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Construction of a New Immune-Related Competing Endogenous RNA Network with Prognostic Value in Lung Adenocarcinoma

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A Correction to this article was published on 27 June 2023

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Abstract

Tumor microenvironment has significant influence on the gene expression of tumor tissues and on the clinical outcomes in lung adenocarcinoma. Infiltrating immune and stromal cells not only perturb the tumor signal in molecular studies, but also play crucial roles in cancer biology. The competing endogenous RNAs (ceRNAs) are useful to explain the post-transcriptional layer regulated by gene translation and play an important role in the occurrence and progression of lung adenocarcinoma. Therefore, identifying novel molecular markers by constructing ceRNA associated with immune infiltration is of great significance to guide the treatment of lung adenocarcinoma in the future. According to the immune and stromal scores of lung adenocarcinoma samples in The Cancer Genome Atlas (TCGA) database calculated by the ESTIMATE algorithm, we identified differentially expressed lncRNAs, miRNAs and mRNAs associated with immune infiltration, including 60 dysregulated lncRNAs, 38 dysregulated mRNAs, and 29 dysregulated miRNAs. Based on the PPI network and Cox regression analysis, 5 mRNAs including CNR2, P2RY12, ZNF831, RSPO1, and F2 were identified to be related to immune infiltration and prognosis in lung adenocarcinoma, and their differential expression, prognosis and correlation with immune cells were verified. Next, through target binding prediction, pearson correlation analysis and expression analysis, a novel immune-related ceRNA network containing 6 lncRNAs, 4 miRNAs, and 3 mRNAs was finally constructed. The present study constructed a novel immune-associated lncRNA-miRNA-mRNA ceRNA network, which deepens our understanding on the molecular network mechanism of lung adenocarcinoma and provides potential prognostic markers and novel therapeutic targets for the patients with lung adenocarcinoma.

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Data Availability

The datasets generated and analyzed for this study can be found in The Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) database.

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References

  1. Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., & Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 68, 394–424.

    PubMed  Google Scholar 

  2. Denisenko, T. V., Budkevich, I. N., & Zhivotovsky, B. (2018). Cell death-based treatment of lung adenocarcinoma. Cell Death & Disease, 9, 117.

    Article  Google Scholar 

  3. Guo, L., Liu, S., Zhang, S., Chen, Q., Zhang, M., Quan, P., & Sun, X. (2017). Human papillomavirus infection as a prognostic marker for lung adenocarcinoma: a systematic review and meta-analysis. Oncotarget, 8, 34507–34515.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Calvayrac, O., Pradines, A., Pons, E., Mazières, J., & Guibert, N. (2017). Molecular biomarkers for lung adenocarcinoma. European Respiratory Journal. https://doi.org/10.1183/13993003.01734-2016

    Article  PubMed  Google Scholar 

  5. Gan, T. Q., Chen, W. J., Qin, H., Huang, S. N., Yang, L. H., Fang, Y. Y., Pan, L. J., Li, Z. Y., & Chen, G. (2017). Clinical value and prospective pathway signaling of MicroRNA-375 in lung adenocarcinoma: a study based on the cancer genome atlas (TCGA), gene expression omnibus (GEO) and bioinformatics analysis. Medical Science Monitor, 23, 2453–2464.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Ahn, M. J., Sun, J. M., Lee, S. H., Ahn, J. S., & Park, K. (2017). EGFR TKI combination with immunotherapy in non-small cell lung cancer. Expert Opinion on Drug Safety, 16, 465–469.

    Article  PubMed  CAS  Google Scholar 

  7. Oshima, Y., Tanimoto, T., Yuji, K., & Tojo, A. (2018). EGFR-TKI-Associated interstitial pneumonitis in nivolumab-treated patients with non-small cell lung cancer. JAMA oncology, 4, 1112–1115.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Zhang, M., Wang, Q., Ding, Y., Wang, G., Chu, Y., He, X., Wu, X., Shao, Y. W., & Lu, K. (2018). CUX1-ALK, a novel ALK rearrangement that responds to Crizotinib in non-small cell lung cancer. Journal of Thoracic Oncology, 13, 1792–1797.

    Article  PubMed  Google Scholar 

  9. Dou, Y., Zhu, Y., Ai, J., Chen, H., Liu, H., Borgia, J. A., Li, X., Yang, F., Jiang, B., Wang, J., et al. (2018). Plasma small ncRNA pair panels as novel biomarkers for early-stage lung adenocarcinoma screening. BMC Genomics, 19, 545.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Dong, H. X., Wang, R., Jin, X. Y., Zeng, J., & Pan, J. (2018). LncRNA DGCR5 promotes lung adenocarcinoma (LUAD) progression via inhibiting hsa-mir-22-3p. Journal of Cellular Physiology, 233, 4126–4136.

    Article  PubMed  CAS  Google Scholar 

  11. Flynt, A. S., & Lai, E. C. (2008). Biological principles of microRNA-mediated regulation: Shared themes amid diversity. Nature Reviews Genetics, 9, 831–842.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  12. Inamura, K. (2017). Diagnostic and therapeutic potential of MicroRNAs in lung cancer. Cancers (Basel), 9(12), 49.

    Article  PubMed  Google Scholar 

  13. Geisler, S., & Coller, J. (2013). RNA in unexpected places: Long non-coding RNA functions in diverse cellular contexts. Nature Reviews Molecular Cell Biology, 14, 699–712.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Yang, Z., Li, H., Wang, Z., Yang, Y., Niu, J., Liu, Y., Sun, Z., & Yin, C. (2018). Microarray expression profile of long non-coding RNAs in human lung adenocarcinoma. Thoracic cancer, 9, 1312–1322.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Yuan, S., Liu, Q., Hu, Z., Zhou, Z., Wang, G., Li, C., Xie, W., Meng, G., Xiang, Y., Wu, N., et al. (2018). Long non-coding RNA MUC5B-AS1 promotes metastasis through mutually regulating MUC5B expression in lung adenocarcinoma. Cell Death & Disease, 9, 450.

    Article  Google Scholar 

  16. Di, X., Jin, X., Li, R., Zhao, M., & Wang, K. (2019). CircRNAs and lung cancer: Biomarkers and master regulators. Life Sciences, 220, 177–185.

    Article  PubMed  CAS  Google Scholar 

  17. Karreth, F. A., & Pandolfi, P. P. (2013). ceRNA cross-talk in cancer: When ce-bling rivalries go awry. Cancer Discovery, 3, 1113–1121.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Wu, T., & Dai, Y. (2017). Tumor microenvironment and therapeutic response. Cancer Letters, 387, 61–68.

    Article  PubMed  CAS  Google Scholar 

  19. Quail, D. F., & Joyce, J. A. (2013). Microenvironmental regulation of tumor progression and metastasis. Nature Medicine, 19, 1423–1437.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  20. Hui, L., & Chen, Y. (2015). Tumor microenvironment: Sanctuary of the devil. Cancer Letters, 368, 7–13.

    Article  PubMed  CAS  Google Scholar 

  21. Liu, X., Wu, S., Yang, Y., Zhao, M., Zhu, G., & Hou, Z. (2017). The prognostic landscape of tumor-infiltrating immune cell and immunomodulators in lung cancer. Biomedicine & Pharmacotherapy, 95, 55–61.

    Article  CAS  Google Scholar 

  22. Becht, E., Giraldo, N. A., Lacroix, L., Buttard, B., Elarouci, N., Petitprez, F., Selves, J., Laurent-Puig, P., Sautès-Fridman, C., Fridman, W. H., et al. (2016). Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biology, 17, 218.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yoshihara, K., Shahmoradgoli, M., Martínez, E., Vegesna, R., Kim, H., Torres-Garcia, W., Treviño, V., Shen, H., Laird, P. W., Levine, D. A., et al. (2013). Inferring tumour purity and stromal and immune cell admixture from expression data. Nature communications, 4, 2612.

    Article  PubMed  Google Scholar 

  24. Galon, J., Pagès, F., Marincola, F. M., Thurin, M., Trinchieri, G., Fox, B. A., Gajewski, T. F., & Ascierto, P. A. (2012). The immune score as a new possible approach for the classification of cancer. Journal of Translational Medicine, 10, 1.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Zhang, M., Wang, X., Chen, X., Zhang, Q., & Hong, J. (2020). Novel immune-related gene signature for risk stratification and prognosis of survival in lower-grade glioma. Frontiers in genetics, 11, 363.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Ma, J., Cheng, P., Chen, X., Zhou, C., & Zheng, W. (2020). Mining of prognosis-related genes in cervical squamous cell carcinoma immune microenvironment. PeerJ, 8, e9627.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Wen, J., Wang, Y., Luo, L., Peng, L., Chen, C., Guo, J., Ge, Y., Li, W., & Jin, X. (2020). Identification and verification on prognostic index of lower-grade glioma immune-related LncRNAs. Frontiers in Oncology, 10, 578809.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019). STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47, D607-d613.

    Article  PubMed  CAS  Google Scholar 

  29. Smoot, M. E., Ono, K., Ruscheinski, J., Wang, P. L., & Ideker, T. (2011). Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics, 27, 431–432.

    Article  PubMed  CAS  Google Scholar 

  30. Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M., & Alizadeh, A. A. (2018). Profiling tumor infiltrating immune cells with CIBERSORT. Methods in Molecular Biology, 1711, 243–259.

    Article  PubMed  CAS  Google Scholar 

  31. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Research, 13, 2498–2504.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  32. Wakeam, E., Acuna, S. A., Leighl, N. B., Giuliani, M. E., Finlayson, S. R. G., Varghese, T. K., & Darling, G. E. (2017). Surgery versus chemotherapy and radiotherapy for early and locally advanced small cell lung cancer: a propensity-matched analysis of survival. Lung Cancer, 109, 78–88.

    Article  PubMed  CAS  Google Scholar 

  33. Rau, A., Maugis-Rabusseau, C., Martin-Magniette, M. L., & Celeux, G. (2015). Co-expression analysis of high-throughput transcriptome sequencing data with Poisson mixture models. Bioinformatics, 31, 1420–1427.

    Article  PubMed  CAS  Google Scholar 

  34. Ling, B., Huang, Z., Huang, S., Qian, L., Li, G., & Tang, Q. (2020). Microenvironment analysis of prognosis and molecular signature of immune-related genes in lung adenocarcinoma. Oncology Research, 28(6), 561–578.

    Article  PubMed  Google Scholar 

  35. Qi, X., Qi, C., Qin, B., Kang, X., Hu, Y., & Han, W. (2020). Immune-stromal score signature: novel prognostic tool of the tumor microenvironment in lung adenocarcinoma. Frontiers in Oncology, 10, 541330.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Shao, T., Wu, A., Chen, J., Chen, H., Lu, J., Bai, J., Li, Y., Xu, J., & Li, X. (2015). Identification of module biomarkers from the dysregulated ceRNA-ceRNA interaction network in lung adenocarcinoma. Molecular bioSystems, 11, 3048–3058.

    Article  PubMed  CAS  Google Scholar 

  37. Fan, C. N., Ma, L., & Liu, N. (2018). Systematic analysis of lncRNA-miRNA-mRNA competing endogenous RNA network identifies four-lncRNA signature as a prognostic biomarker for breast cancer. Journal of Translational Medicine, 16, 264.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Wang, S., Yang, L., Liu, Y., Xu, Y., Zhang, D., Jiang, Z., Wang, C., & Liu, Y. (2020). A novel immune-related competing endogenous rna network predicts prognosis of acute myeloid leukemia. Frontiers in Oncology, 10, 1579.

    Article  PubMed  PubMed Central  Google Scholar 

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Correspondence to Wuzhang Wang.

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Liu, L., Li, J., Fan, C. et al. Construction of a New Immune-Related Competing Endogenous RNA Network with Prognostic Value in Lung Adenocarcinoma. Mol Biotechnol 66, 300–310 (2024). https://doi.org/10.1007/s12033-023-00754-7

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