scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients With Renal Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits

BackgroundRenal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients. Here, we aim to construct an immune prognostic predictive model to assist clinician to predict RCC prognosis.MethodsHerein, an immune prognostic signature was developed, and its predictive ability was confirmed in the kidney renal clear cell carcinoma (KIRC) cohorts based on The Cancer Genome Atlas (TCGA) dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.ResultsThe immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.ConclusionsThe immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Immunogenomic Analyses of the Prognostic Predictive Model for Patients with Renal Cancer

2021 ◽  
Author(s):  
Tao Feng ◽  
Jiahui Zhao ◽  
Dechao Wei ◽  
Pengju Guo ◽  
Xiaobing Yang ◽  
...  
Keyword(s):  
Predictive Model ◽  
Risk Score ◽  
Immune Cell ◽  
Predictive Ability ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Survival Outcomes ◽  
Prognostic Signature ◽  
Therapeutic Benefits ◽  
Oncogenic Pathways

Abstract Background: Renal cell carcinoma (RCC) is associated with poor prognostic outcomes. The current stratifying system does not predict prognostic outcomes and therapeutic benefits precisely for RCC patients.Methods: Herein, an immune prognostic signature was developed, and its predictive ability was confirmed in the cohorts based on TCGA-KIRC dataset. Several immunogenomic analyses were conducted to investigate the correlations between immune risk scores and immune cell infiltrations, immune checkpoints, cancer genotypes, tumor mutational burden, and responses to chemotherapy and immunotherapy.Results: The immune prognostic signature contained 14 immune-associated genes and was found to be an independent prognostic factor for KIRC. Furthermore, the immune risk score was established as a novel marker for predicting the overall survival outcomes for RCC. The risk score was correlated with some significant immunophenotypic factors, including T cell infiltration, antitumor immunity, antitumor response, oncogenic pathways, and immunotherapeutic and chemotherapeutic response.Conclusions: The immune prognostic, predictive model can be effectively and efficiently used in the prediction of survival outcomes and immunotherapeutic responses of RCC patients.

scholarly journals Identification of Molecular Subtypes and a Prognostic Signature Based on Inflammation-Related Genes in Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Chenjie Qiu ◽  
Wenxiang Shi ◽  
Huili Wu ◽  
Shenshan Zou ◽  
Jianchao Li ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Cancer Patients ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Differential Expression Analysis ◽  
Human Leukocyte ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Prognostic Signature

Both tumour-infiltrating immune cells and inflammation-related genes that can mediate immune infiltration contribute to the initiation and prognosis of patients with colon cancer. In this study, we developed a method to predict the survival outcomes among colon cancer patients and direct immunotherapy and chemotherapy. We obtained patient data from The Cancer Genome Atlas (TCGA) and captured inflammation-related genes from the GeneCards database. The package “ConsensusClusterPlus” was used to generate molecular subtypes based on inflammation-related genes obtained by differential expression analysis and univariate Cox analysis. A prognostic signature including four genes (PLCG2, TIMP1, BDNF and IL13) was also constructed and was an independent prognostic factor. Cluster 2 and higher risk scores meant worse overall survival and higher expression of human leukocyte antigen and immune checkpoints. Immune cell infiltration calculated by the estimate, CIBERSORT, TIMER, ssGSEA algorithms, tumour immune dysfunction and exclusion (TIDE), and tumour stemness indices (TSIs) were also compared on the basis of inflammation-related molecular subtypes and the risk signature. In addition, analyses of stratification, somatic mutation, nomogram construction, chemotherapeutic response prediction and small-molecule drug prediction were performed based on the risk signature. We finally used qRT–PCR to detect the expression levels of four genes in colon cancer cell lines and obtained results consistent with the prediction. Our findings demonstrated a four-gene prognostic signature that could be useful for prognostication in colon cancer patients and designing personalized treatments, which could provide new versions of personalized management for these patients.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals TYROBP, TLR4 and ITGAM regulated macrophages polarization and immune checkpoints expression in osteosarcoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuo Liang ◽  
Jiarui Chen ◽  
GuoYong Xu ◽  
Zide Zhang ◽  
Jiang Xue ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Risk Model ◽  
Low Risk ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Kegg Pathways ◽  
Gene Sets ◽  
Macrophages Polarization ◽  
Immune Related Genes

AbstractWe established a relationship among the immune-related genes, tumor-infiltrating immune cells (TIICs), and immune checkpoints in patients with osteosarcoma. The gene expression data for osteosarcoma were downloaded from UCSC Xena and GEO database. Immune-related differentially expressed genes (DEGs) were detected to calculate the risk score. “Estimate” was used for immune infiltrating estimation and “xCell” was used to obtain 64 immune cell subtypes. Furthermore, the relationship among the risk scores, immune cell subtypes, and immune checkpoints was evaluated. The three immune-related genes (TYROBP, TLR4, and ITGAM) were selected to establish a risk scoring system based on their integrated prognostic relevance. The GSEA results for the Hallmark and KEGG pathways revealed that the low-risk score group exhibited the most gene sets that were related to immune-related pathways. The risk score significantly correlated with the xCell score of macrophages, M1 macrophages, and M2 macrophages, which significantly affected the prognosis of osteosarcoma. Thus, patients with low-risk scores showed better results with the immune checkpoints inhibitor therapy. A three immune-related, gene-based risk model can regulate macrophage activation and predict the treatment outcomes the survival rate in osteosarcoma.

scholarly journals Integrated analysis of the underlying immunomodulator and survival signature of STON1 gene in kidney renal clear cell carcinoma

2021 ◽  
Author(s):  
Axiu Zheng ◽  
Jianrong Bai ◽  
Yanping Ha ◽  
Bingshu Wang ◽  
Yuan Zou ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Clinicopathological Characteristics ◽  
Risk Scores ◽  
Integrated Analysis

Abstract Background Stonin 1 (STON1) is an endocytic protein but its role in cancer remains unclear. Here, we investigated the role of STON1 in kidney renal clear cell carcinoma (KIRC). Methods We undertook bioinformatics analyses of a series of public databases to determine the expression and clinical significance of STON1 in KIRC and focused on STON1-related immunomodulator and survival signatures. A nomogram model integrating clinicopathological characteristics and risk scores for KIRC was established and validated. Results Through TGCA and GEO databases, STON1 mRNA was found to be significantly downregulated in KIRC compared with normal controls, and decreased STON1 was related to grade, TNM stage, distant metastasis, and vital status of KIRC. Furthermore, OncoLnc, UALCAN, Kaplan–Meier, and GEPIA2 analyses supported that KIRC patients with high STON1 expression had better overall survival. STON1 was positively associated with mismatch proteins including MLH1, PMS2, MSH2, MSH6 and EpCAM, and was negatively correlated with tumor mutational burden. Interestingly, arm-level deletion of STON1 was clearly related to the abundance of immune cells and the infiltration abundance in the majority of 26 immune cell types that were positively related to STON1 mRNA level in the TIMER database. The TISIDB database revealed 21 immunostimulators and 10 immunoinhibitors that were obviously related to STON1 in KIRC. In univariate and multivariate Cox regression analyses, CTLA4 , KDR , LAG3 , PDCD1 , HHLA2 , TNFRSF25 , and TNFSF14 were screened to establish a risk score model, and the low-risk group had a better prognosis for KIRC. Furthermore, a nomogram integrating clinicopathological characteristics and risk score had better accuracy and practicability in predicating the survival of KIRC patients. Conclusions Decreased STON1 expression in KIRC leads to clinical progression and poor survival. Mechanically, loss of STON1 is associated with the aberrant immune microenvironment in KIRC. Integrated clinicopathological characteristics and risk score derived from STON1 -related immunomodulators can aid the prediction of KIRC survival.

scholarly journals A Novel TGF-β Risk Score Predicts the Clinical Outcomes and Tumour Microenvironment Phenotypes in Bladder Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhi Liu ◽  
Tiezheng Qi ◽  
Xiaowen Li ◽  
Yiyan Yao ◽  
Belaydi Othmane ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Score ◽  
Tumour Microenvironment ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Immune Checkpoints ◽  
Normal Tissues ◽  
Cox Analysis

BackgroundThe TGF-β pathway plays critical roles in numerous malignancies. Nevertheless, its potential role in prognosis prediction and regulating tumour microenvironment (TME) characteristics require further elucidation in bladder cancer (BLCA).MethodsTGF-β-related genes were comprehensively summarized from several databases. The TCGA-BLCA cohort (training cohort) was downloaded from the Cancer Genome Atlas, and the independent validation cohorts were gathered from Xiangya Hospital (Xinagya cohort) and Gene Expression Omnibus. Initially, we identified differentially expressed TGF-β genes (DEGs) between cancer and normal tissues. Subsequently, univariate Cox analysis was applied to identify prognostic DEGs, which were further used to develop the TGF-β risk score by performing LASSO and multivariate Cox analyses. Then, we studied the role of the TGF-β risk score in predicting prognosis and the TME phenotypes. In addition, the role of the TGF-β risk score in guiding precision treatments for BLCA has also been assessed.ResultsWe successfully constructed a TGF-β risk score with an independent prognostic prediction value. A high TGF-β risk score indicated an inflamed TME, which was supported by the positive relationships between the risk score, enrichment scores of anticancer immunity steps, and the infiltration levels of tumour-infiltrating immune cells. In addition, the risk score positively correlated with the expression of several immune checkpoints and the T cell inflamed score. Consistently, the risk score was positively related to the enrichment scores of most immunotherapy-positive pathways. In addition, the sensitivities of six common chemotherapeutic drugs were positively associated with the risk score. Furthermore, higher risk score indicated higher sensitivity to radiotherapy and EGFR-targeted therapy. On the contrary, patients with low-risk scores were more sensitive to targeted therapies, including the blockade of FGFR3 and WNT-β-catenin networks.ConclusionsWe first constructed and validated a TGF-β signature that could predict the prognosis and TME phenotypes for BLCA. More importantly, the TGF-β risk score could aid in individual precision treatment for BLCA.

scholarly journals Diagnostic and Immunotherapeutic Value of CD248 in Renal Cell Carcinoma

2020 ◽  
Author(s):  
Keying Zhang ◽  
Yao Jiang ◽  
Xiaolong Zhao ◽  
Shaojie Liu ◽  
Chao Xu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clinical Specimen ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Renal cell carcinoma (RCC) is the most common malignancy in urinary system. Despite substantial improvements in available treatments, survival outcome of advanced RCC is unsatisfied. Identifying novel biomarker to assist early diagnosis and screen immunotherapy sensitive patients would be beneficial. CD248 is a promising candidate that deserves to be investigated.Methods: The Cancer Genome Atlas (TCGA) dataset and clinical specimen were adopted to analyze CD248 expression between normal and tumor tissues. Univariate and multivariate Cox regression analysis was employed to identify independent prognostic factors and construct a CD248-based prognostic signature. The correlation among present signature, tumor infiltrating immune cells, tumor mutation burden (TMB), and immunomodulatory molecules were evaluated. Weighted gene co-expression network analysis (WGCNA) and enrichment analysis was performed to explore the underlying mechanism of CD248 in RCC progression.Results: CD248 overexpressed in RCC was related with a poor prognosis, and CD248-based prognostic signature could precisely stratify RCC patients with different survival outcomes regardless of training or testing cohort. Present signature could reflect immunosuppressive landscape of RCC (i.e. increased regulatory T cells infiltration and upregulated immune checkpoints), which accompanied with deteriorated clinicopathologic indexes. TMB and immunostimulatory molecules expression also increased with the risk score generated from present signature. CD248 co-expressed gene sets were identified through WGCNA algorithm, and several immunosuppressive GO terms and KEGG pathways were significantly enriched.Conclusion: CD248 is a valuable biomarker to improve diagnostic and therapeutic efficiency of RCC. Immunosuppressive effect of CD248 co-expressed genes may provide insight for present study.

scholarly journals An immune-related pseudogene signature to improve prognosis prediction of endometrial carcinoma patients

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Shanshan Tang ◽  
Yiyi Zhuge
Keyword(s):  
Cancer Treatment ◽  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Multiple Cancer

Abstract Background Pseudogenes show multiple functions in various cancer types, and immunotherapy is a promising cancer treatment. Therefore, this study aims to identify immune-related pseudogene signature in endometrial cancer (EC). Methods Gene transcriptome data of EC tissues and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) through UCSC Xena browser. Spearman correlation analysis was performed to identify immune-related pseudogenes (IRPs) between the immune genes and pseudogenes. Univariate Cox regression, LASSO, and multivariate were performed to develop a risk score signature to investigate the different overall survival (OS) between high- and low-risk groups. The prognostic significance of the signature was assessed by the Kaplan–Meier curve, time-dependent receiver operating characteristic (ROC) curve. The abundance of 22 immune cell subtypes of EC patients was evaluated using CIBERSORT. Results Nine IRPs were used to build a prognostic signature. Survival analysis revealed that patients in the low-risk group presented longer OS than those in the high-risk group as well as in multiple subgroups. The signature risk score was independent of other clinical covariates and was associated with several clinicopathological variables. The prognostic signature reflected infiltration by multiple types of immune cells and revealed the immunotherapy response of patients with anti-programmed death-1 (PD-1) and anti-programmed cell death 1 ligand 1 (PD-L1) therapy. Function enrichment analysis revealed that the nine IRPs were mainly involved in multiple cancer-related pathways. Conclusion We identified an immune-related pseudogene signature that was strongly correlated with the prognosis and immune response to EC. The signature might have important implications for improving the clinical survival of EC patients and provide new strategies for cancer treatment.

scholarly journals Pyroptosis-associated molecular classification and prognostic signature in glioma

2021 ◽  
Author(s):  
Yanyan Li ◽  
Lin Shen ◽  
Na Li ◽  
Yajie Zhao ◽  
Qin Zhou ◽  
...  
Keyword(s):  
High Risk ◽  
Clinical Features ◽  
Immune Cell ◽  
Risk Groups ◽  
Molecular Classification ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Molecular Alterations ◽  
Genome Atlas

Purpose: Integrative analysis was performed in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas to describe the pyroptosis-associated molecular classification and prognostic signature in glioma. Experimental Design: Pyroptosis-related genes were used for consensus clustering and to develop a prognostic signature. The immune statuses, molecular alterations and clinical features of differentially expressed genes were analyzed among different subclasses and risk groups. A lncRNA-miRNA-mRNA network was built, and drug sensitivity analysis was used to identify small molecular drugs for the identified genes. Results: Glioma can be divided into two subclasses using 30 pyroptosis-related genes. Cluster 1 displayed high immune signatures and poor prognosis as well as high immune-related function scores. A prognostic signature based on 15 pyroptosis-related genes of the CGGA cohort can predict the overall survival of glioma and was well validated in the TCGA cohort. Cluster 1 had higher risk scores. The high-risk group had high immune cell and function scores and low DNA methylation of pyroptosis-related genes. The differences in pyroptosis-related gene mutations and somatic copy numbers were significant between the high-risk and low-risk groups. The ceRNA regulatory network uncovered the regulatory patterns of different risk groups in glioma. Nine pairs of target genes and drugs were identified. Conclusions: Pyroptosis-related genes can reflect the molecular biological and clinical features of glioma subclasses. The established prognostic signature can predict prognosis and distinguish molecular alterations in glioma patients. Our comprehensive analyses provide valuable guidelines for improving glioma patient management and individualized therapy.

scholarly journals Risk score model of autophagy-related genes in osteosarcoma

2021 ◽  
Author(s):  
Wentao Qin ◽  
Mingyang Jiang ◽  
Yang Hu ◽  
Mingjing Xie ◽  
Yiji Jike ◽  
...  
Keyword(s):  
Risk Score ◽  
Immune Cell ◽  
Cox Regression ◽  
Mapk Pathway ◽  
Pi3k Pathway ◽  
The Cancer Genome Atlas ◽  
Sensitivity Analyses ◽  
Risk Scores ◽  
Primary Malignancy ◽  
Score Model

Abstract Background Osteosarcoma (OS) is the most common primary malignancy in children and adolescents, with a high mortality and disability rate. Autophagy plays an important role in the regulation of apoptosis, invasion and metastasis of tumor cells. Hence, construction of a risk score model of autophagy related genes (ARGs) of OS would benefit the treatment and prognosis evaluation. Methods We downloaded a dataset of OS from The Cancer Genome Atlas (TCGA) database, and found the OS-related ARGs through Human Autophagy Database (HADb). Five hub ARGs (CCL2, AMBRA1, VEGFA, MYC and EGFR) were obtained by using multivariate Cox regression model. Then we calculated the risk scores and constructed a prediction model. Another two datasets downloaded from GEO were combined to verify the accuracy and validity of the model. The role of immune cell infiltration was systematically explored, and prediction of response to targeted drugs was assessed. Immunohistochemistry was carried out to verify the expression of the key ARGs. Results Based on these five hub ARGs, we constructed a risk score model related to OS. High accuracy and validity were demonstrated by datasets downloaded from GEO. These five ARGs played a role in cancer-related biological processes, such as MAPK pathway and PI3K pathway. The results of targeted drug sensitivity analyses coincided with the pathway analysis. Immunohistochemistry showed that the expression of 5 ARGs in OS group was more obvious than that in paracancerous group. Conclusion This study constructs a risk score model related to autophagy of OS, explores the prognostic value of autophagy related genes, and finds possible therapeutic targets.

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