Molecular Subtypes and Prognostic Models for Predicting Prognosis
of Lung Adenocarcinoma based on MiRNA-related Genes

Yuxi      Wei; Wei      Zhong; Yalan      Bi; Xiaoyan      Liu; Qing      Zhou; Jia      Liu; Mengzhao      Wang; Hong      Zhang; Minjiang      Chen
doi:10.2174/0929867331666230914151943
Abstract

Background: MicroRNAs (miRNAs) are crucial in cancer development and progression, and therapies targeting miRNAs demonstrate great therapeutic promise.
Aim: We sought to predict the prognosis and therapeutic response of lung adenocarcinoma (LUAD) by classifying molecular subtypes and constructing a prognostic model based on miRNA-related genes.
Methods: This study was based on miRNA-mRNA action pairs and ceRNA networks in the Cancer Genome Atlas (TCGA) database. Three molecular subtypes were determined based on 64 miRNA-associated target genes identified in the ceRNA network. The S3 subtype had the best prognosis, and the S2 subtype had the worst prognosis. The S2 subtype had a higher tumor mutational load (TMB) and a lower immune score. The S2 subtype was more suitable for immunotherapy and sensitive to chemotherapy. The least absolute shrinkage and selection operator (LASSO) algorithm was performed to determine eight miRNA-associated target genes for the construction of prognostic models.
Result: High-risk patients had a poorer prognosis, lower immune score, and lower response to immunotherapy. Robustness was confirmed in the Gene-Expression Omnibus (GEO) database cohort (GSE31210, GSE50081, and GSE37745 datasets). Overall, our study deepened the understanding of the mechanism of miRNA-related target genes in LUAD and provided new ideas for classification.
Conclusion: Such miRNA-associated target gene characterization could be useful for prognostic prediction and contribute to therapeutic decision-making in LUAD.
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[1]
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]

[2]
Thandra, K. C.; Barsouk, A.; Saginala, K.; Aluru, J. S.; Barsouk, A. Epidemiology of lung cancer. Contemp Oncol.,  2021, 25(1), 45-52.

[3]
Myers, D.J.; Wallen, J.M. Lung adenocarcinoma.StatPearls; StatPearls Publishing, 2022. 

[4]
Saito, M.; Suzuki, H.; Kono, K.; Takenoshita, S.; Kohno, T. Treatment of lung adenocarcinoma by molecular-targeted therapy and immunotherapy. Surg. Today,  2018, 48(1), 1-8.
 [http://dx.doi.org/10.1007/s00595-017-1497-7] [PMID: 28280984]

[5]
Cesana, M.; Daley, G.Q. Deciphering the rules of ceRNA networks. Proc. Natl. Acad. Sci.,  2013, 110(18), 7112-7113.
 [http://dx.doi.org/10.1073/pnas.1305322110] [PMID: 23620514]

[6]
Hill, M.; Tran, N. miRNA interplay: Mechanisms and consequences in cancer. Dis. Model. Mech.,  2021, 14(4), dmm047662.
 [http://dx.doi.org/10.1242/dmm.047662] [PMID: 33973623]

[7]
Saliminejad, K.; Khorram Khorshid, H.R.; Soleymani Fard, S.; Ghaffari, S.H. An overview of microRNAs: Biology, functions, therapeutics, and analysis methods. J. Cell. Physiol.,  2019, 234(5), 5451-5465.
 [http://dx.doi.org/10.1002/jcp.27486] [PMID: 30471116]

[8]
Ali Syeda, Z.; Langden, S.S.S.; Munkhzul, C.; Lee, M.; Song, S.J. Regulatory mechanism of MicroRNA expression in cancer. Int. J. Mol. Sci.,  2020, 21(5), 1723.
 [http://dx.doi.org/10.3390/ijms21051723] [PMID: 32138313]

[9]
He, B.; Zhao, Z.; Cai, Q.; Zhang, Y.; Zhang, P.; Shi, S.; Xie, H.; Peng, X.; Yin, W.; Tao, Y.; Wang, X. miRNA-based biomarkers, therapies, and resistance in Cancer. Int. J. Biol. Sci.,  2020, 16(14), 2628-2647.
 [http://dx.doi.org/10.7150/ijbs.47203] [PMID: 32792861]

[10]
Yoon, A.J.; Wang, S.; Kutler, D.I.; Carvajal, R.D.; Philipone, E.; Wang, T.; Peters, S.M.; LaRoche, D.; Hernandez, B.Y.; McDowell, B.D.; Stewart, C.R.; Momen-Heravi, F.; Santella, R.M. MicroRNA-based risk scoring system to identify early-stage oral squamous cell carcinoma patients at high-risk for cancer-specific mortality. Head Neck,  2020, 42(8), 1699-1712.
 [http://dx.doi.org/10.1002/hed.26089] [PMID: 31981257]

[11]
Si, W.; Shen, J.; Zheng, H.; Fan, W. The role and mechanisms of action of microRNAs in cancer drug resistance. Clin. Epigenetics,  2019, 11(1), 25.
 [http://dx.doi.org/10.1186/s13148-018-0587-8] [PMID: 30744689]

[12]
Baldassari, F.; Zerbinati, C.; Galasso, M.; Corrà, F.; Minotti, L.; Agnoletto, C.; Previati, M.; Croce, C.M.; Volinia, S. Screen for MicroRNA and drug interactions in breast cancer cell lines points to miR-126 as a modulator of CDK4/6 and PIK3CA inhibitors. Front. Genet.,  2018, 9, 174.
 [http://dx.doi.org/10.3389/fgene.2018.00174] [PMID: 29868122]

[13]
Tu, M.J.; Ho, P.Y.; Zhang, Q.Y.; Jian, C.; Qiu, J.X.; Kim, E.J.; Bold, R.J.; Gonzalez, F.J.; Bi, H.; Yu, A.M. Bioengineered miRNA-1291 prodrug therapy in pancreatic cancer cells and patient-derived xenograft mouse models. Cancer Lett.,  2019, 442, 82-90.
 [http://dx.doi.org/10.1016/j.canlet.2018.10.038] [PMID: 30389433]

[14]
Gautier, L.; Cope, L.; Bolstad, B.M.; Irizarry, R.A. affy—analysis of Affymetrix GeneChip data at the probe level. Bioinformatics,  2004, 20(3), 307-315.
 [http://dx.doi.org/10.1093/bioinformatics/btg405] [PMID: 14960456]

[15]
Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res.,  2015, 43(7), e47-e47.
 [http://dx.doi.org/10.1093/nar/gkv007] [PMID: 25605792]

[16]
Krek, A.; Grün, D.; Poy, M.N.; Wolf, R.; Rosenberg, L.; Epstein, E.J.; MacMenamin, P.; da Piedade, I.; Gunsalus, K.C.; Stoffel, M.; Rajewsky, N. Combinatorial microRNA target predictions. Nat. Genet.,  2005, 37(5), 495-500.
 [http://dx.doi.org/10.1038/ng1536] [PMID: 15806104]

[17]
Enright, A.J.; John, B.; Gaul, U.; Tuschl, T.; Sander, C.; Marks, D.S. MicroRNA targets in drosophila. Genome Biol.,  2003, 5(1), R1.
 [http://dx.doi.org/10.1186/gb-2003-5-1-r1] [PMID: 14709173]

[18]
John, B.; Enright, A.J.; Aravin, A.; Tuschl, T.; Sander, C.; Marks, D.S. Human microRNA targets. PLoS Biol.,  2004, 2(11), e363.
 [http://dx.doi.org/10.1371/journal.pbio.0020363] [PMID: 15502875]

[19]
Kiriakidou, M.; Nelson, P.T.; Kouranov, A.; Fitziev, P.; Bouyioukos, C.; Mourelatos, Z.; Hatzigeorgiou, A. A combined computational-experimental approach predicts human microRNA targets. Genes Dev.,  2004, 18(10), 1165-1178.
 [http://dx.doi.org/10.1101/gad.1184704] [PMID: 15131085]

[20]
Bandyopadhyay, S.; Mitra, R. TargetMiner: MicroRNA target prediction with systematic identification of tissue-specific negative examples. Bioinformatics,  2009, 25(20), 2625-2631.
 [http://dx.doi.org/10.1093/bioinformatics/btp503] [PMID: 19692556]

[21]
Huang, H.Y.; Lin, Y.C.; Li, J.; Huang, K.Y.; Shrestha, S.; Hong, H.C.; Tang, Y.; Chen, Y.G.; Jin, C.N.; Yu, Y.; Xu, J.T.; Li, Y.M.; Cai, X.X.; Zhou, Z.Y.; Chen, X.H.; Pei, Y.Y.; Hu, L.; Su, J.J.; Cui, S.D.; Wang, F.; Xie, Y.Y.; Ding, S.Y.; Luo, M.F.; Chou, C.H.; Chang, N.W.; Chen, K.W.; Cheng, Y.H.; Wan, X.H.; Hsu, W.L.; Lee, T.Y.; Wei, F.X.; Huang, H.D. miRTarBase 2020: Updates to the experimentally validated microRNA-target interaction database. Nucleic Acids Res.,  2020, 48(D1), D148-D154.
 [PMID: 31647101]

[22]
Vejnar, C. E.; Blum, M.; Zdobnov, E. M. MiRmap web: Comprehensive microRNA target prediction online. Nucleic Acids Res,  2013, 41(Web Server issue), W165-W168.

[23]
Chen, Y.; Wang, X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res.,  2020, 48(D1), D127-D131.
 [http://dx.doi.org/10.1093/nar/gkz757] [PMID: 31504780]

[24]
Miranda, K.C.; Huynh, T.; Tay, Y.; Ang, Y.S.; Tam, W.L.; Thomson, A.M.; Lim, B.; Rigoutsos, I. A pattern-based method for the identification of microRNA binding sites and their corresponding heteroduplexes. Cell,  2006, 126(6), 1203-1217.
 [http://dx.doi.org/10.1016/j.cell.2006.07.031] [PMID: 16990141]

[25]
Jeggari, A.; Marks, D.S.; Larsson, E. miRcode: A map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics,  2012, 28(15), 2062-2063.
 [http://dx.doi.org/10.1093/bioinformatics/bts344] [PMID: 22718787]

[26]
Kertesz, M.; Iovino, N.; Unnerstall, U.; Gaul, U.; Segal, E. The role of site accessibility in microRNA target recognition. Nat. Genet.,  2007, 39(10), 1278-1284.
 [http://dx.doi.org/10.1038/ng2135] [PMID: 17893677]

[27]
Long, D.; Lee, R.; Williams, P.; Chan, C.Y.; Ambros, V.; Ding, Y. Potent effect of target structure on microRNA function. Nat. Struct. Mol. Biol.,  2007, 14(4), 287-294.
 [http://dx.doi.org/10.1038/nsmb1226] [PMID: 17401373]

[28]
Zhao, Y.; Samal, E.; Srivastava, D. Serum response factor regulates a muscle-specific microRNA that targets Hand2 during cardiogenesis. Nature,  2005, 436(7048), 214-220.
 [http://dx.doi.org/10.1038/nature03817] [PMID: 15951802]

[29]
Lewis, B.P.; Shih, I.; Jones-Rhoades, M.W.; Bartel, D.P.; Burge, C.B. Prediction of mammalian microRNA targets. Cell,  2003, 115(7), 787-798.
 [http://dx.doi.org/10.1016/S0092-8674(03)01018-3] [PMID: 14697198]

[30]
Li, J.H.; Liu, S.; Zhou, H.; Qu, L.H.; Yang, J.H. starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res.,  2014, 42(D1), D92-D97.
 [http://dx.doi.org/10.1093/nar/gkt1248] [PMID: 24297251]

[31]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res.,  2003, 13(11), 2498-2504.
 [http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]

[32]
Liao, Y.; Wang, J.; Jaehnig, E.J.; Shi, Z.; Zhang, B. WebGestalt 2019: Gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res.,  2019, 47(W1), W199-W205.
 [http://dx.doi.org/10.1093/nar/gkz401] [PMID: 31114916]

[33]
Wilkerson, M.; Waltman, P.; Wilkerson, M. M. Package ‘ConsensusClusterPlus’. 2013. Available from: https://bioconductor.riken.jp/packages/2.14/bioc/html/ConsensusClusterPlus.html

[34]
Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS,  2012, 16(5), 284-287.
 [http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]

[35]
Hastie, T.; Qian, J.; Tay, K. An Introduction to glmnet.  CRAN R Repositary, 2021. Available from: https://cran.r-project.org/web/packages/glmnet/vignettes/glmnet.pdf

[36]
Blanche, P.  Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med Res Methodol, 2015, 17(53). Available from: https://cran.r-project.org/web/packages/glmnet/vignettes/glmnet.pdf

[37]
Mariathasan, S.; Turley, S.J.; Nickles, D.; Castiglioni, A.; Yuen, K.; Wang, Y.; Kadel, E.E., III; Koeppen, H.; Astarita, J.L.; Cubas, R.; Jhunjhunwala, S.; Banchereau, R.; Yang, Y.; Guan, Y.; Chalouni, C.; Ziai, J.; Şenbabaoğlu, Y.; Santoro, S.; Sheinson, D.; Hung, J.; Giltnane, J.M.; Pierce, A.A.; Mesh, K.; Lianoglou, S.; Riegler, J.; Carano, R.A.D.; Eriksson, P.; Höglund, M.; Somarriba, L.; Halligan, D.L.; van der Heijden, M.S.; Loriot, Y.; Rosenberg, J.E.; Fong, L.; Mellman, I.; Chen, D.S.; Green, M.; Derleth, C.; Fine, G.D.; Hegde, P.S.; Bourgon, R.; Powles, T. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature,  2018, 554(7693), 544-548.
 [http://dx.doi.org/10.1038/nature25501] [PMID: 29443960]

[38]
Kassambara, A. ggcorrplot: Visualization of a Correlation Matrix using ggplot2 (0.1. 3). 2019. Available from: https://github.com/kassambara/ggcorrplot

[39]
Balar, A.V.; Galsky, M.D.; Rosenberg, J.E.; Powles, T.; Petrylak, D.P.; Bellmunt, J.; Loriot, Y.; Necchi, A.; Hoffman-Censits, J.; Perez-Gracia, J.L.; Dawson, N.A.; van der Heijden, M.S.; Dreicer, R.; Srinivas, S.; Retz, M.M.; Joseph, R.W.; Drakaki, A.; Vaishampayan, U.N.; Sridhar, S.S.; Quinn, D.I.; Durán, I.; Shaffer, D.R.; Eigl, B.J.; Grivas, P.D.; Yu, E.Y.; Li, S.; Kadel, E.E., III; Boyd, Z.; Bourgon, R.; Hegde, P.S.; Mariathasan, S.; Thåström, A.; Abidoye, O.O.; Fine, G.D.; Bajorin, D.F. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: A single-arm, multicentre, phase 2 trial. Lancet,  2017, 389(10064), 67-76.
 [http://dx.doi.org/10.1016/S0140-6736(16)32455-2] [PMID: 27939400]

[40]
Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One,  2014, 9(9), e107468.
 [http://dx.doi.org/10.1371/journal.pone.0107468] [PMID: 25229481]

[41]
Riaz, N.; Havel, J. J.; Makarov, V.; Desrichard, A.; Urba, W. J.; Sims, J. S.; Hodi, F. S.; Martín-Algarra, S.; Mandal, R.; Sharfman, W. H. Tumor and microenvironment evolution during immunotherapy with nivolumab. Cell,  2017, 171(4), 934-949.
 [http://dx.doi.org/10.1016/j.cell.2017.09.028]

[42]
Hugo, W.; Zaretsky, J.M.; Sun, L.; Song, C.; Moreno, B.H.; Hu-Lieskovan, S.; Berent-Maoz, B.; Pang, J.; Chmielowski, B.; Cherry, G.; Seja, E.; Lomeli, S.; Kong, X.; Kelley, M.C.; Sosman, J.A.; Johnson, D.B.; Ribas, A.; Lo, R.S. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell,  2016, 165(1), 35-44.
 [http://dx.doi.org/10.1016/j.cell.2016.02.065] [PMID: 26997480]

[43]
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]

[44]
Sherafatian, M.; Arjmand, F. Decision tree-based classifiers for lung cancer diagnosis and subtyping using TCGA miRNA expression data. Oncol. Lett.,  2019, 18(2), 2125-2131.
 [http://dx.doi.org/10.3892/ol.2019.10462] [PMID: 31423286]

[45]
Sui, J.; Yang, R.S.; Xu, S.Y.; Zhang, Y.Q.; Li, C.Y.; Yang, S.; Yin, L.H.; Pu, Y.P.; Liang, G.Y. Comprehensive analysis of aberrantly expressed microRNA profiles reveals potential biomarkers of human lung adenocarcinoma progression. Oncol. Rep.,  2017, 38(4), 2453-2463.
 [http://dx.doi.org/10.3892/or.2017.5880] [PMID: 28791371]

[46]
Yao, Y.; Zhang, T.; Qi, L.; Liu, R.; Liu, G.; Wang, J.; Song, Q.; Sun, C. Comprehensive analysis of prognostic biomarkers in lung adenocarcinoma based on aberrant lncRNA–miRNA–mRNA networks and Cox regression models. Biosci. Rep.,  2020, 40(1), BSR20191554.
 [http://dx.doi.org/10.1042/BSR20191554] [PMID: 31950990]

[47]
Tiwari, A.; Mukherjee, B.; Dixit, M. MicroRNA key to angiogenesis regulation: MiRNA biology and therapy. Curr. Cancer Drug Targets,  2018, 18(3), 266-277.
 [http://dx.doi.org/10.2174/1568009617666170630142725] [PMID: 28669338]

[48]
Yang, W.; Yin, Y.; Bi, L.; Wang, Y.; Yao, J.; Xu, L.; Jiao, L. MiR-182-5p promotes the metastasis and epithelial-mesenchymal transition in non-small cell lung cancer by targeting EPAS1. J. Cancer,  2021, 12(23), 7120-7129.
 [http://dx.doi.org/10.7150/jca.60419] [PMID: 34729113]

[49]
Nai, A.; Ma, F.; He, Z.; Zeng, S.; Bashir, S.; Song, J.; Xu, M. Development and validation of a 7-gene inflammatory signature forecasts prognosis and diverse immune landscape in lung adenocarcinoma. Front. Mol. Biosci.,  2022, 9, 822739.
 [http://dx.doi.org/10.3389/fmolb.2022.822739] [PMID: 35372503]

[50]
Reddy, D.; Kumavath, R.; Tan, T.Z.; Ampasala, D.R.; Kumar, A.P. Peruvoside targets apoptosis and autophagy through MAPK Wnt/β-catenin and PI3K/AKT/mTOR signaling pathways in human cancers. Life Sci.,  2020, 241, 117147.
 [http://dx.doi.org/10.1016/j.lfs.2019.117147] [PMID: 31830480]

[51]
Chen, D.; Qiu, Y.; Gao, Z.; Wu, Y.X.; Wan, B.; Liu, G.; Chen, J.; Zhou, Q.; Yu, R.; Pang, Q. Sodium propionate attenuates the lipopolysaccharide-induced epithelial–mesenchymal transition via the PI3K/Akt/mTOR signaling pathway. J. Agric. Food Chem.,  2020, 68(24), 6554-6563.
 [http://dx.doi.org/10.1021/acs.jafc.0c01302] [PMID: 32452677]

[52]
Freudenstein, D.; Litchfield, C.; Caramia, F.; Wright, G.; Solomon, B.J.; Ball, D.; Keam, S.P.; Neeson, P.; Haupt, Y.; Haupt, S. TP53 status, patient sex, and the immune response as determinants of lung cancer patient survival. Cancers ,  2020, 12(6), 1535.
 [http://dx.doi.org/10.3390/cancers12061535] [PMID: 32545367]

[53]
Lin, X.; Wang, L.; Xie, X.; Qin, Y.; Xie, Z.; Ouyang, M.; Zhou, C. Prognostic biomarker TP53 mutations for immune checkpoint blockade therapy and its association with tumor microenvironment of lung adenocarcinoma. Front. Mol. Biosci.,  2020, 7, 602328.
 [http://dx.doi.org/10.3389/fmolb.2020.602328] [PMID: 33330629]

[54]
Sun, H.; Liu, S.Y.; Zhou, J.Y.; Xu, J.T.; Zhang, H.K.; Yan, H.H.; Huan, J.J.; Dai, P.P.; Xu, C.R.; Su, J.; Guan, Y.F.; Yi, X.; Yu, R.S.; Zhong, W.Z.; Wu, Y.L. Specific TP53 subtype as biomarker for immune checkpoint inhibitors in lung adenocarcinoma. EBioMedicine,  2020, 60, 102990.
 [http://dx.doi.org/10.1016/j.ebiom.2020.102990] [PMID: 32927274]

[55]
Zhang, L.; Jiang, B.; Lan, Z.; Yang, C.; Yao, Y.; Lin, J.; Wei, Q. Immune infiltration landscape on prognosis and therapeutic response and relevant epigenetic and transcriptomic mechanisms in lung adenocarcinoma. Front. Immunol.,  2022, 13, 983570.
 [http://dx.doi.org/10.3389/fimmu.2022.983570] [PMID: 36275753]

[56]
Hellmann, M.D.; Paz-Ares, L.; Bernabe Caro, R.; Zurawski, B.; Kim, S.W.; Carcereny Costa, E.; Park, K.; Alexandru, A.; Lupinacci, L.; de la Mora Jimenez, E.; Sakai, H.; Albert, I.; Vergnenegre, A.; Peters, S.; Syrigos, K.; Barlesi, F.; Reck, M.; Borghaei, H.; Brahmer, J.R.; O’Byrne, K.J.; Geese, W.J.; Bhagavatheeswaran, P.; Rabindran, S.K.; Kasinathan, R.S.; Nathan, F.E.; Ramalingam, S.S. Nivolumab plus ipilimumab in advanced non–small-cell lung cancer. N. Engl. J. Med.,  2019, 381(21), 2020-2031.
 [http://dx.doi.org/10.1056/NEJMoa1910231] [PMID: 31562796]

[57]
Xu, X.; Wang, K.; Vera, O.; Verma, A.; Jasani, N.; Bok, I.; Elemento, O.; Du, D.; Yu, X.; Karreth, F.A. Gain of chromosome 1q perturbs a competitive endogenous RNA network to promote melanoma metastasis. Cancer Res.,  2022, 82(17), 3016-3031.
 [http://dx.doi.org/10.1158/0008-5472.CAN-22-0283] [PMID: 36052492]

[58]
Kikutake, C.; Yoshihara, M.; Sato, T.; Saito, D.; Suyama, M. Intratumor heterogeneity of HMCN1 mutant alleles associated with poor prognosis in patients with breast cancer. Oncotarget,  2018, 9(70), 33337-33347.
 [http://dx.doi.org/10.18632/oncotarget.26071] [PMID: 30279964]

[59]
Liu, H.T.; Liu, S.; Liu, L.; Ma, R.R.; Gao, P. EGR1-mediated transcription of lncRNA-HNF1A-AS1 promotes cell-cycle progression in gastric cancer. Cancer Res.,  2018, 78(20), 5877-5890.
 [http://dx.doi.org/10.1158/0008-5472.CAN-18-1011] [PMID: 30185552]

[60]
Ricciuti, B.; Wang, X.; Alessi, J.V.; Rizvi, H.; Mahadevan, N.R.; Li, Y.Y.; Polio, A.; Lindsay, J.; Umeton, R.; Sinha, R.; Vokes, N.I.; Recondo, G.; Lamberti, G.; Lawrence, M.; Vaz, V.R.; Leonardi, G.C.; Plodkowski, A.J.; Gupta, H.; Cherniack, A.D.; Tolstorukov, M.Y.; Sharma, B.; Felt, K.D.; Gainor, J.F.; Ravi, A.; Getz, G.; Schalper, K.A.; Henick, B.; Forde, P.; Anagnostou, V.; Jänne, P.A.; Van Allen, E.M.; Nishino, M.; Sholl, L.M.; Christiani, D.C.; Lin, X.; Rodig, S.J.; Hellmann, M.D.; Awad, M.M. Association of high tumor mutation burden in non–small cell lung cancers with increased immune infiltration and improved clinical outcomes of PD-L1 blockade across PD-L1 expression levels. JAMA Oncol.,  2022, 8(8), 1160-1168.
 [http://dx.doi.org/10.1001/jamaoncol.2022.1981] [PMID: 35708671]

[61]
Zarrei, M.; MacDonald, J.R.; Merico, D.; Scherer, S.W. A copy number variation map of the human genome. Nat. Rev. Genet.,  2015, 16(3), 172-183.
 [http://dx.doi.org/10.1038/nrg3871] [PMID: 25645873]

[62]
Chen, W.; Zhang, J.; Fu, H.; Hou, X.; Su, Q.; He, Y.; Yang, D. KLF5 is activated by gene amplification in gastric cancer and is essential for gastric cell proliferation. Cells,  2021, 10(5), 1002.
 [http://dx.doi.org/10.3390/cells10051002] [PMID: 33923166]

[63]
Xu, X.; Yu, Y.; Zong, K.; Lv, P.; Gu, Y. Up-regulation of IGF2BP2 by multiple mechanisms in pancreatic cancer promotes cancer proliferation by activating the PI3K/Akt signaling pathway. J. Exp. Clin. Cancer Res.,  2019, 38(1), 497.
 [http://dx.doi.org/10.1186/s13046-019-1470-y] [PMID: 31852504]

[64]
Chen, G.; Wang, Q.; Wang, K. MicroRNA-218-5p affects lung adenocarcinoma progression through targeting endoplasmic reticulum oxidoreductase 1 alpha. Bioengineered,  2022, 13(4), 10061-10070.
 [http://dx.doi.org/10.1080/21655979.2022.2063537] [PMID: 35441565]

[65]
Roy, S.K.; Shrivastava, A.; Srivastav, S.; Shankar, S.; Srivastava, R.K. SATB2 is a novel biomarker and therapeutic target for cancer. J. Cell. Mol. Med.,  2020, 24(19), 11064-11069.
 [http://dx.doi.org/10.1111/jcmm.15755] [PMID: 32885593]

[66]
Chen, Q.Y.; Des Marais, T.; Costa, M. Deregulation of SATB2 in carcinogenesis with emphasis on miRNA-mediated control. Carcinogenesis,  2019, 40(3), 393-402.
 [http://dx.doi.org/10.1093/carcin/bgz020] [PMID: 30916759]

[67]
Müller, S.; Glaß, M.; Singh, A.K.; Haase, J.; Bley, N.; Fuchs, T.; Lederer, M.; Dahl, A.; Huang, H.; Chen, J.; Posern, G.; Hüttelmaier, S. IGF2BP1 promotes SRF-dependent transcription in cancer in a m6A- and miRNA-dependent manner. Nucleic Acids Res.,  2019, 47(1), 375-390.
 [http://dx.doi.org/10.1093/nar/gky1012] [PMID: 30371874]

[68]
Müller, S.; Bley, N.; Glaß, M.; Busch, B.; Rousseau, V.; Misiak, D.; Fuchs, T.; Lederer, M.; Hüttelmaier, S. IGF2BP1 enhances an aggressive tumor cell phenotype by impairing miRNA-directed downregulation of oncogenic factors. Nucleic Acids Res.,  2018, 46(12), 6285-6303.
 [http://dx.doi.org/10.1093/nar/gky229] [PMID: 29660014]

[69]
Dastjerdi, S.; Haghparast, A.; Amroabadi, J.M.; Dolatabadi, N.F.; Mirzaei, S.; Zamani, A.; Hashemi, M.; Mahdevar, M.; Ghaedi, K. Elevated CDK5R1 expression associated with poor prognosis, proliferation, and drug resistance in colorectal and breast malignancies: CDK5R1 as an oncogene in cancers. Chem. Biol. Interact.,  2022, 368, 110190.
 [http://dx.doi.org/10.1016/j.cbi.2022.110190] [PMID: 36162454]

[70]
Song, D.; Zhao, L.; Zhao, G.; Hao, Q.; Wu, J.; Ren, H.; Zhang, B. Identification and validation of eight lysosomes-related genes signatures and correlation with immune cell infiltration in lung adenocarcinoma. SSRN,  , 4318397.
 [http://dx.doi.org/10.2139/ssrn.4318397]

[71]
Geng, Y.; Guan, R.; Hong, W.; Huang, B.; Liu, P.; Guo, X.; Hu, S.; Yu, M.; Hou, B. Identification of m6A-related genes and m6A RNA methylation regulators in pancreatic cancer and their association with survival. Ann. Transl. Med.,  2020, 8(6), 387.
 [http://dx.doi.org/10.21037/atm.2020.03.98] [PMID: 32355831]

[72]
Deng, L.; Petrek, H.; Tu, M.J.; Batra, N.; Yu, A.X.; Yu, A.M. Bioengineered miR-124-3p prodrug selectively alters the proteome of human carcinoma cells to control multiple cellular components and lung metastasis in vivo. Acta Pharm. Sin. B,  2021, 11(12), 3950-3965.
 [http://dx.doi.org/10.1016/j.apsb.2021.07.027] [PMID: 35024318]

[73]
Chen, Y.; Shen, L.; Chen, B.; Han, X.; Yu, Y.; Yuan, X.; Zhong, L. The predictive prognostic values of CBFA2T3, STX3, DENR, EGLN1, FUT4, and PCDH7 in lung cancer. Ann. Transl. Med.,  2021, 9(10), 843.
 [http://dx.doi.org/10.21037/atm-21-1392] [PMID: 34164477]

[74]
Sun, J.; Zhao, T.; Zhao, D.; Qi, X.; Bao, X.; Shi, R.; Su, C. Development and validation of a hypoxia-related gene signature to predict overall survival in early-stage lung adenocarcinoma patients. Ther. Adv. Med. Oncol.,  2020, 12
 [http://dx.doi.org/10.1177/1758835920937904] [PMID: 32655701]

[75]
Woo, S.Y.; Lee, S.Y.; Yu, S.L.; Park, S.J.; Kang, D.; Kim, J.S.; Jeong, I.B.; Kwon, S.J.; Hwang, W.J.; Park, C.R.; Son, J.W. MicroRNA-7-5p′s role in the O-GlcNAcylation and cancer metabolism. Noncoding RNA Res.,  2020, 5(4), 201-207.
 [http://dx.doi.org/10.1016/j.ncrna.2020.11.003] [PMID: 33251387]
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DOI https://dx.doi.org/10.2174/0929867331666230914151943	Print ISSN 0929-8673
Publisher Name Bentham Science Publisher	Online ISSN 1875-533X
Current Medicinal Chemistry

Molecular Subtypes and Prognostic Models for Predicting Prognosis of Lung Adenocarcinoma based on MiRNA-related Genes

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