Selection of pathogenic genes for validation

The full analysis of the genes in each group can be found in Additional file 1. We found 35 unique genes combining G1 and G2 groups (off course it is not an exhaustive list). The selected genes in each group and its corresponding Entrez Gene ID identifier are:

G1 (n = 27): ADA (100), ADORA2A (135), ADORA2B (136), AGTR1 (185), APOH (350), CD73 (4907), CRP (1401), ENG (2022), EDN1 (1606), FLT1 (2321), GADD45A (1647), HADHA (3030), HIF1A (3091), IDO1 (3620), IL10 (3586), IL17A (3605), IL6 (2569), NOS1 (4842), NOS2 (4843), NOS3 (4846), PGF (5228), ROS1 (6098), TACR3 (6870), TGFB1 (7040), TNF (7124), TNFSF14 (8740), VEGFA (7422).

G2 (n = 13): F5 (2153), F2 (2147), AGT (183), MTHFR (4524), NOS3 (4846), ACE (1636), SERPINE1 (5054), VEGFA (7422), LEPR (3953), TGFB1 (7040), AGTR1 (185), HLA-G (3135), IL10 (3586).

Prioritization algorithms and consensus strategy

From the prioritization portal [15, 16] we selected the methods according to the following criteria: 1) fully available in web service platform and 2) requiring only the disease name for gene prioritization. Under these conditions we found 12 methods: Biograph [17], Candid [18], Glad4U [19], PolySearch [20], Cipher [21], Guildify [22], DisgeNet [23], GeneProspector [24], Genie [25], SNPs3D [26], GeneDistiller [27] and MetaRanker [28]. The following methods: Cipher, Guildify and DisgeNet were not selected from the prioritization portal but from literature, however, fulfilling the same two requirements. These methods have several characteristics that had being fully comprised by other authors previously [15, 29].

The Eq. 1 correspond with the geometrical mean between the average score of each gene obtained in each method and the normalized score according to the number of methods which predict the association between the gene and the disease. However, this formula will be zero if the gene is only predicted by only one method. Therefore we sort the genes according to the Gene _i values and according to the average (\( \left[\left(\frac{\left({n}_i-1\right)}{\left(12-1\right)}\right)+\left(\frac{1}{j}{\varSigma}_j\kern0.5em {GeneN}_{i,j}\right)\right]/2 \)). This sorting will produce a ranking that further normalized leading to the final score of each gene (ConsenScore _i ). If two genes are predicted by only one method and also have the same normalized scores then will also have the same value of ConsenScore _i .

The final list of prioritized genes is actually very long (more than 18,000). We needed a strategy to create a rational cutoff in the number of genes comprising the major pathogenic information with minimal noise. To accomplish this task we used the same pathogenic genes already defined. We defined the following index: \( {I}_i=\frac{TP_i}{FP_i+1}{ConsenScore}_i \) where, TP and FP are the true and false positive values (up to the ranking value of the gene i) respectively. The maximal value of I _i can be understood as the maximal compromise between the true positive and false positive rate compensated with the ranking index of each gene.

Early recognition analysis in prioritization

Several enrichment metrics have been proposed in the chemoinformatics literature to measure the enrichment ability of a virtual screening protocol [32] and had being recently applied in gene prioritization [33]. In this work and similar to [33], we used some of the most extended metrics to estimate the enrichment ability in order to compare different gene prioritization strategies. The overall enrichment metrics include the area under the accumulation curve (AUAC); the area under the ROC curve (ROC); and the enrichment factor (EF) evaluated at the top 1, 5, 10 and 20% of the ranked list. At the same time, the early recognition metrics used were the robust initial enhancement (RIE) and the Boltzmann-enhanced discrimination of ROC (BEDROC) evaluated at the top 1, 5, 10 and 20% of the ranked list [32]. The calculation of both classic and early recognition enrichment metrics was conducted by using the perl scriptCresset_VS [34].

Enrichment analysis

We used David Bioinformatics Resource [35, 36] for gene ontology (GO) and pathways enrichment analysis. The number of GO terms could be very big considering the amount of genes. Therefore we used Revigo [37] in order to simplify the GO terms keeping those with highest specificity. We additionally used RSpider [38], to obtain an integrated pathway combining Reactome and KEGG databases. In these databases the pathways are not the same so any enrichment will produce different pathways that otherwise could be connected or even very similar in the two databases. The use of RSpider will produce not only a statistical analysis of the enrichment but also a network representation integrating the information in both databases. The main goal in RSpider is to connect into non-interrupted sub-network component as many input genes as possible using minimal number of missing genes.

Protein-protein interaction network and analysis

We used String Database [39] to create the protein-protein interactions network with a confidence cutoff of 0.9 and zero node addition. We also used Cytoscape [40] for centrality indexes calculation and network visualization.

Communality (or cliques) network analysis by clique percolation method was applied using CFinder [41]. The communality analysis provides a better topology description of the network including the location of highly connected sub-graphs (cliques) and/or overlapping modules that usually correspond with relevant biological information. The selection of the value “k-cliques” will affect the number of community and also the number of genes in each community. We create a rational cutoff by balancing the number of communities and the genes distribution across them. In general higher values of k-cliques imply few communities while lower values lead to many communities. In our network both extremes (too small or to high k-cliques values) result in an unbalanced distribution of the genes across communities. Therefore we create the following index “S” as: \( {S}^k=\frac{\left| mean\left({N}_g^k\right)- median\left({N}_g^k\right)\right|}{N_c^k} \) where \( {N}_g^k \) and \( {N}_c^k \) are the number of genes in each community and the number of communities for a defined k-clique cutoff value.

Microarray analysis

A total of five studies were considered in microarray data integration and were named as: A1 [7], A2 [6], A3 [14], A4 [8] and A5 [10]. In each study we extracted the significant up-regulated/down-regulated genes considering the procedure of each author in the correspondent articles. In any case was considered the fold-expression as significant cutoff but the adjusted p-value reported by the authors. The criterion was an adjusted p-value < 0.05. The strategies in the reported articles considering: microarrays integration, gene expression correction and annotations where different (we will discuss more about it in results section and a brief description can be found in Additional file 2). The adjusted p-values were used to create a ranking of genes in each study followed by independent normalization.

We could go through a meta-analysis cross-normalization approach as in [10, 33]. However, because different strategies are possible to accomplish this analysis leading to different results we choose to consider each study separately. In each study (“j”) a particular up-regulated or down-regulated gene (“i”) will have a normalized score according to ranking (GeneS _i , j , the normalized score of the gene “i” in the study “j”). The consensus scoring of each gene in the microarray data was carried out similarly to consensus prioritization strategy. This means, the final score of each gene was calculated as \( {GeneAS}_i=\sqrt{\left(\frac{\left({Narray}_i-1\right)}{\left(5-1\right)}\right)\left(\frac{1}{j}{\varSigma}_j{GeneS}_{i,j}\right)} \)where, Narray_i correspond with the number of studies reporting the gene “i”. Combining all genes in the selected studies we found 1944 genes: 916 always reported up-regulated, 1013 always reported as down-regulated and 13 genes with ambiguous expression. The full list of genes is presented in Additional file 2 as well as the calculated scores. This final score (GeneAS _i ) will have a double meaning 1) inter-studies agreement and 2) the measure of the statistical significance of the gene in the study. Therefore, highest values of the score imply that the gene was identifying in several studies and also with highest statistical significance.

Consensus prioritization

Consensus strategy identify the entire G2 set in the first 1% of the final gene list (>18,000 genes) and in all cases remain as the method with higher identification of pathogenic genes. Very close to consensus strategy approach is the MetaRanker [28] method. The identification of the pathogenic genes is important but even more relevant is the early recognition ability.

The indexes related with the early enrichment clearly state that consensus strategy over perform the result of MetaRanker in pathogenic genes detection locating more genes with a significant lower rank. We compare the rank of the pathogenic genes between the two methods for G1, G2 and G1,2 using signed Wilconson test. The p-value was lower than 0.01 in the three groups indicating statically significant differences in the ranking obtained by the two methods.

The density distribution (using Gaussian kernel of function “density” in R) of the 1000 values in both methods is presented in the Fig. 1.

As we can noticed, consensus strategies compared to MetaRanker provides more frequently the lower rank for the genes in agreement with our previous results in Table 3 after evaluation in 1000 samples modifications.

Enrichment analysis of preeclampsia related genes and protein-protein interaction network

Using G1 and G2 unique genes (n = 35), we can notice (Table 1) that consensus strategy already identify the 89% in the 10% of the data, this means that the 89% of the 35 genes are in the initial 1800 genes obtained from prioritization. This is a very big number; therefore a strategy for a rational cutoff was designed (see Methods). The implementation of I_i considering the true positive and false positive ratio could be used as a rational cutoff to reduce the amount of genes. This procedure is showed in Fig. 2.

The maximal (Fig. 2) value of I _i is 0.76085 and correspond with a ranking value of 476, therefore the reduced list for PE comprise the first 476 genes. The entire gene list as well as their scores and ranking can be found in Additional file 3. In the 476 genes there are 30 of 35 predefined pathogenic genes.

The pathways presented in Table 5 are only a partial list but it is entirely presented for Reactome and KEGG in Additional file 4 .

The biological processes and enriched pathways are consistent between them and also with the scientific knowledge about PE, however would be hard to establish some kind of relevance between them without further consideration. In this way we carried on a network analysis.

With the indicated cutoff of 0.9, the final protein-protein interaction network has 417 nodes, corresponding with the 87.6% of the initial genes (476). The S ^k index (as proposed in the Methods section to identify a rational k-clique number) will rich a minimum either by an increment in the number of communities and/or by an increment in the similarity between the mean and median values of the number of genes in all communities. We can notice (Fig. 3) that the desired values will be between 8 and 10. The number of communities for k = 8 is 16 compared to 9 and 5 for k = 9 and 10 respectively. Considering that in each community several biological analyses will be carried on, 16 communities will be difficult to study. Additionally in k = 8, one of the communities have almost twofold the number of genes with respect the remaining communities. For this reason we select the k = 9 in our analysis (Fig. 4. Left).

Communities 2 and 6 could be considered as the more relevant showing. However could be useful the prioritization of the metabolic pathways by an enrichment analysis in each community (full list presented in Additional file 4) and weighted as presented in the Methods section.

Microarray data integration

From the 1944 genes collected in microarrays data only 80 are present in our 476 obtained in consensus prioritization representing only a 4%. The worst gene overlapping with respect to other microarrays is with the study A1. The A2, A3, A4, A5 conserve 40 genes in common but drastically reduced to 2 by adding A1 (Fig. 5 Left). The agreement between selected microarray studies is not good in terms of genes identifications as we can see in the Venn diagrams (Fig. 5, Left). It is a direct consequence of the differences in initial microarray data and processing strategies (presented in Additional file 2). The study A1 is the only one with any meta-analysis strategy. Both A2 and A3 carried out a meta-analysis, while A4 and A5 go specifically through cross-platform normalization. The differences between both strategies in microarray data integration had being explored previously [42]. Actually A2 and A3 share 111 genes and similarly A4 and A5 share 237 genes. This gene space is reduced 40 genes when all four studies are combined. Moreover as we can see in Additional file 2, A4 and A5 share a number of similarities regarding initial microarray data.

Analyzing the amount of genes that each study independently shares with the initial 476 prioritized genes (Fig. 5. Right) we found that: A1 (n = 12, 3.8%), A2 (n = 30, 7.7%), A3 (n = 30, 7.9%), A4 (n = 53, 4.2%) and A5 (n = 26, 7.5%). The result indicates that the methodology of Moslehi et al. [14] will represent better our prioritized genes (even when very close to A2 and A5). Moreover considering the average consensus score of those genes we found: A1 (0.396), A2 (0.561), A3 (0.597), A4 (0.111) and A5 (0.540). This average scoring also suggests that the work of Moslehi et al. [14] also cover better ranked genes (even not so distant of A2 and A5). These values will be discussed later.

There are a total of 41 up-regulated and 39 down-regulated genes commonly found between all integrated genes in microarray data and the 476 already prioritized genes (a total of 80 genes). The up-regulated are: VEGFA, FLT1, STOX1, SERPINE1, LEP, INHA, INHBA, ENG, HMOX1, VWF, TGFB1, TFPI, ADAM12, CRH, PAPPA2, VEGFC, CP, MMP14, FN1, SERPINA3, SIGLEC6, ACE2, PREP, FABP4, EGFR, FSTL3, IL6ST, VDR, IGFBP5, MMP15, ITGA5, TRIM24, CGA, MET, DUSP1, MIF, TAPBP, NR1H2, MMP11, HPN, GLRX and the down-regulated are: ACVRL1, ADRB3, AGTR1, ANGPT1, CD4, CD14, COL1A1, COL1A2, F5, F13A1, FCER1G, FGF2, GHR, CFH, HGF, HSD11B2, CFI, IGF1, IGFBP7, IL10RA, IDO1, JAK1, KLRD1, MMP1, NEDD4, ENPP1, PLAUR, MAPK1, CCL2, SOD1, SPP1, TGFBR3, THBS1, TLR4, VCAM1, APLN, HGS, ROCK2, PLAC1. From these 80 genes 34 (42.5%) were located with a ranking less than 180 (around the first 1% of the list) in the consensus strategy prioritization.

Comparing the scores of consensus strategy and scores obtained from the microarrays studies (Fig. 6) we can arrive to some interesting results. Moreover, from these 80 genes 72 are also present in the 417 forming the interaction network and 19 are also part of some community. We can evaluate the contribution of these 19 genes in each community using the average GeneAS _i of the genes which belong to a particular community in a similar way as we did previously (Table 6). The corresponding weights for each community are: 1 (0.062), 2(0.140), 3(0.072), 4(0.033), 5(0.014), 6(0.132), 7(0.054), 8(0.031) and 9(0.048). These weights also confirm that communities 2 and 6 could be the more relevant as previously presented.

Integrated metabolic network

Using RSpider [38] from the 476 genes only 272 were mapped to a reference global network obtaining three significant models (Table 8).

The p-value indicates the probability for a random gene/protein list to have a maximal connected component of the same or larger size. This p-value is computed by Monte Carlo simulation as described in [38]. Beside this statistical analysis, we should also consider that in the initial data (476) there are 80 genes also matching with microarray data while in the smallest network 23 of 98 are also present in the microarray information. This enrichment is statistically different (p-value = 0.036) compared to random gene extraction. The 23 genes are: ANGPT1, COL1A1, COL1A2, F5, F13A1, FCER1G, FLT1, FN1, IGF1, IGFBP5, ITGA5, JAK1, MET, MMP1, SERPINE1, PLAUR, SPP1, TFPI, THBS1, VEGFA, VEGFC, VWF and PAPPA2. The network associated with Model 1 is presented in Fig. 7. The network in Model 3 of Table 8 is presented in Additional file 5.

The expanded integrated metabolic network (Model 3) allows the entrance of 114 genes in order to bring connections between initial genes. However, it also incorporates 32 compounds that also act as connectors. These compounds obtained from the integrated network are presented in Table 8, some of them could be very generic like “fatty acids” o could be very specifics as “serotonin” or “L-Homocysteine”. These compounds can be easily grouped mainly in lipids, steroids, amino acids, and purine metabolisms and will be further explored in the discussion.

Consensus prioritization and enrichment analysis

Our results confirm that the consensus strategy actually improve the detection and prioritization of pathogenic genes. Application of early recognition measures are important and should be considered together with identification capabilities. The ability to rank the relevant genes on the top of a long prioritized list will directly reduce de cost of experimental validation. Previous authors had being probed that consensual strategy in prioritization improve the detection of genes related with specific pathology [15, 16, 33]. However, we are proving here for the first time that the consensus strategy also improves the early enrichment ability of genes related with pathogenesis (at least in PE).

Any study in gene-disease association is intrinsically focused into pathogenesis discovery. During this process obviously some relations could be established and not necessarily because of pathogenesis but a secondary modification (the experimental design will be directly related with this type of result). If several prioritization strategies are combined, then, the possibility of removing noisy relationships (in pathogenesis terms) increases as well as the agreement in relevant genes.

The biological processes as well as metabolic pathways enrichment analysis lead us to already expected information. Some of the biological processes, like those related to blood pressure or vasoconstrictions have a direct association with PE clinical development. Biological processes associated with inflammation, angiogenesis, cytokine, immune system and hormones regulation could also be associated with PE clinical manifestation or even pathogenesis [6, 7, 43, 44] and there are also well related with the metabolic pathways enrichment results (Table 5). The pathway analysis also reflects a good agreement with previous works. The cytokine pathway, VEGF and PDGF signaling, immune system and even some of the cancer related pathways were previously reported by other authors [6, 7, 9, 14, 44, 45]. Signaling pathways in general, are highly relevant as well as several routes connected with cancer (see Additional file 4) which also agree with the previous studies [14, 46, 47].

Protein-protein interaction network, communality analysis and microarrays integration

The enrichment analysis can be helpful. However it is hard to establish a ranking of the pathways according to their implications in pathogenesis without further analysis. It is the main reason to combine the analysis of the protein-protein interaction network. The entire network contains 417 nodes but only 111 are part of some community. The network with 417 already comprises 29 of the 35 predefined pathogenic genes. The sub-network containing only genes which belong to some community have 12 of 29 predefined pathogenic genes. Moreover only 3 genes (HADHA, IDO1 and HLA-G) of the remaining 17 genes are not directly connected with the sub-network. On the other hand, the average degree of the pathogenic genes is 23.6 which is statistically significant higher than non-pathogenic genes (14.2) at p-value <0.05. This result indicates that the node degree could be associated with pathogenesis in this network. The black colored nodes represent those genes that are present in more than one community and therefore are usually those with higher connectivity degree as we can also see in the Fig. 3 Right (and Table 6).

Actually we can notice that tops pathways primarily involve communities 2 and 6. It indicates that those communities as well as their genes are highly relevant in PE. Additionally, the community number 5 is exclusively related with the Renin-angiotensin system and considering that it is also enriched in neuroactive ligand-receptor interaction and vascular smooth muscle contraction, we can suspect that this community has a strong connection with the hypertensive disorder. Interestingly community 8 have the major number of associated pathways. However, most of them are related with signaling pathways like TGF-beta signaling. The enrichment in signaling processes could indicate that it is probably a central group of genes acting as connectors between several metabolic processes and therefore would be relevant to comprehend PE heterogeneity.

Following the importance of community 2 and 6, the major pathways ordered by relevance connected to both communities are: VEGF signaling pathway, mTOR signaling pathway, Adipocytokine signaling pathway, Intestinal immune network for IgA production, Leukocyte transendothelial migration, Progesterone-mediated oocyte maturation, Cytokine-cytokine receptor interaction, Jak-STAT signaling pathway, complement and coagulation cascades, TGF-beta signaling pathway, focal adhesion and regulation of actin cytoskeleton.

In order to explore our results using additional experimental information we included the microarrays analysis. The worst genes overlapping with respect to prioritized list is with the study A1 (Fig. 5), while the other four studies show more consistent results. The reason for this difference in A1 is mainly because is the only study that is not a meta-analysis. We included it because it is the largest independent study.

Our result indicates that the study of Moslehi et al. [14] identify more common genes and also better ranked in our consensus strategy. Both studies using meta-analysis (A2 and A3) shown a better agreement with consensus than A4 and A5. The A5 is better that A4, and similar to A2 and A3 probably because the use of combat [48] and an increased number of arrays. Also, A2 is the study that carried out the largest microarray integration related with PE. The use of combat in cross-platform normalization had being favored in term of clinical and biological meaning agreement [42]. The differences in the percentage of genes (Fig. 5 Righ) shared with consensus strategy is really small comparing A3 with A2 and A5 studies. The A3 study also comprise some similarities with A4 and A5 regarding initial microarray data. However, A3 study exclusively considers meta-analysis in microarrays of early-onset preeclampsia. It is a very important difference regarding other studies. We had probed that consensus prioritization actually improve pathogenic early recognition and we also known that genes involved in early onset preeclampsia are probably closer to pathogenesis than late-onset preeclampsia. This could explain why the A3 have the highest average score with our prioritized consensus list. Therefore it is a logical result considering the previous analysis and also an indirect validation of our consensus strategy. The A5 and A4, consider similar microarrays than A3 but including other do not exclusively related with early-onset preeclampsia.

Regarding all differences between microarrays studies, we should remember that all genes extracted from microarray data were statistically significant up or down-regulated in each corresponding study. Moreover we carried out a complete integration of the gene space between all microarrays data considered, so, to our effects we didn’t exclude any genes because be part or not of a particular study. Therefore, this disparity in microarrays studies could only affect the GeneAS _i scoring. The score should be interpreted as a commitment between agreement across methods and their statistical significance. Even when is reasonable to assume that a gene with a simultaneously high agreement and high statistical significance could be very important (i.e. LEP, FLT1, INHA) (Fig. 6), also the condition of high agreement with low statistically significance is equally relevant (because actually leads to highest score). In other words, a statistical significance don’t necessarily means that the gene is more relevant to the disease than any other with a reduced but significant change.

Previously we presented evidence indicating that consensus prioritization is capable to identify genes with high pathogenic probabilities in the first portion of the data. It is clearly presented in Fig. 6 with VEGFA, AGTR1, F5 and TGFB1 which are well related with pathogenesis [49–55]; however, the score obtained from microarray data is relatively low in these cases (less than 0.5). Considering a high cutoff value (i.e. >0.7) we can identify: LEP, FLT1, INHA, ENG, PAPPA2, and CRH. There are sufficient evidences to associate these genes with PE pathogenesis or clinical manifestation [14, 52, 53, 56–60]. Our calculations also indicate that communities 6 and 2 are those with highest enrichment of genes coming from microarray data confirming our previous results using the network and consensus prioritization (Table 6). This consistency support that the prioritization strategy is actually pointing us in the correct direction and also justify the idea that the associated pathways could also be highly relevant.

Metabolic involvement

In all previous analysis, the VEGF signaling pathways had being selected as the most relevant in PE. This pathway is presented (Fig. 6) with the genes: VEGFA, VEGFB, VEGFC, FLT1, KDR, FLT4, PGF, NRP2 and NRP1. These genes are directly connected with arginine (NOS1, NOS2 and NOS3) and nitric oxide metabolisms (NOSIP and HSP90AA1). Considering the previous results in communities and pathways enrichment analysis we should conclude that this processes will be actually the most significant for PE pathogenesis. Interestingly the involvement of VEGF, FLT1 and several elements in arginine metabolism, including NO production, was proposed in [61] as the primary mechanism in placenta leading to PE.

In the protein-protein interaction network analysis we can noticed that genes like VEGFA, NOS3, SRC and AKT1 are highly connected (community 2 in Table 6 and Fig. 4) but in the integrated metabolic network (Model 1, Fig. 7) their connectivity is not so elevated (or even in the extended integrated metabolic network showed in Additional file 5). The reason for these differences is a direct consequent of the pathway representation in KEGG and Reactome. For example, VEGF mediates the ezrin/calpain/PI3K/Akt pathway-dependent stimulation of NOS3 phosphorylation leading to Ca2+ independent NO generation [62–64]. This connection will be reflected in the PPI as an edge between VEGFA and NOS3 or even between VEGFA and PIK3R1 (distant nodes in Fig. 6) and these both interactions will not be seen in the Fig. 7. This is why we should additionally use the Model 3 for integrated pathway (presented in Additional file 5) and consider the pathway in Fig. 7 as the simplest representation of the biological meaning of genes involved in PE. Form the metabolic integrated network (Fig. 6) we can clearly identify some well-known mechanisms related with VEGF and PE and other relevant effects that will be discussed further.

The increment in FLT1 production (noticed by the microarray data) could lead to the increment in the soluble-Flt1 rescuing the extracellular VEGFA [65, 66]. Therefore the increment in VEGFA expression in placenta could be a compensatory response to restore normal angiogenesis [67]. This mechanism of interaction between soluble-Flt1 and VEGFA as well as soluble-Eng and TGFB1 (also present in community 2) had being long term related with PE pathogenesis [66]. The increment in VEGFA could also be associated with an increment in HSP90, also acting with SRC in the NOS3 expression and NO production. As we previously explained, this can also be accomplished through PI3K and the involvement of AKT1. The expression of HSP90 could be polemic because apparently it is related with the disease progression and also placenta location [68]. However several authors had being found an increment in the placenta expression of HSP90 in PE [68–70] at mRNA and protein levels. Moreover, this increment can be a protective reaction that is stimulated by HIF1A [71] and consequently connected to disease progression as described in [68]. Actually, there are differences in HIF1A placenta expression in early-onset PE and late-onset PE [72] showing that both stages have different hypoxia compensatory mechanism. Interestingly the HIF1A gene is part of our prioritized list (ranking at 46) and PPI network. HIF1A is not part of any community but connected to several of them, especially with community 2 (HIF1A is not connected with communities 1 and 7) and it is also missing in the integrated metabolic models. There are not studies of HSP90 (or closely related HSP70) gene variations or promoter polymorphism in PE to know for sure if the protective roll could be compromised leading to early or late PE manifestation.

In the extended model (Model 3, in Additional file 5) the VEGF pathways is connected with several members of the HLA family through CD247 and PAK2 but also to another variety of pathways through ROCK2 and FGD3. Both connections can be related with apoptosis, trophoblastic affectation and endothelial cells organization [73, 74]. Even when PAK2 had being poorly studied in PE, we know that it is directly involved in gestational trophoblastic disease [45] and that endothelial cells PAK and/or CDC42 are directly involved with KDR and consequently essential for endothelial cells organization [74, 75].

The roll of angiogenesis in PE pathogenesis is clearly revealed in our theoretical analysis as well as scientific literature. However, we should discuss other aspects related with pathogenesis, specially the renin-angiotensin pathway and the roll of catechol-O-methyltransferase (COMT).

We can notice in our prioritization list that AGT and AGTR1 are the first two genes in our ranking (ACE and AGTR2 were also found in position 7 and 19 respectively) but interestingly only AGTR1 was found down-regulated in our microarray data. The down-regulation of AGTR1 from microarray data was only identified (considering our microarray data) in [6, 8], however, other authors [76, 77] found an increment in AGTR1 expression. In any case there are evidences that renin-angiotensin system is modified in hypoxic conditions [77, 78] and it is well connected with the HIF1A previously discussed. In our list of pathogenic genes, we should notice the AGT derive from 1) the AT₁-AA auto-antibody that interact with AGTR1 or 2) from some polymorphism in AGT that had being associated with increased risk of preeclampsia [54, 79, 80]. The origin of AT₁-AA in PE is quite unknown [50, 51, 81] but some authors shown that it is related to B-cells and it is connected with IL10 during pregnancy as well as with other cytokines (i.e. TNF) [82, 83]. Other authors indicates that an increment in CD4(+) T-cells and a decrement in T regulatory cells stimulates TNF, IL6, endothelin (EDN1), IL-17 and B-cells production of AT₁-AA [83–85]. Some of these genes are clearly involved in our networks and communities, especially EDN1 and IL6. We can’t clearly state that renin-angiotensin pathway is not part of PE pathogenesis but our network based results reduce the importance of this pathway. These suggest that its finding in the top prioritized gene list is actually a consequence of the hypertension effect more than the PE pathogenesis.

Our results confirm that consensus prioritization strategy lead us to genes with pathogenic involvement, at least in PE. Moreover, the introductions of network and enrichment analysis are capable to narrow the metabolic and gene space leading us toward reasonable conclusions in agreement with our scientific knowledge of the disease. However, the proposed strategies need to be further improved in several topics. For instance: a) the inclusion of prioritization algorithms based in learning strategies, b) the inclusion of other network processing methods to reduce the gene lost and 3) the differentiation between early and late-onset preeclampsia. Additionally, as previously stated, there are several genes relevant in our analysis with poor or almost no information in their PE involvement. Therefore further experimental analysis will needed to validate the participation of these genes in PE pathogenesis or clinical manifestation.