Molecular diagnosis of sentinel lymph node metastases in cervical cancer using squamous cell carcinoma antigen.
OBJECTIVE
To clarify the prognostic value of molecular diagnosis of SLN metastases in cervical cancer using SCCA.


METHODS
All SLNs and primary tumors, part of non-SLNs, were harvested from 36 patients with cervical cancer. Expression levels of SCCA, cytokeratin 19 (CK19), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA in 178 samples (29 primary tumors, 5 histologic positive nodes, 60 histologic negative SLNs, 69 non-SLNs, and 15 normal nodes) were assessed by quantitative reverse transcription-PCR assay. The quantitative value of SCCA or CK19 mRNA was described as each value relative to GAPDH mRNA. The cutoff value was set at the upper limit of the quantitative value of nodes from noncancer patients, and those above this value constituted the molecular metastasis group.


RESULTS
The SCCA mRNA expression values were more than 1 x 10(3) in 28 primary tumors and all histologic positive nodes, and its expression levels in SLNs were higher than in non-SLNs. SLNs from patients with adverse prognostic features had higher SCCA mRNA expression levels. Four histologic negative SLNs were diagnosed molecular metastases based on SCCA mRNA. Two cases with histologically uninvolved pelvic nodes recurred. Survival analysis indicates that molecular lymphatic metastasis based on elevated SCCA mRNA level is the best predictor of recurrence. However, CK19 is not a suitable marker due to its low specificity and relative higher baseline expression in normal nodes.


CONCLUSIONS
SCCA mRNA levels for molecular diagnosis of SLN metastases in cervical cancer more accurately identifies patients at risk for recurrence than the routine histology does.
Yuan
Song-Hua
SH
Department of Gynecology, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, People's Republic of China.
Liang
Xue-Fang
XF
Jia
Wei-Hua
WH
Huang
Jian-Ling
JL
Wei
Mei
M
Deng
Ling
L
Liang
Li-Zhi
LZ
Wang
Xiao-Yun
XY
Zeng
Yi-Xin
YX