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.

A novel dual-function SERS-based identification strategy for preliminary screening and accurate diagnosis of circulating tumor cells.

Non-specific adsorption of bioprobes based on surface-enhanced Raman spectroscopy (SERS) technology inevitably endows white blood cells (WBC) in the peripheral blood with Raman signals, which greatly interfere the identification accuracy of circulating tumor cells (CTCs). In this study, an innovative strategy was proposed to effectively identify CTCs by using SERS technology assisted by a receiver operating characteristic (ROC) curve. Firstly, a magnetic Fe3O4-Au complex SERS bioprobe was developed, which could effectively capture the triple negative breast cancer (TNBC) cells and endow the tumor cells with distinct SERS signals. Then, the ROC curve obtained based on the comparison of SERS intensity of TNBC cells and WBC was used to construct a tumor cell identification model. The merit of the model was that the detection sensitivity and specificity could be intelligently switched according to different identification purposes such as accurate diagnosis or preliminary screening of tumor cells. Finally, the difunctional recognition ability of the model for accurate diagnosis and preliminary screening of tumor cells was further validated by using the healthy human blood added with TNBC cells and blood samples of real tumor patients. This novel difunctional identification strategy provides a new perspective for identification of CTCs based on the SERS technology.

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.

Association of DW/DCE-MRI features with prognostic factors in breast cancer.

BACKGROUND: Through analyzing apparent diffusion coefficient (ADC) values and morphological evaluations, this research aimed to study how magnetic resonance imaging (MRI)-based breast lesion characteristics can enhance the diagnosis and prognosis of breast cancer. METHODS:: A total of 118 breast lesions, including 50 benign and 68 malignant lesions, from 106 patients were analyzed. All lesions were measured with both diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI. The average ADC of breast lesions was analyzed at b values of 600, 800 and 1,000 s/mm2. Lesion margins, lesion enhancement patterns, and dynamic curves were also investigated. The relations between MRI-based features and molecular prognostic factors were evaluated using Spearman's rank correlation analysis. RESULTS:: A b value of 800 s/mm2 was used to distinguish malignant from benign breast lesions, with an ADC cutoff value of 1.365 × 10-3 mm2/s. The average ADC value between invasive ductal carcinoma (IDC) and ductal carcinoma in situ (DCIS) was significantly different. Malignant lesions were more likely to have spiculated margins, heterogeneous enhancement and washout curves. On the other hand, DCIS was more likely to have spiculated margins, heterogeneous/rim enhancement and plateau/washout dynamic curves. A significant negative correlation was found between progesterone receptor (PR) status and dynamic imaging (p = 0.027), while a significant positive correlation was found between Ki-67 status and lesion enhancement (p = 0.045). CONCLUSIONS:: Both ADC values and MRI morphological assessment could be used to distinguish malignant breast lesions from benign ones.

Contactin 1 as a potential biomarker promotes cell proliferation and invasion in thyroid cancer.

Contactin 1 (CNTN1) as a member of the immunoglobulin superfamily plays important role in the development of nervous system. Recent studies find that elevated CNTN1 can promote the metastasis of cancer. However, the expression and function of CNTN1 in thyroid cancer are still unknown. Here, we firstly find CNTN1 is a new gene which can be regulated by RET/PTC3 (Ret proto-oncogene and Ret-activating protein ELE1) rearrangement gene and the protein level of CNTN1 is increasing in thyroid cancer. Besides this change is positively associated with the TNM stage and tumor size. Moreover, we confirm that knockdown of CNTN1 significantly inhibits the tumor proliferation, invasiveness and represses the expression of cyclin D1 (CCND1). In conclusion, CNTN1 will be a potential diagnosis biomarker and therapy target for thyroid cancer.