Figure 1.
Network structure of P-Net, R-Net and O-Net (a) P-Net; (b) R-Net; (c) O-Net.
WW domain-containing transcription regulator 1 (TAZ, or WWTR1) and Yes-associated protein 1 (YAP) are both important effectors of the Hippo pathway and exhibit different functions. However, few studies have explored their co-regulatory mechanisms in kidney renal clear cell carcinoma (KIRC). Here, we used bioinformatics approaches to evaluate the co-regulatory roles of TAZ/YAP and screen novel biomarkers in KIRC. GSE121689 and GSE146354 were downloaded from the GEO. The limma was applied to identify the differential expression genes (DEGs) and the Venn diagram was utilized to screen co-expressed DEGs. Co-expressed DEGs obtained the corresponding pathways through GO and KEGG analysis. The protein-protein interaction (PPI) network was constructed using STRING. The hub genes were selected applying MCODE and CytoHubba. GSEA was further applied to identify the hub gene-related signaling pathways. The expression, survival, receiver operating character (ROC), and immune infiltration of the hub genes were analyzed by HPA, UALCAN, GEPIA, pROC, and TIMER. A total of 51 DEGs were co-expressed in the two datasets. The KEGG results showed that the enriched pathways were concentrated in the TGF-β signaling pathway and endocytosis. In the PPI network, the hub genes (STAU2, AGO2, FMR1) were identified by the MCODE and CytoHubba. The GSEA results revealed that the hub genes were correlated with the signaling pathways of metabolism and immunomodulation. We found that STAU2 and FMR1 were weakly expressed in tumors and were negatively associated with the tumor stages. The overall survival (OS) and disease-free survival (DFS) rate of the high-expressed group of FMR1 was greater than that of the low-expressed group. The ROC result exhibited that FMR1 had certainly a predictive ability. The TIMER results indicated that FMR1 was positively correlated to immune cell infiltration. The abovementioned results indicated that TAZ/YAP was involved in the TGF-β signaling pathway and endocytosis. FMR1 possibly served as an immune-related novel prognostic gene in KIRC.
Citation: Sufang Wu, Hua He, Jingjing Huang, Shiyao Jiang, Xiyun Deng, Jun Huang, Yuanbing Chen, Yiqun Jiang. FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9295-9320. doi: 10.3934/mbe.2022432
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WW domain-containing transcription regulator 1 (TAZ, or WWTR1) and Yes-associated protein 1 (YAP) are both important effectors of the Hippo pathway and exhibit different functions. However, few studies have explored their co-regulatory mechanisms in kidney renal clear cell carcinoma (KIRC). Here, we used bioinformatics approaches to evaluate the co-regulatory roles of TAZ/YAP and screen novel biomarkers in KIRC. GSE121689 and GSE146354 were downloaded from the GEO. The limma was applied to identify the differential expression genes (DEGs) and the Venn diagram was utilized to screen co-expressed DEGs. Co-expressed DEGs obtained the corresponding pathways through GO and KEGG analysis. The protein-protein interaction (PPI) network was constructed using STRING. The hub genes were selected applying MCODE and CytoHubba. GSEA was further applied to identify the hub gene-related signaling pathways. The expression, survival, receiver operating character (ROC), and immune infiltration of the hub genes were analyzed by HPA, UALCAN, GEPIA, pROC, and TIMER. A total of 51 DEGs were co-expressed in the two datasets. The KEGG results showed that the enriched pathways were concentrated in the TGF-β signaling pathway and endocytosis. In the PPI network, the hub genes (STAU2, AGO2, FMR1) were identified by the MCODE and CytoHubba. The GSEA results revealed that the hub genes were correlated with the signaling pathways of metabolism and immunomodulation. We found that STAU2 and FMR1 were weakly expressed in tumors and were negatively associated with the tumor stages. The overall survival (OS) and disease-free survival (DFS) rate of the high-expressed group of FMR1 was greater than that of the low-expressed group. The ROC result exhibited that FMR1 had certainly a predictive ability. The TIMER results indicated that FMR1 was positively correlated to immune cell infiltration. The abovementioned results indicated that TAZ/YAP was involved in the TGF-β signaling pathway and endocytosis. FMR1 possibly served as an immune-related novel prognostic gene in KIRC.
In recent years, artificial intelligence technique has advanced rapidly [1,2,3]. Biometric recognition [4,5,6], which includes face recognition, voice recognition, fingerprint recognition, iris recognition, eye pattern recognition, etc. will occupy a very important position in the field of artificial intelligence in the future. At present, technologies such as smart cards based on radio frequency identification, second-generation ID cards, and user passwords are mostly used in identification, and biometrics will gradually occupy an important market share.
Due to the superiority of biometrics, it is widely used in bank payment, securities, transportation, e-commerce, airport subway entrance access control, attendance, and criminal investigation by public security and judicial departments [7,8,9]. Major enterprises, institutions, companies, and government agencies have established their own biometric-based access control systems and attendance systems to improve the informatization and intelligence of management, greatly improve management efficiency, and effectively liberate the labor force [10,11,12,13].
Face recognition technology started relatively late, but the technology related to face recognition [37] has developed rapidly, and has achieved remarkable results in recognition accuracy, etc., and related technical achievements have attracted worldwide attention. Due to the lack of prior knowledge of face images, large illumination changes, complex backgrounds, and variable face angles, the demand for face images is large, expression changes are large, and face occlusion leads to low accuracy of face recognition.
Deep learning applications often use convolutional neural networks to achieve image processing and recognition with high efficiency and accuracy [14,15,16,17]. Facial images are highly structured images. Combining with prior facial knowledge is a very popular method in face recognition.
Dong et al. proposed an image super-resolution method based on deep convolutional neural network [18], which realizes the mapping from the low-resolution end to the high-resolution end of the image, and extends the traditional super-resolution method based on coding coefficients. Since then, the research of neural network combined with image super-resolution has continued to deepen. Kim et al. [19] proposed the use of very deep convolutional neural network on the basis of VGG network to improve the super-resolution accuracy by using multiple filters in the neural network, and realize the use of image context information.
Yu et al. proposed a transforming and distinguishing neural network [20] for the serious problems of multi-posture and degradation, and solving the problem of multi-posture and image misalignment. As the depth of the network increases, the features gradually disappear during the transmission process. In response to this problem, the multi-scale residual network image super-resolution (MSRN) algorithm [21] uses the combination of local multi-scale features and global features to maximize utilizing the features of low-resolution images; the problem of feature disappearance during transmission is solved. There are various existing algorithms and deep learning models [38,39,40] in which not only can they facilitate face recognition, but also perform human activity recognition and motion prediction.
In this paper, we propose a face detection and recognition model based on multi-task convolutional neural network (MTCNN) [22,23,24]. The recognition model combined with deep learning has a high accuracy rate, and has a shorter recognition time, which can reduce the waste of human resources.
MTCNN implements the face area detection and face key point detection together, and its subject framework is similar to cascade. The whole can be divided into a three-layer network structure of Proposal Network (P-Net), Refine Network (R-Net), and Output Network (O-Net) [25,26]. It is a multi-task neural network model for face detection tasks which mainly uses three cascaded networks and the idea of candidate boxes plus classifiers.
The three cascaded networks are P-Net for quickly generating candidate windows, R-Net for filtering and selecting high-precision candidate windows, and O-Net for generating final bounding boxes and face key points. And many convolutional neural network models that deal with image problems; the model also uses image pyramids, border regression, non-maximum suppression and other technologies. The network structure of P-Net, R-Net and O-Net is shown in Figure 1.
To balance computational expenses and performance, MTCNN avoids the huge performance consumption caused by traditional ideas such as sliding windows and classifiers, we first use a small model to generate a candidate frame for the target area with a certain possibility, and then use a more complex model for fine classification. Then we return the higher-precision area box, and let this step be executed recursively. The network structure of MTCNN is presented by Figure 2.
R-CNN [27] draws on the idea of sliding window and adopts the scheme of area recognition. The specific identification scheme is the first step. Given an input image, extract 2000 independent candidate regions from the image; the second step is to use CNN to extract a fixed-length feature vector for each region; the third step, we use SVM to classify each area. Figure 3 shows the process of R-CNN in the recognition of faces.
Faster R-CNN [28,29] creatively uses the convolutional network to generate the suggestion frame by itself, and shares the convolutional network with the target detection network, so that the number of suggestion frames is reduced from about 2000 to 300. This framework can even be combined with nonlinear anisotropic diffusion filtering and other morphological methods to perform denoising of image. To ensure excellent face recognition, the noise and the other unrelated background areas can be removed. The process of Faster R-CNN to recognize faces is shown in Figure 4.
The commonly used kernel functions [30] include linear kernel functions [31,32], Gaussian kernel functions [33,34], and other similar algorithms. One of the most commonly used is the Gaussian kernel function, which can map feature data to infinite dimensions.
The linear kernel function is mainly used in the case of linear separability, which can achieve a good classification effect. The mathematical expression of the linear kernel function is shown in Eq (1). There are few parameters in the function, so the computation rate is very fast, and the dimension of its input space is the same as the dimension of the feature space, which is suitable for the first attempt in the classification task.
| K(X,Y)=(XTY+C) | (1) |
In Eq (1), X and Y represent eigenvectors, and C represents a constant.
The Gaussian kernel function can map the sample from the input space to the higher-dimensional feature space, and it can achieve good results regardless of the sample size. When the classification task cannot determine which kernel function to use, the Gaussian kernel function is the most widely used one of the kernel functions. The mathematical expression of the Gaussian kernel function is shown in Eq (2).
| K(X,Y)=exp(−‖X−Y‖22σ2) | (2) |
In particular, σ in Eq (2) is the width parameter of the function, which controls the radial range of the function.
We collect 2500 face images for our face recognition research in this paper. During the test, the data set is divided into the original image data set and the test data set according to the parity position. The two images at the corresponding positions of the two data sets are the comparison objects, as shown in Figure 5. Then, we apply the algorithm of this paper to these two data sets, and extract the images out in turn. Table 1 show the gender ratio and picture size of the two data sets.
| Characteristics | Original image data set | Test data set |
| Male: Female | 10: 15 | 13: 12 |
| Image size | 1897×1897 | 1897×1897 |
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The high-resolution face image reconstructed after the algorithm processing requires a certain measurement standard to examine the performance of the algorithm. The earlier evaluation standard was subjective evaluation through naked eye observation. This method is simple and direct. The objective evaluation calculates the similarity between the synthesized image and the original image, and has a specific value to measure the reconstruction result of the image. Compared with the subjective evaluation, its advantage is that the comparison result is more concise and accurate. At present, the commonly used objective evaluation methods include Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) [35,36].
PSNR is an image quality evaluation method based on the error between corresponding pixels. It is the most common image quality evaluation index, and its expression is as Eq (3).
| PSNR=10log10((2b−1)2MSE) | (3) |
In Eq (3), b is the number of bits of the pixel, which is usually 8. The unit of PSNR is decibel (dB). The larger the value means higher quality of the reconstructed image. Mean Square Error (MSE) represents the mean square error between images. The symbol h represents the height of the image, and w represents the width of the image. The expression is shown in Eq (4).
| MSE=1h×w∑hi=1∑wj=1[X1(i,j)−X2(i,j)]2 | (4) |
However, because the human perception of an area will be affected by the surrounding area and other reasons, this method does not take into account the human visual characteristics, but only calculates the difference between pixels, so the result of the peak signal-to-noise ratio evaluation method will appear to be different from that of humans.
The value of SSIM is a value between 0 and 1. The larger the value of SSIM, explant the smaller the difference between both photos of the faces. Therefore, the larger the value of SSIM replies the better the image reconstruction quality. The SSIM method measures image similarity from three aspects: brightness (L), contrast (C), and structure (S). The expression of SSIM is shown in Eq (5).
| SSIM(X,Y)=L(X,Y)×C(X,Y)×S(X,Y) | (5) |
SSIM simulates the human perception of changes in image information, uses the image average to model the image brightness, the image standard deviation to model the image contrast, and the image covariance to model the image structure, which makes up for the shortcomings of the PSNR method.
Receiver operating characteristic (ROC) curve is a comprehensive indicator that reflects the sensitivity and specificity of continuous variables. Each point on the ROC curve reflects the susceptibility to the same signal stimulus.
The abscissa is false positive rate (FPR), that is, the proportion of all negative samples that are predicted to be positive but actually negative. The larger the FPR, the positive prediction the more negative classes in the class. The computational FPR is as Eq (6).
| FPR=FPTN+FN | (6) |
The ordinate is true positive rate (TPR), the proportion of all positive samples that are predicted to be positive and actually positive. The larger the TPR stands the more actual positive classes in the predicted positive class. The computational TPR is as Eq (7).
| TPR=TPTP+FP | (7) |
In Eqs (6) and (7), True Positive (TP), the prediction is a positive sample and the actual number of features is also a positive sample. False Positive (FP), the number of features predicted to be a positive sample and actually a negative sample. True Negative (TN) is predicted to be a negative sample and is actually the number of features of a negative sample. False Negative (FN), the number of features predicted to be negative samples and actually positive samples.
In order to better recognize the face image, this paper implements the Gaussian kernel function as the mapping function in the model. In order to achieve the best results, we need to adjust three parameters, which are the local constraint parameter λ, the kernel function similarity parameter σ, and the high resolution layer constraint parameter k.
We fix the value of k and the kernel function similarity parameter σ, and set the value range of λ to be 0.01 to 0.12. The mean value of PSNR and mean value of SSIM as the evaluation criteria change with the value of λ as shown in Figure 6(a), (b). As shown in the figure, when λ = 0.08, the average PSNR of MTCNN and Faster R-CNN is the largest, and when λ = 0.08, the average PSNR of R-CNN is the largest. The average value of SSIM reaches its peak. Since the difference between the two values is very small, the value of λ is selected here as 0.08.
We fix the values of k and λ, and set the value of the kernel function similarity parameter σ from 200 to 1000. The average value of PSNR and the average value of SSIM as the evaluation criteria change with the value of σ as shown in Figure 6(c), (d). We find that when the σ value is 400, the average PSNR of the three models reaches the maximum, while the average SSIM reaches the peak at σ = 400 and 500. Combining the two objective evaluation criteria, we choose σ = 400.
We set the values of λ and the kernel function similarity parameter σ, and set the value of the high-resolution layer error term parameter k to 0.01 to 0.10. As the evaluation criteria, the mean value of PSNR and mean value of SSIM change with the value of k as shown in Figure 6(e), (f). It can be clearly seen from the following line chart that both the mean PSNR and the mean SSIM peak when k takes 0.05, so we determine the optimal value of k to be 0.05.
From the optimization parameters, we know that when our parameters are set to λ as 0.08, σ as 400 and k as 0.05, the three models are in the optimal state. Table 2 shows the average values of PSNR and SSIM of the three models.
| Model | PSNR (dB) | SSIM |
| MTCNN | 36.245 | 0.954 |
| R-CNN | 35.005 | 0.927 |
| Faster R-CNN | 35.305 | 0.938 |
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From Table 2, the mean PSNR and SSIM pertaining to the MTCNN are better than R-CNN and Faster R-CNN. The average PSNR of our method is 1.24 dB higher than that of R-CNN and 0.94 dB higher than that of Faster R-CNN. The average SSIM of MTCNN is 10.3% higher than R-CNN and 8.7% higher than Faster R-CNN.
Figure 7 shows the ROC curves of MTCNN, R-CNN and Faster R-CNN. The Area Under Curve (AUC) of MTCNN is 97.56%, the AUC of R-CNN is 91.24%, and the AUC of Faster R-CNN is 92.01%. MTCNN has the best overall face recognition performance. For detection of faces, MTCNN still has the best effect.
In actual scenes, the acquired face images are usually of poor resolution and low-quality, which is caused by a variety of reasons: first, the location of the surveillance camera is high, the shooting range is large, and the target face image is small; second, the monitoring The device is limited by storage space and highly compresses video images, so the image loses detailed information; third, external environments such as rainy weather and poor lighting will further reduce the quality of the captured images. In response to these problems, the face recognition technology combined with convolutional neural network realizes its practical application value.
Although this paper has conducted considerable research worthy of investigation, and performed exploration on learning-based image classification methods, there are still significant limitations that need to be addressed. Despite the sound conclusion of this research in this field, there are still some issues worthy of attention and future implementation of other relevant networks is required for a thorough analysis.
With the continuous deepening of research on convolutional neural networks, vector-based convolution and pooling processing have been fully studied. In fact, in a network, we can apply the Riemannian manifold geometry to the data in the middle layer for processing. This pooling and iterative process in the form of a matrix can have a positive effect on the final output of the network.
MTCNN mainly uses three cascaded networks, and uses the idea of candidate box plus classifier to execute fast and efficient face recognition. Among the three evaluation indicators, MTCNN has the best overall face recognition performance, and for defective faces, MTCNN still has the best effect as well as performance.
The technology based on MTCNN has a good development prospect, which greatly improves the accuracy of face recognition. While improving accuracy, it also improves the security of the image recognition system.
The authors declare no conflict of interest.
This research is funded by the National Natural Science Foundation of China (No. 62006102).
| [1] |
Y. Wang, Y. Zhang, P. Wang, X. Fu, W. Lin, Circular RNAs in renal cell carcinoma: implications for tumorigenesis, diagnosis, and therapy, Mol. Cancer, 19 (2020), 149. https://doi.org/10.1186/s12943-020-01266-7 doi: 10.1186/s12943-020-01266-7
|
| [2] |
H. Sung, J. Ferlay, R. L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, et al., Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA Cancer J. Clin., 71 (2021), 209–249. https://doi.org/10.3322/caac.21660 doi: 10.3322/caac.21660
|
| [3] |
M. He, F. Hu, TF-RBP-AS Triplet analysis reveals the mechanisms of aberrant alternative splicing events in kidney cancer: implications for their possible clinical use as prognostic and therapeutic biomarkers, Int. J. Mol. Sci., 22 (2021), 8789. https://doi.org/10.3390/ijms22168789 doi: 10.3390/ijms22168789
|
| [4] |
A. Znaor, J. Lortet-Tieulent, M. Laversanne, A. Jemal, F. Bray, International variations and trends in renal cell carcinoma incidence and mortality, Eur. Urol., 67 (2015), 519–530. https://doi.org/10.1016/j.eururo.2014.10.002 doi: 10.1016/j.eururo.2014.10.002
|
| [5] |
Z. Sun, C. Jing, X. Guo, M. Zhang, F. Kong, Z. Wang, et al., Comprehensive analysis of the immune infiltrates of pyroptosis in kidney renal clear cell carcinoma, Front. Oncol., 11 (2021), 716854. https://doi.org/10.3389/fonc.2021.716854 doi: 10.3389/fonc.2021.716854
|
| [6] |
X. Mao, J. Xu, W. Wang, C. Liang, J. Hua, J. Liu, et al., Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives, Mol. Cancer, 20 (2021), 131. https://doi.org/10.1186/s12943-021-01428-1 doi: 10.1186/s12943-021-01428-1
|
| [7] |
L. F. S. Patterson, S. A. Vardhana, Metabolic regulation of the cancer-immunity cycle, Trends Immunol., 42 (2021), 975–993. https://doi.org/10.1016/j.it.2021.09.002 doi: 10.1016/j.it.2021.09.002
|
| [8] |
Y. Senbabaoglu, R. S. Gejman, A. G. Winer, M. Liu, E. M. Van Allen, G. de Velasco, et al., Tumor immune microenvironment characterization in clear cell renal cell carcinoma identifies prognostic and immunotherapeutically relevant messenger RNA signatures, Genome Biol., 17 (2016), 231. https://doi.org/10.1186/s13059-016-1092-z doi: 10.1186/s13059-016-1092-z
|
| [9] |
B. Wang, D. Chen, H. Hua, TBC1D3 family is a prognostic biomarker and correlates with immune infiltration in kidney renal clear cell carcinoma, Mol. Ther. Oncolytics, 22 (2021), 528–538. https://doi.org/10.1016/j.omto.2021.06.014 doi: 10.1016/j.omto.2021.06.014
|
| [10] |
G. Liao, P. Wang, Y. Wang, Identification of the prognosis value and potential mechanism of immune checkpoints in renal clear cell carcinoma microenvironment, Front. Oncol., 11 (2021), 720125. https://doi.org/10.3389/fonc.2021.720125 doi: 10.3389/fonc.2021.720125
|
| [11] |
A. D. Janiszewska, S. Poletajew, A. Wasiutyński, Reviews Spontaneous regression of renal cell carcinoma, Współczesna Onkologia, 2 (2013), 123–127. https://doi.org/10.5114/wo.2013.34613 doi: 10.5114/wo.2013.34613
|
| [12] |
B. A. Inman, M. R. Harrison, D. J. George, Novel immunotherapeutic strategies in development for renal cell carcinoma, Eur, Urol, , 63 (2013), 881–889. https://doi.org/10.1016/j.eururo.2012.10.006 doi: 10.1016/j.eururo.2012.10.006
|
| [13] |
A. Kulkarni, M. T. Chang, J. H. A. Vissers, A. Dey, K. F. Harvey, The Hippo pathway as a driver of select human cancers, Trends Cancer, 6 (2020), 781–796. https://doi.org/10.1016/j.trecan.2020.04.004 doi: 10.1016/j.trecan.2020.04.004
|
| [14] |
Y. Zheng, D. Pan, The hippo signaling pathway in development and disease, Dev. Cell, 50 (2019), 264–282. https://doi.org/10.1016/j.devcel.2019.06.003 doi: 10.1016/j.devcel.2019.06.003
|
| [15] |
M. Moloudizargari, M. H. Asghari, S. F. Nabavi, D. Gulei, I. Berindan-Neagoe, A. Bishayee, et al., Targeting Hippo signaling pathway by phytochemicals in cancer therapy, Semin. Cancer Biol., 80 (2020), 183–194. https://doi.org/10.1016/j.semcancer.2020.05.005 doi: 10.1016/j.semcancer.2020.05.005
|
| [16] |
F. Reggiani, G. Gobbi, A. Ciarrocchi, V. Sancisi, YAP and TAZ are not identical twins, Trends Biochem. Sci., 46 (2021), 154–168. https://doi.org/10.1016/j.tibs.2020.08.012 doi: 10.1016/j.tibs.2020.08.012
|
| [17] |
H. Zhang, C. Y. Liu, Z. Y. Zha, B. Zhao, J. Yao, S. Zhao, et al., TEAD transcription factors mediate the function of TAZ in cell growth and epithelial-mesenchymal transition, J. Biol. Chem., 284 (2009), 13355–13362. https://doi.org/10.1074/jbc.M900843200 doi: 10.1074/jbc.M900843200
|
| [18] |
B. Zhao, X. Ye, J. Yu, L. Li, W. Li, S. Li, et al., TEAD mediates YAP-dependent gene induction and growth control, Genes Dev., 22 (2008), 1962–1971. https://doi.org/10.1101/gad.1664408 doi: 10.1101/gad.1664408
|
| [19] |
M. Murakami, J. Tominaga, R. Makita, Y. Uchijima, Y. Kurihara, O. Nakagawa, et al., Transcriptional activity of Pax3 is co-activated by TAZ, Biochem. Biophys. Res. Commun., 339 (2006), 533–539. https://doi.org/10.1016/j.bbrc.2005.10.214 doi: 10.1016/j.bbrc.2005.10.214
|
| [20] |
Z. Miskolczi, M. P. Smith, E. J. Rowling, J. Ferguson, J. Barriuso, C. Wellbrock, et al., Collagen abundance controls melanoma phenotypes through lineage-specific microenvironment sensing, Oncogene, 37 (2018), 3166–3182. https://doi.org/10.1038/s41388-018-0209-0 doi: 10.1038/s41388-018-0209-0
|
| [21] |
M. Murakami, M. Nakagawa, E. Olson, O. Nakagawa, A WW domain protein TAZ is a critical coactivator for TBX5 a transcription factor implicated in Holt Oram syndrome, Proc. Natl. Acad. Sci., 102 (2005), 18034–18039. https://doi.org/10.1073/pnas.0509109102 doi: 10.1073/pnas.0509109102
|
| [22] |
J. Rosenbluh, D. Nijhawan, A. G. Cox, X. Li, J. T. Neal, E. J. Schafer, et al., beta-Catenin-driven cancers require a YAP1 transcriptional complex for survival and tumorigenesis, Cell, 151 (2012), 1457–1473. https://doi.org/10.1016/j.cell.2012.11.026 doi: 10.1016/j.cell.2012.11.026
|
| [23] |
F. Zanconato, M. Forcato, G. Battilana, L. Azzolin, E. Quaranta, B. Bodega, et al., Genome-wide association between YAP/TAZ/TEAD and AP-1 at enhancers drives oncogenic growth, Nat. Cell Biol., 17 (2015), 1218–1227. https://doi.org/10.1038/ncb3216 doi: 10.1038/ncb3216
|
| [24] |
H. L. Li, Q. Y. Li, M. J. Jin, C. F. Lu, Z. Y. Mu, W. Y. Xu, et al., A review: hippo signaling pathway promotes tumor invasion and metastasis by regulating target gene expression, J. Cancer Res. Clin. Oncol., 147 (2021), 1569–1585. https://doi.org/10.1007/s00432-021-03604-8 doi: 10.1007/s00432-021-03604-8
|
| [25] |
G. D. Chiara, F. Gervasoni, M. Fakiola, C. Godano, C. D'Oria, L. Azzolin, et al., Epigenomic landscape of human colorectal cancer unveils an aberrant core of pan-cancer enhancers orchestrated by YAP/TAZ, Nat. Commun., 12 (2021), 2340. https://doi.org/10.1038/s41467-021-22544-y doi: 10.1038/s41467-021-22544-y
|
| [26] |
Y. Wang, X. Xu, D. Maglic, M. T. Dill, K. Mojumdar, P. K. S. Ng, et al., Comprehensive molecular characterization of the hippo signaling pathway in cancer, Cell Rep., 25 (2018), 1304–1317. https://doi.org/10.1016/j.celrep.2018.10.001 doi: 10.1016/j.celrep.2018.10.001
|
| [27] |
W. H. Yang, C. K. C. Ding, T. Sun, G. Rupprecht, C. C. Lin, D. Hsu, et al., The hippo pathway effector taz regulates ferroptosis in renal cell carcinoma, Cell Rep., 28 (2019), 2501–2508. https://doi.org/10.1016/j.celrep.2019.07.107 doi: 10.1016/j.celrep.2019.07.107
|
| [28] |
W. H. Yang, Z. Huang, J. Wu, C. K. C. Ding, S. K. Murphy, J. T. Chi, A TAZ-ANGPTL4-NOX2 axis regulates ferroptotic cell death and chemoresistance in epithelial ovarian cancer, Mol. Cancer Res., 18 (2020), 79–90. https://doi.org/10.1158/1541-7786.MCR-19-0691 doi: 10.1158/1541-7786.MCR-19-0691
|
| [29] |
W. H. Yang, C. C. Lin, J. Wu, P. Y. Chao, K. Chen, P. H. Chen, et al., The hippo pathway effector YAP promotes ferroptosis via the E3 Ligase SKP2, Mol. Cancer Res., 19 (2021), 1005–1014. https://doi.org/10.1158/1541-7786.MCR-20-0534 doi: 10.1158/1541-7786.MCR-20-0534
|
| [30] |
M. Pavel, M. Renna, S. J. Park, F. M. Menzies, T. Ricketts, J. Füllgrabe, et al., Contact inhibition controls cell survival and proliferation via YAP/TAZ-autophagy axis, Nat. Commun., 9 (2018), 2961. https://doi.org/10.1038/s41467-018-05388-x doi: 10.1038/s41467-018-05388-x
|
| [31] |
M. Toth, L. Wehling, L. Thiess, F. Rose, J. Schmitt, S. M. Weiler, et al., Co-expression of YAP and TAZ associates with chromosomal instability in human cholangiocarcinoma, BMC Cancer, 21 (2021), 1079. https://doi.org/10.1186/s12885-021-08794-5 doi: 10.1186/s12885-021-08794-5
|
| [32] |
S. M. White, M. L. Avantaggiati, I. Nemazanyy, C. Di Poto, Y. Yang, M. Pende, et al., YAP/TAZ inhibition induces metabolic and signaling rewiring resulting in targetable vulnerabilities in NF2-deficient tumor cells, Dev. Cell, 49 (2019), 425–443. https://doi.org/10.1016/j.devcel.2019.04.014 doi: 10.1016/j.devcel.2019.04.014
|
| [33] |
S. W. Zhang, N. Zhang, N. Wang, Role of COL3A1 and POSTN on pathologic stages of esophageal cancer, Technol. Cancer Res. Treat., 19 (2020), 1533033820977489. https://doi.org/10.1177/1533033820977489 doi: 10.1177/1533033820977489
|
| [34] |
D. Xu, Y. Xu, Y. Lv, F. Wu, Y. Liu, M. Zhu, et al., Identification of four pathological stage-relevant genes in association with progression and prognosis in clear cell renal cell carcinoma by integrated bioinformatics analysis, Biomed. Res. Int., 2020 (2020), 2137319. https://doi.org/10.1155/2020/2137319 doi: 10.1155/2020/2137319
|
| [35] |
S. Bai, L. Chen, Y. Yan, X. Wang, A. Jiang, R. Li, et al., Identification of hypoxia-immune-related gene signatures and construction of a prognostic model in kidney renal clear cell carcinoma, Front. Cell Dev. Biol., 9 (2021), 796156. https://doi.org/10.3389/fcell.2021.796156 doi: 10.3389/fcell.2021.796156
|
| [36] |
S. Sun, W. Mao, L. Wan, K. Pan, L. Deng, L. Zhang, et al., Metastatic immune-related genes for affecting prognosis and immune response in renal clear cell carcinoma, Front. Mol. Biosci., 8 (2021), 794326. https://doi.org/10.3389/fmolb.2021.794326 doi: 10.3389/fmolb.2021.794326
|
| [37] |
J. Jing, J. Sun, Y. Wu, N. Zhang, C. Liu, S. Chen, et al., AQP9 is a prognostic factor for kidney cancer and a promising indicator for M2 TAM polarization and CD8+ T-cell recruitment, Front. Oncol., 11 (2021), 770565. https://doi.org/10.3389/fonc.2021.770565 doi: 10.3389/fonc.2021.770565
|
| [38] |
J. Song, Y. D. Liu, J. Su, D. Yuan, F. Sun, J. Zhu, Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma, J. Cell Physiol., 234 (2019), 22753–22764. https://doi.org/10.1002/jcp.28840 doi: 10.1002/jcp.28840
|
| [39] |
G. Du, X. Yan, Z. Chen, R. J. Zhang, K. Tuoheti, X. J. Bai, et al., Identification of transforming growth factor beta induced (TGFBI) as an immune-related prognostic factor in clear cell renal cell carcinoma (ccRCC), Aging (Albany NY), 12 (2020), 8484–8505. https://doi.org/10.18632/aging.103153 doi: 10.18632/aging.103153
|
| [40] |
G. Chen, Y. Wang, L. Wang, W. Xu, Identifying prognostic biomarkers based on aberrant DNA methylation in kidney renal clear cell carcinoma, Oncotarget, 8 (2017), 5268–5280. https://doi.org/10.18632/oncotarget.14134 doi: 10.18632/oncotarget.14134
|
| [41] |
G. Lin, Q. Feng, F. Zhan, F. Yang, Y. Niu, G. Li, Generation and analysis of pyroptosis-based and immune-based signatures for kidney renal clear cell carcinoma patients, and cell experiment, Front. Genet., 13 (2022), 809794. https://doi.org/10.3389/fgene.2022.809794 doi: 10.3389/fgene.2022.809794
|
| [42] |
X. L. Xing, Y. Liu, J. Liu, H. Zhou, H. Zhang, Q. Zuo, et al., Comprehensive analysis of ferroptosis- and immune-related signatures to improve the prognosis and diagnosis of kidney renal clear cell carcinoma, Front. Immunol., 13 (2022), 851312. https://doi.org/10.3389/fimmu.2022.851312 doi: 10.3389/fimmu.2022.851312
|
| [43] |
Y. Hong, M. Lin, D. Ou, Z. Huang, P. Shen, A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma, BMC Cancer, 21 (2021), 831. https://doi.org/10.1186/s12885-021-08559-0 doi: 10.1186/s12885-021-08559-0
|
| [44] |
Y. Zhang, M. Tang, Q. Guo, H. Xu, Z. Yang, D. Li, The value of erlotinib related target molecules in kidney renal cell carcinoma via bioinformatics analysis, Gene, 816 (2022), 146173. https://doi.org/10.1016/j.gene.2021.146173 doi: 10.1016/j.gene.2021.146173
|
| [45] |
Y. L. Wang, H. Liu, L. L. Wan, K. H. Pan, J. X. Ni, Q. Hu, et al., Characterization and function of biomarkers in sunitinib-resistant renal carcinoma cells, Gene, 832 (2022), 146514. https://doi.org/10.1016/j.gene.2022.146514 doi: 10.1016/j.gene.2022.146514
|
| [46] |
X. Che, X. Qi, Y. Xu, Q. Wang, G. Wu, Using genomic and transcriptome analyses to identify the role of the oxidative stress pathway in renal clear cell carcinoma and its potential therapeutic significance, Oxid. Med. Cell Longev., 2021 (2021), 5561124. https://doi.org/10.1155/2021/5561124 doi: 10.1155/2021/5561124
|
| [47] |
X. Che, X. Qi, Y. Xu, Q. Wang, G. Wu, Genomic and transcriptome analysis to identify the role of the mtor pathway in kidney renal clear cell carcinoma and its potential therapeutic significance, Oxid. Med. Cell Longev., 2021 (2021), 6613151. https://doi.org/10.1155/2021/6613151 doi: 10.1155/2021/6613151
|
| [48] |
G. Tan, Z. Xuan, Z. Li, S. Huang, G. Chen, Y. Wu, et al., The critical role of BAP1 mutation in the prognosis and treatment selection of kidney renal clear cell carcinoma, Transl. Androl. Urol., 9 (2020), 1725–1734. https://doi.org/10.21037/tau-20-1079 doi: 10.21037/tau-20-1079
|
| [49] |
M. Huang, T. Zhang, Z. Y. Yao, C. Xing, Q. Wu, Y. W. Liu, et al., MicroRNA related prognosis biomarkers from high throughput sequencing data of kidney renal clear cell carcinoma, BMC Med. Genomics, 14 (2021), 72. https://doi.org/10.1186/s12920-021-00932-z doi: 10.1186/s12920-021-00932-z
|
| [50] |
L. Peng, Z. Chen, Y. Chen, X. Wang, N. Tang, MIR155HG is a prognostic biomarker and associated with immune infiltration and immune checkpoint molecules expression in multiple cancers, Cancer Med., 8 (2019), 7161–7173. https://doi.org/10.1002/cam4.2583 doi: 10.1002/cam4.2583
|
| [51] |
D. Zhang, S. Zeng, X. Hu, Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma, Cancer Cell Int., 20 (2020), 319. https://doi.org/10.1186/s12935-020-01423-4 doi: 10.1186/s12935-020-01423-4
|
| [52] |
S. Khadirnaikar, P. Kumar, S. N. Pandi, R. Malik, S. M. Dhanasekaran, S. K. Shukla, Immune associated LncRNAs identify novel prognostic subtypes of renal clear cell carcinoma, Mol. Carcinog., 58 (2019), 544–553. https://doi.org/10.1002/mc.22949 doi: 10.1002/mc.22949
|
| [53] |
E. Clough, T. Barrett, The gene expression omnibus database, Methods Mol. Biol., 1418 (2016), 93–110. https://doi.org/10.1007/978-1-4939-3578-9_5 doi: 10.1007/978-1-4939-3578-9_5
|
| [54] |
M. E. Ritchie, B. Phipson, D. I. Wu, Y. Hu, C. W. Law, W. Shi, et al., Limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43 (2015), e47. https://doi.org/10.1093/nar/gkv007 doi: 10.1093/nar/gkv007
|
| [55] |
Z. Jiang, M. Shao, X. Dai, Z. Pan, D. Liu, Identification of diagnostic biomarkers in systemic lupus erythematosus based on bioinformatics analysis and machine learning, Front. Genet., 13 (2022), 865559. https://doi.org/10.3389/fgene.2022.865559 doi: 10.3389/fgene.2022.865559
|
| [56] |
T. Wu, E. Hu, S. Xu, M. Chen, P. Guo, Z. Dai, et al., clusterProfiler 4.0: A universal enrichment tool for interpreting omics data, Innovation, 2 (2021), 100141. https://doi.org/10.1016/j.xinn.2021.100141 doi: 10.1016/j.xinn.2021.100141
|
| [57] |
Gene Ontology Consortium, The Gene Ontology (GO) database and informatics resource, Nucleic Acids Res., 32 (2004), D258–D261. https://doi.org/10.1093/nar/gkh036 doi: 10.1093/nar/gkh036
|
| [58] |
D. Szklarczyk, A. L. Gable, D. Lyon, A. Junge, S. Wyder, J. Huerta-Cepas, et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47 (2019), D607–D613. https://doi.org/10.1093/nar/gky1131 doi: 10.1093/nar/gky1131
|
| [59] |
P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498–2504. https://doi.org/10.1101/gr.1239303 doi: 10.1101/gr.1239303
|
| [60] |
W. Lin, Y. Tang, M. Zhang, B. Liang, M. Wang, L. Zha, et al., Integrated bioinformatic analysis reveals txnrd1 as a novel biomarker and potential therapeutic target in idiopathic pulmonary arterial hypertension, Front. Med., 9 (2022), 894584. https://doi.org/10.3389/fmed.2022.894584 doi: 10.3389/fmed.2022.894584
|
| [61] |
A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, et al., Gene set enrichment analysis A knowledge-based approach for interpreting genome-wide expression profiles, Proc. Natl. Acad. Sci., 102 (2005), 15545–15550. https://doi.org/10.1073/pnas.0506580102 doi: 10.1073/pnas.0506580102
|
| [62] |
Z. Zhuang, D. Li, M. Jiang, Y. Wang, Q. Cao, S. Li, et al., An integrative bioinformatics analysis of the potential mechanisms involved in propofol affecting hippocampal neuronal cells, Comput. Intell. Neurosci., 2022 (2022), 4911773. https://doi.org/10.1155/2022/4911773 doi: 10.1155/2022/4911773
|
| [63] |
F. Ponten, K. Jirstrom, M. Uhlen, The Human Protein Atlas--a tool for pathology, J. Pathol., 216 (2008), 387–393. https://doi.org/10.1002/path.2440 doi: 10.1002/path.2440
|
| [64] |
D. S. Chandrashekar, B. Bashel, S. A. H. Balasubramanya, C. J. Creighton, I. Ponce-Rodriguez, B. V. Chakravarthi, et al., UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses, Neoplasia, 19 (2017), 649–658. https://doi.org/10.1016/j.neo.2017.05.002 doi: 10.1016/j.neo.2017.05.002
|
| [65] |
Z. Tang, C. Li, B. Kang, G. Gao, C. Li, Z. Zhang, GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses, Nucleic Acids Res., 45 (2017), W98–W102. https://doi.org/10.1093/nar/gkx247 doi: 10.1093/nar/gkx247
|
| [66] |
Y. C. Yang, M. Y. Zhang, J. Y. Liu, Y. Y. Jiang, X. L. Ji, Y. Q. Qu, Identification of ferroptosis-related hub genes and their association with immune infiltration in chronic obstructive pulmonary disease by bioinformatics analysis, Int. J. Chron. Obstruct. Pulmon. Dis., 17 (2022), 1219–1236. https://doi.org/10.2147/COPD.S348569 doi: 10.2147/COPD.S348569
|
| [67] |
T. Li, J. Fu, Z. Zeng, D. Cohen, J. Li, Q. Chen, et al., TIMER2.0 for analysis of tumor-infiltrating immune cells, Nucleic Acids Res, 48 (2020), W509–W514. https://doi.org/10.1093/nar/gkaa407 doi: 10.1093/nar/gkaa407
|
| [68] |
B. A. Teicher, TGFbeta-directed therapeutics: 2020, Pharmacol. Ther., 217 (2021), 107666. https://doi.org/10.1016/j.pharmthera.2020.107666 doi: 10.1016/j.pharmthera.2020.107666
|
| [69] |
A. E. Vilgelm, A. Richmond, Chemokines modulate immune surveillance in tumorigenesis, metastasis, and response to immunotherapy, Front. Immunol., 10 (2019), 333. https://doi.org/10.3389/fimmu.2019.00333 doi: 10.3389/fimmu.2019.00333
|
| [70] |
R. Wang, B. Zheng, H. Liu, X. Wan, Long non-coding RNA PCAT1 drives clear cell renal cell carcinoma by upregulating YAP via sponging miR-656 and miR-539, Cell Cycle, 19 (2020), 1122–1131. https://doi.org/10.1080/15384101.2020.1748949 doi: 10.1080/15384101.2020.1748949
|
| [71] |
S. Nagashima, J. Maruyama, K. Honda, Y. Kondoh, H. Osada, M. Nawa, et al., CSE1L promotes nuclear accumulation of transcriptional coactivator TAZ and enhances invasiveness of human cancer cells, J. Biol. Chem., 297 (2021), 100803. https://doi.org/10.1016/j.jbc.2021.100803 doi: 10.1016/j.jbc.2021.100803
|
| [72] |
P. Chen, Y. Duan, X. Lu, L. Chen, W. Zhang, H. Wang, et al., RB1CC1 functions as a tumor-suppressing gene in renal cell carcinoma via suppression of PYK2 activity and disruption of TAZ-mediated PDL1 transcription activation, Cancer Immunol. Immunother., 70 (2021), 3261–3275. https://doi.org/10.1007/s00262-021-02913-8 doi: 10.1007/s00262-021-02913-8
|
| [73] |
S. Xu, H. Zhang, Y. Chong, B. Guan, P. Guo, YAP promotes VEGFA expression and tumor angiogenesis though Gli2 in human renal cell carcinoma, Arch. Med. Res., 50 (2019), 225–233. https://doi.org/10.1016/j.arcmed.2019.08.010 doi: 10.1016/j.arcmed.2019.08.010
|
| [74] |
P. Carter, U. Schnell, C. Chaney, B. Tong, X. Pan, J. Ye, et al., Deletion of Lats1/2 in adult kidney epithelia leads to renal cell carcinoma, J. Clin. Invest., 131 (2021), e144108. https://doi.org/10.1172/JCI144108 doi: 10.1172/JCI144108
|
| [75] |
S. Xu, H. Zhang, T. Liu, Z. Wang, W. Yang, T. Hou, et al., 6-Gingerol suppresses tumor cell metastasis by increasing YAP(ser127) phosphorylation in renal cell carcinoma, J. Biochem. Mol. Toxicol., 35 (2021), e22609. https://doi.org/10.1002/jbt.22609 doi: 10.1002/jbt.22609
|
| [76] |
S. Xu, Z. Yang, Y. Fan, B. Guan, J. Jia, Y. Gao, et al., Curcumin enhances temsirolimus-induced apoptosis in human renal carcinoma cells through upregulation of YAP/p53, Oncol. Lett., 12 (2016), 4999–5006. https://doi.org/10.3892/ol.2016.5376 doi: 10.3892/ol.2016.5376
|
| [77] |
M. D. Robinson, D. J. McCarthy, G. K. Smyth, edgeR: a Bioconductor package for differential expression analysis of digital gene expression data, Bioinformatics, 26 (2010), 139–140. https://doi.org/10.1093/bioinformatics/btp616 doi: 10.1093/bioinformatics/btp616
|
| [78] |
S. Anders, W. Huber, Differential expression analysis for sequence count data, Genome Biol, , 11 (2010), R106. https://doi.org/10.1186/gb-2010-11-10-r106 doi: 10.1186/gb-2010-11-10-r106
|
| [79] |
M. I. Love, W. Huber, S. Anders, Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2, Genome Biol., 15 (2014), 550. https://doi.org/10.1186/s13059-014-0550-8 doi: 10.1186/s13059-014-0550-8
|
| [80] |
D. W. Huang, B. T. Sherman, R. A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources, Nat. Protoc., 4 (2009), 44–57. https://doi.org/10.1038/nprot.2008.211 doi: 10.1038/nprot.2008.211
|
| [81] |
C. Xie, X. Mao, J. Huang, Y. Ding, J. Wu, S. Dong, et al., KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases, Nucleic Acids Res., 39 (2011), W316–W322. https://doi.org/10.1093/nar/gkr483 doi: 10.1093/nar/gkr483
|
| [82] |
Y. Zhou, B. Zhou, L. Pache, M. Chang, A. H. Khodabakhshi, O. Tanaseichuk, et al., Metascape provides a biologist-oriented resource for the analysis of systems-level datasets, Nat. Commun., 10 (2019), 1523. https://doi.org/10.1038/s41467-019-09234-6 doi: 10.1038/s41467-019-09234-6
|
| [83] |
Y. Liao, J. Wang, E. J. Jaehnig, Z. Shi, B. Zhang, WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs, Nucleic Acids Res., 47 (2019), W199–W205. https://doi.org/10.1093/nar/gkz401 doi: 10.1093/nar/gkz401
|
| [84] |
M. D. Paraskevopoulou, G. Georgakilas, N. Kostoulas, M. Reczko, M. Maragkakis, T. M. Dalamagas, et al., DIANA-LncBase: experimentally verified and computationally predicted microRNA targets on long non-coding RNAs, Nucleic Acids Res., 41 (2013), D239–D245. https://doi.org/10.1093/nar/gks1246 doi: 10.1093/nar/gks1246
|
| [85] |
S. D. Hsu, F. M. Lin, W. Y. Wu, C. Liang, W. C. Huang, W. L. Chan, et al., miRTarBase: a database curates experimentally validated microRNA–target interactions, Nucleic Acids Res., 39 (2011), D163–D169. https://doi.org/10.1093/nar/gkq1107 doi: 10.1093/nar/gkq1107
|
| [86] |
J. H. Yang, J. H. Li, P. Shao, H. Zhou, Y. Q. Chen, starBase: a database for exploring microRNA-mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data, Nucleic Acids Res., 39 (2011), D202–D209. https://doi.org/10.1093/nar/gkq1056 doi: 10.1093/nar/gkq1056
|
| [87] |
W. Liu, Y. Jiang, L. Peng, X. Sun, W. Gan, Q. Zhao, et al., Inferring gene regulatory networks using the improved markov blanket discovery algorithm, Interdiscip. Sci., 14 (2022), 168–181. https://doi.org/10.1007/s12539-021-00478-9 doi: 10.1007/s12539-021-00478-9
|
| [88] |
L. Zhang, P. Yang, H. Feng, Q. Zhao, H. Liu, Using Network Distance Analysis to Predict lncRNA-miRNA Interactions, Interdiscip. Sci., 13 (2021), 535–545. https://doi.org/10.1007/s12539-021-00458-z doi: 10.1007/s12539-021-00458-z
|
| [89] |
H. Liu, G. Ren, H. Chen, Q. Liu, Y. Yang, Q. Zhao, Predicting lncRNA–miRNA interactions based on logistic matrix factorization with neighborhood regularized, Knowledge Based Syst., 191 (2020), 105261. https://doi.org/10.1016/j.knosys.2019.105261 doi: 10.1016/j.knosys.2019.105261
|
| [90] |
W. Liu, H. Lin, L. Huang, L. Peng, T. Tang, Q. Zhao, et al., Identification of miRNA-disease associations via deep forest ensemble learning based on autoencoder, Brief Bioinform., 23 (2022), bbac104. https://doi.org/10.1093/bib/bbac104 doi: 10.1093/bib/bbac104
|
| [91] |
C. C. Wang, C. D. Han, Q. Zhao, X. Chen, Circular RNAs and complex diseases: from experimental results to computational models, Brief Bioinform., 22 (2021), bbab286. https://doi.org/10.1093/bib/bbab286 doi: 10.1093/bib/bbab286
|
| [92] |
A. Reustle, M. Di Marco, C. Meyerhoff, A. Nelde, J. S. Walz, S. Winter, et al., Integrative -omics and HLA-ligandomics analysis to identify novel drug targets for ccRCC immunotherapy, Genome Med., 12 (2020), 32. https://doi.org/10.1186/s13073-020-00731-8 doi: 10.1186/s13073-020-00731-8
|
| [93] |
K. Dong, W. Chen, X. Pan, H. Wang, Y. Sun, C. Qian, et al., FCER1G positively relates to macrophage infiltration in clear cell renal cell carcinoma and contributes to unfavorable prognosis by regulating tumor immunity, BMC Cancer, 22 (2022), 140. https://doi.org/10.1186/s12885-022-09251-7 doi: 10.1186/s12885-022-09251-7
|
| [94] |
Y. Chen, F. He, R. Wang, M. Yao, Y. Li, D. Guo, et al., NCF1/2/4 are prognostic biomarkers related to the immune infiltration of kidney renal clear cell carcinoma, Biomed. Res. Int., 2021 (2021), 5954036. https://doi.org/10.1155/2021/5954036 doi: 10.1155/2021/5954036
|
| [95] |
B. G. Kim, E. Malek, S. H. Choi, J. J. Ignatz-Hoover, J. J. Driscoll, Novel therapies emerging in oncology to target the TGF-beta pathway, J. Hematol. Oncol., 14 (2021), 55. https://doi.org/10.1186/s13045-021-01053-x doi: 10.1186/s13045-021-01053-x
|
| [96] |
J. D. Richter, X. Zhao, The molecular biology of FMRP: new insights into fragile X syndrome, Nat. Rev. Neurosci., 22 (2021), 209–222. https://doi.org/10.1038/s41583-021-00432-0 doi: 10.1038/s41583-021-00432-0
|
| [97] |
Y. Laitman, L. Ries-Levavi, M. Berkensdadt, J. Korach, T. Perri, E. Pras, et al., FMR1 CGG allele length in Israeli BRCA1/BRCA2 mutation carriers and the general population display distinct distribution patterns, Genet. Res., 96 (2014), e11. https://doi.org/10.1017/S0016672314000147 doi: 10.1017/S0016672314000147
|
| [98] |
W. Li, L. Zhang, B. Guo, J. Deng, S. Wu, F. Li, et al., Exosomal FMR1-AS1 facilitates maintaining cancer stem-like cell dynamic equilibrium via TLR7/NFkappaB/c-Myc signaling in female esophageal carcinoma, Mol. Cancer, 18 (2019), 22. https://doi.org/10.1186/s12943-019-0949-7 doi: 10.1186/s12943-019-0949-7
|
| [99] |
Y. Jiang, Z. Wang, C. Ying, J. Hu, T. Zeng, L. Gao, FMR1/circCHAF1A/miR-211-5p/HOXC8 feedback loop regulates proliferation and tumorigenesis via MDM2-dependent p53 signaling in GSCs, Oncogene, 40 (2021), 4094–4110. https://doi.org/10.1038/s41388-021-01833-2 doi: 10.1038/s41388-021-01833-2
|
| [100] |
Z. Shen, B. Liu, B. Wu, H. Zhou, X. Wang, J. Cao, et al., FMRP regulates STAT3 mRNA localization to cellular protrusions and local translation to promote hepatocellular carcinoma metastasis, Commun. Biol., 4 (2021), 540. https://doi.org/10.1038/s42003-021-02071-8 doi: 10.1038/s42003-021-02071-8
|
| [101] |
Y. Higuchi, M. Ando, A. Yoshimura, S. Hakotani, Y. Koba, Y. Sakiyama, et al., Prevalence of fragile X-associated tremor/ataxia syndrome in patients with cerebellar ataxia in Japan, Cerebellum, (2021), 1–10. https://doi.org/10.1007/s12311-021-01323-x doi: 10.1007/s12311-021-01323-x
|
| [102] |
K. H. Yu, N. Palmer, K. Fox, L. Prock, K. D. Mandl, I. S. Kohane, et al., The phenotypical implications of immune dysregulation in fragile X syndrome, Eur. J. Neurol., 27 (2020), 590–593. https://doi.org/10.1111/ene.14146 doi: 10.1111/ene.14146
|
| [103] |
M. Careaga, D. Rose, F. Tassone, R. F. Berman, R. Hagerman, P. Ashwood, Immune dysregulation as a cause of autoinflammation in fragile X premutation carriers: link between FMRI CGG repeat number and decreased cytokine responses, PLoS One, 9 (2014), e94475. https://doi.org/10.1371/journal.pone.0094475 doi: 10.1371/journal.pone.0094475
|
| [104] |
S. L. Hodges, S. O. Nolan, L. A. Tomac, I. D. Muhammad, M. S. Binder, J. H. Taube, et al., Lipopolysaccharide-induced inflammation leads to acute elevations in pro-inflammatory cytokine expression in a mouse model of Fragile X syndrome, Physiol. Behav., 215 (2020), 112776. https://doi.org/10.1016/j.physbeh.2019.112776 doi: 10.1016/j.physbeh.2019.112776
|
| [105] |
S. L. Hodges, S. O. Nolan, J. H. Taube, J. N. Lugo, Adult Fmr1 knockout mice present with deficiencies in hippocampal interleukin-6 and tumor necrosis factor-alpha expression, Neuroreport, 28 (2017), 1246–1249. https://doi.org/10.1097/WNR.0000000000000905 doi: 10.1097/WNR.0000000000000905
|
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Figures(10) / Tables(2)
Sufang Wu, Hua He, Jingjing Huang, Shiyao Jiang, Xiyun Deng, Jun Huang, Yuanbing Chen, Yiqun Jiang. FMR1 is identified as an immune-related novel prognostic biomarker for renal clear cell carcinoma: A bioinformatics analysis of TAZ/YAP[J]. Mathematical Biosciences and Engineering, 2022, 19(9): 9295-9320. doi: 10.3934/mbe.2022432
| Characteristics | Original image data set | Test data set |
| Male: Female | 10: 15 | 13: 12 |
| Image size | 1897×1897 | 1897×1897 |
DownLoad:
CSV
| Model | PSNR (dB) | SSIM |
| MTCNN | 36.245 | 0.954 |
| R-CNN | 35.005 | 0.927 |
| Faster R-CNN | 35.305 | 0.938 |
DownLoad:
CSV
