Association of Tumor Size with Risk of Lymph Node Metastasis in Clear Cell Renal Cell Carcinoma: A Population-Based Study

Zhi, Yunlai; Li, Xiao; Qi, Feng; Hu, Xin; Xu, Wenbo

doi:10.1155/2020/8887782

Cited by 11 publications

(10 citation statements)

References 25 publications

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“…We suggested a whole-body CT scan, even PET/CT is necessary for identifying LNM. Furthermore, we also found a tight correlation between tumor size and the incidence of LNM in RCC, and this correlation has been revealed previously (17,39,40). The web calculator for predicting lymph node metastasis in patients with RCC.…”

Section: Discussionsupporting

confidence: 89%

“…In our study, T stage, M stage, and pathological grade were independent predictors of LNM, Patients with high-level pathological grade, and advanced T and M stages had a higher risk of LNM, probably indicating that tumors were closely related to much more drastic aggressive, which was similar to previous studies (17,18,(36)(37)(38). Meanwhile, Figure 5 also showed that the T stage and M stage were the top two important variables in our five models.…”

Section: Discussionsupporting

confidence: 88%

“…Great efforts had been paid in developing predictive methods to identify LNM risk factors (16)(17)(18)(19)(20)(21). As a novel and popular artificial intelligence tool, ML plays a vital role in improving predictive accuracy in diagnosis and prognosis (22,23) and has been widely used in medical data analysis (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

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Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Feng¹,

Hong²,

Liu³

et al. 2022

Front. Endocrinol.

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Simple summaryStudies have shown that about 30% of kidney cancer patients will have metastasis, and lymph node metastasis (LNM) may be related to a poor prognosis. Our retrospective study aims to provide a reliable machine learning-based model to predict the occurrence of LNM in kidney cancer. We screened the pathological grade, liver metastasis, M staging, primary site, T staging, and tumor size from the training group (n=39016) formed by the SEER database and the validation group (n=771) formed by the medical center. Independent predictors of LNM in cancer patients. Using six different algorithms to build a prediction model, it is found that the prediction performance of the XGB model in the training group and the validation group is significantly better than any other machine learning model. The results show that prediction tools based on machine learning can accurately predict the probability of LNM in patients with kidney cancer and have satisfactory clinical application prospects.BackgroundLymph node metastasis (LNM) is associated with the prognosis of patients with kidney cancer. This study aimed to provide reliable machine learning-based (ML-based) models to predict the probability of LNM in kidney cancer.MethodsData on patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology and Outcomes (SEER) database from 2010 to 2017, and variables were filtered by least absolute shrinkage and selection operator (LASSO), univariate and multivariate logistic regression analyses. Statistically significant risk factors were used to build predictive models. We used 10-fold cross-validation in the validation of the model. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the model. Correlation heat maps were used to investigate the correlation of features using permutation analysis to assess the importance of predictors. Probability density functions (PDFs) and clinical utility curves (CUCs) were used to determine clinical utility thresholds.ResultsThe training cohort of this study included 39,016 patients, and the validation cohort included 771 patients. In the two cohorts, 2544 (6.5%) and 66 (8.1%) patients had LNM, respectively. Pathological grade, liver metastasis, M stage, primary site, T stage, and tumor size were independent predictive factors of LNM. In both model validation, the XGB model significantly outperformed any of the machine learning models with an AUC value of 0.916.A web calculator (https://share.streamlit.io/liuwencai4/renal_lnm/main/renal_lnm.py) were built based on the XGB model. Based on the PDF and CUC, we suggested 54.6% as a threshold probability for guiding the diagnosis of LNM, which could distinguish about 89% of LNM patients.ConclusionsThe predictive tool based on machine learning can precisely indicate the probability of LNM in kidney cancer patients and has a satisfying application prospect in clinical practice.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 88%

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Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Feng¹,

Hong²,

Liu³

et al. 2022

Front. Endocrinol.

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“…We also identi ed the positive relationship of tumor size with the ODF3B expression level (Table 3). According to a study by Zhi et al, an increase in tumor size has been reported in the progression and high risk of lymph node metastases (LNM) in ccRCC (21).…”

Section: Discussionmentioning

confidence: 99%

PGGHG and ODF3B as New Tumor Suppressor Genes in Renal Cell Carcinoma

Mohammadisoleimani

Firoozi

Haghi‐Aminjan

et al. 2021

Preprint

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Background Renal cell carcinoma (RCC) is the most frequent heterogeneous type of kidney cancer. Although the role of many genes in the development of RCC has been shown, its specific molecular mechanism is still unknown. In this research, we studied the expression profile of two poorly studied genes, PGGHG and ODF3B, with possible role in renal cancers in our RCC patients' tumors and adjacent normal tissues, as well as their clinicopathological features. Materials and Methods The PGGHG and ODF3B expression was determined using the quantitative real-time PCR in 40 tumor samples and their adjacent non-cancerous tissues. Moreover, Enrichr was used to investigate their pathways, ontologies, and possible upstream transcription factors. Furthermore, STRING was applied to search for interaction between PGGHG and ODF3B. Results The expression level of PGGHG and ODF3B was lower in the tumor tissues compared to their adjacent normal tissues. The expression level of ODF3B in the patients who had tumor size > 4cm was significant. Our data found the possible upstream transcription factor of ODF3B and PGGHG regulating their expression in biological pathways, mainly PPARG as the same upstream transcription factor affecting both genes. Moreover, our data revealed connection between protein networks of PGGHG and ODF3B. We found several main KEGG, biological process (BP), molecular function (MF), and cellular component (CC) for proteins involved in these networks. Conclusion PGGHG and ODF3B downregulation in RCC tumors suggests their possible tumor-suppressive role in RCC. More research is needed to determine the precise mechanism of their involvement in RCC pathogenesis.

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“…It was in 2019 that there were about 14,000 patients died of RCC in America [ 1 ]. Clear cell renal cell cancer (ccRCC) is the major RCC subtype, whose patients made up for the majority of the diagnosed cases [ 2 ]. ccRCC is a high-risk vascular tumor with no clinical symptoms of manifestation at its early stage, and more than 25-30% of the patients were found along with cancer metastasis as soon as their initial diagnosis of ccRCC, resulted in patients' poor survival prognoses [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Overexpression of TP53INP2 Promotes Apoptosis in Clear Cell Renal Cell Cancer via Caspase-8/TRAF6 Signaling Pathway

Liu

et al. 2022

Journal of Immunology Research

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Clear cell renal cell cancer (ccRCC) is a tumor of high malignancy, which can escape apoptosis. The tumor protein p53-inducible nuclear protein 2 (TP53INP2), known as an autophagy protein, is the essential part for autophagosome formation and sensitizes cells to apoptosis. Our study is aimed at exploring the role of TP53INP2 in ccRCC. We have identified the autophagy-related genes (ARGs) of differential expression in ccRCC patients with the help of the TCGA database by bioinformatics analysis. Our assays of quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were for the determination on the both levels of mRNA and protein. Overexpression of TP53INP2 on cellular proliferation, migration, and apoptosis of ccRCC was verified in the ways of performing CCK-8, wound scrape, transwell and flow cytometry assays in vitro, and a mice tumor model in vivo. Transmission electron microscopy was used to measure autophagy formation. The underlying mechanisms of TP53INP2 on ccRCC were determined via coimmunoprecipitation. TP53INP2 was found highly associated with an outcome of worse overall survival (OS) in Kaplan-Meier curves, and this parameter in ccRCC tissues was also lower than the normal tissues. Overexpression of TP53INP2 inhibited ccRCC cellular proliferation, migration, and invasion, as well as the tumor growth of mice. Those cells treated with autophagy inhibitor chloroquine (CQ) or TP53INP2 increased the apoptosis rate. TP53INP2 promoted autophagy formation and elevated the ratio of LC3 II/LC3 I. However, TP53INP2 did not significantly decrease the p-mTOR level. In addition, TP53INP2 activates the expressions of caspase-3, caspase-8, and PARP. Caspase-8 and TNF receptor associated factor 6 (TRAF6) were found to bind to each other in the presence of TP53INP2. TP53INP2 induces apoptosis in ccRCC cells through caspase-8/TRAF6 pathway, rather than the autophagy-dependent pathway.

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Association of Tumor Size with Risk of Lymph Node Metastasis in Clear Cell Renal Cell Carcinoma: A Population-Based Study

Cited by 11 publications

References 25 publications

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

Development and validation of a machine learning model to predict the risk of lymph node metastasis in renal carcinoma

PGGHG and ODF3B as New Tumor Suppressor Genes in Renal Cell Carcinoma

Overexpression of TP53INP2 Promotes Apoptosis in Clear Cell Renal Cell Cancer via Caspase-8/TRAF6 Signaling Pathway

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