Skip to content
2000
image of Investigating the Unique Transcriptional miRNA-mRNA Regulatory Network of ALK-positive Lung Adenocarcinoma Using Machine Learning Methods

Abstract

Introduction

Non-small Cell Lung Cancer (NSCLC) is characterized by key gene mutations, such as EGFR, KRAS, and ALK. ALK rearrangement occurs in 3–5% of patients with non-small cell lung adenocarcinoma and is related to different clinical characteristics. Although ALK tyrosine kinase inhibitors have shown efficacy, drug resistance remains a challenge. This current study aims to determine the unique molecular characteristics of ALK-positive lung adenocarcinoma to improve detection and prognosis.

Methods

GSE128311 integrates expression profiling data by array from GSE128309 and noncoding RNA profiling data by array from GSE128310, including 42 patients with ALK-positive lung adenocarcinoma and 35 patients with ALK-negative lung adenocarcinoma. This data was analyzed by eight feature ranking algorithms, yielding eight feature lists. These lists were fed into incremental feature selection to extract essential features.

Results

Key differentially expressed genes and miRNAs were identified, and functional enrichment analysis was carried out.

Discussion

Results of the imbalance of the cell cycle pathway, FOXM1 transcription factor network, and immune response process in ALK-positive tumors were emphasized. It is worth noting that CX3CL1, MMS22L, DSG3, RUFY1, miR-652-5p, and miR-1288 are potentially important markers. Gene set enrichment analysis revealed the low expression of the cell cycle pathway in ALK-positive samples.

Conclusion

This comprehensive computational analysis provides new insights into the molecular basis of ALK-positive lung adenocarcinoma and determines promising biomarkers for further research.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936362646250501185409
2025-05-08
2025-08-17

  • From This Site

    /content/journals/cbio/10.2174/0115748936362646250501185409
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Lin X. Ma Q. Chen L. Guo W. Huang Z. Huang T. Cai Y.D. Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods. Biochim. Biophys. Acta, Gen. Subj. 2023 1867 12 130484 10.1016/j.bbagen.2023.130484 37805078
    [Google Scholar]
  2. Liu B. Quan X. Xu C. Lv J. Li C. Dong L. Liu M. Lung cancer in young adults aged 35 years or younger: A full-scale analysis and review. J. Cancer 2019 10 15 3553 3559 10.7150/jca.27490 31293660
    [Google Scholar]
  3. Koivunen J.P. Mermel C. Zejnullahu K. Murphy C. Lifshits E. Holmes A.J. Choi H.G. Kim J. Chiang D. Thomas R. Lee J. Richards W.G. Sugarbaker D.J. Ducko C. Lindeman N. Marcoux J.P. Engelman J.A. Gray N.S. Lee C. Meyerson M. Jänne P.A. EML4-ALK fusion gene and efficacy of an ALK kinase inhibitor in lung cancer. Clin. Cancer Res. 2008 14 13 4275 4283 10.1158/1078‑0432.CCR‑08‑0168 18594010
    [Google Scholar]
  4. Fallet V. Cadranel J. Doubre H. Toper C. Monnet I. Chinet T. Oliviero G. Foulon G. De Cremoux H. Vieira T. Antoine M. Wislez M. Prospective screening for ALK: Clinical features and outcome according to ALK status. Eur. J. Cancer 2014 50 7 1239 1246 10.1016/j.ejca.2014.02.001 24589437
    [Google Scholar]
  5. Shaw A.T. Yeap B.Y. Mino-Kenudson M. Digumarthy S.R. Costa D.B. Heist R.S. Solomon B. Stubbs H. Admane S. McDermott U. Settleman J. Kobayashi S. Mark E.J. Rodig S.J. Chirieac L.R. Kwak E.L. Lynch T.J. Iafrate A.J. Clinical features and outcome of patients with non-small-cell lung cancer who harbor EML4-ALK. J. Clin. Oncol. 2009 27 26 4247 4253 10.1200/JCO.2009.22.6993 19667264
    [Google Scholar]
  6. Shaw A.T. Kim D.W. Mehra R. Tan D.S.W. Felip E. Chow L.Q.M. Camidge D.R. Vansteenkiste J. Sharma S. De Pas T. Riely G.J. Solomon B.J. Wolf J. Thomas M. Schuler M. Liu G. Santoro A. Lau Y.Y. Goldwasser M. Boral A.L. Engelman J.A. Ceritinib in ALK-rearranged non-small-cell lung cancer. N. Engl. J. Med. 2014 370 13 1189 1197 10.1056/NEJMoa1311107 24670165
    [Google Scholar]
  7. Lin J.J. Riely G.J. Shaw A.T. Targeting ALK: Precision medicine takes on drug resistance. Cancer Discov. 2017 7 2 137 155 10.1158/2159‑8290.CD‑16‑1123 28122866
    [Google Scholar]
  8. Dagogo-Jack I. Rooney M. Lin J.J. Nagy R.J. Yeap B.Y. Hubbeling H. Chin E. Ackil J. Farago A.F. Hata A.N. Lennerz J.K. Gainor J.F. Lanman R.B. Shaw A.T. Treatment with next-generation ALK inhibitors fuels plasma ALK mutation diversity. Clin. Cancer Res. 2019 25 22 6662 6670 10.1158/1078‑0432.CCR‑19‑1436 31358542
    [Google Scholar]
  9. Golding B. Luu A. Jones R. Viloria-Petit A.M. The function and therapeutic targeting of anaplastic lymphoma kinase (ALK) in non-small cell lung cancer (NSCLC). Mol. Cancer 2018 17 1 52 10.1186/s12943‑018‑0810‑4 29455675
    [Google Scholar]
  10. Solomon B. Varella-Garcia M. Camidge D.R. ALK gene rearrangements: A new therapeutic target in a molecularly defined subset of non-small cell lung cancer. J. Thorac. Oncol. 2009 4 12 1450 1454 10.1097/JTO.0b013e3181c4dedb 20009909
    [Google Scholar]
  11. Rodig S.J. Mino-Kenudson M. Dacic S. Yeap B.Y. Shaw A. Barletta J.A. Stubbs H. Law K. Lindeman N. Mark E. Janne P.A. Lynch T. Johnson B.E. Iafrate A.J. Chirieac L.R. Unique clinicopathologic features characterize ALK-rearranged lung adenocarcinoma in the western population. Clin. Cancer Res. 2009 15 16 5216 5223 10.1158/1078‑0432.CCR‑09‑0802 19671850
    [Google Scholar]
  12. Okayama H. Kohno T. Ishii Y. Shimada Y. Shiraishi K. Iwakawa R. Furuta K. Tsuta K. Shibata T. Yamamoto S. Watanabe S. Sakamoto H. Kumamoto K. Takenoshita S. Gotoh N. Mizuno H. Sarai A. Kawano S. Yamaguchi R. Miyano S. Yokota J. Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 2012 72 1 100 111 10.1158/0008‑5472.CAN‑11‑1403 22080568
    [Google Scholar]
  13. Chuang T.P. Lai W.Y. Gabre J.L. Lind D.E. Umapathy G. Bokhari A.A. Bergman B. Kristenson L. Thorén F.B. Le A. Doebele R.C. Van den Eynden J. Palmer R.H. Hallberg B. ALK fusion NSCLC oncogenes promote survival and inhibit NK cell responses via SERPINB4 expression. Proc. Natl. Acad. Sci. USA 2023 120 8 e2216479120 10.1073/pnas.2216479120 36791109
    [Google Scholar]
  14. Li J. Tang Z. Wang H. Wu W. Zhou F. Ke H. Lu W. Zhang S. Zhang Y. Yang S. Ni S. Huang J. CXCL6 promotes non-small cell lung cancer cell survival and metastasis via down-regulation of miR-515-5p. Biomed. Pharmacother. 2018 97 1182 1188 10.1016/j.biopha.2017.11.004 29136957
    [Google Scholar]
  15. Gasparini P. Landi L. Carasi S. Tibaldi C. Cascione L. Alì G. D’Incecco A. Minuti G. Chella A. Fontanini G. Cappuzzo F. Croce C.M. Abstract 3061: Micro-RNA signature differences in lung cancer patients with ALK translocation, EGFR mutations and KRAS mutations. Cancer Res. 2013 73 8_Supplement 3061 10.1158/1538‑7445.AM2013‑3061
    [Google Scholar]
  16. Chen M.F. Chaft J.E. Early-stage anaplastic lymphoma kinase (ALK)-positive lung cancer: A narrative review. Transl. Lung Cancer Res. 2023 12 2 337 345 10.21037/tlcr‑22‑631 36895922
    [Google Scholar]
  17. Schneider J.L. Lin J.J. Shaw A.T. ALK-positive lung cancer: A moving target. Nat. Cancer 2023 4 3 330 343 10.1038/s43018‑023‑00515‑0 36797503
    [Google Scholar]
  18. Dziadziuszko R. Peters S. Mok T. Camidge D.R. Gadgeel S.M. Ou S.H.I. Konopa K. Noé J. Nowicka M. Bordogna W. Morcos P.N. Smoljanovic V. Shaw A.T. Circulating cell-free DNA as a prognostic biomarker in patients with advanced ALK+ non–small cell lung cancer in the global phase III ALEX trial. Clin. Cancer Res. 2022 28 9 1800 1808 10.1158/1078‑0432.CCR‑21‑2840 35275991
    [Google Scholar]
  19. Sakamoto T. Matsubara T. Takahama T. Yokoyama T. Nakamura A. Tokito T. Okamoto T. Akamatsu H. Oki M. Sato Y. Tobino K. Ikeda S. Mori M. Mimura C. Maeno K. Miura S. Harada T. Nishimura K. Hiraoka M. Kenmotsu H. Fujimoto J. Shimokawa M. Yamamoto N. Nakagawa K. Biomarker testing in patients with unresectable advanced or recurrent non–small cell lung cancer. JAMA Netw. Open 2023 6 12 e2347700 10.1001/jamanetworkopen.2023.47700 38100106
    [Google Scholar]
  20. Freund Y. Schapire R.E. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997 55 1 119 139 10.1006/jcss.1997.1504
    [Google Scholar]
  21. Dorogush A. Ershov V. Gulin A. CatBoost: Gradient boosting with categorical features support. arXiv 2018 10.48550/arXiv.1810.11363
    [Google Scholar]
  22. Geurts P. Ernst D. Wehenkel L. Extremely randomized trees. Mach. Learn. 2006 63 1 3 42 10.1007/s10994‑006‑6226‑1
    [Google Scholar]
  23. Tibshirani R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Series B Stat. Methodol. 1996 58 1 267 288 10.1111/j.2517‑6161.1996.tb02080.x
    [Google Scholar]
  24. Ke G. Meng Q. Finley T. Wang T. Chen W. Ma W. Lightgbm: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst. 2017 30 3146 3154
    [Google Scholar]
  25. Breiman L. Random forests. Mach. Learn. 2001 45 1 5 32 10.1023/A:1010933404324
    [Google Scholar]
  26. Hoerl A.E. Kennard R.W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970 12 1 55 67 10.1080/00401706.1970.10488634
    [Google Scholar]
  27. Chen T. Guestrin C. XGBoost: A scalable tree boosting system. KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. California, San Francisco, USA, Aug. 13-17, 2016, pp. 785-794. 10.1145/2939672.2939785
    [Google Scholar]
  28. Liu H. Setiono R. Incremental feature selection. Appl. Intell. 1998 9 3 217 230 10.1023/A:1008363719778
    [Google Scholar]
  29. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence Stanford, CA, Jan. 1995, pp. 1137-1143.
    [Google Scholar]
  30. Chen L. Xu J. Zhou Y. PDATC-NCPMKL: Predicting drug’s Anatomical Therapeutic Chemical (ATC) codes based on network consistency projection and multiple kernel learning. Comput. Biol. Med. 2024 169 107862 10.1016/j.compbiomed.2023.107862 38150886
    [Google Scholar]
  31. Chen L. Gu J. Zhou B. PMiSLocMF: Predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs. Brief. Bioinform. 2024 25 5 bbae386 10.1093/bib/bbae386 39154195
    [Google Scholar]
  32. Chen L. Chen Y. RMTLysPTM: Recognizing multiple types of lysine PTM sites by deep analysis on sequences. Brief. Bioinform. 2023 25 1 bbad450 10.1093/bib/bbad450 38066710
    [Google Scholar]
  33. Chen L. Zhang S. Zhou B. Herb-disease association prediction model based on network consistency projection. Sci. Rep. 2025 15 1 3328 10.1038/s41598‑025‑87521‑7 39865145
    [Google Scholar]
  34. Chawla N.V. Bowyer K.W. Hall L.O. Kegelmeyer W.P. SMOTE: Synthetic minority over-sampling technique. J. Artif. Intell. Res. 2002 16 321 357 10.1613/jair.953
    [Google Scholar]
  35. Powers D. Evaluation: From precision, recall and f-measure to roc., informedness, markedness & correlation. J. Mach. Learn. Technol. 2011 2 1 37 63
    [Google Scholar]
  36. Chen L. Li J. PDTDAHN: Predicting drug-target-disease associations using a heterogeneous network. Curr. Bioinform. 2025 20 10.2174/0115748936359702250120114240
    [Google Scholar]
  37. Chen L. Zhao X. PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path. Math. Biosci. Eng. 2023 20 12 20553 20575 10.3934/mbe.2023909 38124565
    [Google Scholar]
  38. Chen L. Zhang C. Xu J. PredictEFC: A fast and efficient multi-label classifier for predicting enzyme family classes. BMC Bioinformatics 2024 25 1 50 10.1186/s12859‑024‑05665‑1 38291384
    [Google Scholar]
  39. Ren J.X. Chen L. Guo W. Feng K.Y. Cai Y.D. Huang T. Patterns of gene expression profiles associated with colorectal cancer in colorectal mucosa by using machine learning methods. Comb. Chem. High Throughput Screen. 2024 27 19 2921 2934 10.2174/0113862073266300231026103844 37957897
    [Google Scholar]
  40. Ren J. Gao Q. Zhou X. Chen L. Guo W. Feng K. Huang T. Cai Y.D. Identification of key gene expression associated with quality of life after recovery from COVID-19. Med. Biol. Eng. Comput. 2024 62 4 1031 1048 10.1007/s11517‑023‑02988‑8 38123886
    [Google Scholar]
  41. Ren J. Zhou X. Huang K. Chen L. Guo W. Feng K. Huang T. Cai Y.D. Identification of key genes associated with persistent immune changes and secondary immune activation responses induced by influenza vaccination after COVID-19 recovery by machine learning methods. Comput. Biol. Med. 2024 169 107883 10.1016/j.compbiomed.2023.107883 38157776
    [Google Scholar]
  42. Matthews B.W. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta Protein Struct. 1975 405 2 442 451 10.1016/0005‑2795(75)90109‑9 1180967
    [Google Scholar]
  43. Zhou Y. Zhou B. Pache L. Chang M. Khodabakhshi A.H. Tanaseichuk O. Benner C. Chanda S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 2019 10 1 1523 10.1038/s41467‑019‑09234‑6 30944313
    [Google Scholar]
  44. Wu T. Hu E. Xu S. Chen M. Guo P. Dai Z. Feng T. Zhou L. Tang W. Zhan L. Fu X. Liu S. Bo X. Yu G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation 2021 2 3 100141 10.1016/j.xinn.2021.100141 34557778
    [Google Scholar]
  45. Subramanian A. Tamayo P. Mootha V.K. Mukherjee S. Ebert B.L. Gillette M.A. Paulovich A. Pomeroy S.L. Golub T.R. Lander E.S. Mesirov J.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 2005 102 43 15545 15550 10.1073/pnas.0506580102 16199517
    [Google Scholar]
  46. Chen Y. Wang X. miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020 48 D1 D127 D131 10.1093/nar/gkz757 31504780
    [Google Scholar]
  47. Szklarczyk D. Kirsch R. Koutrouli M. Nastou K. Mehryary F. Hachilif R. Gable A.L. Fang T. Doncheva N.T. Pyysalo S. Bork P. Jensen L.J. von Mering C. The STRING database in 2023: Protein–protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023 51 D1 D638 D646 10.1093/nar/gkac1000 36370105
    [Google Scholar]
  48. Wang S. Lou N. Luo R. Hao X. Liu Y. Wang L. Shi Y. Han X. Role of chemokine-mediated angiogenesis in resistance towards crizotinib and its reversal by anlotinib in EML4-ALK positive NSCLC. J. Transl. Med. 2022 20 1 248 10.1186/s12967‑022‑03451‑2 35642002
    [Google Scholar]
  49. Srivastava S. Mohanty A. Nam A. Singhal S. Salgia R. Chemokines and NSCLC: Emerging role in prognosis, heterogeneity, and therapeutics. Semin. Cancer Biol. 2022 86 Pt 2 233 246 10.1016/j.semcancer.2022.06.010 35787939
    [Google Scholar]
  50. Sarode P. Schaefer M.B. Grimminger F. Seeger W. Savai R. Macrophage and tumor cell cross-talk is fundamental for lung tumor progression: We need to talk. Front. Oncol. 2020 10 324 10.3389/fonc.2020.00324 32219066
    [Google Scholar]
  51. Liu Y. Wu H. Luo T. Luo Q. Meng Z. Shi Y. Li F. Liu M. Peng X. Liu J. Xu C. Tang W. The SOX9-MMS22L axis promotes oxaliplatin resistance in colorectal cancer. Front. Mol. Biosci. 2021 8 646542 10.3389/fmolb.2021.646542 34124145
    [Google Scholar]
  52. Guo Z. Liu F. Gong Q. Integrative pan-cancer landscape of MMS22L and its potential role in hepatocellular carcinoma. Front. Genet. 2022 13 1025970 10.3389/fgene.2022.1025970 36276962
    [Google Scholar]
  53. Fukuoka J. Dracheva T. Shih J.H. Hewitt S.M. Fujii T. Kishor A. Mann F. Shilo K. Franks T.J. Travis W.D. Jen J. Desmoglein 3 as a prognostic factor in lung cancer. Hum. Pathol. 2007 38 2 276 283 10.1016/j.humpath.2006.08.006 17084439
    [Google Scholar]
  54. Hao X. Han F. Ma B. Zhang N. Chen H. Jiang X. Yin L. Liu W. Ao L. Cao J. Liu J. SOX30 is a key regulator of desmosomal gene suppressing tumor growth and metastasis in lung adenocarcinoma. J. Exp. Clin. Cancer Res. 2018 37 1 111 10.1186/s13046‑018‑0778‑3 29855376
    [Google Scholar]
  55. Yang J. Kim O. Wu J. Qiu Y. Interaction between tyrosine kinase Etk and a RUN domain- and FYVE domain-containing protein RUFY1. A possible role of ETK in regulation of vesicle trafficking. J. Biol. Chem. 2002 277 33 30219 30226 10.1074/jbc.M111933200 11877430
    [Google Scholar]
  56. Yang W. Zhou C. Luo M. Shi X. Li Y. Sun Z. Zhou F. Chen Z. He J. MiR-652-3p is upregulated in non-small cell lung cancer and promotes proliferation and metastasis by directly targeting Lgl1. Oncotarget 2016 7 13 16703 16715 10.18632/oncotarget.7697 26934648
    [Google Scholar]
  57. Wang B. Lv F. Zhao L. Yang K. Gao Y.S. Du M.J. MicroRNA-652 inhibits proliferation and induces apoptosis of non-small cell lung cancer A549 cells. Int. J. Clin. Exp. Pathol. 2017 10 6719 6726
    [Google Scholar]
  58. Hou R. Liu Y. Su Y. Shu Z. Overexpression of long non-coding RNA FGF14-AS2 inhibits colorectal cancer proliferation via the RERG/Ras/ERK signaling by sponging microRNA-1288-3p. Pathol. Oncol. Res. 2020 26 4 2659 2667 10.1007/s12253‑020‑00862‑8 32654025
    [Google Scholar]
  59. Gopalan V. Islam F. Pillai S. Tang J.C.O. Tong D.K.H. Law S. Chan K.W. Lam A.K.Y. Overexpression of microRNA-1288 in oesophageal squamous cell carcinoma. Exp. Cell Res. 2016 348 2 146 154 10.1016/j.yexcr.2016.09.010 27658568
    [Google Scholar]
  60. Xiao Z. Yang Z. Xu M. Li W. Chen X. Chen K. Li M. Lu X. Jiang Y. ling Y. The Circ_CARM1 controls cell migration by regulating CTNNBIP1 in anti-benzo[a]pyrene-trans-7,8-dihydrodiol-9,10-epoxide-transformed 16HBE cells. Toxicol. Lett. 2021 348 40 49 10.1016/j.toxlet.2021.05.007 34052308
    [Google Scholar]
  61. Berry M.A. Bland A.R. Ashton J.C. Mechanisms of synergistic suppression of ALK-positive lung cancer cell growth by the combination of ALK and SHP2 inhibitors. Sci. Rep. 2023 13 1 10041 10.1038/s41598‑023‑37006‑2 37339995
    [Google Scholar]
  62. Camidge D.R. Cell cycle-associated kinases as targets for therapy in lung cancer. J. Thorac. Oncol. 2010 5 12 Suppl 6 S461 S462 10.1097/01.JTO.0000391366.63882.30 21102239
    [Google Scholar]
  63. Berry M. Bland A. Ashton J. EP02.01-01 differing cell cycle responses for alectinib and SHP099 in variant 1 and variant 3 ALK+ non-small cell lung cancer cell lines. J. Thorac. Oncol. 2023 18 11 S424 S425 10.1016/j.jtho.2023.09.766
    [Google Scholar]
  64. Liao G.B. Li X.Z. Zeng S. Liu C. Yang S.M. Yang L. Hu C.J. Bai J.Y. Regulation of the master regulator FOXM1 in cancer. Cell Commun. Signal. 2018 16 1 57 10.1186/s12964‑018‑0266‑6 30208972
    [Google Scholar]
  65. Liang S.K. Hsu C.C. Song H.L. Huang Y.C. Kuo C.W. Yao X. Li C.C. Yang H.C. Hung Y.L. Chao S.Y. Wu S.C. Tsai F.R. Chen J.K. Liao W.N. Cheng S.C. Tsou T.C. Wang I.C. FOXM1 is required for small cell lung cancer tumorigenesis and associated with poor clinical prognosis. Oncogene 2021 40 30 4847 4858 10.1038/s41388‑021‑01895‑2 34155349
    [Google Scholar]
  66. Wang K. Zhu X. Zhang K. Zhu L. Zhou F. FoxM1 inhibition enhances chemosensitivity of docetaxel-resistant A549 cells to docetaxel via activation of JNK/mitochondrial pathway. Acta Biochim. Biophys. Sin. (Shanghai) 2016 48 9 804 809 10.1093/abbs/gmw072 27521795
    [Google Scholar]
  67. Shrestha N. Nimick M. Dass P. Rosengren R.J. Ashton J.C. Mechanisms of suppression of cell growth by dual inhibition of ALK and MEK in ALK-positive non-small cell lung cancer. Sci. Rep. 2019 9 1 18842 10.1038/s41598‑019‑55376‑4 31827192
    [Google Scholar]
  68. Kong F.F. Qu Z.Q. Yuan H.H. Wang J.Y. Zhao M. Guo Y.H. Shi J. Gong X.D. Zhu Y.L. Liu F. Zhang W.Y. Jiang B. Overexpression of FOXM1 is associated with EMT and is a predictor of poor prognosis in non-small cell lung cancer. Oncol. Rep. 2014 31 6 2660 2668 10.3892/or.2014.3129 24715097
    [Google Scholar]
  69. Jhunjhunwala S. Hammer C. Delamarre L. Antigen presentation in cancer: Insights into tumour immunogenicity and immune evasion. Nat. Rev. Cancer 2021 21 5 298 312 10.1038/s41568‑021‑00339‑z 33750922
    [Google Scholar]
  70. Oh C.Y. Klatt M.G. Bourne C. Dao T. Dacek M.M. Brea E.J. Mun S.S. Chang A.Y. Korontsvit T. Scheinberg D.A. ALK and RET inhibitors promote HLA class I antigen presentation and unmask new antigens within the tumor immunopeptidome. Cancer Immunol. Res. 2019 7 12 1984 1997 10.1158/2326‑6066.CIR‑19‑0056 31540894
    [Google Scholar]
  71. Gettinger S. Choi J. Hastings K. Truini A. Datar I. Sowell R. Wurtz A. Dong W. Cai G. Melnick M.A. Du V.Y. Schlessinger J. Goldberg S.B. Chiang A. Sanmamed M.F. Melero I. Agorreta J. Montuenga L.M. Lifton R. Ferrone S. Kavathas P. Rimm D.L. Kaech S.M. Schalper K. Herbst R.S. Politi K. Impaired HLA class I antigen processing and presentation as a mechanism of acquired resistance to immune checkpoint inhibitors in lung cancer. Cancer Discov. 2017 7 12 1420 1435 10.1158/2159‑8290.CD‑17‑0593 29025772
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936362646250501185409
Loading
Investigating the Unique Transcriptional miRNA-mRNA Regulatory Network of ALK-positive Lung Adenocarcinoma Using Machine Learning Methods
Bentham Science Publishers ; https://doi.org/10.2174/0115748936362646250501185409
/content/journals/cbio/10.2174/0115748936362646250501185409
/content/journals/cbio/10.2174/0115748936362646250501185409
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keywords: miRNA ; machine learning ; Non-small cell lung cancer ; ALK-positive lung adenocarcinoma ; feature ranking ; gene

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test