High Expression of ERK-related RASGRF2 predicts Poor prognosis in patients with Stomach Adenocarcinoma and correlates with M2 macrophage

Exosomal miR-181a-5p derived from SAOS-2 cells promotes macrophages M2 polarization by targeting RORA

Although the interaction between tumor cells and tumor-associated macrophages (TAMs) has been widely studied; however, the mechanism of osteosarcoma cells in regulating the polarization of TAMs remains unclear. Exosomes from SAOS-2 cells were isolated and validated by electron microscopy and Western blot. Transfection of indicated plasmids was applied to modify the expressions of miR-181a-5p and RAR-related orphan receptor alpha (RORA). Flow cytometric analysis was carried out to analyze M1/M2 macrophage polarization. Quantitative real-time PCR was performed to determine the levels of miR-181a-5p and RORA. Protein levels of CD63, CD81, RORA, CD163, CD206, IL-10, CXCL10, and IL-1β were evaluated by Western blot. The direct interaction of miR-181a-5p and RORA was validated by dual-luciferase activity assay. The expression of miR-181a-5p was upregulated in osteosarcoma tissues and presented in SAOS-2-derived exosomes. SAOS-2-derived exosomes promoted the polarization of M2 macrophages by transferring miR-181a-5p. In addition, RORA was downregulated in osteosarcoma tissues and showed a negative correlation with miR-181a-5p. RORA was found to be the downstream target of miR-181a-5p in SAOS-2 cells. Inhibition of RORA reversed the effects of miR-181a-5p knockdown on the polarization of M2 macrophages. The results showed that exosomal miR-181a-5p derived from osteosarcoma cells induced polarization of M2 macrophages via targeting RORA.

An Unsupervised Deep Learning-Based Model Using Multiomics Data to Predict Prognosis of Patients with Stomach Adenocarcinoma

METHODS

Patients (363 in total) with stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) cohort were included. An autoencoder was constructed to integrate the RNA sequencing, miRNA sequencing, and methylation data. The features of the bottleneck layer were used to perform the k-means clustering algorithm to obtain different subgroups for evaluating the prognosis-related risk of stomach adenocarcinoma. The model's robustness was verified using a 10-fold cross-validation (CV). Survival was analyzed by the Kaplan-Meier method. Univariate and multivariate Cox regression was used to estimate hazard risk. The model was validated in three independent cohorts with different endpoints.

RESULTS

The patients were divided into low-risk and high-risk groups according to the k-means clustering algorithm. The high-risk group had a significantly higher risk of poor survival (log-rank P value = 2.80e - 06; adjusted hazard ratio = 2.386, 95% confidence interval: 1.607~3.543), a concordance index (C-index) of 0.714, and a Brier score of 0.184. The model performed well both in the 10-fold CV procedure and three independent cohorts from the Gene Expression Omnibus (GEO) repository.

CONCLUSIONS

A robust and generalizable model based on the autoencoder was proposed to integrate multiomics data and predict the prognosis of patients with stomach adenocarcinoma. The model demonstrates better performance than two alternative approaches on prognosis prediction. The results might provide the grounds for further exploring the potential biomarkers to predict the prognosis of patients with stomach adenocarcinoma.

GraphLoc: a graph neural network model for predicting protein subcellular localization from immunohistochemistry images

Motivation

Recognition of protein subcellular distribution patterns and identification of location biomarker proteins in cancer tissues are important for understanding protein functions and related diseases. Immunohistochemical (IHC) images enable visualizing the distribution of proteins at the tissue level, providing an important resource for the protein localization studies. In the past decades, several image-based protein subcellular location prediction methods have been developed, but the prediction accuracies still have much space to improve due to the complexity of protein patterns resulting from multi-label proteins and variation of location patterns across cell types or states.

Results

Here, we propose a multi-label multi-instance model based on deep graph convolutional neural networks, GraphLoc, to recognize protein subcellular location patterns. GraphLoc builds a graph of multiple IHC images for one protein, learns protein-level representations by graph convolutions, and predicts multi-label information by a dynamic threshold method. Our results show that GraphLoc is a promising model for image-based protein subcellular location prediction with model interpretability. Furthermore, we apply GraphLoc to the identification of candidate location biomarkers and potential members for protein networks. A large portion of the predicted results have supporting evidence from the existing literatures and the new candidates also provide guidance for further experimental screening.

Availability

The dataset and code are available at: www.csbio.sjtu.edu.cn/bioinf/GraphLoc.

Supplementary information

Supplementary data are available at Bioinformatics online.

Comprehensive analysis of expression signature and immune microenvironment signature of biomarker Endothelin Receptor Type A in stomach adenocarcinoma

Background: EDNRA (Endothelin Receptor Type A) is closely associated with tumor progression in many tumor types. However, the functional mechanism of EDNRA in stomach adenocarcinoma (STAD) remains to be elucidated.

Methods: ENDRA expression levels in STAD were assessed. A Receiver Operating Characteristic (ROC) curve was constructed to measure the diagnostic value of EDNRA. The correlation between ENDRA expression levels and patient clinical-pathological characteristics was analyzed. The survival and prognostic significance were validated using Kaplan-Meier and Cox regression and confirmed by the immunohistochemistry cohorts. Differentially expressed genes of EDNRA in STAD were determined, and EDNRA related functional enrichment and biological pathways involved in STAD were obtained by Gene-Set Enrichment Analysis (GSEA). The correlation between EDNRA expression in STAD and immune cell infiltration was assessed using the CIBERSORT and Spearman correlation analysis, and the correlation between EDNRA and TMB, MSI, IC50, and immune checkpoints was examined.

Results: EDNRA expression was significantly higher in STAD than in normal tissues (P < 0.001) and associated with worse overall survival (OS). EDNRA expression was significantly associated with T stage, histological type, histologic grade, and TP53 status. Cox regression analysis revealed that primary therapy outcome, age, tumor status, and EDNRA were independent prognostic factors for OS. Multivariate analysis revealed that EDNRA expression, tumor status, age, and primary therapy outcome influenced patient prognosis. GSEA was significantly enriched in several pathways and biological processes, which include Immunoregulatory, Hedgehog, WNT, PI3K-AKT.NK cells, Tem, macrophages, and mast cells were substantially positively correlated with EDNRA expression in the STAD microenvironment. Notably, high EDNRA expression may promote M2 macrophages to block PD-1-mediated immunotherapy and induce immunosuppression. In addition, patients with high expression of EDNRA might be resistant to the treatment of several anti-tumor drugs.

Conclusion: Our results suggest that EDNRA was closely related to clinicopathologic characteristics, poor prognosis, and promoted macrophage differentiation and synergistic role in immunosuppression.