Serum Indicators of Oxidative Damage from Embedded Metal Fragments in a Rat Model

A novel PCDscore based on programmed cell death-related genes can effectively predict prognosis and therapy responses of colon adenocarcinoma

Emerging evidence suggests a correlation between oncogenesis and programmed cell death (PCD). However, comprehensive studies that incorporate all identified PCD-related genes to guide colon adenocarcinoma (COAD) prognosis and precision treatment strategies are lacking. In this study, a series of bioinformatics analyses were comprehensively conducted using data from the TCGA-COAD, GSE17538, and GSE39582 cohorts. A total of 21 PCD-associated prognostic genes were identified through univariate Cox analysis. LASSO and multivariate Cox methods were employed to establish a prognostic gene signature (ALOX12, HSPA1A, IL13, MID2, RFFL, and SLC39A8) and the corresponding scoring system, termed PCDscore, which exhibited robust predictive ability. The ssGSEA and ESTIMATE algorithms were utilized to evaluate the tumor microenvironment of COAD. The high PCDscore group demonstrated a poorer prognosis, characterized by lower CD4+ T cell infiltration and a higher stromal score. In contrast, the low PCDscore group exhibited sensitivity to common chemotherapy drugs such as Cisplatin and 5-Fluorouracil. Single-cell sequencing analysis further revealed that the high-PCDscore group displayed a lower proportion of CD4+ T cells. Colorectal cancer samples from the years 2013-2017 were employed to validate the PCDscore, while those from 2018 to 2019 served as a temporal external validation set for the PCDscore. In vitro experimental results indicated that the overexpression of SLC39A8 inhibited the proliferation and invasion of colorectal cancer cells. The study developed a novel PCDscore system based on the analysis of genes related to all identified PCD types, providing valuable insights into clinical prognosis and drug sensitivity for patients with COAD.

Early transcriptomic signatures and biomarkers of renal damage due to prolonged exposure to embedded metal

BACKGROUND

Prolonged exposure to toxic heavy metals leads to deleterious health outcomes including kidney injury. Metal exposure occurs through both environmental pathways including contamination of drinking water sources and from occupational hazards, including the military-unique risks from battlefield injuries resulting in retained metal fragments from bullets and blast debris. One of the key challenges to mitigate health effects in these scenarios is to detect early insult to target organs, such as the kidney, before irreversible damage occurs.

METHODS

High-throughput transcriptomics (HTT) has been recently demonstrated to have high sensitivity and specificity as a rapid and cost-effective assay for detecting tissue toxicity. To better understand the molecular signature of early kidney damage, we performed RNA sequencing (RNA-seq) on renal tissue using a rat model of soft tissue-embedded metal exposure. We then performed small RNA-seq analysis on serum samples from the same animals to identify potential miRNA biomarkers of kidney damage.

RESULTS

We found that metals, especially lead and depleted uranium, induce oxidative damage that mainly cause dysregulated mitochondrial gene expression. Utilizing publicly available single-cell RNA-seq datasets, we demonstrate that deep learning-based cell type decomposition effectively identified cells within the kidney that were affected by metal exposure. By combining random forest feature selection and statistical methods, we further identify miRNA-423 as a promising early systemic marker of kidney injury.

CONCLUSION

Our data suggest that combining HTT and deep learning is a promising approach for identifying cell injury in kidney tissue. We propose miRNA-423 as a potential serum biomarker for early detection of kidney injury.

The ratio of monocyte count and high-density lipoprotein cholesterol mediates the association between urinary tungsten and cardiovascular disease: a study from NHANES 2005-2018

Tungsten (W) is an emerging contaminant that can damage multiple systems in humans. However, studies of its effects on cardiovascular disease (CVD) are limited. The monocyte count to high-density lipoprotein cholesterol ratio (MHR) is a composite inflammatory index of great concern in recent years, derived from lipid and cell inflammation parameters, that is used to indicate the risk of CVD. This study aimed to investigate the association between urinary W and CVD in the general population and compare the mediating effects of lipids, cell inflammatory parameters, and MHR to find a better target for intervention. We analyzed data from 9137 (≥ 20 years) participants in the National Health and Nutrition Examination Survey (NHANES), from 2005 to 2018. Restricted cubic splines (RCS) and survey-weighted generalized linear models (SWGLMs) were used to assess the relationship between W and CVD. Mediated analyses were used to explore lipids, cell inflammatory parameters, and MHR in the possible mediating pathways between W and CVD. In SWGLM, we found that W enhances the risk of CVD, especially congestive heart failure (CHF), coronary heart disease (CHD), and angina pectoris (AP). Women, higher age groups (≥ 55 years), and those with hypertension were vulnerable to W in the subgroup analysis. Mediation analysis showed that monocyte count (MC), white blood cell count (WBC), high-density lipoprotein cholesterol (HDL), and MHR played a mediating role between W and CVD in proportions of 8.49%, 3.70%, 5.18%, and 12.95%, respectively. In conclusion, our study shows that urinary W can increase the risk of CVD, especially for CHF, CHD, and AP. Women, older age groups, and people with hypertension seem to be more vulnerable to W. In addition, MC, WBC, HDL, and MHR mediated the association between W and CVD, especially MHR, which suggests that we should consider it as a priority intervention target in the future.