# MicroRNA dysregulational synergistic network: discovering microRNA dysregulatory modules across subtypes in non-small cell lung cancers

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## Abstract

### Background

The majority of cancer-related deaths are due to lung cancer, and there is a need for reliable diagnostic biomarkers to predict stages in non-small cell lung cancer cases. Recently, microRNAs were found to have potential as both biomarkers and therapeutic targets for lung cancer. However, some of the microRNA’s functions are unknown, and their roles in cancer stage progression have been mostly undiscovered in this clinically and genetically heterogeneous disease. As evidence suggests that microRNA dysregulations are implicated in many diseases, it is essential to consider the changes in microRNA-target regulation across different lung cancer subtypes.

### Results

We proposed a pipeline to identify microRNA synergistic modules with similar dysregulation patterns across multiple subtypes by constructing the MicroRNA Dysregulational Synergistic Network. From the network, we extracted microRNA modules and incorporated them as prior knowledge to the Sparse Group Lasso classifier. This leads to a more relevant selection of microRNA biomarkers, thereby improving the cancer stage classification accuracy. We applied our method to the TCGA Lung Adenocarcinoma and the Lung Squamous Cell Carcinoma datasets. In cross-validation tests, the area under ROC curve rate for the cancer stages prediction has increased considerably when incorporating the learned microRNA dysregulation modules. The extracted modules from multiple independent subtypes differential analyses were found to have high agreement with microRNA family annotations, and they can also be used to identify mutual biomarkers between different subtypes. Among the top-ranked candidate microRNAs selected by the model, 87% were reported to be related to Lung Adenocarcinoma. The overall result demonstrates that clustering microRNAs from the dysregulation pattern between microRNAs and their targets leads to biomarkers with high precision and recall rate to known differentially expressed disease-associated microRNAs.

### Conclusions

The results indicated that our method improves microRNA biomarker selection by detecting similar microRNA dysregulational synergistic patterns across the multiple subtypes. Since microRNA-target dysregulations are implicated in many cancers, we believe this tool can have broad applications for discovery of novel microRNA biomarkers in heterogeneous cancer diseases.

## Keywords

microRNA dysregulation Differential analysis Biomarker discovery Scale-free network Synergistic module## Abbreviations

- AUC
Area under the curve

- Dys
Dysregulation

- FDR
False discovery rate

- LUAD
Lung adenocarcinoma

- LUSC
Lung squamous cell carcinoma

- MDSN
MicroRNA dysregulational synergistic network

- miRNA
microRNA

- mRNA
messengerRNA

- NMI
Normalized mutual information

- ROC
Receiver operating characteristic

- RPKM
Reads per million mapped

- SCC
Squamous cell carcinoma

- SGL
Sparse group lasso

- TCGA
The cancer genome atlas

- WGCNA
Weighted gene co-expression network analysis

## Background

Lung cancer accounts for more than 1.5 million deaths globally per year and is the leading cause of cancer-related mortality. About 87% of the lung cancer cases are classified as Non-Small Cell Lung Cancer, and the 5-year survival rate of all stages is below 17% because the majority of lung cancer patients (57%) are diagnosed at later stages because early disease is typically asymptomatic [1]. Even when diagnosed early, the only recommended treatment is surgical resection, despite that up to 30% of those successfully treated will still die within five years of initial diagnosis [2]. Therefore, the development of early diagnosis and treatment strategy is critical and essential for the control of this deadly disease. Recently, it has been found that microRNAs have the potential as both biomarkers and therapeutic targets for lung cancer [3, 4].

MicroRNAs (miRNAs) are a recently discovered class of small noncoding RNA. Approximately 22nt, miRNAs post-transcriptionally target messenger-RNAs (mRNAs) to regulate the translation of target genes. They have been found to play a critical role in various biological functions such as proliferation, differentiation, and apoptosis [5]. Thus, abnormal miRNA regulatory events can cause a significant impact on various cellular functions, ultimately resulting in complex events leading to cancer. Increasing evidence suggests that miRNAs can have a causal role in tumorigenesis [6].

Due to the significant role of miRNAs found in cancer biology, many existing lung cancer studies use miRNA expression profiles for accurate prediction of lung cancer stages or subtypes [7, 8]. In a typical differential expression analysis, a univariate statistical method (e.g., student’s *t*-test, false discovery rate threshold) is performed to select miRNAs with a significant deviation between normal and tumor sample groups. However, the results are not always satisfactory, as large-scale multi-omics analysis of non-small cell lung adenocarcinoma (LUAD) revealed distinct interactions of miRNA to target mRNA that are specific to histological subtypes [9]. In other words, an identified miRNA biomarker may correctly classify tumor based on analyses done on one particular subtype but may misclassify cases of other subtypes, where it may target a different set of mRNAs. Therefore, for a more robust selection of miRNA biomarker, analysis of the deviation in miRNA-target interactions between various lung cancer subtypes should be considered to assess their potential as predictor to this heterogeneous disease.

Experimental evidence has shown that multiple miRNAs can potentially target a gene through synergism, in which two or more miRNAs can cooperatively co-regulate an individual gene [10]. Studying the synergism of miRNAs within a specific cellular environment is another critical step to determine their disease-specific functions at the system level. Construction of the miRNA co-regulation network by considering regulatory targets with similar functions [11] revealed a miRNA-miRNA functional synergistic network; however, the study of the changes in miRNA-target interactions between different cancer subtypes has mainly left uncovered.

To further our understanding of the role of miRNAs in lung cancers, we aim to identify differentially expressed miRNAs while considering miRNA-target dysregulations among different cancer subtypes. We extended the brilliant miRNA-target dysregulation idea from Xu et al. [12] and proposed a novel miRNA clustering strategy to identify miRNA dysregulatory modules. We hypothesize that by identifying the context-specific group structures among the miRNAs, the differential analysis procedure can benefit from a more robust selection of miRNA biomarkers that can accurately predict cancer stages across different subtypes.

## Methods

### Dataset and notations

We denote the miRNA and mRNA expression profiles as column vectors \(\mathbf {x_{i}}=\left [x_{i}^{1}, x_{i}^{2},\ldots,x_{i}^{s}\right ]^{\top }\) and \(\mathbf {y_{j}}=\left [y_{j}^{1}, y_{j}^{2},\ldots,y_{j}^{s}\right ]^{\top }\) to represent the expression level of miRNA *i* and mRNA *j* across *s* samples, respectively. To represent miRNA and mRNA expressions for a specific group of samples, we denote column vectors \(\mathbf {x_{i}^{C}}=\left [x_{i}^{1}, x_{i}^{2}, \ldots, x_{i}^{n_{C}}\right ]^{\top }\) and \(\mathbf {y_{j}^{C}}=\left [y_{j}^{1}, y_{j}^{2}, \ldots, y_{j}^{n_{C}}\right ]^{\top }\), respectively, where *n*_{C} is the number of samples attributed with a particular phenotype *C*, e.g., normal, stage I cancer, stage II cancer, etc. Note, boldface variables are to represent vectors and non-boldface for scalars. Also, for expression data, we use subscripts to identify a specific miRNA or mRNA expression level, and superscripts to identify a sample group.

### Identification of miRNA biomarkers for lung cancer

### Step 1: Identifying miRNA-target dysregulations between subtypes

*i*and target

*j*as:

where \(\sigma _{x_{i}^{A}}\) and \(\sigma _{x_{i}^{B}}\) denote the standard deviation of miRNA *i* expressions of sample groups *A* and *B*, respectively. To determine whether the deviation of the correlation between the two groups is significant, Xu et al. randomly assigned patients to the two groups and recalculated *Dys* 10,000 times, and obtained a *p*-value by the frequency of the random *Dys* being higher than the actual *Dys*.

*i*and target

*j*, we calculated the two Pearson’s correlation values

*r*

_{A}and

*r*

_{B}from each sample group then obtained their corresponding z-values

*z*

_{A}and

*z*

_{B}through Fisher’s transformation \(z=\frac {1}{2}\ln \left (\frac {1+r}{1-r}\right)\). The z-value for the difference between

*z*

_{A}and

*z*

_{B}is obtained by

*z*

_{AB}to a

*p*-value (two-tailed) and thereby obtain a statistical significance of the difference between two miRNA-target correlations. The cut-off for the

*p*-value threshold was chosen at 0.001, as it has been commonly used as a threshold in several correlation studies.

### Step 2: Building the miRNA-target dysregulation association matrix

One primary function of miRNAs is the cleavage of the transcript of its target gene to regulate gene expression. Thus, in the task of identifying aberrant miRNA-target interactions, the inverse correlation should be a prerequisite for candidate miRNA and target pairs to avoid false-positives. In other words, only miRNA-target pairs which have a negative Pearson’s correlation in at least one of the sample groups, *A* or *B*, were considered.

Furthermore, since the primary goal of this study is to discover novel miRNA biomarkers to help understand cancer stage progression, it is essential to consider as many miRNAs as possible. In this study, the miRNA-target relationship prediction algorithms, e.g., TargetScan 7.1 [15] and miRanda [16], were not utilized as the interaction databases only covered a total of 263 miRNAs out of 1881 miRNAs present in the miRNA expression profiles.

For each putative miRNA *i* and target *j* considered, we repeated the dysregulation analysis procedure in Step 1 between all pairs of different lung cancer subtypes as independent dysregulation analyses. Then, all miRNA-target dysregulations found significant were encoded by constructing a matrix **A** with entry *A*_{ij} equal to 1 if the *p*-value of the miRNA *i* and target *j* dysregulation passes the *p*-value threshold and 0 otherwise. For each independent dysregulation analyses, the matrix **A** is concatenated. This matrix is interpreted as a new feature set, where each row characterizes a miRNA’s dysregulation targets that were present across multiple cancer subtypes dysregulation analyses.

### Step 3: Calculating miRNA-miRNA dysregulation functional similarity

*p*and

*q*is calculated by cosine similarity, defined as

where **A**_{i.} is a row vector indicating the dysregulated targets of miRNA *i*. The cosine similarity value ranges [0,1] and can be interpreted as the number of mutual dysregulation targets shared between two miRNAs normalized by their total connections. By calculating the similarity between every miRNA-miRNA pairs, an adjacency matrix is produced to construct a miRNA-miRNA similarity network. Since it is difficult to uncover cluster structures when the network is dense, it is necessary to prune the weaker miRNA-miRNA connections.

### Step 4: Constructing the MDSN and pruning with scale-free thresholding

The scale-free topology property exists in most biological graphs, including miRNAs [17], which indicates that the miRNA-miRNA network connections follow a power-law distribution in which more miRNAs tend to have fewer neighbors and fewer miRNAs tend to have more neighbors. A well-known framework, Weighted Gene Co-expression Network Analysis (WGCNA) is utilized to prune lower weight edges with a threshold chosen such that the graph’s scale-free property still holds while preserving as many edges as possible.

After all miRNA-miRNA pairs’ cosine similarity scores are computed, they are used as edge weights in the MDSN. This is constructed by an adjacency matrix **M** with entries *M*_{pq}=*s*(*p,q*) for all miRNAs *p, g*. Similar to the approach used in most biological networks, the miRNA node degrees is expected to exhibit a scale-free distribution under some thresholding. We applied the hard-thresholding technique in WGCNA [18] by removing from the network any edge with weight lower than the threshold, which was chosen to be the least stringent threshold such that the degree distribution maintains a desirable power-law fitting score.

### Step 5: Identifying miRNA dysregulation modules with community detection

After pruning of the MDSN, we utilized the graph partitioning approach to extract miRNA modules by assigning miRNA nodes into communities using a modularity objective proposed in the Louvain method [19]. Using a fast greedy iterative procedure, the Louvain method assigns nodes into communities by optimization of the modularity objective, which measures the density of links inside communities compared to links between communities.

To summarize the algorithm, initially, each node is assigned to its own community. At the first phase, node *i* consider each of its neighbor *j* and evaluate the gain of modularity if *i* is placed in *j*’s community, and then selects the neighbor *j* with the maximum modality gain. This first phase repeats iteratively until convergence. The algorithm then alternates to the second phase to build a new network whose nodes are the newly formed communities found in the first phase. The first and second phase are repeated iteratively until there is only one community that includes all nodes. In the final result, the algorithm gives a hierarchical community structure of all nodes in the MDSN network. The partition in this dendrogram with the highest modularity value by the Louvain algorithm is selected as the miRNA modules assignment.

### Step 6: Classification of cancer stage with identified miRNA modules

It is known that a classifier with *ℓ*- 1 norm regularization is typically used for feature selection in problems with "small n, large p." However, for problems known to have grouped features, adding group information as prior knowledge can improve feature selection and classification performance. We applied a multi-class logistic classifier with Sparse Group Lasso (SGL) with the intuition that if a miRNA predictor to cancer stage is found relevant, other miRNAs in the same group are also likely relevant since they share similar dysregulation targets across the cancer subtypes.

*ℓ*- 1 and Group Lasso

*ℓ*- 2 norm regularization to achieve a sparse solution at both the group and within group level [20]. We used an indicator vector

*c*

_{i}∈{0,1}

^{k}to represent the

*i*

^{th}sample’s reported cancer stage. In this study,

*k*is 5, indicating whether a sample is labeled as normal, stage I, II, III, or IV. The objective function is as follows:

*λ*is the sparsity coefficient,

*α*is the mixing coefficient between

*ℓ*- 1 and Group Lasso

*ℓ*- 2 norm, which is defined as:

where |*g*| is the size of the group. The Python package *pylearn*-*parsimony* was used to train the logistic regression classifier with SGL regularization.

## Result

### Applications in TCGA non-small cell lung adenocarcinoma dataset

Sample size characteristics of the TGCA LUAD dataset

Phenotype | Sample size |
---|---|

Normal (matched) | 20 |

Stage I | 277 |

Stage II | 121 |

Stage III | 84 |

Stage IV | 24 |

Acinar | 18 |

Bronchioloalveolar | 24 |

Clear Cell | 2 |

Colloid | 10 |

Micropapillary | 3 |

Mucinous | 2 |

Papillary | 23 |

Signet Ring | 1 |

Solid | 5 |

Mixed subtype | 107 |

Not otherwise specified | 320 |

#### Identified miRNA-target dysregulations between LUAD subtypes

We identified significant dysregulations for every miRNA-target pair between 1881 miRNAs and 20,484 mRNAs. Each miRNA-target pair is tested for significant change in correlations between different subtype sample groups. Due to insufficient sample size in some subtypes, only four histological LUAD subtypes were selected for subtypes dysregulation analysis, as outlined in Table 1. To build the miRNA-target dysregulation matrix, we performed an independent dysregulation analysis for each pair-wise combination of the four subtypes.

Setting the *p*-value threshold parameter at *p*<0.001, we obtained a sum of 1,896,631 miRNA-target dysregulations from a union of six independent dysregulation analyses for the Acinar, Bronchioloalveolar, Colloid, and Papillary subtypes. In other words, we identified miRNA-target dysregulations between Acinar vs. Bronchioloalveolar, Bronchioloalveolar vs. Colloid, Acinar vs. Colloid, and so on. Since it is very likely that false-positives exist among the identified miRNA-target dysregulations, we accounted for this by careful selection of the threshold parameter to prune weaker miRNA synergism similarities.

#### Selection of threshold parameter for the scale-free topology of MDSN for LUAD cohort

After identifying miRNA-target dysregulations among the lung cancer subtypes, we computed the miRNA-miRNA cosine similarity score for every pair of miRNAs to construct the MDSN. For every pair of the 1314 miRNAs (found dysregulated), we computed a total of 754,086 cosine similarity scores. The power law fitting score [18] is defined as *corr*(*log*_{10}(*s*),*log*_{10}(*p*(*s*)))^{2} where *s* is the similarity scores and the distribution *p*(*s*) is modeled by a histogram of binned data samples. The *R*^{2} score computed over all miRNA-miRNA pairs was 0.9135, which satisfies the *R*^{2}>0.8 criterion and indicates the network has a scale-free topology. The similarity score power parameter was kept at *β*=1.

### Applications in the TCGA lung squamous cell carcinoma dataset

Sample size characteristics of the TGCA LUSC dataset

Phenotype | Sample size |
---|---|

Normal (matched) | 37 |

Stage I | 155 |

Stage II | 125 |

Stage III | 50 |

Stage IV | 3 |

Lung Basaloid SCC | 10 |

Lung Papillary SCC | 5 |

Lung small cell SCC | 2 |

Not otherwise specified | 353 |

Primitive | 59 |

Classical | 96 |

Basal | 156 |

Secretory | 53 |

One reason for this issue is that it has been known the lung squamous cell carcinoma is clinically and genetically heterogeneous, and it is challenging to sub-stratify this heterogeneity. However, a study by Wilkerson et al. [23] discovered reproducible and clinically significant LUSC subtypes that can be predicted from the mRNA expression profiles. A representative expression profile for each of the four subtypes, Primitive, Classical, Basal, and Secretory, were summarized by a cluster centroid consisting of 196 genes. Using the cluster centroids representing the four LUSC subtypes, we performed subtype prediction for all LUSC samples using the nearest-centroid classification algorithm proposed in [24].

#### Identified miRNA-target dysregulations between LUSC subtypes

After the subtype prediction of the LUSC samples were obtained, we tested for significant dysregulation for every miRNA-target pair between 1870 miRNAs and 20,472 mRNAs. Six independent dysregulation analyses were performed for every pairwise combination of the four subtypes, e.g., Primitive vs. Classical, Basal vs. Secretory, Primitive vs. Basal, and so on. A union of the six analyses revealed a sum of 1,560,419 miRNA-target dysregulations found at the *p*-value cut-off of 0.001.

#### Selection of threshold parameter for the scale-free topology of the MDSN for LUSC cohort

For every pair of the 1490 miRNAs found with dysregulation patterns across multiple LUSC subtypes, we computed a total of 754,086 cosine similarity scores. Similar to the procedure applied to the network in LUAD cohort, we selected the edge-prune threshold at 0.50, where the scale-free topology criterion *R*^{2} score is higher than 0.8, shown in Fig. 2b. The number non-isolate miRNA nodes that remained in the MDSN is 391.

### Extracted miRNA modules are consistent between independent subtypes dysregulation analyses

### Incorporating miRNA modules information improves prediction of LUAD lung cancer stage

We applied the logistic classifier with SGL using the extracted miRNA modules as prior information to the Sparse Group Lasso regularization. Using a one-vs-rest scheme for multi-class classification, SGL classifies between normal, stage I, stage II, stage III, and stage IV samples, with numbers of samples corresponding to the first column of Table 1. We empirically set the sparsity parameters *λ*=1.0 and *α*=0.5 that were found to give the best prediction performance from 5-fold cross-validation tests.

*ℓ*- 1 regularization. With each classifier, we computed the area under the ROC curve rates for each stage from a train-test split of 20%, as shown in Fig. 5.

### MicroRNA groups lead to higher recall and precision of candidate miRNA biomarkers

## Discussion

In this study, we integrated paired miRNA and mRNA expression data to detect aberrant miRNA-target interactions between lung cancer subtypes to discover novel miRNA biomarkers to predict lung cancer stages. We have developed an efficient method to identify dysregulations among millions of potential regulatory relationships between 1,881 miRNAs and more than 20,000 mRNAs across multiple lung cancer subtypes. Among all the regulatory relationships considered, 4.9% of the miRNA-target pairs were found to have aberrant behavior across the different subtypes of the lung cancer diseases. Since the LUAD and LUSC are clinically and genetically heterogeneous diseases, utilizing this information would provide a glimpse into the miRNAs’ role in cancer pathogenesis in some specific lung cancer subtypes. This was apparent in Fig. 4, where it is apparent that some specific lung cancer subtypes possessed similar groups of dysregulated miRNA modules across multiple independent subtypes dysregulation analyses. For instance, note that the Primitive subtype in LUSC has high NMI values between the Secretory vs. Primitive, Classical vs. Primitive, and Basal vs. Primitive analyses. This indicates that in the Primitive subtype samples, there are possibly a few groups of miRNAs that have a consistent set of dysregulated targets, exclusive to all other LUSC subtypes. It would be interesting to report an analysis on such group of miRNA-target dysregulations in this Primitive subtype, which coincidently has the worst survival outcome (*p*<0.05) than the other three subtypes [23]. Such an observation may not be apparent with only a normal vs. tumor differential analysis, as it is shown in Fig. 4 where the NMI values are near zero in the normal vs. tumor dysregulation analysis compared to all other subtypes dysregulation analyses.

Despite that a growing number of miRNAs have been rigorously studied, the functions of most miRNAs are still unknown. Furthermore, only a small fraction of miRNAs were considered in the target prediction algorithms that provide a database of putative miRNA-mRNA relationships. By considering all potential miRNAs and their targets, our method can be used for novel miRNA functions discovery. However, a primary concern of this task is that selection of various thresholding hyper-parameters may produce unstable results. We performed the miRNA-target dysregulation analysis with varying *p*-value threshold at 0.01 and 0.001 and found similar patterns in the NMI similarity comparison from extracted miRNA modules in Fig. 4. Furthermore, all subtypes dysregulation analyses showed high NMI similarity with the miRNA family assignments without having incorporated this prior knowledge. This implies that despite possible false-positives in identifying miRNA-target dysregulations, the pruned MDSN can still be an excellent tool to reveal miRNA-miRNA functional synergism when inferring novel miRNA functions.

## Conclusions

By utilizing a dysregulation metric that allows for analysis of multiple cancer subtypes, we proposed a pipeline to cluster miRNAs with high functional synergism. The extracted miRNA modules, when applied to grouped feature selection, can improve phenotype prediction and result in biomarkers with high precision and recall rate to known LUAD-associated miRNAs. Furthermore, the predicted miRNA modules extracted from different subtype analyses can be used to reveal common miRNA dysregulations across multiple subtypes in heterogeneous cancer types. Since miRNA-target dysregulations are implicated in many cancers, where multi-modal differential analyses between multiple cancer subtypes have mainly left undiscovered, we believe this tool can have broad applications in the development of new diagnosis and treatment strategies.

## Notes

### Acknowledgements

The authors thank the editor and the anonymous reviewers for their constructive comments to improve this work.

### Funding

The authors are grateful for the GAANN support from the Department of Education under the grant P200A150192.

### Availability of data and materials

The TCGA LUAD and LUSC data were obtained using the TCGA-Assembler tool at http://www.compgenome.org/TCGA-Assembler/.

### About this supplement

This article has been published as part of *BMC Bioinformatics Volume 19 Supplement 20, 2018: Selected articles from the IEEE BIBM International Conference on Bioinformatics & Biomedicine (BIBM) 2017: bioinformatics*. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume-19-supplement-20.

### Authors’ contributions

JG directed the study and experimental procedure, reviewed the manuscript, and supervised the project. NT conceived the idea, designed the coding work, performed the computational experiments, and drafted the manuscript. VA, KN, and JW guided the aim of study and interpreted early results of the project. All authors read and approved the final version of the manuscript.

### Ethics approval and consent to participate

Not applicable.

### Consent for publication

Not applicable.

### Competing interests

The authors declare that they have no competing interests.

### Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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