Studying the Role and Molecular Mechanisms of MAP4K3 in Sorafenib Resistance of Hepatocellular Carcinoma

A Retrospective and Multicenter Study on COVID-19 in Inner Mongolia: Evaluating the Influence of Sampling Locations on Nucleic Acid Test and the Dynamics of Clinical and Prognostic Indexes

COVID-19 is spreading widely, and the pandemic is seriously threatening public health throughout the world. A comprehensive study on the optimal sampling types and timing for an efficient SARS-CoV-2 test has not been reported. We collected clinical information and the values of 55 biochemical indices for 237 COVID-19 patients, with 37 matched non-COVID-19 pneumonia patients and 131 healthy people in Inner Mongolia as control. In addition, the results of dynamic detection of SARS-CoV-2 using oropharynx swab, pharynx swab, and feces were collected from 197 COVID-19 patients. SARS-CoV-2 RNA positive in feces specimen was present in approximately one-third of COVID-19 patients. The positive detection rate of SARS-CoV-2 RNA in feces was significantly higher than both in the oropharynx and nasopharynx swab (P < 0.05) in the late period of the disease, which is not the case in the early period of the disease. There were statistically significant differences in the levels of blood LDH, CRP, platelet count, neutrophilic granulocyte count, white blood cell number, and lymphocyte count between COVID-19 and non-COVID-19 pneumonia patients. Finally, we developed and compared five machine-learning models to predict the prognosis of COVID-19 patients based on biochemical indices at disease onset and demographic characteristics. The best model achieved an area under the curve of 0.853 in the 10-fold cross-validation.

A Systematic Review of Lymphangioleiomyomatosis on Diagnosis and Molecular Mechanism

Objective

Lymphangioleiomyomatosis (LAM) is a rare low-grade metastatic tumor; however, LAM patients were always found in young age with difficulty for diagnosis. Our study is aimed at observing the clinical characteristics of patients with lymphangiomatosis, including the clinical manifestations, imaging findings, histopathological features, and immunophenotype.

Methods

We did a systematic review on LAM/PLAM cases, especially on male cases, and collected the clinical features and molecular mechanisms of PLAM based on previous findings.

Results

Diagnosis criteria were summarized by combining CT scans, MRI, immunohistochemistry results, and gene sequencing results for effectively distinguishing between PLAM and similar diseases. Moreover, our study illustrated the molecular mechanism of PLAM as well as the signaling pathway involved in the disease initials. In addition, a male case was reported with differential diagnosis on the clinical manifestations, microscopic features, immunophenotypes, and genotypes.

Conclusion

Our review will definitely improve the understanding of diagnosis and treatment in PLAM cases.

Potential Drug Prediction of Glioblastoma Based on Drug Perturbation-Induced Gene Expression Signatures

OBJECTIVES

Glioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures.

METHODS

We first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings.

RESULTS

We identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset.

CONCLUSIONS

In this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.