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Abstract：
UNASSIGNED: Advanced breast cancer commonly metastasises to bone; however, the molecular mechanisms underlying the affinity for breast cancer cells to bone remains unclear. Thus, we developed nomograms based on a competing endogenous RNA (ceRNA) network and analysed tumour-infiltrating immune cells to elucidate the molecular pathways that may predict prognosis in patients with breast cancer.
UNASSIGNED: We obtained the RNA expression profile of 1091 primary breast cancer samples included in The Cancer Genome Atlas database, 58 of which were from patients with bone metastasis. We analysed the differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established based on the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and a Cox proportional risk model.
UNASSIGNED: We found significant differences in long non-coding RNAs (lncRNAs), 18 microRNAs (miRNAs), and 20 messenger RNAs (mRNAs) between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes GJB3, CAMMV, PTPRZ1, and FBN3 were significantly differentially expressed by Kaplan-Meier analysis. We also observed significant differences in the abundance of plasma cell and follicular helper T cell populations between the two groups. In addition, the proportion of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis showed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that DLX6-AS1, Wnt6, and GABBR2 expression may be associated with bone metastasis in breast cancer.
UNASSIGNED: We developed a bioinformatic tool for exploring the molecular mechanisms of bone metastasis in patients with breast cancer and identified factors that may predict the occurrence of bone metastasis.
UNASSIGNED: We obtained the RNA expression profile of 1091 primary breast cancer samples included in The Cancer Genome Atlas database, 58 of which were from patients with bone metastasis. We analysed the differential RNA expression patterns between breast cancer with and without bone metastasis and developed a ceRNA network. Cibersort was employed to differentiate between immune cell types based on tumour transcripts. Nomograms were then established based on the ceRNA network and immune cell analysis. The value of prognostic factors was evaluated by Kaplan-Meier survival analysis and a Cox proportional risk model.
UNASSIGNED: We found significant differences in long non-coding RNAs (lncRNAs), 18 microRNAs (miRNAs), and 20 messenger RNAs (mRNAs) between breast cancer with and without bone metastasis, which were used to construct a ceRNA network. We found that the protein-coding genes GJB3, CAMMV, PTPRZ1, and FBN3 were significantly differentially expressed by Kaplan-Meier analysis. We also observed significant differences in the abundance of plasma cell and follicular helper T cell populations between the two groups. In addition, the proportion of mast cells, gamma delta T cells, and plasma cells differed depending on disease location and stage. Our analysis showed that a high proportion of follicular helper T cells and a low proportion of eosinophils promoted survival and that DLX6-AS1, Wnt6, and GABBR2 expression may be associated with bone metastasis in breast cancer.
UNASSIGNED: We developed a bioinformatic tool for exploring the molecular mechanisms of bone metastasis in patients with breast cancer and identified factors that may predict the occurrence of bone metastasis.
摘要：
■晚期乳腺癌通常转移到骨骼；然而，乳腺癌细胞与骨亲和力的分子机制尚不清楚.因此，我们建立了基于竞争性内源性RNA(ceRNA)网络的列线图,并分析了肿瘤浸润免疫细胞,以阐明可能预测乳腺癌患者预后的分子通路.
■我们获得了癌症基因组图谱数据库中包含的1091个原发性乳腺癌样本的RNA表达谱，其中58例来自骨转移患者。我们分析了有和没有骨转移的乳腺癌之间的差异RNA表达模式，并开发了一个ceRNA网络。Cibersort用于基于肿瘤转录物区分免疫细胞类型。然后基于ceRNA网络和免疫细胞分析建立列线图。通过Kaplan-Meier生存分析和Cox比例风险模型评估预后因素的价值。
■我们发现长链非编码RNA（lncRNAs）存在显著差异，18microRNAs(miRNAs),和20个信使RNA（mRNA）之间有和没有骨转移的乳腺癌，用于构建ceRNA网络。我们发现蛋白质编码基因GJB3，CAMMV，通过Kaplan-Meier分析,PTPRZ1和FBN3显著差异表达。我们还观察到两组之间浆细胞和滤泡辅助性T细胞群的丰度存在显着差异。此外,肥大细胞的比例，γδT细胞，和浆细胞根据疾病的位置和阶段而有所不同。我们的分析表明，高比例的滤泡辅助性T细胞和低比例的嗜酸性粒细胞促进了生存，DLX6-AS1，Wnt6和GABBR2的表达可能与乳腺癌的骨转移有关。
■我们开发了一种生物信息学工具，用于探索乳腺癌患者骨转移的分子机制，并确定了可能预测骨转移发生的因素。
■我们获得了癌症基因组图谱数据库中包含的1091个原发性乳腺癌样本的RNA表达谱，其中58例来自骨转移患者。我们分析了有和没有骨转移的乳腺癌之间的差异RNA表达模式，并开发了一个ceRNA网络。Cibersort用于基于肿瘤转录物区分免疫细胞类型。然后基于ceRNA网络和免疫细胞分析建立列线图。通过Kaplan-Meier生存分析和Cox比例风险模型评估预后因素的价值。
■我们发现长链非编码RNA（lncRNAs）存在显著差异，18microRNAs(miRNAs),和20个信使RNA（mRNA）之间有和没有骨转移的乳腺癌，用于构建ceRNA网络。我们发现蛋白质编码基因GJB3，CAMMV，通过Kaplan-Meier分析,PTPRZ1和FBN3显著差异表达。我们还观察到两组之间浆细胞和滤泡辅助性T细胞群的丰度存在显着差异。此外,肥大细胞的比例，γδT细胞，和浆细胞根据疾病的位置和阶段而有所不同。我们的分析表明，高比例的滤泡辅助性T细胞和低比例的嗜酸性粒细胞促进了生存，DLX6-AS1，Wnt6和GABBR2的表达可能与乳腺癌的骨转移有关。
■我们开发了一种生物信息学工具，用于探索乳腺癌患者骨转移的分子机制，并确定了可能预测骨转移发生的因素。
