RANK ligand converts the NCoR/HDAC3 co-repressor to a PGC1ß- and RNA-dependent co-activator of osteoclast gene expression.

The nuclear receptor co-repressor (NCoR) complex mediates transcriptional repression dependent on histone deacetylation by histone deacetylase 3 (HDAC3) as a component of the complex. Unexpectedly, we found that signaling by the receptor activator of nuclear factor κB (RANK) converts the NCoR/HDAC3 co-repressor complex to a co-activator of AP-1 and NF-κB target genes that are required for mouse osteoclast differentiation. Accordingly, the dominant function of NCoR/HDAC3 complexes in response to RANK signaling is to activate, rather than repress, gene expression. Mechanistically, RANK signaling promotes RNA-dependent interaction of the transcriptional co-activator PGC1ß with the NCoR/HDAC3 complex, resulting in the activation of PGC1ß and inhibition of HDAC3 activity for acetylated histone H3. Non-coding RNAs Dancr and Rnu12, which are associated with altered human bone homeostasis, promote NCoR/HDAC3 complex assembly and are necessary for RANKL-induced osteoclast differentiation in vitro. These findings may be prototypic for signal-dependent functions of NCoR in other biological contexts.

Lymph Node Mesenchymal and Endothelial Stromal Cells Cooperate via the RANK-RANKL Cytokine Axis to Shape the Sinusoidal Macrophage Niche.

Tissue-resident macrophages are receptive to specific signals concentrated in cellular niches that direct their cell differentiation and maintenance genetic programs. Here, we found that deficiency of the cytokine RANKL in lymphoid tissue organizers and marginal reticular stromal cells of lymph nodes resulted in the loss of the CD169+ sinusoidal macrophages (SMs) comprising the subcapsular and the medullary subtypes. Subcapsular SM differentiation was impaired in mice with targeted RANK deficiency in SMs. Temporally controlled RANK removal in lymphatic endothelial cells (LECs) revealed that lymphatic RANK activation during embryogenesis and shortly after birth was required for the differentiation of both SM subtypes. Moreover, RANK expression by LECs was necessary for SM restoration after inflammation-induced cell loss. Thus, cooperation between mesenchymal cells and LECs shapes a niche environment that supports SM differentiation and reconstitution after inflammation.

irGSEA: the integration of single-cell rank-based gene set enrichment analysis.

irGSEA is an R package designed to assess the outcomes of various gene set scoring methods when applied to single-cell RNA sequencing data. This package incorporates six distinct scoring methods that rely on the expression ranks of genes, emphasizing relative expression levels over absolute values. The implemented methods include AUCell, UCell, singscore, ssGSEA, JASMINE and Viper. Previous studies have demonstrated the robustness of these methods to variations in dataset size and composition, generating enrichment scores based solely on the relative gene expression of individual cells. By employing the robust rank aggregation algorithm, irGSEA amalgamates results from all six methods to ascertain the statistical significance of target gene sets across diverse scoring methods. The package prioritizes user-friendliness, allowing direct input of expression matrices or seamless interaction with Seurat objects. Furthermore, it facilitates a comprehensive visualization of results. The irGSEA package and its accompanying documentation are accessible on GitHub (https://github.com/chuiqin/irGSEA).

LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing.

Single-cell sequencing has revolutionized our ability to dissect the heterogeneity within tumor populations. In this study, we present LoRA-TV (Low Rank Approximation with Total Variation), a novel method for clustering tumor cells based on the read depth profiles derived from single-cell sequencing data. Traditional analysis pipelines process read depth profiles of each cell individually. By aggregating shared genomic signatures distributed among individual cells using low-rank optimization and robust smoothing, the proposed method enhances clustering performance. Results from analyses of both simulated and real data demonstrate its effectiveness compared with state-of-the-art alternatives, as supported by improvements in the adjusted Rand index and computational efficiency.

DRONet: effectiveness-driven drug repositioning framework using network embedding and ranking learning.

As one of the most vital methods in drug development, drug repositioning emphasizes further analysis and research of approved drugs based on the existing large amount of clinical and experimental data to identify new indications of drugs. However, the existing drug repositioning methods didn't achieve enough prediction performance, and these methods do not consider the effectiveness information of drugs, which make it difficult to obtain reliable and valuable results. In this study, we proposed a drug repositioning framework termed DRONet, which make full use of effectiveness comparative relationships (ECR) among drugs as prior information by combining network embedding and ranking learning. We utilized network embedding methods to learn the deep features of drugs from a heterogeneous drug-disease network, and constructed a high-quality drug-indication data set including effectiveness-based drug contrast relationships. The embedding features and ECR of drugs are combined effectively through a designed ranking learning model to prioritize candidate drugs. Comprehensive experiments show that DRONet has higher prediction accuracy (improving 87.4% on Hit@1 and 37.9% on mean reciprocal rank) than state of the art. The case analysis also demonstrates high reliability of predicted results, which has potential to guide clinical drug development.

LncRNA-disease association identification using graph auto-encoder and learning to rank.

Discovering the relationships between long non-coding RNAs (lncRNAs) and diseases is significant in the treatment, diagnosis and prevention of diseases. However, current identified lncRNA-disease associations are not enough because of the expensive and heavy workload of wet laboratory experiments. Therefore, it is greatly important to develop an efficient computational method for predicting potential lncRNA-disease associations. Previous methods showed that combining the prediction results of the lncRNA-disease associations predicted by different classification methods via Learning to Rank (LTR) algorithm can be effective for predicting potential lncRNA-disease associations. However, when the classification results are incorrect, the ranking results will inevitably be affected. We propose the GraLTR-LDA predictor based on biological knowledge graphs and ranking framework for predicting potential lncRNA-disease associations. Firstly, homogeneous graph and heterogeneous graph are constructed by integrating multi-source biological information. Then, GraLTR-LDA integrates graph auto-encoder and attention mechanism to extract embedded features from the constructed graphs. Finally, GraLTR-LDA incorporates the embedded features into the LTR via feature crossing statistical strategies to predict priority order of diseases associated with query lncRNAs. Experimental results demonstrate that GraLTR-LDA outperforms the other state-of-the-art predictors and can effectively detect potential lncRNA-disease associations. Availability and implementation: Datasets and source codes are available at http://bliulab.net/GraLTR-LDA.

Improving the performance of single-cell RNA-seq data mining based on relative expression orderings.

The advent of single-cell RNA-sequencing (scRNA-seq) provides an unprecedented opportunity to explore gene expression profiles at the single-cell level. However, gene expression values vary over time and under different conditions even within the same cell. There is an urgent need for more stable and reliable feature variables at the single-cell level to depict cell heterogeneity. Thus, we construct a new feature matrix called the delta rank matrix (DRM) from scRNA-seq data by integrating an a priori gene interaction network, which transforms the unreliable gene expression value into a stable gene interaction/edge value on a single-cell basis. This is the first time that a gene-level feature has been transformed into an interaction/edge-level for scRNA-seq data analysis based on relative expression orderings. Experiments on various scRNA-seq datasets have demonstrated that DRM performs better than the original gene expression matrix in cell clustering, cell identification and pseudo-trajectory reconstruction. More importantly, the DRM really achieves the fusion of gene expressions and gene interactions and provides a method of measuring gene interactions at the single-cell level. Thus, the DRM can be used to find changes in gene interactions among different cell types, which may open up a new way to analyze scRNA-seq data from an interaction perspective. In addition, DRM provides a new method to construct a cell-specific network for each single cell instead of a group of cells as in traditional network construction methods. DRM's exceptional performance is due to its extraction of rich gene-association information on biological systems and stable characterization of cells.

Exercise and osteoimmunology in bone remodeling.

Bones can form the scaffolding of the body, support the organism, coordinate somatic movements, and control mineral homeostasis and hematopoiesis. The immune system plays immune supervisory, defensive, and regulatory roles in the organism, which mainly consists of immune organs (spleen, bone marrow, tonsils, lymph nodes, etc.), immune cells (granulocytes, platelets, lymphocytes, etc.), and immune molecules (immune factors, interferons, interleukins, tumor necrosis factors, etc.). Bone and the immune system have long been considered two distinct fields of study, and the bone marrow, as a shared microenvironment between the bone and the immune system, closely links the two. Osteoimmunology organically combines bone and the immune system, elucidates the role of the immune system in bone, and creatively emphasizes its interdisciplinary characteristics and the function of immune cells and factors in maintaining bone homeostasis, providing new perspectives for skeletal-related field research. In recent years, bone immunology has gradually become a hot spot in the study of bone-related diseases. As a new branch of immunology, bone immunology emphasizes that the immune system can directly or indirectly affect bones through the RANKL/RANK/OPG signaling pathway, IL family, TNF-α, TGF-ß, and IFN-γ. These effects are of great significance for understanding inflammatory bone loss caused by various autoimmune or infectious diseases. In addition, as an external environment that plays an important role in immunity and bone, this study pays attention to the role of exercise-mediated bone immunity in bone reconstruction.

RANK/RANKL axis promotes migration, invasion, and metastasis of osteosarcoma via activating NF-κB pathway.

Osteosarcoma (OS) is one of the most prevalent primary bone tumors with a high degree of metastasis and poor prognosis. Epithelial-to-mesenchymal transition (EMT) is a cellular mechanism that contributes to the invasion and metastasis of cancer cells, and OS cells have been reported to exhibit EMT-like characteristics. Our previous studies have shown that the interaction between tumor necrosis factor superfamily member 11 (TNFRSF11A; also known as RANK) and its ligand TNFSF11 (also known as RANKL) promotes the EMT process in breast cancer cells. However, whether the interaction between RANK and RANKL enhances aggressive behavior by inducing EMT in OS cells has not yet been elucidated. In this study, we showed that the interaction between RANK and RANKL increased the migration, invasion, and metastasis of OS cells by promoting EMT. Importantly, we clarified that the RANK/RANKL axis induces EMT by activating the nuclear factor-kappa B (NF-κB) pathway. Furthermore, the NF-κB inhibitor dimethyl fumarate (DMF) suppressed migration, invasion, and EMT in OS cells. Our results suggest that the RANK/RANKL axis may serve as a potential tumor marker and promising therapeutic target for OS metastasis. Furthermore, DMF may have clinical applications in the treatment of lung metastasis in patients with OS.

CD169+ macrophages in lymph node and spleen critically depend on dual RANK and LTbetaR signaling.

CD169+ macrophages reside in lymph node (LN) and spleen and play an important role in the immune defense against pathogens. As resident macrophages, they are responsive to environmental cues to shape their tissue-specific identity. We have previously shown that LN CD169+ macrophages require RANKL for formation of their niche and their differentiation. Here, we demonstrate that they are also dependent on direct lymphotoxin beta (LTß) receptor (R) signaling. In the absence or the reduced expression of either RANK or LTßR, their differentiation is perturbed, generating myeloid cells expressing SIGN-R1 in LNs. Conditions of combined haploinsufficiencies of RANK and LTßR revealed that both receptors contribute equally to LN CD169+ macrophage differentiation. In the spleen, the Cd169-directed ablation of either receptor results in a selective loss of marginal metallophilic macrophages (MMMs). Using a RANKL reporter mouse, we identify splenic marginal zone stromal cells as a source of RANKL and demonstrate that it participates in MMM differentiation. The loss of MMMs had no effect on the splenic B cell compartments but compromised viral capture and the expansion of virus-specific CD8+ T cells. Taken together, the data provide evidence that CD169+ macrophage differentiation in LN and spleen requires dual signals from LTßR and RANK with implications for the immune response.

Workplace inequality is associated with status-signaling expenditure.

Regional inequality is known to magnify sensitivity to social rank. This, in turn, is shown to increase people's propensity to acquire luxury goods as a means to elevate their perceived social status. Yet existing research has focused on broad, aggregated datasets, and little is known about how individual-level measures of income interact with inequality within peer groups to affect status signaling. Using detailed financial transaction data, we construct 32,008 workplace peer groups and explore the longitudinal spending and salary data associated with 683,677 individuals. These data reveal links between people's status spending, their absolute salary, salary rank within their workplace peer group, and the inequality of their workplace salary distribution. Status-signaling luxury spending is found to be greatest among those who have higher salaries, whose workplaces exhibit higher inequality, and who occupy a lower rank position within the workplace. We propose that low-rank individuals in unequal workplaces suffer status anxiety and, if they can afford it, spend to signal higher status.

RANK rewires energy homeostasis in lung cancer cells and drives primary lung cancer.

Lung cancer is the leading cause of cancer deaths. Besides smoking, epidemiological studies have linked female sex hormones to lung cancer in women; however, the underlying mechanisms remain unclear. Here we report that the receptor activator of nuclear factor-kB (RANK), the key regulator of osteoclastogenesis, is frequently expressed in primary lung tumors, an active RANK pathway correlates with decreased survival, and pharmacologic RANK inhibition reduces tumor growth in patient-derived lung cancer xenografts. Clonal genetic inactivation of KRasG12D in mouse lung epithelial cells markedly impairs the progression of KRasG12D -driven lung cancer, resulting in a significant survival advantage. Mechanistically, RANK rewires energy homeostasis in human and murine lung cancer cells and promotes expansion of lung cancer stem-like cells, which is blocked by inhibiting mitochondrial respiration. Our data also indicate survival differences in KRasG12D -driven lung cancer between male and female mice, and we show that female sex hormones can promote lung cancer progression via the RANK pathway. These data uncover a direct role for RANK in lung cancer and may explain why female sex hormones accelerate lung cancer development. Inhibition of RANK using the approved drug denosumab may be a therapeutic drug candidate for primary lung cancer.

The miR-29-3p family suppresses inflammatory osteolysis.

Osteoclasts are the cells primarily responsible for inflammation-induced bone loss, as is particularly seen in rheumatoid arthritis. Increasing evidence suggests that osteoclasts formed under homeostatic versus inflammatory conditions may differ in phenotype. While microRNA-29-3p family members (miR-29a-3p, miR-29b-3p, miR-29c-3p) promote the function of RANKL-induced osteoclasts, the role of miR-29-3p during inflammatory TNF-α-induced osteoclastogenesis is unknown. We used bulk RNA-seq, histology, qRT-PCR, reporter assays, and western blot analysis to examine bone marrow monocytic cell cultures and tissue from male mice in which the function of miR-29-3p family members was decreased by expression of a miR-29-3p tough decoy (TuD) competitive inhibitor in the myeloid lineage (LysM-cre). We found that RANKL-treated monocytic cells expressing the miR-29-3p TuD developed a hypercytokinemia/proinflammatory gene expression profile in vitro, which is associated with macrophages. These data support the concept that miR-29-3p suppresses macrophage lineage commitment and may have anti-inflammatory effects. In correlation, when miR-29-3p activity was decreased, TNF-α-induced osteoclast formation was accentuated in an in vivo model of localized osteolysis and in a cell-autonomous manner in vitro. Further, miR-29-3p targets mouse TNF receptor 1 (TNFR1/Tnfrsf1a), an evolutionarily conserved regulatory mechanism, which likely contributes to the increased TNF-α signaling sensitivity observed in the miR-29-3p decoy cells. Whereas our previous studies demonstrated that the miR-29-3p family promotes RANKL-induced bone resorption, the present work shows that miR-29-3p dampens TNF-α-induced osteoclastogenesis, indicating that miR-29-3p has pleiotropic effects in bone homeostasis and inflammatory osteolysis. Our data supports the concept that the knockdown of miR-29-3p activity could prime myeloid cells to respond to an inflammatory challenge and potentially shift lineage commitment toward macrophage, making the miR-29-3p family a potential therapeutic target for modulating inflammatory response.

Automated individual cortical parcellation via consensus graph representation learning.

Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.

High-fidelity intravoxel incoherent motion parameter mapping using locally low-rank and subspace modeling.

PURPOSE: Intravoxel incoherent motion (IVIM) is a quantitative magnetic resonance imaging (MRI) method used to quantify perfusion properties of tissue non-invasively without contrast. However, clinical applications are limited by unreliable parameter estimates, particularly for the perfusion fraction (f) and pseudodiffusion coefficient (D*). This study aims to develop a high-fidelity reconstruction for reliable estimation of IVIM parameters. The proposed method is versatile and amenable to various acquisition schemes and fitting methods. METHODS: To address current challenges with IVIM, we adapted several advanced reconstruction techniques. We used a low-rank approximation of IVIM images and temporal subspace modeling to constrain the magnetization dynamics of the bi-exponential diffusion signal decay. In addition, motion-induced phase variations were corrected between diffusion directions and b-values, facilitating the use of high SNR real-valued diffusion data. The proposed method was evaluated in simulations and in vivo brain acquisitions in six healthy subjects and six individuals with a history of SARS-CoV-2 infection and compared with the conventionally reconstructed magnitude data. Following reconstruction, IVIM parameters were estimated voxel-wise. RESULTS: Our proposed method reduced noise contamination in simulations, resulting in a 60%, 58.9%, and 83.9% reduction in the NRMSE for D, f, and D*, respectively, compared to the conventional reconstruction. In vivo, anisotropic properties of D, f, and D* were preserved with the proposed method, highlighting microvascular differences in gray matter between individuals with a history of COVID-19 and those without (p = 0.0210), which wasn't observed with the conventional reconstruction. CONCLUSION: The proposed method yielded a more reliable estimation of IVIM parameters with less noise than the conventional reconstruction. Further, the proposed method preserved anisotropic properties of IVIM parameter estimates and demonstrated differences in microvascular perfusion in COVID-affected subjects, which weren't observed with conventional reconstruction methods.

Recent advances of NFATc1 in rheumatoid arthritis-related bone destruction: mechanisms and potential therapeutic targets.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by inflammation of the synovial tissue and joint bone destruction, often leading to significant disability. The main pathological manifestation of joint deformity in RA patients is bone destruction, which occurs due to the differentiation and proliferation of osteoclasts. The transcription factor nuclear factor-activated T cell 1 (NFATc1) plays a crucial role in this process. The regulation of NFATc1 in osteoclast differentiation is influenced by three main factors. Firstly, NFATc1 is activated through the upstream nuclear factor kappa-B ligand (RANKL)/RANK signaling pathway. Secondly, the Ca2+-related co-stimulatory signaling pathway amplifies NFATc1 activity. Finally, negative regulation of NFATc1 occurs through the action of cytokines such as B-cell Lymphoma 6 (Bcl-6), interferon regulatory factor 8 (IRF8), MAF basic leucine zipper transcription factor B (MafB), and LIM homeobox 2 (Lhx2). These three phases collectively govern NFATc1 transcription and subsequently affect the expression of downstream target genes including TRAF6 and NF-κB. Ultimately, this intricate regulatory network mediates osteoclast differentiation, fusion, and the degradation of both organic and inorganic components of the bone matrix. This review provides a comprehensive summary of recent advances in understanding the mechanism of NFATc1 in the context of RA-related bone destruction and discusses potential therapeutic agents that target NFATc1, with the aim of offering valuable insights for future research in the field of RA. To assess their potential as therapeutic agents for RA, we conducted a drug-like analysis of potential drugs with precise structures.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

idenMD-NRF: a ranking framework for miRNA-disease association identification.

Identifying miRNA-disease associations is an important task for revealing pathogenic mechanism of complicated diseases. Different computational methods have been proposed. Although these methods obtained encouraging performance for detecting missing associations between known miRNAs and diseases, how to accurately predict associated diseases for new miRNAs is still a difficult task. In this regard, a ranking framework named idenMD-NRF is proposed for miRNA-disease association identification. idenMD-NRF treats the miRNA-disease association identification as an information retrieval task. Given a novel query miRNA, idenMD-NRF employs Learning to Rank algorithm to rank associated diseases based on high-level association features and various predictors. The experimental results on two independent test datasets indicate that idenMD-NRF is superior to other compared predictors. A user-friendly web server of idenMD-NRF predictor is freely available at http://bliulab.net/idenMD-NRF/.

CBLRR: a cauchy-based bounded constraint low-rank representation method to cluster single-cell RNA-seq data.

The rapid development of single-cel+l RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for exploring biological phenomena at the single-cell level. The discovery of cell types is one of the major applications for researchers to explore the heterogeneity of cells. Some computational methods have been proposed to solve the problem of scRNA-seq data clustering. However, the unavoidable technical noise and notorious dropouts also reduce the accuracy of clustering methods. Here, we propose the cauchy-based bounded constraint low-rank representation (CBLRR), which is a low-rank representation-based method by introducing cauchy loss function (CLF) and bounded nuclear norm regulation, aiming to alleviate the above issue. Specifically, as an effective loss function, the CLF is proven to enhance the robustness of the identification of cell types. Then, we adopt the bounded constraint to ensure the entry values of single-cell data within the restricted interval. Finally, the performance of CBLRR is evaluated on 15 scRNA-seq datasets, and compared with other state-of-the-art methods. The experimental results demonstrate that CBLRR performs accurately and robustly on clustering scRNA-seq data. Furthermore, CBLRR is an effective tool to cluster cells, and provides great potential for downstream analysis of single-cell data. The source code of CBLRR is available online at https://github.com/Ginnay/CBLRR.

Metrics for evaluating differentially methylated region sets predicted from BS-seq data.

Investigating differentially methylated regions (DMRs) presented in different tissues or cell types can help to reveal the mechanisms behind the tissue-specific gene expression. The identified tissue-/disease-specific DMRs also can be used as feature markers for spotting the tissues-of-origins of cell-free DNA (cfDNA) in noninvasive diagnosis. In recent years, many methods have been proposed to detect DMRs. However, due to the lack of benchmark DMRs, it is difficult for researchers to choose proper methods and select desirable DMR sets for downstream studies. The application of DMRs, used as feature markers, can be benefited by the longer length of DMRs containing more CpG sites when a threshold is given for the methylation differences of DMRs. According to this, two metrics ($Qn$ and $Ql$), in which the CpG numbers and lengths of DMRs with different methylation differences are weighted differently, are proposed in this paper to evaluate the DMR sets predicted by different methods on BS-seq data. DMR sets predicted by eight methods on both simulated datasets and real BS-seq datasets are evaluated by the proposed metrics, the benchmark-based metrics, and the enrichment analysis of biological data, including genomic features, transcription factors and histones. The rank correlation analysis shows that the $Qn$ and $Ql$ are highly correlated to the benchmark metrics for simulated datasets and the biological data enrichment analysis for real BS-seq data. Therefore, with no need for additional biological data, the proposed metrics can help researchers selecting a more suitable DMR set on a certain BS-seq dataset.