Integrating spatial and single-cell transcriptomics to characterize the molecular and cellular architecture of the ischemic mouse brain.

Neuroinflammation is acknowledged as a pivotal pathological event after cerebral ischemia. However, there is limited knowledge of the molecular and spatial characteristics of nonneuronal cells, as well as of the interactions between cell types in the ischemic brain. Here, we used spatial transcriptomics to study the ischemic hemisphere in mice after stroke and sequenced the transcriptomes of 19,777 spots, allowing us to both visualize the transcriptional landscape within the tissue and identify gene expression profiles linked to specific histologic entities. Cell types identified by single-cell RNA sequencing confirmed and enriched the spatial annotation of ischemia-associated gene expression in the peri-infarct area of the ischemic hemisphere. Analysis of ligand-receptor interactions in cell communication revealed galectin-9 to cell-surface glycoprotein CD44 (LGALS9-CD44) as a critical signaling pathway after ischemic injury and identified microglia and macrophages as the main source of galectins after stroke. Extracellular vesicle-mediated Lgals9 delivery improved the long-term functional recovery in photothrombotic stroke mice. Knockdown of Cd44 partially reversed these therapeutic effects, inhibiting oligodendrocyte differentiation and remyelination. In summary, our study provides a detailed molecular and cellular characterization of the peri-infact area in a murine stroke model and revealed Lgals9 as potential treatment target that warrants further investigation.

DRdriver: identifying drug resistance driver genes using individual-specific gene regulatory network.

Drug resistance is one of principal limiting factors for cancer treatment. Several mechanisms, especially mutation, have been validated to implicate in drug resistance. In addition, drug resistance is heterogeneous, which makes an urgent need to explore the personalized driver genes of drug resistance. Here, we proposed an approach DRdriver to identify drug resistance driver genes in individual-specific network of resistant patients. First, we identified the differential mutations for each resistant patient. Next, the individual-specific network, which included the genes with differential mutations and their targets, was constructed. Then, the genetic algorithm was utilized to identify the drug resistance driver genes, which regulated the most differentially expressed genes and the least non-differentially expressed genes. In total, we identified 1202 drug resistance driver genes for 8 cancer types and 10 drugs. We also demonstrated that the identified driver genes were mutated more frequently than other genes and tended to be associated with the development of cancer and drug resistance. Based on the mutational signatures of all driver genes and enriched pathways of driver genes in brain lower grade glioma treated by temozolomide, the drug resistance subtypes were identified. Additionally, the subtypes showed great diversity in epithelial-mesenchyme transition, DNA damage repair and tumor mutation burden. In summary, this study developed a method DRdriver for identifying personalized drug resistance driver genes, which provides a framework for unlocking the molecular mechanism and heterogeneity of drug resistance.

LncRNA HCP5-Encoded Protein Regulates Ferroptosis to Promote the Progression of Triple-Negative Breast Cancer.

BACKGROUND: Long non-coding RNAs (lncRNAs) are a class of RNA molecules that are longer than 200 nucleotides and were initially believed to lack encoding capability. However, recent research has found open reading frames (ORFs) within lncRNAs, suggesting that they may have coding capacity. Despite this discovery, the mechanisms by which lncRNA-encoded products are involved in cancer are not well understood. The current study aims to investigate whether lncRNA HCP5-encoded products promote triple-negative breast cancer (TNBC) by regulating ferroptosis. METHODS: We used bioinformatics to predict the coding capacity of lncRNA HCP5 and conducted molecular biology experiments and a xenograft assay in nude mice to investigate the mechanism of its encoded products. We also evaluated the expression of the HCP5-encoded products in a breast cancer tissue microarray. RESULTS: Our analysis revealed that the ORF in lncRNA HCP5 can encode a protein with 132-amino acid (aa), which we named HCP5-132aa. Further experiments showed that HCP5-132aa promotes TNBC growth by regulating GPX4 expression and lipid ROS level through the ferroptosis pathway. Additionally, we found that the breast cancer patients with high levels of HCP5-132aa have poorer prognosis. CONCLUSIONS: Our study suggests that overexpression of lncRNA HCP5-encoded protein is a critical oncogenic event in TNBC, as it regulates ferroptosis. These findings could provide new therapeutic targets for the treatment of TNBC.

Genomic Landscape and Potential Regulation of RNA Editing in Drug Resistance.

Adenosine-to-inosine RNA editing critically affects the response of cancer therapies. However, comprehensive identification of drug resistance-related RNA editing events and systematic understanding of how RNA editing mediates anticancer drug resistance remain unclear. Here, 7157 differential editing sites (DESs) are identified from 98 127 informative RNA editing sites in tumor tissues, many of which are validated in cancer cell lines. Diverse editing patterns of DESs are discovered in resistant samples, which could not be fully explained by adenosine deaminase acting on RNA enzymes. Some RNA-binding proteins are identified that potentially regulate these editing events. Notably, the DESs are significantly enriched in 3'-untranslated regions (3'-UTRs). The impact of DESs in 3'-UTR on the microRNA (miRNA) regulations is explored, and some triplets (DES, miRNA, and gene) that may contribute to drug resistance are identified. In addition, it is determined that the functions of genes enriched with DESs are associated with drug resistance, such as apoptosis, drug metabolism, and DNA synthesis involved in DNA repair. An online resource (http://www.jianglab.cn/REDR/) to support convenient retrieval of DESs is also built. The findings reveal the landscape and potential regulatory mechanism of RNA editing in drug resistance, providing new therapeutic targets for reversing drug resistance.

ncFO: A Comprehensive Resource of Curated and Predicted ncRNAs Associated with Ferroptosis.

Ferroptosis is a form of regulated cell death driven by the accumulation of lipid hydroperoxides. Regulation of ferroptosis might be beneficial to cancer treatment. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play critical roles in regulating ferroptosis. Herein, we developed ncFO, the ncRNA-ferroptosis association database, to document the manually curated and predicted ncRNAs that are associated with ferroptosis. Collectively, ncFO contains 90 experimentally verified entries, including 46 microRNAs (miRNAs), 21 long non-coding RNAs (lncRNAs), and 17 circular RNAs (circRNAs). In addition, ncFO also incorporates two online prediction tools based on the regulation and co-expression of ncRNA and ferroptosis genes. Using default parameters, we obtained 3260 predicted entries, including 598 miRNAs and 178 lncRNAs, by regulation, as well as 2,592,661 predicted entries, including 967 miRNAs and 9632 lncRNAs, by ncRNA-ferroptosis gene co-expression in more than 8000 samples across 20 cancer types. The detailed information of each entry includes ncRNA name, disease, species, tissue, target, regulation, publication time, and PubMed identifier. ncFO also provides survival analysis and differential expression analysis for ncRNAs. In summary, ncFO offers a user-friendly platform to search and predict ferroptosis-associated ncRNAs, which might facilitate research on ferroptosis and discover potential targets for cancer treatment. ncFO can be accessed at http://www.jianglab.cn/ncFO/.

Estimating Metastatic Risk of Pancreatic Ductal Adenocarcinoma at Single-Cell Resolution.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by intra-tumoral heterogeneity, and patients are always diagnosed after metastasis. Thus, finding out how to effectively estimate metastatic risk underlying PDAC is necessary. In this study, we proposed scMetR to evaluate the metastatic risk of tumor cells based on single-cell RNA sequencing (scRNA-seq) data. First, we identified diverse cell types, including tumor cells and other cell types. Next, we grouped tumor cells into three sub-populations according to scMetR score, including metastasis-featuring tumor cells (MFTC), transitional metastatic tumor cells (TransMTC), and conventional tumor cells (ConvTC). We identified metastatic signature genes (MSGs) through comparing MFTC and ConvTC. Functional enrichment analysis showed that up-regulated MSGs were enriched in multiple metastasis-associated pathways. We also found that patients with high expression of up-regulated MSGs had worse prognosis. Spatial mapping of MFTC showed that they are preferentially located in the cancer and duct epithelium region, which was enriched with the ductal cells' associated inflammation. Further, we inferred cell-cell interactions, and observed that interactions of the ADGRE5 signaling pathway, which is associated with metastasis, were increased in MFTC compared to other tumor sub-populations. Finally, we predicted 12 candidate drugs that had the potential to reverse expression of MSGs. Taken together, we have proposed scMetR to estimate metastatic risk in PDAC patients at single-cell resolution which might facilitate the dissection of tumor heterogeneity.

CTpathway: a CrossTalk-based pathway enrichment analysis method for cancer research.

BACKGROUND: Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated. METHODS: To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes. RESULTS: Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy. CONCLUSIONS: Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here http://www.jianglab.cn/CTpathway/ . The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway .

DNA methylation dynamics associated with long-term isolation of simulated space travel.

Long-term isolation is one of the risk factors that astronauts will encounter in spaceflight. At present, few researches have explored DNA methylation dynamics during long-term isolation. In this study, using time series DNA methylation data from "Mars-500" mission, we conducted a multi-step analysis to investigate the characteristics and dynamic patterns of DNA methylation as well as their functional insights during long-term isolation. The results showed that genome-wide methylation changes were minimal. In the six identified DNA methylation dynamic patterns, most of significantly fluctuating CpG sites could be returned to the baseline in post-isolation, and the remaining sites persistently decreased during isolation. Next, functional enrichment analysis of genes with each pattern revealed strong functional specificity. Some patterns were also significantly associated with nervous system diseases, digestive system diseases and cancers. In conclusion, the DNA methylation dynamics during long-term isolation have great functional significance, and might be helpful for protection of astronaut health.

Single-cell RNA-seq dissects the intratumoral heterogeneity of triple-negative breast cancer based on gene regulatory networks.

Triple-negative breast cancer (TNBC) is a subtype of breast cancer with high intratumoral heterogeneity. Recent studies revealed that TNBC patients might comprise cells with distinct molecular subtypes. In addition, gene regulatory networks (GRNs) constructed based on single-cell RNA sequencing (scRNA-seq) data have demonstrated the significance for decoding the key regulators. We performed a comprehensive analysis of the GRNs for the intrinsic subtypes of TNBC patients using scRNA-seq. The copy number variations (CNVs) were inferred from scRNA-seq data and identified 545 malignant cells. The subtypes of the malignant cells were assigned based on the PAM50 model. The cell-cell communication analysis revealed that the macrophage plays a dominant role in the tumor microenvironment. Next, the GRN for each subtype was constructed through integrating gene co-expression and enrichment of transcription-binding motifs. Then, we identified the critical genes based on the centrality metrics of genes. Importantly, the critical gene ETV6 was ubiquitously upregulated in all subtypes, but it exerted diverse roles in each subtype through regulating different target genes. In conclusion, the construction of GRNs based on scRNA-seq data could help us to dissect the intratumoral heterogeneity and identify the critical genes of TNBC.

Integrative Analysis of Regulatory Module Reveals Associations of Microgravity with Dysfunctions of Multi-body Systems and Tumorigenesis.

Previous studies have demonstrated that microgravity could lead to health risks. The investigation of the molecular mechanisms from the aspect of systems biology has not been performed yet. Here, we integratively analyzed transcriptional and post-transcriptional regulations based on gene and miRNA expression profiles in human peripheral blood lymphocytes cultured in modeled microgravity. Two hundred and thirty dysregulated TF-miRNA (transcription factor and microRNA) feed-forward loops (FFLs) were identified in microgravity. The immune, cardiovascular, endocrine, nervous and skeletal system subnetworks were constructed according to the functions of dysregulated FFLs. Taking the skeletal system as an example, most of genes and miRNAs in the subnetwork were involved in bone loss. In addition, several drugs have been predicted to have potential to reduce bone loss, such as traditional Chinese medicines Emodin and Ginsenoside Rh2. Furthermore, we investigated the relationships between microgravity and 20 cancer types, and found that most of cancers might be promoted by microgravity. For example, rectum adenocarcinoma (READ) might be induced by microgravity through reducing antigen presentation and suppressing IgA-antibody-secreting cells' migration. Collectively, TF-miRNA FFL might provide a novel mechanism to elucidate the changes induced by microgravity, serve as drug targets to relieve microgravity effects, and give new insights to explore the relationships between microgravity and cancers.

ncRI: a manually curated database for experimentally validated non-coding RNAs in inflammation.

BACKGROUND: Inflammation has been considered to be central to the onset, progression, and outcome of infectious diseases, especially as one of the hallmarks of cancer. Non-coding RNAs (ncRNAs), such as miRNAs and lncRNAs, have emerged as vital regulators in control of immune and inflammatory processes, and also play important roles in the inflammatory disease and immunotherapy. RESULTS: In this study, we presented a database ncRI, which documented experimentally verified ncRNAs in inflammatory diseases, from published articles. Each entry contained the detailed information about ncRNA name, inflammatory diseases, mechanism, experimental techniques (e.g., microarray, RNA-seq, qRT-PCR), experimental samples (cell line and/or tissue), expression patterns of ncRNA (up-regulated or down-regulated), reference information (PubMed ID, year of publication, title of paper) and so on. Collectively, ncRI recorded 11,166 entries that include 1976 miRNAs, 1377 lncRNAs and 107 other ncRNAs across 3 species (human, mouse, and rat) from more than 2000 articles. All these data are free for users to search, browse and download. CONCLUSION: In summary, the presented database ncRI provides a relatively comprehensive credible repository about ncRNAs and their roles in inflammatory diseases, and will be helpful for research on immunotherapy. The ncRI is now freely available to all users at http://www.jianglab.cn/ncRI/.

ncEP: A Manually Curated Database for Experimentally Validated ncRNA-encoded Proteins or Peptides.

Noncoding RNAs (ncRNAs), such as lncRNAs, circRNAs and pri-miRNAs, play important roles in physiological and pathological processes. Recently, it was demonstrated that they could encode proteins or peptides. However, relevant information is scattered across numerous published articles, which is inconvenient for the exploration of ncRNA translation by researchers. In this study, we presented an ncEP database, which records the low-throughput experimentally validated (LTEV) proteins or peptides encoded by ncRNAs, from published articles. Collectively, ncEP contains 80 entries including 74 proteins or peptides, 22 lncRNAs, 11 circRNAs, 9 pri-miRNAs and 37 other ncRNAs across 18 species from more than 50 articles of over 2000 candidate articles. We have provided a user-friendly interface for users to search, browse, visualize, download and submit data. In summary, ncEP provides a relatively comprehensive repository of the LTEV proteins or peptides encoded by ncRNAs and will enrich the knowledge for translation process. ncEP is freely available at http://www.jianglab.cn/ncEP/.

Identification of drug resistance associated ncRNAs based on comprehensive heterogeneous network.

AIMS: Chemotherapy and molecularly targeted therapy are main strategies for treatment of cancers. However, long-term treatment makes cancer cells acquire resistance to anti-cancer drugs, which severely limits the effects of cancer treatment. NcRNAs, especially miRNAs and lncRNAs, have been reported to play key roles in drug resistance and could restore drug responses in resistant cells. MAIN METHODS: We presented a network-based framework to systematically identify drug resistance associated miRNAs and lncRNAs. First, we constructed a comprehensive heterogeneous miRNA-lncRNA regulatory network through integrating curated miRNA regulations to lncRNA, and significantly co-expressed miRNA-miRNA, lncRNA-lncRNA and miRNA-lncRNA interactions for each cancer type. Second, random walk with restart (RWR) was utilized to identify novel drug resistance associated ncRNAs. KEY FINDINGS: We predicted 470 associations of 34 miRNAs and 79 lncRNAs for 27 drugs in 10 cancer types. In addition, leave-one-out cross validation (LOOCV) demonstrated the effectiveness of the proposed approach. Next, we also demonstrated that the integrated heterogeneous cancer-specific network achieved better performance than the general curated miRNA-lncRNA regulatory network. What's more, we found that the drug resistance associated ncRNAs validated by high-throughput technology was also a reliable source for prediction. SIGNIFICANCE: We proposed a new framework to identify novel and reliable drug resistance associated ncRNAs, which provides new perspectives for drug resistance mechanism and new guidance for clinical cancer treatment.

Dissecting the m6A methylation affection on afatinib resistance in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is one of the leading causes of cancer deaths. Afatinib is the first-line anti-cancer agent for treatment of NSCLC. However, unexpected resistance has been a major obstacle for its clinical efficacy. In this study, we dissected afatinib resistance from the perspective of N6-Methyladenosine (m6A) modification. First, we depicted the m6A modification profiles for the afatinib resistant and sensitive NSCLC cell lines (H1299 and A549). We found that the sum enrichment scores of the resistant cell line (H1299) was much higher than that of the sensitive cell line (A549). Next, we identified the functionally m6A-modified genes, which were the intersection of the differentially m6A methylated genes and the differentially expressed genes between H1299 and A549, as well as negative correlation between m6A modification levels and gene expression levels. In addition, functional enrichment analysis of the functionally m6A-modified genes indicated that m6A methylation might modify cell cycle to affect afatinib response. Furthermore, the functionally m6A-modified genes were over-represented in the putative drug resistance-associated genes and the FDA-approved drug targets, and had significantly higher average degree and clustering coefficient than other genes in protein-protein interaction (PPI) network. We also identified five network modules, which were all related to drug resistance functions. Finally, survival analysis demonstrated that m6A modification could affect prognosis of NSCLC patients. In conclusion, we conducted a first attempt to dissect m6A methylation affection on afatinib resistance in NSCLC, and brought inspiration for the study of epigenetic roles in drug resistance.

LncRNA HCP5 promotes triple negative breast cancer progression as a ceRNA to regulate BIRC3 by sponging miR-219a-5p.

Emerging evidence has suggested that long noncoding RNAs (lncRNA) involved in the development and progression of cancer. Triple negative breast cancer (TNBC) was an aggressive type of breast cancer with high rates of cancer recurrence and metastasis. The pathogenesis of TNBC is largely unknown. Recent studies suggested that lncRNA HCP5 plays an important role in carcinogenesis. The purpose of this study was to examine the function and mechanism of HCP5 in TNBC. We observed that HCP5 was upregulated in TNBC cell lines and specimens. HCP5 knockdown induced TNBC cell apoptosis, and inhibited cell proliferation and orthotopic xenograft tumor growth. RNA sequencing and antibody array suggested that HCP5 achieves its functions through regulating apoptosis pathway. Bioinformatics, luciferase and RIP experiments proved that both HCP5 and BIRC3 could competitively bind to miR-219a-5p. Increased BIRC3 and decreased miR-219a-5p were observed in TNBC tissues and cell lines. We then performed gain- and loss-of-function studies as well as rescue experiments in TNBC cells. The decrease of proliferation and migration due to HCP5 knockdown could be rescued when miR-219a-5p inhibitor or BIRC3 was transfected and vice versa. Our study suggested that lncRNA HCP5 promotes TNBC progression as a ceRNA to regulate BIRC3 by sponging miR-219a-5p. In a word, we revealed a new signaling pathway to mediate TNBC, and provided HCP5 as a new target for improving treatment of TNBC.

Drug Resistance-Related Competing Interactions of lncRNA and mRNA across 19 Cancer Types.

Drug resistance is a common cause of treatment failure in cancer therapy, and molecular mechanisms need further exploration. Competing endogenous RNAs (ceRNAs) can influence drug response by participating in various biological processes, including regulation of cell cycle, signal transduction, and so on. In this study, we systematically explored resistance from the perspective of ceRNA modules. First, we constructed a general ceRNA network, involving 83 long non-coding RNAs (lncRNAs) and 379 mRNAs. Next, we identified the drug resistance-related modules for 138 drugs and 19 cancer types, totaling 758 drug-cancer conditions. Function analysis showed that resistance-related biological processes were enriched in these modules, such as regulation of cell proliferation, DNA damage repair, and so on. Pan-drug and pan-cancer analyses revealed some common and specific modules across multiple drugs or cancers. In addition, we also found that drug pairs with common modules have similar structure, indicating high risk for multidrug resistance (MDR). Finally, we speculated that ceRNA pair GAS5-RPL8 could regulate drug resistance because low expression of GAS5 would enhance microRNA (miRNA)-mediated inhibition of RPL8. In total, we investigated the drug resistance by using ceRNA modules and proposed that ceRNA modules may be new markers for drug resistance that indicated a possible novel mechanism.

Pathway enrichment analysis approach based on topological structure and updated annotation of pathway.

Pathway enrichment analysis has been widely used to identify cancer risk pathways, and contributes to elucidating the mechanism of tumorigenesis. However, most of the existing approaches use the outdated pathway information and neglect the complex gene interactions in pathway. Here, we first reviewed the existing widely used pathway enrichment analysis approaches briefly, and then, we proposed a novel topology-based pathway enrichment analysis (TPEA) method, which integrated topological properties and global upstream/downstream positions of genes in pathways. We compared TPEA with four widely used pathway enrichment analysis tools, including database for annotation, visualization and integrated discovery (DAVID), gene set enrichment analysis (GSEA), centrality-based pathway enrichment (CePa) and signaling pathway impact analysis (SPIA), through analyzing six gene expression profiles of three tumor types (colorectal cancer, thyroid cancer and endometrial cancer). As a result, we identified several well-known cancer risk pathways that could not be obtained by the existing tools, and the results of TPEA were more stable than that of the other tools in analyzing different data sets of the same cancer. Ultimately, we developed an R package to implement TPEA, which could online update KEGG pathway information and is available at the Comprehensive R Archive Network (CRAN): https://cran.r-project.org/web/packages/TPEA/.

Identification of transcription factor-miRNA-lncRNA feed-forward loops in breast cancer subtypes.

Previous studies have demonstrated that transcription factor-miRNA-gene feed-forward loops (FFLs) played important roles in tumorigenesis. However, the lncRNA-involved FFLs have not been explored very well. Understanding the characteristics of lncRNA-involved FFLs in breast cancer subtypes may be a key question with clinical implications. In this study, we firstly constructed an integrated background regulatory network. Then, based on mRNA, miRNA, and lncRNA differential expression, we identified 147, 140, 284, 1031 dysregulated FFLs for luminal A, luminal B, HER2+ and basal-like subtype of breast cancer, respectively. Importantly, the known breast cancer-associated lncRNAs and miRNAs were enriched in the identified dysregulated FFLs. Through merging the dysregulated FFLs, we constructed the regulatory sub-network for each subtype. We found that all sub-networks were enriched in the well-known cancer-related pathways, such as cell cycle, pathways in cancer. Next, we also identified potential prognostic FFLs for subtypes of breast cancer, such as the hsa-miR-182-5p_JUN_XIST in basal-like subtype. Finally, we also discussed the potential application of inferring the candidate drugs for breast cancer treatment through modulating the lncRNA expression in the dysregulated FFLs. Collectively, this study elucidated the roles of lncRNA-involved FFLs in breast cancer subtypes, which could contribute to understanding breast cancer pathogenesis and improving the treatment.

Inferring Novel Autophagy Regulators Based on Transcription Factors and Non-Coding RNAs Coordinated Regulatory Network.

Autophagy is a complex cellular digestion process involving multiple regulators. Compared to post-translational autophagy regulators, limited information is now available about transcriptional and post-transcriptional regulators such as transcription factors (TFs) and non-coding RNAs (ncRNAs). In this study, we proposed a computational method to infer novel autophagy-associated TFs, micro RNAs (miRNAs) and long non-coding RNAs (lncRNAs) based on TFs and ncRNAs coordinated regulatory (TNCR) network. First, we constructed a comprehensive TNCR network, including 155 TFs, 681 miRNAs and 1332 lncRNAs. Next, we gathered the known autophagy-associated factors, including TFs, miRNAs and lncRNAs, from public data resources. Then, the random walk with restart (RWR) algorithm was conducted on the TNCR network by using the known autophagy-associated factors as seeds and novel autophagy regulators were finally prioritized. Leave-one-out cross-validation (LOOCV) produced an area under the curve (AUC) of 0.889. In addition, functional analysis of the top 100 ranked regulators, including 55 TFs, 26 miRNAs and 19 lncRNAs, demonstrated that these regulators were significantly enriched in cell death related functions and had significant semantic similarity with autophagy-related Gene Ontology (GO) terms. Finally, extensive literature surveys demonstrated the credibility of the predicted autophagy regulators. In total, we presented a computational method to infer credible autophagy regulators of transcriptional factors and non-coding RNAs, which would improve the understanding of processes of autophagy and cell death and provide potential pharmacological targets to autophagy-related diseases.