A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Gui, Chengpeng; Wei, Jinhuan; Chen, Yuhang; Fu, Liangmin; Tang, Yuanyan; Cao, Jianping; Chen, Wei; Luo, Junhang

doi:10.1093/bib/bbab173

Cited by 41 publications

(45 citation statements)

References 66 publications

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“…found that the hypoxia-immune risk score was negatively associated with EREG-mRNAsi and ENHsi. Higher EREG-mRNAsi and ENHsi levels meant better prognosis ( 10 ). In this study, Cluster 2 was found to be relevant to lower TSIs ( Supplementary Figure S2C ).…”

Section: Resultsmentioning

confidence: 99%

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

Qiu

Shi

et al. 2021

Front. Immunol.

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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.

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Section: Resultsmentioning

confidence: 99%

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

Qiu

Shi

et al. 2021

Front. Immunol.

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“…Different metabolism signatures may cause prognosis heterogeneity in ccRCCs, suggesting the possibility to classify ccRCCs from metabolic perspective. To date, variable immune-related classification methods based on gene signatures or immune components in transcriptome studies showed prediction values in ccRCC immunotherapy ( 18 – 20 ). However, the interplay of immune activity with metabolism is crucial for the regulation of the TME network.…”

Section: Introductionmentioning

confidence: 99%

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma

et al. 2022

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Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

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“…Gui et al. ( 16 ) developed a hypoxia-immune–based multiomics signature since they noticed both the hypoxia and immune status of the tumor microenvironment in ccRCC. A predictive model consisting of 13 glycolysis-related genes was also constructed by Zhang et al.…”

Section: Discussionmentioning

confidence: 99%

Adipogenic Transdifferentiation and Regulatory Factors Promote the Progression and the Immunotherapy Response of Renal Cell Carcinoma: Insights From Integrative Analysis

Wang

Wei

et al. 2022

Front. Oncol.

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BackgroundAdipogenic transdifferentiation was an important carcinogenic factor in various tumors, while studies on its role in clear cell renal cell carcinoma (ccRCC) were still relatively few. This study aimed to investigate its prognostic value and mechanism of action in ccRCC.MethodsGene expression profiles and clinical data of ccRCC patients were obtained from The Cancer Genome Atlas database. Nonnegative matrix factorization was used for clustering. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to analyze the pathways and biological process activities. single-sample GSEA (ssGSEA) was utilized to quantify the relative abundance of each immune cell. Tumor Immune Estimation Resource (TIMER) was used to evaluate the proportion of various immune infiltrating cells across diverse cancer types. Real-Time PCR was performed to examine the gene expression. R software was utilized to analyze the expression and prognostic role of genes in ccRCC.ResultsA total of 49 adipose-related genes (ARGs) were screened for differential expression between normal and ccRCC tissues. Based on differentially expressed ARGs, patients with ccRCC were divided into two adipose subtypes with different clinical, molecular, and pathway characteristics. Patients in cluster A exhibited more advanced pathological stages, higher expressions of RARRES2 and immune checkpoint genes, higher immune infiltration scores, and less nutrient metabolism pathways. Adipose differentiation index (ADI) was constructed according to the above ARGs and survival data, and its robustness and accuracy was validated in different cohorts. In addition, it was found that the expression of ARGs was associated with immune cell infiltration and immune checkpoint in ccRCC, among which GBP2 was thought to be the most relevant gene to the tumor immune microenvironment and play a potential role in carcinogenesis and invasion of tumor cells.ConclusionOur analysis revealed the consistency of higher adipogenic transdifferentiation of tumor cells with worse clinical outcomes in ccRCC. The 16-mRNA signature could predict the prognosis of ccRCC patients with high accuracy. ARGs such as GBP2 might shed light on the development of novel biomarkers and immunotherapies of ccRCC.

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A new thinking: extended application of genomic selection to screen multiomics data for development of novel hypoxia-immune biomarkers and target therapy of clear cell renal cell carcinoma

Cited by 41 publications

References 66 publications

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

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

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma

Adipogenic Transdifferentiation and Regulatory Factors Promote the Progression and the Immunotherapy Response of Renal Cell Carcinoma: Insights From Integrative Analysis

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