Journal of Microbiology and Biotechnology

The Korean Society for Microbiology and Biotechnology (한국미생물·생명공학회)
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Construction of a Novel Mitochondria-Associated Gene Model for Assessing ESCC Immune Microenvironment and Predicting Survival

  • Xiu Wang (Department of General Practice, Shandong Provincial Hospital Affiliated to Shandong First Medical University) ;
  • Zhenhu Zhang (Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University) ;
  • Yamin Shi (School of Foreign Languages, Shandong University of Finance and Economics) ;
  • Wenjuan Zhang (Department of Surgical, Shandong Provincial Hospital Affiliated to Shandong First Medical University) ;
  • Chongyi Su (Department of Emergency, Shandong Provincial Hospital Affiliated to Shandong First Medical University) ;
  • Dong Wang (Department of Thoracic Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University)
  • Received : 2023.11.09
  • Accepted : 2024.02.02
  • Published : 2024.05.28
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Abstract

Esophageal squamous cell carcinoma (ESCC) is among the most common malignant tumors of the digestive tract, with the sixth highest fatality rate worldwide. The ESCC-related dataset, GSE20347, was downloaded from the Gene Expression Omnibus (GEO) database, and weighted gene co-expression network analysis was performed to identify genes that are highly correlated with ESCC. A total of 91 transcriptome expression profiles and their corresponding clinical information were obtained from The Cancer Genome Atlas database. A mitochondria-associated risk (MAR) model was constructed using the least absolute shrinkage and selection operator Cox regression analysis and validated using GSE161533. The tumor microenvironment and drug sensitivity were explored using the MAR model. Finally, in vitro experiments were performed to analyze the effects of hub genes on the proliferation and invasion abilities of ESCC cells. To confirm the predictive ability of the MAR model, we constructed a prognostic model and assessed its predictive accuracy. The MAR model revealed substantial differences in immune infiltration and tumor microenvironment characteristics between high- and low-risk populations and a substantial correlation between the risk scores and some common immunological checkpoints. AZD1332 and AZD7762 were more effective for patients in the low-risk group, whereas Entinostat, Nilotinib, Ruxolutinib, and Wnt.c59 were more effective for patients in the high-risk group. Knockdown of TYMS significantly inhibited the proliferation and invasive ability of ESCC cells in vitro. Overall, our MAR model provides stable and reliable results and may be used as a prognostic biomarker for personalized treatment of patients with ESCC.

Keywords

Acknowledgement

The authors are grateful for the help from the Department of Thoracic Surgery and Central Laboratory of Shandong Provincial Hospital, affiliated to Shandong First Medical University, Jinan, China. This study is supported by the National Science Foundation of Shandong Province of China (NO. ZR2019MH092).

References

  1. Arnold M, Abnet CC, Neale RE, Vignat J, Giovannucci EL, McGlynn KA, et al. 2020. Global burden of 5 major types of gastrointestinal cancer. Gastroenterology 159: 335-349.  https://doi.org/10.1053/j.gastro.2020.02.068
  2. Morgan E, Soerjomataram I, Rumgay H, Coleman HG, Thrift AP, Vignat J, et al. 2022obal Landscape of eophageal suamous cll crcinoma and eophageal aenocarcinoma icidence and mrtality in 2020 and pojections to 2040: new etimates from GLOBOCAN 2020. Gastroenterology 163: 649-658. 
  3. Tanprasert P, Yamada SL, Chattipakorn SC, Chattipakorn N, Shinlapawittayatorn K. 2022. Targeting mitochondria as a therapeutic anti-gastric cancer approach. Apoptosis 27: 163-183.  https://doi.org/10.1007/s10495-022-01709-0
  4. Zheng ZY, Yang PL, Li RY, Liu LX, Xu XE, Liao LD, et al. 2021. STAT3beta disrupted mitochondrial electron transport chain enhances chemosensitivity by inducing pyroptosis in esophageal squamous cell carcinoma. Cancer Lett. 522: 171-183.  https://doi.org/10.1016/j.canlet.2021.09.035
  5. He JZ, Chen Y, Zeng FM, Huang QF, Zhang HF, Wang SH, et al. 2023. Spatial analysis of stromal signatures has identified invasive front carcinoma-associated fibroblasts as suppressors of the antitumor immune response in esophageal cancer. J. Exp. Clin. Cancer Res. 42: 136. 
  6. Cheng J, Chen F, Cheng Y. 2022. Construction and evaluation of a risk score model for lymph node metastasis-associated circadian clock genes in esophageal squamous carcinoma. Cells 11: 3432. 
  7. Jia W, Huang Z, Zhou L, Liou YC, Di Virgilio F, Ulrich H, et al. 2023. Purinergic signalling in cancer therapeutic resistance: from mechanisms to targeting strategies. Drug Resist. Updat. 70: 100988. 
  8. Sheban F. 2023. It takes two to tango: the role of tumor-associated macrophages in T cell-directed immune checkpoint blockade therapy. Front. Immunol. 14: 1183578. 
  9. Carlisle JW, Steuer CE, Owonikoko TK, Saba NF. 2020. An update on the immune landscape of lung, head, and neck cancers. CA Cancer J. Clin. 70: 505-517.  https://doi.org/10.3322/caac.21630
  10. Hu N, Clifford RJ, Yang HH, Wang C, Goldstein AM, Ding T, et al. 2010. Genome wide analysis of DNA copy number neutral loss of heterozygosity (CNNLOH) and its relationship to gene expression in esophageal squamous cell carcinoma. BMC Genomics 11: 576. 
  11. Tabah A, Buetti N, Staiquly Q, Ruckly S, Akova M, Aslan AT, et al. 2023. Eurobact-2 study group EEE and the ON. epidemiology and outcomes of hospital-acquired bloodstream infections in intensive care unit patients: the EUROBACT-2 international cohort study. Intensive Care Med. 49: 178-190. 
  12. Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, et al. 2016. CGAbiolinks: an R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44: e71. 
  13. Chang J, Wu H, Wu J, Liu M, Zhang W, Hu Y, et al. 2023. Constructing a novel mitochondrial-related gene signature for evaluating the tumor immune microenvironment and predicting survival in stomach adenocarcinoma. J. Transl. Med. 21: 191. 
  14. Langfelder P, Horvath S. 2008. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559. 
  15. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. 2015. limma powers differential expression analyses for RNA sequencing and microarray studies. Nucleic Acids Res. 43: e47. 
  16. Gustavsson EK, Zhang D, Reynolds RH, Garcia-Ruiz S, Ryten M. 2022. ggtranscript: an R. package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics 38: 3844-3846.  https://doi.org/10.1093/bioinformatics/btac409
  17. Chu F, Liang T, Chen CLP, Wang X, Ma X. 2024. Compact broad learning system based on fused lasso and smooth lasso. IEEE Trans. Cybern. 54: 435-448.  https://doi.org/10.1109/TCYB.2023.3267947
  18. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. 2011. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12: 77. 
  19. Shu Q, She H, Chen X, Zhong L, Zhu J, Fang L. 2023. Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis. Front. Immunol. 14: 1184126. 
  20. Yoshihara K, Shahmoradgoli M, Martinez E, Vegesna R, Kim H, Torres-Garcia W, et al. 2013. Inferring tumour purity and stromal and immune cell admixtures from expression data. Nat. Commun. 4: 2612. 
  21. Ye Y, Dai Q, Qi H. 2021. A novel defined a pyroptosis-related gene signature for predicting the prognosis of ovarian cancer.Cell Death Discov. 7: 71. 
  22. Newman AM, Steen CB, Liu CL, Gentles AJ, Chaudhuri AA, Scherer F, et al. 2019. Determining of cell type abundance and expression in bulk tissues using digital cytometry. Nat. Biotechnol. 37: 773-782.  https://doi.org/10.1038/s41587-019-0114-2
  23. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, et al. 2013. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41: 955-961.  https://doi.org/10.1093/nar/gks1111
  24. Geeleher P, Cox N, Huang RS. 2014. pRRophetic: an rpackage for the prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 9: e107468. 
  25. Rao X, Huang X, Zhou Z, Lin X. 2013. An improvement of the 2ˆ (-delta delta CT) method for quantitative real-time polymerase chain reaction data analysis. Biostat. Bioinforma. Biomath. 3: 71-85. 
  26. Ajani JA, D'Amico TA, Almhanna K, Bentrem DJ, Besh S, Chao J, et al. 2015. Esophageal and esophagogastric junction cancers, version 1.2015. J. Natl. Compr. Canc. Netw. 13: 194-227.  https://doi.org/10.6004/jnccn.2015.0028
  27. Pape M, Vissers PAJ, de Vos-Geelen J, Hulshof M, Gisbertz SS, Jeene PM, et al. 2022. Treatment patterns and survival in advanced unresectable esophageal squamous cell cancer: a population-based study. Cancer Sci. 113: 1038-1046.  https://doi.org/10.1111/cas.15262
  28. Wilkinson MS, Dunham-Snary KJ. 2023. Blood-based bioenergetics: a liquid biopsy of mitochondrial dysfunction in disease. Trends Endocrinol. Metab. 34: 554-570.  https://doi.org/10.1016/j.tem.2023.06.004
  29. Vercellino I, Sazanov LA. 2022. Small cell lung cancer: novel treatments beyond immunotherapy. Nat. Rev. Mol. Cell. Biol. 23: 141-161.  https://doi.org/10.1038/s41580-021-00415-0
  30. Wu Q, Xu R, Zhang K, Sun R, Yang M, Li K, et al. 2023. Characterization of early myocardial inflammation in ischemia-reperfusion injury. Front. Immunol. 13:1081719. 
  31. Zampieri LX, Silva-Almeida C, Rondeau JD, Sonveaux P. 2021. Mitochondrial transfer in cancer: a comprehensive review. Int. J. Mol. Sci. 22: 3245. 
  32. Canyelles-Nino M, Gonzalez-Lafont A, Lluch JM. 2023. Hydroperoxidation of docosahexaenoic acid by human ALOX12 and pigALOX15-mini-LOX. Int. J. Mol. Sci. 24: 6064. 
  33. Nie J, Shan D, Li S, Zhang S, Zi X, Xing F, et al. 2021. A novel ferroptosis related gene signature for prognosis prediction in patients with colon cancer. Front. Oncol. 11: 654076. 
  34. Weng S, Liu Z, Xu H, Ge X, Ren Y, Dang Q, et al. 2022. ALOX12: a novel insight into bevacizumab response, immunotherapy effect, and prognosis of colorectal cancer. Front. Immunol. 13: 910582. 
  35. Chen J, Tong W, Liao M, Chen D. 2020. Inhibition of arachidonate lipoxygenase12 targets lung cancer by inhibiting EMT and suppressing RhoA and NF-kappaB activity. Biochem. Biophys. Res. Commun. 524: 803-809.  https://doi.org/10.1016/j.bbrc.2020.01.166
  36. Song J, Liu Y, Guan X, Zhang X, Yu W, Li Q. 2021. A novel ferroptosis-related biomarker signature to predict overall survival of esophageal squamous cell carcinoma. Front. Mol. Biosci. 8: 675193. 
  37. Guijarro MV, Nawab A, Dib P, Burkett S, Luo X, Feely M, et al. 2023. TYMS promotes genomic instability and tumor progression in Ink4a/Arf null background. Oncogene 42: 1926-1939. 
  38. Fu Z, Jiao Y, Li Y, Ji B, Jia B, Liu B. 2019. TYMS presents a novel biomarker for diagnosis and prognosis in patients with pancreatic cancer. Medicine (Baltimore) 98: e18487. 
  39. Song S, Tian B, Zhang M, Gao X, Jie L, Liu P, et al. 2021. Diagnostic and prognostic value of thymidylate synthase expression in breast cancer. Clin. Exp. Pharmacol. Physiol. 48: 279-287.  https://doi.org/10.1111/1440-1681.13415
  40. Wang L, Shi C, Yu J, Xu Y. 2022. Correction: FOXM1-induced TYMS upregulation promotes the progression of hepatocellular carcinoma. Cancer Cell Int. 22: 384. 
  41. Chen W, Li Y, Liu C, Kang Y, Qin D, Chen S, et al. 2023. In situ engineering of tumor-associated macrophages via a nanodrug-delivering-drug (beta-Elemene@Stanene) strategy for enhanced cancer chemo-immunotherapy. Angew. Chem. Int. Ed. Engl. 62: e202308413. 
  42. Serafini P, Mgebroff S, Noonan K, Borrello I. 2008. Myeloid-derived suppressor cells promote cross-tolerance in B-cell lymphoma by expanding regulatory T cells. Cancer Res. 68: 5439-49.  https://doi.org/10.1158/0008-5472.CAN-07-6621
  43. Izzi V, Davis MN, Naba A. P2020. an-Cancer analysis of the genomic alterations and mutations of the matrisome. Cancers (Basel) 12: 2046. 
  44. Guan Q, Han M, Guo Q, Yan F, Wang M, Xi D. 2023. Strategies to reinvigorate exhausted CD8(+) T cells in tumor microenvironment. Front. Immunol. 14: 1204363. 
  45. Ko KP, Huang Y, Zhang S, Zou G, Kim B, Zhang J, et al. 2023. Key Genetic determinants of Cell Carcinoma initiation and immune evasion. Gastroenterology 165: 613-628.  https://doi.org/10.1053/j.gastro.2023.05.030
  46. Bolf EL, Beadnell TC, Rose MM, D'Alessandro A, Nemkov T, Hansen KC, et al. 2023. Dasatinib and trametinib promote antitumor metabolic activity. Cells 12: 1374. 
  47. Xu B, Zhang Q, Hu X, Li Q, Sun T, Li W, et al. 2023. Entinostat, a class I selective histone deacetylase inhibitor, plus exemestane for Chinese patients with hormone receptor-positive advanced breast cancer: a multicenter, randomized, double-blind, placebo-controlled, phase 3 trial. Acta Pharm Sin B. 13: 2250-2258. 
  48. Sausville E, Lorusso P, Carducci M, Carter J, Quinn MF, Malburg L, et al. 2014. Phase I dose-escalation study of AZD7762, a checkpoint kinase inhibitor, in combination with gemcitabine in US patients with advanced solid tumors. Cancer Chemother. Pharmacol. 73: 539-549. https://doi.org/10.1007/s00280-014-2380-5