This study is designed to illuminate the specific role and underlying mechanism of collagen type III alpha 1 chain (COL3A1) in triple negative breast cancer (TNBC).Quantitative real-time polymerase chain reaction was applied to examine mRNA expression of COL3A1. Western blot analysis was employed to determine protein levels of COL3A1, programmed death ligand 1 (PD-L1), Bcl-2, and cleaved caspase-3. Immunohistochemistry staining was utilized for assessing protein expression of Ki67 and COL3A1 in tissues. The proliferous capacity of cells was assessed through CCK-8 assay and 5-Ethynyl-2′-deoxyuridine assay. Cell apoptosis and the percentage of CD8 + T cells were measured using flow cytometry. Migration and invasion of TNBC cells were examined via transwell assay. Lactate dehydrogenase (LDH) release was measured via a LDH assay kit. For establishing a xenograft tumor model, MDA-MB-231 cells were injected into the flank of mice through subcutaneous injection. COL3A1 expression was raised in TNBC tissues and cells, and it was inversely associated with overall survival data of TNBC patients. COL3A1 downregulation repressed proliferation, invasion, migration, and immune escape of TNBC cells along with tumor growth of xenograft mice. In TNBC cells and tumor tissues of mice, protein expression of PD-L1 was reduced by COL3A1 knockdown. COL3A1 knockdown-mediated inhibitory effects on cell proliferation, migration, invasion, and immune escape were reversed by PD-L1 upregulation in vitro. Silencing of COL3A1 exerted an antitumor role in TNBC, implying its potential as a therapeutic target for TNBC.
Cell-to-cell variability is orchestrated by transcriptional variations participating in different biological processes. However, the dissection of transcriptional variability in specific biological process at single-cell level remains unavailable. Here, we present a deep generative model scPheno to integrate scRNA-seq with disease phenotypes to unravel the invisible phenotype-related transcriptional variations. We applied scPheno on COVID-19 blood scRNA-seq to separate transcriptional variations in regulating COVID-19 host immunity and transcriptional variations in maintaining cell-type identity. In silico, we found CLU+IFI27+S100A9+ monocyte as the efficient cellular marker for the prediction of COVID-19 diagnosis. Inspiringly, using only 4 genes upregulated in CLU+IFI27+S100A9+ monocytes can predict the COVID-19 diagnosis of individuals from different country with an accuracy up to 81.3%. We also found C1+CD163+ monocyte and 8 C1+CD163+ monocyte-upregulated genes as the efficient biomarkers for the prediction of severity assessment. Overall, scPheno is an effective method in dissecting the transcriptional basis of phenotype variations at single-cell level.
Background Accurate precision approaches have far not been developed for modeling mortality risk in intensive care unit (ICU) patients. Conventional mortality risk prediction methods can hardly extract the information in longitudinal electronic medical records (EHRs) effectively, since they simply aggregate the heterogeneous variables in EHRs, ignoring the complex relationship and interactions between variables and the time dependence in longitudinal records. Recently deep learning approaches have been widely used in modeling longitudinal EHR data. However, most existing deep learning-based risk prediction approaches only use the information of a single disease, neglecting the interactions between multiple diseases and different conditions. Results In this paper, we address this unmet need by leveraging disease and treatment information in EHRs to develop a mortality risk prediction model based on deep learning (DeepMPM). DeepMPM utilizes a two-level attention mechanism, i.e. visit-level and variable-level attention, to derive the representation of patient risk status from patient’s multiple longitudinal medical records. Benefiting from using EHR of patients with multiple diseases and different conditions, DeepMPM can achieve state-of-the-art performances in mortality risk prediction. Conclusions Experiment results on MIMIC III database demonstrates that with the disease and treatment information DeepMPM can achieve a good performance in terms of Area Under ROC Curve (0.85). Moreover, DeepMPM can successfully model the complex interactions between diseases to achieve better representation learning of disease and treatment than other deep learning approaches, so as to improve the accuracy of mortality prediction. A case study also shows that DeepMPM offers the potential to provide users with insights into feature correlation in data as well as model behavior for each prediction.
TCP1, one of the subunits of molecular chaperone containing tailless complex polypeptide 1 (CCT) (CCT1), maybe play an essential role in cell proliferation and tumorigenesis. However, the biological functions and the underlying mechanisms of TCP1 in tumors are not fully understood. Here, we found that TCP1 levels increased with successive generations of xenografted tumors and were associated with the tumorigenicity of HL-60 cells. TCP1 expression was significantly higher in de novo and recurrent leukemia clinical samples and in various solid tumor tissues than in their related normal tissues. High expression of TCP1 was significantly associated with poor outcomes in patients with pancreatic ductal adenocarcinoma (PDAC) and HCC. Functional assays and mechanistic studies revealed that TCP1 suppression not only decreased the proliferation and invasion of cancer cells in vitro but also inhibited tumor growth and metastatic spread in vivo. Ubiquitination assays and mechanistic studies revealed that TCP1 regulated the stability of c-Myc through the AKT/GSK-3β and ERK signaling pathways. Moreover, TCP1 knockin (TCP1-KI) mice, which generated by CRISPR/Cas9-mediated genome engineering, dramatically facilitated the occurrence of DEN-induced HCC. Our results demonstrate a novel mechanism of TCP1 regulation in multiple malignant tumors, and targeting TCP1 may provide new therapeutic strategies for the cancer treatment.
Identifying different types of cells in scRNA-seq data is a critical task in single-cell data analysis. In this paper, we propose a method called ProgClust for the decomposition of cell populations and detection of rare cells. ProgClust represents the single-cell data with clustering trees where a progressive searching method is designed to select cell population-specific genes and cluster cells. The obtained trees reveal the structure of both abundant cell populations and rare cell populations. Additionally, it can automatically determine the number of clusters. Experimental results show that ProgClust outperforms the baseline method and is capable of accurately identifying both common and rare cells. Moreover, when applied to real unlabeled data, it reveals potential cell subpopulations which provides clues for further exploration. In summary, ProgClust shows potential in identifying subpopulations of complex single-cell data.
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