An immunotherapy response prediction model derived from proliferative CD4+ T cells and antigen-presenting monocytes in ccRCC

Zheng, Kun; Gao, Lianchong; Hao, Jie; Zou, Xin; Hu, Xiaoyong

doi:10.3389/fimmu.2022.972227

Cited by 12 publications

(13 citation statements)

References 90 publications

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“…Immune checkpoint blockade (ICB) therapies based on the immune microenvironment have been developed to improve the treatment of advanced ccRCC significantly. However, most ccRCC patients do not respond to ICB therapies (Zheng et al, 2022). This study was conducted by predicting the correlation between immune cells and hub genes, uncovering potential links between m6A methylation modulation, ferroptosis, and the varying immunotherapy responses in ccRCC patients.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of m6A‐associated ferroptosis genes in renal clear cell carcinoma

Pang,

Zhao,

Dongye

et al. 2024

Cell Biology International

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Urinary cancer is synonymous with clear cell renal cell carcinoma (ccRCC). Unfortunately, existing treatments for this illness are ineffective and unpromising. Finding novel ccRCC biomarkers is crucial to creating successful treatments.The Cancer Genome Atlas provided clear cell renal cell carcinoma transcriptome data. Functional enrichment analysis was performed on ccRCC and control samples' differentially expressed N6‐methyladenosine RNA methylation and ferroptosis‐related genes (DEMFRGs). Machine learning was used to find and model ccRCC patients' predicted genes. A nomogram was created for clear cell renal cell carcinoma patients. Prognostic genes were enriched. We examined patients' immune profiles by risk score. Our prognostic genes predicted ccRCC treatment drugs.We found 37 DEMFRGs by comparing 1913 differentially expressed ccRCC genes to 202 m6A RNA methylation FRGs. Functional enrichment analysis showed that hypoxia‐induced cell death and metabolism pathways were the most differentially expressed methylation functional regulating genes. Five prognostic genes were found by machine learning: TRIB3, CHAC1, NNMT, EGFR, and SLC1A4. An advanced renal cell carcinoma nomogram with age and risk score accurately predicted the outcome. These five prognostic genes were linked to various cancers. Immunological cell number and checkpoint expression differed between high‐ and low‐risk groups. The risk model successfully predicted immunotherapy outcome, showing high‐risk individuals had poor results. NIACIN, TAE‐684, ROCILETINIB, and others treat ccRCC. We found ccRCC prognostic genes that work. This discovery may lead to new ccRCC treatments.

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Section: Discussionmentioning

confidence: 99%

Identification and validation of m6A‐associated ferroptosis genes in renal clear cell carcinoma

Pang,

Zhao,

Dongye

et al. 2024

Cell Biology International

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“…Although numerous researchers have developed many biomarkers to predict the survival time of patients with ccRCC, they have not been able to predict the survival time of patients receiving immunotherapy because the patients included did not receive anti-PD-1 treatment [ 55 , 56 , 57 ]. There are also quite a few biomarkers that can predict the responsiveness of ccRCC to anti-PD-1, for example, a novel signature composed of 47 genes can predict the response to anti-PD-1 therapy, and the AUC value is as high as 0.93 [ 58 ]. Long et al developed a mutation-based gene set to predict immunotherapy results, which provides a good direction for accurate immunotherapy for ccRCC [ 59 ].…”

Section: Discussionmentioning

confidence: 99%

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

Lin

Cai

et al. 2023

IJMS

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Immunotherapy has greatly improved the survival time and quality of life of patients with renal cell carcinoma, but the benefits are limited to a small portion of patients. There are too few new biomarkers that can be used to identify molecular subtypes of renal clear cell carcinoma and predict survival time with anti-PD-1 treatment. Single-cell RNA data of clear cell renal cell carcinoma (ccRCC) treated with anti-PD-1 were obtained from public databases, then 27,707 high-quality CD4 + T and CD8 + T cells were obtained for subsequent analysis. Firstly, genes set variation analysis and CellChat algorithm were used to explore potential molecular pathway differences and intercellular communication between the responder and non-responder groups. Additionally, differentially expressed genes (DEGs) between the responder and non-responder groups were obtained using the “edgeR” package, and ccRCC samples from TCGA-KIRC (n = 533) and ICGA-KIRC (n = 91) were analyzed by the unsupervised clustering algorithm to recognize molecular subtypes with different immune characteristics. Finally, using univariate Cox analysis, least absolute shrinkage and selection operator (Lasso) regression, and multivariate Cox regression, the prognosis model of immunotherapy was established and verified to predict the progression-free survival of ccRCC patients treated with anti-PD-1. At the single cell level, there are different signal pathways and cell communication between the immunotherapy responder and non-responder groups. In addition, our research also confirms that the expression level of PDCD1/PD-1 is not an effective marker for predicting the response to immune checkpoint inhibitors (ICIs). The new prognostic immune signature (PIS) enabled the classification of ccRCC patients with anti-PD-1 therapy into high- and low-risk groups, and the progression-free survival times (PFS) and immunotherapy responses were significantly different between these two groups. In the training group, the area under the ROC curve (AUC) for predicting 1-, 2- and 3-year progression-free survival was 0.940 (95% CI: 0.894–0.985), 0.981 (95% CI: 0.960–1.000), and 0.969 (95% CI: 0.937–1.000), respectively. Validation sets confirm the robustness of the signature. This study revealed the heterogeneity between the anti-PD-1 responder and non-responder groups from different angles and established a robust PIS to predict the progression-free survival of ccRCC patients receiving immune checkpoint inhibitors.

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“…Also, new RNA-based biosignatures, generated using AI transcriptomics and DNA methylation profiles, have been found to have potential in predicting the ICI response across cancers [ 86 , 87 , 88 , 89 , 90 , 91 ].…”

Section: Ai: the New Frontiermentioning

confidence: 99%

Immunotherapy and Cancer: The Multi-Omics Perspective

Donisi,

Pretta,

Pusceddu

et al. 2024

IJMS

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Immunotherapies have revolutionized cancer treatment approaches. Because not all patients respond positively to immune therapeutic agents, it represents a challenge for scientists who strive to understand the mechanisms behind such resistance. In-depth exploration of tumor biology, using novel technologies such as omics science, can help decode the role of the tumor immune microenvironment (TIME) in producing a response to the immune blockade strategies. It can also help to identify biomarkers for patient stratification and personalized treatment. This review aims to explore these new models and highlight their possible pivotal role in changing clinical practice.

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An immunotherapy response prediction model derived from proliferative CD4+ T cells and antigen-presenting monocytes in ccRCC

Cited by 12 publications

References 90 publications

Identification and validation of m6A‐associated ferroptosis genes in renal clear cell carcinoma

Identification and validation of m6A‐associated ferroptosis genes in renal clear cell carcinoma

A New Signature That Predicts Progression-Free Survival of Clear Cell Renal Cell Carcinoma with Anti-PD-1 Therapy

Immunotherapy and Cancer: The Multi-Omics Perspective

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