scholarly journals Integrative framework of cross-module deep biomarker for the prognosis of clear cell renal cell carcinoma

2019 ◽  
Author(s):  
Zhenyuan Ning ◽  
Weihao Pan ◽  
Qing Xiao ◽  
Yuting Chen ◽  
Xinsen Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Functional Analysis ◽  
Deep Learning ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Integrative Framework ◽  
Histopathological Images ◽  
Gene Data ◽  
The Relationship ◽  
Cross Module

AbstractPurposeWe aimed to integrate cross-module data for predicting the prognosis of clear cell renal cell carcinoma (ccRCC) based on deep learning and to explore the relationship between deep features from images and eigengenes form gene data.Experimental designA total of 209 patients with ccRCC with computed tomography (CT), histopathological images and RNA sequences were enrolled. A deep biomarker-based integrative framework was proposed to construct a prognostic model. Deep features extracted from CT and histopathological images by using deep learning combined with eigengenes generated from functional genomic data were used to predict ccRCC prognosis. Furthermore, the relationship between deep features and eigengenes was explored, and two survival subgroups identified by integrative cross-module biomarkers were subjected to functional analysis.ResultsThe model based on the integrative framework stratified two subgroups of patients with a significant prognostic difference (P = 6.51e-6, concordance index [C-index] = 0.808, 95% confidence interval [CI] = 0.728-0.888) and outperformed the prediction based on their individual biomarkers in the independent validation cohort (n = 70, gene data: C-index = 0.452, CI = 0.336-0.567; histopathological images: C-index = 0.677, CI = 0.577-0.776; CT images: C-index = 0.774, CI = 0.670-0.879). On the basis of statistical relationship, deep features correlated or complemented with eigengenes both enhanced the predictive performance of eigengenes (P = 0.439, correlated: C-index = 0.785, CI = 0.685-0.886; complemented: C-index = 0.778, CI = 0.683-0.872). The functional analysis of subgroups also exhibited reasonable results.ConclusionThe model based on the integrative framework of cross-module deep biomarkers can efficiently predict ccRCC prognosis, and the framework with a code is shared to act as a reliable and powerful tool for further studies.

scholarly journals Integrative analysis of cross-modal features for the prognosis prediction of clear cell renal cell carcinoma

2020 ◽  
Vol 36 (9) ◽  
pp. 2888-2895 ◽  
Author(s):  
Zhenyuan Ning ◽  
Weihao Pan ◽  
Yuting Chen ◽  
Qing Xiao ◽  
Xinsen Zhang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Genomic Data ◽  
Image Features ◽  
Cell Renal Cell Carcinoma ◽  
Multimodal Data ◽  
Integrative Framework ◽  
Deep Image

Abstract Motivation As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance. Results We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728–0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data. Availability and implementation https://github.com/zhang-de-lab/zhang-lab? from=singlemessage Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Fructose 1,6-Bisphosphatase 1 Expression Reduces 18F-FDG Uptake in Clear Cell Renal Cell Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Ruohua Chen ◽  
Xiang Zhou ◽  
Gang Huang ◽  
Jianjun Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Primary Tumor ◽  
Renal Cell ◽  
Clear Cell ◽  
Low Grade ◽  
High Grade ◽  
Cell Renal Cell Carcinoma ◽  
Fdg Uptake ◽  
Fructose 1,6 Bisphosphatase ◽  
The Relationship

Purpose. To determine the relationship between fructose 1,6-bisphosphatase 1 (FBP1) expression and fluorine 18 (18F) fluorodeoxyglucose (FDG) uptake in patients with clear cell renal cell carcinoma (ccRCC), and to investigate how 18F-FDG uptake and FBP1 expression are related to tumor metabolism and tumor differentiation grade. Materials and Methods. A total of 54 patients with ccRCC underwent 18F-FDG combined positron emission tomography and computed tomography (PET/CT) before tumor resection. The maximum standardized uptake value (SUVmax) for the primary tumor was calculated from the 18F-FDG uptake. The relationship between SUVmax of primary tumor and the expression of FBP1, hexokinase 2 (HK2), and glucose transporter 1 (GLUT1) was analyzed via immunohistochemical analysis. Results. We identified an inverse relationship between FBP1 expression and SUVmax (P=0.031). SUVmax was higher in patients with high-grade ccRCC (mean, 11.6 ± 5.0) than in those with low-grade ccRCC (mean, 3.8 ± 1.6, P<0.001). FBP1 expression was significantly lower in patients with high-grade ccRCC (mean, 0.23 ± 0.1) than in those with low-grade ccRCC (mean, 0.57 ± 0.08; P=0.018). FBP1 status could be predicted with an accuracy of 66.7% when a SUVmax cutoff value of 3.55 was used. GLUT1 expression in ccRCC was positively correlated with 18F-FDG uptake and FBP1 status, whereas HK2 expression was not. Conclusion. SUVmax in patients with ccRCC is inversely associated with the expression of FBP1, and FBP1 may inhibit 18F-FDG uptake via regulating GLUT1. SUVmax is higher in patients with high-grade ccRCC than in those with low-grade ccRCC, which could be the result of lower FBP1 expression in patients with high-grade ccRCC.

scholarly journals A pyramidal deep learning pipeline for kidney whole-slide histology images classification

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hisham Abdeltawab ◽  
Fahmi Khalifa ◽  
Mohammed Mohammed ◽  
Liang Cheng ◽  
Dibson Gondim ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Correct Diagnosis ◽  
Papillary Renal Cell Carcinoma ◽  
Automated Classification ◽  
Cell Renal Cell Carcinoma ◽  
Whole Slide Images

AbstractRenal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist’s experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist’s capabilities by providing automated classification for histopathological images.

Deep learning and radiomics: the utility of Google TensorFlow™ Inception in classifying clear cell renal cell carcinoma and oncocytoma on multiphasic CT

2019 ◽  
Vol 44 (6) ◽  
pp. 2009-2020 ◽  
Author(s):  
Heidi Coy ◽  
Kevin Hsieh ◽  
Willie Wu ◽  
Mahesh B. Nagarajan ◽  
Jonathan R. Young ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

scholarly journals High kinesin family member 11 expression predicts poor prognosis in patients with clear cell renal cell carcinoma

2019 ◽  
Vol 72 (5) ◽  
pp. 354-362 ◽  
Author(s):  
Qin Jin ◽  
Yanfeng Dai ◽  
Yan Wang ◽  
Shu Zhang ◽  
Gang Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Family Member ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Qrt Pcr ◽  
The Relationship ◽  
Kinesin Family

AimsKinesin family member 11 (Kif11) is a member of the kinesin family motor proteins, which is associated with spindle formation and tumour genesis. In this study, we investigated the relationship between Kif11 expression and clear cell renal cell carcinoma (CCRCC) development.MethodsThe relationship between Kif11 expression and CCRCC development was analysed by quantitative real-time (qRT)-PCR analyses, and tissue immunohistochemistry. The prognostic significance of Kif11 expression was explored by univariable and multivariable survival analyses of 143 included patients. Furthermore, SB743921 was used as a specific Kif11 inhibitor to treat 786-O cells with the epithelial to mesenchymal transition (EMT) process analysed by qRT-PCR, and cell survival rates analysed with Annexin V-FITC/PI staining followed by flow cytometric analyses. Disease-free survival curves of Kif11 with different cancers and the relationships between Kif11 and the von Hippel-Lindau disease tumour suppressor gene (VHL), and proliferating cell nuclear antigen (PCNA) in kidney cancer were further analysed using the GEPIA database.ResultsThe levels of Kif11 mRNA were significantly higher in CCRCC tissues compared with corresponding non-cancerous tissues. The results of immunohistochemistry demonstrated that the expression of Kif11 protein was significantly associated with clinicopathologial parameters, including nuclear grade and TNM stage. The Kaplan-Meier survival curve indicated that high Kif11 expression, nuclear grade and TNM stage were independent factors to predict poor prognosis in patients with CCRCC. In addition, inhibition of Kif11 expression by SB743921 suppressed cell proliferation, migration and the EMT process with increased apoptosis rate.ConclusionsThese results combined with bioinformation analyses suggest that high Kif11 expression was associated with unfavourable prognosis in CCRCC and could be used as a potential prognostic marker in the clinical diagnosis of CCRCC.

863 Identification and functional analysis of novel long non-coding RNAs in clear cell renal cell carcinoma

2015 ◽  
Vol 14 (2) ◽  
pp. e863 ◽  
Author(s):  
J. Ellinger ◽  
J.J.C. Blondeau ◽  
M. Deng ◽  
I. Syring ◽  
S. Schrödter ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Functional Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Non Coding Rnas

scholarly journals Deep learning based prediction of prognosis in nonmetastatic clear cell renal cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Seok-Soo Byun ◽  
Tak Sung Heo ◽  
Jeong Myeong Choi ◽  
Yeong Seok Jeong ◽  
Yu Seop Kim ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Deep Learning ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
External Validation ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cell Renal Cell Carcinoma ◽  
Test Dataset

AbstractSurvival analyses for malignancies, including renal cell carcinoma (RCC), have primarily been conducted using the Cox proportional hazards (CPH) model. We compared the random survival forest (RSF) and DeepSurv models with the CPH model to predict recurrence-free survival (RFS) and cancer-specific survival (CSS) in non-metastatic clear cell RCC (nm-cRCC) patients. Our cohort included 2139 nm-cRCC patients who underwent curative-intent surgery at six Korean institutions between 2000 and 2014. The data of two largest hospitals’ patients were assigned into the training and validation dataset, and the data of the remaining hospitals were assigned into the external validation dataset. The performance of the RSF and DeepSurv models was compared with that of CPH using Harrel’s C-index. During the follow-up, recurrence and cancer-specific deaths were recorded in 190 (12.7%) and 108 (7.0%) patients, respectively, in the training-dataset. Harrel’s C-indices for RFS in the test-dataset were 0.794, 0.789, and 0.802 for CPH, RSF, and DeepSurv, respectively. Harrel’s C-indices for CSS in the test-dataset were 0.831, 0.790, and 0.834 for CPH, RSF, and DeepSurv, respectively. In predicting RFS and CSS in nm-cRCC patients, the performance of DeepSurv was superior to that of CPH and RSF. In no distant time, deep learning-based survival predictions may be useful in RCC patients.

scholarly journals Significance of Tumor Mutation Burden Related Immune Gene in the Progression and Prognosis of Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Zhirong Yang ◽  
Duo Yun ◽  
Longmei Dai ◽  
Qinqin Wang ◽  
Xiangli Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Common Mutation ◽  
Immune Gene ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Mutation Burden ◽  
Mutation Burden ◽  
The Relationship

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is a common renal malignant disease with a poor prognosis. There were limited studies focus on the relationship between Tumor mutation burden (TMB) and ccRCC.Methods: Based on TCGA-ccRCC cohort, we summarized the status of gene mutations in ccRCC. Then, we analyzed the relationship between TMB and clinical characteristic. Meanwhile, we identified some TMB-related immune genes through the intersection of TMB-Related differentially expressed genes (DEGs) and immune related genes. Finally, we selected the highest correction and novel genes for the future analysis.Results: The most common mutation of Variant Classification, Variant Type, SNV Class were missense mutations, SNP, C>T, respectively. Higher TMB related to shorter overall survival (OS), lower age and grade. Finally, we identified PAEP gene, a novel TMB-related immune gene in ccRCC, which was significantly overexpression in ccRCC tissues and cells with progression and poor survival in ccRCC patients. Furthermore, PAEP promoted the invasion, migration, and proliferation of ccRCC cells. Mechanistically, PAEP suppressed the PI3K/Akt/NF-κB signaling pathway.Conclusion: Our study suggests that PAEP might represents a potential target of antibody immunotherapy for ccRCC patients.

scholarly journals Increased CDC6 Expression Associates With Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Yao Yicong ◽  
Yi Wang ◽  
Wu Denglong ◽  
Hu Baoying
Keyword(s):  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Checkpoint ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
High Expression ◽  
Cell Renal Cell Carcinoma ◽  
The Relationship

BackgroundCDC6 (Cell division control protein 6), located at chromosome 17q21.3, plays an important role in the early stage of DNA replication and has unique functions in various malignant tumors. Here, we evaluate the relationship between CDC6 expression and oncology outcomes in patients with clear cell renal cell carcinoma (ccRCC).MethodsA retrospective analysis of 118 ccRCC patients in Affiliated Hospital of Nantong University from 2015 to 2017 was performed. Triplicate tissue microarrays (TMA) were prepared from formalin-fixed and paraffin-embedded specimens. Immunohistochemistry (IHC) was conducted to evaluate the relationship between CDC6 expression and standard pathological features and prognosis. The RNA sequencing data and corresponding clinical information were acquired from the TCGA database. GSEA was used to identify signal pathways related to CDC6. Cox regression analysis was used to assess independent prognostic factors. In addition, the relationship between CDC6 and immunity was also investigated.ResultsThe results of Kaplan–Meier curve indicated that the OS of the patients with high expression of CDC6 was shorter than that of the patients with low CDC6 expression. Integrating the TCGA database and IHC staining, the results showed that CDC6 in ccRCC tissue was obviously up-regulated compared with adjacent normal kidney tissue. The results of Logistic regression analysis demonstrated that ccRCC patients with high expression of CDC6 are more likely to develop advanced disease than ccRCC patients with low CDC6 expression. The results of GSEA showed that the high expression of CDC6 was related to multiple signaling pathways. As for immunity, it was also related to TMB, immune checkpoint molecules, tumor microenvironment and immune infiltration. There were significantly correlations with CDC6 and immune cell infiltration levels and tumor microenvironment. The results of further results of the TCGA database showed that CDC6 was obviously related to immune checkpoint molecules and immune cells.ConclusionsIncreased expression of CDC6 is a potentially prognostic factor of poor prognosis in ccRCC patients.

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