|Year : 2022 | Volume
| Issue : 7 | Page : 1926-1930
Investigating the effect of ARHGEF10L gene on tumor growth in gastric cancer in a nude mouse model using quantitative MRI parameters
Junyi Tang1, Xuping Zhang2, Huan Chang3, Dawei Wang4
1 Department of Clinical Laboratory Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key of Laboratory of Laboratory Medicine, Jinan, Shandong, China
2 Department of Medicine Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key of Laboratory of Abdominal Medical Imaging, Jinan, Shandong, China
3 Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, Shandong, China
4 Department of Radiology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key of Laboratory of Abdominal Medical Imaging, Shandong Lung Cancer Institute, Shandong institute of Neuroimmunology, Jinan, Shandong, P. R. China
|Date of Submission||15-Apr-2022|
|Date of Decision||06-Jun-2022|
|Date of Acceptance||24-Jun-2022|
|Date of Web Publication||11-Jan-2023|
Jing Shi Road 16766, Lixia District, Jinan - 250 014
P. R. China
Source of Support: None, Conflict of Interest: None
Background: The quantitative magnetic resonance imaging (MRI) parameters were initially used in the study of central nervous system diseases and has since been widely used in the diagnosis of breast, liver, rectum, and prostate diseases. In our study, we aimed to evaluate the effect of ARHGEF10L gene on tumor growth in gastric cancer in nude mice using quantitative MRI parameters.
Subjects and Methods: A nude mice model of gastric cancer was established, and the mice were divided into a control group and an shARHGEF10L group (N = 10). T2-fs and intravoxel incoherent motions (IVIM) imaging were performed in the mice coil with a 3.0 T MR system. The differences in quantitative parameters (apparent diffusion coefficient [ADC], D, D *, f values) were compared between both groups, and the effect of ARHGEF10L expression on tumor growth in tumor-bearing mice was investigated. The data were analyzed using Statistical Package for the Social Sciences (SPSS) 17.0 software package.
Results: The ADC and D values of tumor imaging in the shARHGEF10L group were higher than those in the control group, and the differences were statistically significant. There was no significant difference in the D* or F values between both groups.
Conclusions: The ADC and D values of the quantitative IVIM imaging parameters can be used to effectively assess the growth of gastric cancer in nude mice, suggesting that ARHGEF10L may promote the growth of tumor cells.
Keywords: ARHGEF10L, gastric cancer, IVIM imaging, MRI
|How to cite this article:|
Tang J, Zhang X, Chang H, Wang D. Investigating the effect of ARHGEF10L gene on tumor growth in gastric cancer in a nude mouse model using quantitative MRI parameters. J Can Res Ther 2022;18:1926-30
|How to cite this URL:|
Tang J, Zhang X, Chang H, Wang D. Investigating the effect of ARHGEF10L gene on tumor growth in gastric cancer in a nude mouse model using quantitative MRI parameters. J Can Res Ther [serial online] 2022 [cited 2023 Jan 27];18:1926-30. Available from: https://www.cancerjournal.net/text.asp?2022/18/7/1926/367478
Authors Junyi Tang and Xuping Zhang contributed equally to this work.
| > Introduction|| |
Gastric cancer (GC) is one of the most common malignant tumors of the digestive tract. GC is the third leading cause of cancer-related deaths worldwide, with more than 723,000 deaths, especially in Asia. The 5-year survival rate of patients with stage 4 GC is about 5%–15%. Accurate preoperative diagnosis and clinical staging of patients with GC are particularly important for the selection of optimal clinical treatment and the evaluation of prognosis., In our previous study, we detected the high expression of ARHGEF10L in gastric tumor tissues. We also found that ARHGEF10 overexpression in SGC7901 cells stimulated tumor cell proliferation, cell migration, and the formation of tube-like structures. Diffusion-weighted imaging (DWI) is a noninvasive functional magnetic resonance imaging (MRI) technique. Le Bihan first proposed the concept of intravoxel incoherent motions (IVIM) in 1986. IVIM imaging was initially used in the study of central nervous system diseases and has since been widely used in the diagnosis of breast, liver, rectum, and prostate diseases.,,,,,, However, the MRI IVIM sequence has rarely been used in nude mice tumor models., The purpose of this study was to evaluate the value of the quantitative parameters of IVIM (apparent diffusion coefficient [ADC], D, D*, and f) on tumor growth in tumor-bearing mice.
| > Subjects and Methods|| |
BALB/c nude mice aged 4–5 weeks were purchased from Beijing Charles River Animal Technology Co., Ltd. (Beijing, China). BALB/c nude female mice were reared in strict accordance with specific pathogen-free conditions. BALB/c nude female mice were randomly divided into an experimental group and a control group, with 10 mice in each group.
The epithelial GC cell line SGC7901 was purchased from Procell Life Science and Technology Company (Wuhan, China). The cell culture medium was high-glucose Dulbecco's modified Eagle medium, with 1% penicillin–streptomycin solution and 10% fetal bovine serum added. SGC7901 cells were infected with a constructed interference ARHGEF10L lentivirus suspension or a control group lentiviral suspension. After 48–72 h, the expression of green fluorescent-labeled protein was observed under a fluorescence microscope. The cell pellet was resuspended in sterile phosphate-buffered saline (PBS) and mixed by pipetting. Then, the cells were centrifuged at 1000 rpm for 5 min at room temperature. SGC7901 cells suspended in 100 μl of sterile PBS were subcutaneously injected into the left armpit of mice to generate tumor-bearing BALB/c nude mice. The progress of tumor formation in the nude mice was observed daily.
The mice were weighed and then anesthetized by intraperitoneal injection of 10% chloral hydrate. After the nude mice were successfully anesthetized, they were placed in a special mouse coil in a prone position and covered with cotton to keep them warm. The MRI instrument was connected such that the center of the mice coil was located at the tumor location of the nude mice. The tumors were scanned using MRI. After routine scanning of the axial, sagittal, and coronal images, the sagittal T2WI (T2-weighted imaging) sequence was scanned (Repetition Time/Echo Time 2200 ms/102 ms; Field of View 8 mm; matrix 256 × 256; slice thickness 1.5 mm; number of excitation 2; scanning layers 18). The tumor was located based on the T2WI imaging, and axial IVIM sequence scanning was performed at the location of the tumor (10 b values were selected as 10, 20, 30, 40, 50, 80, 100, 150, 200, and 400; NEX 2, 2, 2, 2, 2, 2, 1, 1, 1, and 2; TR/TE 2000 ms/110 ms; FOV 6 mm; matrix 64 × 64; slice thickness 1.0 mm; scanning layers 22).
The images were processed using the original image analysis software GE AW4.6. The lesion was located and the region of interest (ROI) delineated. The ROI was selected as the best location to display the tumor, while avoiding hemorrhagic and necrotic areas. The quantitative parameters of the IVIM imaging were analyzed using postprocessing software. These parameters were the standard ADC, simple diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f). Pseudo-color imaging was obtained at the same time. For each tumor area, three layers were selected as the final ROI: the maximum cross-sectional layer, the upper layer, and the lower layer. The average values of the parameters of the three layers in the ROI were taken as the final ADC, D, D*, and f values of the tumor.
Statistical Package for the Social Sciences (SPSS) 17.0 software was used to analyze the data. The distribution of the data was checked for normality and homogeneity of variance. Independent sample t-tests were used to evaluate the statistical significance of differences between the two groups. Statistical differences were considered significant when P < 0.05.
| > Results|| |
BALB/c nude mice were used to construct a tumor model of GC in nude mice (N = 10). SGC7901 cells were transfected with the lentivirus and divided into an interference ARHGEF10L group (shARHGEF10L) and a control group (CON). The transfected SGC7901 cell suspension was prepared and injected subcutaneously into nude mice to construct tumor-bearing mice. MRI of nude mice was performed, and the results are shown in [Figure 1].
|Figure 1: Images of gastric carcinoma in nude mice, obtained using T2WI (n = 10)|
Click here to view
The ADC value of the control group was 2.39 ± 0.24 × 10−4 mm2/s and that of the shARHGEF10L group was 4.13 ± 0.21 × 10−4 mm2/s. The ADC value of the shARHGEF10L group was higher and the difference was statistically significant (P < 0.001), as shown in [Table 1] and [Figure 2]. The D value of the control group was 6.81 ± 0.21 × 10−3 mm2/s and that of the shARHGEF10L group was 8.03 ± 0.13 × 10−3 mm2/s. The D value of the shARHGEF10L group was higher and the difference was statistically significant (P < 0.001), as shown in [Table 1] and [Figure 3]. The D* value of the control group was 4.37 ± 0.15 × 10−2 mm2/s and that of the shARHGEF10L group was 4.55 ± 0.17 × 10−2 mm2/s. There was no significant difference between both groups, as shown in [Table 1] and [Figure 4]. The f value of the control group was 0.21 ± 0.02 and that of the shARHGEF10L group was 0.17 ± 0.01. There was no significant difference between both groups, as shown in [Table 1] and [Figure 5].
|Figure 2: (a) Comparison of ADC values between the CON group and the shARHGEF10L group. (b) Statistical analysis of ADC values. P < 0.001. ADC = apparent diffusion coefficient, CON = control|
Click here to view
|Figure 3: (a) Comparison of D values between the CON group and the shARHGEF10L group. (b) Statistical analysis of D values. P < 0.001. CON = control|
Click here to view
|Figure 4: (a) Comparison of D* values between the CON group and the shARHGEF10L group. (b) Statistical analysis of D* values. P > 0.05. CON = control|
Click here to view
|Figure 5: (a) Comparison of f values between the CON group and the shARHGEF10L group. (b) Statistical analysis of f values. P > 0.05. CON = control|
Click here to view
| > Discussion|| |
DWI is a noninvasive functional MRI technology. It images changes in internal structure and pathophysiology by detecting the diffusion of water molecules in living tissues. ADC is used to quantify the diffusion of water molecules. However, the diffusion of water molecules in tissues is also affected by microcirculation perfusion. DWI ignores the influence of microcirculation perfusion on ADC imaging., An IVIM imaging sequence can be used to quantitatively analyze the diffusion of water molecules and microcirculation perfusion by analyzing multi-b-value DWI images. Le Bihan showed that IVIM imaging can be used to quantitatively analyze DWI images by using different b values and obtain the diffusion correlation coefficient D value and the values of the perfusion-related parameters D* and f.
The ADC and D values of rectal cancer tissues decreased with increasing T stage and the differences were statistically significant (P < 0.05). The ADC and D values of tumor tissues in patients with breast cancer were significantly lower than those in patients with benign lesions. Relevant studies showed that the IVIM quantitative parameters D and ADC can be used to identify benign and malignant tumors and predict the degree of malignant tumors.,,,,,,,,, These differences may be caused by the active proliferation of tumor cells, enlargement of the nucleus, an increased nuclear–cytoplasmic ratio, an increase in the number of tumor cells, the dense packing of tumor cells, and the increased density of tumor microvessels. The decrease in the inner and outer spaces leads to the limited diffusion of water molecules in tumor tissues, thus causing decreased ADC and D values. The proliferation ability and growth activity of benign tumors and well-differentiated tumor tissues are weak; therefore, the degree of restriction of the diffusion of water molecules in tissues is reduced and the ADC and D values are higher. These results showed that the ADC and D values of the tumor tissues in the shARHGEF10L group were higher than those in the control group in a nude mice model of GC. The ADC and D values of the tumor increased after interfering with the gene ARHGEF10L, indicating that the degree of diffusion of water molecules was restricted, and the water molecule diffusion limitation in the tumor tissue of the ARHGEF10L group was lower. The expression of ARHGEF10L is decreased in GC tissues.
Some studies found no significant difference in the D* and f values of IVIM imaging between benign and malignant tumors, and the D value could provide more diagnostic information. Koh et al. also found that the f and D* values were not significantly different between the two groups (P > 0.05). This study also showed that there was no significant difference in the D* and f values between the control and ARHGEF10L groups in a tumor-bearing mouse model, and there was no significant change in the D* and f values after interfering with the expression of ARHGEF10L. The reasons for this may be as follows: first, the blood supply of malignant tumor tissue is rich and the number of microvessels is high, but the number and arrangement of tumor cells are increased and the pressure of intercellular substance is increased, leading to an increase or a decrease of blood perfusion in the tumor. Second, partial necrosis and fibrosis of the tumor, as well as changes in histiocytic structure, can lead to reduced local tumor perfusion. Tumor heterogeneity results in different rates of perfusion in different regions. Finally, respiratory motion artifacts in nude mice may lead to some errors in image analysis and parameter measurement.
After interfering with the expression of ARHGEF10L, the D* and f values representing the microcirculation perfusion of tumor tissue did not change significantly.
There are some deficiencies in this study: first, the number of nude mice was relatively small, and the measurement results may be unilateral because of the limited sample number; so, it is necessary to increase the sample size. Second, there was no clear uniform standard for the selection of multiple b values. The data may have errors affected by the motion artifact of nude mice. We will address these shortcomings in future research.
In a nude mouse model of GC, the ADC and D values of quantitative IVIM parameters can be used to effectively evaluate tumor growth activity. The D* and f values have little significance, and ARHGEF10L may promote the growth of GC cells.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| > References|| |
Tan P, Yeoh KG. Genetics and molecular pathogenesis of gastric adenocarcinoma. Gastroenterology 2015;149:1153-62.e3.
Ushiku T, Ishikawa S, Kakiuchi M, Tanaka A, Katoh H, Aburatani H, et al
., RHOA mutation in diffuse-type gastric cancer: A comparative clinicopathology analysis of 87 cases. Gastric Cancer 2016;19:403-11.
Bertuccio P, Chatenoud L, Levi F, Praud D, Ferlay J, Negri E, et al
. Recent patterns in gastric cancer: A global overview. Int J Cancer 2009;125:666-73.
Wang A, Du L, Jiang K, Kong Q, Zhang X, Li L. Long noncoding RNA microvascular invasion in hepatocellular carcinoma is an indicator of poor prognosis and a potential therapeutic target in gastric cancer. J Cancer Res Ther 2019;15:126-31.
Wang DW, Tang JY, Zhang GQ, Chang XT. ARHGEF10L expression regulates cell proliferation and migration in gastric tumorigenesis. Biosci Biotechnol Biochem 2020;84:1-11.
Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: Application to diffusion and perfusion in neurologic disorders. Radiology 1986;161:401-7.
Chan SW, Chang YC, Huang PW, Ouyang YC, Chang YT, Chang RF, et al
. Breast tumor detection and classification using intravoxel incoherent motion hyperspectral imaging techniques. BioMed Res Int 2019;2019:3843295. doi: 10.1155/2019/3843295.
Baxter GC, Graves MJ, Gilbert FJ, Patterson AJ. A meta-analysis of the diagnostic performance of diffusion MRI for breast lesion characterization. Radiology 2019;291:632-41.
Joo I, Lee JM, Grimm R, Han JK, Choi BI. Monitoring vascular disrupting therapy in a rabbit liver tumor model: Relationship between tumor perfusion parameters at IVIM diffusion-weighted MR imaging and those at dynamic contrast-enhanced MR imaging. Radiology 2016;278:104-13.
Luciani A, Vignaud A, Cavet M, Nhieu JT, Mallat A, Ruel L. Liver cirrhosis: Intravoxel incoherent motion MR imaging—pilot study. Radiology 2008;249:891-9.
Xu Y, Xu Q, Sun H, Liu T, Shi K, Wang W. Could IVIM and ADC help in predicting the KRAS status in patients with rectal cancer? Eur. Radiol 2018;28:3059-65.
Yang X, Xiao X, Lu B, Chen Y, Wen Z, Yu S. Perfusion-sensitive parameters of intravoxel incoherent motion MRI in rectal cancer: Evaluation of reproducibility and correlation with dynamic contrast-enhanced MRI. Acta Radiol 2019;60:569-77.
Hompland T, Hole KH, Ragnum HB, Aarnes EK, Vlatkovic L, Lie AK, et al
. Combined MR imaging of oxygen consumption and supply reveals tumor hypoxia and aggressiveness in prostate cancer patients. Cancer Res 2018;78:4774-85.
Pan JH, Zhu S, Huang J, Liang J, Zhang D, Zhao X, et al
. Monitoring the process of endostar-induced tumor vascular normalization by non-contrast intravoxel incoherent motion diffusion-weighted MRI. Front Oncol 2018;8:524.
Iima M, Nobashi T, Imai H, Koyasu S, Saga T, Nakamoto Y. Effects of diffusion time on non-Gaussian diffusion and intravoxel incoherent motion (IVIM) MRI parameters in breast cancer and hepatocellular carcinoma xenograft models. Acta Radiol Open 2018;7:1114179764:2058460117751565. doi: 10.1177/2058460117751565.
Mürtz P, Sprinkart AM, Reick M, Pieper CC, Schievelkamp AH, König R, et al
. Accurate IVIM model-based liver lesion characterisation can be achieved with only three b-value DWI. Eur Radiol 2018;28:4418-28.
Attenberger UI, Pilz LR, Morelli JN, Hausmann D, Doyon F, Hofheinz R. Multi-parametric MRI of rectal cancer-do quantitative functional MR measurements correlate with radiologic and pathologic tumor stages? Eur J Radiol 2014;83:1036-43.
Liu C, Liang C, Liu Z, Zhang S, Huang B. Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: Comparison with conventional DWI. Eur J Radiol 2013;82:e782-9.
Curvo-Semedo L, Lambregts DMJ, Maas M, Beets GL, Caseiro-Alves F, Beets-Tan RGH. Diffusion-weighted MRI in rectal cancer: Apparent diffusion coefficient as a potential noninvasive marker of tumor aggressiveness. J Magn Reson Imaging 2012;35:1365-71.
Bourillon C, Rahmouni A, Lin C, Belhadj K, Beaussart P, Vignaud A, et al
. Intravoxel incoherent motion diffusion-weighted imaging of multiple myeloma lesions: Correlation with whole-body dynamic contrast agent-enhanced MR imaging. Radiology 2015;277:773-83.
Sun J, Wu G, Shan F, Meng Z. The value of IVIM DWI in combination with conventional MRI in identifying the residual tumor after cone biopsy for early cervical carcinoma. Acad Radiol 2019;26:1040-7.
Klauß M, Maier-Hein K, Tjaden C, Hackert T, Grenacher L, Stieltjes B. IVIM DW-MRI of autoimmune pancreatitis: Therapy monitoring and differentiation from pancreatic cancer. Eur Radiol 2016;26:2099-106.
He XQ, Wei LN. Diagnostic value of lymph node metastasis by diffusion-weighted magnetic resonance imaging in cervical cancer. J Cancer Res Ther 2016;12:77-83.
Zhang Y, Kuang S, Shan Q, Rong D, Zhang Z, Yang H, et al
. Can IVIM help predict HCC recurrence after hepatectomy? Eur Radiol 2019;29:5791-803.
Wei Y, Huang Z, Tang H, Deng L, Yuan Y, Li J, et al
. IVIM improves preoperative assessment of microvascular invasion in HCC. Eur Radiol 2019;29:5403-14.
Akashi M, Nakahusa Y, Yakabe T, Egashira Y, Koga Y, Sumi K, et al
. Assessment of aggressiveness of rectal cancer using 3-T MRI: Correlation between the apparent diffusion coefficient as a potential imaging biomarker and histologic prognostic factors. Acta Radiol 2014;55:524-31.
Zhao W, Liu W, Liu H, Yi X, Hou J, Pei Y, et al
. Preoperative prediction of microvascular invasion of hepatocellular carcinoma with IVIM diffusion-weighted MR imaging and Gd-EOB-DTPA-enhanced MR imaging. PLoS One 2018;13:e0197488.
Wang YX, Yuan MZ, Wen ZX. Application of apparent diffusion coefficient and exponent apparent diffusion coefficient values in magnetic resonance imaging diffusion-weighted imaging to differentiate benign and malignant ovarian epithelial tumors. J Cancer Res Ther 2016;12:401-5.
Long L, Zhang H, He X, Zhou J, Guo D, Liu X. Value of intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic from nonmetastatic mesorectal lymph nodes with different short-axis diameters in rectal cancer. J Cancer Res Ther 2019;15:1508-15.
Koh DM, Collins DJ, Orton MR. Intravoxel incoherent motion in body diffusion-weighted MRI: Reality and challenges. AJR Am J Roentgenol 2011;196:1351-61.
[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]