The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs).
BACKGROUND
Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion.


OBJECTIVE
To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis.


METHODS
Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS).


RESULTS
Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis.


CONCLUSIONS
The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis.
Department of Chemical Engineering and Russell Berrie Nanotechnology Institute, Technion - Israel Institute of Technology, Haifa, Israel.
Haitham
H
Amal
Lu
L
Ding
Bin-bin
BB
Liu
Ulrike
U
Tisch
Zhen-qin
ZQ
Xu
Da-you
DY
Shi
Yan
Y
Zhao
Jie
J
Chen
Rui-xia
RX
Sun
Hu
H
Liu
Sheng-Long
SL
Ye
Zhao-you
ZY
Tang
Hossam
H
Haick
D000818:Animals; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism; D006528:Carcinoma, Hepatocellular / Q000737:chemistry* / Q000378:metabolism / Q000473:pathology*; D016022:Case-Control Studies; D045744:Cell Line, Tumor; D016002:Discriminant Analysis; D008401:Gas Chromatography-Mass Spectrometry / Q000379:methods*; D056945:Hep G2 Cells; D006801:Humans; D008113:Liver Neoplasms / Q000737:chemistry* / Q000378:metabolism / Q000473:pathology*; D008175:Lung Neoplasms / Q000556:secondary; D051379:Mice; D008819:Mice, Nude; D008962:Models, Theoretical; D036103:Nanotechnology / Q000295:instrumentation; D009362:Neoplasm Metastasis; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D018709:Statistics, Nonparametric; D055549:Volatile Organic Compounds / Q000032:analysis* / Q000378:metabolism
D014408:Biomarkers, Tumor; D055549:Volatile Organic Compounds