Zainulabadeen Aamir, Yao Philip, Zare Habil
Department of Computer Science, Texas State University, San Marcos, Texas, United States of America.
Department of Computer Science, Princeton University, Princeton, New Jersey, United States of America.
PLoS One. 2017 Jan 23;12(1):e0170025. doi: 10.1371/journal.pone.0170025. eCollection 2017.
Because the prognosis of melanoma is challenging and inaccurate when using current clinical approaches, clinicians are seeking more accurate molecular markers to improve risk models. Accordingly, we performed a survival analysis on 404 samples from The Cancer Genome Atlas (TCGA) cohort of skin cutaneous melanoma. Using our recently developed gene network model, we identified biological signatures that confidently predict the prognosis of melanoma (p-value < 10-5). Our model predicted 38 cases as low-risk and 54 cases as high-risk. The probability of surviving at least 5 years was 64% for low-risk and 14% for high-risk cases. In particular, we found that the overexpression of specific genes in the mitotic cell cycle pathway and the underexpression of specific genes in the interferon pathway are both associated with poor prognosis. We show that our predictive model assesses the risk more accurately than the traditional Clark staging method. Therefore, our model can help clinicians design treatment strategies more effectively. Furthermore, our findings shed light on the biology of melanoma and its prognosis. This is the first in vivo study that demonstrates the association between the interferon pathway and the prognosis of melanoma.
由于使用当前临床方法时黑色素瘤的预后具有挑战性且不准确，临床医生正在寻找更准确的分子标记来改进风险模型。因此，我们对来自癌症基因组图谱（TCGA）皮肤黑色素瘤队列的404个样本进行了生存分析。使用我们最近开发的基因网络模型，我们确定了能够可靠预测黑色素瘤预后的生物学特征（p值<10-5）。我们的模型将38例预测为低风险，54例预测为高风险。低风险病例至少存活5年的概率为64%，高风险病例为14%。特别是，我们发现有丝分裂细胞周期途径中特定基因的过表达和干扰素途径中特定基因的低表达均与预后不良有关。我们表明，我们的预测模型比传统的克拉克分期方法更准确地评估风险。因此，我们的模型可以帮助临床医生更有效地设计治疗策略。此外，我们的研究结果揭示了黑色素瘤的生物学特性及其预后。这是第一项证明干扰素途径与黑色素瘤预后之间关联的体内研究。
