MicroRNAs Possibly Involved in the Development of Bone Metastasis in Clear-Cell Renal Cell Carcinoma

Kinget, Lisa; Roussel, Eduard; Lambrechts, Diether; Boeckx, Bram; Vanginderhuysen, Loïc; Albersen, Maarten; Rodríguez‐Antona, Cristina; Graña‐Castro, Osvaldo; Inglada-Pérez, Lucía; Verbiest, Annelies; Zucman–Rossi, Jessica; Couchy, Gabrielle; Caruso, Stefano; Laenen, Annouschka; Baldewijns, Marcella; Beuselinck, Benoît

doi:10.3390/cancers13071554

Cited by 9 publications

(6 citation statements)

References 168 publications

(168 reference statements)

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“…Given this feature, 30% of patients will be diagnosed with metastasis even after aggressive treatment of the primary tumor (6). Bone metastases (BMs), with a 40% incidence rate, are a common mechanism of metastasis and have been identified as a major prognostic risk factor associated with poor survival in patients with metastasic RCC (mRCC) (6)(7)(8). The frequent sites of colonization by mRCC cells are the proximal extremities and pelvis, manifesting as bone pain, pathological fractures, and hypercalcemia.…”

Section: Introductionmentioning

confidence: 99%

“…It has been shown previously that metastatic tumors affect bone turnover differently, and radiographic images of bone metastatic tumors from mRCC are osteolytic or osteoclastic due to an imbalance between osteoclasts and osteoblasts mediated by tumor cells. Osteoclasts often increase their activity because of the upregulation of kappa-B ligand (RANKL) induced by mRCC ( 7 ). As a result, the osteoclast inhibitor bisphosphonates and the RANKL blocker denosumab are widely used to treat BMs from mRCC.…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

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BackgroundBone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.MethodsPatients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.ResultsA total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.ConclusionsThe comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

et al. 2021

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“…In ccRCC patients, bone metastases can cause skeletal-related adverse events, lead to substantial morbidity, and often predict poor outcomes (18). Therefore, it is necessary to identify the risk and prognostic factors of bone metastasis in patients with ccRCC to improve their survival.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study

et al. 2022

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BackgroundThis study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.MethodsThe clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.ResultsOur study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.ConclusionsSeveral factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

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“…Besides, it was reported that miRNAs have a great potential for cancer diagnosis [ 61 ]. Such as, Lisa Kinget et.al found that the hsa-miR-30b-3p could suppress the development of bone metastasis in clear-cell renal cell carcinoma [ 62 ]. And the downregulation of hsa-miR-30b-3p leads to the poor prognosis of gastric cancer [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

et al. 2021

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Background Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study. Methods Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells. Results We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes. Conclusion Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

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MicroRNAs Possibly Involved in the Development of Bone Metastasis in Clear-Cell Renal Cell Carcinoma

Cited by 9 publications

References 168 publications

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study

ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

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