Skip to main content
Key Specialties
Anesthesiology Cardiology Dermatology Diabetology Endocrinology Gastroenterology General Medicine Geriatrics Hematology Infectious Diseases Internal Medicine Nephrology Neurology Obstetrics and Gynecology Oncology Pediatrics Psychiatry Radiology Respiratory Medicine Rheumatology Surgery Urology
Congress Coverage
ASH 2024 Annual Meeting 2024 ESMO Congress 2024 ERS Congress 2024 EASD Congress 2024 ESC Congress 2024 EAN Congress 2024 ASCO Annual Meeting 2024 ACC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote series | Spotlight on menopause

Menopause can have a significant impact on the body, with effects ranging beyond the endocrine and reproductive systems. Learn about the systemic effects of menopause, so you can help patients in your clinics through the transition.

Published: Thursday 12th December 2024
ADVANCED SEARCH
Log in
Top
Breast Cancer Research
Published in:

Open Access 01-12-2024 | Breast Cancer | Research

Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study

Authors: Jingyang Liu, Wen Sun, Ning Li, Haibin Li, Lijuan Wu, Huan Yi, Jianguang Ji, Deqiang Zheng

Published in: Breast Cancer Research | Issue 1/2024

Login to get access

Abstract

Background

Breast cancer, which is the most prevalent form of cancer among women globally, encompasses various subtypes that demand distinct treatment approaches. The tumor microenvironment and immune response are of crucial significance in the development and progression of breast cancer. Nevertheless, there has been scant evidence concerning the genes within breast cancer - specific immune cells.

Methods

We utilized summary data-based Mendelian randomization (SMR) to identify genes associated with breast cancer by utilizing expression quantitative trait loci (eQTL) datasets for 14 different immune cell types and genome-wide association studies (GWAS) for overall breast cancer and its subtypes. Furthermore, colocalization analysis was carried out to evaluate whether the observed association in SMR analyses is influenced by the same causal variant. Replication analysis and bulk RNA sequencing (bulkRNA-seq) analysis were employed to validate promising immune genes as potential drug targets.

Results

After correcting for the rate of false discovery, we discovered a total of 17 genes in 9 immune cell types that were significantly associated with overall breast cancer and its subtypes. The genes KCNN4, L3MBTL3, ZBTB38, MDM4, and TNFSF10 were identified in overall breast cancer and its subtypes. Colocalization analyses provided robust evidence in support of these associations. Notably, the KCNN4 gene in non-classical MONOcytes (MONOnc) was further validated through replication analysis and bulkRNA-seq analysis.

Conclusion

In summary, our research has revealed a repertoire of genes within diverse immune cells associated with breast cancer. KCNN4 gene in non-classical MONOcytes (MONOnc) exhibited a negative association with overall breast cancer and its subtypes, which was identified as a potential drug target for breast cancer, opening up new avenues for therapeutic interventions.
Appendix
Available only for authorised users
Literature
1.
Bray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63.CrossRefPubMed
2.
Curigliano G, Burstein HJ, Winer EP, Gnant M, Dubsky P, Loibl S, et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the primary therapy of early breast Cancer 2017. Ann Oncol. 2017;28(8):1700–12.CrossRefPubMedPubMedCentral
3.
Soysal SD, Tzankov A, Muenst SE. Role of the Tumor Microenvironment in breast Cancer. Pathobiology. 2015;82(3–4):142–52.CrossRefPubMed
4.
Harris RE, Chlebowski RT, Jackson RD, Frid DJ, Ascenseo JL, Anderson G, et al. Breast cancer and nonsteroidal anti-inflammatory drugs: prospective results from the women’s Health Initiative. Cancer Res. 2003;63(18):6096–101.PubMed
5.
Kroemer G, Senovilla L, Galluzzi L, André F, Zitvogel L. Natural and therapy-induced immunosurveillance in breast cancer. Nat Med. 2015;21(10):1128–38.CrossRefPubMed
6.
Adams S, Gray RJ, Demaria S, Goldstein L, Perez EA, Shulman LN, et al. Prognostic value of tumor-infiltrating lymphocytes in triple-negative breast cancers from two phase III randomized adjuvant breast cancer trials: ECOG 2197 and ECOG 1199. J Clin Oncol. 2014;32(27):2959–66.CrossRefPubMedPubMedCentral
7.
8.
Abbott M, Ustoyev Y. Cancer and the Immune System: the history and background of Immunotherapy. Semin Oncol Nurs. 2019;35(5):150923.CrossRefPubMed
9.
Thomas A, Routh ED, Pullikuth A, Jin G, Su J, Chou JW, et al. Tumor mutational burden is a determinant of immune-mediated survival in breast cancer. Oncoimmunology. 2018;7(10):e1490854.CrossRefPubMedPubMedCentral
10.
Dieci MV, Miglietta F, Guarneri V. Immune infiltrates in breast Cancer: recent updates and clinical implications. Cells. 2021;10(2).
11.
12.
Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian randomization as an Approach to assess causality using Observational Data. J Am Soc Nephrol. 2016;27(11):3253–65.CrossRefPubMedPubMedCentral
13.
Yazar S, Alquicira-Hernandez J, Wing K, Senabouth A, Gordon MG, Andersen S, et al. Single-cell eQTL mapping identifies cell type-specific genetic control of autoimmune disease. Science. 2022;376(6589):eabf3041.CrossRefPubMed
14.
Zhang H, Ahearn TU, Lecarpentier J, Barnes D, Beesley J, Qi G, et al. Genome-wide association study identifies 32 novel breast cancer susceptibility loci from overall and subtype-specific analyses. Nat Genet. 2020;52(6):572–81.
15.
Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–7.CrossRefPubMed
16.
Giambartolomei C, Vukcevic D, Schadt EE, Franke L, Hingorani AD, Wallace C, Plagnol V. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 2014;10(5):e1004383.CrossRefPubMedPubMedCentral
17.
Zhu X, You S, Du X, Song K, Lv T, Zhao H, Yao Q. LRRC superfamily expression in stromal cells predicts the clinical prognosis and platinum resistance of ovarian cancer. BMC Med Genomics. 2023;16(1):10.CrossRefPubMedPubMedCentral
18.
Speyer CL, Bukhsh MA, Jafry WS, Sexton RE, Bandyopadhyay S, Gorski DH. Riluzole synergizes with paclitaxel to inhibit cell growth and induce apoptosis in triple-negative breast cancer. Breast Cancer Res Treat. 2017;166(2):407–19.CrossRefPubMedPubMedCentral
19.
Speyer CL, Nassar MA, Hachem AH, Bukhsh MA, Jafry WS, Khansa RM, Gorski DH. Riluzole mediates anti-tumor properties in breast cancer cells independent of metabotropic glutamate receptor-1. Breast Cancer Res Treat. 2016;157(2):217–28.CrossRefPubMed
20.
Dolfi SC, Medina DJ, Kareddula A, Paratala B, Rose A, Dhami J, et al. Riluzole exerts distinct antitumor effects from a metabotropic glutamate receptor 1-specific inhibitor on breast cancer cells. Oncotarget. 2017;8(27):44639–53.CrossRefPubMedPubMedCentral
21.
Speyer CL, Smith JS, Banda M, DeVries JA, Mekani T, Gorski DH. Metabotropic glutamate receptor-1: a potential therapeutic target for the treatment of breast cancer. Breast Cancer Res Treat. 2012;132(2):565–73.CrossRefPubMed
22.
Teh JL, Shah R, La Cava S, Dolfi SC, Mehta MS, Kongara S, et al. Metabotropic glutamate receptor 1 disrupts mammary acinar architecture and initiates malignant transformation of mammary epithelial cells. Breast Cancer Res Treat. 2015;151(1):57–73.CrossRefPubMedPubMedCentral
23.
Ghanshani S, Wulff H, Miller MJ, Rohm H, Neben A, Gutman GA, et al. Up-regulation of the IKCa1 potassium channel during T-cell activation. Molecular mechanism and functional consequences. J Biol Chem. 2000;275(47):37137–49.CrossRefPubMed
24.
Chou CC, Lunn CA, Murgolo NJ. KCa3.1: target and marker for cancer, autoimmune disorder and vascular inflammation? Expert Rev Mol Diagn. 2008;8(2):179–87.CrossRefPubMed
25.
Sugunan S, Nampoothiri SS, Garg T, Krishnamurthy RG. Role of KCa3.1 channels in CNS diseases: a concise review. CNS Neurol Disord Drug Targets. 2016;15(10):1299–305.CrossRefPubMed
26.
Köhler R, Wulff H, Eichler I, Kneifel M, Neumann D, Knorr A, et al. Blockade of the intermediate-conductance calcium-activated potassium channel as a new therapeutic strategy for restenosis. Circulation. 2003;108(9):1119–25.CrossRefPubMed
27.
Zhang N, Li Y, Sundquist J, Sundquist K, Ji J. Identifying actionable druggable targets for breast cancer: mendelian randomization and population-based analyses. EBioMedicine. 2023;98:104859.CrossRefPubMedPubMedCentral
28.
Ho PJ, Khng A, Tan BK, Khor CC, Tan EY, Lim GH et al. Characterizing the relationship between expression quantitative trait loci (eQTLs), DNA methylation quantitative trait loci (mQTLs), and breast Cancer risk variants. Cancers (Basel). 2024;16(11).
29.
Pillozzi S, D’Amico M, Bartoli G, Gasparoli L, Petroni G, Crociani O, et al. The combined activation of K(ca)3.1 and inhibition of K(v)11.1/hERG1 currents contribute to overcome cisplatin resistance in colorectal cancer cells. Br J Cancer. 2018;118(2):200–12.CrossRefPubMed
30.
Muratori L, Petroni G, Antonuzzo L, Boni L, Iorio J, Lastraioli E, et al. hERG1 positivity and Glut-1 negativity identifies high-risk TNM stage I and II colorectal cancer patients, regardless of adjuvant chemotherapy. Onco Targets Ther. 2016;9:6325–32.CrossRefPubMedPubMedCentral
31.
Eil R, Vodnala SK, Clever D, Klebanoff CA, Sukumar M, Pan JH, et al. Ionic immune suppression within the tumour microenvironment limits T cell effector function. Nature. 2016;537(7621):539–43.CrossRefPubMedPubMedCentral
32.
Chandy KG, Norton RS, Immunology. Channelling potassium to fight cancer. Nature. 2016;537(7621):497–9.CrossRefPubMed
33.
Liu BS, Ferreira R, Lively S, Schlichter LC. Microglial SK3 and SK4 currents and activation state are modulated by the neuroprotective drug, riluzole. J Neuroimmune Pharmacol. 2013;8(1):227–37.CrossRefPubMed
34.
Xu T, Park SS, Giaimo BD, Hall D, Ferrante F, Ho DM, et al. RBPJ/CBF1 interacts with L3MBTL3/MBT1 to promote repression of notch signaling via histone demethylase KDM1A/LSD1. Embo j. 2017;36(21):3232–49.CrossRefPubMedPubMedCentral
35.
Gan L, Yang C, Zhao L, Wang S, Ye Y, Gao Z. L3MBTL3 is a potential prognostic biomarker and correlates with Immune infiltrations in gastric Cancer. Cancers (Basel). 2023;16(1).
36.
Kar SP, Beesley J, Amin Al Olama A, Michailidou K, Tyrer J, Kote-Jarai Z, et al. Genome-wide Meta-analyses of breast, ovarian, and prostate Cancer Association studies identify multiple new susceptibility loci Shared by at least two Cancer types. Cancer Discov. 2016;6(9):1052–67.CrossRefPubMedPubMedCentral
37.
Toth R, Scherer D, Kelemen LE, Risch A, Hazra A, Balavarca Y, et al. Genetic variants in epigenetic pathways and risks of multiple cancers in the GAME-ON Consortium. Cancer Epidemiol Biomarkers Prev. 2017;26(6):816–25.CrossRefPubMedPubMedCentral
38.
Ding G, Lu W, Zhang Q, Li K, Zhou H, Wang F, et al. ZBTB38 suppresses prostate cancer cell proliferation and migration via directly promoting DKK1 expression. Cell Death Dis. 2021;12(11):998.CrossRefPubMedPubMedCentral
39.
de Dieuleveult M, Marchal C, Jouinot A, Letessier A, Miotto B. Molecular and clinical relevance of ZBTB38 expression levels in prostate Cancer. Cancers (Basel). 2020;12(5).
40.
Wang S, El-Deiry WS. TRAIL and apoptosis induction by TNF-family death receptors. Oncogene. 2003;22(53):8628–33.CrossRefPubMed
41.
Han YJ, Zhang J, Hardeman A, Liu M, Karginova O, Romero R, et al. An enhancer variant associated with breast cancer susceptibility in black women regulates TNFSF10 expression and antitumor immunity in triple-negative breast cancer. Hum Mol Genet. 2023;32(1):139–50.CrossRefPubMed
42.
43.
Haupt S, Buckley D, Pang JM, Panimaya J, Paul PJ, Gamell C, et al. Targeting mdmx to treat breast cancers with wild-type p53. Cell Death Dis. 2015;6(7):e1821.CrossRefPubMedPubMedCentral
Metadata
Title
Uncovering immune cell-associated genes in breast cancer: based on summary data-based Mendelian randomized analysis and colocalization study
Authors
Jingyang Liu
Wen Sun
Ning Li
Haibin Li
Lijuan Wu
Huan Yi
Jianguang Ji
Deqiang Zheng
Publication date
01-12-2024
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2024
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-024-01928-0

Other articles of this Issue 1/2024

Effect of testosterone therapy on breast tissue composition and mammographic breast density in trans masculine individuals

A genome-wide association study of contralateral breast cancer in the Women’s Environmental Cancer and Radiation Epidemiology Study

Increased risk of contralateral breast cancer for BRCA1/2 wild-type, high-risk Korean breast cancer patients: a retrospective cohort study

HER2-low and tumor infiltrating lymphocytes in triple-negative breast cancer: Are they connected?

NSABP FB-10: a phase Ib/II trial evaluating ado-trastuzumab emtansine (T-DM1) with neratinib in women with metastatic HER2-positive breast cancer

Menopausal hormone therapy and incidence, mortality, and survival of breast cancer subtypes: a prospective cohort study

ASH 2024 Annual Meeting Coverage

inMIND supports tafasitamab addition in follicular lymphoma

Combining tafasitamab with lenalidomide and rituximab significantly improves progression-free survival for patients with relapsed or refractory follicular lymphoma.

Featuring the official presentation video

Read more

Transplantation can be omitted for certain MCL patients

Add-on blinatumomab improves DFS in children with ALL

Promising response to loncastuximab tesirine in lymphoma

» View more highlights on the ASH 2024 hub page

SPONSORED

Recent advances in the use of CAR T-cell therapies in relapsed/refractory diffuse large B-cell lymphoma and follicular lymphoma

In this webinar, Professor Martin Dreyling and an esteemed international panel of CAR T-cell therapy experts discuss the latest data on the safety, efficacy, and clinical impact of CAR T-cell therapies in the treatment of r/r DLBCL and r/r FL.

Please note, this webinar is not intended for healthcare professionals based in the US and UK.

Sponsored by:
  • Novartis Pharma AG
Chaired by: Prof. Martin Dreyling
Developed by: Springer Healthcare
Watch now
Image Credits