Increased Apolipoprotein A1 Expression Correlates with Tumor-Associated Neutrophils and T Lymphocytes in Upper Tract Urothelial Carcinoma.

An increased neutrophil-to-lymphocyte ratio (NLR) is a poor prognostic biomarker in various types of cancer, because it reflects the inhibition of lymphocytes in the circulation and tumors. In urologic cancers, upper tract urothelial carcinoma (UTUC) is known for its aggressive features and lack of T cell infiltration; however, the association between neutrophils and suppressed T lymphocytes in UTUC is largely unknown. In this study, we examined the relationship between UTUC-derived factors and tumor-associated neutrophils or T lymphocytes. The culture supernatant from UTUC tumor tissue modulated neutrophils to inhibit T cell proliferation. Among the dominant factors secreted by UTUC tumor tissue, apolipoprotein A1 (Apo-A1) exhibited a positive correlation with NLR. Moreover, tumor-infiltrating neutrophils were inversely correlated with tumor-infiltrating T cells. Elevated Apo-A1 levels in UTUC were also inversely associated with the population of tumor-infiltrating T cells. Our findings indicate that elevated Apo-A1 expression in UTUC correlates with tumor-associated neutrophils and T cells. This suggests a potential immunomodulatory effect on neutrophils and T cells within the tumor microenvironment, which may represent therapeutic targets for UTUC treatment.

Semiparametric estimation of the transformation model by leveraging external aggregate data in the presence of population heterogeneity.

Leveraging information in aggregate data from external sources to improve estimation efficiency and prediction accuracy with smaller scale studies has drawn a great deal of attention in recent years. Yet, conventional methods often either ignore uncertainty in the external information or fail to account for the heterogeneity between internal and external studies. This article proposes an empirical likelihood-based framework to improve the estimation of the semiparametric transformation models by incorporating information about the t-year subgroup survival probability from external sources. The proposed estimation procedure incorporates an additional likelihood component to account for uncertainty in the external information and employs a density ratio model to characterize population heterogeneity. We establish the consistency and asymptotic normality of the proposed estimator and show that it is more efficient than the conventional pseudopartial likelihood estimator without combining information. Simulation studies show that the proposed estimator yields little bias and outperforms the conventional approach even in the presence of information uncertainty and heterogeneity. The proposed methodologies are illustrated with an analysis of a pancreatic cancer study.

Estimations of the joint distribution of failure time and failure type with dependent truncation.

In biomedical studies involving survival data, the observation of failure times is sometimes accompanied by a variable which describes the type of failure event (Kalbeisch and Prentice, 2002). This paper considers two specific challenges which are encountered in the joint analysis of failure time and failure type. First, because the observation of failure times is subject to left truncation, the sampling bias extends to the failure type which is associated with the failure time. An analytical challenge is to deal with such sampling bias. Second, in case that the joint distribution of failure time and failure type is allowed to have a temporal trend, it is of interest to estimate the joint distribution of failure time and failure type nonparametrically. This paper develops statistical approaches to address these two analytical challenges on the basis of prevalent survival data. The proposed approaches are examined through simulation studies and illustrated by using a real data set.

Assessment of hepatic fatty infiltration using dual-energy computed tomography: a phantom study.

The purpose of this study was to examine the performance of dual-energy computed tomography (DECT) for the quantification of liver fat content (LFC). We prepared two phantoms: homogenized mixtures of porcine liver and fat and homogeneous mixtures of liver- and fat-equivalent solutions. Tubes containing mixtures with known fat concentrations were scanned on a dual-source CT scanner using two DE scanning protocols (80 kV/Sn140 kV and 100 kV/Sn140 kV). Attenuation curves obtained from DECT were used to describe attenuations of various degrees of LFC at different energies. LFC was calculated from DECT data and compared with the known LFC. The phantom made of liver/fat mixtures was not used for liver fat quantification because the increase of fat content did not show a decline of CT numbers. This may be due to inhomogeneity as observed in CT images. Attenuation curves obtained from two DE scanning protocols had the ability to discriminate small differences in fat concentrations. Our results also showed a strong correlation between DECT measurements and known LFC (R(2) > 0.99, P < 0.005). DECT will be a reliable tool for liver fat quantification. Furthermore, attenuation curves obtained from DECT data can be used for discriminating various degrees of LFC.