Tumor shrinkage rate as a potential marker for the prediction of long-term outcome in advanced non-small cell lung cancer treated with first-line tyrosine kinase inhibitors

Context: Tyrosine kinase inhibitors (TKIs) targeting epidermal growth factor receptor (EGFR) play an indispensable role in the treatment of non-small cell lung cancer (NSCLC), leading to a survival major breakthrough, but there remains no uniform standard for predicting the efficacy of TKI therapy. Aims: We retrospectively reviewed the use of EGFR-TKIs for advanced NSCLC between January 2009 and December 2017 in a hospital, which 169 patients who treated with first-line TKIs were enrolled. Subjects and Methods: Multiple clinical factors, including histology, age, and sex, were analyzed. We calculated the tumor shrinkage rate (TSR) by measuring the longest diameters of the main mass by computed tomography (CT) before TKI therapy and the first CT after TKI therapy. We evaluated overall survival (OS) and progression-free survival (PFS) after first-line TKI therapy, and we assessed factors predicting survival using the Kaplan–Meier method. Results: Eligible patients were sorted into higher (n = 83) and lower (n = 86) TSR groups according to the mean TSR of 0.49%. The 83 patients with a higher TSR had longer PFS and OS than those in the 86 patients with a lower TSR (14.83 vs. 8.40 months, P < 0.001, and 31.03 vs. 20.10 months, P < 0.001, respectively). Multivariate analyses revealed that TSR was an independent predictor of PFS and OS (PFS hazard ratio [HR]: 0.506, P < 0.001, and OS HR: 0.291, P < 0.001). Conclusions: These cumulative data support that TSR may be an early predictor of the treatment efficacy in NSCLC with EGFR mutations treated with first-line TKIs

Identification of genes related to high royal jelly production in the honey bee (Apis mellifera) using microarray analysis

Abstract China is the largest royal jelly producer and exporter in the world, and high royal jelly-yielding strains have been bred in the country for approximately three decades. However, information on the molecular mechanism underlying high royal jelly production is scarce. Here, a cDNA microarray was used to screen and identify differentially expressed genes (DEGs) to obtain an overview on the changes in gene expression levels between high and low royal jelly producing bees. We developed a honey bee gene chip that covered 11,689 genes, and this chip was hybridised with cDNA generated from RNA isolated from heads of nursing bees. A total of 369 DEGs were identified between high and low royal jelly producing bees. Amongst these DEGs, 201 (54.47%) genes were up-regulated, whereas 168 (45.53%) were down-regulated in high royal jelly-yielding bees. Gene ontology (GO) analyses showed that they are mainly involved in four key biological processes, and pathway analyses revealed that they belong to a total of 46 biological pathways. These results provide a genetic basis for further studies on the molecular mechanisms involved in high royal jelly production.