Top

BMC Cancer

Published in:

Open Access 01-12-2021 | Research article

Butyrophilin-like 9 expression is associated with outcome in lung adenocarcinoma

Authors: Weishuang Ma, Jiaming Liang, Junjian Mo, Siyuan Zhang, Ningdong Hu, Dongbo Tian, Zisheng Chen

Published in: BMC Cancer | Issue 1/2021

Abstract

Background

Lung adenocarcinoma (LUAD) is the most prevalent non-small cell lung cancer (NSCLC). Patients with LUAD have a poor 5-year survival rate. The use of immune checkpoint inhibitors (ICIs) for the treatment of LUAD has been on the rise in the past decade. This study explored the prognostic role of butyrophilin-like 9 (BTNL9) in LUAD.

Methods

Gene expression profile of buytrophilins (BTNs) was determined using the GEPIA database. The effect of BTNL9 on the survival of LUAD patients was assessed using Kaplan-Meier plotter and OncoLnc. Correlation between BTNL9 expression and tumor-infiltrating immune cells (TILs) was explored using TIMER and GEPIA databases. Further, the relationship between BTNL9 expression and drug response was evaluated using CARE. Besides, construction and evaluation of nomogram based on BTNL9 expression and TNM stage.

Results

BTNL9 expression was downregulated in LUAD and was associated with a poor probability of 1, 3, 5-years overall survival (OS). In addition, BTNL9 expression was regulated at epigenetic and post-transcriptional modification levels. Moreover, BTNL9 expression was significantly positively correlated with ImmuneScore and ESTIMATEScore. Furthermore, BTNL9 expression was positively associated with infiltration levels of B cells, CD4⁺ T cells, and macrophages. Kaplan-Meier analysis showed that BTNL9 expression in B cells and dendritic cells (DCs) was significantly associated with OS. BTNL9 expression was significantly positively correlated with CARE scores.

Conclusions

These findings show that BTNL9 is a potential prognostic biomarker for LUAD. Low BTNL9 expression levels associated with low infiltration levels of naïve B cells, and DCs in the tumor microenvironment are unfavorable for OS in LUAD patients.

Available only for authorised users

Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, et al. Cancer statistics in China, 2015. CA Cancer J Clin. 2016;66(2):115–32. https://doi.org/10.3322/caac.21338.CrossRefPubMed

Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, 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. https://doi.org/10.3322/caac.21492.CrossRefPubMed

Lu T, Yang X, Huang Y, Zhao M, Li M, Ma K, et al. Trends in the incidence, treatment, and survival of patients with lung cancer in the last four decades. Cancer Manag Res. 2019;11:943–53. https://doi.org/10.2147/CMAR.S187317.CrossRefPubMedPubMedCentral

Garon EB, Hellmann MD, Rizvi NA, Carcereny E, Leighl NB, Ahn MJ, et al. Five-year overall survival for patients with advanced non–small-cell lung Cancer treated with Pembrolizumab: results from the phase I KEYNOTE-001 study. J Clin Oncol. 2019;37(28):2518–27. https://doi.org/10.1200/JCO.19.00934.CrossRefPubMedPubMedCentral

Davern M, Lysaght J. Cooperation between chemotherapy and immunotherapy in gastroesophageal cancers. Cancer Lett. 2020;495:89–99. https://doi.org/10.1016/j.canlet.2020.09.014.CrossRefPubMed

Guo Y, Wang AY. Novel Immune Check-Point Regulators in Tolerance Maintenance. Front Immunol. 2015;6:421. https://doi.org/10.3389/fimmu.2015.00421.CrossRefPubMedPubMedCentral

Abeler-Dörner L, Swamy M, Williams G, Hayday AC, Bas A. Butyrophilins: an emerging family of immune regulators. Trends Immunol. 2012;33(1):34–41. https://doi.org/10.1016/j.it.2011.09.007.CrossRefPubMed

Malinowska M, Tokarz-Deptuła B, Deptuła W. Butyrophilins: an important new element of resistance. Cent-Eur J Immunol. 2017;42(4):399–403. https://doi.org/10.5114/ceji.2017.72806.CrossRefPubMedPubMedCentral

Arnett HA, Viney JL. Immune modulation by butyrophilins. Nat Rev Immunol. 2014;14(8):559–69. https://doi.org/10.1038/nri3715.CrossRefPubMed

10.

Zhou C. Lung cancer molecular epidemiology in China: recent trends. Transl Lung Cancer Res. 2014;3(5):270–9. https://doi.org/10.3978/j.issn.2218-6751.2014.09.01.CrossRefPubMedPubMedCentral

11.

Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–W102. https://doi.org/10.1093/nar/gkx247.CrossRefPubMedPubMedCentral

12.

Clough E, Barrett T. The Gene Expression Omnibus Database. Methods Mol Biol. 2016;1418:93–110.CrossRefPubMedPubMedCentral

13.

Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, et al. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–w514. https://doi.org/10.1093/nar/gkaa407.CrossRefPubMedPubMedCentral

14.

Győrffy B, Surowiak P, Budczies J, Lánczky A. Online survival analysis software to assess the prognostic value of biomarkers using transcriptomic data in non-small-cell lung cancer. PLoS One. 2013;8(12):e82241. https://doi.org/10.1371/journal.pone.0082241.CrossRefPubMedPubMedCentral

15.

Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi B, et al. UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses. Neoplasia. 2017;19(8):649–58.CrossRefPubMedPubMedCentral

16.

Anaya J. OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. PeerJ Computer Science. 2016;2:e67. https://doi.org/10.7717/peerj-cs.67.CrossRef

17.

Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004;6(1):1–6.CrossRefPubMedPubMedCentral

18.

Kim P, Park A, Han G, Sun H, Jia P, Zhao Z. TissGDB: tissue-specific gene database in cancer. Nucleic Acids Res. 2017;46(D1):D1031–8. https://doi.org/10.1093/nar/gkx850.CrossRefPubMedCentral

19.

Tang Z, Kang B, Li C, Chen T, Zhang Z. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019;47(W1):W556–60. https://doi.org/10.1093/nar/gkz430.CrossRefPubMedPubMedCentral

20.

Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, et al. TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res. 2017;77(21):e108–10. https://doi.org/10.1158/0008-5472.CAN-17-0307.CrossRefPubMedPubMedCentral

21.

Dai D, Chen B, Feng Y, Wang W, Jiang Y, Huang H, et al. Prognostic value of prostaglandin I2 synthase and its correlation with tumor-infiltrating immune cells in lung cancer, ovarian cancer, and gastric cancer. Aging. 2020;12(10):9658–85. https://doi.org/10.18632/aging.103235.CrossRefPubMedPubMedCentral

22.

Vejnar CE, Blum M, Zdobnov EM. miRmap web: Comprehensive microRNA target prediction online. Nucleic Acid Res. 2013;41(Web Server issue):W165–8.CrossRefPubMedPubMedCentral

23.

Agarwal V, Bell GW, Nam JW, Bartel DP. Predicting effective microRNA target sites in mammalian mRNAs. eLife. 2015;4:e05005. https://doi.org/10.7554/eLife.05005.CrossRefPubMedCentral

24.

Sticht C, De La Torre C, Parveen A, Gretz N. miRWalk: an online resource for prediction of microRNA binding sites. PLoS One. 2018;13(10):e0206239-e0206239. https://doi.org/10.1371/journal.pone.0206239.CrossRef

25.

Li J-H, Liu S, Zhou H, Qu L-H, Yang J-H. starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 2013;42(D1):D92–7. https://doi.org/10.1093/nar/gkt1248.CrossRefPubMedPubMedCentral

26.

Jiang P, Lee W, Li X, Johnson C, Liu JS, Brown M, et al. Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies. Cell Syst. 2018;6(3):343–354.e345.CrossRefPubMedPubMedCentral

27.

Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genet. 2009;2(1):18. https://doi.org/10.1186/1755-8794-2-18.CrossRef

28.

Li Y, Li L, Wang Z, Pan T, Sahni N, Jin X, et al. LncMAP: Pan-cancer atlas of long noncoding RNA-mediated transcriptional network perturbations. Nucleic Acids Res. 2018;46(3):1113–23. https://doi.org/10.1093/nar/gkx1311.CrossRefPubMedPubMedCentral

29.

Wang P, Li X, Gao Y, Guo Q, Wang Y, Fang Y, et al. LncACTdb 2.0: an updated database of experimentally supported ceRNA interactions curated from low-and high-throughput experiments. Nucleic Acids Res. 2019;47(D1):D121–7. https://doi.org/10.1093/nar/gky1144.CrossRefPubMed

30.

Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, et al. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2018;47(D1):D607–13. https://doi.org/10.1093/nar/gky1131.CrossRefPubMedCentral

31.

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504. https://doi.org/10.1101/gr.1239303.CrossRefPubMedPubMedCentral

32.

Li Y, Xie P, Lu L, Wang J, Diao L, Liu Z, et al. An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network. Nat Commun. 2017;8(1):347. https://doi.org/10.1038/s41467-017-00299-9.CrossRefPubMedPubMedCentral

33.

Faubert B, Solmonson A, DeBerardinis RJ. Metabolic reprogramming and cancer progression. Science. 2020;368(6487):eaaw5473.CrossRefPubMedPubMedCentral

34.

Yuan H, Yan M, Zhang G, Liu W, Deng C, Liao G, et al. CancerSEA: a cancer single-cell state atlas. Nucleic Acids Res. 2018;47(D1):D900–8. https://doi.org/10.1093/nar/gky939.CrossRefPubMedCentral

35.

Giraldo NA, Sanchez-Salas R, Peske JD, Vano Y, Becht E, Petitprez F, et al. The clinical role of the TME in solid cancer. Br J Cancer. 2019;120(1):45–53. https://doi.org/10.1038/s41416-018-0327-z.CrossRefPubMed

36.

Brown CC, Gudjonson H, Pritykin Y, Deep D, Lavallée V-P, Mendoza A, et al. Transcriptional Basis of Mouse and Human Dendritic Cell Heterogeneity. Cell. 2019;179(4):846–863.e824.CrossRefPubMedPubMedCentral

37.

Chen J, Tan Y, Sun F, Hou L, Zhang C, Ge T, et al. Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer. Genome Biol. 2020;21(1):152. https://doi.org/10.1186/s13059-020-02064-6.CrossRefPubMedPubMedCentral

38.

Chu T, Zhong R, Zhong H, Zhang B, Zhang W, Shi C, et al. Phase Ib study of Sintilimab plus Anlotinib as first-line therapy in patients with advanced non-small cell lung Cancer. J Thorac Oncol. 2021;16(4):643-52. https://doi.org/10.1016/j.jtho.2020.11.026.

39.

Collisson EA, Campbell JD, Brooks AN, Berger AH, Lee W, Chmielecki J, et al. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511(7511):543–50. https://doi.org/10.1038/nature13385.CrossRef

40.

Mo Q, Xu K, Luo C, Zhang Q, Wang L, Ren G. BTNL9 is frequently downregulated and inhibits proliferation and metastasis via the P53/CDC25C and P53/GADD45 pathways in breast cancer. Biochem Biophys Res Commun. 2021;553:17–24. https://doi.org/10.1016/j.bbrc.2021.03.022.CrossRefPubMed

41.

Cheng Y, He C, Wang M, Ma X, Mo F, Yang S, et al. Targeting epigenetic regulators for cancer therapy: mechanisms and advances in clinical trials. Signal Transduct Target Ther. 2019;4(1):62. https://doi.org/10.1038/s41392-019-0095-0.CrossRefPubMedPubMedCentral

42.

Zhong K, Chen K, Han L, Li B. MicroRNA-30b/c inhibits non-small cell lung cancer cell proliferation by targeting Rab18. BMC Cancer. 2014;14(1):703. https://doi.org/10.1186/1471-2407-14-703.CrossRefPubMedPubMedCentral

43.

Gao D, Zhou Z, Huang H. miR-30b-3p Inhibits Proliferation and Invasion of Hepatocellular Carcinoma Cells via Suppressing PI3K/Akt Pathway. Front Genet. 2019;10:1274. https://doi.org/10.3389/fgene.2019.01274.CrossRefPubMedPubMedCentral

44.

Hsu Y-L, Hung J-Y, Lee Y-L, Chen F-W, Chang K-F, Chang W-A, et al. Identification of novel gene expression signature in lung adenocarcinoma by using next-generation sequencing data and bioinformatics analysis. Oncotarget. 2017;8(62):104831–54. https://doi.org/10.18632/oncotarget.21022.CrossRefPubMedPubMedCentral

45.

Miller DM, Thomas SD, Islam A, Muench D, Sedoris K. C-Myc and cancer metabolism. Clin Cancer Res. 2012;18(20):5546–53. https://doi.org/10.1158/1078-0432.CCR-12-0977.CrossRefPubMedPubMedCentral

46.

Valvezan AJ, Manning BD. Molecular logic of mTORC1 signalling as a metabolic rheostat. Nat Metab. 2019;1(3):321–33. https://doi.org/10.1038/s42255-019-0038-7.CrossRefPubMed

47.

Öjlert ÅK, Halvorsen AR, Nebdal D, Lund-Iversen M, Solberg S, Brustugun OT, et al. The immune microenvironment in non-small cell lung cancer is predictive of prognosis after surgery. Mol Oncol. 2019;13(5):1166–79. https://doi.org/10.1002/1878-0261.12475.CrossRefPubMedPubMedCentral

48.

Mao Y, Feng Q, Zheng P, Yang L, Liu T, Xu Y, et al. Low tumor purity is associated with poor prognosis, heavy mutation burden, and intense immune phenotype in colon cancer. Cancer Manag Res. 2018;10:3569–77. https://doi.org/10.2147/CMAR.S171855.CrossRefPubMedPubMedCentral

49.

Hu X, Zhang J, Wang J, Fu J, Li T, Zheng X, et al. Landscape of B cell immunity and related immune evasion in human cancers. Nat Genet. 2019;51(3):560–7. https://doi.org/10.1038/s41588-018-0339-x.CrossRefPubMedPubMedCentral

50.

Böttcher JP, Reis e Sousa C. The role of type 1 conventional dendritic cells in Cancer immunity. Trends Cancer. 2018;4(11):784–92. https://doi.org/10.1016/j.trecan.2018.09.001.CrossRefPubMedPubMedCentral

51.

Perez CR, De Palma M. Engineering dendritic cell vaccines to improve cancer immunotherapy. Nat Commun. 2019;10(1):5408–5408. https://doi.org/10.1038/s41467-019-13368-y.CrossRef

Title: Butyrophilin-like 9 expression is associated with outcome in lung adenocarcinoma
Authors: Weishuang Ma
Jiaming Liang
Junjian Mo
Siyuan Zhang
Ningdong Hu
Dongbo Tian
Zisheng Chen
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Cancer / Issue 1/2021
Electronic ISSN: 1471-2407
DOI: https://doi.org/10.1186/s12885-021-08790-9

2024 ASCO Annual Meeting Coverage

Highlights of the Day: Breast cancer – metastatic

Breast Cancer Video

In this session, Dr. Wolff provides his key takeaways from the data presented in the metastatic breast cancer oral abstract session at ASCO 2024.

Watch now

Highlights of the Day: Gynecologic cancer

Gynecologic Cancer Video

Highlights of the Day: Breast Cancer – local/regional/adjuvant

Breast Cancer Video

Neoadjuvant dual immunotherapy improves melanoma outcomes

04-06-2024 Melanoma News

» View more highlights on the ASCO 2024 congress hub page

Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras

Prof. Fabrice Barlesi

Developed by: Springer Medicine

Image Credits

ASCO 2024 | Highlights of the Day: Breast cancer – metastatic/© 2024 American Society of Clinical Oncology, all rights reserved., ASCO 2024 | Highlights of the Day: Gynecologic Cancer/© 2024 American Society of Clinical Oncology, all rights reserved., ASCO 2024 | Highlights of the Day: Breast Cancer – local/regional/adjuvant/© 2024 American Society of Clinical Oncology, all rights reserved., Melanoma/© selvanegra / Getty Images / iStock, Antibody drug conjugated with cytotoxic payload/© Love Employee / Getty images / iStock

2024 ASCO Annual Meeting

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

Please log in to get access to this content

Other articles of this Issue 1/2021

Association of ZNF331 and WIF1 methylation in peripheral blood leukocytes with the risk and prognosis of gastric cancer

AXL receptor tyrosine kinase: a possible therapeutic target in acute promyelocytic leukemia

TINCR inhibits the proliferation and invasion of laryngeal squamous cell carcinoma by regulating miR-210/BTG2

Competing risk analysis of cardiovascular/cerebrovascular death in T1/2 kidney cancer: a SEER database analysis

Association of Single Nucleotide Polymorphism LEP-R c.668A>G (p.Gln223Arg, rs1137101) of leptin receptor gene with endometrial cancer