Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

Jiang, Aimin; Pang, Qingyang; Gan, Xinxin; Wang, Anbang; Wu, Zhenjie; Liu, Bing; Luo, Peng; Qu, Le; Wang, Linhui

doi:10.1002/cai2.25

Cited by 3 publications

(2 citation statements)

References 79 publications

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“…Differences in estimates, immune scores, and immune components were also qualified and compared between the ccRCC subtypes. A detailed description of the parameters employed are described in previous studies ( 15 , 16 ).…”

Section: Methodsmentioning

confidence: 99%

“…Moreover, several studies have suggested that renal carcinoma occurs after an AKI episode or after years of chronic kidney disease, as kidney injury is an initial factor in renal cancer (8,13). Indeed, ccRCCs account for approximately 75% of renal carcinoma cases (14). However, there is no satisfactory AKI-based prognostic model for accurate risk stratification in these patients.…”

Section: Introductionmentioning

confidence: 99%

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Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Wang

Peng

et al. 2023

Front. Med.

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BackgroundAcute kidney injury can be mitigated if detected early. There are limited biomarkers for predicting acute kidney injury (AKI). In this study, we used public databases with machine learning algorithms to identify novel biomarkers to predict AKI. In addition, the interaction between AKI and clear cell renal cell carcinoma (ccRCC) remain elusive.MethodsFour public AKI datasets (GSE126805, GSE139061, GSE30718, and GSE90861) treated as discovery datasets and one (GSE43974) treated as a validation dataset were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AKI and normal kidney tissues were identified using the R package limma. Four machine learning algorithms were used to identify the novel AKI biomarkers. The correlations between the seven biomarkers and immune cells or their components were calculated using the R package ggcor. Furthermore, two distinct ccRCC subtypes with different prognoses and immune characteristics were identified and verified using seven novel biomarkers.ResultsSeven robust AKI signatures were identified using the four machine learning methods. The immune infiltration analysis revealed that the numbers of activated CD4 T cells, CD56dim natural killer cells, eosinophils, mast cells, memory B cells, natural killer T cells, neutrophils, T follicular helper cells, and type 1 T helper cells were significantly higher in the AKI cluster. The nomogram for prediction of AKI risk demonstrated satisfactory discrimination with an Area Under the Curve (AUC) of 0.919 in the training set and 0.945 in the testing set. In addition, the calibration plot demonstrated few errors between the predicted and actual values. In a separate analysis, the immune components and cellular differences between the two ccRCC subtypes based on their AKI signatures were compared. Patients in the CS1 had better overall survival, progression-free survival, drug sensitivity, and survival probability.ConclusionOur study identified seven distinct AKI-related biomarkers based on four machine learning methods and proposed a nomogram for stratified AKI risk prediction. We also confirmed that AKI signatures were valuable for predicting ccRCC prognosis. The current work not only sheds light on the early prediction of AKI, but also provides new insights into the correlation between AKI and ccRCC.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Wang

Peng

et al. 2023

Front. Med.

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Transcriptome analysis revealed a novel nine-gene prognostic risk score of clear cell renal cell carcinoma

Al Sharie,

Al Masoud,

Jadallah

et al. 2024

Medicine

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Clear cell renal cell carcinoma (ccRCC) continues to pose a significant global health concern, with rising incidence and high mortality rate. Accordingly, identifying molecular alternations associated with ccRCC is crucial to boost our understanding of its onset, persistence, and progression as well as developing prognostic biomarkers and novel therapies. Bulk RNA sequencing data and its associated clinicopathological variables of ccRCC were obtained from The Cancer Genome Atlas Program. Atypical differential gene expression analysis of advanced disease states using the extreme categories of staging and grading components was performed. Upregulated differentially expressed genes shared across the aforementioned components were selected. The risk-score construction pipeline started with univariate Cox logistic regression analysis, least absolute shrinkage and selection operator, and multivariate Cox logistic regression analysis in sequence. The generated risk score classified patients into low- vs high-risk groups. The predictive power of the constructed risk score was assessed using Kaplan–Meier curves analysis, multivariate Cox logistic regression analysis, and receiver operator curve of the overall survival. External validation of the risk score was performed using the E-MTAB-1980 cohort. The analysis work scheme established a novel nine-gene prognostic risk score composed of the following genes: ZIC2, TNNT1, SAA1, OTX1, C20orf141, CDHR4, HOXB13, IGFL2, and IGFN1. The high-risk group was associated with shortened overall survival and possessed an independent predictive power (hazard ratio: 1.942, 95% CI: 1.367–2.758, P < .0001, area under the curve = 0.719). In addition, the high-risk score was associated with advance clinicopathological parameters. The same pattern was observed within the external validation dataset (E-MTAB-1980 cohort), in which the high-risk score held a poor prognostic signature as well as independent predictive potential (hazard ratio: 5.121, 95% CI: 1.412–18.568, P = .013, area under the curve = 0.787). In the present work, a novel nine-gene prognostic risk score was constructed and validated. The risk score correlated with tumor immune microenvironment, somatic mutation patterns, and altered molecular pathways involved in tumorigenesis. Further experimental data are warranted to expand the work.

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Downregulation of RNA binding protein 47 predicts low survival in patients and promotes the development of renal cell malignancies through RNA stability modification

Wang,

Li,

Meng

et al. 2023

Mol Biomed

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RNA binding proteins (RBPs) are crucial for cell function, tissue growth, and disease development in disease or normal physiological processes. RNA binding motif protein 47 (RBM47) has been proven to have anti-tumor effects on many cancers, but its effect is not yet clear in renal cancer. Here, we demonstrated the expression and the prognostic role of RBM47 in public databases and clinical samples of clear cell renal carcinoma (ccRCC) with bioinformatics analysis. The possible mechanism of RBM47 in renal cancer was verified by gene function prediction and in vitro experiments. The results showed that RBM47 was downregulated in renal cancers when compared with control groups. Low RBM47 expression indicated poor prognosis in ccRCC. RBM47 expression in renal cancer cell lines was reduced significantly when compared to normal renal tubular epithelial cells. Epithelial-mesenchymal transition (EMT) and transforming growth factor-β signaling pathway was associated with RBM47 in ccRCC by Gene set enrichment analysis. RBM47 expression had a positive correlation with e-cadherin, but a negative correlation with snail and vimentin. RBM47 overexpression could repress the migration, invasion activity, and proliferation capacity of renal cancer cells, while RBM47 inhibition could promote the development of the malignant features through EMT signaling by RNA stability modification. Therefore, our results suggest that RBM47, as a new molecular biomarker, may play a key role in the cancer development of ccRCC.

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Definition and verification of novel metastasis and recurrence related signatures of ccRCC: A multicohort study

Cited by 3 publications

References 79 publications

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Identification of AKI signatures and classification patterns in ccRCC based on machine learning

Transcriptome analysis revealed a novel nine-gene prognostic risk score of clear cell renal cell carcinoma

Downregulation of RNA binding protein 47 predicts low survival in patients and promotes the development of renal cell malignancies through RNA stability modification

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