Alzheimer’s disease (AD), an elderly neurodegenerative disorder with a high incidence and progressive memory decline, is one of the most expensive, lethal, and burdening diseases. To date, the pathogenesis of AD has not been fully illustrated. Emerging studies have revealed that cellular senescence and abnormal glucose metabolism in the brain are the early hallmarks of AD. Moreover, cellular senescence and glucose metabolism disturbance in the brain of AD patients may precede amyloid-β deposition or Tau protein phosphorylation. Thus, metabolic reprogramming targeting senescent microglia and astrocytes may be a novel strategy for AD intervention and treatment. Here, we recapitulate the relationships between neural cell senescence and abnormal glucose metabolism (e.g., insulin signaling, glucose and lactate metabolism) in AD. We then discuss the potential perspective of metabolic reprogramming towards an AD intervention, providing a theoretical basis for the further exploration of the pathogenesis of and therapeutic approach toward AD.

Long noncoding RNAs (lncRNAs) play a crucial role in tumorigenesis. The aim of this study is to identify lncRNA signature that can predict breast cancer patient survival. RNA expression data from 1064 patients were downloaded from The Cancer Genome Atlas project. Cox regression, Kaplan–Meier, and receiver operating characteristic (ROC) analyses were performed to construct a model for predicting the overall survival (OS) of patients and evaluate it. A model consisting of three lncRNA genes (CAT104, LINC01234, and STXBP5-AS1) was identified. The Kaplan–Meier analysis and ROC curves proved that the model could predict the prognostic survival with good sensitivity and specificity in both the validation set (AUC = 0.752, 95% confidence intervals (CI): 0.651–0.854) and the microarray dataset (AUC = 0.714, 95%CI: 0.615–0.814). Further study showed the three-lncRNA signature was not only pervasive in different breast cancer stages, subtypes and age groups, but also provides more accurate prognostic information than some widely known biomarkers. The results suggested that RNA-seq transcriptome profiling provides that the three-lncRNA signature is an independent prognostic biomarker, and have clinical significance. In addition, lncRNA, miRNA, and mRNA interaction network indicated lncRNAs may intervene in breast cancer pathogenesis by binding to miR-190b, acting as competing endogenous RNAs.

Cutaneous melanoma (CM) is a life-threatening form of skin cancer. Prognostic biomarkers can reliably stratify patients at initial melanoma diagnosis according to risk, and may inform clinical decisions. Here, we performed a retrospective, cohort-based study analyzing genome-wide DNA methylation of 461 patients with CM from the TCGA database. Cox regression analyses were conducted to establish a four-DNA methylation signature that was significantly associated with the overall survival (OS) of patients with CM, and that was validated in an independent cohort. Corresponding Kaplan–Meier analysis displayed a distinct separation in OS. The ROC analysis confirmed that the predictive signature performed well. Notably, this signature exhibited much higher predictive accuracy in comparison with known biomarkers. This signature was significantly correlated with immune checkpoint blockade (ICB) immunotherapy-related signatures, and may have potential as a guide for measures of responsiveness to ICB immunotherapy.

Ovarian cancer is the most fatal tumor of the female reproductive system and the fifth leading cause of cancer death among women in the USA. The prognosis is poor due to the lack of biomarkers for treatment options. The methylation array data of 551 patients with ovarian serous cystadenocarcinoma (OSC) in The Cancer Genome Atlas (TCGA) database were assessed in this study to explore the methylation biomarkers associated with prognosis and improve the prognosis of patients. These patients were divided into training (first two thirds) and validation datasets (remaining one third). A five-DNA methylation signature was found to be significantly associated with the overall survival of patients with OSC using the Cox regression analysis in the training dataset. The Kaplan–Meier analysis showed that the five-DNA methylation signature could significantly distinguish the high- and low-risk patients in both training and validation sets. The receiver operating characteristic (ROC) analysis further confirmed that the five-DNA methylation signature exhibited high sensitivity and specificity to predict the prognostic survival of patients. Also, the five-DNA methylation signature was not only applicable in patients of different ages, stages, histologic grade, and size of residual tumor after surgery but also more accurate in predicting OSC prognosis compared with known biomarkers. This five-DNA methylation signature demonstrated the potential of being a novel independent prognostic indicator and served as an important tool for guiding the clinical treatment of OSC to improve outcome prediction and management for patients. Hence, the findings of this study might have potential clinical significance.

// <![CDATA[ $('.header-date').hide();$('#titleAuthors').hide();$('#abstractHeader').hide(); // ]]> Wenna Guo1, Xijia Chen2, Liucun Zhu2 and Qiang Wang1 1State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, China 2School of Life Sciences, Shanghai University, Shanghai, China Correspondence to: Qiang Wang, email: wangq@nju.edu.cn Liucun Zhu, email: zhuliucun@shu.edu.cn Keywords: biomarker, HNSCC, prognosis, risk-stratification, RNA-seq Received: August 18, 2017 Accepted: September 22, 2017 Published: October 10, 2017 ABSTRACT Head and neck squamous cell carcinoma (HNSCC), one of the most common cancers with high morbidity and mortality rates worldwide, has a poor prognosis. The transcriptome sequencing data of 500 patients with HNSCC in the TCGA dataset were assessed to find biomarkers associated with HNSCC prognosis so as to improve the prognosis of patients with HNSCC. The patients were divided into the training and testing sets. A model of six mRNAs (FRMD5, PCMT1, PDGFA, TMC8, YIPF4, ZNF324B) that could predict patient prognosis was identified in the training set using the Cox regression analysis. According to this model, the patients were divided into high-risk and low-risk groups. The Kaplan-Meier analysis showed that the high-risk group showed significantly shorter overall survival time compared with the low-risk group in both training and testing sets. The receiver operating characteristic analysis further confirmed high sensitivity and specificity for the model, which was more accurate compared with some known biomarkers in predicting HNSCC prognosis. Moreover, the model was applicable to patients of different ages, genders, clinical stages, tumor locations, smoking history, and human papillomavirus (HPV) status, as well as to microarray dataset. This model could be used as a novel biomarker for the prognosis of HNSCC and a significant tool for guiding the clinical treatment of HNSCC. The risk score acquired from the model might contribute to improving outcome prediction and management for patients with HNSCC, indicating its clinical significance.