Pathway analysis of kidney cancer using proteomics and metabolic profiling

Perroud, Bertrand; Lee, Jinoo; Valkova, Nelly; Dhirapong, Amy; Lin, P. Y.; Fiehn, Oliver; Kültz, Dietmar; Weiss, Robert H.

doi:10.1186/1476-4598-5-64

Cited by 203 publications

(117 citation statements)

References 31 publications

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“…Although these methods have been studied and published extensively, they are not readily amenable to the discovery of diagnostic tests because the results of such analyses are not generally secreted into biofluids [14]. Metabolomics, on the other hand, is ideally suited for such analysis because small molecular metabolites frequently appear, even if transiently, in both the serum and urine [20]. Therefore, metabolomics research ultimately may be more clinically translatable than other ''omic'' endeavors [21,22].…”

Section: Discussionmentioning

confidence: 97%

Ultra-High Performance Liquid Chromatography–Mass Spectrometry for the Metabolomic Analysis of Urine in Colorectal Cancer

Qin

Liu

et al. 2009

Dig Dis Sci

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We report here the results of a pilot study in which ultra-high performance liquid chromatography/time-of- flight-mass spectrometry (UPLC/TOF-MS) and multivariate statistical analysis (supervised partial least squares discriminant analysis, PLS-DA) were applied for urinary metabolite profiling and data interpretation. The results of the PLS-DA indicated that the metabolic pattern as a whole was significantly different between the groups of preoperative colorectal cancer (CRC) patients, postoperative CRC patients, and healthy volunteers, respectively. The preoperative group of patients showed significantly increased levels of low-molecular weight compounds (LMC) MW 283 and MW 234 in comparison to the group of healthy volunteers group. After the operation, the levels of these two LMC significantly decreased. These preliminary results suggest that the UPLC-MS-based method coupled with pattern recognition will likely lead to procedures with the potential to be clinically applicable for the diagnosis of CRC and, consequently, to an improvement in patient prognosis.

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Section: Discussionmentioning

confidence: 97%

Ultra-High Performance Liquid Chromatography–Mass Spectrometry for the Metabolomic Analysis of Urine in Colorectal Cancer

Qin

Liu

et al. 2009

Dig Dis Sci

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“…RCC is the sixth leading cause of cancer death and approximately one-third of patients present disease that is already metastatic and for which there is currently no adequate treatment (Perroud et al, 2006). Investigating biomarkers in complex diseases such as RCC will contribute to the understanding of the pathogenesis and diagnosis of the disease, potentially reducing its mortality and morbidity substantially.…”

Section: Discussionmentioning

confidence: 99%

Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma

Li¹,

Yang²,

Wang³

2016

Genet. Mol. Res.

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ABSTRACT. The purpose of this study was to screen the key genes and pathways of renal cell carcinoma (RCC) and lay the foundation for its diagnosis and therapy. Microarray data of normal subjects and RCC patients at different stages of disease were used to screen differentially expressed genes (DEGs). Based on the DEGs in the four disease stages, four co-expression networks were constructed using the Empirical Bayes method and hub genes were obtained by centrality analysis. The enriched pathways of the DEGs and the mutual hub genes in the cluster of each disease stage were investigated. The mutual hub genes of the four disease stages in RCC tissue were validated using reverse transcription-polymerase chain reaction (RT-PCR) and western blot analysis. A total of 432 DEGs were screened, including 233 upregulated and 199 downregulated genes, by statistical analysis. Centrality analysis of co-expression networks in different disease stages suggested that PLXDC1, IKZF1, RUNX2, and RNF125 were mutual hub genes. Pathway analysis showed that the DEGs were significantly enriched in seven terms. The hub modules in stage I disease were significantly enriched in the complement coagulation cascade pathway and the hub modules of the other three disease stages were enriched in natural killer cell-mediated cytotoxicity. The expression levels of PLXDC1, IKZF1, RUNX2, and RNF125 were significantly different between normal subjects and RCC patients by RT-PCR and western blot. Our study revealed four hub genes (PLXDC1, IKZF1, RUNX2, and RNF125) and two biological pathways that might be underlying biomarkers involved in RCC.

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“…Annexin A1 protein is reported to be downregulated in ductal carcinoma [37] and squamous cell carcinoma [38] and upregulated in bladder cancer (H. J. Issaq, manuscript in preparation). In human laryngeal tumors, annexin A1 was upregulated in the nuclei and cytoplasmic granule matrix from larynx mast cells and downregulated in larynx epithelial cells [39]. Another example is carcinoembryonic antigen (CEA), which is used mainly to monitor the treatment of cancer patients, especially those with colon cancer.…”

Section: Proteins As Biomarkersmentioning

confidence: 99%

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

2011

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Many diseases result in specific and characteristic changes in the chemical and biochemical profiles of biological fluids and tissues prior to development of clinical symptoms. These changes are often useful diagnostic and prognostic biomarkers. Identifying biomarkers that can be used for the early detection of cancer will result in more efficient treatments, reduction in suffering, and lower mortality rates. An ideal screening test should be non-invasive with high sensitivity and specificity. Proteomic and metabolomic analyses of biological samples can reveal changes in abundance levels of metabolites and proteins that when validated and confirmed through clinical trials can function as clinical tests for early detection, diagnosis, monitoring disease progression, and predicting therapeutic response. While the past decade has seen great advancements in proteomics and metabolomics research producing potential biomarkers for cancer, most of the identified biomarkers have failed to replace existing clinical tests. To become a clinically approved test, a potential biomarker should be confirmed and validated using hundreds of specimens and should be reproducible, specific, and sensitive. A search of the scientific and medical literature indicates that many studies report the discovery of potential biomarkers without proper validation and/or they do not meet the above criteria. In this manuscript, we will discuss the successes and the pitfalls of biomarker research and comment on study and experimental design, which in most cases is lacking, resulting in suboptimal biomarkers.

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Pathway analysis of kidney cancer using proteomics and metabolic profiling

Cited by 203 publications

References 31 publications

Ultra-High Performance Liquid Chromatography–Mass Spectrometry for the Metabolomic Analysis of Urine in Colorectal Cancer

Ultra-High Performance Liquid Chromatography–Mass Spectrometry for the Metabolomic Analysis of Urine in Colorectal Cancer

Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma

Cancer biomarker discovery: Opportunities and pitfalls in analytical methods

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