NOD2 maybe a biomarker for the survival of kidney cancer patients

Construction of a Prognosis Model of the Pyroptosis-Related Gene in Multiple Myeloma and Screening of Core Genes

Pyroptosis is an important factor affecting the proliferation, invasion, and metastasis of tumor cells. However, in multiple myeloma (MM), there are few studies on whether the occurrence of pyroptosis is related to the occurrence and prognosis of the disease. Based on the Gene Expression Omnibus and Cancer Genome Atlas database search dataset, this study identified pyroptosis-related genes with a specific prognosis, constructed and verified the prediction model by stepwise Cox regression analysis and time receiver operating characteristic curve analysis, and predicted specific functions by single-sample gene set enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes. Dataset analysis identified key genes, which were used to construct a risk scoring system for the prognosis of MM. The entire test set and external verification set verified the results. The expression levels of related genes in the clinical samples were detected using fluorescence quantitative PCR. A prognostic gene model based on six pyroptosis-related genes (CYCS, NLRP9, AIM2, NOD2, CHMP3, and GSDME) was constructed. The model has an excellent prognostic ability and can be popularized in the external validation set. The predictive prognostic nomogram integrating clinical information can effectively evaluate the risk score of each patient and predict their survival. After sample validation, our study found three potential key pyroptosis-related genes in multiple myeloma. GSDME, NOD2, and CHMP3 were significantly different between MM and healthy subjects, suggesting that they are pyroptosis-related protective genes. This study shows that the key pyroptosis-related gene in the model can be used as a marker for predicting the prognosis of myeloma, which may provide a basis for clinical individualized stratification therapy.

Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile

Ovarian cancer is a common cause of death among women worldwide. The current diagnostic and prognostic procedures available for the treatment of ovarian cancer are either not specific or are very expensive. Gene expression profiling has proved to be a very effective tool in the exploration of new molecular markers in patients with ovarian cancer, although the link between such markers and patient survival and clinical outcomes is still elusive. We are looking for genes that may function in the development and progression of ovarian cancer. The aim of our study was to evaluate the expression of selected suppressor genes (ATM, BRCA1, BRCA2), proto-oncogenes (KRAS, c-JUN, c-FOS), pro-apoptotic genes (NOXA, PUMA), genes related to chromatin remodeling (MEN1), and genes related to carcinogenesis (NOD2, CHEK2, EGFR). Tissue samples from 30 normal ovaries and 60 ovarian carcinoma tumors were provided for analysis of the gene and protein expression. Gene expression analysis was performed using the real-time PCR method. The protein concentrations from tissue homogenates were determined using the ELISA technique according to the manufacturers’ protocols. An increase in the expression level of mRNA and protein in women with ovarian cancer was observed for KRAS, c-FOS, PUMA, and EGFR. No significant changes in the transcriptional levels we observed for BRCA1, BRCA2, NOD2, or CHEK2. In conclusion, we suggest that KRAS, NOXA, PUMA, c-FOS, and c-JUN may be associated with poor prognosis in ovarian cancer.

Construction and evaluation of a prognostic risk assessment model of gastric cancer by using hypoxia features

In this study, mRNA expression of gastric cancer tissue and clinical data of patients in TCGA-STAD dataset were used, together with the hypoxia-related gene sets in the MsigDB database, to screen hypoxia-related differentially expressed genes (DEGs) in GC. Thereafter, univariate and multivariate Cox regression analyses were carried out on hypoxia-related DEGs. The optimal feature genes related to prognosis were obtained to construct a prognostic risk assessment model. According to the model, the riskScore of GC patients was measured, and GC samples were assigned into high- and low-risk groups in accordance with the median riskScore. Based on the Kaplan-Meier curve and Receiver operating characteristic curve, validity of the prognostic risk assessment model was measured. Gene set enrichment analysis was performed on the two risk groups through Gene set enrichment analysis software. The results revealed that in the high-risk group, 9 signaling pathways were remarkably activated in several terms, like focal adhesion, extracellular matrix receptor interaction, Cell adhesion molecules cams, Cytokine-cytokine receptor interaction, TGF-beta signaling pathway, NOD-like receptor signaling pathway, JAK-STAT signaling pathway, Toll-like receptor signaling pathway and MAPK signaling pathway. In combination with riskScore and clinical factors, univariate and multivariate Cox regression analyses verified the independence of the model. Meanwhile, a nomogram was constructed to predict the 1-, 3- and 5-year survival of GC patients. The calibration curve indicated that the survival status predicted by the nomogram fitted better with actual survival status. On the whole, the prognostic risk model of GC on the basis of hypoxia-related genes demonstrated good predictive ability. It can provide more powerful technical support for clinicians to make prognostic determination and therapeutic plans.

Bladder cancer survival in patients with NOD2 or CDKN2A variants

Purpose: The association between the NOD2 c.3020insC allele and CDKN2A missense variant c.442G>A (p.P.A148T) and survival of patients with bladder or kidney cancer remains controversial.

Materials and Methods: We compared the allele frequencies of NOD2 c.3020insC and CDKN2A p.A148T allele in 706 patients with bladder cancer, 410 cases with kidney cancer against two control groups. The Cox proportional hazards model was used to determine whether there were any survival differences between carriers of the NOD2 c.3020insC or the CDKN2A p.A148T variant.

Results: Among the three patient subgroups: patients under 60 years of age, non-smokers and a third with histological features of low grade noninvasive papillary bladder cancer, we observed that the c.3020insC allele had a nominal statistically significant effect on survival. We also observed that the NOD2 c.3020insC variant was more frequent in patients with bladder cancer aged between 51 and 60 years.

There was some nominal evidence that the CDKN2A p.A148T polymorphism reduced survival in the subgroup of bladder cancer patients under 60 years of age. We observed that in kidney cancer patients, the incidence of the NOD2 variant appeared to be lower in the group aged between 60 and 70 years, however, this was not statistically significant. In addition, in patients with histological features of grade III chromophobic kidney cancer, the c.3020insC allele also appeared to be over-represented but this too was not statistically significant.

Conclusion: We have shown that the NOD2 c.3020insC allele and the CDKN2A p.A148T polymorphism does not play a role in the survival of patients with bladder cancer.

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.

Upregulation of NOD1 and NOD2 contribute to cancer progression through the positive regulation of tumorigenicity and metastasis in human squamous cervical cancer

BACKGROUND

Metastatic cervical squamous cell carcinoma (CSCC) has poor prognosis and is recalcitrant to the current treatment strategies, which warrants the necessity to identify novel prognostic markers and therapeutic targets. Given that CSCC is a virus-induced malignancy, we hypothesized that the pattern recognition receptors (PRRs) involved in the innate immune response likely play a critical role in tumor development.

METHODS

A bioinformatics analysis, qPCR, IHC, immunofluorescence, and WB were performed to determine the expression of NOD1/NOD2. The biological characteristics of overexpression NOD1 or NOD2 CSCC cells were compared to parental cells: proliferation, migration/invasion and cytokines secretion were examined in vitro through CCK8/colony formation/cell cycle profiling/cell counting, wound healing/transwell, and ELISA assays, respectively. The proliferative and metastatic capacity of overexpression NOD1 or NOD2 CSCC cells were also evaluated in vivo. FCM, mRNA and protein arrays, ELISA, and WB were used to identify the mechanisms involved, while novel pharmacological treatment were evaluated in vitro and in vivo. Quantitative variables between two groups were compared by Student's t test (normal distribution) or Mann-Whitney U test (non-normal distribution), and one-way or two-way ANOVA was used for comparing multiple groups. Pearson χ² test or Fisher's exact test was used to compare qualitative variables. Survival curves were plotted by the Kaplan-Meier method and compared by the log-rank test. P values of < 0.05 were considered statistically significant.

RESULTS

NOD1 was highly expressed in CSCC with lymph-vascular space invasion (LVSI, P < 0.01) and lymph node metastasis (LM, P < 0.01) and related to worse overall survival (OS, P = 0.016). In vitro and in vivo functional assays revealed that the upregulation of NOD1 or NOD2 in CSCC cells promoted proliferation, invasion, and migration. Mechanistically, NOD1 and NOD2 exerted their oncogenic effects by activating NF-κb and ERK signaling pathways and enhancing IL-8 secretion. Inhibition of the IL-8 receptor partially abrogated the effects of NOD1/2 on CSCC cells.

CONCLUSIONS

NOD1/2-NF-κb/ERK and IL-8 axis may be involved in the progression of CSCC; the NOD1 significantly enhanced the progression of proliferation and metastasis, which leads to a poor prognosis. Anti-IL-8 was identified as a potential therapeutic target for patients with NOD1^high tumor.

Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

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.

New insights into genetic characteristics between multiple myeloma and COVID-19: An integrative bioinformatics analysis of gene expression omnibus microarray and the cancer genome atlas data

Background

Multiple myeloma (MM) is a hematological malignancy. Coronavirus disease 2019 (COVID‐19) infection correlates with MM features. This study aimed to identify MM prognostic biomarkers with potential association with COVID‐19.

Methods

Differentially expressed genes (DEGs) in five MM data sets (GSE47552, GSE16558, GSE13591, GSE6477, and GSE39754) with the same expression trends were screened out. Functional enrichment analysis and the protein‐protein interaction network were performed for all DEGs. Prognosis‐associated DEGs were screened using the stepwise Cox regression analysis in the cancer genome atlas (TCGA) MMRF‐CoMMpass cohort and the GSE24080 data set. Prognosis‐associated DEGs associated with COVID‐19 infection in the GSE164805 data set were also identified.

Results

A total of 98 DEGs with the same expression trends in five data sets were identified, and 83 DEGs were included in the protein‐protein interaction network. Cox regression analysis identified 16 DEGs were associated with MM prognosis in the TCGA cohort, and only the cytochrome c oxidase subunit 6C (COX6C) gene (HR = 1.717, 95% CI 1.231–2.428, p = .002) and the nucleotide‐binding oligomerization domain containing 2 (NOD2) gene (HR = 0.882, 95% CI 0.798–0.975, p = .014) were independent factors related to MM prognosis in the GSE24080 data set. Both of them were downregulated in patients with mild COVID‐19 infection compared with controls but were upregulated in patients with severe COVID‐19 compared with patients with mild illness.

Conclusions

The NOD2 and COX6C genes might be used as prognostic biomarkers in MM. The two genes might be associated with the development of COVID‐19 infection.

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module ‘yellow’ were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module ‘green’ mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (LINC00472, AL590094.1, AL365203.3, and AC147651.3) and RPL27A and RPL22L1 in the module ‘yellow’ and two lncRNAs (LINC00426 and AC129507.2) and five protein-coding genes (CSF1, NOD2, ITGAE, CD7, and PDCD1) in the module ‘green’ represented independent prognostic values in patients with ccRCC. Expression of LINC0042, NOD2, CD7, and PDCD1 were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). LINC00426, with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.

A Two-DNA Methylation Signature to Improve Prognosis Prediction of Clear Cell Renal Cell Carcinoma

PURPOSE

Effective biomarkers and models are needed to improve the prognostic prospects of clear cell renal cell carcinoma (ccRCC). The purpose of this work was to identify DNA methylation biomarkers and to evaluate the utility of DNA methylation analysis for ccRCC prognosis.

MATERIALS AND METHODS

An overview of genome-wide methylation of ccRCC tissues derived from The Cancer Genome Atlas (TCGA) database was download for analysis. DNA methylation signatures were identified using Cox regression methods. The potential clinical significance of methylation biomarkers acting as a novel prognostic markers was analyzed using the Kaplan-Meier method and receiver operating characteristic (ROC) curves.

RESULTS

This study analyzed data for 215 patients with information on 23171 DNA methylation sites and identified a two-DNA methylation signature (cg18034859, cg24199834) with the help of a step-wise multivariable Cox regression model. The area under the curve of ROCs for the two-DNA methylation signature was 0.819. The study samples were stratified into low- and high-risk classifications based on an optimal threshold, and the two groups showed markedly different survival rates. Moreover, the two-DNA methylation marker was suitable for patients of varying ages, sex, stages (I and IV), and histologic grade (G2).

CONCLUSION

The two-DNA methylation signature was deemed to be a potential novel prognostic biomarker of use in increasing the accuracy of predicting overall survival of ccRCC patients.

Construction and comprehensive analysis of dysregulated long non-coding RNA-associated competing endogenous RNA network in clear cell renal cell carcinoma

OBJECTIVE

This study aimed to assess the long noncoding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in clear cell renal cell carcinoma (ccRCC) by gene expression analyses.

MATERIALS AND METHODS

LncRNA, miRNA, and mRNA expression profiles in ccRCC were obtained from The Cancer Genome Atlas. Differentially expressed lncRNAs, mRNAs (cut-off: |log 2 [fold change, FC])| > 2.0 and adjusted P < 0.01) and miRNAs (|log 2FC| > 1.5 and adjusted P < 0.01) were unveiled using R. Cox regression analysis was performed to identify prognostic factors of ccRCC related to overall survival (OS). A protein-protein interaction (PPI) network was constructed for differentially expressed mRNAs (DEmRNAs) by Search Tool for the Retrieval of Interacting Genes (STRING). Key hub genes were screened from top 300 DEmRNAs. LncRNA-miRNA and miRNA-mRNA regulatory network were constructed and combined into the competing endogenous RNA regulatory network. Gene ontology biological terms were screened by STRING; Kyoto Encyclopedia of Genes and Genomes pathways were identified using the "clusterProfiler" package in R.

RESULTS

A total of 2331, 1517, and 83 DEmRNAs, lncRNAs, and miRNAs were identified, respectively. Eleven lncRNAs (AC016773.1, HOTTIP, LINC00460, NALCN-AS1, PVT1, TRIM36-IT1, WT1-AS, COL18A1-AS1, LINC00443, LINC00472, and TCL6), three miRNAs (hsa-mir-21, hsa-mir-144, and hsa-mir-155), and three mRNAs (COL4A4, NOD2, and GOLGA8B) were associated with OS. Specifically, four lncRNAs (PVT1, LINC00472, TCL6, and WT1-AS1) and one mRNA (Collagen Type IV Alpha 4 Chain) were verified as independent prognostic factors by Gene Expression Profiling Interactive Analysis. Eleven key hub genes were obtained by PPI analysis. "Cell adhesion molecules (CAMs)," "chemical carcinogenesis," and "cytokine-cytokine receptor interaction" were significantly enriched in the network.

CONCLUSION

The findings clarify the pathogenesis of ccRCC and might provide potential therapeutic targets.

Hyper expression of MTBP may be an adverse signal for the survival of some malignant tumors: A data-based analysis and clinical observation

To explore the relationship between mouse double minute 2 binding protein (MTBP) and the prognosis of cancer patients, a databank-based reanalysis was conducted and a clinical observation about lung adenocarcinoma was taken to verify the result of data analysis.We reanalyzed all the downloaded data in order to make a conclusion about the relationship between MTBP and the prognosis of cancer patients. At last, we collected 112 lung cancer patients with MTBP information to verify the results of data analysis (GSE30219).The overall Kaplan-Meier curve results of 6 eligible data groups were shown in Fig. 1. The Kaplan-Meier curve result of GSE16011 was shown in Fig. 1A (concordance index = 59.48, Log-Rank Equal Curves [P = 5.942e-05], R = 0.045/1, risk groups hazard ratio = 1.69 [conf. int. 1.3-2.9], P = 7.344e-05), while the stratification results were displayed independently in Figs. 2 and 3. The similar results could be seen in other 5 data groups. The tissue sections of 112 patients with lung adenocarcinoma were collected and immunohistochemically stained. The hyper expression rate of MTBP in adenocarcinoma was 23.21% (26/112). The results showed that patients with hyper expression of MTBP had significantly worse prognosis than the control group, and the survival curves were clearly separated from each other (Fig. 4B, P = .000).Hyper expression of MTBP maybe an adverse event for the survival of some cancer patients, especially in glioblastoma, kidney cancer, and lung cancer patients, which has been verified in 112 lung cancer patients with MTBP status.