- Timetable
- Download all abstracts
- Plenary Lectures
- MSSJ Special Program
- Award Lecture
- Symposium Sessions（Day1, Day2）
- Fundamental Sessions（Day1, Day3）
- Young Researchers' Sessions (Int'l)（Day1, Day3）
- Young Researchers' Sessions（Day1, Day3）
- Poster Presentations（Day1, Day2, Day3）
- Evening Workshop
- Corporate Program
Young Researchers' Sessions (Int'l)
Day 3, June 12（Wed.） 10:00-10:15 Room A (Convention Hall 300)
- 3A-O1-1000（3P-27）
Tissue Metabolomic Analysis of Renal Cell Carcinoma using Differential 12C2-/13C2-Isotope Dansylation Labeling combine with LC-QTOF-MS and LC-MRM-MS
(1Department of Biomedical Science, Chang Chung University, 2Biological Mass Spectrometry & Translational Proteomic/Metabolomic Laboratory, Chang Chung University, 3Molecular and Medicine Center, Chang Chung University, 4Chang Gung Memorial Hospital)
oHsiang-Cheng Tu1, Yi-Ting Chen2, Ya-Ju Hsieh3, Chien‐Lun Chen4, Ying‐Hsu Chang4
Renal cell carcinoma (RCC) is associated with poor prognosis at the metastatic stage. However, RCC usually does not cause obvious symptoms, especially in the early stages. To better understand the metabolic changes associated with RCC, this study investigated the tissue metabolomic profiles of 34 RCC patients, using <SUP>12<SUP>C<SUB>2<SUB>-/<SUP>13<SUP>C<SUB>2<SUB> dansylation labeling combine with LC-QTOF-MS and LC- multiple reaction monitoring (MRM)-MS.
In our findings, we identified 225 metabolites showing differential expressions and subsequently selected 9 targets for further absolute quantification using MRM-MS. We have validated the identities of these candidates using metabolite standards. Our aim is to perform absolute quantification of these metabolites to obtain their concentrations in tissues of RCC patients for biomarker development.
Furthermore, some metabolite signals in MS spectra show significant changes although without confirmed identification due to the absence of standard metabolites. It makes absolute quantification unfeasible at present. Therefore, we also aim to develop quantitative methods for verifying the RCC diagnosis performance of these unidentified metabolites using MRM-profiling. By integrating data from both known and unknown metabolites, this study offers deeper insights into the metabolic changes observed in RCC tissue metabolome. Additionally, it aids in enhancing the prediction and diagnosis of RCC in the future.