Winslow Sofia, Lindquist Kajsa Ericson, Edsjö Anders, Larsson Christer
Department of Laboratory Medicine, Lund University Cancer Center, Translational Cancer Research, Lund University, Lund, Sweden.
Institute of Biochemistry I, Faculty of Medicine, Goethe-University Frankfurt, Frankfurt, Germany.
BMC Cancer. 2016 Nov 4;16(1):841. doi: 10.1186/s12885-016-2864-2.
There are several indications that the composition of the tumor stroma can contribute to the malignancy of a tumor. Here we utilized expression data sets to identify metagenes that may serve as surrogate marker for the extent of matrix production and vascularization of a tumor and to characterize prognostic molecular components of the stroma.
TCGA data sets from six cancer forms, two breast cancer microarray sets and one mRNA data set of xenografted tumors were downloaded. Using the mean correlation as distance measure compact clusters with genes representing extracellular matrix production (ECM metagene) and vascularization (endothelial metagene) were defined. Explorative Cox modeling was used to identify prognostic stromal gene sets.
Clustering of stromal genes in six cancer data sets resulted in metagenes, each containing three genes, representing matrix production and vascularization. The ECM metagene was associated with poor prognosis in renal clear cell carcinoma and in lung adenocarcinoma but not in other cancers investigated. Explorative Cox modeling using gene pairs identified gene sets that in multivariate models were prognostic in breast cancer. This was validated in two microarray sets. Two notable genes are TCF4 and P4HA3 which were included in the sets associated with positive and negative prognosis, respectively. Data from laser-microdissected tumors, a xenografted tumor data set and from correlation analyses demonstrate the stroma specificity of the genes.
It is possible to construct ECM and endothelial metagenes common for several cancer forms. The molecular composition of matrix-producing cells, rather than the extent of matrix production seem to be important for breast cancer prognosis.
有多项迹象表明肿瘤基质的组成可能促成肿瘤的恶性程度。在此，我们利用表达数据集来识别可能作为肿瘤基质产生和血管生成程度替代标志物的元基因，并对基质的预后分子成分进行表征。
下载了来自六种癌症类型的TCGA数据集、两个乳腺癌微阵列集和一个异种移植肿瘤的mRNA数据集。以平均相关性作为距离度量，定义了代表细胞外基质产生（ECM元基因）和血管生成（内皮元基因）的基因紧密簇。使用探索性Cox模型来识别预后性基质基因集。
六个癌症数据集中基质基因的聚类产生了元基因，每个元基因包含三个基因，代表基质产生和血管生成。ECM元基因与肾透明细胞癌和肺腺癌的不良预后相关，但在其他所研究的癌症中并非如此。使用基因对的探索性Cox模型识别出在多变量模型中对乳腺癌具有预后意义的基因集。这在两个微阵列集中得到了验证。两个值得注意的基因是TCF4和P4HA3，它们分别包含在与阳性和阴性预后相关的基因集中。来自激光显微切割肿瘤、异种移植肿瘤数据集和相关性分析的数据证明了这些基因的基质特异性。
有可能构建几种癌症类型共有的ECM和内皮元基因。对于乳腺癌预后而言，产生基质的细胞的分子组成而非基质产生的程度似乎更为重要。
