Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma

Zhang, Cong; Xu, Tiantian; Ji, Kun; Cao, Shoujin; Ai, Jing; Pan, Junhan; Cao, Yunbo; Yang, Yuning; Jing, Li; Sun, Jun-Hui

doi:10.1007/s10495-023-01900-x

Cited by 10 publications

(2 citation statements)

References 46 publications

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“…To investigate the biological functions and pathways associated with DRLs, Gene Set Enrichment Analysis (GSEA) was utilized for assessing differences in enrichment ( Zhang C. et al, 2023 ). RNA methylation can impact RNA processing, translation, and degradation, thereby regulating cellular processes such as cell self-renewal, apoptosis, differentiation, tumorigenesis, and immune cell infiltration in the tumor microenvironment, thereby influencing the physiological and pathological processes of cancer cells ( Chen J et al, 2023 ).…”

Section: Methodsmentioning

confidence: 99%

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer

Wei,

Wang,

2024

Front. Genet.

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ObjectiveDisulfidptosis is a newly recognized form of regulated cell death that has been linked to cancer progression and prognosis. Despite this association, the prognostic significance, immunological characteristics and treatment response of disulfidptosis-related lncRNAs (DRLs) in ovarian cancer have not yet been elucidated.MethodsThe lncRNA data and clinical information for ovarian cancer and normal samples were obtained from the UCSC XENA. Differential expression analysis and Pearson analysis were utilized to identify core DRLs, followed by LASSO algorithm. Random Survival Forest was used to construct a prognostic model. The relationships between risk scores, RNA methylation, immune cell infiltration, mutation, responses to immunotherapy and drug sensitivity analysis were further examined. Additionally, qRT-PCR experiments were conducted to validate the expression of the core DRLs in human ovarian cancer cells and normal ovarian cells and the scRNA-seq data of the core DRLs were obtained from the GEO dataset, available in the TISCH database.ResultsA total of 8 core DRLs were obtained to construct a prognostic model for ovarian cancer, categorizing all patients into low-risk and high-risk groups using an optimal cutoff value. The AUC values for 1-year, 3-year and 5-year OS in the TCGA cohort were 0.785, 0.810 and 0.863 respectively, proving a strong predictive capability of the model. The model revealed the high-risk group patients exhibited lower overall survival rates, higher TIDE scores and lower TMB levels compared to the low-risk group. Variations in immune cell infiltration and responses to therapeutic drugs were observed between the high-risk and low-risk groups. Besides, our study verified the correlations between the DRLs and RNA methylation. Additionally, qRT-PCR experiments and single-cell RNA sequencing data analysis were conducted to confirm the significance of the core DRLs at both cellular and scRNA-seq levels.ConclusionWe constructed a reliable and novel prognostic model with a DRLs cluster for ovarian cancer, providing a foundation for further researches in the management of this disease.

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Section: Methodsmentioning

confidence: 99%

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer

Wei,

Wang,

2024

Front. Genet.

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“…These models provide valuable guidelines for cancer immunotherapy and targeted therapy. Currently, the development of cancer prognostic models that integrate the analysis of ferroptosis, cuproptosis, and disul dptosis is at the forefront of oncological research, aiming to predict drug sensitivity and validate crucial factors through in vitro experimentation (43,44). In this study, we utilized a machine learning algorithm, LASSO regression, to construct a risk-prognosis model based on CDRLRs.…”

Section: Disscusionmentioning

confidence: 99%

Machine Learning-Driven Prognostic Analysis of Cuproptosis and Disulfidptosis-related lncRNAs in Clear Cell Renal Cell Carcinoma: A Step Towards Precision Oncology

Chen,

Wu,

Che

et al. 2023

Preprint

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Background Clear cell renal cell carcinoma (ccRCC), the most prevalent type of kidney malignancy, is noted for its high fatality rate, underscoring the imperative for reliable diagnostic and prognostic indicators. The mechanisms of cell death, cuproptosis and disulfidptosis, recently identified, along with the variable expression of associated genes and long non-coding RNAs (lncRNAs), have been linked to the progression of cancer and resistance to treatment. The objective of this research is to delineate the functions of lncRNAs associated with cuproptosis and disulfidptosis (CDRLRs) in ccRCC, thereby enhancing the precision of prognostic evaluations and contributing to the development of targeted therapeutic approaches. Methods We applied the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a prognostic signature from a set of CDRLRs. The data from The Cancer Genome Atlas (TCGA) was segmented into high and low-risk groups based on median risk scores from the signature, to investigate their prognostic disparities. Results The derived signature, which includes four CDRLRs—ACVR2B-AS1, AC095055.1, AL161782.1, and MANEA-DT—was confirmed to be predictive for ccRCC patient outcomes, as evidenced by receiver operating characteristic (ROC) curves and Kaplan-Meier (K-M) survival analysis. The prognostic model enabled the graphical prediction of 1-, 3-, and 5-year survival rates for ccRCC patients, with calibration plots affirming the concordance between anticipated and observed survival rates. Additionally, the study assessed tumor mutation burden (TMB) and the immune microenvironment (TME) using oncoPredict and Immunophenoscore (IPS) algorithms, uncovering that patients in the high-risk group presented with increased TMB and distinctive TME profiles, which may influence their response to targeted and immune therapies. Notably, marked differences in the sensitivity to anticancer drugs were observed between the risk groups. Conclusion This investigation introduces a prognostic signature comprising cuproptosis and disulfidptosis-associated lncRNAs as a viable biomarker for ccRCC. Beyond enhancing prognostic accuracy, this signature holds the promise for steering personalized treatments, thereby advancing precision oncology for ccRCC. However, it is imperative to pursue further clinical validation to adopt these insights into clinical practice.

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Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments

Zhou,

Wang,

Cai

et al. 2024

Urolithiasis

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Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma

Cited by 10 publications

References 46 publications

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer

A disulfidptosis-related lncRNAs cluster to forecast the prognosis and immune landscapes of ovarian cancer

Machine Learning-Driven Prognostic Analysis of Cuproptosis and Disulfidptosis-related lncRNAs in Clear Cell Renal Cell Carcinoma: A Step Towards Precision Oncology

Comprehensive analysis and validation of TP73 as a biomarker for calcium oxalate nephrolithiasis using machine learning and in vivo and in vitro experiments

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