Bioinformatics and functional analyses of key genes and pathways in human clear cell renal cell carcinoma

Comprehensive analysis of gene expression profiles of annulus fibrosus subtypes and hub genes in intervertebral disc degeneration

Intervertebral disc degeneration (IVDD) has been considered a major cause of low back pain. Therefore, further molecular subtypes of IVDD and identification of potential critical genes are urgently needed. First, consensus clustering was used to classify patients with IVDD into two subtypes and key module genes for subtyping were identified using weighted gene co-expression network analysis (WGCNA). Then, key module genes for the disease were identified by WGCNA. Subsequently, SVM and GLM were used to identify hub genes. Based on the above genes, a nomogram was constructed to predict the subtypes of IVDD. Finally, we find that ROM1 is lowered in IVDD and is linked to various cancer prognoses. The present work offers innovative diagnostic and therapeutic biomarkers for molecular subtypes of IVDD.

DLK2 Acts as a Potential Prognostic Biomarker for Clear Cell Renal Cell Carcinoma Based on Bioinformatics Analysis

Clear cell renal cell carcinoma (ccRCC) is the most common RCC subtype with a high mortality. It has been reported that delta-like 1 homologue (DLK1) participates in the tumor microenvironmental remodeling of ccRCC, but the relationship between delta-like 2 homologue (DLK2, a DLK1 homologue) and ccRCC is still unclear. Thus, this study aims to investigate the role of DLK2 in the biological function and disease prognosis of ccRCC using bioinformatics analysis. The TNMplot database showed that DLK2 was upregulated in ccRCC tissues. From the UALCAN analysis, the overexpression of DLK2 was associated with advanced stage and high grade in ccRCC. Moreover, the Kaplan-Meier plotter (KM Plotter) database showed that DLK2 upregulation was associated with poor survival outcome in ccRCC. By the LinkedOmics analysis, DLK2 signaling may participated in the modulation of ccRCC extracellular matrix (ECM), cell metabolism, ribosome biogenesis, TGF-β signaling and Notch pathway. Besides, Tumor Immune Estimation Resource (TIMER) analysis showed that the macrophage and CD8⁺ T cell infiltrations were associated with good prognosis in ccRCC patients. Finally, DLK2 overexpression was associated with the reduced macrophage recruitments and the M1–M2 polarization of macrophage in ccRCC tissues. Together, DLK2 may acts as a novel biomarker, even therapeutic target in ccRCC. However, this study lacks experimental validation, and further studies are required to support this viewpoint.

Identification of CXCL10 as a Prognostic Biomarker for Clear Cell Renal Cell Carcinoma

Background

One of the widespread forms of kidney tumor is clear cell renal cell carcinoma (ccRCC), with poor prognosis and insensitivity to radio chemotherapy as there is limited capacity to understand the disease mechanism. This study aims at identifying potential biomarkers and the underlying processes of ccRCC using bioinformatics analysis.

Methods

Transcriptome data of relevant samples were downloaded from The Cancer Genome Atlas (TCGA) database. R software was used to screen differentially expressed genes (DEGs) using the “edgeR” package. Two types of analysis—Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment—were accomplished by applying Database for Annotation, Visualization, and Integrated Discovery (DAVID) and Search Tool for the Retrieval of Interacting Genes database (STRING) online bioinformatics tools. A protein–protein interaction (PPI) network of the identified DEGs was constructed using Cytoscape software, and hub genes were subsequently selected via the Cytohubba plug-in. The selected genes were input into Oncomine for verification. Finally, selected hub genes were analyzed by doing survival analysis to notice the relationship between survival (OS) rate and the selected genes’ level of expression.

Results

There were 1,855 DEGs found connected to ccRCC, with 1,207 upregulated genes and 648 downregulated genes. G-protein-coupled receptor signaling pathway, integral component of membrane, calcium ion binding, and cytokine–cytokine receptor interaction were among the DEGs discovered. Oncomine confirmed the top six hub genes from the PPI network (C3, CXCR3, CXCL10, CCR5, CCL4, and CCL5). A high level of expression of CXCL10, one of these hub genes, was linked to a poor prognosis in individuals with ccRCC. The results of survival analysis showed that the expression level of CXCL10 was significantly correlated with the prognosis of ccRCC patients (p < 0.05).

Conclusions

From the analysis, the following results were drawn: CXCL10 might be a potential prognostic biomarker and novel therapeutic target for ccRCC.

The Role of CXCL12 in Kidney Diseases: A Friend or Foe?

Background

Chemokines are a family of proteins mainly mediating the homing and migration of various cells. The CXC chemokine CXCL12 is a member of low-weight-molecular chemokines. In the kidney, CXCL12 is pivotal for renal development and exerts a modulatory effect in kidney diseases under different etiologic settings by binding with CXC chemokine receptor 4 (CXCR4) or CXC chemokine receptor 7 (CXCR7). Besides, CXCL12 also exerts homeostasis influence in diverse physical conditions and various pathological situations. Thus, we conclude the complicated relationship between CXCL12 and kidney diseases in this review.

Summary

In renal development, CXCL12 contributes a lot to nephrogenesis and the formation of renal vasculature via correlating with CXCR4. CXCL12 also plays an essential role in renal recovery from acute kidney injury. However, the CXCL12/CXCR4 axis plays a dual regulatory role in the initiation and development of diabetic kidney disease as well as chronic allogeneic nephropathy after kidney transplantation through dialectical consideration. Additionally, the CXCL12/CXCR4 link is considered as a new risk factor for lupus nephritis and renal cell carcinoma.

Key Messages

Plenty of studies have presented the influence of CXCL12 and the relation with corresponding receptors in diverse biological and pathological statuses. Simultaneously, some drugs and antagonists targeting CXCL12/CXCR4 axis effectively treat various kidney diseases. However, more researches are needed to explore thorough influence and mechanisms, providing more cues for clinical treatments.

The Outer Retinal Membrane Protein 1 Could Inhibit Lung Cancer Progression as a Tumor Suppressor

Some related reports indicate that the outer retinal membrane protein 1 (ROM1) functions importantly in the regulation of the biological process of tumor. Nevertheless, studies towards the role of ROM1 in lung cancer are few. Here, our data demonstrated that ROM1 displayed a relation with lung cancer tumorigenesis and development. In the Tumor Genome Atlas (TCGA) cohort, reduced ROM1 level was observed in lung cancer tissues, instead of normal tissues. After bioinformatics analysis, the data revealed that ROM1 level was associated with the tumor stage. Additional results indicated that highly expressed ROM1 exhibited a positive correlation with the overall survival rate, and ROM1 was probably a promising prognostic biomarker of lung cancer. Additionally, our results indicated that knocking out ROM1 could promote cell proliferation, migration, and invasion. Our data conclusively demonstrated that ROM1 modulated lung cancer tumorigenesis and development, as a prognosis and treatment biomarker.

Identification of Seven Cell Cycle-Related Genes with Unfavorable Prognosis and Construction of their TF-miRNA-mRNA regulatory network in Breast Cancer

Breast cancer (BC), with complex tumorigenesis and progression, remains the most common malignancy in women. We aimed to explore some novel and significant genes with unfavorable prognoses and potential pathways involved in BC initiation and progression via bioinformatics methods. BC tissue-specific microarray datasets of GSE42568, GSE45827 and GSE54002, which included a total of 651 BC tissues and 44 normal breast tissues, were obtained from the Gene Expression Omnibus (GEO) database, and 124 differentially expressed genes (DEGs) were identified between BC tissues and normal breast tissues via R software and an online Venn diagram tool. Database for Annotation, Visualization and Integration Discovery (DAVID) software showed that 65 upregulated DEGs were mainly enriched in the regulation of the cell cycle, and Search Tool for the Retrieval of Interacting Genes (STRING) software identified the 39 closest associated upregulated DEGs in protein-protein interactions (PPIs), which validated the high expression of genes in BC tissues by the Gene Expression Profiling Interactive Analysis (GEPIA) tool. In addition, 36 out of 39 BC patients showed significantly worse outcomes by Kaplan-Meier plotter (KM plotter), and an additional Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that seven genes (cyclin E2 (CCNE2), cyclin B1 (CCNB1), cyclin B2 (CCNB2), mitotic checkpoint serine/threonine kinase B (BUB1B), dual-specificity protein kinase (TTK), cell division cycle 20 (CDC20), and pituitary tumor transforming gene 1 (PTTG1)) were markedly enriched in the cell cycle pathway. Analysis of the clinicopathological characteristics of hub genes revealed that seven cell cycle-related genes (CCRGs) were significantly highly expressed in four BC subtypes (luminal A, luminal B, HER2-positive and triple-negative (TNBC)), and except for the CCNE2 gene, high expression levels were significantly associated with tumor pathological grade and stage and metastatic events of BC. Furthermore, genetic mutation analysis indicated that genetic alterations of CCRGs could also significantly affect BC patients' prognosis. A quantitative real-time polymerase chain reaction (qRT-PCR) assay found that the seven CCRGs were significantly differentially expressed in BC cell lines. Integration of published multilevel expression data and a bioinformatics computational approach were used to predict and construct a regulation mechanism: a transcription factor (TF)-microRNA (miRNA)-messenger RNA (mRNA) regulation network. The present work is the first to construct a regulatory network of TF-miRNA-mRNA in BC for CCRGs and provides new insights into the molecular mechanism of BC.

Expression and Prognostic Significance of Cadherin 4 (CDH4) in Renal Cell Carcinoma

Background

Aberrant expression of cadherin family members and their possible biological function have been widely studied in renal cell carcinoma (RCC). However, the expression of cadherin 4 (CDH4) and its value in RCC diagnosis and prognosis remains elusive.

Material/Methods

The TCGA database was used to analyze the expression of CDH4 and its clinical parameters and prognosis in 891 RCC patients. In addition, real-time PCR was used to verify the transcription of CDH4 in renal clear cell carcinoma tissue, and the distribution of protein was observed by immunohistochemical staining.

Results

We found that the mRNA level of CDH4 was elevated in primary RCC in contrast with normal kidney samples using bioinformatics analysis based on the TCGA database. Among the 3 main subtypes of RCC, transcriptional CDH4 was significantly increased in KIRC and KIRP, while it was downregulated in KICH. Interestingly, CDH4 mRNA gradually decreased with the progression of KIRC and KIRP. The transcription of CDH4 in the primary tumor of KIRP patients at T3–T4 stages and KIRC patients with lymph node and distant metastasis were decreased significantly. Overall survival (OS) showed that KIRC and KICH patients with lower expression of CDH4 had worse outcomes.

Conclusions

The transcriptional level of CDH4 may serve as an effective diagnostic and prognostic biomarker for RCC patients.

Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.