Dataset Information

Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature.

ABSTRACT: Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflammatory response and an immunosuppressive microenvironment. A seven-gene inflammation-related risk signature was designed employing the LASSO and Cox regression analyses for the inflammation-related genes. The ccRCC specimens were classified into two groups with high and low risk by calculating the risk scores. The specimens in the group with high risk showed a poor prognosis and were positively correlated with immune inhibitory factors. Moreover, a nomogram was created by incorporating inflammation-related risk signatures and clinical characteristics. The ROC and DCA curves indicated a satisfactory efficiency of the nomogram for predicting the survival outcomes. Furthermore, we identified the potential therapeutic drug molecules through CMap analysis. The findings of our study may act as a guide for further research on new prognostic biomarkers and therapies.

SUBMITTER: Zhang Y

PROVIDER: S-EPMC9602121 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature.

Zhang Yuke Y Shi Chunliu C Chen Yue Y Wang Hongwei H Chen Feng F Han Ping P

Genes 20221019 10

Clear cell renal cell carcinoma (ccRCC) is the most frequent kind of kidney malignancy. Inflammation is a physiological response of the immune system to harmful stimuli. Notably, the role inflammation plays in ccRCC is still unknown. In this study, consensus clustering analysis sorted the ccRCC specimens from the TCGA dataset into C1 and C2 clusters. The C2 cluster comprised ccRCC specimens with a high TNM stage and tumor grade. These specimens were characterized by the activation of the inflamm ...[more]

PMID: 36292781

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Project description:Background: Oxidative stress is related to oncogenic transformation in kidney renal clear cell carcinoma (KIRC). We intended to identify a prognostic antioxidant gene signature and investigate its relationship with immune infiltration in KIRC. Methods: With the support of The Cancer Genome Atlas (TCGA) database, we researched the gene expression and clinical data of KIRC patients. Antioxidant related genes with significant differences in expression between KIRC and normal samples were then identified. Through univariate and multivariate Cox analysis, a prognostic gene model was established and all patients were divided into high- and low-risk subgroups. Single sample gene set enrichment analysis was adopted to analyze the immune infiltration, HLA expression, and immune checkpoint genes in different risk groups. Finally, the prognostic nomogram model was established and evaluated. Results: We identified six antioxidant genes significantly correlated with the outcome of KIRC patients as independent predictors, namely DPEP1 (HR = 0.97, P < 0.05), GSTM3 (HR = 0.97, P < 0.05), IYD (HR = 0.33, P < 0.05), KDM3B (HR = 0.96, P < 0.05), PRDX2 (HR = 0.99, P < 0.05), and PRXL2A (HR = 0.96, P < 0.05). The high- and low-risk subgroups of KIRC patients were grouped according to the six-gene signature. Patients with higher risk scores had poorer prognosis, more advanced grade and stage, and more abundance of M0 macrophages, regulatory T cells, and follicular helper T cells. There were statistically significant differences in HLA and checkpoint gene expression between the two risk subgroups. The performance of the nomogram was favorable (concordance index = 0.766) and reliably predicted the 3-year (AUC = 0.792) and 5-year (AUC = 0.766) survival of patients with KIRC. Conclusion: The novel six antioxidant related gene signature could effectively forecast the prognosis of patients with KIRC, supply insights into the interaction between cellular antioxidant mechanisms and cancer, and is an innovative tool for selecting potential patients and targets for immunotherapy.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a prevalent urogenital malignancy characterized by heterogeneous patterns. Stemness is a pivotal factor in tumor progression, recurrence, and metastasis. Nevertheless, the impact of stemness-related long non-coding RNAs (SRlncRNAs) on the prognosis of ccRCC remains elusive. In this study, we aimed to delve into the SRlncRNAs of ccRCC and develop a signature for risk stratification and prognosis prediction.MethodGene-expression and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We calculated RNA stemness scores (RNAss) for the samples to evaluate their stemness. SRlncRNAs and stemness-related mRNAs (SRmRNAs) in ccRCC were identified through weighted correlation network analysis (WGCNA), which employed sophisticated statistical methodologies to identify interconnected modules of related genes. Enrichment analysis was performed to explore the potential functions of SRmRNAs. Multiple machine learning algorithms were employed to construct a prognostic signature. Samples from TCGA-KIRC and GSE29609 cohorts were designated as the training and validation cohorts, respectively. Based on their risk scores, samples were stratified into low- and high-risk groups. Prognosis analysis, immune infiltration assessment, drug sensitivity prediction, mutation landscape, and gene set enrichment analysis (GSEA) were conducted to investigate the distinct characteristics of the low- and high-risk groups. Additionally, a web-based calculator was developed to facilitate clinical application. Expression and effects of SRlncRNAs in ccRCC were further corroborated through the utilization of single-cell RNA-seq (scRNA-seq), as well as in vitro and in vivo experiments.ResultsSRlncRNAs and SRmRNAs were identified based on RNAss and WGCNA. The least absolute shrinkage and selection operator (LASSO) in combination with multivariate Cox regression was selected as the optimal approach. Six SRlncRNAs were used to construct the prognostic signature. Samples in the low- and high-risk groups exhibited distinct characteristics in terms of prognosis, GSEA pathways, immune infiltration profiles, drug sensitivity, and mutation status. A nomogram and a web-based calculator were developed to facilitate the clinical application of the model. ScRNA-seq and RT-qPCR demonstrated the differential expression of SRlncRNAs between ccRCC tumors and normal tissues. In vitro and in vivo experiments demonstrated that downregulation of EMX2OS and LINC00944 affected the proliferation, migration, invasion, apoptosis, and metastasis of ccRCC cells.ConclusionWe uncovered the crucial associations between SRlncRNAs and the prognosis of ccRCC. By leveraging these findings, we developed a novel SRlncRNA-related signature and a user-friendly web calculator. This signature holds great potential in facilitating risk stratification and guiding tailored treatment strategies for ccRCC patients. Both in vitro and in vivo experiments confirmed the role of SRlncRNAs in the progression of ccRCC.

Project description:Background: Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cell carcinoma (RCC). Anoikis plays an essential function in tumourigenesis, whereas the role of anoikis in ccRCC remains unclear. Methods: Anoikis-related genes (ARGs) were collected from the MSigDB database. According to univariate Cox regression analysis, the least absolute shrinkage and selection operator (LASSO) algorithm was utilized to select the ARGs associated with the overall rate (OS). Multivariate Cox regression analysis was conducted to identify 5 prognostic ARGs, and a risk model was established. The Kaplan-Meier survival analysis was used to evaluate the OS rate of ccRCC patients. Gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSVA) were utilized to investigate the molecular mechanism of patients in the low- and high-risk group. ESTIMATE, CIBERSOT, and single sample gene set enrichment analysis (ssGSEA) algorithms were conducted to estimate the immune infiltration landscape. Consensus clustering analysis was performed to divide the patients into different subgroups. Results: A fresh risk model was constructed based on the 5 prognostic ARGs (CHEK2, PDK4, ZNF304, SNAI2, SRC). The Kaplan-Meier survival analysis indicated that the OS rate of patients with a low-risk score was significantly higher than those with a high-risk score. Consensus clustering analysis successfully clustered the patients into two subgroups, with a remarkable difference in immune infiltration landscape and prognosis. The ESTIMATE, CIBERSORT, and ssGSEA results illustrated a significant gap in immune infiltration landscape of patients in the low- and high-risk group. Enrichment analysis and GSVA revealed that immune-related signaling pathways might mediate the role of ARGs in ccRCC. The nomogram results illustrated that the ARGs prognostic signature was an independent prognostic predictor that distinguished it from other clinical characteristics. TIDE score showed a promising immunotherapy response of ccRCC patients in different risk subgroups and cluster subgroups. Conclusion: Our study revealed that ARGs play a carcinogenic role in ccRCC. Additionally, we firstly integrated multiple ARGs to establish a risk-predictive model. This study highlights that ARGs could be implemented as a stratification factor for individualized and precise treatment in ccRCC patients.

Project description:Given the importance of tripartite motif (TRIM) proteins in diverse cellular biological processes and that their dysregulation contributes to cancer progression, we constructed a robust TRIM family signature to stratify patients with kidney renal clear cell carcinoma (KIRC). Transcriptomic profiles and corresponding clinical information of KIRC patients were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. Prognosis-related TRIM family genes were screened and used to construct a novel TRIM family-based signature for the training cohort. The accuracy and generalizability of the prognostic signature were assessed in testing, entire, and external ICGC cohorts. We analyzed correlations among prognostic signatures, tumor immune microenvironment, and immune cell infiltration. The results of univariate Cox regression and Kaplan-Meier survival analyses revealed 27 TRIMs that were robustly associated with the prognosis of patients with KIRC. We applied Lasso regression and multivariate Cox regression analyses to develop a prognostic signature containing the TRIM1, 13, 35, 26, 55, 2, 47, and 27 genes to predict the survival of patients with KIRC. The accuracy and generalizability of this signature were confirmed in internal and external validation cohorts. We also constructed a predictive nomogram based on the signature and the clinicopathological characteristics of sex, age, and T and M status to aid clinical decision-making. We analyzed immune cell infiltration analysis and found that CD8 T cells, memory resting CD4 T cells, and M2 macrophages were the most enriched components in the KIRC tumor immune microenvironment. A higher level of immune infiltration by plasma cells, follicular helper T cells, and activated NK cells, and a lower level of immune infiltration by memory resting CD4 T cells, M1 and M2 macrophages, and resting dendritic cells were associated with higher risk scores. Overall, our eight-gene TRIM family signature has sufficient accuracy and generalizability for predicting the overall survival of patients with KIRC. Furthermore, this prognostic signature is associated with tumor immune status and distinct immune cell infiltrates in the tumor microenvironment.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is known as the most common and malignant histologic subtype of renal carcinoma. Sorting nexin 4 (SNX4) plays a regulatory role in recycling from endosomes to the plasma membrane and promotes autophagosome assembly and transport, which may exert the cancerous growth and progression. This study aimed to assess the biological role of SNX4 in ccRCC and their clinical association via public biological data platforms combined with experimental verification.MethodsIn our study, we analyzed the mRNA and protein expression of SNX4 in ccRCC under different clinicopathological characteristics through The Cancer Genome Atlas (TCGA), Human Protein Atlas (HPA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases. We used the Gene Expression Profiling Interactive Analysis (GEPIA) platform to conduct the survival analysis and figure out the immune cell infiltration level under different expression levels of SNX4 combined with Tumor Immune Estimation Resource (TIMER) database. Furthermore, we predicted competing endogenous RNA (ceRNA) regulatory network using TargetScan, miRDB, starBase and miRTarBase online databases. We totally collected six paired ccRCC tissues and adjacent tissues and applied quantitative real-time polymerase chain reaction (qRT-PCR) and western blot (WB) to detect the expression of SNX4 in the collected clinical specimens.ResultsThe mRNA and protein expression level of SNX4 was significantly lower in ccRCC than those in normal tissues. The results proposed that lower SNX4 was expressed in patients with higher histologic grade and in male patients. Kaplan-Meier analysis demonstrated that lower mRNA expression level of SNX4 was correlated with poorer prognosis. SNX4 had positive correlation with immune cell infiltrating levels and programmed cell death-ligand 1 (PD-L1) expression. Furthermore, we constructed the SNX4/miR-221-3p/miR-222-3p/DHRS4-AS1 axis, which may be the underlying ceRNA interaction network. Finally, we verified the reduced expression of SNX4 in ccRCC by qRT-PCR and WB.ConclusionThe expression of SNX4 in ccRCC was lower than adjacent tissues and its downregulated expression was associated with poor prognosis of ccRCC patients. SNX4 may exert critical roles in the tumorigenesis, development and migration of ccRCC via various mechanisms.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is the most common kidney cancer. The crosstalk between tumor tissue and adjacent adipose tissue has been appreciated recently. This study examines the predictive usefulness of brown adipocyte-related genes (BARGs) in ccRCC.MethodsThe transcriptome and clinical data of ccRCC patients were obtained from TCGA-KIRC and USA-ccRCC cohorts (848 tumor samples; 72 normal samples). Lasso-Cox methods were used to construct the risk prognostic signature model. We used Kaplan-Meier survival analysis to evaluate the prognostic significance of the risk model with ROC curves ascertaining prediction accuracy. The differences in immune cell infiltrates and signature risk scores between different risk categories were analyzed. Finally, biological experiments were performed to explore the functions of candidate genes.ResultsTCGA-KIRC patients were classified into two clusters that differed significantly regarding overall survival (OS) and tumor microenvironment. After screening BARGs candidates, a signature consisting of PPP1R1A, DPYSL3, and PTPRM was created to calculate risk score. Patients were assigned to the high or low-risk group, and the high-risk group had a significantly worse prognosis. Consistent trend was validated in external USA-ccRCC cohort. Meanwhile, the signature risk score affected immune cell infiltrates within the ccRCC microenvironment, positively correlated with the infiltration of CD4+ T cells, CD8+ T cells, CD56dim, CD56bright NK cells, MDSCs, and macrophage cells, while negatively correlated with neutrophil, iDCs, mast cells, and eosinophil. Finally, knockdown of PPP1R1A and DPYSL3 in renal cancer cells showed impairment in tumor proliferation ability of ccRCC in vitro and in vivo. Conversely, knockdown of PTPRM exhibited a promotive effect.ConclusionWe developed a predictive BARGs-related risk signature for early diagnosis and classifying ccRCC patients, which offers potential targets for individualized treatment of ccRCC.

Dataset Information

Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature.

Publications

Systematic Analysis of Immune Infiltration and Predicting Prognosis in Clear Cell Renal Cell Carcinoma Based on the Inflammation Signature.

Similar Datasets

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