Cervical cancer remains one of the most common causes of gynecological cancer-associated death. Long non-coding RNA Loc554202 (lncRNA Loc554202) has been reported to be involved in the development of several types of cancer. However, the role of Loc554202 in cervical cancer remains unclear. In this study, we measured the expression levels of Loc554202 in cervical cancer tissues from 120 patients. The quantitative real-time PCR analysis showed the expression levels of Loc554202 were significantly higher in cervical cancer tissues compared with the adjacent non-tumor tissues. Elevated expression levels of Loc554202 were significantly associated with tumor size (p = 0.006), FIGO stage (p = 0.015), HPV (p = 0.001), and lymph node metastasis (p = 0.002). Kaplan-Meier analysis clearly illustrated that patients with high expression levels of Loc554202 had a lower overall survival rate compared to patients with lower expression (p = 0.0013). Furthermore, we show that Loc554202 is an independent poor prognostic factor through multivariate analysis. Subsequently, using cervical cancer cell lines, HeLa and ME-180, we decreased the expression levels of Loc554202 with siRNA. As results, the proliferation ability of cervical cancer cells was inhibited and apoptosis was induced after Loc554202 knockdown, as judged by viability assay, colony formation, and flow cytometry. Moreover, knockdown of Loc554202 expression downregulated Bcl-2 expression and conversely up-regulated Bax expression in cervical cancer cells using Western blotting analysis. In conclusion, elevated levels of Loc554202 are predictive of poor prognosis in cervical cancer. We suggest that Loc554202 may serve as a potential therapeutic target for cervical cancer.

Purpose : To develop and validate nomogram models using noninvasive imaging parameters with related clinical variables to predict the extent of axillary nodal involvement and stratify treatment options based on the essential cut-offs for axillary surgery according to the ACOSOG Z0011 criteria. Materials and Methods : From May 2007 to December 2017, 1799 patients who underwent preoperative breast and axillary magnetic resonance imaging (MRI) were retrospectively studied. Patients with data on axillary ultrasonography (AUS) were enrolled. The MRI images were interpreted according to Breast Imaging Reporting and Data system (BI-RADS). Using logistic regression analyses, nomograms were developed to visualize the associations between the predictors and each lymph node (LN) status endpoint. Predictive performance was assessed based on the area under the receiver operating characteristic curve (AUC). Bootstrap resampling was performed for internal validation. Goodness-of-fit of the models was evaluated using the Hosmer-Lemeshow test. Results : Of 397 early breast cancer patients, 200 (50.4%) had disease-free axilla, 119 (30.0%) had 1 or 2 positive LNs, and 78 (19.6%) had ≥3 positive LNs. Patient age, MRI features (mass margin, LN margin, presence/absence of LN hilum, and LN symmetry/asymmetry), and AUS descriptors (presence of cortical thickening or hilum) were identified as predictors of nodal disease. Nomograms with these predictors showed good calibration and discrimination; the AUC was 0.809 for negative axillary node (N0) vs. any LN metastasis, 0.749 for 1 or 2 involved nodes vs. N0, and 0.874 for ≥3 nodes vs. ≤2 metastatic nodes. The predictive ability of the 3 nomograms with additional pathological variables was significantly greater. Conclusion : The nomograms could predict the extent of ALN metastasis and facilitate decision-making preoperatively.