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Current Genomics

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ISSN (Print): 1389-2029
ISSN (Online): 1875-5488

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Research Article

A Novel Methylation-based Model for Prognostic Prediction in Lung Adenocarcinoma

Author(s): Manyuan Li, Xufeng Deng, Dong Zhou, Xiaoqing Liu, Jigang Dai and Quanxing Liu*

Volume 25, Issue 1, 2024

Published on: 22 January, 2024

Page: [26 - 40] Pages: 15

DOI: 10.2174/0113892029277397231228062412

Price: $65

Abstract

Objectives: Specific methylation sites have shown promise in the early diagnosis of lung adenocarcinoma (LUAD). However, their utility in predicting LUAD prognosis remains unclear. This study aimed to construct a reliable methylation-based predictor for accurately predicting the prognosis of LUAD patients.

Methods: DNA methylation data and survival data from LUAD patients were obtained from the TCGA and a GEO series. A DNA methylation-based signature was developed using univariate least absolute shrinkage and selection operators and multivariate Cox regression models.

Results: Eight CpG sites were identified and validated as optimal prognostic signatures for the overall survival of LUAD patients. Receiver operating characteristic analysis demonstrated the high predictive ability of the eight-site methylation signature combined with clinical factors for overall survival.

Conclusion: This research successfully identified a novel eight-site methylation signature for predicting the overall survival of LUAD patients through bioinformatic integrated analysis of gene methylation markers used in the early diagnosis of lung cancer.

Keywords: Lung adenocarcinoma, DNA methylation, diagnosis, methylated sites, overall survival, signature.

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[1]
Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin., 2023, 73(1), 17-48.
[http://dx.doi.org/10.3322/caac.21763] [PMID: 36633525]
[2]
Zhou, Y.; Gao, S.; Yang, R.; Du, C.; Wang, Y.; Wu, Y. Identification of a three-gene expression signature and construction of a prognostic nomogram predicting overall survival in lung adenocarcinoma based on TCGA and GEO databases. Transl. Lung Cancer Res., 2022, 11(7), 1479-1496.
[http://dx.doi.org/10.21037/tlcr-22-444] [PMID: 35958325]
[3]
Travis, W.D. Pathology of lung cancer. Clin. Chest Med., 2011, 32(4), 669-692.
[http://dx.doi.org/10.1016/j.ccm.2011.08.005] [PMID: 22054879]
[4]
Chen, W.; Zheng, R.; Baade, P.D.; Zhang, S.; Zeng, H.; Bray, F.; Jemal, A.; Yu, X.Q.; He, J. Cancer statistics in China, 2015. CA Cancer J. Clin., 2016, 66(2), 115-132.
[http://dx.doi.org/10.3322/caac.21338] [PMID: 26808342]
[5]
Hao, C.; Xu, C.; Zhao, X.; Luo, J.; Wang, G.; Zhao, L.; Ge, X.; Ge, X. Up-regulation of VANGL1 by IGF2BPs and miR-29b-3p attenuates the detrimental effect of irradiation on lung adenocarcinoma. J. Exp. Clin. Cancer Res., 2020, 39(1), 256.
[http://dx.doi.org/10.1186/s13046-020-01772-y] [PMID: 33228740]
[6]
Buddharaju, L.N.R.; Ganti, A.K. Immunotherapy in lung cancer: The chemotherapy conundrum. Chin. Clin. Oncol., 2020, 9(4), 59.
[http://dx.doi.org/10.21037/cco.2020.01.05] [PMID: 32036674]
[7]
Wang, H.; Wei, C.; Pan, P.; Yuan, F.; Cheng, J. Identification of a methylomics-associated nomogram for predicting overall survival of stage I–II lung adenocarcinoma. Sci. Rep., 2021, 11(1), 9938.
[http://dx.doi.org/10.1038/s41598-021-89429-4] [PMID: 33976305]
[8]
Liu, Y.; Wang, B.; Shi, S.; Li, Z.; Wang, Y.; Yang, J. Construction of methylation-associated nomogram for predicting the recurrence-free survival risk of stage I–III lung adenocarcinoma. Future Oncol., 2021, fon-2020-1270.
[http://dx.doi.org/10.2217/fon-2020-1270] [PMID: 34476982]
[9]
Kim, J.Y.; Choi, J.K.; Jung, H. Genome-wide methylation patterns predict clinical benefit of immunotherapy in lung cancer. Clin. Epigenetics, 2020, 12(1), 119.
[http://dx.doi.org/10.1186/s13148-020-00907-4] [PMID: 32762727]
[10]
Wang, X.; Zhou, B.; Xia, Y.; Zuo, J.; Liu, Y.; Bi, X.; Luo, X.; Zhang, C. A methylation-based nomogram for predicting survival in patients with lung adenocarcinoma. BMC Cancer, 2021, 21(1), 801.
[http://dx.doi.org/10.1186/s12885-021-08539-4] [PMID: 34247575]
[11]
Luo, W.M.; Wang, Z.Y.; Zhang, X. Identification of four differentially methylated genes as prognostic signatures for stage I lung adenocarcinoma. Cancer Cell Int., 2018, 18(1), 60.
[http://dx.doi.org/10.1186/s12935-018-0547-6] [PMID: 29713243]
[12]
Orooji, M.; Alilou, M.; Rakshit, S.; Beig, N.; Khorrami, M.H.; Rajiah, P.; Thawani, R.; Ginsberg, J.; Donatelli, C.; Yang, M.; Jacono, F.; Gilkeson, R.; Velcheti, V.; Linden, P.; Madabhushi, A. Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography. J. Med. Imaging, 2018, 5(2), 1.
[http://dx.doi.org/10.1117/1.JMI.5.2.024501] [PMID: 29721515]
[13]
Liang, W.; Chen, Z.; Li, C.; Liu, J.; Tao, J.; Liu, X.; Zhao, D.; Yin, W.; Chen, H.; Cheng, C.; Yu, F.; Zhang, C.; Liu, L.; Tian, H.; Cai, K.; Liu, X.; Wang, Z.; Xu, N.; Dong, Q.; Chen, L.; Yang, Y.; Zhi, X.; Li, H.; Tu, X.; Cai, X.; Jiang, Z.; Ji, H.; Mo, L.; Wang, J.; Fan, J.B.; He, J. Accurate diagnosis of pulmonary nodules using a noninvasive DNA methylation test. J. Clin. Invest., 2021, 131(10), e145973.
[http://dx.doi.org/10.1172/JCI145973] [PMID: 33793424]
[14]
Qi, J.; Hong, B.; Tao, R.; Sun, R.; Zhang, H.; Zhang, X.; Ji, J.; Wang, S.; Liu, Y.; Deng, Q.; Wang, H.; Zhao, D.; Nie, J. Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning. Cancer Sci., 2021, 112(9), 3918-3923.
[http://dx.doi.org/10.1111/cas.15052] [PMID: 34251068]
[15]
Liu, Q.X.; Zhou, D.; Han, T.C.; Lu, X.; Hou, B.; Li, M.Y.; Yang, G.X.; Li, Q.Y.; Pei, Z.H.; Hong, Y.Y.; Zhang, Y.X.; Chen, W.Z.; Zheng, H.; He, J.; Dai, J.G. A noninvasive multianalytical approach for lung cancer diagnosis of patients with pulmonary nodules. Adv. Sci., 2021, 8(13), 2100104.
[http://dx.doi.org/10.1002/advs.202100104] [PMID: 34258160]
[16]
Zheng, R.; Xu, H.; Mao, W.; Du, Z.; Wang, M.; Hu, M.; Gu, X. A novel CpG-based signature for survival prediction of lung adenocarcinoma patients. Exp. Ther. Med., 2020, 19(1), 280-286.
[PMID: 31853300]
[17]
Wang, R.; Zhu, H.; Yang, M.; Zhu, C. DNA methylation profiling analysis identifies a DNA methylation signature for predicting prognosis and recurrence of lung adenocarcinoma. Oncol. Lett., 2019, 18(6), 5831-5842.
[http://dx.doi.org/10.3892/ol.2019.10931] [PMID: 31788056]
[18]
Shen, N.; Du, J.; Zhou, H.; Chen, N.; Pan, Y.; Hoheisel, J.D.; Jiang, Z.; Xiao, L.; Tao, Y.; Mo, X. A diagnostic panel of DNA methylation biomarkers for lung adenocarcinoma. Front. Oncol., 2019, 9, 1281.
[http://dx.doi.org/10.3389/fonc.2019.01281] [PMID: 31850197]
[19]
Seok, Y.; Lee, W.K.; Park, J.Y.; Kim, D.S. TGFBI promoter methylation is associated with poor prognosis in lung adenocarcinoma patients. Mol. Cells, 2019, 42(2), 161-165.
[PMID: 30726660]
[20]
Pan, X.; Ji, P.; Deng, X.; Chen, L.; Wang, W.; Li, Z. Genome-wide analysis of methylation CpG sites in gene promoters identified four pairs of CpGs-mRNAs associated with lung adenocarcinoma prognosis. Gene, 2022, 810, 146054.
[http://dx.doi.org/10.1016/j.gene.2021.146054] [PMID: 34737001]
[21]
Tavassoly, I.; Hu, Y.; Zhao, S.; Mariottini, C.; Boran, A.; Chen, Y.; Li, L.; Tolentino, R.E.; Jayaraman, G.; Goldfarb, J.; Gallo, J.; Iyengar, R. Genomic signatures defining responsiveness to allopurinol and combination therapy for lung cancer identified by systems therapeutics analyses. Mol. Oncol., 2019, 13(8), 1725-1743.
[http://dx.doi.org/10.1002/1878-0261.12521] [PMID: 31116490]
[22]
Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2021, 71(3), 209-249.
[http://dx.doi.org/10.3322/caac.21660] [PMID: 33538338]
[23]
Karlsson, A.; Jönsson, M.; Lauss, M.; Brunnström, H.; Jönsson, P.; Borg, Å.; Jönsson, G.; Ringnér, M.; Planck, M.; Staaf, J. Genome-wide DNA methylation analysis of lung carcinoma reveals one neuroendocrine and four adenocarcinoma epitypes associated with patient outcome. Clin. Cancer Res., 2014, 20(23), 6127-6140.
[http://dx.doi.org/10.1158/1078-0432.CCR-14-1087] [PMID: 25278450]
[24]
Liang, W.; Zhao, Y.; Huang, W.; Gao, Y.; Xu, W.; Tao, J.; Yang, M.; Li, L.; Ping, W.; Shen, H.; Fu, X.; Chen, Z.; Laird, P.W.; Cai, X.; Fan, J.B.; He, J. Non-invasive diagnosis of early-stage lung cancer using high-throughput targeted DNA methylation sequencing of circulating tumor DNA (ctDNA). Theranostics, 2019, 9(7), 2056-2070.
[http://dx.doi.org/10.7150/thno.28119] [PMID: 31037156]
[25]
Chen, C.; Huang, X.; Yin, W.; Peng, M.; Wu, F.; Wu, X.; Tang, J.; Chen, M.; Wang, X.; Hulbert, A.; Brock, M.V.; Liu, W.; Herman, J.G.; Yu, F. Ultrasensitive DNA hypermethylation detection using plasma for early detection of NSCLC: A study in Chinese patients with very small nodules. Clin. Epigenetics, 2020, 12(1), 39.
[http://dx.doi.org/10.1186/s13148-020-00828-2] [PMID: 32138766]
[26]
Xie, C.; Mao, X.; Huang, J.; Ding, Y.; Wu, J.; Dong, S.; Kong, L.; Gao, G.; Li, C. Y.; Wei, L. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res,, 2011, 39, 316-322.
[27]
De Angelis, G.; De Angelis, R.; Frova, L.; Verdecchia, A. MIAMOD: A computer package to estimate chronic disease morbidity using mortality and survival data. Comput. Methods Programs Biomed., 1994, 44(2), 99-107.
[http://dx.doi.org/10.1016/0169-2607(94)90091-4] [PMID: 7988122]
[28]
Robin, X.; Turck, N.; Hainard, A.; Tiberti, N.; Lisacek, F.; Sanchez, J.C.; Müller, M. pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics, 2011, 12(1), 77.
[http://dx.doi.org/10.1186/1471-2105-12-77] [PMID: 21414208]
[29]
Wu, T.H.; Chang, S.Y.; Shih, Y.L.; Chian, C.F.; Chang, H.; Lin, Y.W. Epigenetic silencing of LMX1A contributes to cancer progression in lung cancer cells. Int. J. Mol. Sci., 2020, 21(15), 5425.
[http://dx.doi.org/10.3390/ijms21155425] [PMID: 32751497]
[30]
Savci-Heijink, C.D.; Halfwerk, H.; Koster, J.; van de Vijver, M.J. A novel gene expression signature for bone metastasis in breast carcinomas. Breast Cancer Res. Treat., 2016, 156(2), 249-259.
[http://dx.doi.org/10.1007/s10549-016-3741-z] [PMID: 26965286]
[31]
Gao, Q.; Zhang, G.; Zheng, Y.; Yang, Y.; Chen, C.; Xia, J.; Liang, L.; Lei, C.; Hu, Y.; Cai, X.; Zhang, W.; Tang, H.; Chen, Y.; Huang, A.; Wang, K.; Tang, N. SLC27A5 deficiency activates NRF2/TXNRD1 pathway by increased lipid peroxidation in HCC. Cell Death Differ., 2020, 27(3), 1086-1104.
[http://dx.doi.org/10.1038/s41418-019-0399-1] [PMID: 31367013]
[32]
Elango, R.; Vishnubalaji, R.; Shaath, H.; Alajez, N.M. Molecular subtyping and functional validation of TTK, TPX2, UBE2C, and LRP8 in sensitivity of TNBC to paclitaxel. Mol. Ther. Methods Clin. Dev., 2021, 20, 601-614.
[http://dx.doi.org/10.1016/j.omtm.2021.01.013] [PMID: 33665229]
[33]
Cieply, B.; Farris, J.; Denvir, J.; Ford, H.L.; Frisch, S.M. Epithelial-mesenchymal transition and tumor suppression are controlled by a reciprocal feedback loop between ZEB1 and Grainyhead- like-2. Cancer Res., 2013, 73(20), 6299-6309.
[http://dx.doi.org/10.1158/0008-5472.CAN-12-4082] [PMID: 23943797]
[34]
Munakata, K.; Uemura, M.; Tanaka, S.; Kawai, K.; Kitahara, T.; Miyo, M.; Kano, Y.; Nishikawa, S.; Fukusumi, T.; Takahashi, Y.; Hata, T.; Nishimura, J.; Takemasa, I.; Mizushima, T.; Ikenaga, M.; Kato, T.; Murata, K.; Carethers, J.M.; Yamamoto, H.; Doki, Y.; Mori, M. Cancer stem-like properties in colorectal cancer cells with low proteasome activity. Clin. Cancer Res., 2016, 22(21), 5277-5286.
[http://dx.doi.org/10.1158/1078-0432.CCR-15-1945] [PMID: 27166395]
[35]
Roslan, Z.; Muhamad, M.; Selvaratnam, L.; Ab-Rahim, S. The roles of low-density lipoprotein receptor-related proteins 5, 6, and 8 in cancer: A review. J. Oncol., 2019, 2019, 1-6.
[http://dx.doi.org/10.1155/2019/4536302] [PMID: 31031810]
[36]
Dun, B.; Sharma, A.; Teng, Y.; Liu, H.; Purohit, S.; Xu, H.; Zeng, L.; She, J.X. Mycophenolic acid inhibits migration and invasion of gastric cancer cells via multiple molecular pathways. PLoS One, 2013, 8(11), e81702.
[http://dx.doi.org/10.1371/journal.pone.0081702] [PMID: 24260584]
[37]
Lu, J.; Ma, Y.; Zhao, Z. MiR-142 suppresses progression of gastric carcinoma via directly targeting LRP8. Clin. Res. Hepatol. Gastroenterol., 2021, 45(4), 101520.
[http://dx.doi.org/10.1016/j.clinre.2020.08.001] [PMID: 33268037]
[38]
Qiu, H.; Shen, X.; Chen, B.; Chen, T.; Feng, G.; Chen, S.; Feng, D.; Xu, Q. miR-30b-5p inhibits cancer progression and enhances cisplatin sensitivity in lung cancer through targeting LRP8. Apoptosis, 2021, 26(5-6), 261-276.
[http://dx.doi.org/10.1007/s10495-021-01665-1] [PMID: 33779882]
[39]
Passarella, D.; Ciampi, S.; Di Liberto, V.; Zuccarini, M.; Ronci, M.; Medoro, A.; Foderà, E.; Frinchi, M.; Mignogna, D.; Russo, C.; Porcile, C. Low-density lipoprotein receptor-related protein 8 at the crossroad between cancer and neurodegeneration. Int. J. Mol. Sci., 2022, 23(16), 8921.
[http://dx.doi.org/10.3390/ijms23168921] [PMID: 36012187]
[40]
Lin, C.C.; Lo, M.C.; Moody, R.; Jiang, H.; Harouaka, R.; Stevers, N.; Tinsley, S.; Gasparyan, M.; Wicha, M.; Sun, D. Targeting LRP8 inhibits breast cancer stem cells in triple-negative breast cancer. Cancer Lett., 2018, 438, 165-173.
[http://dx.doi.org/10.1016/j.canlet.2018.09.022] [PMID: 30227220]
[41]
Fang, Z.; Zhong, M.; Zhou, L.; Le, Y.; Wang, H.; Fang, Z. Low-density lipoprotein receptor-related protein 8 facilitates the proliferation and invasion of non-small cell lung cancer cells by regulating the Wnt/β-catenin signaling pathway. Bioengineered, 2022, 13(3), 6807-6818.
[http://dx.doi.org/10.1080/21655979.2022.2036917] [PMID: 35246020]
[42]
Maire, V.; Mahmood, F.; Rigaill, G.; Ye, M.; Brisson, A.; Némati, F.; Gentien, D.; Tucker, G.C.; Roman-Roman, S.; Dubois, T. LRP8 is overexpressed in estrogen-negative breast cancers and a potential target for these tumors. Cancer Med., 2019, 8(1), 325-336.
[http://dx.doi.org/10.1002/cam4.1923] [PMID: 30575334]
[43]
Du, S.; Wang, H.; Cai, J.; Ren, R.; Zhang, W.; Wei, W.; Shen, X. Apolipoprotein E2 modulates cell cycle function to promote proliferation in pancreatic cancer cells via regulation of the c-Myc–p21 Waf1 signalling pathway. Biochem. Cell Biol., 2020, 98(2), 191-202.
[http://dx.doi.org/10.1139/bcb-2018-0230] [PMID: 32167787]
[44]
Arai, T.; Kojima, S.; Yamada, Y.; Sugawara, S.; Kato, M.; Yamazaki, K.; Naya, Y.; Ichikawa, T.; Seki, N. Pirin: A potential novel therapeutic target for castration-resistant prostate cancer regulated by miR‐455‐5p. Mol. Oncol., 2019, 13(2), 322-337.
[http://dx.doi.org/10.1002/1878-0261.12405] [PMID: 30444038]
[45]
Cai, J.; Chen, J.; Wu, T.; Cheng, Z.; Tian, Y.; Pu, C.; Shi, W.; Suo, X.; Wu, X.; Zhang, K. Genome-scale CRISPR activation screening identifies a role of LRP8 in Sorafenib resistance in Hepatocellular carcinoma. Biochem. Biophys. Res. Commun., 2020, 526(4), 1170-1176.
[http://dx.doi.org/10.1016/j.bbrc.2020.04.040] [PMID: 32312520]
[46]
Sun, J.C.; Wang, L.; Zhu, X.H.; Shen, M.L. Hsa_circ_0006427 suppresses multiplication, migration and invasion of non-small cell lung cancer cells through miR-346/VGLL4 pathway. Cell J., 2022, 24(5), 245-254.
[PMID: 35717572]
[47]
Deng, X.; Fang, L. VGLL4 is a transcriptional cofactor acting as a novel tumor suppressor via interacting with TEADs. Am. J. Cancer Res., 2018, 8(6), 932-943.
[PMID: 30034932]
[48]
Geng, H.; Liu, G.; Hu, J.; Li, J.; Wang, D.; Zou, S.; Xu, X. HOXB13 suppresses proliferation, migration and invasion, and promotes apoptosis of gastric cancer cells through transcriptional activation of VGLL4 to inhibit the involvement of TEAD4 in the Hippo signaling pathway. Mol. Med. Rep., 2021, 24(4), 722.
[http://dx.doi.org/10.3892/mmr.2021.12361] [PMID: 34396425]
[49]
Mickle, M.; Adhikary, G.; Shrestha, S.; Xu, W.; Eckert, R.L. VGLL4 inhibits YAP1/TEAD signaling to suppress the epidermal squamous cell carcinoma cancer phenotype. Mol. Carcinog., 2021, 60(7), 497-507.
[http://dx.doi.org/10.1002/mc.23307] [PMID: 34004031]
[50]
Zheng, S.; Wei, Y.; Jiang, Y.; Hao, Y. LRP8 activates STAT3 to induce PD-L1 expression in osteosarcoma. Tumori, 2021, 107(3), 238-246.
[PMID: 33054597]
[51]
Liu, M.; Wang, W.; Zhang, H.; Bi, J.; Zhang, B.; Shi, T.; Su, G.; Zheng, Y.; Fan, S.; Huang, X.; Chen, B.; Song, Y.; Zhao, Z.; Shi, J.; Li, P.; Lu, W.; Zhang, L. Three-dimensional gene regulation network in glioblastoma ferroptosis.. Int. J. Mol. Sci., 2023, 24(19), 14945.
[52]
Cadenas, C.; Franckenstein, D.; Schmidt, M.; Gehrmann, M.; Hermes, M.; Geppert, B.; Schormann, W.; Maccoux, L.J.; Schug, M.; Schumann, A.; Wilhelm, C.; Freis, E.; Ickstadt, K.; Rahnenführer, J.; Baumbach, J.I.; Sickmann, A.; Hengstler, J.G. Role of thioredoxin reductase 1 and thioredoxin interacting protein in prognosis of breast cancer. Breast Cancer Res., 2010, 12(3), R44.
[http://dx.doi.org/10.1186/bcr2599] [PMID: 20584310]
[53]
Leone, A.; Roca, M.S.; Ciardiello, C.; Costantini, S.; Budillon, A. Oxidative stress gene expression profile correlates with cancer patient poor prognosis: Identification of crucial pathways might select novel therapeutic approaches. Oxid. Med. Cell. Longev., 2017, 2017, 1-18.
[http://dx.doi.org/10.1155/2017/2597581] [PMID: 28770020]
[54]
Bhatia, M.; McGrath, K.L.; Di Trapani, G.; Charoentong, P.; Shah, F.; King, M.M.; Clarke, F.M.; Tonissen, K.F. The thioredoxin system in breast cancer cell invasion and migration. Redox Biol., 2016, 8, 68-78.
[http://dx.doi.org/10.1016/j.redox.2015.12.004] [PMID: 26760912]
[55]
Guo, W.; Wu, Z.; Chen, J.; Guo, S.; You, W.; Wang, S.; Ma, J.; Wang, H.; Wang, X.; Wang, H.; Ma, J.; Yang, Y.; Tian, Y.; Shi, Q.; Gao, T.; Yi, X.; Li, C. Nanoparticle delivery of miR-21-3p sensitizes melanoma to anti-PD-1 immunotherapy by promoting ferroptosis. J. Immunother. Cancer, 2022, 10(6), e004381.
[http://dx.doi.org/10.1136/jitc-2021-004381] [PMID: 35738798]
[56]
Kim, J.Y.; Kim, E.K.; Lee, W.M.; Hong, Y.O.; Lee, H. VGLL4 with low YAP expression is associated with favorable prognosis in colorectal cancer. Acta Pathol. Microbiol. Scand. Suppl., 2020, 128(10), 543-551.
[http://dx.doi.org/10.1111/apm.13070] [PMID: 32794608]
[57]
Goeman, J.J. L1 penalized estimation in the Cox proportional hazards model. Biom. J., 2010, 52(1), 70-84.
[http://dx.doi.org/10.1002/bimj.200900028] [PMID: 19937997]

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