Dataset Information

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma.

ABSTRACT:

Aim

The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.

Method

Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Therapeutic potential was profiled using genome-drug sensitivity datasets.

Results

ADCP genes were selected from a recent CRISPR/Cas9 screen report. Following a four-module panel based on clinical traits, we generated a six-gene signature (ARPC3, PHF19, FKBP11, MS4A14, KDELR3, and CD1C), which showed a strong correlation with advanced grade and stage and worsened prognosis, with a nomogram showing predictive efficacies of 0.911, 0.845, and 0.867 (AUC) at 1, 3, and 5 years, respectively. Signatures were further dichotomized, and groups with a higher risk score showed a positive correlation with tumor mutation burden, higher expressions of inhibitory checkpoint molecules, and increased antitumor immune infiltrates and were enriched for antitumor immune pathways. The high risk-score group showed better response to ICI and could benefit from TKIs of axitinib, tivozanib, or sorafenib, preferentially in combination, whereas sunitinib and pazopanib would better fit the low risk-score group.

Conclusion

Here we showed a six-gene ADCP signature that correlated with prognosis and immune modulation in ccRCC. The signature-based risk stratification was associated with response to both ICI and tyrosine kinase inhibition in ccRCC.

SUBMITTER: Li K

PROVIDER: S-EPMC9148997 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma.

Li Kunping K Li Yuqing Y Lyu Yinfeng Y Tan Linyi L Zheng Xinyi X Jiang Haowen H Wen Hui H Feng Chenchen C

Frontiers in immunology 20220516

<h4>Aim</h4>The action of immune checkpoint inhibition (ICI) largely depends on antibody-dependent cellular phagocytosis (ADCP). We thus aim to develop ADCP-based ccRCC risk stratification as both prognostic and therapeutic markers of ICI.<h4>Method</h4>Genomic data from multiple public datasets (TCGA, etc.) were integrated. A cancer-intrinsic ADCP gene set for ccRCC tailored from a recent report was constructed based on the association with prognosis, immune infiltrates, and response to ICI. Th ...[more]

PMID: 35651604

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Project description:Background: Amino acid metabolism (AAM) deregulation, an emerging metabolic hallmark of malignancy, plays an essential role in tumour proliferation, invasion, and metastasis. However, the expression of AAM-related genes and their correlation with prognosis in clear cell renal cell carcinoma (ccRCC) remain elusive. This study aims to develop a novel consensus signature based on the AAM-related genes. Methods: The RNA-seq expression data and clinical information for ccRCC were downloaded from the TCGA (KIRC as training dataset) and ArrayExpress (E-MTAB-1980 as validation dataset) databases. The AAM-related differentially expressed genes were screened via the "limma" package in TCGA cohorts for further analysis. The machine learning algorithms (Lasso and stepwise Cox (direction = both)) were then utilised to establish a novel consensus signature in TCGA cohorts, which was validated by the E-MTAB-1980 cohorts. The optimal cutoff value determined by the "survminer" package was used to categorise patients into two risk categories. The Kaplan-Meier curve, the receiver operating characteristic (ROC) curve, and multivariate Cox regression were utilised to evaluate the prognostic value. The nomogram based on the gene signature was constructed, and its performance was analysed using ROC and calibration curves. Gene Set Enrichment Analysis (GSEA) and immune cell infiltration analysis were conducted on its potential mechanisms. The relationship between the gene signature and key immune checkpoint, N6-methyladenosine (m6A)-related genes, and sensitivity to chemotherapy was assessed. Results: A novel consensus AMM-related gene signature consisting of IYD, NNMT, ACADSB, GLDC, and PSAT1 is developed to predict prognosis in TCGA cohorts. Kaplan-Meier survival shows that overall survival in the high-risk group was more dismal than in the low-risk group in the TCGA cohort, validated by the E-MTAB-1980 cohort. Multivariate regression analysis also demonstrates that the gene signature is an independent predictor of ccRCC. Immune infiltration analysis highlighted that the high-risk group indicates an immunosuppressive microenvironment. It is also closely related to the level of key immune checkpoints, m6A modification, and sensitivity to chemotherapy drugs. Conclusion: In this study, a novel consensus AAM-related gene signature is developed and validated as an independent predictor to robustly predict the overall survival from ccRCC, which would further improve the clinical outcomes.

Project description:Background Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancers. As cuproptosis, a new cell death mechanism proposed recently, differs from all other known mechanisms regulating cell death, we aimed to create prognostic markers using cuproptosis-related long non-coding ribonucleic acids (RNAs; lncRNAs) and elucidate the molecular mechanism. Methods Data from transcriptome RNA sequencing of ccRCC samples and the relevant clinical data were downloaded from The Cancer Genome Atlas, and Pearson's correlation analysis was implemented to obtain the cuproptosis-related lncRNAs. Then, univariate Cox, multivariate Cox, and Least Absolute Shrinkage and Selection Operator Cox analyses were performed to construct the risk signatures. The cuproptosis-related lncRNAs predictive signature was evaluated with receiver operating characteristic curves and subgroup analysis. Finally, Gene Set Enrichment Analysis (GSEA), single-sample GSEA (ssGSEA), tumor immune microenvironment (TIME), and immune checkpoints were performed to explore the relationship between immunity and patient prognosis. Results Five cuproptosis-related lncRNAs, including FOXD2-AS1, LINC00460, AC091212.1, AC007365.1, and AC026401.3, were used to construct the signature. In the training and test sets, low-risk groups (as identified by a risk score lower than the median) demonstrated a better prognosis with an area under the curve for 1-, 3-, and 5-year survival being 0.793, 0.716, and 0.719, respectively. GSEA analysis suggested significant enrichment of the tricarboxylic acid cycle and metabolism-related pathways in the low-risk group. Besides, both ssGSEA and TIME suggested that the high-risk group exhibited more active immune infiltration. Conclusion We proposed a cuproptosis-related lncRNAs signature, which had the potential for prognoses and prediction. Our findings might contribute to elucidating potential genomic biomarkers and targets for future therapies in the cuproptosis-related signaling pathways.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is a malignant disease containing tumor-infiltrating lymphocytes. Reactive oxygen species (ROS) are present in the tumor microenvironment and are strongly associated with cancer development. Nevertheless, the role of ROS-related genes in ccRCC remains unclear.MethodsWe describe the expression patterns of ROS-related genes in ccRCC from The Cancer Genome Atlas and their alterations in genetics and transcription. An ROS-related gene signature was constructed and verified in three datasets and immunohistochemical staining (IHC) analysis. The immune characteristics of the two risk groups divided by the signature were clarified. The sensitivity to immunotherapy and targeted therapy was investigated.ResultsOur signature was constructed on the basis of glutamate-cysteine ligase modifier subunit (GCLM), interaction protein for cytohesin exchange factors 1 (ICEF1), methionine sulfoxide reductase A (MsrA), and strawberry notch homolog 2 (SBNO2) genes. More importantly, protein expression levels of GCLM, MsrA, and SBNO2 were detected by IHC in our own ccRCC samples. The high-risk group of patients with ccRCC suffered lower overall survival rates. As an independent predictor of prognosis, our signature exhibited a strong association with clinicopathological features. An accurate nomogram for improving the clinical applicability of our signature was constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that the signature was closely related to immune response, immune activation, and immune pathways. The comprehensive results revealed that the high-risk group was associated with high infiltration of regulatory T cells and CD8+ T cells and more benefited from targeted therapy. In addition, immunotherapy had better therapeutic effects in the high-risk group.ConclusionOur signature paved the way for assessing prognosis and developing more effective strategies of immunotherapy and targeted therapy in ccRCC.

Project description:Methylation is one of the most extensive modifications of biological macromolecules and affects cell-fate determination, development, aging, and cancer. Several methylation modifications, including 5-methylcytosine and N6-methyladenosine, play an essential role in many cancers. However, little is known about the relationship between methylation and the prognosis of clear cell renal cell carcinoma (ccRCC). Here, we established a methylation-regulating genes prognostic signature (MRGPS) to predict the prognoses of ccRCC patients. We obtained ccRCC samples from The Cancer Genome Atlas and identified methylation-regulatingd genes (MRGs) from the Gene Set Enrichment Analysis database. We also determined differentially expressed genes (DEGs) and performed cluster analysis to identify candidate genes. Subsequently, we established and validated an MRGPS to predict the overall survival of ccRCC patients. This was also verified in 15 ccRCC samples collected from the Fujian Provincial Hospital via quantitative real-time transcription (qRT-PCR). While 95 MRGs were differentially expressed (DEGs1) between tumor and normal tissues, 17 MRGs were differentially expressed (DEGs2) between cluster 1 and 2. Notably, 13 genes common among DEGs1 and DEGs2 were identified as hub genes. In fact, we established three genes (NOP2, NSUN6, and TET2) to be an MRGPS based on their multivariate Cox regression analysis coefficients (p < 0.05). A receiver operating characteristic curve analysis confirmed this MRGPS to have a good prognostic performance. Moreover, the MRGPS was associated with characteristics of the tumor immune microenvironment and responses to inhibitor checkpoint inhibitors. Data from "IMvigor 210" demonstrated that patients with a low MRGPS would benefit more from atelozumab (p < 0.05). Furthermore, a multivariate analysis revealed that MRGPS was an independent risk factor associated with ccRCC prognosis (p < 0.05). Notably, a nomogram constructed by combining with clinical characteristics (age, grade, stage, and MRGPS risk score) to predict the overall survival of a ccRCC patient had a favorable predictive value. Eventually, our qRT-PCR results showed that tumor tissues had higher NOP2 and NSUN6 expression levels and lower TET2 expression than normal tissues of ccRCC samples. While the proposed MRGPS comprising NOP2, NSUN6, and TET2 can be an alternative prognostic biomarker for ccRCC patients, it is a promising index for personalized ICI treatments against ccRCC.

Project description:BackgroundImmune checkpoint inhibitor (ICI) treatment has enhanced survival outcomes in clear cell renal cell carcinoma (ccRCC) patients. Nevertheless, the effectiveness of immunotherapy in ccRCC patients is restricted and we intended to develop and characterize an immune response prediction signature (IRPS) to forecast the efficacy of immunotherapy.MethodsRNA-seq expression profile and clinicopathologic characteristics of 539 kidney cancer and 72 patients with normal specimens, were downloaded from the Cancer Genome Atlas (TCGA) database, while the Gene Expression Omnibus (GEO) database was used as the validation set, which included 24 ccRCC samples. Utilization of the TCGA data and immune genes databases (ImmPort and the InnateDB), we explored through Weighted Gene Co-expression Network Analysis (WGCNA), along with Least Absolute Shrinkage and Selection Operator method (LASSO), and constructed an IRPS for kidney cancer patients. GSEA and CIBERSORT were performed to declare the molecular and immunologic mechanism underlying the predictive value of IRPS. The Human Protein Atlas (HPA) was deployed to verify the protein expressions of IRPS genes. Tumor immune dysfunction and exclusion (TIDE) score and immunophenoscore (IPS) were computed to determine the risk of immune escape and value the discrimination of IRPS. A ccRCC cohort with anti-PD-1 therapy was obtained as an external validation data set to verify the predictive value of IRPS.ResultsWe constructed a 10 gene signature related to the prognosis and immune response of ccRCC patients. Considering the IRPS risk score, patients were split into high and low risk groups. Patients with high risk in the TCGA cohort tended towards advanced tumor stage and grade with poor prognosis (p < 0.001), which was validated in GEO database (p = 0.004). High-risk group tumors were related with lower PD-L1 expression, higher TMB, higher MSIsensor score, lower IPS, higher TIDE score, and enriched Treg cells, which might be the potential mechanism of immune dysfunction and exclusion. Patients in the IRPS low risk group had better PFS (HR:0.73; 95% CI: 0.54-1.0; P = 0.047).ConclusionA novel biomarker of IRPS was constructed to predict the benefit of immunotherapy, which might lead to more individualized prognoses and tailored therapy for kidney cancer patients.

Project description:BackgroundTransforming growth factor (TGF)-β signaling is strongly related to the development and progression of tumor. We aimed to construct a prognostic gene signature based on TGF-β signaling-related genes for predicting clinical prognosis and immunotherapy responses of patients with clear cell renal cell carcinoma (ccRCC).MethodsThe gene expression profiles and corresponding clinical information of ccRCC were collected from the TCGA and the ArrayExpress (E-MTAB-1980) databases. LASSO, univariate and multivariate Cox regression analyses were conducted to construct a prognostic signature in the TCGA cohort. The E-MTAB-1980 cohort were used for validation. Kaplan-Meier (K-M) survival and time-dependent receiver operating characteristic (ROC) were conducted to assess effectiveness and reliability of the signature. The differences in gene enrichments, immune cell infiltration, and expression of immune checkpoints in ccRCC patients showing different risks were investigated.ResultsWe constructed a seven gene (PML, CDKN2B, COL1A2, CHRDL1, HPGD, CGN and TGFBR3) signature, which divided the ccRCC patients into high risk group and low risk group. The K-M analysis indicated that patients in the high risk group had a significantly shorter overall survival (OS) time than that in the low risk group in the TCGA (p < 0.001) and E-MTAB-1980 (p = 0.012). The AUC of the signature reached 0.77 at 1 year, 0.7 at 3 years, and 0.71 at 5 years in the TCGA, respectively, and reached 0.69 at 1 year, 0.72 at 3 years, and 0.75 at 5 years in the E-MTAB-1980, respectively. Further analyses confirmed the risk score as an independent prognostic factor for ccRCC (p < 0.001). The results of ssGSEA that immune cell infiltration degree and the scores of immune-related functions were significantly increased in the high risk group. The CIBERSORT analysis indicated that the abundance of immune cell were significantly different between two risk groups. Furthermore, The risk score was positively related to the expression of PD-1, CTLA4 and LAG3.These results indicated that patients in the high risk group benefit more from immunotherapy.ConclusionWe constructed a novel TGF-β signaling-related genes signature that could serve as an promising independent factor for predicting clinical prognosis and immunotherapy responses in ccRCC patients.

Dataset Information

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma.

Aim

Method

Results

Conclusion

Publications

Development of a Phagocytosis-Dependent Gene Signature to Predict Prognosis and Response to Checkpoint Inhibition in Clear-Cell Renal Cell Carcinoma.

Similar Datasets

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