Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model.

OBJECTIVES: This study was designed to explore and validate the value of different machine learning models based on ultrasound image-omics features in the preoperative diagnosis of lymph node metastasis in pancreatic cancer (PC). METHODS: This research involved 189 individuals diagnosed with PC confirmed by surgical pathology (training cohort: n = 151; test cohort: n = 38), including 50 cases of lymph node metastasis. Image-omics features were extracted from ultrasound images. After dimensionality reduction and screening, eight machine learning algorithms, including logistic regression (LR), support vector machine (SVM), K-nearest neighbors (KNN), random forest (RF), extra trees (ET), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), were used to establish image-omics models to predict lymph node metastasis in PC. The best omics prediction model was selected through ROC curve analysis. Machine learning models were used to analyze clinical features and determine variables to establish a clinical model. A combined model was constructed by combining ultrasound image-omics and clinical features. Decision curve analysis (DCA) and a nomogram were used to evaluate the clinical application value of the model. RESULTS: A total of 1561 image-omics features were extracted from ultrasound images. 15 valuable image-omics features were determined by regularization, dimension reduction, and algorithm selection. In the image-omics model, the LR model showed higher prediction efficiency and robustness, with an area under the ROC curve (AUC) of 0.773 in the training set and an AUC of 0.850 in the test set. The clinical model constructed by the boundary of lesions in ultrasound images and the clinical feature CA199 (AUC = 0.875). The combined model had the best prediction performance, with an AUC of 0.872 in the training set and 0.918 in the test set. The combined model showed better clinical benefit according to DCA, and the nomogram score provided clinical prediction solutions. CONCLUSION: The combined model established with clinical features has good diagnostic ability and can be used to predict lymph node metastasis in patients with PC. It is expected to provide an effective noninvasive method for clinical decision-making, thereby improving the diagnosis and treatment of PC.

Proteomics profiling of nontumor liver tissues identifies prognostic biomarkers in hepatitis B-related hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) often occurs following chronic hepatitis B virus (HBV) infection, leading to high recurrence and a low 5-year survival rate. We developed an overall survival (OS) prediction model based on protein expression profiles in HBV-infected nontumor liver tissues. We aimed to demonstrate the feasibility of using protein expression profiles in nontumor liver tissues for survival prediction. A univariate Cox and differential expression analysis were performed to identify candidate prognostic factors. A multivariate Cox analysis was performed to develop the liver gene prognostic index (LGPI). The survival differences between the different risk groups in the training and validation cohorts were also estimated. A total of 363 patients, 159 in the training cohort, and 204 in the validation cohort were included. Of the 6478 proteins extracted from nontumor liver tissues, we identified 1275 proteins altered between HCC and nontumor liver tissues. A total of 1090 out of 6478 proteins were significantly related to OS. The prognostic values of the proteins in nontumor tissues were mostly positively related to those in the tumor tissues. Protective proteins were mainly enriched in the metabolism-related pathways. From the differentially expressed proteins, the top 10 most significant prognosis-related proteins were submitted for LGPI construction. In the training and validation cohorts, this LGPI showed a great ability for distinguishing patients' OS risk stratifications. After adjusting for clinicopathological features, the LGPI was an independent prognostic factor in the training and validation cohorts. We demonstrated the prognostic value of protein expression profiling in nontumor liver tissues. The proposed LGPI was a promising predictive model for estimating OS in HBV-related HCC.

Comparison of the Feasibility and Diagnostic Performance of ACR CEUS LI-RADS and a Modified CEUS LI-RADS for HCC in Examinations Using Sonazoid.

OBJECTIVES: The present study aimed to determine the feasibility of the American College of Radiology's (ACR) contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS) (version 2017) in examinations using Sonazoid and compare its diagnostic performance with that of modified LI-RADS in patients at high risk of hepatocellular carcinoma (HCC). METHODS: This retrospective study's sample population consisted of 137 participants with a total of 140 nodules who underwent CEUS with Sonazoid and pathological confirmation via surgery or biopsy from January 2020 to February 2022. The lesions were evaluated and classified based on the reference standards (ie, ACR CEUS LI-RADS and modified LI-RADS). The overall diagnostic capabilities of the two systems were evaluated in terms of accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) with 95% confidence intervals (CIs). RESULTS: The participants had a median age of 51 years and an interquartile range of 43-58 years. Regarding LR-5 as a predictor of HCC, the accuracy results of the ACR LI-RADS and modified LI-RADS algorithms were 72.9 and 71.4%, respectively (P = .50). The sensitivity of both systems was the same (69.7%; 95% CI: 60.7-77.8%). Regarding LR-M as a predictor of non-HCC malignancy, the diagnostic performance of the algorithms was the same, with accuracy and sensitivity results of 76.4 and 73.3%, respectively (95% CI: 44.9-92.2%). CONCLUSION: The findings indicate that modified LI-RADS had a moderate level of diagnostic performance for HCC in examinations using Sonazoid, which was comparable to ACR LI-RADS.

Ultrasound-based radiomics for predicting different pathological subtypes of epithelial ovarian cancer before surgery.

OBJECTIVE: To evaluate the value of ultrasound-based radiomics in the preoperative prediction of type I and type II epithelial ovarian cancer. METHODS: A total of 154 patients with epithelial ovarian cancer were enrolled retrospectively. There were 102 unilateral lesions and 52 bilateral lesions among a total of 206 lesions. The data for the 206 lesions were randomly divided into a training set (53 type I + 71 type II) and a test set (36 type I + 46 type II) by random sampling. ITK-SNAP software was used to manually outline the boundary of the tumor, that is, the region of interest, and 4976 features were extracted. The quantitative expression values of the radiomics features were normalized by the Z-score method, and the 7 features with the most differences were screened by using the Lasso regression tenfold cross-validation method. The radiomics model was established by logistic regression. The training set was used to construct the model, and the test set was used to evaluate the predictive efficiency of the model. On the basis of multifactor logistic regression analysis, combined with the radiomics score of each patient, a comprehensive prediction model was established, the nomogram was drawn, and the prediction effect was evaluated by analyzing the area under the receiver operating characteristic curve (AUC), calibration curve and decision curve. RESULTS: The AUCs of the training set and test set in the radiomics model and comprehensive model were 0.817 and 0.731 and 0.982 and 0.886, respectively. The calibration curve showed that the two models were in good agreement. The clinical decision curve showed that both methods had good clinical practicability. CONCLUSION: The radiomics model based on ultrasound images has a good predictive effect for the preoperative differential diagnosis of type I and type II epithelial ovarian cancer. The comprehensive model has higher prediction efficiency.

Radiomics Analysis Based on Ultrasound Images to Distinguish the Tumor Stage and Pathological Grade of Bladder Cancer.

OBJECTIVES: To identify the clinical value of ultrasound radiomic features in the preoperative prediction of tumor stage and pathological grade of bladder cancer (BLCA) patients. METHODS: We retrospectively collected patients who had been diagnosed with BLCA by pathology. Ultrasound-based radiomic features were extracted from manually segmented regions of interest. Participants were randomly assigned to a training cohort and a validation cohort at a ratio of 7:3. Radiomic features were Z-score normalized and submitted to dimensional reduction analysis (including Spearman's correlation coefficient analysis, the random forest algorithm, and statistical testing) for core feature selection. Classifiers for tumor stage and pathological grade prediction were then constructed. Prediction performance was estimated by the area under the curve (AUC) of the receiver operating characteristic curve and was verified by the validation cohort. RESULTS: A total of 5936 radiomic features were extracted from each of the ultrasound images obtained from 157 patients. The BLCA tumor stage and pathological grade prediction models were developed based on 30 and 35 features, respectively. Both models showed good predictive ability. For the tumor stage prediction model, the AUC was 0.94 in the training cohort and 0.84 in the validation cohort. For the pathological grade model, the AUCs obtained were 0.84 in the training cohort and 0.75 in the validation cohort. CONCLUSIONS: The ultrasound-based radiomics models performed well in the preoperative tumor staging and pathological grading of BLCA. These findings should be applied clinically to optimize treatment and to assess prognoses for BLCA.

A radiogenomics signature for predicting the clinical outcome of bladder urothelial carcinoma.

OBJECTIVES: To determine the integrative value of contrast-enhanced computed tomography (CECT), transcriptomics data and clinicopathological data for predicting the survival of bladder urothelial carcinoma (BLCA) patients. METHODS: RNA sequencing data, radiomics features and clinical parameters of 62 BLCA patients were included in the study. Then, prognostic signatures based on radiomics features and gene expression profile were constructed by using least absolute shrinkage and selection operator (LASSO) Cox analysis. A multi-omics nomogram was developed by integrating radiomics, transcriptomics and clinicopathological data. More importantly, radiomics risk score-related genes were identified via weighted correlation network analysis and submitted to functional enrichment analysis. RESULTS: The radiomics and transcriptomics signatures significantly stratified BLCA patients into high- and low-risk groups in terms of the progression-free interval (PFI). The two risk models remained independent prognostic factors in multivariate analyses after adjusting for clinical parameters. A nomogram was developed and showed an excellent predictive ability for the PFI in BLCA patients. Functional enrichment analysis suggested that the radiomics signature we developed could reflect the angiogenesis status of BLCA patients. CONCLUSIONS: The integrative nomogram incorporated CECT radiomics, transcriptomics and clinical features improved the PFI prediction in BLCA patients and is a feasible and practical reference for oncological precision medicine. KEY POINTS: • Our radiomics and transcriptomics models are proved robust for survival prediction in bladder urothelial carcinoma patients. • A multi-omics nomogram model which integrates radiomics, transcriptomics and clinical features for prediction of progression-free interval in bladder urothelial carcinoma is established. • Molecular functional enrichment analysis is used to reveal the potential molecular function of radiomics signature.

An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma.

BACKGROUND The tumor microenvironment is largely orchestrated by the immune cells. Considerable evidence has shown their excellent clinicopathological application value in assessment of clinical outcomes and immunotherapy efficacy. Hence, a moderate, individualized prognostic signature based on immune cells that can estimate prognosis and reflect the immune microenvironment in hepatocellular carcinoma (HCC) patients is greatly needed. MATERIAL AND METHODS Here, we systematically analyzed the expression differences and survival prediction value of tumor infiltrating immune cells by analyzing 638 HCC patients from 3 public cohorts, including 2 microarray datasets and 1 RNA sequencing dataset. CIBERSORT software, a computational algorithm, was used to calculate the relative levels of immune cells. Three immune microenvironment subtypes were defined via ConsensuClusterPlus package. Univariate and multivariate survival analyses were used to develop an individualized immune prognostic index based on immune cell pairs. RESULTS Notably, HCC patients with higher immune signatures score, utterly appreciable, suffered inferior prognosis (hazard ratio=2.742; 95% confidence interval: 1.887-3.983; P.

Genome-Wide Analysis of Prognostic lncRNAs, miRNAs, and mRNAs Forming a Competing Endogenous RNA Network in Hepatocellular Carcinoma.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) is the most prevalent subtype of primary liver tumor worldwide. Growing evidence has led to a consensus that long non-coding RNAs (lncRNAs) have considerable influence on tumorigenesis and tumor progression of HCC via the mechanism of competing endogenous RNAs (ceRNAs). METHODS: Here, we systematically investigated the expression landscape and clinical prognostic value of lncRNAs, micorRNAs (miRNAs), and mRNAs from The Cancer Genome Atlas. Differentially expressed RNAs were submitted to Cox regression analysis and the construction of prognostic indexes. A lncRNA-miRNA-mRNA regulatory network was then constructed based on interaction information derived from miRcode, TargetScan, miRTarBase, and miRDB. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed to reveal and determine the functional roles of the ceRNA network in the prognosis of HCC. RESULTS: We detected 77 differentially expressed lncRNAs, 29 differentially expressed miRNAs, and 1014 differentially expressed mRNAs in HCC, which were significantly associated with the overall survival of patients with HCC. We developed three prognostic prediction models that showed moderate predicting prognosis performance and were highly correlated with tumor burden, histological grade and pathological stage. Additionally, 10 survival-related lncRNAs, 6 survival-related miRNAs, and 31 survival-related mRNAs were included to develop a ceRNA network. Further functional enrichment analysis suggested that the ceRNA network was associated with a dismal prognosis for patients with HCC by disturbing the homeostasis of the cell cycle. CONCLUSION: Together, our study highlights the significant roles of lncRNAs in the development and implementation of monitoring surveillance and prognosis of HCC and provides a deeper understanding of the lncRNA-related ceRNA regulatory mechanism in the pathogenesis of HCC.

Comprehensive and Integrative Analysis Reveals the Diagnostic, Clinicopathological and Prognostic Significance of Polo-Like Kinase 1 in Hepatocellular Carcinoma.

BACKGROUND/AIMS: Liver cancer has the second highest cancer-related death rate globally and has relatively few targeted therapeutics. Polo-like kinase 1 (PLK1) is a fascinating trigger of the cell cycle; however, the still-rudimentary understanding of PLK1 at present is a significant barrier to its clinical applications. Here, we comprehensively clarified the clinicopathological value and potential functions of PLK1 in hepatocellular carcinoma (HCC). METHODS: HCC-related microarrays, RNA-sequencing datasets and published studies were deeply mined and integrated from The Cancer Genome Atlas, Gene Expression Omnibus, ArrayExpress, Oncomine, literature databases, and immunohistochemistry experiments. Meanwhile, the associations between PLK1 expression and its clinicopathological implications and prognostic value in HCC patients were assessed. The standardized mean difference, summary receiver operating characteristic curve and the corresponding area under the curve, hazard ratios, odds ratios (ORs), and their 95% confidence intervals (CIs) were examined by STATA 12.0. Additionally, several bioinformatics methods were used to identify the potential function of PLK1 in HCC. RESULTS: Comprehensive analyses revealed that PLK1 was significantly increased in HCC (standardized mean difference = 1.34, 95% CI: 1.03-1.65, P < 0.001). The results of diagnostic tests specified that in the summary receiver operating characteristic curve, the area under the curve was 0.88 (95% CI: 0.85-0.90). Furthermore, an elevated PLK1 level significantly predicted unfavorable overall survival (hazard ratio = 1.78, 95% CI: 1.10-2.88, P = 0.019) and was correlated with female gender (OR = 0.73, 95% CI: 0.56-0.95, P = 0.017), tumor thrombus (OR = 3.97, 95% CI: 1.46-10.78, P < 0.001), metastasis (OR = 3.46, 95% CI: 1.33-9.01, P = 0.011), pathologic stage (OR = 1.56, 95% CI: 1.17-2.07, P = 0.002), Barcelona Clinic Liver Cancer stage (OR = 5.76, 95% CI: 2.17-15.28, P < 0.001) and histologic grade (OR = 2.33, 95% CI: 1.12-487, P = 0.024). Through bioinformatics methods, we determined that enhancing the proliferative effect of PLK1 in HCC was associated with a series of hub genes and the activation of the cell cycle pathway. CONCLUSIONS: These findings substantiated that PLK1 may be an independent prognostic biomarker in HCC and may facilitate the development of targeted precision oncology.

A Preliminary Investigation of PVT1 on the Effect and Mechanisms of Hepatocellular Carcinoma: Evidence from Clinical Data, a Meta-Analysis of 840 Cases, and In Vivo Validation.

BACKGROUND/AIMS: Hepatocellular carcinoma (HCC) remains a difficult problem that significantly affects the survival of the afflicted patients. Accumulating evidence has demonstrated the functions of long non-coding RNA (lncRNA) in HCC. In the present study, we aimed to explore the potential roles of PVT1 in the tumorigenesis and progression of HCC. METHODS: In this study, quantitative reverse transcription-polymerase chain reaction (RT-qPCR) was applied to detect the differences between PVT1 expression in HCC tissues and cell lines. Then, the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were searched to confirm the relationship between PVT1 expression and HCC. Moreover, a meta-analysis comprising TCGA, GEO, and RT-qPCR was applied to estimate the expression of PVT1 in HCC. Then, cell proliferation was evaluated in vitro. A chicken chorioallantoic membrane (CAM) model of HCC was constructed to measure the effect on tumorigenicity in vivo. To further explore the sponge microRNA (miRNA) of PVT1 in HCC, we used TCGA, GEO, a gene microarray, and target prediction algorithms. TCGA and GEO and the gene microarray were used to select the differentially expressed miRNAs, and the different target prediction algorithms were applied to predict the target miRNAs of PVT1. RESULTS: We found that PVT1 was markedly overexpressed in HCC tissue than in normal liver tissues based on both RT-qPCR and data from TCGA, and the overexpression of PVT1 was closely related to the gender and race of the patient as well as to higher HCC tumor grades. Also, a meta-analysis of 840 cases from multiple sources (TCGA, GEO and the results of our in-house RT-qPCR) showed that PVT1 gained moderate value in discriminating HCC patients from normal controls, confirming the results of RT-qPCR. Additionally, the upregulation of PVT1 could promote HCC cell proliferation in vitro and vivo. Based on the competing endogenous RNA (ceRNA) theory, the PVT1/miR-424-5p/INCENP axis was finally selected for further research. The in silico prediction revealed that there were complementary sequences between PVT1 and miR-424-5p as well as between miR-424-5p and INCENP. Furthermore, a negative correlation trend was found between miR-424-5p and PVT1 based on RT-qPCR, whereas a positive correlation trend was found between PVT1 and INCENP based on data from TCGA. Also, INCENP small interfering RNA (siRNA) could significantly inhibit cell proliferation and viability. CONCLUSIONS: We hypothesized that PVT1 could affect the biological function of HCC cells via targeting miR-424-5p and regulating INCENP. Focusing on the new insight of the PVT1/miR-424-5p/INCENP axis, this study provides a novel perspective for HCC therapeutic strategies.

A circRNA-miRNA-mRNA network identification for exploring underlying pathogenesis and therapy strategy of hepatocellular carcinoma.

BACKGROUND: Circular RNAs (circRNAs) have received increasing attention in human tumor research. However, there are still a large number of unknown circRNAs that need to be deciphered. The aim of this study is to unearth novel circRNAs as well as their action mechanisms in hepatocellular carcinoma (HCC). METHODS: A combinative strategy of big data mining, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and computational biology was employed to dig HCC-related circRNAs and to explore their potential action mechanisms. A connectivity map (CMap) analysis was conducted to identify potential therapeutic agents for HCC. RESULTS: Six differently expressed circRNAs were obtained from three Gene Expression Omnibus microarray datasets (GSE78520, GSE94508 and GSE97332) using the RobustRankAggreg method. Following the RT-qPCR corroboration, three circRNAs (hsa_circRNA_102166, hsa_circRNA_100291 and hsa_circRNA_104515) were selected for further analysis. miRNA response elements of the three circRNAs were predicted. Five circRNA-miRNA interactions including two circRNAs (hsa_circRNA_104515 and hsa_circRNA_100291) and five miRNAs (hsa-miR-1303, hsa-miR-142-5p, hsa-miR-877-5p, hsa-miR-583 and hsa-miR-1276) were identified. Then, 1424 target genes of the above five miRNAs and 3278 differently expressed genes (DEGs) on HCC were collected. By intersecting the miRNA target genes and the DEGs, we acquired 172 overlapped genes. A protein-protein interaction network based on the 172 genes was established, with seven hubgenes (JUN, MYCN, AR, ESR1, FOXO1, IGF1 and CD34) determined from the network. The Gene Oncology, Kyoto Encyclopedia of Genes and Genomes and Reactome enrichment analyses revealed that the seven hubgenes were linked with some cancer-related biological functions and pathways. Additionally, three bioactive chemicals (decitabine, BW-B70C and gefitinib) based on the seven hubgenes were identified as therapeutic options for HCC by the CMap analysis. CONCLUSIONS: Our study provides a novel insight into the pathogenesis and therapy of HCC from the circRNA-miRNA-mRNA network view.

Up-regulation of CTD-2547G23.4 in hepatocellular carcinoma tissues and its prospective molecular regulatory mechanism: a novel qRT-PCR and bioinformatics analysis study.

BACKGROUND: Dysregulated expression of long non-coding RNAs (lncRNAs) has been reported in the pathogenesis and progression of multiple cancers, including hepatocellular carcinoma (HCC). LncRNA CTD-2547G23.4 is a novel lncRNA, and its role in HCC is still unknown. Here, we aimed to clarify the expression pattern and clinical value of CTD-2547G23.4 and to investigate the prospective regulatory mechanism via bioinformatics analysis in HCC. METHODS: To identify differentially expressed lncRNAs in HCC, we downloaded RNA-Seq data for HCC and adjacent non-tumour tissues via The Cancer Genome Atlas (TCGA). CTD-2547G23.4 was selected by using the R language and receiver operating characteristic curve analysis. Furthermore, we validated the differential expression of CTD-2547G23.4 via Gene Expression Omnibus (GEO), ArrayExpress, Oncomine databases and quantitative real-time polymerase chain reaction (qRT-PCR). The relationship between the CTD-2547G23.4 level and clinic pathological parameters was also assessed. To further probe the role of CTD-2547G23.4 in HCC cell cycle, lentivirus-mediated small interfering RNA was applied to silence CTD-2547G23.4 expression in Huh-7 cell line. In addition, the related genes of CTD-2547G23.4 gathered from The Atlas of Noncoding RNAs in Cancer (TANRIC) database and Multi Experiment Matrix (MEM) were assessed with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes, Protein Analysis Through Evolutionary Relationships and protein-protein interaction (PPI) networks. RESULTS: CTD-2547G23.4 expression was remarkably higher in 370 HCC tissue samples than that in adjacent non-tumour liver tissues (48.762 ± 27.270 vs. 14.511 ± 8.341, P < 0.001) from TCGA dataset. The relative expression level of CTD-2547G23.4 in HCC was consistently higher than that in adjacent non-cancerous tissues (2.464 ± 0.833 vs. 1.813 ± 0.784, P = 0.001) as assessed by real time RT-qPCR. The area under the curve of the summary receiver operating characteristic curve was 0.8720 based on TCGA, qRT-PCR and GEO data. Further analysis indicated that the increased expression levels of CTD-2547G23.4 were associated with the neoplasm histologic grade and vascular tumour cell type. The expression of CTD-2547G23.4 was significantly downregulated in CTD-2547G23.4 knockdown cells. Moreover, cell cycle analysis revealed that CTD-2547G23.4 depletion in Huh-7 cell line led to S phase arrest. Furthermore, 314 related genes identified by TANRIC and MEM databases were processed with a pathway analysis. The bioinformatics analysis indicated that CTD-2547G23.4 might play a key role in the progress of HCC through four hub genes, SRC, CREBBP, ADCY8 and PPARA. CONCLUSIONS: Collectively, we put forward the hypothesis that the novel lncRNA CTD-2547G23.4 may act as an exceptional clinical index and promote the HCC tumourigenesis and progression via various related genes.

Expression of the Long Intergenic Non-Protein Coding RNA 665 (LINC00665) Gene and the Cell Cycle in Hepatocellular Carcinoma Using The Cancer Genome Atlas, the Gene Expression Omnibus, and Quantitative Real-Time Polymerase Chain Reaction.

BACKGROUND Long non-coding RNAs (lncRNAs) have a role in physiological and pathological processes, including cancer. The aim of this study was to investigate the expression of the long intergenic non-protein coding RNA 665 (LINC00665) gene and the cell cycle in hepatocellular carcinoma (HCC) using database analysis including The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and quantitative real-time polymerase chain reaction (qPCR). MATERIAL AND METHODS Expression levels of LINC00665 were compared between human tissue samples of HCC and adjacent normal liver, clinicopathological correlations were made using TCGA and the GEO, and qPCR was performed to validate the findings. Other public databases were searched for other genes associated with LINC00665 expression, including The Atlas of Noncoding RNAs in Cancer (TANRIC), the Multi Experiment Matrix (MEM), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction (PPI) networks. RESULTS Overexpression of LINC00665 in patients with HCC was significantly associated with gender, tumor grade, stage, and tumor cell type. Overexpression of LINC00665 in patients with HCC was significantly associated with overall survival (OS) (HR=1.47795%; CI: 1.046-2.086). Bioinformatics analysis identified 469 related genes and further analysis supported a hypothesis that LINC00665 regulates pathways in the cell cycle to facilitate the development and progression of HCC through ten identified core genes: CDK1, BUB1B, BUB1, PLK1, CCNB2, CCNB1, CDC20, ESPL1, MAD2L1, and CCNA2. CONCLUSIONS Overexpression of the lncRNA, LINC00665 may be involved in the regulation of cell cycle pathways in HCC through ten identified hub genes.

Overexpression of LncRNA HOTAIR is Associated with Poor Prognosis in Thyroid Carcinoma: A Study Based on TCGA and GEO Data.

The role of long non-coding RNA (lncRNA) HOX transcript antisense RNA (HOTAIR) in thyroid carcinoma (TC) remains unclear. The current study was aimed to assess the clinical value of HOTAIR expression levels in TC based on publically available data and to evaluate its potential signaling pathways. The expression data of HOTAIR and clinical information concerning TC were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. Furthermore, 3 online biological databases, Starbase, Cbioportal, and Multi Experiment Matrix, were used to identify HOTAIR-related genes in TC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Panther pathway analyses were then undertaken to study the most enriched signaling pathways in TC (EASE score<0.1, Bonferroni<0.05). The TCGA results demonstrated that the expression level of HOTAIR in TC tissues was significantly increased compared with non-cancerous tissues (p<0.001). HOTAIR over-expression was significantly associated with poor survival in TC patients (p=0.03). Meta-analyses of GEO datasets revealed a trend consistent with the above results on HOTAIR expression levels in TC (SMD=0.23; 95%CI, 0.00-0.45; p=0.047). Finally, the results of functional analysis for HOTAIR-related genes indicated that HOTAIR might participate in tumorigenesis via the Wnt signaling pathway. In conclusion, our study demonstrates that HOTAIR may be involved in thyroid carcinogenesis, and the over-expression of HOTAIR could act as a biomarker associated with a poor outcome in TC patients. Moreover, the Wnt signaling pathway may be the key pathway regulated by HOTAIR in TC.

Investigation of Circular RNA Expression Profiles in Ultrasound-guided Incomplete Radiofrequency Ablation Transplanted Tumor Models of Human Liver Cancer.

BACKGROUND: Abnormally expressed circular RNAs (circRNAs) are associated with many diseases and have important biological effects on the regulation of gene expression. However, the circRNA expression profile in incomplete radiofrequency ablation (RFA)-treated liver cancer (LC) patients has not been characterized. This study investigated the potential biological effects of differentially expressed (DE) circRNAs in an incomplete RFA-treated transplantation tumor model of human LC. MATERIAL/METHODS: A circRNA microarray was utilized to analyze changes in the circRNA expression profiles. CircRNA host gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also conducted using computational biology. Quantitative real-time PCR (qPCR) was also performed on the selected DE-circRNAs to verify the reliability of the microarray. The circRNA/miRNA interactions were predicted by Arraystar software and confirmed by a dual-luciferase assay. RESULTS: Following RFA incomplete ablation, 76 DE-circRNAs were detected (|fold change |>1.5, P-value < 0.05), 21 of which were upregulated and 55 of which were downregulated. Computational biological analysis revealed that the T-cell receptor signaling pathway was the most significantly enriched pathway of the genes related to altered expression, as indicated by enrichment of LCK, AKT3 and DLG1. PCR results for the upregulated hsa_circRNA_103595 and downregulated hsa_circRNA_001264 indicated that the circRNA microarray sequencing results were reliable. Double luciferase reporter assays confirmed that hsa-miR-185-3p was the target miRNA of hsa_circRNA_103595. CONCLUSIONS: The current study confirmed the changes in the expression profiles of circRNAs in tumor transplantation models after incomplete ablation, these changes may play a crucial role in the pathophysiological process of residual cancer transplantation tumors. These findings could lead to new directions for investigating the molecular biological mechanisms underlying RFA-treated LC as well as new ideas for treating LC by regulating circRNAs.

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.

RAD21: A Key Transcriptional Regulator in the Development of Residual Liver Cancer.

Objective: Thermal ablation is a commonly used therapy for hepatocellular carcinoma (HCC). Nevertheless, inadequate ablation can lead to the survival of residual HCC, potentially causing rapid progression. The underlying mechanisms for this remain unclear. This study explores the molecular mechanism responsible for the rapid progression of residual HCC. Methods: We established an animal model of inadequate ablation in BALB/c nude mice and identified a key transcriptional regulator through high-throughput sequencing. Subsequently, we conducted further investigations on RAD21. We evaluated the expression and clinical significance of RAD21 in HCC and studied its impact on HCC cell function through various assays, including CCK-8, wound healing, Transwell migration and invasion. In vitro experiments established an incomplete ablation model verifying RAD21 expression and function. Using ChIP-seq, we determined potential molecules regulated by RAD21 and investigated how RAD21 influences residual tumor development. Results: High RAD21 expression in HCC was confirmed and correlated with low tumor cell differentiation, tumor growth, and portal vein thrombosis. Silencing RAD21 inhibited the migration, invasion, and proliferation significantly in liver cancer cells. Patients with high RAD21 levels showed elevated multiple inhibitory immune checkpoint levels and a lower response rate to immune drugs. Heat treatment intensified the malignant behavior of liver cancer cells, resulting in increased migration, invasion, and proliferation. After subjecting it to heat treatment, the results indicated elevated RAD21 levels in HCC. Differentially expressed molecules regulated by RAD21 following incomplete ablation were primarily associated with the VEGF signaling pathway, focal adhesion, angiogenesis, and hepatocyte growth factor receptor signaling pathway etc. Conclusion: The upregulation of RAD21 expression after incomplete ablation may play a crucial role in the rapid development of residual tumors and could serve as a novel therapeutic target.

Diagnostic performance and interreader agreement of CEUS LI-RADS in ≤ 30 mm liver nodules with different experienced radiologists.

PURPOSE: To explore the diagnostic performance and interreader agreement of CEUS LI-RADS in diagnosing ≤ 30 mm liver nodules with different experienced radiologists. METHODS: Between January 2018 and October 2020, 244 patients at high-risk for HCC who underwent CEUS were enrolled. Two novice radiologists and two expert radiologists independently evaluated LI-RADS categories and main features. Kappa (κ) and Kendall's tests were employed to evaluate the interreader agreement of CEUS LI-RADS. The diagnostic performance was determined based on sensitivity, specificity, accuracy, PPV and NPV. RESULTS: The interreader agreement for arterial phase hyperenhancement, late and mild washout, early washout, and rim hyperenhancement was moderate to almost perfect (κ, 0.44-0.93) among the different levels of radiologists. The interreader agreement for the LI-RADS categories was substantial to almost perfect (κ, 0.78-0.88). However, the interreader agreement for marked washout was fair to moderate (κ, 0.28-0.50). When CEUS LR-5 was used as a diagnostic criterion for HCC, there were no statistical differences in sensitivity, specificity, accuracy, PPV and NPV among the radiologists (p > 0.05), except for the differences between Reader 4 and the remaining three radiologists in terms of accuracy and sensitivity (p < 0.05). CONCLUSION: CEUS LI-RADS has good diagnostic agreement for ≤ 30 mm liver nodules among experienced radiologists.

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.

Incomplete thermal ablation-induced up-regulation of transcription factor nuclear receptor subfamily 2, group F, member 6 (NR2F6) contributes to the rapid progression of residual liver tumor in hepatoblastoma.

Hepatoblastoma is a kind of extreme malignancy frequently diagnosed in children. Although surgical resection is considered as the first-line treatment for hepatoblastoma, a relatively large population of patients have lost the preferred opportunity for surgery. Administration of locoregional ablation enables local tumor control but with the deficiency of insufficient ablation, residual tumor, and rapid progression. In this study, we integrated 219 hepatoblastoma and 121 non-cancer liver tissues to evaluate the expression of NR2F6, from which a higher NR2F6 level was found in hepatoblastoma compared with non-cancer livers with a standard mean difference (SMD) of 1.04 (95% CI: 0.79, 1.29). The overexpression of NR2F6 also appeared to be an efficient indicator in distinguishing hepatoblastoma tissues from non-cancer liver tissues from the indication of a summarized AUC of 0.90, with a pooled sensitivity of 0.76 and a pooled specificity of 0.89. Interestingly, nude mouse xenografts provided direct evidence that overexpressed NR2F6 was also detected in residual tumor compared to untreated hepatoblastoma. Chromatin immunoprecipitation-binding data in HepG2 cells and transcriptome analysis of HepG2 xenografts were combined to identify target genes regulated by NR2F6. We finally selected 150 novel target genes of NR2F6 in residual tumor of incomplete ablation, and these genes appeared to be associated with the biological regulation of lipid metabolism-related pathway. Accordingly, targeting NR2F6 holds a therapeutic promise in treating residual recurrent hepatoblastoma after incomplete ablation.