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m7GDisAI: N7-methylguanosine (m7G) sites and diseases associations inference based on heterogeneous network



Recent studies have confirmed that N7-methylguanosine (m7G) modification plays an important role in regulating various biological processes and has associations with multiple diseases. Wet-lab experiments are cost and time ineffective for the identification of disease-associated m7G sites. To date, tens of thousands of m7G sites have been identified by high-throughput sequencing approaches and the information is publicly available in bioinformatics databases, which can be leveraged to predict potential disease-associated m7G sites using a computational perspective. Thus, computational methods for m7G-disease association prediction are urgently needed, but none are currently available at present.


To fill this gap, we collected association information between m7G sites and diseases, genomic information of m7G sites, and phenotypic information of diseases from different databases to build an m7G-disease association dataset. To infer potential disease-associated m7G sites, we then proposed a heterogeneous network-based model, m7G Sites and Diseases Associations Inference (m7GDisAI) model. m7GDisAI predicts the potential disease-associated m7G sites by applying a matrix decomposition method on heterogeneous networks which integrate comprehensive similarity information of m7G sites and diseases. To evaluate the prediction performance, 10 runs of tenfold cross validation were first conducted, and m7GDisAI got the highest AUC of 0.740(± 0.0024). Then global and local leave-one-out cross validation (LOOCV) experiments were implemented to evaluate the model’s accuracy in global and local situations respectively. AUC of 0.769 was achieved in global LOOCV, while 0.635 in local LOOCV. A case study was finally conducted to identify the most promising ovarian cancer-related m7G sites for further functional analysis. Gene Ontology (GO) enrichment analysis was performed to explore the complex associations between host gene of m7G sites and GO terms. The results showed that m7GDisAI identified disease-associated m7G sites and their host genes are consistently related to the pathogenesis of ovarian cancer, which may provide some clues for pathogenesis of diseases.


The m7GDisAI web server can be accessed at, which provides a user-friendly interface to query disease associated m7G. The list of top 20 m7G sites predicted to be associted with 177 diseases can be achieved. Furthermore, detailed information about specific m7G sites and diseases are also shown.


Over 150 types of RNA modifications have been identified in RNA molecules [1, 2], and N7-methylguanosine (m7G), which refers to methylation of guanosine(G) on position N7 is a typical positively charged modification present in tRNA [3], rRNA [4], mRNA 5′cap [5] and internal mRNA regions [6], playing a critical role in regulating RNA processing, metabolism,and function. As a positively charged RNA modification, m7G could tune RNA secondary structures or protein-RNA interactions through a combination of electrostatic and steric effects [7]. m7G sites in several tRNAs variable loops, which are installed by the heterodimers METTL1-WDR4 in mammals [3], have been reported to stabilize tRNA tertiary fold [8, 9]. m7G sites that install at 5′cap stabilize transcripts against exonucleolytic degradation [10], and modulate nearly every stage of the mRNA life cycle, including transcription elongation [11], pre-mRNA splicing [12], polyadenylation [13], nuclear export [14], and translation [15].

Mutations in m7G methyltransferase are associated with various diseases. To be more specific, a mutation in the methyltransferase complex WDR4 (WD Repeat Domain 4) in humans has been reported to cause primordial dwarfism characterized by facial dysmorphism, brain malformation, and severe encephalopathy with seizures [16, 17]. Lin et al. [18] reported that knockout of the m7G46 tRNA WDR4 in embryonic stem cells impairs neural lineage differentiation and affects translation on a global scale. Besides, overexpression of WDR4 has been discovered to influence learning and memory in Down syndrome [19]. Moreover, the m7G tRNA methyltransferase METTL1 (Methyltransferase like 1) was reported to influence cancer cell viability [20]. Therefore, identification of disease-associated m7G sites will accelerate the understanding of disease pathogenesis at the molecular level, and will further benefit the prognosis, diagnosis, evaluation, treatment, and prevention of human complex diseases. However, it is time-consuming and expensive to explore the association between m7G sites and various diseases by only conducting wet experiments. Fortunately, m7G-MeRIP-Seq [21], m7G-miCLIP-seq [6], and m7G-Seq [21] have generated vast amounts of biological data about m7G, so computational methods are urgently needed to uncover potential disease-associated m7G sites effectively. Researchers can then select the most probable m7G sites and the host genes of these sites for further analysis, streamlining their wet-lab experiments. To our knowledge, no computational models for finding disease-associated m7G sites have been developed.

In this study, we extracted 768 validated associations among 741 m7G sites and 177 diseases from m7GHub to construct the m7G disease association dataset [22]. Then we proposed a heterogeneous network-based m7G-disease associations inference method m7GDisAI to prioritize candidate m7G sites for a disease of interest. Furthermore, experiments of cross validation and case study on ovarian cancer have been carried out to prove the effectiveness and stability of our method. To facilitate the exploration and direct query of our predicted results, we developed an online database m7GDisAI. The website hosts the top 20 m7G sites predicted to be associated with 177 diseases with high prediction scores and supports queries with diseases which you are interested. The m7GDisAI website is freely available at



Source of datasets

m7GHub is a comprehensive m7G online platform, which deciphers the location, regulation, and pathogenesis of m7G modification [22]. It consists of four parts, including m7GDB, m7GFinder, m7GSNPer, and m7GdiseaseDB. It provides 69,159 m7G sites which are classified into three confidence levels: high confidence level sites reported by m7G-seq, medium confidence level sites reported by m7G-MeRIP-Seq as well as m7G-miCLIP-Seq, and low confidence level sites predicted by m7GFinder. As a subpart of m7GHub, m7GDiseaseDB collects 1218 disease-associated genetic variants that may lead to gain/loss of m7G sites, with implications for disease pathogenesis involving m7G RNA methylation. It provides us sufficient information to construct the m7G-variant dataset and further build the m7G-disease association dataset.

m7G-variant dataset

In the m7G-variant dataset, m7G-associated variants refer to those mutated at or close to G sites and cause gain/loss of m7G sites simultaneously. For each m7G site-variant pair, the association of them was measured by the association levels as well as the confidence levels. The association level qualifies the influence that variants exert on m7G sites into the range [0,1]. The closer the association level is to 1, the stronger influence that variant exerts on the exact site. Initially, 812 m7G site-variant pairs with high confidence level were first extracted, then ranked according to the association level. Then 741 m7G site-disease pairs were further picked out with association levels higher than 0.8. Meanwhile, the sequence and genomic location information of m7G-variant pairs were collected correspondingly in this dataset. Specifically, it contains the genomic locations, host genes of m7G sites, site-centered 41 bp reference sequences as well as site-centered 41 bp alternative sequences.

m7G-disease association dataset

In the m7G-disease association dataset, 741 m7G sites were associated with 177 diseases via 741 variants in the m7G-variant dataset. Specifically, these variants are both m7G-associated and disease-associated. In other words, they cause the gain/loss of the m7G site and involve in various disease pathogenesis. Taking these variants as linkages, 177 diseases in ClinVar and GWAS were found to be associated with 741 variants, with implications for disease pathogenesis in m7G RNA methylation.


m7G-disease association network reconstruction can be transformed into predicting the unknown entries in the m7G-disease association matrix, which can be solved by traditional matrix decomposition methods. However, the number of known associations is so small that matrix decomposition methods cannot achieve satisfactory performance in this case. Thus, we proposed a heterogeneous network-based m7G-disease association prediction method m7GDisAI which will be detailed in the next. The framework of m7GDisAI is shown in Fig. 1.

Fig. 1
figure 1

The framework of m7GDisAI. m7GDisAI mainly consists of four steps. The first step is to extract m7G sequence-derived features with m7G-variant data to construct m7G chemical similarity network (CSN) and CNF similarity network (CNFSN). The second step is to fuse CSN and CNFSN together by taking linear combinations of chemical similarities and CNF similarities, and then form a series of m7G integrated similarity networks. The third step is to build heterogeneous networks with m7G-similarity networks, m7G-disease association network, and disease semantic network. The fourth step is to predict associations between unknown m7G site-disease pairs

m7G-Disease Association Network

Based on the m7G-disease association dataset, the m7G-disease adjacency network was constructed to record their associations. To be more specific, let S = {s1, s2, …, sm} and D = {d1, d2, … dn} denote m m7G sites and n diseases respectively. Let \({\varvec{A}}_{{{\varvec{SD}}}} \in R^{m \times n}\) indicate the adjacency network, \({\varvec{A}}_{{{\varvec{SD}}_{{{\mathbf{ij}}}} }}\) is 1 if there exists a validated association between m7G-disease pair \((s_{i} ,d_{j} )\). The m7G-disease association matrix ASD was provided in Additional file 4: Table S4.

m7G similarity networks

As a kind of auxiliary information, m7G similarity information plays a critical role in m7G-disease association prediction. To make full advantages of the information of m7G sites, a series of m7G similarity networks were constructed for further use in the heterogeneous network.

m7G chemical similarity network

m7G chemical similarity network (CSN) depicts the m7G similarities in terms of the chemical properties extracted from m7G site-centered sequences [23, 24]. Specifically, either sequence is a combination of four nucleotides A, T, C, G. Each nucleotide can be characterized by three distinct structural chemical properties, such as ring structures, hydrogen bonds, and functional groups. In terms of ring structures, A and G have two benzene rings, while C and T have only one. As for the number of hydrogen bonds formed during hybridization, A and T have two, while G and C have three. Regarding the functional groups they contain, A and C contain amino groups, whereas G and T contain keto groups. Therefore, the i-th nucleotide in sequence N can be encoded by a vector \((x_{i} ,y_{i} ,z_{i} )\).

$$x_{i} = \left\{ {\begin{array}{*{20}c} 1 & {{\text{if }}\;N_{i} \in \{ A,G\} } \\ 0 & {{\text{if }}\;N_{i} \in \{ C,T\} } \\ \end{array} } \right\},\quad y_{i} = \left\{ {\begin{array}{*{20}c} 1 & {{\text{if }}\;N_{i} \in \{ A,T\} } \\ 0 & {{\text{if }}\;N_{i} \in \{ G,C\} } \\ \end{array} } \right\},\quad z_{i} = \left\{ {\begin{array}{*{20}c} 1 & {{\text{if}}\; \, N_{i} \in \{ A,C\} } \\ 0 & {{\text{if}}\; \, N_{i} \in \{ G,T\} } \\ \end{array} } \right\}$$

Therefore, A, C, G, T can be encoded as (1,1,1), (0,0,1), (1,0,0) and (0,1,0) respectively. Thus, the chemical feature of site si, denoted as CF (si), is the combination of these four vectors, in the form of a sequence consisting of {0,1}. Considering the binary numerical properties of the m7G chemical features, the Jaccard coefficient was applied to them. To be specific, for two sites si and sj, their pairwise chemical similarity is defined as (1)

$$che\_sim_{ij} = \frac{{|CF(s_{i} ) \cap CF(s_{j} )|}}{{|CF(s_{i} ) \cup CF(s_{j} )|}}$$

Then in the m7G CSN, s1, s2, …, sm are nodes, and the edges between them are weighted by the pairwise chemical similarity above. For convenience, the adjacency matrix was indicated as ACSN (Additional file 5: Table S5).

m7G Cumulative Nucleotide Frequency Similarity Network

Similar to the construction of CSN, m7G cumulative nucleotide frequency (CNF) features were extracted for further similarity calculation. To be specific, CNF of the i-th nucleotide in a sequence is defined as the sum of all the instances of this nucleotide before the i + 1 position dividing i. Taking the sequence ‘TAAGTCCA’ as an example, the CNF for A is 0.5(1/2),0.667(2/3),0.375(3/8) at the 2nd, 3rd and 8th positions respectively. Thus, the CNF features of site si are denoted as CNF (si). Comparing with the m7G chemical features, CNF features pay more attention to the sequence context around the m7G site. Then the Cosine coefficient was adopted to calculate similarities of CNF since it reflects the similarity in trend rather than absolute values. For sites si and sj, the pairwise CNF similarity is defined as (2).

$$CNF\_sim_{ij} = \frac{{|CNF(s_{i} ) \cdot CNF(s_{j} )|}}{{||CNF(s_{i} )||_{2} ||CNF(s_{j} )||_{2} }}$$

Then m7G CNF similarity network (CNFSN) was obtained with the weights between nodes si and sj, (i = 1,2…m, j = 1,2…m), and the adjacency matrix was indicated as ACNFSN (Additional file 6: Table S6).

m7G integrated similarity network

Since m7G chemical similarity and CNF similarity measure m7G similarities from their own views, we took a linear combination of those two similarities to form an integrated similarity, and the contribution of m7G chemical similarity and CNF similarity is weighted by α. For sites si and sj, the integrated similarity is defined as (3).

$$int\_sim_{ij} = (1 - \alpha ) \cdot che\_sim_{ij} + \alpha \cdot CNF\_sim_{ij}$$

The value of α was chosen from 0 to 1 with step 0.1, and was determined by tenfold cross validation experiments. Then a series of m7G integrated similarity networks were obtained via taking (3) as weights between nodes si and sj, (i = 1,2…m, j = 1,2…m), and its adjacency matrix was indicated as ASS. In addition, if α is 0, then ASS is ACSN, while if α is 1, then ASS is ACNFSN.

Disease semantic similarity network

Disease semantic similarity network (DSSN), indicated by adjacency matrix ADD, was also constructed by calculating pairwise disease semantic similarities. Generally speaking, functional similarity between molecules results in similar phenotypes, such as diseases. Based on this fact, many researchers [15, 25,26,27] utilized functional similarities of the disease-associated molecules for semantic disease similarities. We followed Wang’s PBPA method, which was implemented to calculate pairwise disease semantic similarities [28, 29]. Additionally, the “DisSetSim” web server can be accessed from By calculating all pairwise semantic similarities in D, a disease semantic similarity network was obtained and the adjacency matrix was indicated as ADD (Additional file 7: Table S7).

m7G-disease heterogeneous network

The m7G-disease heterogeneous network and its adjacency matrix are shown in Fig. 2. The m7G-disease heterogeneous network was constructed by incorporating m7G-disease adjacency network, disease semantic similarity network DSSN, and m7G integrated similarity networks. It was represented by adjacency matrix A and mask matrix W, as (4).

$${\varvec{A}} = \left( {\begin{array}{*{20}c} {{\varvec{A}}_{{{\mathbf{SS}}}} } & {{\varvec{A}}_{{{\mathbf{SD}}}} } \\ {{\varvec{A}}_{{{\mathbf{SD}}}}^{{\mathbf{T}}} } & {{\varvec{A}}_{{{\mathbf{DD}}}} } \\ \end{array} } \right),{\varvec{W}} = \left( {\begin{array}{*{20}c} {{\varvec{W}}_{{{\mathbf{SS}}}} } & {{\varvec{W}}_{{{\mathbf{SD}}}} } \\ {{\varvec{W}}_{{{\mathbf{SD}}}}^{{\mathbf{T}}} } & {{\varvec{W}}_{{{\mathbf{DD}}}} } \\ \end{array} } \right)$$
Fig. 2
figure 2

m7G-disease heterogeneous network and its adjacency matrix

where WSS and WDD are all one’s matrix. For WSD, Wij = 1 if the association of the i-th site to the j-th disease is known, 0, vice versa.

By incorporating DSSN and m7G integrated similarity networks into the m7G-disease adjacency network, cold start issue is avoided, while information of sites and diseases is fully be used.

m7G-disease association inference based on heterogeneous network

Based on the m7G-disease heterogeneous network constructed above, the goal of recovering ASD is transformed into completing A. Underpinned by the fact that similar sites have similar molecular pathways for similar diseases, the matrix completion model assumes that the underlying latent factors determining m7G-disease associations are highly correlated. In addition, if two sites are similar, then they would have similar patterns with any other sites, and it is true for diseases. The number of independent factors that govern the pattern of A is much smaller than that of sites and diseases. In a mathematical view, the number of independent factors is the rank, here we used k to denote it. Thus, the goal of completing A can be achieved by the classical matrix decomposition method, which achieved positive results in many cases and is easy to realize. The primary idea of matrix decomposition is to map the adjacency matrix A into a k dimensional space, where k <  < m + n, so dimension reduction is achieved and a lower-dimensional representation of A in a k-dimensional space is given by two matrices \({\mathbf{U}} \in {\mathbb{R}}^{(m + n) \times k}\) and \({\mathbf{V}} \in {\mathbb{R}}^{(m + n) \times k}\). Then A can be approximated by (5).

$${\varvec{A}} \approx {\varvec{UV}}^{{\mathbf{T}}}$$

The fundamental idea of finding suitable factor matrices U, V is to minimize the objective function defined as (6):

$$\mathop {min}\limits_{{{\varvec{U,V}}}} ||{\varvec{W}} \odot ({\varvec{A}} - {\varvec{UV}}^{{\mathbf{T}}} )||_{F}^{2}$$

where \(|| * ||_{F}\) is the Frobenius norm, \({\varvec{W}} \odot ({\varvec{A}} - {\varvec{UV}}^{{\mathbf{T}}} )\) denotes the Hadamard product of two matrices W and A-UVT.

Furthermore, regularization terms should be considered, and the loss function is defined as (7), while the objective function is (8).

$$L = ||{\varvec{W}} \odot \left( {{\varvec{A}} - {\varvec{UV}}^{{\mathbf{T}}} } \right)||_{F}^{2} + \lambda_{{1}} {||}{\varvec{U}}||_{F}^{2} + \lambda_{2} {||}{\varvec{V}}||_{F}^{2}$$
$$\mathop {\min }\limits_{{{\varvec{U,V}}}} \, L$$

where \(\lambda_{{1}} {||}{\varvec{U}}||_{F}^{2} + \lambda_{2} {||}{\varvec{V}}||_{F}^{2}\) is the regularization term to avoid overfitting, with λ1 and λ2 being the regularization parameters.

λ1 and λ2, which were optimized by cross validation, help to achieve the trade-off between fitting and generalization. The Alternating Least Square method [30, 31] was then followed to reach the global minimum concerning to U and V. Finally, unknown entries in ASD were predicted. The implementation process of m7GDisAI is given below.

figure a


Experimental design

To systematically evaluate the prediction performance of m7GDisAI on the m7G-disease association dataset, tenfold cross validation and LOOCV strategies were adopted for the experiments.

As for tenfold cross validation, in the m7G-disease association dataset, there are 768 validated known associations, and the others that haven’t been validated are considered as candidate associations. All known associations are randomly divided into 10 sets that are roughly equal size. Each set is taken as test set in turn, in other words, pretends to be unknown ones, while the remaining nine sets serve as the training set. After performing m7GDisAI on training set, the test associations were ranked together with the candidate associations in descending order according to the predicted value obtained by m7GDisAI. Additionally, two types of LOOCV, global LOOCV and local LOOCV, were further carried out on the m7G-disease association dataset. At each iteration, each validated known m7G-disease association was treated as the test data and all the remaining associations as the training data. The only difference between them is the selection of candidate samples. To be specific, in global LOOCV, the candidate samples are all unknown m7G-disease associations, while in local LOOCV, candidate samples are only those associations under the disease of interest. In each scheme of LOOCV, the test sample was ranked with candidate samples in descending order.

Regardless of tenfold cross validation, global LOOCV and local LOOCV, for a given threshold τ, a test association is regarded as true positive (TP) if it ranks above the threshold, false negative (FN) otherwise. Similarly, a candidate sample is considered as false position (FP) if it ranks above the threshold, true negative (TN) otherwise. By varying τ, true positive rate (TPR), false positive rate (FPR) can be calculated for Receiver Operating Characteristic (ROC) curve. It depicts the relative tradeoffs of prediction performance between TP and FP [32]. The area under ROC curve (AUC), ranging from 0 to 1, can be used to evaluate the overall performance [32, 33].

Parameter setting

There are four parameters, rank k, linear combination coefficient α, regularization parameters λ1 and λ2, that are required to be optimized to enhance the performance of m7GDisAI. To be specific, k is the number of independent factors that govern the pattern of the heterogeneous matrix A, and if k is too large, then the algorithm would be time-consuming. Then k is chosen from {70,90,110}. The linear combination coefficient α weights the contribution of m7G chemical similarity and m7G CNF similarity in m7G integrated similarity network, and it was taken from 0 to 1.0 with the step 0.1. In addition, regularization parameters λ1 and λ2 control the relative penalty extent of the factor matrices U and V respectively, and they were chosen from {2–2,2–1,20,21,22}. It is apparent that k, λ1 and λ2 directly influence the optimal solution of the two factor matrices U and V, while α only has an impact on the m7G similarity matrix ASS. Thus, α was first fixed to 0.5 or any other specific value between 0 to 1, and a grid search strategy was performed on k, λ1 and λ2. tenfold cross validation experiments were performed with all combination of k, λ1 and λ2 on the training set. m7GDisAI performed best when k is 90, λ1 is -2 and λ2 is -2 with AUC of 0. 728. For fairness, the impact of α on m7GDisAI was measured via tenfold cross validation experiments with fixed k, λ1 and λ2. To be specific, α is 0 means that ASS is ACHN, and m7GDisAI only utilizes m7G chemical similarities, while α is 1 indicates that ASS is ACNFHN, and m7GDisAI only utilizes m7G CNF similarities. Table 1 reports the AUC scores with all α, and the highest AUC score is marked in bold.

Table 1 AUC scores of different α inthe10-fold cross validation experiments

In Table 1, As α increases, AUC scores generally show an increased tendency except when α is 0.4, and reaches its maximum at 0.742 when α is 1. In other words, the more CNF similarities contribute, the higher the AUC scores achieved, and m7GDisAI has the best performance when only utilizes CNFHN. Table 1 validates the effectiveness of the CNF features and Cosine coefficient to some extent. Specifically, chemical features decode the nucleotides of m7G site-centered sequence individually, while CNF features pay more attention to the context of site-centered sequence. Meanwhile, the Cosine coefficient reflects the similarity in trend instead of absolute value as the Jaccard coefficient calculates.

Performance evaluation

To further evaluate the robustness of m7GDisAI, we conducted 10 runs of tenfold cross validation experiments by taking α as 1, which has the best performance in the Table 1. The mean value of AUC scores is 0.740 with standard variance at 0.0024, showing the effectiveness and stability of m7GDisAI. Figure 3a clearly displays the ROC curves with respect to the best performance in tenfold cross validation experiments. Additionally, LOOCV experiments were further conducted to comprehensively evaluate the performance of m7GDisAI. The AUC of global LOOCV was 0.769 while that of local LOOCV was 0.635. The ROC curves of LOOCV experiments are illustrated in the Fig. 3b.

Fig. 3
figure 3

The best performance of m7GDisAI for tenfold cross validation and LOOCV experiments. a. ROC curves generated by tenfold cross validation. b. ROC curves generated by global LOOCV and local LOOCV

As we can see from Fig. 3b, local LOOCV experiment performs worse than global LOOCV. The key factor contributing to this phenomenon is the number of candidate samples that the test sample were ranked with. To be specific, the number of candidate samples participating in global LOOCV is much larger than those involved in the local LOOCV. In other words, the local LOOCV experiments have more rigorous requirements for positive results.

Case study

Ovarian cancer is the most common cause of gynecological cancer-associated death [34]. Over the past decades, the overall cure rate remains approximately 30% [35]. The reason for low cure rate is the late presentation in most cases. 80% of patients have symptoms, however, these symptoms are shared with many more common gynecological conditions [35]. Given the heterogeneity of this disease, it is necessary to explore the disease pathogenesis at molecular and cellular levels. Then taking all known associations as training samples, while other unknown ones as candidate samples. Since CNFHN has the best performance in the tenfold cross validation experiments, then we performed it on the training samples to score the candidate samples, especially those under ovarian cancer. Furthermore, all the m7G sites were ranked in descending order according to their association scores with ovarian cancer, and the top 100 m7G sites were selected as potential ovarian cancer-associated sites. 98 host genes of these sites were further mapped out. To predict potential cellular processes and molecular functions that involve m7G methylation, we used the R package “clusterProfiler” to analyze and visualize the functional profiles of m7G host genes.

GO terms include three subontologies, cellular component (CC), biological process (BP) and molecular function (MF), and they can be conducted via enrichGO function. In the parameter setting of the enrichGO function, we set the parameter “ont” to “ALL”, aiming at performing CC, BP and MF together. Additionally, the p-value cutoff was set as 0.05, q-value cutoff 0.2, indicating statistical significance of associations between host genes and GO terms. Furthermore, “BH” method was used to adjust the p-value to control the false discovery rate, which was considered to be statistically significant. Considering the potentially biological complexities in which a gene may belong to multiple annotation categories, we utilized a gene-concept network to depict the linkages of gene and GO terms as a network. Figure 4 provides a visualization of the gene-concept network by cnetplot function.

Fig. 4
figure 4

The gene-concept network of functional GO enrichment results. The connection between a gene and a term means that the gene is involved in this GO term

In Fig. 4, ten most significantly enriched terms including CC, BP and MF were shown to be associated with 26 genes. The enrichment analysis results have been verified by published literature. Specifically, TP53 is the most widely studied tumor suppressor gene [36], and it is the host gene of m7G_ID_194615, m7G_ID_203640, m7G_ID_202781 m7G_ID_194736 and m7G_ID_280795 as Additional file 1: Table S1 shows. TP53 functions in ovarian cancer by arresting the cell cycle at G1 phase and by triggering apoptosis [37]. In addition, Lang et al. [38] found that UV radiation leads to base-pair changes of p53, the protein product of the TP53 gene, and further leads to tumor formation. Furthermore, Jeremy et al. [39] experimentally showed that the dynamic patterns of TP53 vary depending on the stimulus. For example, the levels of p53 exhibit a series of pulses with fixed amplitude and frequency in response to DNA breaks caused by γ-irradiation. These discoveries prove that TP53 is enriched into “negative regulation of mitotic cell cycle”, “response to UV” and “cellular response to environmental stimulus” terms [40].

To data, hereditary nonpolyposis colorectal cancer (HNPCC) is the third major cause of hereditary ovarian cancer, and HNPCC is caused by mutations in genes involved in DNA mismatch repair [41]. MLH1 [42] (host gene of m7G_ID_137019, m7G_ID_137020, m7G_ID_151088, m7G_ID_220822), MSH2 [43] (host gene of m7G_ID_161433, m7G_ID_192868, m7G_ID_253317), MSH6 [44] (host gene of m7G_ID_200227, m7G_ID_317794) and PMS2 [45] (host gene of m7G_ID_155289) are all reported to be mismatch repair genes. To be specific, the MLH1 and MSH2 genes are the most common genes for HNPCC-associated ovarian cancer, and account for 80%-90% of observed mutations [46]. What’s more, Cederquist et al. [47] reported that ovarian cancer is in the MSH6 tumor spectrums. Besides, PIK3CA was also known to be oncogenes of ovarian cancer [48], and they are the host genes of m7G_ID_2249, m7G_ID_9238 in Additional file 1: Table S1 respectively. Notably, PIK3CA activated mutation participates in the PI3K pathway which is activated in approximately 70% of ovarian cancer [49], and is enriched in regulation of protein kinase B signaling, which is activated by autocrine or paracrine signaling through protein kinase signaling in many kinds of cancers [49].

Numerical cases [50,51,52] have suggested that ERBB family of receptor tyrosine kinases has a significant contribution to the initiation and progression of ovarian cancer. EGFR and ERBB2 in Fig. 4 are members of the ERBB family of receptor tyrosine kinases. EGFR is the host gene of m7G_ID_149119 and its overexpression has been observed in 30%-98% of epithelial ovarian cancer in all histologic subtypes, and enhanced expression of EGFR is correlated with advanced-stage disease as well as poor response to chemotherapies. Additionally, Ginath reported [53] that ERBB2 (host gene of m7G_ID_268139) activates multiple downstream signaling pathways, and then promotes the proliferation, invasion, and metastasis of tumor cells.


This research into identifying potential m7G-disease association prediction will help us understand the pathogenesis of diseases and promote the treatment of diseases. In this paper, we extracted 768 associations between 741 m7G sites and 177 diseases to construct the m7G-disease association dataset. To predict the m7G-disease association based on the m7G-disease dataset, we proposed a heterogeneous network-based association inference method m7GDisAI. For m7GDisAI, we performed m7G-disease association inference on a series of heterogeneous networks which contain m7G-disease adjacency network and disease semantic similarity network, but different m7G similarity networks, CHN, CNFHN and their combinations.10-fold cross validation, global and local LOOCV were performed with m7GDisAI. CNFHN outperforms the CHN and other heterogeneous networks, which proves the effectiveness of CNF features. Then a case study of ovarian cancer was later conducted by CNFHN. It is worth mentioning that the constructed m7G-variant pair dataset and m7G-disease association dataset may play important role in further investigation of disease-associated m7G sites discovery. To our knowledge, m7GDisAI is the first algorithm that connects m7G sites, variants as well as diseases together to uncover potential cancer-related functions of m7G, which may provide some valuable hints for wet experiments guidance. However, there remains limitations in this study. Firstly, the research of m7G and diseases is an ongoing topic and the m7G-disease dataset is far from completed. Secondly, more feature selection methods could be taken into consideration to construct m7G similarity networks and further improve the accuracy of m7GDisAI.


m7GDisAI is a heterogeneous network-based m7G-disease association inference method and is freely acessible at m7GDisAI uncovers disease-associated m7G sites by applying matrix decomposition method on a heterogeneous network-based m7G-disease association matrix. m7GDisAI provides users a function to query related m7G sites of disease which the users are interested in. The website hosts the top 20 m7G sites predicted to be associted with 177 diseases with high prediction scores,which may provide some clues for pathogenesis of diseases. The front-end is implemented in JavaScript while the back-end is implemented in Python as well as R. We will continue updating m7GDisAI by adding additional information, improving the implementation, and incorporating new measures for infering disease-associated m7G sites. The user can always access the latest version of m7GDisAI.

Availability and requirements

Project name: m7GDisAI. Project home page: Operating system(s): Linux, Windows. Programming language: Python, R, JavaScript. Other requirements: Not specified. Python version 3.8.0 or higher, R version 4.0.3 or higher. License: GNU GPL. Any restrictions to use by non-academics: None.

Availability of data and materials

The detailed information of m7G-variant dataset is listed in Additional file 1: Table S1. For each m7G-disease pair, information for their sequence and genomic location is included. Additional file 2: Table S2 shows diseases we collected with their names and DOID. Additional file 3: Table S3 provides the information for m7G-disease association dataset with 768 known m7G-disease associations. In addition, Additional file 4: Table S4 is the m7G-disease matrix ASD where the validated associations are all one. Additional files 5: Table S5–Additional file 6: Table S6 are m7G simialrity networks ACSN, ACNFSN respectively, while Additional file 7: Table S7 is the disease semantic similarity network ADD. Furthermore, Additional file 8: Table S8 presents the recommended m7G sites and their host gene of ovarian cancer. The website m7GDisAI implemented to query related m7G sites of the disease which you are interested in is deposited at





N7-methylguanosine Methylated RNA immunoprecipitation sequencing


N7-methylguanosineIndividual-Nucleotide-Resolution Crosslinking and Immunoprecipitation


N7-methylguanosine-disease association inference


Chemical Heterogeneous Network


Cumulative Nucleotide Frequency


Cumulative Nucleotide Frequency Heterogeneous Network


Leave-one-out cross validation


Disease Semantic Similarity Network


Chemical Similarity Network


Cumulative Nucleotide Frequency Similarity Network


Integrated Similarity Network


Most Informative Common Ancestor


Alternating Least Squares


False Positive


True Negative


False Negative


Receiver Operating Characteristic Curves


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This work has been supported by Postgraduate Student Education Reform Research and Practice Funds (Research Projects No. 2019YJSJG045 to LZ), the National Natural Science Foundation of China (Research Projects Nos. 61971422 to LZ, 31871337 to HL). The funding body did not play any roles in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

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Authors and Affiliations



JM and LZ built the architecture for m7GDisAI, designed and implemented the experiments, analyzed the result, and wrote the paper. JC analyzed the result, and revised the paper. BS prepared the data. CZ built up the webserver. HL supervised the project, analyzed the result, and revised the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Lin Zhang.

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Supplementary Information

Additional file 1

. Table S1: m7G-variant dataset.

Additional file 2

. Table S2: The detailed information of diseases we collected.

Additional file 3

. Table S3: m7G-disease association dataset.

Additional file 4

. Table S4: m7G-disease association matrix ASD.

Additional file 5

. Table S5: m7G chemical similarity matrix ACSN.

Additional file 6

. Table S6: m7G CNF similarity matrix ACNFSN.

Additional file 7

. Table S7: Disease semantic similarity network ADD.

Additional file 8

. Table S8: Predicted ovarian cancer related m7G sites and their host genes.

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Ma, J., Zhang, L., Chen, J. et al. m7GDisAI: N7-methylguanosine (m7G) sites and diseases associations inference based on heterogeneous network. BMC Bioinformatics 22, 152 (2021).

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