Identification of Vital Genes for NSCLC Integrating Mutual Information and Synergy

Xiaobo Yang, Zhilong Mi (), Qingcai He, Binghui Guo and Zhiming Zheng
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Xiaobo Yang: School of Mathematical Sciences, Beihang University, Beijing 100191, China
Zhilong Mi: Zhongguancun Laboratory, Beijing 100094, China
Qingcai He: School of Mathematical Sciences, Beihang University, Beijing 100191, China
Binghui Guo: Zhongguancun Laboratory, Beijing 100094, China
Zhiming Zheng: Zhongguancun Laboratory, Beijing 100094, China

Mathematics, 2023, vol. 11, issue 6, 1-15

Abstract: Lung cancer, amongst the fast growing malignant tumors, has become the leading cause of cancer death, which deserves attention. From a prevention and treatment perspective, advances in screening, diagnosis, and treatment have driven a reduction in non-small-cell lung cancer (NSCLC) incidence and improved patient outcomes. It is of benefit that the identification of key genetic markers contributes to the understanding of disease initiation and progression. In this work, information theoretical measures are proposed to determine the collaboration between genes and specific NSCLC samples. Top mutual information observes genes of high sample classification accuracy, such as STX11, S1PR1, TACC1, LRKK2, and SRPK1. In particular, diversity exists in different gender, histology, and smoking situations. Furthermore, leading synergy detects a high-accuracy combination of two ordinary individual genes, bringing a significant gain in accuracy. We note a strong synergistic effect of genes between COL1A2 and DCN, DCN and MMP2, and PDS5B and B3GNT8. Apart from that, RHOG is revealed to have quite a few functions in coordination with other genes. The results provide evidence for gene-targeted therapy as well as combined diagnosis in the context of NSCLC. Our approach can also be extended to find synergistic biomarkers associated with different diseases.

Keywords: NSCLC; gene interaction; mutual information; synergy; classification accuracy (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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