본문 바로가기
KERD
논문KCI 등재

Impact of Mitophagy-Related Genes on the Diagnosis and Development of Esophageal Squamous Cell Carcinoma via Single-Cell RNA-seq Analysis and Machine Learning Algorithms

Mo Xuzhi|Ji Feng|Chen Jianguang|Yi Chengcheng|Wang Fang
한국미생물·생명공학회 (The Korean Society For Microbiology And Biotechnology)|2024.11|Journal of Microbiology and Biotechnology|제34권 제11호|pp. 2362-2375|DOI 10.4014/jmb.2407.07052

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

machine learningprediction modelESCCmitophagy-related genessingle-cell RNA-seq
주제분류생물학

초록

As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively removes damaged mitochondria and is crucial for cellular homeostasis. While tumor development and resistance to anticancer therapies are related to ESCC, their role in ESCC remains unclear. Here, we investigated the relationship between mitophagy-related genes (MRGs) and ESCC to provide novel insights into the role of mitophagy in ESCC prognosis and diagnosis prediction. First, we identified MRGs from the GeneCards database and examined them at both the single-cell and transcriptome levels. Key genes were selected and a prognostic model was constructed using least absolute shrinkage and selection operator analysis. External validation was performed using the GSE53624 dataset and Kaplan–Meier survival analysis was performed to identify PYCARD as a gene significantly associated with survival in ESCC. We then examined the effect of PYCARD on ESCC cell proliferation and migration and identified 169 MRGs at the single-cell and transcriptome levels, as well as the high-risk groups associated with cancer-related pathways. Thirteen key genes were selected for model construction via multiple machine learning algorithms. PYCARD, which is upregulated in patients with ESCC, was negatively correlated with prognosis and its knockdown inhibited ESCC cell proliferation and migration. Our ESCC prediction model based on mitophagy-related genes demonstrated promising results and provides more options for the management and clinical treatment of ESCC patients. Moreover, targeting or regulating PYCARD levels might offer new therapeutic strategies for ESCC patients in clinical settings.

Abstract

As a treatment for esophageal squamous cell carcinoma (ESCC), which is common and fatal, mitophagy is a conserved cellular mechanism that selectively removes damaged mitochondria and is crucial for cellular homeostasis. While tumor development and resistance to anticancer therapies are related to ESCC, their role in ESCC remains unclear. Here, we investigated the relationship between mitophagy-related genes (MRGs) and ESCC to provide novel insights into the role of mitophagy in ESCC prognosis and diagnosis prediction. First, we identified MRGs from the GeneCards database and examined them at both the single-cell and transcriptome levels. Key genes were selected and a prognostic model was constructed using least absolute shrinkage and selection operator analysis. External validation was performed using the GSE53624 dataset and Kaplan–Meier survival analysis was performed to identify PYCARD as a gene significantly associated with survival in ESCC. We then examined the effect of PYCARD on ESCC cell proliferation and migration and identified 169 MRGs at the single-cell and transcriptome levels, as well as the high-risk groups associated with cancer-related pathways. Thirteen key genes were selected for model construction via multiple machine learning algorithms. PYCARD, which is upregulated in patients with ESCC, was negatively correlated with prognosis and its knockdown inhibited ESCC cell proliferation and migration. Our ESCC prediction model based on mitophagy-related genes demonstrated promising results and provides more options for the management and clinical treatment of ESCC patients. Moreover, targeting or regulating PYCARD levels might offer new therapeutic strategies for ESCC patients in clinical settings.

참고문헌 (58)

  1. Morgan E, "The global landscape of esophageal squamous cell carcinoma and esophageal adenocarcinoma incidence and mortality in 2020 and projections to 2040 : new estimates from GLOBOCAN 2020", Gastroenterology, 163, pp.649~658 (2022)
  2. Bray F, "Global cancer statistics 2022 : GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries", CA Cancer J. Clin, 74, pp.229~263 (2024)
  3. Wang N, "Selenium may be involved in esophageal squamous cancer prevention by affecting GPx3 and FABP1 expression : a case-control study based on bioinformatic analysis", Nutrients, 16, pp.1322~ (2024)
  4. Uhlenhopp DJ, "Epidemiology of esophageal cancer : update in global trends, etiology and risk factors", Clin. J. Gastroenterol, 13, pp.1010~1021 (2020)
  5. Miller KD, "Cancer treatment and survivorship statistics, 2022", CA Cancer J. Clin, 72, pp.409~436 (2022)
  6. Lagergren J, "Oesophageal cancer", Lancet, 390, pp.2383~2396 (2017)
  7. Nunnari J, "Mitochondria : in sickness and in health", Cell, 148, pp.1145~1159 (2012)
  8. Denisenko TV, "Mitophagy in carcinogenesis and cancer treatment", Discov. Oncol, 12, pp.58~ (2021)
  9. Trotta AP, "Mitochondrial dynamics as regulators of cancer biology", Cell Mol. Life Sci, 74, pp.1999~2017 (2017)
  10. Wang X, "Construction of a novel mitochondria-associated gene model for assessing ESCC immune microenvironment and predicting survival", J. Microbiol. Biotechnol, 34, pp.1164~1177 (2024)
  11. Ao X, "Non-coding RNAs regulating mitochondrial function in cardiovascular diseases", J. Mol. Med. (Berl), 101, pp.501~526 (2023)
  12. Liu Y, "Non-coding RNA-mediated modulation of ferroptosis in cardiovascular diseases", Biomed. Pharmacother, 164, pp.114993~ (2023)
  13. Xu Y, "Emerging views of mitophagy in immunity and autoimmune diseases", Autophagy, 16, pp.3~17 (2020)
  14. Chen F, "Inhibiting Pink1", Parkin-mediated mitophagy enhances the anticancer effects of quercetin in hepatocellular carcinomaf, 2024, pp.712-713, 149899~
  15. Wang Y, "13-oxyingenol dodecanoate derivatives induce mitophagy and ferroptosis through targeting TMBIM6 as potential anti-NSCLC agents", Eur. J. Med. Chem, 270, pp.116312~ (2024)
  16. Colaprico A, "TCGAbiolinks : an R", Bioconductor package for integrative analysis of TCGA data, 2016, pp.44, e71~
  17. Ritchie ME, "limma powers differential expression analyses for RNA-sequencing and microarray studies", Nucleic Acids Res, 43, pp.e47~ (2015)
  18. Davis S, "GEOquery : a bridge between the Gene Expression Omnibus(GEO)and BioConductor", Bioinformatics, 23, pp.1846~1847 (2007)
  19. Shi K, "Profiling transcriptional heterogeneity of epithelium, fibroblasts, and immune cells in esophageal squamous cell carcinoma by single-cell RNA sequencing", FASEB J, 36, pp.e22620~ (2022)
  20. Hao Y, "Integrated analysis of multimodal single-cell data", Cell, 184, pp.3573~3587 (2021)
  21. Korsunsky I, "Fast, sensitive and accurate integration of single-cell data with harmony", Nat. Methods, 16, pp.1289~1296 (2019)
  22. Langfelder P, "WGCNA : an R package for weighted correlation network analysis", BMC Bioinformatics, 9, pp.559~ (2008)
  23. Jin S, "Inference and analysis of cell-cell communication using CellChat", Nat. Commun, 12, pp.1088~ (2021)
  24. Gene Ontology C, "Gene ontology consortium : going forward", Nucleic Acids Res, 43, pp.D1049~D1056 (2015)
  25. Kanehisa M, "KEGG : Kyoto encyclopedia of genes and genomes", Nucleic Acids Res, 28, pp.27~30 (2000)
  26. Yu G, "clusterProfiler : an R package for comparing biological themes among gene clusters", OMICS, 16, pp.284~287 (2012)
  27. Kim S, "Margin-maximised redundancy-minimised SVM-RFE for diagnostic classification of mammograms", Int. J. Data Min. Bioinform, 10, pp.374~390 (2014)
  28. Ambale-Venkatesh B, "Cardiovascular event prediction by machine learning : the multi-ethnic study of atherosclerosis", Circ. Res, 121, pp.1092~1101 (2017)
  29. Van Calster B, "Regression shrinkage methods for clinical prediction models do not guarantee improved performance: simulation study", Stat. Methods Med. Res, 29, pp.3166~3178 (2020)
  30. Yoshihara K, "Inferring tumour purity and stromal and immune cell admixture from expression data", Nat. Commun, 4, pp.2612~ (2013)
  31. Chen B, "Profiling tumor infiltrating immune cells with CIBERSORT", Methods Mol. Biol, 1711, pp.243~259 (2018)
  32. Chen Y, "A novel immune-related gene signature to identify the tumor microenvironment and prognose disease among patients with oral squamous cell carcinoma patients using ssGSEA : a bioinformatics and biological validation study", Front. Immunol, 13, pp.922195~ (2022)
  33. Mayakonda A, "Maftools : efficient and comprehensive analysis of somatic variants in cancer", Genome Res, 28, pp.1747~1756 (2018)
  34. Geeleher P, "pRRophetic : an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels", PLoS One, 9, pp.e107468~ (2014)
  35. Yan Q, "Roles of long non-coding RNAs in esophageal cell squamous carcinoma(Review)", Int. J. Mol. Med, 54, pp.72~ (2024)
  36. Zhang Y, "Decoding mitochondria's role in immunity and cancer therapy", Biochim. Biophys. Acta Rev. Cancer, 1879, pp.189107~ (2024)
  37. Zorova LD, "Targeting mitochondria for cancer treatment", Pharmaceutics, 16 (2024)
  38. Lei T, "Mitochondria transcription and cancer", Cell Death Discov, 10, pp.168~ (2024)
  39. Yuan L, "Rational design of mitochondria-targeted fluorescent biosensors for in vivo elucidation of the interaction between breast cancer metastasis and mitochondrial autophagy", Biosens Bioelectron, 251, pp.116123~ (2024)
  40. Cheung KJ, "Collective invasion in breast cancer requires a conserved basal epithelial program", Cell, 155, pp.1639~1651 (2013)
  41. Marsh T, "Autophagic degradation of NBR1 restricts metastatic outgrowth during mammary tumor progression", Dev. Cell, 52, pp.591~604 (2020)
  42. Xie Y, "Mefloquine inhibits esophageal squamous cell carcinoma tumor growth by inducing mitochondrial autophagy", Front. Oncol, 10, pp.1217~ (2020)
  43. Yamashita K, "High expression of the mitophagy-related protein pink1 is associated with a poor response to chemotherapy and a poor prognosis for patients treated with neoadjuvant chemotherapy for esophageal squamous cell carcinoma", Ann. Surg. Oncol, 24, pp.4025~4032 (2017)
  44. Loibl S, "HER2-positive breast cancer", Lancet, 389, pp.2415~2429 (2017)
  45. Cuzick J, "Prevention and early detection of prostate cancer", Lancet Oncol, 15, pp.e484~e492 (2014)
  46. Mariathasan S, "Differential activation of the inflammasome by caspase-1 adaptors ASC and Ipaf", Nature, 430, pp.213~218 (2004)
  47. Geng XL, "Serum PYCARD may become a new diagnostic marker for rheumatoid arthritis patients", Eur. J. Med. Res, 29, pp.218~ (2024)
  48. Ippagunta SK, "The inflammasome adaptor ASC regulates the function of adaptive immune cells by controlling Dock2-mediated Rac activation and actin polymerization", Nat. Immunol, 12, pp.1010~1016 (2011)
  49. Ellebedy AH, "Inflammasome-independent role of the apoptosis-associated speck-like protein containing CARD(ASC)in the adjuvant effect of MF59", Proc. Natl. Acad. Sci. USA, 108, pp.2927~2932 (2011)
  50. Liang A, "High expression of PYCARD is an independent predictor of unfavorable prognosis and chemotherapy resistance in glioma", Ann. Transl. Med, 9, pp.986~ (2021)
  51. Brunetto E, "The IL-1", IL-1 receptor axis and tumor cell released inflammasome adaptor ASC are key regulators of TSLP secretion by cancer associated fibroblasts in pancreatic cancer, 2019, pp.7, 45~
  52. Zahraeifard S, "Loss of tumor suppressors promotes inflammatory tumor microenvironment and enhances LAG3+T cell mediated immune suppression", Nat. Commun, 15, pp.5873~ (2024)
  53. Zhou X, "Inhibition of DUSP18 impairs cholesterol biosynthesis and promotes anti-tumor immunity in colorectal cancer", Nat. Commun, 15, pp.5851~ (2024)
  54. Zarlashat Y, "Advancements in immunotherapeutic treatments for hepatocellular carcinoma : potential of combination therapies", Int. J. Mol. Sci, 25, pp.6830~ (2024)
  55. Salcher S, "High-resolution single-cell atlas reveals diversity and plasticity of tissue-resident neutrophils in non-small cell lung cancer", Cancer Cell, 40, pp.1503~1520 (2022)
  56. Zhang Z, "Cathepsin L promotes oesophageal squamous cell carcinoma development and may be associated with tumour-associated macrophages", Heliyon, 10, pp.e29273~ (2024)
  57. He S, "RSK4 promotes the macrophage recruitment and M2 polarization in esophageal squamous cell carcinoma", Biochim. Biophys. Acta Mol. Basis Dis, 1870, pp.166996~ (2024)
  58. Gao LR, "Tumor-derived exosomal miR-143-3p induces macrophage M2polarization to cause radiation resistance in locally advanced esophageal squamous cell carcinoma", Int. J. Mol. Sci, 25, pp.6082~ (2024)

이 간행물의 다른 논문 (24건)

Understanding Metabolic Pathway Rewiring by Oncogenic Gamma Herpesvirus이승현, Choi Un Yungpp. 2143-2152Bacterial Pigments as a Promising Alternative to Synthetic Colorants: From Fundamentals to ApplicationsHe Zhicheng, Huang Xin, Jiang Guangyang, Gan Longzhan, He Tengxiapp. 2153-2165Impacts of Senolytic Phytochemicals on Gut Microbiota: A Comprehensive ReviewJung Chang Hwa, Kim Hee Soopp. 2166-2172Efficacy of Jasminum subtriplinerve Extract against 7,12-Dimethylbenz[ a ]anthracene-Induced Cancer in MiceTung Bui Thanh, Vinh Le Ba, Hang Nguyen Thi, Phong Nguyen Viet, Minh Phan Hong, Anh Pham Thi Van, Dung Ho My, Trang Tran Thi Thu, Nhung Pham Thi Hong, Nguyet Nguyen Thi Minh, Thanh Mai Phuongpp. 2173-2183Establishment of an Apical-Out Organoid Model for Directly Assessing the Function of PostbioticsLee Jong Hun, Sung Moon-Hee, Cho Yeonoh, Kang Hee-Taikpp. 2184-2191
원문 보기