HPIP: a predictor of lymph node metastasis and poor survival in cervical cancer

Two decades of a protooncogene HPIP/PBXIP1: Uncovering the tale from germ cell to cancer

Hematopoietic PBX interacting protein (HPIP or pre-B-cell leukemia transcription factor interacting protein (PBXIP1) was discovered two decades ago as a corepressor of pre-B-cell leukemia homeobox (PBX) 1 with a vital functional role in hematopoiesis. Later it emerged as a potential biomarker of poor prognosis and tumorigenesis for more than a dozen different cancers. It regulates aggressive cancer phenotypes, cell proliferation, metastasis, EMT, etc. The anomaly in the regulation of HPIP is linked with physiological disorders like renal fibrosis, chronic kidney disease and osteoarthritis. Scientists have unraveled more than twenty interacting proteins of HPIP and its functional role in various physiological and cellular processes that involves normal neuronal development, embryogenesis, endometrium decidualization, and germ cell proliferation. Over the past 20 years, we have witnessed the emerging role of HPIP and its association with a myriad of cellular activities ranging from germ cell proliferation to cancer aggressiveness, modulating multitude of signaling cascades like TGF-β1, PI3K/AKT, Wnt, mTOR, and Sonic hedgehog signaling pathways. This review will give the current understanding of HPIP, in terms of its diverse functions, theoretical ideas, and further explore cellular links and promising areas that need to be investigated. We also provide a comprehensive overview of the transcript variants of HPIP and distinct sets of transcription factors regulating their expression, which may help to understand the role of HPIP in various cellular or physiological conditions.

Dynamic prediction and prognostic analysis of patients with cervical cancer: a landmarking analysis approach

PURPOSE

Providing up-to-date information on patient prognosis is important in determining the optimal treatment strategies. The currently available prediction models, such as the Cox model, are limited to making predictions from baseline and do not consider the time-varying effects of covariates.

METHODS

A total of 1501 cervical cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database were included. We introduced three landmark dynamic prediction models (models 1-3) that explore the dynamic effects of prognostic factors to obtain 5-year dynamic survival rate predictions at different prediction times. The performances of these models were evaluated by Harrell's C-index and the Brier score using cross-validation.

RESULTS

Some variables did not meet the proportional hazards assumption, indicating that the constant hazard ratios were unreliable. Model 3, which showed the best performance for prediction, was selected as the final model. Significant time-varying effects were observed for age at diagnosis, International Federation of Gynecology and Obstetrics (FIGO) stage, lymph node metastasis, and histological subtypes. Three patients were as examples used to illustrate how the predicted probabilities change at different prediction times during follow-up.

CONCLUSIONS

Model 3 can effectively incorporate covariates with time-varying effects and update the probability of surviving an additional 5 years at different prediction times. The use of the landmark approach may provide evidence for clinical decision making by updating personalized information for patients.

Nomograms for predicting the survival rate for cervical cancer patients who undergo radiation therapy: a SEER analysis

Aim: To integrate multiple independent risk factors to establish prognostic nomograms for better predicting overall survival and disease-specific survival in patients with cervical cancer receiving radiation therapy. Materials & methods: Cox analysis used to construct nomograms. The C-index, time-dependent receiver operating characteristic and calibration plots were used to evaluate the performance. The discrimination abilities were compared using the decision curve analysis, net reclassification improvement and integrated discrimination improvement. Results: After randomization, 2869 and 1230 cervical cancer patients were included in the training and validation sets, respectively. Nomograms that incorporated all of the significant independent factors for predicting the 3- and 5-year overall survival and disease-specific survival in the training cohort were established. Conclusion: Compared with the International Federation of Gynecology and Obstetrics staging system, the proposed nomograms exhibit superior prognostic discrimination and survival prediction.

Overexpression of HPIP as a biomarker for metastasis and prognosis prediction in endometrial cancer patients

BACKGROUND

Hematopoietic pre-B-cell leukemia transcription factor (PBX)-interacting protein (HPIP) has shown to be overexpressed in several human cancers. The purpose of this study was to explore the expression of HPIP in endometrial cancer (EC) and its associated effects on disease.

METHODS

A total of 113 EC patients at the Harbin Medical University Cancer Hospital between August 2011 and September 2012 were studied for immunohistochemistry analysis. HPIP expression was detected using real-time reverse transcription PCR, Western blotting, and immunohistochemistry. Prognostic value of HPIP expression was examined using multivariate Cox regression analysis and Kaplan-Meier method.

RESULTS

The result of Western blotting indicated that HPIP protein expression is significantly high in normal tissues compared to EC tissues (P < 0.001). The expression of HPIP was significantly associated with FIGO stage (P < 0.001), histological grade (P < 0.001), depth of myometrial invasion (P < 0.001), and lymph node metastasis (P = 0.033). Kaplan-Meier analysis demonstrated that there was a significant difference in overall survival and disease-free survival between the two groups of patients stratified by HPIP expression level (log-rank, both P = 0.002). Patients with HPIP high expression had significantly shorter median survival time than those with HPIP low expression. Moreover, results of the multivariate analysis revealed that HPIP expression was an independent prognostic factor for predicting overall survival (P = 0.015) and disease-free survival (P = 0.017) in patients with EC.

CONCLUSION

The present study provides evidence that HPIP predicts EC progression and poor survival, highlighting its potential as a therapeutic target for EC.

A Prognostic Nomogram for Cervical Cancer after Surgery from SEER Database

Background: To develop and validate a nomogram based on the conventional measurements and log of odds between the number of positive lymph node and the number of negative lymph node (LODDS) in predicting prognosis for cervical cancer patients after surgery. Methods: A total of 8202 cervical cancer patients with pathologically confirmed between 2004 and 2014 were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. All the patients were divided into training (n=3603) and validation (n=4599) cohorts based on consecutive age of diagnosis. Demographic and clinical pathological factors were evaluated the association with overall survival (OS). Parameters significantly correlating with OS were used to create a nomogram. An independent external validation cohort was subsequently used to assess the predictive performance of the model. Results: In the training set, age at diagnosis, race, marital status, tumor grade, FIGO stage, histology, size and LODDS were correlated significantly with outcome and used to develop a nomogram. The calibration curve for probability of survival showed excellent agreement between prediction by nomogram and actual observation in the training cohort, with a bootstrap-corrected concordance index of 0.749(95% CI, 0.731-0.767). Importantly, our nomogram performed favorably compared to the currently utilized FIGO model, with concordance indices of 0.786 (95% CI, 0.764 to 0.808) vs 0.685 (95%CI, 0.660 to 0.710) for OS in the validation cohort, respectively. Conclusions: By incorporating LODDS, our nomogram may be superior to the currently utilized FIGO staging system in predicting OS in cervical cancer patients after surgery.

Progress in the Study of Lymph Node Metastasis in Early-stage Cervical Cancer

Spread into regional lymph node is the major route of metastasis in cervical cancer. Although lymph node status is not involved in the International Federation of Gynecology and Obstetrics staging system of uterine cervical cancer, the presence or absence of lymph node metastasis provides important information for prognosis and treatment. In this review, we have attempted to focus on the incidence and patterns of lymph node metastasis, and the issues surrounding surgical assessment of lymph nodes. In addition, the preoperative prediction of lymph node status, as well as the intraoperative assessment by sentinel nodes will be reviewed. Finally, lymph node micrometastasis also will be discussed.