MicroRNAs (miRNAs) are highly conserved noncoding RNAs of about 19-25 nucleotides. Through specifically pairing with complementary sites in 3' untranslated regions (UTRs) of target mRNAs, they mediate post-transcriptional silencing. MicroRNAs have been implicated in many physiological processes including proliferation, differentiation, development, apoptosis, and metabolism. In recent years many studies have revealed that the aberrant expression of miRNA is closely related to oncogenesis and is now an intense field of study. Mir-148a is aberrantly expressed in various cancers and has been identified as an oncogenic or tumor suppressor with crucial roles in the molecular mechanisms of oncogenesis. In this review, we have summarized the role of mir-148a in the oncogenic pathways of gastric, liver, breast and urogenital cancers, and in neurogliocytoma oncogenesis. Studying the functional role of mir-148a is crucial in discovering novel tumor molecular markers and identifying potential therapeutic targets.

Resilience is a fundamental concept for vegetation health. The increasing drought frequency and severity may pose severe threat to vegetation resilience. However, it is still not clear how vegetation resilience is evolving in response to climate change in pivotal biographical zones. Here, we examined the resilience changes in terms of leaf area index (LAI, an indicator of canopy structure) and gross primary productivity (GPP, an indicator of carbon uptake) in responding to the Standardized Precipitation‐Evapotranspiration Index (SPEI) and vapor pressure deficit (VPD) over China's Loess Plateau and Qinling Mountains. Linking remote sensing variables and tree ring width allows the upscaling of plot‐based vegetation growth information. We further explored potential explanatory factors associated with the heterogeneous spatial distributions of resilience changes. Results revealed that the resilience of GPP weakened more than LAI in response to drought, suggesting that compared to LAI, productivity requires more time to recover to the pre‐drought levels. Regionally, the change of vegetation resilience on the Loess Plateau and in high‐altitude areas was highly susceptible to SPEI and VPD, respectively. The observed spatial heterogeneity in resilience changes was mainly attributed by climate zone, water deficit, and their interactions. Our findings provide direct and empirical evidence that the vegetation in the Loess Plateau and Qinling Mountains is gradually losing resilience. The results indicate that sustained ecosystem water deficit and atmospheric dryness will continue to threaten vegetation survival and terrestrial ecosystem service.

Recent studies have highlighted the value of microRNA-21 (miR-21) as a prognostic biomarker in gliomas. However, the role of miR-21 in predicting prognosis remains controversial. We performed a comprehensive study based upon a meta-analysis and The Cancer Genome Atlas (TCGA) glioma dataset validation to clarify the prognostic significance of miR-21 in glioma patients. In this study, we searched Embase, PubMed, Web of science, CNKI, SinoMed, and Wanfang databases for records up to May 2018. Relevant data were extracted to assess the correlation between miR-21 expression and survival in glioma patients. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were used to describe association strength. We further used multivariate Cox regression analysis to assess miR-21 expression in the TCGA glioma dataset to validate the relationship between miR-21 expression and survival. Nine studies were included in the meta-analysis. Among them, eight studies provided data on overall survival (OS) with a pooled HR of 1.91 (95% CI: 1.34, 2.73), indicating that higher expression of miR-21 was significantly associated with worse OS in glioma patients; for the other study, which provided data on progression-free survival (PFS), no statistically significant HR was reported for PFS in the glioma patients (HR = 1.23, 95% CI: 0.41, 3.72). A multivariate Cox regression analysis of the miR-21 expression in the TCGA glioma dataset revealed that overexpression of miR-21 was a potential independent prognostic biomarker of poorer OS (HR = 1.27, 95% CI: 1.01, 1.59) and poorer PFS (HR = 1.46, 95% CI: 1.17, 1.82). Our findings suggest that higher expression of miR-21 is correlated with poorer glioma prognosis.

In patients with gliomas, depression is a common complication that may cause severe psychological barriers and deteriorate the patient’s quality of life (QoL). Currently, the Hospital Anxiety and Depression Scale (HADS) is the most commonly used tool to diagnose depression in patients with gliomas. Female sex, unmarried status, low education level, high tumor grade, and a history of mental illness may increase the risks of depression and depressive symptoms in patients with gliomas. The QoL of patients with gliomas can be directly reduced by depression. Therefore, the evaluation and intervention of mood disorders could improve the overall QoL of patients with gliomas. Antidepressant use has become a treatment strategy for patients with gliomas and comorbid depression. This narrative review summarizes the current issues related to depression in patients with gliomas, including the prevalence, risk factors, and diagnostic criteria of depression as well as changes in QoL caused by comorbid depression and antidepressant use. The purpose of this review is to guide clinicians to assess the psychological status of patients with gliomas and to provide clinicians and oncologists with a new treatment strategy to improve the prognosis of such patients.

In biomedical data mining, the gene dimension is often much larger than the sample size. To solve this problem, we need to use a feature selection algorithm to select feature gene subsets with a strong correlation with phenotype to ensure the accuracy of subsequent analysis. This paper presents a new three-stage hybrid feature gene selection method, that combines a variance filter, extremely randomized tree, and whale optimization algorithm. First, a variance filter is used to reduce the dimension of the feature gene space, and an extremely randomized tree is used to further reduce the feature gene set. Finally, the whale optimization algorithm is used to select the optimal feature gene subset. We evaluate the proposed method with three different classifiers in seven published gene expression profile datasets and compare it with other advanced feature selection algorithms. The results show that the proposed method has significant advantages in a variety of evaluation indicators.