Identifying Potential Prognostic Markers for Muscle-Invasive Bladder Urothelial Carcinoma by Weighted Gene Co-Expression Network Analysis

PDZ and LIM Domain-Encoding Genes: Their Role in Cancer Development

PDZ-LIM family proteins (PDLIMs) are a kind of scaffolding proteins that contain PDZ and LIM interaction domains. As protein-protein interacting molecules, PDZ and LIM domains function as scaffolds to bind to a variety of proteins. The PDLIMs are composed of evolutionarily conserved proteins found throughout different species. They can participate in cell signal transduction by mediating the interaction of signal molecules. They are involved in many important physiological processes, such as cell differentiation, proliferation, migration, and the maintenance of cellular structural integrity. Studies have shown that dysregulation of the PDLIMs leads to tumor formation and development. In this paper, we review and integrate the current knowledge on PDLIMs. The structure and function of the PDZ and LIM structural domains and the role of the PDLIMs in tumor development are described.

Identifying stage-associated hub genes in bladder cancer via weighted gene co-expression network and robust rank aggregation analyses

BACKGROUND

Bladder cancer (BC) is among the most frequent cancers globally. Although substantial efforts have been put to understand its pathogenesis, its underlying molecular mechanisms have not been fully elucidated.

METHODS

The robust rank aggregation approach was adopted to integrate 4 eligible bladder urothelial carcinoma microarray datasets from the Gene Expression Omnibus. Differentially expressed gene sets were identified between tumor samples and equivalent healthy samples. We constructed gene co-expression networks using weighted gene co-expression network to explore the alleged relationship between BC clinical characteristics and gene sets, as well as to identify hub genes. We also incorporated the weighted gene co-expression network and robust rank aggregation to screen differentially expressed genes.

RESULTS

CDH11, COL6A3, EDNRA, and SERPINF1 were selected from the key module and validated. Based on the results, significant downregulation of the hub genes occurred during the early stages of BC. Moreover, receiver operating characteristics curves and Kaplan-Meier plots showed that the genes exhibited favorable diagnostic and prognostic value for BC. Based on gene set enrichment analysis for single hub gene, all the genes were closely linked to BC cell proliferation.

CONCLUSIONS

These results offer unique insight into the pathogenesis of BC and recognize CDH11, COL6A3, EDNRA, and SERPINF1 as potential biomarkers with diagnostic and prognostic roles in BC.

Comprehensive Analysis of PDLIM3 Expression Profile, Prognostic Value, and Correlations with Immune Infiltrates in Gastric Cancer

Protein PDZ and LIM domain 3 (PDLIM3) is a cytoskeletal protein, colocalizing with α-actinin on the Z line of mature muscle fibers. It plays a key role in dilated cardiomyopathy (DCM), muscular dystrophy, and tumor progression. However, correlations between PDLIM3 expression, prognosis, and tumor-infiltrating immune cells in gastric cancer are unknown. Therefore, we leveraged the Oncomine, GEPIA, GEO, and HPA databases to evaluate PDLIM3 expression in tumors. We also quantified PDLIM3 expression in 15 matched pairs of gastric tumor and nontumor tissues by immunohistochemistry. The Kaplan-Meier method was employed to determine the relationship between PDLIM3 expression and clinical outcomes. GO and KEGG analyses were performed to illuminate the molecular mechanisms of action of PDLIM3. TIMER2.0 and GEPIA were applied to investigate correlations between PDLIM3 expression and gene marker subsets signifying immune infiltration, with TIMER2.0 exploring the correlations between PDLIM3 and related signaling pathways. Gastric cancer tissues were found to express more PDLIM3 than nontumor tissues. PDLIM3 overexpression was associated with shorter OS and PFS of gastric cancer patients (OS $\text{HR}=2.02$ , $P=9.8\text{e}-10$ ; PFS $\text{HR}=1.77$ , $P=7.5\text{e}-06$ ). PDLIM3 was also positively correlated with worse OS and PFS according to gastric cancer staging, Her-2 overexpression, differentiation grade, and Lauren classification. PDLIM3 was shown to be associated with immunological responses by GO, while it was related to PI3K/Akt signal pathways by KEGG analysis. Furthermore, increased PDLIM3 expression was significantly correlated with greater infiltration of CD4+ T cells, CD8+ T cells, macrophages, neutrophils, and dendritic cells. PDLIM3 expression had significant positive correlations with a variety of immune marker subsets. Finally, correlations were found between PDLIM3 and crucial markers of signaling pathways involving PI3K/Akt and p38 MAPK. Thus, upregulation of PDLIM3 was significantly associated with poor prognosis, immune cell infiltration, and activation of two key signal pathways in gastric cancer. We propose that PDLIM3 could be used as a biomarker to predict prognosis and immune cell infiltration in gastric cancer.

Bioinformatical analysis identifies PDLIM3 as a potential biomarker associated with immune infiltration in patients with endometriosis

Background

Endometriosis is a chronic systemic disease, whose classic symptoms are pelvic pain and infertility. This disease seriously reduces the life quality of patients. The pathogenesis, recognition and treatment of endometriosis is still unclear, and cannot be over emphasized. The aim of our study was to investigate the potential biomarker of endometriosis for the mechanism and treatment.

Methods

Using GSE11691, GSE23339 and GSE5108 datasets, differentially expressed genes (DEGs) were identified between endometriosis and normal samples. The functions of DEGs were reflected by the analysis of gene ontology (GO), pathway enrichment and gene set enrichment analysis (GSEA). The LASSO regression model was performed to identify candidate biomarkers. The receiver operating characteristic curve (ROC) was used to evaluate discriminatory ability of candidate biomarkers. The predictive value of the markers in endometriosis were further validated in the GSE120103 dataset. Then, the expression level of biomarkers was detected by qRT-PCR and Western blot. Finally, the relationship between candidate biomarker expression and immune infiltration was estimated using CIBERSORT.

Results

A total of 42 genes were identified, which were mainly involved in cytokine-cytokine receptor interaction, systemic lupus erythematosus and chemokine signaling pathway. We confirmed PDLIM3 was a specific biomarker in endometriosis (AUC = 0.955) and validated in the GSE120103 dataset (AUC = 0.836). The mRNA and protein expression level of PDLIM3 in endometriosis tissue was significantly higher than normal. Immune cell infiltration analysis revealed that PDLIM3 was correlated with M2 macrophages, neutrophils, CD4+ memory resting T cells, gamma delta T cells, M1 Macrophages, resting mast cells, follicular helper T cells, activated NK cells, CD8+ T cells, regulatory T cells (Tregs), naive B cells, plasma cells and resting NK cells.

Identification of Lymph Node Metastasis-Related Key Genes and Prognostic Risk Model in Bladder Cancer by Co-Expression Analysis

Background: Lymph node metastasis (LNM) is an important pathological characteristic of bladder cancer (BCa). However, the molecular mechanism underlying LNM was not thoroughly elaborated. Identification for LNM-related biomarkers may contribute to making suitable therapies. So, the current study was aimed to identify key genes and construct a prognostic signature. Methods: Based on the Cancer Genome Atlas (TCGA) database, gene expression and clinical information were obtained. Then, the weighted gene co-expression network analysis (WGCNA) was performed to identify the key modules and hub genes. A function analysis and a gene set enrichment analysis were applied to explore biological functions and pathways of interested genes. Furthermore, a prognostic model based on LNM-related genes was constructed by using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results: Finally, nine co-expression modules were constructed, and two modules (turquoise and green) were significantly associated with LNM. Three hub genes were identified as DACT3, TNS1, and MSRB3, which were annotated in actin binding, actin cytoskeleton, adaptive immune response, and cell adhesion molecular binding by the GSEA method. Further analysis demonstrated that three hub genes were associated with the overall survival of BCa patients. In addition, we built a prognostic signature based on the genes from LNM-related modules and evaluated the prognostic value of this signature. Conclusion: In general, this study revealed the key genes related to LNM and prognostic signature, which might provide new insights into therapeutic target of BCa.

Integrated Transcriptomic Analysis Reveals the Molecular Mechanism of Meningiomas by Weighted Gene Coexpression Network Analysis

Meningiomas are the most common primary intracranial tumor in adults. However, to date, systemic coexpression analyses for meningiomas fail to explain its pathogenesis. The aim of the present study was to construct coexpression modules and identify potential biomarkers associated with meningioma progression. Weighted gene coexpression network analysis (WGCNA) was performed based on GSE43290, and module preservation was tested by GSE74385. Functional annotations were performed to analyze biological significance. Hub genes were selected for efficacy evaluations and correlation analyses using two independent cohorts. A total of 14 coexpression modules were identified, and module lightcyan was significantly associated with WHO grades. Functional enrichment analyses of module lightcyan were associated with tumor pathogenesis. The top 10 hub genes were extracted. Ten biomarkers, particularly AHCYL2, FGL2, and KCNMA1, were significantly related to grades and prognosis of meningioma. These findings not only construct coexpression modules leading to the better understanding of its pathogenesis but also provide potential biomarkers that represent specific on tumor grades and identify recurrence, predicting prognosis and progression of meningiomas.

Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis

Peritoneal metastasis is the most common pattern in advanced gastric cancer and can predict poor disease prognosis. Early detection of peritoneal tumor dissemination is restricted by small peritoneal deposits. Therefore, it is critical to identify a novel predictive marker and to explore the potential mechanism associated with this process. In the present study, one module that correlated with peritoneal metastasis was identified. Enrichment analysis indicated that the Focal adhesion and the PI3K-Akt signaling pathway were the most significant pathways. Following network and Molecular Complex Detection (MCODE) analysis, the hub-gene cluster that consisted of 19 genes was selected. Methionine sulfoxide reductase B3 (MSRB3) was identified as a seed gene. Survival analysis indicated that high expression levels of MSRB3 were independent predictors of peritoneal disease-free survival (pDFS) as determined by univariate (HR 8.559, 95% CI; 3.339-21.937; P<.001) and multivariate Cox analysis (HR 3.982, 95% CI; 1.509-10.509; P=.005). Furthermore, patients with high levels of MSRB3 exhibited a significantly lower Overall Survival (OS) (log-rank P = 0.007). The external validation was performed by the (The Cancer Genome Atlas (TCGA)) (log-rank P = 0.037) and Kaplan Meier-plotter (KMplotter) (log-rank P = 0.031) data. In vitro experiments confirmed that MSRB3 was a critical protein in regulating gastric cancer cell proliferation and migration. In conclusion, High expression levels of MSRB3 in GC can predict peritoneal metastasis and recurrence as well as poor prognosis. Furthermore, MSRB3 was involved in the regulation of the proliferation and migration of GC cells.