Abstract
Objectives
Non-small cell lung cancer (NSCLC) accounts for more than 80 % of all lung cancers, and its 5-year survival rate can be greatly improved by early diagnosis. However, early diagnosis remains elusive because of the lack of effective biomarkers. In this study, we aimed to develop an effective diagnostic model for NSCLC based on a combination of circulating biomarkers.
Methods
Tissue-deregulated long noncoding RNAs (lncRNAs) in NSCLC were identified in datasets retrieved from the Gene Expression Omnibus (GEO, n=727) and The Cancer Genome Atlas (TCGA, n=1,135) databases, and their differential expression was verified in paired local plasma and exosome samples from NSCLC patients. Subsequently, LASSO regression was used to screen for biomarkers in a large clinical population, and a logistic regression model was used to establish a multi-marker diagnostic model. The area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), clinical impact curves, and integrated discrimination improvement (IDI) were used to evaluate the efficiency of the diagnostic model.
Results
Three lncRNAs-PGM5-AS1, SFTA1P, and CTA-384D8.35 were consistently expressed in online tissue datasets, plasma, and exosomes from local patients. LASSO regression identified nine variables (Plasma CTA-384D8.35, Plasma PGM5-AS1, Exosome CTA-384D8.35, Exosome PGM5-AS1, Exosome SFTA1P, Log10CEA, Log10CA125, SCC, and NSE) in clinical samples that were eventually included in the multi-marker diagnostic model. Logistic regression analysis revealed that Plasma CTA-384D8.35, exosome SFTA1P, Log10CEA, Exosome CTA-384D8.35, SCC, and NSE were independent risk factors for NSCLC (p<0.01), and their results were visualized using a nomogram to obtain personalized prediction outcomes. The constructed diagnostic model demonstrated good NSCLC prediction ability in both the training and validation sets (AUC=0.97).
Conclusions
In summary, the constructed circulating lncRNA-based diagnostic model has good NSCLC prediction ability in clinical samples and provides a potential diagnostic tool for NSCLC.
Funding source: Creative Research Groups of Hubei Provincial Natural Science Foundation
Award Identifier / Grant number: No.2022CFA005
Funding source: Zhongnan Hospital of Wuhan University Medical Science and Technology Innovation Platform Construction Support Project
Award Identifier / Grant number: No. PTXM2021019
Funding source: medical Sci-Tech innovation platform of Zhongnan Hospital
Award Identifier / Grant number: No. PTXM2021001
Funding source: Medical Top-talented youth development project of Hubei Province and the Health Commission of Hubei Province scientific research project
Award Identifier / Grant number: No. WJ2021M172
-
Research funding: This work was supported by the research fund from Creative Research Groups of Hubei Provincial Natural Science Foundation (No.2022CFA005), medical Sci-Tech innovation platform of Zhongnan Hospital (No. PTXM2021001), the Fundamental Research Funds for the Central Universities (No. 2042021kf0227), Medical Top-talented youth development project of Hubei Province and the Health Commission of Hubei Province scientific research project (No. WJ2021M172) and Zhongnan Hospital of Wuhan University Medical Science and Technology Innovation Platform Construction Support Project (No. PTXM2021019).
-
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
-
Competing interests: Authors state no conflict of interest.
-
Informed consent: Informed consent was obtained from all individuals included in this study.
-
Ethical approval: Research involving human subjects complied with all relevant national regulations, institutional policies and is in accordance with the tenets of the Helsinki Declaration (as revised in 2013), and has been approved by the Research Ethics Committees of Zhongnan Hospital and Renmin Hospital of Wuhan University (Ethical Approval number: 2021054).
-
Data availability: The datasets generated and/or analyzed in the current study are available from the corresponding author upon reasonable request.
References
1. Ganti, AK, Klein, AB, Cotarla, I, Seal, B, Chou, E. Update of incidence, prevalence, survival, and initial treatment in patients with non-small cell lung cancer in the US. JAMA Oncol 2021;7:1824–32. https://doi.org/10.1001/jamaoncol.2021.4932.Search in Google Scholar PubMed PubMed Central
2. American Society of Clinical Oncology. Lung cancer – non-small cell – statistics: cancer. Net Editorial Board; 2022. https://www.cancer.net/cancer-types/lung-cancer-non-small-cell/statistics [Accessed 8 Aug 2022].Search in Google Scholar
3. Duma, N, Santana-Davila, R, Molina, JR. Non-small cell lung cancer: epidemiology, screening, diagnosis, and treatment. Mayo Clin Proc 2019;94:1623–40. https://doi.org/10.1016/j.mayocp.2019.01.013.Search in Google Scholar PubMed
4. Ma, C, Hu, K, Ullah, I, Zheng, Q-K, Zhang, N, Sun, Z-G. Molecular mechanisms involving the sonic hedgehog pathway in lung cancer therapy: recent advances. Front Oncol 2022;12:729088. https://doi.org/10.3389/fonc.2022.729088.Search in Google Scholar PubMed PubMed Central
5. Mendoza, TR, Kehl, KL, Bamidele, O, Williams, LA, Shi, Q, Cleeland, CS, et al.. Assessment of baseline symptom burden in treatment-naïve patients with lung cancer: an observational study. Support Care Cancer 2019;27:3439–47. https://doi.org/10.1007/s00520-018-4632-0.Search in Google Scholar PubMed
6. Gridelli, C, Rossi, A, Carbone, DP, Guarize, J, Karachaliou, N, Mok, T, et al.. Non-small-cell lung cancer. Nat Rev Dis Prim 2015;1:15009. https://doi.org/10.1038/nrdp.2015.9.Search in Google Scholar PubMed
7. Aberle, DR, Adams, AM, Berg, CD, Black, WC, Clapp, JD, Fagerstrom, RM, et al.. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011;365:395–409. https://doi.org/10.1056/nejmoa1102873.Search in Google Scholar PubMed PubMed Central
8. Humphrey, LL, Deffebach, M, Pappas, M, Baumann, C, Artis, K, Mitchell, JP, et al.. Screening for lung cancer with low-dose computed tomography: a systematic review to update the US Preventive services task force recommendation. Ann Intern Med 2013;159:411–20. https://doi.org/10.7326/0003-4819-159-6-201309170-00690.Search in Google Scholar PubMed
9. Gerlinger, M, Rowan, AJ, Horswell, S, Math, M, Larkin, J, Endesfelder, D, et al.. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012;366:883–92. https://doi.org/10.1056/nejmoa1113205.Search in Google Scholar PubMed PubMed Central
10. Ten Haaf, K, Jeon, J, Tammemägi, MC, Han, SS, Kong, CY, Plevritis, SK, et al.. Risk prediction models for selection of lung cancer screening candidates: a retrospective validation study. PLoS Med 2017;14:e1002277. https://doi.org/10.1371/journal.pmed.1002277.Search in Google Scholar PubMed PubMed Central
11. Li, K, Hüsing, A, Sookthai, D, Bergmann, M, Boeing, H, Becker, N, et al.. Selecting high-risk individuals for lung cancer screening: a prospective evaluation of existing risk models and eligibility criteria in the German EPIC cohort. Cancer Prev Res 2015;8:777–85. https://doi.org/10.1158/1940-6207.capr-14-0424.Search in Google Scholar PubMed
12. Crosbie, PA, Balata, H, Evison, M, Atack, M, Bayliss-Brideaux, V, Colligan, D, et al.. Second round results from the Manchester ‘Lung Health Check’ community-based targeted lung cancer screening pilot. Thorax 2019;74:700–4. https://doi.org/10.1136/thoraxjnl-2018-212547.Search in Google Scholar PubMed PubMed Central
13. Weber, M, Yap, S, Goldsbury, D, Manners, D, Tammemagi, M, Marshall, H, et al.. Identifying high risk individuals for targeted lung cancer screening: independent validation of the PLCO(m2012) risk prediction tool. Int J Cancer 2017;141:242–53. https://doi.org/10.1002/ijc.30673.Search in Google Scholar PubMed
14. Ostrowski, M, Marjanski, T, Dziedzic, R, Kocot, S, Rzyman, W. P1.11-08 are risk prediction models superior over standard criteria for lung cancer screening in europe? Macroscale simulation on large polish cohort. J Thorac Oncol 2019;14:S518. https://doi.org/10.1016/j.jtho.2019.08.1081.Search in Google Scholar
15. Tammemägi, MC, Katki, HA, Hocking, WG, Church, TR, Caporaso, N, Kvale, PA, et al.. Selection criteria for lung-cancer screening. N Engl J Med 2013;368:728–36. https://doi.org/10.1056/nejmoa1211776.Search in Google Scholar PubMed PubMed Central
16. Muller, DC, Johansson, M, Brennan, P. Lung cancer risk prediction model incorporating lung function: development and validation in the UK biobank prospective cohort study. J Clin Oncol 2017;35:861–9. https://doi.org/10.1200/jco.2016.69.2467.Search in Google Scholar PubMed
17. Sin, DD, Tammemagi, CM, Lam, S, Barnett, MJ, Duan, X, Tam, A, et al.. Pro-surfactant protein B as a biomarker for lung cancer prediction. J Clin Oncol 2013;31:4536–43. https://doi.org/10.1200/jco.2013.50.6105.Search in Google Scholar
18. Shiels, MS, Pfeiffer, RM, Hildesheim, A, Engels, EA, Kemp, TJ, Park, JH, et al.. Circulating inflammation markers and prospective risk for lung cancer. J Natl Cancer Inst 2013;105:1871–80. https://doi.org/10.1093/jnci/djt309.Search in Google Scholar PubMed PubMed Central
19. Cecchini, MJ, Yi, ES. Liquid biopsy is a valuable tool in the diagnosis and management of lung cancer. J Thorac Dis 2020;12:7048–56. https://doi.org/10.21037/jtd.2020.04.20.Search in Google Scholar PubMed PubMed Central
20. Han, J, LaVigne, CA, Jones, BT, Zhang, H, Gillett, F, Mendell, JT. A ubiquitin ligase mediates target-directed microRNA decay independently of tailing and trimming. Science 2020;370. https://doi.org/10.1126/science.abc9546.Search in Google Scholar PubMed PubMed Central
21. Bridges, MC, Daulagala, AC, Kourtidis, A. LNCcation: lncRNA localization and function. J Cell Biol 2021;220. https://doi.org/10.1083/jcb.202009045.Search in Google Scholar PubMed PubMed Central
22. Zhou, M, Guo, M, He, D, Wang, X, Cui, Y, Yang, H, et al.. A potential signature of eight long non-coding RNAs predicts survival in patients with non-small cell lung cancer. J Transl Med 2015;13:231. https://doi.org/10.1186/s12967-015-0556-3.Search in Google Scholar PubMed PubMed Central
23. Schmidt, LH, Spieker, T, Koschmieder, S, Schäffers, S, Humberg, J, Jungen, D, et al.. The long noncoding MALAT-1 RNA indicates a poor prognosis in non-small cell lung cancer and induces migration and tumor growth. J Thorac Oncol 2011;6:1984–92. https://doi.org/10.1097/jto.0b013e3182307eac.Search in Google Scholar PubMed
24. Li, C, Lv, Y, Shao, C, Chen, C, Zhang, T, Wei, Y, et al.. Tumor-derived exosomal lncRNA GAS5 as a biomarker for early-stage non-small-cell lung cancer diagnosis. J Cell Physiol 2019;234:20721–7. https://doi.org/10.1002/jcp.28678.Search in Google Scholar PubMed
25. Tao, Y, Tang, Y, Yang, Z, Wu, F, Wang, L, Yang, L, et al.. Exploration of serum exosomal LncRNA TBILA and AGAP2-AS1 as promising biomarkers for diagnosis of non-small cell lung cancer. Int J Biol Sci 2020;16:471–82. https://doi.org/10.7150/ijbs.39123.Search in Google Scholar PubMed PubMed Central
26. Chen, Q, Zhu, C, Jin, Y, Si, X, Jiao, W, He, W, et al.. Plasma long non-coding RNA RP11-438N5.3 as a novel biomarker for non-small cell lung cancer. Cancer Manag Res 2020;12:1513–21. https://doi.org/10.2147/cmar.s237024.Search in Google Scholar
27. Lin, Y, Leng, Q, Zhan, M, Jiang, F. A plasma long noncoding RNA signature for early detection of lung cancer. Transl Oncol 2018;11:1225–31. https://doi.org/10.1016/j.tranon.2018.07.016.Search in Google Scholar PubMed PubMed Central
28. Huang, L, Rong, Y, Tang, X, Yi, K, Qi, P, Hou, J, et al.. Engineered exosomes as an in situ DC-primed vaccine to boost antitumor immunity in breast cancer. Mol Cancer 2022;21:45. https://doi.org/10.1186/s12943-022-01515-x.Search in Google Scholar PubMed PubMed Central
29. Ren, S, Wang, F, Shen, J, Sun, Y, Xu, W, Lu, J, et al.. Long non-coding RNA metastasis associated in lung adenocarcinoma transcript 1 derived miniRNA as a novel plasma-based biomarker for diagnosing prostate cancer. Eur J Cancer 2013;49:2949–59. https://doi.org/10.1016/j.ejca.2013.04.026.Search in Google Scholar PubMed
30. Chakraborty, S, Andrieux, G, Hasan, AMM, Ahmed, M, Hosen, MI, Rahman, T, et al.. Harnessing the tissue and plasma lncRNA-peptidome to discover peptide-based cancer biomarkers. Sci Rep 2019;9:12322. https://doi.org/10.1038/s41598-019-48774-1.Search in Google Scholar PubMed PubMed Central
31. Riffo-Campos, AL, Perez-Hernandez, J, Martinez-Arroyo, O, Ortega, A, Flores-Chova, A, Redon, J, et al.. Biofluid specificity of long non-coding RNA profile in hypertension: relevance of exosomal fraction. Int J Mol Sci 2022;23:5199. https://doi.org/10.3390/ijms23095199.Search in Google Scholar PubMed PubMed Central
32. Wang, J, Cao, B, Gao, Y, Chen, Y-H, Feng, J. Exosome-transported lncRNA H19 regulates insulin-like growth factor-1 via the H19/let-7a/insulin-like growth factor-1 receptor axis in ischemic stroke. Neural Regener Res 2023;18. https://doi.org/10.4103/1673-5374.357901.Search in Google Scholar PubMed PubMed Central
33. Sun, Z, Yang, S, Zhou, Q, Wang, G, Song, J, Li, Z, et al.. Emerging role of exosome-derived long non-coding RNAs in tumor microenvironment. Mol Cancer 2018;17:82. https://doi.org/10.1186/s12943-018-0831-z.Search in Google Scholar PubMed PubMed Central
34. Luo, A, Lan, X, Qiu, Q, Zhou, Q, Li, J, Wu, M, et al.. LncRNA SFTA1P promotes cervical cancer progression by interaction with PTBP1 to facilitate TPM4 mRNA degradation. Cell Death Dis 2022;13:936. https://doi.org/10.1038/s41419-022-05359-7.Search in Google Scholar PubMed PubMed Central
35. Wang, M, Zhang, Z, Pan, D, Xin, Z, Bu, F, Zhang, Y, et al.. Circulating lncRNA UCA1 and lncRNA PGM5-AS1 act as potential diagnostic biomarkers for early-stage colorectal cancer. Biosci Rep 2021;41. https://doi.org/10.1042/bsr20211115.Search in Google Scholar PubMed PubMed Central
36. Groeper, C, Gambazzi, F, Zajac, P, Bubendorf, L, Adamina, M, Rosenthal, R, et al.. Cancer/testis antigen expression and specific cytotoxic T lymphocyte responses in non small cell lung cancer. Int J Cancer 2007;120:337–43. https://doi.org/10.1002/ijc.22309.Search in Google Scholar PubMed
37. Chen, Y, Zitello, E, Guo, R, Deng, Y. The function of LncRNAs and their role in the prediction, diagnosis, and prognosis of lung cancer. Clin Transl Med 2021;11:e367. https://doi.org/10.1002/ctm2.367.Search in Google Scholar PubMed PubMed Central
38. Weber, DG, Johnen, G, Casjens, S, Bryk, O, Pesch, B, Jöckel, KH, et al.. Evaluation of long noncoding RNA MALAT1 as a candidate blood-based biomarker for the diagnosis of non-small cell lung cancer. BMC Res Notes 2013;6:518. https://doi.org/10.1186/1756-0500-6-518.Search in Google Scholar PubMed PubMed Central
39. Zhao, T, Khadka, VS, Deng, Y. Identification of lncRNA biomarkers for lung cancer through integrative cross-platform data analyses. Aging 2020;12:14506–27. https://doi.org/10.18632/aging.103496.Search in Google Scholar PubMed PubMed Central
40. Guida, F, Sun, N, Bantis, LE, Muller, DC, Li, P, Taguchi, A, et al.. Assessment of lung cancer risk on the basis of a biomarker panel of circulating proteins. JAMA Oncol 2018;4:e182078. https://doi.org/10.1001/jamaoncol.2018.2078.Search in Google Scholar PubMed PubMed Central
41. Badowski, C, He, B, Garmire, LX. Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty. NPJ Precis Oncol 2022;6:40. https://doi.org/10.1038/s41698-022-00283-7.Search in Google Scholar PubMed PubMed Central
42. Li, MX, Wang, HY, Yuan, CH, Ma, ZL, Jiang, B, Li, L, et al.. KLHDC7B-DT aggravates pancreatic ductal adenocarcinoma development via inducing cross-talk between cancer cells and macrophages. Clin Sci 2021;135:629–49. https://doi.org/10.1042/cs20201259.Search in Google Scholar
43. Ge, X, Peng, X, Li, M, Ji, F, Chen, J, Zhang, D. OGT regulated O-GlcNacylation promotes migration and invasion by activating IL-6/STAT3 signaling in NSCLC cells. Pathol Res Pract 2021;225:153580. https://doi.org/10.1016/j.prp.2021.153580.Search in Google Scholar PubMed
44. Du, D, Shen, X, Zhang, Y, Yin, L, Pu, Y, Liang, G. Expression of long non-coding RNA SFTA1P and its function in non-small cell lung cancer. Pathol Res Pract 2020;216:153049. https://doi.org/10.1016/j.prp.2020.153049.Search in Google Scholar PubMed
45. Zhu, B, Finch-Edmondson, M, Leong, KW, Zhang, X, Mitheera, V, Lin, QXX, et al.. LncRNA SFTA1P mediates positive feedback regulation of the Hippo-YAP/TAZ signaling pathway in non-small cell lung cancer. Cell Death Dis 2021;7:369. https://doi.org/10.1038/s41420-021-00761-0.Search in Google Scholar PubMed PubMed Central
46. Liu, W, Liu, P, Gao, H, Wang, X, Yan, M. Long non-coding RNA PGM5-AS1 promotes epithelial-mesenchymal transition, invasion and metastasis of osteosarcoma cells by impairing miR-140-5p-mediated FBN1 inhibition. Mol Oncol 2020;14:2660–77. https://doi.org/10.1002/1878-0261.12711.Search in Google Scholar PubMed PubMed Central
47. Zhuo, E, Cai, C, Liu, W, Li, K, Zhao, W. Downregulated microRNA-140-5p expression regulates apoptosis, migration and invasion of lung cancer cells by targeting zinc finger protein 800. Oncol Lett 2020;20:390. https://doi.org/10.3892/ol.2020.12253.Search in Google Scholar PubMed PubMed Central
48. Du, L, Gao, Y. PGM5-AS1 impairs miR-587-mediated GDF10 inhibition and abrogates progression of prostate cancer. J Transl Med 2021;19:12. https://doi.org/10.1186/s12967-020-02572-w.Search in Google Scholar PubMed PubMed Central
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/cclm-2023-0291).
© 2023 Walter de Gruyter GmbH, Berlin/Boston
