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Unveiling the hidden role of disulfidptosis in kidney renal clear cell carcinoma: a prognostic signature for personalized treatment

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Abstract

The role of disulfidptosis in kidney renal clear cell carcinoma (KIRC) remains unknown. This study investigated disulfidptosis-related biomarkers for KIRC prognosis prediction and individualized treatment. KIRC patients were clustered by disulfidptosis profiles. Differential expression analysis, survival models, and machine learning were used to construct the disulfidptosis-related prognostic signature (DRPS). Characterizations of the tumor immune microenvironment, genetic drivers, drug sensitivity, and immunotherapy response were explored according to the DRPS risk stratification. Markers included in the signature were validated using single-cell, spatial transcriptomics, quantitative RT-qPCR, and immunohistochemistry. In the discovery cohort, we unveiled two clusters of KIRC patients that differed significantly in disulfidptosis regulator expressions and overall survival (OS). After multiple feature selection steps, a DRPS prognostic model with four features (CHAC1, COL7A1, FOXM1, SHOX2) was constructed and validated. Combined with clinical factors, the model demonstrated robust performance in the discovery and external validation cohorts (5-year AUC = 0.793 and 0.846, respectively). KIRC patients with high-risk scores are characterized by inferior OS, less tumor purity, and increased infiltrations of fibroblasts, M1 macrophages, and B cells. High-risk patients also have higher frequencies of BAP1 and AHNAK2 mutation. Besides, the correlation between the DRPS score and the chemotherapy-response signature indicated the potential effect of Gefitinib for high-risk patients. Among the signature genes, FOXM1 is highly expressed in cycling tumor cells and exhibits spatial aggregation, while others are expressed sparsely within tumor samples. The DRPS model enables improved clinical management and personalized KIRC therapy. The identified biomarkers and immune characteristics offer new mechanistic insight into disulfidptosis in KIRC.

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Data availability

This study did not generate any original sequencing data. The utilized data sources are cited where appropriate throughout the manuscript. All other relevant data is contained within the article and Supplementary Files or available from the authors upon reasonable request. The datasets analyzed in this study can be found in the following public repositories: The TCGA-KIRC RNA, SNP, and clinical data are available on the Genomic Data Commons portal (https://portal.gdc.cancer.gov). The expression matrix and metadata for the E-MTAB-1980 validation cohort are available in the Supplementary Files of the original publication. The single-cell transcriptomic data was obtained from the Single Cell Portal (https://singlecell.broadinstitute.org) under the study "Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma". The spatial transcriptomic data is available on the Gene Expression Omnibus database under accession number GSE175540.

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Acknowledgements

We are grateful to the researchers and study participants for their contributions to this study.

Funding

No funding was received for conducting this study.

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Author notes

  1. Yang Ye and Song Zeng have contributed equally to this work.

Authors and Affiliations

  1. Department of Urology, Beijing Chaoyang Hospital, Capital Medical University, NO.8 GongTi South Road, Beijing, 100020, China

    Yang Ye, Song Zeng & Xiaopeng Hu

  2. Institute of Urology, Capital Medical University, Beijing, China

    Yang Ye, Song Zeng & Xiaopeng Hu

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Contributions

YY: Data curation, Formal analysis, Writing—original draft, Visualization. SZ: Investigation, Methodology, Writing—original draft, Visualization. XH: Conceptualization, Project administration, Writing—review & editing.

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Correspondence to Xiaopeng Hu.

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Ethical approval and consent to participate

This study complied with the ethical guidelines outlined in The Code of Ethics of the World Medical Association (Declaration of Helsinki). Informed consent was obtained from all patients participating in this study, and the ethics committees of Beijing Chao-Yang Hospital approved this study.

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Supplementary file1 (DOCX 14 KB)

Supplementary file2 (XLSX 176 KB)

10495_2023_1933_MOESM3_ESM.jpg

Supplementary file3 (JPG 242 KB)—Supplementary Fig. 1 External validation of the DRPS model in the E-MTAB-1980 cohort. A Nomogram to predict the 1-, 3-, and 5-year survival probability. B Calibration curves of the established nomogram at the given follow-up time. C The decision curve analyses for the established nomogram. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns not statistically significant. ROC Receiver Operating Characteristic. OS Overall Survival. DCA Decision Curve Analysis

10495_2023_1933_MOESM4_ESM.jpg

Supplementary file4 (JPG 232 KB)—Supplementary Fig. 2 Quantifying metabolic reprogramming at pathway level in KIRC bulk samples. A Measuring and comparing metabolic pathway activities in disulfidptosis high-risk and low-risk samples. In the volcano plot, red dots denote pathways that are up-regulated in disulfidptosis high-risk samples. B Bar plot showing the metabolic pathways that are differentially expressed. Terms colored in blue are up-regulated in disulfidptosis high-risk samples, while those in green are down-regulated

10495_2023_1933_MOESM5_ESM.jpg

Supplementary file5 (JPG 189 KB)—Supplementary Fig. 3 Marker validation using in-house samples RT-PCT and public datasets. A Comparison of marker RNA expression between KIRC tumor samples and adjacent normal samples using RT-PCR. B Differential expression of the enrolled markers in TCGA and GTEx samples. *p < 0.05; **p < 0.01; ***p < 0.001; ****p < 0.0001; ns not statistically significant

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Ye, Y., Zeng, S. & Hu, X. Unveiling the hidden role of disulfidptosis in kidney renal clear cell carcinoma: a prognostic signature for personalized treatment. Apoptosis 29, 693–708 (2024). https://doi.org/10.1007/s10495-023-01933-2

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