Li Junyi, Wu Sujuan, Yang Liguang, Li Yi-Xue, Liu Bing-Ya, Li Yuan-Yuan
Key Lab of Computational Biology，CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
Shanghai Center for Bioinformation Technology, 1278 Keyuan Road, Shanghai 201203, China.
Comb Chem High Throughput Screen. 2017;20(2):174-181. doi: 10.2174/1386207320666170117121543.
Gastric cancer is one of the most common cancers and has very high incidence and mortality rate in Asian population. To tackle the problems of infiltration and heterogeneity, more accurate biomarkers for diagnosis and prognosis as well as effective targets for treatment are needed to achieve better outcomes of gastric cancer patients. Recently, methods and algorithms for analyzing high-throughput sequencing data have greatly facilitated the molecular profiling of gastric cancer. Nevertheless, prognostic biomarkers for gastric cancer that can be potentially applied in clinic are still lacking.
In this study, we performed differential regulatory analysis based on gene co-expression network for four different cohorts of Asian gastric cancer samples and their clinical data.
We identified a 36-gene prognostic signature specific for gastric cancer, particularly for Asian population. We further analyzed differential regulatory patterns related to these featured genes, such as C1S, and suggested hypotheses for investigating their roles in gastric cancer pathogenesis.
Findings from present study suggest a 36-gene signature which is based on differential regulatory analysis and can predict the prognosis of gastric cancer. Our research explores molecular mechanism of gastric cancer at transcriptional regulation level and provides potential drug targets. This integrated biomarker searching scheme is extendable to other cancer study for not only prognostic prediction, but also pathogenesis.
胃癌是最常见的癌症之一，在亚洲人群中发病率和死亡率极高。为解决浸润和异质性问题，需要更准确的诊断和预后生物标志物以及有效的治疗靶点，以改善胃癌患者的治疗效果。近年来，分析高通量测序数据的方法和算法极大地促进了胃癌的分子特征分析。然而，仍缺乏可潜在应用于临床的胃癌预后生物标志物。
在本研究中，我们基于基因共表达网络对四组不同的亚洲胃癌样本及其临床数据进行了差异调控分析。
我们鉴定出一个针对胃癌的36基因预后特征，尤其适用于亚洲人群。我们进一步分析了与这些特征基因（如C1S）相关的差异调控模式，并提出了研究它们在胃癌发病机制中作用的假设。
本研究结果表明，基于差异调控分析的36基因特征可预测胃癌预后。我们的研究在转录调控水平上探索了胃癌的分子机制，并提供了潜在的药物靶点。这种综合生物标志物搜索方案不仅可扩展到其他癌症研究用于预后预测，还可用于发病机制研究。
