Cancer Cell International最新文献

Non-small cell lung cancer (NSCLC) constitutes a significant proportion of lung cancers and poses a serious threat to human health. Osimertinib is the first-line drug for treating NSCLC, but long-term use can lead to drug resistance. Exploring the mechanism of drug resistance and effectively selecting treatment plans based on the mechanism of resistance are urgent issues to be addressed. In this study, dryness characteristics were evaluated by measuring cell activity, cell spheroid formation and cloning conditions, and the levels of stem cell marker molecules. The sensitivity of SPP1 to osimertinib was also assessed in mice. The results showed that SPP1 regulates cancer stem cells (CSCs) by interacting with CD44, thereby generating osimertinib resistance. These findings provide a basis for clinical research.

Breast cancer progression is facilitated by the epithelial to mesenchymal transition (EMT), generating cancer cells with enhanced metastatic capacity and resistance to chemotherapeutics. The fungus-derived sesterterpenoid natural produce compound, ophiobolin A (OpA), possesses nanomolar cytotoxic activity and a high therapeutic index, although its molecular targets and mechanism of action are not well characterized. Herein, we utilized a model of mammary epithelial cells and breast cancer cell lines with and without EMT features to characterize the mechanism of selectivity towards EMT(+) cells by OpA. Proteins interacting with OpA in EMT(+) cells, including mitochondrial glutathione transporter SLC25A40, were identified through via mass spectrometry. We utilized trans-mitochondrial cybrids to determine that mitochondria mediate sensitivity to OpA. Furthermore, we report effects on glycolysis, oxidative metabolism, and disruption of metabolite abundance in the TCA cycle. Antioxidant mechanisms are activated by OpA in EMT(+) cells via the NRF2-ARE pathway, verified by decreased cytotoxicity in EMT(+) cells pretreated with the NRF2 activator CDDO. Collectively, we conclude that OpA selectivity toward EMT is mediated by the mitochondria, and at sub-cytotoxic levels, generates a metabolic shift leading to cell death countered by antioxidant mechanisms.

Background: Gastric cancer (GC) remains a major global health challenge, characterized by high morbidity and mortality rates. Early diagnosis is essential for improving patient outcome. This study aims to develop a diagnostic model based on specific signature genes by investigating the association between double-negative (DN) T cells and GC.

Methods: A bidirectional Mendelian randomization (MR) analysis was conducted to assess the causal relationship between immune cell phenotypes and GC pathogenesis. Three machine learning (ML) algorithms, combined with logistic regression, were employed to identify featured genes. Real-world cohorts and animal experiments were applied to validate the expression levels of DN T cells and selected model genes. Virtual screening was further performed to identify potential therapeutic candidates.

Results: DN T cells were identified as significant risk factors for GC. A diagnostic model incorporating four genes-EML4, IL32, FXYD5, and TTC39C-was constructed using ML algorithms and demonstrated high predictive accuracy across multiple clinical cohorts. External validation and experimental analyses confirmed elevated DN T cell levels and increased expression of all model genes in GC tissues, correlating with poor prognosis. Virtual screening identified potential therapeutic compounds with strong binding affinity to target proteins, indicating their potential for GC treatment.

Conclusions: The study established a novel diagnostic model for GC based on DN T cell signature genes, which shows robust predictive performance and significant clinical benefit. The findings underscore the important role of DN T cells and model genes in GC, providing new insights into early diagnosis and potential therapeutic targets for effective management of GC.

Colorectal cancer (CRC) metastasis is critically mediated by pre-metastatic niche (PMN) formation, a process driven by tumor-derived exosomes. This study establishes a highly invasive CT-26 subline (HI CT-26) through iterative in vivo selection, demonstrating enhanced metastatic potential via epithelial-mesenchymal transition (EMT) activation and transendothelial migration. HI CT-26-derived exosomes were found to enrich miR-188-5p, which directly suppresses TIMP2 and TIMP3, key regulators of extracellular matrix (ECM) homeostasis and immune modulation. This suppression is associated with the establishment of a PMN, characterized by an increased relative abundance of M2-like macrophages and myeloid-derived suppressor cell (MDSC) accumulation in target organs. Mechanistically, Notch signaling activates miR-188-5p transcription through RBP-J binding to its promoter, forming a regulatory axis that links Notch pathway activation to metastatic progression. Clinical validation using TCGA data confirmed elevated RBP-J and miR-188-5p expression, alongside reduced TIMP2/3 levels, as prognostic biomarkers for poor CRC outcomes. These findings reveal a novel Notch-miR-188-5p-TIMP2/3 signaling cascade driving exosome-mediated PMN formation, offering insights into therapeutic strategies targeting the metastatic microenvironment.

Uveal melanoma (UM), a prevalent intraocular malignancy with a high rate of metastasis, particularly to the liver, presents a significant therapeutic challenge due to the absence of effective treatments. Long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) are under scrutiny for their roles in cancer, with lncRNA-NEAT1 identified as a key contributor to tumor growth. Our study delves into the aberrant expression of NEAT1, miR-506-3p, and STAT3 in UM cells compared with retinal pigment epithelial cells, revealing their impact on UM cell proliferation, migration, and invasion. Interventions targeting NEAT1 inhibition or miR-506-3p overexpression restrict UM cell viability, migration, and invasion. Conversely, increasing NEAT1 expression or suppressing miR-506-3p enhances these biological behaviors. Bioinformatic tools and dual-luciferase assays validated the specific binding of miR-506-3p to NEAT1 and its regulatory effect on STAT3. Rescue experiments further confirmed these interactions, contributing to a comprehensive understanding of the NEAT1/miR-506-3p/STAT3 axis in UM. The NEAT1/miR-506-3p/STAT3 axis has emerged as a promising diagnostic and therapeutic target for UM, providing a novel perspective on the pathogenesis of this challenging malignancy.

Background: Palmitoylation, a key post-translational modification, plays a crucial role in tumor progression, yet its landscape in clear cell renal cell carcinoma (ccRCC) remains poorly characterized. This study aims to systematically identify and validate key palmitoylation-modifying enzymes in ccRCC and explore their clinical significance.

Methods: We integrated multi-omics data from TCGA-KIRC and GEO datasets to evaluate palmitoylation levels using the PalmScore system. Machine learning algorithms were applied to identify diagnostic and prognostic key genes. Functional roles of ZDHHC11 were validated in vitro using siRNA-mediated knockdown in ccRCC cell lines. Single-cell RNA sequencing data further confirmed expression patterns.

Results: PalmScore effectively stratified ccRCC patients into high- and low-risk groups, with the high PalmScore group showing enriched immune infiltration and poorer survival outcomes. Machine learning identified ZDHHC2 and ABHD17C as diagnostic markers, while ZDHHC11 emerged as a prognostic key gene. In vitro experiments demonstrated that ZDHHC11 knockdown significantly suppressed proliferation, migration, and invasion in ccRCC cells. Single-cell analysis validated the expression patterns of these key genes across different cell types.

Discussion: Our study unveils the critical roles of palmitoylation-modifying enzymes in ccRCC progression and immune regulation. The identified key genes hold promise as biomarkers for diagnosis and prognosis, offering potential targets for future therapeutic strategies.

Background: Treg infiltration and programmed cell death are important factors influencing cancer progression, and they interact with each other. However, the significance of Treg-related programmed cell death (PCD) characteristics in clear cell renal cell carcinoma remains unclear.

Methods: Through Mendelian randomization, we identified PCD genes and Treg markers that are highly associated with ccRCC outcomes. Subsequently, based on Treg-related PCD genes, we constructed a diagnostic model utilizing a multi-layer perceptron (MLP) and integrated 10 machine learning algorithms to construct a prognostic model, which was then explained by the SHAP method. After exploring functional differences and chemotherapy sensitivity differences between high- and low-risk groups in the prognostic model, we validated the core gene of the model through in vitro cell experiments. Finally, we screened molecular drugs targeting the core genes using the DSigDB database and performed molecular docking and molecular dynamics validation.

Results: Utilizing Mendelian randomization (MR), we first established causal links between specific Treg subtypes and PCD gene CASP9 with renal cancer outcomes. Leveraging shared Treg-PCD molecular features, we developed a MLP-based diagnostic model achieving an AUC of 0.987 in external validation. Further, a robust prognostic index Treg-Programmed Cell Death Score (TPCDS) was constructed using 101 machine learning combinations, demonstrating superior stratification across multi-cohort data. High TPCDS correlated with immunosuppressive microenvironments including increased Tregs, T-cell exhaustion, HLA downregulation and poor immunotherapy response, while guiding chemotherapy sensitivity. Functional assays confirmed the core gene SLC11A1 as an oncogenic driver promoting proliferation, migration, and invasion. Molecular docking and dynamics simulations identified Atovaquone as a high-affinity inhibitor of SLC11A1.

Conclusion: We explored the significance of Treg and programmed cell death characteristics in the ccRCC tumor microenvironment and established clinically translatable tools for ccRCC diagnosis, prognosis, and personalized therapy selection, thus promoted the application of explainable machine learning models in precision oncology. Furthermore, We have identified SLC11A1 as a highly promising therapeutic target for ccRCC.