Identification of Key Biomarkers and Candidate Molecules in Non-Small-Cell Lung Cancer by Integrated Bioinformatics Analysis

被引:8
|
作者
Yu, Liyan [1 ]
Liang, Xuemei [2 ]
Wang, Jianwei [3 ]
Ding, Guangxiang [3 ]
Tang, Jinhai [3 ]
Xue, Juan [3 ]
He, Xin [2 ]
Ge, Jingxuan [2 ]
Jin, Xianzhang [2 ]
Yang, Zhiyi [2 ]
Li, Xianwei [2 ]
Yao, Hehuan [2 ]
Yin, Hongtao [2 ]
Liu, Wu [2 ]
Yin, Shengchen [2 ]
Sun, Bing [2 ]
Sheng, Junxiu [3 ]
机构
[1] Dalian Med Univ, Affiliated Hosp 1, Dept Resp, Dalian 116044, Liaoning, Peoples R China
[2] Dalian Med Univ, Affiliated Hosp 1, Dept Thorac Surg, Dalian 116044, Peoples R China
[3] Dalian Med Univ, Affiliated Hosp 1, Dept Radiat Oncol, Dalian 116044, Peoples R China
关键词
PD-L1; EXPRESSION; GENE-EXPRESSION; IMMUNE ESCAPE; UP-REGULATION; WEB SERVER; PATHWAY; COMBINATION; P85-ALPHA; MIR-146A; SURVIVAL;
D O I
10.1155/2023/6782732
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Background. Non-small cell lung cancer (NSCLC) is the most prevalent malignant tumor of the lung cancer, for which the molecular mechanisms remain unknown. In this study, we identified novel biomarkers associated with the pathogenesis of NSCLC aiming to provide new diagnostic and therapeutic approaches for NSCLC by bioinformatics analysis. Methods. From the Gene Expression Omnibus database, GSE118370 and GSE10072 microarray datasets were obtained. Identifying the differentially expressed genes (DEGs) between lung adenocarcinoma and normal samples was done. By using bioinformatics tools, a protein-protein interaction (PPI) network was constructed, modules were analyzed, and enrichment analyses were performed. The expression and prognostic values of 14 hub genes were validated by the GEPIA database, and the correlation between hub genes and survival in lung adenocarcinoma was assessed by UALCAN, cBioPortal, String and Cytoscape, and Timer tools. Results. We found three genes (PIK3R1, SPP1, and PECAM1) that have a clear correlation with OS in the lung adenocarcinoma patient. It has been found that lung adenocarcinoma exhibits high expression of SPP1 and that this has been associated with poor prognosis, while low expression of PECAM1 and PIK3R1 is associated with poor prognosis P < 0.05. We also found that the expression of SPP1 was associated with miR-146a-5p, while the high expression of miR-146a-5p was related to good prognosis P < 0.05. On the contrary, the lower miR-21-5p on upstream of PIK3R1 is associated with a higher surviving rate in cancer patients P < 0.05. Finally, we found that the immune checkpoint genes CD274(PD-L1) and PDCD1LG2(PD-1) were also related to SPP1 in lung adenocarcinoma. Conclusions. The results indicated that SPP1 is a cancer promoter (oncogene), while PECAM1 and PIK3R1 are cancer suppressor genes. These genes take part in the regulation of biological activities in lung adenocarcinoma, which provides a basis for improving detection and immunotherapeutic targets for lung adenocarcinoma.
引用
收藏
页数:19
相关论文
共 50 条
  • [1] Identification of key candidate tumor biomarkers in non-small-cell lung cancer by in silico analysis
    Chen, Weiping
    Zhu, Song
    Zhang, Yifei
    Xiao, Jinghua
    Tian, Dongbo
    ONCOLOGY LETTERS, 2020, 19 (01) : 1008 - 1016
  • [2] Identification of novel biomarkers and candidate small molecule drugs in non-small-cell lung cancer by integrated microarray analysis
    Wu, Qiong
    Zhang, Bo
    Sun, Yidan
    Xu, Ran
    Hu, Xinyi
    Ren, Shiqi
    Ma, Qianqian
    Chen, Chen
    Shu, Jian
    Qi, Fuwei
    He, Ting
    Wang, Wei
    Wang, Ziheng
    ONCOTARGETS AND THERAPY, 2019, 12 : 3545 - 3563
  • [3] Bioinformatics analysis reveals 6 key biomarkers associated with non-small-cell lung cancer
    Dai, Bai
    Ren, Li-qing
    Han, Xiao-yu
    Liu, Dong-jun
    JOURNAL OF INTERNATIONAL MEDICAL RESEARCH, 2020, 48 (03)
  • [4] Identification and validation of key genes with prognostic value in non-small-cell lung cancer via integrated bioinformatics analysis
    Wang, Li
    Qu, Jialin
    Liang, Yu
    Zhao, Deze
    Rehman, Faisal U. L.
    Qin, Kang
    Zhang, Xiaochun
    THORACIC CANCER, 2020, 11 (04) : 851 - 866
  • [5] Identification of Candidate Biomarkers Correlated With the Pathogenesis and Prognosis of Non-small Cell Lung Cancer via Integrated Bioinformatics Analysis
    Ni, Mengwei
    Liu, Xinkui
    Wu, Jiarui
    Zhang, Dan
    Tian, Jinhui
    Wang, Ting
    Liu, Shuyu
    Meng, Ziqi
    Wang, Kaihuan
    Duan, Xiaojiao
    Zhou, Wei
    Zhang, Xiaomeng
    FRONTIERS IN GENETICS, 2018, 9
  • [6] Identification of Prognostic Gene Biomarkers in Non-Small Cell Lung Cancer Progression by Integrated Bioinformatics Analysis
    Giannos, Panagiotis
    Kechagias, Konstantinos S.
    Gal, Annamaria
    BIOLOGY-BASEL, 2021, 10 (11):
  • [7] Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer
    Liu, Xingyuan
    Liu, Xuefeng
    Li, Jingyuan
    Ren, Fu
    MEDICAL SCIENCE MONITOR, 2019, 25 : 9280 - 9289
  • [8] Identification of key genes in non-small cell lung cancer by bioinformatics analysis
    Zhang, Li
    Peng, Rui
    Sun, Yan
    Wang, Jia
    Chong, Xinyu
    Zhang, Zheng
    PEERJ, 2019, 7
  • [9] Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis
    Gong, Ke
    Zhou, Huiling
    Liu, Haidan
    Xie, Ting
    Luo, Yong
    Guo, Hui
    Chen, Jinlan
    Tan, Zhiping
    Yang, Yifeng
    Xie, Li
    TECHNOLOGY IN CANCER RESEARCH & TREATMENT, 2021, 20
  • [10] Identification of candidate genes associated with the pathogenesis of small cell lung cancer via integrated bioinformatics analysis
    Liao, Yi
    Yin, Guofang
    Wang, Xue
    Zhong, Ping
    Fan, Xianming
    Huang, Chengliang
    ONCOLOGY LETTERS, 2019, 18 (04) : 3723 - 3733