Literature DB >> 33680914

Mutational Pattern in Multiple Pulmonary Nodules Are Associated With Early Stage Lung Adenocarcinoma.

Shao-Wei Dong1,2, Rong Li3, Zhiqiang Cheng4, Dong-Cheng Liu1, Jinquan Xia1, Jing Xu4, Shixuan Li5, Jian Wang5, Yongjian Yue6, Yingrui Fan3, Yundi Cao3, Lingyun Dai7, Jigang Wang7, Pan Zhao1, Xin Wang8, Zhangang Xiao9, Chen Qiu6, Guang-Suo Wang5, Chang Zou1,2.   

Abstract

The clinical significance of mutation in multiple pulmonary nodules is largely limited by single gene mutation-directed analysis and lack of validation of gene expression profiles. New analytic strategy is urgently needed for comprehensive understanding of genomic data in multiple pulmonary nodules. In this study, we performed whole exome sequencing in 16 multiple lung nodules and 5 adjacent normal tissues from 4 patients with multiple pulmonary nodules and decoded the mutation information from a perspective of cellular functions and signaling pathways. Mutated genes as well as mutation patterns shared in more than two lesions were identified and characterized. We found that the number of mutations or mutated genes and the extent of protein structural changes caused by different mutations is positively correlated with the degree of malignancy. Moreover, the mutated genes in the nodules are associated with the molecular functions or signaling pathways related to cell proliferation and survival. We showed a developing pattern of quantity (the number of mutations/mutated genes) and quality (the extent of protein structural changes) in multiple pulmonary nodules. The mutation and mutated genes in multiple pulmonary nodules are associated with cell proliferation and survival related signaling pathways. This study provides a new perspective for comprehension of genomic mutational data and might shed new light on deciphering molecular evolution of early stage lung adenocarcinoma.
Copyright © 2021 Dong, Li, Cheng, Liu, Xia, Xu, Li, Wang, Yue, Fan, Cao, Dai, Wang, Zhao, Wang, Xiao, Qiu, Wang and Zou.

Entities:  

Keywords:  functional analysis; mutation; pathway analysis; pulmonary nodules; whole-exome sequencing

Year:  2021        PMID: 33680914      PMCID: PMC7934775          DOI: 10.3389/fonc.2020.571521

Source DB:  PubMed          Journal:  Front Oncol        ISSN: 2234-943X            Impact factor:   6.244


  1 in total

1.  Deep Learning-Based Computed Tomography Imaging to Diagnose the Lung Nodule and Treatment Effect of Radiofrequency Ablation.

Authors:  Xixi Guo; Yuze Li; Chunjie Yang; Yanjiang Hu; Yun Zhou; Zhenhua Wang; Liguo Zhang; Hongjun Hu; Yuemin Wu
Journal:  J Healthc Eng       Date:  2021-10-20       Impact factor: 2.682

  1 in total

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