Metabolomics in oncology – A fascinating travel into the mechanisms of metabolic disturbances during carcinogenesis

Q4 Medicine Forum of Clinical Oncology Pub Date : 2021-05-01 DOI:10.2478/fco-2021-0017
E. Karamitrousis, M. Liontos, N. Tsoukalas
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引用次数: 0

Abstract

cancerous transformation, among them cellular metabolism. Intrinsic and extrinsic factors, such as environmental and genetic factors, play a key role in the metabolic pathway alterations of cancer cells and contribute to the oncogenic processes altering the cellular phenotype and molecular physiology (1, 2). Metabolomics is the large-scale study of small molecules, known as metabolites. Collectively, these small molecules and their interactions within a biological system are known as the metabolome. Metabolomics are currently used in biomarker identification for the diagnosis and prognosis of cancer and can play a crucial role in the evaluation of therapeutic interventions (3, 4). The most prominent alteration in cancer cell metabolism is the phenomenon called “Warburg effect” or aerobic glycolysis. Cancer cells uptake glucose that in the presence of oxygen is not used through the Krebs cycle, but is instead incompletely oxidized or fermented to lactate. Unlike other oncogenic pathways that can be completely abrogated by using small molecular inhibitors or antibodies, this is not applicable to fundamental metabolic pathways. The latter would have immediate detrimental effects to the whole organism. Targeting through the level of enzymatic activity of nodal enzymes in metabolic pathways could provide therapeutic opportunities. Metabolomics can help us to identify the exact processes which a cancer cell can use a biological molecule (such as glucose) in order to produce energy or all the other essential biological molecules (such as nucleotides, lipids, and amino acids) (6)(7). Metabolomics could have several applications in oncology. Metabolomics can be used for the development of sensitive biomarkers to help in early diagnosis of several tumors. For example, the metabolic profile in urine samples could be used for the early and precise diagnosis of renal carcinoma, which is a tumor that is often diagnosed at advanced stages (8). In ovarian cancer, metabolomics can be used in order to detect the metabolic profile of serum proteins, which is different from the metabolic profile in healthy postmenopausal women (9). Also, prostate cancer cells carry alterations in their metabolites, such as increased amounts of phosphatidylcholine and decreased amounts of branched amino acids (10). Metabolomics can also assist to develop new therapeutic interventions for several tumors. A decade ago it was shown that mutations in the isocitrate dehydrogenase 1 enzyme (IDH1) lead to the intracellular accumulation of the metabolite 2-hydroxyglutarate (2HG) (11). 2HG contributes to the malignant transformation of glial cells. Currently, IDH1/2 inhibitors – namely ivosidenib and enasidenib – have been approved for patients with relapsed or refractory acute myeloid leukemia. Cervical cancer is another area that the use of metabolomics could have therapeutic implications. Through this Forum of Clinical Oncology
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肿瘤学中的代谢组学——深入了解致癌过程中代谢紊乱的机制
癌变,其中包括细胞代谢。内在和外在因素,如环境和遗传因素,在癌症细胞的代谢途径改变中起着关键作用,并有助于改变细胞表型和分子生理学的致癌过程(1,2)。代谢组学是对小分子,即代谢产物的大规模研究。总的来说,这些小分子及其在生物系统中的相互作用被称为代谢组。代谢组学目前用于癌症诊断和预后的生物标志物鉴定,并可在治疗干预措施的评估中发挥关键作用(3,4)。癌症细胞代谢最显著的变化是所谓的“Warburg效应”或有氧糖酵解现象。癌症细胞摄入的葡萄糖在有氧的情况下不会通过克雷布斯循环使用,而是不完全氧化或发酵成乳酸盐。与其他可以通过使用小分子抑制剂或抗体完全消除的致癌途径不同,这不适用于基本代谢途径。后者会对整个生物体产生直接的有害影响。通过代谢途径中节点酶的酶活性水平进行靶向可以提供治疗机会。代谢组学可以帮助我们确定癌症细胞使用生物分子(如葡萄糖)产生能量或所有其他必要生物分子(例如核苷酸、脂质和氨基酸)的确切过程(6)(7)。代谢组学在肿瘤学中可能有几个应用。代谢组学可用于开发敏感的生物标志物,以帮助早期诊断几种肿瘤。例如,尿液样本中的代谢谱可用于肾癌的早期精确诊断,肾癌是一种经常在晚期诊断的肿瘤(8)。在卵巢癌症中,代谢组学可用于检测血清蛋白质的代谢谱,这与健康绝经后妇女的代谢谱不同(9)。此外,前列腺癌症细胞的代谢产物发生变化,如磷脂酰胆碱含量增加和支链氨基酸含量减少(10)。代谢组学还可以帮助开发针对几种肿瘤的新的治疗干预措施。十年前,研究表明异柠檬酸脱氢酶1酶(IDH1)的突变导致代谢产物2-羟基戊二酸(2HG)的细胞内积累(11)。2HG有助于神经胶质细胞的恶性转化。目前,IDH1/2抑制剂,即伊沃西替尼和埃纳西替尼,已被批准用于复发或难治性急性髓系白血病患者。癌症是另一个使用代谢组学可能具有治疗意义的领域。通过这个临床肿瘤学论坛
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来源期刊
Forum of Clinical Oncology
Forum of Clinical Oncology Medicine-Oncology
CiteScore
0.50
自引率
0.00%
发文量
3
审稿时长
6 weeks
期刊最新文献
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