Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.

Objective: Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy. Methods: We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC. Results: We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines. Conclusion: The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

An Ultra-Low Modulus of Ductile TiZrHfTa Biomedical High-Entropy Alloys through Deformation Induced Martensitic Transformation/Twinning/Amorphization.

Biomedical alloys are paramount materials in biomedical applications, particularly in crafting biological artificial replacements. In traditional biomedical alloys, a significant challenge is simultaneously achieving an ultra-low Young's modulus, excellent biocompatibility, and acceptable ductility. A multi-component body-centered cubic (BCC) biomedical high-entropy alloy (Bio-HEA), which is composed of non-toxic elements, is noteworthy for its outstanding biocompatibility and compositional tuning capabilities. Nevertheless, the aforementioned challenges still remain. Here, a method to achieve a single phase with the lowest Young's modulus among the constituent phases by precisely tuning the stability of the BCC phase in the Bio-HEA, is proposed. The subtle tuning of the BCC phase stability also enables the induction of stress-induced martensite transformation with extremely low trigger stress. The transformation-induced plasticity and work hardening capacity are achieved via the stress-induced martensite transformation. Additionally, the hierarchical stress-induced martensite twin structure and crystalline-to-amorphous phase transformation provide robust toughening mechanisms in the Bio-HEA. The cytotoxicity test confirms that this Bio-HEA exhibits excellent biocompatibility without cytotoxicity. In conclusion, this study provides new insights into the development of biomedical alloys with a combination of ultra-low Young's modulus, excellent biocompatibility, and decent ductility.

Inactivation of ERK1/2 in cancer-associated hepatic stellate cells suppresses cancer-stromal interaction by regulating extracellular matrix in fibrosis.

The ERK1/2 pathway is involved in epithelial-mesenchymal transformation and cell cycle of tumor cells in hepatocellular carcinoma (HCC). In the present study, we investigated the involvement of ERK1/2 activation on hepatic stellate cells (HSCs). We identified ERK1/2 phosphorylation in activated HSCs of HCC samples. We found that tumor cells promoted the migration and invasion capacity of HSCs by activating ERK1/2 phosphorylation. Using high throughput transcriptome sequencing analysis, we found that ERK1/2 inhibition altered genes significantly correlated to signaling pathways involved in extracellular matrix remodeling. We screened genes and demonstrated that the ERK1/2 inhibition-related gene set significantly correlated to cancer-associated fibroblast infiltration in TCGA HCC tumor samples. Moreover, inhibition of ERK1/2 suppressed tumor cell-induced enhancement of HSC migration and invasion by regulating expression of fibrosis markers FAP, FN1 and COL1A1. In a tumor cell and HSC splenic co-transplanted xenograft mouse model, inhibition of ERK1/2 suppressed liver tumor formation by downregulating fibrosis, indicating ERK1/2 inhibition suppresses tumor-stromal interactions in vivo. Taken together, our data indicate that inhibition of ERK1/2 in tumor-associated HSCs suppresses tumor-stromal interactions and progression. Furthermore, inhibition of ERK1/2 may be a potential target for HCC treatment.

HGCTNet: Handcrafted Feature-Guided CNN and Transformer Network for Wearable Cuffless Blood Pressure Measurement.

Biosignals collected by wearable devices, such as electrocardiogram and photoplethysmogram, exhibit redundancy and global temporal dependencies, posing a challenge in extracting discriminative features for blood pressure (BP) estimation. To address this challenge, we propose HGCTNet, a handcrafted feature-guided CNN and transformer network for cuffless BP measurement based on wearable devices. By leveraging convolutional operations and self-attention mechanisms, we design a CNN-Transformer hybrid architecture to learn features from biosignals that capture both local information and global temporal dependencies. Then, we introduce a handcrafted feature-guided attention module that utilizes handcrafted features extracted from biosignals as query vectors to eliminate redundant information within the learned features. Finally, we design a feature fusion module that integrates the learned features, handcrafted features, and demographics to enhance model performance. We validate our approach using two large wearable BP datasets: the CAS-BP dataset and the Aurora-BP dataset. Experimental results demonstrate that HGCTNet achieves an estimation error of 0.9 ± 6.5 mmHg for diastolic BP (DBP) and 0.7 ± 8.3 mmHg for systolic BP (SBP) on the CAS-BP dataset. On the Aurora-BP dataset, the corresponding errors are -0.4 ± 7.0 mmHg for DBP and -0.4 ± 8.6 mmHg for SBP. Compared to the current state-of-the-art approaches, HGCTNet reduces the mean absolute error of SBP estimation by 10.68% on the CAS-BP dataset and 9.84% on the Aurora-BP dataset. These results highlight the potential of HGCTNet in improving the performance of wearable cuffless BP measurements.

Effect of Bioactive Black Phosphorus Nanomaterials on Cancer-Associated Fibroblast Heterogeneity in Pancreatic Cancer.

Tumor-stromal interactions and stromal heterogeneity in the tumor microenvironment are critical factors that influence the progression, metastasis, and chemoresistance of pancreatic ductal adenocarcinoma (PDAC). Here, we used spatial transcriptome technology to profile the gene expression landscape of primary PDAC and liver metastatic PDAC after bioactive black phosphorus nanomaterial (bioactive BP) treatment using a murine model of PDAC (LSL-KrasG12D/+; LSL-Trp53R172H/+; and Pdx-1-Cre mice). Bioinformatic and biochemical analyses showed that bioactive BP contributes to the tumor-stromal interplay by suppressing cancer-associated fibroblast (CAF) activation. Our results showed that bioactive BP contributes to CAF heterogeneity by decreasing the amount of inflammatory CAFs and myofibroblastic CAFs, two CAF subpopulations. Our study demonstrates the influence of bioactive BP on tumor-stromal interactions and CAF heterogeneity and suggests bioactive BP as a potential PDAC treatment.