OMICS insights into cancer histology; Metabolomics and proteomics approach

Tayanloo-Beik, Akram; Sarvari, Masoumeh; Payab, Moloud; Gilany, Kambiz; Alavi-Moghadam, Sepideh; Gholami, Mahdi; Goodarzi, Parisa; Larijani, Bagher; Arjmand, Babak

doi:10.1016/j.clinbiochem.2020.06.008

Cited by 37 publications

(25 citation statements)

References 72 publications

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“…Cancer is a major global health problem issue, which exacts a heavy toll on healthcare systems [46] . Metabolomics defines a post-genomic research field, embracing small molecule analysis applied to utilizing biomarkers for cancer detection, monitoring, and prognostication [47] .…”

Section: Cancermentioning

confidence: 99%

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Lonardo¹,

Byrne²,

Targher³

2021

MTOD

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In this review, we discuss selected topics which are relevant to implementing precision medicine in metabolic disorders. Personalization of diet and exercise may help in preventing obesity and type 2 diabetes (T2D). Weight loss should be personalized based on age, sex, ethnicity, and coexisting comorbidities. Advances in our understanding of the pathophysiology, genetics, and epigenetics of obesity promise to offer tailored management options. Careful risk assessment is necessary prior to intervention. Risk may be underestimated, e.g., in women, in different ethnic groups, and in people with T2D. More personalized approaches could be useful among persons who failed to respond to traditional risk factor management, such as pharmacological treatment for dyslipidemia and arterial hypertension. Nonalcoholic fatty liver disease/metabolic-associated fatty liver disease (NAFLD/MAFLD) is both a cause and an effect of altered glucose and lipid metabolism. Personalized medicine approaches could be key to identify more effective pharmacological strategies as well as to reverse this common and burdensome metabolic liver disease. Finally, metabolomics could be used to identify relevant biomarkers for cancer diagnosis, staging, and prognostication. Cancers of the colon and rectum, breast, prostate, thyroid, and ovaries illustrate the notion that cancer cell metabolic derangements may be utilized in clinical practice. A true personalization of pharmacotherapies should be pursued especially in obese patients with cancer.

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Section: Cancermentioning

confidence: 99%

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Lonardo¹,

Byrne²,

Targher³

2021

MTOD

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show abstract

“…With the rapid development of modern biological analysis technology, metabolomics has been successfully applied in many fields, such as cancer research ( Tayanloo-Beik et al, 2020 ). Metabolomics is one of the “omics” techniques, and is complementary to genomics, transcriptomics, and proteomics ( Xuan et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Serum Metabolic Biomarkers as Indicators in the Progression of Intravenous Leiomyomatosis: A High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Study

Feng

Zhang

et al. 2021

Front. Cell Dev. Biol.

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BackgroundIntravenous leiomyomatosis (IVL) is a rare estrogen-dependent neoplasm. However, identifiable and reliable biomarkers are still not available for clinical application, especially for the diagnosis and prognosis of the disease.MethodsIn the present study, 30 patients with IVL and 30 healthy controls were recruited. Serum samples were isolated from these participants for further high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) analysis to study metabolomics alterations and identify differentially expressed metabolites based on orthogonal partial least-squares discriminant analysis (OPLS-DA). Subsequently, lasso regression analysis and a generalized linear regression model were applied to screen out hub metabolites associated with the progression of IVL.ResultsFirst, 16 metabolites in the positive ion mode were determined from the 240 identifiable metabolites at the superclass level, with ten metabolites upregulated in the IVL group and the remaining six metabolites downregulated. Our data further proved that four metabolites [hypoxanthine, acetylcarnitine, glycerophosphocholine, and hydrocortisone (cortisol)] were closely related to the oncogenesis of IVL. Hypoxanthine and glycerophosphocholine might function as protective factors in the development of IVL (OR = 0.19 or 0.02, respectively). Nevertheless, acetylcarnitine and hydrocortisone (cortisol), especially the former, might serve as risk indicators for the disease to promote the development or recurrence of IVL (OR = 18.16 or 2.10, respectively). The predictive accuracy of these hub metabolites was further validated by the multi-class receiver operator characteristic curve analysis (ROC) with the Scikit-learn algorithms.ConclusionFour hub metabolites were finally determined via comprehensive bioinformatics analysis, and these substances could potentially serve as novel biomarkers in predicting the prognosis or progression of IVL.

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“…Metabolomics has been a classic theory in cancer research based on the well known fact that even in the presence of oxygen, cancer cells perform less energy-e cient glycolysis process termed as aerobic glycolysis or Warburg effect [40,41]. Although the detailed reasons for Warburg effect are still unclear, one of the theories is that increased glycolysis may provide cancer cells easier access to accumulation of essential metabolic precursors they need for rapid cell proliferation [42][43][44]. The mainly focus of DEGs on the metabolism related biological processes reveal the importance of the elaborate network of energy consuming in cancer development.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic Analysis Identifing FGF1 Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma 

Zhang

Wang

Zeng

et al. 2020

Preprint

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Background clear cell renal cell carcinoma (ccRCC) has been the commonest renal cell carcinoma (RCC). Although the disease classification, diagnosis and targeted therapy of RCC has been increasingly evolving attributing to the rapid development of current molecular pathology, the current situation is still challenge considering the comprehensive and progressively developing nature of malignant cancer. The study is to identify more potential responsible genes during the development of ccRCC using bioinformatic analysis, thus aiding more precise interpretation of the disease. Methods Firstly, different cDNA expression profiles from Gene Expression Omnibus (GEO) online database were used to screen the abnormal differently expressed genes (DEGs) between ccRCC and normal renal tissues. Then, based on the protein-protein interaction network (PPI) of all DEGs, the module analysis was performed to scale down the potential genes, and further survival analysis assisted our proceeding to the next step for selecting a credible key gene. Thirdly, immunohistochemistry (IHC) and quantitative real-time PCR (QPCR) were conducted to validate the expression change of the key gene in ccRCC comparing to normal tissues, meanwhile the prognostic value was verified using TCGA clinical data. Lastly, the potential biological function and signaling mechanism of gene regulation during ccRCC development was preliminary explored. Results Four cDNA expression profiles were picked from GEO database based on the number of containing sample cases, and a total of 192 DEGs, including 39 up-regulated and 153 down-regulated genes were shared in four profiles. Based on the DEGs PPI network, four function modules were identified highlighting a FGF1 gene involving PI3K-AKT signaling pathway which was shared in 3/4 modules. Further, both the IHC performed on 104 local ccRCC samples containing tissue microarray and QPCR conducted using 30 different samples confirmed that FGF1 was aberrant lost in ccRCC. And Kaplan-Meier overall survival analysis revealed that FGF1 gene loss was related to worse ccRCC patients survival. Lastly, the pathological clinical features of FGF1 gene and the probable biological functions and signaling pathways it involved were analyzed using TCGA clinical data. Conclusions Using bioinformatic analysis, we revealed that FGF1 expression was aberrant lost in ccRCC which correlated statistical significantly with patients survival, and the gene’s clinical features and potential biological functions were also explored. However, more detailed experiments and clinical trials are needed to support its potential drug-target role in clinical medical use.

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OMICS insights into cancer histology; Metabolomics and proteomics approach

Cited by 37 publications

References 72 publications

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Identification of Novel Serum Metabolic Biomarkers as Indicators in the Progression of Intravenous Leiomyomatosis: A High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Study

Bioinformatic Analysis Identifing FGF1 Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma

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OMICS insights into cancer histology; Metabolomics and proteomics approach

Cited by 37 publications

References 72 publications

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Precision medicine approaches in metabolic disorders and target organ damage: where are we now, and where are we going?

Identification of Novel Serum Metabolic Biomarkers as Indicators in the Progression of Intravenous Leiomyomatosis: A High Performance Liquid Chromatography-Tandem Mass Spectrometry-Based Study

Bioinformatic Analysis Identifing FGF1&nbsp;Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma&nbsp;

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Bioinformatic Analysis Identifing FGF1 Gene as a New Prognostic Indicator in Clear Cell Renal Cell Carcinoma