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IMI-driver: Integrating multi-level gene networks and multi-omics for cancer driver gene identification
Integrating multi-level gene networks for cancer driver gene identification
Shi Peiting Data curation Formal analysis Investigation Methodology Software Visualization Writing – original draft Writing – review & editing 1
Han Junmin Data curation Formal analysis Visualization Writing – original draft Writing – review & editing 1
Zhang Yinghao Data curation Formal analysis Writing – review & editing 1
Li Guanpu Data curation Formal analysis Writing – review & editing 1
https://orcid.org/0000-0003-1234-1091
Zhou Xionghui Conceptualization Methodology Project administration Supervision Writing – original draft Writing – review & editing 1 2 *
1 Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, People’s Republic of China
2 Key Laboratory of Smart Farming for Agricultural Animals, Ministry of Agriculture and Rural Affairs, People’s Republic of China
Mora Rodrigo Editor
Universidad de Costa Rica, COSTA RICA
The authors have declared that no competing interests exist.

* E-mail: zhouxionghui@mail.hzau.edu.cn, zhouxionghui6@gmail.com
26 8 2024
8 2024
20 8 e101238919 4 2024
5 8 2024
© 2024 Shi et al
2024
Shi et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. Cancer is caused by the dysregulation of several genes at various levels of regulation. However, current techniques only capture a limited amount of regulatory information, which may hinder their efficacy. In this study, we present IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low-dimensional vector space to identify cancer drivers. We apply IMI-driver to 29 cancer types from The Cancer Genome Atlas (TCGA) and compare its performance with nine other methods on nine benchmark datasets. IMI-driver outperforms the other methods, demonstrating that multi-level network integration enhances prediction accuracy. We also perform a pan-cancer analysis using the genes identified by IMI-driver, which confirms almost all our selected candidate genes as known or potential drivers. Case studies of the new positive genes suggest their roles in cancer development and progression.

Author summary

Cancer driver identification is of great value in cancer research. Recent advancements have demonstrated that incorporating the intricate regulatory relationships among genes can facilitate driver genes identification. However, current methods do not fully capture the regulatory relationships at different layers. In this study, we propose a new method for predicting cancer driver genes (IMI-driver), which integrates multi-omics data into eight biological networks (including common networks such as protein-protein interaction networks and various cancer-specific networks) and embeds multi-level network information as features to predict driver gene sets. Experiment demonstrates that our method outperforms mainstream tools, and can discover new drivers.

Biological Breeding-Major Projects 2023ZD04061 Xiong-Hui Zhou http://dx.doi.org/10.13039/501100012226 Fundamental Research Funds for the Central Universities 2662023XXPY003 Xiong-Hui Zhou the National Training Program of Innovation and Entrepreneurship for Undergraduates of Huazhong Agricultural University 202210504100 Shi Peiting This work was supported by Biological Breeding-Major Projects (2023ZD04061), the Fundamental Research Funds for the Central Universities to X. Z. (2662023XXPY003) and the National Training Program of Innovation and Entrepreneurship for Undergraduates of Huazhong Agricultural University (202210504100) to P.S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. No authors have received a salary from the funders. PLOS Publication Stagevor-update-to-uncorrected-proof
Publication Update2024-09-06
Data AvailabilityThe data and scripts can be downloaded from https://github.com/coding0lion/IMI-driver.git.
Data Availability

The data and scripts can be downloaded from https://github.com/coding0lion/IMI-driver.git.
==== Body
pmc1 Introduction

Cancer results from critical genetic changes that disrupt the balance between cell proliferation and death [1]. These changes affect cancer driver genes that regulate cell growth, cell cycle, and DNA replication. Finding these genes is essential for early detection, effective therapy, and precision medicine of cancer. However, this is a challenging task due to cancer’s complexity and computational and experimental limitations. Large-scale cancer genomics projects, such as the Cancer Genome Atlas (TCGA) [2] and the International Cancer Genome Consortium (ICGC) [3], have produced massive genomic data across various cancer types [4], revealing the causes of cancer. Identifying driver genes that contribute to cancer development is a key challenge in cancer genomics. Different principles have been proposed to distinguish driver genes from passenger genes that accumulate mutations by chance.

One principle involves mutation frequency-based methods, such as DriverML [5] and WITER [6], which assume that high-frequency mutations are indicative of driver genes. However, this principle faces challenges in accurately estimating the background mutation rate [7] and in identifying driver genes with low-frequency or non-coding mutations. In fact, driver genes are not only those that are frequently mutated but also those that play critical roles in cancer development and progression [8], even if their mutation frequencies are relatively low. Another principle focuses on functional impact, as exemplified by methods like MutPanning [9] and OncodriveFML [10]. Machine learning models have been successfully applied to predict key regulatory elements in the genome [11–14]. In this context, such methods utilize machine learning models to predict driver genes based on the functional impact of mutations. While this approach can detect rare mutant genes, it relies on high-quality datasets of both functional and nonfunctional mutations, which are often challenging to obtain [15]. Cancer arises from the dysregulation of multiple cellular mechanisms, including gene expression, protein interactions, and epigenetic modifications, which collectively influence cancer development [16]. Therefore, integrating multi-omics data is crucial for comprehensively identifying potential driver genes, distinguishing them from passenger genes, and providing precise targets for cancer research [17]. A third principle is network analysis, which considers gene interactions and addresses some of the limitations of the previous two principles. Network analysis can identify low-frequency driver genes that mutation frequency-based methods might overlook and can integrate multi-omics data to elucidate the complex mechanisms underlying cancer development.

Cancer progression may result from mutations mediated via protein interactions, illuminated by the protein-protein interaction (PPI) network [18]. Numerous approaches prioritize cancer genes based on PPI networks; some integrate somatic mutation data (e.g., MaxMIF [19], MUFFINN [20], DriverRWH [21]), while others use multi-omics data for network representation learning and model building (e.g., EMOGI [22], MTGCN [23]). Current PPI networks are still incomplete and noisy [24], limiting their ability to capture complex biological relationships and impacting prediction accuracy. Most methods focus on a single PPI network, overlooking interactions across different layers. Constructing a model to integrate multiple networks is crucial for a comprehensive gene interaction landscape. MODIG [25] combines heterogeneous information into multi-dimensional gene networks and integrates multi-omics data. However, it lacks tumor-specific networks, missing tumor-specific regulation information, and doesn’t use methylation data, somatic mutations, and other data for constructing comprehensive biological networks representing cancer interactions at various layers.

Here we present IMI-Driver, a novel framework that integrates multi-level network embedding and machine learning to predict cancer driver genes. IMI-Driver constructs various networks based on multi-omics data (gene expression, miRNA expression, DNA methylation, and somatic mutations), such as gene dependency network (GDN) [26], competing endogenous RNA (ceRNA) network [27], gene co-expression network (GCN) [28], DNA methylation interaction network (DMIN) [29] and co-mutations network (DCMN), to capture the multidimensional interactions of carcinogenesis for each cancer type. It also incorporates other networks that reflect common gene interactions, such as the protein-protein interaction (PPI) network [18], gene pathway similarity network (GPSN) [30], and transcriptional regulatory network (TRN) [31]. Then, it applies Multi-view collAborative Network Embedding (MANE) [32] to extract gene interaction pairs in different networks and embed them into a low-dimensional vector space. In addition, it also integrates biological characteristics extracted from multi-omics data of each gene as features. Finally, it employs XGBoost [33] to build a prediction model based on the embedded vectors. We demonstrate that IMI-Driver outperforms existing methods on several benchmark datasets and reveals some novel driver genes with biological evidence.

2 Results

2.1 Overall framework

IMI-Driver is a novel method that uses multi-omics data and network features to identify driver genes in various cancers based on the TCGA database. Fig 1 illustrates the IMI-Driver framework. Firstly, we constructed eight biological networks for each cancer by combining gene expression, miRNA expression, DNA methylation, and somatic mutation data with gene regulatory relationships from known databases (Method). These networks include common networks (e.g., PPI, co-pathway network, and TF-target network) and cancer specific networks (e.g., co-expression network, co-methylation network, co-mutation network, ceRNA network, and gene dependency network). Secondly, we extracted three types of relationship pairs: diversity, first-order, and second-order collaboration, from these heterogeneous networks and applied a multiplex heterogeneous network embedding algorithm (MANE) to learn a low-dimensional continuous vector representation for each gene. Thirdly, we enhanced the network features by integrating other omics features such as mutation, genome, epigenetics, and phenotype features (Method) to obtain a comprehensive feature representation for each gene. Finally, an XGBoost model was employed to predict the driver scores for each gene.

10.1371/journal.pcbi.1012389.g001 Fig 1 Overall Framework.

(a) Eight networks were constructed using multi-omics data (five tumor-specific networks) and known regulatory relationships (three common networks). (b) Extracting the eight networks’ relation to catch various collaborations between genes. (c) Obtaining the Embedding of each network. (d) Combining the feature in (c) with multi-omics features. (e) All features we generated. (f) Using all features in (e) to train the XGBoost model.

2.2 Performance assessment of IMI-Driver

We evaluated the performance of IMI-Driver by comparing it with nine representative methods on nine established gold-standard sets of driver genes, comprising five network-based algorithms (DriverRWH [21], MaxMIF [19], DNsum and Dnmax [20], EMOGI [22]), two function-based algorithms (MutPanning [9] and DORGE [34]) and two frequency-based algorithms (WITER [6] and DriverML [5]). We used STRING PPI as the network input for the network-based algorithms [35]. Each method produces a ranked list of individual genes as potential driver genes. Accordingly, we selected the top 100 genes (results for other thresholds are discussed in the "Discussion" section) as potential drivers for each method on nine established gold-standard benchmark datasets (CGC [36], CGCpointMut [35], 20/20Rule [37], HCD [38], oncogene [39], CTAT [40], MouseMut [41], ConsistentSet [35] and IntOGen [42]) (The prediction scores of all genes on nine datasets are available at https://github.com/coding0lion/IMI-Driver.git), and calculated the p-values of the KS-test, MCC, AUROC, F1, and Precision for each method (S1–S4 Figs).

To compare the performance of the different methods, we calculated the performance of each method for each cancer in each benchmark dataset. We will then calculate the average of all benchmark datasets as an overall evaluation of the method. Table 1 shows the ranking of each method’s performance among the nine methods. The main evaluation metrics MCC for all methods on the nine benchmark datasets are shown in Fig 2. From both the overall evaluation (S1 Table shows the evaluation scores of each method on different benchmarks) and MCC comparison (Fig 2), our model outperformed other methods on different benchmarks and criteria. Therefore, IMI-driver can identify cancer drivers with high accuracy and robustness.

10.1371/journal.pcbi.1012389.t001 Table 1 Performance rankings of all the models on the benchmark datasets (MCC).

method	IntOGen	ConsistentSet	CGC	CGCpointMut	CTAT	MouseMut	CD	oncoGene	20/20Rule	Average Rank	
DNmax	9	7	7	7	7	6	7	7	7	7.11	
DNsum	1	6	6	6	6	5	6	5	6	5.22	
DORGE	8	2	2	2	2	7	2	4	2	3.44	
DriverML	7	8	8	8	8	8	8	8	8	7.89	
EMOGI	5	3	3	3	3	4	3	6	3	3.67	
driverRWH	3	4	4	4	4	2	4	2	4	3.44	
MaxMIF	4	5	5	5	5	3	5	3	5	4.44	
MutPanning	6	9	9	9	9	9	9	9	9	8.67	
WITER	10	10	10	10	10	10	10	10	10	10.00	
IMI-Driver	2	1	1	1	1	1	1	1	1	1.11	

10.1371/journal.pcbi.1012389.g002 Fig 2 Performance comparison of IMI-driver with other methods (MCC).

The classification performance of IMI-driver was compared with nine other methods across nine gold-standard datasets. Each subplot shows the classification performance comparison (MCC) of the 10 methods on one benchmark dataset.

2.3 Contribution of each component

Our manuscript leverages multi-omics data, employing eight networks (three common and five tumor-specific, with most specific networks generated using in-house algorithms) to depict cancer regulatory mechanisms. These networks are integrated and embedded to represent genes, contributing to a stable tumor driver gene model. We analyze the substantial contributions of biological networks and assess each feature category’s impact on model performance. Validation was performed on 9 benchmark datasets, including IntOGen [42] with tumor-specific driver genes. Results present mean performance across all datasets and specifically focus on IntOGen. If a biological network can be utilized for the exploration of cancer driver genes, then these genes should occupy important topological positions within the network. Consequently, we utilized PageRank [43] to rank all genes within each network, followed by conducting enrichment analysis (KS-test) between known driver genes and the ranked gene list to examine whether driver genes are essential nodes within the biological network (i.e., whether they are ranked on the top of the network’s gene list ranked by PageRank). The results demonstrate that all networks could facilitate the identification of driver genes (Fig 3A and 3B).

10.1371/journal.pcbi.1012389.g003 Fig 3 Contribution of each component in the IMI-driver.

(a) Performance of each network on all benchmarks (each metric is the average across nine benchmark datasets); (b) Performance of each network on IntOGen; (c) Contribution of each component on all benchmarks (each metric is the average across nine benchmark datasets); (d) Contribution of each component on IntOGen.

In addition to the eight networks, our method incorporated enhanced features from DORGE [34]. We categorized features into three classes (common network, specific network, DORGE and validated their contributions through ablation experiments. Fig 3 illustrates that removing any feature type decreases predictive performance, confirming their collective contribution. Interestingly, in all benchmark datasets, the specificity network contributes less compared to DORGE (Fig 3C). Yet, when using the cancer-specific driver genes as the benchmark, the cancer-specific network’s contribution significantly increases (Fig 3D). Particularly in terms of AUROC, the tumor-specific network is the predominant contributor to global prediction accuracy, affirming its role in revealing regulatory mechanisms in specific cancers and facilitating the exploration of cancer driver genes.

2.4 Prediction of novel cancer driver genes

Cancer is a highly heterogeneous disease and a list of cancer-specific driver genes is useful for finding each cancer-specific driver gene, we used IntOGen [42], a list of tumor-specific driver genes with abundant cancer-specific information, to find out new driver genes.

We screened genes that appeared in more than five cancer types (See S1 Text for details of the process) and a total of 93 genes were obtained (Fig 4A and S2 Table). Among these genes, 76 were included in the known driver datasets. Among these 76 genes, there are many well-known cancer driver genes, such as BRCA1, BRCA2, KRAS, and TP53BP1 (Tumor Protein P53 Binding Protein 1). This indicates that our method can identify well-known cancer driver genes. For the remaining 17 genes, DNMT3B, RARB, SMAD5, SMARCA5, SMARCC1 and TGFBRAP1 were predicted only by our method (S2 Table), but various evidence suggests that they have a driving role in cancer development.

10.1371/journal.pcbi.1012389.g004 Fig 4 The top genes identified by IMI-driver and Pan-caner analysis of the novel drivers.

(a) Top genes predicted by IMI-driver; (b-g) Differential expression analysis of the six genes.

Finally, we performed a differential expression analysis of the six genes. For this analysis, only 14 cancer types were used because some cancer data lacked the gene expression data of control samples. The results showed significant expression differences for the six potential drivers (Fig 4B–4G). Notably, DNMT3B was differentially expressed in all cancers.

These results suggest that DNMT3B is the most closely associated novel driver gene with cancer among the six genes. DNMT3B is a DNA methyltransferase that catalyzes de novo methylation of CpG islands in the genome, an epigenetic modification that controls gene expression and genome stability [44]. By altering the methylation status of genes involved in various cellular processes, such as DNA damage response, cell cycle regulation, mismatch repair, cell proliferation, differentiation, and survival, DNMT3B can affect tumorigenesis [44]. For instance, DNMT3B overexpression can result in hypermethylation and silencing of BRCA1, CDKN2A, and MLH1, while DNMT3B deficiency can cause hypomethylation and activation of MYC, CCND1, and EGFR [45]. Therefore, DNMT3B may be a novel driver gene for cancer.

The other five genes have also been reported in some literature to be closely associated with cancer. RARB, functioning as a receptor for retinoic acid, primarily orchestrates gene expression activation [46]. SMAD5, a transcription factor integral to the bone morphogenetic protein (BMP) signaling pathway, governs an array of cellular processes, including differentiation, migration, and apoptosis [47]. SMARCA5, the product of a gene encoding a SWI/SNF-associated chromatin remodeling factor, intricately regulates cell cycle progression, DNA damage repair, and gene expression modulation [48]. Another key contributor, SMARCC1, encoded by a gene pivotal to the SWI/SNF complex, participates in chromatin remodeling, gene transcription, and cellular differentiation [49]. Additionally, TGFBRAP1 plays a pivotal role in modulating the TGF-beta/activin signaling pathway, a pathway intricately linked to tumor development and immune system diseases [50]. This multifaceted portfolio of identified driver genes underscores their significance in diverse cellular pathways and affirms their potential as key players in the intricate landscape of cancer biology.

For each gene, we performed survival analysis across TCGA datasets to assess its prognostic potential. For each cancer dataset, we divided patients into two groups with the same number of samples based on each gene’s expression levels (high-expression samples vs. Low-expression samples) and used the log-rank test to evaluate the prognostic risk between the two groups. The results show that, except for SMAD5 and SMARCA5, the remaining four genes have significant prognostic ability in multiple cancers, while SMAD5 and SMARCA5 exhibit marginal statistical significance in survival analysis for some cancer datasets. S3 Table presents the results for all cancer datasets, while Fig 5 shows the results for each gene in a case dataset.

10.1371/journal.pcbi.1012389.g005 Fig 5 Survival analysis of the new driver candidates.

In each dataset, patients are divided into two groups based on the expression level of the specific gene (using the median expression value of this gene as the threshold). The log-rank test is then used to assess whether there is a significant difference in prognostic risk between the two groups.

In conclusion, we identified six novel cancer genes that previous computational methods missed, and case studies showed they had roles in cancer development and progression. These novel candidates warrant further functional and cancer genomics studies to confirm their role as cancer drivers.

3 Discussion and conclusion

Identifying cancer-associated genes that drive the cancer phenotype is a major challenge in cancer genome sequencing. To construct a complete list of cancer driver genes, it is important to integrate high-throughput functional genomics data from diverse sources. Network-based methods can facilitate this integration, but existing methods generally suffer from three problems: (1) Most methods do not make full use of multi-omics data in cancer; (2) most of the methods only used one level of the biological network; (3) The biological networks do not contain cancer-specific regulatory information. We proposed IMI-Driver, a model that integrates cancer-specific and common networks and multi-omics data to predict potential drivers.

IMI-Driver outperformed nine existing tools on nine gold standard gene sets for 29 cancer types from the TCGA, using metrics such as MCC, AUROC, accuracy and KS test. Notably, IMI-Driver also predicted driver genes consistently across different cancer types. Pan-cancer analysis of high-frequency genes predicted as drivers in more than five cancers confirmed most of them as known or potential drivers. Among them, IMI-Driver identified six novel candidates. Differential expression analysis on various cancer datasets and literature review suggests its potential role in the occurrence and progression of cancer.

We assessed the robustness of our method by randomly shuffling gene class labels and rebuilding the prediction model. The results (S5 Fig) indicate that our outstanding performance is not a result of overfitting to noise signals but reflects the identification of crucial drivers in cancer initiation. In the results section, we considered the top 100 genes from each method as driver genes. To evaluate model robustness with changing thresholds (100, 200, 300, 400, and 500), we found that our method consistently outperformed others across nearly all benchmark datasets, demonstrating its robustness (See S6 Fig and S2 Text for details). To evaluate whether the good performance of our method is solely due to the use of the XGBoost classifier, rather than the effective utilization of multi-omics data and the effective representation of multi-level network information by our model framework, we used several other major classifiers (Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes) to construct driver gene prediction models based on the gene representation matrix. The results (S4 Table) show that all these models have relatively good classification performance, especially Logistic Regression, which, as a very simple classifier model, only performs slightly worse than XGBoost. This indicates the effectiveness of our model framework.

In summary, IMI-Driver emerges as a robust and effective model for identifying cancer driver genes, offering scalability to integrate diverse biological networks and datasets. Additionally, we provide a scalable research framework that constructs multi-level networks based on multi-omics data and represents the network nodes for classification. The technical details within the framework can be adjusted, and it can be applied to other biomedical fields beyond driver gene prediction. In this study, we used gene expression data, miRNA expression data, somatic mutation data, and methylation data. Other high-throughput sequencing data, such as proteomics data, can also be incorporated into our framework. At the network level, we integrated eight biological networks, including five cancer-specific networks and three common networks. Of course, other networks, such as metabolic networks, can also be included in the analysis in practical applications. In terms of multi-network embedding, besides the methods we used, other approaches suitable for multi-layer graph embedding can also be applied. For the final model prediction, in addition to driver gene prediction, theoretically, any other type of node class prediction, such as drug target prediction, can also use our framework. In summary, we provide a scalable framework that can be used for various biomedical problems. However, our method has some limitations. For instance, due to the network reconstruction and multi-layer graph embedding calculations, it requires substantial computational resources. For example, using 5 threads (2.90GHz per thread) and 25 GB of memory, our model takes approximately 29 hours. If further reduction in runtime is needed, we recommend using 10 threads and 50GB memory, which can reduce the required time to 22 hours. In future work, we will explore how to optimize our algorithm framework to consume fewer computational resources. Additionally, although our framework can theoretically be applied to other fields, its performance in these areas requires further investigation. This will be a focus of our future work.

4 Materials and methods

4.1 Data collection

All the data sets we used, including the multi-omics data of 29 cancer types from TCGA, and the known biological network data, were described in S3 Text and S5 Table.

To evaluate the IMI-Driver, an unbiased and comprehensive set of known cancer genes is needed. Unfortunately, there is no such gold standard set of cancer genes, and each cancer gene set is biased toward particular features or study methods. In this work, we used nine cancer driver datasets (see S6 Table for more details) to validate our approach.

Cancer Gene Census (CGC; Tier 1; January 2019), a widely used gold-standard set of genes with causal mutations in cancer [36];

CGCpointMut, a subset of CGC that contains genes with point mutations in cancer [35];

20/20 Rule, a set of genes with characteristic mutation patterns of oncogenes and tumour suppressors [37];

HCD, a consensus set of genes from multiple frequency-based methods [38];

oncoGene, a manually curated set of oncogenes from literature and public databases [39];

CTAT, an aggregated set of genes from previously reported methods using a combination of tools [40];

MouseMut, a set of human homologs of mouse cancer genes identified by insertional mutagenesis [41];

ConsistentSet, a non-redundant set of common driver genes from at least three of the above data sets [35];

IntOGen [42], a tumour-specific set of driver genes that integrates different prediction methods and provides the best trade-off between sensitivity and specificity. We selected 17 cancers that were shared between IntOGen and TCGA data when this data set was used to validate IMI-driver.

4.2 Construction of gene networks

We built eight biological networks from existing studies and classified them into two categories: common networks, using the same data for all cancers; and cancer-specific networks, using the multi-omics data of each cancer type. S4 Text described the three common networks: PPI, gene pathway similarity network, and transcriptional regulatory network. We showed the pipelines of the cancer-specific networks below.

4.2.1 Gene dependency network

We used our previous method [26], which calculates the conditional mutual information (CMI) of each gene pair (i.e. gene A and gene B) using their expression data and matched clinical data in cancer patients, to assess gene dependency relationships in each cancer type. The pipeline to construct the gene dependency is shown as follow:

(1) Discretizing the clinical information and gene expression levels. We set the phenotypic status to 1 if distant metastasis occurred within 5 years; if distant metastasis occurred after 5 years, we set the phenotypic status to 0; other data were discarded. The gene expression value was set to 0 if it was below the median gene expression level of all samples; otherwise, it was set to 1.

(2) Inferring the dependency relationship between gene A and gene B by calculating the CMI of each gene pairs.

CMI(GeneA;outcome|GeneB)=Ihigh(GeneA,outcome)−Ilow(GeneA,outcome) (1)

Where Ihigh is the top 35% triplets (i.e. Gene A’s value, clinic information, Gene B’s value), Ilow is the bottom 35% of triplets.

(3) Evaluating the significance of every gene dependency relation using the permutation method.

(4) Constructing a gene dependency network using the significant gene dependency pairs, where the threshold of p-value is 0.05 in this work.

4.2.2 Competing endogenous RNA network

Based on the ceRNA hypothesis [51], we screened significant gene pairs sharing the miRNA sponges by using matched gene and miRNA expression data from cancer patients based on our previous method [27]. This method can facilitate biomarker discovery by revealing key ceRNAs that regulate each other through shared miRNAs.

(1) Collecting the miRNA-gene pairs from database targetScan (https://www.targetscan.org/).

(2) Calculating the Pearson correlation coefficient for each miRNA-gene pair using the miRNA and gene expression data, and we will select the negatively correlated pairs with p-values less than 0.05.

(3) Extracting all miRNA from the filtered miRNA-gene pairs.

(4) Testing whether the common miRNA sponges between the two genes were significant using a hypergeometric cumulative distribution function test.

p−value=1−F(xU,M,N)=1−∑i=0x−1 (Mi)(U−MN−i)(UN) (2)

where x stands for the number of common miRNAs that regulate both of the two genes, U is the number of all the miRNAs in this work, M is the size of the miRNA set and N is the size of the miRNA set.

(5) The patient’s gene expression data were used to further confirm the ceRNA relationship, and the positively correlated pairs with p-value<0.05 were retained.

(6) Combining all the ceRNA pairs to construct the ceRNA network.

4.2.3 Co-expression network and co-methylation network

Rank-based co-expression network [28] was constructed for each cancer type. For each gene, the top k (k = 4 in this work) genes that were the most correlated ones (Pearson correlation coefficient) for each gene were selected as neighbors in the network. Regarding the co-methylation network, we utilized DNA methylation data to compute the correlation between each pair of genes, employing the same approach as in our previous study [29].

rij(1)=|cov(ei,ej)σ(ei)σ(ej)| (3)

As to the co-methylation network, DNA methylation data was used to calculate the correlation of each gene pair.

4.2.4 Co-mutations network

Based on TCGA somatic mutation data, for each cancer type, we calculated the cosine similarity between every gene pair. Then we construct the Somatic mutation network using the rank-based method [28]. rij(2)=mi∙mj|mi||mj| (4)

where rij(2) is the cosine similarity of the mutation vectors of the i-th and j-th genes, mi and mj are the mutation vectors of the i-th and j-th genes, respectively.

4.3 Integrating multi-level gene network

Different biological networks can capture different aspects of the cancer mechanism. Network representation learning (NRL) can integrate these networks into low-dimensional feature vectors for each node [52], enabling cancer driver gene identification by graph convolutional neural networks (GCNs). However, many cancers have small sample sizes and build networks that are too sparse, which poses a challenge to the training and performance of deep learning models. To overcome this limitation, we used advanced multi-view collaborative network embedding (MANE) [32] to embed multiple heterogeneous networks, which has demonstrated excellent performance on biomedical data. The method captures these three categories of relationships to integrate different biological network information and obtain a low-dimensional and continuous vector representation of each gene (node). In this work, we apply MANE to our study as follows.

Diversity: intra-network pairs. To retain the diversity of each network, each node has a network-specific representation such that two nodes associated in the same network should have similar representations for that network. For each network, we generate topologically associated gene pairs from random walks, by the skipgram model [53] to construct the loss function LDiv.

First-order collaboration: cross-network, intra-gene pairs. While different network exhibit diversity, the two network-specific embeddings of the same gene should become similar, by optimizing the loss LC1.

Second-order collaboration: cross-network, cross-gene pairs. Gene pairs that interact in one network may also interact in other networks. Thus, we optimize the loss LC2.

Based on the losses of the three types of gene pairs, the final loss is obtained. L=LDiv+α·LC1+β·LC2 (5)

where LDiv, LC2 and LC3 are the losses of three types of relationships respectively and α, β≥0 are hyperparameters to control the relative importance among the three components.

Network attention. However, in real-world scenarios, the importance or relevance of each network is often non-uniform and varies from gene to gene. Thus, MANE+ is the algorithm that introduces the neural attention mechanism. We first define a score function s: U×V→ℝ to compute the similarity between any gene i and any network n: s(i,n)=z2(n)·tanh(z1(n)∘fi+b1(n)1)+b2(n) (6)

where z1(n),z2(n)∈RD,b1(n),b2(n)∈R are trainable parameters specific to network n, and ∘ denotes the Hadamard product. The scores are further normalized to obtain the attention value ai(n), i.e., the weight of network v w.r.t. gene i: ai(n)=exps(i,n)∑n′∈Nexps(i,n′) (7)

Subsequently, for every gene i, we aggregate its network-specific embeddings into a vector fia∈RD, weighted by its network attention: fia=tanh(⊕n∈Nai(n)fi(n)) (8)

The attention-based embeddings, {fia:i∈U}, can be used as features for downstream applications, where some supervision is often available to guide the learning of network-specific attention parameters. Thus the following losses in gene classification can be minimized: LAtt=1|Utrain|∑i∈UtrainCrossEntropy(yi,h(fia)) (9)

where Utrain is the set of training genes, yi is the class of gene i, and h(fia) is the classification output for gene i using the attention-based embedding fia. Since Utrain⊂U, this is a semi-supervised setting.

The final loss function with the attention mechanism is obtained: L+=LDiv+α·LC1+β·LC2+γ·LAtt (10)

where γ>0 is a hyperparameter to control the contribution of the attention-based loss.

4.4 Feature enhancement

To integrate multifaceted data to predict cancer driver genes, we used DORGE [34]. The 75 features mentioned in the algorithm were used as our source of enhanced features. We selected 45 features from this set, based on KL divergence Ranking [54], for our analysis. See S7 Table for details of feature enhancements.

4.5 Assessment criteria

Due to the severe imbalance between positive and negative samples in the dataset, we chose the Matthews Correlation Coefficient (MCC) as the primary evaluation metric. In previous studies, the KS test was used to assess whether a gene set is significantly enriched at the top of a ranked gene list [55]. Following this strategy, we used it to evaluate whether the list of driver genes is significantly enriched in our ranked gene list (where higher ranks indicate a higher likelihood of being predicted as driver genes by our model). In this context, a smaller p-value (from the KS test) indicates a higher degree of enrichment of true driver genes within the model-predicted gene list. Therefore, we have included the KS test along with accuracy, the Receiver Operating Characteristic Curve (AUROC), recall, F1-score, and precision as additional evaluation metrics for the model. S5 Text provides a detailed description of these metrics.

4.6 Driver prediction model

In this study, we constructed an XGBoost classifier to predict cancer driver genes using a combination of a network embedding matrix and enhanced features as features. To address the challenge of class imbalance, we employed the SMOTE algorithm [56] to balance the samples in the training set. The key steps of our approach, are detailed in S6 Text. K-fold cross-validation (K = 10 in this work) was applied to validate our model (S7 Text and S7 Fig).

Supporting information

S1 Text Prediction of novel cancer driver genes.

(DOCX)

S2 Text Stability evaluation of IMI-driver.

(DOCX)

S3 Text Data collection.

(DOC)

S4 Text The pipeline of constructing common networks.

(DOCX)

S5 Text Description of assessment criteria.

(DOCX)

S6 Text The pipeline of predicting model.

(DOCX)

S7 Text Details of K-fold cross-validation.

(DOCX)

S1 Fig Performance comparison of ten methods on nine benchmarks (AUROC).

We compared the classification performance of IMI-driver with nine other methods across nine gold-standard datasets. Each subplot shows the classification performance comparison (AUROC) of the 10 methods on one benchmark dataset.

(TIF)

S2 Fig Performance comparison of ten methods on nine benchmarks (Precision).

We compared the classification performance of IMI-driver with nine other methods across nine gold-standard datasets. Each subplot shows the classification performance comparison (Precision) of the 10 methods on one benchmark dataset.

(TIF)

S3 Fig Performance comparison of ten methods on nine benchmarks (F1-score).

We compared the classification performance of IMI-driver with nine other methods across nine gold-standard datasets. Each subplot shows the classification performance comparison (F1-score) of the 10 methods on one benchmark dataset.

(TIF)

S4 Fig Performance comparison of ten methods on nine benchmarks (KS test p-value).

We compared the classification performance of IMI-driver with nine other methods across nine gold-standard datasets. Each subplot shows the classification performance comparison (KS test p-value) of the 10 methods on one benchmark dataset.

(TIF)

S5 Fig Comparing the performance of IMI-driver and models based on randomly labeled datasets.

The models were compared across 9 benchmark datasets, with the x-axis representing the models at different thresholds (n) and the y-axis depicting the corresponding results (MCC).

(TIF)

S6 Fig Comparing the performance of IMI-driver and other models at various thresholds.

The models were compared across 9 benchmark datasets, with the x-axis representing the models at different thresholds (n) and the y-axis showing the MCC of our method minus the MCC of the best-performing model among other models.

(TIF)

S7 Fig K-fold cross-validation.

(a) Diagram of data partitioning for K-fold cross-validation. (b) Diagram of model training and validation for each fold.

(TIF)

S1 Table Performance of all the models on the benchmarks.

(XLSX)

S2 Table The high-confidence genes predicted by IMI-driver.

(XLSX)

S3 Table Survival analysis of the potential driver genes on TCGA (Log-rank P-value).

(XLSX)

S4 Table Performance comparison of different classifiers.

(XLSX)

S5 Table 29 cancer types used in this work.

(XLSX)

S6 Table Nine benchmark datasets.

(XLSX)

S7 Table 45 biological features.

(XLSX)

10.1371/journal.pcbi.1012389.r001
Decision Letter 0
Mendes Pedro Section Editor
Mora Rodrigo Academic Editor
© 2024 Mendes, Mora
2024
Mendes, Mora
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version0
13 May 2024

Dear Dr., Zhou,

Thank you very much for submitting your manuscript "IMI-driver: integrating multi-level gene networks and multi-omics for cancer driver gene identification" for consideration at PLOS Computational Biology.

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Pedro Mendes

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***********************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The identification of cancer driver genes is crucial for early detection, effective therapy, and precision medicine of cancer. This study introduces IMI-driver, a novel method that integrates multi-level gene networks and multi-omics data for the identification of cancer driver genes. The authors provide insights into the importance of integrating diverse biological networks (including common networks and tumor-specific networks) and multi-omics data for accurate cancer driver gene identification, and demonstrated the superior performance of IMI-driver compared to existing methods in predicting cancer driver genes.

While IMI-driver presents several strengths in integrating multi-level gene networks and multi-omics data for cancer driver gene identification, the manuscript also have some potential weaknesses need to be improved. My additional comments are as follows:

(1)In this paper, the authors used Top100 genes as the screening threshold. I don't think that's a fair way to compare different methods. As many methods do not use rankings as the screening threshold.

(2)As the field of cancer driver gene identification is rapidly evolving, IMI-driver should compare with the latest state-of-the-art network-based methods, such as MODIG, MTGCN, MNGCL, and so on, to ensure its competitiveness and relevance in the current landscape.

(3)The interpretability of the results and the biological relevance of the identified novel driver genes could be further elaborated to provide a clearer understanding of the implications for cancer research and precision medicine.

(4)The figure legends are too simple in such as Fig. 2 and Supplementary Figures, which don't show the message clearly

(5) The sample number of the multi-omics data of 29 cancer types from TCGA should be provided in the materials and methods, as well as the details resource or version of TCGA.

Reviewer #2: Considering that cancer is caused by the dysregulation of several genes at various levels of regulation, the authors present IMI-driver, a model that integrates multi-omics data into eight biological networks to predict cancer drivers. The experimental results confirm the effectiveness of this method. The major highlight of this work is the integration of different levels of biological networks, especially the incorporation of cancer-specific networks into the gene representation process, which has not been addressed in other methods. Therefore, I believe this work is quite innovative. Additionally, the manuscript is well-structured and written, and the results are promising. However, some points need to be taken into consideration.

Major issues:

1.The authors define the top 100 genes as driver genes and define six new cancer driver genes by comparing them with known driver gene sets. Experimental findings suggest that these genes might indeed be potential driver genes. I am curious whether selecting the top 150 or 200 genes for each cancer type as driver genes would still yield potential driver genes. The authors should demonstrate this result to show the robustness of their method.

2.Regarding Figure 2, the evaluation metrics seem limited, and additional evaluation metrics may be needed.

3.Evaluate the computational efficiency of the IMI-driver model, particularly in terms of runtime and memory usage. Discuss strategies employed to optimize performance, such as parallel processing or algorithmic optimizations, and provide recommendations for researchers seeking to apply the model to large-scale datasets.

4.Discuss avenues for refining the IMI-driver model, such as incorporating additional omics data modalities or exploring alternative network integration techniques. This would guide researchers in extending the proposed methodology and addressing remaining challenges in cancer genomics.

Minor issues:

1.Besides some conventional evaluation metrics, the authors also use the Ks-test to assess the performance of each method. However, the authors did not explain why the Ks-test can serve as an effective evaluation metric.

2.The clarity of Figure 2 is insufficient, especially the text inside it is quite blurry, which affects its readability. I suggest the authors remake Figure 2.

3.There are too few figures in the main text, and more details may need to be presented in the main text.

4.Provide more detailed explanations of the IMI-driver model, including the specific algorithms utilized for multi-view collaborative network embedding and the integration of multi-omics data into biological networks.

Reviewer #3: In this study, the authors propose IMI-driver, a model that integrates multi-omics data into eight biological networks and applies Multi-view Collaborative Network Embedding to embed the gene regulation information from the biological networks into a low dimensional vector space to identify cancer drivers. However, the following issues need to be resolved by the authors before consideration for publication.

1. The authors should give a clear definition of what is cancer driver gene. Regarding the concept of a cancer driver gene, if the author's definition is solely based on genomic mutations, it raises the question of why it is necessary to integrate data from multiple omics. If, however, the impact of mutations on the system is considered, please provide specific experiments to demonstrate the extent of influence that driver mutations identified by IMI-driver methods have on cancer-related biological processes. If the scope of cancer driver genes extends beyond genomic mutations, why are comparative methods predominantly derived from driver mutation approaches? In summary, the fundamental issue is that the authors need to clarify their clear definition of driver gene in this paper, otherwise the various downstream evaluations are meaningless.

2 A single gene usually have multiple methylation sites, and similarly, a gene has multiple sites and types of mutations. Please elucidate specifically how the co-methylation network and co-mutation network are constructed.

3. The authors should provide detailed information on the process of obtaining network-specific embeddings for each gene. Is this a supervised or unsupervised learning approach? If it's the former, how is the label for each gene determined?

**********

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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10.1371/journal.pcbi.1012389.r002
Author response to Decision Letter 0
Submission Version1
16 Jul 2024

Attachment Submitted filename: response letter_v4.pdf

10.1371/journal.pcbi.1012389.r003
Decision Letter 1
Mendes Pedro Section Editor
Mora Rodrigo Academic Editor
© 2024 Mendes, Mora
2024
Mendes, Mora
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Submission Version1
5 Aug 2024

Dear Dr. Zhou,

We are pleased to inform you that your manuscript 'IMI-driver: integrating multi-level gene networks and multi-omics for cancer driver gene identification' has been provisionally accepted for publication in PLOS Computational Biology.

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PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The authors have addressed all my concerns. I have no more comments.

Reviewer #2: The authors have adequately addressed all of my questions in this revised version. I have no further concerns or new questions. Based on the authors' responses, I recommend that this paper be accepted.

Reviewer #3: The author's responses and revisions have addressed all of my concerns. I think it is now ready for publication.

**********

Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data and code should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Huiyan Sun

10.1371/journal.pcbi.1012389.r004
Acceptance letter
Mendes Pedro Section Editor
Mora Rodrigo Academic Editor
© 2024 Mendes, Mora
2024
Mendes, Mora
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
17 Aug 2024

PCOMPBIOL-D-24-00654R1

IMI-driver: integrating multi-level gene networks and multi-omics for cancer driver gene identification

Dear Dr Zhou,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

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==== Refs
Reference

1 Hanahan D. Hallmarks of Cancer: New Dimensions. Cancer Discov. 2022 Jan; 12 (1 ): 31–46. doi: 10.1158/2159-8290.CD-21-1059 35022204
2 Tomczak K , Czerwińska P , Wiznerowicz M . Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp Oncol (Pozn). 2015 Jan 20; 19 (1A ):A68–77. doi: 10.5114/wo.2014.47136 25691825
3 Zhang J , Bajari , Andric D , Gerthoffert F , Lepsa A , Nahal-Bose H et al. The International Cancer Genome Consortium Data Portal. Nat Biotechnol. 2019 March 15; 37 : 367–369. doi: 10.1038/s41587-019-0055-9 30877282
4 Zhang P. , Wu Y. , Zhou H. , Zhou B. , Zhang H. , Wu H. CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types. Bioinformatics. 2022 Sep 30;38 (19 ):4497–4504. doi: 10.1093/bioinformatics/btac575 35997565
5 Han Y , Yang J , Qian X , Cheng WC , Liu , Hua X , Zhou L et al. DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies. Nucleic Acids Res. 2019 May 7; 47 (8 ):e45. doi: 10.1093/nar/gkz096 30773592
6 Jiang L , Zheng J , Him Kwan JS , Dai S , Li C , Li MJ et al. WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts. Nucleic Acids Res. 2019; 47 : e96. doi: 10.1093/nar/gkz566 31287869
7 Ostroverkhova D. , Przytycka T.M. , Panchenko A.R. Cancer driver mutations: predictions and reality. Trends Mol Med., 2023 Jul; 29 (7 ): 554–566. doi: 10.1016/j.molmed.2023.03.007 .37076339
8 Lawrence M. S. , Stojanov P. , Polak P. , Kryukov G. V. , Cibulskis K. , Sivachenko A. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013 Jul 11; 499 : 214–218. doi: 10.1038/nature12213 23770567
9 F Dietlein , Weghorn D , Taylor-Weiner A , Richters A , Reardon B , Liu D et al. Identification of cancer driver genes based on nucleotide context. Nat Genet. 2020 Feb; 52 (2 ): 208–218. doi: 10.1038/s41588-019-0572-y 32015527
10 LL Mularoni R Sabarinathan, J Deu-Pons, A Gonzalez-Perez, N López-Bigas. OncodriveFML: a general framework to identify coding and non-coding regions with cancer driver mutations. Genome Biology. 2016 Jun 16; 17 (1 ):128. doi: 10.1186/s13059-016-0994-0 27311963
11 Zhang P. , Zhang H. , Wu H. iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species. Nucleic Acids Research. 2022 Oct 14; 50 (18 ): 10278–10289. doi: 10.1093/nar/gkac824 36161334
12 Liu H. , Li D. , Wu H. Lnclocator-imb: An Imbalance-tolerant Ensemble Deep Learning Framework for Predicting Long Non-coding RNA Subcellular Localization. IEEE J Biomed Health Inform. 2023 Oct 16; 28 (1 ):538–547. doi: 10.1109/JBHI.2023.3324709 37843994
13 Zhang P. , Wu H. IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions. IEEE J Biomed Health Inform. 2023 Sep 6; 27 (9 ): 4559–4568. doi: 10.1109/JBHI.2023.3292299 37402191
14 Zhang Y. , Zhang P. , Wu H. Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers. Brief Bioinform. 2024 Jan 22; 25 (2 ): bbae083. doi: 10.1093/bib/bbae083 38485768
15 F Cheng J Zhao, Z Zhao. Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes. Briefings in Bioinformatics. 2016 Jul;17 (4 ): 642–656. doi: 10.1093/bib/bbv068 26307061
16 Bradner J. E. , Hnisz D. , & Young R. A. Transcriptional addiction in cancer. Cell. 2017 Feb 9; 168 (4 ): 629–643. doi: 10.1016/j.cell.2016.12.013 28187285
17 Liu C. H. , Lai Y. L. , Shen P. C. , Liu H. C. , Tsai M. H. , Wang Y. D. ,… & Cheng, W. C. DriverDBv4: a multi-omics integration database for cancer driver gene research. Nucleic Acids Research. 2024 Jan 5; 52 (D1 ): D1246–D1252. doi: 10.1093/nar/gkad1060 37956338
18 D Szklarczyk D. , AL Gable KC Nastou, D Lyon, R Kirsch, S Pyysalo et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021 Jan 8; 49 (D1 ): D605–D612. doi: 10.1093/nar/gkaa1074 33237311
19 Y Hou B Gao, G Li, Z Su. MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration. Advanced Science. 2018 Jul 23; 5 :1800640. doi: 10.1002/advs.201800640 30250803
20 Cho A. , Shim J. E. , Kim E. , Supek F. , Lehner B. , & Lee I. MUFFINN: cancer gene discovery via network analysis of somatic mutation data. Genome Biol. 2016 Jun 23; 17 (1 ):129. doi: 10.1186/s13059-016-0989-x 27333808
21 C Wang J Shi, J Cai, Y Zhang, X Zheng, N Zhang. DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph. BMC Bioinformatics. 2022 Jul 13; 23 (1 ):277. doi: 10.1186/s12859-022-04788-7 35831792
22 R Schulte-Sasse S Budach, D Hnisz, A Marsico. Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms. Nat Mach Intell. 2021 April 12; 3 : 513–526.
23 W Peng Q Tang, W Dai, T Chen. Improving cancer driver gene identification using multi-task learning on graph convolutional network. Briefings in Bioinformatics. 2022 Jan 17; 23 (1 ): bbab432. doi: 10.1093/bib/bbab432 34643232
24 Xiao Z. , & Deng Y. Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network. PloS one. 2020 Sep 24; 15 (9 ):e0238915. doi: 10.1371/journal.pone.0238915 32970681
25 W Zhao X Gu, S Chen, J Wu, Z Zhou. MODIG: integrating multi-omics and multi-dimensional gene network for cancer driver gene identification based on graph attention network model. Bioinformatics. 2022 Oct 31; 38 (21 ):4901–4907. doi: 10.1093/bioinformatics/btac622 36094338
26 Zhou X. and Liu J. Inferring Gene Dependency Network Specific to Phenotypic Alteration Based on Gene Expression Data and Clinical Information of Breast Cancer. PLOS ONE. 2014 Mar 17; 9 (3 ):e92023. doi: 10.1371/journal.pone.0092023 24637666
27 Zhou X , Liu J , Wang W . Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data. IET Systems Biology. 2014 Jun; 8 (3 ):96–103. doi: 10.1049/iet-syb.2013.0025 25014376
28 Kammerer R. and Zimmermann W. Coevolution of activating and inhibitory receptors within mammalian carcinoembryonic antigen families. BMC Biol. 2010 Feb 4; 8 :12. doi: 10.1186/1741-7007-8-12 20132533
29 Hu W.-L. and Zhou X.-H. Identification of prognostic signature in cancer based on DNA methylation interaction network. BMC Medical Genomics. 2017 Dec 21; 10 (Suppl 4 ):63. doi: 10.1186/s12920-017-0307-9 29322932
30 Y Chen J Hao, W Jiang, T He, X Zhang, T Jiang, R Jiang. Identifying potential cancer driver genes by genomic data integration. Sci Rep. 2013 Dec 18; 3 :3538. doi: 10.1038/srep03538 24346768
31 H Han JW Cho, S Lee, A Yun, H Kim, D Bae. TRRUST v2: an expanded reference database of human and mouse transcriptional regulatory interactions. Nucleic Acids Res. 2018 Jan 4; 46 (D1 ):D380–D386. doi: 10.1093/nar/gkx1013 29087512
32 Ata S. K. , Fang Y. , Wu M. , Shi J. , Kwoh C. K. , Li X. Multi-View Collaborative Network Embedding. ACM Trans. Knowl. Discov. Data. 2021 April 21; 15 (3 ):39.
33 Chen T. and Guestrin C. XGBoost: A Scalable Tree Boosting System. In, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ‘16. Association for Computing Machinery, New York, NY, USA, 2016 August; pp. 785–794.
34 J Lyu JJ Li, J Su, F Peng, YE Chen, X Ge. et al. DORGE: Discovery of Oncogenes and tumoR suppressor genes using Genetic and Epigenetic features. Science Advances. 2020 Nov 11; 6 (46 ):eaba6784. doi: 10.1126/sciadv.aba6784 33177077
35 X Shi H Teng, L Shi, W Bi, W Wei, F Mao, Z Sun. Comprehensive evaluation of computational methods for predicting cancer driver genes. Briefings in Bioinformatics. 2022 Mar 10; 23 (2 ):bbab548. doi: 10.1093/bib/bbab548 35037014
36 Z Sondka S Bamford, CG Cole, SA Ward, I Dunham, SA Forbes. The COSMIC Cancer Gene Census: describing genetic dysfunction across all human cancers. Nat Rev Cancer. Nov; 18 (11 ):696–705. doi: 10.1038/s41568-018-0060-1 30293088
37 Vogelstein B. , Papadopoulos N. , Velculescu V. E. , Zhou S. , Diaz L. A. Jr , & Kinzler K. W. Cancer Genome Landscapes. Science. 2013 Mar 29; 339 (6127 ):1546–58. doi: 10.1126/science.1235122 23539594
38 Tamborero D. , Gonzalez-Perez A. , Perez-Llamas C. , Deu-Pons J. , Kandoth C. , Reimand J. Comprehensive identification of mutational cancer driver genes across> 12 tumor types. Sci Rep. 2013 Oct 2; 3 :2650. doi: 10.1038/srep02650 24084849
39 Y Liu J Sun, M Zhao. ONGene: A literature-based database for human oncogenes. J Genet Genomics. 2017 Feb 20; 44 (2 ): 119–121. doi: 10.1016/j.jgg.2016.12.004 28162959
40 Bailey M. H. , Tokheim C. , Porta-Pardo E. , et al . Comprehensive Characterization of Cancer Driver Genes and Mutations. Cell. 2018 Aug 9;174 (4 ):1034–1035. doi: 10.1016/j.cell.2018.07.034 30096302
41 Kim E. , Hwang S. , Kim H. , Shim H. , Kang B. , Yang S. ,et. al . MouseNet v2: a database of gene networks for studying the laboratory mouse and eight other model vertebrates. Nucleic Acids Res. 2016 Jan 4; 44 (D1 ):D848–854. doi: 10.1093/nar/gkv1155 26527726
42 Gonzalez-Perez A. , Perez-Llamas C. , Deu-Pons J. , Tamborero D. , Schroeder M. P. , Jene-Sanz A. et al. IntOGen-mutations identifies cancer drivers across tumor types. Nat Methods. 2013 Nov; 10 (11 ): 1081–1082. doi: 10.1038/nmeth.2642 24037244
43 L Page , Brin S , Motwani R , Winograd T . The pagerank citation ranking: Bringing order to the web. 1999.
44 P Liu , Yang F , Zhang L , Hu Y , Chen B , Wang J . et al . Emerging role of different DNA methyltransferases in the pathogenesis of cancer. Front Pharmacol. 2022 Aug 25;13 :958146. doi: 10.3389/fphar.2022.958146 36091786
45 C Hu , Liu X , Zeng Y , Liu J , Wu F . DNA methyltransferase inhibitors combination therapy for the treatment of solid tumor: mechanism and clinical application. Clinical Epigenetics. 2021 Aug 27;13 (1 ):166. doi: 10.1186/s13148-021-01154-x 34452630
46 Butsri S , Kukongviriyapan V , Senggunprai L , Kongpetch S , Prawan A . All-trans-retinoic acid induces RARB-dependent apoptosis via ROS induction and enhances cisplatin sensitivity by NRF2 downregulation in cholangiocarcinoma cells. Oncology Letters. 2022 Jun; 23 (6 ):179. doi: 10.3892/ol.2022.13299 35464301
47 Yin D. and Lu X. Silencing of long non-coding RNA HCP5 inhibits proliferation, invasion, migration, and promotes apoptosis via regulation of miR-299-3p/SMAD5 axis in gastric cancer cells. Bioengineered. 2021 Dec;12 (1 ):225–239. doi: 10.1080/21655979.2020.1863619 33371778
48 X Xu , Zhang J , Tian Y , Gao Y , Dong X , Chen W et al. CircRNA inhibits DNA damage repair by interacting with host gene. Molecular Cancer. 2020 Aug 24;19 (1 ):128. doi: 10.1186/s12943-020-01246-x 32838810
49 X Cai , Zhou J , Deng J , Chen Z . Prognostic biomarker SMARCC1 and its association with immune infiltrates in hepatocellular carcinoma. Cancer Cell International. 2021 Dec 22;21 (1 ):701. doi: 10.1186/s12935-021-02413-w 34937564
50 W Song , Dai WJ , Zhang M , Wang H , Yang XZ . Comprehensive Analysis of the Expression of TGF-β Signaling Regulators and Prognosis in Human Esophageal Cancer. Computational and Mathematical Methods in Medicine. 2021 Oct 23; 2021 :1812227. doi: 10.1155/2021/1812227 34725559
51 L Salmena , Poliseno L , Tay Y , Kats L , Pandolfi PP . A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language? Cell. 2011 Aug 5;146 (3 ): 353–358. doi: 10.1016/j.cell.2011.07.014 21802130
52 W Peng , Yi S , Dai W , Wang J . Identifying and ranking potential cancer drivers using representation learning on attributed network. Methods. 2021 Aug; 192 :13–24. doi: 10.1016/j.ymeth.2020.07.013 32758683
53 B Perozzi , Al-Rfou R , Skiena S . DeepWalk: online learning of social representations. In, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ‘14. Association for Computing Machinery, New York, NY, USA, 2014 August; pp. 701–710.
54 F Li , Chu X , Dai L , Wang J , Liu J , Shang J . Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms. Genes. 2022 Apr 19; 13 :716. doi: 10.3390/genes13050716 35627101
55 Lamb J , Crawford ED , Peck D , Modell JW , Blat IC , Wrobel MJ et al . The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006 Sep 29; 313 (5795 ):1929–35. doi: 10.1126/science.1132939 17008526
56 NV Chawla , Bowyer KW , Hall LO , Kegelmeyer WP . SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research. 2002; 16 :321–357.
