Long-term exposure to cadmium (Cd) can severely damage the kidney, where orally absorbed Cd accumulates. However, the molecular mechanisms of Cd-induced kidney damage, especially the early biomarkers of Cd-induced renal carcinogenesis, are unclear. In the present study, we established a rat kidney injury model by intragastric administration of Cd to evaluate the morphological and biochemical aspects of kidney injury. We randomly divided Sprague-Dawley rats into control, low Cd (3 mg/kg), and high Cd (6 mg/kg) groups and measured biochemical indices associated with renal toxicity after 2, 4, and 8 weeks of treatment. The Cd-exposed mice had significantly higher Cd concentrations in blood and renal tissues as well as blood urea nitrogen (BUN), β2-microglobulin (β2-MG), urinary protein excretion, and tumor necrosis factor-α (TNF-α) levels. Furthermore, histopathological and transmission electron microscopy (TEM) observations revealed structural disruption of renal tubules and glomeruli after 8 weeks of exposure to the high Cd regimen. Besides, microarray technology experiments showed that Cd increased the expression of genes related to the chemical carcinogenesis pathway in kidney tissue. Finally, combining the protein–protein interaction (PPI) network of the Cd carcinogenesis pathway genes with the microarray and Comparative Toxicogenomics Database (CTD) results revealed two overlapping genes, CYP1B1 and UGT2B. Therefore, the combined molecular and bioinformatics experiments’ results suggest that CYP1B1 and UGT2B are biomarkers of Cd-induced kidney injury with precancerous lesions.

Background: Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction. Methods: A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram. Results: Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61–3.53) in the validation set I, 1.35 (95% CI, 1.06–1.71) in the validation set II, and 2.71 (95% CI, 1.77–4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type ( p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival. Conclusions: In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.