Cited by Decellularized extracellular matrix-based disease models for drug screening

Application of 3D, 4D, 5D, and 6D bioprinting in cancer research: what does the future look like? DOI

Danial Khorsandi,

Dorsa Rezayat,

Serap Sezen

et al.

Journal of Materials Chemistry B, Journal Year: 2024, Volume and Issue: 12(19), P. 4584 - 4612

Published: Jan. 1, 2024

Recent advancements pertaining to the application of 3D, 4D, 5D, and 6D bioprinting in cancer research are discussed, focusing on important challenges future perspectives.

Language: Английский

Citations

Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach DOI

Tianshu Chen, Yuhan Yang, Zhizhong Huang

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: March 8, 2025

Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop robust predictive model integrating programmed cell death-related genes advanced machine learning techniques. Utilizing transcriptomic data from TCGA-UCEC GSE119041 datasets, we employed comprehensive approach involving 117 algorithms. Key methodologies included differential gene expression analysis, weighted co-expression network functional enrichment studies, immune landscape evaluation, multi-dimensional risk stratification. We identified 10 critical (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) constructed superior performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent accuracy (AUC > 0.8). Kaplan–Meier analysis revealed survival differences between high- low-risk groups in both training (HR = 3.37, p < 0.001) validation cohorts 2.05, 0.021). showed strong correlations clinical characteristics, infiltration patterns, potential therapeutic responses. presents novel, endometrial prognosis, molecular insights provide more precise stratification tool translation.

Language: Английский

Citations

Chalcones induce apoptosis, autophagy and reduce spreading in osteosarcoma 3D models DOI

Michela Rossi, C. Pellegrino,

Martyna Malgorzata Rydzyk

et al.

Biomedicine & Pharmacotherapy, Journal Year: 2024, Volume and Issue: 179, P. 117284 - 117284

Published: Aug. 15, 2024

Osteosarcoma is the most common primary bone malignancy with a challenging prognosis marked by high rate of metastasis. The limited success current treatments may be partially attributed to an incomplete understanding osteosarcoma pathophysiology and absence reliable in vitro models select best molecules for vivo studies. Among natural compounds relevant treatment, Licochalcone A (Lic-A) chalcone derivatives are particularly interesting. Here, Lic-A selected have been evaluated their anticancer effect on multicellular tumor spheroids from MG63 143B cell lines. metabolic activity assay revealed Lic-A, 1i, 1k as promising candidates. To delve into mechanism action, caspase was conducted 2D 3D models. Notably, apoptosis autophagic induction generally observed 1k. invasion demonstrated that possess ability mitigate spread cells within matrix. effectiveness scaffold generating potential antiproliferative agents against has demonstrated. In particular, chalcones exert inducing autophagy, addition they capable reducing invasion. These findings suggest antitumor cells.

Language: Английский

Citations

Decellularized extracellular matrix-based disease models for drug screening DOI

Zhoujiang Chen, Ji Wang, Ranjith Kumar Kankala

et al.

Materials Today Bio, Journal Year: 2024, Volume and Issue: 29, P. 101280 - 101280

Published: Sept. 28, 2024

Language: Английский

Citations