Unraveling pathogenesis, biomarkers and potential therapeutic agents for endometriosis associated with disulfidptosis based on bioinformatics analysis, machine learning and experiment validation

Zondervan KT, Becker CM, Koga K, Missmer SA, Taylor RN. Viganò P: Endometriosis. Nat Rev Dis Primers. 2018;4(1):9.

Article  Google Scholar 

Taylor HS, Kotlyar AM, Flores VA. Endometriosis is a chronic systemic disease: clinical challenges and novel innovations. Lancet. 2021;397(10276):839–52.

Article  Google Scholar 

Peiris AN, Chaljub E, Medlock D. Endometriosis. JAMA. 2018;320(24):2608.

Article  Google Scholar 

Asghari S, Valizadeh A, Aghebati-Maleki L, Nouri M, Yousefi M. Endometriosis: Perspective, lights, and shadows of etiology. Biomed Pharmacother. 2018;106:163–74.

Article  Google Scholar 

Zheng T, Liu Q, Xing F, Zeng C, Wang W. Disulfidptosis: a new form of programmed cell death. J Exp Clin Cancer Res. 2023;42(1):137.

Article  Google Scholar 

Liu X, Nie L, Zhang Y, Yan Y, Wang C, Colic M, et al. Actin cytoskeleton vulnerability to disulfide stress mediates disulfidptosis. Nat Cell Biol. 2023;25(3):404–14.

Article  Google Scholar 

Xue W, Qiu K, Dong B, Guo D, Fu J, Zhu C, et al. Disulfidptosis-associated long non-coding RNA signature predicts the prognosis, tumor microenvironment, and immunotherapy and chemotherapy options in colon adenocarcinoma. Cancer Cell Int. 2023;23(1):218.

Article  Google Scholar 

Shao D, Shi L, Ji H. Disulfidptosis: Disulfide Stress Mediates a Novel Cell Death Pathway via Actin Cytoskeletal Vulnerability. Mol Cells. 2023;46(7):414–6.

Article  Google Scholar 

Xu K, Zhang Y, Yan Z, Wang Y, Li Y, Qiu Q, et al. Identification of disulfidptosis related subtypes, characterization of tumor microenvironment infiltration, and development of DRG prognostic prediction model in RCC, in which MSH3 is a key gene during disulfidptosis. Front Immunol. 2023;14:1205250.

Article  Google Scholar 

Wang Z, Chen X, Zhang J, Chen X, Peng J, Huang W. Based on disulfidptosis-related glycolytic genes to construct a signature for predicting prognosis and immune infiltration analysis of hepatocellular carcinoma. Front Immunol. 2023;14:1204338.

Article  Google Scholar 

Yu X, Guo Z, Fang Z, Yang K, Liu C, Dong Z, et al. Identification and validation of disulfidptosis-associated molecular clusters in non-alcoholic fatty liver disease. Front Genet. 2023;14:1251999.

Article  Google Scholar 

Ma S, Wang D, Xie D. Identification of disulfidptosis-related genes and subgroups in Alzheimer's disease. Front Aging Neurosci. 2023;15:1236490.

Article  Google Scholar 

Guo SW. Endometriosis and ovarian cancer: potential benefits and harms of screening and risk-reducing surgery. Fertil Steril. 2015;104(4):813–30.

Article  Google Scholar 

Maignien C, Santulli P, Chouzenoux S, Gonzalez-Foruria I, Marcellin L, Doridot L, et al. Reduced α-2,6 sialylation regulates cell migration in endometriosis. Hum Reprod. 2019;34(3):479–90.

Article  Google Scholar 

Li Y, Liu H, Ye S, Zhang B, Li X, Yuan J, et al. The effects of coagulation factors on the risk of endometriosis: a Mendelian randomization study. BMC Med. 2023;21(1):195.

Article  Google Scholar 

Chen C, Ye C, Xia J, Zhou Y, Wu R. Ezrin T567 phosphorylation regulates migration and invasion of ectopic endometrial stromal cells by changing actin cytoskeleton. Life Sci. 2020;254:117681.

Article  Google Scholar 

Toniyan KA, Povorova VV, Gorbacheva EY, Boyarintsev VV, Ogneva IV. Organization of the Cytoskeleton in Ectopic Foci of the Endometrium with Rare Localization. Biomedicines. 2021;9(8):998.

Ping S, Ma C, Liu P, Yang L, Yang X, Wu Q, et al. Molecular mechanisms underlying endometriosis pathogenesis revealed by bioinformatics analysis of microarray data. Arch Gynecol Obstet. 2016;293(4):797–804.

Article  Google Scholar 

Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10.

Article  Google Scholar 

Goji T, Takahara K, Negishi M, Katoh H. Cystine uptake through the cystine/glutamate antiporter xCT triggers glioblastoma cell death under glucose deprivation. J Biol Chem. 2017;292(48):19721–32.

Article  Google Scholar 

Koppula P, Zhang Y, Shi J, Li W, Gan B. The glutamate/cystine antiporter SLC7A11/xCT enhances cancer cell dependency on glucose by exporting glutamate. J Biol Chem. 2017;292(34):14240–9.

Article  Google Scholar 

Liu X, Olszewski K, Zhang Y, Lim EW, Shi J, Zhang X, et al. Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer. Nat Cell Biol. 2020;22(4):476–86.

Article  Google Scholar 

Shin CS, Mishra P, Watrous JD, Carelli V, D'Aurelio M, Jain M, et al. The glutamate/cystine xCT antiporter antagonizes glutamine metabolism and reduces nutrient flexibility. Nat Commun. 2017;8:15074.

Article  Google Scholar 

Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019;10(1):1523.

Article  Google Scholar 

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. 2019;47(D1):D607–13.

Article  Google Scholar 

Chin CH, Chen SH, Wu HH, Ho CW, Ko MT, Lin CY. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol. 2014;8(Suppl 4):S11.

Article  Google Scholar 

Wu C, Jin X, Tsueng G, Afrasiabi C, Su AI. BioGPS: building your own mash-up of gene annotations and expression profiles. Nucleic Acids Res. 2016;44(D1):D313–316.

Article  Google Scholar 

Sjöstedt E, Zhong W, Fagerberg L, Karlsson M, Mitsios N, Adori C, et al. An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. 2020;367(6482):eaay5947.

Stelzer G, Rosen N, Plaschkes I, Zimmerman S, Twik M, Fishilevich S, et al. The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Curr Protoc Bioinformatics. 2016;54:1.30.31-31.30.33.

Article  Google Scholar 

Alliance of Genome Resources Consortium. Harmonizing model organism data in the Alliance of Genome Resources. Genetics. 2022;220(4):iyac022.

UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480–9.

Sam SA, Teel J, Tegge AN, Bharadwaj A, Murali TM. XTalkDB: a database of signaling pathway crosstalk. Nucleic Acids Res. 2017;45(D1):D432–9.

Article  Google Scholar 

Errington N, Iremonger J, Pickworth JA, Kariotis S, Rhodes CJ, Rothman AM, et al. A diagnostic miRNA signature for pulmonary arterial hypertension using a consensus machine learning approach. EBioMedicine. 2021;69:103444.

Article  Google Scholar 

Zheng Y, Wang J, Ling Z, Zhang J, Zeng Y, Wang K, et al. A diagnostic model for sepsis-induced acute lung injury using a consensus machine learning approach and its therapeutic implications. J Transl Med. 2023;21(1):620.

Article  Google Scholar 

Wu Z, Wang X, Liang H, Liu F, Li Y, Zhang H, et al. Identification of Signature Genes of Dilated Cardiomyopathy Using Integrated Bioinformatics Analysis. Int J Mol Sci. 2023;24(8):7339.

Motamedi F, Pérez-Sánchez H, Mehridehnavi A, Fassihi A, Ghasemi F. Accelerating Big Data Analysis through LASSO-Random Forest Algorithm in QSAR Studies. Bioinformatics. 2022;38(2):469–75.

Article  Google Scholar 

Huang ML, Hung YH, Lee WM, Li RK, Jiang BR. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal. 2014;2014:795624.

Article  Google Scholar 

Yi F, Yang H, Chen D, Qin Y, Han H, Cui J, et al. XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease. BMC Med Inf Decis Mak. 2023;23(1):137.

Article  Google Scholar 

Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Wiegers TC, et al. Comparative Toxicogenomics Database (CTD): update 2021. Nucleic Acids Res. 2021;49(D1):D1138–43.

Article  Google Scholar 

Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1):D1074–82.

Article  Google Scholar 

Cotto KC, Wagner AH, Feng YY, Kiwala S, Coffman AC, Spies G, et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 2018;46(D1):D1068–73.

Article  Google Scholar 

Arifin WN, Zahiruddin WM. Sample Size Calculation in Animal Studies Using Resource Equation Approach. Malays J Med Sci. 2017;24(5):101–5.

Google Scholar 

Festing MF. Design and statistical methods in studies using animal models of development. Ilar j. 2006;47(1):5–14.

Article  Google Scholar 

Festing MF, Altman DG. Guidelines for the design and statistical analysis of experiments using laboratory animals. Ilar j. 2002;43(4):244–58.

Article  Google Scholar 

Kaptchuk TJ. The double-blind, randomized, placebo-controlled trial: gold standard or golden calf? J Clin Epidemiol. 2001;54(6):541–9.

Article  Google Scholar 

Ozer H, Boztosun A, Açmaz G, Atilgan R, Akkar OB, Kosar MI. The efficacy of bevacizumab, sorafenib, and retinoic acid on rat endometriosis model. Reprod Sci. 2013;20(1):26–32.

Article  Google Scholar 

Maharati A, Moghbeli M. PI3K/AKT signaling pathway as a critical regulator of epithelial-mesenchymal transition in colorectal tumor cells. Cell Commun Signal. 2023;21(1):201.

Article  Google Scholar 

McKinnon BD, Kocbek V, Nirgianakis K, Bersinger NA, Mueller MD. Kinase signalling pathways in endometriosis: potential targets for non-hormonal therapeutics. Hum Reprod Update. 2016;22(3):382–403.

Article 

留言 (0)

沒有登入
gif