Learning the cellular activity representation based on gene regulatory networks for prediction of tumor response to drugs.

Predicting the response of tumor cells to anti-tumor drugs is critical to realizing cancer precision medicine. Currently, most existing methods ignore the regulatory relationships between genes and thus have unsatisfactory predictive performance. In this paper, we propose to predict anti-tumor drug efficacy via learning the activity representation of tumor cells based on a priori knowledge of gene regulation networks (GRNs). Specifically, the method simulates the cellular biosystem by synthesizing a cell-gene activity network and then infers a new low-dimensional activity representation for tumor cells from the raw high-dimensional expression profile. The simulated cell-gene network mainly comprises known gene regulatory networks collected from multiple resources and fuses tumor cells by linking them to hotspot genes that are over- or under-expressed in them. The resulting activity representation could not only reflect the shallow expression profile (hotspot genes) but also mines in-depth information of gene regulation activity in tumor cells before treatment. Finally, we build deep learning models on the activity representation for predicting drug efficacy in tumor cells. Experimental results on the benchmark GDSC dataset demonstrate the superior performance of the proposed method over SOTA methods with the highest AUC of 0.954 in the efficacy label prediction and the best R2 of 0.834 in the regression of half maximal inhibitory concentration (IC50) values, suggesting the potential value of the proposed method in practice.

Investigation of the feasibility of NRAV as a biomarker for hepatocellular carcinoma.

The long non-coding RNA, Negative Regulator of Antiviral Response (NRAV) has been identified as a participant in both respiratory virus replication and immune checkpoints, however, its involvement in pan-cancer immune regulation and prognosis, particularly those of hepatocellular carcinoma (HCC), remains unclear. To address this knowledge gap, we analyzed expression profiles obtained from The Cancer Genome Atlas (TCGA) database, comparing normal and malignant tumor tissues. We found that NRAV expression is significantly upregulated in tumor tissues compared to adjacent nontumor tissues. Kaplan-Meier (K-M) analysis revealed the prognostic power of NRAV, wherein overexpression was significantly linked to reduced overall survival in a diverse range of tumor patients. Furthermore, noteworthy associations were observed between NRAV, immune checkpoints, immune cell infiltration, genes related to autophagy, epithelial-mesenchymal transition (EMT), pyroptosis, tumor mutational burden (TMB), and microsatellite instability (MSI) across different cancer types, including HCC. Moreover, NRAV upregulation expression was associated with multiple pathological stages by clinical observations. Furthermore, our investigation revealed a substantial elevation in the expression of NRAV in both HCC tumor tissues and cells compared to normal tissues and cells. The inhibition of NRAV resulted in the inhibition of cell proliferation, migration, and invasion in HCC cells, while also influencing the expression of CD274 (PD-L1) and CD44, along with various biomarkers associated with EMT, autophagy, and pyroptosis. The aforementioned results propose NRAV as a promising prognostic biomarker for HCC.