BackgroundHOX transcript antisense intergenic RNA (HOTAIR), a long non-coding RNA transcribed from the antisense strand of the HOXC gene locus, has been shown to be overexpressed in various carcinomas and is thought to be an indicator of poor prognosis. Recently, HOTAIR was found to be an estrogen-responsive gene. We therefore conducted a meta-analysis to systematically summarize and clarify the association between HOTAIR expression and prognosis in the four main estrogen-dependent tumors.MethodsA systematic search of studies that examined the association and prognostic impact of HOTAIR in four of the main estrogen-dependent tumors was conducted in PubMed and Embase. Hazard ratios (HRs) and 95% confidence intervals (95% CIs) were calculated to pool the effect size.ResultsA total of 1,200 patients from eight eligible studies were included. The current study found an association between HOTAIR expression and overall survival (OS) in four estrogen-dependent tumor types (HR, 1.99; 95% CI: 1.02–3.90; PHeterogeneity=0.001). Subgroup analyses indicated that high HOTAIR expression appeared to be a potential prognostic biomarker in non-breast cancer patients (HR, 2.72; 95% CI: 1.65–4.48). There was also an increased risk in Asian populations (HR, 2.55; 95% CI: 1.62–4.00) compared with Caucasian populations (HR, 1.19; 95% CI: 0.16–8.83) and in patients without preoperative treatment (HR, 2.55; 95% CI: 1.62–4.00) compared with patients with preoperative treatment (HR, 1.19; 95% CI: 0.16–8.83). In addition, the HRs of patients with high HOTAIR expression for metastasis-free survival (MFS), relapse-free survival (RFS), and disease-free survival (DFS) were 2.30 (P=0.120), 1.39 (P=0.000), and 2.53 (P=0.714), respectively, but there were insufficient data to fully confirm these associations.ConclusionHOTAIR may be a predictor of poor prognosis in four of the main estrogen-dependent tumors, especially in cervical, ovarian, and endometrial cancer patients without preoperative treatment in Asian populations. It is important to note that the prognostic value of HOTAIR in MFS, RFS, and DFS should be interpreted with caution due to the limited sample size and sample heterogeneity. Well-designed and larger-scale studies are needed to validate our findings.

Objective:Although the prognostic value of programmed cell death-ligand 1 (PD-L1) expression in non-Hodgkin lymphoma (NHL) has been evaluated in many studies, the results remain controversial. To investigate the prognostic role of PD-L1 expression and the association between PD-L1 expression and clinicopathological features of NHL, we performed a meta-analysis.Methods:The PubMed, EMBASE, and Cochrane Library databases were searched up to November 30, 2017. The hazard ratio (HR), 95% confidence interval (CI), and odds ratios (OR) with 95% CIs were combined to evaluate the association of PD-L1 expression with overall survival (OS) and clinicopathological features. Review manager 5.3 and STATA 12.0 were used in this meta-analysis.Results:A total of 2,005 patients across nine studies were enrolled in our meta-analysis, and the pooled results showed that high PD-L1 expression was associated with a poor prognosis (HR=2.04, 95% CI: 1.18–3.54, P=0.01). In the subgroup analysis according to histology types, pooled results demonstrated that an increased PD-L1 expression was an unfavorable prognostic factor for diffuse large B-cell lymphoma (HR=1.92, 95% CI: 1.06–3.48, P=0.03) but not for natural killer/T-cell lymphoma (HR=2.41, 95% CI: 0.47–12.22, P=0.29). Pooled ORs indicated that PD-L1 expression was higher in NHL with international prognostic indices of ≥3. However, PD-L1 expression had no correlation with gender, age, disease stage, lactate dehydrogenase level, B symptoms, and germinal center B-cell-like lymphoma. Conclusions:High PD-L1 expression was a poor prognostic biomarker in patients with NHL. Because of our limited sample size, high-quality studies with larger sample sizes are needed to validate our results.

Objective In this study, based on PET/CT radiomics features, we developed and validated a nomogram to predict progression-free survival (PFS) for cases with diffuse large B cell lymphoma (DLBCL) treated with immunochemotherapy. Methods This study retrospectively recruited 129 cases with DLBCL. Among them, PET/CT scans were conducted and baseline images were collected for radiomics features along with their clinicopathological features. Radiomics features related to recurrence were screened for survival analysis using univariate Cox regression analysis with p < 0.05. Next, a weighted Radiomics-score (Rad-score) was generated and independent risk factors were obtained from univariate and multivariate Cox regressions to build the nomogram. Furthermore, the nomogram was tested for their ability to predict PFS using time-dependent receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results Blood platelet, Rad-score, and gender were included in the nomogram as independent DLBCL risk factors for PFS. We found that the training cohort areas under the curve (AUCs) were 0.79, 0.84, and 0.88, and validation cohort AUCs were 0.67, 0.83, and 0.72, respectively. Further, the DCA and calibration curves confirmed the predictive nomogram’s clinical relevance. Conclusion Using Rad-score, blood platelet, and gender of the DLBCL patients, a PET/CT radiomics-based nomogram was developed to guide cases’ recurrence risk assessment prior to treatment. The developed nomogram can help provide more appropriate treatment plans to the cases. Key Points • DLBCL cases can be classified into low- and high-risk groups using PET/CT radiomics based Rad-score. • When combined with other clinical characteristics (gender and blood platelet count), Rad-score can be used to predict the outcome of the pretreatment of DLBCL cases with a certain degree of accuracy. • A prognostic nomogram was established in this study in order to aid in assessing prognostic risk and providing more accurate treatment plans for DLBCL cases.

PeneloPET is a PET-dedicated Monte Carlo simulation toolkit, which is based on PENELOPE. This article describes the characteristics and the general process of PeneloPET simulation. Then we compare the simulation results of PeneloPET and GATE to model the GE Healthcare eXplore Vista microPET system respectively, including sensitivity and noise equivalent count rate. The results show that PeneloPET simulation data corresponds with the data from real scanners and GATE simulation, and proves PeneloPET is an accurate toolkit for PET simulation.