Dataset Information

A novel classification based on non-apoptosis cell death predicts clinical outcomes and immunotherapy response of clear renal cell carcinoma.

ABSTRACT:

Background

Non-apoptosis cell death could be a secondary consequence of the immune response, which profoundly influences tumor microenvironment (TME), escaping from chemotherapy/immunotherapy-induced apoptosis resistance effects. Whereas, systemic analysis of non-apoptosis regulated cell death associated with TME and clinical outcomes remains unveiled.

Methods

Our kidney clear carcinoma (KIRC) samples from The Cancer Genome Atlas (TCGA) were stratified into three clusters based on the activity of autophagic cell death, ferroptosis, pyroptosis and necroptosis. Clinical prognosis, TME landscape, biological functions and somatic mutation frequency were compared among the clusters. Additionally, to identify a gene signature highly correlated with clinical prognosis, a risk score model was constructed, and the clinical prognosis, immune infiltration, somatic mutation and biological pathways of risk score subgroups were investigated.

Results

Our non-apoptosis cell death clusters are robustly predictive of immunotherapy responses. Patients in Cluster B are the most sensitive to immune checkpoint blockades-depended immunotherapy. Our risk score model was also verified as a promising biomarker for clinical prognosis and immunotherapy efficiency. Where, the High-risk score group was more sensitive to immunotherapy.

Conclusions

The novel non-apoptosis cell death-based classification and risk score model could predict the outcome of immunotherapy, and highly associate with immune infiltration. These findings may provide a novel strategy to aid in identificatin of biomarkers and selecting personalized therapeutic strategies.

SUBMITTER: Chen L

PROVIDER: S-EPMC9730061 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

A novel classification based on non-apoptosis cell death predicts clinical outcomes and immunotherapy response of clear renal cell carcinoma.

Chen Liuxun L Liu Xvdong X Zeng Shenjie S Zhu Huimin H Wang Jiawu J Jiang Qing Q

American journal of translational research 20221115 11

<h4>Background</h4>Non-apoptosis cell death could be a secondary consequence of the immune response, which profoundly influences tumor microenvironment (TME), escaping from chemotherapy/immunotherapy-induced apoptosis resistance effects. Whereas, systemic analysis of non-apoptosis regulated cell death associated with TME and clinical outcomes remains unveiled.<h4>Methods</h4>Our kidney clear carcinoma (KIRC) samples from The Cancer Genome Atlas (TCGA) were stratified into three clusters based on ...[more]

PMID: 36505323

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Project description:Immunogenic cell death (ICD) is the trigger of adaptive immune responses. However, the role of ICD-related genes in clear cell renal carcinoma (ccRCC) remains unclear. We aimed to identify biomarkers associated with ICD and develop an ICD-related predictive model that predicts the immune microenvironment, prognosis, and response to immunotherapy in ccRCC. Our study included 739 patients (603 in the training set and 136 in the validation set) with clinicopathologic information and transcriptome sequencing data. Consensus clustering, principal component analysis (PCA), weighted gene co-expression network analysis (WGCNA), univariate COX analysis, multivariate COX analysis, and the Lasso-Cox algorithm were applied to shrink predictors and construct a predictive signature of overall survival (OS). We used CIBERSORT, ESTIMATE, and TIMER in the R package IOBR to evaluate the tumor microenvironment and immune infiltration pattern of each sample. Finally, the single cell sequencing results of immune cells in ccRCC were used to verify the results of immune infiltration analysis, and the performance of the prognostic model was evaluated by calibration curves and c-index. This study revealed that inability of the initial immune response and primary immunodeficiency were significantly enriched in the ICD subgroup with poor prognosis. We found that the ten candidate ICD genes (CALR, ENTPD1, FOXP3, HSP90AA1, IFNB1, IFNG, IL6, LY96, PIK3CA, and TLR4) could affect the prognosis of ccRCC (p < 0.05). The prediction model (PRE) we constructed can not only predict the long-term survival probability but also evaluate the landscape of immune infiltration in ccRCC. Our study demonstrated that low infiltration of dendritic cells in ccRCC implies a poor prognosis, whereas the degree of CTL infiltration is less important. An individualized prediction model was created to predict the 1-, 2-, 3-, and 5-year survival and responsiveness of ccRCC patients to immunotherapy, which may serve as a potent tool for clinicians to make better treatment decisions and thus improve the overall survival (OS) of ccRCC patients in the future.

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Project description:BackgroundClear cell carcinoma of the kidney is a common urological malignancy characterized by poor patient prognosis and treatment outcomes. Modulation of vasculogenic mimicry in tumor cells alters the tumor microenvironment and the influx of tumor-infiltrating lymphocytes, and the combination of its inducers and immune checkpoint inhibitors plays a synergistic role in enhancing antitumor effects.MethodsWe downloaded the data from renal clear cell carcinoma samples and vasculogenic mimicry-related genes to establish a new vasculogenic mimicry-related index (VMRI) using a machine learning approach. Based on VMRI, patients with renal clear cell carcinoma were divided into high VMRI and low VMRI groups, and patients' prognosis, clinical features, tumor immune microenvironment, chemotherapeutic response, and immunotherapeutic response were systematically analyzed. Finally, the function of CDH5 was explored in renal clear cell carcinoma cells.ResultsVMRI can be used for prognostic and immunotherapy efficacy prediction in a variety of cancers, which consists of four vasculogenic mimicry-related genes (CDH5, MMP9, MAPK1, and MMP13), is a reliable predictor of survival and grade in patients with clear cell carcinoma of the kidney and has been validated in multiple external datasets. We found that the high VMRI group presented higher levels of immune cell infiltration, which was validated by pathological sections. We performed molecular docking prediction of vasculogenic mimicry core target proteins and identified natural small molecule drugs with the highest affinity for the target protein. Knockdown of CDH5 inhibited the proliferation and migration of renal clear cell carcinoma.ConclusionsThe VMRI identified in this study allows for accurate prognosis assessment of patients with renal clear cell carcinoma and identification of patient populations that will benefit from immunotherapy, providing valuable insights for future precision treatment of patients with renal clear cell carcinoma.

Dataset Information

A novel classification based on non-apoptosis cell death predicts clinical outcomes and immunotherapy response of clear renal cell carcinoma.

Background

Methods

Results

Conclusions

Publications

A novel classification based on non-apoptosis cell death predicts clinical outcomes and immunotherapy response of clear renal cell carcinoma.

Similar Datasets

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