Cadmium (Cd) is a heavy metal and environmental toxin. Exposure to Cd has been associated with a variety of human cancers. In this study, we performed in vitro assays to examine the effects of cadmium chloride (CdCl₂) on A549 cells, a human lung adenocarcinoma cell line. Cd does not affect proliferation, migration, or apoptosis of A549 cells at concentrations of 0.1–10 μM. At 0.5 and 1 μM, Cd increases the expression of vascular endothelial growth factor (VEGF) (p < 0.05, p < 0.01, respectively), but not basic fibroblast growth factor (b-FGF) in A549 cells. The conditioned media were collected from the A549 cells treated with 1 μM Cd and were co-cultured with human umbilical vein endothelial cells (HUVECs). Upon treatment with the conditioned media, the proliferation and migration of HUVECs significantly increased (p < 0.01, p < 0.05, respectively), while apoptosis remained unchanged. In addition, 1 μM Cd increases the level of hypoxia inducible factor 1-α (HIF1-α), which is a positive regulator of VEGF expression. Although low-dose Cd does not directly affect the growth of lung adenocarcinoma cells, it might facilitate the development of tumors through its pro-angiogenic effects.

Background: The relationship between Hashimoto’s thyroiditis (HT) and papillary thyroid microcarcinoma (PTMC) is controversial. These include central lymph node metastasis (CLNM), which affects the prognosis of PTMC patients. This study aimed to establish a predictive model combining ultrasonography and clinicopathological features to accurately evaluate latent CLNM in PTMC patients with HT at the clinical lymph node-negative (cN0) stage.Methods: In this study, 1102 PTMC patients who received thyroidectomy and central cervical lymph node dissection (CLND) from the First Affiliated Hospital of Shandong First Medical University from January 2021 to December 2022 and the 960th Hospital of PLA from January 2021 to December 2022 were jointly collected. The clinical differences between PTMCs with HT and those without HT were compared. A total of 373 PTMCs with HT in cN0 were randomly divided into a training cohort and a validation cohort. By analyzing and screening the risk factors of CLNM, a nomogram model was established and verified. The predictive performance was measured by the receiver operating characteristic (ROC) curve, calibration curve, and clinical decision curve analysis (DCA).Results: The ratio of central lymph node metastasis (CLNMR) in PTMCs with HT was 0.0% (0.0%, 15.0%) and 7.7% (0.0%, 40.0%) in the non-HT group (P<0.001). Multivariate logistic regression analysis showed that age, gender, calcification, adjacent to trachea or capsule, and TPOAB were predictors of CLNM in PTMCs with HT. The areas under the curve (AUC) of the prediction models in the training cohort and the validation cohort were 0.835 and 0.825, respectively, which showed good differentiation ability. DCA indicates that the prediction model also has high net benefit and clinical practical value.Conclusion: This study found that CLN involvement was significantly reduced in PTMC patients with HT, suggesting that different methods should be used to predict CLNM in PTMC patients with HT and without HT, to more accurately assist preoperative clinical evaluation. The actual CLNM situation of PTMCs with HT in cN0 can be accurately predicted by the combination of ultrasonography and clinicopathological features.

Wilms tumor (WT) is the most common type of malignant abdominal tumor in children; it exhibits a high degree of malignancy, grow rapidly, and is prone to metastasis. This study aimed to construct a prognosis model based on anoikis-related genes (ARGs) and epithelial-mesenchymal transition (EMT)-related genes (ERGs) for WT patients; we assessed the characteristics of the tumor microenvironment and treatment efficacy, as well as identifying potential therapeutic targets. To this end, we downloaded transcriptome sequencing data and clinical data for WT and normal renal cortices and used R to construct and validate the prognostic model based on ARGs and ERGs. Additionally, we performed clinical feature analysis, nomogram construction, mutation analysis, drug sensitivity analysis, Connectivity Map (cMAP) analysis, functional enrichment analysis, and immune infiltration analysis. Finally, we screened the hub gene using the STRING database and validated it via experiments. In this way, we constructed a model with good accuracy and robustness, which was composed of seven anoikis- and EMT-related genes. Paclitaxel and mesna were selected as potential chemotherapeutic drugs and adjuvant chemotherapeutic drugs for the WT high-risk group by using the Genomics of Drug Sensitivity in Cancer (GDSC) and cMAP compound libraries, respectively. We proved the existence of a strong correlation between invasive immune cells and prognostic genes and risk scores. Next, we selected NTRK2 as the hub gene, and in vitro experiments confirmed that its inhibition can significantly inhibit the proliferation and migration of tumor cells and promote late apoptosis. In summary, we screened out the potential biomarkers and chemotherapeutic drugs that can improve the prognosis of patients with WT.

Objective: The primary objective was to establish a radiomics model utilizing longitudinal +cross-sectional ultrasound (US) images of lymph nodes (LNs) to predict cervical lymph node metastasis (CLNM) following differentiated thyroid carcinoma (DTC) surgery. Methods: A retrospective collection of 211 LNs from 211 postoperative DTC patients who underwent neck US with suspicious LN fine needle aspiration cytopathology findings at our institution was conducted between June 2021 and April 2023. Conventional US and clinicopathological information of patients were gathered. Based on the pathological results, patients were categorized into CLNM and non-CLNM groups. The database was randomly divided into a training cohort (n = 147) and a test cohort (n = 64) at a 7:3 ratio. The least absolute shrinkage and selection operator algorithm was applied to screen the most relevant radiomic features from the longitudinal + cross-sectional US images, and a radiomics model was constructed. Univariate and multivariate analyses were used to assess US and clinicopathological significance features. Subsequently, a combined model for predicting CLNM was constructed by integrating radiomics, conventional US, and clinicopathological features and presented as a nomogram. Results: The area under the curves (AUCs) of the longitudinal + cross-sectional radiomics models were 0.846 and 0.801 in the training and test sets, respectively, outperforming the single longitudinal and cross-sectional models (p < 0.05). In the testing cohort, the AUC of the combined model in predicting CLNM was 0.901, surpassing that of the single US model (AUC, 0.731) and radiomics model (AUC, 0.801). Conclusions: The US-based radiomics model exhibits the potential to accurately predict CLNM following DTC surgery, thereby enhancing diagnostic accuracy.