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

IF 1.4 4区生物学 Q4 GENETICS & HEREDITY Genetics research Pub Date : 2023-01-03 eCollection Date: 2023-01-01 DOI:10.1155/2023/6782732

Liyan Yu, Xuemei Liang, Jianwei Wang, Guangxiang Ding, Jinhai Tang, Juan Xue, Xin He, Jingxuan Ge, Xianzhang Jin, Zhiyi Yang, Xianwei Li, Hehuan Yao, Hongtao Yin, Wu Liu, Shengchen Yin, Bing Sun, Junxiu Sheng

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Abstract

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.

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通过综合生物信息学分析鉴定非小细胞肺癌的关键生物标记物和候选分子。

背景：非小细胞肺癌（NSCLC非小细胞肺癌（NSCLC）是肺癌中发病率最高的恶性肿瘤，其分子机制尚不清楚。在这项研究中，我们发现了与 NSCLC 发病机制相关的新型生物标记物，旨在通过生物信息学分析为 NSCLC 提供新的诊断和治疗方法：方法：从基因表达总库数据库中获得GSE118370和GSE10072微阵列数据集。方法：从基因表达总库数据库中获取 GSE118370 和 GSE10072 微阵列数据集，确定肺腺癌样本与正常样本之间的差异表达基因（DEGs）。利用生物信息学工具，构建了蛋白质-蛋白质相互作用（PPI）网络，分析了模块，并进行了富集分析。GEPIA数据库验证了14个中心基因的表达和预后价值，UALCAN、cBioPortal、String和Cytoscape以及Timer工具评估了中心基因与肺腺癌生存的相关性：结果：我们发现三个基因（PIK3R1、SPP1和PECAM1）与肺腺癌患者的OS有明显的相关性。研究发现，肺腺癌表现出 SPP1 的高表达，这与预后不良有关，而 PECAM1 和 PIK3R1 的低表达与预后不良有关（P < 0.05）。我们还发现，SPP1的表达与miR-146a-5p有关，而miR-146a-5p的高表达与预后良好有关（P＜0.05）。相反，PIK3R1上游的miR-21-5p表达量越低，癌症患者的存活率越高（P < 0.05）。最后，我们发现免疫检查点基因CD274（PD-L1）和PDCD1LG2（PD-1）也与肺腺癌中的SPP1有关：结果表明，SPP1是癌症启动子（癌基因），而PECAM1和PIK3R1是抑癌基因。这些基因参与了肺腺癌的生物活性调控，为改善肺腺癌的检测和免疫治疗靶点提供了依据。

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来源期刊

Genetics research 生物-遗传学

自引率

6.70%

发文量

审稿时长

>12 weeks

期刊介绍： Genetics Research is a key forum for original research on all aspects of human and animal genetics, reporting key findings on genomes, genes, mutations and molecular interactions, extending out to developmental, evolutionary, and population genetics as well as ethical, legal and social aspects. Our aim is to lead to a better understanding of genetic processes in health and disease. The journal focuses on the use of new technologies, such as next generation sequencing together with bioinformatics analysis, to produce increasingly detailed views of how genes function in tissues and how these genes perform, individually or collectively, in normal development and disease aetiology. The journal publishes original work, review articles, short papers, computational studies, and novel methods and techniques in research covering humans and well-established genetic organisms. Key subject areas include medical genetics, genomics, human evolutionary and population genetics, bioinformatics, genetics of complex traits, molecular and developmental genetics, Evo-Devo, quantitative and statistical genetics, behavioural genetics and environmental genetics. The breadth and quality of research make the journal an invaluable resource for medical geneticists, molecular biologists, bioinformaticians and researchers involved in genetic basis of diseases, evolutionary and developmental studies.