NOD1 and NOD2 of the innate immune system is differently expressed in human clear cell renal cell carcinoma, corresponding healthy renal tissue, its vasculature and primary isolated renal tubular epithelial cells

Functional characterization of NOD1 from golden pompano Trachinotus ovatus

Fish rely on innate immune system for immunity, and nucleotide-binding oligomerization domain-like receptors (NLRs) are a vital group of receptor for recognition. In the present study, NOD1 gene was cloned and characterized from golden pompano Trachinotus ovatus, a commercially important aquaculture fish species. The ORF of T. ovatus NOD1 was 2820 bp long, encoding 939 amino acid residues with a highly conserved domains containing CARD-NACHT-LRRs. Phylogenetic analysis revealed that the T. ovatus NOD1 clustered with those of fish and separated from those of birds and mammals. T. ovatus NOD1 has wide tissue distribution with the highest expression in gills. Bacterial challenges (Streptococcus agalactiae and Vibrio alginolyticus) significantly up-regulated the expression of NOD1 with different response time. The results of T. ovatus NOD1 ligand recognition and signaling pathway analysis revealed that T. ovatus NOD1 could recognize iE-DAP at the concentration of ≧ 100 ng/mL and able to activate NF-κB signaling pathway. This study confirmed that NOD1 play a crucial role in the innate immunity of T. ovatus. The findings of this study improve our understanding on the immune function of NOD1 in teleost, especially T. ovatus.

Investigation into the Role of Long-Non-Coding RNA MIAT in Leukemia

Myocardial Infarction Associated Transcript (MIAT) is a nuclear long non-coding RNA (LncRNA) with four different splicing variants. MIAT dysregulation is associated with carcinogenesis, mainly acting as an oncogene regulating cellular growth, invasion, and metastasis. The aim of the current study is to investigate the role of MIAT in the regulation of T and chronic myeloid leukemic cell survival. To this end, MIAT was silenced using MIAT-specific siRNAs in leukemic cell lines, and functional assays were performed thereafter. This investigation also aims to investigate the effects of MIAT silencing on the expression of core genes involved in cancer. Functional studies and gene expression determination confirm that MIAT knockdown not only affects short- and long-term survival and the apoptosis of leukemic cells but also plays a pivotal role in the alteration of key genes involved in cancer, including c-MYC and HIF-1A. Our observations suggest that MIAT could act as an oncogene and it has the potential to be used not only as a reliable biomarker for leukemia, but also be employed for prognostic and therapeutic purposes.

Novel insights into NOD-like receptors in renal diseases

Nucleotide-binding oligomerization domain-like receptors (NLRs), including NLRAs, NLRBs (also known as NAIPs), NLRCs, and NLRPs, are a major subfamily of pattern recognition receptors (PRRs). Owing to a recent surge in research, NLRs have gained considerable attention due to their involvement in mediating the innate immune response and perpetuating inflammatory pathways, which is a central phenomenon in the pathogenesis of multiple diseases, including renal diseases. NLRs are expressed in different renal tissues during pathological conditions, which suggest that these receptors play roles in acute kidney injury, obstructive nephropathy, diabetic nephropathy, IgA nephropathy, lupus nephritis, crystal nephropathy, uric acid nephropathy, and renal cell carcinoma, among others. This review summarises recent progress on the functions of NLRs and their mechanisms in the pathophysiological processes of different types of renal diseases to help us better understand the role of NLRs in the kidney and provide a theoretical basis for NLR-targeted therapy for renal diseases.

A New Prognostic Risk Score: Based on the Analysis of Autophagy-Related Genes and Renal Cell Carcinoma

Introduction: Clear cell renal cell carcinoma (ccRCC) patients suffer from its high recurrence and metastasis rate, and a new prognostic risk score to predict individuals with high possibility of recurrence or metastasis is in urgent need. Autophagy has been found to have a dual influence on tumorigenesis. In this study we aim to analyze autophagy related genes (ATGs) and ccRCC patients and find a new prognostic risk score. Method: Analyzing differential expression genes (DEGs) in TCGA-KIRC dataset, and took intersection with ATGs. Through lasso, univariate, and multivariate cox regression, DEGs were chosen, and the coefficients and expression levels of them were components constructing the formula of risk score. We analyzed mRNA expression of DEGs in tumor and normal tissue in ONCOMINE database and TCGA-KIRC dataset. The Human Protein Atlas (HPA) was used to analyze protein levels of DEGs. The protein-protein interaction (PPI) network was examined in STRING and visualized in cytoscape. Functional enrichment analysis was performed in RStudio. To prove the ability and practicibility of risk score, we analyzed univariate and multivariate cox regression, Kaplan-Meier curve (K-M curve), risk factor association diagram, receiver operating characteristic curve (ROC curve) of survival and nomogram, and the performance of nomogram was evaluated by calibration curve. Then we further explored functional enrichment related to risk groups through Gene Set Enrichment Analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and Metascape database. At last, we investigated immune cell infiltration of DEGs and two risk groups through TIMER database and “Cibersort” algorithm.

Result: We identified 7 DEGs (BIRC5, CAPS, CLDN7, CLVS1, GMIP, IFI16, and TCIRG1) as components of construction of risk score. All 7 DEGs were differently expressed in ccRCC and normal tissue according to ONCOMINE database and TCGA-KIRC dataset. Functional enrichment analysis indicated DEGs, and their most associated genes were shown to be abundant in autophagy-related pathways and played roles in tumorigenesis and progression processes. A serious analysis proved that this risk score is independent from the risk signature of ccRCC patients.

Conclusion: The risk score constructed by 7 DEGs had the ability of predicting prognosis of ccRCC patients and was conducive to the identification of novel prognostic molecular markers. However, further experiment is still needed to verify its ability and practicability.

Activation of RIPK2-mediated NOD1 signaling promotes proliferation and invasion of ovarian cancer cells via NF-κB pathway

The goal of this study was to investigate the role and mechanism of action of nucleotide oligomerization domain receptor 1 (NOD1) in ovarian cancer. Results showed that the expressions of NOD1 and receptor interacting serine/threonine kinase 2 (RIPK2) were notably upregulated in non-metastatic and metastatic ovarian tumors compared with matched non-tumor tissues, and their expression in metastatic tumor tissues was higher than that in non-metastatic tumors. Overexpression of NOD1 facilitated the expression of proliferation-related proteins (PCNA and Ki67) and proliferation and invasion of ovarian cancer cells. Overexpression of NOD1 promoted NF-κB expression and phosphorylation. Importantly, NOD1 bound with RIPK2, and silencing of RIPK2 partly rescued the promotion of NOD1 to NF-κB expression and its phosphorylation. The promotion of NOD1 to ovarian cancer cell proliferation and invasion was partly reversed by RIPK2 silencing. Results from our in vivo study indicate that overexpression of NOD1 accelerated the growth of ovarian cancer tumors, expression of proliferation-related proteins, and activation of NF-κB. However, silencing of NOD1 suppressed tumor growth. In summary, NOD1 facilitates ovarian cancer progression by activating NF-κB signaling by binding to RIPK2. We suggest a new strategy for the treatment of ovarian cancer.

Development and Validation of an IL6/JAK/STAT3-Related Gene Signature to Predict Overall Survival in Clear Cell Renal Cell Carcinoma

Background: Traditional clinicopathological features (TNM, pathology grade) are often insufficient in predictive prognosis accuracy of clear cell renal cell carcinoma (ccRCC). The IL6-JAK-STAT3 pathway is aberrantly hyperactivated in many cancer types, and such hyperactivation is generally associated with a poor clinical prognosis implying that it can be used as a promising prognosis indicator. The relation between the IL6-JAK-STAT3 pathway and ccRCC remains unknown.

Methods: We evaluated the levels of various cancer hallmarks and filtered out the promising risk hallmarks in ccRCC. Subsequently, a prognosis model based on these hallmark-related genes was established via weighted correlation network analysis and Cox regression analysis. Besides, we constructed a nomogram based on the previous model with traditional clinicopathological features to improve the predictive power and accuracy.

Results: The IL6-JAK-STAT3 pathway was identified as the promising risk hallmarks in ccRCC, and the pathway-related prognosis model based on five genes was built. Also, the nomogram we developed demonstrated the strongest and most stable survival predictive ability.

Conclusion: Our study would provide new insights for guiding individualized treatment of ccRCC patients.

NOD1-Targeted Immunonutrition Approaches: On the Way from Disease to Health

Immunonutrition appears as a field with great potential in modern medicine. Since the immune system can trigger serious pathophysiological disorders, it is essential to study and implement a type of nutrition aimed at improving immune system functioning and reinforcing it individually for each patient. In this sense, the nucleotide-binding oligomerization domain-1 (NOD1), one of the members of the pattern recognition receptors (PRRs) family of innate immunity, has been related to numerous pathologies, such as cancer, diabetes, or cardiovascular diseases. NOD1, which is activated by bacterial-derived peptidoglycans, is known to be present in immune cells and to contribute to inflammation and other important pathways, such as fibrosis, upon recognition of its ligands. Since immunonutrition is a significant developing research area with much to discover, we propose NOD1 as a possible target to consider in this field. It is relevant to understand the cellular and molecular mechanisms that modulate the immune system and involve the activation of NOD1 in the context of immunonutrition and associated pathological conditions. Surgical or pharmacological treatments could clearly benefit from the synergy with specific and personalized nutrition that even considers the health status of each subject.

Silencing lncRNA TUG1 Alleviates LPS-Induced Mouse Hepatocyte Inflammation by Targeting miR-140/TNF

Hepatitis is a major public health problem that increases the risk of liver cirrhosis and liver cancer. Numerous studies have revealed that long non-coding RNAs (lncRNAs) exert essential function in the inflammatory response of multiple organs. Herein, we aimed to explore the effect of lncRNA TUG1 in LPS-induced hepatocyte inflammation response and further illuminate the underlying mechanisms. Mice were intraperitoneally injected with LPS, and the liver inflammation was evaluated. Microarray showed that lncRNA TUG1 was upregulated in LPS-induced hepatocyte inflammation. qRT-PCR and immunofluorescence assay indicated a significant increase of TUG1 in mice with LPS injection. Functional analysis showed that si-TUG1 inhibited LPS-induced inflammation response in mice liver, inhibited apoptosis level, and protected liver function. Then, we knock down TUG1 in normal human hepatocyte AML12. Consistent with in vivo results, si-TUG1 removed the injury of LPS on AML12 cells. Furthermore, TUG1 acted as a sponge of miR-140, and miR-140 directly targeted TNFα (TNF). MiR-140 or si-TNF remitted the beneficial effects of TUG1 on LPS-induced hepatocyte inflammation response both in vitro and in vivo. Our data revealed that deletion of TUG1 protected against LPS-induced hepatocyte inflammation via regulating miR-140/TNF, which might provide new insight for hepatitis treatment.

A Novel Immune-Related Prognostic Signature for Thyroid Carcinoma

Background:

Recent studies have confirmed that immune-associated genes perform a crucial function in recurrence and metastasis of thyroid carcinoma. A reliable immune-related prognostic signature for patients with thyroid cancer is needed. This study constructed a novel immune-related prognostic signature for thyroid cancer and evaluated its prognostic value by bioinformatics analysis.

Methods:

In this study, we anatomized differentially expressed immune-associated genes from The Cancer Genome Atlas database. The samples from The Cancer Genome Atlas database were randomly divided into training set and test set. A novel immune-related prognostic signature for thyroid cancer was developed by least absolute shrinkage and selection operator and Cox regression analysis: Risk score = (0.6846 × expression value of C-X-C motif chemokine ligand 5 [CXCL5]) + (1.1556 × expression value of Azurocidin 1 [AZU1]) + (−0.3156 × expression value of nucleotide binding oligomerization domain containing 1 [NOD1] + (0.0542 × expression value of TNF Receptor Superfamily Member 11b [TNFRSF11B]) + (0.0952 × expression value of VGF nerve growth factor inducible [VGF]). The established prognostic signature was evaluated based on training set and test set by survival curves, receiver–operator characteristic curves, risk score, survival status, heatmap, and independent prognostic analysis. Meanwhile, we appraised the correlation between target immune-associated genes and clinical stage, tumor-infiltrating immune cells respectively.

Results:

Five immune-associated genes were used for constructing an immune-related prognostic signature by least absolute shrinkage and selection operator, univariate, and multivariate analysis. Survival curves, receiver–operator characteristic curves, and independent prognostic analysis showed the signature had significant prediction value. Clinical and immune cell correlation analyses indicated that target immune-associated genes may participate in tumor immune infiltration and tumor progression.

Conclusions:

We constructed a novel 5 immune-associated genes signature for predicting the prognosis of patients with thyroid cancer, which may help clinical workers evaluate individualized therapy and prognosis.

The Oncocytic Variant of Poorly Differentiated Thyroid Carcinoma Shows a Specific Immune-Related Gene Expression Profile

BACKGROUND

Poorly differentiated thyroid cancer (PDTC) is a rare, follicular cell-derived neoplasm with an unfavorable prognosis. The oncocytic variant of PDTC may be associated with even more adverse outcome than classical PDTC cases, but its specific molecular features are largely unknown. Our aim was to explore the immune-related gene expression profile of oncocytic and classical PDTC, in correlation with clinical and pathological characteristics (including programmed death ligand 1 [PD-L1] expression) and outcome, and in comparison with a control group of well-differentiated follicular carcinomas (WDFCs), including conventional follicular carcinomas (FTCs) and Hürthle cell carcinomas (HCCs).

METHODS

A retrospective series of 48 PDTCs and 24 WDFCs was analyzed by means of NanoString technology employing the nCounter PanCancer Immune Profiling panel. Gene expression data were validated using quantitative real-time polymerase chain reaction.

RESULTS

Oncocytic PDTCs showed a specific immune-related gene expression profile, with higher expression of LAIR2, CD274, DEFB1, IRAK1, CAMP, LCN2, LY96, and APOE, and lower expression of NOD1, as compared to conventional PDTCs. This molecular signature was associated with increased intratumoral lymphocytic infiltration, PD-L1 expression, and adverse outcome. Three of these genes, CD274, DEFB1, and IRAK1, as well as PD-L1 expression, were also the hallmarks of HCCs as compared to FTCs. By contrast, the panel of genes differentially regulated in PDTCs as compared to WDFCs was unrelated to the oncocytic phenotype.

CONCLUSIONS

Our results revealed a distinctive immune-related gene expression profile of oncocytic PDTC and confirmed a more aggressive outcome in this cancer subtype. These findings may provide guidance when exploring novel immunotherapeutic options for oncocytic PDTC patients.

Epigenetic signature predicts overall survival clear cell renal cell carcinoma

BACKGROUND

Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC).

METHODS

We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC.

RESULT

Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0-21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2-7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9-10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871.

CONCLUSIONS

Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

Development and validation of an integrative methylation signature and nomogram for predicting survival in clear cell renal cell carcinoma

Background

Growing evidence has shown that genetic or epigenetic alterations are highly involved in the initiation and progression of renal cell carcinoma (RCC). This study aimed to find prognostic methylation markers in clear cell RCC (ccRCC).

Methods

In this study, we developed and confirmed an integrated and comprehensive methylation signature by integrating DNA methylation, gene expression, and The Cancer Genome Atlas (TCGA) survival data. First, the methylation signature was found and checked based on data analysis of published datasets. Then, independent predictive factors were selected using the Cox proportional model and incorporated into the nomogram. Finally, the predictive nomogram was derived and validated using a concordance index and calibration plots.

Results

A series of differentially expressed and methylated genes were identified. After intersection analysis, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) analysis, and correlation analysis, FCGR1A, F2, and NOD2 were established as a predictive signature. According to the Kaplan-Meier survival analysis, the risk score system based on the predictive signature was able to stratify the patients into high- and low-risk groups with significantly different overall survival. The receiver operating characteristic (ROC) analysis further showed that the predictive signature yielded high sensitivity and specificity in predicting the prognosis outcome of ccRCC patients. Moreover, univariate and multivariate Cox regression analysis confirmed that the three-gene methylation signature was an independent prognostic factor in ccRCC. Finally, a nomogram comprising the predictive signature and several independent variables were constructed and proved to effectively predict ccRCC patient survival.

Conclusions

The three-gene methylation signature was revealed to be a potential novel and independent adverse predictor of prognosis for ccRCC patients and may serve as a promising marker for treatment management and survival outcome improvement. However, substantial validation experiments are required to characterize the molecular background of the predictive signature.

The ceRNA Network Has Potential Prognostic Value in Clear Cell Renal Cell Carcinoma: A Study Based on TCGA Database

Clear cell renal cell carcinoma (ccRCC) is a very common cancer in urology. Many evidences suggest that complex changed pathways take a nonnegligible part in the occurrence and development of ccRCC. Nevertheless, the underlying mechanism is not clear. In this study, the expression data between ccRCC and normal tissue samples in TCGA database were compared to distinguish differentially expressed genes (DEGs: mRNAs, miRNAs, and lncRNAs). Afterwards, we used GO enrichment and KEGG pathway enrichment analyses to explore the functions of these DEGs. We also found the correlation between three RNAs and created a competing endogenous RNA (ceRNA) network. Moreover, we used univariate Cox regression analysis to select DEGs that are connected with overall survival (OS) of ccRCC patients. We found 1652 mRNAs, 1534 lncRNAs, and 173 miRNAs that were distinguished in ccRCC compared with normal tissues. According to GO analysis, the maladjusted mRNAs are mainly concentrated in immune cell activation and kidney development, while according to KEGG, they are mainly concentrated in pathways related to cancer. A total of 5 mRNAs, 1 miRNA, and 4 lncRNAs were connected with patients' OS. In this article, a network of lncRNA-miRNA-mRNA was established; it is expected to be able to indicate possible molecular mechanisms for initial of ccRCC and provide a new viewpoint for diagnosis of ccRCC.