Metabolic Profiling of Human Lung Cancer Tissue by H-1 High Resolution Magic Angle Spinning (HRMAS) NMR Spectroscopy

• Type: Article in International Scientific Journal
• Date: 2010

Authors

Rocha, CM (Author) Other; Barros, AS (Author) Other; Gil, AM (Author) Other; Goodfellow, BJ (Author) Other; Humpfer, E (Author) Other; Spraul, M (Author) Other; Carreira, IM (Author) Other; Melo, JB (Author) Other; Bernardo, J (Author) Other; Gomes, A (Author) Other; Sousa, V (Author) Other; Carvalho, L (Author) Other; Duarte, IF (Author) Other

Journal

Title: Journal of Proteome Research

Vol. 9

Pages: 319-332

ISSN: 1535-3893

Publisher: American Chemical Society

Indexing

ISI Web of Knowledge - 130 Citations

Scopus - 136 Citations

Other information

Authenticus ID: P-003-BR0

DOI: 10.1021/pr9006574

Abstract (EN): This work aims at characterizing the metabolic profile of human lung cancer, to gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic value in the future. Paired samples of tumor and noninvolved adjacent tissues from 12 lung tumors have been directly analyzed by H-1 HRMAS NMR (500/600 MHz) enabling, for the first time to our knowledge, the identification of over 50 compounds. The effect of temperature on tissue stability during acquisition time has also been investigated, demonstrating that analysis should be performed within less than two hours at low temperature (277 K), to minimize glycerophosphocholine (GPC) and phosphocholine (PC) conversion to choline and reduce variations in some amino acids. The application of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) to the standard 1D H-1 spectra resulted in good separation between tumor and control samples, showing that inherently different metabolic signatures characterize the two tissue types. On the basis of spectral integration measurements, lactate, PC, and GPC were found to be elevated in tumors, while glucose, myo-inositol, inosine/adenosine, and acetate were reduced. These results show the valuable potential of HRMAS NMR-metabonomics for investigating the metabolic phenotype of lung cancer.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14