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Sauer TJ, Bejan A, Segars P, Samei E. Development and CT image-domain validation of a computational lung lesion model for use in virtual imaging trials. Med Phys 2023; 50:4366-4378. [PMID: 36637206 PMCID: PMC10338637 DOI: 10.1002/mp.16222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Revised: 11/03/2022] [Accepted: 12/14/2022] [Indexed: 01/14/2023] Open
Abstract
PURPOSE Computational abnormalities (e.g., lesion models) for use in medical imaging simulation studies are frequently generated using data collected from clinical images. Although this approach allows for highly-customizable lesion detectability studies on clinical computed tomography (CT) data, the ground-truth lesion models produced with this method do not provide a sufficiently realistic lesion morphology for use with current anthropomorphic simulation studies. This work is intended to demonstrate that the new anatomically-informed lesion model presented here is not inferior to the previous lesion model under CT imaging, and can therefore provide a more biologically-informed model for use with simulated CT imaging studies. METHODS The lesion model was simulated initially from a seed cell with 10 μm diameter placed in an anatomical location within segmented lung CT and was allowed to reproduce locally within the available solid angle in discrete time-intervals (corresponding to synchronous cell cycles) up to a size of ∼200 μm in diameter. Daughter cells of generation G were allowed also to reproduce on the next available time-step given sufficient space. At lesion sizes beyond 200 μm in diameter, the health of subregions of cells were tracked with a Markov chain technique, indicating which regions were likely to continue growing, which were likely stable, and which were likely to develop necrosis given their proximity to anatomical features and other lesion cells. For lesion sizes beyond 500 μm, the lesion was represented with three nested, triangulated surfaces (corresponding to proliferating, dormant, and necrotic regions), indicating how discrete volumes of the lesion were behaving at a particular time. Lesions were then assigned smoothly-varying material properties based on their cellular level health in each region, resulting in a multi-material lesion model. The lesions produced with this model were then voxelized and placed into lung CT images for comparison with both prior work and clinical data. This model was subject to an observer study in which cardiothoracic imaging radiologists assessed the realism of both clinical and synthetic lesions in CT images. RESULTS The useable outputs of this work were voxel- or surface-based, validated, computational lesions, at a scale clearly visible on clinical CT (3-4 cm). Analysis of the observer study results indicated that the computationally-generated lesions were indistinguishable from clinical lesions (AUC = 0.49, 95% CI = [0.36, 0.61]) and non-inferior to an earlier image-based lesion model-indicating the advantage of the model for use in both hybrid CT images and in simulated CT imaging of the lungs. CONCLUSIONS Results indicated the non-inferiority of this model as compared to previous methods, indicating the utility of the model for use in both hybrid CT images and in simulated CT imaging.
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Affiliation(s)
- Thomas J. Sauer
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Adrian Bejan
- Department of Mechanical Engineering, Duke University, Durham, North Carolina, USA
| | - Paul Segars
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
| | - Ehsan Samei
- Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, North Carolina, USA
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Sabeti S, Ternifi R, Larson NB, Olson MC, Atwell TD, Fatemi M, Alizad A. Morphometric analysis of tumor microvessels for detection of hepatocellular carcinoma using contrast-free ultrasound imaging: A feasibility study. Front Oncol 2023; 13:1121664. [PMID: 37124492 PMCID: PMC10134399 DOI: 10.3389/fonc.2023.1121664] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction A contrast-free ultrasound microvasculature imaging technique was evaluated in this study to determine whether extracting morphological features of the vascular networks in hepatic lesions can be beneficial in differentiating benign and malignant tumors (hepatocellular carcinoma (HCC) in particular). Methods A total of 29 lesions from 22 patients were included in this work. A post-processing algorithm consisting of clutter filtering, denoising, and vessel enhancement steps was implemented on ultrasound data to visualize microvessel structures. These structures were then further characterized and quantified through additional image processing. A total of nine morphological metrics were examined to compare different groups of lesions. A two-sided Wilcoxon rank sum test was used for statistical analysis. Results In the malignant versus benign comparison, six of the metrics manifested statistical significance. Comparing only HCC cases with the benign, only three of the metrics were significantly different. No statistically significant distinction was observed between different malignancies (HCC versus cholangiocarcinoma and metastatic adenocarcinoma) for any of the metrics. Discussion Obtained results suggest that designing predictive models based on such morphological characteristics on a larger sample size may prove helpful in differentiating benign from malignant liver masses.
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Affiliation(s)
- Soroosh Sabeti
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Redouane Ternifi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Michael C. Olson
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Thomas D. Atwell
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Mostafa Fatemi
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
| | - Azra Alizad
- Department of Physiology and Biomedical Engineering, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
- Department of Radiology, Mayo Clinic College of Medicine and Science, Rochester, MN, United States
- *Correspondence: Azra Alizad,
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Sauer TJ, Abadi E, Segars P, Samei E. Anatomically and physiologically informed computational model of hepatic contrast perfusion for virtual imaging trials. Med Phys 2022; 49:2938-2951. [PMID: 35195901 PMCID: PMC9547339 DOI: 10.1002/mp.15562] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 02/02/2022] [Accepted: 02/02/2022] [Indexed: 12/10/2022] Open
Abstract
PURPOSE Virtual (in silico) imaging trials (VITs), involving computerized phantoms and models of the imaging process, provide a modern alternative to clinical imaging trials. VITs are faster, safer, and enable otherwise-impossible investigations. Current phantoms used in VITs are limited in their ability to model functional behavior such as contrast perfusion which is an important determinant of dose and image quality in CT imaging. In our prior work with the XCAT computational phantoms, we determined and modeled inter-organ (organ to organ) intravenous contrast concentration as a function of time from injection. However, intra-organ concentration, heterogeneous distribution within a given organ, was not pursued. We extend our methods in this work to model intra-organ concentration within the XCAT phantom with a specific focus on the liver. METHODS Intra-organ contrast perfusion depends on the organ's vessel network. We modeled the intricate vascular structures of the liver, informed by empirical and theoretical observations of anatomy and physiology. The developed vessel generation algorithm modeled a dual-input-single-output vascular network as a series of bifurcating surfaces to optimally deliver flow within the bounding surface of a given XCAT liver. Using this network, contrast perfusion was simulated within voxelized versions of the phantom by using knowledge of the blood velocities in each vascular structure, vessel diameters and length, and the time since the contrast entered the hepatic artery. The utility of the enhanced phantom was demonstrated through a simulation study with the phantom voxelized prior to CT simulation with the relevant liver vasculature prepared to represent blood and iodinated contrast media. The spatial extent of the blood-contrast mixture was compared to clinical data. RESULTS The vascular structures of the liver were generated with size and orientation which resulted in minimal energy expenditure required to maintain blood flow. Intravenous contrast was simulated as having known concentration and known total volume in the liver as calibrated from time-concentration curves. Measurements of simulated CT ROIs were found to agree with clinically observed values of early arterial phase contrast enhancement of the parenchyma (∼ 5 $ \sim 5$ HU). Similarly, early enhancement in the hepatic artery was found to agree with average clinical enhancement( 180 $(180$ HU). CONCLUSIONS The computational methods presented here furthered the development of the XCAT phantoms allowing for multi-timepoint contrast perfusion simulations, enabling more anthropomorphic virtual clinical trials intended for optimization of current clinical imaging technologies and applications.
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Affiliation(s)
- Thomas J. Sauer
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Ehsan Abadi
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Paul Segars
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
| | - Ehsan Samei
- Center for Virtual Imaging Trials (CVIT), Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center
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Dai Y, Jiang H, Feng ST, Xia Y, Li J, Zhao S, Wang D, Zeng X, Chen Y, Xin Y, Liu D. Noninvasive Imaging Evaluation Based on Computed Tomography of the Efficacy of Initial Transarterial Chemoembolization to Predict Outcome in Patients with Hepatocellular Carcinoma. J Hepatocell Carcinoma 2022; 9:273-288. [PMID: 35411303 PMCID: PMC8994626 DOI: 10.2147/jhc.s351077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/18/2022] [Indexed: 11/23/2022] Open
Abstract
Purpose This study aims to develop a new model to more comprehensively and accurately predict the survival of patients with HCC after initial TACE. Patients and Methods The whole cohort (n = 102) was randomly divided into a training cohort and a validation cohort in the ratio of 8:2. The optimal radiomics signatures were screened using the least absolute shrinkage and selection operator algorithm (LASSO) regression for constructing the radscore to predict overall survival (OS). The C-index (95% confidence interval, CI), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the models. The independent risk factors (hazard ratio, HR) for predicting OS were stratified by Kaplan–Meier (K-M) analysis and the Log rank test. Results The median OS was 439 days (95% CI: 215.795–662.205) in whole cohort, and in the training cohort and validation cohort, the median OS was 552 days (95% CI: 171.172–932.828), 395 days (95% CI: 309.415–480.585), respectively (P = 0.889). After multivariate cox regression, the combined radscore-clinical model was consisted of radscore (HR: 2.065, 95% CI: 1.285–3.316; P = 0.0029) and post-response (HR: 1.880, 95% CI: 1.310–2.697; P = 0.0007), both of which were independent risk factors for the OS. In the validation cohort, the efficacy of both the radscore (C-index: 0.769, 95% CI: 0.496–1.000) and combined model (C-index: 0.770, 95% CI: 0.581–0.806) were higher than that of the clinical model (C-index: 0.655, 95% CI: 0.508–0.802). The calibration curve of the combined model for predicting OS presented good consistency between observations and predictions in both the training cohort and validation cohort. Conclusion Noninvasive imaging has a good prediction performance of survival after initial TACE in patients with HCC. The combined model consisting of post-response and radscore may be able to better predict outcome.
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Affiliation(s)
- Yanmei Dai
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
- Correspondence: Huijie Jiang; Shi-Ting Feng, Tel +86 86605576, Email ;
| | - Shi-Ting Feng
- Department of Radiology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, Guangdong Province, 510030, People’s Republic of China
| | - Yuwei Xia
- Huiying Medical Technology Co., Ltd, Beijing City, 100192, People’s Republic of China
| | - Jinping Li
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Sheng Zhao
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Dandan Wang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Xu Zeng
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Yusi Chen
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Yanjie Xin
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
| | - Dongmin Liu
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, 150086, People’s Republic of China
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Predicting the stages of liver fibrosis with multiphase CT radiomics based on volumetric features. Abdom Radiol (NY) 2021; 46:3866-3876. [PMID: 33751193 DOI: 10.1007/s00261-021-03051-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/05/2021] [Accepted: 03/09/2021] [Indexed: 12/14/2022]
Abstract
PURPOSES To develop and externally validate a multiphase computed tomography (CT)-based machine learning (ML) model for staging liver fibrosis (LF) by using whole liver slices. MATERIALS AND METHODS The development dataset comprised 232 patients with pathological analysis for LF, and the test dataset comprised 100 patients from an independent outside institution. Feature extraction was performed based on the precontrast (PCP), arterial (AP), portal vein (PVP) phase, and three-phase CT images. CatBoost was utilized for ML model investigation by using the features with good reproducibility. The diagnostic performance of ML models based on each single- and three-phase CT image was compared with that of radiologists' interpretations, the aminotransferase-to-platelet ratio index, and the fibrosis index based on four factors (FIB-4) by using the receiver operating characteristic curve with the area under the curve (AUC) value. RESULTS Although the ML model based on three-phase CT image (AUC = 0.65-0.80) achieved higher AUC value than that based on PCP (AUC = 0.56-0.69) and PVP (AUC = 0.51-0.74) in predicting various stage of LF, significant difference was not found. The best CT-based ML model (AUC = 0.65-0.80) outperformed the FIB-4 in differentiating advanced LF and cirrhosis and radiologists' interpretation (AUC = 0.50-0.76) in the diagnosis of significant and advanced LF. CONCLUSION All PCP, PVP, and three-phase CT-based ML models can be an acceptable in assessing LF, and the performance of the PCP-based ML model is comparable to that of the enhanced CT image-based ML model.
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Fiz F, Costa G, Gennaro N, la Bella L, Boichuk A, Sollini M, Politi LS, Balzarini L, Torzilli G, Chiti A, Viganò L. Contrast Administration Impacts CT-Based Radiomics of Colorectal Liver Metastases and Non-Tumoral Liver Parenchyma Revealing the "Radiological" Tumour Microenvironment. Diagnostics (Basel) 2021; 11:diagnostics11071162. [PMID: 34202253 PMCID: PMC8305553 DOI: 10.3390/diagnostics11071162] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/11/2021] [Accepted: 06/22/2021] [Indexed: 12/29/2022] Open
Abstract
The impact of the contrast medium on the radiomic textural features (TF) extracted from the CT scan is unclear. We investigated the modification of TFs of colorectal liver metastases (CLM), peritumoral tissue, and liver parenchyma. One hundred and sixty-two patients with 409 CLMs undergoing resection (2017–2020) into a single institution were considered. We analyzed the following volumes of interest (VOIs): The CLM (Tumor-VOI); a 5-mm parenchyma rim around the CLM (Margin-VOI); and a 2-mL sample of parenchyma distant from CLM (Liver-VOI). Forty-five TFs were extracted from each VOI (LIFEx®®). Contrast enhancement affected most TFs of the Tumor-VOI (71%) and Margin-VOI (62%), and part of those of the Liver-VOI (44%, p = 0.010). After contrast administration, entropy increased and energy decreased in the Tumor-VOI (0.93 ± 0.10 vs. 0.85 ± 0.14 in pre-contrast; 0.14 ± 0.03 vs. 0.18 ± 0.04, p < 0.001) and Margin-VOI (0.89 ± 0.11 vs. 0.85 ± 0.12; 0.16 ± 0.04 vs. 0.18 ± 0.04, p < 0.001), while remaining stable in the Liver-VOI. Comparing the VOIs, pre-contrast Tumor and Margin-VOI had similar entropy and energy (0.85/0.18 for both), while Liver-VOI had lower values (0.76/0.21, p < 0.001). In the portal phase, a gradient was observed (entropy: Tumor > Margin > Liver; energy: Tumor < Margin < Liver, p < 0.001). Contrast enhancement affected TFs of CLM, while it did not modify entropy and energy of parenchyma. TFs of the peritumoral tissue had modifications similar to the Tumor-VOI despite its radiological aspect being equal to non-tumoral parenchyma.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
| | - Guido Costa
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
| | - Nicolò Gennaro
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Ludovico la Bella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Alexandra Boichuk
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Martina Sollini
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Letterio S. Politi
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Balzarini
- Department of Diagnostic Imaging, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (N.G.); (L.S.P.); (L.B.)
| | - Guido Torzilli
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Arturo Chiti
- Nuclear Medicine Unit, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (M.S.); (A.C.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
| | - Luca Viganò
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, 20089 Milan, Italy; (G.C.); (G.T.)
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20089 Milan, Italy; (L.l.B.); (A.B.)
- Correspondence: (F.F.); (L.V.); Tel.: +39-02-8224-7361 (L.V.)
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Wu H, Chen G, Wang J, Deng M, Yuan F, Gong J. TIM-4 interference in Kupffer cells against CCL4-induced liver fibrosis by mediating Akt1/Mitophagy signalling pathway. Cell Prolif 2019; 53:e12731. [PMID: 31755616 PMCID: PMC6985653 DOI: 10.1111/cpr.12731] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/13/2019] [Accepted: 10/31/2019] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES T-cell immunoglobulin domain and mucin domain-4 (TIM-4) is selectively expressed on antigen-presenting cells (APCs) and modulates various immune responses. However, the role of TIM-4 expressed by Kupffer cells (KCs) in liver fibrosis remains unclear. The present study aimed to explore whether and how TIM-4 expressed by KCs is involved in liver fibrosis. MATERIALS AND METHODS Mice chronic liver fibrosis models were established and divided into the olive-induced control group, CCL4-induced control group, olive-induced TIM-4 interference group and CCL4-induced TIM-4 interference group. Different techniques were used to monitor the fibrotic effects of TIM-4, including histopathological assays, Western blotting, ELISA and transmission electron microscopy. Additionally, mice liver transplant models were established to determine the fibrotic effects of TIM-4 on fibrosis after liver transplantation (LT). RESULTS We found that the induction of liver fibrosis by CCL4 was associated with TIM-4 expression in KCs. TIM-4 interference essentially contributed to liver fibrosis resolution. KCs from the TIM-4 interference group had decreased levels of pro-fibrotic markers, reduced TGF-β1 secretion and inhibited hepatic stellate cell (HSC) differentiation into myofibroblast-like cells. In addition, we used GdCl3 to verify that KCs are the primary source of TGF-β1 during fibrosis progression. Moreover, KCs from CCL4-induced mice showed increased ROS production, mitophagy activation and TGF-β1 secretion. However, TIM-4 interference in the KCs inhibited Akt1-mediated ROS production, resulting in the suppression of PINK1, Parkin and LC3-II/I activation and the reduction of TGF-β1 secretion during liver fibrosis. Additionally, TIM-4 interference potentially attenuated development of fibrosis after LT. CONCLUSIONS Our findings revealed the underlying mechanisms of TIM-4 interference in KCs to mitigate liver fibrosis.
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Affiliation(s)
- Hao Wu
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Guoyong Chen
- Department of Hepatobiliary and pancreatic surgery, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, China
| | - Jingyuan Wang
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Minghua Deng
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Fangchao Yuan
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Jianping Gong
- Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
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Ma Y, Ma M, Ma L, Zhang F, Liu Y, Ma X. Downregulation of miR-552 in hepatocellular carcinoma inhibits cell migration and invasion, and promotes cell apoptosis via RUNX3. Exp Ther Med 2019; 18:3829-3836. [PMID: 31656538 PMCID: PMC6812473 DOI: 10.3892/etm.2019.8061] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 08/07/2019] [Indexed: 12/27/2022] Open
Abstract
Research conducted previously has indicated that microRNAs (miRs) have potential effects on the pathogenesis of hepatocellular carcinoma (HCC). The biological functions of miR-552 have been well documented in colon cancer; however, the role of miR-552 in HCC remains unclear. The present study evaluated the effects of miR-552 in HCC physiology, using HCC cell lines as model. An miR-552 inhibitor was transfected into HCC cell lines to knock down the expression of miR-552. Reverse transcription-quantitative PCR and western blot analysis were used to detect the expression of miR-552 and Runt-related transcription factor 3 (RUNX3), respectively. MTT assay was used to analyze cell viability, whilst Transwell and wound-healing assay were used to investigate cell migration. Flow cytometry was performed to measure cell apoptosis. The direct association between RUNX3 and miR-552 was evaluated using dual luciferase reporter assay. The expression of miR-552 was significantly elevated in HCC tumor tissues compared with the adjacent healthy samples. Additionally, transfection with the miR-552 inhibitor decreased cell viability and migration. miR-552 knockdown also increased HCC cell apoptosis in vitro. In conclusion, these results suggest that miR-552 has an oncogenic function in HCC and is a potential biomarker for detecting HCC.
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Affiliation(s)
- Ying Ma
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang 830054, P.R. China
| | - Ming Ma
- Department of Hepatobiliary Surgery, People's Hospital of Xinjiang Uygur Autonomous Region, Urumchi, Xinjiang 830001, P.R. China
| | - Ling Ma
- Department of Pharmacy, Thoracic Hospital of Xinjiang Uygur Autonomous Region, Urumchi, Xinjiang 830049, P.R. China
| | - Fengbo Zhang
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang 830054, P.R. China
| | - Yumei Liu
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang 830054, P.R. China
| | - Xiumin Ma
- Department of Clinical Laboratory, The First Affiliated Hospital of Xinjiang Medical University, Urumchi, Xinjiang 830054, P.R. China
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