Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment.

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

Systemic immune-inflammation index predicts postoperative acute kidney injury in hepatocellular carcinoma patients after hepatectomy.

ABSTRACT: The systemic immune-inflammation index (SII) is an independent prognostic predictor of hepatocellular carcinoma (HCC). The present investigation examined whether an association exists between preoperative SII value and postoperative acute kidney injury (pAKI) in HCC patients.The study included 479 hepatitis B virus (HBV)-associated HCC patients undergoing hepatectomy. The SII was calculated as P × N/L, where P, N, and L represent the counts of platelets, neutrophils, and lymphocytes in routine blood test, respectively. After propensity score matching, logistic regression analysis was used to explore independent predictors of pAKI in HCC patients.pAKI was confirmed in 51 patients (10.8%). The average SII value was higher in patients with pAKI than patients without pAKI. After multivariate logistic regression analysis, SII, history of hypertension, and tumor size, among others, were found to be predictors of pAKI. The optimal threshold value of SII for predicting pAKI was found to be 547.84 × 109/L. Multivariate analysis performed after propensity score matching confirmed that SII ≥ 547.84 × 109/L was an independent predictor of pAKI.The preoperative SII qualifies as a novel, independent predictor of pAKI in HCC patients with HBV infection who underwent hepatectomy.