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Prognostic significance of CHAC1 expression in breast cancer

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Abstract

Background

An emerging component of Unfolded Protein Response (UPR) pathway, cation transport regulator homolog 1 (CHAC1) has been conferred with the ability to degrade intracellular glutathione and induce apoptosis, however, many reports have suggested a role of CHAC1 in cancer progression. Our study aimed to investigate CHAC1 mRNA levels in large breast cancer datasets using online tools and both mRNA and protein levels in different breast cancer cell lines.

Methods and results

Analysis of clinical information from various online tools (UALCAN, GEPIA2, TIMER2, GENT2, UCSCXena, bcGenExMiner 4.8, Km Plotter, and Enrichr) was done to elucidate the CHAC1 mRNA expression in large breast cancer patient dataset and its correlation with disease progression. Later, in vitro techniques were employed to explore the mRNA and protein expression of CHAC1 in breast cancer cell lines. Evidence from bioinformatics analysis as well as in vitro studies indicated a high overall expression of CHAC1 in breast tumor samples and had a significant impact on the prognosis and survival of patients. Enhanced CHAC1 levels in the aggressive breast tumor subtypes such as Human Epidermal growth factor receptor 2 (HER2) and Triple Negative Breast Cancer (TNBC) were evident. Our findings hint toward the possible role of CHAC1 in facilitating the aggressiveness of breast cancer and the disease outcome.

Conclusion

In summary, CHAC1 is constantly up-regulated in breast cancer leading to a poor prognosis. CHAC1, therefore, could be a promising candidate in the analysis of breast cancer diagnosis and prognosis.

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Data availability

The datasets used in the present study are available in the public database and have been listed in the manuscript.

Abbreviations

BC:

Breast cancer

CHAC1:

Cation transport regulator homolog 1

DMFS:

Distant metastasis free survival

HER2:

Human epidermal growth factor receptor 2

RFS:

Recurrence-free survival

TNBC:

Triple-negative breast cancer

UPR:

Unfolded Protein Response

References

  1. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71(3):209–249

    Article  PubMed  Google Scholar 

  2. Urra H, Dufey E, Avril T, Chevet E, Hetz C (2016) Endoplasmic reticulum stress and the hallmarks of cancer. Trends Cancer 2(5):252–262

    Article  PubMed  Google Scholar 

  3. Muz B, de la Puente P, Azab F, Azab AK (2015) The role of hypoxia in cancer progression, angiogenesis, metastasis, and resistance to therapy. Hypoxia 3:83

    Article  PubMed  PubMed Central  Google Scholar 

  4. Mehta V, Chander H, Munshi A (2021) Complex roles of discoidin domain receptor tyrosine kinases in cancer. Clin Transl Oncol 23(8):1497–1510

    Article  CAS  PubMed  Google Scholar 

  5. McGrath EP, Logue SE, Mnich K, Deegan S, Jäger R, Gorman AM, Samali A (2018) The unfolded protein response in breast cancer. Cancers 10(10):344

    Article  CAS  PubMed Central  Google Scholar 

  6. Binet F, Sapieha P (2015) ER stress and angiogenesis. Cell Metab 22(4):560–575

    Article  CAS  PubMed  Google Scholar 

  7. Chen X, Iliopoulos D, Zhang Q, Tang Q, Greenblatt MB, Hatziapostolou M, Lim E, Tam WL, Ni M, Chen Y (2014) XBP1 promotes triple-negative breast cancer by controlling the HIF1α pathway. Nature 508(7494):103–107

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Zhang K, Liu H, Song Z, Jiang Y, Kim H, Samavati L, Nguyen HM, Yang Z-Q (2020) The UPR transducer IRE1 promotes breast cancer malignancy by degrading tumor suppressor microRNAs. Iscience 23(9):101503

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Logue SE, McGrath EP, Cleary P, Greene S, Mnich K, Almanza A, Chevet E, Dwyer RM, Oommen A, Legembre P (2018) Inhibition of IRE1 RNase activity modulates the tumor cell secretome and enhances response to chemotherapy. Nat Commun 9(1):1–14

    Article  CAS  Google Scholar 

  10. Zhao N, Cao J, Xu L, Tang Q, Dobrolecki LE, Lv X, Talukdar M, Lu Y, Wang X, Hu DZ (2018) Pharmacological targeting of MYC-regulated IRE1/XBP1 pathway suppresses MYC-driven breast cancer. J Clin Investig 128(4):1283–1299

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhang X, Zhou Y, Mao F, Lin Y, Shen S, Sun Q (2020) lncRNA AFAP1-AS1 promotes triple negative breast cancer cell proliferation and invasion via targeting miR-145 to regulate MTH1 expression. Sci Rep 10(1):1–11

    Google Scholar 

  12. Feng Y-X, Jin DX, Sokol ES, Reinhardt F, Miller DH, Gupta PB (2017) Cancer-specific PERK signaling drives invasion and metastasis through CREB3L1. Nat Commun 8(1):1–10

    Article  Google Scholar 

  13. Chi Z, Zhang J, Tokunaga A, Harraz MM, Byrne ST, Dolinko A, Xu J, Blackshaw S, Gaiano N, Dawson TM (2012) Botch promotes neurogenesis by antagonizing notch. Dev Cell 22(4):707–720

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Mungrue IN, Pagnon J, Kohannim O, Gargalovic PS, Lusis AJ (2009) CHAC1/MGC4504 is a novel proapoptotic component of the unfolded protein response, downstream of the ATF4-ATF3-CHOP cascade. J Immunol 182(1):466–476

    Article  CAS  PubMed  Google Scholar 

  15. Chen M-S, Wang S-F, Hsu C-Y, Yin P-H, Yeh T-S, Lee H-C, Tseng L-M (2017) CHAC1 degradation of glutathione enhances cystine-starvation-induced necroptosis and ferroptosis in human triple negative breast cancer cells via the GCN2-eIF2α-ATF4 pathway. Oncotarget 8(70):114588

    Article  PubMed  PubMed Central  Google Scholar 

  16. Goebel G, Berger R, Strasak A, Egle D, Müller-Holzner E, Schmidt S, Rainer J, Presul E, Parson W, Lang S (2012) Elevated mRNA expression of CHAC1 splicing variants is associated with poor outcome for breast and ovarian cancer patients. Br J Cancer 106(1):189–198

    Article  CAS  PubMed  Google Scholar 

  17. Liu Y, Li M, Shi D, Zhu Y (2019) Higher expression of cation transport regulator-like protein 1 (CHAC1) predicts of poor outcomes in uveal melanoma (UM) patients. Int Ophthalmol 39(12):2825–2832

    Article  PubMed  Google Scholar 

  18. Ogawa T, Wada Y, Takemura K, Board PG, Uchida K, Kitagaki K, Tamura T, Suzuki T, Tokairin Y, Nakajima Y (2019) CHAC1 overexpression in human gastric parietal cells with Helicobacter pylori infection in the secretory canaliculi. Helicobacter 24(4):e12598

    Article  PubMed  PubMed Central  Google Scholar 

  19. Wada Y, Takemura K, Tummala P, Uchida K, Kitagaki K, Furukawa A, Ishige Y, Ito T, Hara Y, Suzuki T (2018) Helicobacter pylori induces somatic mutations in TP 53 via overexpression of CHAC 1 in infected gastric epithelial cells. FEBS Open Bio 8(4):671–679

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Parrales A, Iwakuma T (2015) Targeting oncogenic mutant p53 for cancer therapy. Front Oncol 5:288

    Article  PubMed  PubMed Central  Google Scholar 

  21. Tang Z, Kang B, Li C, Chen T, Zhang Z (2019) GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res 47(W1):W556–W560

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BV, Varambally S (2017) UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia 19(8):649–658

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Park S-J, Yoon B-H, Kim S-K, Kim S-Y (2019) GENT2: an updated gene expression database for normal and tumor tissues. BMC Med Genomics 12(5):1–8

    CAS  Google Scholar 

  24. Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS (2017) TIMER: a web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res 77(21):e108–e110

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Jézéquel P, Frénel J-S, Campion L, Guérin-Charbonnel C, Gouraud W, Ricolleau G, Campone M (2013) bc-GenExMiner 3.0: new mining module computes breast cancer gene expression correlation analyses. Database 2013

  26. Goldman MJ, Craft B, Hastie M, Repečka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN (2020) Visualizing and interpreting cancer genomics data via the xena platform. Nat Biotechnol 38(6):675–678

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Xie Z, Bailey A, Kuleshov MV, Clarke DJ, Evangelista JE, Jenkins SL, Lachmann A, Wojciechowicz ML, Kropiwnicki E, Jagodnik KM (2021) Gene set knowledge discovery with enrichr. Curr Protoc 1(3):e90

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Győrffy B (2021) Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer. Comput Struct Biotechnol J 19:4101–4109

    Article  PubMed  PubMed Central  Google Scholar 

  29. Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A (2015) Tissue-based map of the human proteome. Science 347(6220):1260419

    Article  PubMed  Google Scholar 

  30. 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

    Article  PubMed  PubMed Central  Google Scholar 

  31. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma’ayan A (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14(1):1–14

    Article  Google Scholar 

  32. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A (2016) Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res 44(W1):W90–W97

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Kaur M, Mehta V, Wani AA, Arora S, Bharatam PV, Sharon A, Singh S, Kumar R (2021) Synthesis of 1, 4-dihydropyrazolo [4, 3-b] indoles via intramolecular C (sp2)-N bond formation involving nitrene insertion, DFT study and their anticancer assessment. Bioorg Chem 114:105114

    Article  CAS  PubMed  Google Scholar 

  34. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real-time quantitative PCR and the 2− ΔΔCT method. Methods 25(4):402–408

    Article  CAS  PubMed  Google Scholar 

  35. Kaur M, Mehta V, Arora S, Munshi A, Singh S, Kumar R (2021) Design, synthesis and biological evaluation of new 5-(2-Nitrophenyl)-1-aryl-1H-pyrazoles as Topoisomerase inhibitors. ChemistrySelect 6(26):6644–6651

    Article  CAS  Google Scholar 

  36. Li X, Yang J, Peng L, Sahin AA, Huo L, Ward KC, O’Regan R, Torres MA, Meisel JL (2017) Triple-negative breast cancer has worse overall survival and cause-specific survival than non-triple-negative breast cancer. Breast Cancer Res Treat 161(2):279–287

    Article  PubMed  Google Scholar 

  37. Hennigs A, Riedel F, Gondos A, Sinn P, Schirmacher P, Marmé F, Jäger D, Kauczor H-U, Stieber A, Lindel K (2016) Prognosis of breast cancer molecular subtypes in routine clinical care: a large prospective cohort study. BMC Cancer 16(1):1–9

    Article  Google Scholar 

  38. Duffy MJ, Synnott NC, Crown J (2018) Mutant p53 in breast cancer: potential as a therapeutic target and biomarker. Breast Cancer Res Treat 170(2):213–219

    Article  CAS  PubMed  Google Scholar 

  39. Suman P, Mehta V, Craig AW, Chander H (2022) Wild type p53 suppresses formin binding protein 17 (FBP17) to reduce invasion. Carcinogenesis 43(5):494–503. https://doi.org/10.1093/carcin/bgac015

  40. Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D, Ng S, Leiserson MD, Niu B, McLellan MD, Uzunangelov V (2014) Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158(4):929–944

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Muller PA, Vousden KH (2014) Mutant p53 in cancer: new functions and therapeutic opportunities. Cancer Cell 25(3):304–317

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Ding W, Chen G, Shi T (2019) Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis. Epigenetics 14(1):67–80

    Article  PubMed  PubMed Central  Google Scholar 

  43. Chander H, Truesdell P, Meens J, Craig AW (2013) Transducer of Cdc42-dependent actin assembly promotes breast cancer invasion and metastasis. Oncogene 32(25):3080–3090

    Article  CAS  PubMed  Google Scholar 

  44. Blanco MA, Kang Y (2011) Signaling pathways in breast cancer metastasis-novel insights from functional genomics. Breast Cancer Res 13(2):1–9

    Article  Google Scholar 

  45. Hsu JL, Hung M-C (2016) The role of HER2, EGFR, and other receptor tyrosine kinases in breast cancer. Cancer Metastasis Rev 35(4):575–588

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Suman P, Mishra S, Chander H (2018) High expression of FBP17 in invasive breast cancer cells promotes invadopodia formation. Med Oncol 35(5):1–6

    Article  CAS  Google Scholar 

  47. Mehta V, Chander H, Munshi A (2021) Mechanisms of anti-tumor activity of Withania somnifera (Ashwagandha). Nutr Cancer 73(6):914–926

    Article  CAS  PubMed  Google Scholar 

  48. Kumar A, Tikoo S, Maity S, Sengupta S, Sengupta S, Kaur A, Kumar Bachhawat A (2012) Mammalian proapoptotic factor ChaC1 and its homologues function as γ-glutamyl cyclotransferases acting specifically on glutathione. EMBO Rep 13(12):1095–1101

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Reich S, Nguyen CD, Has C, Steltgens S, Soni H, Coman C, Freyberg M, Bichler A, Seifert N, Conrad D (2020) A multi-omics analysis reveals the unfolded protein response regulon and stress-induced resistance to folate-based antimetabolites. Nat Commun 11(1):1–15

    Article  Google Scholar 

  50. Madden E, Logue SE, Healy SJ, Manie S, Samali A (2019) The role of the unfolded protein response in cancer progression: from oncogenesis to chemoresistance. Biol Cell 111(1):1–17

    Article  PubMed  Google Scholar 

  51. Nagelkerke A, Bussink J, Mujcic H, Wouters BG, Lehmann S, Sweep FC, Span PN (2013) Hypoxia stimulates migration of breast cancer cells via the PERK/ATF4/LAMP3-arm of the unfolded protein response. Breast Cancer Res 15(1):1–13

    Article  Google Scholar 

  52. Lin HH, Chung Y, Cheng C-T, Ouyang C, Fu Y, Kuo C-Y, Chi KK, Sadeghi M, Chu P, Kung H-J (2018) Autophagic reliance promotes metabolic reprogramming in oncogenic KRAS-driven tumorigenesis. Autophagy 14(9):1481–1498

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Yang H, He X, Zheng Y, Feng W, Xia X, Yu X, Lin Z (2014) Down-regulation of asparagine synthetase induces cell cycle arrest and inhibits cell proliferation of breast cancer. Chem Biol Drug Des 84(5):578–584

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors highly acknowledge the developers of various databases and web servers used in the study. The current research was in part supported by extramural research funding from the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), India (Grant Nos. EMR/2015/000761 & EEQ/2017/000794). V.M. is thankful to ICMR, New Delhi for providing the Senior Research Fellowship (SRF).

Funding

The study was funded by the Department of Science and Technology-Science and Engineering Research Board (DST-SERB), India (Grant Nos. EMR/2015/000761 & EEQ/2017/000794).

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Authors and Affiliations

  1. Department of Human Genetics and Molecular Medicine, Central University of Punjab, Bathinda, 151401, India

    Vikrant Mehta & Anjana Munshi

  2. Biotherapeutics Division, National Institute of Biologicals, Sector 62, Noida, 201309, India

    Jaipal Meena, Harit Kasana & Harish Chander

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  1. Vikrant Mehta
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  2. Jaipal Meena
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  3. Harit Kasana
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  4. Anjana Munshi
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Contributions

VM and HC: designed the study. VM: performed the experimental work. VM and HC: drafted the figures and prepared the manuscript. JM, HK, and AM: performed data analysis in part. All the authors analyzed the data and reviewed the manuscript.

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Correspondence to Harish Chander.

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Mehta, V., Meena, J., Kasana, H. et al. Prognostic significance of CHAC1 expression in breast cancer. Mol Biol Rep 49, 8517–8526 (2022). https://doi.org/10.1007/s11033-022-07673-x

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