scholarly journals A Novel Defined Ferroptosis-Related Gene Signature for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Ming Chen ◽  
Hui Liu ◽  
Ming-Hao Zhang ◽  
Li-Lin Wan ◽  
Nai-Peng Shi ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cell Carcinoma ◽  
Risk Prediction ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Go Enrichment ◽  
Tumor Group ◽  
Go Enrichment Analysis

Abstract Objective: Currently, the mechanism of ferroptosis in the progression of kidney renal clear cell carcinoma (KIRC) is still unclear. This paper aims to explore the potential mechanism of ferroptosis-related genes in KIRC.Methods: Using KIRC chip data in Gene Expression Synthesis (GEO) database, the differentially expressed genes (DEGs) between normal and tumor group were screened in GSE168845, GSE105261 and GSE11151 by limma package. Ferroptosis-related DEGs were gained by the intersection of DEGs and ferroptosis-related genes, which from the FerrDb database. Gene ontology (GO) enrichment analysis of ferroptosis-related DEGs was carried out by gene set enrichment analysis (GSEA). Univariate and multivariate Cox risk regression model was used to screen and establish gene prognosis risk prediction model. For ferroptosis-related DEGs, targeted small molecules are predicted for the treatment of KIRC.Results: In GSE168845, GSE105261 and GSE11151, 2532 DEGs were screened from normal group and tumor group. And 149 ferroptosis-related genes were obtained from the FerrDb database. Through the intersection of DEGs and ferroptosis-related genes, 17 ferroptosis-related DEGs were obtained. GO enrichment analysis indicated that primary biological processes of 17 ferroptosis-related DEGs enrichment had iron ion binding, microvillus membrane and regulation of transcription from RNA polymerase II promoter in response to stress. Based on univariate and multivariate Cox regression analysis, the multivariate prognostic risk prediction model composed of three ferroptosis DEGs including MT1G, LAMP2 and MIOX was constructed. The results of patient risk score indicated that the prognosis with high score was worse than those with low score. Meanwhile, we found that the exprssion of MT1G, LAMP2 and MIOX were related with methylation and immune infiltration in KIRC. Terroptosis-gene interaction and terroptosis-miRNA coregulatory network of MT1G, LAMP2 and MIOX were collected by Network Analyst. Then, the ferroptosis-related prognosis nomogram, including age, gender, grade, TNM and risk score, was found to predict the overall survival (OS) of KIRC patients. Finally, according to ferroptosis related DEGs, the potential therapeutic effects of emetine, cephaeline,scoulerline, sanguinarine, cicloheximide, tolfenamic acid, phenoxybenzamine and calmidazolium were predicted in KIRC.Conclusion: The risk prediction models of MT1G, LAMP2 and MIOX can effectively predict the prognosis of patients with KIRC. And MT1G, LAMP2 and MIOX are related to methylation and immune infiltration in KIRC, which is expected to play a guiding role in the clinical treatment of KIRC. Targeted these three genes, potential therapeutic drugs of emetine, cephaeline,scoulerline, sanguinarine, cicloheximide, tolfenamic acid, phenoxybenzamine and calmidazolium were also predicted in KIRC.

scholarly journals Identification of Tumor Microenvironment-Related Genes in Kidney Renal Clear Cell Carcinoma Patients via Bioinformatic Analysis

2021 ◽  
Author(s):  
Rongjiong Zheng ◽  
Yaosen SHao ◽  
Mingming Wang ◽  
Yeli Tang ◽  
Meiling Hu
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Abstract BackgroundTumor microenvironment has been implicated in the development and progression of cancers. However, the prognostic significance of tumor microenvironment-related genes in kidney renal clear cell carcinoma (KIRC) remains unclear. MethodsIn this study, we obtained and analyzed gene expression profiles from The Cancer Genome Atlas database. Stromal and immune scores were calculated based on the ESTIMATE algorithm. ResultsIn the discovery series of 537 patients, we identified a list of differentially expressed genes which was significantly associated with prognosis in KIRC patients. Protein-protein interaction networks and functional enrichment analysis were both performed, indicating that these identified genes were related to the immune response. ConclusionsThe tumor microenvironment-related genes could serve as the potential biomarkers for KIRC.

scholarly journals Abnormal Expression and Prognostic Significance of EPB41L1 in Kidney Renal Clear Cell Carcinoma Based on Data Mining

2020 ◽  
Author(s):  
Taotao Liang ◽  
Siyao Sang ◽  
Qi Shao ◽  
Zhichao Deng ◽  
Ting Wang ◽  
...  
Keyword(s):  
Data Mining ◽  
Cell Adhesion ◽  
Cell Carcinoma ◽  
Cancer Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma

Abstract Background: EPB41L1 gene (erythrocyte membrane protein band 4.1 like 1) encodes the protein 4.1N, a member of 4.1 family, playing a vital role in cell adhesion and migration, which is associated with the malignant progression of various human cancers. However, the expression and prognostic significance of EPB41L1 in kidney renal clear cell carcinoma (KIRC) remains to be investigated.Methods: In this study, we collected the mRNA expression of EPB41L1 in KIRC through the Oncomine platform, and used the HPA database to perform the pathological tissue immunohistochemistry in patients. Then, the sub-groups and prognosis of KIRC were performed by UALCAN and GEPIA web-tool, respectively. Further, the mutation of EPB41L1 in KIRC were analyzed by c-Bioportal. The co-expression genes of EPB41L1 in KIRC were displayed from the LinkedOmics database, and function enrichment analysis was used by LinkFinder module in LinkedOmics. Co-expression gene network was constructed through the STRING database, and the MCODE plug-in of which was used to build the gene modules, both of them were visualized by Cytoscape software. Finally, the top modular genes in the same patient cohort were constructed through data mining in TCGA by using the UCSC Xena browser.Results: The results indicated that EPB41L1 was down-expressed in KIRC, leading to a poor prognosis. Moreover, there is a mutation in the FERM domain of EPB41L1, but it has no significant effect on the prognosis of KIRC. The co-expressed genes of EPB41L1 was associated with cell adhesion. Further analysis suggested that EPB41L1 and amyloid beta precursor protein (APP) were coordinated to regulated cancer cell adhesion, thereby increasing the incidence of cancer cell metastasis and tumor invasion.Conclusions: In summary, EPB41L1 is constantly down-expressed in KIRC tissues, resulting a poor prognosis. Therefore, we suggest that it can be an effective biomarker for the diagnosis of KIRC.

scholarly journals Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weihao Tang ◽  
Yiling Cao ◽  
Xiaoke Ma
Keyword(s):  
Prediction Model ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Epigenetic Modification ◽  
Clinical Information ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Prognosis Model

Abstract Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

scholarly journals Alternative splicing associated with cancer stemness in kidney renal clear cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lixing Xiao ◽  
Guoying Zou ◽  
Rui Cheng ◽  
Pingping Wang ◽  
Kexin Ma ◽  
...  
Keyword(s):  
Stem Cells ◽  
Logistic Regression ◽  
Alternative Splicing ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Cancer Stemness ◽  
Alternative Splicing Events

Abstract Backgroud Cancer stemness is associated with metastases in kidney renal clear cell carcinoma (KIRC) and negatively correlates with immune infiltrates. Recent stemness evaluation methods based on the absolute expression have been proposed to reveal the relationship between stemness and cancer. However, we found that existing methods do not perform well in assessing the stemness of KIRC patients, and they overlooked the impact of alternative splicing. Alternative splicing not only progresses during the differentiation of stem cells, but also changes during the acquisition of the stemness features of cancer stem cells. There is an urgent need for a new method to predict KIRC-specific stemness more accurately, so as to provide help in selecting treatment options. Methods The corresponding RNA-Seq data were obtained from the The Cancer Genome Atlas (TCGA) data portal. We also downloaded stem cell RNA sequence data from the Progenitor Cell Biology Consortium (PCBC) Synapse Portal. Independent validation sets with large sample size and common clinic pathological characteristics were obtained from the Gene Expression Omnibus (GEO) database. we constructed a KIRC-specific stemness prediction model using an algorithm called one-class logistic regression based on the expression and alternative splicing data to predict stemness indices of KIRC patients, and the model was externally validated. We identify stemness-associated alternative splicing events (SASEs) by analyzing different alternative splicing event between high- and low- stemness groups. Univariate Cox and multivariable logistic regression analysisw as carried out to detect the prognosis-related SASEs respectively. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of our model. Results Here, we constructed a KIRC-specific stemness prediction model with an AUC of 0.968,and to provide a user-friendly interface of our model for KIRC stemness analysis, we have developed KIRC Stemness Calculator and Visualization (KSCV), hosted on the Shiny server, can most easily be accessed via web browser and the url https://jiang-lab.shinyapps.io/kscv/. When applied to 605 KIRC patients, our stemness indices had a higher correlation with the gender, smoking history and metastasis of the patients than the previous stemness indices, and revealed intratumor heterogeneity at the stemness level. We identified 77 novel SASEs by dividing patients into high- and low- stemness groups with significantly different outcome and they had significant correlations with expression of 17 experimentally validated splicing factors. Both univariate and multivariate survival analysis demonstrated that SASEs closely correlated with the overall survival of patients. Conclusions Basing on the stemness indices, we found that not only immune infiltration but also alternative splicing events showed significant different at the stemness level. More importantly, we highlight the critical role of these differential alternative splicing events in poor prognosis, and we believe in the potential for their further translation into targets for immunotherapy.

scholarly journals Overexpressing PTTG family genes predict poor prognosis in kidney renal clear cell carcinoma

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Yonghui Gui ◽  
Xueni Liu ◽  
Chao Wang ◽  
Peng Yang
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
Expression Level ◽  
Pathway Enrichment Analysis ◽  
Term Survival ◽  
Clinical Indicators

Abstract Background Pituitary tumor transforming genes (PTTG1, PTTG2, and PTTG3P) play key roles in the pathogenesis and development of human cancers. The studies show that overexpression of the PTTG genes is associated with tumor progression and migration. However, the function of the PTTG genes in the prognostic value of kidney renal clear cell carcinoma is rarely known by people. Methods The expression of PTTG family genes was analyzed by the ONCOMINE, Human Protein Atlas, GEPIA2, and UALCAN database. The relationship between PTTG family genes expression level and clinical indicators including prognostic data in kidney renal clear cell carcinoma was analyzed by GEPIA2, TCGA portal, and UALCAN. cBioPortal database was used to analyze the genetic mutations of differentially expressed PTTG family members. Similar genes of the PTTG family (90 in total) obtained from GEPIA2 and Metascape were used for GO enrichment to explore the interaction among similar genes. The online tools of Metascape and STRING were used for functional and pathway enrichment analysis. Results PTTG1, 2, and 3P mRNA and protein expression upregulated in kidney renal clear cell carcinoma kidney renal clear cell carcinoma patients compared with normal tissues. And higher expression level of PTTG family genes was associated with shorter overall survival (OS) and disease-free survival (DFS). Furthermore, overexpression of the PTTG family genes had been found correlated with individual cancer stages and pathological tumor grades. In addition, 18% of mutations in the PTTG family genes were associated with short-term survival in kidney renal clear cell carcinoma patients. Conclusions A single PTTG gene or PTTG family genes as a whole may be a potential prognostic biomarker for kidney renal clear cell carcinoma.

Delayed Pancreatic Metastasis of Renal Clear Cell Carcinoma

2013 ◽  
Vol 13 (2) ◽  
pp. 79-80
Author(s):  
Zane Simtniece ◽  
Gatis Kirsakmens ◽  
Ilze Strumfa ◽  
Andrejs Vanags ◽  
Maris Pavars ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Surgical Treatment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Primary Tumour ◽  
Renal Clear Cell Carcinoma ◽  
Pancreatic Metastasis ◽  
Distal Pancreatic Resection

Abstract Here, we report surgical treatment of a patient presenting with pancreatic metastasis (MTS) of renal clear cell carcinoma (RCC) 11 years after nephrectomy. RCC is one of few cancers that metastasise in pancreas. Jaundice, abdominal pain or gastrointestinal bleeding can develop; however, asymptomatic MTS can be discovered by follow-up after removal of the primary tumour. The patient, 67-year-old female was radiologically diagnosed with a clinically silent mass in the pancreatic body and underwent distal pancreatic resection. The postoperative period was smooth. Four months after the surgery, there were no signs of disease progression.

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