Identification of TMEM178 as a Potential Prognostic Biomarker and Therapeutic Target for Breast Cancer
Background: The transmembrane protein (TMEM) family plays important roles in cancer. However, the expression pattern and biological roles of TMEM178, a member of TMEM family, remains unclear in breast cancer (BRCA).
Methods: Methylation and RNA-seq data were obtained to explore methylation level. Expression of TMEM178, methylation inhibitor 5-Aza-CdR was used to verify the effect of methylation status on the expression of TMEM178. We comprehensively investigated the prognostic outcomes, biological functions and effects on immune cell infiltration of the TMEM178 in BRCA using multiple bioinformatics methods.
Results: The expression of TMEM178 was downregulated and negatively correlated with the level of DNA methylation and DNA methyltransferase (DNMT1, DNMT3A, and DNMT3B) in BRCA. Consistently, TMEM178 mRNA were confirmed to be downregulated, while upregulated in response to treatment with methylation inhibitor 5-Aza-CdR by RT-qPCR. Patients with high expression of TMEM178 have better prognosis and are more sensitive to targeted drug Pazopanib. Immune infiltration analysis showed that the infiltration levels of CD4+ T cell subsets were reduced in BRAC tissues with high TMEM178 expression, and immunosuppressive molecules of T-cell exhaustion were lower expression level.
Conclusion: Hypermethylation of the TMEM178 promoter region was a contributing factor to the downregulation of its expression, and TMEM178 may reflect a prognostic and immunosuppressive situation in BRCA.
2. Nishiyama A, Nakanishi M (2021). Navigating the DNA methylation landscape of cancer. Trends Genet, 37 (11):1012–1027.
3. Sher G, Salman NA, Khan AQ, et al (2022). Epigenetic and breast cancer therapy: Promising diagnostic and therapeutic applications. Semin Cancer Biol, 83:152–165.
4. Schmit K, Michiels C (2018). TMEM Proteins in Cancer: A Review. Front Pharmacol, 9: 1345.
5. So CL, Saunus JM, Roberts-Thomson SJ, Monteith GR (2019). Calcium signalling and breast cancer. Semin Cell Dev Biol, 94:74–83.
6. Das D, Karthik N, Taneja R (2021). Crosstalk Between Inflammatory Signaling and Methylation in Cancer. Front cell Dev Biol, 9: 756458.
7. Lan T, Chen L, Wei X (2021). Inflammatory Cytokines in Cancer: Comprehensive Understanding and Clinical Progress in Gene Therapy. Cells, 10:1–16.
8. Yang Z, Yan H, Dai W, et al (2019). Tmem178 negatively regulates store-operated calcium entry in myeloid cells via association with STIM1. J Autoimmun, 101:94–108.
9. Wu L, Lian W, Zhao L (2021). Calcium signaling in cancer progression and therapy. FEBS J, 288:6187–6205.
10. Carvalho D, Mackay A, Bjerke L, et al (2014). The prognostic role of intragenic copy number breakpoints and identification of novel fusion genes in paediatric high grade glioma. Acta Neuropathol Commun, 2:23.
11. Rhodes DR, Yu J, Shanker K, et al (2004). ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia, 6:1–6.
12. Li T, Fu J, Zeng Z, et al (2020). TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res, 48:W509–W514.
13. Tang Z, Kang B, Li C, et al (2019). GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res, 47:W556–W560.
14. Vasaikar S V., Straub P, Wang J, Zhang B (2018). LinkedOmics: analyzing multi-omics data within and across 32 cancer types. Nucleic Acids Res, 46:D956–D963.
15. Chandrashekar DS, Karthikeyan SK, Korla PK, et al (2022). UALCAN: An update to the integrated cancer data analysis platform. Neoplasia, 25:18–27.
16. Ding W, Chen J, Feng G, et al (2020). DNMIVD: DNA methylation interactive visualization database. Nucleic Acids Res, 48:D856–D862.
17. Koch A, Jeschke J, Van Criekinge W, et al (2019). MEXPRESS update 2019. Nucleic Acids Res, 47:W561–W565.
18. Zhou Y, Zeng P, Li YH, et al (2016). SRAMP: prediction of mammalian N6-methyladenosine (m6A). sites based on sequence-derived features. Nucleic Acids Res, 44(10): e91-e91.
19. Digre A, Lindskog C (2021). The Human Protein Atlas-Spatial localization of the human proteome in health and disease. Protein Sci, 30:218–233.
20. Lánczky A, Győrffy B (2021). Web-Based Survival Analysis Tool Tailored for Medical Research (KMplot).: Development and Implementation. J Med Internet Res, 23(7): e27633.
21. Zhang C, Zhao N, Zhang X, et al (2021). SurvivalMeth: a web server to investigate the effect of DNA methylation-related functional elements on prognosis. Brief Bioinform, 22(3): bbaa162.
22. Modhukur V, Iljasenko T, Metsalu T, et al (2018). MethSurv: a web tool to perform multivariable survival analysis using DNA methylation data. Epigenomics, 10(3):277–288.
23. Hänzelmann S, Castelo R, Guinney J (2013). GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics, 14: 1-15.
24. Reinhold WC, Sunshine M, Varma S, et al (2015). Using CellMiner 1.6 for Systems Pharmacology and Genomic Analysis of the NCI-60. Clin Cancer Res, 21(17):3841–3852.
25. Yang W, Soares J, Greninger P, et al (2013). Genomics of Drug Sensitivity in Cancer (GDSC).: a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res, 41(D1).: D955-D961.
26. Hanahan D (2022). Hallmarks of Cancer: New Dimensions. Cancer Discov, 12:31–46.
27. Marx S, Dal Maso T, Chen JW, et al (2020). Transmembrane (TMEM). protein family members: Poorly characterized even if essential for the metastatic process. Semin Cancer Biol, 60:96–106.
28. Subramaniam D, Thombre R, Dhar A, Anant S (2014). DNA methyltransferases: a novel target for prevention and therapy. Front Oncol, 4: 80.
29. Vasan N, Baselga J, Hyman DM (2019). A view on drug resistance in cancer. Nature, 575:299–309.
30. Lee ATJ, Jones RL, Huang PH (2019). Pazopanib in advanced soft tissue sarcomas. Signal Transduct Target Ther,4(1): 16.
31. Yamada H, Takahashi M, Watanuki M, et al (2021). lncRNA HAR1B has potential to be a predictive marker for pazopanib therapy in patients with sarcoma. Oncol Lett, 21(6).: 1-14.
32. Tie Y, Tang F, Wei Y quan, Wei X wei (2022). Immunosuppressive cells in cancer: mechanisms and potential therapeutic targets. BioMed Central, 15(1): 61.
33. Zalfa C, Paust S (2021). Natural Killer Cell Interactions With Myeloid Derived Suppressor Cells in the Tumor Microenvironment and Implications for Cancer Immunotherapy. Front Immunol, 12: 633205.
34. Richardson JR, Schöllhorn A, Gouttefangeas C, Schuhmacher J (2021). CD4+ T cells: Multitasking cells in the duty of cancer immunotherapy. Cancers (Basel), 13:1–19.
35. Wang Y, Zhang H, Liu C, et al (2022). Immune checkpoint modulators in cancer immunotherapy : recent advances and emerging concepts. BioMed Central, 15(1): 1-53.
36. Saillard M, Cenerenti M, Romero P, Jandus C (2021). Impact of immunotherapy on cd4 t cell phenotypes and function in cancer. Vaccines, 9:1–21.
37. Scherwitzl I, Opp S, Hurtado AM, et al (2020). Sindbis Virus with Anti-OX40 Overcomes the Immunosuppressive Tumor Microenvironment of Low-Immunogenic Tumors. Mol Ther – Oncolytics, 17:431–447.
|Issue||Vol 52 No 11 (2023)|
|Methylation modification CD4 T subsets Immunosuppressive molecules|
|Rights and permissions|
|This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.|