A Prognosis and Immunological Study of Cellular Aging Related lncRNAs in Lung Adenocarcinoma

Kaiwei Yang; Baixue Li; Yi Liu

doi:10.54097/z4wyrp23

Authors

Kaiwei Yang
Baixue Li
Yi Liu

DOI:

https://doi.org/10.54097/z4wyrp23

Abstract

Background: Recently, cellular senescence has been recognized as a novel hallmark of cancer. However, the mechanisms that control cellular senescence remain poorly understood. This study seeks to discover long non-coding RNAs (lncRNAs) that are linked to senescence and can predict outcomes in lung adenocarcinoma (LUAD) patients. Methods: Using RNA sequencing data from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) and senescence genes from the CellAge database, a group of lncRNAs related to senescence was initially identified. Subsequently, through univariate and multivariate Cox regression analyses, a senescence-related lncRNA signature (SenLncSig) linked to the prognosis of LUAD was developed. LUAD patients were categorized into high-risk and low-risk groups based on the median risk score of SenLncSig. Kaplan-Meier analysis was employed to assess differences in overall survival (OS) between these subgroups. Additionally, disparities in Gene Set Enrichment Analysis (GSEA), immune infiltration, scores from the tumor immune dysfunction and exclusion (TIDE) module, and choices of chemotherapy and targeted therapy were also evaluated between the high-risk and low-risk groups. Results: Based on univariate and multivariate Cox regression analyses, we established a prognostic model composed of seven senescence-related lncRNAs, named SenLncSig, which includes AL031775.2, AC026355.1, AL365181.2, AC090559.1, AL606489.1, AL513550.1, and C20orf197. This model divides patients into high-risk and low-risk groups, with the high-risk group showing significantly shorter overall survival compared to the low-risk group, with a hazard ratio (HR) of 1.326. The predictive accuracy of SenLncSig was validated through ROC curves and principal component analysis (PCA). Additionally, we developed an intuitive survival prediction tool that combines SenLncSig with clinical pathological features to assess patients' overall survival (OS). Enrichment analysis (GSEA) revealed that SenLncSig is involved in multiple pathways related to tumor immune modulation. Risk model analysis indicated significant differences between the high and low-risk groups in terms of immune status and responses to immunotherapy, chemotherapy, and targeted therapy. Conclusion: In this study, a lncRNA signature named as SenLncSig was developed that not only identifies the senescence phenotype and predicts prognosis but also has the capability to forecast the response of LUAD to immunotherapy.

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References

Avelar, R. A., Ortega, J. G., Tacutu, R., Tyler, E. J., Bennett, D., & Binetti, P. (2020). A multidimensional systems biology analysis of cellular senescence in aging and disease. Genome Biology, 21, 91.

Bonanno, L., Pavan, A., Dieci, M. V., Di Liso, E., et al. (2018). The role of immune microenvironment in small-cell lung cancer: distribution of PD-L1 expression and prognostic role of FOXP3-positive tumour infiltrating lymphocytes. European Journal of Cancer.

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(6), 394-424.

Campisi, J. (2013). Aging, cellular senescence, and cancer. Annual Review of Physiology, 75, 685-705.

Degirmenci, U., Lei, S. (2016). Role of lncRNAs in cellular aging. Frontiers in Endocrinology.

Do, H., & Kim, W. (2018). Roles of oncogenic long non-coding RNAs in cancer development. Genomics & Informatics, 16(1), e18.

Genova, C., Dellepiane, C., Carrega, P., et al. (2022). Therapeutic implications of tumor microenvironment in lung cancer: focus on immune checkpoint blockade. Frontiers in Immunology.

Hoang, P. H., & Landi, M. T. (2022). DNA methylation in lung cancer: mechanisms and associations with histological subtypes, molecular alterations, and major epidemiological factors. Cancers, 14(4), 961.

Huo, J., Wu, L., Zang, Y., Dong, H., Liu, X., He, F., & Zhang, X. (2021). Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma. BMC Cancer, 21, 77.

Jenkins, R. W., Barbie, D. A., Flaherty, K. T. (2018). Mechanisms of resistance to immune checkpoint inhibitors. British Journal of Cancer.

Jin, D., Song, Y., Chen, Y., & Zhang, P. (2020). Identification of three lncRNAs as potential predictive biomarkers of lung adenocarcinoma. BioMed Research International, 2020, Article 7573689.

Kim, C., Kang, D., Lee, E. K., Lee, J. S. (2017). Long noncoding RNAs and RNA-binding proteins in oxidative stress, cellular senescence, and age-related diseases. Oxidative Medicine and Cellular Longevity.

Lecot, P., Alimirah, F., Desprez, P. Y., Campisi, J., & Wiley, C. D. (2016). Context-dependent effects of cellular senescence in cancer development. British Journal of Cancer, 114(11), 1180-1184.

Lin, W., Wang, X., Xu, Z., Wang, Z., Liu, T., Cao, Z., et al. (2021). Identification and validation of cellular senescence patterns to predict clinical outcomes and immunotherapeutic responses in lung adenocarcinoma. Cancer Cell International.

Liu, A., Wang, X., Hu, L., Yan, D., Yin, Y., Zheng, H., Liu, G. et al. (2023). A predictive molecular signature consisting of lncRNAs associated with cellular senescence for the prognosis of lung adenocarcinoma. Plos One.

Liu, H., Zhao, H., Sun, Y. (2022). Tumor microenvironment and cellular senescence: Understanding therapeutic resistance and harnessing strategies. Seminars in Cancer Biology.

Liu, J., Nie, S., Wu, Z., Jiang, Y., Wan, S., Li, S., Meng, H. (2020). Exploration of a novel prognostic risk signatures and immune checkpoint molecules in endometrial carcinoma microenvironment. Genomics.

Miranda-Filho, A., Piñeros, M., Ferlay, J., Soerjomataram, I., Monnereau, A., & Bray, F. (2019). Epidemiology and patterns of care for invasive breast and cervical cancer in 57 low- and middle-income countries: a population-based study. The Lancet Global Health, 7(7), e951-e961.

Montes, M., Lubas, M., Arendrup, F. S., Mentz, B., et al. (2021). The long non-coding RNA MIR31HG regulates the senescence associated secretory phenotype. Nature Communications.

Muppa, P., Terra, S. B. S. P., Sharma, A., Mansfield, A. S., et al. (2019). Immune cell infiltration may be a key determinant of long-term survival in small cell lung cancer. Journal of Thoracic Oncology.

Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., ... & Alizadeh, A. A. (2015). Robust enumeration of cell subsets from tissue expression profiles. Nature Methods, 12(5), 453-457.

Peng, C., Hu, W., Weng, X., Tong, R., Cheng, S., Ding, C., et al. (2017). Over expression of long non-coding RNA PANDA promotes hepatocellular carcinoma by inhibiting senescence associated inflammatory factor IL8. Scientific Reports.

Peng, H., Wu, X., Zhong, R., Yu, T., Cai, X., Liu, J., et al. (2021). Profiling tumor immune microenvironment of non-small cell lung cancer using multiplex immunofluorescence. Frontiers in Immunology.

Pennock, G. K., Chow, L. Q. M. (2015). The evolving role of immune checkpoint inhibitors in cancer treatment. The Oncologist.

Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G., & Hacohen, N. (2015). Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell, 160(1-2), 48-61.

Schosserer, M., Grillari, J., & Breitenbach, M. (2017). The dual role of cellular senescence in developing tumors and their response to cancer therapy. Frontiers in Oncology, 7, 278.

Serviss, J. T., Johnsson, P., & Grandér, D. (2014). An emerging role for long non-coding RNAs in cancer metastasis. Frontiers in Genetics, 5, 234.

Shi, L., Ginn, L., La Montagna, M., & Garofalo, M. (2020). LncRNAs in non-small-cell lung cancer. Non-coding RNA, 6(3), 25.

Shiravand, Y., Khodadadi, F., Kashani, S.M.A. (2022). Immune checkpoint inhibitors in cancer therapy. Current Oncology Reports.

Silva, A., Bullock, M., & Calin, G. (2015). The clinical relevance of long non-coding RNAs in cancer. Cancers, 7(4), 884.

Tang, P., Qu, W., Wang, T., Liu, M., Wu, D., & Tan, L. (2021). Identifying a hypoxia-related long non-coding RNAs signature to improve the prediction of prognosis and immunotherapy response in hepatocellular carcinoma. Frontiers in Genetics, 12, 785185.

Topalian, S. L., Taube, J. M., Anders, R. A., et al. (2016). Mechanism-driven biomarkers to guide immune checkpoint blockade in cancer therapy. Nature Reviews Cancer.

Twomey, J.D., Zhang, B. (2021). Cancer immunotherapy update: FDA-approved checkpoint inhibitors and companion diagnostics. The AAPS Journal.

Wagner, M., Jasek, M., Karabon, L. (2021). Immune checkpoint molecules—inherited variations as markers for cancer risk. Frontiers in Immunology.

Wei, S. C., Duffy, C. R., Allison, J. P. (2018). Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discovery.

Wen, S., Peng, W., Chen, Y., Du, X., Xia, J., Shen, B., Zhou, G. (2022). Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO. BMC Cancer.

West, L., Vidwans, S. J., Campbell, N. P., Shrager, J., Simon, G. R., Bueno, R., ... & Chen, H. (2012). A novel classification of lung cancer into molecular subtypes. PLoS One, 7(2), e31906.

Wu, Q., Qian, W., Sun, X., Jiang, S. (2022). Small-molecule inhibitors, immune checkpoint inhibitors, and more: FDA-approved novel therapeutic drugs for solid tumors from 1991 to 2021. Journal of Hematology & Oncology.

Xu, X., Zhou, X., Chen, Z., Gao, C., Zhao, L., & Cui, Y. (2020). Silencing of lncRNA XIST inhibits non-small cell lung cancer growth and promotes chemosensitivity to cisplatin. Aging (Albany NY), 12(5), 6720-6737.

Yi, M., Li, A., Zhou, L., Chu, Q., Luo, S., Wu, K. (2021). Immune signature-based risk stratification and prediction of immune checkpoint inhibitor's efficacy for lung adenocarcinoma. Cancer Immunology Research.

Yuan, S., Xiang, Y., Guo, X., Zhang, C., Li, C., & Xie, M. (2020). Circulating long noncoding RNAs act as diagnostic biomarkers in non-small cell lung cancer. Frontiers in Oncology, 10, Article 537120.

Zhang, X. Z., Liu, H., & Chen, S. R. (2020). Mechanisms of long non-coding RNAs in cancers and their dynamic regulations. Cancers, 12(5), 1245.