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.

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.

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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.

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-1β, 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.