Skip to content
2000
Volume 20, Issue 3
  • ISSN: 1574-8928
  • E-ISSN: 2212-3970

Abstract

Background

Accumulated evidence suggest that tumor microenvironment (TME) plays a crucial role in breast cancer (BRCA) progression and therapeutic effects.

Objective

This study aimed to characterize immune-related BRCA subtypes in TME, and identify genes with prognostic value.

Methods

RNA sequencing profiles with corresponding clinical data from The Cancer Genome Atlas (TCGA) database of BRCA patients were downloaded to evaluate immune infiltration using the single-sample gene set enrichment (ssGAEA) algorithm. Further, BRCA was clustered according to immune infiltration status by consensus clustering analysis. Using Venn analysis, differentially expressed genes (DEGs) were overlapped to obtain candidate genes. Kaplan–Meier (K-M) analysis was performed to identify prognostic genes, and the results were verified in the GEO and METABRIC datasets. RT-qPCR was conducted to detect the mRNA expression of prognostic genes.

Results

In the TCGA database, 3 immune-related BRCA subtypes were identified [cluster1 (C1), cluster2 (C2), and cluster3 (C2)]. The C2 subtype had better overall survival (OS) compared to the C1 subtype. Higher levels of immune markers and checkpoint protein were found in the C2 subtype than in others. By combining DEGs between BRCA and normal tissues, with the C1 and C2 subtypes associated with different OS, 25 BRCA candidate genes were identified. Among these, 8 genes were identified as prognostic genes for BRCA. RT-qPCR showed that the expressions of 2 genes were significantly elevated in BRCA tissues, while that of other genes were decreased.

Conclusion

Three BRCA subtypes were identified with the immune index, which may help design advanced treatment of BRCA. The data code used for the analysis in this article was available on GitHub (https://github.com/tangzhn/BRCA1.git).

Loading

Article metrics loading...

/content/journals/pra/10.2174/0115748928258157231128103337
2025-07-01
2025-09-21
Loading full text...

Full text loading...

References

  1. BehlA. ChhillarA.K. Nano-based drug delivery of anticancer chemotherapeutic drugs targeting breast cancer.Recent Patents Anticancer Drug Discov.202318332534210.2174/157489281703220610170559 35702804
    [Google Scholar]
  2. FerlayJ. ColombetM. SoerjomataramI. Cancer statistics for the year 2020: An overview.Int. J. Cancer2021149477878910.1002/ijc.33588 33818764
    [Google Scholar]
  3. ChenW. ZhengR. BaadeP.D. Cancer statistics in China, 2015.CA Cancer J. Clin.201666211513210.3322/caac.21338 26808342
    [Google Scholar]
  4. GreavesM. MaleyC.C. Clonal evolution in cancer.Nature2012481738130631310.1038/nature10762 22258609
    [Google Scholar]
  5. ShackletonM. QuintanaE. FearonE.R. MorrisonS.J. Heterogeneity in cancer: Cancer stem cells versus clonal evolution.Cell2009138582282910.1016/j.cell.2009.08.017 19737509
    [Google Scholar]
  6. JiangJ. PanW. XuY. Tumour-infiltrating immune cell-based subtyping and signature gene analysis in breast cancer based on gene expression profiles.J. Cancer20201161568158310.7150/jca.37637 32047563
    [Google Scholar]
  7. KimI.S. ZhangX.H.F. One microenvironment does not fit all: Heterogeneity beyond cancer cells.Cancer Metastasis Rev.201635460162910.1007/s10555‑016‑9643‑z 27858305
    [Google Scholar]
  8. PiriniF. VergaraD. ParrellaP. Editorial: Tumor microenvironment signaling networks in pathophysiology and therapeutics.Front. Oncol.202212100918710.3389/fonc.2022.1009187 36158695
    [Google Scholar]
  9. WangQ. ShaoX. ZhangY. Role of tumor microenvironment in cancer progression and therapeutic strategy.Cancer Med.20231210111491116510.1002/cam4.5698 36807772
    [Google Scholar]
  10. WuT. DaiY. Tumor microenvironment and therapeutic response.Cancer Lett.2017387616810.1016/j.canlet.2016.01.043 26845449
    [Google Scholar]
  11. BaxevanisC.N. FortisS.P. PerezS.A. The balance between breast cancer and the immune system: Challenges for prognosis and clinical benefit from immunotherapies.Semin. Cancer Biol.202172768910.1016/j.semcancer.2019.12.018 31881337
    [Google Scholar]
  12. HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑7 23323831
    [Google Scholar]
  13. RooneyM.S. ShuklaS.A. WuC.J. GetzG. HacohenN. Molecular and genetic properties of tumors associated with local immune cytolytic activity.Cell20151601-2486110.1016/j.cell.2014.12.033 25594174
    [Google Scholar]
  14. ZhangL. ZhaoY. DaiY. Immune landscape of colorectal cancer tumor microenvironment from different primary tumor location.Front. Immunol.20189157810.3389/fimmu.2018.01578 30042763
    [Google Scholar]
  15. MuroK. ChungH.C. ShankaranV. Pembrolizumab for patients with PD-L1-positive advanced gastric cancer (KEYNOTE-012): A multicentre, open-label, phase 1b trial.Lancet Oncol.201617671772610.1016/S1470‑2045(16)00175‑3 27157491
    [Google Scholar]
  16. MayakondaA. LinD.C. AssenovY. PlassC. KoefflerH.P. Maftools: Efficient and comprehensive analysis of somatic variants in cancer.Genome Res.201828111747175610.1101/gr.239244.118 30341162
    [Google Scholar]
  17. Couzin-FrankelJ. Cancer Immunotherapy.Science201334261651432143310.1126/science.342.6165.1432 24357284
    [Google Scholar]
  18. SavasP. SalgadoR. DenkertC. Clinical relevance of host immunity in breast cancer: From TILs to the clinic.Nat. Rev. Clin. Oncol.201613422824110.1038/nrclinonc.2015.215 26667975
    [Google Scholar]
  19. AbduS. JuaidN. AminA. MoulayM. MiledN. Therapeutic Effects of Crocin Alone or in Combination with Sorafenib against Hepatocellular Carcinoma: In Vivo & In Vitro Insights.Antioxidants20221110.3390/antiox11091645
    [Google Scholar]
  20. Abdel-latifR. HeebaG.H. HassaninS.O. WazS. AminA. TLRs-JNK/NF-κB pathway underlies the protective effect of the sulfide salt against liver toxicity.Front. Pharmacol.20221385006610.3389/fphar.2022.850066 35517830
    [Google Scholar]
  21. XieY. MuC. KazybayB. Network pharmacology and experimental investigation of Rhizoma polygonati extract targeted kinase with herbzyme activity for potent drug delivery.Drug Deliv.20212812187219710.1080/10717544.2021.1977422 34662244
    [Google Scholar]
  22. BouabdallahS. Al-MaktoumA. AminA. Steroidal saponins: Naturally occurring compounds as inhibitors of the hallmarks of cancer.Cancers20231515390010.3390/cancers15153900 37568716
    [Google Scholar]
  23. AwadB. HamzaA.A. Al-MaktoumA. Al-SalamS. AminA. Combining crocin and sorafenib improves their tumor-inhibiting effects in a rat model of diethylnitrosamine-induced cirrhotic-hepatocellular carcinoma.Cancers20231516406310.3390/cancers15164063 37627094
    [Google Scholar]
  24. AbdallaA. MuraliC. AminA. Safranal inhibits angiogenesis via targeting hif-1α/vegf machinery: in vitro and ex vivo insights.Front. Oncol.20221178917210.3389/fonc.2021.789172 35211395
    [Google Scholar]
  25. HamzaA.A. MohamedM.G. LashinF.M. AminA. Dandelion prevents liver fibrosis, inflammatory response, and oxidative stress in rats.J. Basic Appl. Zool.20208114310.1186/s41936‑020‑00177‑9
    [Google Scholar]
  26. AbdallaY. AbdallaA. HamzaA.A. AminA. Safranal prevents liver cancer through inhibiting oxidative stress and alleviating inflammation.Front. Pharmacol.20221277750010.3389/fphar.2021.777500 35177980
    [Google Scholar]
  27. HamzaA.A. HeebaG.H. HassaninS.O. ElwyH.M. BekhitA.A. AminA. Hibiscus-cisplatin combination treatment decreases liver toxicity in rats while increasing toxicity in lung cancer cells via oxidative stress- apoptosis pathway.Biomed. Pharmacother.202316511514810.1016/j.biopha.2023.115148 37450997
    [Google Scholar]
  28. NelsonD.R. HroutA.A. AlzahmiA.S. ChaiboonchoeA. AminA. Salehi-AshtianiK. Molecular mechanisms behind safranal’s toxicity to hepg2 cells from dual omics.Antioxidants2022116112510.3390/antiox11061125 35740022
    [Google Scholar]
  29. OthmanE.M. HabibH.A. ZahranM.E. AminA. HeebaG.H. Mechanistic protective effect of cilostazol in cisplatin-induced testicular damage via regulation of oxidative stress and tnf-α/nf-κb/caspase-3 pathways.Int. J. Mol. Sci.202324161265110.3390/ijms241612651 37628836
    [Google Scholar]
  30. HannaE.M. ZakiN. AminA. Detecting protein complexes in protein interaction networks modeled as gene expression biclusters.PLoS One20151012e014416310.1371/journal.pone.0144163 26641660
    [Google Scholar]
  31. MillerL.D. ChouJ.A. BlackM.A. Immunogenic subtypes of breast cancer delineated by gene classifiers of immune responsiveness.Cancer Immunol. Res.20164760061010.1158/2326‑6066.CIR‑15‑0149 27197066
    [Google Scholar]
  32. YangA. ZhouY. KongY. Identification and validation of immune-related methylation clusters for predicting immune activity and prognosis in breast cancer.Front. Immunol.20211270455710.3389/fimmu.2021.704557 34276701
    [Google Scholar]
  33. XuM. LiY. LiW. Immune and stroma related genes in breast cancer: a comprehensive analysis of tumor microenvironment based on the cancer genome atlas (TCGA) database.Front. Med.202076410.3389/fmed.2020.00064 32195260
    [Google Scholar]
  34. ThompsonJ.C. DavisC. DeshpandeC. Gene signature of antigen processing and presentation machinery predicts response to checkpoint blockade in non-small cell lung cancer (NSCLC) and melanoma.J. Immunother. Cancer202082e00097410.1136/jitc‑2020‑000974 33028693
    [Google Scholar]
  35. DieciM.V. MigliettaF. GuarneriV. Immune infiltrates in breast cancer: Recent updates and clinical implications.Cells202110222310.3390/cells10020223 33498711
    [Google Scholar]
  36. ZhengS. SongQ. ZhangP. Metabolic modifications, inflammation, and cancer immunotherapy.Front. Oncol.20211170368110.3389/fonc.2021.703681 34631531
    [Google Scholar]
  37. HeimesA.S. HärtnerF. AlmstedtK. Prognostic significance of interferon-γ and its signaling pathway in early breast cancer depends on the molecular subtypes.Int. J. Mol. Sci.20202119717810.3390/ijms21197178 33003293
    [Google Scholar]
  38. (12) standard patent application (11) application no. au 2023202654 al (19) australian patent office.
    [Google Scholar]
  39. JiangP. GuS. PanD. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.Nat. Med.201824101550155810.1038/s41591‑018‑0136‑1 30127393
    [Google Scholar]
  40. FifeB.T. BluestoneJ.A. Control of peripheral T‐cell tolerance and autoimmunity via the CTLA‐4 and PD‐1 pathways.Immunol. Rev.2008224116618210.1111/j.1600‑065X.2008.00662.x 18759926
    [Google Scholar]
  41. TokumaruY. JoyceD. TakabeK. Current status and limitations of immunotherapy for breast cancer.Surgery2020167362863010.1016/j.surg.2019.09.018 31623855
    [Google Scholar]
  42. LiuC. JinY. FanZ. The mechanism of warburg effect-induced chemoresistance in cancer.Front. Oncol.20211169802310.3389/fonc.2021.698023 34540667
    [Google Scholar]
  43. JuaidN. AminA. AbdallaA. Anti-hepatocellular carcinoma biomolecules: Molecular targets insights.Int. J. Mol. Sci.202122191077410.3390/ijms221910774 34639131
    [Google Scholar]
  44. ChenL. YangL. YaoL. Characterization of PIK3CA and PIK3R1 somatic mutations in Chinese breast cancer patients.Nat. Commun.201891135710.1038/s41467‑018‑03867‑9 29636477
    [Google Scholar]
  45. EllisH. MaC.X. PI3K inhibitors in breast cancer therapy.Curr. Oncol. Rep.2019211211010.1007/s11912‑019‑0846‑7 31828441
    [Google Scholar]
  46. DuffyM.J. SynnottN.C. CrownJ. Mutant p53 in breast cancer: Potential as a therapeutic target and biomarker.Breast Cancer Res. Treat.2018170221321910.1007/s10549‑018‑4753‑7 29564741
    [Google Scholar]
  47. LiuZ. JiangZ. GaoY. WangL. ChenC. WangX. TP53 mutations promote immunogenic activity in breast cancer.J. Oncol.2019201911910.1155/2019/5952836 31275382
    [Google Scholar]
  48. NittaT. NasreenM. SeikeT. IAN family critically regulates survival and development of T lymphocytes.PLoS Biol.200644e10310.1371/journal.pbio.0040103 16509771
    [Google Scholar]
  49. ShiaoY.M. ChangY.H. LiuY.M. Dysregulation of GIMAP genes in non-small cell lung cancer.Lung Cancer200862328729410.1016/j.lungcan.2008.03.021 18462827
    [Google Scholar]
  50. UsmanM. IlyasA. HashimZ. ZarinaS. Identification of GIMAP7 and Rabl3 as putative biomarkers for oral squamous cell carcinoma through comparative proteomic approach.Pathol. Oncol. Res.20202631817182210.1007/s12253‑019‑00775‑1 31748878
    [Google Scholar]
  51. XiY. JingZ. HaihongL. YizhenJ. WeiliG. ShuwenH. Analysis of T lymphocyte-related biomarkers in pancreatic cancer.Pancreatology20202071502151010.1016/j.pan.2020.09.005 32952042
    [Google Scholar]
/content/journals/pra/10.2174/0115748928258157231128103337
Loading
/content/journals/pra/10.2174/0115748928258157231128103337
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