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Our results demonstrate that AUY922 potently induces cytotoxicity with concomitant modulation of hsp90 client proteins in ATC cells. Moreover, AUY922 has a synergistic activity with PXD101 in induction of cytotoxicity in conjunction with the inactivation of PI3K/Akt signaling and survivin and the activation of DNA damage response in ATC cells.

The effect of 17-allylamino-17-demethoxygeldanamycin (17-AAG), an hsp90 inhibitor, alone or in combination with paclitaxel on survival of anaplastic thyroid carcinoma (ATC) was evaluated. In 8505C and CAL62 cells, after treatment of 17-AAG, cell viability decreased, and the percentage of dead cells increased. 17-AAG did not cause cleavage of caspase-3 protein, and change expression of IAPs. Pretreatment of z-VAD-fmk did not alter cell viability and the percentage of dead cells. In 17-AAG-treated cells, knockdown of p53 rescued growth inhibition, while cycloheximide attenuated cell death. When cells were treated with both 17-AAG and paclitaxel, all of the combination index values were higher than 1, indicating antagonism between 17-AAG and paclitaxel. In 17-AAG- and paclitaxel-treated cells, compared with paclitaxel alone-treated cells, the protein levels of hsp90, hsp70, and hsc70 increased. In conclusion, our results suggest that 17-AAG induces non-apoptotic cell death requiring de novo protein synthesis in ATC cells. Moreover, these results demonstrate that 17-AAG antagonizes paclitaxel with concomitant alterations in hsp90 client proteins in ATC cells.

Background The atherogenic index of plasma (AIP) is composed of triglycerides and high-density lipoprotein cholesterol and is a novel marker for assessing the risk of atherogenicity and cardiometabolic health. An association between AIP and greater frequency of major adverse cardiovascular events (MACEs) in patients with type 2 diabetes mellitus and high cardiovascular (CV) disease risk has been reported. However, only few studies have examined the correlation between AIP and CV risk in general populations. We thus aimed to evaluate the relationship between AIP and CV diseases using a large-scale population dataset from the Korean National Health Insurance Service-National Health Screening Cohort (NHIS-HEALS). Methods A total of 514,866 participants were enrolled from the NHIS-HEALS and classified according to the AIP quartiles. We performed univariate and multivariate Cox proportional hazards regression analyses to determine the association between AIP and MACEs, CV events, and CV mortality. Results During follow-up, we documented 12,133, 11,055, and 1942 cases of MACEs, CV events, and CV mortality, respectively. The multivariate-adjusted hazard ratios [HRs; 95% confidence interval (CI)] for MACEs gradually and significantly increased with the AIP quartiles [1.113 (1.054–1.175) in Q2, 1.175 (1.113–1.240) in Q3, and 1.278 (1.209–1.350) in Q4], following an adjustment for the conventional CV risk factors, including age, sex, body mass index, smoking, alcohol drinking, physical activities, household income, fasting glucose, systolic blood pressure, low-density lipoprotein cholesterol, and estimated glomerular filtration rate. In subgroup analyses, the association of AIP with MACEs and CV events was particularly outstanding in patients with diabetes. Conclusions AIP was significantly associated with CV risks after adjusting for the traditional risk factors. Therefore, it may be used as an effective mass screening method to identify patients at a high risk of CV events.