上海交通大学学报（医学版）

上海交通大学学报(医学版) ›› 2020, Vol. 40 ›› Issue (12): 1598-1606.doi: 10.3969/j.issn.1674-8115.2020.12.005

• 论著·基础研究 • 上一篇    下一篇

转录组分析鉴定肺腺癌潜在的生物标志物

张伟然1, 2，林雪峰3，李 鑫2，张 浩2，王 猛2，孙 伟2，韩兴鹏2，孙大强1, 4   

  1. 1. 天津医科大学研究生院，天津 300070；2. 天津市胸科医院胸外科，天津 300222；3. 天津医学高等专科学校护理系，天津300222；4. 天津市南开医院胸外科，天津 300100
  • 出版日期:2020-12-28 发布日期:2021-02-02
  • 通讯作者: 孙大强，电子信箱：sdqmd@163.com。
  • 作者简介:张伟然（1986—），男，博士生；电子信箱：wrzhang1986@126.com。
  • 基金资助:
    天津市津南区科技计划项目（201805006）；天津市科学技术委员会科技计划项目（17YFZCSY00850）。

Transcriptional identification of potential biomarkers of lung adenocarcinoma

ZHANG Wei-ran1, 2, LIN Xue-feng3, LI Xin2, ZHANG Hao2, WANG Meng2, SUN Wei2, HAN Xing-peng2, SUN Da-qiang1, 4   

  1. 1.Graduate School, Tianjin Medical University, Tianjin 300070, China; 2.Department of Thoracic Surgery, Tianjin Chest Hospital, Tianjin 300222, China; 3.Department of Nursing, Tianjin Medical College, Tianjin 300222, China; 4.Department of Thoracic Surgery, Tianjin Hospital of ITCWM, Nankai Hospital, Tianjin 300100, China
  • Online:2020-12-28 Published:2021-02-02
  • Supported by:
    Science and Technology Project of Tianjin Jinnan District (201805006); Key Science and Technology Support Project of Tianjin Science and Technology Commission (17YFZCSY00850).

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摘要： 目的·通过转录组数据分析，寻找相关的分子标志物，用于肺腺癌的诊断和治疗。方法·通过差异表达分析，对癌组织相对于正常组织的差异表达基因（differentially expressed genes，DEGs）、差异表达miRNAs（differentially expressed miRNAs，DEMs）和差异表达circRNAs（differentially expressed circRNAs，DECs）进行筛选。对DEGs进行功能富集和通路分析，并对DEMs进行靶向预测。构建DEGs和DEMs的调控网络，并选取部分节点作为潜在的生物标志物。通过癌症基因图谱（the Cancer Genome Atlas，TCGA）数据库和实时荧光定量PCR（qRT-PCR）对获得的生物标志物进行验证，并分析其表达水平与总生存期和肿瘤分期的相关性。结果·在3个基因表达谱的3组DEGs中有61个重叠DEGs，富集在32个基因本体（gene ontology，GO）和10个通路中。共鉴定出24个DEMs，并筛选出612个miRNA-DEGs靶向关系对。在circRNAs表达谱中得到92个DECs。ADRA1A、hsa-miR-141-5p和hsa-miR-191-3p是调控网络中重要的节点。TCGA和qRT-PCR验证结果与分析结果一致，hsa-miR-191-3p与肿瘤分期间存在相关性。结论·ADRA1A、hsa-miR-141-5p、hsa-miR-191-3p、SFTPC、ITLN2和SLC6A4可能是肺腺癌潜在的生物标志物，hsa-miR-191-3p可能与肿瘤进展有关。

关键词: 肺腺癌, 转录组分析, 生物信息学分析, 生物标志物

Abstract:

Objective · To identify some related molecular markers for the diagnosis and treatment of lung adenocarcinoma by transcriptome analysis. Methods · The differentially expressed analyses were performed to identify the differentially expressed genes (DEGs), the differentially expressed miRNAs (DEMs), and the differentially expressed circRNAs (DECs). Functional and pathway enrichment analyses were conducted for DEGs, and the targets prediction for DEMs. Regulated network of the DEGs and DEMs was constructed, and some candidates were selected. The biomarkers obtained were verified by the Cancer Genome Atlas (TCGA) database and the qRT-PCR, and the correlation between their expression levels and overall survival and tumor stage were analyzed. Results · Sixty-one overlaps were contained in the 3 sets of DEGs in 3 gene expression profiles, which were enriched in 32 gene ontology (GO) terms and 10 pathways. Twenty-four DEMs were identified, and 612 miRNA-target pairs were screened out that the target genes were DEGs. In the circRNA microarray, 92 DECs were obtained. ADRA1A, hsa-miR-141-5p and hsa-miR-191-3p were important nodes in the network. TCGA and qRT-PCR results were consistent with the microarray analysis results, in addition, hsa-miR-191-3p was significantly correlated with tumor stage. Conclusion · ADRA1A, hsa-miR-141-5p, hsa-miR-191-3p, SFTPC, ITLN2 and SLC6A4 might be potential biomarkers of lung adenocarcinoma, and hsa-miR-191-3p might be associated with tumor progression.

Key words: lung adenocarcinoma, transcriptome analysis, bioinformatics analysis, potential biomarker

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