Disulfidptosis, a new form of cell death triggered by disulfide stress, is characterized by the collapse of cytoskeleton proteins and F-actin due to the intracellular accumulation of disulfides. This discovery will eventually aid in the development of therapeutic strategies against cancer.

Background. Cancerous embryo antigen (CEA) and carbohydrate antigen 19-9 (CA19-9) are commonly used in clinical practice to assist in diagnosing CRC. However, their sensitivity is very low. This study aims to investigate the clinical significance of circulating tumor cells (CTCs) and circulating tumor endothelial cells (CTECs) compared with CEA and CA19-9 in the auxiliary diagnosis of colorectal cancer (CRC) patients. Methods. 115 pathologically confirmed CRC patients and 20 healthy controls were enrolled in this study. CTCs and CTECs were enriched and identified by subtraction enrichment and immunostaining-fluorescence in situ hybridization (SE-iFISH). A logistic regression was used to establish a model for the receiver-operating characteristic (ROC) curve analysis, and the diagnostic efficacy of CTCs, CTECs, CEA, CA19-9, and their combinations was analyzed. Results. The CTC ( ) and CTEC ( ) level was significantly higher in CRC patients than that in healthy controls. For CRC patients, CTC and CTEC level was significantly correlated with tumor stage and lymph node metastasis status, but not with sex, age, tumor location, and degree of differentiation. The positive rate of CTCs, CTECs, CEA, and CA19-9 in CRC patients was 87.8%, 39.1%, 28.7%, and 26.1%, respectively. To distinguish CRC patients from controls, the area under the curve (AUC) of CTC was 0.889, which was much higher than 0.695 of CTEC, 0.696 of CEA, and 0.695 of CA19-9. Establishing ROC curve by logistic regression algorithm, the highest AUC was 0.935, which combined CTCs with CTEC, CEA, and CA19-9. Conclusions. CTCs combined with CTEC, CEA, and CA19-9 are useful to improve the diagnostic efficiency, which has high clinical significance in the diagnosis of colorectal cancer.

Potentiometric immunoassays have been utilized for the quantitative detection of alpha-fetoprotein (AFP) in hepatocellular carcinoma, but most of them involve low sensitivity and enzyme labels, and thus are unfavorable for routine use. In this work, we report the proof-of-concept of a sensitive and powerful ion-selective potentiometric sensing method for AFP detection with an in situ amplified signal readout. This potentiometric immunoassay mainly contains a silver nanocluster-functionalized single-stranded DNA (AgNC-DNA), a short DNA primer and two antibodies. A sandwich-type immunoreaction is employed for AFP determination on an anti-AFP capture antibody-coated microplate using a biotinylated human AFP secondary antibody. Coupling with a typical biotin–avidin system, the biotinylated DNA initiator strands are conjugated on the microplate in the presence of AFP to induce the rolling cycle amplification (RCA) reaction, followed by AgNC-DNA hybridization. Upon addition of HNO₃, the hybridized AgNCs are dissolved into numerous Ag(I) ions, which can be readily determined on a portable handheld silver-ion selective electrode (Ag-ISE). Under optimal conditions, the electrode potential increases with an increase in AFP concentration and exhibits a good linear range of 0.01–100 ng mL⁻¹ at a detection limit of 7.9 pg mL⁻¹. Moreover, the Ag-ISE-based potentiometric immune assay also shows good reproducibility, high specificity and long-term storage stability. Importantly, 18 human serum specimens containing the AFP analyte are screened using the potentiometric immunoassay, giving well-matched experimental results relative to the referenced enzyme-linked immunosorbent assay (ELISA) method.

Background: Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Dysregulated small nucleolar RNAs (snoRNAs) have recently been demonstrated to participate in cancer activity through their influence on cellular transformation and carcinogenesis. Specifically, SNORD93 exhibits consistent dysregulation across multiple malignancies and demonstrates significant correlation with adverse clinical outcomes, but it remains unclear how SNORD93 is involved in the development of CRC. In this study, we examined the role of SNORD93 in CRC pathogenesis in order to provide novel diagnostic and treatment targets for CRC. Methods: The expression levels of SNORD93 were quantified in CRC tissue samples (n=13) and paired adjacent normal tissue samples (n=13) from the same patients, as well as in human CRC cell lines (HT-29, SW480, and HCT-15) and the normal human intestinal epithelial crypt cell (HIEC) line via quantitative real-time polymerase chain reaction (qRT-PCR). The biological significance of SNORD93 in the proliferation, migration, cell cycle progression, and cell apoptosis was examined through gain-of-function analyses via Cell Counting Kit-8 (CCK-8) assay, colony formation assay, wound healing assay, and flow cytometry. Additionally, the expression of related proteins was quantified through Western blotting (WB). Results: A significant downregulation of SNORD93 was identified in CRC tissues and cells. Moreover, downregulation of SNORD93 was significantly associated with the poor survival of patients with CRC. In contrast, the overexpression of SNORD93 suppressed the growth of CRC cells in vitro but did not affect CRC cell migration. In terms of mechanism, SNORD93 inhibited cellular proliferation by arresting cells at the G1/S transition phase, while simultaneously inducing apoptosis. Moreover, SNORD93 overexpression correlated with elevated phosphorylation of both receptor-interacting protein (RIP) and mixed lineage kinase domain-like protein (MLKL), suggesting potential involvement of necroptosis pathways. Conclusions: Collectively, these results establish SNORD93 as a functionally significant tumor-suppressive snoRNA in CRC.

Renal cell carcinoma (RCC) is the leading cause of urinary system morbidity and mortality. Early identification is crucial for improving RCC patient outcomes. This study aims to construct and validate an RCC prediction model for at-risk individuals using machine learning (ML) based on routine clinical data. Data from the Quanzhou First Hospital Affiliated with Fujian Medical University between March 2014 and March 2024 were retrospectively collected, with 70% randomly assigned to the training cohort and 30% to the validation cohort. Univariate and hierarchical clustering methods were employed to identify discriminatory features to enable optimal ML algorithm selection. The performance of 7 kinds of ML algorithms-based models was evaluated based on sensitivity (recall), accuracy, F1-score, area under the receiver operating curve (AUC), discrimination, calibration, and clinical net benefit. The algorithm achieving the best AUC was selected for combination with recursive feature elimination to identify features that maximize model performance and stability. After that, the RCC prediction model was finally constructed, and the Shapley Additive Explanations method was used to visualize model characteristics and individual case predictions. Among those algorithms, the eXtreme Gradient Boosting algorithm achieving the best performance was selected for final construction. Combined with the recursive feature elimination method, it identified 21 clinically relevant variables, including age, total protein, albumin, total bilirubin, alanine aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase, glucose, lactate dehydrogenase, creatine kinase-MB, creatinine, potassium-chloride ratio, sodium ion, calcium ion, eosinophil count, hemoglobin, platelet count, Systemic Immune-Inflammation Index, Pan-Immune-Inflammation Value, platelet–lymphocyte ratio, and sodium–chloride ratio for RCC model construction. Subsequently, a RCC prediction model and eXtreme Gradient Boosting using these 21 variables was built, achieving AUC of 0.955 (95% CI: 0.938–0.976) and an average precision of 0.923 in the validation cohort. The additional calibration curve showed high agreement between predicted and observed risks. Finally, the Shapley Additive Explanations method well demonstrated the importance of all model features and provided case-specific interpretation for clinicians. We developed and validated an ML model using routine clinical data for large-scale RCC screening. This cost-effective approach facilitates the early detection of and intervention for RCC, which may lead to improved clinical outcomes.