Auslander-Reiten translations in monomorphism categories

We generalize Ringel and Schmidmeier's theory on the Auslander-Reiten translation of the submodule category $\mathcal S_2(A)$ to the monomorphism category $\mathcal S_n(A)$. As in the case of $n=2$, $\mathcal S_n(A)$ has Auslander-Reiten sequences, and the Auslander-Reiten translation $\tau_{\mathcal{S}}$ of $\mathcal S_n(A)$ can be explicitly formulated via $\tau$ of $A$-mod. Furthermore, if $A$ is a selfinjective algebra, we study the periodicity of $\tau_{\mathcal{S}}$ on the objects of $\mathcal S_n(A)$, and of the Serre functor $F_{\mathcal S}$ on the objects of the stable monomorphism category $\underline{\mathcal{S}_n(A)}$. In particular, $\tau_{\mathcal S}^{2m(n+1)}X\cong X$ for X\in\mathcal{S}_n(\A(m, t)); and $F_{\mathcal S}^{m(n+1)}X\cong X$ for X\in\underline{\mathcal{S}_n(\A(m, t))}, where \A(m, t), \ m\ge1, \ t\ge2, are the selfinjective Nakayama algebras.Comment: 33 pages, 1 figure

Improving the prediction of overall survival for head and neck cancer patients using image biomarkers in combination with clinical parameters

Purpose: To develop and validate prediction models of overall survival (OS) for head and neck cancer (HNC) patients based on image biomarkers (IBMs) of the primary tumor and positive lymph nodes (Ln) in combination with clinical parameters. Material and methods: The study cohort was composed of 289 nasopharyngeal cancer (NPC) patients from China and 298 HNC patients from the Netherlands. Multivariable Cox-regression analysis was performed to select clinical parameters from the NPC and HNC datasets, and IBMs from the NPC dataset. Final prediction models were based on both IBMs and clinical parameters. Results: Multivariable Cox-regression analysis identified three independent IBMs (tumor Volume density, Run Length Non-uniformity and Ln Major-axis-length). This IBM model showed a concordance (c)-index of 0.72 (95%Cl: 0.65-0.79) for the NPC dataset, which performed reasonably with a c-index of 0.67 (95%Cl: 0.62-0.72) in the external validation HNC dataset. When IBMs were added in clinical models, the c-index of the NPC and HNC datasets improved to 0.75 (95%Cl: 0.68-0.82; p = 0.019) and 0.75 (95%Cl: 0.70-0.81; p <0.001), respectively. Conclusion: The addition of IBMs from the primary tumor and Ln improved the prognostic performance of the models containing clinical factors only. These combined models may improve pre-treatment individualized prediction of OS for HNC patients. (C) 2017 The Authors. Published by Elsevier Ireland Ltd

Upregulation of MIAT Regulates LOXL2 Expression by Competitively Binding MiR-29c in Clear Cell Renal Cell Carcinoma

Background/Aims: MIAT is a long noncoding RNA (lncRNA) involved in cell proliferation and the development of tumor. However, the exact effects and molecular mechanisms of MIAT in clear cell renal cell carcinoma (ccRCC) progression are still unknown. Methods: We screened the lncRNAs’ profile of ccRCC in The Cancer Genome Atlas database, and then examined the expression levels of lncRNA MIAT in 45 paired ccRCC tissue specimens and in cell lines by q-RT-PCR. MTS, colony formation, EdU, and Transwell assays were performed to examine the effect of MIAT on proliferation and metastasis of ccRCC. Western blot and luciferase assays were performed to determine whether MIAT can regulate Loxl2 expression by competitively binding miR-29c in ccRCC. Results: MIAT was up-regulated in ccRCC tissues and cell lines. High MIAT expression correlated with worse clinicopathological features and shorter survival rate. Functional assays showed that knockdown of MIAT inhibited renal cancer cell proliferation and metastasis in vitro and in vivo. Luciferase and western blot assays further confirmed that miR-29c binds with MIAT. Additionally, the correlation of miR-29c with MIAT and Loxl2 was further verified in patients' samples. Conclusion: Our data indicated that MIAT might be an oncogenic lncRNA that promoted proliferation and metastasis of ccRCC, and could be a potential therapeutic target in human ccRCC

A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods.The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis.The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use