Human Cytogenetics Laboratory, Department of Human Genetics, Guru Nanak Dev University, Amritsar, Punjab, India.
Department of Surgery, Sri Guru Ram Das Institute of Medical Sciences and Research, Vallah, Amritsar, Punjab, India.
BMC Med Genomics. 2020 Nov 9;13(1):168. doi: 10.1186/s12920-020-00811-z.
Complex genomic changes that arise in tumors are a consequence of chromosomal instability. In tumor cells genomic aberrations disrupt core signaling pathways involving various genes, thus delineating of signaling pathways can help understand the pathogenesis of cancer. The bioinformatics tools can further help in identifying networks of interactions between the genes to get a greater biological context of all genes affected by chromosomal instability.
Karyotypic analyses was done in 150 clinically confirmed breast cancer patients and 150 age and gender matched healthy controls after 72 h Peripheral lymphocyte culturing and GTG-banding. Reactome database from Cytoscape software version 3.7.1 was used to perform in-silico analysis (functional interaction and gene enrichment).
Frequency of chromosomal aberrations (structural and numerical) was found to be significantly higher in patients as compared to controls. The genes harbored by chromosomal regions showing increased aberration frequency in patients were further analyzed in-silico. Pathway analysis on a set of genes that were not linked together revealed that genes HDAC3, NCOA1, NLRC4, COL1A1, RARA, WWTR1, and BRCA1 were enriched in the RNA Polymerase II Transcription pathway which is involved in recruitment, initiation, elongation and dissociation during transcription.
The current study employs the information inferred from chromosomal instability analysis in a non-target tissue for determining the genes and the pathways associated with breast cancer. These results can be further extrapolated by performing either mutation analysis in the genes/pathways deduced or expression analysis which can pinpoint the relevant functional impact of chromosomal instability.
肿瘤中出现的复杂基因组变化是染色体不稳定性的结果。在肿瘤细胞中，基因组异常会破坏涉及各种基因的核心信号通路，因此信号通路的描绘可以帮助理解癌症的发病机制。生物信息学工具还可以帮助识别基因之间的相互作用网络，从而获得受染色体不稳定性影响的所有基因的更大生物学背景。
对 150 例经临床证实的乳腺癌患者和 150 例年龄和性别匹配的健康对照者进行外周血淋巴细胞培养和 GTG 带型分析，培养时间为 72 小时。使用 Cytoscape 软件版本 3.7.1 的 Reactome 数据库进行计算机分析（功能相互作用和基因富集）。
与对照组相比，患者的染色体异常（结构和数量）频率明显更高。对患者中染色体区域显示增加的畸变频率的基因进行进一步的计算机分析。对一组未相互连接的基因进行途径分析表明，基因 HDAC3、NCOA1、NLRC4、COL1A1、RARA、WWTR1 和 BRCA1 在 RNA 聚合酶 II 转录途径中富集，该途径参与转录过程中的募集、起始、延伸和解离。
本研究利用非靶向组织中染色体不稳定性分析得出的信息来确定与乳腺癌相关的基因和途径。这些结果可以通过对推导的基因/途径进行突变分析或表达分析来进一步推断，这可以指出染色体不稳定性的相关功能影响。
