Xia Wang-Xiao, Yu Qin, Li Gong-Hua, Liu Yao-Wen, Xiao Fu-Hui, Yang Li-Qin, Rahman Zia Ur, Wang Hao-Tian, Kong Qing-Peng
State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China.
Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, China.
PeerJ. 2019 Mar 14;7:e6555. doi: 10.7717/peerj.6555. eCollection 2019.
Adrenocortical carcinoma (ACC) is a rare and aggressive malignant cancer in the adrenal cortex with poor prognosis. Though previous research has attempted to elucidate the progression of ACC, its molecular mechanism remains poorly understood.
Gene transcripts per million (TPM) data were downloaded from the UCSC Xena database, which included ACC (The Cancer Genome Atlas, = 77) and normal samples (Genotype Tissue Expression, = 128). We used weighted gene co-expression network analysis to identify gene connections. Overall survival (OS) was determined using the univariate Cox model. A protein-protein interaction (PPI) network was constructed by the search tool for the retrieval of interacting genes.
To determine the critical genes involved in ACC progression, we obtained 2,953 significantly differentially expressed genes and nine modules. Among them, the blue module demonstrated significant correlation with the "Stage" of ACC. Enrichment analysis revealed that genes in the blue module were mainly enriched in cell division, cell cycle, and DNA replication. Combined with the PPI and co-expression networks, we identified four hub genes (i.e., , , , and ) that were highly expressed in ACC and negatively correlated with OS. Thus, these identified genes may play important roles in the progression of ACC and serve as potential biomarkers for future diagnosis.
肾上腺皮质癌（ACC）是肾上腺皮质中一种罕见且侵袭性强的恶性肿瘤，预后较差。尽管先前的研究试图阐明ACC的进展，但其分子机制仍知之甚少。
从UCSC Xena数据库下载每百万基因转录本（TPM）数据，其中包括ACC样本（癌症基因组图谱，n = 77）和正常样本（基因型组织表达，n = 128）。我们使用加权基因共表达网络分析来识别基因关联。使用单变量Cox模型确定总生存期（OS）。通过检索相互作用基因的搜索工具构建蛋白质-蛋白质相互作用（PPI）网络。
为了确定参与ACC进展的关键基因，我们获得了2953个显著差异表达基因和9个模块。其中，蓝色模块与ACC的“分期”显示出显著相关性。富集分析表明，蓝色模块中的基因主要富集于细胞分裂、细胞周期和DNA复制。结合PPI和共表达网络，我们确定了四个在ACC中高表达且与OS呈负相关的枢纽基因（即 、 、 和 ）。因此，这些鉴定出的基因可能在ACC的进展中发挥重要作用，并可作为未来诊断的潜在生物标志物。
