Abstract
Purpose
Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients.
Methods
In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA.
Results
Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001).
Conclusion
In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD.
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Data availability
The results of this study are based on the TCGA Research Network: https://www.cancer.gov/tcga.
References
Al-Dherasi A, Liao Y, Al-Mosaib S et al (2021) Allele frequency deviation (AFD) as a new prognostic model to predict overall survival in lung adenocarcinoma (LUAD). Cancer Cell Int 21(1):451. https://doi.org/10.1186/s12935-021-02127-z
Cao M, Li H, Sun D, Chen W (2020) Cancer burden of major cancers in China: a need for sustainable actions. Cancer Commun (Lond) 40(5):205–210. https://doi.org/10.1002/cac2.12025
Che CL et al (2013) DNA microarray reveals different pathways responding to paclitaxel and docetaxel in non-small cell lung cancer cell line. Int J Clin Exp Pathol 6(8):1538–1548
Chen B, Khodadoust MS, Liu CL, Newman AM, Alizadeh AA (2018) Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol 1711:243–259. https://doi.org/10.1007/978-1-4939-7493-1_12
Chen H, Chong W, Teng C, Yao Y, Wang X, Li X (2019) The immune response-related mutational signatures and driver genes in non-small-cell lung cancer. Cancer Sci 110:2348–2356. https://doi.org/10.1111/cas.14113
Chen C, Yang L, Li H et al (2020) Raman spectroscopy combined with multiple algorithms for analysis and rapid screening of chronic renal failure. Photodiagn Photodyn Ther 30:101792. https://doi.org/10.1016/j.pdpdt.2020.101792
Chen Y, Li ZY, Zhou GQ, Sun Y (2021a) An immune-related gene prognostic index for head and neck squamous cell carcinoma. Clin Cancer Res 27(1):330–341. https://doi.org/10.1158/1078-0432.CCR-20-2166
Chen Y, Zhang X, Li J, Zhou M (2021b) Immune-related eight-lncRNA signature for improving prognosis prediction of lung adenocarcinoma. J Clin Lab Anal 35(11):e24018. https://doi.org/10.1002/jcla.24018
Chen C, Chen F, Yang B, Zhang K, Lv X, Chen C (2022) A novel diagnostic method: FT-IR, Raman and derivative spectroscopy fusion technology for the rapid diagnosis of renal cell carcinoma serum. Spectrochim Acta A Mol Biomol Spectrosc 269:120684. https://doi.org/10.1016/j.saa.2021.120684
Cheng CA et al (2020) Urine Raman spectroscopy for rapid and inexpensive diagnosis of chronic renal failure (CRF) using multiple classification algorithms. Optik 203:164043
Cheung WK, Nguyen DX (2015) Lineage factors and differentiation states in lung cancer progression. Oncogene 34(47):5771–5780. https://doi.org/10.1038/onc.2015.85
Choi B, Lee HJ, Min J et al (2017) Plasma expression of the intestinal metaplasia markers CDH17 and TFF3 in patients with gastric cancer. Cancer Biomark 19(3):231–239. https://doi.org/10.3233/CBM-160147
Doni A, Stravalaci M, Inforzato A et al (2019) The long pentraxin PTX3 as a link between innate immunity, tissue remodeling, and cancer. Front Immunol 10:712. https://doi.org/10.3389/fimmu.2019.00712
Finotello F, Trajanoski Z (2018) Quantifying tumor-infiltrating immune cells from transcriptomics data. Cancer Immunol Immunother 67(7):1031–1040. https://doi.org/10.1007/s00262-018-2150-z
García-Martínez JM, Wang S, Weishaeupl C et al (2021) Selective tumor cell apoptosis and tumor regression in CDH17-positive colorectal cancer models using BI 905711, a novel liver-sparing TRAILR2 agonist. Mol Cancer Ther 20(1):96–108. https://doi.org/10.1158/1535-7163.MCT-20-0253
Hanzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform 14:7. https://doi.org/10.1186/1471-2105-14-7
Herbst RS, Morgensztern D, Boshoff C (2018) The biology and management of non-small cell lung cancer. Nature 553:446. https://doi.org/10.1038/nature25183
Hinshaw DC, Shevde LA (2019) The tumor microenvironment innately modulates cancer progression. Cancer Res 79(18):4557–4566. https://doi.org/10.1158/0008-5472.CAN-18-3962
Hollander LL et al (2015) The novel tumor angiogenic factor, adrenomedullin-2 (ADM2) predicts survival in pancreatic adenocarcinoma. J Surg Res 17:2
Huang DP et al (2021) Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma. Aging 13(22):24621–24639. https://doi.org/10.1863/aging.203698
Jia D, Chen C, Chen C et al (2021) Breast cancer case identification based on deep learning and bioinformatics analysis[J]. Front Genet 12
Li J, Zhou D, Qiu W, Shi Y, Yang JJ, Chen S et al (2018) Application of weighted gene co-expression network analysis for data from paired design. Sci Rep 8:622
Li Y, Shen R, Wang A et al (2021) Construction of a prognostic immune-related LncRNA risk model for lung adenocarcinoma. Front Cell Dev Biol 9:648806. https://doi.org/10.3389/fcell.2021.648806
Li F, Wan B, Li XQ (2022) Expression profile and prognostic values of CDH family members in lung adenocarcinoma. Dis Markers 2022:9644466. https://doi.org/10.1155/2022/9644466
Lin JJ, Cardarella S, Lydon CA, Dahlberg SE, Jackman DM, Janne PA et al (2016) Five-Year survival in EGFR-mutant metastatic lung adenocarcinoma treated with EGFR-TKIs. J Thorac Oncol 114:556–565. https://doi.org/10.1016/j.jtho.2015.12.103
Lin J, Wu C, Ma D, Hu Q (2021) Identification of P2RY13 as an immune-related prognostic biomarker in lung adenocarcinoma: a public database-based retrospective study. PeerJ 9:e11319. https://doi.org/10.7717/peerj.11319
Liu Z, Mi M, Li X, Zheng X, Wu G, Zhang L (2020) A lncRNA prognostic signature associated with immune infiltration and tumour mutation burden in breast cancer. J Cell Mol Med 24(21):12444–12456. https://doi.org/10.1111/jcmm.15762
Liu Z, Li H, Pan S (2021) Discovery and validation of key biomarkers based on immune infiltrates in Alzheimer’s disease. Front Genet 12:658323. https://doi.org/10.3389/fgene.2021.658323
Liu J, Peng Y, Wei W (2022) Cell cycle on the crossroad of tumorigenesis and cancer therapy. Trends Cell Biol 32(1):30–44. https://doi.org/10.1016/j.tcb.2021.07.001
Long J, Wang A, Bai Y, Lin J, Yang X, Wang D et al (2019) Development and validation of a TP53-associated immune prognostic model for hepatocellular carcinoma. EBioMedicine 42:363–374. https://doi.org/10.1016/j.ebiom.2019.03.022
Luo X, Feng L, Xu W, Bai X, Wu M (2021) Weighted gene co-expression network analysis of hub genes in lung adenocarcinoma. Evol Bioinform Online 17:11769343211009898. https://doi.org/10.1177/11769343211009898
Qiao L, Xu Z-L, Zhao T-J, Ye L-H, Zhang X-D (2008) Dkk-1 secreted by mesenchymal stem cells inhibits growth of breast cancer cells via depression of Wnt signalling. Cancer Lett 269:67–77. https://doi.org/10.1016/j.canlet.2008.04.032
Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PPA (2011) ceRNA hypothesis: the rosetta stone of a hidden RNA language? Cell 146:353–358. https://doi.org/10.1016/j.cell.2011.07.014
Shang S, Li X, Gao Y et al (2021) MeImmS: predict clinical benefit of Anti-PD-1/PD-L1 treatments based on DNA methylation in non-small cell lung cancer. Front Genet 12:676449
Shen S, Wang G, Zhang R, Zhao Y, Yu H, Wei Y et al (2019) Development and validation of an immune gene-set based prognostic signature in ovarian cancer. EBioMedicine 40:318–326. https://doi.org/10.1016/j.ebiom.2018.12.054
Shi J et al (2021) Identification of a three-gene signature based on epithelial-mesenchymal transition of lung adenocarcinoma through construction and validation of a risk-prediction model. Front Oncol 11:726834. https://doi.org/10.3389/fonc.2021.726834
Su Y, Tian X, Gao R et al (2022) Colon cancer diagnosis and staging classification based on machine learning and bioinformatics analysis. Comput Biol Med 145:105409. https://doi.org/10.1016/j.compbiomed.2022.105409
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660. (Epub 2021 Feb 4)
Topalian SL, Weiner GJ, Pardoll DM (2011) Cancer immunotherapy comes of age. J Clin Oncol 29(36):4828–4836. https://doi.org/10.1200/JCO.2011.38.0899
Wang L, Zhao H, Xu Y, Li J, Deng C, Deng Y, Bai J, Li X, Xiao Y, Zhang Y (2019) Systematic identification of lincRNA-based prognostic biomarkers by integrating lincRNA expression and copy number variation in lung adenocarcinoma. Int J Cancer 144:1723–1734. https://doi.org/10.1002/ijc.31865
Wu J, Zhao Y, Zhang J, Wu Q, Wang W (2019) Development and validation of an immune-related gene pairs signature in colorectal cancer. Oncoimmunology 8:1596715. https://doi.org/10.1080/2162402x.2019.1596715
Wu X, Sui Z, Zhang H, Wang Y, Yu Z (2020) Integrated analysis of lncRNA-mediated ceRNA network in lung adenocarcinoma. Front Oncol 10:554759. https://doi.org/10.3389/fonc.2020.554759
Xin P, Xu X, Deng C et al (2020) The role of JAK/STAT signaling pathway and its inhibitors in diseases. Int Immunopharmacol 80:106210. https://doi.org/10.1016/j.intimp.2020.106210
Yao Y, Zhou Y, Hua Q (2021) circRNA hsa_circ_0018414 inhibits the progression of LUAD by sponging miR-6807-3p and upregulating DKK1. Mol Ther Nucleic Acids 23:783–796. https://doi.org/10.1016/j.omtn.2020.12.031
Yin X, Wang P, Yang T et al (2020) Identification of key modules and genes associated with breast cancer prognosis using WGCNA and ceRNA network analysis. Aging (Albany NY) 13(2):2519–2538. https://doi.org/10.18632/aging.202285
Yue F, Chen C, Yan Z et al (2020) Fourier transform infrared spectroscopy combined with deep learning and data enhancement for quick diagnosis of abnormal thyroid function. Photodiagn Photodyn Ther 32:101923. https://doi.org/10.1016/j.pdpdt.2020.101923
Zhang J, Zhang X, Zhao X, Jiang M, Gu M, Wang Z et al (2017) DKK1 promotes migration and invasion of non-small cell lung cancer via β-catenin signaling pathway. Tumor Biol 39:1010428317703820
Zhang F, Yu X, Lin Z et al (2021) Using tumor-infiltrating immune cells and a ceRNA network model to construct a prognostic analysis model of thyroid carcinoma. Front Oncol 11:658165. https://doi.org/10.3389/fonc.2021.658165
Zhou L, Tang H, Wang F et al (2018) Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma. Mol Med Rep 18(5):4185–4196. https://doi.org/10.3892/mmr.2018.9411
Zhuang X, Zhang H, Li X, Li X, Cong M, Peng F et al (2017) Differential effects on lung and bone metastasis of breast cancer by Wnt signalling inhibitor DKK1. Nat Cell Biol 19:1274–1285. https://doi.org/10.1038/ncb3613
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XH: Conception and design, acquisition of data, analysis and interpretation of data, drafting the article. YS and CC: Reviewed submitted version of the manuscript. PL and HG: Statistical analysis. XL, WG and CC: Study supervision. All authors reviewed the manuscript.
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He, X., Su, Y., Liu, P. et al. Machine learning-based immune prognostic model and ceRNA network construction for lung adenocarcinoma. J Cancer Res Clin Oncol 149, 7379–7392 (2023). https://doi.org/10.1007/s00432-023-04609-1
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DOI: https://doi.org/10.1007/s00432-023-04609-1