Department of Computer Science and Engineering, Heritage Institute of Technology, Kolkata, India.
Department of Computer Science and Engineering, Techno International New Town, Kolkata, India.
Protein J. 2022 Dec;41(6):591-595. doi: 10.1007/s10930-022-10081-6. Epub 2022 Oct 11.
Microarray technology has been successfully used in many biology studies to solve the protein-protein interaction (PPI) prediction computationally. For normal tissue, the cell regulation process begins with transcription and ends with the translation process. However, when cell regulation activity goes wrong, cancer occurs. Microarray data can precisely give high accuracy expression levels at normal and cancer-affected cells, which can be useful for the identification of disease-related genes. First, the differentially expressed genes (DEGs) are extracted from the cancer microarray dataset in order to identify the genes that are up-regulated and down-regulated during cancer progression in the human body. Then, proteins corresponding to these genes are collected from NCBI, and then the STRING web server is used to build the PPI network of these proteins. Interestingly, up-regulated proteins have always a higher number of PPIs compared to down-regulated proteins, although, in most of the datasets, the majority of these DEGs are down-regulated. We hope this study will help to build a relevant model to analyze the process of cancer progression in the human body.
微阵列技术已成功应用于许多生物学研究中，用于计算蛋白质-蛋白质相互作用（PPI）的预测。对于正常组织，细胞调节过程从转录开始，以翻译过程结束。然而，当细胞调节活动出错时，癌症就会发生。微阵列数据可以精确地给出正常细胞和受癌症影响的细胞的高准确度表达水平，这对于识别与疾病相关的基因非常有用。首先，从癌症微阵列数据集中提取差异表达基因（DEGs），以鉴定在人体癌症进展过程中上调和下调的基因。然后，从 NCBI 收集对应于这些基因的蛋白质，然后使用 STRING 网络服务器构建这些蛋白质的 PPI 网络。有趣的是，与下调蛋白相比，上调蛋白的 PPI 数量总是更高，尽管在大多数数据集，这些 DEGs 中的大多数都是下调的。我们希望这项研究将有助于建立一个相关的模型来分析人体癌症进展的过程。
