Recent studies have reported that microRNAs (miRNAs) often function as negative post-transcriptional regulators with altered expression levels found in colorectal cancer (CRC). There have been few studies on miRNAs that regulate the oncogenic alterations in CRC. Here, we aim to explore the anti-cancer miRNA and the potential mechanisms by which miRNAs modulate CRC progression. We performed an integrated analysis of CRC miRNA expression datasets in The Cancer Genome Atlas (TCGA). The miRNA with the lowest expression, miR-760, was validated in an independent validation sample cohort of 76 CRC tissues. Functional assays, such as CCK-8 assay, colony formation assay, and CFSE staining, were used to determine the oncogenic role of miR-760 in human CRC progression. Furthermore, western blotting and dual-luciferase reporter assay were used to determine the mechanism by which miR-760 promotes proliferation of CRC cells. Xenograft nude mouse models were used to determine the role of miR-760 in CRC tumorigenicity in vivo. Immunohistochemical assays were conducted to study the relationship between miR-760 expression and basic leucine zipper transcriptional factor ATF-like 3 (BATF3) expression in human CRC samples. miR-760 was markedly downregulated in CRC tissues, and low miR-760 expression was associated with poor prognosis among CRC patients. Upregulation of miR-760 suppressed CRC cell proliferation, whereas downregulation of miR-760 promoted CRC proliferation in vitro. Additionally, we identified BATF3 as a direct target of miR-760, and that the essential biological function of miR-760 during CRC progression both in vitro and in vivo is to suppress the expression of BATF3 and downstream cyclinD1 via AP-1 transcription factor. Finally, we showed a significant correlation between miR-760 and BATF3 expression in CRC tissues. miR-760 inhibited CRC growth by downregulating BATF3/AP-1/ cyclinD1 signaling.

Gliomas are the most aggressive phenotypes of brain tumors and are classified into four grades according to the malignancy degree by the World Health Organization. Metabolic profiling can provide an overview of metabolic reprogramming at a specific stage of tumor initiation and development. Studies about metabolic alterations related to different grades of gliomas are helpful to understand the molecular mechanism for progression of glioma. In the current study, metabolomics and lipidomics analyses based on chromatography-mass spectrometry were performed on different grades of glioma tissues. Differential metabolites between glioma and para-tumor tissues were studied and used as the basis to explore metabolic alterations related to glioma grading. It was found that short-chain acylcarnitines were elevated, whereas lysophosphatidylethanolamines (LPEs) were decreased in high-grade gliomas. Furthermore, the gene expression of short/branched-chain acyl-coenzyme dehydrogenase (ACADSB), which is involved in fatty acid oxidation, was found down-regulated with glioma progression by analyzing related genes and pathways. In addition, LPE metabolism showed a significant difference among different grades of gliomas. These important metabolic pathways related to glioma progression may provide potential clues for further study on the mechanisms and treatment of glioma.

The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response. We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found. We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations. We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.