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.

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.

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.

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.

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.

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.

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.

Downregulation of miR­193a­3p via targeting cyclin D1 in thyroid cancer.

Thyroid cancer (TC) is a frequently occurring malignant tumor with a rising steadily incidence. microRNA (miRNA/miR)­193a­3p is an miRNA that is associated with tumors, playing a crucial role in the genesis and progression of various cancers. However, the expression levels of miR­193a­3p and its molecular mechanisms in TC remain to be elucidated. The present study aimed to probe the expression of miR­193a­3p and its clinical significance in TC, including its underlying molecular mechanisms. Microarray and RNA sequencing data gathered from three major databases, specifically Gene Expression Omnibus (GEO), ArrayExpress and The Cancer Genome Atlas (TCGA) databases, and the relevant data from the literature were used to examine miR­193a­3p expression. Meta­analysis was also conducted to evaluate the association between clinicopathological parameters and miR­193a­3p in 510 TC and 59 normal samples from the TCGA database. miRWalk 3.0, and the TCGA and GEO databases were used to predict the candidate target genes of miR­193a­3p. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes and protein­protein interaction network enrichment analyses were conducted by using the predicted candidate target genes to investigate the underlying carcinogenic mechanisms. A dual luciferase assay was performed to validate the targeting regulatory association between the most important hub gene cyclin D1 (CCND1) and miR­193a­3p. miR­193a­3p expression was considerably downregulated in TC compared with in the non­cancer controls (P<0.001). The area under the curve of the summary receiver operating characteristic was 0.80. Downregulation of miR­193a­3p was also significantly associated with age, sex and metastasis (P=0.020, 0.044 and 0.048, respectively). Bioinformatics analysis indicated that a low miR­193a­3p expression may augment CCND1 expression to affect the biological processes of TC. In addition, CCND1, as a straightforward target, was validated through a dual luciferase assay. miR­193a­3p and CCND1 may serve as prognostic biomarkers of TC. Finally, miR­193a­3p may possess a crucial role in the genesis and progression of TC by altering the CCND1 expression.

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.

Predictive value of hypoxia, metabolism and immune factors for prognosis in hepatocellular carcinoma: a retrospective analysis and multicenter validation study.

The tumor microenvironment (TME), as a potent and pervasive factor of tumorigenesis and tumor progression, has a profound impact on the clinical outcomes of hepatocellular carcinoma (HCC). A systematic analysis of TME factors in HCC is still lacking and urgently needed. In this retrospective analysis and multicenter validation study, a total of 987 HCC patients with RNA-seq or microarray data and the corresponding clinical information from five cohorts were included. A TME risk score was developed based on five factors (hypoxia, nucleotide, TCA cycle, T helper cells and activated CD8 T cells). We also identified various types of clinical parameters and molecular features associated with the TME risk score. The TME risk factor network depicts close associations among the factors. Our TME risk score could be a practical and reliable predictor that can stratify patients according to distinct clinical outcomes and was validated by integrating five HCC patient cohorts (HR= 2.27, 95% CI: 1.79-2.86, P<0.001). Pan-cancer analysis also suggested that the prognostic signature was an effective prognostic indicator in 9,122 patients across 30 types of cancer. Correlation analysis revealed that the TME risk score was significantly associated with tumor progression-related clinical factors and molecular factors. TME factors are perturbations in HCC patients, and these alterations are vital determinants of both clinical outcomes and biological characteristics. The TME risk score we proposed is valuable for deciphering the molecular characteristics of the TME in HCC and is an effective prognostic predictor for HCC prognosis evaluation.

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.

Preoperative Ultrasound Radiomics Signatures for Noninvasive Evaluation of Biological Characteristics of Intrahepatic Cholangiocarcinoma.

RATIONALE AND OBJECTIVES: The purpose of this study was to establish and validate radiomics signatures based on ultrasound (US) medicine images to assess the biological behaviors of intrahepatic cholangiocarcinoma (ICC) in a noninvasive manner. MATERIALS AND METHODS: This study consisted of 128 ICC patients. We focused on evaluating six pathological features: microvascular invasion, perineural invasion, differentiation, Ki-67, vascular endothelial growth factor, and cytokeratin 7. Region of interest (ROI) of ICC was identified by manually plotting the tumor contour on the grayscale US image. We extracted radiomics features from medical US imaging. Then, dimensionality reduction methods and classifiers were used to develop radiomic signatures for evaluating six pathological features in ICC. Finally, independent validation datasets were used to assess the radiomic signatures performance. RESULTS: We extracted 1076 quantitative characteristic parameters on the US medicine images. Based on extracted radiomics features, the best performing radiomic signatures for evaluating microvascular invasion features were produced by hypothetical test + support vector machine (SVM), perineural invasion subgroup were least absolute shrinkage and selection operator + principal component analysis + support vector machine, differentiation subgroup were hypothetical test + decision tree, Ki-67 subgroup were hypothetical test + logistic regression, vascular endothelial growth factor subgroup were hypothetical test + Gradient Boosting Decision Tree (GBDT), and cytokeratin 7 subgroup were hypothetical test + bagging, respectively. CONCLUSION: Through the high-throughput radiomics analysis based on US medicine images, we proposed radiomics signatures that have moderate efficiency in predicting the biological behaviors of ICC noninvasively.

Reduced expression of microRNA-139-5p in hepatocellular carcinoma results in a poor outcome: An exploration the roles of microRNA-139-5p in tumorigenesis, advancement and prognosis at the molecular biological level using an integrated meta-analysis and bioinformatic investigation.

Hepatocellular carcinoma (HCC) is generally considered one of the most common gastrointestinal malignant tumors, characterized by high invasiveness and metastatic rate, as well as insidious onset. A relationship between carcinogenicity and aberrant microRNA-139-5p (miR-139-5p) expression has been identified in multiple tumors while the specific molecular mechanisms of miR-139-5p in HCC have not yet been thoroughly elucidated. A meta-analysis of available data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, ArrayExpress and Oncomine databases, as well as the published literature, was comprehensively conducted with the aim of examining the impact of miR-139-5p expression on HCC. Additionally, predicted downstream target genes were confirmed using a series of bioinformatics tools. Moreover, a correlative biological analysis was performed to ascertain the precise function of miR-139-5p in HCC. The results revealed that the expression of miR-139-5p was noticeably lower in HCC compared with non-tumor liver tissues according to the pooled standard mean difference, which was -0.84 [95% confidence interval (CI): -1.36 to -0.32; P<0.001]. Furthermore, associations were detected between miR-139-5p expression and certain clinicopathological characteristics of TCGA samples, including tumor grade, pathological stage and T stage. Moreover, the pooled hazard ratio (HR) for overall survival (HR=1.37; 95% CI: 1.07-1.76; P=0.001) indicated that decreased miR-139-5p expression was a risk factor for adverse outcomes. Additionally, 382 intersecting genes regulated by miR-139-5p were obtained and assembled in signaling pathways, including 'transcription factor activity, sequence-specific DNA binding', 'pathways in cancer' and 'Ras signaling pathway'. Notably, four targeted genes that were focused in 'pathways in cancer' were identified as hub genes and immunohistochemical staining of the proteins encoded by these four hub genes in liver tissues, explored using the Human Protein Atlas database, confirmed their expression patterns in HCC and normal liver tissues Findings of the present study suggest that reduced miR-139-5p expression is capable of accelerating tumor progression and is associated with a poor clinical outcome by modulating the expression of downstream target genes involved in tumor-associated signaling pathways.

Oncogenic value of microRNA-15b-5p in hepatocellular carcinoma and a bioinformatics investigation.

miR-15b-5p has frequently been reported to function as a biomarker in some malignancies; however, the function of miR-15b-5p in hepatocellular carcinoma (HCC) and its molecular mechanism are still not well understood. The present study was designed to confirm the clinical value of miR-15b-5p and further explore its underlying molecular mechanism. A comprehensive investigation of the clinical value of miR-15b-5p in HCC was investigated by data mining The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets as well as literature. In addition, intersected target genes of miR-15b-5p were predicted using the miRWalk database and differentially expressed genes of HCC from TCGA. Furthermore, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out. Then, a protein-protein interaction network (PPI) was constructed to reveal the interactions between some hub target genes of miR-15b-5p. The miR-15b-5p expression level in HCC was predominantly overexpressed compared with non-HCC tissues samples (SMD=0.618, 95% CI: 0.207, 1.029; P<0.0001) based on 991 HCC and 456 adjacent non-HCC tissue samples. The pooled summary receiver operator characteristic (SROC) of miR-15b-5p was 0.81 (Q*=0.74), and the pooled sensitivity and specificity of miR-15b-5p in HCC were 72% (95% CI: 69-75%) and 68% (95% CI: 65-72%), respectively. Bioinformatically, 225 overlapping genes were selected as prospective target genes of miR-15b-5p in HCC, and profoundly enriched GO terms and KEGG pathway investigation in silico demonstrated that the target genes were associated with prostate cancer, proximal tubule bicarbonate reclamation, heart trabecula formation, extracellular space, and interleukin-1 receptor activity. Five genes (ACACB, RIPK4, MAP2K1, TLR4 and IGF1) were defined as hub genes from the PPI network. The high expression of miR-15b-5p could play an essential part in hepatocarcinogenesis through diverse regulation approaches.

Integrated assessment of CDK1 upregulation in thyroid cancer.

Cyclin-dependent kinase 1 (CDK1) has a unique role in cell cycle regulation, as it is crucial for cell cycle progression and cell division. The aim of the present study was to use a combination of various detection methods to examine the expression and clinical significance of CDK1 in thyroid cancer (THCA). We used in-house tissue microarrays, immunohistochemistry, public RNA-sequencing, gene microarrays, and meta-analyses to conduct a comprehensive analysis of the role of CDK1 in the occurrence and development of THCA. CDK1 protein expression was notably higher in THCA tissues than in non-cancer tissues as evidenced by the in-house tissue microarrays. The expression of CDK1 protein was also significantly higher in pathologic T3-T4 than in T1-T2 samples. The pooled standardized mean difference (SMD) for CDK1 was 0.71 (95% CI, 0.46-0.95) including a total of 931 THCA and 585 non-cancerous thyroid tissue samples. An aggregation of the immunohistochemistry results and the RNA-sequencing/microarray findings gave a pooled SMD for CDK1 expression of 2.13 (95% CI, 1.30-2.96). The final area under curve (AUC) for the summarized receiver operating characteristic (sROC) was 0.7941 using all 1102 cases of THCA and 672 cases of controls. KEGG analysis with the co-expressed genes of CDK1 in THCA demonstrated the top enriched pathways to be the cell cycle, thyroid hormone synthesis, autoimmune thyroid disease, etc. In summary, we reveal the overexpression of CDK1 in THCA based on multiple detection methods that combine independent cohorts. However, further studies are required to elucidate the molecular mechanisms of CDK1 that promotes the biological aggressiveness of THCA cells.

Potential clinical value and putative biological function of miR-122-5p in hepatocellular carcinoma: A comprehensive study using microarray and RNA sequencing data.

In order to determine the diagnostic efficacy of microRNA (miR)-122-5p and to identify the potential molecular signaling pathways underlying the function of miR-122-5p in hepatocellular carcinoma (HCC), the expression profiles of data collected from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and literature databases were analyzed, along with any associations between clinicopathological characteristics and the diagnostic value of miR-122-5p in HCC. The intersection of 12 online prediction databases and differentially expressed genes from TCGA and GEO were utilized in order to select the prospective target genes of miR-122-5p in HCC. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction network (PPI) analyses were subsequently performed based on the selected target genes. The average expression level of miR-122-5p was decreased in HCC patients compared with controls from TCGA database (P<0.001), and the downregulation of miR-122-5p was significantly associated with HCC tissues (P<0.001), tumor vascular invasion (P<0.001), metastasis (P=0.001), sex (P=0.006), virus infection status (P=0.001) and tissue (compared with serum; P<0.001) in cases from the GEO database. The pooled sensitivity and specificity for miR-122-5p to diagnose HCC were 0.60 [95% confidence interval (CI), 0.48-0.71] and 0.81 (95% CI, 0.70-0.89), respectively. The area under the curve (AUC) value was 0.76 (95% CI, 0.72-0.80), while in Meta-DiSc 1.4, the AUC was 0.76 (Q*=0.70). The pooled sensitivity and specificity were 0.60 (95% CI, 0.57-0.62) and 0.79 (95% CI, 0.76-0.81), respectively. A total of 198 overlapping genes were selected as the potential target genes of miR-122-5p, and 7 genes were defined as the hub genes from the PPI network. Cell division cycle 6 (CDC6), minichromosome maintenance complex component 4 (MCM4) and MCM8, which serve pivotal functions in the occurrence and development of HCC, were the most significant hub genes. The regulation of cell proliferation for cellular adhesion and the biosynthesis of amino acids was highlighted in the GO and KEGG pathway analyses. The downregulation of miR-122-5p in HCC demonstrated diagnostic value, worthy of further attention. Therefore, miR-122-5p may function as a tumor suppressor by modulating genome replication.

Exploration of the diagnostic value and molecular mechanism of miR­1 in prostate cancer: A study based on meta­analyses and bioinformatics.

Prostate cancer (PCa) remains a principal issue to be addressed in male cancer­associated mortality. Therefore, the present study aimed to examine the clinical value and associated molecular mechanism of microRNA (miR)­1 in PCa. A meta­analysis was conducted to evaluate the diagnosis of miR­1 in PCa via Gene Expression Omnibus and ArrayExpress datasets, The Cancer Genome Atlas miR­1 expression data and published literature. It was identified that expression of miR­1 was significantly downregulated in PCa. Decreased miR­1 expression possessed moderate diagnostic value, with area under the curve, sensitivity, specificity and odds ratio values at 0.73, 0.77, 0.57 and 4.60, respectively. Using bioinformatics methods, it was revealed that a number of pathways, including the 'androgen receptor signaling pathway', 'androgen receptor activity', 'transcription factor binding' and 'protein processing in the endoplasmic reticulum', were important in PCa. A total of seven hub genes, including phosphoribosylaminoimidazole carboxylase and phosphoribosylaminoimidazolesuccinocarboxamide synthase (PAICS), cadherin 1 (CDH1), SRC proto­oncogene, non­receptor tyrosine kinase, twist family bHLH transcription factor 1 (TWIST1), ZW10 interacting kinetochore protein (ZWINT), PCNA clamp associated factor (KIAA0101) and androgen receptor, among which, five (PAICS, CDH1, TWIST1, ZWINT and KIAA0101) were significantly upregulated and negatively correlated with miR­1, were identified as key miR­1 target genes in PCa. Additionally, it was investigated whether miR­1 and its hub genes were associated with clinical features, including age, tumor status, residual tumor, lymph node metastasis, pathological T stage and prostate specific antigen level. Collectively the results suggest that miR­1 may be involved in the progression of PCa, and consequently be a promising diagnostic marker. The 'androgen receptor signaling pathway', 'androgen receptor activity', 'transcription factor binding' and 'protein processing in the endoplasmic reticulum' may be crucial interactive pathways in PCa. Furthermore, PAICS, CDH1, TWIST1, ZWINT and KIAA0101 may serve as crucial miR­1 target genes in PCa.

Identification of down-regulated microRNAs in thyroid cancer and their potential functions.

BACKGROUND: The mechanism of microRNAs (miRNAs) in thyroid cancer is still unclear. We identified miRNAs with differential expression in thyroid cancer versus normal tissues. METHODS: Microarray datasets were obtained from the GEO and ArrayExpress databases, and from publications found via PubMed, EMBASE, and Web of Science. Differentially expressed miRNAs were identified using the limma package, and their targets predicted using miRWalk. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI) network analyses were performed using these target genes to explore potential carcinogenic mechanisms. Correlations between target gene and miRNA expression levels were examined. Changes in target protein expression were confirmed using data from The Human Protein Atlas and the Cancer Genome Atlas. RESULTS: We ultimately included five datasets, and further analyzed the four miRNAs that were down-regulated in at least four datasets (miR-7-2-3p, miR-138-5p, miR-144-5p, miR-486-5p). Predicted targets were enriched in GO terms including extracellular matrix organization, cell surface, and receptor binding, and in KEGG cancer pathways. PPI analysis identified 10 hub genes as key potential targets of these miRNAs. The expression levels of eight target genes were negatively correlated with those of their respective miRNAs. Furthermore, eight predicted target genes in cancer-related pathways showed up-regulated protein and mRNA expression in thyroid cancer. CONCLUSION: Low miRNA expression in thyroid cancer might influence tumorigenesis via critical pathways. The genes identified here may act as a starting point for further investigation of the carcinogenic mechanisms of these miRNAs.