SaO032PAPILLARY RENAL CELL CARCINOMA ORIGINATES FROM A POPULATION OF RENAL PROGENITOR CELLS AND IS PROMOTED BY ACUTE KIDNEY INJURY

Acute tissue injury causes DNA damage and repair processes involving increased cell mitosis and polyploidization, leading to cell function alterations that may potentially drive cancer development. Here, we show that acute kidney injury (AKI) increased the risk for papillary renal cell carcinoma (pRCC) development and tumor relapse in humans as confirmed by data collected from several single-center and multicentric studies. Lineage tracing of tubular epithelial cells (TECs) after AKI induction and long-term follow-up in mice showed time-dependent onset of clonal papillary tumors in an adenoma-carcinoma sequence. Among AKI-related pathways, NOTCH1 overexpression in human pRCC associated with worse outcome and was specific for type 2 pRCC. Mice overexpressing NOTCH1 in TECs developed papillary adenomas and type 2 pRCCs, and AKI accelerated this process. Lineage tracing in mice identified single renal progenitors as the cell of origin of papillary tumors. Single-cell RNA sequencing showed that human renal progenitor transcriptome showed similarities to PT1, the putative cell of origin of human pRCC. Furthermore, NOTCH1 overexpression in cultured human renal progenitor cells induced tumor-like 3D growth. Thus, AKI can drive tumorigenesis from local tissue progenitor cells. In particular, we find that AKI promotes the development of pRCC from single progenitors through a classical adenoma-carcinoma sequence.

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Postoperative acute kidney injury in patients with papillary renal cell carcinoma as a novel risk factor for local recurrence. Results from a large multicentric, observational study (the record 1 project)

European Urology Supplements ◽

10.1016/s1569-9056(19)33745-5 ◽

2019 ◽

Vol 18 (9) ◽

pp. e3315

Author(s):

P. Romagnani ◽

F. Di Maida ◽

A. Mari ◽

G. Novara ◽

A. Antonelli ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Acute Kidney Injury ◽

Risk Factor ◽

Local Recurrence ◽

Observational Study ◽

Cell Carcinoma ◽

Renal Cell ◽

Kidney Injury ◽

Papillary Renal Cell Carcinoma

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Machine learning-based prediction of acute kidney injury after nephrectomy in patients with renal cell carcinoma

Scientific Reports ◽

10.1038/s41598-021-95019-1 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yeonhee Lee ◽

Jiwon Ryu ◽

Min Woo Kang ◽

Kyung Ha Seo ◽

Jayoun Kim ◽

...

Keyword(s):

Machine Learning ◽

Renal Cell Carcinoma ◽

Acute Kidney Injury ◽

Cell Carcinoma ◽

Renal Cell ◽

Kidney Injury ◽

Gradient Boosting ◽

Scoring Model ◽

Postoperative Aki ◽

Better Than

AbstractThe precise prediction of acute kidney injury (AKI) after nephrectomy for renal cell carcinoma (RCC) is an important issue because of its relationship with subsequent kidney dysfunction and high mortality. Herein we addressed whether machine learning (ML) algorithms could predict postoperative AKI risk better than conventional logistic regression (LR) models. A total of 4104 RCC patients who had undergone unilateral nephrectomy from January 2003 to December 2017 were reviewed. ML models such as support vector machine, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM) were developed, and their performance based on the area under the receiver operating characteristic curve, accuracy, and F1 score was compared with that of the LR-based scoring model. Postoperative AKI developed in 1167 patients (28.4%). All the ML models had higher performance index values than the LR-based scoring model. Among them, the LightGBM model had the highest value of 0.810 (0.783–0.837). The decision curve analysis demonstrated a greater net benefit of the ML models than the LR-based scoring model over all the ranges of threshold probabilities. The application of ML algorithms improves the predictability of AKI after nephrectomy for RCC, and these models perform better than conventional LR-based models.

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Post-operative acute kidney injury in patients with renal cell carcinoma is a potent risk factor for new-onset chronic kidney disease after radical nephrectomy

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfr094 ◽

2011 ◽

Vol 26 (11) ◽

pp. 3496-3501 ◽

Cited By ~ 53

Author(s):

A. Cho ◽

J. E. Lee ◽

G.-Y. Kwon ◽

W. Huh ◽

H. M. Lee ◽

...

Keyword(s):

Chronic Kidney Disease ◽

Renal Cell Carcinoma ◽

Acute Kidney Injury ◽

Risk Factor ◽

Kidney Disease ◽

Cell Carcinoma ◽

Renal Cell ◽

Radical Nephrectomy ◽

Kidney Injury ◽

New Onset

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Predicting the risk of postoperative acute kidney injury: development and assessment of a novel predictive nomogram

Journal of International Medical Research ◽

10.1177/03000605211032838 ◽

2021 ◽

Vol 49 (8) ◽

pp. 030006052110328

Author(s):

Yukun Wu ◽

Junxing Chen ◽

Cheng Luo ◽

Lingwu Chen ◽

Bin Huang

Keyword(s):

Renal Cell Carcinoma ◽

Acute Kidney Injury ◽

Cell Carcinoma ◽

Renal Cell ◽

Filtration Rate ◽

Estimated Glomerular Filtration Rate ◽

Kidney Injury ◽

Surgical Time ◽

Blood Uric Acid ◽

Postoperative Aki

Objective This study aimed to establish and internally verify the risk nomogram of postoperative acute kidney injury (AKI) in patients with renal cell carcinoma. Methods We retrospectively collected data from 559 patients with renal cell carcinoma from June 2016 to May 2019 and established a prediction model. Twenty-six clinical variables were examined by least absolute shrinkage and selection operator regression analysis, and variables related to postoperative AKI were determined. The prediction model was established by multiple logistic regression analysis. Decision curve analysis was conducted to evaluate the nomogram. Results Independent predictors of postoperative AKI were smoking, hypertension, surgical time, blood glucose, blood uric acid, alanine aminotransferase, estimated glomerular filtration rate, and radical nephrectomy. The C index of the nomogram was 0.825 (0.790–0.860) and 0.814 was still obtained in the internal validation. The nomogram had better clinical benefit when the intervention was decided at the threshold probabilities of >4% and <79% for patients and doctors, respectively. Conclusions This novel postoperative AKI nomogram incorporating smoking, hypertension, the surgical time, blood glucose, blood uric acid, alanine aminotransferase, the estimated glomerular filtration rate, and radical nephrectomy is convenient for facilitating the individual postoperative risk prediction of AKI in patients with renal cell carcinoma.

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