Determination of Genetic Alteration in Pancreatic Ductal Adenocarcinoma Tissues by Analysis of Gene Expression Data

Öz

Objective: It is very important to determine the molecular infrastructure of pancreatic ductal adenocarcinoma, which has a very high mortality rate, limited treatment options, and does not have an option for targeted therapy, and to understand the disease by clinicians. Therefore, in this study, the gene expression dataset was used to determine the differences in transcriptome levels between tissues with pancreatic ductal adenocarcinoma and normal tissues. Methods: In the current study, gene expression data set obtained from 10 pancreatic ductal adenocarcinoma tissues and 5 normal tissues were used. The limma package available in the R programming language was used to identify transcripts with differential expression in pancreatic ductal adenocarcinoma compared to normal tissues. The log2FC and adj-p values were used to identify genes that showed differential (up or down) regulation. Results: According to the results of gene expression analysis, 7098 transcripts showed different regulation in pancreatic ductal adenocarcinoma tissue compared to normal tissue. With the UMAP graph, normal and pancreatic ductal adenocarcinoma tissues are distributed differently from each other, indicating that there is a difference in transcript between these two tissues. Conclusion: As a result of the gene expression analysis performed in the study, transcripts differing between pancreatic ductal adenocarcinoma tissues and normal tissues were found. With the help of studies with these transcripts, targeted treatment strategies can be developed for the treatment of the disease, and the status of this disease, which has a very high mortality rate, can be changed.

Anahtar Kelimeler

• : pancreatic ductal adenocarcinoma
• gene expression
• differential expression

Pankreatik Duktal Adenokarsinom Dokularında Genetik Değişikliklerin Gen Ekspresyon Verileri Analizi ile Belirlenmesi

Öz

Amaç: Ölüm oranı çok yüksek, tedavi seçenekleri kısıtlı ve hedefe yönelik tedavi seçeneği bulunmayan pankreatik duktal adenokarsinomun moleküler altyapısının belirlenmesi ve hastalığın klinisyenler tarafından anlaşılması oldukça önemlidir. Bu nedenle, bu çalışmada, pankreatik duktal adenokarsinomlu dokular ile normal dokular arasındaki transkriptom seviyelerindeki farklılıkları belirlemek için gen ekspresyon veri seti kullanılmıştır. Metod: Bu çalışmada, 10 pankreatik duktal adenokarsinom dokusu ve 5 normal dokudan elde edilen gen ekspresyon veri seti kullanılmıştır. R programlama dilinde bulunan limma paketi, normal dokulara kıyasla pankreatik duktal adenokarsinomda diferansiyel ekspresyona sahip transkriptleri tanımlamak için kullanılmıştır. Log2FC ve adj-p değerleri, diferansiyel (yukarı veya aşağı) düzenleme gösteren genleri tanımlamak için kullanılmıştır. Bulgular: Gen ekspresyon analizi sonuçlarına göre, 7098 transkript pankreatik duktal adenokarsinom dokusunda normal dokuya kıyasla farklı düzenleme göstermiştir. UMAP grafiği ile normal ve pankreatik duktal adenokarsinom dokularının birbirinden farklı dağılım göstermesi, bu iki doku arasında transkript farklılığı olduğunu göstermektedir. Sonuç: Çalışmada yapılan gen ekspresyon analizi sonucunda pankreatik duktal adenokarsinom dokuları ile normal dokular arasında farklılık gösteren transkriptler bulunmuştur. Bu transkriptler ile yapılacak çalışmalar sayesinde hastalığın tedavisi için hedefe yönelik tedavi stratejileri geliştirilebilir ve ölüm oranı çok yüksek olan bu hastalığın durumu değiştirilebilir.

Anahtar Kelimeler

• Pankreatik duktal adenokarsinom
• gen ekspresyonu
• diferansiyel ekspresyon

Kaynakça

1. 1. Howlader N, Noone A, Krapcho M, Miller D, Brest A, Yu M, et al. SEER Cancer Statistics Review, 1975-2017, National Cancer Institute. Bethesda, MD. 2020.
2. 2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020 Jan;70(1):7-30. doi: 10.3322/caac.21590. Epub 2020 Jan 8. PMID: 31912902.
3. 3. Cives M, Strosberg JR. Gastroenteropancreatic Neuroendocrine Tumors. CA Cancer J Clin. 2018 Nov;68(6):471-487. doi: 10.3322/caac.21493. Epub 2018 Oct 8. PMID: 30295930.
4. 4. Li D, Xie K, Wolff R, Abbruzzese JL. Pancreatic cancer. Lancet. 2004 Mar 27;363(9414):1049-57. doi: 10.1016/S0140-6736(04)15841-8. PMID: 15051286.
5. 5. Conroy T, Hammel P, Hebbar M, Ben Abdelghani M, Wei AC, Raoul JL, Choné L, Francois E, Artru P, Biagi JJ, Lecomte T, Assenat E, Faroux R, Ychou M, Volet J, Sauvanet A, Breysacher G, Di Fiore F, Cripps C, Kavan P, Texereau P, Bouhier-Leporrier K, Khemissa-Akouz F, Legoux JL, Juzyna B, Gourgou S, O'Callaghan CJ, Jouffroy-Zeller C, Rat P, Malka D, Castan F, Bachet JB; Canadian Cancer Trials Group and the Unicancer-GI–PRODIGE Group. FOLFIRINOX or Gemcitabine as Adjuvant Therapy for Pancreatic Cancer. N Engl J Med. 2018 Dec 20;379(25):2395-2406. doi: 10.1056/NEJMoa1809775. PMID: 30575490.
6. 6. Neoptolemos JP, Stocken DD, Friess H, Bassi C, Dunn JA, Hickey H, Beger H, Fernandez-Cruz L, Dervenis C, Lacaine F, Falconi M, Pederzoli P, Pap A, Spooner D, Kerr DJ, Büchler MW; European Study Group for Pancreatic Cancer. A randomized trial of chemoradiotherapy and chemotherapy after resection of pancreatic cancer. N Engl J Med. 2004 Mar 18;350(12):1200-10. doi: 10.1056/NEJMoa032295. Erratum in: N Engl J Med. 2004 Aug 12;351(7):726. PMID: 15028824.
7. 7. Danai LV, Babic A, Rosenthal MH, Dennstedt EA, Muir A, Lien EC, Mayers JR, Tai K, Lau AN, Jones-Sali P, Prado CM, Petersen GM, Takahashi N, Sugimoto M, Yeh JJ, Lopez N, Bardeesy N, Fernandez-Del Castillo C, Liss AS, Koong AC, Bui J, Yuan C, Welch MW, Brais LK, Kulke MH, Dennis C, Clish CB, Wolpin BM, Vander Heiden MG. Altered exocrine function can drive adipose wasting in early pancreatic cancer. Nature. 2018 Jun;558(7711):600-604. doi: 10.1038/s41586-018-0235-7. Epub 2018 Jun 20. PMID: 29925948; PMCID: PMC6112987.
8. 8. Andersen DK, Korc M, Petersen GM, Eibl G, Li D, Rickels MR, Chari ST, Abbruzzese JL. Diabetes, Pancreatogenic Diabetes, and Pancreatic Cancer. Diabetes. 2017 May;66(5):1103-1110. doi: 10.2337/db16-1477. PMID: 28507210; PMCID: PMC5399609.

9. 9. Kays JK, Shahda S, Stanley M, Bell TM, O'Neill BH, Kohli MD, Couch ME, Koniaris LG, Zimmers TA. Three cachexia phenotypes and the impact of fat-only loss on survival in FOLFIRINOX therapy for pancreatic cancer. J Cachexia Sarcopenia Muscle. 2018 Aug;9(4):673-684. doi: 10.1002/jcsm.12307. Epub 2018 Jul 5. PMID: 29978562; PMCID: PMC6104116.
10. 10. He J, Blair AB, Groot VP, Javed AA, Burkhart RA, Gemenetzis G, Hruban RH, Waters KM, Poling J, Zheng L, Laheru D, Herman JM, Makary MA, Weiss MJ, Cameron JL, Wolfgang CL. Is a Pathological Complete Response Following Neoadjuvant Chemoradiation Associated With Prolonged Survival in Patients With Pancreatic Cancer? Ann Surg. 2018 Jul;268(1):1-8. doi: 10.1097/SLA.0000000000002672. PMID: 29334562; PMCID: PMC6178802.
11. 11. Van Cutsem E, Tempero MA, Sigal D, Oh DY, Fazio N, Macarulla T, Hitre E, Hammel P, Hendifar AE, Bates SE, Li CP, Hingorani SR, de la Fouchardiere C, Kasi A, Heinemann V, Maraveyas A, Bahary N, Layos L, Sahai V, Zheng L, Lacy J, Park JO, Portales F, Oberstein P, Wu W, Chondros D, Bullock AJ; HALO 109-301 Investigators. Randomized Phase III Trial of Pegvorhyaluronidase Alfa With Nab-Paclitaxel Plus Gemcitabine for Patients With Hyaluronan-High Metastatic Pancreatic Adenocarcinoma. J Clin Oncol. 2020 Sep 20;38(27):3185-3194. doi: 10.1200/JCO.20.00590. Epub 2020 Jul 24. PMID: 32706635; PMCID: PMC7499614.
12. 12. Tempero M, Oh DY, Tabernero J, Reni M, Van Cutsem E, Hendifar A, Waldschmidt DT, Starling N, Bachet JB, Chang HM, Maurel J, Garcia-Carbonero R, Lonardi S, Coussens LM, Fong L, Tsao LC, Cole G Jr, James D, Macarulla T. Ibrutinib in combination with nab-paclitaxel and gemcitabine for first-line treatment of patients with metastatic pancreatic adenocarcinoma: phase III RESOLVE study. Ann Oncol. 2021 May;32(5):600-608. doi: 10.1016/j.annonc.2021.01.070. Epub 2021 Feb 1. PMID: 33539945.
13. 13. Hosein AN, Dougan SK, Aguirre AJ, Maitra A. Translational advances in pancreatic ductal adenocarcinoma therapy. Nat Cancer. 2022 Mar;3(3):272-286. doi: 10.1038/s43018-022-00349-2. Epub 2022 Mar 29. PMID: 35352061
14. 14. Akalin PK. Introduction to bioinformatics. Molecular nutrition & food research. 2006;50(7):610-9.
15. 15. van Hal NL, Vorst O, van Houwelingen AM, Kok EJ, Peijnenburg A, Aharoni A, van Tunen AJ, Keijer J. The application of DNA microarrays in gene expression analysis. J Biotechnol. 2000 Mar 31;78(3):271-80. doi: 10.1016/s0168-1656(00)00204-2. PMID: 10751688.
16. 16. Smyth GK. Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor: Springer; 2005. p. 397-420.
17. 17. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin. 2019 Jan;69(1):7-34. doi: 10.3322/caac.21551. Epub 2019 Jan 8. PMID: 30620402.
18. 18. O'Neil NJ, Bailey ML, Hieter P. Synthetic lethality and cancer. Nat Rev Genet. 2017 Oct;18(10):613-623. doi: 10.1038/nrg.2017.47. Epub 2017 Jun 26. PMID: 28649135.
19. 19. Qian Y, Gong Y, Fan Z, Luo G, Huang Q, Deng S, Cheng H, Jin K, Ni Q, Yu X, Liu C. Molecular alterations and targeted therapy in pancreatic ductal adenocarcinoma. J Hematol Oncol. 2020 Oct 2;13(1):130. doi: 10.1186/s13045-020-00958-3. PMID: 33008426; PMCID: PMC7532113.
20. 20. Grossberg AJ, Chu LC, Deig CR, Fishman EK, Hwang WL, Maitra A, Marks DL, Mehta A, Nabavizadeh N, Simeone DM, Weekes CD, Thomas CR Jr. Multidisciplinary standards of care and recent progress in pancreatic ductal adenocarcinoma. CA Cancer J Clin. 2020 Sep;70(5):375-403. doi: 10.3322/caac.21626. Epub 2020 Jul 19. PMID: 32683683; PMCID: PMC7722002.