ABSTRACT
As an underrecognized route of cancer metastasis, perineural invasion (PNI) is defined as the neoplastic invasion of nerves, which can be targeted to inhibit the metastasis of malignant cancer. However, the mechanism underlying PNI in cancer is largely unknown. We constructed a PNI gene signature based on a Pathway Studio-mediated literature screen and investigated the relevant genes in a gastric cancer model. Thus, a total of 467 studies/datasets were retrieved from the Gene Expression Omnibus database using the keyword "gastric cancer," among which 13 studies that focused on gene expression profiling were further manually inspected and selected. Furthermore, the constructed PNI gene signature (104 genes) expression was meta-analyzed, and the consensus-expressed C-X-C motif chemokine ligand 8 (CXCL8) and matrix metallopeptidase 9 (MMP9) (p < 0.01, |log fold change| >1) were detected. Importantly, the disease-free survival was significantly worse in patients with high expressions of CXCL8 and MMP9 than in those with low expressions (p = 0.05). Moreover, multiple linear regression analysis showed that the population region (country) was associated with the expressions of both CXCL8 and MMP9. In conclusion, these data suggest that the coexpression of CXCL8 and MMP9 could be an early detection marker for PNI, with a potential to be utilized as individual therapy targets for early treatment to prevent PNI-related cancer metastasis.
ABSTRACT
RNAs may act as competing endogenous RNAs (ceRNAs), a critical mechanism in determining gene expression regulations in many cancers. However, the roles of ceRNAs in thyroid carcinoma remains elusive. In this study, we have developed a novel pipeline called Molecular Network-based Identification of ceRNA (MNIceRNA) to identify ceRNAs in thyroid carcinoma. MNIceRNA first constructs micro RNA (miRNA)-messenger RNA (mRNA)long non-coding RNA (lncRNA) networks from miRcode database and weighted correlation network analysis (WGCNA), based on which to identify key drivers of differentially expressed RNAs between normal and tumor samples. It then infers ceRNAs of the identified key drivers using the long non-coding competing endogenous database (lnCeDB). We applied the pipeline into The Cancer Genome Atlas (TCGA) thyroid carcinoma data. As a result, 598 lncRNAs, 1025 mRNAs, and 90 microRNA (miRNAs) were inferred to be differentially expressed between normal and thyroid cancer samples. We then obtained eight key driver miRNAs, among which hsa-mir-221 and hsa-mir-222 were key driver RNAs identified by both miRNA-mRNA-lncRNA and WGCNA network. In addition, hsa-mir-375 was inferred to be significant for patients' survival with 34 associated ceRNAs, among which RUNX2, DUSP6 and SEMA3D are known oncogenes regulating cellular proliferation and differentiation in thyroid cancer. These ceRNAs are critical in revealing the secrets behind thyroid cancer progression and may serve as future therapeutic biomarkers.
ABSTRACT
Although domesticated tomato is cultivated by wild tomato, there are a lot of differences between cultivated tomato and wild tomato, such as shape, physiological function and life history. Many studies show that wild tomato has better salt resistance and drought resistance. In addition to, domesticated tomato's fruit is bigger and has more nutritious than wild tomato. The different features are closely related to differentially expressed genes. We identified 126 up-regulated differentially expressed genes and 87 down-regulated differentially expressed genes in cultivated tomato and wild tomato by RNA-Seq. These differentially expressed genes may be associated with salt resistance, drought resistance and fruit nutrition. These differentially expressed genes also further highlight the large-scale reconstruction between wild and cultivated species. In this paper, we mainly study GO enrichment analysis and pathway analysis of the differentially expressed genes. After GO and pathway enrichment analysis, a set of significantly enriched GO annotations and pathways were identified for the differentially expressed genes. What's more, we also identified long non-coding RNAs and mRNAs in the two species and analyzed its essential features. In addition to, we construct a co-expression network of long non-coding RNAs and mRNAs, and annotate mRNAs associated with long non-coding RNAs as target genes, and speculate the regulation function of long non-coding RNAs. In total, our results reveal the effects of artificial and natural selection on tomato's transcript, providing scientific basis for tomato's research in the future.
