Immune Signatures Combined With BRCA1-Associated Protein 1 Mutations Predict Prognosis and Immunotherapy Efficacy in Clear Cell Renal Cell Carcinoma

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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A Novel Defined Pyroptosis-Related Gene Prognostic Index for Clear Cell Renal Cell Carcinoma

10.21203/rs.3.rs-919283/v1 ◽

2021 ◽

Author(s):

xuexiang Li ◽

Yarong Song ◽

Bing Liu ◽

Liang Chen ◽

dingheng Lu ◽

...

Keyword(s):

Cell Carcinoma ◽

Risk Score ◽

Immune Checkpoint Inhibitor ◽

Cox Regression ◽

Clear Cell ◽

Checkpoint Inhibitor ◽

Cell Renal Cell Carcinoma ◽

Checkpoint Inhibitor Therapy ◽

Score Model ◽

Immune Score

Abstract Background: Clear cell renal cell carcinoma (ccRCC), a common pathological subtype of renal cancer with high aggressiveness, has been reported to be associated with chronic inflammation. Pyroptosis, a newly discovered inflammatory form of programmed cell death, can aggravate the inflammatory response. However, the influence of pyroptosis-related genes on ccRCC patient outcomes is yet unknown.Methods: In this study, 43 differentially expressed pyroptosis-related hub genes were identified by analysing The Cancer Genome Atlas–Kidney Renal Clear Cell Carcinoma dataset. The risk-score model was selected using the least absolute shrinkage and selection operator Cox regression and Cox multivariate methods, and all patients were divided into two risk subgroups based on the risk score. Prognostic value of the risk-score model was verified through survival curve, receiver operating characteristic curve and risk curve. Gene ontology and Kyoto Encyclopaedia of Genes and Genomes analyses suggested that the differentially expressed genes between the two subgroups were enriched in immune-mediated categories. Furthermore, the relationship between the risk-score model and ESTIMATE immune score and immunophenoscore was analysed. Finally, Nomogram was constructed based on the results of cox regression analyses. Results: The training cohort and the validation cohort enrolled 346 and 148 ccRCC patients respectively. The risk-score model was constructed by two genes (AIM2 and GSDMB). The area under curve of the ROC curve in two cohorts were both greater than 0.6. The grade and risk score were selected as independent factors and used to construct a nomogram to predict ccRCC patients' survival rate with the c-index of 0.68. Moreover, high-risk score subgroup was associated with a higher immune score and a lower percentage of PBRM1 mutations. The risk score was positively related to the degree of immune infiltration of CD8+ T, T follicular helper, gamma delta T, and regulatory T cells, and patients with a higher risk score were more likely to benefit from immune checkpoint inhibitor therapy. Conclusion: The risk-score model based on pyroptosis-related genes constructed in our study is a promising biomarker to predict the prognosis, molecular and immune characteristics, and immune benefit from immune checkpoint inhibitor therapy in ccRCC patients.

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m6A modification patterns and tumor immune landscape in clear cell renal carcinoma

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-001646 ◽

2021 ◽

Vol 9 (2) ◽

pp. e001646

Author(s):

Jiehui Zhong ◽

Zezhen Liu ◽

Chao Cai ◽

Xiaolu Duan ◽

Tuo Deng ◽

...

Keyword(s):

Prognostic Value ◽

Cox Regression ◽

Clear Cell ◽

Comprehensive Evaluation ◽

Principal Component ◽

Intratumor Heterogeneity ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis ◽

Specific Tumor ◽

Cell Renal Carcinoma

BackgroundRecent studies have focused on the correlation between N6-methyladenosine (m6A) modification and specific tumor-infiltrating immune cells. However, the potential roles of m6A modification in the tumor immune landscape remain elusive.MethodsWe comprehensively evaluated the m6A modification patterns and tumor immune landscape of 513 clear cell renal cell carcinoma (ccRCC) patients, and correlated the m6A modification patterns with the immune landscape. The m6Ascore was established using principal component analysis. Multivariate Cox regression analysis was performed to evaluate the prognostic value of the m6Ascore.ResultsWe identified three m6Aclusters—characterized by differences in Th17 signature, extent of intratumor heterogeneity, overall cell proliferation, aneuploidy, expression of immunomodulatory genes, overall somatic copy number alterations, and prognosis. The m6Ascore was established to quantify the m6A modification pattern of individual ccRCC patients. Further analyses revealed that the m6Ascore was an independent prognostic factor of ccRCC. Finally, we verified the prognostic value of the m6Ascore in the programmed cell death protein 1 (PD-1) blockade therapy of patients with advanced ccRCC.ConclusionsThis study demonstrated the correlation between m6A modification and the tumor immune landscape in ccRCC. The comprehensive evaluation of m6A modification patterns in individual ccRCC patients enhances our understanding of the tumor immune landscape and provides a new approach toward new and improved immunotherapeutic strategies for ccRCC patients.

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Bioinformatics-Based Identification of Tumor Microenvironment-Related Prognostic Genes in Pancreatic Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.632803 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shaojie Chen ◽

Feifei Huang ◽

Shangxiang Chen ◽

Yinting Chen ◽

Jiajia Li ◽

...

Keyword(s):

Pancreatic Cancer ◽

Overall Survival ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Operating Characteristic ◽

Cox Regression ◽

Time Dependent ◽

Concordance Index ◽

Cox Regression Analysis ◽

Score Model

ObjectiveGrowing evidence has highlighted that the immune and stromal cells that infiltrate in pancreatic cancer microenvironment significantly influence tumor progression. However, reliable microenvironment-related prognostic gene signatures are yet to be established. The present study aimed to elucidate tumor microenvironment-related prognostic genes in pancreatic cancer.MethodsWe applied the ESTIMATE algorithm to categorize patients with pancreatic cancer from TCGA dataset into high and low immune/stromal score groups and determined their differentially expressed genes. Then, univariate and LASSO Cox regression was performed to identify overall survival-related differentially expressed genes (DEGs). And multivariate Cox regression analysis was used to screen independent prognostic genes and construct a risk score model. Finally, the performance of the risk score model was evaluated by Kaplan-Meier curve, time-dependent receiver operating characteristic and Harrell’s concordance index.ResultsThe overall survival analysis demonstrated that high immune/stromal score groups were closely associated with poor prognosis. The multivariate Cox regression analysis indicated that the signatures of four genes, including TRPC7, CXCL10, CUX2, and COL2A1, were independent prognostic factors. Subsequently, the risk prediction model constructed by those genes was superior to AJCC staging as evaluated by time-dependent receiver operating characteristic and Harrell’s concordance index, and both KRAS and TP53 mutations were closely associated with high risk scores. In addition, CXCL10 was predominantly expressed by tumor associated macrophages and its receptor CXCR3 was highly expressed in T cells at the single-cell level.ConclusionsThis study comprehensively investigated the tumor microenvironment and verified immune/stromal-related biomarkers for pancreatic cancer.

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A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhentao Liu ◽

Hao Zhang ◽

Hongkang Hu ◽

Zheng Cai ◽

Chengyin Lu ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Cox Regression ◽

Methylation Status ◽

Survival Rates ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

BioMed Research International ◽

10.1155/2020/2719739 ◽

2020 ◽

Vol 2020 ◽

pp. 1-43

Author(s):

Beilei Wu ◽

Lijun Tao ◽

Daqing Yang ◽

Wei Li ◽

Hongbo Xu ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Immune Cells ◽

Clinical Characteristics ◽

Cox Regression ◽

Roc Curves ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

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Discovery and validation of immune- related long non-coding RNA biomarkers associated with prognosis in hepatocellular carcinoma

10.21203/rs.3.rs-17851/v1 ◽

2020 ◽

Author(s):

Li Liu ◽

She Tian ◽

Zhu Li ◽

Yongjun Gong ◽

Hao Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

Survival Curve ◽

Principal Component ◽

High Risk Group ◽

Low Risk ◽

Cox Regression Analysis ◽

Immune Related Genes

Abstract Background : Hepatocellular carcinoma (HCC) is one of the most common clinical malignant tumors, resulting in high mortality and poor prognosis. Studies have found that LncRNA plays an important role in the onset, metastasis and recurrence of hepatocellular carcinoma. The immune system plays a vital role in the development, progression, metastasis and recurrence of cancer. Therefore, immune-related lncRNA can be used as a novel biomarker to predict the prognosis of hepatocellular carcinoma. Methods : The transcriptome data and clinical data of HCC patients were obtained by using The Cancer Genome Atlas-Liver Hepatocellular Carcinoma (TCGA‑LIHC), and immune-related genes were extracted from the Molecular Signatures Database (IMMUNE RESPONSE M19817 and IMMUNE SYSTEM PROCESS M13664). By constructing the co-expression network and Cox regression analysis, 13 immune-lncRNAs was identified to predict the prognosis of HCC patients. Patients were divided into high risk group and low risk group by using the risk score formula, and the difference in overall survival (OS) between the two groups was reflected by Kaplan-Meier survival curve. The time - dependent receiver operating characteristics (ROC) analysis and principal component analysis (PCA) were used to evaluate 13 immune -lncRNAs signature. Results : Through TCGA - LIHC extracted from 343 cases of patients with hepatocellular carcinoma RNA - Seq data and clinical data, 331 immune-related genes were extracted from the Molecular Signatures Database , co-expression networks and Cox regression analysis were constructed, 13 immune-lncRNAs signature was identified as biomarkers to predict the prognosis of patients. At the same time using the risk score median divided the patients into high risk and low risk groups, and through the Kaplan-Meier survival curve analysis found that high-risk group of patients' overall survival (OS) less low risk group of patients. The AUC value of the ROC curve is 0.828, and principal component analysis (PCA) results showed that patients could be clearly divided into two parts by immune-lncRNAs, which provided evidence for the use of 13 immune-lncRNAs signature as prognostic markers. Conclusion : Our study identified 13 immune-lncRNAs signature that can effectively predict the prognosis of HCC patients, which may be a new prognostic indicator for predicting clinical outcomes.

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N6-Methyladenosine-Related Long Non-coding RNA Signature Associated With Prognosis and Immunotherapeutic Efficacy of Clear-Cell Renal Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.726369 ◽

2021 ◽

Vol 12 ◽

Author(s):

Tianming Ma ◽

Xiaonan Wang ◽

Jiawen Wang ◽

Xiaodong Liu ◽

Shicong Lai ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Differential Expression ◽

Renal Cell ◽

Cox Regression ◽

Clear Cell ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cell Renal Cell Carcinoma ◽

Cox Regression Analysis

Increasing evidence suggests that N6-methyladenosine (m6A) and long non-coding RNAs (lncRNAs) play important roles in cancer progression and immunotherapeutic efficacy in clear-cell renal cell carcinoma (ccRCC). In this study, we conducted a comprehensive ccRCC RNA-seq analysis using The Cancer Genome Atlas data to establish an m6A-related lncRNA prognostic signature (m6A-RLPS) for ccRCC. Forty-four prognostic m6A-related lncRNAs (m6A-RLs) were screened using Pearson correlation analysis (|R| > 0.7, p < 0.001) and univariable Cox regression analysis (p < 0.01). Using consensus clustering, the patients were divided into two clusters with different overall survival (OS) rates and immune status according to the differential expression of the lncRNAs. Gene set enrichment analysis corroborated that the clusters were enriched in immune-related activities. Twelve prognostic m6A-RLs were selected and used to construct the m6A-RLPS through least absolute shrinkage and selection operator Cox regression. We validated the differential expression of the 12 lncRNAs between tumor and non-cancerous samples, and the expression levels of four m6A-RLs were further validated using Gene Expression Omnibus data and Lnc2Cancer 3.0 database. The m6A-RLPS was verified to be an independent and robust predictor of ccRCC prognosis using univariable and multivariable Cox regression analyses. A nomogram based on age, tumor grade, clinical stage, and m6A-RLPS was generated and showed high accuracy and reliability at predicting the OS of patients with ccRCC. The prognostic signature was found to be strongly correlated to tumor-infiltrating immune cells and immune checkpoint expression. In conclusion, we established a novel m6A-RLPS with a favorable prognostic value for patients with ccRCC. The 12 m6A-RLs included in the signature may provide new insights into the tumorigenesis and allow the prediction of the treatment response of ccRCC.

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

10.21203/rs.3.rs-25880/v1 ◽

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.

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A novel prognostic risk score model based on immune-related genes in patients with stage IV colorectal cancer

Bioscience Reports ◽

10.1042/bsr20201725 ◽

2020 ◽

Vol 40 (10) ◽

Author(s):

Ke Xu ◽

Jie He ◽

Jie Zhang ◽

Tao Liu ◽

Fang Yang ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Predictive Power ◽

Cox Regression ◽

External Validation ◽

Stage Iv ◽

Regression Analyses ◽

Stage Iv Colorectal Cancer ◽

Score Model ◽

Immune Related Genes

Abstract Purpose: The aims of the present study were to explore immune-related genes (IRGs) in stage IV colorectal cancer (CRC) and construct a prognostic risk score model to predict patient overall survival (OS), providing a reference for individualized clinical treatment. Methods: High-throughput RNA-sequencing, phenotype, and survival data from patients with stage IV CRC were downloaded from TCGA. Candidate genes were identified by screening for differentially expressed IRGs (DE-IRGs). Univariate Cox regression, LASSO, and multivariate Cox regression analyses were used to determine the final variables for construction of the prognostic risk score model. GSE17536 from the GEO database was used as an external validation dataset to evaluate the predictive power of the model. Results: A total of 770 candidate DE-IRGs were obtained, and a prognostic risk score model was constructed by variable screening using the following 12 genes: FGFR4, LGR6, TRBV12-3, NUDT6, MET, PDIA2, ORM1, IGKV3D-20, THRB, WNT5A, FGF18, and CCR8. In the external validation set, the survival prediction C-index was 0.685, and the AUC values were 0.583, 0.731, and 0.837 for 1-, 2- and 3-year OS, respectively. Univariate and multivariate Cox regression analyses demonstrated that the risk score model was an independent prognostic factor for patients with stage IV CRC. High- and low-risk patient groups had significant differences in the expression of checkpoint coding genes (ICGs). Conclusion: The prognostic risk score model for stage IV CRC developed in the present study based on immune-related genes has acceptable predictive power, and is closely related to the expression of ICGs.

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