Genome-Wide Transcriptomic Analysis of Non-Tumorigenic Tissues Reveals Aging-Related Prognostic Markers and Drug Targets in Renal Cell Carcinoma

The relationship between expression of aging-related genes in normal tissues and cancer patient survival has not been assessed. We developed a genome-wide transcriptomic analysis approach for normal tissues adjacent to the tumor to identify aging-related transcripts associated with survival outcome, and applied it to 12 cancer types. As a result, five aging-related genes (DUSP22, MAPK14, MAPKAPK3, STAT1, and VCP) in normal tissues were found to be significantly associated with a worse survival outcome in patients with renal cell carcinoma (RCC). This computational approach was investigated using nontumorigenic immune cells purified from young and aged mice. Aged immune cells showed upregulated expression of all five aging-related genes and promoted RCC invasion compared to young immune cells. Further studies revealed DUSP22 as a regulator and druggable target of metastasis. DUSP22 gene knockdown reduced RCC invasion and the small molecule inhibitor BML-260 prevented RCC dissemination in a tumor/immune cell xenograft model. Overall, these results demonstrate that deciphering the relationship between aging-related gene expression in normal tissues and cancer patient survival can provide new prognostic markers, regulators of tumorigenesis and novel targets for drug development.

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Differential Gene Expression Analysis In Renal Cell Carcinoma As Compared To Pair Matched Patient Normal Tissues; A Novel Method In Identification Of New Diagnostic And Or Prognostic Markers In Renal Cell Carcinoma

Journal of Surgical Research ◽

10.1016/j.jss.2010.11.774 ◽

2011 ◽

Vol 165 (2) ◽

pp. 176

Author(s):

T.L. Tello ◽

F.J. Kim ◽

S. Koul ◽

H. Koul ◽

Campagna ◽

...

Keyword(s):

Gene Expression ◽

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Gene Expression Analysis ◽

Prognostic Markers ◽

Differential Gene Expression Analysis ◽

Normal Tissues ◽

Novel Method ◽

Differential Gene

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CD31, EDNRB and TSPAN7 are promising prognostic markers in clear-cell renal cell carcinoma revealed by genome-wide expression analyses of primary tumors and metastases

International Journal of Cancer ◽

10.1002/ijc.27419 ◽

2012 ◽

Vol 131 (5) ◽

pp. E693-E704 ◽

Cited By ~ 36

Author(s):

Daniela Wuttig ◽

Stefan Zastrow ◽

Susanne Füssel ◽

Marieta I. Toma ◽

Matthias Meinhardt ◽

...

Keyword(s):

Renal Cell Carcinoma ◽

Cell Carcinoma ◽

Renal Cell ◽

Prognostic Markers ◽

Clear Cell ◽

Cell Renal Cell Carcinoma ◽

Primary Tumors ◽

Genome Wide ◽

Genome Wide Expression

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A Deep learning approach for Pan-Renal Cell Carcinoma classification and survival prediction from histopathology images

10.1101/559401 ◽

2019 ◽

Cited By ~ 1

Author(s):

Sairam Tabibu ◽

P.K. Vinod ◽

C.V. Jawahar

Keyword(s):

Renal Cell Carcinoma ◽

Deep Learning ◽

Cell Carcinoma ◽

Classification Accuracy ◽

Renal Cell ◽

Clear Cell ◽

Patient Survival ◽

Survival Outcome ◽

Support Vector ◽

Histopathological Images

ABSTRACTHistopathological images contain morphological markers of disease progression that have diagnostic and predictive values. However, complex morphological information remains unutilized in unaided approach to histopathology. In this study, we demonstrate how deep learning framework can be used for an automatic classification of Renal Cell Carcinoma (RCC) subtypes, and for identification of features that predict survival outcome from digital histopathological images. Convolutional neural networks (CNN’s) trained on whole-slide images distinguish clear cell and chromophobe RCC from normal tissue with a classification accuracy of 93.39 % and 87.34 %, respectively. Further, a CNN trained to distinguish clear cell, chromophobe and papillary RCC achieves a classification accuracy of 92.61 %. Here, we introduced a novel support vector machine based method to deal with data imbalance in multi-class classification to improve the accuracy. Finally, we extracted the morphological features from high probability tumor regions identified by the CNN to predict patient survival outcome of most common clear cell RCC. The generated risk index based on both tumor shape and nuclei features are significantly associated with patient survival outcome. These results highlight that deep learning can play a role in both cancer diagnosis and prognosis.

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