Wu Kejia, Wang Wen, Ye Yaqi, Huang Junhong, Zhou Yinghui, Zhang Yue, Zhang Xuewenjun, Wu Wenyu
Department of Dermatology, Huashan Hospital Affiliated of Fudan University.
Department of Internal Medicine, Florida Hospital, Orlando, USA.
Melanoma Res. 2019 Apr;29(2):126-133. doi: 10.1097/CMR.0000000000000525.
Cutaneous melanoma is an aggressive form of skin cancer that causes death worldwide. Although much has been learned about the molecular basis of melanoma genesis and progression, there is also increasing appreciation for the continuing discovery of melanoma genes to improve the genetic understanding of this malignancy. In the present study, melanoma candidate genes were identified by analysis of the common network from cancer type-specific RNA-Seq co-expression data and protein-protein interaction profiles. Then, an integrated network containing the known melanoma-related genes represented as seed genes and the putative genes represented as linker genes was generated using the subnetwork extraction algorithm. According to the network topology property of the putative genes, we selected seven key genes (CREB1, XPO1, SP3, TNFRSF1B, CD40LG, UBR1, and ZNF484) as candidate genes of melanoma. Subsequent analysis showed that six of these genes are melanoma-associated genes and one (ZNF484) is a cancer-associated gene on the basis of the existing literature. A signature comprising these seven key genes was developed and an overall survival analysis of 461 cutaneous melanoma cases was carried out. This seven-gene signature can accurately determine the risk profile for cutaneous melanoma tumors (log-rank P=3.27E-05) and be validated on an independent clinical cohort (log-rank P=0.028). The presented seven genes might serve as candidates for studying the molecular mechanisms and help improve the prognostic risk assessment, which have clinical implications for melanoma patients.
皮肤黑色素瘤是一种侵袭性皮肤癌,在全球范围内导致死亡。尽管我们对黑色素瘤发生和发展的分子基础已经有了很多了解,但人们也越来越认识到,不断发现黑色素瘤基因对于增进对这种恶性肿瘤的遗传学理解具有重要意义。在本研究中,通过分析癌症类型特异性RNA测序共表达数据和蛋白质-蛋白质相互作用图谱的共同网络,鉴定出黑色素瘤候选基因。然后,使用子网提取算法生成一个包含以已知黑色素瘤相关基因为种子基因、以推定基因为连接基因的整合网络。根据推定基因的网络拓扑特性,我们选择了七个关键基因(CREB1、XPO1、SP3、TNFRSF1B、CD40LG、UBR1和ZNF484)作为黑色素瘤的候选基因。后续分析表明,根据现有文献,这些基因中有六个是黑色素瘤相关基因,一个(ZNF484)是癌症相关基因。开发了一个包含这七个关键基因的特征,并对461例皮肤黑色素瘤病例进行了总生存分析。这个七基因特征可以准确确定皮肤黑色素瘤肿瘤的风险特征(对数秩检验P=3.27E-05),并在一个独立的临床队列中得到验证(对数秩检验P=0.028)。所提出的这七个基因可能作为研究分子机制的候选基因,并有助于改善预后风险评估,这对黑色素瘤患者具有临床意义。