Differential Co-expression Research Articles

The Correlation Patterns of miRNA Expression with Targeted mRNA Transcripts in Glioma Patients with Wild-Type and Mutated Isocitrate Dehydrogenase (IDH) Genotypes.

Low-grade gliomas are divided into two main genetic phenotypes based on the presence or absence of mutations in the isocitrate dehydrogenase (IDH) genes. The mutated IDH phenotype (IDHmut), in contrast to the wild-type phenotype (IDHwt), is characterized by a more positive response to pharmacological intervention and a significantly longer survival time. In this study, we analyzed the differential co-expression of 225,000 microRNA-mRNA pairs at the level of correlations between microRNA levels and their potential mRNA targets. Analysis of the associative relationships of individual representatives of the selected pairs revealed that the level of mRNAs encoded by the ELN, ARL4C, C9orf64, PLAT, and FKBP9 genes associated with aggressive progression of glioma was increased in the IDHwt group. Meanwhile, the levels of miRNA-182, miRNA-455, and miRNA-891a associated with the negative prognosis in glioma were generally increased in the IDHmut group. Most (16/21) of the detected 21 microRNA-mRNA pairs with significant difference in regulation between IDHwt and IDHmut glioma samples had a weak or moderate positive correlation in IDHmut samples and a negative correlation in IDHwt samples. Therefore, our findings indicate that glioma samples from the IDHmut group with a positive prognosis potentially have a significantly less pronounced ability to microRNA-mediated regulation. We further suggest that such physiological disorders can lead to reduced tumor viability, resulting in an increased ability of the host to resist the spread of a malignant transformation of this genetic phenotype.

Differential expression and co-expression reveal cell types relevant to genetic disorder phenotypes.

Knowledge of the specific cell types affected by genetic alterations in rare diseases is crucial for advancing diagnostics and treatments. Despite significant progress, the cell types involved in the majority of rare disease manifestations remain largely unknown. In this study, we integrated scRNA-seq data from non-diseased samples with known genetic disorder genes and phenotypic information to predict the specific cell types disrupted by pathogenic mutations for 482 disease phenotypes. We found significant phenotype-cell type associations focusing on differential expression and co-expression mechanisms. Our analysis revealed that 13% of the associations documented in the literature were captured through differential expression, while 42% were elucidated through co-expression analysis, also uncovering potential new associations. These findings underscore the critical role of cellular context in disease manifestation and highlight the potential of single-cell data for the development of cell-aware diagnostics and targeted therapies for rare diseases. All code generated in this work is available at https://github.com/SergioAlias/sc-coex. Supplementary data are available at Bioinformatics online.

Partial correlation network analysis identifies coordinated gene expression within a regional cluster of COPD genome-wide association signals.

Chronic obstructive pulmonary disease (COPD) is a complex disease influenced by well-established environmental exposures (most notably, cigarette smoking) and incompletely defined genetic factors. The chromosome 4q region harbors multiple genetic risk loci for COPD, including signals near HHIP, FAM13A, GSTCD, TET2, and BTC. Leveraging RNA-Seq data from lung tissue in COPD cases and controls, we estimated the co-expression network for genes in the 4q region bounded by HHIP and BTC (~70MB), through partial correlations informed by protein-protein interactions. We identified several co-expressed gene pairs based on partial correlations, including NPNT-HHIP, BTC-NPNT and FAM13A-TET2, which were replicated in independent lung tissue cohorts. Upon clustering the co-expression network, we observed that four genes previously associated to COPD: BTC, HHIP, NPNT and PPM1K appeared in the same network community. Finally, we discovered a sub-network of genes differentially co-expressed between COPD vs controls (including FAM13A, PPA2, PPM1K and TET2). Many of these genes were previously implicated in cell-based knock-out experiments, including the knocking out of SPP1 which belongs to the same genomic region and could be a potential local key regulatory gene. These analyses identify chromosome 4q as a region enriched for COPD genetic susceptibility and differential co-expression.

Integrative genomics approach identifies glial transcriptomic dysregulation and risk in the cortex of individuals with Alcohol Use Disorder.

Alcohol use disorder (AUD) is a prevalent neuropsychiatric disorder that is a major global health concern, affecting millions of people worldwide. Past molecular studies of AUD used underpowered single cell analysis or bulk homogenates of postmortem brain tissue, which obscures gene expression changes in specific cell types. Here we performed single nuclei RNA-sequencing analysis of 73 post-mortem samples from individuals with AUD (N=36, Nnuclei= 248,873) and neurotypical controls (N=37, Nnuclei= 210,573) in both sexes across two institutional sites. We identified 32 clusters and found widespread cell type-specific transcriptomic changes across the cortex in AUD, particularly affecting glia. We found the greatest dysregulation in novel microglial and astrocytic subtypes that accounted for the majority of differential gene expression and co-expression modules linked to AUD. Analysis for cell type-specific enrichment of aggregate genetic risk for AUD identified subtypes of glial and neuronal cell types as potential key players not only affected by but causally linked to the progression of AUD. Most of the enrichments for genetic risk for AUD were in glial cell types. These results highlight the importance of cell-type specific molecular changes in AUD and offer opportunities to identify novel targets for treatment.

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

Comprehensive analysis of aging-related gene expression patterns and identification of potential intervention targets.

This study aims to understand the molecular mechanisms underlying the aging process and identify potential interventions to mitigate age-related decline and diseases. This study utilized the GSE168753 dataset to conduct comprehensive differential gene expression analysis and co-expression module analysis. Machine learning and Mendelian randomization analyses were employed to identify core aging-associated genes and potential drug targets. Molecular docking simulations and mediation analysis were also performed to explore potential compounds and mediators involved in the aging process. The analysis identified 4164 differentially expressed genes, with 1893 upregulated and 2271 downregulated genes. Co-expression analysis revealed 21 modules, including both positively and negatively correlated modules between older age and younger age groups. Further exploration identified 509 aging-related genes with distinct biological functions. Machine learning and Mendelian randomization analyses identified eight core genes associated with aging, including DPP9, GNAZ, and RELL2. Molecular docking simulations suggested resveratrol, folic acid, and ethinyl estradiol as potential compounds capable of attenuating aging through modulation of RELL2 expression. Mediation analysis indicated that eosinophil counts and neutrophil count might act as mediators in the causal relationship between genes and aging-related indicators. This comprehensive study provides valuable insights into the molecular mechanisms of aging and offers important implications for the development of anti-aging therapeutics. Key Messages What is already known on this topic - Prior research outlines aging's complexity, necessitating precise molecular targets for intervention. What this study adds - This study identifies novel aging-related genes, potential drug targets, and therapeutic compounds, advancing our understanding of aging mechanisms. How this study might affect research, practice, or policy - Findings may inform targeted therapies for age-related conditions, influencing future research and clinical practices.

PSVI-24 Gene expression profiles of granulosa and peripheral white blood cells from fertile and sub-fertile beef heifers

Abstract Beef heifer fertility issues contribute to a major economic loss in the cow-calf production industry. Therefore, identifying beef heifers with superior genetic potential for improved fertility would increase profitability. This study aimed to identify differences in the transcriptome profiles from granulosa and peripheral white blood cells (pWBCs) of beef heifers with varying reproductive potential. For this, Angus-Simmental crossbred heifers were subjected to an estrus synchronization and fixed-time artificial insemination (AI) protocol (7-D CO-Synch + CIDR) followed by exposure for 60-d to a fertile bull. Depending on the presence or absence of conceptus 120 d post-AI, heifers were classified as fertile (pregnant by AI) or sub-fertile (non-pregnant by AI or bull-breeding). Pregnancies were terminated, and all animals in both groups (fertile, n = 8; and sub-fertile, n = 5) were cycling when the blood and ovaries were collected from each heifer. Total RNA was extracted from the pWBCs and granulosa cells and subjected to library preparation and sequencing on the Nova-Seq platform. The read counts were obtained after data quality control using FastQC v0.11.9 and MultiQC v1.12 and alignment to the Ensemble’s ARS UCD1.2 Bos taurus genome reference using STAR aligner v2.7.5. The filtered data were subjected to differential expression analysis using DESeq2. We identified 1,061 and 72 significantly differentially expressed genes (DEGs) with P-values ≤ 0.05 and absolute (log2 fold change) ≥ 0.5 from pWBC and granulosa, respectively. Notably, 12 targets including 9 protein coding genes (PLCL1, DNER, GNAS, CDH3, PER1, ITGA2B, CXCL12, ENSBTAG00000048613, ENSBTAG00000051519), bta-mir-2887-1, 5-8S-rRNA and U5 were found as DEGs in both the tissues. Based on a differential co-expression analysis using PCIT, we identified GNAS and DNER as hub genes in pWBC and the granulosa cells of the sub-fertile heifer group. The 12 shared genes were over-represented for pathways such as NF kappa B and chemokine signaling, regulation of actin cytoskeleton, and platelet activation. Some of the identified genes have been previously associated with fertility, while others are novel. A detailed understanding of the underlying biological mechanisms of the top genes and a follow-up study with a larger sample size at different time points could validate the candidates identified in this study for their role as potential therapeutic targets.

Prognosis evaluation and efficacy analysis of different treatment options for patients with visceral pleural invasion in stage IIA–IIB lung cancer

ObjectiveControversy surrounds the treatment of visceral pleural invasion in lung cancer, and no studies have compared the efficacy of its four main treatment options (i.e., surgery, chemotherapy, targeted therapy, and immunotherapy). This study aims to compare and analyze surgery, chemotherapy, targeted therapy, and immunotherapy outcomes and explore the optimal treatment of visceral pleural invasion in lung cancer.MethodsWe searched electronic databases (i.e., Pubmed, Embase, Cochrane Library, CNKI, and Chinese Biomedical Literature Database Search) for relevant studies of treatment options for patients with visceral pleural invasion in stage IIA–IIB lung cancer. Searches times were limited to studies published between January 1, 2000 and February 20, 2021. Meta analysis was performed using RevMan 5.3 software We also downloaded original RNA transcription data about lung cancer invasion in the GEO and TCGA tumor databases, and used R 4.0.3 software to perform differential expression and co-expression gene network analyses.ResultsWe included a total of 25 high-quality (i.e., Jadad score 4–7) studies. Meta-analysis found that surgical treatment was associated with a 3-year survival rate OR = 3.80 (95% CI 3.53, 4.09; P < 0.0001), 5-year survival rate OR = 4.10 (95% CI 3.72, 4.53; P < 0.0001), and median survival time OR = 2.71 (95% CI 2.53, 2.89; P < 0.0001). Chemotherapy was associated with a 3-year survival rate OR = 2.08 (95% CI 1.93, 2.25; P < 0.0001), 5-year survival rate OR = 1.68 (95% CI 1.49, 1.89; P < 0.0001), and median survival time OR = 1.84 (95% CI 1.66, 2.04; P < 0.0001). Targeted therapy was associated with a 3-year survival rate OR = 2.91 (95% CI 2.65, 3.19; P < 0.0001), 5-year survival rate OR = 1.83 (95% CI 1.39, 2.33; P < 0.0001), and median survival time OR = 1.76 (95% CI 1.59, 1.94; P < 0.0001). Finally, immunotherapy was associated with a 3-year survival rate OR = 1.89 (95% CI 1.73, 2.07; P < 0.0001), 5-year survival rate OR = 1.66 (95% CI 1.46, 1.88; P < 0.0001), and median survival time OR = 2.53 (95% CI 2.27, 2.82; P < 0.0001). After screening differential genes and co-expressed genes in tumor gene databases, we found that AC245595.1, ITGB1-DT and AL606489.1 may be involved in the process of lung cancer invasion, and macrophages M1 and M2, CD4+-Th1, CD8+-Th1 may participate in immune infiltration.ConclusionsIn patients with visceral pleural invasion of stage IIA-IIB lung cancer, chemotherapy has shown a significant effect on improving prognosis and enhancing efficacy. However, surgical treatment did not significantly improve the overall prognosis. Therefore, the individual situation of the patient and the comprehensive benefits of the treatment program should be fully considered when developing the treatment program.

Co-Expression Network and Machine Learning Analysis of Transcriptomics Data Identifies Distinct Gene Signatures and Pathways in Lesional and Non-Lesional Atopic Dermatitis.

Atopic dermatitis (AD) is a common inflammatory skin condition with complex origins. Current treatments often yield suboptimal results due to an incomplete understanding of its underlying mechanisms. This study aimed to identify pathway and gene signatures that distinguish between lesional AD, non-lesional AD, and healthy skin. We conducted differential gene expression and co-expression network analyses to identify differentially co-expressed genes (DCEGs) in lesional AD vs. healthy skin, lesional vs. non-lesional AD, and non-lesional AD vs. healthy skin. Modules associated with lesional and non-lesional AD were identified based on the correlation coefficients between module eigengenes and clinical phenotypes (|R| ≥ 0.5, p-value < 0.05). Subsequently, we employed Ingenuity Pathway Analysis (IPA) on the identified DCEGs, followed by machine learning (ML) analysis within the pathway expression framework. The ML analysis of pathway expressions, selected by IPA and derived from gene expression data, identified relevant pathway signatures, which were validated using an independent dataset and correlated with AD severity measures (EASI and SCORAD). We identified 975, 441, and 40 DCEGs in lesional vs. healthy skin, lesional vs. non-lesional, and non-lesional vs. healthy skin, respectively. IPA and ML analyses revealed 25 relevant pathway signatures, including wound healing, glucocorticoid receptor signaling, and S100 gene family signaling pathways. Validation confirmed the significance of 10 pathway signatures, which were correlated with the AD severity measures. DCEGs such as MMP12 and S100A8 demonstrated high diagnostic efficacy (AUC > 0.70) in both the discovery and validation datasets. Differential gene expression, co-expression networks and ML analyses of pathway expression have unveiled relevant pathways and gene signatures that distinguish between lesional, non-lesional, and healthy skin, providing valuable insights into AD pathogenesis.

Higher-order correction of persistent batch effects in correlation networks.

Systems biology analyses often use correlations in gene expression profiles to infer co-expression networks that are then used as input for gene regulatory network inference or to identify functional modules of co-expressed or putatively co-regulated genes. While systematic biases, including batch effects, are known to induce spurious associations and confound differential gene expression analyses (DE), the impact of batch effects on gene co-expression has not been fully explored. Methods have been developed to adjust expression values, ensuring conditional independence of mean and variance from batch or other covariates for each gene, resulting in improved fidelity of DE analysis. However, such adjustments do not address the potential for spurious differential co-expression (DC) between groups. Consequently, uncorrected, artifactual DC can skew the correlation structure, leading to the identification of false, non-biological associations, even when the input data are corrected using standard batch correction. In this work, we demonstrate the persistence of confounders in covariance after standard batch correction using synthetic and real-world gene expression data examples. We then introduce Co-expression Batch Reduction Adjustment (COBRA), a method for computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters expressing the co-expression matrix as a function of the sample covariates, allowing control for continuous and categorical covariates. COBRA is computationally efficient, leveraging the inherently modular structure of genomic data to estimate accurate gene regulatory associations and facilitate functional analysis for high-dimensional genomic data. COBRA is available under the GLP3 open source license in R and Python in netZoo (https://netzoo.github.io).

Unveiling differential gene co-expression networks and its effects on levodopa-induced dyskinesia

Levodopa-induced dyskinesia (LID) refers to involuntary motor movements of chronic use of levodopa in Parkinson's disease (PD) that negatively impact the overall well-being of people with this disease. The molecular mechanisms involved in LID were investigated through whole-blood transcriptomic analysis for differential gene expression and identification of new co-expression and differential co-expression networks. We found six differentially expressed genes in patients with LID, and 13 in patients without LID. We also identified 12 co-expressed genes exclusive to LID, and six exclusive hub genes involved in 23 gene-gene interactions in patients with LID. Convergently, we identified novel genes associated with PD and LID that play roles in mitochondrial dysfunction, dysregulation of lipid metabolism, and neuroinflammation. We observed significant changes in disease progression, consistent with previous findings of maladaptive plastic changes in the basal ganglia leading to the development of LID, including a chronic pro-inflammatory state in the brain.

Mutual exclusivity and co-occurrence patterns of immune checkpoints indicate NKG2A relates to anti-PD-1 resistance in gastric cancer

BackgroundAn increasing number of clinical studies have begun to explore combination strategies with immune checkpoint inhibitors, aiming to present new opportunities for overcoming anti-PD-1 treatment resistance in gastric cancer. Unfortunately, the exploration of certain immune checkpoint inhibitor combination strategies has yielded suboptimal results. Therefore, it is necessary to comprehensively analyze the expression patterns of immune checkpoints and identify optimal combination regimens of anti-PD-1 inhibitors with other immune checkpoint inhibitors.MethodsLeveraging single-cell RNA sequencing (scRNA-seq) and multivariate linear regression interaction models, we dissected the immune checkpoint expression characteristics of CD8+ T cells in gastric cancer and the immune checkpoint expression pattern (ICEP) mediating anti-PD-1 treatment resistance. Furthermore, we employed transcription factor analysis and CellOracle to explore the transcriptional regulatory mechanisms governing CD8+ T cell differentiation fates. Finally, we utilized Nichenet and spatial transcriptomic analysis to investigate the spatial expression patterns of immune checkpoints.ResultsInteraction analysis indicated that, among the known immune checkpoints, co-expression of NKG2A and PD-1 might exert a more profound inhibitory effect on the proliferative capacity of CD8+ T cells. The co-expression analysis revealed differential co-expression pattern of PD-1 and NKG2A, defined as ICEP1 (CD8+ T cells co-expressing PD-1, CTLA-4, TIGIT, LAG-3 or CD38) and ICEP2 (CD8+ T cells solely expressing NKG2A or co-expressing with other immune checkpoints), reflecting the co-occurrence pattern of PD-1 and the mutual exclusivity of NKG2A. Further, these two ICEP CD8+ T cell subsets represented distinct CD8+ T cell differentiation fates governed by MSC and RUNX3. Notably, ICEP2 CD8+ T cells were associated with anti-PD-1 therapy resistance in gastric cancer. This phenomenon may be attributed to the recruitment of LGMN+ macrophages mediated by the CXCL16-CXCR6 signaling pathway.ConclusionThis study unveiled two distinct ICEPs and the mutually exclusivity and co-occurrence characteristics of CD8+ T cells in gastric cancer. The ICEP2 CD8+ T cell subset, highly expressed in gastric cancer patients resistant to anti-PD-1 therapy, may be recruited by LGMN+ macrophages through CXCL16-CXCR6 axis. These findings provide evidence for NKG2A as a novel immunotherapeutic target in gastric cancer and offer new insights into combination strategies for immune checkpoint inhibitors in gastric cancer.Graphical

Constructing the biomolecular networks associated with diabetic nephropathy and dissecting the effects of biomolecule variation underlying pathogenesis.

Diabetic nephropathy (DN) is a common and serious complication of diabetes, contributing significantly to patient mortality. Complication of DN (CDN) ranks as the second leading cause of end-stage renal disease globally. To address this, understanding the genetic regulation underlying DN is crucial for personalized treatment strategies. In this study, we identified genes and lncRNAs associated with diabetes and diabetic nephropathy constructing a DN-related lncRNA-mRNA network (DNLMN). This network, characterized by scale-free biomolecular properties, generated through the study of topological properties, elucidates key regulatory interactions. Enrichment analysis of important network modules revealed critical biological processes and pathways involved in DN pathogenesis. In the second step, we investigated the differential expression and co-expression of hub nodes in diseased and normal individuals, identifying lncRNA-mRNA relationships implicated in disease regulation. Finally, we gathered DN-related single nucleotide polymorphisms (SNPs) and lncRNAs from the LincSNP 3.0 database. The DNLMN encompasses SNP-associated lncRNAs, and transcription factors (TFs) linked to differentially expressed lncRNAs between diseased and normal samples. These results underscore the significance of biomolecular networks in disease progression and highlighting the role of biomolecular variability contributes to personalized disease phenotyping and treatment.

Identification of a novel cellular senescence-related lncRNA signature for prognosis and immune response in osteosarcoma.

Cellular senescence, a novel hallmark of cancer, is associated with patient outcomes and tumor immunotherapy. However, at present, there is no systematic study on the use of cellular senescence-related long non-coding RNAs (CSR-lncRNAs) to predict survival in patients with osteosarcoma. In this study, we aimed to identify a CSR-lncRNAs signature and to evaluate its potential use as a survival prognostic marker and predictive tool for immune response of osteosarcoma. We downloaded a cohort of patients with osteosarcoma from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We performed differential expression and co-expression analyses to identify CSR-lncRNAs. We performed univariate and multivariate Cox regression analyses along with the random forest algorithm to identify lncRNAs significantly correlated with senescence. Subsequently, we assessed the predictive models using survival curves, receiver operating characteristic curves, nomograms, C-index, and decision curve analysis. Based on this model, patients with osteosarcoma were divided into two groups according to their risk scores. Then, using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses, we compared their clinical characteristics to uncover functional differences. We further conducted immune infiltration analyses using estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE), cell-type identification by estimating relative subsets of rna transcripts (CIBERSORT), and single-sample gene set enrichment analysis for the two groups. We also evaluated the expression of the target genes of immune checkpoint inhibitors (ICIs). We identified six lncRNAs that were significantly correlated with senescence and accordingly established a novel cellular senescence-related lncRNA prognostic signature incorporating these lncRNAs. The nomogram indicated that the risk model was an independent prognostic factor that could predict the survival of patients with osteosarcoma. This model demonstrated high accuracy upon validation. Further analysis revealed that patients with osteosarcoma in the low-risk group exhibited better clinical outcomes and enhanced immune infiltration. The six-CSR-lncRNA prognostic signature effectively predicted survival outcomes and patients in the low-risk group might have improved immune infiltration.

Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression.

Pooled designs for single-cell RNA sequencing, where many cells from distinct samples are processed jointly, offer increased throughput and reduced batch variation. This study describes expression-aware demultiplexing (EAD), a computational method that employs differential co-expression patterns between individuals to demultiplex pooled samples without any extra experimental steps. We use synthetic sample pools and show that the top interindividual differentially co-expressed genes provide a distinct cluster of cells per individual, significantly enriching the regulation of metabolism. Our application of EAD to samples of six isogenic inbred mice demonstrated that controlling genetic and environmental effects can solve interindividual variations related to metabolic pathways. We utilized 30 samples from both sepsis and healthy individuals in six batches to assess the performance of classification approaches. The results indicate that combining genetic and EAD results can enhance the accuracy of assignments (Min. 0.94, Mean 0.98, Max. 1). The results were enhanced by an average of 1.4% when EAD and barcoding techniques were combined (Min. 1.25%, Median 1.33%, Max. 1.74%). Furthermore, we demonstrate that interindividual differential co-expression analysis within the same cell type can be used to identify cells from the same donor in different activation states. By analysing single-nuclei transcriptome profiles from the brain, we demonstrate that our method can be applied to nonimmune cells. EAD workflow is available at https://isarnassiri.github.io/scDIV/ as an R package called scDIV (acronym for single-cell RNA-sequencing data demultiplexing using interindividual variations).

Dysregulation of long non-coding RNA gene expression pathways in monocytes of type 2 diabetes patients with cardiovascular disease

BackgroundMonocytes play a central role in the pathophysiology of cardiovascular complications in type 2 diabetes (T2D) patients through different mechanisms. We investigated diabetes-induced changes in lncRNA genes from T2D patients with cardiovascular disease (CVD), long-duration diabetes, and poor glycemic control.MethodsWe performed paired-end RNA sequencing of monocytes from 37 non-diabetes controls and 120 patients with T2D, of whom 86 had either macro or microvascular disease or both. Monocytes were sorted from peripheral blood using flow cytometry; their RNA was purified and sequenced. Alignments and gene counts were obtained with STAR to reference GRCh38 using Gencode (v41) annotations followed by batch correction with CombatSeq. Differential expression analysis was performed with EdgeR and pathway analysis with IPA software focusing on differentially expressed genes (DEGs) with a p-value < 0.05. Additionally, differential co-expression analysis was done with csdR to identify lncRNAs highly associated with diabetes-related expression networks with network centrality scores computed with Igraph and network visualization with Cytoscape.ResultsComparing T2D vs. non-T2D, we found two significantly upregulated lncRNAs (ENSG00000287255, FDR = 0.017 and ENSG00000289424, FDR = 0.048) and one significantly downregulated lncRNA (ENSG00000276603, FDR = 0.017). Pathway analysis on DEGs revealed networks affecting cellular movement, growth, and development. Co-expression analysis revealed ENSG00000225822 (UBXN7-AS1) as the highest-scoring diabetes network-associated lncRNA. Analysis within T2D patients and CVD revealed one lncRNA upregulated in monocytes from patients with microvascular disease without clinically documented macrovascular disease. (ENSG00000261654, FDR = 0.046). Pathway analysis revealed DEGs involved in networks affecting metabolic and cardiovascular pathologies. Co-expression analysis identified lncRNAs strongly associated with diabetes networks, including ENSG0000028654, ENSG00000261326 (LINC01355), ENSG00000260135 (MMP2-AS1), ENSG00000262097, and ENSG00000241560 (ZBTB20-AS1) when we combined the results from all patients with CVD. Similarly, we identified from co-expression analysis of diabetes patients with a duration ≥ 10 years vs. <10 years two lncRNAs: ENSG00000269019 (HOMER3-AS10) and ENSG00000212719 (LINC02693). The comparison of patients with good vs. poor glycemic control also identified two lncRNAs: ENSG00000245164 (LINC00861) and ENSG00000286313.ConclusionWe identified dysregulated diabetes-related genes and pathways in monocytes of diabetes patients with cardiovascular complications, including lncRNA genes of unknown function strongly associated with networks of known diabetes genes.

CRCDB: A comprehensive database for integrating and analyzing multi-omics data of early-onset and late-onset colorectal cancer

The incidence of early-onset colorectal cancer (EOCRC) has increased significantly worldwide. Uncovering biomarkers that are unique to EOCRC is of great importance to facilitate the prevention and detection of this growing cancer subtype. Although efforts have been made in the data curation about CRC, there is no integrated platform that gives access to data specifically related to young CRC patients. Here, we constructed a user-friendly open integrated resource called CRCDB (URL: http://crcdb-hust.com) which contains multi-omics data of 785 EOCRC, 4898 late-onset CRCs (LOCRC), and 1110 normal control samples from tissue, whole blood, platelets, and serum exosomes. CRCDB manages the differential analysis, survival analysis, co-expression analysis, and immune cell infiltration comparison analysis results in different CRC groups. Meta-analysis results were also provided for users for further data interpretation. Using the resource in CRCDB, we identified that genes associated with the metabolic process were less expressed in EOCRC patients, while up regulated genes most associated with the mitosis process might play an important role in the molecular pathogenesis of LOCRC. Survival-related genes were most enriched in oxidoreduction pathways in EOCRC while in immune-related pathways in LOCRC. With all the data gathered and processed, we anticipate that CRCDB could be a practical data mining platform to help explore potential applications of omics data and develop effective prevention and therapeutic strategies for the specific group of CRC patients.

Single-cell RNA analysis of chromodomain-encoding genes in mesenchymal stromal cells of the mouse dental pulp.

The chromodomain helicase DNA-binding (CHD) and chromobox (CBX) families of proteins play crucial roles in cell fate decisions, differentiation, and cell proliferation in a broad variety of tissues and cell types. CHD proteins are ATP-dependent epigenetic enzymes actively engaged in transcriptional regulation, DNA replication, and DNA damage repair, whereas CBX proteins are transcriptional repressors mainly involved in the formation of heterochromatin. The pleiotropic effects of CHD and CBX proteins are largely dependent on their versatility to interact with other key components of the epigenetic and transcriptional machinery. Although the function and regulatory modes of CHD and CBX factors are well established in many cell types, little is known about their roles during osteogenic differentiation. A single-cell RNA-sequencing analysis of the mouse incisor dental pulp revealed distinct spatiotemporal expression patterns of CHD- and CBX-encoding genes within different clusters of mesenchymal stromal cells (MSCs) representing various stages of osteogenic differentiation. Additionally, genes encoding interaction partners of CHD and CBX proteins, such as subunits of the trithorax-COMPASS and polycomb chromatin remodeling complexes, exhibited differential co-expression behaviors within MSC subpopulations. Thus, CHD- and CBX-encoding genes show partially overlapping but distinct expression patterns in MSCs, suggesting their differential roles in osteogenic cell fate decisions.

Increased co-expression of 4-1BB with PD-1 on CD8+ tumor-infiltrating lymphocytes is associated with improved prognosis and immunotherapy response in cervical cancer.

The combination of agonistic antibodies with immune checkpoint inhibitors presents a promising avenue for cancer immunotherapy. Our objective is to explore the co-expression of 4-1BB, ICOS, CD28, with PD-1 on CD8+ T cells in the peripheral blood and tumor tissue of cervical cancer(CC) patients, with a specific focus on the association between the co-expression levels of 4-1BB with PD-1 and clinical features, prognosis as well as immunotherapy response. The goal is to offer valuable insights into cervical cancer immunotherapy. In this study, 50 treatment-naive patients diagnosed with CC were enrolled. Flow cytometry was used to detect PD-1/4-1BB, PD-1/ICOS and PD-1/CD28 co-expression on CD8+ T cells. Subsequent analysis aimed to investigate the differential co-expression between peripheral blood and cancer tissue, and also the correlation between co-expression and clinical features in these patients. Gene Expression Omnibus (GEO) datasets, The Cancer Genome Atlas (TCGA) cohort, The IMvigor210 cohort, The BMS038cohort and Immunophenoscores were utilized to investigate the correlation between PD-1/4-1BB and the immune microenvironment, prognosis, immunotherapy, and drug sensitivity in cervical cancer. The co-expression levels of PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 on CD8+ tumor-infiltrating lymphocytes (TILs) were significantly higher in cervical cancer patients compared to those in peripheral blood. Clinical feature analysis reveals that on CD8+ TILs, the co-expression of PD-1/4-1BB is more closely correlated with clinical characteristics compared to PD-1/ICOS, PD-1/CD28, PD-1, and 4-1BB. Pseudo-time analysis and cell communication profiling reveal close associations between the subgroups harboring 4-1BB and PD-1. The prognosis, tumor mutation burden, immune landscape, and immunotherapy response exhibit statistically significant variations between the high and low co-expression groups of PD-1/4-1BB. The high co-expression group of PD-1/4-1BB is more likely to benefit from immunotherapy. PD-1/4-1BB, PD-1/ICOS, and PD-1/CD28 exhibit elevated co-expression on CD8+TILs of cervical cancer, while demonstrating lower expression in circulating T cells. The co-expression patterns of PD-1/4-1BB significantly contributed to the prediction of immune cell infiltration characteristics, prognosis, and tailored immunotherapy tactics. PD-1/4-1BB exhibits potential as a target for combination immunotherapy in cervical cancer.

Construction of a TP53 mutation-associated ceRNA network as prognostic biomarkers in hepatocellular carcinoma

BackgroundHepatocellular carcinoma (HCC) continues to endanger human health worldwide. Regulatory networks of competing endogenous RNAs (ceRNAs) play important roles in HCC. TP53 is the second most often altered gene in HCC and has a significant role in regulating target genes such as miRNAs and lncRNAs. MethodsData from patients with TP53 mutation were collected through the cBioPortal database and differential analysis was performed to screen RNAs related to TP53 mutation. The lncRNA-miRNA–mRNA relationship was predicted by the miRcode, miRDB, and TargetScan databases. The ceRNA networks were screened and visualized by Cytoscape. Core ceRNA networks were generated by differential analysis, coexpression analysis, prognostic analysis and subcellular localization. Finally, methylation, mutation, PPI, GSEA, immunity and drug sensitivity analyses of MEX3A were performed to determine the role of MEX3A in HCC. ResultsWe identified 1508 DEmRNAs, 85 DEmiRNAs and 931 DElncRNAs and obtained a ceRNA network including 28 lncRNAs, 4 miRNAs and 31 mRNAs. Twenty hub DERNAs in the TP53-altered-related ceRNA network were screened out by Cytoscape and the core ceRNA network (LINC00491/TCL6-hsa-miR-139-5p-MEX3A) was obtained by multiple analyses. In addition, we discovered that the methylation level of MEX3A was decreased and the mutation frequency was raised in HCC. Furthermore, elevated MEX3A expression was associated with alterations in the HCC immunological microenvironment. ConclusionWe successfully constructed a reciprocal ceRNA network, which could provide new ideas for exploring HCC mechanisms and therapeutic approaches.