in colon cancer ［J］. Cell Biosci ， 2020 ， 10 （ 1 ）： 144 . DOI： 10.1186/s13578-020-00506-z http://dx.doi.org/10.1186/s13578-020-00506-z .

WANG Y C ， WU Y S ， HUNG C Y ， et al . USP24 induces IL-6 in tumor-associated microenvironment by stabilizing p300 and β-TrCP and promotes cancer malignancy ［J］. Nat Commun ， 2018 ， 9 （ 1 ）： 3996 . DOI： 10.1038/s41467-018-06178-1 http://dx.doi.org/10.1038/s41467-018-06178-1 .

LI X Y ， CAO Y ， YU X M ， et al . A novel autophagy-related genes prognostic risk model and validation of autophagy-related oncogene VPS35 in breast cancer ［J］. Cancer Cell Int ， 2021 ， 21 （ 1 ）： 265 . DOI： 10.1186/s12935-021-01970-4 http://dx.doi.org/10.1186/s12935-021-01970-4 .

JAISWAL P K ， KOUL S ， SHANMUGAM P S T ， et al . Eukaryotic Translation Initiation Factor 4 Gamma 1 （eIF4G1） is upregulated during Prostate cancer progression and modulates cell growth and metastasis ［J］. Sci Rep ， 2018 ， 8 ： 7459 . DOI： 10.1038/s41598-018-25798-7 http://dx.doi.org/10.1038/s41598-018-25798-7 .

ZACKSENHAUS E ， SHRESTHA M ， LIU J C ， et al . Mitochondrial OXPHOS induced by RB1 deficiency in breast cancer： implications for anabolic metabolism， stemness， and metastasis ［J］. Trends Cancer ， 2017 ， 3 （ 11 ）： 768 - 779 . DOI： 10.1016/j.trecan.2017.09.002 http://dx.doi.org/10.1016/j.trecan.2017.09.002 .

MATTIONI A ， CASTAGNOLI L ， SANTONICO E . RNF11 at the crossroads of protein ubiquitination ［J］. Biomolecules ， 2020 ， 10 （ 11 ）： 1538 . DOI： 10.3390/biom10111538 http://dx.doi.org/10.3390/biom10111538 .

LI Y Q ， LU X ， ZHANG J ， et al . Significance of Parkinson family genes in the prognosis and treatment outcome prediction for lung adenocarcinoma ［J］. Front Mol Biosci ， 2021 ， 8 ： 735263 . DOI： 10.3389/fmolb.2021.735263 http://dx.doi.org/10.3389/fmolb.2021.735263 .

GARBE C ， AMARAL T ， PERIS K ， et al . European consensus-based interdisciplinary guideline for melanoma. Part 2： Treatment - Update 2022 ［J］. Eur J Cancer ， 2022 ， 170 ： 256 - 284 . DOI： 10.1016/j.ejca.2022.04.018 http://dx.doi.org/10.1016/j.ejca.2022.04.018 .

LEONG S P L ， GERSHENWALD J E ， SOONG S J ， et al . Cutaneous melanoma： a model to study cancer metastasis ［J］. J Surg Oncol ， 2011 ， 103 （ 6 ）： 538 - 549 . DOI： 10.1002/jso.21816 http://dx.doi.org/10.1002/jso.21816 .

SCHADENDORF D ， FISHER D E ， GARBE C ， et al . Melanoma ［J］. Nat Rev Dis Primers ， 2015 ， 1 ： 15003 . DOI： 10.1038/nrdp.2015.3 http://dx.doi.org/10.1038/nrdp.2015.3 .

ROZEMAN E A ， DEKKER T J A ， HAANEN J B A G ， et al . Advanced melanoma： current treatment options， biomarkers， and future perspectives ［J］. Am J Clin Dermatol ， 2018 ， 19 （ 3 ）： 303 - 317 . DOI： 10.1007/s40257-017-0325-6 http://dx.doi.org/10.1007/s40257-017-0325-6 .

CONIC R Z ， CABRERA C I ， KHORANA A A ， et al . Determination of the impact of melanoma surgical timing on survival using the National Cancer Database ［J］. J Am Acad Dermatol ， 2018 ， 78 （ 1 ）： 40 - 46.e7 . DOI： 10.1016/j.jaad.2017.08.039 http://dx.doi.org/10.1016/j.jaad.2017.08.039 .

LEONG Y Q ， LEE S W H ， NG K Y . Cancer risk in Parkinson disease： an updated systematic review and meta-analysis ［J］. Eur J Neurol ， 2021 ， 28 （ 12 ）： 4219 - 4237 . DOI： 10.1111/ene.15069 http://dx.doi.org/10.1111/ene.15069 .

ZHOU L X ， ZHENG H ， TIAN Y ， et al . SNCA inhibits epithelial–mesenchymal transition and correlates to favorable prognosis of breast cancer ［J］. Carcinogenesis ， 2022 ， 43 （ 11 ）： 1071 - 1082 . DOI： 10.1093/carcin/bgac078 http://dx.doi.org/10.1093/carcin/bgac078 .

LI L L ， TAO Q ， JIN H C ， et al . The tumor suppressor UCHL1 forms a complex with p53/MDM2/ARF to promote p53 signaling and is frequently silenced in nasopharyngeal carcinoma ［J］. Clin Cancer Res ， 2010 ， 16 （ 11 ）： 2949 - 2958 . DOI： 10.1158/1078-0432.CCR-09-3178 http://dx.doi.org/10.1158/1078-0432.CCR-09-3178 .

GOTO Y ， ZENG L H ， YEOM C J ， et al . UCHL1 provides diagnostic and antimetastatic strategies due to its deubiquitinating effect on HIF-1α ［J］. Nat Commun ， 2015 ， 6 ： 6153 . DOI： 10.1038/ncomms7153 http://dx.doi.org/10.1038/ncomms7153 .

LU Y ， YU S S ， WANG G X ， et al . Elevation of EIF4G1 promotes non-small cell lung cancer progression by activating mTOR signalling ［J］. J Cell Mol Med ， 2021 ， 25 （ 6 ）： 2994 - 3005 . DOI： 10.1111/jcmm.16340 http://dx.doi.org/10.1111/jcmm.16340 .

YAN J ， ZHAO W H ， YU W ， et al . LRRK2 correlates with macrophage infiltration in pan-cancer ［J］. Genomics ， 2022 ， 114 （ 1 ）： 316 - 327 . DOI： 10.1016/j.ygeno.2021.11.037 http://dx.doi.org/10.1016/j.ygeno.2021.11.037 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

NK细胞特异性敲除UTX以性别差异方式调控黑色素瘤肺转移

Effects of PRKCH on CD8⁺T cell function and melanoma immunotherapy

Anti-melanoma SK-Mel-103 cells effect of a natural small molecule, HEP-14

Effects of melanoma cell exosomes on fibroblast invasion and expression of MMP2 and MMP9 in vitro

Effect of tyrosine kinase inhibitor dasatinib on melanoma cells

Related Author

WANG Hongchen

HUANG He

XIE Jiaxin

WU Yu

ZHOU Simin

LIAO Xinyi

GUAN Xiao

HUANG Pei

Related Institution

Department of Oncology, The First Affiliated Hospital of Wenzhou Medical University

Department of Oncology, Second Affiliated Hospital, Army Medical University (Third Military Medical University)

Center of Growth, Metabolism and Aging, College of Life Sciences, Sichuan University

Glorious Med Clinical Laboratory Co., Ltd

⁰