Background: Estrogen replacement therapy (ERT) is a common treatment method for menopausal syndrome; however, its therapeutic value for the treatment of neurological diseases is still unclear. Epidemiological studies were performed, and the effect of postmenopausal ERT on treating neurodegenerative diseases, including Alzheimer's disease (AD) and Parkinson's disease (PD), was summarized through a meta-analysis. Methods: Twenty-one articles were selected using a systematic searching of the contents listed on PubMed and Web of Science before June 1, 2019. Epidemiological studies were extracted, and relevant research data were obtained from the original articles based on the predefined inclusion criteria and data screening principles. The Comprehensive Meta-Analysis Version 2 software was used to pool effective size, test heterogeneity, conduct meta-regression and subgroup analysis, and to calculate publication bias. Results: Our results showed that ERT significantly decreased the risk of onset and/or development of AD [odds ratio (OR): 0.672; 95% CI: 0.581-0.779; P < 0.001] and PD (OR: 0.470; 95% CI: 0.368-0.600; P < 0.001) compared with the control group. A subgroup and meta-regression analysis showed that study design and measure of effect were the source of heterogeneity. Age, sample size, hormone therapy ascertainment, duration of the treatment, or route of administration did not play a significant role in affecting the outcome of the meta-analysis. Conclusion: We presented evidence here to support the use of estrogen therapy for the treatment of AD and PD.

The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m5C), as an important post-transcriptional modification, has been identified to be involved in a variety of biological processes such as subcellular localization and translational fidelity. Though high-throughput experimental technologies have been developed and applied to profile m5C modifications under certain conditions, transcriptome-wide studies of m5C modifications are still hindered by the dynamic nature of m5C and the lack of computational prediction methods. In this study, we introduced PEA-m5C, a machine learning-based m5C predictor trained with features extracted from the flanking sequence of m5C modifications. PEA-m5C yielded an average AUC (area under the receiver operating characteristic) of 0.939 in 10-fold cross-validation experiments based on known Arabidopsis m5C modifications. A rigorous independent testing showed that PEA-m5C (Accuracy [Acc] = 0.835, Matthews correlation coefficient [MCC] = 0.688) is remarkably superior to the recently developed m5C predictor iRNAm5C-PseDNC (Acc = 0.665, MCC = 0.332). PEA-m5C has been applied to predict candidate m5C modifications in annotated Arabidopsis transcripts. Further analysis of these m5C candidates showed that 4nt downstream of the translational start site is the most frequently methylated position. PEA-m5C is freely available to academic users at: https://github.com/cma2015/PEA-m5C.

Background: Long non-coding RNAs (lncRNAs) are involved in many fundamental biological processes, such as transcription regulation, protein degradation, and cell differentiation. Information on lncRNA in the melon fly, Zeugodacus cucurbitae (Coquillett) is currently limited. Results: We constructed 24 RNA-seq libraries from eight tissues (midgut, Malpighian tubules, fat body, ovary, and testis) of Z. cucurbitae adults. A total of 3124 lncRNA transcripts were identified. Among those, 1464 were lincRNAs, 1037 were intronic lncRNAs, 301 were anti-sense lncRNAs, and 322 were sense lncRNAs. The majority of lncRNAs contained two exons and one isoform. Differentially expressed lncRNAs were analyzed between tissues, and Malpighian tubules versus testis had the largest number. Some lncRNAs exhibited strong tissue specificity. Specifically expressed lncRNAs were identified and filtered in tissues of female and male Z. cucurbitae based on their expression levels. Four midgut-specific lncRNAs were validated by quantitative real-time polymerase chain reaction (RT-qPCR), and the data were consistent with RNA-seq data. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of targets of midgut-specific lncRNAs indicated an enrichment of the metabolic process. Conclusions: This was the first systematic identification of lncRNA in the melon fly. Expressions of lncRNAs in multiple adult tissues were evaluated by quantitative transcriptomic analysis. These qualitative and quantitative analyses of lncRNAs, especially the tissue-specific lncRNAs in Z. cucurbitae, provide useful data for further functional studies.

Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis and prognosis prediction in patients with LUAD. Invasion‐related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion‐related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi‐gene risk model was constructed by Lasso‐Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features. 3 subtypes (C1, C2 and C3) based on the expression of 97 invasion‐related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signalling pathway and other tumour‐related pathways. A 5‐gene signature (KRT6A, MELTF, IRX5, MS4A1 and CRTAC1) was identified by using Lasso‐Cox analysis. The training, validation and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumour tissues were higher than in normal tissues, while CRTAC1 expression in tumour tissues was lower than in normal tissues. The 5‐gene signature prognostic stratification system based on invasion‐related genes could be used to assess prognostic risk in patients with LUAD.