mRNA expression insights: Unraveling the relationship between COPD and lung cancer

IF 3.2 4区医学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY

Journal of Gene Medicine Pub Date : 2024-08-25 DOI:10.1002/jgm.3728

Zhan Gu, Jijia Sun, Lixin Wang

{"title":"mRNA expression insights: Unraveling the relationship between COPD and lung cancer","authors":"Zhan Gu, Jijia Sun, Lixin Wang","doi":"10.1002/jgm.3728","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to.</p>\n </section>\n \n <section>\n \n <h3> Method</h3>\n \n <p>Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups.</p>\n </section>\n \n <section>\n \n <h3> Result</h3>\n \n <p>After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log<sub>2</sub> fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3 × 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × −0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.</p>\n </section>\n </div>","PeriodicalId":56122,"journal":{"name":"Journal of Gene Medicine","volume":null,"pages":null},"PeriodicalIF":3.2000,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Gene Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/jgm.3728","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Background

Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to.

Method

Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups.

Result

After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log₂ fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3 × 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × −0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion.

Conclusion

In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.

查看原文本刊更多论文

mRNA 表达见解：揭示慢性阻塞性肺病与肺癌之间的关系

背景：肺癌是全球流行的一种癌症。最近的研究表明，肺癌与慢性阻塞性肺病（COPD）之间可能存在联系。我们的研究目的是分析这两种情况下的 mRNA 表达模式，重点是它们的生物学功能及其相关途径：方法：慢性阻塞性肺病 mRNA 表达数据来自 NCBI-GEO 数据库，肺癌 mRNA 信息来自癌症基因组图谱数据库。为了研究肺癌患者 COPD 相关评分的关联性，我们使用了 ssGSEA 算法进行单样本基因组富集分析。通过基因本体和京都基因与基因组百科全书的富集分析，研究了可能的途径。利用 Cox 和最小绝对收缩和选择算子（LASSO）回归分析建立了风险模型。此外，还进行了GSEA分析，以研究不同风险组之间的重要通路：结果：在确定了 17 个与慢性阻塞性肺病相关的差异表达基因后，我们发现这些基因符合假发现率 2 折变化> 0.585 的标准。通过使用 ssGSEA 算法，我们可以根据慢性阻塞性肺病的状况将肺癌患者分为两个不同的组别。因此，专门为这些患者开发了一个七基因风险模型。风险分值通过以下公式确定：风险分值 = AC022784.1 × 0.0423737993775888 + CRISP3 × 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × -0.309489953363417 + ITGA6 × 0.01813978449589。与慢性阻塞性肺病相关的风险模型显示，与肺癌样本中发现的不同免疫细胞，包括 M0/M1/M2 型巨噬细胞、造血干细胞、肥大细胞、NK T 细胞和调节性 T 细胞有显著联系。在肺癌样本中，关键基因的过度表达会增强细胞增殖和侵袭潜力。在肺癌样本中，观察到 ZNF506 表达的增加增强了细胞增殖和侵袭能力：总之，本研究有效地探讨了慢性阻塞性肺病与肺癌之间的潜在关联。基于七个慢性阻塞性肺病相关基因的预后模型在肺癌样本中显示出强大的预测潜力。我们的分析为肺癌患者提供了全面的见解。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Gene Medicine 医学-生物工程与应用微生物

CiteScore

6.40

自引率

0.00%

发文量

审稿时长

6-12 weeks

期刊介绍： The aims and scope of The Journal of Gene Medicine include cutting-edge science of gene transfer and its applications in gene and cell therapy, genome editing with precision nucleases, epigenetic modifications of host genome by small molecules, siRNA, microRNA and other noncoding RNAs as therapeutic gene-modulating agents or targets, biomarkers for precision medicine, and gene-based prognostic/diagnostic studies. Key areas of interest are the design of novel synthetic and viral vectors, novel therapeutic nucleic acids such as mRNA, modified microRNAs and siRNAs, antagomirs, aptamers, antisense and exon-skipping agents, refined genome editing tools using nucleic acid /protein combinations, physically or biologically targeted delivery and gene modulation, ex vivo or in vivo pharmacological studies including animal models, and human clinical trials. Papers presenting research into the mechanisms underlying transfer and action of gene medicines, the application of the new technologies for stem cell modification or nucleic acid based vaccines, the identification of new genetic or epigenetic variations as biomarkers to direct precision medicine, and the preclinical/clinical development of gene/expression signatures indicative of diagnosis or predictive of prognosis are also encouraged.