scholarly journals Identification of an Independent Immune-genes Prognostic Index for Renal Cell Carcinoma

2020 ◽  
Author(s):  
Chao Qin ◽  
Guangyao Li ◽  
Xiyi Wei ◽  
Shifeng Su ◽  
Shangqian Wang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model

Abstract Objective: Increasing evidence has indicated an association between immune micro-environment in clear cell renal cell carcinoma (ccRCC) and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of ccRCC patients. Methods: 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with ccRCC from the TCGA database through the R package, as well as relevant clinical information. We apply certain survival R package to analyse the survival of hub-genes before analyzing the effect of immune genes on the prognosis of clear cell renal cell carcinoma (ccRCC) utilizing Cox regression analysis. Based on the statistical correlation between hub immune gene and survival ,immune risk score model was set up.We finally constructed a nomogram to predict the survival rate of ccRCC overally. In addition, whether the immune gene risk score model is an independent prognostic factor for ccRCC is comprehensively considered applying multivariate cox regression analysis. It is worth noting that throughout the data analysis, P< 0.05 was recognized to be of significance statistically. Results: The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and ccRCC tissues (p<0. 05). Univariate cox regression analysis revealed 43 immune genes statistically correlated with ccRCC related survival risk (P<0.05). In addition, a 18-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.802). Our model showed satisfying AUC and survival correlation in the validation dataset ( 5-year OS AUC=0.705, P<0.001). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of ccRCC. A nomogram was established to comprehensively predict the survival of ccRCC patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways.Conclusion: Collectively, tumor immune genes played an essential role in the prognosis of ccRCC. Furthermore, immune risk score was an independent predictive factor of ccRCC, indicating a poor survival.

scholarly journals Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Chengjian Ji ◽  
Yichun Wang ◽  
Liangyu Yao ◽  
Jiaochen Luan ◽  
Rong Cong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

scholarly journals Identification of an independent immune-genes prognostic index for renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Guangyao Li ◽  
Xiyi Wei ◽  
Shifeng Su ◽  
Shangqian Wang ◽  
Wei Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Roc Curve ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Set Up ◽  
Immune Related Genes ◽  
Survival Risk

Abstract Background Considerable evidence has indicated an association between the immune microenvironment and clinical outcome in ccRCC. The purpose of this study is to extensively figure out the influence of immune-related genes of tumors on the prognosis of patients with ccRCC. Methods Files containing 2498 immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort), and the transcriptome data and clinical information relevant to patients with ccRCC were identified and downloaded from the TCGA data-base. Univariate and multivariate Cox regression analyses were used to screen out prognostic immune genes. The immune risk score model was established in light of the regression coefficient between survival and hub immune-related genes. We eventually set up a nomogram for the prediction of the overall survival for ccRCC. Kaplan-Meier (K-M) and ROC curve was used in evaluating the value of the predictive risk model. A P value of < 0.05 indicated statistically significant differences throughout data analysis. Results Via differential analysis, we found that 556 immune-related genes were expressed differentially between tumor and normal tissues (p < 0. 05). The analysis of univariate Cox regression exhibited that there was a statistical correlation between 43 immune genes and survival risk in patients with ccRCC (p < 0.05). Through Lasso-Cox regression analysis, we established an immune genetic risk scoring model based on 18 immune-related genes. The high-risk group showed a bad prognosis in K-M analysis. (p < 0.001). ROC curve showed that it was reliable of the immune risk score model to predict survival risk (5 year over survival, AUC = 0.802). The model indicated satisfactory AUC and survival correlation in the validation data set (5 year OS, Area Under Curve = 0.705, p < 0.05). From Multivariate regression analysis, the immune-risk score model plays an isolated role in the prediction of the prognosis of ccRCC. Under multivariate-Cox regression analysis, we set up a nomogram for comprehensive prediction of ccRCC patients’ survival rate. At last, it was identified that 18 immune-related genes and risk scores were not only tremendously related to clinical prognosis but also contained in a variety of carcinogenic pathways. Conclusion In general, tumor immune-related genes play essential roles in ccRCC development and progression. Our research established an unequal 18-immune gene risk index to predict the prognosis of ccRCC visually. This index was found to be an independent predictive factor for ccRCC.

scholarly journals Identification of Prognosis-related RNA Binding Proteins to Reveal the Role of RNA Binding Proteins in the Progression and Prognosis of Colon Cancer

2020 ◽  
Author(s):  
Qi Zou ◽  
Yue Ding ◽  
Yuxiang Dong ◽  
Dejun Wu ◽  
Junyi Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Colon Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Binding Proteins ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Survival Risk

Abstract Background: RNA binding proteins (RBPs) are now under discussion as novel promising bio-markers for patients with colon cancer. The purpose of our study is to identify several RBPs related to the progression and prognosis of colon cancer, and to further investigate the mechanism of their influence on tumor progression. Methods: The transcriptome data of colon cancer as well as clinical characteristics used in this study were downloaded from the The Cancer Genome Atlas (TCGA) database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA) were performed to elucidate the gene functions and relative pathways. Cox and lasso regression analysis were used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P< 0.05 was considered to be statistically significant. Results: The results of the difference analysis showed that 473 RBPs exhibited differential expression between normal and colon cancer tissues (p<0. 05). Univariate cox regression analysis revealed 25 RBPs statistically correlated with colon cancer related survival risk (P<0.05). In addition, a 10-RBPs based risk scoring model was constructed through multivariate cox regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p<0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC=0.782). Our model showed satisfying AUC and survival correlation in the validation dataset (5-year OS AUC=0.744). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of colon cancer. A nomogram was established to comprehensively predict the survival of colon cancer patients with the results of multivariate cox regression analysis. Finally, we found that 10 RBPs and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion: Collectively, RBPs played an essential role in the progression and prognosis of colon cancer by regulating multiple biological pathways. Furthermore, RBPs risk score was an independent predictive factor of colon cancer, indicating a poor survival.

Identification of an independent immune-genes prognostic index for breast cancer

2020 ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Chen Chen ◽  
Junyi Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
R Package ◽  
Breast Cancer Patients ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Score Model ◽  
Survival Risk

Abstract Objective Increasing evidence has indicated an association between immune micro-environment in breast cancer and clinical outcomes. The aim of this research is to comprehensively investigate the effect of tumor immune genes on the prognosis of breast cancer patients. Methods 2498 immune genes were downloaded from ImmPort database. Additionally, we identified and downloaded the transcriptome data of patients with breast cancer from the TCGA database through the R package, as well as relevant clinical information. Survival R package was applied in survival analyses for hub-genes. Cox regression analysis was used to analyze the effect of immune genes on the prognosis of breast cancer. Immune risk scoring model was constructed based on the statistical correlation between hub immune genes and survival. Meanwhile, multivariate cox regression analysis was utilized to investigate whether the immune genes risk score model was an independent factor for predicting the prognosis of breast cancer. Nomogram was constructed to comprehensively predict the survival rate of breast cancer. P < 0.05 was considered to be statistically significant. Results The results of the difference analysis showed that 556 immune genes exhibited differential expression between normal and breast cancer tissues (p < 0. 05). Univariate cox regression analysis revealed 66 immune genes statistically correlated with breast cancer related survival risk, of which 30 were associated with overall survival (P < 0.05). In addition, a 15-genes based immune genes risk scoring model was constructed through lasso COX regression analysis. KM curve indicated that patients in high-risk were associated with poor outcomes (p < 0.001). ROC curve indicated that the immune risk score model was reliable in predicting survival risk (5-year OS, AUC = 0.752). Our model showed satisfying AUC and survival correlation in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Furthermore, multivariate cox regression analysis confirmed that the immune risk score model was an independent factor for predicting the prognosis of breast cancer. A nomogram was established to comprehensively predict the survival of breast cancer patients with the results of multivariate cox regression analysis. Finally, we found that 15 immune genes and risk scores were significantly associated with clinical factors and prognosis, and were involved in multiple oncogenic pathways. Conclusion Collectively, tumor immune genes played an essential role in the prognosis of breast cancer. Furthermore, immune risk score was an independent predictive factor of breast cancer, indicating a poor survival.

scholarly journals Expression of PD-1 and CTLA-4 Are Negative Prognostic Markers in Renal Cell Carcinoma

2019 ◽  
Vol 8 (5) ◽  
pp. 743 ◽  
Author(s):  
Andreas Kahlmeyer ◽  
Christine G. Stöhr ◽  
Arndt Hartmann ◽  
Peter J. Goebell ◽  
Bernd Wullich ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Prognostic Marker ◽  
Renal Cell ◽  
Mononuclear Cells ◽  
Cox Regression ◽  
Cancer Specific Survival ◽  
Cox Regression Analysis ◽  
Ci Therapy

Immuno-oncological therapy with checkpoint inhibition (CI) has become a new standard treatment in metastatic renal cell carcinoma (RCC), but the prognostic value of the expression of CI therapy target molecules is still controversial. 342 unselected consecutive RCC tumor samples were analyzed regarding their PD-1, PD-L1, and CTLA-4 expression by immunohistochemistry (IHC). The prognostic values for cancer-specific survival (CSS) and overall survival (OS) were analyzed for those not exposed to CI therapy. The expression of PD-1 in tumor-infiltrating mononuclear cells (TIMC) and PD-L1 in tumor cells was detected in 9.4% and 12.3%, respectively (Immune reactive score (IRS) > 0). Furthermore, PD-L1 expression in TIMC (IRS > 0) and CTLA-4 expression in TIMC (>1% positive cells) was detected in 4.8% and 6.3%. PD-1 expression and CTLA-4 expression were significantly associated with a worse OS and CSS in log rank survival analysis and univariate Cox regression analysis. CTLA-4 expression is a prognostic marker that is independently associated with a worse outcome in multivariate Cox regression analysis in the whole cohort (OS: p = 0.013; CSS: p = 0.048) as well as in a non-metastatic subgroup analysis (OS: p = 0.028; CSS: p = 0.022). Patients with combined CTLA-4 expression and PD-1-expression are at highest risk in OS and CSS. In RCC patients, PD-1 expression in TIMC and CTLA-4 expression in TIMC are associated with a worse OS and CSS. The combination of PD-1 expression in TIMC and CTLA-4 expression in TIMC might identify high risk patients. This is, to our knowledge, the first description of CTLA-4 expression to be a prognostic marker in RCC.

scholarly journals Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8827 ◽  
Author(s):  
Yiqiao Zhao ◽  
Zijia Tao ◽  
Xiaonan Chen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Experimental Studies ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

Background Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC. Methods We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS. Results A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis. Conclusion To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

scholarly journals Increased Expression of TICRR Predicts Poor Clinical Outcomes: A Potential Therapeutic Target for Papillary Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Shuang Xia ◽  
Yan Lin ◽  
Jiaqiong Lin ◽  
Xiaoyong Li ◽  
Xuexian Tan ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Clinical Outcomes ◽  
Renal Cell ◽  
Cox Regression ◽  
Papillary Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Clinical Stages

Background: Papillary renal cell carcinoma (PRCC), although the second-most common type of renal cell carcinoma, still lacks specific biomarkers for diagnosis, treatment, and prognosis. TopBP1-interacting checkpoint and replication regulator (TICRR) is a DNA replication initiation regulator upregulated in various cancers. We aimed to evaluate the role of TICRR in PRCC tumorigenesis and prognosis.Methods: Based on the Kidney Renal Papillary cell carcinoma Project (KIRP) on The Cancer Genome Atlas (TCGA) database, we determined the expression of TICRR using the Wilcoxon rank sum test. The biological functions of TICRR were evaluated using the Metascape database and Gene Set Enrichment Analysis (GSEA). The association between TICRR and immune cell infiltration was investigated by single sample GSEA. Logistic analysis was applied to study the correlation between TICRR expression and clinicopathological characteristics. Finally, Cox regression analysis, Kaplan–Meier analysis, and nomograms were used to determine the predictive value of TICRR on clinical outcomes in PRCC patients.Results:TICRR expression was significantly elevated in PRCC tumors (P &lt; 0.001). Functional annotation indicated enrichment with negative regulation of cell division, cell cycle, and corresponding pathways in the high TICRR expression phenotype. High TICRR expression in PRCC was associated with female sex, younger age, and worse clinical stages. Cox regression analysis revealed that TICRR was a risk factor for overall survival [hazard ratio (HR): 2.80, P = 0.002], progression-free interval (HR: 2.86, P &lt; 0.001), and disease-specific survival (HR: 7.03, P &lt; 0.001), especially in patients with male sex, age below 60 years, clinical stages II–IV and clinical T stage T1–T2.Conclusion: Increased TICRR expression in PRCC might play a role in tumorigenesis by regulating the cell cycle and has prognostic value for clinical outcomes.

scholarly journals Prognostic value and immunological role of AXL gene in clear cell renal cell carcinoma associated with identifying LncRNA/RBP/AXL mRNA networks

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yi Wang ◽  
Ye Tian ◽  
Shouyong Liu ◽  
Zengjun Wang ◽  
Qianwei Xing
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Set Enrichment Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Tcga Dataset

Abstract Backgrounds This article aimed to explore the prognostic and immunological roles of AXL gene in clear cell renal cell carcinoma (ccRCC) for overall survival (OS) and to identify the LncRNA/RBP/AXL mRNA networks. Methods AXL-related gene expression matrix and clinical data were obtained from The Cancer Genome Atlas (TCGA) dataset and AXL-related pathways were identified by gene set enrichment analysis (GSEA). We performed univariate/multivariate Cox regression analysis to evaluate independent prognostic factors and the relationships between AXL and immunity were also investigated. Results The outcomes of us indicated that the AXL mRNA expression was up-regulated in ccRCC samples and high expression of AXL was associated with worse OS in TCGA dataset (P < 0.01). Further external verification results from HPA, UALCAN, ICGC dataset, GSE6344, GSE14994, and qRT-PCR remained consistent (all P < 0.05). AXL was also identified as an independent prognostic factor for ccRCC by univariate/multivariate Cox regression analysis (both P < 0.05). A nomogram including AXL expression and clinicopathological factors was established by us and GSEA results found that elevated AXL expression was associated with the JAK-STAT, P53, WNT, VEGF and MAPK signaling pathways. In terms of immunity, AXL was dramatically linked to tumor microenvironment, immune cells, immune infiltration, immune checkpoint molecules and tumor mutational burden (TMB). As for its potential mechanisms, we also identified several LncRNA/RBP/AXL mRNA axes. Conclusions AXL was revealed to play prognostic and immunological roles in ccRCC and LncRNA/RBP/AXL mRNA axes were also identified by us for its potential mechanisms.

scholarly journals MCM2-7 in Clear Cell Renal Cell Carcinoma: MCM7 Promotes Tumor Cell Proliferation

2021 ◽  
Vol 11 ◽  
Author(s):  
Junneng Zhang ◽  
Huanzong Zhang ◽  
Yinghui Wang ◽  
Qingshui Wang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Treatment Strategies ◽  
Functional Enrichment ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis

BackgroundClear cell renal cell carcinoma (ccRCC) accounts for 60-70% of renal cell carcinoma (RCC) cases. Finding more therapeutic targets for advanced ccRCC is an urgent mission. The minichromosome maintenance proteins 2-7 (MCM2-7) protein forms a stable heterohexamer and plays an important role in DNA replication in eukaryotic cells. In the study, we provide a comprehensive study of MCM2-7 genes expression and their potential roles in ccRCC.MethodsThe expression and prognosis of the MCM2-7 genes in ccRCC were analyzed using data from TCGA, GEO and ArrayExpress. MCM2-7 related genes were identified by weighted co-expression network analysis (WGCNA) and Metascape. CancerSEA and GSEA were used to analyze the function of MCM2–7 genes in ccRCC. The gene effect scores (CERES) of MCM2-7, which reflects carcinogenic or tumor suppressor, were obtained from DepMap. We used clinical and expression data of MCM2-7 from the TCGA dataset and the LASSO Cox regression analysis to develop a risk score to predict survival of patients with ccRCC. The correlations between risk score and other clinical indicators such as gender, age and stage were also analyzed. Further validation of this risk score was engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset.ResultsThe mRNA and protein expression of MCM2-7 were increased in ccRCC compared with normal tissues. High MCM2, MCM4, MCM6 and MCM7 expression were associated with a poor prognosis of ccRCC patients. Functional enrichment analysis revealed that MCM2-7 might influence the progress of ccRCC by regulating the cell cycle. Knockdown of MCM7 can inhibit the proliferation of ccRCC cells. A two-gene risk score including MCM4 and MCM6 can predict overall survival (OS) of ccRCC patients. The risk score was successfully verified by further using Arrayexpress cohort.ConclusionWe analyze MCM2-7 mRNA and protein levels in ccRCC. MCM7 is determined to promote tumor proliferation. Meanwhile, our study has determined a risk score model composed of MCM2-7 can predict the prognosis of ccRCC patients, which may help future treatment strategies.

scholarly journals Identification and development of an independent immune-related genes prognostic model for breast cancer

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lin Chen ◽  
Yuxiang Dong ◽  
Yitong Pan ◽  
Yuhan Zhang ◽  
Ping Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Validation Dataset ◽  
Immune Gene ◽  
Cox Regression Analysis ◽  
Immune Genes ◽  
Immune Related Genes

Abstract Background Breast cancer is one of the main malignant tumors that threaten the lives of women, which has received more and more clinical attention worldwide. There are increasing evidences showing that the immune micro-environment of breast cancer (BC) seriously affects the clinical outcome. This study aims to explore the role of tumor immune genes in the prognosis of BC patients and construct an immune-related genes prognostic index. Methods The list of 2498 immune genes was obtained from ImmPort database. In addition, gene expression data and clinical characteristics data of BC patients were also obtained from the TCGA database. The prognostic correlation of the differential genes was analyzed through Survival package. Cox regression analysis was performed to analyze the prognostic effect of immune genes. According to the regression coefficients of prognostic immune genes in regression analysis, an immune risk scores model was established. Gene set enrichment analysis (GSEA) was performed to probe the biological correlation of immune gene scores. P < 0.05 was considered to be statistically significant. Results In total, 556 immune genes were differentially expressed between normal tissues and BC tissues (p < 0. 05). According to the univariate cox regression analysis, a total of 66 immune genes were statistically significant for survival risk, of which 30 were associated with overall survival (P < 0.05). Finally, a 15 immune genes risk scores model was established. All patients were divided into high- and low-groups. KM survival analysis revealed that high immune risk scores represented worse survival (p < 0.001). ROC curve indicated that the immune genes risk scores model had a good reliability in predicting prognosis (5-year OS, AUC = 0.752). The established risk model showed splendid AUC value in the validation dataset (3-year over survival (OS) AUC = 0.685, 5-year OS AUC = 0.717, P = 0.00048). Moreover, the immune risk signature was proved to be an independent prognostic factor for BC patients. Finally, it was found that 15 immune genes and risk scores had significant clinical correlations, and were involved in a variety of carcinogenic pathways. Conclusion In conclusion, our study provides a new perspective for the expression of immune genes in BC. The constructed model has potential value for the prognostic prediction of BC patients and may provide some references for the clinical precision immunotherapy of patients.

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