Skip to content
2000
  1. Home
  2. A-Z Publications
  3. MicroRNA
  4. Volume 14, Issue 1
  5. Article
Volume 14, Issue 1
  • ISSN: 2211-5366
  • E-ISSN: 2211-5374

Abstract

Introduction

The differential expression of miRNAs, a key regulator in many cell signaling pathways, has been studied in various malignancies and may have an important role in cancer progression, including colorectal cancer (CRC).

Methods

The present study used machine learning and gene interaction study tools to explore the prognostic and diagnostic value of miRNAs in CRC. Integrative analysis of 353 CRC samples and normal tissue data was obtained from the TCGA database and further analyzed by R packages to define the deferentially expressed miRNAs (DEMs). Furthermore, machine learning and Kaplan Meier survival analysis helped better specify the significant prognostic value of miRNAs. A combination of online databases was then used to evaluate the interactions between target genes, their molecular pathways, and the correlation between the DEMs.

Results

The results indicated that miR-19b and miR-20a have a significant prognostic role and are associated with CRC progression. The ROC curve analysis discovered that miR-20a alone and combined with other miRNAs, including hsa-mir-21 and hsa-mir-542, are diagnostic biomarkers in CRC. In addition, 12 genes, including NTRK2, CDC42, EGFR, AGO2, PRKCA, HSP90AA1, TLR4, IGF1, ESR1, SMAD2, SMAD4, and NEDD4L, were found to be the highest score targets for these miRNAs. Pathway analysis identified the two correlated tyrosine kinase and MAPK signaling pathways with the key interaction genes, , EGFR, CDC42, and HSP90AA1.

Conclusion

To better define the role of these miRNAs, the ceRNA network, including lncRNAs, was also prepared. In conclusion, the combination of R data analysis and machine learning provides a robust approach to resolving complicated interactions between miRNAs and their targets.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/mirna/10.2174/0122115366320538240912080053
2024-09-24
2025-05-12

  • From This Site

    /content/journals/mirna/10.2174/0122115366320538240912080053
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. SawickiT. RuszkowskaM. DanielewiczA. NiedźwiedzkaE. ArłukowiczT. PrzybyłowiczK.E. A review of colorectal cancer in terms of epidemiology, risk factors, development, symptoms and diagnosis.Cancers 2021139202510.3390/cancers13092025 33922197
     [Google Scholar]
  2. AngiusA. UvaP. PiraG. Integrated analysis of miRNA and mRNA endorses a twenty miRNAs signature for colorectal carcinoma.Int. J. Mol. Sci.20192016406710.3390/ijms20164067 31434359
     [Google Scholar]
  3. AkimotoN. UgaiT. ZhongR. Rising incidence of early-onset colorectal cancer - A call to action.Nat. Rev. Clin. Oncol.202118423024310.1038/s41571‑020‑00445‑1 33219329
     [Google Scholar]
  4. LiuJ. LiH. ZhengB. SunL. YuanY. XingC. Competitive endogenous RNA (ceRNA) regulation network of lncRNA–miRNA–mRNA in colorectal carcinogenesis.Dig. Dis. Sci.20196471868187710.1007/s10620‑019‑05506‑9 30734239
     [Google Scholar]
  5. DuB.M.W. DThe expression and significance of microRNA in different stages of colorectal cancer.Medicine 2018
     [Google Scholar]
  6. Alves MartinsB.A. de BulhõesG.F. CavalcantiI.N. MartinsM.M. de OliveiraP.G. MartinsA.M.A. Biomarkers in colorectal cancer: The role of translational proteomics research.Front. Oncol.20199128410.3389/fonc.2019.01284 31828035
     [Google Scholar]
  7. KoncinaE. HaanS. RauhS. LetellierE. Prognostic and predictive molecular biomarkers for colorectal cancer: Updates and challenges.Cancers 202012231910.3390/cancers12020319 32019056
     [Google Scholar]
  8. ZhuJ. XuY. LiuS. QiaoL. SunJ. ZhaoQ. MicroRNAs associated with colon cancer: New potential prognostic markers and targets for therapy.Front. Bioeng. Biotechnol.2020817610.3389/fbioe.2020.00176 32211396
     [Google Scholar]
  9. HwangJ. MinB.H. JangJ. MicroRNA expression profiles in gastric carcinogenesis.Sci. Rep.2018811439310.1038/s41598‑018‑32782‑8 30258124
     [Google Scholar]
  10. PawelkaD. LaczmanskaI. KarpinskiP. Machine-learning-based analysis identifies miRNA expression profile for diagnosis and prediction of colorectal cancer: A preliminary study.Cancer Genomics Proteomics202219450351110.21873/cgp.20336 35732322
     [Google Scholar]
  11. PaulS. LakatosP. HartmannA. Schneider-StockR. VeraJ. Identification of miRNA-mRNA modules in colorectal cancer using rough hypercuboid based supervised clustering.Sci. Rep.2017714280910.1038/srep42809 28220871
     [Google Scholar]
  12. NazariE. Khalili-TanhaG. AsadniaA. Bioinformatics analysis and machine learning approach applied to the identification of novel key genes involved in non-alcoholic fatty liver disease.Sci. Rep.20231312048910.1038/s41598‑023‑46711‑x 37993474
     [Google Scholar]
  13. PalM. MatherP.M. An assessment of the effectiveness of decision tree methods for land cover classification.Remote Sens. Environ.200386455456510.1016/S0034‑4257(03)00132‑9
     [Google Scholar]
  14. MenardS. Logistic regression: From introductory to advanced concepts and applications.Sage publications2009
     [Google Scholar]
  15. ZakariahM. Classification of large datasets using random forest algorithm in various applications: Survey.Int J Eng Innov Technol201443
     [Google Scholar]
  16. BhavsarH. PanchalM.H. A review on support vector machine for data classification.Int. J. Adv. Res. Comput. Eng. Technol.2012110185189
     [Google Scholar]
  17. GuoG. WangH. BellD. BiY. GreerK. KNN model-based approach in classification. On the move to meaningful internet systems 2003: CoopIS, DOA, and ODBASE OTM 2003. Springer, Berlin200398699610.1007/978‑3‑540‑39964‑3_62
     [Google Scholar]
  18. LemenkovaP. Processing oceanographic data by Python libraries NumPy, SciPy and Pandas.Aquatic Research201922739110.3153/AR19009
     [Google Scholar]
  19. MaharatiA. ZangueiA.S. Khalili-TanhaG. MoghbeliM. MicroRNAs as the critical regulators of tyrosine kinase inhibitors resistance in lung tumor cells.Cell Commun. Signal.202220127
     [Google Scholar]
  20. Højgaard JensenK. IzarzugazaJ.M.G. Sierakowska JunckerA. Analysis of a gene panel for targeted sequencing of colorectal cancer samples.Oncotarget20189109043906010.18632/oncotarget.24138 29507673
     [Google Scholar]
  21. LiJ. SunA. ZhongG. HeY. XiongH. YuanX. Mutation analysis of a 10-gene panel for colorectal cancer in Huizhou, Guangdong Province of China.J. Int. Med. Res.2021491110.1177/03000605211061040 34851763
     [Google Scholar]
  22. WanM. WangY. ZengZ. Colorectal cancer (CRC) as a multifactorial disease and its causal correlations with multiple signaling pathways.Biosci. Rep.2020403BSR2020026510.1042/BSR20200265 32149326
     [Google Scholar]
  23. HassanM. AwanF.M. NazA. Innovations in genomics and big data analytics for personalized medicine and health care: A review.Int. J. Mol. Sci.2022239464510.3390/ijms23094645 35563034
     [Google Scholar]
  24. Khalili-TanhaG. MohitR. AsadniaA. Identification of ZMYND19 as a novel biomarker of colorectal cancer: RNA-sequencing and machine learning analysis.J. Cell Commun. Signal.20231741469148510.1007/s12079‑023‑00779‑2 37428302
     [Google Scholar]
  25. Khojasteh-LeylakoohiF. MohitR. Khalili-TanhaN. Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer.Sci. Rep.20231311667810.1038/s41598‑023‑42928‑y 37794108
     [Google Scholar]
  26. AsadniaA. NazariE. GoshayeshiL. The prognostic value of ASPHD1 and ZBTB12 in colorectal cancer: A machine learning-based integrated bioinformatics approach.Cancers 20231517430010.3390/cancers15174300 37686578
     [Google Scholar]
  27. MogilyanskyE. RigoutsosI. The miR-17/92 cluster: A comprehensive update on its genomics, genetics, functions and increasingly important and numerous roles in health and disease.Cell Death Differ.201320121603161410.1038/cdd.2013.125 24212931
     [Google Scholar]
  28. HuangL. CaiJ.L. HuangP.Z. miR19b-3p promotes the growth and metastasis of colorectal cancer via directly targeting ITGB8.Am. J. Cancer Res.201771019962008 29119049
     [Google Scholar]
  29. SunT. YinY.F. JinH.G. LiuH.R. TianW.C. Exosomal microRNA ‐19b targets FBXW7 to promote colorectal cancer stem cell stemness and induce resistance to radiotherapy.Kaohsiung J. Med. Sci.202238210811910.1002/kjm2.12449 34520626
     [Google Scholar]
  30. JiangT. YeL. HanZ. miR-19b-3p promotes colon cancer proliferation and oxaliplatin-based chemoresistance by targeting SMAD4: Validation by bioinformatics and experimental analyses.J. Exp. Clin. Cancer Res.201736113110.1186/s13046‑017‑0602‑5 28938919
     [Google Scholar]
  31. ZhangJ. WangZ. HanX. Up-regulation of microRNA-19b is associated with metastasis and predicts poor prognosis in patients with colorectal cancer.Int. J. Clin. Exp. Pathol.201811839523960 31949783
     [Google Scholar]
  32. SelvenH. AndersenS. PedersenM.I. LombardiA.P.G. BusundL.T.R. KilværT.K. High expression of miR-17-5p and miR-20a-5p predicts favorable disease-specific survival in stage I-III colon cancer.Sci. Rep.2022121708010.1038/s41598‑022‑11090‑2 35490164
     [Google Scholar]
  33. SongK. LiuC. ZhangJ. Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer.Heliyon202283e0906810.1016/j.heliyon.2022.e09068 35284668
     [Google Scholar]
  34. ZhangG.J. LiY. ZhouH. XiaoH.X. ZhouT. miR-20a is an independent prognostic factor in colorectal cancer and is involved in cell metastasis.Mol. Med. Rep.201410128329110.3892/mmr.2014.2144 24737193
     [Google Scholar]
  35. XiaoZ. ChenS. FengS. Function and mechanisms of microRNA 20a in colorectal cancer (Review).Exp. Ther. Med.20201931605161610.3892/etm.2020.8432 32104211
     [Google Scholar]
  36. XuT. JingC. ShiY. microRNA-20a enhances the epithelial-to-mesenchymal transition of colorectal cancer cells by modulating matrix metalloproteinases.Exp. Ther. Med.201510268368810.3892/etm.2015.2538 26622375
     [Google Scholar]
  37. ChengD. ZhaoS. TangH. MicroRNA-20a-5p promotes colorectal cancer invasion and metastasis by downregulating Smad4.Oncotarget2016729451994521310.18632/oncotarget.9900 27286257
     [Google Scholar]
  38. HuangG. miR-20a-directed regulation of BID is associated with trail sensitivity in colorectal cancer.Oncol. Rep.2017371571578
     [Google Scholar]
  39. MoodyL. The efficacy of miR-20a as a diagnostic and prognostic biomarker for colorectal cancer: A systematic review and meta-analysis.Cancers 2019118111110.3390/cancers11081111
     [Google Scholar]
  40. YauT.O. WuC.W. TangC.M. microRNA-20a in human faeces as a non-invasive biomarker for colorectal cancer.Oncotarget2016721559156810.18632/oncotarget.6403 26621842
     [Google Scholar]
  41. García-ArandaM. RedondoM. Targeting receptor kinases in colorectal cancer.Cancers 201911443310.3390/cancers11040433 30934752
     [Google Scholar]
  42. HuangC. ZhouQ. HuY. Hepatocyte growth factor is a prognostic marker in patients with colorectal cancer: A meta-analysis.Oncotarget2017814234592346910.18632/oncotarget.15589 28423584
     [Google Scholar]
  43. VigneriP.G. TirròE. PennisiM.S. The Insulin/IGF system in colorectal cancer development and resistance to therapy.Front. Oncol.2015523010.3389/fonc.2015.00230 26528439
     [Google Scholar]
  44. AhsanA. RamanandS.G. WhiteheadC. Wild-type EGFR is stabilized by direct interaction with HSP90 in cancer cells and tumors.Neoplasia2012148670IN110.1593/neo.12986 22952420
     [Google Scholar]
  45. SzczukaI. WierzbickiJ. SerekP. Szczęśniak-SięgaB.M. Krzystek-KorpackaM. Heat shock proteins HSPA1 and HSP90AA1 are upregulated in colorectal polyps and can be targeted in cancer cells by anti-inflammatory oxicams with arylpiperazine pharmacophore and benzoyl moiety substitutions at thiazine ring.Biomolecules20211111158810.3390/biom11111588 34827586
     [Google Scholar]
  46. Gómez del PulgarT. Valdés-MoraF. BandrésE. Cdc42 is highly expressed in colorectal adenocarcinoma and downregulates ID4 through an epigenetic mechanism.Int. J. Oncol.200833118519310.3892/ijo.33.1.185 18575765
     [Google Scholar]
  47. ZhangX. BandyopadhyayS. AraujoL.P. Elevating EGFR-MAPK program by a nonconventional Cdc42 enhances intestinal epithelial survival and regeneration.JCI Insight2020516e13592310.1172/jci.insight.135923 32686657
     [Google Scholar]
  48. FangJ.Y. RichardsonB.C. The MAPK signalling pathways and colorectal cancer.Lancet Oncol.20056532232710.1016/S1470‑2045(05)70168‑6 15863380
     [Google Scholar]
  49. WangD. FanZ. LiuF. ZuoJ. Hsa-miR-21 and Hsa-miR-29 in tissue as potential diagnostic and prognostic biomarkers for gastric cancer.Cell. Physiol. Biochem.20153741454146210.1159/000438514 26509997
     [Google Scholar]
  50. LiH. WuQ. LiT. The miR-17-92 cluster as a potential biomarker for the early diagnosis of gastric cancer: Evidence and literature review.Oncotarget2017828450604507110.18632/oncotarget.15023 28178677
     [Google Scholar]
  51. Jafarzadeh-SamaniZ. SohrabiS. ShirmohammadiK. EffatpanahH. YadegarazariR. SaidijamM. Evaluation of miR-22 and miR-20a as diagnostic biomarkers for gastric cancer. Chin Clin Oncol2017621610.21037/cco.2017.03.01 28482669
     [Google Scholar]
  52. LiuM. MoF. SongX. Exosomal hsa-miR-21-5p is a biomarker for breast cancer diagnosis.PeerJ20219e1214710.7717/peerj.12147 34616615
     [Google Scholar]
  53. LvS. WangY. XuW. DongX. Serum Exosomal miR-17-5p as a promising biomarker diagnostic biomarker for breast cancer.Clinical. Laboratory.20209
     [Google Scholar]
  54. EldosokyM. HammadR. ElmadboulyA. Diagnostic significance of hsa-miR-21-5p, hsa-miR-192-5p, hsa-miR-155-5p, hsa-miR-199a-5p panel and ratios in hepatocellular carcinoma on top of liver cirrhosis in HCV-infected patients.Int. J. Mol. Sci.2023244315710.3390/ijms24043157 36834570
     [Google Scholar]
  55. ZhangY. ZhangY. YinY. LiS. Detection of circulating exosomal miR-17-5p serves as a novel non-invasive diagnostic marker for non-small cell lung cancer patients.Pathol. Res. Pract.2019215815246610.1016/j.prp.2019.152466 31146974
     [Google Scholar]
  56. ZhangH. MaoF. ShenT. Plasma miR-145, miR-20a, miR-21 and miR-223 as novel biomarkers for screening early-stage non-small cell lung cancer.Oncol. Lett.201713266967610.3892/ol.2016.5462 28356944
     [Google Scholar]
  57. QuK. ZhangX. LinT. Circulating miRNA-21-5p as a diagnostic biomarker for pancreatic cancer: evidence from comprehensive miRNA expression profiling analysis and clinical validation.Sci. Rep.201771169210.1038/s41598‑017‑01904‑z 28490741
     [Google Scholar]
  58. LiQ. SongS. NiG. LiY. WangX. Serum miR-542-3p as a prognostic biomarker in osteosarcoma.Cancer Biomark.201821352152610.3233/CBM‑170255 29103020
     [Google Scholar]
  59. ZhuQ.N. RenaudH. GuoY. Bioinformatics-based identification of miR-542-5p as a predictive biomarker in breast cancer therapy.Hereditas201815511710.1186/s41065‑018‑0055‑7 29371858
     [Google Scholar]
  60. SilvaC.M.S. Barros-FilhoM.C. WongD.V.T. Circulating let-7e-5p, miR-106a-5p, miR-28-3p, and miR-542-5p as a promising microRNA signature for the detection of colorectal cancer.Cancers 2021137149310.3390/cancers13071493 33804927
     [Google Scholar]
  61. NanS. ZhangS. JinR. WangJ. LINC00665 up-regulates SIN3A expression to modulate the progression of colorectal cancer via sponging miR-138-5p.Cancer Cell Int.20222215110.1186/s12935‑021‑02176‑4 35101035
     [Google Scholar]
  62. HanT. GaoM. WangX. LINC00665 activates Wnt/β-catenin signaling pathway to facilitate tumor progression of colorectal cancer via upregulating CTNNB1.Exp. Mol. Pathol.202112010463910.1016/j.yexmp.2021.104639 33865827
     [Google Scholar]
  63. DuanQ. CaiL. ZhengK. lncRNA KCNQ1OT1 knockdown inhibits colorectal cancer cell proliferation, migration and invasiveness via thePI3K/AKT pathway.Oncol. Lett.202020160161010.3892/ol.2020.11619 32565985
     [Google Scholar]
  64. MiniE. LapucciA. PerroneG. RNA sequencing reveals PNN and KCNQ1OT1 as predictive biomarkers of clinical outcome in stage III colorectal cancer patients treated with adjuvant chemotherapy.Int. J. Cancer201914592580259310.1002/ijc.32326 30973654
     [Google Scholar]
  65. LiuX. ChenJ. ZhangS. LINC00839 promotes colorectal cancer progression by recruiting RUVBL1/Tip60 complexes to activate NRF1.EMBO Rep.2022239e5412810.15252/embr.202154128 35876654
     [Google Scholar]
  66. ZhouM. BianZ. LiuB. Long noncoding RNA MCM3AP‐AS1 enhances cell proliferation and metastasis in colorectal cancer by regulating miR‐193a‐5p/SENP1.Cancer Med.20211072470248110.1002/cam4.3830 33686713
     [Google Scholar]
  67. Ghafouri-FardS. KhoshbakhtT. HussenB.M. TaheriM. SamadianM. A review on the role of MCM3AP-AS1 in the carcinogenesis and tumor progression.Cancer Cell Int.202222122510.1186/s12935‑022‑02644‑5 35790972
     [Google Scholar]
  68. MaX. LuoJ. ZhangY. SunD. LinY. LncRNA MCM3AP-AS1 upregulates CDK4 by sponging miR-545 to suppress G1 arrest in colorectal cancer.Cancer Manag. Res.2020128117812410.2147/CMAR.S247330 32982409
     [Google Scholar]
  69. KogoR. ShimamuraT. MimoriK. Long noncoding RNA HOTAIR regulates polycomb-dependent chromatin modification and is associated with poor prognosis in colorectal cancers.Cancer Res.201171206320632610.1158/0008‑5472.CAN‑11‑1021 21862635
     [Google Scholar]
  70. SvobodaM. SlyskovaJ. SchneiderovaM. HOTAIR long non-coding RNA is a negative prognostic factor not only in primary tumors, but also in the blood of colorectal cancer patients.Carcinogenesis20143571510151510.1093/carcin/bgu055 24583926
     [Google Scholar]
  71. KimJ.O. JunH.H. KimE.J. Genetic variants of HOTAIR associated with colorectal cancer susceptibility and mortality.Front. Oncol.2020107210.3389/fonc.2020.00072 32117729
     [Google Scholar]
  72. TianJ. HuD. LncRNA SLC16A1-AS1 is upregulated in hepatocellular carcinoma and predicts poor survival.Clin. Res. Hepatol. Gastroenterol.202145210149010.1016/j.clinre.2020.07.001 33744723
     [Google Scholar]
  73. PeiS. ChenZ. TanH. FanL. ZhangB. ZhaoC. SLC16A1-AS1 enhances radiosensitivity and represses cell proliferation and invasion by regulating the miR-301b-3p/CHD5 axis in hepatocellular carcinoma.Environ. Sci. Pollut. Res. Int.20202734427784279010.1007/s11356‑020‑09998‑1 32748357
     [Google Scholar]
  74. CaiJ ChenZ ChenX HuangH LinX MiaoB Coexpression network analysis identifies a novel nine-RNA signature to improve prognostic prediction for prostate cancer patients.. BioMed Res Int 202010.1155/2020/4264291
     [Google Scholar]
  75. ZhaoJ MaH FengR LiD LiuB CaoX. A novel oxidative stressrelated lncrna signature that predicts the prognosis and tumor immune microenvironment of breast cancer. J Oncol 2022;202210.1155/2022/9766954
     [Google Scholar]
/content/journals/mirna/10.2174/0122115366320538240912080053
Loading
Identification of miR-20a as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: MicroRNA Sequencing and Machine Learning Analysis
MicroRNA 14, 73 (2025); https://doi.org/10.2174/0122115366320538240912080053
/content/journals/mirna/10.2174/0122115366320538240912080053
/content/journals/mirna/10.2174/0122115366320538240912080053
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Colorectal cancer; data analysis; machine learning; miRNA; signaling pathway
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