Identification of disulfidptosis-related subtypes and development of a prognosis model based on stacking framework in renal clear cell carcinoma.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is a common malignant tumor with an unsatisfactory prognosis. This study aims to identify the expression patterns of disulfidptosis-related genes (DRGs), develop a prognostic model, and predict immunological profiles. METHODS: First, we identified differentially expressed DRGs in TCGA-KIRC cohort and analyzed their mutational profiles, methylation levels, and interaction networks. Subsequently, we identified disulfidptosis-associated molecular subtypes and investigated their prognostic and immunological characteristics. Simultaneously, a disulfidptosis-related prognostic signature (DRPS) was developed using a two-stage stacking framework consisting of 5 machine learning models. The effect of DRPS on immune cell infiltration levels was explored using seven different algorithms, and the status and function of T cells for distinct risk-score groups were evaluated based on T cell exhaustion and dysfunction scores. Additionally, the study also examined differences in clinical characteristics and therapy efficacy between high- and low-risk groups. RESULTS: We found two disulfidptosis-associated clusters, one of which had a poor prognosis and was linked to high immune cell infiltration but impaired T cell function. DRPS showed excellent predictive performance in all four cohorts and could accurately identified disulfidptosis-related molecular subtypes. The DRPS-based risk score was positively associated with poor prognosis, malignant pathological features, high immune cell infiltration levels, and T cell exhaustion or dysfunction, and better respond to immunotherapy and targeted therapy. Additionally, we have identified a close association between ISG20 and disulfidptosis as well as tumor immunity. CONCLUSION: Our study identified distinct disulfidptosis-related subtypes in ccRCC patients, and constructed the highly accurate and robust DRPS based on an ensemble learning framework, which has critical reference value in clinical decision-making and individualized treatment. And this work also revealed ISG20 exhibits promising potential as a therapeutic target for ccRCC.

Ratiometric system based on graphene quantum dots and Eu3+ for selective detection of tetracyclines.

In this study, a system was established for the detection of tetracyclines (TCs) by simply mixing graphene quantum dots (GQDs) and Eu3+. The GQDs-Eu3+ system exhibits both strong emission of GQDs and weak characteristic emission of Eu3+ upon 360 nm excitation. In the presence of TCs, the luminescence of GQDs was quenched, while the luminescence of Eu3+ was significantly enhanced. It is worth to mention that the GQDs we prepared possess a lot of carboxyl groups on the surface and edge. This carboxyl groups play an important role in fluorescent enhancement of Eu3+. On the basis of this effect, a novel label free ratiometric sensor was developed for the detection of TCs. Under the optimized conditions, linear concentration ranges from 0 to 20 µM for tetracycline was found, and the detection limits was 8.2 nM. This system also exhibited excellent selectivity toward TCs over other antibiotics. In addition, it was successfully applied to the determination of tetracycline in river water and milk samples. The proposed system was simple in design, fast in operation, rapid in response and cost-effective. In view of the advantages, the developed system demonstrates great potential for the detection of TCs in environmental and biological samples.