Skip to content
2000
Volume 32, Issue 27
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Aims

The present study aimed todevelop a prognostic model for HNSCC treatment on the basis of angiogenesis-related signatures.

Background

Head and Neck Squamous Cell Carcinoma (HNSCC) is the most frequent malignancy with poor prognostic outcomes in the head and neck. Angiogenesis plays a critical role in tumorigenesis and is expected to be an effective therapeutic target.

Objective

The RNA-seq dataset TCGA-HNSCC and the hallmark gene set were used for angiogenesis-related RiskScore model construction.

Methods

The RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA), and the hallmark gene set was used to measure the angiogenesis score using the GSVA R package. Then, the optimal cutoff point for prognostic classification was calculated by the survminer package, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify angiogenesis gene modules. Multi/univariable and Lasso Cox analyses were performed to develop the RiskScore model, and the classifier efficiency was evaluated by the Receiver Operating Characteristic curve (ROC). Furthermore, a nomogram was designed for survival probability prediction, and the immune infiltration and immunotherapy differences among different risk patients were assessed.

Results

After calculating the angiogenesis score, we found that this indicator and patients’ prognosis were closely correlated, especially when patients with a high angiogenesis score had a poor prognosis. Then, WGCNA identified a blue gene module positively correlated with angiogenesis. Multivariate and Lasso Cox analysis further identified 9 risk model genes for developing a RiskScore, which was used to divide low- and high-risk groups of patients. Those with a high risk tended to show poor prognosis, immune infiltration, and higher immune escape. Finally, a nomogram was developed to optimize the risk model, and it exhibited excellent short- and long-term survival prediction performance.

Conclusion

We constructed a reliable RiskScore model for the prognostic prediction of HNSCC patients, contributing to precise therapeutic intervention of the cancer.

Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673306245240514064119
2024-05-15
2025-09-06
Loading full text...

Full text loading...

References

  1. LeemansC.R. BraakhuisB.J.M. BrakenhoffR.H. The molecular biology of head and neck cancer.Nat. Rev. Cancer201111192210.1038/nrc298221160525
    [Google Scholar]
  2. SiegelR.L. MillerK.D. WagleN.S. JemalA. Cancer statistics, 2023.CA Cancer J. Clin.2023731174810.3322/caac.2176336633525
    [Google Scholar]
  3. BlotW.J. McLaughlinJ.K. WinnD.M. AustinD.F. GreenbergR.S. Preston-MartinS. BernsteinL. SchoenbergJ.B. StemhagenA. FraumeniJ.F.J.r. Smoking and drinking in relation to oral and pharyngeal cancer.Cancer Res.19884811328232873365707
    [Google Scholar]
  4. GuhaN. WarnakulasuriyaS. VlaanderenJ. StraifK. Betel quid chewing and the risk of oral and oropharyngeal cancers: A meta-analysis with implications for cancer control.Int. J. Cancer201413561433144310.1002/ijc.2864324302487
    [Google Scholar]
  5. MehannaH. BeechT. NicholsonT. El-HariryI. McConkeyC. PaleriV. RobertsS. Prevalence of human papillomavirus in oropharyngeal and nonoropharyngeal head and neck cancer-systematic review and meta-analysis of trends by time and region.Head Neck201335574775510.1002/hed.2201522267298
    [Google Scholar]
  6. SolomonB. YoungR.J. RischinD. Head and neck squamous cell carcinoma: Genomics and emerging biomarkers for immunomodulatory cancer treatments.Semin. Cancer Biol.201852Pt 222824010.1016/j.semcancer.2018.01.00829355614
    [Google Scholar]
  7. De FeliceF. MusioD. TerenziV. ValentiniV. CassoniA. TomboliniM. De VincentiisM. TomboliniV. Treatment improvement and better patient care: Which is the most important one in oral cavity cancer?Radiat. Oncol.20149126310.1186/s13014‑014‑0263‑x25479896
    [Google Scholar]
  8. MachielsJ.P. René LeemansC. GolusinskiW. GrauC. LicitraL. GregoireV. Reprint of “Squamous cell carcinoma of the oral cavity, larynx, oropharynx and hypopharynx: EHNS-ESMO-ESTRO Clinical Practice Guidelines for diagnosis, treatment and follow-up”.Oral Oncol.202111310504210.1016/j.oraloncology.2020.10504233583513
    [Google Scholar]
  9. ChauhanS.S. KaurJ. KumarM. MattaA. SrivastavaG. AlyassA. AssiJ. LeongI. MacMillanC. WitterickI. ColganT.J. ShuklaN.K. ThakarA. SharmaM.C. SiuK.W.M. WalfishP.G. RalhanR. Prediction of recurrence-free survival using a protein expression-based risk classifier for head and neck cancer.Oncogenesis201544e14710.1038/oncsis.2015.725893634
    [Google Scholar]
  10. JiangY. LiY. GeH. WuY. ZhangY. GuoS. ZhangP. Cheng.J. WangY. Identification of an autophagy-related prognostic signature in head and neck squamous cell carcinoma.J. Oral Pathol. Med.202150101040104910.1111/jop.13231
    [Google Scholar]
  11. ZhouG. WangX. The potential of oxidative stress related genes as prognostic biomarkers in head and neck squamous cell carcinoma.Comb. Chem. High Throughput Screen.202225111952196510.2174/138620732566621120715443634875987
    [Google Scholar]
  12. Ghafouri-FardS. GholipourM. TaheriM. Shirvani FarsaniZ. MicroRNA profile in the squamous cell carcinoma: prognostic and diagnostic roles.Heliyon2020611e0543610.1016/j.heliyon.2020.e0543633204886
    [Google Scholar]
  13. QiangW. DaiY. XingX. SunX. Identification and validation of a prognostic signature and combination drug therapy for immunotherapy of head and neck squamous cell carcinoma.Comput. Struct. Biotechnol. J.2021191263127610.1016/j.csbj.2021.01.04633717423
    [Google Scholar]
  14. ViallardC. LarrivéeB. Tumor angiogenesis and vascular normalization: Alternative therapeutic targets.Angiogenesis201720440942610.1007/s10456‑017‑9562‑928660302
    [Google Scholar]
  15. AhirB.K. EngelhardH.H. LakkaS.S. Tumor development and angiogenesis in adult brain tumor: Glioblastoma.Mol. Neurobiol.20205752461247810.1007/s12035‑020‑01892‑832152825
    [Google Scholar]
  16. KuczynskiE.A. VermeulenP.B. PezzellaF. KerbelR.S. ReynoldsA.R. Vessel co-option in cancer.Nat. Rev. Clin. Oncol.201916846949310.1038/s41571‑019‑0181‑930816337
    [Google Scholar]
  17. FolkmanJ. Tumor angiogenesis: A possible control point in tumor growth.Ann. Intern. Med.19758219610010.7326/0003‑4819‑82‑1‑96799908
    [Google Scholar]
  18. FidlerI.J. Angiogenesis and cancer metastasis.Cancer J.20006Suppl. 2S134S14110803828
    [Google Scholar]
  19. LuganoR. RamachandranM. DimbergA. Tumor angiogenesis: Causes, consequences, challenges and opportunities.Cell. Mol. Life Sci.20207791745177010.1007/s00018‑019‑03351‑731690961
    [Google Scholar]
  20. RahmaO.E. HodiF.S. The intersection between tumor angiogenesis and immune suppression.Clin. Cancer Res.201925185449545710.1158/1078‑0432.CCR‑18‑154330944124
    [Google Scholar]
  21. SaitoK. MatsuoY. ImafujiH. OkuboT. MaedaY. SatoT. ShamotoT. TsuboiK. MorimotoM. TakahashiH. IshiguroH. TakiguchiS. Xanthohumol inhibits angiogenesis by suppressing nuclear factor-κB activation in pancreatic cancer.Cancer Sci.2018109113214010.1111/cas.1344129121426
    [Google Scholar]
  22. WhiteJ.R. HarrisR.A. LeeS.R. CraigonM.H. BinleyK. PriceT. BeardG.L. MundyC.R. NaylorS. Genetic amplification of the transcriptional response to hypoxia as a novel means of identifying regulators of angiogenesis.Genomics20048311810.1016/S0888‑7543(03)00215‑514667803
    [Google Scholar]
  23. ChenY. HuangL. WeiZ. LiuX. ChenL. WangB. Development and validation of a nomogram model to predict the prognosis of intrahepatic cholangiocarcinoma.Oncologie202224232934010.32604/oncologie.2022.022521
    [Google Scholar]
  24. DanaherP. WarrenS. LuR. SamayoaJ. SullivanA. PekkerI. WalldenB. MarincolaF.M. CesanoA. Pan- cancer adaptive immune resistance as defined by the tumor inflammation signature (TIS): Results from The Cancer Genome Atlas (TCGA).J. Immunother. Cancer2018616310.1186/s40425‑018‑0367‑129929551
    [Google Scholar]
  25. ChiH. XieX. YanY. PengG. StrohmerD.F. LaiG. ZhaoS. XiaZ. TianG. Natural killer cell-related prognosis signature characterizes immune landscape and predicts prognosis of HNSCC.Front. Immunol.202213101868510.3389/fimmu.2022.101868536263048
    [Google Scholar]
  26. LiberzonA. BirgerC. ThorvaldsdóttirH. GhandiM. MesirovJ.P. TamayoP. The molecular signatures database (MSigDB) hallmark gene set collection.Cell Syst.20151641742510.1016/j.cels.2015.12.00426771021
    [Google Scholar]
  27. HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
    [Google Scholar]
  28. WangS. SuW. ZhongC. YangT. ChenW. ChenG. LiuZ. WuK. ZhongW. LiB. MaoX. LuJ. An eight-circRNA assessment model for predicting biochemical recurrence in prostate cancer.Front. Cell Dev. Biol.2020859949410.3389/fcell.2020.59949433363156
    [Google Scholar]
  29. ChiH. ZhaoS. YangJ. GaoX. PengG. ZhangJ. XieX. SongG. XuK. XiaZ. ChenS. ZhaoJ. T- cell exhaustion signatures characterize the immune landscape and predict HCC prognosis via integrating single-cell RNA-seq and bulk RNA-sequencing.Front. Immunol.202314113702510.3389/fimmu.2023.113702537006257
    [Google Scholar]
  30. LangfelderP. HorvathS. WGCNA: An R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑55919114008
    [Google Scholar]
  31. YueT. ChenS. ZhuJ. GuoS. HuangZ. WangP. ZuoS. LiuY. The aging-related risk signature in colorectal cancer.Aging (Albany NY)20211357330734910.18632/aging.20258933658390
    [Google Scholar]
  32. AlyS.A. ZurakowskiD. GlassP. Skurow-ToddK. JonasR.A. DonofrioM.T. Cerebral tissue oxygenation index and lactate at 24 hours postoperative predict survival and neurodevelopmental outcome after neonatal cardiac surgery.Congenit. Heart Dis.201712218819510.1111/chd.1242627862979
    [Google Scholar]
  33. ZhaoK. MaZ. ZhangW. Comprehensive analysis to identify SPP1 as a prognostic biomarker in cervical cancer.Front. Genet.20221273282210.3389/fgene.2021.73282235058964
    [Google Scholar]
  34. QiuC. ShiW. WuH. ZouS. LiJ. WangD. LiuG. SongZ. XuX. HuJ. GengH. Identification of molecular subtypes and a prognostic signature based on inflammation-related genes in colon adenocarcinoma.Front. Immunol.20211276968510.3389/fimmu.2021.76968535003085
    [Google Scholar]
  35. LiM. XinS. GuR. ZhengL. HuJ. ZhangR. DongH. Novel diagnostic biomarkers related to oxidative stress and macrophage ferroptosis in atherosclerosis.Oxid. Med. Cell. Longev.2022202211810.1155/2022/891794736035208
    [Google Scholar]
  36. KourkoveliP. RammosS. ParissisJ. MaillisA. KremastinosD. ParaskevaidisI. Depressive symptoms in patients with congenital heart disease: Incidence and prognostic value of self-rating depression scales.Congenit. Heart Dis.201510324024710.1111/chd.1220024975053
    [Google Scholar]
  37. XiaoY. ZhouP. ZhengY. ZhengC. LiuG. LiuW. A nomogram for predicting lateral lymph node metastasis in cases of papillary thyroid micro-carcinoma with suspected lymph node metastasis.Oncologie202123221922810.32604/Oncologie.2021.016480
    [Google Scholar]
  38. BhatA.A. YousufP. WaniN.A. RizwanA. ChauhanS.S. SiddiqiM.A. BedognettiD. El-RifaiW. FrenneauxM.P. BatraS.K. HarisM. MachaM.A. Tumor microenvironment: An evil nexus promoting aggressive head and neck squamous cell carcinoma and avenue for targeted therapy.Signal Transduct. Target. Ther.2021611210.1038/s41392‑020‑00419‑w33436555
    [Google Scholar]
  39. MiyauchiS. KimS.S. PangJ. GoldK.A. GutkindJ.S. CalifanoJ.A. MellL.K. CohenE.E.W. SharabiA.B. Immune modulation of head and neck squamous cell carcinoma and the tumor microenvironment by conventional therapeutics.Clin. Cancer Res.201925144211422310.1158/1078‑0432.CCR‑18‑087130814108
    [Google Scholar]
  40. KudoM. FinnR.S. EdelineJ. CattanS. OgasawaraS. PalmerD.H. VerslypeC. ZagonelV. FartouxL. VogelA. SarkerD. VersetG. ChanS.L. KEYNOTE-224 Investigators. Updated efficacy and safety of KEYNOTE-224: A phase II study of pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib.Eur J Cancer2022167112
    [Google Scholar]
  41. ChengA.L. KangY.K. ChenZ. TsaoC.J. QinS. KimJ.S. LuoR. FengJ. YeS. YangT.S. XuJ. SunY. LiangH. LiuJ. WangJ. TakW.Y. PanH. BurockK. ZouJ. VoliotisD. GuanZ. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: A phase III randomised, double-blind, placebo-controlled trial.Lancet Oncol.2009101253410.1016/S1470‑2045(08)70285‑719095497
    [Google Scholar]
  42. HaasN.B. ManolaJ. UzzoR.G. FlahertyK.T. WoodC.G. KaneC. JewettM. DutcherJ.P. AtkinsM.B. PinsM. WildingG. CellaD. WagnerL. MatinS. KuzelT.M. SextonW.J. WongY.N. ChoueiriT.K. PiliR. PuzanovI. KohliM. StadlerW. CarducciM. CoomesR. DiPaolaR.S. Adjuvant sunitinib or sorafenib for high-risk, non-metastatic renal-cell carcinoma (ECOG-ACRIN E2805): A double-blind, placebo-controlled, randomised, phase 3 trial.Lancet2016387100322008201610.1016/S0140‑6736(16)00559‑626969090
    [Google Scholar]
  43. KangJ. XiangX. ChenX. JiangJ. ZhangY. LiL. TangJ. Angiogenesis-related gene signatures reveal the prognosis of cervical cancer based on single cell sequencing and co-expression network analysis.Front. Cell Dev. Biol.202310108683510.3389/fcell.2022.108683536712973
    [Google Scholar]
  44. BaghbanR. RoshangarL. Jahanban-EsfahlanR. SeidiK. Ebrahimi-KalanA. JaymandM. KolahianS. JavaheriT. ZareP. Tumor microenvironment complexity and therapeutic implications at a glance.Cell Commun. Signal.20201815910.1186/s12964‑020‑0530‑432264958
    [Google Scholar]
  45. De PalmaM. BiziatoD. PetrovaT.V. Microenvironmental regulation of tumour angiogenesis.Nat. Rev. Cancer201717845747410.1038/nrc.2017.5128706266
    [Google Scholar]
  46. NiC. YangL. XuQ. YuanH. WangW. XiaW. GongD. ZhangW. YuK. CD68- and CD163-positive tumor infiltrating macrophages in non-metastatic breast cancer: A retrospective study and meta-analysis.J. Cancer201910194463447210.7150/jca.3391431528210
    [Google Scholar]
  47. YeJ. WangX. ShiJ. YinX. ChenC. ChenY. WuH.Y. JiongS. sunQ. ZhangM. ShiX. ZhouG. HassanS. FengJ. XuX. ZhangW. Tumor-associated macrophages are associated with response to neoadjuvant chemotherapy and poor outcomes in patients with triple-negative breast cancer.J. Cancer202112102886289210.7150/jca.4756633854589
    [Google Scholar]
  48. WangY.C. HeF. FengF. LiuX.W. DongG.Y. QinH.Y. HuX.B. ZhengM.H. LiangL. FengL. LiangY.M. HanH. Notch signaling determines the M1 versus M2 polarization of macrophages in antitumor immune responses.Cancer Res.201070124840484910.1158/0008‑5472.CAN‑10‑026920501839
    [Google Scholar]
  49. FranklinR.A. LiaoW. SarkarA. KimM.V. BivonaM.R. LiuK. PamerE.G. LiM.O. The cellular and molecular origin of tumor-associated macrophages.Science2014344618692192510.1126/science.125251024812208
    [Google Scholar]
  50. KanedaM.M. MesserK.S. RalainirinaN. LiH. LeemC.J. GorjestaniS. WooG. NguyenA.V. FigueiredoC.C. FoubertP. SchmidM.C. PinkM. WinklerD.G. RauschM. PalombellaV.J. KutokJ. McGovernK. FrazerK.A. WuX. KarinM. SasikR. CohenE.E.W. VarnerJ.A. PI3Kγ is a molecular switch that controls immune suppression.Nature2016539762943744210.1038/nature1983427642729
    [Google Scholar]
  51. SuG. WangW. XuL. LiG. Progress of EGFL6 in angiogenesis and tumor development.Int. J. Clin. Exp. Pathol.2022151143644336507067
    [Google Scholar]
  52. WangX. YuanW. WangX. QiJ. QinY. ShiY. ZhangJ. GongJ. DongZ. LiuX. SunC. ChaiR. Le NobleF. LiuD. The somite-secreted factor Maeg promotes zebrafish embryonic angiogenesis.Oncotarget2016747777497776310.18632/oncotarget.1279327780917
    [Google Scholar]
  53. KongR. YiF. WenP. LiuJ. ChenX. RenJ. LiX. ShangY. NieY. WuK. FanD. ZhuL. FengW. WuJ.Y. Myo9b is a key player in SLIT/ROBO-mediated lung tumor suppression.J. Clin. Invest.2015125124407442010.1172/JCI8167326529257
    [Google Scholar]
  54. NakanoS. NishikawaM. KobayashiT. HarlinE.W. ItoT. SatoK. SugiyamaT. YamakawaH. NagaseT. UedaH. The Rho guanine nucleotide exchange factor PLEKHG1 is activated by interaction with and phosphorylation by Src family kinase member FYN.J. Biol. Chem.2022298210157910.1016/j.jbc.2022.10157935031323
    [Google Scholar]
  55. BilgenF. UralA. KurutasE.B. BekereciogluM. The effect of oxidative stress and Raftlin levels on wound healing.Int. Wound J.20191651178118410.1111/iwj.1317731407472
    [Google Scholar]
  56. SuY. LiuJ. ZhengZ. ShiL. HuangW. HuangX. YeC. QiJ. WangW. ZhuangH. NSUN5-FTH1 axis inhibits ferroptosis to promote the growth of gastric cancer cells.Cell Biochem. Biophys.202381355356010.1007/s12013‑023‑01152‑137528314
    [Google Scholar]
  57. JinY. QiuJ. LuX. MaY. LiG. LncRNA CACNA1G-AS1 up-regulates FTH1 to inhibit ferroptosis and promote malignant phenotypes in ovarian cancer cells.Oncol. Res.202331216917910.32604/or.2023.02781537304234
    [Google Scholar]
  58. AliA. ShafarinJ. Abu JabalR. AljabiN. HamadM. Sualeh MuhammadJ. UnnikannanH. HamadM. Ferritin heavy chain (FTH1) exerts significant antigrowth effects in breast cancer cells by inhibiting the expression of c-MYC.FEBS Open Bio202111113101311410.1002/2211‑5463.1330334551213
    [Google Scholar]
  59. XuX. ShenL. LiW. LiuX. YangP. CaiJ. ITGA5 promotes tumor angiogenesis in cervical cancer.Cancer Med.20231210119831199910.1002/cam4.587336999964
    [Google Scholar]
  60. WangJ. ChenY. ZhangS. ZhaoK. QiuY. WangY. WangJ. YuZ. LiB. WangZ. ChenJ. ITGA5 promotes tumor progression through the activation of the FAK/AKT signaling pathway in human gastric cancer.Oxid. Med. Cell. Longev.2022202211810.1155/2022/861130636193075
    [Google Scholar]
  61. ZhangX. ChenF. HuangP. WangX. ZhouK. ZhouC. YuL. PengY. FanJ. ZhouJ. LuZ. HuJ. WangZ. Exosome-depleted MiR-148a-3p derived from hepatic stellate cells promotes tumor progression via ITGA5/PI3K/Akt axis in hepatocellular carcinoma.Int. J. Biol. Sci.20221862249226010.7150/ijbs.6618435414782
    [Google Scholar]
  62. HaratakeN. HuQ. OkamotoT. JogoT. ToyokawaG. KinoshitaF. TakenakaT. TagawaT. IsedaN. ItohS. YamadaY. OdaY. ShimokawaM. KikutakeC. SuyamaM. UnokiM. SasakiH. MoriM. Identification of SLC38A7 as a prognostic marker and potential therapeutic target of lung squamous cell carcinoma.Ann. Surg.2021274350050710.1097/SLA.000000000000500134171866
    [Google Scholar]
  63. MaX. GuL. LiH. GaoY. LiX. ShenD. GongH. LiS. NiuS. ZhangY. FanY. HuangQ. LyuX. ZhangX. Hypoxia-induced overexpression of stanniocalcin-1 is associated with the metastasis of early stage clear cell renal cell carcinoma.J. Transl. Med.20151315610.1186/s12967‑015‑0421‑425740019
    [Google Scholar]
  64. ChangA.C.M. DohertyJ. HuschtschaL.I. RedversR. RestallC. ReddelR.R. AndersonR.L. STC1 expression is associated with tumor growth and metastasis in breast cancer.Clin. Exp. Metastasis2015321152710.1007/s10585‑014‑9687‑925391215
    [Google Scholar]
  65. CorreiaJ.C. JannigP.R. GosztylaM.L. CervenkaI. DucommunS. PræstholmS.M. DumontK. LiuZ. LiangQ. EdsgärdD. EmanuelssonO. GregorevicP. WesterbladH. Zfp697 is an RNA-binding protein that regulates skeletal muscle inflammation and regeneration.bioRxiv202310.1101/2023.06.12.544338
    [Google Scholar]
/content/journals/cmc/10.2174/0109298673306245240514064119
Loading
/content/journals/cmc/10.2174/0109298673306245240514064119
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test