A network approach to quantifying radiotherapy effect on cancer: Radiosensitive gene group centrality
Yu-Xiang Yao, Zhi-Tong Bing, Liang Huang, Zi-Gang Huang, Ying-Cheng, Lai

TL;DR
This paper introduces RSGGC, a network-based indicator that quantifies tumor radiosensitivity by analyzing gene group centrality, aiding in prognosis and treatment planning for cancer radiotherapy.
Contribution
The study proposes a novel gene group centrality measure, RSGGC, that integrates gene correlation networks with clinical data to assess radiosensitivity.
Findings
Higher RSGGC scores correlate with better radiotherapy outcomes.
RSGGC effectively predicts patient survival and cell line response.
The method offers a new clinical tool for classifying radiosensitive patients.
Abstract
Radiotherapy plays a vital role in cancer treatment, for which accurate prognosis is important for guiding sequential treatment and improving the curative effect for patients. An issue of great significance in radiotherapy is to assess tumor radiosensitivity for devising the optimal treatment strategy. Previous studies focused on gene expression in cells closely associated with radiosensitivity, but factors such as the response of a cancer patient to irradiation and the patient survival time are largely ignored. For clinical cancer treatment, a specific pre-treatment indicator taking into account cancer cell type and patient radiosensitivity is of great value but it has been missing. Here, we propose an effective indicator for radiosensitivity: radiosensitive gene group centrality (RSGGC), which characterizes the importance of the group of genes that are radiosensitive in the whole gene…
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Taxonomy
TopicsBioinformatics and Genomic Networks · Gene Regulatory Network Analysis · Computational Drug Discovery Methods
