Increased KIF15 Expression Predicts a Poor Prognosis in Patients with Lung Adenocarcinoma

Background/Aims: Lung cancer is the leading cause of cancer-related deaths worldwide. The outcome of patients with non-small cell lung cancer remains poor; the 5-year survival rate for stage IV non-small cell lung cancer is only 1.0%. KIF15 is a tetrameric kinesin spindle motor that has been investigated for its regulation of mitosis. While the roles of kinesin motor proteins in the regulation of mitosis and their potentials as therapeutic targets in pancreatic cancer have been described previously, the role of KIF15 in lung cancer development remains unknown. Methods: Paired lung carcinoma specimens and matched adjacent normal tissues were used for protein analysis. Clinical data were obtained from medical records. We first examined KIF15 messenger RNA expression in The Cancer Genome Atlas database, and then determined KIF15 protein levels using immunohistochemistry and western blotting. Differences between the groups were analyzed using repeated measures analysis of variance. Overall survival was analyzed using the Kaplan–Meier method. Cell-cycle and proliferation assays were conducted using A549, NCI-H1299, and NCI-H226 cells. Results: KIF15 was significantly upregulated at both the messenger RNA and protein levels in human lung tumor tissues. In patients with lung adenocarcinoma, KIF15 expression was positively associated with disease stages; high KIF15 expression predicted a poor prognosis. KIF15 knockdown using short hairpin RNA in two human lung adenocarcinoma cell lines induced G1/S phase cell cycle arrest and inhibited cell growth, but there was no effect in human lung squamous cell carcinoma. Conclusion: Our findings show that KIF15 is involved in lung cancer carcinogenesis. KIF15 could therefore serve as a specific prognostic marker for patients with lung adenocarcinoma

BmC/EBPZ gene is essential for the larval growth and development of silkworm, Bombyx mori

The genetic male sterile line (GMS) of the silkworm Bombyx mori is a recessive mutant that is naturally mutated from the wild-type 898WB strain. One of the major characteristics of the GMS mutant is its small larvae. Through positional cloning, candidate genes for the GMS mutant were located in a region approximately 800.5 kb long on the 24th linkage group of the silkworm. One of the genes was Bombyx mori CCAAT/enhancer-binding protein zeta (BmC/EBPZ), which is a member of the basic region-leucine zipper transcription factor family. Compared with the wild-type 898WB strain, the GMS mutant features a 9 bp insertion in the 3′end of open reading frame sequence of BmC/EBPZ gene. Moreover, the high expression level of the BmC/EBPZ gene in the testis suggests that the gene is involved in the regulation of reproduction-related genes. Using the CRISPR/Cas9-mediated knockout system, we found that the BmC/EBPZ knockout strains had the same phenotypes as the GMS mutant, that is, the larvae were small. However, the larvae of BmC/EBPZ knockout strains died during the development of the third instar. Therefore, the BmC/EBPZ gene was identified as the major gene responsible for GMS mutation

Analyzing Capacity Utilization and Travel Patterns of Chinese High-Speed Trains: An Exploratory Data Mining Approach

Train capacity utilization (TCU), usually represented by passenger load factor (PLF), is a critical measure of effectiveness for rail operation. In literature, efforts are usually made to improve capacity utilization by optimizing rail operation and management strategies. Comparably little attention is paid to analyzing the factors that affect TCU and to understanding the behavioral patterns behind it. This paper applies exploratory data mining techniques to a 3-month long real world train operation data of the Beijing-Shanghai High-Speed Railway. Principal component analysis (PCA) is conducted to find the principal components that can efficiently represent the collected data. Clustering techniques are then applied to understand the unique characteristics that affect PLF and the travel pattern. The findings can be further used to guide train operation planning and facilitate better decision-making