國家衛生研究院 NHRI:Item 3990099045/15055
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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/15055


    Title: Supervised learning and multi-omics integration reveals clinical significance of Inner Membrane Mitochondrial Protein (IMMT) in prognostic prediction, tumor immune microenvironment and precision medicine for kidney renal clear cell carcinoma
    Authors: Chen, CC;Chu, PY;Lin, HY
    Contributors: National Institute of Cancer Research
    Abstract: Kidney renal clear cell carcinoma (KIRC) accounts for approximately 75% of all renal cancers. The prognosis for patients with metastatic KIRC is poor, with less than 10% surviving five years after diagnosis. Inner membrane mitochondrial protein (IMMT) plays a crucial role in shaping the inner mitochondrial membrane (IMM), regulation of metabolism and innate immunity. However, the clinical relevance of IMMT in KIRC is not yet fully understood, and its role in shaping the tumor immune microenvironment (TIME) remains unclear. This study aimed to investigate the clinical significance of IMMT in KIRC using a combination of supervised learning and multi-omics integration. The supervised learning principle was applied to analyze a TCGA dataset, which was downloaded and split into training and test datasets. The training dataset was used to train the prediction model, while the test and the entire TCGA dataset were used to evaluate its performance. Based on the risk score, the cutoff between the low and high IMMT group was set at median value. A Kaplan-Meier curve, receiver operating characteristic (ROC) curve, principal component analysis (PCA) and Spearman's correlation were conducted to evaluate the prediction ability of the model. Gene Set Enrichment Analysis (GSEA) was used to investigate the critical biological pathways. Immunogenicity, immunological landscape and single-cell analysis were performed to examine the TIME. Databases including Gene Expression Omnibus (GEO), Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were employed for inter-database verification. Pharmacogenetic prediction was analyzed via single-guide RNA (sgRNA)-based drug sensitivity screening using Q-omics v.1.30. Low expressions of IMMT in tumor predicted dismal prognosis in KIRC patients and correlated with KIRC progression. GSEA revealed that low expressions of IMMT were implicated in mitochondrial inhibition and angiogenetic activation. In addition, low IMMT expressions had associations with reduced immunogenicity and an immunosuppressive TIME. Inter-database verification corroborated the correlation between low IMMT expressions, KIRC tumors and the immunosuppressive TIME. Pharmacogenetic prediction identified lestaurtinib as a potent drug for KIRC in the context of low IMMT expressions. This study highlights the potential of IMMT as a novel biomarker, prognostic predictor and pharmacogenetic predictor to inform the development of more personalized and effective cancer treatments. Additionally, it provides important insights into the role of IMMT in the mechanism underlying mitochondrial activity and angiogenesis development in KIRC, which suggests IMMT as a promising target for the development of new therapies.
    Date: 2023-05-15
    Relation: International Journal of Molecular Sciences. 2023 May 15;24(10):Article number 8807.
    Link to: http://dx.doi.org/10.3390/ijms24108807
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1422-0067&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000997276500001
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85160376506
    Appears in Collections:[Others] Periodical Articles

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