Intermediate complex morphophysiological dormancy in seeds of Aconitum barbatum (Ranunculaceae).

BACKGROUND: Seed dormancy and germination are key components of plant regeneration strategies. Aconitum barbatum is a plant commonly found in northeast China. Although it has potential for use in gardening and landscaping, its seed dormancy and regeneration strategy, which adapt to its natural habitat, are not well understood. Our aim was to identify conditions for breaking A. barbatum seed dormancy and determine its dormancy type. Embryo growth and germination were determined by collecting seeds over time in the field. Laboratory experiments that control light, temperature, and stratification period were conducted to assess dormancy breaking and germination, and GA3 was used to identify dormancy type. RESULTS: Seeds of A. barbatum have undeveloped embryos with physiological dormancy at maturity in autumn. The embryo-to-seed length ratio increases from 0.33 to 0.78 before the emergence of the radical. Under natural environmental conditions, embryo development begins in early winter. Laboratory experiments have shown that long-term incubation under 4 °C (cold stratification) promotes embryo development and seed dormancy break. With an extension of cold stratification, an increase in germination percentages was observed when seeds were transferred from 4 °C to warmer temperatures. Seeds exposed to light during incubation show a higher germination percentage than those kept in the dark. Seed germination can also be enhanced by a 100 mg/L GA3 concentration. CONCLUSIONS: Seeds of A. barbatum display intermediate complex morphophysiological dormancy at maturity. In addition to the underdeveloped embryo, there are also physiological barriers that prevent the embryo from germinating. Dormancy breaking of A. barbatum seeds can be achieved by natural winter cold stratification, allowing seeds to germinate and sprout seedlings at the beginning of the following growing season. Our findings provide valuable insights into the seed dormancy and regeneration strategy of A. barbatum, which could facilitate its effective utilization in gardening and landscaping.

Systematical identification of cell-specificity of CTCF-gene binding based on epigenetic modifications.

The CCCTC-binding factor (CTCF) mediates transcriptional regulation and implicates epigenetic modifications in cancers. However, the systematically unveiling inverse regulatory relationship between CTCF and epigenetic modifications still remains unclear, especially the mechanism by which histone modification mediates CTCF binding. Here, we developed a systematic approach to investigate how epigenetic changes affect CTCF binding. Through integration analysis of CTCF binding in 30 cell lines, we concluded that CTCF generally binds with higher intensity in normal cell lines than that in cancers, and higher intensity in genome regions closed to transcription start sites. To facilitate the better understanding of their associations, we constructed linear mixed-effect models to analyze the effects of the epigenetic modifications on CTCF binding in four cancer cell lines and six normal cell lines, and identified seven epigenetic modifications as potential epigenetic patterns that influence CTCF binding intensity in promoter regions and six epigenetic modifications in enhancer regions. Further analysis of the effects in different locations revealed that the epigenetic regulation of CTCF binding was location-specific and cancer cell line-specific. Moreover, H3K4me2 and H3K9ac showed the potential association with immune regulation of disease. Taken together, our method can contribute to improve the understanding of the epigenetic regulation of CTCF binding and provide potential therapeutic targets for treating tumors associated with CTCF.

Identification of Prognostic Gene Signatures by Developing a scRNA-Seq-Based Integration Approach to Predict Recurrence and Chemotherapy Benefit in Stage II-III Colorectal Cancer.

Prospective identification of robust biomarkers related to prognosis and adjuvant chemotherapy has become a necessary and critical step to predict the benefits of adjuvant therapy for patients with stage II-III colorectal cancer (CRC) before clinical treatment. We proposed a single-cell-based prognostic biomarker recognition approach to identify and construct CRC up- and down-regulated prognostic signatures (CUPsig and CDPsig) by integrating scRNA-seq and bulk datasets. We found that most genes in CUPsig and CDPsig were known disease genes, and they had good prognostic abilities in CRC validation datasets. Multivariate analysis confirmed that they were two independent prognostic factors of disease-free survival (DFS). Significantly, CUPsig and CDPsig could effectively predict adjuvant chemotherapy benefits in drug-treated validation datasets. Additionally, they also performed well in patients with CMS4 subtype. Subsequent analysis of drug sensitivity showed that expressions of these two signatures were significantly associated with the sensitivities of CRC cell lines to multiple drugs. In summary, we proposed a novel prognostic biomarker identification approach, which could be used to identify novel prognostic markers for stage II-III CRC patients who will undergo adjuvant chemotherapy and facilitate their further personalized treatments.

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.

Identification of a Combined RNA Prognostic Signature in Adenocarcinoma of the Lung.

BACKGROUND Adenocarcinoma of the lung is a type of non-small cell lung cancer (NSCLC). Clinical outcome is associated with tumor grade, stage, and subtype. This study aimed to identify RNA expression profiles, including long noncoding RNA (lncRNA), microRNA (miRNA), and mRNA, associated with clinical outcome in adenocarcinoma of the lung using bioinformatics data. MATERIAL AND METHODS The miRNA and mRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and lncRNA expression profiles were downloaded from The Atlas of Noncoding RNAs in Cancer (TANRIC) database. The independent dataset, the Gene Expression Omnibus (GEO) accession dataset, GSE81089, was used. RNA expression profiles were used to identify comprehensive prognostic RNA signatures based on patient survival time. RESULTS From 7,704 lncRNAs, 787 miRNAs, and 28,937 mRNAs of 449 patients, four joint RNA molecular signatures were identified, including RP11-909N17.2, RP11-14N7.2 (lncRNAs), MIR139 (miRNA), KLHDC8B (mRNA). The random forest (RF) classifier was used to test the prediction ability of patient survival risk and showed a good predictive accuracy of 71% and also showed a significant difference in overall survival (log-rank P=0.0002; HR, 3.54; 95% CI, 1.74-7.19). The combined RNA signature also showed good performance in the identification of patient survival in the validation and independent datasets. CONCLUSIONS This study identified four RNA sequences as a prognostic molecular signature in adenocarcinoma of the lung, which may also provide an increased understanding of the molecular mechanisms underlying the pathogenesis of this malignancy.

Cooperative genomic alteration network reveals molecular classification across 12 major cancer types.

The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification.

Systematical Identification of Breast Cancer-Related Circular RNA Modules for Deciphering circRNA Functions Based on the Non-Negative Matrix Factorization Algorithm.

Circular RNA (circRNA), a kind of special endogenous RNA, has been shown to be implicated in crucial biological processes of multiple cancers as a gene regulator. However, the functional roles of circRNAs in breast cancer (BC) remain to be poorly explored, and relatively incomplete knowledge of circRNAs handles the identification and prediction of BC-related circRNAs. Towards this end, we developed a systematic approach to identify circRNA modules in the BC context through integrating circRNA, mRNA, miRNA, and pathway data based on a non-negative matrix factorization (NMF) algorithm. Thirteen circRNA modules were uncovered by our approach, containing 4164 nodes (80 circRNAs, 2703 genes, 63 miRNAs and 1318 pathways) and 67,959 edges in total. GO (Gene Ontology) function screening identified nine circRNA functional modules with 44 circRNAs. Within them, 31 circRNAs in eight modules having direct relationships with known BC-related genes, miRNAs or disease-related pathways were selected as BC candidate circRNAs. Functional enrichment results showed that they were closely related with BC-associated pathways, such as 'KEGG (Kyoto Encyclopedia of Genes and Genomes) PATHWAYS IN CANCER', 'REACTOME IMMUNE SYSTEM' and 'KEGG MAPK SIGNALING PATHWAY', 'KEGG P53 SIGNALING PATHWAY' or 'KEGG WNT SIGNALING PATHWAY', and could sever as potential circRNA biomarkers in BC. Comparison results showed that our approach could identify more BC-related functional circRNA modules in performance. In summary, we proposed a novel systematic approach dependent on the known disease information of mRNA, miRNA and pathway to identify BC-related circRNA modules, which could help identify BC-related circRNAs and benefits treatment and prognosis for BC patients.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer.

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Identification of a five-lncRNA signature for predicting the risk of tumor recurrence in patients with breast cancer.

Long non-coding RNAs (lncRNAs) are a major class of non-coding RNAs, and the functional deregulations of lncRNAs have been shown to be associated with the development and progression of BC. In this work, we conduct an integrative analysis on five re-annotated lncRNA expression datasets from the Gene Expression Omnibus (GEO) which included a total of 891 BC samples. We identified a five-lncRNA signature that was significantly associated with DFS in the training cohort of 327 patients. We found the five-lncRNA signature could effectively stratify patients in the training dataset into high- and low-risk groups with significantly different DFS (p = 3.29 × 10-5 , log-rank test). The five-lncRNA signature was effectively validated in four independent cohorts, and prognostic analysis results showed that the five-lncRNA signature was independent of clinical prognostic factors, such as BC subtypes and adjuvant treatments. Furthermore, GSEA suggested that the five-lncRNA signature was involved in BC metastasis-related pathways. Our findings indicate that these five lncRNAs may be implicated in BC pathogenesis, and further, these lncRNAs may potentially serve as novel candidate biomarkers for the identification of BC patients at high risk for tumor recurrence.

Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data.

The driver genetic aberrations collectively regulate core cellular processes underlying cancer development. However, identifying the modules of driver genetic alterations and characterizing their functional mechanisms are still major challenges for cancer studies. Here, we developed an integrative multi-omics method CMDD to identify the driver modules and their affecting dysregulated genes through characterizing genetic alteration-induced dysregulated networks. Applied to glioblastoma (GBM), the CMDD identified a core gene module of 17 genes, including seven known GBM drivers, and their dysregulated genes. The module showed significant association with shorter survival of GBM. When classifying driver genes in the module into two gene sets according to their genetic alteration patterns, we found that one gene set directly participated in the glioma pathway, while the other indirectly regulated the glioma pathway, mostly, via their dysregulated genes. Both of the two gene sets were significant contributors to survival and helpful for classifying GBM subtypes, suggesting their critical roles in GBM pathogenesis. Also, by applying the CMDD to other six cancers, we identified some novel core modules associated with overall survival of patients. Together, these results demonstrate integrative multi-omics data can identify driver modules and uncover their dysregulated genes, which is useful for interpreting cancer genome.

Integrating epigenetic marks for identification of transcriptionally active miRNAs.

MicroRNAs have been identified as important regulators involved in biological processes and human diseases. We proposed a computational approach to systematic identification of active promoters of miRNAs by active models using epigenetic characteristics at active promoters of protein-coding genes together with a genomic context-based filtering step in nine human cell types, which were validated to exhibit greater conservation, more overlap with CAGE-identified TSSs, more conserved TFBSs and higher transcription factor binding signal intensities. Furthermore, expression analysis showed discordance between transcriptional activation of miRNAs and expression of their precursor and mature forms, indicating that precursor and mature miRNA expression is insufficient to account for transcriptional activation of miRNAs. Compared to other methods, our approach identified higher percentages of active miRNAs with CAGE-detected TSS activity and primary transcript expression, further supporting the validity of our approach, which will be valuable to understand the biological roles of miRNAs in specific cell contexts.

Prioritizing cancer-related key miRNA-target interactions by integrative genomics.

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.

Differential whole-genome doubling based signatures for improvement on clinical outcomes and drug response in patients with breast cancer.

Whole genome doublings (WGD), a hallmark of human cancer, is pervasive in breast cancer patients. However, the molecular mechanism of the complete impact of WGD on survival and treatment response in breast cancer remains unclear. To address this, we performed a comprehensive and systematic analysis of WGD, aiming to identify distinct genetic alterations linked to WGD and highlight its improvement on clinical outcomes and treatment response for breast cancer. A linear regression model along with weighted gene co-expression network analysis (WGCNA) was applied on The Cancer Genome Atlas (TCGA) dataset to identify critical genes related to WGD. Further Cox regression models with random selection were used to optimize the most useful prognostic markers in the TCGA dataset. The clinical implication of the risk model was further assessed through prognostic impact evaluation, tumor stratification, functional analysis, genomic feature difference analysis, drug response analysis, and multiple independent datasets for validation. Our findings revealed a high aneuploidy burden, chromosomal instability (CIN), copy number variation (CNV), and mutation burden in breast tumors exhibiting WGD events. Moreover, 247 key genes associated with WGD were identified from the distinct genomic patterns in the TCGA dataset. A risk model consisting of 22 genes was optimized from the key genes. High-risk breast cancer patients were more prone to WGD and exhibited greater genomic diversity compared to low-risk patients. Some oncogenic signaling pathways were enriched in the high-risk group, while primary immune deficiency pathways were enriched in the low-risk group. We also identified a risk gene, ANLN (anillin), which displayed a strong positive correlation with two crucial WGD genes, KIF18A and CCNE2. Tumors with high expression of ANLN were more prone to WGD events and displayed worse clinical survival outcomes. Furthermore, the expression levels of these risk genes were significantly associated with the sensitivities of BRCA cell lines to multiple drugs, providing valuable insights for targeted therapies. These findings will be helpful for further improvement on clinical outcomes and contribution to drug development in breast cancer.

Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data.

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.

Establishment of comorbidity target pools and prediction of drugs candidate for multiple sclerosis and autoimmune thyroid diseases based on GWAS and transcriptome data.

BACKGROUND: Clinical observation has revealed that multiple sclerosis (MS) and autoimmune thyroid disease (AITD) are strongly correlated. The aim of this study was to explore the shared molecular causes of MS and AITD, and to conduct drug rearrangement on this basis, search for comorbidity drugs and feasible drugs for mutual reference between the two diseases. METHODS: Based on genome-wide association study (GWAS) data and transcriptome data, susceptibility genes and differentially expressed genes related to MS and AITD were identified by bioinformatics analysis. Pathway enrichment, gene ontology (GO), protein-protein interaction analysis, and gene-pathway network analysis of the above genes were performed to identify a common target pool, including common genes, common hub genes, and common pathways, and to explore the specific pathogenesis of the two diseases, respectively. Drugs that target the common pathways/genes were identified through the Comparative Toxicogenomics Database (CTD), DrugBank database, and Drug-Gene Interaction (DGI) Database. Common hub genes were compared with the target genes of drugs approved for treating MS/AITD and drugs under investigation identified by DrugBank and ClinicalTrials, respectively. RESULTS: We identified a pool of shared targets containing genes and pathways, including 46 common genetic susceptibility pathways and 9 common differentially expressed pathways, including JAK-STAT signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, PD-L1 expression and PD-1 checkpoint pathway in cancer, etc. In addition, a total of 29 hub genes, including TYK2, JAK1, STAT3, IL2RA, HLA-DRB1, and TLR3, were identified. Drugs approved for treating MS or AITD, such as methylprednisolone, cyclophosphamide, glatiramer, natalizumab, and methimazole, can target the shared genes and pathways, among which methylprednisolone and cyclophosphamide have been shown to be beneficial for the treatment of the two diseases, indicating that these drugs have the potential to become a priority in the treatment of comorbidities. Moreover, drugs targeting multiple common genes and pathways, including tacrolimus, deucravacitinib, and nivolumab, were identified as potential drugs for the treatment of MS, AITD, and their comorbidities. CONCLUSION: We observed that T-cell activation-related genes and pathways play a major role in the pathogenesis of both MS and AITD, which may be the molecular basis of the comorbidity. Moreover, we identified a variety of drugs which may be used as priority or potential treatments for comorbidities.

Single-cell RNA sequencing identifies critical transcription factors of tumor cell invasion induced by hypoxia microenvironment in glioblastoma.

Rationale: Glioblastoma (GBM) is an aggressive malignant primary brain cancer with poor survival. Hypoxia is a hallmark of GBM, which promotes tumor cells spreading (invasion) into the healthy brain tissue. Methods: To better elucidate the influence of hypoxia on GBM invasion, we proposed a data-driven modeling framework for predicting cellular hypoxia (CHPF) by integrating single cell transcriptome profiling and hypoxia gene signatures. Results: We characterized the hypoxia status landscape of GBM cells and observed that hypoxic cells were only present in the tumor core. Then, by investigating the cell-cell communication between immune cells and tumor cells, we discovered significant interaction between macrophages and tumor cells in hypoxic microenvironment. Notably, we dissected the functional heterogeneity of tumor cells and identified a hypoxic subpopulation that had highly invasive potential. By constructing cell status specific gene regulatory networks, we further identified 14 critical regulators of tumor invasion induced by hypoxic microenvironment. Finally, we confirmed that knocking down two critical regulators CEBPD and FOSL1 could reduce the invasive ability of GBM under hypoxic conditions. Additionally, we revealed the therapeutic effect of Axitinib and Entinostat through the mice model. Conclusion: Our work revealed the critical regulators in hypoxic subpopulation with high invasive potential in GBM, which may have practical implications for clinical targeted-hypoxia cancer drug therapy.

Differential expression pattern-based prioritization of candidate genes through integrating disease-specific expression data.

Expression data can reveal subtle transcriptional changes that mediate the clinical phenotype of the disease resulting from interaction between genetic and environmental factors, which offers us a new perspective to prioritize candidate genes. Here, we proposed a novel differential expression pattern (DEP)-based approach integrating numerous disease-specific expression data sets for prioritizing candidate genes. Using breast cancer as a case study, we validated the efficiency of our approach through integrating 12 breast cancer-related expression data sets based on the leave-one-out cross-validation. Particularly, prioritization based on subtype-specific expression data sets could generate significantly higher performance. The performance could be continually improved with the increasing expression data sets regardless of platform heterogeneity. We further validated the robustness of this approach by application to prostate cancer. Additionally, our approach showed higher performance in comparison with other expression-based approaches and better capability of identification of less well-studied disease genes in comparison with other integration-based approaches.

Identification of multiple sclerosis-related genes regulated by EBV-encoded microRNAs in B cells.

BACKGROUND: Multiple sclerosis (MS) is driven by the interaction between genetic susceptibility and environmental triggers, particularly to Epstein-Barr virus (EBV) infection. EBV-encoded microRNAs (miRNAs) are abundantly expressed in all stages of EBV infection and latency, which can target both viral and host cellular mRNAs, allowing EBV-infected B cells to evade the host immune response. However, it remains a big gap to understand the roles of EBV miRNAs and their target genes in MS pathogenesis. METHODS: We investigated the correlation between MS-related viruses infection and MS risk quantitatively by systematic analysis. All MS-related genes in B cells were obtained by integrating MS susceptibility genes and differentially expressed genes from B cells. In comparison with differentially expressed genes from B cells after EBV infection in vitro, we confirmed EBV-regulated, MS-related genes. Subsequently, we obtained target EBV miRNAs which can regulate these genes from several online databases. By constructing pathway-pathway, pathway-gene and protein-protein interaction networks, we further screened out MS-related genes and risk pathways regulated by EBV miRNAs. Finally, we identified target EBV miRNAs which may directly regulate MS-related genes through bioinformatic prediction. RESULTS: EBV infection showed the strongest correlation with MS risk. A total of 568 MS-related genes and 80 risk pathways in B cells were obtained. We then identified 112 MS-related genes and 18 associated risk pathways that EBV was involved in. In addition, 33 human target genes regulated by 33 EBV miRNAs overlapped with EBV-regulated, MS-related genes. Finally, 15 target EBV miRNAs and their regulated, 7 MS-related genes (MALT1, BCL10, IFNGR2, STAT3, CXCR4, PTK2B and FOXP1) have been confirmed as crucial pathogenic molecules, which could promote the initiation and development of MS through NF-kappa B (MALT1 and BCL10) and PD-L1/PD-1 (IFNGR2 and STAT3) pathways. Surprisingly, ebv-miR-BHRF1-2-5p directly targeting MALT1 was confirmed by our experiments, and FOXP1 was identified as a target gene of ebv-miR-BART11. CONCLUSIONS: This work identified the target EBV miRNAs and their regulated, MS-related genes as well as risk pathways, which may provide a novel insight into discovering diagnostic biomarkers and therapeutic targets for MS.

Identification of common susceptibility genes and drug target genes in multiple sclerosis, systemic lupus erythematosus, and rheumatoid arthritis and its value to guide clinical treatment.

BACKGROUND: Multiple sclerosis (MS) is an autoimmune-mediated demyelinating disease of the white matter in the central nervous system (CNS). In clinical practice, it was found that MS is associated with a variety of autoimmune diseases, such as systemic lupus erythematosus (SLE), rheumatoid arthritis (RA). The aim of this study was to identify common susceptibility genes and drug target genes in MS, SLE, and RA and to provide new insights into treatment. METHODS: The common susceptibility genes of MS, SLE, and RA were obtained by searching the GWAS database and using microarray data to validate. The Genome Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, and the common KEGG pathways were selected. All the genes enriched in the common pathways were obtained and intersected with the susceptibility genes of MS, SLE, and RA to obtain the pathway genes of them respectively, and found the common pathogenesis-related genes of the three diseases. By reviewing the literature and the DrugBank database, the drugs and drug target genes that have been approved for the treatment of the three diseases were obtained. Finally, the DGIdb database was searched to predict potential drugs or molecular compounds that interact with susceptibility genes common to MS, SLE, and RA. RESULTS: In MS, SLE, and RA, there were 46 common susceptibility genes, of which 23 were significantly differentially expressed in the microarray expression profile. Then, 2117 genes were obtained in the 42 common pathways, among which 17 pathogenesis-related genes were common in MS, SLE, and RA. The Drugbank database was used to obtain 29 drug target genes for MS, 43 drug target genes for RA, and 20 drug target genes for SLE. DHODH is a common drug target gene for MS, SLE, and RA, and its corresponding drugs are Leflunomide and Teriflunomide. A total of 13 genes and 366 potential drugs or molecular compounds were predicted to have interaction relationships after searching the DGIdb database. CONCLUSION: The common susceptibility genes and drug target genes among MS, SLE, and RA provide a theoretical basis for the co-morbidity phenomenon of the three diseases in clinical practice and may guide the clinical treatment.

Identification of potential key genes and immune infiltration in Multiple sclerosis.

BACKGROUND: Multiple sclerosis (MS) is an extremely serious autoimmune disease of the nervous system. Extensive evidence indicated that immune system activation plays a crucial role in the development of MS. However, the exact mechanism of MS is still not well understood. Our objective was to identify potential key genes of Multiple sclerosis (MS) via bioinformatic analysis and apply CIBERSORT algorithms to calculate the proportion of infiltrating immune cells. METHODS: The differentially expressed genes (DEGs) were analyzed from two public datasets, which included 99 MS, 45 controls and 133 MS, 79 controls. Then the common DEGs were obtained (p < 0.05). LASSO regression analysis was performed on common DEGs of GSE17048. The receiver operating characteristic (ROC) curves were created. The key genes were screened based on area under the receiver operating characteristic curve (AUC). CIBERSORT algorithms were used to explore the immune infiltration in MS. RESULTS: 516 common DEGs were screened from two public datasets. And then 54 signature genes were obtained by constructing LASSO model. MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 with the largest AUC values were selected as the key genes. Neutrophils, Monocytes, resting memory CD4+ T cells, CD8+ T cells and resting NK cells accounted for a large proportion of infiltrating immune cells in MS. CONCLUSION: MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 may be closely related pathogenesis of MS, and may represent new candidate biomarkers. In addition, immune cell infiltration may also play an important role in the progression of MS.