Open Access Journal Article

A prognostic aging-related lncRNA risk model correlates with the immune microenvironment in HCC

by Kun Mei a,1 Zilu Chen b,1 Qin Wang c Akbar Ali d Yan Huang e,* orcid  and  Luo Yi f, g,*
a
Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China
b
Center for Molecular Imaging and Nuclear Medicine, Soochow University, School of Radiological & Interdisciplinary Sciences, Soochow University (RAD-X), Suzhou, China
c
Nanjing University of Chinese Medicine, Nanjing 210023, China
d
Nishtar Medial College, Multan, Pakistan
e
Department of Ultrasound, Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine, Nanjing 210001, China
f
Department of Oncology, Jiangsu Province Hospital on Integration of Chinese and Western Medicine, Nanjing 210028, China
g
Department of Oncology, Affiliated Hospital of Integrated Traditional Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
*
Author to whom correspondence should be addressed.
CI  2024, 35; 3(2), 35; https://doi.org/10.58567/ci03020003
Received: 28 October 2023 / Accepted: 27 December 2023 / Published: 9 January 2024

Abstract

Background: Hepatocellular carcinoma (HCC) stands out as one of the most lethal cancers globally, given its complexity, recurrence following surgical resection, metastatic potential, and inherent heterogeneity. In recent years, researchers have systematically elucidated the significance of long non-coding RNA (lncRNA) in the initiation and progression of HCC. The introduction of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases has significantly enhanced the prognostic assessment of HCC. However, the association between HCC and cell senescence has been infrequently explored in the literature. Method: We downloaded liver hepatocellular carcinoma (LIHC)-related messenger RNA and lncRNA expression levels from TCGA. Correlation analysis, Cox regression, and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to validate the lncRNA risk model associated with cellular aging. Comparing the infiltration of diverse immune cells enabled the identification of distinct differences in the immunological microenvironments of the two risk groups. Subsequently, we conducted a real-time polymerase chain reaction (qPCR) experiment to confirm the accuracy of the selected lncRNAs. Results: A predictive framework for HCC was constructed based on the expression levels of five lncRNAs. Multivariate and univariate Cox regression analyses revealed that lncRNA signatures associated with senescence were independently correlated with an increased risk of HCC. Additionally, the nomogram also provides a more refined and sensitive model. Further investigation into the variations in immune cells and functions between the high-risk and low-risk groups was conducted. Subsequently, a qPCR experiment results revealed underexpression of AC068756.1, AC090578.1, AC145343.1, and LINC0022 in Huh7 and LM3 cells. In contrast, AP003392.4 did not exhibit a significant difference between Huh7 and control cells. Conclusion: The prognostic features and nomogram, consisting of five aging-related lncRNAs (AC068756.1, AC090578.1, AC145343.1, AP003392.4, and LINC00221), may be useful in predicting the overall survival of HCC.


Copyright: © 2024 by Mei, Chen, Wang, Ali, Huang and Yi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Show Figures

Funding

National Natural Science Foundation of China (81773947) , Foundation for The Top Talent Program of Jiangsu Commission of Health’s “Six-One Project” for High Level Personnels (LGY2020003)

Share and Cite

ACS Style
Mei, K.; Chen, Z.; Wang, Q.; Ali, A.; Huang, Y.; Yi, L. A prognostic aging-related lncRNA risk model correlates with the immune microenvironment in HCC. Cancer Insight, 2024, 3, 35. https://doi.org/10.58567/ci03020003
AMA Style
Mei K, Chen Z, Wang Q, Ali A, Huang Y, Yi L. A prognostic aging-related lncRNA risk model correlates with the immune microenvironment in HCC. Cancer Insight; 2024, 3(2):35. https://doi.org/10.58567/ci03020003
Chicago/Turabian Style
Mei, Kun; Chen, Zilu; Wang, Qin; Ali, Akbar; Huang, Yan; Yi, Luo 2024. "A prognostic aging-related lncRNA risk model correlates with the immune microenvironment in HCC" Cancer Insight 3, no.2:35. https://doi.org/10.58567/ci03020003
APA style
Mei, K., Chen, Z., Wang, Q., Ali, A., Huang, Y., & Yi, L. (2024). A prognostic aging-related lncRNA risk model correlates with the immune microenvironment in HCC. Cancer Insight, 3(2), 35. https://doi.org/10.58567/ci03020003

Article Metrics

Article Access Statistics

References

  1. Sung, H., et al., Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin, 2021. 71(3): p. 209-249. https://doi.org/10.3322/caac.21660
  2. Wang, Z., et al., Microwave ablation versus laparoscopic resection as first-line therapy for solitary 3-5-cm HCC. Hepatology, 2022. 76(1): p. 66-77. https://doi.org/10.1002/hep.32323
  3. Benson, A.B., et al., Hepatobiliary Cancers, Version 2.2021, NCCN Clinical Practice Guidelines in Oncology. J Natl Compr Canc Netw, 2021. 19(5): p. 541-565. https://doi.org/10.6004/jnccn.2021.0022
  4. Hao, K., et al., Expression and prognostic signatures of m6A-related lncRNAs in hepatocellular carcinoma. J Cancer Res Clin Oncol, 2022. https://doi.org/10.1007/s00432-022-04338-x
  5. Seo, E., et al., Reactive oxygen species-induced changes in glucose and lipid metabolism contribute to the accumulation of cholesterol in the liver during aging. Aging Cell, 2019. 18(2): p. e12895. https://doi.org/10.1111/acel.12895
  6. Chinta, S.J., et al., Cellular senescence and the aging brain. Exp Gerontol, 2015. 68: p. 3-7. https://doi.org/10.1016/j.exger.2014.09.018
  7. Hanahan, D. and R.A. Weinberg, Hallmarks of cancer: the next generation. Cell, 2011. 144(5): p. 646-74. https://doi.org/10.1016/j.cell.2011.02.013
  8. Johnson, A.A. and A. Stolzing, The role of lipid metabolism in aging, lifespan regulation, and age-related disease. Aging Cell, 2019. 18(6): p. e13048. https://doi.org/10.1111/acel.13048
  9. Vaiserman, A., et al., Anti-ageing gene therapy: Not so far away? Ageing Res Rev, 2019. 56: p. 100977. https://doi.org/10.1016/j.arr.2019.100977
  10. Sullivan, J., L. Mirbahai, and J.M. Lord, Major trauma and acceleration of the ageing process. Ageing Res Rev, 2018. 48: p. 32-39. https://doi.org/10.1016/j.arr.2018.10.001
  11. Campisi, J., Aging, cellular senescence, and cancer. Annu Rev Physiol, 2013. 75: p. 685-705. https://doi.org/10.1146/annurev-physiol-030212-183653
  12. He, X., et al., Single-cell omics in ageing: a young and growing field. Nat Metab, 2020. 2(4): p. 293-302. https://doi.org/10.1038/s42255-020-0196-7
  13. Gong, L., et al., FBXW7 inactivation induces cellular senescence via accumulation of p53. Cell Death Dis, 2022. 13(9): p. 788. https://doi.org/10.1038/s41419-022-05229-2
  14. Chao, H.H., et al., Regulatory mechanisms and function of hypoxia-induced long noncoding RNA NDRG1-OT1 in breast cancer cells. Cell Death Dis, 2022. 13(9): p. 807. https://doi.org/10.1038/s41419-022-05253-2
  15. Farzaneh, M., et al., Functional roles of lncRN-TUG1 in hepatocellular carcinoma. Life Sci, 2022: p. 120974. https://doi.org/10.1016/j.lfs.2022.120974
  16. Shi, W., et al., Five-mRNA Signature for the Prognosis of Breast Cancer Based on the ceRNA Network. Biomed Res Int, 2020. 2020: p. 9081852. https://doi.org/10.1155/2020/9081852
  17. Khanbabaei, H., et al., Non-coding RNAs and epithelial mesenchymal transition in cancer: molecular mechanisms and clinical implications. J Exp Clin Cancer Res, 2022. 41(1): p. 278. https://doi.org/10.1186/s13046-022-02488-x
  18. Chen, L., J. Wang, and Q. Liu, Long noncoding RNAs as therapeutic targets to overcome chemoresistance in ovarian cancer. Front Cell Dev Biol, 2022. 10: p. 999174. https://doi.org/10.3389/fcell.2022.999174
  19. Li, Y., et al., A review of literature: role of long noncoding RNA TPT1-AS1 in human diseases. Clin Transl Oncol, 2022. https://doi.org/10.1007/s12094-022-02947-z
  20. Jafari-Raddani, F., et al., An overview of long noncoding RNAs: Biology, functions, therapeutics, analysis methods, and bioinformatics tools. Cell Biochem Funct, 2022. https://doi.org/10.1002/cbf.3748
  21. Bruix, J., M. Reig, and M. Sherman, Evidence-Based Diagnosis, Staging, and Treatment of Patients With Hepatocellular Carcinoma. Gastroenterology, 2016. 150(4): p. 835-53. https://doi.org/10.1053/j.gastro.2015.12.041
  22. Foerster, F. and P.R. Galle, Comparison of the current international guidelines on the management of HCC. JHEP Rep, 2019. 1(2): p. 114-119. https://doi.org/10.1016/j.jhepr.2019.04.005
  23. Huang, Z., et al., The role of long noncoding RNAs in hepatocellular carcinoma. Mol Cancer, 2020. 19(1): p. 77. https://doi.org/10.1186/s12943-020-01188-4
  24. Di Micco, R., et al., Cellular senescence in ageing: from mechanisms to therapeutic opportunities. Nat Rev Mol Cell Biol, 2021. 22(2): p. 75-95. https://doi.org/10.1038/s41580-020-00314-w
  25. Schmitt, C.A., B. Wang, and M. Demaria, Senescence and cancer - role and therapeutic opportunities. Nat Rev Clin Oncol, 2022. 19(10): p. 619-636. https://doi.org/10.1038/s41571-022-00668-4
  26. Kang, T.W., et al., Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature, 2011. 479(7374): p. 547-51. https://doi.org/10.1038/nature10599
  27. Calcinotto, A., et al., Cellular Senescence: Aging, Cancer, and Injury. Physiol Rev, 2019. 99(2): p. 1047-1078. https://doi.org/10.1152/physrev.00020.2018
  28. Hu, D.J., et al., High WDR34 mRNA expression as a potential prognostic biomarker in patients with breast cancer as determined by integrated bioinformatics analysis. Oncol Lett, 2019. 18(3): p. 3177-3187. https://doi.org/10.3892/ol.2019.10634
  29. Kamel, M.M., et al., Investigation of long noncoding RNAs expression profile as potential serum biomarkers in patients with hepatocellular carcinoma. Transl Res, 2016. 168: p. 134-145. https://doi.org/10.1016/j.trsl.2015.10.002
  30. Zheng, Z.K., et al., Serum long noncoding RNA urothelial carcinoma-associated 1: A novel biomarker for diagnosis and prognosis of hepatocellular carcinoma. J Int Med Res, 2018. 46(1): p. 348-356. https://doi.org/10.1177/0300060517726441