Choroidal neovascularization (CNV) is a severe eye disease that leads to blindness, especially in the elderly population. Various endogenous and exogenous regulatory factors promote its pathogenesis. However, the detailed molecular biological mechanisms of CNV have not been fully revealed. In this study, by using advanced computational tools, a number of key gene ontology (GO) terms and KEGG pathways were selected for CNV. A total of 29 validated genes associated with CNV and 17,639 nonvalidated genes were encoded based on the features derived from the GO terms and KEGG pathways by using the enrichment theory. The widely accepted feature selection method—maximum relevance and minimum redundancy (mRMR)—was applied to analyze and rank the features. An extensive literature review for the top 45 ranking features was conducted to confirm their close associations with CNV. Identifying the molecular biological mechanisms of CNV as described by the GO terms and KEGG pathways may contribute to improving the understanding of the pathogenesis of CNV.
1. Introduction
Choroidal neovascularization (CNV) is a serious eye disease involving the abnormal growth of blood vessels in the choroid region [1–3]. The growth originates from a break in Bruch’s membrane, and subsequently the new blood vessels penetrate into the subretinal pigment epithelium [4]. From an epidemiological perspective, CNV is a major cause of pathological visual loss in aging populations [5]. Clinically, age-related macular degeneration (ARMD), myopia, and presumed ocular histoplasmosis syndrome (POHS) are the three major pathogeneses attributed to CNV [6–8]. The Wisconsin Beaver Dam Eye Study [9] confirmed that up to 90% of visual loss in ARMD is secondary to CNV. Given that ARMD is the most common cause of visual loss in people older than 50 years, CNV is speculated to be directly linked to such pathological visual loss. Aside from ARMD, the other two pathological processes—myopia [10] and POHS [11]—are also linked to pathological visual loss. This finding validates the specific role of CNV in pathological visual loss.
Clinically, most patients with CNV share a group of characteristic signs and symptoms, including painless loss of vision, metamorphopsia, paracentral or central scotoma, and apparent changes in image size perception [12, 13]. Generally, patients with these complaints need further physical examinations on blood, fluid, lipid exudation, and retinal pigment epithelial detachment for accurate diagnoses [14]. However, for the final differential diagnosis, laboratory tests are the golden criteria. Generally, the laboratory studies for CNV involve three main techniques, namely, fluorescein angiography [15], indocyanine green angiography [16], and spectral domain optical coherence tomography [17]. For the patients with confirmed diagnosis, due to the unclear pathological mechanisms of CNV, anti-VEGF treatment that counters angiogenesis is the only preferred clinical therapeutic approach [17]. However, the injection burden limits the long-term application of such anti-VEGF treatment. Therefore, more detailed pathological mechanisms of CNV need to be revealed to promote the development and application of new drugs against the disease.
Recent publications have partially revealed the detailed pathological mechanisms of CNV, which involve the interactions between genetic factors and exogenous environments. For the environmental factors, the personal physical factors induced by the exogenous factors are directly involved in the pathogenesis [18]. Age, obesity, high cholesterol, and high blood pressure aggravate the progression of CNV and further contribute to the occurrence of complications [19, 20]. Aside from these so-called physical exogenous factors, various genetic factors are also connected to the initiation and progression of CNV. Given that CNV is a highly specific disease with an abnormal angiogenesis, genes associated with angiogenesis, such as VEGF [21] and FGF2 [22], definitely participate in the pathological processes, which have been widely confirmed by reliable experiments. In addition to these genes, a specific gene called CFI participates in CNV and induces gradual visual loss and myopia; this finding is based on the sequencing data of CNV families [23]. Furthermore, a specific study [24] on the East Asian population with 2119 patients and 5691 controls revealed a group of effective hereditary and sporadic virulence genes that participate in CNV, mapping out the detailed genetic blueprint of CNV. Some trials were also conducted in the bioinformatics field. Zhang et al. [25] presented a specific computational routine for the identification of CNV-associated genes, indicating the efficacy and accuracy of computational application in such field.
As mentioned earlier, the genetic basis and the environmental influences of CNV have been revealed. However, its biological molecular mechanisms have not been explained thoroughly. Here, the detailed biological processes, cellular components, and molecular functions that may participate in the pathogenesis of CNV were screened out by using computational methods. In this study, GO [26] and KEGG [26, 27] pathways were introduced as two effective bioinformatics tools to accurately describe such items [27]. Based on widely known biological processes associated with CNV, an effective network was rebuilt, and novel biological processes described by the GO and KEGG items were screened out. Recent publications have validated these highly correlative biological processes, thus supporting the efficacy and accuracy of our prediction. With the use of computational methods, a group of functional biological processes that may participate in the potential pathogenesis of CNV were screened out, and for the first time, the detailed pathological mechanisms of CNV were described at the level of comprehensive biological processes instead of genes. The results contributed to the understanding of the development and progression of CNV.
2. Materials and Methods
This study aimed to extract some key GO terms and KEGG pathways that share close biological associations with CNV by using a computational framework. The flowchart of our method is illustrated in Figure 1 for the easy understanding of this work.
Flowchart of selecting the key GO terms and KEGG pathways related to CNV.
2.1. Materials
In 2012, Newman et al. [28] reported a number of genes that are related to AMD. We downloaded the “Additional file 3” in their study [28], in which genes associated with AMD in literature either by genetic linkage or as expression biomarker were listed. Since CNV was a subtype of AMD, we further filtered the genes. Only the genes in CNV Up or CNV Down modules from “Additional file 5” in Newman et al.’s study were kept and at last, 35 CNV genes were obtained. CNV Up or CNV Down modules were generated by network clustering of differentially expressed genes with a permuted p<0.1 and fold change ⩾ 1.5 among 31 normal, 7 MD1, 4 MD2, 17 Dry AMD, 2 GA, 4 CNV, and 3 GA/CNV samples. Therefore, the final CNV genes we used were both reported by literature and differentially expressed.
The obtained 35 CNV genes were mapped onto their Ensembl IDs. We excluded IDs that are not in the PPI network reported in STRING (Version 10.0) [29]. 38 Ensembl IDs were accessed. The GO terms and KEGG pathways were used to investigate the difference between CNV-related genes and others; thus, the Ensembl IDs without a GO term and KEGG pathway information were excluded. A total of 29 Ensembl IDs were left. These IDs were the positive samples in this study. The other 17,639 Ensembl IDs were the negative samples and comprised the dataset together with the positive samples in this study. The genes belonging to the positive and negative samples are provided in Supplementary Material S1.
2.2. Feature Vector
The goal of this study was to refine important GO terms and KEGG pathways that are associated with CNV genes. To fulfill that goal, all the genes in the dataset were needed to be represented by all the GO terms and KEGG pathways. Here, the enrichment theory [30] of the GO term and KEGG pathway was used to transform the genes into numeric values, which indicated the biological relationships between the genes and GO terms (KEGG pathways). Comparing with the direct binary annotation of whether a gene has a specific GO term or KEGG pathway, the score obtained by the enrichment theory can indicate the significance of overlap between a gene set and a GO or KEGG function in the genome background. It is more robust than the binary qualitative measurement [31]. To date, this theory has been widely applied to investigate different gene- or protein-related problems [30, 32–41]. After each gene was represented by a larger number of features, by applying a feature selection method described in Section 2.3, the key GO term or KEGG pathway features were extracted to distinguish the difference between the positive and negative samples. The encoding procedure follows.
GO Enrichment Score. The GO enrichment score was utilized to represent the association between a GO term and an involved gene as a numeric value. For a given GO term, such as GOj, and a gene g, the gene set G1 consisted of genes annotated to GOj and gene set G2 consisted of the neighbor genes of g in the protein–protein interaction network reported in STRING (http://string-db.org/) [29], a well-organized database providing known and predicted protein–protein interactions. On the basis of the preceding items, the GO enrichment score of GOj and g can be defined as the −log10 of the hypergeometric test p value [30, 32–35] of G1 and G2 according to the following equation:(1)ESGOg,GOj=-log10∑k=mnMkN-Mn-kNn,where N is the total number of genes in humans, M is the number of genes in G1, n is the number of genes in G2, and m is the number of the common genes of G1 and G2. A large enrichment score of GOj and g indicated a close relationship between them. In this study, 20,686 GO terms were considered. Thus, 20,686 GO enrichment scores were calculated for each gene in the dataset, which were obtained by using an in-house program using R function phyper. The R code is “score ← −log10(phyper(numWdrawn − 1, numW, numB, numDrawn, lower.tail = FALSE)),” where numW, numB, and numDrawn correspond to the number of genes annotated to GOj, number of genes not annotated to GOj, and number of neighbor genes g, respectively.
KEGG Enrichment Score. Similar to the GO enrichment score, the KEGG pathway score was calculated using the same theory to represent the quantitative associations between the KEGG pathways and genes in the dataset. For a given KEGG pathway Kj and a gene g, G1 was a gene set containing genes in Kj and G2 had the same meaning as described in preceding paragraph. The KEGG enrichment score shared a similar definition with the GO enrichment score between Kj and g, which was formulated as(2)ESKEGGg,Kj=-log10∑k=mnMkN-Mn-kNn,where N, M, n, and m share similar definitions as described in (1). In addition, a high score yielded by a KEGG pathway Kj and a gene g indicated their strong associations. Here, 297 KEGG pathways were considered and resulted in 297 KEGG enrichment scores for each gene, which were also obtained by using an in-house program using R function phyper.
Accordingly, each gene in the dataset was encoded by a combination of 20,686 GO term and 297 KEGG pathway features and was defined as a feature vector with a total of 20,983 elements:(3)fg=ESGOg,GO1,…,ESGOg,GO20686,ESKEGGg,K1,…,ESKEGGg,K291T.
2.3. Feature Selection
As described in Section 2.2, each gene in the dataset was encoded with 20,983 features derived from the GO terms and KEGG pathways. Some of them shared closer biological associations with CNV. Thus, advanced tools were necessary to extract these important features that played essential roles in the development of CNV. Here, a reliable and widely accepted feature selection method, namely, maximum relevance and minimum redundancy (mRMR) [42], was adopted to analyze all 20,983 features. The mRMR method, proposed by Peng et al. [42], is a useful tool to analyze the feature space of complicated biological problems. To date, many investigations related to complicated biological systems or problems have applied this method to analyze their feature space and extract important information [34, 36, 43–52].
In the mRMR method, two excellent criteria were proposed to rank the features: (1) maximum relevance and (2) minimum redundancy. According to their names, the former criterion measures the importance of features by relying on their correlation to target variable, whereas the latter criterion provides a guarantee that the selected features also have minimum redundancies. If one decides to construct an optimal feature subspace, both maximum relevance and minimum redundancy should be used. In this study, the purpose was to extract important features that are closely related to CNV rather than construct an optimal feature subspace. Therefore, only the criterion of maximum relevance was employed to rank the features in this study. The maximum relevance of each feature was measured by the mutual information (MI) between the feature and the target variable. For each feature, f was a variable representing the values in all samples and c was the target variable. The MI was calculated as follows:(4)Ic,f=∬pc,flogpc,fpcpfdcdf,where p(c) and p(f) are the marginal probabilities of c and f and p(c,f) is their joint probabilistic distribution. According to (4), MI measures the mutual dependence between two variables.
Based on the MI value assigned to each feature, the feature ranking list called MaxRel feature list was obtained and formulated as follows:(5)F=f1,f2,…,fN,where N is the total number of features in the feature space. A high rank received by a feature indicates a strong association with CNV. Based on the properties of the top ranked features, a new insight into the CNV can be proposed for the investigation of the corresponding GO terms and KEGG pathways.
3. Results and Discussion3.1. Results
As described in Section 2.2, a total of 20,983 GO terms and KEGG pathway features were encoded in each gene in dataset. Then, according to their relevance to the target variables, these features were ranked in the descending order by using the maximum relevance criterion described in Section 2.3. The output feature list, called MaxRel feature list, was built and obtained (Supplementary Material S2).
As mentioned in the preceding paragraphs, not all GO terms or KEGG pathways shared equal roles on influencing the progression of CNV. Thus, extracting the key GO terms or KEGG pathways was necessary. By applying the maximum relevance criterion, all the features were ranked by their relevance to the target variables, and the rank of a corresponding feature in the output MaxRel feature list for a GO term or KEGG pathway indicated its association with CNV. According to their MI values, some GO terms or KEGG pathways received high MI values in the MaxRel feature list; these features were extracted and their importance was further investigated. To determine the cut-off of MI value, a curve was plotted in Figure 2, which shows the number of selected features under different cut-offs of MI value. It can be observed that the cut-off 0.003 was a proper choice, resulting in 45 features. These features would be given further literature review. Their detailed information is listed in Table 1. All the 45 features corresponded to important GO terms. The following section provides a detailed discussion on these GO terms.
Top 45 key GO terms associated with CNV.
GO term ID
GO term
GO description
MI value
Rank
GO: 0031091
Platelet alpha-granule
Cellular component
0.003
1
GO: 0031093
Platelet alpha-granule lumen
Cellular component
0.003
2
GO: 0060205
Cytoplasmic membrane-bounded vesicle lumen
Cellular component
0.003
3
GO: 0038133
ERBB2-ERBB3 signaling pathway
Biological process
0.003
4
GO: 0038129
ERBB3 signaling pathway
Biological process
0.003
5
GO: 1902847
Regulation of neuronal signal transduction
Biological process
0.003
6
GO: 0061517
Macrophage proliferation
Biological process
0.003
7
GO: 1902949
Positive regulation of tau protein kinase activity
Biological process
0.003
8
GO: 0061518
Microglial cell proliferation
Biological process
0.003
9
GO: 0031983
Vesicle lumen
Cellular component
0.003
10
GO: 0005576
Extracellular region
Cellular component
0.003
11
GO: 0035767
Endothelial cell chemotaxis
Biological process
0.003
12
GO: 0002580
Regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II
Biological process
0.003
13
GO: 0044421
Extracellular region part
Cellular component
0.003
14
GO: 0007603
Phototransduction, visible light
Biological process
0.003
15
GO: 0001948
Glycoprotein binding
Molecular function
0.003
16
GO: 0072562
Blood microparticle
Cellular component
0.003
17
GO: 0044650
Adhesion of symbiont to host cell
Biological process
0.003
18
GO: 0019062
Virion attachment to host cell
Biological process
0.003
19
GO: 0010466
Negative regulation of peptidase activity
Biological process
0.003
20
GO: 0009584
Detection of visible light
Biological process
0.003
21
GO: 0010951
Negative regulation of endopeptidase activity
Biological process
0.003
22
GO: 0001654
Eye development
Biological process
0.003
23
GO: 0002581
Negative regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II
Positive regulation of nephron tubule epithelial cell differentiation
Biological process
0.003
33
GO: 1903002
Positive regulation of lipid transport across blood brain barrier
Biological process
0.003
34
GO: 1903000
Regulation of lipid transport across blood brain barrier
Biological process
0.003
35
GO: 1903001
Negative regulation of lipid transport across blood brain barrier
Biological process
0.003
36
GO: 1902951
Negative regulation of dendritic spine maintenance
Biological process
0.003
37
GO: 1902999
Negative regulation of phospholipid efflux
Biological process
0.003
38
GO: 1901627
Negative regulation of postsynaptic membrane organization
Biological process
0.003
39
GO: 2001139
Negative regulation of phospholipid transport
Biological process
0.003
40
GO: 0046911
Metal chelating activity
Molecular function
0.003
41
GO: 0030574
Collagen catabolic process
Biological process
0.003
42
GO: 0007423
Sensory organ development
Biological process
0.003
43
GO: 0001968
Fibronectin binding
Molecular function
0.003
44
GO: 0051346
Negative regulation of hydrolase activity
Biological process
0.003
45
The number of selected features under different cut-offs of MI values.
3.2. Analysis of Key GO Terms
As mentioned earlier, based on our current computational methods, a group of functional biological processes that may directly contribute to the initiation and progression of CNV as a pathological mechanism were screened out. In the prediction list, the top 45 biological processes described by the GO terms as optimal CNV-associated biological processes were selected. Due to the limitation of such manuscript, an individual analysis of all the items was not feasible. Therefore, the top terms were chosen, and their respective connection with CNV according to recent publications was discussed. According to recent publications, such GO terms can be summarized into three major subgroups: angiogenesis, local neural metabolism, and immune-associated biological processes. The detailed discussion follows.
3.2.1. Analysis of Angiogenesis Associated Biological Processes
The two GO terms in our prediction list—GO: 0031091 and GO: 0031093—both describe the functional cellular components of platelet alpha-granules. In 2015, a specific study [53] on proangiogenic responses confirmed that the release of platelet alpha-granules promotes angiogenesis. No direct connections were revealed between platelet alpha-granules and CNV; however, abnormal angiogenesis plays an irreplaceable role and may be the core pathological biological process during the initiation and progression of CNV [54]. Therefore, GO items associated with platelet alpha-granules, such as cellular components GO: 0031091 and GO: 0031093, are definitely associated with CNV. This result validated the efficacy and accuracy of our prediction.
GO term GO: 0038133 describes a detailed pathway called the ERBB2-ERBB3 signaling pathway. According to recent publications, this signaling pathway contributes to the regulation of cell survival and tumorigenesis [55, 56]. As for the detailed connections between the ERBB2-ERBB3 signaling pathway and CNV, mediated by miR-199a and miR-125b, ERBB2 and ERBB3 as two functional components of our predicted biological process have been confirmed to contribute to the regulation of vascular endothelial growth factor secretion and the stimulation of angiogenesis in multiple tissues, including the eyes [57–59]. Given the core initiative functions of angiogenesis for CNV, the predicted biological process called the ERBB2-ERBB3 signaling pathway is a potential CNV-associated GO term. Moreover, the next predicted GO term, called GO: 0038129, also describes the ERBB3-associated signaling pathway. This finding not only implied the prediction consistency of the current computational methods but also further confirmed the specific role of such pathways during the initiation and progression of CNV.
GO: 0031983 was the next predicted GO term and describes the vesicle lumen as a functional cellular component. As the parent term of GO: 0060205 describing the cytoplasmic vesicle lumen, such cellular component definitely is associated with the initiation and progression of CNV. As for detailed literature evidence, in 2009, a specific study on the vascular permeability and pathological angiogenesis of CNV confirmed that the vesicle lumen in living cells is related to the vascular hyperpermeability and abnormal angiogenesis [60]. Vascular hyperpermeability [61] and abnormal angiogenesis are both specific symptoms of CNV [62]; thus, such biological processes are potential CNV-associated biological processes.
The next GO term, called GO: 0005576, describes a general term called extracellular region. Various extracellular substances participate in the pathogenesis of CNV, including LOX [63], LOXL2 [63], Thy-1 [64], and integrins [65]. Such specific substances may play irreplaceable roles during the initiation and progression of CNV; thus, this GO term that describes the extracellular regions of a certain focus is a potential CNV-associated biological process. GO: 0035767, as the next predicted GO in our prediction list, describes an effective biological process called endothelial cell chemotaxis. Based on recent publications, such biological process is involved in the activation of platelets [66] and exosome-mediated antiangiogenesis [67]. Platelet activation [68] and angiogenesis [69] are directly connected to the initiation and progression of CNV; therefore, this predicted GO term is quite significant in the pathogenesis of CNV.
3.2.2. Analysis of Local Neural Metabolism Associated Biological Processes
GO: 0060205, as another cellular component associated item, describes the cytoplasmic vesicle lumen. Based on recent publications, such cellular component participates in autophagy and secretion-associated biological processes in living cells [70, 71]. As for the biological connections between the cytoplasmic vesicle lumen and CNV, the predicted GO-associated biological processes, such as autophagy and substance secretion, have all been confirmed to be involved in the initiation and progression of CNV [72, 73]. This result implied the accuracy and efficacy of our prediction. GO: 1902847 describes the regulation of neuronal signal transduction. In the biological process of neuronal signal transduction, a specific gene called IKK2 has been confirmed to be significant [74]. Coincidentally, the inhibition of IKK2 has been widely used in the treatment against CNV, indicating the specific role of IKK2 during the pathogenesis of CNV [75, 76]. Therefore, connected by such functional gene IKK2, the predicted biological processes associated with neuronal signal transduction may also be related to CNV. This finding validates the efficacy and accuracy of our prediction. As the next predicted GO, GO: 1902949 describes the positive regulation of tau protein kinase activity. Tau protein is a major pathological factor that contributes to the initiation and progression of Alzheimer’s disease (AD) [77–79]. During the initiation and progression of AD, another specific protein called apolipoprotein E4 (apoE4) interacts with our predicted tau protein [80] and participates in the pathogenesis of AD [81]. Given that recent studies also validated the specific role of apoE4 in neovascularization [82] and its potential functions in CNV [23], tau protein associated kinase activity is reasonably connected to CNV-associated biological characteristics.
3.2.3. Analysis of Immune-Associated Biological Processes
The GO term GO: 0061517 describes the proliferation of a specific immune-associated cell subgroup: macrophage. Based on recent publications, macrophages contribute to CNV by regulating CCR2-dependent and proangiogenic biological processes [83, 84], indicating that the proliferation of such gene is definitely related to the progression of the disease [85]. Apart from the proliferation of macrophage, the proliferation of another effective cell subgroup called microglial cells is also predicted to contribute to CNV by GO: 0061518 in our prediction list. Mediated by neuroprotectin D1, microglial ramifications and redistribution participate in the pathological processes of CNV [86]. Therefore, as a functional neuronal cell subtype with specific microglial ramifications [86], this predicted GO is reasonably connected to CNV [87]. Besides these predicted biological processes, several functional GOs in the top 45 predicted GO terms have been reported to participate in CNV-associated biological processes. These functional GO terms include GO: 0002580 (regulation of antigen processing and presentation of peptide or polysaccharide antigen via MHC class II) [88], GO: 0044421 (extracellular region) [89], and GO: 0007603 (phototransduction, visible light) [90, 91]. These results confirmed the efficacy and accuracy of our prediction.
4. Conclusion
Based on our presented computational method, a group of functional biological functions that have been confirmed by recent publications to be related to the pathogenesis of CNV were screened out. Such predicted biological processes not only further revealed the detailed pathological mechanisms of CNV but also provided a new tool to identify potential functional disease-associated biological processes in multiple categories of the disease. Finally, we will try our best to develop a computational method based on some extracted features in this study to predict novel CNV genes in the future.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this article.
Authors’ Contributions
YuanYuan Luo and Yan Yan contributed equally to this work.
Supplementary Materials
The supplementary materials consist of two files. Supplementary Material S1: positive and negative samples in the dataset represented by their gene symbols. Supplementary Material S2: MaxRel feature list as an output of the mRMR method with ranked 20,983 features.
PereiraF. B.VelosoC. E.KokameG. T.NehemyM. B.Characteristics of Neovascular Age-Related Macular Degeneration in Brazilian Patients201523442332422-s2.0-8494549692510.1159/000439359KaragiannisD.KontadakisG. A.KaprinisK.GiarmoukakisA.GeorgalasI.ParikakisE. A.TsilimbarisM. K.Treatment of myopic choroidal neovascularization with intravitreal ranibizumab injections: The role of age201711119712012-s2.0-8502140144110.2147/OPTH.S135174CheungC. M. G.ArnoldJ. J.HolzF. G.Myopic choroidal neovascularization: review, guidance, and consensus statement on management2017JungJ. J.ChenC. Y.MrejenS.Gallego-PinazoR.XuL.MarsigliaM.BodduS.FreundK. B.The incidence of neovascular subtypes in newly diagnosed neovascular age-related macular degeneration20141584769779.e22-s2.0-8490850625810.1016/j.ajo.2014.07.006NeelamK.CheungC. M. G.Ohno-MatsuiK.LaiT. Y. Y.WongT. Y.Choroidal neovascularization in pathological myopia20123154955252-s2.0-8486383631110.1016/j.preteyeres.2012.04.001HanD. P.McAllisterJ. T.WeinbergD. V.KimJ. E.WirostkoW. J.Combined intravitreal anti-VEGF and verteporfin photodynamic therapy for juxtafoveal and extrafoveal choroidal neovascularization as an alternative to laser photocoagulation20102447137162-s2.0-7795110729210.1038/eye.2009.122El MellaouiM.El OuafiA.El HansaliZ.BouzidiA.IferkhasS.LaktaouiA.Presumed ocular histoplasmosis syndrome20153898928932-s2.0-8494856846010.1016/j.jfo.2015.01.019LaghmariM.LezrekO.Presumed ocular histoplasmosis syndrome (POHS)201418, article no. 2682682-s2.0-8490731770810.11604/pamj.2014.18.268.4692KleinR.KleinB. E. K.LintonK. L. P.Prevalence of age-related maculopathy. The Beaver Dam Eye Study199299693394310.1016/S0161-6420(92)31871-82-s2.0-0026681119GhafourI. M.AllanD.FouldsW. S.Common causes of blindness and visual handicap in the west of Scotland19836742092132-s2.0-002054080510.1136/bjo.67.4.209FemanS. S.PodgorskiS. F.PennM. K.Blindness from presumed ocular histoplasmosis in Tennessee19828912129512982-s2.0-002041399210.1016/S0161-6420(82)34630-8HatzK.PrünteC.Polypoidal choroidal vasculopathy in Caucasian patients with presumed neovascular age-related macular degeneration and poor ranibizumab response20149821881942-s2.0-8489261721010.1136/bjophthalmol-2013-303444CheungC. M. G.LohB. K.LiX.MathurR.WongE.LeeS. Y.WongD.WongT. Y.Choroidal thickness and risk characteristics of eyes with myopic choroidal neovascularization2013917e580e5812-s2.0-8488595898510.1111/aos.12117Bonini FilhoM. A.de CarloT. E.FerraraD.AdhiM.BaumalC. R.WitkinA. J.ReichelE.DukerJ. S.WaheedN. K.Association of choroidal neovascularization and central serous chorioretinopathy with optical coherence tomography angiography2015133889990610.1001/jamaophthalmol.2015.1320LiuW.LiH.ShahR. S.ShuX.LinsenmeierR. A.FawziA. A.ZhangH. F.Simultaneous optical coherence tomography angiography and fluorescein angiography in rodents with normal retina and laser-induced choroidal neovascularization20154024578257852-s2.0-8495660740610.1364/OL.40.005782KawamuraA.YuzawaM.MoriR.HaruyamaM.TanakaK.Indocyanine green angiographic and optical coherence tomographic findings support classification of polypoidal choroidal vasculopathy into two types2013916e474e4812-s2.0-8488258643710.1111/aos.12110IaconoP.Battaglia ParodiM.PapayannisA.KontadakisS.Da PozzoS.CascavillaM. L.La SpinaC.VaranoM.BandelloF.Fluorescein angiography and spectral-domain optical coherence tomography for monitoring Anti-VEGF therapy in myopic choroidal neovascularization201452125312-s2.0-8490114114210.1159/000358331MunchI. C.LinnebergA.LarsenM.Precursors of age-related macular degeneration: associations with physical activity, obesity, and serum lipids in the Inter99 Eye Study20135463932394010.1167/iovs.12-107852-s2.0-84878782248HsuC.-C.ChenS.-J.LiA.-F.LeeF.-L.Systolic blood pressure, choroidal thickness, and axial length in patients with myopic maculopathy20147794874912-s2.0-8491207463810.1016/j.jcma.2014.06.009ZhangX.LiM.WenF.ZuoC.ChenH.WuK.ZengR.Different impact of high-density lipoprotein-related genetic variants on polypoidal choroidal vasculopathy and neovascular age-related macular degeneration in a Chinese Han population201310816222-s2.0-8487241736510.1016/j.exer.2012.12.005MuetherP. S.NeuhannI.BuhlC.HermannM. M.KirchhofB.FauserS.Intraocular growth factors and cytokines in patients with dry and neovascular age-related macular degeneration2013339180918142-s2.0-8488501864610.1097/IAE.0b013e318285cd9eDe DiasJ. R. O.RodriguesE. B.MaiaM.Magalhat̃esO.Jr.PenhaF. M.FarahM. E.Cytokines in neovascular age-related macular degeneration: Fundamentals of targeted combination therapy20119512163116372-s2.0-8155521446010.1136/bjo.2010.186361LevezielN.YuY.ReynoldsR.TaiA.MengW.CaillauxV.CalvasP.RosnerB.MalecazeF.SouiedE. H.SeddonJ. M.Genetic factors for choroidal neovascularization associated with high myopia2012538500450092-s2.0-8486739301410.1167/iovs.12-9538ChengC. Y.YamashiroK.ChenL. J.New loci and coding variants confer risk for age-related macular degeneration in East Asians201566063ZhangJ.SuoY.ZhangY.-H.ZhangQ.ChenX.XuX.LuW.Mining for genes related to choroidal neovascularization based on the shortest path algorithm and protein interaction information2016186011274027492-s2.0-8496127598810.1016/j.bbagen.2016.03.015The Gene Ontology ConsortiumGene ontology consortium: going forward2015431D1049D105610.1093/nar/gku1179KanehisaM.SatoY.KawashimaM.FurumichiM.TanabeM.KEGG as a reference resource for gene and protein annotation2016441D457D46210.1093/nar/gkv1070NewmanA. M.GalloN. B.HancoxL. S.MillerN. J.RadekeC. M.MaloneyM. A.CooperJ. B.HagemanG. S.AndersonD. H.JohnsonL. V.RadekeM. J.Systems-level analysis of age-related macular degeneration reveals global biomarkers and phenotype-specific functional networks201242, article 1610.1186/gm3152-s2.0-84863410211SzklarczykD.FranceschiniA.WyderS.ForslundK.HellerD.Huerta-CepasJ.SimonovicM.RothA.SantosA.TsafouK. P.KuhnM.BorkP.JensenL. J.von MeringC.STRING v10: protein-protein interaction networks, integrated over the tree of life201543D447D45210.1093/nar/gku1003Carmona-SaezP.ChagoyenM.TiradoF.CarazoJ. M.Pascual-MontanoA.GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists200781, article R310.1186/gb-2007-8-1-r32-s2.0-33847205295HuangT.WangC.ZhangG.XieL.LiY.SySAP: a system-level predictor of deleterious single amino acid polymorphisms201231384310.1007/s13238-011-1130-22-s2.0-84862245195HuangT.ChenL.CaiY.ChouK.Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property20116910.1371/journal.pone.0025297e252972-s2.0-80053220447HuangT.ZhangJ.XuZ.HuL.ChenL.ShaoJ.ZhangL.KongX.CaiY.ChouK.Deciphering the effects of gene deletion on yeast longevity using network and machine learning approaches2012944101710252-s2.0-8486279220810.1016/j.biochi.2011.12.024ChenL.ZhangY.-H.LuG.HuangT.CaiY.-D.Analysis of cancer-related lncRNAs using gene ontology and KEGG pathways20177627362-s2.0-8501304016510.1016/j.artmed.2017.02.001ChenL.ZhangY.-H.WangS.ZhangY.HuangT.CaiY.-D.Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways20171292-s2.0-8502894099910.1371/journal.pone.0184129e0184129ChenL.ZhangY.ZhengM.HuangT.CaiY.Identification of compound–protein interactions through the analysis of gene ontology, KEGG enrichment for proteins and molecular fragments of compounds201629162065207910.1007/s00438-016-1240-xLiJ.ChenL.WangS.ZhangY.KongX.HuangT.CaiY.-D.A computational method using the random walk with restart algorithm for identifying novel epigenetic factors2018293129330110.1007/s00438-017-1374-52-s2.0-85029582823LuS.YanY.LiZ.ChenL.YangJ.ZhangY.WangS.LiuL.Determination of genes related to uveitis by utilization of the random walk with restart algorithm on a protein–protein interaction network2017185, article no. 10452-s2.0-8501954504110.3390/ijms18051045ChenL.YangJ.XingZ.YuanF.ShuY.ZhangY.KongX.HuangT.LiH.CaiY.ZouQ.An integrated method for the identification of novel genes related to oral cancer2017124e017518510.1371/journal.pone.0175185ZhouY.LiB.ZhangY.ChenL.KongX.Feature classification and analysis of lung cancer related genes through gene ontology and KEGG pathways201611140502-s2.0-8496189951210.2174/1574893611666151119220803YangJ.ChenL.KongX.HuangT.CaiY. D.Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway20149910.1371/journal.pone.0107202e107202PengH.LongF.DingC.Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy20052781226123810.1109/TPAMI.2005.1592-s2.0-24344458137WangS.ZhangY.LuJ.CuiW.HuJ.CaiY.Analysis and identification of aptamer-compound interactions with a maximum relevance minimum redundancy and nearest neighbor algorithm201620169835120410.1155/2016/8351204ChenL.ChuC.HuangT.KongX.CaiY.Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models20154771485149310.1007/s00726-015-1974-5HuangT.WangM.CaiY.-D.Analysis of the preferences for splice codes across tissues20156129049072-s2.0-8494838217110.1007/s13238-015-0226-5LiZ.ZhouX.DaiZ.ZouX.Classification of G-protein coupled receptors based on support vector machine with maximum relevance minimum redundancy and genetic algorithm201011, article 32510.1186/1471-2105-11-3252-s2.0-77954864967NiQ.ChenL.A feature and algorithm selection method for improving the prediction of protein structural classes2017207612621LiB.-Q.ZhengL.-L.FengK.-Y.HuL.-L.HuangG.-H.ChenL.Prediction of linear B-cell epitopes with mRMR feature selection and analysis201611122312-s2.0-8496189876510.2174/1574893611666151119215131MohabatkarH.BeigiM. M.AbdolahiK.MohsenzadehS.Prediction of allergenic proteins by means of the concept of Chou's pseudo amino acid composition and a machine learning approach2013911331372-s2.0-8487357257310.2174/1573406411309010133ChenL.WangS.ZhangY.LiJ.XingZ.YangJ.HuangT.CaiY.Identify Key Sequence Features to Improve CRISPR sgRNA Efficacy20175265822659010.1109/ACCESS.2017.2775703ChenL.ChuC.FengK.Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization201619213614310.2174/13862073196661511101224532-s2.0-84959298727ChenL.ZhangY.-H.HuangG.PanX.WangS.HuangT.CaiY.-D.Discriminating cirRNAs from other lncRNAs using a hierarchical extreme learning machine (H-ELM) algorithm with feature selection2018293113714910.1007/s00438-017-1372-72-s2.0-85029548353EtulainJ.MenaH. A.NegrottoS.SchattnerM.Stimulation of PAR-1 or PAR-4 promotes similar pattern of VEGF and endostatin release and pro-angiogenic responses mediated by human platelets20152687998042-s2.0-8494299196810.3109/09537104.2015.1051953XuH.ZengF.ShiD.SunX.ChenX.BaiY.Focal choroidal excavation complicated by choroidal neovascularization2014121124625010.1016/j.ophtha.2013.08.0142-s2.0-84891623509McDonaghC. F.HuhalovA.HarmsB. D.AdamsS.ParagasV.OyamaS.ZhangB.LuusL.OverlandR.NguyenS.GuJ.KohliN.WallaceM.FeldhausM. J.KudlaA. J.SchoeberlB.NielsenU. B.Antitumor activity of a novel bispecific antibody that targets the ErbB2/ErbB3 oncogenic unit and inhibits heregulin-induced activation of ErbB320121135825932-s2.0-8485940975610.1158/1535-7163.MCT-11-0820FockV.PlesslK.DraxlerP.OttiG. R.FialaC.KnöflerM.PollheimerJ.Neuregulin-1-mediated ErbB2-ErbB3 signalling protects human trophoblasts against apoptosis to preserve differentiation201512823430643162-s2.0-8494984473510.1242/jcs.176933HeJ.JingY.LiW.QianX.XuQ.LiF.LiuL.JiangB.JiangY.Roles and Mechanism of miR-199a and miR-125b in Tumor Angiogenesis2013822-s2.0-8487427433310.1371/journal.pone.0056647e56647YenL.YouX.Al MoustafaA.BatistG.HynesN. E.MaderS.MelocheS.Alaoui-JamaliM. A.Heregulin selectively upregulates vascular endothelial growth factor secretion in cancer cells and stimulates angiogenesis200019313460346910.1038/sj.onc.12036852-s2.0-0034691674RussellK. S.SternD. F.PolveriniP. J.BenderJ. R.Neuregulin activation of ErbB receptors in vascular endothelium leads to angiogenesis19992776H2205H221110.1152/ajpheart.1999.277.6.H2205ChangS.-H.FengD.NagyJ. A.SciutoT. E.DvorakA. M.DvorakH. F.Vascular permeability and pathological angiogenesis in caveolin-1-null mice20091754176817762-s2.0-7354910001710.2353/ajpath.2009.090171DaullP.PatersonC. A.KuppermannB. D.GarrigueJ.-S.A preliminary evaluation of dexamethasone palmitate emulsion: A novel intravitreal sustained delivery of corticosteroid for treatment of macular Edema20132922582692-s2.0-8487503245210.1089/jop.2012.0044DuH.SunX.GumaM.LuoJ.OuyangH.ZhangX.ZengJ.QuachJ.NguyenD. H.ShawP. X.KarinM.ZhangK.JNK inhibition reduces apoptosis and neovascularization in a murine model of age-related macular degeneration20131106237723822-s2.0-8487342093910.1073/pnas.1221729110BergenT. V.SpanglerR.MarshallD.HollandersK.Van de VeireS.VandewalleE.MoonsL.HermanJ.SmithV.StalmansI.The role of LOX and LOXL2 in the pathogenesis of an experimental model of choroidal neovascularization2015569528052892-s2.0-8493982426410.1167/iovs.14-15513WangH.HanX.KunzE.Elizabeth HartnettM.Thy-1 regulates VEGF-mediated choroidal endothelial cell activation and migration: Implications in neovascular age-related macular degeneration20165713552555342-s2.0-8499242445810.1167/iovs.16-19691NakajimaT.HirataM.ShearerT. R.AzumaM.Mechanism for laser-induced neovascularization in rat choroid: accumulation of integrin alpha chain-positive cells and their ligands201420864871Janowska-WieczorekA.WysoczynskiM.KijowskiJ.Marquez-CurtisL.MachalinskiB.RatajczakJ.RatajczakM. Z.Microvesicles derived from activated platelets induce metastasis and angiogenesis in lung cancer200511357527602-s2.0-1194424967610.1002/ijc.20657HajrasoulihaA. R.JiangG.LuQ.LuH.KaplanH. J.ZhangH.-G.ShaoH.Exosomes from retinal astrocytes contain antiangiogenic components that inhibit laser-induced choroidal neovascularization20132883928058280672-s2.0-8488476671810.1074/jbc.M113.47076523926109BirkeK.LipoE.BirkeM. T.Kumar-SinghR.Topical Application of PPADS Inhibits Complement Activation and Choroidal Neovascularization in a Model of Age-Related Macular Degeneration20138102-s2.0-8488516626210.1371/journal.pone.0076766e76766QiJ. H.EbrahemQ.AliM.CutlerA.BellB.PraysonN.SearsJ.KnauperV.MurphyG.Anand-ApteB.Tissue Inhibitor of Metalloproteinases-3 Peptides Inhibit Angiogenesis and Choroidal Neovascularization in Mice2013832-s2.0-8487457174310.1371/journal.pone.0055667e55667ZhangM.KennyS. J.GeL.XuK.SchekmanR.Translocation of interleukin-1beta into a vesicle intermediate in autophagy-mediated secretion2015410.7554/eLife.11205MalhotraV.ErlmannP.The Pathway of Collagen Secretion2015311091242-s2.0-8494723562810.1146/annurev-cellbio-100913-013002KlettnerA.KauppinenA.BlasiakJ.RoiderJ.SalminenA.KaarnirantaK.Cellular and molecular mechanisms of age-related macular degeneration: from impaired autophagy to neovascularization20134571457149710.1016/j.biocel.2013.04.0132-s2.0-84877628853ZhangR.LiuZ.ZhangH.ZhangY.LinD.The COX-2-selective antagonist (NS-398) inhibits choroidal neovascularization and subretinal fibrosis20161112-s2.0-8495546783210.1371/journal.pone.0146808e0146808TsaousidouE.PaegerL.BelgardtB. F.PalM.WunderlichC. M.BrönnekeH.CollienneU.HampelB.WunderlichF. T.Schmidt-SupprianM.KloppenburgP.BrüningJ. C.Distinct Roles for JNK and IKK Activation in Agouti-Related Peptide Neurons in the Development of Obesity and Insulin Resistance201494149515062-s2.0-8491211182710.1016/j.celrep.2014.10.045GaddipatiS.LuQ.KasettiR. B.MillerM. C.LuQ.TrentJ. O.KaplanH. J.LiQ.IKK2 inhibition using TPCA-1-Loaded PLGA microparticles attenuates laser-induced choroidal neovascularization and macrophage recruitment20151032-s2.0-8492588001810.1371/journal.pone.0121185e0121185LuH.LuQ.GaddipatiS.KasettiR. B.WangW.PasparakisM.KaplanH. J.LiQ.IKK2 inhibition attenuates laser-induced choroidal neovascularization2014912-s2.0-8490031298710.1371/journal.pone.0087530e87530CrunkhornS.Antisense oligonucleotide reverses tau pathology201716316616610.1038/nrd.2017.37UchiharaT.EndoK.KondoH.OkabayashiS.ShimozawaN.YasutomiY.AdachiE.KimuraN.Tau pathology in aged cynomolgus monkeys is progressive supranuclear palsy/corticobasal degeneration- but not Alzheimer disease-like -Ultrastructural mapping of tau by EDX20164111810.1186/s40478-016-0385-5MalkkiH.Alzheimer disease: BACE1 inhibition could block CSF tau increase2017131610.1038/nrneurol.2016.1702-s2.0-84994143487LirazO.Boehm-CaganA.MichaelsonD. M.ApoE4 induces Aβ42, tau, and neuronal pathology in the hippocampus of young targeted replacement apoE4 mice201381, article no. 162-s2.0-8487779264210.1186/1750-1326-8-16YuJ.-T.TanL.HardyJ.Apolipoprotein e in Alzheimer's disease: An update201437791002-s2.0-8490467818510.1146/annurev-neuro-071013-014300AntesR.Salomon-ZimriS.BeckS. C.GarridoM. G.LivnatT.MaharshakI.KadarT.SeeligerM.WeinbergerD.MichaelsonD. M.VEGF mediates apoE4-induced neovascularization and synaptic pathology in the choroid and retina20151243233342-s2.0-8492961608510.2174/1567205012666150325182504KrauseT. A.AlexA. F.EngelD. R.KurtsC.EterN.VEGF-production by CCR2-dependent macrophages contributes to laser-induced choroidal neovascularization2014942-s2.0-8489945475010.1371/journal.pone.0094313e94313HorieS.RobbieS. J.LiuJ.WuW.-K.AliR. R.BainbridgeJ. W.NicholsonL. B.MochizukiM.DickA. D.CoplandD. A.CD200R signaling inhibits pro-angiogenic gene expression by macrophages and suppresses choroidal neovascularization20133, article no. 30722-s2.0-8488699712210.1038/srep03072HeL.MarnerosA. G.Doxycycline inhibits polarization of macrophages to the proangiogenic M2-type and subsequent neovascularization201428912801980282-s2.0-8489699920010.1074/jbc.M113.535765SheetsK. G.JunB.ZhouY.etal.Microglial ramification and redistribution concomitant with the attenuation of choroidal neovascularization by neuroprotectin D120131917471759CarrascoM.-C.NavascuésJ.CuadrosM. A.CalventeR.Martín-OlivaD.SantosA. M.SierraA.Ferrer-MartínR. M.Marín-TevaJ. L.Migration and ramification of microglia in quail embryo retina organotypic cultures20117142963152-s2.0-7995249165610.1002/dneu.20860PenfoldP. L.WongJ. G.GyoryJ.BillsonF. A.Effects of triamcinolone acetonide on microglial morphology and quantitative expression of MHC-II in exudative age-related macular degeneration20012931881922-s2.0-003494543110.1046/j.1442-9071.2001.00407.xBinderS.StanzelB. V.KrebsI.GlittenbergC.Transplantation of the RPE in AMD20072655165542-s2.0-3454812461510.1016/j.preteyeres.2007.02.002PapastefanouV. P.NogueiraV.HayG.AndrewsR. M.HarrisM.CohenV. M. L.SagooM. S.Choroidal naevi complicated by choroidal neovascular membrane and outer retinal tubulation2013978101410192-s2.0-8488004577810.1136/bjophthalmol-2013-303234ParodM. B.IaconoP.BandelloF.Correspondence of leakage on fluorescein angiography and optical coherence tomography parameters in diagnosis and monitoring of myopic choroidal neovascularization treated with bevacizumab20163611041092-s2.0-8495292814910.1097/IAE.0000000000000684