Identification of SARS-CoV-2 Proteins Binding Human mRNAs As a Novel Signature Predicting Overall Survival in Hepatocellular Carcinoma

Single-cell analysis reveals changes in BCG vaccine-injected mice modeling tuberculous meningitis brain infection

Tuberculous meningitis (TBM) is the most severe and deadly manifestation of tuberculosis. Neurological complications are observed in up to 50% of patients affected. Here, attenuated Mycobacterium bovis are injected into the cerebellum of mice, and histopathological images and cultured colonies confirm successful brain infection. Then, whole-brain tissue is dissected for 10X Genomics single-cell sequencing, and we acquire 15 cell types. Transcriptional changes of inflammation processes are found in multiple cell types. Specifically, Stat1 and IRF1 are shown to mediate inflammation in macrophages and microglia. For neurons, decreased oxidative phosphorylation activity in neurons is observed, which corresponds to TBM clinical symptoms of neurodegeneration. Finally, ependymal cells present prominent transcriptional changes, and decreased FERM domain containing 4A (Frmd4a) may contribute to TBM clinical symptoms of hydrocephalus and neurodegeneration. This study shows a single-cell transcriptome of M. bovis infection in mice and improves the understanding of brain infection and neurological complications in TBM.

Integrated Proteomics and Bioinformatics to Identify Potential Prognostic Biomarkers in Hepatocellular Carcinoma

BACKGROUND

Liver hepatocellular carcinoma (HCC) is the third most common cause of death by cancer and has a high mortality world-widely. Approximately 75-85% of primary liver cancers are caused by HCC. Uncovering novel genes with prognostic significance would shed light on improving the HCC patient's outcome.

OBJECTIVE

In this research, we aim to identify novel prognostic biomarkers in hepatocellular carcinoma.

METHODS

Integrated proteomics and bioinformatics analysis were performed to investigate the expression landscape of prognostic biomarkers in 24 paired HCC patients.

RESULTS

As a result, eight key genes related to prognosis, including ACADS, HSD17B13, PON3, AMDHD1, CYP2C8, CYP4A11, SLC27A5, CYP2E1, were identified by comparing the weighted gene co-expression network analysis (WGCNA), proteomic differentially expressed genes (DEGs), proteomic turquoise module, The Cancer Genome Atlas (TCGA) cohort DEGs of HCC. Furthermore, we trained and validated eight pivotal genes integrating these independent clinical variables into a nomogram with superior accuracy in predicting progression events, and their lower expression was associated with a higher stage/risk score. The Gene Set Enrichment Analysis (GSEA) further revealed that these key genes showed enrichment in the HCC regulatory pathway.

CONCLUSION

All in all, we found that these eight genes might be the novel potential prognostic biomarkers for HCC and also provide promising insights into the pathogenesis of HCC at the molecular level.