Identification and validation of a risk signature based on extracellular matrix-related genes in gliomas

Extracellular Matrix Structure and Interaction with Immune Cells in Adult Astrocytic Tumors

The extracellular matrix (ECM) is a dynamic set of molecules produced by the cellular component of normal and pathological tissues of the embryo and adult. ECM acts as critical regulator in various biological processes such as differentiation, cell proliferation, angiogenesis, and immune control. The most frequent primary brain tumors are gliomas and by far the majority are adult astrocytic tumors (AATs). The prognosis for patients with these neoplasms is poor and the treatments modestly improves survival. In the literature, there is a fair number of studies concerning the composition of the ECM in AATs, while the number of studies relating the composition of the ECM with the immune regulation is smaller. Circulating ECM proteins have emerged as a promising biomarker that reflect the general immune landscape of tumor microenvironment and may represent a useful tool in assessing disease activity. Given the importance it can have for therapeutic and prognostic purposes, the aim of our study is to summarize the biological properties of ECM components and their effects on the tumor microenvironment and to provide an overview of the interactions between major ECM proteins and immune cells in AATs. As the field of immunotherapy in glioma is quickly expanding, we retain that current data together with future studies on ECM organization and functions in glioma will provide important insights into the tuning of immunotherapeutic approaches.

Identification and validation of an ECM organization-related gene signature as a prognostic biomarker and therapeutic target for glioma patients

BACKGROUND

Glioma is the most common and devastating form of malignant brain tumor, with a poor prognosis. Extracellular matrix (ECM) organization is a crucial determinant of glioma invasion and progression. However, the clinical significance of ECM organization in glioma patients remains unclear.

OBJECTIVE

To evaluate the prognostic value of ECM organization-related genes in glioma patients and identify potential therapeutic targets.

METHODS

Bulk RNA-sequencing and corresponding clinical data for patients with glioma were downloaded from TCGA and GEO databases. Differentially expressed ECM organization genes were identified, and an ECM organization-related gene prognostic model was then generated. Furthermore, the prognostic model has validated in the Chinese Glioma Genome Atlas (CGGA) dataset. The role of TIMP1 in glioma cells by using various functional assays revealed their underlying mechanism in vitro.

RESULTS

We identified and validated a nine-gene signature (TIMP1, SERPINE1, PTX3, POSTN, PLOD3, PDPN, LOXL1, ITGA2, and COL8A1) related to ECM organization as a robust prognostic biomarker for glioma. Time-dependent ROC curve analysis confirmed the specificity and sensitivity of the signature. The signature was closely related to an immunosuppressive phenotype, and its combination with immune checkpoints served as a good predictor for patients' clinical outcomes. Notably, single-cell RNA sequencing analysis revealed high expression of TIMP1 in astrocytes and oligodendrocyte progenitor cells in glioma patients. Last, we show that TIMP1 regulates glioma cell growth and invasion via the AKT/GSK3β signaling pathway.

CONCLUSION

This study provides promising insights into predicting glioma prognosis and identifying a potential therapeutic target in TIMP1.

Identification of Prognostic Genes in Gliomas Based on Increased Microenvironment Stiffness

With a median survival time of 15 months, glioblastoma multiforme is one of the most aggressive primary brain cancers. The crucial roles played by the extracellular matrix (ECM) stiffness in glioma progression and treatment resistance have been reported in numerous studies. However, the association between ECM-stiffness-regulated genes and the prognosis of glioma patients remains to be explored. Thus, using bioinformatics analysis, we first identified 180 stiffness-dependent genes from an RNA-Seq dataset, and then evaluated their prognosis in The Cancer Genome Atlas (TCGA) glioma dataset. Our results showed that 11 stiffness-dependent genes common between low- and high-grade gliomas were prognostic. After validation using the Chinese Glioma Genome Atlas (CGGA) database, we further identified four stiffness-dependent prognostic genes: FN1, ITGA5, OSMR, and NGFR. In addition to high-grade glioma, overexpression of the four-gene signature also showed poor prognosis in low-grade glioma patients. Moreover, our analysis confirmed that the expression levels of stiffness-dependent prognostic genes in high-grade glioma were significantly higher than in low-grade glioma, suggesting that these genes were associated with glioma progression. Based on a pathophysiology-inspired approach, our findings illuminate the link between ECM stiffness and the prognosis of glioma patients and suggest a signature of four stiffness-dependent genes as potential therapeutic targets.

Prediction of Prognosis and Recurrence of Bladder Cancer by ECM-Related Genes

Background

Bladder cancer (BLCA) is one of the most common cancers and ranks ninth among all cancers. Extracellular matrix (ECM) genes activate a number of pathways that facilitate tumor development. This study is aimed at providing models to predict BLCA survival and recurrence by ECM genes.

Methods

Expression data from BLCA samples in GSE32894, GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts were downloaded and analyzed. The ECM-related genes were obtained by differentially expressed gene analysis, stage-associated gene analysis, and random forest variable selection. The ECM was constructed in GSE32894 by the hub ECM-related genes and validated in GSE13507, GSE31684, GSE32548, and TCGA-BLCA cohorts. The correlations of the ECM score with cells (T cells, fibroblasts, etc.) and the response to immunotherapeutic drugs were investigated. Four machine learning models were selected and used to construct models to predict the recurrence of BLCA. A total of 15 paired BLCA and normal tissue specimens, human immortalized uroepithelial cell lines, and bladder cancer cell lines were selected for the validation of the difference in expression of FSTL1 between normal tissues and BLCA.

Results

Six ECM genes (CTHRC1, MMP11, COL10A1, FSTL1, SULF1, and COL5A3) were recognized to be the hub ECM-related genes. The ECM score of each BLCA patient was calculated using these six selected ECM-related genes. BLCA patients with a high ECM score group had significantly lower overall survival rates than patients in the low ECM score group. We found that the ECM score was positively associated with immune cells and fibroblasts and negatively correlated with tumor purity. When treated with immunotherapy, BLCA patients with a high ECM score presented a high response rate and better prognosis. We also found that the combination of FSTL1, stage, age, and gender achieved an AUC value of 0.76 in predicting bladder cancer recurrence. Based on the RT-qPCR results of FSTL1 gene expression, there was an overall decrease in the mRNA expression of FSTL1 in cancer tissues compared to their adjacent normal tissues. Subsequent in vitro validation demonstrated that the FSTL1 expression was downregulated at the gene and protein level compared to that in SVH cells.

Conclusion

Taken together, our results indicate that ECM-related genes correlate with immune cells, overall survival, and recurrence of BLCA. This study provides a machine learning model for predicting the survival and recurrence of BLCA patients.

OUP accepted manuscript

This study aimed to investigate the molecular mechanisms through which the intestinal microbiota and microRNAs (miRNAs) participate in colon cancer metastasis. Intestinal flora data, and the GSE29621 (messenger RNA/long non-coding RNA [mRNA/lncRNA]) and GSE29622 (miRNA) datasets, were downloaded from The Cancer Gene Atlas and Gene Expression Omnibus databases, respectively. Immune-related cells in M1 vs. M0 samples were analyzed using the Wilcoxon test. Furthermore, an lncRNA-miRNA-mRNA (competing endogenous RNA [ceRNA]) network was constructed, and survival analysis of RNAs in the network was performed. A total of 16 miRNA-genus co-expression pairs containing eight microbial genera and 15 miRNAs were screened; notably, Porphyromonas and Bifidobacterium spp. were found to be associated with most miRNAs, and has-miR-3943 was targeted by most microbial genera. Furthermore, five immune cell types, including activated natural killer cells, M1 macrophages, resting mast cells, activated mast cells and neutrophils, were differentially accumulated between the M1 and M0 groups. Enrichment analysis suggested that mRNAs related to colon cancer metastasis were mainly involved in pathways related to bacterial and immune responses. Survival analysis revealed that TMEM176A and PALM3 in the ceRNA network were significantly associated with the prognosis of patients with colon cancer. In conclusion, this study revealed a potential mechanism by which the intestinal microbiota influences the colon cancer microenvironment by targeting miRNAs.