Identifying a Novel Eight-NK Cell-related Gene Signature for Ovarian Cancer Prognosis Prediction


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Abstract

Background:Ovarian cancer (OVC) is the most common and costly tumor in the world with unfavorable overall survival and prognosis. This study is aimed to explore the prognostic value of natural killer cells related genes for OVC treatment.

Methods:RNA-seq and clinical information were acquired from the TCGA-OVC dataset (training dataset) and the GSE51800 dataset (validation dataset). Genes linked to NK cells were obtained from the immPort dataset. Moreover, ConsensusClusterPlus facilitated the screening of molecular subtypes. Following this, the risk model was established by LASSO analysis, and immune infiltration and immunotherapy were then detected by CIBERSORT, ssGSEA, ESTIMATE, and TIDE algorithms.

Results:Based on 23 NK cell-related genes with prognosis, TCGA-OVC samples were classified into two clusters, namely C1 and C2. Of these, C1 had better survival outcomes as well as enhanced immune infiltration and tumor stem cells. Additionally, it was more suitable for immunotherapy and was also sensitive to traditional chemotherapy drugs. The eight-gene prognosis model was constructed and verified via the GSE51800 dataset. Additionally, a high infiltration level of immune cells was observed in low-risk patients. Low-risk samples also benefited from immunotherapy and chemotherapy drugs. Finally, a nomogram and ROC curves were applied to validate model accuracy.

Conclusion:The present study identified a RiskScore signature, which could stratify patients with different infiltration levels, immunotherapy, and chemotherapy drugs. Our study provided a basis for precisely evaluating OVC therapy and prognosis.

About the authors

Nan Li

Reproductive Medicine Center, Liuzhou Maternal and Child Health Hospital

Email: info@benthamscience.net

Kai Yu

College of Animal Science and Technology,, Guangxi University

Email: info@benthamscience.net

Delun Huang

Department of Physiology,, Guangxi University of Chinese Medicine

Email: info@benthamscience.net

Hui Zhou

Department of Gynecologic Oncology, Sun Yat-Sen Memorial Hospital of Sun Yatsen University,

Author for correspondence.
Email: info@benthamscience.net

Dingyuan Zeng

Reproductive Medicine Center, Liuzhou Maternal and Child Health Hospital

Author for correspondence.
Email: info@benthamscience.net

References

  1. 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 PMID: 33538338
  2. Lheureux, S.; Gourley, C.; Vergote, I.; Oza, A.M. Epithelial ovarian cancer. Lancet, 2019, 393(10177), 1240-1253. doi: 10.1016/S0140-6736(18)32552-2 PMID: 30910306
  3. Giampaolino, P.; Foreste, V.; Della Corte, L.; Di Filippo, C.; Iorio, G.; Bifulco, G. Role of biomarkers for early detection of ovarian cancer recurrence. Gland Surg., 2020, 9(4), 1102-1111. doi: 10.21037/gs-20-544 PMID: 32953625
  4. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin., 2018, 68(6), 394-424. doi: 10.3322/caac.21492 PMID: 30207593
  5. Chiossone, L.; Dumas, P.Y.; Vienne, M.; Vivier, E. Natural killer cells and other innate lymphoid cells in cancer. Nat. Rev. Immunol., 2018, 18(11), 671-688. doi: 10.1038/s41577-018-0061-z PMID: 30209347
  6. Hoogstad-van Evert, J.S.; Maas, R.J.; van der Meer, J.; Cany, J.; van der Steen, S.; Jansen, J.H.; Miller, J.S.; Bekkers, R.; Hobo, W.; Massuger, L.; Dolstra, H. Peritoneal NK cells are responsive to IL-15 and percentages are correlated with outcome in advanced ovarian cancer patients. Oncotarget, 2018, 9(78), 34810-34820. doi: 10.18632/oncotarget.26199 PMID: 30410679
  7. Lukesova, S.; Vroblova, V.; Tosner, J.; Kopecky, J.; Sedlakova, I.; Čermáková, E.; Vokurkova, D.; Kopecky, O. Comparative study of various subpopulations of cytotoxic cells in blood and ascites from patients with ovarian carcinoma. Contemp. Oncol., 2015, 4(4), 290-299. doi: 10.5114/wo.2015.54388 PMID: 26557777
  8. Lai, P.; Rabinowich, H.; Crowley-Nowick, P.A.; Bell, M.C.; Mantovani, G.; Whiteside, T.L. Alterations in expression and function of signal-transducing proteins in tumor-associated T and natural killer cells in patients with ovarian carcinoma. Clin. Cancer Res., 1996, 2(1), 161-173. PMID: 9816103
  9. Pinto, M.P.; Balmaceda, C.; Bravo, M.L.; Kato, S.; Villarroel, A.; Owen, G.I.; Roa, J.C.; Cuello, M.A.; Ibañez, C. Patient inflammatory status and CD4+/CD8+ intraepithelial tumor lymphocyte infiltration are predictors of outcomes in high-grade serous ovarian cancer. Gynecol. Oncol., 2018, 151(1), 10-17. doi: 10.1016/j.ygyno.2018.07.025 PMID: 30078505
  10. Mariya, T.; Hirohashi, Y.; Torigoe, T.; Asano, T.; Kuroda, T.; Yasuda, K.; Mizuuchi, M.; Sonoda, T.; Saito, T.; Sato, N. Prognostic impact of human leukocyte antigen class I expression and association of platinum resistance with immunologic profiles in epithelial ovarian cancer. Cancer Immunol. Res., 2014, 2(12), 1220-1229. doi: 10.1158/2326-6066.CIR-14-0101 PMID: 25324403
  11. Liberzon, A.; Birger, C.; Thorvaldsdóttir, H.; Ghandi, M.; Mesirov, J.P.; Tamayo, P. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst., 2015, 1(6), 417-425. doi: 10.1016/j.cels.2015.12.004 PMID: 26771021
  12. Newman, A.M.; Liu, C.L.; Green, M.R.; Gentles, A.J.; Feng, W.; Xu, Y.; Hoang, C.D.; Diehn, M.; Alizadeh, A.A. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods, 2015, 12(5), 453-457. doi: 10.1038/nmeth.3337 PMID: 25822800
  13. Wilkerson, M.D.; Hayes, D.N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking. Bioinformatics, 2010, 26(12), 1572-1573. doi: 10.1093/bioinformatics/btq170 PMID: 20427518
  14. Engebretsen, S.; Bohlin, J. Statistical predictions with glmnet. Clin. Epigenetics, 2019, 11(1), 123. doi: 10.1186/s13148-019-0730-1 PMID: 31443682
  15. Yang, P.; Chen, W.; Xu, H.; Yang, J.; Jiang, J.; Jiang, Y.; Xu, G. Correlation of CCL8 expression with immune cell infiltration of skin cutaneous melanoma: Potential as a prognostic indicator and therapeutic pathway. Cancer Cell Int., 2021, 21(1), 635. doi: 10.1186/s12935-021-02350-8 PMID: 34844613
  16. Charoentong, P.; Finotello, F.; Angelova, M.; Mayer, C.; Efremova, M.; Rieder, D.; Hackl, H.; Trajanoski, Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep., 2017, 18(1), 248-262. doi: 10.1016/j.celrep.2016.12.019 PMID: 28052254
  17. Jiang, P.; Gu, S.; Pan, D.; Fu, J.; Sahu, A.; Hu, X.; Li, Z.; Traugh, N.; Bu, X.; Li, B.; Liu, J.; Freeman, G.J.; Brown, M.A.; Wucherpfennig, K.W.; Liu, X.S. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat. Med., 2018, 24(10), 1550-1558. doi: 10.1038/s41591-018-0136-1 PMID: 30127393
  18. Geeleher, P.; Cox, N.; Huang, R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One, 2014, 9(9), e107468. doi: 10.1371/journal.pone.0107468 PMID: 25229481
  19. Oberg, H.H.; Kellner, C.; Gonnermann, D.; Sebens, S.; Bauerschlag, D.; Gramatzki, M.; Kabelitz, D.; Peipp, M.; Wesch, D. Tribody (HER2)2xCD16 is more effective than trastuzumab in enhancing γδ t cell and natural killer cell cytotoxicity against HER2-expressing cancer cells. Front. Immunol., 2018, 9, 814. doi: 10.3389/fimmu.2018.00814 PMID: 29725336
  20. Guzzo, F.; Bellone, S.; Buza, N.; Hui, P.; Carrara, L.; Varughese, J.; Cocco, E.; Betti, M.; Todeschini, P.; Gasparrini, S.; Schwartz, P. E.; Rutherford, T. J.; Angioli, R.; Pecorelli, S.; Santin, A. D. HER2/neu as a potential target for immunotherapy in gynecologic carcinosarcomas. Int. J. Gynecol. Pathol., 2012, 31(3), 211-221.
  21. Smyth, M.J.; Hayakawa, Y.; Takeda, K.; Yagita, H. New aspects of natural-killer-cell surveillance and therapy of cancer. Nat. Rev. Cancer, 2002, 2(11), 850-861. doi: 10.1038/nrc928 PMID: 12415255
  22. Kaur, K.; Nanut, M.P.; Ko, M.W.; Safaie, T.; Kos, J.; Jewett, A. Natural killer cells target and differentiate cancer stem-like cells/undifferentiated tumors: Strategies to optimize their growth and expansion for effective cancer immunotherapy. Curr. Opin. Immunol., 2018, 51, 170-180. doi: 10.1016/j.coi.2018.03.022 PMID: 29653339
  23. Uppendahl, L.D.; Felices, M.; Bendzick, L.; Ryan, C.; Kodal, B.; Hinderlie, P.; Boylan, K.L.M.; Skubitz, A.P.N.; Miller, J.S.; Geller, M.A. Cytokine-induced memory-like natural killer cells have enhanced function, proliferation, and in vivo expansion against ovarian cancer cells. Gynecol. Oncol., 2019, 153(1), 149-157. doi: 10.1016/j.ygyno.2019.01.006 PMID: 30658847
  24. Yahata, T.; Mizoguchi, M.; Kimura, A.; Orimo, T.; Toujima, S.; Kuninaka, Y.; Nosaka, M.; Ishida, Y.; Sasaki, I.; Fukuda-Ohta, Y.; Hemmi, H.; Iwahashi, N.; Noguchi, T.; Kaisho, T.; Kondo, T.; Ino, K. Programmed cell death ligand 1 d isruption by clustered regularly interspaced short palindromic repeats/Cas9-genome editing promotes antitumor immunity and suppresses ovarian cancer progression. Cancer Sci., 2019, 110(4), 1279-1292. doi: 10.1111/cas.13958 PMID: 30702189
  25. Dong, W.; Wu, X.; Ma, S.; Wang, Y.; Nalin, A.P.; Zhu, Z.; Zhang, J.; Benson, D.M.; He, K.; Caligiuri, M.A.; Yu, J. The mechanism of Anti–PD-L1 antibody efficacy against PD-L1–negative tumors identifies NK cells expressing PD-L1 as a cytolytic effector. Cancer Discov., 2019, 9(10), 1422-1437. doi: 10.1158/2159-8290.CD-18-1259 PMID: 31340937
  26. Klapdor, R.; Wang, S.; Morgan, M.; Dörk, T.; Hacker, U.; Hillemanns, P.; Büning, H.; Schambach, A. Characterization of a novel third-generation anti-CD24-CAR against ovarian cancer. Int. J. Mol. Sci., 2019, 20(3), 660. doi: 10.3390/ijms20030660 PMID: 30717444
  27. Hung, C.F.; Xu, X.; Li, L.; Ma, Y.; Jin, Q.; Viley, A.; Allen, C.; Natarajan, P.; Shivakumar, R.; Peshwa, M.V.; Emens, L.A. Development of anti-human mesothelin-targeted chimeric antigen receptor messenger RNA–transfected peripheral blood lymphocytes for ovarian cancer therapy. Hum. Gene Ther., 2018, 29(5), 614-625. doi: 10.1089/hum.2017.080 PMID: 29334771
  28. Travers, M.; Brown, S.M.; Dunworth, M.; Holbert, C.E.; Wiehagen, K.R.; Bachman, K.E.; Foley, J.R.; Stone, M.L.; Baylin, S.B.; Casero, R.A., Jr; Zahnow, C.A. DFMO and 5-azacytidine increase M1 macrophages in the tumor microenvironment of murine ovarian cancer. Cancer Res., 2019, 79(13), 3445-3454. doi: 10.1158/0008-5472.CAN-18-4018 PMID: 31088836
  29. Siew, Y.Y.; Neo, S.Y.; Yew, H.C.; Lim, S.W.; Ng, Y.C.; Lew, S.M.; Seetoh, W.G.; Seow, S.V.; Koh, H.L. Oxaliplatin regulates expression of stress ligands in ovarian cancer cells and modulates their susceptibility to natural killer cell-mediated cytotoxicity. Int. Immunol., 2015, 27(12), 621-632. doi: 10.1093/intimm/dxv041 PMID: 26138671
  30. Gordon, D.J.; Resio, B.; Pellman, D. Causes and consequences of aneuploidy in cancer. Nat. Rev. Genet., 2012, 13(3), 189-203. doi: 10.1038/nrg3123 PMID: 22269907
  31. Pietragalla, A.; Arcieri, M.; Marchetti, C.; Scambia, G.; Fagotti, A. Ovarian cancer predisposition beyond BRCA1 and BRCA2 genes. Int. J. Gynecol. Cancer, 2020, 30(11), 1803-1810. doi: 10.1136/ijgc-2020-001556 PMID: 32895312
  32. Gomes, F.C.; Figueiredo, E.R.L.; Araújo, E.N.D.; Andrade, E.M.D.; Carneiro, C.D.L.; Almeida, G.M.D.; Dias, H.A.A.L.; Teixeira, L.I.B.; Almeida, M.T.; Farias, M.F.D.; Linhares, N.A.; Fonseca, N.L.D.; Pereira, Y.D.S.; Melo-Neto, J.S. Social, genetics and histopathological factors related to Titin (TTN) gene mutation and survival in women with ovarian serous cystadenocarcinoma: Bioinformatics analysis. Genes., 2023, 14(5), 1092. doi: 10.3390/genes14051092 PMID: 37239452
  33. Lu, N.; Liu, J.; Xu, M.; Liang, J.; Wang, Y.; Wu, Z.; Xing, Y.; Diao, F. CSMD3 is associated with tumor mutation burden and immune infiltration in ovarian cancer patients. Int. J. Gen. Med., 2021, 14, 7647-7657. doi: 10.2147/IJGM.S335592 PMID: 34764678
  34. Akbarzadeh, M.; Akbarzadeh, S.; Majidinia, M. Targeting Notch signaling pathway as an effective strategy in overcoming drug resistance in ovarian cancer. Pathol. Res. Pract., 2020, 216(11), 153158. doi: 10.1016/j.prp.2020.153158 PMID: 32829107
  35. Wicks, E.E.; Semenza, G.L. Hypoxia-inducible factors: Cancer progression and clinical translation. J. Clin. Invest., 2022, 132(11), e159839. doi: 10.1172/JCI159839 PMID: 35642641
  36. Peng, D.; Fu, M.; Wang, M.; Wei, Y.; Wei, X. Targeting TGF-β signal transduction for fibrosis and cancer therapy. Mol. Cancer, 2022, 21(1), 104. doi: 10.1186/s12943-022-01569-x PMID: 35461253
  37. Tauriello, D.V.F.; Sancho, E.; Batlle, E. Overcoming TGFβ-mediated immune evasion in cancer. Nat. Rev. Cancer, 2022, 22(1), 25-44. doi: 10.1038/s41568-021-00413-6 PMID: 34671117
  38. Wang, K.; Guan, C.; Shang, X.; Ying, X.; Mei, S.; Zhu, H.; Xia, L.; Chai, Z. A bioinformatic analysis: The overexpression and clinical significance of FCGBP in ovarian cancer. Aging., 2021, 13(5), 7416-7429. doi: 10.18632/aging.202601 PMID: 33686968
  39. Jiang, E.; He, X.; Chen, X.; Sun, G.; Wu, H.; Wei, Y.; Zhao, X. Expression of CD40 in ovarian cancer and adenovirus-mediated CD40 ligand therapy on ovarian cancer in vitro. Tumori, 2008, 94(3), 356-361. doi: 10.1177/030089160809400312 PMID: 18705404
  40. Zong, S.; Xu, P.; Xu, Y.; Guo, Y. A bioinformatics analysis: ZFHX4 is associated with metastasis and poor survival in ovarian cancer. J. Ovarian Res., 2022, 15(1), 90. doi: 10.1186/s13048-022-01024-x PMID: 35915456
  41. Singh, S.K.; Mishra, M.K.; Singh, R. Hypoxia-inducible factor-1α induces CX3CR1 expression and promotes the epithelial to mesenchymal transition (EMT) in ovarian cancer cells. J. Ovarian Res., 2019, 12(1), 42. doi: 10.1186/s13048-019-0517-1 PMID: 31077234
  42. Wang, H.; Wang, D.; Gu, T.; Zhu, M.; Cheng, L.; Dai, W. AADAC promotes therapeutic activity of cisplatin and imatinib against ovarian cancer cells. Histol. Histopathol., 2022, 37(9), 899-907. PMID: 35451495
  43. Zhou, S.; Wang, R.; Xiao, H. Adipocytes induce the resistance of ovarian cancer to carboplatin through ANGPTL4. Oncol. Rep., 2020, 44(3), 927-938. doi: 10.3892/or.2020.7647 PMID: 32705217

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