EFNA3 is a prognostic biomarker for the overall survival of patients with hepatocellular carcinoma

TMEM101 expression and its impact on immune cell infiltration and prognosis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a cancer caused by inflammation, which affects the immune response and treatment outcomes. Finding new immune-related targets could improve HCC immunotherapy. New research suggests that TMEM family proteins can act as either tumor suppressors or oncogenes, but the role of TMEM101 in HCC development is unclear. This study conducted an analysis of TMEM101 mRNA expression and its correlation with clinical outcomes in HCC patients using RNA sequencing data from various open databases. Additionally, differences in TMEM101 expression in HCC cell lines and HCC tissue microarrays were examined using RT-qPCR, western blotting, and in situ hybridization staining. The findings presented herein offer initial evidence indicating a significant upregulation of TMEM101 mRNA expression in HCC, which is linked to a poorer prognosis. Furthermore, TMEM101 expression was found to be positively associated with the histological grade and clinical stage of HCC patients. Moreover, a notable reduction in promoter methylation of TMEM101 was observed in HCC patients. Cox regression analysis indicated that TMEM101 was an independent prognostic factor for overall survival (OS) in HCC patients. A nomogram incorporating TMEM101 and tumor stage was constructed and assessed. Comparative analysis with four established HCC diagnostic biomarkers (AFP, EFNA3, MDK, and SMYD5) using ROC curve and time-dependent ROC curves demonstrated the diagnostic potential of TMEM101 in HCC. Gene set enrichment analysis (GSEA) revealed a correlation between TMEM101 and the cell cycle, DNA replication, and repair signaling pathways, which were differentially enriched in the TMEM101 high expression phenotype. The findings from CIBERSORT analysis suggest that TMEM101's pro-tumor effect may be due to decreasing the number of anti-tumor immune cells (M1 macrophages and resting memory CD4+ T cells) and promoting M0 macrophage infiltration in the tumor microenvironment (TME). Overall, our study indicates that TMEM101 could serve as a promising diagnostic and prognostic biomarker for HCC.

GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection

Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecules from different biological modalities and the limited sample size. Here, we introduce GD-Net, a Graph Deep learning algorithm to enhance the accuracy of survival prediction with an average accuracy of 72% by early fusing of multimodal information, which includes an interpretable and lightweight XGBoost module to efficiently extract informative features. First, we applied GD-Net to eight cancer datasets and achieved superior performance compared to benchmarking methods, with an average 7.9% higher C-index value. The ablation experiments strongly supported that multi-modal integration could significantly improve accuracy over the single-modality model. In the deep case study of liver cancer, 319 differential genes, 15 differential miRNAs and 155 methylated differential genes based on the predicted risk subgroups are identified as the informative features, and then we have statistically and biologically validated the efficacy of these key molecules in internal and external test datasets. The comprehensive independent validations demonstrated that GD-Net is accurate and competitive in predicting different cancer outcomes in real-time, and it is an effective tool for identifying new multimodal prognosis biomarkers.

Artesunate attenuates the tumorigenesis of choroidal melanoma via inhibiting EFNA3 through Stat3/Akt signaling pathway

PURPOSE

Choroidal melanoma (CM), a kind of malignant tumor, is the main type of Uveal melanoma and one half of CM patients develop metastases. As a member of Eph/ephrin pathway that plays vital role in tumors, EphrinA3 (EFNA3) has been proved to promote tumorigenesis in many tumors. But the effect of EFNA3 in CM has not been studied yet. Through inhibiting angiogenesis, inducing apoptosis and autophagy and so on, Artesunate (ART) plays a key anti-tumor role in many tumors, including CM. However, the exact mechanisms of anti-tumor in CM remain unclear.

METHODS

The UALCAN and TIMER v2.0 database analyzed the role of EFNA3 in CM patients. Quantitative real time polymerase chain reaction (qPCR) and Western blot were used to detect the expression of EFNA3 in CM. The growth ability of CM was tested by clonogenic assay and Cell counting kit-8 assay, and the migration ability using Transwell assay.

RESULTS

Our results found EFNA3 boosted CM cells' growth and migration through activating Stat3/Akt signaling pathway, while ART inhibited the tumor promoting effect of CM via downregulating EFNA3. In xenograft tumor model, EFNA3 knockdown and ART significantly inhibited tumor growth.

CONCLUSION

EFNA3 could be a valuable prognostic factor in CM.

Hub gene identification and molecular subtype construction for Helicobacter pylori in gastric cancer via machine learning methods and NMF algorithm

Helicobacter pylori (HP) is a gram-negative and spiral-shaped bacterium colonizing the human stomach and has been recognized as the risk factor of gastritis, peptic ulcer disease, and gastric cancer (GC). Moreover, it was recently identified as a class I carcinogen, which affects the occurrence and progression of GC via inducing various oncogenic pathways. Therefore, identifying the HP-related key genes is crucial for understanding the oncogenic mechanisms and improving the outcomes of GC patients. We retrieved the list of HP-related gene sets from the Molecular Signatures Database. Based on the HP-related genes, unsupervised non-negative matrix factorization (NMF) clustering method was conducted to stratify TCGA-STAD, GSE15459, GSE84433 samples into two clusters with distinct clinical outcomes and immune infiltration characterization. Subsequently, two machine learning (ML) strategies, including support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF), were employed to determine twelve hub HP-related genes. Beyond that, receiver operating characteristic and Kaplan-Meier curves further confirmed the diagnostic value and prognostic significance of hub genes. Finally, expression of HP-related hub genes was tested by qRT-PCR array and immunohistochemical images. Additionally, functional pathway enrichment analysis indicated that these hub genes were implicated in the genesis and progression of GC by activating or inhibiting the classical cancer-associated pathways, such as epithelial-mesenchymal transition, cell cycle, apoptosis, RAS/MAPK, etc. In the present study, we constructed a novel HP-related tumor classification in different datasets, and screened out twelve hub genes via performing the ML algorithms, which may contribute to the molecular diagnosis and personalized therapy of GC.