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Bioinformatics and Machine Learning Methods Identified MGST1 and QPCT as Novel Biomarkers for Severe Acute Pancreatitis



  • Lankisch, P. G., Apte, M., & Banks, P. A. (2015). Acute pancreatitis. Lancet (London, England), 386(9988), 85–96.

    Article 
    PubMed 

    Google Scholar
     

  • Song, Y., Zhang, Z., Yu, Z., Xia, G., Wang, Y., Wang, L., et al. (2021). Wip1 aggravates the cerulein-induced cell autophagy and inflammatory injury by targeting STING/TBK1/IRF3 in acute pancreatitis. Inflammation, 44(3), 1175–1183.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, G. J., Gao, C. F., Wei, D., Wang, C., & Ding, S. Q. (2009). Acute pancreatitis: Etiology and common pathogenesis. World Journal of Gastroenterology, 15(12), 1427–1430.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Valdés Lacasa, T., Duarte Borges, M. A., García Marín, A., & Gómez, C. C. (2017). Acute pancreatitis caused by Mycoplasma pneumoniae: An unusual etiology. Clinical Journal of Gastroenterology., 10(3), 279–282.

    Article 
    PubMed 

    Google Scholar
     

  • Gliem, N., Ammer-Herrmenau, C., Ellenrieder, V., & Neesse, A. (2021). Management of severe acute pancreatitis: An update. Digestion, 102(4), 503–507.

    Article 
    PubMed 

    Google Scholar
     

  • Szatmary, P., Grammatikopoulos, T., Cai, W., Huang, W., Mukherjee, R., Halloran, C., et al. (2022). Acute pancreatitis: Diagnosis and treatment. Drugs, 82(12), 1251–1276.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kandasamy, C., Shah, I., Yakah, W., Ahmed, A., Tintara, S., Sorrento, C., et al. (2022). The impact of an inpatient pancreatitis service and educational intervention program on the outcome of acute pancreatitis. The American Journal of Medicine, 135(3), 350–9.e2.

    Article 
    PubMed 

    Google Scholar
     

  • Baldursdottir, M. B., Andresson, J. A., Jonsdottir, S., Benediktsson, H., Kalaitzakis, E., & Bjornsson, E. S. (2023). Ischemic pancreatitis is an important cause of acute pancreatitis in the intensive care unit. Journal of Clinical Gastroenterology, 57(1), 97–102.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Lodewijkx, P. J., Besselink, M. G., Witteman, B. J., Schepers, N. J., Gooszen, H. G., van Santvoort, H. C., et al. (2016). Nutrition in acute pancreatitis: A critical review. Expert Review of Gastroenterology & Hepatology, 10(5), 571–580.

    Article 
    CAS 

    Google Scholar
     

  • Zwicker, A., Denovan-Wright, E. M., & Uher, R. (2018). Gene-environment interplay in the etiology of psychosis. Psychological Medicine, 48(12), 1925–1936.

    Article 
    PubMed 

    Google Scholar
     

  • Song, Z. Y., Chao, F., Zhuo, Z., Ma, Z., Li, W., & Chen, G. (2019). Identification of hub genes in prostate cancer using robust rank aggregation and weighted gene co-expression network analysis. Aging, 11(13), 4736–4756.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Dai, Y., Sun, X., Wang, C., Li, F., Zhang, S., Zhang, H., et al. (2021). Gene co-expression network analysis reveals key pathways and hub genes in Chinese cabbage (Brassica rapa L.) during vernalization. BMC Genomics, 22(1), 236.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Langfelder, P., & Horvath, S. (2008). WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics, 9, 559.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, F., Petersen, M., Johnson, L., Hall, J., & O’Bryant, S. E. (2021). Recursive support vector machine biomarker selection for Alzheimer’s disease. Journal of Alzheimer’s Disease, 79(4), 1691–1700.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Sanz, H., Valim, C., Vegas, E., Oller, J. M., & Reverter, F. (2018). SVM-RFE: Selection and visualization of the most relevant features through non-linear kernels. BMC Bioinformatics, 19(1), 432.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kang, J., Choi, Y. J., Kim, I. K., Lee, H. S., Kim, H., Baik, S. H., et al. (2021). LASSO-based machine learning algorithm for prediction of lymph node metastasis in T1 colorectal cancer. Cancer Research and Treatment, 53(3), 773–783.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, B., & Horvath, S. (2005). A general framework for weighted gene co-expression network analysis. Statistical Applications in Genetics and Molecular Biology, 4(10), 1544–6115.


    Google Scholar
     

  • Zhu, Y., Yang, X., & Zu, Y. (2022). Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure. Frontiers in Cell and Developmental Biology, 10, 1089915.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhou, Y., Shi, W., Zhao, D., Xiao, S., Wang, K., & Wang, J. (2022). Identification of immune-associated genes in diagnosing aortic valve calcification with metabolic syndrome by integrated bioinformatics analysis and machine learning. Frontiers in Immunology, 13, 937886.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Nesvaderani, M., Dhillon, B. K., Chew, T., Tang, B., Baghela, A., Hancock, R. E., et al. (2022). Gene expression profiling: Identification of novel pathways and potential biomarkers in severe acute pancreatitis. Journal of the American College of Surgeons, 234(5), 803–815.

    Article 
    PubMed 

    Google Scholar
     

  • Carpenter, C. M., Frank, D. N., Williamson, K., Arbet, J., Wagner, B. D., Kechris, K., et al. (2021). tidyMicro: A pipeline for microbiome data analysis and visualization using the tidyverse in R. BMC Bioinformatics, 22(1), 021–03967.

    Article 

    Google Scholar
     

  • Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., et al. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids research., 43(7), e47.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Song, Y., Feng, T., Cao, W., Yu, H., & Zhang, Z. (2022). Identification of key genes in nasopharyngeal carcinoma based on bioinformatics analysis. Computational Intelligence and Neuroscience, 2022, 9022700.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Ito, K., & Murphy, D. (2013). Application of ggplot2 to pharmacometric graphics. CPT: Pharmacometrics & Systems Pharmacology, 2(10), e79.

    CAS 

    Google Scholar
     

  • Langfelder, P., Zhang, B., & Horvath, S. (2008). Defining clusters from a hierarchical cluster tree: The dynamic tree cut package for R. Bioinformatics, 24(5), 719–720.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2014). cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Systems Biology, 4(Suppl 4), 1752–2509.


    Google Scholar
     

  • Warde-Farley, D., Donaldson, S. L., Comes, O., Zuberi, K., Badrawi, R., Chao, P., et al. (2010). The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Research. https://doi.org/10.1093/nar/gkq537

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yu, G., Wang, L. G., Han, Y., & He, Q. Y. (2012). clusterProfiler: An R package for comparing biological themes among gene clusters. Omics: A Journal of Integrative Biology, 16(5), 284–287.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • McEligot, A. J., Poynor, V., Sharma, R., & Panangadan, A. (2020). Logistic LASSO regression for dietary intakes and breast cancer. Nutrients. https://doi.org/10.3390/nu12092652

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, L., Qu, Q., Hao, Z., Sha, K., Li, Z., & Li, S. (2022). Powerful identification of large quantitative trait loci using genome-wide R/glmnet-based regression. The Journal of heredity., 113(4), 472–478.

    Article 
    PubMed 

    Google Scholar
     

  • Xu, N., Guo, H., Li, X., Zhao, Q., & Li, J. (2021). A five-genes based diagnostic signature for sepsis-induced ARDS. Pathology Oncology Research, 27, 580801.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., et al. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences USA, 102(43), 15545–15550.

    Article 
    CAS 

    Google Scholar
     

  • Fehringer, G., Liu, G., Briollais, L., Brennan, P., Amos, C. I., Spitz, M. R., et al. (2012). Comparison of pathway analysis approaches using lung cancer GWAS data sets. PLoS ONE, 7(2), 21.

    Article 

    Google Scholar
     

  • Mootha, V. K., Lindgren, C. M., Eriksson, K. F., Subramanian, A., Sihag, S., Lehar, J., et al. (2003). PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics, 34(3), 267–273.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • G. Yu. (2023). Enrichplot: Visualization of Functional Enrichment Result. R package version 1220.

  • Heng, L., Jia, Z., Bai, J., Zhang, K., Zhu, Y., Ma, J., et al. (2017). Molecular characterization of metastatic osteosarcoma: Differentially expressed genes, transcription factors and microRNAs. Molecular Medicine Reports, 15(5), 2829–2836.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • McGeary, S. E., Lin, K. S., Shi, C. Y., Pham, T. M., Bisaria, N., Kelley, G. M., et al. (2019). The biochemical basis of microRNA targeting efficacy. Science, 366(6472), 5.

    Article 

    Google Scholar
     

  • Chen, Y., & Wang, X. (2020). miRDB: An online database for prediction of functional microRNA targets. Nucleic Acids Research, 48(D1), D127–D131.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Team RC. (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing.


    Google Scholar
     

  • Beger, H. G., & Rau, B. M. (2007). Severe acute pancreatitis: Clinical course and management. World journal of gastroenterology., 13(38), 5043–5051.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Date, K., Satoh, A., Iida, K., & Ogawa, H. (2015). Pancreatic α-amylase controls glucose assimilation by duodenal retrieval through N-glycan-specific binding, endocytosis, and degradation. Journal of Biological Chemistry, 290(28), 17439–17450.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, R. W., Shao, Z. X., Chen, Y. Y., Yin, Z., & Wang, W. J. (2005). Lipase and pancreatic amylase activities in diagnosis of acute pancreatitis in patients with hyperamylasemia. Hepatobiliary & pancreatic diseases international: HBPD INT, 4(4), 600–603.

    CAS 

    Google Scholar
     

  • Bettac, L., Denk, S., Seufferlein, T., & Huber-Lang, M. (2017). Complement in pancreatic disease-perpetrator or savior? Frontiers in Immunology, 8, 15.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Hartwig, W., Klafs, M., Kirschfink, M., Hackert, T., Schneider, L., Gebhard, M. M., et al. (2006). Interaction of complement and leukocytes in severe acute pancreatitis: Potential for therapeutic intervention. American Journal of Physiology Gastrointestinal and Liver Physiology, 291(5), G844–G850.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, X., Li, Z., Liu, W., Du, J., Liu, Y., Yu, N., et al. (2022). The complement and coagulation cascades pathway is associated with acute necrotizing pancreatitis by genomics and proteomics analysis. Journal of Inflammation Research, 15, 2349–2363.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Yang, X., Yao, L., Yuan, M., Zhang, X., Jakubowska, M. A., Ferdek, P. E., et al. (2022). Transcriptomics and network pharmacology reveal the protective effect of Chaiqin Chengqi decoction on obesity-related alcohol-induced acute pancreatitis via oxidative stress and PI3K/Akt signaling pathway. Frontiers in Pharmacology., 13, 896523.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zhang, D., Li, L., Li, J., Wei, Y., Tang, J., Man, X., et al. (2022). Colchicine improves severe acute pancreatitis-induced acute lung injury by suppressing inflammation, apoptosis and oxidative stress in rats. Biomedicine & Pharmacotherapy, 153, 113461.

    Article 
    CAS 

    Google Scholar
     

  • Wu, B. U., & Banks, P. A. (2013). Clinical management of patients with acute pancreatitis. Gastroenterology, 144(6), 1272–1281.

    Article 
    PubMed 

    Google Scholar
     

  • Morgenstern, R., Zhang, J., & Johansson, K. (2011). Microsomal glutathione transferase 1: Mechanism and functional roles. Drug Metabolism Reviews, 43(2), 300–306.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kuang, F., Liu, J., Xie, Y., Tang, D., & Kang, R. (2021). MGST1 is a redox-sensitive repressor of ferroptosis in pancreatic cancer cells. Cell Chemical Biology, 28(6), 765–75.e5.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Dodson, M., Anandhan, A., & Zhang, D. D. (2021). MGST1, a new soldier of NRF2 in the battle against ferroptotic death. Cell chemical biology., 28(6), 741–742.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kehlen, A., Haegele, M., Böhme, L., Cynis, H., Hoffmann, T., & Demuth, H. U. (2017). N-terminal pyroglutamate formation in CX3CL1 is essential for its full biologic activity. Bioscience Reports. https://doi.org/10.1042/BSR20170712

  • Zhao, T., Zhou, Y., Wang, Q., Yi, X., Ge, S., He, H., et al. (2021). QPCT regulation by CTCF leads to sunitinib resistance in renal cell carcinoma by promoting angiogenesis. International Journal of Oncology. https://doi.org/10.3892/ijo.2021.5228

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liang, T., Wu, X., Wang, L., Ni, Z., Fan, Y., Wu, P., et al. (2023). Clinical significance and diagnostic value of QPCT, SCEL and TNFRSF12A in papillary thyroid cancer. Pathology, research and practice., 245, 154431.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Peng, C., Li, Z., & Yu, X. (2021). The role of pancreatic infiltrating innate immune cells in acute pancreatitis. International journal of medical sciences., 18(2), 534–545.

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Karabuga, B., Gemcioglu, E., Konca Karabuga, E., Baser, S., & Ersoy, O. (2022). Comparison of the predictive values of CRP, CRP/albumin, RDW, neutrophil/lymphocyte, and platelet/lymphocyte levels in determining the severity of acute pancreatitis in patients with acute pancreatitis according to the BISAP score. Bratislavske Lekarske Listy, 123(2), 129–135.

    CAS 
    PubMed 

    Google Scholar
     

  • Kolaczkowska, E., & Kubes, P. (2013). Neutrophil recruitment and function in health and inflammation. Nature reviews Immunology., 13(3), 159–175.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Manohar, M., Jones, E. K., Rubin, S. J. S., Subrahmanyam, P. B., Swaminathan, G., Mikhail, D., et al. (2021). Novel circulating and tissue monocytes as well as macrophages in pancreatitis and recovery. Gastroenterology, 161(6), 2014–29.e14.

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Hu, F., Lou, N., Jiao, J., Guo, F., Xiang, H., & Shang, D. (2020). Macrophages in pancreatitis: Mechanisms and therapeutic potential. Biomedicine & Pharmacotherapy, 131, 110693.

    Article 
    CAS 

    Google Scholar
     

  • Glaubitz, J., Wilden, A., Frost, F., Ameling, S., Homuth, G., Mazloum, H., et al. (2023). Activated regulatory T-cells promote duodenal bacterial translocation into necrotic areas in severe acute pancreatitis. Gut, 72(7), 1355–1369.

    Article 
    CAS 
    PubMed 

    Google Scholar
     



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