Integrated Single-cell and Transcriptome Sequencing Analyses Identified PREX1 as an Immune-related Prognostic Biomarker for Liver Hepatocellular Carcinoma.

Background: PtdIns (3,4,5) P3-dependent Rac exchanger 1 (PREX1), also known as PREX1, a member of the Rac guanine nucleotide exchange factors (Rac-GEF) family. Studies have suggested that PREX1 plays a role in mediating oncogenic pathway activation and controlling various biological mechanisms in different types of cancer, including liver hepatocellular carcinoma (LIHC). However, the function of PREX1 in the pathogenesis of LIHC and its potential role on immunological regulation is not clearly elucidated. Methods: The expression level and the clinical role of PREX1 in LIHC was analyzed based on database from the Cancer Genome Atlas (TCGA), TNM plotter and University of Alabama Cancer Database (UALCAN). We investigated the relationship between PREX1 and immunity in LIHC by TISIDB, CIBERSORT and single cell analysis. Immunotherapy responses were assessed by the immunophenoscores (IPS). Moreover, biological functional assays were performed to further investigate the roles of PREX1 in liver cancer cell lines. Results: Higher expression of PREX1 in LIHC tissues than in normal liver tissues was found based on public datasets. Further analysis revealed that PREX1 was associated with worse clinical characteristics and dismal prognosis. Pathway enrichment analysis indicated that PREX1 participated in immune-related pathways. Through CIBERSORT and single cell analysis, we found a remarkable correlation between the expression of PREX1 and various immune cells, especially macrophages. In addition, high PREX1 expression was found to be associated with a stronger response to immunotherapy. Furthermore, in vitro assays indicated that depletion of PREX1 can suppress invasion and proliferation of LIHC cells. Conclusion: Elevated expression of PREX1 indicates poor prognosis, influences immune modulation and predicts sensitivity of immunosuppression therapy in LIHC. Our results suggested that PREX1 may be a prognostic biomarker and therapeutic target, offering new treatment options for LIHC.

AI detection of mild COVID-19 pneumonia from chest CT scans.

OBJECTIVES: An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated. METHODS: In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model. RESULTS: The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2-93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0-92.9%) and the general AUC value was 0.955 (p < 0.001). CONCLUSIONS: A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription-polymerase chain reaction (RT-PCR) test. KEY POINTS: • The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible. • The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified. • This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.