Comprehensive analysis and contrast analysis of circulating protein in blood from cervical squamous cell carcinoma

Nowadays, using computers to perform data analysis is inevitable. In the meantime, programming languages for performing computational operations have been developed, so that they have more facilities every day. Meanwhile, computational programming languages such as R very popular.In this study, relying on the knowledge of artificial intelligence and machine learning, we try to use the metastatic uterine cancer dataset number GSE۲۰۸۰۸۹ on the reliable website WWW.NCBI.NLM.NIH.GOV. In this way, after executing the commands and packages related to this data platform in the R software environment, the protein sequence related to the uterine tumor is identified, and then its modules and Proteins IDs corresponding to the protein sequence are obtained by the relevant programs and packages. comes and the protein network is drawn with the WGCNA command, and the protein biomarker panel is obtained. Then the proteins obtained from the protein network based on artificial intelligence are compared with the proteins obtained from gel electrophoresis (the practical part of the work) and thus we can identify the specific proteins of the uterine metastatic tumor tissue and finally with Gene Ontology studies. We examine the biological processes and molecular agents of metastasis.In this study, we found a common proteome and biomarker panel for cervical squamous cell carcinoma tissues, which is very valuable for tracking metastatic pathways. It is possible to find protein IDs that are completely specific for cervical tumor, and It is possible to detect specific proteins from the panel in the blood of metastatic uterine tumor patients, moreover It is possible to finally reach a prognosis in the process of metastasis and prevent the increase in the staging of cervical cancer with the new methods of artificial intelligence.

کلیدواژه ها:

Cervical cancer ، R software ، protein-protein interaction network ، gene ontology