Cuproptosis is a new type of copper-induced cell death that is characterized by the aggregation of lipoylated tricarboxylic acid (TCA) cycle proteins. However, its role in hepatocellular carcinoma (HCC) remains unclear. The goal of this research is to develop a cuproptosis-related signature predicting the prognosis of HCC.The cuproptosis-related genes were defined using Pearson correlation coefficients. LASSO-Cox regression analysis was used to evaluate the prognostic values of cuproptosis-related genes to construct a cuproptosis-related prognostic model. The immune microenvironment analysis was performed by "ssGSEA" to reveal the associated immune cell infiltration patterns with the cuproptosis-related genes signature. The expression levels of one of the prognostic genes PDXK were then verified in HCC samples by Western Blot and immunohistochemistry. The potential roles of target genes in cuproptosis were further explored during in-vitro experiments.A total of 136 cuproptosis-related genes were discovered using Pearson correlation analysis in HCC. A cuproptosis-related signature that included 5 cuproptosis-related genes (PDXK, HPN, SLC25A28, RNFT1, CLEC3B) was established in the TCGA-LIHC training cohort. TCGA validation cohort and another two external validation cohorts confirmed the robustness of the signature's predictive value. Moreover, a nomogram using the risk score was created to best predict the survival of HCC patients. The immune microenvironment analysis revealed distinct immune infiltrations patterns between different risk groups based on the signature model. Furthermore, the upregulation of PDXK was confirmed in HCC tumor tissues in 30 clinical HCC specimens. The knockdown of PDXK reduced the proliferation, migration and invasion of HCC cells. Besides, the expression of PDXK was upregulated after the induction of cuproptosis by elesclomol-CuCL2, which could be suppressed when pretreated with a copper ion chelator. And PDXK deficiency increased the sensitivity of HCC cells to cuproptosis inducer.Our study identified a new cuproptosis-related gene signature that could predict the prognosis of HCC patient. Besides, the upregulated PDXK could promote the proliferation and metastasis of HCC. And PDXK deficiency facilities cuproptosis in HCC. Therefore, these fundings highlighted that PDXK might serve as a potential diagnostic and therapeutic target for HCC. 

/pdf/identification-of-a-prognostic-cuproptosis-related-signature-pj36slzy.pdf

Identification of a prognostic cuproptosis-related signature in hepatocellular carcinoma

Artificial intelligence (AI) is transforming spinal imaging and patient care through automated analysis and enhanced decision-making. This review presents a clinical task-based evaluation, highlighting the specific impact of AI techniques on different aspects of spinal imaging and patient care. We first discuss how AI can potentially improve image quality through techniques like denoising or artifact reduction. We then explore how AI enables efficient quantification of anatomical measurements, spinal curvature parameters, vertebral segmentation, and disc grading. This facilitates objective, accurate interpretation and diagnosis. AI models now reliably detect key spinal pathologies, achieving expert-level performance in tasks like identifying fractures, stenosis, infections, and tumors. Beyond diagnosis, AI also assists surgical planning via synthetic computed tomography generation, augmented reality systems, and robotic guidance. Furthermore, AI image analysis combined with clinical data enables personalized predictions to guide treatment decisions, such as forecasting spine surgery outcomes. However, challenges still need to be addressed in implementing AI clinically, including model interpretability, generalizability, and data limitations. Multicenter collaboration using large, diverse datasets is critical to advance the field further. While adoption barriers persist, AI presents a transformative opportunity to revolutionize spinal imaging workflows, empowering clinicians to translate data into actionable insights for improved patient care. 

Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances

The focal adhesion protein, Hic-5 plays a key role in promoting extracellular matrix deposition and remodeling by cancer associated fibroblasts within the tumor stroma to promote breast tumor cell invasion. However, whether stromal matrix gene expression is regulated by Hic-5 is still unknown. Utilizing a constitutive Hic-5 knockout, Mouse Mammary Tumor Virus-Polyoma Middle T-Antigen spontaneous breast tumor mouse model, bulk RNAseq analysis was performed on cancer associated fibroblasts isolated from Hic-5 knockout mammary tumors. Functional network analysis highlighted a key role for Hic-5 in extracellular matrix organization, with both structural matrix genes, as well as matrix remodeling genes being differentially expressed in relation to Hic-5 expression. The subcellular distribution of the MRTF-A transcription factor and expression of a subset of MRTF-A responsive genes was also impacted by Hic-5 expression. Additionally, cytokine array analysis of conditioned media from the Hic-5 and Hic-5 knockout cancer associated fibroblasts revealed that Hic-5 is important for the secretion of several key factors that are associated with matrix remodeling, angiogenesis and immune evasion. Together, these data provide further evidence of a central role for Hic-5 expression in cancer associated fibroblasts in regulating the composition and organization of the tumor stroma microenvironment to promote breast tumor progression. 

Hic-5 regulates extracellular matrix-associated gene expression and cytokine secretion in cancer associated fibroblasts.

Programmed cell death protein-1 (PD-1) inhibitors are increasingly utilized in the treatment of lung cancer (LC). Combination therapy has recently gained popularity in treating LC. This study aimed to assess the efficacy of combining Astragaloside IV (AS-IV) and anti-PD-1 in LC. C57BL/6J mice were subcutaneously injected with Lewis lung carcinoma (LLC) cells. After 3 weeks, the animals were sacrificed, and the tumors were harvested for analysis. Ki-67 immuno-labeling and TUNEL assay were used for evaluating cell proliferation and apoptosis in tumor tissues. In addition, anti-cleaved caspase 3 was used for immunolabelling of apoptotic cells. Immune cell infiltration (macrophages and T cells) and gene expression in tumor tissues were also investigated by using immunofluorescence staining. Compared to treatment with anti-PD-1 or AS-IV, the combination of AS-IV and anti-PD-1 notably reduced tumor volume and weight of LLC-bearing mice. Additionally, the combination treatment strongly induced the apoptosis and suppressed the proliferation in tumor tissues through inactivating PI3K/Akt and ERK signaling pathways, compared to single treatment group. Moreover, the combination treatment elevated levels of the M1 macrophage marker mCD86, reduced levels of the M2 macrophage marker mCD206, as well as upregulated levels of the T cell activation marker mCD69 in tumor tissues. Collectively, the combination treatment effectively inhibited tumor growth in LLC mice through promoting M1 macrophage polarization and T cell activation. These findings showed that combining AS-IV with anti-PD-1 therapy could be a promising therapeutic approach for LC. 

Astragaloside IV augments anti-PD-1 therapy to suppress tumor growth in lung cancer by remodeling the tumor microenvironment

The objective of this study is to investigate whether the combination of bone turnover marker and radiomic features extracted from chest CT images can improve the diagnostic accuracy for predicting osteoporosis in patients with hip injury. A group of 249 patients who suffered hip injury participated in this study. They underwent various diagnostic tests, including chest CT scans, bone density measurements, and bone turnover marker. The clinical data collected from these patients was used to create clinical models. To extract radiomics features, one thoracic vertebra was selected from each patient using ITK-SNAP. The feature extraction was conducted using a pyramid library tool that runs on the Python programming language. By randomly selecting patients, a training set consisting of 201 individuals and a testing set comprising of 48 participants were formed. A radiomics model was established after extracting radiomics features. The nomogram model was developed by combining radiomics models with clinical models. Finally, the sensitivity, specificity, accuracy, and area under the curve (AUC) of the three models were calculated separately. The clinical model developed by our research team incorporated variables such as gender, age, vitamin D, and β B-Cross Laps. In the training group, the accuracy of Clinic model recognition was 0.731, AUC was 0.735. In the test group, the accuracy of Clinic model recognition was 0.708, AUC was 0.769. In the training group, the accuracy of nomogram model recognition was 0.891, AUC was 0.943. In the test group, the accuracy of nomogram model recognition was 0.812, AUC was 0.862. By combining the radiological features extracted from thoracic CT scans with the clinical features of patients, it is possible to distinguish hip joint injury patients with or without osteoporosis before surgery. 

Integration of bone turnover markers and radiomic features from chest CT imaging for osteoporosis diagnosis in patients with hip joint injury

Lung adenocarcinoma (LUAD) belongs to non-small cell lung carcinoma, and the metastasis is the main cause of death in LUAD patients. It is generally accepted that cell adhesion is closely associated with tumor metastasis. Accordingly, the purpose of this study is to investigate the functions of CLEC3B to the invasion, adhesion, and migration of LUAD cells.Through bioinformatic analysis, CLEC3B level was found markedly down-regulated in LUAD tissue. Parallel-plate flow chamber, wound-healing and Transwell assays were taken to detect the cell adhesion, invasion, and migration. qRT-PCR and western blot analyzed expression of CLEC3B, p53 and epithelial-mesenchymal transition (EMT) marker.When CLEC3B was lowly-expressed in LUAD cell lines, the cell adhesion capability was also lowered, with the EMT, migration and invasion of the cells progressed. Abnormal expression of CLEC3B was related to p53 signaling pathway.Above all, a further investigation of the function of CLEC3B in LUAD helps us further understand the molecular mechanism of the tumor metastasis. 

Down-regulation of CLEC3B facilitates epithelial-mesenchymal transition, migration and invasion of lung adenocarcinoma cells.

To develop and evaluate a fully automated method based on deep learning and phantomless internal calibration for bone mineral density (BMD) measurement and opportunistic low BMD (osteopenia and osteoporosis) screening using chest low-dose CT (LDCT) scans.A total of 1175 individuals were enrolled in this study, who underwent both chest LDCT and BMD examinations with quantitative computed tomography (QCT), by two different CT scanners (Siemens and GE). Two convolutional neural network (CNN) models were employed for vertebral body segmentation and labeling, respectively. A histogram technique was applied for vertebral BMD calculation using paraspinal muscle and surrounding fat as references. 195 cases (by Siemens scanner) as fitting cohort were used to build the calibration function. 698 cases as validation cohort I (VCI, by Siemens scanner) and 282 cases as validation cohort II (VCII, by GE scanner) were performed to evaluate the performance of the proposed method, with QCT as the standard for analysis.The average BMDs from the proposed method were strongly correlated with QCT (in VCI: r = 0.896, in VCII: r = 0.956, p < 0.001). Bland-Altman analysis showed a small mean difference of 1.1 mg/cm3, and large interindividual differences as seen by wide 95% limits of agreement (-29.9 to +32.0 mg/cm3) in VCI. The proposed method measured BMDs were higher than QCT measured BMDs in VCII (mean difference = 15.3 mg/cm3, p < 0.001). Osteoporosis and low BMD were diagnosed by proposed method with AUCs of 0.876 and 0.903 in VCI, 0.731 and 0.794 in VCII, respectively. The AUCs of the proposed method were increased to over 0.920 in both VCI and VCII after adjusting the cut-off.Without manual selection of the region of interest of body tissues, the proposed method based on deep learning and phantomless internal calibration has the potential for preliminary screening of patients with low BMD using chest LDCT scans. However, the agreement between the proposed method and QCT is insufficient to allow them to be used interchangeably in BMD measurement.This study proposed an automated vertebral BMD measurement method based on deep learning and phantomless internal calibration with paraspinal muscle and fat as reference. 

Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning.

Lung cancer originating from the bronchial epithelium is the most common lung malignancy. It has been reported that programmed cell death 1 ligand 1 (PD-L1) and tumor-associated macrophages are closely related to the development of lung cancer. However, whether tumor-derived exosomal PD-L1 could mediate the regulation of macrophage polarization in lung cancer remains unclear. For this research, the level of PD-L1 in normal tissues and lung cancer tissues was evaluated using RT-qPCR. Next, the apoptosis of lung cancer cells was evaluated using flow cytometry assay. Then, the structure and morphology of vesicles were observed using transmission electron microscopy and nanoparticle tracking analysis. Later on, the internalization of exosomes by macrophage was observed using fluorescence microscopy. Our results showed that the level of PD-L1 was upregulated in tumor tissues and lung cancer cells. Knockdown of PD-L1 notably inhibited the viability, migration and invasion of lung cancer cells. In addition, lung cancer cells-derived exosomal PD-L1 could be absorbed by macrophages. Meanwhile, exosomal PD-L1 was able to promote macrophages M2 polarization. Moreover, macrophages M2 polarization induced by exosomal PD-L1 further remarkably promoted the viability, migration, invasion, and epithelial-mesenchymal transition process of lung cancer cells. Collectively, knockdown of PD-L1 notably inhibited the viability, migration and invasion of lung cancer cells. Tumor cell-derived exosomal PD-L1 could promote the growth of lung cancer cells by mediating macrophages M2 polarization. Thus, inhibiting macrophages M2 polarization might be a promoting therapy for the treatment of lung cancer.

Tumor cells-derived exosomal PD-L1 promotes the growth and invasion of lung cancer cells in vitro via mediating macrophages M2 polarization

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Down-regulation of CLEC3B facilitates epithelial-mesenchymal transition, migration and invasion of lung adenocarcinoma cells.

Automated vertebral bone mineral density measurement with phantomless internal calibration in chest LDCT scans using deep learning.

Tumor cells-derived exosomal PD-L1 promotes the growth and invasion of lung cancer cells in vitro via mediating macrophages M2 polarization