Multiomics-based molecular subtyping based on the commensal microbiome predicts molecular characteristics and the therapeutic response in breast cancer.

The gut microbiota has been demonstrated to be correlated with the clinical phenotypes of diseases, including cancers. However, there are few studies on clinical subtyping based on the gut microbiota, especially in breast cancer (BC) patients. Here, using machine learning methods, we analysed the gut microbiota of BC, colorectal cancer (CRC), and gastric cancer (GC) patients to identify their shared metabolic pathways and the importance of these pathways in cancer development. Based on the gut microbiota-related metabolic pathways, human gene expression profile and patient prognosis, we established a novel BC subtyping system and identified a subtype called "challenging BC". Tumours with this subtype have more genetic mutations and a more complex immune environment than those of other subtypes. A score index was proposed for in-depth analysis and showed a significant negative correlation with patient prognosis. Notably, activation of the TPK1-FOXP3-mediated Hedgehog signalling pathway and TPK1-ITGAE-mediated mTOR signalling pathway was linked to poor prognosis in "challenging BC" patients with high scores, as validated in a patient-derived xenograft (PDX) model. Furthermore, our subtyping system and score index are effective predictors of the response to current neoadjuvant therapy regimens, with the score index significantly negatively correlated with both treatment efficacy and the number of immune cells. Therefore, our findings provide valuable insights into predicting molecular characteristics and treatment responses in "challenging BC" patients.

Novel risk model based on angiogenesis-related lncRNAs for prognosis prediction of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death due to early metastasis or recurrence. Tumor angiogenesis plays an essential role in the tumorigenesis of HCC. Accumulated studies have validated the crucial role of lncRNAs in tumor angiogenesis. Here, we established an angiogenesis-related multi-lncRNAs risk model based on the machine learning for HCC prognosis prediction. Firstly, a total of 348 differential expression angiogenesis-related lncRNAs were identified by correlation analysis. Then, 20 of these lncRNAs were selected through univariate cox analysis and used for in-depth study of machine learning. After 1,000 random sampling cycles calculating by random forest algorithm, four lncRNAs were found to be highly associated with HCC prognosis, namely LUCAT1, AC010761.1, AC006504.7 and MIR210HG. Subsequently, the results from both the training and validation sets revealed that the four lncRNAs-based risk model was suitable for predicting HCC recurrence. Moreover, the infiltration of macrophages and CD8 T cells were shown to be closely associated with risk score and promotion of immune escape. The reliability of this model was validated by exploring the biological functions of lncRNA MIR210HG in HCC cells. The results showed that MIR210HG silence inhibited HCC growth and migration through upregulating PFKFB4 and SPAG4. Taken together, this angiogenesis-related risk model could serve as a reliable and promising tool to predict the prognosis of HCC.

Engineered Apoptosis-Bioinspired Nanoparticles Initiate Immune Cascade for Cancer Immunotherapy of Malignant Ascites.

Malignant ascites (MA) is a common symptom of peritoneal metastasis in liver cancer. Cancer immunotherapy can modulate immune cells to induce antitumor immune efficiency. Reprogramming tumor immune microenvironment (TIME) is a momentous strategy to overcome immunosuppression and achieve immune functional normalization. Inspired by the inherent apoptotic bodies and vesicles, we proposed and systematically studied engineered apoptosis-bioinspired nanoparticles (EBN) for cancer immunotherapy of MA. Using both in vitro and in vivo experimental validations, we elucidated that EBN could be efficiently engulfed by the tumor-associated macrophages (TAMs) and manipulate their polarization. Moreover, a boosted immune cascade response as a result of heightening cytotoxic T-lymphocytes (CTLs) activity was investigated. Based on these results, EBN was confirmed to have strong immune cascade activation capability. Remarkably, the injection of EBN further reduced ascites volume and reformed immune cell subtypes, compared to the injection of either PBS or free TMP195 alone. In short, this novel nanodrug delivery system (NDDS) represents a prospective immunotherapeutic approach for clinical therapeutics of hepatoma ascites and other malignant effusion.

Generalization of learned variability across multiple dimensions in humans.

This study examined whether trained variability would generalize across dimensions of the target response. Two experiments used a computerized rectangle drawing task that required participants to click and drag a mouse cursor to create rectangles on a computer screen. In Experiment 1, one group received points when successive rectangles varied in their size, shape and location (VAR), another group were yoked to the VAR group and received points that were allocated to them using a yoking procedure (YOKE), regardless of the variability in the size, shape or location of the rectangle drawn. Variability was higher for a dimension when variability on that dimension was directly reinforced. In Experiment 2, three groups of participants received points when rectangles varied on two dimensions; each group differed in the two dimensions that required variation. Variability was again higher for the reinforced dimensions for two of the three groups. Comparison with the YOKE group showed that the variability on those dimensions where variability was not directly reinforced was affected by reinforcement for variability on the other dimensions. Specifically, the variability in Shape and Location was significantly higher when these two dimensions occurred with other dimensions where variability was reinforced (as in Experiment 2) compared to when they were not required to vary (as in the YOKE group). This suggests that, for these two groups, the reinforced variability on the other two dimensions generalized to the third dimension. Implications of this finding to our understanding of factors that promote behavioral variability are discussed.