BACKGROUND: Pelvic lymph node metastasis (PLNM) is an important prognostic factor for patients with cervical carcinoma. The objective of this study was to identify a gene-expression signature that could predict PLNM in cervical carcinoma. METHODS: Eighty-eight women with cervical carcinoma with PLNM (n ¼ 23) and without PLNM (n ¼ 65) were divided randomly into a training group and a test group. An oligonucleotide microarray that contained probes for 1440 human cancer-related genes was fabricated in-house and was used to detect the gene expression profile of cervical carcinoma. The gene expression levels detected in the microarray were verified by quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR). RESULTS: A gene-expression signature for predicting PLNM was developed in patients from the training group, including 11 genes: ribosomal protein L35 (RPL35); thymosin b 10 (TMSB10); tyrosine 3-mono-oxytenase/tryptophan 5-mono-oxygenase activation protein, f polypeptide (YWHAZ); biotinidase (BTD); lactate dehydrogenase A (LDHA); glucuronidase b (GUSB); superoxide dismutase 2 (SOD2); nuclear receptor subfamily 3, group C, member 2 (NR3C2); fructosamine 3 kinase (FN3K); x-ray repair cross-complementing 4 (XRCC4); and wingless-type mouse mammary tumor virus integration site family member 2 (WNT2). In the test group, the signature's accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 91%, 90.9%, 93.9%, 83.3%, and 96.9%, respectively, for predicting PLNM. The expression levels of 5 genes in the signature were confirmed by qRT-PCR. A multivariate analysis demonstrated that patients with 11-gene high-risk scores were had a 33-fold increased risk for PLNM compared with patients who had low-risk scores. The 5-year overall and disease-free survival rates for patients who had 11-gene high-risk scores were marginally significantly lower than the rates for patients who had 11-gene low-risk scores (P ¼ .087 and P ¼ .174, respectively). CONCLUSIONS: In this study, 11-gene signature for predicting PLNM in cervical carcinoma was identified that may help clinicians in planning therapy for patients with cervical carcinoma.