Proteomic, single-cell and bulk transcriptomic analysis of plasma and tumor tissues unveil core proteins in response to anti-PD-L1 immunotherapy in triple negative breast cancer.

BACKGROUND: Anti-PD-1/PD-L1 treatment has achieved durable responses in TNBC patients, whereas a fraction of them showed non-sensitivity to the treatment and the mechanism is still unclear. METHODS: Pre- and post-treatment plasma samples from triple negative breast cancer (TNBC) patients treated with immunotherapy were measured by tandem mass tag (TMT) mass spectrometry. Public proteome data of lung cancer and melanoma treated with immunotherapy were employed to validate the findings. Blood and tissue single-cell RNA sequencing (scRNA-seq) data of TNBC patients treated with or without immunotherapy were analyzed to identify the derivations of plasma proteins. RNA-seq data from IMvigor210 and other cancer types were used to validate plasma proteins in predicting response to immunotherapy. RESULTS: A random forest model constructed by FAP, LRG1, LBP and COMP could well predict the response to immunotherapy. The activation of complement cascade was observed in responders, whereas FAP and COMP showed a higher abundance in non-responders and negative correlated with the activation of complements. scRNA-seq and bulk RNA-seq analysis suggested that FAP, COMP and complements were derived from fibroblasts of tumor tissues. CONCLUSIONS: We constructe an effective plasma proteomic model in predicting response to immunotherapy, and find that FAP+ and COMP+ fibroblasts are potential targets for reversing immunotherapy resistance.

METnet: A novel deep learning model predicting MET dysregulation in non-small-cell lung cancer on computed tomography images.

BACKGROUND: Mesenchymal epithelial transformation (MET) is a key molecular target for diagnosis and treatment of non-small cell lung cancer (NSCLC). The corresponding molecularly targeted therapeutics have been approved by Food and Drug Administration (FDA), achieving promising results. However, current detection of MET dysregulation requires biopsy and gene sequencing, which is invasive, time-consuming and difficult to obtain tumor samples. METHODS: To address the above problems, we developed a noninvasive and convenient deep learning (DL) model based on Computed tomography (CT) imaging data for prediction of MET dysregulation. We introduced the unsupervised algorithm RK-net for automated image processing and utilized the MedSAM large model to achieve automated tissue segmentation. Based on the processed CT images, we developed a DL model (METnet). The model based on the grouped convolutional block. We evaluated the performance of the model over the internal test dataset using the area under the receiver operating characteristic curve (AUROC) and accuracy. We conducted subgroup analysis on the basis of clinical data of the lung cancer patients and compared the performance of the model in different subgroups. RESULTS: The model demonstrated a good discriminative ability over the internal test dataset. The accuracy of METnet was 0.746 with an AUC value of 0.793 (95% CI 0.714-0.871). The subgroup analysis revealed that the model exhibited similar performance across different subgroups. CONCLUSIONS: METnet realizes prediction of MET dysregulation in NSCLC, holding promise for guiding precise tumor diagnosis and treatment at the molecular level.

AKD Emulsions Stabilized by Guar Gel: A Highly Efficient Agent to Improve the Hydrophobicity of Cellulose Paper.

The aim of the present study was to investigate highly efficient alkyl ketene dimer (AKD) emulsions to improve the hydrophobicity of cellulose paper. AKD emulsions stabilized by guar gel were obtained; the guar gel was prepared by hydrogen bond cross-linking sodium tetraborate and guar gum. The cross-linking was confirmed by combining FTIR and SEM. The effect of guar gel on the performance of the AKD emulsions was also studied by testing AKD emulsions stabilized by different guar gel concentrations. The results showed that with increasing guar gel concentration, the stability of the AKD emulsions improved, the droplet diameter decreased, and the hydrophobicity and water resistance of the sized packaging paper were gradually enhanced. Through SEM, the guar gel film covering the AKD emulsion droplet surface and the three-dimensional structure in the aqueous dispersion phase were assessed. This study constructed a scientific and efficient preparation method for AKD emulsions and provided a new method for the application of carbohydrate polymer gels which may avoid the adverse effect of surfactant on paper sizing and environmental problems caused by surfactant bioaccumulation.

Exosomes-regulated lipid metabolism in tumorigenesis and cancer progression.

Increasing evidence highlights the role of lipid metabolism in tumorigenesis and tumor progression. Targeting the processes of lipid metabolism, including lipogenesis, lipid uptake, fatty acid oxidation, and lipolysis, is an optimal strategy for anti-cancer therapy. Beyond cell-cell membrane surface interaction, exosomes are pivotal factors that transduce intercellular signals in the tumor microenvironment (TME). Most research focuses on the role of lipid metabolism in regulating exosome biogenesis and extracellular matrix (ECM) remodeling. The mechanisms of exosome and ECM-mediated reprogramming of lipid metabolism are currently unclear. We summarize several mechanisms associated with the regulation of lipid metabolism in cancer, including transport of exosomal carriers and membrane receptors, activation of the PI3K pathway, ECM ligand-receptor interactions, and mechanical stimulation. This review aims to highlight the significance of these intercellular factors in TME and to deepen the understanding of the functions of exosomes and ECM in the regulation of lipid metabolism.

Extracellular matrix remodeling in tumor progression and immune escape: from mechanisms to treatments.

The malignant tumor is a multi-etiological, systemic and complex disease characterized by uncontrolled cell proliferation and distant metastasis. Anticancer treatments including adjuvant therapies and targeted therapies are effective in eliminating cancer cells but in a limited number of patients. Increasing evidence suggests that the extracellular matrix (ECM) plays an important role in tumor development through changes in macromolecule components, degradation enzymes and stiffness. These variations are under the control of cellular components in tumor tissue via the aberrant activation of signaling pathways, the interaction of the ECM components to multiple surface receptors, and mechanical impact. Additionally, the ECM shaped by cancer regulates immune cells which results in an immune suppressive microenvironment and hinders the efficacy of immunotherapies. Thus, the ECM acts as a barrier to protect cancer from treatments and supports tumor progression. Nevertheless, the profound regulatory network of the ECM remodeling hampers the design of individualized antitumor treatment. Here, we elaborate on the composition of the malignant ECM, and discuss the specific mechanisms of the ECM remodeling. Precisely, we highlight the impact of the ECM remodeling on tumor development, including proliferation, anoikis, metastasis, angiogenesis, lymphangiogenesis, and immune escape. Finally, we emphasize ECM "normalization" as a potential strategy for anti-malignant treatment.

Combining Immune-Related Genes For Delineating the Extracellular Matrix and Predicting Hormone Therapy and Neoadjuvant Chemotherapy Benefits In Breast Cancer.

Breast cancer (BC) is the most prevalent cancer in women worldwide. A systematic approach to BC treatment, comprising adjuvant and neoadjuvant chemotherapy (NAC), as well as hormone therapy, forms the foundation of the disease's therapeutic strategy. The extracellular matrix (ECM) is a dynamic network that exerts a robust biological effect on the tumor microenvironment (TME), and it is highly regulated by several immunological components, such as chemokines and cytokines. It has been established that the ECM promotes the development of an immunosuppressive TME. Therefore, while analyzing the ECM of BC, immune-related genes must be considered. In this study, we used bioinformatic approaches to identify the most valuable ECM-related immune genes. We used weighted gene co-expression network analysis to identify the immune-related genes that potentially regulate the ECM and then combined them with the original ECM-related gene set for further analysis. Least absolute shrinkage and selection operator (LASSO) regression and SurvivalRandomForest were used to narrow our ECM-related gene list and establish an ECM index (ECMI) to better delineate the ECM signature. We stratified BC patients into ECMI high and low groups and evaluated their clinical, biological, and genomic characteristics. We found that the ECMI is highly correlated with long-term BC survival. In terms of the biological process, this index is positively associated with the cell cycle, DNA damage repair, and homologous recombination but negatively with processes involved in angiogenesis and epithelial-mesenchymal transition. Furthermore, the tumor mutational burden, copy number variation, and DNA methylation levels were found to be related to the ECMI. In the Metabric cohort, we demonstrated that hormone therapy is more effective in patients with a low ECMI. Additionally, differentially expressed genes from the ECM-related gene list were extracted from patients with a pathologic complete response (pCR) to NAC and with residual disease (RD) to construct a neural network model for predicting the chance of achieving pCR individually. Finally, we performed qRT-PCR to validate our findings and demonstrate the important role of the gene OGN in predicting the pCR rate. In conclusion, delineation of the ECM signature with immune-related genes is anticipated to aid in the prediction of the prognosis of patients with BC and the benefits of hormone therapy and NAC in BC patients.

The effect of MiR-497 on the expression of target genes BCL-2 and LC3B on cardiomyocytes injured by hypoxia/reoxygenation.

Myocardial ischemia is easy to cause hypoxia or necrosis of myocardial cells. At present, the performance of various patients is different. Basically, it is mainly caused by chest pain or chest discomfort. Severe patients may die suddenly. Therefore, looking for effective drugs or methods to prevent and treat Cardiomyocyte injury is of great significance for clinical practice, in which the expression of regulatory gene BCL-2 and microtubule-associated protein light chain 3B (LC3B) has a certain effect on hypoxia/reoxygenation injured cardiomyocytes. To this end, the team designed a study on the effect of miR-497 on the expression of target genes BCL-2 and LC3B on cardiomyocytes injured by hypoxia/reoxygenation. In this study, a control group experiment was set up for the study. During the experiment, the cells were treated with hypoxia-reoxygenation and transfected with the corresponding miR-497 treated cells. By detecting apoptosis, the kit was used to detect cell activity and RT-PCR detection. Gene expression levels and other methods are comparatively judged. The results of this study showed that compared with the normal group 14.50±0.78, the viability of cardiomyocytes in the model group was significantly reduced (P<0.01), the amount of NO released by cardiomyocytes was reduced (P<0.01), and the protein expression in cardiomyocytes was significantly reduced (P<0.01). The experimental results of this study prove that miR-497 can alleviate the damage caused by hypoxia-reoxygenation of cardiomyocytes by regulating target genes BCL-2 and LC3B.

Analysis of Macrophage Chemotactic Activity and TLR9 Signaling Pathway in the Mouse Model of Viral Acute Lung Injury.

This study focused on the chemotactic activity of macrophages and the role of the TLR9 signaling pathway in the pathogenesis of viral Acute Lung Injury (ALI). For this purpose, a total of 40 male SPF mice were used, aged 5-8 weeks. They were randomly divided into an experimental group and a control group. The experimental group was further divided into S1 and S2, and the control group was further divided into D1 and D2, with 10 in each. The different groups were detected for the expression of inflammatory cytokines and chemokines and the expression of alveolar macrophages. Results showed that as for the weight, survival status, arterial blood gas analysis, lung index and wet-to-dry value of lung tissue, and lung histopathological analysis results, the S2 group showed more obvious changes versus the D2 group, and the difference was statistically significant (P<0.05). S2 had higher levels of the inflammatory factors TNF-α, IL-1ß, IL-6 and the chemokine CCL3 in the BALF supernatant versus the D2 Group, and the difference is statistically significant (P<0.05). S2 had higher expression levels of chemokines CCR5, TLR9, and JMJD1A mRNA versus the D2 group, and the difference was statistically significant (P<0.05). In conclusion,  the establishment of a mouse ALI model induced by poly l:C was successful; AM has a certain chemotactic activity on CCL3; polyI:C can promote the expression activity and chemotactic activity of macrophages CCR5 through signal pathways, such as TLR9.

ZNF217: the cerberus who fails to guard the gateway to lethal malignancy.

The aberrant expression of the zinc finger protein 217 (ZNF217) promotes multiple malignant phenotypes, such as replicative immortality, maintenance of proliferation, malignant heterogeneity, metastasis, and cell death resistance, via diverse mechanisms, including transcriptional activation, mRNA N6-methyladenosine (m6A) regulation, and protein interactions. The induction of these cellular processes by ZNF217 leads to therapeutic resistance and patients' poor outcomes. However, few ZNF217 related clinical applications or trials, have been reported. Moreover, looming observations about ZNF217 roles in m6A regulation and cancer immune response triggered significant attention while lacking critical evidence. Thus, in this review, we revisit the literature about ZNF217 and emphasize its importance as a prognostic biomarker for early prevention and as a therapeutic target.

Evaluating the Accuracy of Breast Cancer and Molecular Subtype Diagnosis by Ultrasound Image Deep Learning Model.

Background: Breast ultrasound is the first choice for breast tumor diagnosis in China, but the Breast Imaging Reporting and Data System (BI-RADS) categorization routinely used in the clinic often leads to unnecessary biopsy. Radiologists have no ability to predict molecular subtypes with important pathological information that can guide clinical treatment. Materials and Methods: This retrospective study collected breast ultrasound images from two hospitals and formed training, test and external test sets after strict selection, which included 2,822, 707, and 210 ultrasound images, respectively. An optimized deep learning model (DLM) was constructed with the training set, and the performance was verified in both the test set and the external test set. Diagnostic results were compared with the BI-RADS categorization determined by radiologists. We divided breast cancer into different molecular subtypes according to hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression. The ability to predict molecular subtypes using the DLM was confirmed in the test set. Results: In the test set, with pathological results as the gold standard, the accuracy, sensitivity and specificity were 85.6, 98.7, and 63.1%, respectively, according to the BI-RADS categorization. The same set achieved an accuracy, sensitivity, and specificity of 89.7, 91.3, and 86.9%, respectively, when using the DLM. For the test set, the area under the curve (AUC) was 0.96. For the external test set, the AUC was 0.90. The diagnostic accuracy was 92.86% with the DLM in BI-RADS 4a patients. Approximately 70.76% of the cases were judged as benign tumors. Unnecessary biopsy was theoretically reduced by 67.86%. However, the false negative rate was 10.4%. A good prediction effect was shown for the molecular subtypes of breast cancer with the DLM. The AUC were 0.864, 0.811, and 0.837 for the triple-negative subtype, HER2 (+) subtype and HR (+) subtype predictions, respectively. Conclusion: This study showed that the DLM was highly accurate in recognizing breast tumors from ultrasound images. Thus, the DLM can greatly reduce the incidence of unnecessary biopsy, especially for patients with BI-RADS 4a. In addition, the predictive ability of this model for molecular subtypes was satisfactory,which has specific clinical application value.

Neurofilament proteins in axonal regeneration and neurodegenerative diseases.

Neurofilament protein is a component of the mature neuronal cytoskeleton, and it interacts with the zygosome, which is mediated by neurofilament-related proteins. Neurofilament protein regulates enzyme function and the structure of linker proteins. In addition, neurofilament gene expression plays an important role in nervous system development. Previous studies have shown that neurofilament gene transcriptional regulation is crucial for neurofilament protein expression, especially in axonal regeneration and degenerative diseases. Post-transcriptional regulation increased neurofilament protein gene transcription during axonal regeneration, ultimately resulting in a pattern of neurofilament protein expression. An expression imbalance of post-transcriptional regulatory proteins and other disorders could lead to amyotrophic lateral sclerosis or other neurodegenerative diseases. These findings indicated that after transcription, neurofilament protein regulated expression of related proteins and promoted regeneration of damaged axons, suggesting that regulation disorders could lead to neurodegenerative diseases.

Pedicle screw placement in the thoracic spine: a comparison study of computer-assisted navigation and conventional techniques.

The technique of computer-assisted pedicle screw installation and its clinical benefit as compared with conventional pedicle screw installation was evaluated. Twenty-two patients had thoracic screw insertion under 3-dimentional computer-assisted navigation (92 screws) and 20 patients under conventional fluoroscopic control (84 screws). The 2 groups were compared for accuracy of screw placement, screw insertion time by postoperative thin-cut computed tomography scans, and statistical analysis. The cortical perforations were graded by 2-mm increments. In the computer group, 88 (95.65%) were grade I (good), 4 (4.35%) were grade II (<2 mm), and 0 were grade III (>2 mm) violations. There were 4 cortical violations (3.57%). In the conventional group, there were 14 cortical violations (16.67%), 70 (83.33%) were grade I (good), 11 (13.1%) were grade II (<2 mm), and 3 (3.57%) were grade III (>2 mm) violations (P<.001). The number (19.57%) of upper thoracic pedicle screws (T1-T4) inserted under 3-dimensional computer-assisted navigation was significantly higher than that (3.57%) by conventional fluoroscopic control (P<.001). Average screw insertion time in the conventional group was more than in the computer group (P<.001). Three-dimensional computer-assisted navigation pedicle screw placement can increase accuracy, reduce surgical time, and be performed safely and effectively at all levels of the thoracic spine, particularly the upper thoracic spine.

Pedicle screw placement in the thoracic spine: a randomized comparison study of computer-assisted navigation and conventional techniques.

OBJECTIVE: To evaluate the accuracy of computer-assisted pedicle screw installation and its clinical benefit as compared with conventional pedicle screw installation techniques. METHODS: Total 176 thoracic pedicle screws placed in 42 thoracic fracture patients were involved in the study randomly, 20 patients under conventional fluoroscopic control (84 screws) and 22 patients had screw insertion under three dimensional (3D) computer-assisted navigation (92 screws). The 2 groups were compared for accuracy of screw placement, time for screw insertion by postoperative thin-cut CT scans and statistical analysis by X(2) test. The cortical perforations were then graded by 2-mm increments: Grade I (good, no cortical perforation), Grade II (screw outside the pedicle less than 2 mm), Grade III (screw outside the pedicle larger than 2 mm). RESULTS: In computer assisted group, 88 (95.65%) were Grade I (good), 4 (4.35%) were Grade II (less than 2mm), no Grade III (larger than 2 mm) violations. In conventional group, there were 14 cortical violations (16.67%), 70 (83.33%) were Grade I (good), 11 (13.1%) were Grade II (less than 2 mm), and 3 (3.57%) were Grade III (larger than 2 mm) violations (P less than 0.001). The number (19.57%) of upper thoracic pedicle screws ( T(1)-T(4) ) inserted under 3D computer-assisted navigation was significantly higher than that (3.57%) by conventional fluoroscopic control (P less than 0.001). Average screw insertion time in conventional group was (4.56+/-1.03) min and (2.54+/-0.63) min in computer assisted group (P less than 0.001). In the conventional group, one patient had pleura injury and one had a minor dura violation. CONCLUSIONS: This study provides further evidence that 3D computer-assisted navigation placement of pedicle screws can increase accuracy, reduce surgical time, and be performed safely and effectively at all levels of the thoracic spine, particularly upper thoracic spine.