Machine learning-based pan-cancer study of classification and mechanism of BRAF inhibitor resistance DOI Open Access

Yuhang Zhao,

Kai Yang, Yujun Chen

и другие.

Translational Cancer Research, Год журнала: 2024, Номер 13(12), С. 6645 - 6660

Опубликована: Дек. 1, 2024

V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim the study was develop a machine-learning based method identify patients who are at high-risk BRAFi potential biomarker. From Cancer Cell Line Encyclopedia (CCLE) Genomics Drug Sensitivity in (GDSC) databases, we collected RNA sequencing half maximal inhibitory concentration (IC50) data from 235 pan-cancer cell lines then identified 37 significant differential expression genes associated with resistance. Employing machine learning (ML) models, successfully classified into resistant sensitive groups, achieving robust performance external validation datasets. AOX1 may play vital part metabolism Further, found that higher mRNA OXTR, H2AC13, TBX2, lower SLC2A4, as detected by PCR WM983B SKMEL-5 lines, were independent risk factors for We established gene-expression model using ML methods, consisting variables on RNA-seq database, which externally validated could be used predict Meanwhile, our findings provide valuable insights molecular mechanisms resistance, enabling identification patients.

Язык: Английский

Recent Advances in Small Molecule Inhibitors of Deubiquitinating Enzymes DOI

Pengwei Liu,

Zhengyang Chen,

Yiting Guo

и другие.

European Journal of Medicinal Chemistry, Год журнала: 2025, Номер unknown, С. 117324 - 117324

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

3
Suppression of CYLD by HER3 confers ovarian cancer platinum resistance via inhibiting apoptosis and by inducing drug efflux DOI Creative Commons
Ye Zhang, Jian‐Ge Qiu, Wei Wang

и другие.

Experimental Hematology and Oncology, Год журнала: 2025, Номер 14(1)

Опубликована: Фев. 26, 2025

Abstract Background Ovarian cancer (OC) is the most pathogenic gynecological malignant tumor in world. Due to difficulty of early diagnosis, patients developed chemo-resistance and recurrence during/after chemotherapy. Methods CCK8 flow cytometry were utilized assess drug sensitivity apoptosis parental resistant cell lines. CYLD knockdown or overexpressed cells employed investigate its regulatory involvement DDP resistance. Clinical samples have been clinical relevance CYLD. The synergistic effects investigated through combination methods a nude mice model with ABCB1 inhibitor HER3 inhibitor. Results In this study, we found that levels significantly reduced DDP-resistant tissues compared normal cells. DDP-sensitive was sufficient converse become by reducing increasing Bcl-XL inhibiting Bax, efflux via upregulating expression. expression substantially higher cells, upstream facilitator suppressing STAT3 signaling. Furthermore, overexpression increased platinum-based chemotherapy both vitro vivo. key downstream target for regulating growth therapeutic resistance vivo, promoted translocation p65 nucleus which transcriptional activation. High rendered suppression, consequently, mediated blocking pathways promoting ovarian cancer. Conclusions Our findings identify novel HER3/CYLD/ABCB1 axis regulate resistance, may be used as potential target(s) overcome

Язык: Английский

Процитировано

1
Post-translational modifications of immune checkpoints: unlocking new potentials in cancer immunotherapy DOI Creative Commons
Qiongjie Hu, Yueli Shi, Huang Wang

и другие.

Experimental Hematology and Oncology, Год журнала: 2025, Номер 14(1)

Опубликована: Март 14, 2025

Abstract Immunotherapy targeting immune checkpoints has gained traction across various cancer types in clinical settings due to its notable advantages. Despite this, the overall response rates among patients remain modest, alongside issues of drug resistance and adverse effects. Hence, there is a pressing need enhance checkpoint blockade (ICB) therapies. Post-translational modifications (PTMs) are crucial for protein functionality. Recent research emphasizes their pivotal role regulation, directly impacting expression function these key proteins. This review delves into influence significant PTMs—ubiquitination, phosphorylation, glycosylation—on signaling. By modifications, novel immunotherapeutic strategies have emerged, paving way advancements optimizing therapies future.

Язык: Английский

Процитировано

0
Programmed Cell Death Ligand as a Biomarker for Response to Immunotherapy: Contribution of Mass Spectrometry-Based Analysis DOI Open Access
Marco Agostini, Pietro Traldi,

Mahmoud Hamdan

и другие.

Cancers, Год журнала: 2025, Номер 17(6), С. 1001 - 1001

Опубликована: Март 17, 2025

Immune checkpoint inhibition is a major component in today’s cancer immunotherapy. In recent years, the FDA has approved number of immune inhibitors (ICIs) for treatment melanoma, non-small-cell lung, breast and gastrointestinal cancers. These inhibitors, which target cytotoxic T-lymphocyte antigen-4, programmed cell death (PD-1), ligand (PD-L1) checkpoints have assumed leading role The same exert significant antitumor effects by overcoming tumor evasion reversing T-cell exhaustion. initial impact this therapy was justly described as revolutionary, however, clinical well research data followed demonstrated that these innovative drugs are costly, associated with potentially severe adverse effects, only benefit small subset patients. limitations encouraged enhanced efforts to identify predictive biomarkers stratify patients who most likely from form therapy. discovery characterization class pivotal guiding individualized against various forms cancer. Currently, there three FDA-approved biomarkers, none on its own can deliver reliable precise response Present literature identifies absence poor understanding mechanisms behind resistance main obstacles facing ICIs present text, we discuss dual PD-L1 biomarker immunotherapy an checkpoint. contribution mass spectrometry-based analysis, particularly protein post-translational modifications performance underlined.

Язык: Английский

Процитировано

0
Machine learning-based pan-cancer study of classification and mechanism of BRAF inhibitor resistance DOI Open Access

Yuhang Zhao,

Kai Yang, Yujun Chen

и другие.

Translational Cancer Research, Год журнала: 2024, Номер 13(12), С. 6645 - 6660

Опубликована: Дек. 1, 2024

V-raf murine sarcoma viral oncogene homolog B1 (BRAF) inhibitor (BRAFi) therapy resistance affects approximately 15% of cancer patients, leading to disease recurrence and poor prognosis. The aim the study was develop a machine-learning based method identify patients who are at high-risk BRAFi potential biomarker. From Cancer Cell Line Encyclopedia (CCLE) Genomics Drug Sensitivity in (GDSC) databases, we collected RNA sequencing half maximal inhibitory concentration (IC50) data from 235 pan-cancer cell lines then identified 37 significant differential expression genes associated with resistance. Employing machine learning (ML) models, successfully classified into resistant sensitive groups, achieving robust performance external validation datasets. AOX1 may play vital part metabolism Further, found that higher mRNA OXTR, H2AC13, TBX2, lower SLC2A4, as detected by PCR WM983B SKMEL-5 lines, were independent risk factors for We established gene-expression model using ML methods, consisting variables on RNA-seq database, which externally validated could be used predict Meanwhile, our findings provide valuable insights molecular mechanisms resistance, enabling identification patients.

Язык: Английский

Процитировано

0