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Research Paper

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

, , , , , , , & ORCID Icon show all
Article: 2345977 | Received 19 Feb 2024, Accepted 17 Apr 2024, Published online: 24 Apr 2024

References

  • Saad AM, Gad MM, Al-Husseini MJ, Ruhban IA, Sonbol MB, Ho TH. Trends in renal-cell carcinoma incidence and mortality in the United States in the last 2 decades: a SEER-Based study. Clin Genitourin Cancer. 2019;17(1):46–57.e5. doi:10.1016/j.clgc.2018.10.002.
  • Cohen HT, McGovern FJ. Renal-cell carcinoma. N Engl J Med. 2005;353(23):2477–20. doi:10.1056/NEJMra043172.
  • Lieder A, Guenzel T, Lebentrau S, Schneider C, Franzen A. Diagnostic relevance of metastatic renal cell carcinoma in the head and neck: an evaluation of 22 cases in 671 patients. Int Braz J Urol: Off J Braz Soc Urol. 2017;43(2):202–8. doi:10.1590/s1677-5538.ibju.2015.0665.
  • Janzen NK, Kim HL, Figlin RA, Belldegrun AS. Surveillance after radical or partial nephrectomy for localized renal cell carcinoma and management of recurrent disease. Urol Clin North Am. 2003;30(4):843–52. doi:10.1016/S0094-0143(03)00056-9.
  • Wei H, Miao J, Cui J, Zheng W, Chen X, Zhang Q, Liu F, Mao Z, Qiu S, Zhang D. The prognosis and clinicopathological features of different distant metastases patterns in renal cell carcinoma: analysis based on the SEER database. Sci Rep. 2021;11(1):17822. doi:10.1038/s41598-021-97365-6.
  • Swami U, Nussenzveig RH, Haaland B, Agarwal N. Revisiting AJCC TNM staging for renal cell carcinoma: quest for improvement. Ann Transl Med. 2019;7(Suppl 1):S18. doi:10.21037/atm.2019.01.50.
  • Sankin A, Hakimi AA, Mikkilineni N, Ostrovnaya I, Silk MT, Liang Y, Mano R, Chevinsky M, Motzer RJ, Solomon SB, et al. The impact of genetic heterogeneity on biomarker development in kidney cancer assessed by multiregional sampling. Cancer Med. 2014; 3(6):1485–92. doi:10.1002/cam4.293.
  • Brannon AR, Reddy A, Seiler M, Arreola A, Moore DT, Pruthi RS, Wallen EM, Nielsen ME, Liu H, Nathanson KL, et al. Molecular stratification of clear cell renal cell carcinoma by consensus clustering reveals distinct subtypes and survival patterns. Genes Cancer. 2010;1(2):152–63. doi:10.1177/1947601909359929.
  • Baghban R, Roshangar L, Jahanban-Esfahlan R, Seidi K, Ebrahimi-Kalan A, Jaymand M, Kolahian S, Javaheri T, Zare P. Tumor microenvironment complexity and therapeutic implications at a glance. Cell commun signaling: CCS. 2020;18(1):59. doi:10.1186/s12964-020-0530-4.
  • Tang T, Huang X, Zhang G, Hong Z, Bai X, Liang T. Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72. doi:10.1038/s41392-020-00449-4.
  • Mergener S, Peña-Llopis S. A new perspective on immune evasion: escaping immune surveillance by inactivating tumor suppressors. Signal Transduct Target Ther. 2022;7(1):15. doi:10.1038/s41392-022-00875-6.
  • Borcherding N, Vishwakarma A, Voigt AP, Bellizzi A, Kaplan J, Nepple K, Salem AK, Jenkins RW, Zakharia Y, Zhang W, et al. Mapping the immune environment in clear cell renal carcinoma by single-cell genomics. Commun Biol. 2021; 4(1):122. doi:10.1038/s42003-020-01625-6.
  • Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013; 4(1):2612. doi:10.1038/ncomms3612.
  • Tung I, Sahu A. 2021. Immune checkpoint inhibitor in first-line treatment of metastatic renal cell carcinoma: a review of current evidence and future directions. Front Oncol. 11:707214. doi:10.3389/fonc.2021.707214.
  • Jiang A, Meng J, Bao Y, Wang A, Gong W, Gan X, Wang J, Bao Y, Wu Z, Lu J, et al. Establishment of a prognosis prediction model based on pyroptosis-related signatures associated with the immune microenvironment and molecular heterogeneity in clear cell renal cell carcinoma. Front Oncol. 2021;11:4486. doi:10.3389/fonc.2021.755212.
  • Tomczak K, Czerwińska P, Wiznerowicz M. The cancer genome atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Poznan, Poland). 2015;19(1A):A68–77. doi:10.5114/wo.2014.47136.
  • Braun DA, Hou Y, Bakouny Z, Ficial M, Sant’ Angelo M, Forman J, Ross-Macdonald P, Berger AC, Jegede OA, Elagina L, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020; 26(6):909–918. doi:10.1038/s41591-020-0839-y.
  • Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–e110. doi:10.1158/0008-5472.CAN-17-0307.
  • Lu X, Meng, J, Zhou, Y, Jiang, L, Yan, F. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. 8
  • Jiang A, Bao Y, Wang A, Gong W, Gan X, Wang J, Bao Y, Wu Z, Liu B, Lu J, et al. Establishment of a prognostic prediction and drug selection model for patients with clear cell renal cell carcinoma by multiomics data analysis. Oxid Med Cell Longev. 2022;2022:1–30. doi:10.1155/2022/3617775.
  • Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi:10.1186/s13059-014-0550-8.
  • Yu G, Wang L-G, Han Y, He Q-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: J Integr Biol. 2012;16(5):284–287. doi:10.1089/omi.2011.0118.
  • Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinform (Oxford, England). 2011;27(12):1739–40. doi:10.1093/bioinformatics/btr260.
  • Kamburov A, Wierling C, Lehrach H, Herwig R. ConsensusPathDB—a database for integrating human functional interaction networks. Nucleic Acids Res. 2009;37(Database issue):D623–628. doi:10.1093/nar/gkn698.
  • Chen B, Khodadoust, MS, Liu, CL, Newman, AM, Alizadeh, AA. Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol. 2018;1711:243–259.
  • Aran D, Hu Z, Butte AJ. xCell: digitally portraying the tissue cellular heterogeneity landscape. Genome Biol. 2017;18(1):220. doi:10.1186/s13059-017-1349-1.
  • Racle J, Gfeller D. EPIC: a tool to estimate the proportions of different cell types from bulk gene expression data. Methods Mol Biol. 2020;2120:233–248.
  • Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinf. 2013;14(1):7. doi:10.1186/1471-2105-14-7.
  • Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, Li Z, Traugh N, Bu X, Li B, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nat Med. 2018; 24(10):1550–58. doi:10.1038/s41591-018-0136-1.
  • A M, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28(11):1747–1756. doi:10.1101/gr.239244.118.
  • Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12(4):R41. doi:10.1186/gb-2011-12-4-r41.
  • Cancer Genome Atlas Research, N. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517): 202–9.10.1038/nature13480
  • Cokelaer T, Chen E, Iorio F, Menden MP, Lightfoot H, Saez-Rodriguez J, Garnett MJ. Gdsctools for mining pharmacogenomic interactions in cancer. Bioinform (Oxford, England). 2018;34(7):1226–1228. doi:10.1093/bioinformatics/btx744.
  • Lu X, Meng, J, Zhou, Y, Jiang, L, Yan, F. MOVICS: an R package for multi-omics integration and visualization in cancer subtyping. Bioinform (Oxford, England). 2020.36(22–23):5539–41.
  • Geeleher P, Cox N, Huang RS, Barbour JD. 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.
  • Ingels A, Campi R, Capitanio U, Amparore D, Bertolo R, Carbonara U, Erdem S, Kara Ö, Klatte T, Kriegmair MC, et al. Complementary roles of surgery and systemic treatment in clear cell renal cell carcinoma. Nat Rev Urol. 2022; 19(7):391–418. doi:10.1038/s41585-022-00592-3.
  • Gleeson JP, Motzer RJ, Lee C-H. The current role for adjuvant and neoadjuvant therapy in renal cell cancer. Curr Opin Urol. 2019;29(6):636–642 %L 3. doi:10.1097/MOU.0000000000000666.
  • Larroquette M, Peyraud F, Domblides C, Lefort F, Bernhard J-C, Ravaud A, Gross-Goupil M. 2021. Adjuvant therapy in renal cell carcinoma: Current knowledges and future perspectives. Cancer Treat Rev. 97:102207 %L 1. doi:10.1016/j.ctrv.2021.102207.
  • Wang Y, Yin C, Geng L, Cai W. 2020. Immune infiltration landscape in clear cell renal cell carcinoma implications. Front Oncol. 10:491621. doi:10.3389/fonc.2020.491621.
  • Deleuze A, Saout J, Dugay F, Peyronnet B, Mathieu R, Verhoest G, Bensalah K, Crouzet L, Laguerre B, Belaud-Rotureau M-A, et al. Immunotherapy in renal cell carcinoma: The future is now. Int J Mol Sci. 2020; 21(7):E2532. doi:10.3390/ijms21072532.
  • Kim I-H, Lee HJ. The frontline immunotherapy-based treatment of advanced clear cell renal cell carcinoma: Current evidence and clinical perspective. Biomedicines. 2022;10(2):251. doi:10.3390/biomedicines10020251.
  • Jiang A, Zhou Y, Gong W, Pan X, Gan X, Wu Z, Liu B, Qu L, Wang L. 2022. CCNA2 as an immunological biomarker encompassing tumor microenvironment and therapeutic response in multiple cancer types. Oxid Med Cell Longev. 2022:1–35. doi:10.1155/2022/5910575.
  • Bao Y, Jiang A, Dong K, Gan X, Gong W, Wu Z, Liu B, Bao Y, Wang J, Wang L, et al. DDX39 as a predictor of clinical prognosis and immune checkpoint therapy efficacy in patients with clear cell renal cell carcinoma. Int J Biol Sci. 2021; 17(12):3158–72. doi:10.7150/ijbs.62553.
  • Jiang A, Meng J, Gong W, Zhang Z, Gan X, Wang J, Wu Z, Liu B, Qu L, Wang L, et al. Elevated SNRPA1, as a promising predictor reflecting severe clinical outcome via effecting tumor immunity for ccRCC, is related to Cell Invasion, metastasis, and sunitinib sensitivity. Front Immunol. 2022;13:13. doi:10.3389/fimmu.2022.842069.
  • Motzer RJ, Banchereau R, Hamidi H, Powles T, McDermott D, Atkins MB, Escudier B, Liu L-F, Leng N, Abbas AR, et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell. 2020; 38(6):803–817.e4. doi:10.1016/j.ccell.2020.10.011.
  • Clark DJ, Dhanasekaran SM, Petralia F, Pan J, Song X, Hu Y, da Veiga Leprevost F, Reva B, Lih TSM, Chang H-Y, et al. Integrated proteogenomic characterization of clear cell renal cell carcinoma. Cell. 2019; 179(4):964–983.e31. doi:10.1016/j.cell.2019.10.007.
  • Chen F, Zhang Y, Şenbabaoğlu Y, Ciriello G, Yang L, Reznik E, Shuch B, Micevic G, De Velasco G, Shinbrot E, et al. Multilevel genomics-based taxonomy of renal cell carcinoma. Cell Rep. 2016; 14(10):2476–2489. doi:10.1016/j.celrep.2016.02.024.
  • Rebuzzi SE, Brunelli M, Galuppini F, Vellone VG, Signori A, Catalano F, Damassi A, Gaggero G, Rescigno P, Maruzzo M, et al. Characterization of tumor and immune tumor microenvironment of primary tumors and metastatic sites in advanced renal cell carcinoma patients based on response to nivolumab immunotherapy: preliminary results from the Meet-URO 18 Study. Cancers. 2023; 15(8):2394. doi:10.3390/cancers15082394.
  • Brown LC, Zhu J, Desai K, Kinsey E, Kao C, Lee YH, Pabla S, Labriola MK, Tran J, Dragnev KH, et al. Evaluation of tumor microenvironment and biomarkers of immune checkpoint inhibitor response in metastatic renal cell carcinoma. J Immunother Cancer. 2022; 10(10):e005249. doi:10.1136/jitc-2022-005249.
  • de Vries-Brilland M, Rioux-Leclercq N, Meylan M, Dauvé J, Passot C, Spirina-Menand E, Flippot R, Fromont G, Gravis G, Geoffrois L, et al. Comprehensive analyses of immune tumor microenvironment in papillary renal cell carcinoma. J Immunother Cancer. 2023; 11(11):e006885. doi:10.1136/jitc-2023-006885.
  • Lahl K, Loddenkemper C, Drouin C, Freyer J, Arnason J, Eberl G, Hamann A, Wagner H, Huehn J, Sparwasser T, et al. Selective depletion of Foxp3+ regulatory T cells induces a scurfy-like disease. J Exp Med. 2007; 204(1):57–63. doi:10.1084/jem.20061852.
  • Sakaguchi S, Miyara M, Costantino CM, Hafler DA. FOXP3+ regulatory T cells in the human immune system. Nat Rev Immunol. 2010;10(7):490–500. doi:10.1038/nri2785.
  • Monteran L, Erez N. 2019. The dark side of fibroblasts: cancer-associated fibroblasts as mediators of immunosuppression in the tumor microenvironment. Front Immunol. 10:1835. doi:10.3389/fimmu.2019.01835.
  • Wang S, He Z, Wang X, Li H, Liu X-S. 2019. Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction. eLife. eLife. 8:e49020. doi:10.7554/eLife.49020.
  • Braud VM, Allan DSJ, O’Callaghan CA, Söderström K, D’Andrea A, Ogg GS, Lazetic S, Young NT, Bell JI, Phillips JH, et al. HLA-E binds to natural killer cell receptors CD94/NKG2A, B and C. Nature. 1998; 391(6669):795–799. doi:10.1038/35869.
  • Seliger B, Jasinski-Bergner S, Quandt D, Stoehr C, Bukur J, Wach S, Legal W, Taubert H, Wullich B, Hartmann A. HLA-E expression and its clinical relevance in human renal cell carcinoma. Oncotarget. 2016;7(41):67360–67372. doi:10.18632/oncotarget.11744.
  • Chen Z, Han F, Du Y, Shi H, Zhou W. Hypoxic microenvironment in cancer: molecular mechanisms and therapeutic interventions. Signal Transduct Target Ther. 2023;8(1):70 %L 1. doi:10.1038/s41392-023-01332-8.
  • Liu S, Liu X, Zhang C, Shan W, Qiu X. 2021. T-Cell exhaustion status under high and low levels of hypoxia-inducible factor 1α expression in Glioma. Front Pharmacol. 12:711772. doi:10.3389/fphar.2021.711772.
  • Vignali PDA, DePeaux K, Watson MJ, Ye C, Ford BR, Lontos K, McGaa NK, Scharping NE, Menk AV, Robson SC, et al. Hypoxia drives CD39-dependent suppressor function in exhausted T cells to limit antitumor immunity. Nat Immunol. 2023; 24(2):267–79 %L 1. doi:10.1038/s41590-022-01379-9.
  • Sattiraju A, Kang S, Giotti B, Chen Z, Marallano VJ, Brusco C, Ramakrishnan A, Shen L, Tsankov AM, Hambardzumyan D, et al. Hypoxic niches attract and sequester tumor-associated macrophages and cytotoxic T cells and reprogram them for immunosuppression. Immunity. 2023; 56(8):1825–43.e6. doi:10.1016/j.immuni.2023.06.017.
  • Sanchez DJ, Simon MC. Genetic and metabolic hallmarks of clear cell renal cell carcinoma. Biochimica biophysica Acta. 2018;1870(1):23–31. doi:10.1016/j.bbcan.2018.06.003.
  • Weiss RH. Metabolomics and metabolic reprogramming in kidney cancer. Semin Nephrol. 2018;38(2):175–182. doi:10.1016/j.semnephrol.2018.01.006.
  • Cuvillier O. The therapeutic potential of HIF-2 antagonism in renal cell carcinoma. Transl Androl Urol. 2017;6(1):131–133. doi:10.21037/tau.2017.01.12.
  • Han S, Wang P-F, Cai H-Q, Wan J-H, Li S-W, Lin Z-H, Yu C-J, Yan C-X. Alterations in the RTK/Ras/PI3K/AKT pathway serve as potential biomarkers for immunotherapy outcome of diffuse gliomas. Aging. 2021;13(11):15444–15458. doi:10.18632/aging.203102.
  • Sack GH. Serum amyloid a – a review. Mol Med. 2018;24(1):46. doi:10.1186/s10020-018-0047-0.
  • Eklund KK, Niemi K, Kovanen PT. Immune functions of serum amyloid a. Crit Rev Immunol. 2012;32(4):335–348. doi:10.1615/CritRevImmunol.v32.i4.40.
  • Marhaug G, Dowton SB. Serum amyloid A: an acute phase apolipoprotein and precursor of AA amyloid. Bailliere’s Clin Rheumatol. 1994;8(3):553–573. doi:10.1016/S0950-3579(05)80115-3.
  • Artl A, Marsche G, Lestavel S, Sattler W, Malle E. Role of serum amyloid a during metabolism of acute-phase HDL by macrophages. Arterioscler Thromb Vasc Biol. 2000;20(3):763–772. doi:10.1161/01.ATV.20.3.763.
  • Ignacio RMC, Gibbs CR, Kim S, Lee E-S, Adunyah SE, Son D-S. Serum amyloid A predisposes inflammatory tumor microenvironment in triple negative breast cancer. Oncotarget. 2019;10(4):511–526. doi:10.18632/oncotarget.26566.
  • Lee J, Beatty GL. Serum amyloid a proteins and their impact on metastasis and immune biology in cancer. Cancers. 2021;13(13):3179. doi:10.3390/cancers13133179.