Русская версия English version   
О журнале
Редакционная коллегия
Правила для авторов
Информация для читателей и авторов
Оглавление текущего номера
Архив
Контакты
Поиск

Отправить статью
 


Том 19   Выпуск 1   Год 2024
Фатима А. Абдул Джаббар1, Раваа Аль-Чалаби1, Ахмед Ясин Аль-Тарбули1, Семаа А. Шабан2, Ахмед АбдулДжаббар Сулейман3

Альтернативный сплайсинг при аденокарциноме протоков поджелудочной железы приводит к нарушению регуляции иммунной системы

Математическая биология и биоинформатика. 2024;19(1):15-35.

doi: 10.17537/2024.19.15.

Список литературы

  1. Zhang L., Sanagapalli S., Stoita A. Challenges in diagnosis of pancreatic cancer. World J. Gastroenterol. 2018;24(19):2047–2060. doi: 10.3748/wjg.v24.i19.2047
  2. Rawla P., Sunkara T., Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J. Oncol. 2019;10(1):10–27. doi: 10.14740/wjon1166
  3. 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. doi: 10.3322/caac.21660
  4. Fukushima N., Zamboni G. Histologic Classification and Staging of Cystic Neoplasms. In: The Pancreas: An Integrated Textbook of Basic Science, Medicine, and Surgery. Third Edition. John Wiley & Sons, Ltd, 2018. P. 573–579. doi: 10.1002/9781119188421.ch74
  5. Diab M., Philip P.A. Uncommon Cancers of the Pancreas. In: Textbook of Uncommon Cancer. John Wiley & Sons, Ltd, 2017. P. 429–443. doi: 10.1002/9781119196235.ch29
  6. Rawla P., Sunkara T., Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J. Oncol. 2019;10(1):10–27. doi: 10.14740/wjon1166
  7. Collisson E.A., Bailey P., Chang D.K., Biankin A.V. Molecular subtypes of pancreatic cancer. Nat. Rev. Gastroenterol. Hepatol. 2019;16(4):207–220. doi: 10.1038/s41575-019-0109-y
  8. Gao H.L., Wang W.Q., Yu X.J., Liu L. Molecular drivers and cells of origin in pancreatic ductal adenocarcinoma and pancreatic neuroendocrine carcinoma. Exp. Hematol. Oncol. 2020;22(9):28. doi: 10.1186/s40164-020-00184-0
  9. Mostafa M.E., Erbarut-Seven I., Pehlivanoglu B., Adsay V. Pathologic classification of “pancreatic cancers”: current concepts and challenges. Chin. Clin. Oncol. 2018;6(6):59–59. doi: 10.21037/cco.2017.12.01
  10. Oldfield L.E., Connor A.A., Gallinger S. Molecular Events in the Natural History of Pancreatic Cancer. Trends Cancer. 2017;3(5):336–346. doi: 10.1016/j.trecan.2017.04.005
  11. Grimont A., Leach S.D., Chandwani R. Uncertain Beginnings: Acinar and Ductal Cell Plasticity in the Development of Pancreatic Cancer. Cell. Mol. Gastroenterol. Hepatol. 2022;13(2):369–382. doi: 10.1016/j.jcmgh.2021.07.014
  12. Deshwar A.B., Sugar E., Torto D., Jesus-Acosta A.D., Weiss M.J., Wolfgang C.L, Le D., He J., Burkhart R., Zheng L. et al. Diagnostic intervals and pancreatic ductal adenocarcinoma (PDAC) resectability: a single-center retrospective analysis. Ann. Pancreat. Cancer. 2018;1(2). doi: 10.21037/apc.2018.02.01
  13. Moffat G.T., Epstein A.S., O’Reilly E.M. Pancreatic cancer – A disease in need: Optimizing and integrating supportive care. Cancer. 2019;125(22):3927–3935. doi: 10.1002/cncr.32423
  14. Sahin I.H., Askan G., Hu Z.I., O’Reilly E.M. Immunotherapy in pancreatic ductal adenocarcinoma: an emerging entity? Ann. Oncol. 2017;28(12):2950–61. doi: 10.1093/annonc/mdx503
  15. Karamitopoulou E. Tumour microenvironment of pancreatic cancer: immune landscape is dictated by molecular and histopathological features. Br. J. Cancer. 2019;121(1):5–14. doi: 10.1038/s41416-019-0479-5
  16. Riquelme E., Maitra A., McAllister F. Immunotherapy for Pancreatic Cancer: More than Just a Gut Feeling. Cancer Discov. 2018;8(4):386–388. doi: 10.1158/2159-8290.CD-18-0123
  17. Lu J., Wei S., Lou J., Yin S., Zhou L., Zhang W., Zheng S. Systematic Analysis of Alternative Splicing Landscape in Pancreatic Adenocarcinoma Reveals Regulatory Network Associated with Tumorigenesis and Immune Response. Med. Sci. Monit. 2020;26. doi: 10.12659/MSM.925733
  18. Zhang Y., Velez-Delgado A., Mathew E., Li D., Mendez F.M., Flannagan K., Rhim A.D., Simeone D.M., Beatty G.L., di Magliano M.P. Myeloid cells are required for PD-1/PD-L1 checkpoint activation and the establishment of an immunosuppressive environment in pancreatic cancer. Gut. 2017;66(1):124–136. doi: 10.1136/gutjnl-2016-312078
  19. Chen B., Deng T., Deng L., Yu H., He B., Chen K., Zheng C., Wang D., Wang Y., Chen G. Identification of tumour immune microenvironment-related alternative splicing events for the prognostication of pancreatic adenocarcinoma. BMC Cancer. 2021;21(1):1211. doi: 10.1186/s12885-021-08962-7
  20. Marzese D.M., Manughian-Peter A.O., Orozco J.I.J., Hoon D.S.B. Alternative splicing and cancer metastasis: prognostic and therapeutic applications. Clin. Exp. Metastasis. 2018;35(5):393–402. doi: 10.1007/s10585-018-9905-y
  21. Kawalerski R.R., Leach S.D., Escobar-Hoyos L.F. Pancreatic cancer driver mutations are targetable through distant alternative RNA splicing dependencies. Oncotarget. 2021;12(6):525–533. doi: 10.18632/oncotarget.27901
  22. Venkat S., Alahmari A.A., Feigin M.E. Drivers of Gene Expression Dysregulation in Pancreatic Cancer. Trends Cancer. 2021;7(7):594–605. doi: 10.1016/j.trecan.2021.01.008
  23. Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M., et al. NCBI GEO: archive for functional genomics data sets – update. Nucleic Acids Res. 2013;41(D1):D991–D995. doi: 10.1093/nar/gks1193
  24. Barrett T., Troup D.B., Wilhite S.E., Ledoux P., Rudnev D., Evangelista C., Kim I.F., Soboleva A., Tomashevsky M., Marshall K.A., et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009;37(suppl_1):D885–D890. doi: 10.1093/nar/gkn764
  25. Brown J., Pirrung M., McCue L.A. FQC Dashboard: integrates FastQC results into a web-based, interactive, and extensible FASTQ quality control tool. Bioinformatics. 2017;33(19):3137–3139. doi: 10.1093/bioinformatics/btx373
  26. Chen S., Zhou Y., Chen Y., Gu J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884–i890. doi: 10.1093/bioinformatics/bty560
  27. Tarasov A., Vilella A.J., Cuppen E., Nijman I.J., Prins P. Sambamba: fast processing of NGS alignment formats. Bioinformatics. 2015;31(12):2032–2034. doi: 10.1093/bioinformatics/btv098
  28. Pertea M., Pertea G.M., Antonescu C.M., Chang T.C., Mendell J.T., Salzberg S.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat. Biotechnol. 2015;33(3):290–295. doi: 10.1038/nbt.3122
  29. Papatheodorou I., Moreno P., Manning J., Fuentes A.M.P., George N., Fexova S., Fonseca N.A., Füllgrabe A., Green M., Huang N., et al. Expression Atlas update: from tissues to single cells. Nucleic Acids Res. 2020;48(D1):D77–83. doi: 10.1093/nar/gkz947
  30. Tang Z., Kang B., Li C., Chen T., Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–560. doi: 10.1093/nar/gkz430
  31. Xie Z., Bailey A., Kuleshov M.V., Clarke D.J.B., Evangelista J.E., Jenkins S.L., Lachmann A., Wojciechowicz M.L., Kropiwnicki E., Jagodnik K.M., Jeon M., Ma'ayan A. Gene Set Knowledge Discovery with Enrichr. Curr. Protoc. 2021;1(3):e90. doi: 10.1002/cpz1.90
  32. Ianevski A., Giri A.K., Aittokallio T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat. Commun. 2022;13(1):1246. doi: 10.1038/s41467-022-28803-w
  33. Garcia-Moreno A., López-Domínguez R., Villatoro-García J.A., Ramirez-Mena A., Aparicio-Puerta E., Hackenberg M., Pascual-Montano A., Carmona-Saez P. Functional Enrichment Analysis of Regulatory Elements. Biomedicines. 2022;10(3):590. doi: 10.3390/biomedicines10030590
  34. Chen L., Chen K., Hong Y., Xing L., Zhang J., Zhang K., Zhang Z. The landscape of isoform switches in sepsis: a multicenter cohort study. Sci. Rep. 2022;12(1):10276. doi: 10.1038/s41598-022-14231-9
  35. Finotello F., Mayer C., Plattner C., Laschober G., Rieder D., Hackl H., Krogsdam A., Loncova Z., Posch W., Wilflingseder D. et al. Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data. bioRxiv. 2018. doi: 10.1101/223180
  36. Huang S., Song Z., Zhang T., He X., Huang K., Zhang Q., Shen J., Pan J. Identification of Immune Cell Infiltration and Immune-Related Genes in the Tumor Microenvironment of Glioblastomas. Front. Immunol. 2020;11. doi: 10.3389/fimmu.2020.585034
  37. Sturm G., Finotello F., List M. Immunedeconv: An R Package for Unified Access to Computational Methods for Estimating Immune Cell Fractions from Bulk RNA-Sequencing Data. In: Bioinformatics for Cancer Immunotherapy. Methods in Molecular Biology, vol 2120. Ed. Boegel S. New York, NY: Springer US; 2020. P. 223–332. doi: 10.1007/978-1-0716-0327-7_16
  38. Becht E., Giraldo N.A., Lacroix L., Buttard B., Elarouci N., Petitprez F., Selves J., Laurent-Puig P., Sautès-Fridman C., Fridman W.H., de Reyniès A. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218. doi: 10.1186/s13059-016-1070-5
  39. Le Large T.Y.S, Mantini G., Meijer L.L., Pham T.V., Funel N., van Grieken N.C.T., Kok B., Knol J., van Laarhoven H.W.M., Piersma S.R., et al. Microdissected pancreatic cancer proteomes reveal tumor heterogeneity and therapeutic targets. JCI Insight. 2020;5(15):e138290. doi: 10.1172/jci.insight.138290
  40. Hill W., Zaragkoulias A,. Salvador-Barbero B., Parfitt G.J., Alatsatianos M., Padilha A., Porazinski S., Woolley T.E., Morton J.P., Sansom O.J., Hogan C. EPHA2-dependent outcompetition of KRASG12D mutant cells by wild-type neighbors in the adult pancreas. Curr. Biol. 2021;31(12):2550–2560.e5. doi: 10.1016/j.cub.2021.03.094
  41. Iiizumi M., Hosokawa M., Takehara A., Chung S., Nakamura T., Katagiri T., Eguchi H., Ohigashi H., Ishikawa O., Nakamura Y., Nakagawa H. EphA4 receptor, overexpressed in pancreatic ductal adenocarcinoma, promotes cancer cell growth. Cancer Sci. 2006;97(11):1211–1216. doi: 10.1111/j.1349-7006.2006.00313.x
  42. Haouas H., Haouas S., Uzan G., Hafsia A. Identification of new markers discriminating between myeloid and lymphoid acute leukemia. Hematology. 2010;15(4):193–203. doi: 10.1179/102453310X12647083620769
  43. Iovanna J., Dusetti N. Speeding towards individualized treatment for pancreatic cancer by taking an alternative road. Cancer Lett. 2017;410:63–67. doi: 10.1016/j.canlet.2017.09.016
  44. Luo Y. The characteristic of stem-related genes with pancreatic carcinoma cell after irradiation. Heliyon. 2023;9(6):e17074. doi: 10.1016/j.heliyon.2023.e17074
  45. Yang H., Zhao L., Zhang Y., Li F.F. A comprehensive analysis of immune infiltration in the tumor microenvironment of osteosarcoma. Cancer Med. 2021;10(16):5696–5711. doi: 10.1002/cam4.4117
  46. Zalpoor H., Aziziyan F., Liaghat M., Bakhtiyari M., Akbari A., Nabi-Afjadi M., Forghaniesfidvajani R., Rezaei N. The roles of metabolic profiles and intracellular signaling pathways of tumor microenvironment cells in angiogenesis of solid tumors. Cell Commun. Signal. 2022;20(1):186. doi: 10.1186/s12964-022-00951-y
  47. Sommariva M., Gagliano N. E-Cadherin in Pancreatic Ductal Adenocarcinoma: A Multifaceted Actor during EMT. Cells. 2020;9(4):1040. doi: 10.3390/cells9041040
  48. Li H., Wu M., Zhao X. Role of chemokine systems in cancer and inflammatory diseases. MedComm. 2022;3(2):e147. doi: 10.1002/mco2.147
  49. Tan R., Nie M., Long W. The role of B cells in cancer development. Front. Oncol. 2022;12(958756). doi: 10.3389/fonc.2022.958756
  50. Dong Y., Wan Z., Gao X., Yang G., Liu L. Reprogramming Immune Cells for Enhanced Cancer Immunotherapy: Targets and Strategies. Front. Immunol. 2021;12(609762). doi: 10.3389/fimmu.2021.609762
  51. Nagl L., Horvath L., Pircher A., Wolf D. Tumor Endothelial Cells (TECs) as Potential Immune Directors of the Tumor Microenvironment – New Findings and Future Perspectives. Front. Cell Dev. Biol. 2020;8:766. doi: 10.3389/fcell.2020.00766
  52. Luo N. Editorial: Tumor microenvironment in cancer hallmarks and therapeutics. Front. Mol. Biosci. 2022;9:1019830. doi: 10.3389/fmolb.2022.1019830
  53. van der Leun A.M., Thommen D.S., Schumacher T.N. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer. 2020;20:218–232. doi: 10.1038/s41568-019-0235-4
  54. Li C., Jiang P., Wei S., Xu X., Wang J. Regulatory T cells in tumor microenvironment: new mechanisms, potential therapeutic strategies and future prospects. Mol. Cancer. 2020;19(1):116. doi: 10.1186/s12943-020-01234-1
  55. Karakulak T., Moch H., von Mering C., Kahraman A. Probing Isoform Switching Events in Various Cancer Types: Lessons From Pan-Cancer Studies. Front. Mol. Biosci. 2021;8. Article No. 726902. doi: 10.3389/fmolb.2021.726902
  56. Zhang Y., Weh K.M., Howard C.L., Riethoven J.J., Clarke J.L., Lagisetty K.H., Lin J., Reddy R.M., Chang A.C., Beer D.G. et al. Characterizing isoform switching events in esophageal adenocarcinoma. Mol. Ther – Nucleic Acids. 2022;29:749–768. doi: 10.1016/j.omtn.2022.08.018
  57. Vitting-Seerup K., Sandelin A. The Landscape of Isoform Switches in Human Cancers. Mol. Cancer Res. 2017;15(9):1206–1220. doi: 10.1158/1541-7786.MCR-16-0459
  58. Chen J., Weiss W.A. Alternative splicing in cancer: implications for biology and therapy. Oncogene. 2015;34(1):1–14. doi: 10.1038/onc.2013.570
  59. Potaczek D.P., Przytulska-Szczerbik A., Bazan-Socha S., Nastałek M., Wojas-Pelc A., Okumura K., Nishiyama C., Jurczyszyn A., Undas A., Wypasek E. Interaction between functional polymorphisms in FCER1A and TLR2 and the severity of atopic dermatitis. Hum. Immunol. 2020;81(12):709–713. doi: 10.1016/j.humimm.2020.08.002
  60. Wu H., Tian W., Tai X., Li X., Li Z., Shui J., Yu J., Wang Z., Zhu X. Identification and functional analysis of novel oncogene DDX60L in pancreatic ductal adenocarcinoma. BMC Genomics. 2021;22(1):833. doi: 10.1186/s12864-021-08137-5
  61. Lin J., Wu Y.J., Liang X., Ji M., Ying H.M., Wang X.Y., Sun X., Shao C.H., Zhan L.X., Zhang Y. Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer. Mol. Carcinog. 2019;58(2):206–218. doi: 10.1002/mc.22920
Содержание
Оригинальная статья
Мат. биол. и биоинф.
2024;19(1):15-35
doi: 10.17537/2024.19.15
опубликована на англ. яз.

Аннотация (англ.)
Аннотация (рус.)
Полный текст (англ., pdf)
Список литературы
Доп. материалы

 
  Copyright ИМПБ РАН © 2005-2024