Identification of Key Prognostic Alternative Splicing Events of Costimulatory Molecule-Related Genes in Colon Cancer

  • Authors: Ding H.1, Shi H.2, Chen W.3, Liu Z.4, Yang Z.5, Li X.6, Qiu Z.7, Zhuo H.8
  • Affiliations:
    1. Department of General Surgery, Huadong Hospital Affiliated to Fudan University
    2. Department of General Surgery, No. 971 Hospital of PLA Navy
    3. Department of Oncology, Huangdao District Hospital of Traditional Chinese Medicine
    4. Department of General Surgery, Affiliated Qingdao Hiser Hospital of Qingdao University (Qingdao Hospital of Traditional Chinese Medicine)
    5. The IVD Medical Marketing Department, 3D Medicines Inc.
    6. Department of General Surgery, Qingdao Municipal Hospital
    7. Department of Oncology, Shunde Hospital, Guangzhou University of Chinese Medicine
    8. Department of Gastrointestinal Surgery, Provincial Hospital Affiliated to Shandong First Medical University
  • Issue: Vol 27, No 13 (2024)
  • Pages: 1900-1912
  • Section: Chemistry
  • URL: https://rjeid.com/1386-2073/article/view/645258
  • DOI: https://doi.org/10.2174/0113862073249972231026060301
  • ID: 645258

Cite item

Full Text

Abstract

Objective:This study aimed to explore the key alternative splicing events in costimulatory molecule-related genes in colon cancer and to determine their correlation with prognosis.

Methods:Gene expression RNA-sequencing data, clinical data, and SpliceSeq data of colon cancer were obtained from The Cancer Genome Atlas. Differentially expressed alternative splicing events in genes were identified, Followed by correlation analysis of genes corresponding to differentially expressed alternative splicing events with costimulatory molecule-related genes. Survival analysis was conducted using differentially expressed alternative splicing events in these genes and a prognostic model was constructed. Functional enrichment, proteinprotein interaction network, and splicing factor analyses were performed.

Results:In total, 6504 differentially expressed alternative splicing events in 3949 genes were identified between tumor and normal tissues. Correlation analysis revealed 3499 differentially expressed alternative splicing events in 2168 costimulatory molecule-related genes. Moreover, 328 differentially expressed alternative splicing events in 288 costimulatory molecule-related genes were associated with overall survival. The prognostic models constructed using these showed considerable power in predicting survival. The ubiquitin A-52 residue ribosomal protein fusion product 1 and ribosomal protein S9 were the hub nodes in the protein-protein interaction network. Furthermore, one splicing factor, splicing factor proline and glutamine-rich, was significantly associated with patient prognosis. Four splicing factor-alternative splicing pairs were obtained from four alternative splicing events in three genes: TBC1 domain family member 8 B, complement factor H, and mitochondrial fission 1.

Conclusion:The identified differentially expressed alternative splicing events of costimulatory molecule-related genes may be used to predict patient prognosis and immunotherapy responses in colon cancer.

About the authors

Hao Ding

Department of General Surgery, Huadong Hospital Affiliated to Fudan University

Email: info@benthamscience.net

Huiwen Shi

Department of General Surgery, No. 971 Hospital of PLA Navy

Email: info@benthamscience.net

Weifeng Chen

Department of Oncology, Huangdao District Hospital of Traditional Chinese Medicine

Email: info@benthamscience.net

Zhisheng Liu

Department of General Surgery, Affiliated Qingdao Hiser Hospital of Qingdao University (Qingdao Hospital of Traditional Chinese Medicine)

Email: info@benthamscience.net

Zhi Yang

The IVD Medical Marketing Department, 3D Medicines Inc.

Email: info@benthamscience.net

Xiaochuan Li

Department of General Surgery, Qingdao Municipal Hospital

Email: info@benthamscience.net

Zhichao Qiu

Department of Oncology, Shunde Hospital, Guangzhou University of Chinese Medicine

Author for correspondence.
Email: info@benthamscience.net

Hongqing Zhuo

Department of Gastrointestinal Surgery, Provincial Hospital Affiliated to Shandong First Medical University

Author for correspondence.
Email: info@benthamscience.net

References

  1. Tolba, M.F. Revolutionizing the landscape of colorectal cancer treatment: The potential role of immune checkpoint inhibitors. Int. J. Cancer, 2020, 147(11), 2996-3006. doi: 10.1002/ijc.33056 PMID: 32415713
  2. Arnold, M.; Sierra, M.S.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global patterns and trends in colorectal cancer incidence and mortality. Gut, 2017, 66(4), 683-691. doi: 10.1136/gutjnl-2015-310912 PMID: 26818619
  3. Guo, T.; Liu, D.F.; Peng, S.H.; Xu, A.M. ALKBH5 promotes colon cancer progression by decreasing methylation of the lncRNA NEAT1. Am. J. Transl. Res., 2020, 12(8), 4542-4549. PMID: 32913527
  4. Azvolinsky, A. Colorectal cancer: To stack or sequence therapy? J. Natl. Cancer Inst., 2015, 107(5), djv138. doi: 10.1093/jnci/djv138 PMID: 25957442
  5. van de Donk, P.P.; Kist de Ruijter, L.; Lub-de Hooge, M.N.; Brouwers, A.H.; van der Wekken, A.J.; Oosting, S.F.; Fehrmann, R.S.N.; de Groot, D.J.A.; de Vries, E.G.E. Molecular imaging biomarkers for immune checkpoint inhibitor therapy. Theranostics, 2020, 10(4), 1708-1718. doi: 10.7150/thno.38339 PMID: 32042331
  6. Sahin, I.; George, A.; Zhang, S.; Huntington, K.E.; Ordulu, Z.; Zhou, L.; El-Deiry, W.S. Hyperprogression of a mismatch repair-deficient colon cancer in a humanized mouse model following administration of immune checkpoint inhibitor pembrolizumab. Oncotarget, 2021, 12(21), 2131-2146. doi: 10.18632/oncotarget.28086 PMID: 34676046
  7. Sahin, I.H.; Akce, M.; Alese, O.; Shaib, W.; Lesinski, G.B.; El-Rayes, B.; Wu, C. Immune checkpoint inhibitors for the treatment of MSI-H/MMR-D colorectal cancer and a perspective on resistance mechanisms. Br. J. Cancer, 2019, 121(10), 809-818. doi: 10.1038/s41416-019-0599-y PMID: 31607751
  8. Emambux, S.; Tachon, G.; Junca, A.; Tougeron, D. Results and challenges of immune checkpoint inhibitors in colorectal cancer. Expert Opin. Biol. Ther., 2018, 18(5), 561-573. doi: 10.1080/14712598.2018.1445222 PMID: 29471676
  9. Morse, M.A.; Hochster, H.; Benson, A. Perspectives on treatment of metastatic colorectal cancer with immune checkpoint inhibitor therapy. Oncologist, 2020, 25(1), 33-45. doi: 10.1634/theoncologist.2019-0176 PMID: 31383813
  10. Croft, M.; Benedict, C.A.; Ware, C.F. Clinical targeting of the TNF and TNFR superfamilies. Nat. Rev. Drug Discov., 2013, 12(2), 147-168. doi: 10.1038/nrd3930 PMID: 23334208
  11. Schildberg, F.A.; Klein, S.R.; Freeman, G.J.; Sharpe, A.H. Coinhibitory pathways in the B7-CD28 ligand-receptor family. Immunity, 2016, 44(5), 955-972. doi: 10.1016/j.immuni.2016.05.002 PMID: 27192563
  12. Zhang, C.; Zhang, Z.; Sun, N.; Zhang, Z.; Zhang, G.; Wang, F.; Luo, Y.; Che, Y.; He, J. Identification of a costimulatory molecule-based signature for predicting prognosis risk and immunotherapy response in patients with lung adenocarcinoma. OncoImmunology, 2020, 9(1), 1824641. doi: 10.1080/2162402X.2020.1824641 PMID: 33457102
  13. Wei, S.C.; Duffy, C.R.; Allison, J.P. Fundamental mechanisms of immune checkpoint blockade therapy. Cancer Discov., 2018, 8(9), 1069-1086. doi: 10.1158/2159-8290.CD-18-0367 PMID: 30115704
  14. Huang, W.; Su, D.; Liao, X.; Yang, T.; Lu, Y.; Zhang, Z. Prognostic costimulatory molecule-related signature risk model correlates with immunotherapy response in colon cancer. Sci. Rep., 2023, 13(1), 789. doi: 10.1038/s41598-023-27826-7 PMID: 36646765
  15. Punzo, P.; Grillo, S.; Batelli, G. Alternative splicing in plant abiotic stress responses. Biochem. Soc. Trans., 2020, 48(5), 2117-2126. doi: 10.1042/BST20200281 PMID: 32869832
  16. Sciarrillo, R.; Wojtuszkiewicz, A.; Assaraf, Y.G.; Jansen, G.; Kaspers, G.J.L.; Giovannetti, E.; Cloos, J. The role of alternative splicing in cancer: From oncogenesis to drug resistance. Drug Resist. Updat., 2020, 53(100728), 100728. doi: 10.1016/j.drup.2020.100728 PMID: 33070093
  17. Bonnal, S.C.; López-Oreja, I.; Valcárcel, J. Roles and mechanisms of alternative splicing in cancer-implications for care. Nat. Rev. Clin. Oncol., 2020, 17(8), 457-474. doi: 10.1038/s41571-020-0350-x PMID: 32303702
  18. Cherry, S.; Lynch, K.W. Alternative splicing and cancer: Insights, opportunities, and challenges from an expanding view of the transcriptome. Genes Dev., 2020, 34(15-16), 1005-1016. doi: 10.1101/gad.338962.120 PMID: 32747477
  19. Zhao, D.; Zhang, C.; Jiang, M.; Wang, Y.; Liang, Y.; Wang, L.; Qin, K.; Rehman, F.U.L.; Zhang, X. Survival-associated alternative splicing signatures in non-small cell lung cancer. Aging, 2020, 12(7), 5878-5893. doi: 10.18632/aging.102983 PMID: 32282333
  20. Martinez-Montiel, N.; Rosas-Murrieta, N.; Anaya Ruiz, M.; Monjaraz-Guzman, E.; Martinez-Contreras, R. Alternative splicing as a target for cancer treatment. Int. J. Mol. Sci., 2018, 19(2), 545. doi: 10.3390/ijms19020545 PMID: 29439487
  21. Bessa, C.; Matos, P.; Jordan, P.; Gonçalves, V. Alternative splicing: Expanding the landscape of cancer biomarkers and therapeutics. Int. J. Mol. Sci., 2020, 21(23), 9032. doi: 10.3390/ijms21239032 PMID: 33261131
  22. Liu, J.; Li, H.; Shen, S.; Sun, L.; Yuan, Y.; Xing, C. Alternative splicing events implicated in carcinogenesis and prognosis of colorectal cancer. J. Cancer, 2018, 9(10), 1754-1764. doi: 10.7150/jca.24569 PMID: 29805701
  23. Qu, Y.; Chen, Y.; Zhang, L.; Tian, L. Construction of prognostic predictor by comprehensive analyzing alternative splicing events for colon adenocarcinoma. World J. Surg. Oncol., 2020, 18(1), 236. doi: 10.1186/s12957-020-02010-7 PMID: 32883335
  24. Zhang, Z.; Feng, Q.; Jia, C.; Zheng, P.; Lv, Y.; Mao, Y.; Xu, Y.; He, G.; Xu, J. Analysis of relapse-associated alternative mRNA splicing and construction of a prognostic signature predicting relapse in I–III colon cancer. Genomics, 2020, 112(6), 4032-4040. doi: 10.1016/j.ygeno.2020.07.002 PMID: 32645524
  25. Hamdollah Zadeh, M.A.; Amin, E.M.; Hoareau-Aveilla, C.; Domingo, E.; Symonds, K.E.; Ye, X.; Heesom, K.J.; Salmon, A.; D’Silva, O.; Betteridge, K.B.; Williams, A.C.; Kerr, D.J.; Salmon, A.H.J.; Oltean, S.; Midgley, R.S.; Ladomery, M.R.; Harper, S.J.; Varey, A.H.R.; Bates, D.O. Alternative splicing of TIA‐1 in human colon cancer regulates VEGF isoform expression, angiogenesis, tumour growth and bevacizumab resistance. Mol. Oncol., 2015, 9(1), 167-178. doi: 10.1016/j.molonc.2014.07.017 PMID: 25224594
  26. Goldman, M.J.; Craft, B.; Hastie, M. Repečka, K.; McDade, F.; Kamath, A.; Banerjee, A.; Luo, Y.; Rogers, D.; Brooks, A.N.; Zhu, J.; Haussler, D. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol., 2020, 38(6), 675-678. doi: 10.1038/s41587-020-0546-8 PMID: 32444850
  27. Ryan, M.; Wong, W.C.; Brown, R.; Akbani, R.; Su, X.; Broom, B.; Melott, J.; Weinstein, J. TCGASpliceSeq a compendium of alternative mRNA splicing in cancer. Nucleic Acids Res., 2016, 44(D1), D1018-D1022. doi: 10.1093/nar/gkv1288 PMID: 26602693
  28. Song, J.; Liu, Y.D.; Su, J.; Yuan, D.; Sun, F.; Zhu, J. Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma. J. Cell. Physiol., 2019, 234(12), 22753-22764. doi: 10.1002/jcp.28840 PMID: 31140607
  29. Xiong, Y.; Deng, Y.; Wang, K.; Zhou, H.; Zheng, X.; Si, L.; Fu, Z. Profiles of alternative splicing in colorectal cancer and their clinical significance: A study based on large-scale sequencing data. EBioMedicine, 2018, 36, 183-195. doi: 10.1016/j.ebiom.2018.09.021 PMID: 30243491
  30. Haynes, W. Benjaminihochberg method. Encycloped. Syst. Biol., 2013, 78.
  31. The Gene Ontology Resource. 20 years and still going strong. Nucleic Acids Res., 2019, 47(D1), D330-D338. doi: 10.1093/nar/gky1055 PMID: 30395331
  32. Kanehisa, M.; Goto, S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res., 2000, 28(1), 27-30. doi: 10.1093/nar/28.1.27 PMID: 10592173
  33. Yu, G.; Wang, L.G.; Han, Y.; He, Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS, 2012, 16(5), 284-287. doi: 10.1089/omi.2011.0118 PMID: 22455463
  34. Hao, T.; Peng, W.; Wang, Q.; Wang, B.; Sun, J. Reconstruction and application of protein–protein interaction network. Int. J. Mol. Sci., 2016, 17(6), 907. doi: 10.3390/ijms17060907 PMID: 27338356
  35. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; Kuhn, M.; Bork, P.; Jensen, L.J.; von Mering, C. STRING v10: Protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res., 2015, 43(D1), D447-D452. doi: 10.1093/nar/gku1003 PMID: 25352553
  36. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504. doi: 10.1101/gr.1239303 PMID: 14597658
  37. Smyth, G.K. Limma: linear models for microarray data.Bioinformatics and computational biology solutions using R and Bioconductor; Springer, 2005, pp. 397-420. doi: 10.1007/0-387-29362-0_23
  38. van den Bulk, J.; Verdegaal, E.M.E.; de Miranda, N.F.C.C. Cancer immunotherapy: Broadening the scope of targetable tumours. Open Biol., 2018, 8(6), 180037. doi: 10.1098/rsob.180037 PMID: 29875199
  39. Tintelnot, J.; Stein, A. Immunotherapy in colorectal cancer: Available clinical evidence, challenges and novel approaches. World J. Gastroenterol., 2019, 25(29), 3920-3928. doi: 10.3748/wjg.v25.i29.3920 PMID: 31413527
  40. Hua, X.; Ge, S.; Zhang, J.; Xiao, H.; Tai, S.; Yang, C.; Zhang, L.; Liang, C. A costimulatory molecule-related signature in regard to evaluation of prognosis and immune features for clear cell renal cell carcinoma. Cell Death Discov., 2021, 7(1), 021-00646. doi: 10.1038/s41420-021-00646-2
  41. Loos, M.; Giese, N.A.; Kleeff, J.; Giese, T.; Gaida, M.M.; Bergmann, F.; Laschinger, M.W.; Büchler, M.; Friess, H. Clinical significance and regulation of the costimulatory molecule B7-H1 in pancreatic cancer. Cancer Lett., 2008, 268(1), 98-109. doi: 10.1016/j.canlet.2008.03.056 PMID: 18486325
  42. Geng, Y.; Wang, H.; Lu, C.; Li, Q.; Xu, B.; Jiang, J.; Wu, C. Expression of costimulatory molecules B7-H1, B7-H4 and Foxp3+ Tregs in gastric cancer and its clinical significance. Int. J. Clin. Oncol., 2015, 20(2), 273-281. doi: 10.1007/s10147-014-0701-7 PMID: 24804867
  43. Edner, N.M.; Carlesso, G.; Rush, J.S.; Walker, L.S.K. Targeting co-stimulatory molecules in autoimmune disease. Nat. Rev. Drug Discov., 2020, 19(12), 860-883. doi: 10.1038/s41573-020-0081-9 PMID: 32939077
  44. Kelemen, O.; Convertini, P.; Zhang, Z.; Wen, Y.; Shen, M.; Falaleeva, M.; Stamm, S. Function of alternative splicing. Gene, 2013, 514(1), 1-30. doi: 10.1016/j.gene.2012.07.083 PMID: 22909801
  45. Wang, Z.; Yang, X.; Liu, C.; Li, X.; Zhang, B.; Wang, B.; Zhang, Y.; Song, C.; Zhang, T.; Liu, M.; Liu, B.; Ren, M.; Jiang, H.; Zou, J.; Liu, X.; Zhang, H.; Zhu, W.G.; Yin, Y.; Zhang, Z.; Gu, W.; Luo, J. Acetylation of PHF5A modulates stress responses and colorectal carcinogenesis through alternative splicing-mediated upregulation of KDM3A. Mol. Cell, 2019, 74(6), 1250-1263.e6. doi: 10.1016/j.molcel.2019.04.009 PMID: 31054974
  46. Blencowe, B.J. Alternative splicing: New insights from global analyses. Cell, 2006, 126(1), 37-47. doi: 10.1016/j.cell.2006.06.023 PMID: 16839875
  47. Zhou, Q.; Hou, Z.; Zuo, S.; Zhou, X.; Feng, Y.; Sun, Y.; Yuan, X. LUCAT1 promotes colorectal cancer tumorigenesis by targeting the ribosomal protein L40‐ MDM 2‐p53 pathway through binding withUBA 52. Cancer Sci., 2019, 110(4), 1194-1207. doi: 10.1111/cas.13951 PMID: 30690837
  48. Wang, F.; Chen, X.; Yu, X.; Lin, Q. Degradation of CCNB1 mediated by APC11 through UBA52 ubiquitination promotes cell cycle progression and proliferation of non-small cell lung cancer cells. Am. J. Transl. Res., 2019, 11(11), 7166-7185. PMID: 31814919
  49. Lindström, M.S.; Nistér, M. Silencing of ribosomal protein S9 elicits a multitude of cellular responses inhibiting the growth of cancer cells subsequent to p53 activation. PLoS One, 2010, 5(3), e9578. doi: 10.1371/journal.pone.0009578 PMID: 20221446
  50. Cheng, D.; Zhu, B.; Li, S.; Yuan, T.; Yang, Q.; Fan, C. Down-regulation of RPS9 inhibits osteosarcoma cell growth through inactivation of MAPK signaling pathway. J. Cancer, 2017, 8(14), 2720-2728. doi: 10.7150/jca.19130 PMID: 28928861
  51. Yu, C.; Hong, H.; Zhang, S.; Zong, Y.; Ma, J.; Lu, A.; Sun, J.; Zheng, M. Identification of key genes and pathways involved in microsatellite instability in colorectal cancer. Mol. Med. Rep., 2019, 19(3), 2065-2076. doi: 10.3892/mmr.2019.9849 PMID: 30664178
  52. Jyotsana, N.; Heuser, M. Exploiting differential RNA splicing patterns: A potential new group of therapeutic targets in cancer. Expert Opin. Ther. Targets, 2018, 22(2), 107-121. doi: 10.1080/14728222.2018.1417390 PMID: 29235382
  53. Pellarin, I.; Dall’Acqua, A.; Gambelli, A.; Pellizzari, I.; D’Andrea, S.; Sonego, M.; Lorenzon, I.; Schiappacassi, M.; Belletti, B.; Baldassarre, G. Splicing factor proline- and glutamine-rich (SFPQ) protein regulates platinum response in ovarian cancer-modulating SRSF2 activity. Oncogene, 2020, 39(22), 4390-4403. doi: 10.1038/s41388-020-1292-6 PMID: 32332923
  54. Klotz-Noack, K.; Klinger, B.; Rivera, M.; Bublitz, N.; Uhlitz, F.; Riemer, P.; Lüthen, M.; Sell, T.; Kasack, K.; Gastl, B.; Ispasanie, S.S.S.; Simon, T.; Janssen, N.; Schwab, M.; Zuber, J.; Horst, D.; Blüthgen, N.; Schäfer, R.; Morkel, M.; Sers, C. SFPQ depletion is synthetically lethal with BRAFV600E in colorectal cancer cells. Cell Rep., 2020, 32(12), 108184. doi: 10.1016/j.celrep.2020.108184 PMID: 32966782
  55. Yoon, Y.H.; Hwang, H.J.; Sung, H.J.; Heo, S.H.; Kim, D.S.; Hong, S.H.; Lee, K.H.; Cho, J.Y. Upregulation of complement factor H by SOCS-1/3–STAT4 in lung cancer. Cancers, 2019, 11(4), 471. doi: 10.3390/cancers11040471 PMID: 30987235
  56. Zhou, J.; Shi, M.; Li, M.; Cheng, L.; Yang, J.; Huang, X. RETRACTED ARTICLE: Sirtuin 3 inhibition induces mitochondrial stress in tongue cancer by targeting mitochondrial fission and the JNK-Fis1 biological axis. Cell Stress Chaperones, 2019, 24(2), 369-383. doi: 10.1007/s12192-019-00970-8 PMID: 30656603
  57. Jin, Z.; Yao, J.; Xie, N.; Cai, L.; Qi, S.; Zhang, Z.; Li, B. Melittin constrains the expression of identified key genes associated with bladder cancer. J. Immunol. Res., 2018, 2018(5038172), 1-16. doi: 10.1155/2018/5038172 PMID: 29854840

Supplementary files

Supplementary Files
Action
1. JATS XML

Copyright (c) 2024 Bentham Science Publishers