Identification of clear cell renal cell carcinoma subtypes by integrating radiomics and transcriptomics.

Objective: This study aimed to delineate the clear cell renal cell carcinoma (ccRCC) intrinsic subtypes through unsupervised clustering of radiomics and transcriptomics data and to evaluate their associations with clinicopathological features, prognosis, and molecular characteristics. Methods: Using a retrospective dual-center approach, we gathered transcriptomic and clinical data from ccRCC patients registered in The Cancer Genome Atlas and contrast-enhanced computed tomography images from The Cancer Imaging Archive and local databases. Following the segmentation of images, radiomics feature extraction, and feature preprocessing, we performed unsupervised clustering based on the "CancerSubtypes" package to identify distinct radiotranscriptomic subtypes, which were then correlated with clinical-pathological, prognostic, immune, and molecular characteristics. Results: Clustering identified three subtypes, C1, C2, and C3, each of which displayed unique clinicopathological, prognostic, immune, and molecular distinctions. Notably, subtypes C1 and C3 were associated with poorer survival outcomes than subtype C2. Pathway analysis highlighted immune pathway activation in C1 and metabolic pathway prominence in C2. Gene mutation analysis identified VHL and PBRM1 as the most commonly mutated genes, with more mutated genes observed in the C3 subtype. Despite similar tumor mutation burdens, microsatellite instability, and RNA interference across subtypes, C1 and C3 demonstrated greater tumor immune dysfunction and rejection. In the validation cohort, the various subtypes showed comparable results in terms of clinicopathological features and prognosis to those observed in the training cohort, thus confirming the efficacy of our algorithm. Conclusion: Unsupervised clustering based on radiotranscriptomics can identify the intrinsic subtypes of ccRCC, and radiotranscriptomic subtypes can characterize the prognosis and molecular features of tumors, enabling noninvasive tumor risk stratification.

Comparison of clinical-computed tomography model with 2D and 3D radiomics models to predict occult peritoneal metastases in advanced gastric cancer.

PURPOSE: To compare the ability of a clinical-computed tomography (CT) model vs. 2D and 3D radiomics models for predicting occult peritoneal metastasis (PM) in patients with advanced gastric cancer (AGC). METHODS: In this retrospective study, we included 49 patients with occult PM and 49 control patients (without PM) who underwent preoperative CT and subsequent surgery between January 2016 and December 2018. Clinical information and CT semantic features were collected, and CT radiomics features were extracted. A predictive clinical-CT model was created using multivariate logistic regression. The least absolute shrinkage and selection operator algorithm and logistic regression were used for constructing 2D and 3D radiomics models. These models were validated with an external cohort (n = 30). Receiver operating characteristics curve with area under the curve (AUC), sensitivity, and specificity were used to evaluate predictive performance. RESULTS: Tumor size, mild ascites, and serum CA125 were independent factors predictive of occult PM. The clinical-CT model of these independent factors showed better diagnostic performance than 2D and 3D radiomics models. In the external validation cohort, the AUCs of different models were as follows-clinical-CT model: 0.853 (sensitivity, 66.7%; specificity, 93.3%); 2D radiomics model: 0.622 (sensitivity, 80.0%; specificity, 46.7%); and 3D radiomics model: 0.676 (sensitivity, 60.0%; specificity, 86.0%). The clinical-CT model nomogram showed good clinical predictive efficiency to assess occult PM. CONCLUSION: The clinical-CT model was better than the radiomics models in predicting occult PM in AGC.

CT Radiomics for the Prediction of Synchronous Distant Metastasis in Clear Cell Renal Cell Carcinoma.

PURPOSE: The aim of this study was to construct and verify a computed tomography (CT) radiomics model for preoperative prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC) patients. METHODS: Overall, 172 patients with ccRCC were enrolled in the present research. Contrast-enhanced CT images were manually sketched, and 2994 quantitative radiomic features were extracted. The radiomic features were then normalized and subjected to hypothesis testing. Least absolute shrinkage and selection operator (LASSO) was applied to dimension reduction, feature selection, and model construction. The performance of the predictive model was validated through analysis of the receiver operating characteristic curve. Multivariate and subgroup analyses were performed to verify the radiomic score as an independent predictor of SDM. RESULTS: The patients randomized into a training (n = 104) and a validation (n = 68) cohort in a 6:4 ratio. Through dimension reduction using LASSO regression, 9 radiomic features were used for the construction of the SDM prediction model. The model yielded moderate performance in both the training (area under the curve, 0.89; 95% confidence interval, 0.81-0.97) and the validation cohort (area under the curve, 0.83; 95% confidence interval, 0.69-0.95). Multivariate analysis showed that the CT radiomic signature was an independent risk factor for clinical parameters of ccRCC. Subgroup analysis revealed a significant connection between the SDM and radiomic signature, except for the lower pole of the kidney subgroup. CONCLUSIONS: The CT-based radiomics model could be used as a noninvasive, personalized approach for SDM prediction in patients with ccRCC.

Development and Validation of a Radiomic Nomogram for Predicting the Prognosis of Kidney Renal Clear Cell Carcinoma.

PURPOSE: The present study aims to comprehensively investigate the prognostic value of a radiomic nomogram that integrates contrast-enhanced computed tomography (CECT) radiomic signature and clinicopathological parameters in kidney renal clear cell carcinoma (KIRC). METHODS: A total of 136 and 78 KIRC patients from the training and validation cohorts were included in the retrospective study. The intraclass correlation coefficient (ICC) was used to assess reproducibility of radiomic feature extraction. Univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) as well as multivariate Cox analysis were utilized to construct radiomic signature and clinical signature in the training cohort. A prognostic nomogram was established containing a radiomic signature and clinicopathological parameters by using a multivariate Cox analysis. The predictive ability of the nomogram [relative operating characteristic curve (ROC), concordance index (C-index), Hosmer-Lemeshow test, and calibration curve] was evaluated in the training cohort and validated in the validation cohort. Patients were split into high- and low-risk groups, and the Kaplan-Meier (KM) method was conducted to identify the forecasting ability of the established models. In addition, genes related with the radiomic risk score were determined by weighted correlation network analysis (WGCNA) and were used to conduct functional analysis. RESULTS: A total of 2,944 radiomic features were acquired from the tumor volumes of interest (VOIs) of CECT images. The radiomic signature, including ten selected features, and the clinical signature, including three selected clinical variables, showed good performance in the training and validation cohorts [area under the curve (AUC), 0.897 and 0.712 for the radiomic signature; 0.827 and 0.822 for the clinical signature, respectively]. The radiomic prognostic nomogram showed favorable performance and calibration in the training cohort (AUC, 0.896, C-index, 0.846), which was verified in the validation cohort (AUC, 0.768). KM curves indicated that the progression-free interval (PFI) time was dramatically shorter in the high-risk group than in the low-risk group. The functional analysis indicated that radiomic signature was significantly associated with T cell activation. CONCLUSIONS: The nomogram combined with CECT radiomic and clinicopathological signatures exhibits excellent power in predicting the PFI of KIRC patients, which may aid in clinical management and prognostic evaluation of cancer patients.

Effects of magnetic nanoparticle of Fe3O4 on apoptosis induced by Gambogic acid in U937 leukemia cells.

This study was aimed to explore the potential therapy of Gambogic acid (GA) combined with magnetic nanoparticle of Fe3O4 (Fe3O4-MNP) on leukemia. The proliferation of U937 cells and the cytotoxicity were evaluated by MTT assay. Cell apoptosis was observed and analyzed by microscopy and flow cytometry respectively. The expressions of gene and protein were detected by quantitative real-time polymerase chain reaction and Western blot respectively. The results showed that GA enhanced the cytotoxicity for U937 cells in dose- and time-dependent manners. The Fe3O4-MNP itself had not cytotoxicity, but could enhance the inhibitory effect of GA on proliferation of U937 cells. The apoptotic rate of U937 cells induced by combination of GA with Fe3O4-MNP was higher than that by GA alone. The typical apoptotic features of cells treated with GA and Fe3O4-MNP were observed. The expression levels of caspase-3 and bax after co-treatment of GA and Fe3O4-MNP were higher than that exposed to GA or Fe3O4-MNP alone, but the expressions of bcl-2, NF-kappaB and survivin were down-regulated. It is concluded that Fe3O4-MNP can promote GA-induced apoptosis in U937 cells, and the combination of GA with Fe3O4-MNP may be a safer and less toxic new therapy for leukemia.

[Effect of PDMP, a glucosylceramide synthase inhibitor, on reversion of daunorubicin resistance in human leukemia cell line K562/A02].

This study was purposed to investigate the reversal effect of glucosylceramide synthase (GCS) inhibitor D, L-threo-1-phenyl-2-decanoylamino-3-morpholino-1-propanol (PDMP) hydrochloride, on multidrug resistance in K562/A02 cells and its mechanism. The IC(50) (the half maximal inhibitory concentration) of PDMP was measured by MTT method. Cell apoptosis and intracellular daunorubicin (DNR) concentration were detected by flow cytometry. The expression of GCS and mdr1 genes were assayed by semi-quantitative reverse transcriptase polymerase chain reaction (RT-PCR) and Western blot. The results showed that the IC(50) of DNR in K562 and K562/A02 cells were 0.23 +/- 0.02 and 7.15 +/- 0.24 microg/ml respectively. When the concentration of PDMP was equal to or less than 20 micromol/L ( < / = 20 micromol/L), the obviously inhibitory effect on proliferation of K562 and K562/A02 cells was not observed, but both 20 micromol/L and 10 micromol/L PDMP could enhance the sensitivity of K562/A02 cells to DNR (p < 0.01) and the reversal multiples were 2.59 and 1.69 respectively. After treating with 20 micromol/L and 10 micromol/L PDMP for 48 hours, the concentration of DNR in K562/A02 cells increased (p < 0.05) and the apoptotic rate also was elevated (p < 0.01). The expressions of GCS and mdr1 genes were down-regulated at mRNA and protein levels after treating K562/A02 cells with 20 micromol/L PDMP for 48 hours. It is concluded that PDMP can enhance the sensitivity of K562/A02 cells to DNR by increasing cell apoptosis rate and accumulation concentration of DNR in cells, which may be related to down-regulated expressions of GCS and mdr1 genes.

Synergistic effect of magnetic nanoparticles of Fe(3)O(4) with gambogic acid on apoptosis of K562 leukemia cells.

Gambogic acid (GA) has a significant anticancer effect on a wide variety of solid tumors. Recently, many nanoparticles have been introduced as drug-delivery systems to enhance the efficiency of anticancer drug delivery. The aim of this study was to investigate the potential benefit of combination therapy with GA and magnetic nanoparticles of Fe(3)O(4) (MNPs-Fe(3)O(4)). The proliferation of K562 cells and their cytotoxicity were evaluated by MTT assay. Cell apoptosis was observed and analyzed by microscope and flow cytometry, respectively. Furthermore, real-time polymerase chain reaction and Western blotting analyses were performed to examine gene transcription and protein expression, respectively. The results showed that MNPs-Fe(3)O(4) dramatically enhanced GA-induced cytotoxicity and apoptosis in K562 cells. The typical morphological features of apoptosis treated with GA and MNPs-Fe(3)O(4) were observed under an optical microscope and a fluorescence microscope, respectively. The transcription of caspase-3 and bax gene in the group treated with GA and MNPs-Fe(3)O(4) was higher than that in the GA-alone group or MNPs-Fe(3)O(4)-alone group, but the transcription of bcl-2, nuclear factor-kappaB, and survivin degraded as did the expression of corresponding proteins in K562 cells. Our data suggests a potential clinical application of a combination of GA and MNPs-Fe(3)O(4) in leukemia therapy.

[Research progress on aurora kinase inhibitor MK-0457 in therapy for some hematological malignancies -- review].

Recent work indicates that an Aurora kinase inhibitor MK-0457 (VX-680), a small-molecule inhibitor of Aurora kinases A, B, C and BCR-ABL, FLT-3, JAK-2, can block the progression of cell growth cycle, causing apoptosis in a range of human tumors. MK-0457 has the activity against expressions of wild-type and mutated bcr-abl gene, including the T315I mutant, and can inhibit the activity of FLT-3, JAK-2 and their mutated types as well. Clinical applications suggest that the MK-0457 has therapeutic effect on the highly refractory CML and CML with poor prognosis, Ph(+) ALL with T315I mutant, relapse refractory AML and JAK-2 positive myeloproliferative diseases (MPD). The intensive preclinical studies and the on-going phase II clinical trials will open up a new vista of therapy for some hematological malignancies. This review focuses on the pharmacologic action of MK-0457 and its clinical trial as well as combined application.