Department of Biotechnology, Vignan's Foundation for Science, Technology and Research (Deemed to Be University), Guntur, Andhra Pradesh, India.
Department of Biotechnology, Institute of Life Sciences, Bhubaneswar, Odisha, India.
Cancer Rep (Hoboken). 2024 Sep;7(9):e70003. doi: 10.1002/cnr2.70003.
The bone is among the most frequently chosen sites for the metastatic spread of breast cancer. The prediction of biomarkers for BM (Bone Metastasis) and PDB (Paget's disease of bone) initiated from breast cancer could be critically important in categorizing individuals with a higher risk and providing targeted treatment for PDB and BM.
This research aims to investigate the common key candidate biomarkers that contribute to BM-BCa (Bone metastasis of breast cancer) and PDB by employing network decomposition and functional enrichment studies.
This research analyzed high-throughput transcriptome sequencing (RNA-Seq). For this work, the dataset (GSE121677) was downloaded from GEO (Gene Expression Omnibus), and DEGs were identified using Galaxy and R script 4.3. Using STRING (Search Tool for the Retrieval of Interacting Genes), high-throughput research created a protein-protein interaction network (PPIN). The BM-PDB-interactome was created using Cytoscape 3.9.1 and PDB biomarkers, with the top 3% DEGs from BM-BCa. Functional Enrichment Analysis (Funrich 3.1.3) and DAVID 6.8 performed functional and gene set enrichment analysis (GSEA) of putatively essential biomarkers. TCGA (The Cancer Genome Atlas) validated the discovered genes. Based on our research, we identified 1262 DEGs; among these DEGs, 431 genes were upregulated, and 831 genes were downregulated. During the third growth of the interactome, 20 more genes were pinned to the BM-PDB interactome. RAC2, PIAS1, EP300, EIF2S1, and LRP6 are among the additional 25% of genes identified to interact with the BM-PDB interactome. To corroborate the findings of the research presented, additional functional and gene set enrichment analyses have been performed.
Of the five reported genes (RAC2, PIAS1, EP300, EIF2S1, and LRP6), RAC2 was identified to function as the common key potential biomarker in the BM-PDB interactome analysis and validated by TCGA in the study presented.
骨骼是乳腺癌转移最常发生的部位之一。预测乳腺癌骨转移（BM）和佩吉特病骨（PDB）的生物标志物对于将高风险个体分类并为 PDB 和 BM 提供靶向治疗可能至关重要。
本研究旨在通过网络分解和功能富集研究，探讨导致乳腺癌骨转移（BM-BCa）和 PDB 的常见关键候选生物标志物。
本研究分析了高通量转录组测序（RNA-Seq）。为此，从 GEO（基因表达综合数据库）下载了数据集（GSE121677），并使用 Galaxy 和 R 脚本 4.3 识别差异表达基因（DEGs）。使用 STRING（检索基因交互作用的工具），高通量研究创建了一个蛋白质-蛋白质相互作用网络（PPIN）。使用 Cytoscape 3.9.1 和 PDB 生物标志物创建了 BM-PDB 相互作用组，其中包括来自 BM-BCa 的前 3%的 DEGs。功能富集分析（Funrich 3.1.3）和 DAVID 6.8 对假定的关键生物标志物进行了功能和基因集富集分析（GSEA）。TCGA（癌症基因组图谱）验证了发现的基因。根据我们的研究，我们确定了 1262 个 DEGs；其中 431 个基因上调，831 个基因下调。在相互作用组的第三次生长过程中，又有 20 个基因被固定在 BM-PDB 相互作用组中。RAC2、PIAS1、EP300、EIF2S1 和 LRP6 是与 BM-PDB 相互作用组相互作用的另外 25%的基因之一。为了证实本研究报告的结果，还进行了额外的功能和基因集富集分析。
在报告的五个基因（RAC2、PIAS1、EP300、EIF2S1 和 LRP6）中，RAC2 被确定为 BM-PDB 相互作用组分析中的常见关键潜在生物标志物，并在本研究中通过 TCGA 验证。
