An interactive network of alternative splicing events with prognostic value in geriatric lung adenocarcinoma via the regulation of splicing factors

Overexpression of the QKI Gene Promotes Differentiation of Goat Myoblasts into Myotubes

The QKI genes encode RNA-binding proteins regulating cell proliferation, differentiation, and apoptosis. The Goat QKI has six isoforms, but their roles in myogenesis are unclear. In this study, the six isoforms of the QKI gene were overexpressed in goat myoblast. Immunofluorescence, qPCR and Western blot were used to evaluate the effect of QKI on the differentiation of goat myoblast. An RNA-Seq was performed on the cells with the gain of the function from the major isoforms to screen differentially expressed genes (DEGs). The results show that six isoforms had different degrees of deletion in exons 6 and 7, and caused the appearance of different types of encoded amino acids. The expression levels of the QKI-1 and QKI-5 groups were upregulated in the biceps femoris and latissimus dorsi muscle tissues compared with those of the QKI-4, QKI-7, QKI-3 and QKI-6 groups. After 6 d of myoblast differentiation, QKI-5 and the myogenic differentiators MyoG, MyoD, and MyHC were upregulated. Compared to the negative control group, QKI promoted myotube differentiation and the myoblasts overexpressing QKI-5 formed large, abundant myotubes. In summary, we identified that the overexpression of the QKI gene promotes goat-myoblast differentiation and that QKI-5 is the major isoform, with a key role. The RNA-Seq screened 76 upregulated and 123 downregulated DEGs between the negative control and the QKI-5-overexpressing goat myoblasts after d 6 of differentiation. The GO and KEGG analyses associated the downregulated DEGs with muscle-related biological functions. Only the pathways related to muscle growth and development were enriched. This study provides a theoretical basis for further exploring the regulatory mechanism of QKI in skeletal-muscle development in goats.

Computational detection of a genome instability-derived lncRNA signature for predicting the clinical outcome of lung adenocarcinoma

Evidence has been emerging of the importance of long non-coding RNAs (lncRNAs) in genome instability. However, no study has established how to classify such lncRNAs linked to genomic instability, and whether that connection poses a therapeutic significance. Here, we established a computational frame derived from mutator hypothesis by combining profiles of lncRNA expression and those of somatic mutations in a tumor genome, and identified 185 candidate lncRNAs associated with genomic instability in lung adenocarcinoma (LUAD). Through further studies, we established a six lncRNA-based signature, which assigned patients to the high- and low-risk groups with different prognosis. Further validation of this signature was performed in a number of separate cohorts of LUAD patients. In addition, the signature was found closely linked to genomic mutation rates in patients, indicating it could be a useful way to quantify genomic instability. In summary, this research offered a novel method by through which more studies may explore the function of lncRNAs and presented a possible new way for detecting biomarkers associated with genomic instability in cancers.