Impact of mini-driver genes in the prognosis and tumor features of colorectal cancer samples: a novel perspective to support current biomarkers

Carregando...
Imagem de Miniatura
Citações na Scopus
1
Tipo de produção
article
Data de publicação
2023
Título da Revista
ISSN da Revista
Título do Volume
Editora
PEERJ INC
Autores
SEGURA, Anthony Vladimir Campos
SOTOMAYOR, Mariana Belen Velasquez
ROMAN, Ana Isabel Flor Gutierrez
ROJAS, Cesar Alexander Ortiz
Citação
PEERJ, v.11, article ID e15410, 19p, 2023
Projetos de Pesquisa
Unidades Organizacionais
Fascículo
Resumo
Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths, and its development is associated with the gains and/or losses of genetic material, which leads to the emergence of main driver genes with higher mutational frequency. In addition, there are other genes with mutations that have weak tumor-promoting effects, known as mini-drivers, which could aggravate the development of oncogenesis when they occur together. The aim of our work was to use computer analysis to explore the survival impact, frequency, and incidence of mutations of possible mini-driver genes to be used for the prognosis of CRC. Methods: We retrieved data from three sources of CRC samples using the cBioPortal platform and analyzed the mutational frequency to exclude genes with driver features and those mutated in less than 5% of the original cohort. We also observed that the mutational profile of these mini-driver candidates is associated with variations in the expression levels. The candidate genes obtained were subjected to Kaplan-Meier curve analysis, making a comparison between mutated and wild-type samples for each gene using a p-value threshold of 0.01. Results: After gene filtering by mutational frequency, we obtained 159 genes of which 60 were associated with a high accumulation of total somatic mutations with Log2 (fold change) > 2 and p values < 10-5. In addition, these genes were enriched to oncogenic pathways such as epithelium-mesenchymal transition, hsa-miR-218-5p downregulation, and extracellular matrix organization. Our analysis identified five genes with possible implications as mini-drivers: DOCKS, FN1, PAPPA2, DNAH11, and FBN2. Furthermore, we evaluated a combined classification where CRC patients obtaining a p-value < 0.001 in the evaluation of CRC prognosis.Conclusion: Our study suggests that the identification and incorporation of mini-driver genes in addition to known driver genes could enhance the accuracy of prognostic biomarkers for CRC.
Palavras-chave
Mini-driver genes, Colorectal cancer, Bioinformatics, Molecular biology, Mutational profile, Gene expression
Referências
  1. Abdi E, 2022, ENVIRON MOL MUTAGEN, V63, P98, DOI 10.1002/em.22477
  2. Aburjania Z, 2018, ONCOLOGIST, V23, P900, DOI 10.1634/theoncologist.2017-0677
  3. Al-Koofee DAF, 2020, RECENT TOPICS GENETI
  4. Badr H, 2022, J MOL BIOL, V434, DOI 10.1016/j.jmb.2022.167636
  5. BENJAMINI Y, 1995, J R STAT SOC B, V57, P289, DOI 10.1111/j.2517-6161.1995.tb02031.x
  6. Bennett L, 2018, SCI REP-UK, V8, DOI 10.1038/s41598-018-33276-3
  7. Blighe K., 2018, ENHANCEDVOLCANO PUBL
  8. Borgan O., 2001, STAT MED, V20, P2053, DOI [10.1002/sim.956, DOI 10.1002/SIM.956]
  9. Boyle EA, 2017, CELL, V169, P1177, DOI 10.1016/j.cell.2017.05.038
  10. Brannon AR, 2014, GENOME BIOL, V15, DOI 10.1186/s13059-014-0454-7
  11. Bray F, 2018, CA-CANCER J CLIN, V68, P394, DOI [10.3322/caac.21492, DOI 10.3322/caac.21492, 10.3322/caac.21609]
  12. Campos Segura A, 2022, ANALISIS MUTACIONES
  13. Castro-Giner F, 2015, NAT REV CANCER, V15, P680, DOI 10.1038/nrc3999
  14. Cerami E, 2012, CANCER DISCOV, V2, P401, DOI 10.1158/2159-8290.CD-12-0095
  15. Cuykendall Tawny N, 2017, Curr Opin Syst Biol, V1, P9, DOI 10.1016/j.coisb.2016.12.017
  16. Dressler L, 2022, GENOME BIOL, V23, DOI 10.1186/s13059-022-02607-z
  17. Elliott K, 2021, NAT REV CANCER, V21, P500, DOI 10.1038/s41568-021-00371-z
  18. Fantini D, 2019, TCGARETRIEVER RETRIE
  19. Furuya TK, 2021, CANCERS, V13, DOI 10.3390/cancers13194745
  20. Giannakis M, 2016, CELL REP, V15, P857, DOI 10.1016/j.celrep.2016.03.075
  21. Hanahan D, 2022, CANCER DISCOV, V12, P31, DOI 10.1158/2159-8290.CD-21-1059
  22. Hesson LB, 2016, MOL CANCER RES, V14, P1217, DOI 10.1158/1541-7786.MCR-16-0175
  23. Hofer P, 2017, ONCOTARGET, V8, P98623, DOI 10.18632/oncotarget.21697
  24. Iranmanesh H, 2021, GALEN MED J, V10, DOI [10.31661/gmj.v10i0.2030, 10.31661/gmj.v.1010.2030]
  25. Irmak-Yazicioglu MB, 2016, ONCOL RES TREAT, V39, P136, DOI 10.1159/000443224
  26. Jo YS, 2017, POL J PATHOL, V68, P258, DOI [10.5114/PJP.2017.71534, 10.5114/pjp.2017.71534]
  27. Kotelevets L, 2020, CANCERS, V12, DOI 10.3390/cancers12030665
  28. Lee-Six H, 2019, NATURE, V574, P532, DOI 10.1038/s41586-019-1672-7
  29. Leedham S, 2012, CANCER RES, V72, P3131, DOI 10.1158/0008-5472.CAN-12-1052
  30. Leygo C, 2017, DIS MARKERS, V2017, DOI 10.1155/2017/3726595
  31. Li X, 2016, INTERPLAY DRIVER MIN, V49, P4682, DOI [10.1101/084392, DOI 10.1101/084392]
  32. Liu N, 2020, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.00738
  33. Liu Y, 2018, CANCER CELL, V33, P721, DOI 10.1016/j.ccell.2018.03.010
  34. Lu YP, 2020, ONCOTARGETS THER, V13, P10393, DOI 10.2147/OTT.S255590
  35. Ma R, 2020, J CANCER, V11, P1038, DOI 10.7150/jca.37017
  36. Mermel CH, 2011, GENOME BIOL, V12, DOI 10.1186/gb-2011-12-4-r41
  37. Miao YR, 2022, CELLS-BASEL, V11, DOI 10.3390/cells11182802
  38. Muzny DM, 2012, NATURE, V487, P330, DOI 10.1038/nature11252
  39. Pasto A, 2014, CANCER RES, V74, P2106, DOI 10.1158/0008-5472.CAN-13-2022
  40. Qi JB, 2021, FRONT ONCOL, V10, DOI 10.3389/fonc.2020.618902
  41. R Core Team, 2020, R LANG ENV STAT COMP
  42. Sung H, 2021, CA-CANCER J CLIN, V71, P209, DOI 10.3322/caac.21660
  43. Thierry AR, 2014, NAT MED, V20, P430, DOI 10.1038/nm.3511
  44. Timmermann B, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0015661
  45. van Ginkel J, 2023, GASTROENTEROLOGY, V164, P841, DOI 10.1053/j.gastro.2022.11.049
  46. Wang J, 2022, PATHOL ONCOL RES, V28, DOI 10.3389/pore.2022.1610350
  47. Wang RX, 2015, CANCER BIOMARK, V15, P27, DOI 10.3233/CBM-140442
  48. Wickham H, 2011, WIRES COMPUT STAT, V3, P180, DOI 10.1002/wics.147
  49. Wilk G, 2018, NUCLEIC ACIDS RES, V46, P1089, DOI 10.1093/nar/gkx1250
  50. Wu JJ, 2016, CARCINOGENESIS, V37, P511, DOI 10.1093/carcin/bgw029
  51. Wu P, 2020, MOL CANCER, V19, DOI 10.1186/s12943-020-1147-3
  52. Xie Zhuorui, 2021, Curr Protoc, V1, pe90, DOI 10.1002/cpz1.90
  53. Xiu MX, 2021, FRONT MOL BIOSCI, V8, DOI 10.3389/fmolb.2021.694141
  54. Yang SX, 2018, CANCER MANAG RES, V10, P2249, DOI 10.2147/CMAR.S166308
  55. Zheng X, 2020, J EXTRACELL VESICLES, V9, DOI 10.1080/20013078.2020.1750202