Identification of Two Immune Related Genes Correlated With Aberrant Methylations as Prognosis Signatures for Renal Clear Cell Carcinoma.
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ABSTRACT:
SUBMITTER: Yao ZY
PROVIDER: S-EPMC8674690 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is one of the most common malignancies. Recently, immunotherapy has been considered a promising treatment for metastatic ccRCC. NUF2 is a crucial component of the Ndc80 complex. NUF2 can stabilize microtubule attachment and is closely related to cell apoptosis and proliferation. This research is dedicated to investigating the role of NUF2 in ccRCC and the possible mechanisms.MethodsFirst, analysis of NUF2 mRNA expression levels in ccRCC and normal tissues by The Cancer Genome Atlas (TCGA) database and further verified by analysis of independent multiple microarray data sets in the Gene Expression Omnibus (GEO) database. Moreover, we evaluated and identified correlations between NUF2 expression, clinicopathologic variable, and overall survival (OS) in ccRCC by various methods. We investigated the relationship between NUF2 and tumor immune infiltration and the expression of corresponding immune cell markers via the Gene Expression Profiling Interactive Analysis (GEPIA) and Tumor Immune Estimation Resource (TIMER) databases. Then, we performed functional enrichment analysis of NUF2 co-expressed genes using R software and protein-protein interactions (PPIs) using the search tool used to retrieve interacting genes/proteins (STRING) databases.ResultsWe discovered that NUF2 mRNA expression was upregulated in ccRCC tissues and was associated with sex, grade, pathological stage, lymph node metastasis, and worse prognosis. In addition, NUF2 was positively linked to tumor immune cells in ccRCC. Moreover, NUF2 was closely related to genetic markers of different immune cells. Finally, functional enrichment and protein-protein interaction (PPI) analysis suggested that NUF2 and its closely related genes may be involved in the regulation of the cell cycle and mitosis. Our results suggested that NUF2 is correlated with a poor prognosis and immune infiltration in ccRCC.
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Project description:Background: Early detection and precise prognostic evaluation of clear cell renal cell carcinoma (ccRCC) are crucial for patient life expectancy. Ion channel-related genes (ICRGs) are of great diagnostic and prognostic value as components that maintain the normal structure of the kidney. Therefore, we systematically explored the diagnostic, prognostic, and therapeutic value of ICRGs in ccRCC using the multi-database. Methods: RNA transcriptome profiles and clinical data of ccRCC patients were extracted and integrated from public databases including The Cancer Genome Atlas, ICGC, GEO, and E-MTAB databases. Ion channel-related genes were obtained from the literature collection. The diagnostic signature was performed using the LASSO and SVM-REF analyses. Meanwhile, the prognostic signature was conducted using the LASSO analyses. Molecular subtyping was performed using the ConsensusClusterPlus and the corresponding therapeutic targets were evaluated using the pRRophetic package. In addition, a prognostic nomogram was constructed based on the results of cox regression analyses. Results: We successfully constructed diagnostic signatures for five ICRGs and prognostic signatures for 10 ICRGs with AUC values greater than 0.7, showing good predictive performance. Based on the median risk score, we found that high-risk patients had a significantly worse prognosis. We also divided ccRCC patients into two clusters according to prognostic ICRGs, and there was a significant survival outcome between the two clusters and different sensitivity to diverse clinical therapeutic strategies. Meanwhile, we constructed a nomogram based on clinical molecules and signatures, and its predictive efficacy was better than the signature or the present tumor-node-metastasis staging system. Conclusion: In this study, we established useful signatures for early detection, prognosis evaluation, and individualized treatment for ccRCC. Moreover, KCNJ16 deserves to be explored comprehensively in the future.
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Project description:BackgroundLysophosphatidic acid (LPA) and its receptors play a key role in regulating cancer progression. Upregulation of LPA receptor 2 (LPAR2) plays a role in carcinogenesis; however, the exact role of LPAR2 in tumors remains elusive. This study aims to explore the correlation between LPAR2 expression with tumor prognosis and immune infiltration in pan-cancers.Materials and methodsThe expression of LPAR2 in pan-cancers was analyzed using the Online Cancer Microarray Database (Oncomine), Tumor Immune Estimation Resource (TIMER), and UALCAN databases. The effects of LPAR2 on the clinical prognosis in pan-cancer were examined using the Kaplan-Meier plotter (KM plotter) as well as Gene Expression Profiling Interactive Analysis (GEPIA), UALCAN, and Human Protein Atlas (HPA) databases. Moreover, the R software program was applied for validation of expression and prognostic value of LPAR2 in tumor patients in the Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. The relationship between the expression level of LPAR2 and the clinical and molecular criteria of head and neck squamous cell carcinoma (HNSC) and kidney renal clear cell carcinoma (KIRC) was analyzed using UALCAN, whereas the relationship between LPAR2 expression and prognosis in patients with HNSC and KIRC with different clinical characteristics was examined using the KM plotter. Furthermore, the correlation between LPAR2 expression and tumor immune infiltration was examined using TIMER. The correlation between LPAR2 expression and gene markers of tumor immune infiltrates was analyzed using TIMER and GEPIA. In addition, the cBioPortal for Cancer Genomics was used to calculate the mutations, methylations, and altered neighbor genes of LPAR2.ResultsThe expression of LPAR2 was significantly correlated with the outcome of multiple types of cancer, especially HNSC and KIRC. Furthermore, high expression of LPAR2 was significantly associated with various immune markers in the immune cell subsets of HNSC and KIRC.ConclusionsHigh expression of LPAR2 plays significantly different prognostic roles in HNSC and KIRC possibly owing to its association with different immune markers. LPAR2 is correlated with tumor immune cell infiltration and is a valuable prognostic biomarker for HNSC and KIRC. However, further experiments are required to validate these findings.
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Project description:ObjectivesNever in mitosis gene A-related kinase 2 (NEK2) has been implicated in tumorigenesis in various tissues, but its function in clear cell renal cell carcinoma (ccRCC) tumorigenesis is unclear. We evaluated the correlation between NEK2 expression and ccRCC.MethodsImmunohistochemistry analysis of NEK2 protein was done on high-density multi-organ Human Cancer tissue microarray derived from the patient samples from clear cell renal cell carcinoma. We used multiple clinical cohorts to analyze the NEK2 immunohistochemical staining expression across human cancers. The cancer genome atlas (TCGA) data analysis of NEK2 was done through UALCAN web servers. Association of NEK2 and Kaplan-Meier survival analysis was done on both of our clinical database and available TCGA datasets.ResultsUsing the UALCAN cancer transcriptional data analysis website, we found that NEK2 is overexpressed in ccRCC, and its expression was associated with overall survival. According to the analyses of our own clinical database and immunohistochemical staining, protein levels of NEK2 were elevated in renal carcinoma compared to adjacent normal tissues. Kaplan-Meier survival analysis of both UALCAN and our database showed that high expression of NEK2 was associated with a poor prognosis. Multivariate and univariate analyses showed that NEK2 expression was closely related to a poor prognosis. The findings suggest that NEK2 is associated with ccRCC.ConclusionThese studies show that NEK2 is over-expressed in clear cell renal cell carcinoma and plays an essential role in cancer cell survival, as such NEK2 could serve as a novel potential target for therapeutic intervention in ccRCC.
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Project description:Clear cell renal cell carcinoma (ccRCC) is a kind of lethal cancer. Although there are mature treatment methods, there is still a lack of rigorous and scientific means for cancer diagnosis. Long noncoding RNAs (lncRNAs) are a kind of noncoding RNA (ncRNA). Recent studies find that alteration of lncRNA expression is related to the occurrence of many cancers. In order to find lncRNAs which can effectively predict the prognosis of ccRCC, RNA-seq count data and clinical information were downloaded from TCGA-KIRC, and gene expression profiles from 530 patients were included. Then, K-means was used for clustering, and the number of clusters was determined to be 5. The R-package "edgeR" was used to perform differential expression analysis. Subsequently, a risk model composed of 10 lncRNA biomarkers significantly related to prognosis was identified via Cox and LASSO regression analyses. Then, patients were divided into two groups according to the model-based risk score, and then, GSEA pathway enrichment was performed. The results showed that metabolism- and mTOR-related pathways were activated while immune-related pathways were inhibited in the high-risk patients. Combined with previous studies, it is believed that these 10 lncRNAs are potential targets for the treatment of ccRCC. In addition, Cox regression analysis was used to verify the independence of the risk model, and as results revealed, the risk model can be used to independently predict the prognosis of patients. In conclusion, our study found 10 lncRNAs related to the prognosis of ccRCC and provided new ideas for clinical diagnosis and drug development.
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Project description:Immunotherapy is gradually emerging in the field of tumor treatment. However, because of the complexity of the tumor microenvironment (TME), some patients cannot benefit from immunotherapy. Therefore, we comprehensively analyzed the TME and gene mutations of ccRCC to identify a comprehensive index that could more accurately guide the immunotherapy of patients with ccRCC. We divided ccRCC patients into two groups based on immune infiltration activity. Next, we investigated the differentially expressed genes (DEGs) and constructed a prognostic immune score using univariate Cox regression analysis, unsupervised cluster analysis, and principal component analysis (PCA) and validated its predictive power in both internal and total sets. Subsequently, the gene mutations in the groups were investigated, and patients suitable for immunotherapy were selected in combination with the immune score. The prognosis of the immune score-low group was significantly worse than that of the immune score-high group. The patients with BRCA1-associated protein 1 (BAP1) mutation had a poor prognosis. Thus, this study indicated that establishing an immune score model combined with BAP1 mutation can better predict the prognosis of patients, screen suitable ccRCC patients for immunotherapy, and select more appropriate drug combinations.
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Project description:BackgroundAlmost 40% of patients with kidney renal clear cell carcinoma (KIRC) with advanced cancers eventually develop to metastases, and their 5-year survival rates are approximately 10%. Aberrant DNA methylations are significantly associated with the development of KIRC. The aim of our present study was to identify suitable ferroptosis- and immune-related (FI) biomarkers correlated with aberrant methylations to improve the prognosis and diagnosis of KIRC.MethodsChAMP and DESeq2 in R (3.6.2) were used to screen the differentially expressed methylation probes and differentially expressed genes, respectively. Univariate and multivariate Cox regression were used to identify the overall survival (OS)-related biomarkers.ResultsWe finally identified five FI biomarkers (CCR4, CMTM3, IFITM1, MX2, and NR3C2) that were independently correlated with the OS of KIRC. The area under the curve value of the receiver operating characteristic value of prognosis model was 0.74, 0.68, and 0.72 in the training, validation, and entire cohorts, respectively. The sensitivity and specificity of the diagnosis model were 0.8698 and 0.9722, respectively. In addition, the prognosis model was also significantly correlated with several immune cells and factors.ConclusionOur present study suggested that these five FI-DEGs (CCR4, CMTM3, IFITM1, MX2, and NR3C2) could be used as prognosis and diagnosis biomarkers for patients with KIRC, but further cross-validation clinical studies are still needed to confirm them.
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Project description:BackgroundFerroptosis is a recently recognised new type of cell death which may be a potential target for cancer therapy. In the present study, we aimed to screen ferroptosis-related differentially expressed long non-coding RNAs as biomarkers to predict the outcome of kidney renal clear cell carcinoma.MethodsRNAseq count data and corresponding clinical information were obtained from the Cancer Genome Atlas database. Lists of ferroptosis-related genes and long non-coding RNAs were obtained from the FerrDb and GENCODE databases, respectively. The candidate prognostic signatures were screened by Cox regression analyses and least absolute shrinkage and selection operator analyses.ResultsThree ferroptosis-related long non-coding RNAs (DUXAP8, LINC02609, and LUCAT1) were significantly correlated with the overall survival of kidney renal clear cell carcinoma independently. Kidney renal clear cell carcinoma patients with high-risk values displayed worse OS. Meanwhile, the expression of these three ferroptosis-related long non-coding RNAs and their risk scores were significantly correlated with clinicopathological features. Principal component analyses showed that patients with kidney renal clear cell carcinoma have differential risk values were well distinguished by the three ferroptosis-related long non-coding RNAs.ConclusionsThe present study suggests that the risk assessment model constructed by these three ferroptosis-related long non-coding RNAs could accurately predict the outcome of kidney renal clear cell carcinoma. We also provide a novel perspective for cancer prognosis screening.
| S-EPMC8567554 | biostudies-literature
Project description:BackgroundMammalian STE20-like kinase 1 (MST1/STK4/KRS2) is a highly conserved serine/threonine kinase and a central member of the Hippo signaling pathway. STK4 has been reported to play important roles in various tumors, but a systematic and comprehensive study of its function in clear cell renal cell carcinoma (ccRCC) has not been conducted.MethodsIn this study, we used immunohistochemistry (IHC), western blot (WB), quantitative real-time PCR (qPCR) experiments, and bioinformatics analysis to comprehensively analyze the expression, prognostic value, and immune infiltration of STK4 in ccRCC.ResultsAnalysis of the TCGA database showed that the expression level of the STK4 gene in ccRCC patients depended on tumor stage, grade, and distant lymphatic metastasis. This was further confirmed by the results of IHC, WB, and qPCR. In addition, we used the receiver operating characteristic curve (ROC curve) to elucidate the diagnostic value of STK4 in ccRCC patients. According to the findings of the TIMER database, the high expression of STK4 is significantly associated with the survival of kidney cancer (including ccRCC) patients (p < 0.001), suggesting that STK4 is a reliable prognostic predictor. We then used gene set enrichment analysis (GSEA) to explore the mechanisms behind STK4 function in ccRCC. We found that STK4 may play a role in immune regulation interactions. Subsequently, we performed immune infiltration analysis of STK4. The results showed that STK4 may regulate the development of ccRCC by affecting the immune infiltration of NK and pDC cells.ConclusionsSTK4 may be a prognostic marker for ccRCC and may help identify new strategies for treating ccRCC patients.
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Project description:ObjectiveKidney renal clear cell carcinoma (KIRC, ccRCC) is the most common type of renal cancer with high recurrence and mortality. It has long been recognized that Antizyme inhibitor 1 (AZIN1) serves as a pro-oncogenic molecule in multiple cancers. However, the clinicopathological features of AZIN1 in KIRC remain unexplored.Materials and methodsThe Cancer Genome Atlas (TCGA, TIMER, and GEPIA) were employed for pan-cancer expression and survival analysis of AZIN1, indicating the unique anti-tumor role of AZIN1 in KIRC. The expression and clinical characteristics of AZIN1 in KIRC were further proven via Human Protein Atlas and TCGA. single-sample GSEA was employed to investigate the immune infiltration of AZIN1. Then the downstream pathways were illustrated via the LinkedOmics, Metascape, and Cytoscape databases. The possible upper regulating noncoding RNAs (ncRNAs) were analyzed from five programs-TargetScan, StarBase, miRanda, PITA, and miRmap.ResultsAZIN1 is downregulated in KIRC patients. Lower levels of AZIN1 were linked with unfavorable outcomes in KIRC patients. The AZIN1 expression was positively related to immune cell infiltration in KIRC. We also elucidated a possible upstream regulatory ncRNA of AZIN1 in KIRC namely STK4-AS1/AC068338.2-miR-106b-5p-AZIN1 axis as well as the downstream signaling pathways.ConclusionThis study illustrated the unique anti-tumor role of AZIN1 in KIRC and provided potential value for guiding immunotherapy and targeted therapy.
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