Hepatocellular carcinoma (HCC) is the most common subtype in liver cancer whose prognosis is affected by malignant progression associated with complex gene interactions. However, there is currently no available biomarkers associated with HCC progression in clinical application. In our study, RNA sequencing expression data of 50 normal samples and 374 tumor samples was analyzed and 9225 differentially expressed genes were screened. Weighted gene coexpression network analysis was then conducted and the blue module we were interested was identified by calculating the correlations between 17 gene modules and clinical features. In the blue module, the calculation of topological overlap was applied to select the top 30 genes and these 30 genes were divided into the green group (11 genes) and the yellow group (19 genes) through searching whether these genes were validated by in vitro or in vivo experiments. The genes in the green group which had never been validated by any experiments were recognized as hub genes. These hub genes were subsequently validated by a new data set GSE76427 and KM Plotter Online Tool, and the results indicated that 10 genes (FBXO43, ARHGEF39, MXD3, VIPR1, DNASE1L3, PHLDA1, CSRNP1, ADR2B, C1RL, and CDC37L1) could act as prognosis and progression biomarkers of HCC. In summary, 10 genes who have never been mentioned in HCC were identified to be associated with malignant progression and prognosis of patients. These findings may contribute to the improvement of the therapeutic decision, risk stratification, and prognosis prediction for HCC patients. K E Y W O R D S biomarker, hepatocellular carcinoma, malignant progression, weighted gene coexpression network analysis

Background Brain malignancies encompass gliomas and brain metastases originating from extracranial tumours including lung cancer. Approximately 50% of patients with lung adenocarcinoma (LUAD) will eventually develop brain metastases. However, the specific characteristics of gliomas and lung‐to‐brain metastases (LC) are largely unknown. Methods We applied single‐cell RNA sequencing to profile immune and nonimmune cells in 4 glioma and 10 LC samples. Results Our analysis revealed that tumour microenvironment (TME) cells are present in heterogeneous subpopulations. LC reprogramed cells into immune suppressed state, including microglia, macrophages, endothelial cells, and CD8+ T cells, with unique cell proportions and gene signatures. Particularly, we identified that a subset of macrophages was associated with poor prognosis. ROS (reactive oxygen species)‐producing neutrophils was found to participant in angiogenesis. Furthermore, endothelial cells participated in active communication with fibroblasts. Metastatic epithelial cells exhibited high heterogeneity in chromosomal instability (CIN) and cell population. Conclusions Our findings provide a comprehensive understanding of the heterogenicity of the tumor microenvironment and tumour cells and it will be crucial for successful immunotherapy development for brain metastasis of lung cancer.

Triple‐negative breast cancer is the most aggressive subtype of breast cancer and the incidence of depression in breast cancer patients is high, which leading to worse survival and increased risk of recurrence. The effect of antidepressants on breast cancer patients remains contradictory, which might be due to variations in antidepression targets. Therefore, there is significant value to explore the antitumor potential of antidepressants and discover new therapeutic targets for breast patients. The authors screen antidepressant‐related oncogenes or suppressors by using siRNAs. After combining functional experiments with online database analysis, 5‐hydroxytryptamine receptor 1A (HTR1A is selected with antitumor potential in breast cancer cells in vivo and in vitro. RNA‐seq analysis and coimmunoprecipitation assays indicate that HTR1A interacts with TRIM21 and PSMD7 to inhibit the degradation of T β RII through the ubiquitin‐proteasome pathway, thereby inhibiting the transforming growth factor‐ β (TGF‐β) canonical and noncanonical pathway. In addition, HTR1A is an independent predictive factor for breast cancer patients. The combined treatment of HTR1A agonists with demethylation drugs may significantly improve patient survival. It is of great significance to clarify the function and mechanism of the depression‐related gene HTR1A in breast cancer, which might provide a new approach for triple‐negative breast cancer patients.