Transcriptome RNA-sequencing Data Research Articles

The integrate profiling of single-cell and spatial transcriptome RNA-seq reveals tumor heterogeneity, therapeutic targets, and prognostic subtypes in ccRCC.

Clear-cell renal cell carcinoma (ccRCC) is the most common type of RCC; however, the intratumoral heterogeneity in ccRCC remains unclear. We first identified markers and biological features of each cell cluster using bioinformatics analysis based on single-cell and spatial transcriptome RNA-sequencing data. We found that gene copy number loss on chromosome 3p and amplification on chromosome 5q were common features in ccRCC cells. Meanwhile, NNMT and HILPDA, which are associated with the response to hypoxia and metabolism, are potential therapeutic targets for ccRCC. In addition, CD8+ exhausted T cells (LAG3+ HAVCR2+), CD8+ proliferated T cells (STMN+), and M2-like macrophages (CD68+ CD163+ APOC1+), which are closely associated with immunosuppression, played vital roles in ccRCC occurrence and development. These results were further verified by whole exome sequencing, cell line and xenograft experiments, and immunofluorescence staining. Finally, we divide patients with ccRCC into three subtypes using unsupervised cluster analysis. and generated a classifier to reproduce these subtypes using the eXtreme Gradient Boosting algorithm. Our classifier can help clinicians evaluate prognosis and design personalized treatment strategies for ccRCC. In summary, our work provides a new perspective for understanding tumor heterogeneity and will aid in the design of antitumor therapeutic strategies for ccRCC.

Comprehensive analyses and experimental verification of NETs and an EMT gene signature for prognostic prediction, immunotherapy, and chemotherapy in pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is an aggressive malignancy with high mortality and poor prognosis. Neutrophil extracellular traps (NETs) and the epithelial-mesenchymal transition (EMT) significantly influence on the progression of various cancers. However, the underlying relevance of NETs- and EMT-associated genes on the outcomes of patients with PAAD remains to be elucidated. Transcriptome RNA sequencing data, together with clinical information and single-cell sequencing data of PAAD were collected from public databases. In the TCGA-PAAD cohort, ssGSEA was used to calculate NET and EMT scores. WGCNA was used to determine the key gene modules. A risk model with eight NET- and EMT-related genes (NERGs) was established using LASSO and multivariate Cox regression analysis. Patients in the reduced risk (RR) group showed better prognostic values compared with those in the elevated risk (ER) group. The prognostic model exhibited reliable and robust prediction when validated using an external database. The distributions of risk genes were explored in a single-cell sequencing data set. Immune infiltration, immune cycle, and immune checkpoints were compared between the RR and ER groups. Moreover, potential chemotherapeutic drugs were examined. DCBLD2 was identified as a key gene in PAAD cell lines by qRT-PCR, and was highly expressed in PAAD tissues. GSEA demonstrated that DCBLD2 induced the EMT. Transwell assays and western blotting showed that cell invasion and EMT induction were significantly reduced after DCBLD2 knockdown. Collectively, we constructed a prognosis model based on a NET and EMT gene signature, providing a valuable perspective for the prognostic evaluation and management of PAAD patient.

Multiple programmed cell death patterns and immune landscapes in bladder cancer: Evidence based on machine learning and multi-cohorts.

Bladder cancer (BLCA) is the most prevalent malignant neoplasm of the urinary tract, and ranks seventh as the most frequent systemic neoplasm in males. Dysregulation of programmed cell death (PCD) has been implicated in various stages of cancer progression, including tumorigenesis, invasion, and metastasis. However, the correlation between multiple PCD modes and BLCA is lacking. Thus, a risk prediction model was built based on 12 models of PCD to predict prognosis and immunotherapy response in patients with BLCA. The RNA sequencing transcriptome data of BLCA were collected from the Cancer Genome Atlas Program (TCGA) and GEO datasets. Univariate Cox and LASSO regression analyzes were performed to identify PCD-related genes (PCDRGs) significant for prognosis. Multivariate Cox regression analysis was used to develop a prognostic model for PCD. Survival analysis and chi-squared test were employed to analyze the survival variations between different risk groups. Univariate and multivariate Cox analyses were performed to evaluate the model as an independent prognostic predictor. A nomogram was formulated using both clinical data and the model to predict the survival rates of BLCA patients. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were performed to analyze and elucidate the molecular mechanisms and pathways operating within different risk score groups. Furthermore, the immune landscape was investigated and the efficacy of various anti-tumor drugs was evaluated for BLCA. Finally, consensus clustering analysis was adopted to explore the association between different PCD clusters and clinical characteristics. Assessment of the public datasets and multivariate Cox analysis yielded 1254 PCDRGs, of which 10 PCDRGs for BLCA were identified. Based on the PCDRGs, a prognostic model was built for BLCA patient prognosis. Compared with the low-risk group, the high-risk group had a poorer prognosis. The model predicted area under the curve (AUC) values of 0.751, 0.753, and 0.763, respectively, for 1-, 3-, and 5-year survival of BLCA patients. The nomogram further demonstrated the credibility of the prognosis model. The low-risk group patients exhibited lower TIDE scores and higher TMB scores, implying better response of the low-risk group to immunotherapy. The consensus clustering analysis indicated that compared with PCD cluster A, PCD cluster B was significantly more expressed in PCDRGs, suggesting a closer relation of PCD cluster B to PCDRGs. Patients in PCD cluster B had lower risk scores. To summarize, the effects of 12 PCD patterns on BLCA were synthesized and the correlation between PCD and BLCA was explored. These findings provide new and convincing evidence for individualized treatment of BLCA, and help guide the treatment strategy and improve the prognosis of BLCA patients.

Transcriptomic and genomic identification of spliceosomal genes from Euglena gracilis.

Diverse splicing types in nuclear and chloroplast genes of protist Euglena gracilis have been recognized for decades. However, the splicing machinery responsible for processing nuclear precursor messenger RNA introns, including trans-splicing of the 5' terminal outron and spliced leader (SL) RNA, remains elusive. Here, we identify 166 spliceosomal protein genes and two snRNA genes from E. gracilis by performing bioinformatics analysis from a combination of next-generation and full-length transcriptomic RNA sequencing (RNAseq) data as well as draft genomic data. With the spliceosomal proteins we identified in hand, the insensitivity of E. gracilis to some splicing modulators is revealed at the sequence level. The prevalence of SL RNA-mediated trans-splicing is estimated to be more than 70% from our full-length RNAseq data. Finally, the splicing proteomes between E. gracilis and its three evolutionary cousins within the same Excavata group are compared. In conclusion, our study characterizes the spliceosomal components in E. gracilis and provides the molecular basis for further exploration of underlying splicing mechanisms.

Integrated analysis of transcriptomic and small RNA sequencing data provides miRNA candidates for engineering agronomically important seed traits in Brassica juncea

Brassica juncea L. is an important oilseed crop that yields edible oil and biofuel. Improving B. juncea seed traits is a primary breeding target, but these traits are genetically complex. MicroRNAs (miRNAs) regulate seed development by modulating gene expression at the post-transcriptional or translational level and are excellent candidates for improving seed traits. However, the roles of miRNAs in B. juncea seed development are yet to be investigated. Here, we report small RNA profiling and miRNA identification from developing seeds of two contrasting varieties of B. juncea, Early Heera2 (EH2) and Pusa Jaikisan (PJK). We identified 326 miRNAs, including 127 known and 199 novel miRNAs, of which 103 exhibited inter-varietal differential expression. Integrating miRNAome and our previous transcriptome data identified 13,683 putative miRNA-target modules. Segregation of differentially expressed miRNAs into different groups based on variety-wise upregulation, followed by comprehensive functional analysis of targets using pathway mapping, gene ontology, transcription factor mapping, and candidate gene analysis, revealed at least 11, 6, and 7 miRNAs as robust candidates for the regulation of seed size, seed coat color, and oil content, respectively. Further, co-localization with previously reported quantitative trait loci (QTL) proffered 29 and 15 miRNAs overlapping with seed weight and oil content QTLs, respectively. Our study is the first comprehensive report of miRNAome expression dynamics from developing seeds and provides candidate miRNAs and target genes for engineering seed traits in B. juncea.

Integrated analysis of single-cell sequencing and weighted co-expression network identifies a novel signature based on cellular senescence-related genes to predict prognosis in glioblastoma.

Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated. Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package. A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted. Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

Establishing a prognostic model based on five starvation-related long non-coding RNAs for clear cell renal cell carcinoma.

Starvation-induced tumor microenvironment significantly alters genetic profiles including long non-coding RNAs (lncRNAs), further regulating the malignant biological characteristics (invasion and migration) of clear cell renal cell carcinoma (ccRCC). Transcriptome RNA-sequencing data of 539 ccRCC tumors and 72 normal tissues were acquired from the TCGA and paired clinical samples of 50 ccRCC patients. In vitro experiments, such as qPCR, migration and invasion assays were applied to reveal the clinical relevance of LINC-PINT, AC108449.2 and AC007637.1. 170 lncRNAs were verified as starvation-related lncRNAs (SR-LncRs), of which 25 lncRNAs were associated with overall survival in ccRCC patients. Furthermore, a starvation-related risk score model (SRSM) was built based on the expression levels of LINC-PINT, AC108449.2, AC009120.2, AC008702.2 and AC007637.1. ccRCC patients with high level of LINC-PINT expression were divided into high-risk group and led to higher mortality, but AC108449.2 and AC007637.1 were contrary. Analogously, LINC-PINT was highly expressed in ccRCC cell lines and tumor tissues, especially in patients with advanced stage, T-stage and M-stage, while AC108449.2 and AC007637.1 showed the opposite results. In addition, the increased levels of AC108449.2 and AC007637.1 were significantly correlated with grade. Silencing LINC-PINT reduced the invasion and migration characteristics of ccRCC cells. SiR-AC108449.2 and siR-AC007637.1 enhanced the ability of invasion and migration in ccRCC cells. In this study, we find the clinical significance of LINC-PINT, AC108449.2 and AC007637.1 in predicting the prognosis of ccRCC patients and verify their correlation with various clinical parameters. These findings provide an advisable risk score model for ccRCC clinical decision-making.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data.

We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS. Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication. OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells. Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Integrated clinical analysis and data mining assessed the impact of NOX4 on the immune microenvironment and prognosis of pancreatic cancer.

The tumor microenvironment (TME) of pancreatic cancer is complex. which forms forms a microenvironment with high immunosuppression, ischemia and hypoxia, which promotes tumor proliferation and migration, inhibit the anti-tumor immune response. NOX4 plays an important role in tumor microenvironment and has a significant relationship with the occurrence, development and drug resistance of tumor. Firstly, NOX4 expression in pancreatic cancer tissues under different pathological conditions was detected by applying immunohistochemical staining of tissue microarray (TMA). Transcriptome RNA sequencing data and clinical data of 182 pancreatic cancer samples were downloaded and collated from the UCSC xena database. 986 NOX4-related lncRNAs were filtered by Spearman correlation analysis. prognosis-related NOX4-related lncRNAs and NRlncSig Score were finally obtained by univariate and multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (Lasso) analysis in pancreatic cancer patients. we plotted Kaplan -Meier and time-dependent ROC curves (ROC) to assess the validity in predicting the prognosis of pancreatic cancer. The ssGSEA analysis was applied to explore the immune microenvironment of pancreatic cancer patients as well as to discuss the immune cells and immune status separately. We found that a mature tumor marker, NOX4, play different roles in different clinical subgroups by immunohistochemical analysis and clinical data. Finally, 2 NOX4-related lncRNAs were determined by least absolute shrinkage and selection operator (LASSO) analysis, univariate Cox analysis and multivariate COX analysis. The ROC curve and DCA curve showed that NRS Score had better predictive ability than independent prognosis-related lncRNA and other clinicopathologic indicators. We obtained the relative abundance of 28 infiltrating immune cells by ssGSEA analysis and found a significant positive correlation between the abundance of anti-tumor immune cells and tumor-promoting immune cells in the risk-classified microenvironment. No matter NRS Score or AC092667.2, RP11-349A8.3 was significantly correlated with immune infiltrating cells. Meanwhile, the IC50 of conventional chemotherapeutic agents in high-score group were significantly lower than those in low-score group. As a mature tumor marker, NOX4-related lncRNAs provide new research strategies for prognostic evaluation, molecular mechanism and clinical treatment of pancreatic cancer.

Activated autophagy of innate immune cells during the early stages of major trauma.

Trauma-induced immune dysfunction has been a major barrier to achieving reduced mortality, which is poorly understood. Autophagy is a crucial catabolic mechanism of immune cells during times of stress. Few studies have investigated the immune regulatory effects induced by autophagy after trauma. Here, we use single-cell transcriptomics analysis in a major trauma cohort to demonstrate the dominant role of autophagy in innate immune cells during the early stages of major trauma. Single-cell transcriptional profiling of peripheral blood mononuclear cells (PBMCs) was performed, which were sampled from three control participants and five major trauma patients within 6 hours of injury. In detail, after single-cell RNA-sequence data processing, cell type annotation and cluster marker identification were performed. A genetic toolbox with 604 autophagy-related genes was used to monitor the autophagy levels in immune cells. In addition, all transcriptome RNA sequencing data obtained from PBMCs in a cohort of 167 major trauma patients were downloaded from gene expression omnibus (GEO) datasets (GSE36809). Key deregulated biological processes and important autophagic hub genes involved in immune cells were identified by weighted gene co-expression network analysis and gene ontology enrichment analysis. A total of 20,445 differentially expressed genes were identified and five co-expression modules were constructed. Enrichment analysis indicated that activated autophagy is the most important biological process during the early stages of major trauma, and JMY (autophagy-related genes) were identified as hub genes. The single-cell transcriptional profiling of PBMCs demonstrated that all components of adaptive immune cells were significantly decreased, whereas components of innate immune cells (monocytes and neutrophils) were significantly increased in major trauma patients compared with control participants. Activated autophagy was detected in monocytes and neutrophils by monitoring the dynamic transcriptional signature of the autophagy-related genetic toolbox. Biological process analysis shows that antigen uptake, processing presentation, and major histocompatibility complex (MHC) class II protein complex assembly pathways were up-regulated in autophagy-positive monocytes, whereas antigen processing and presentation of endogenous antigen and type I interferon signaling pathways were up-regulated in autophagy-positive neutrophils during the early stages of major trauma. Our study demonstrated that autophagy is a biological process crucial to the development of immune disorders in the early stages of major trauma. Furthermore, the results of our study generated a comprehensive single-cell immune landscape for major trauma patients, in which we determined that autophagy profoundly affects the main functions of innate immune cells and provides insight into the cellular basis of immune dysregulation after major trauma.

Starvation-induced long non-coding RNAs are significant for prognosis evaluation of bladder cancer.

Starving intratumoral microenvironment prominently alters genic profiles including long non-coding RNAs (lncRNAs), which further regulate bladder cancer (BCa) malignant biological properties, such as invasion and migration. Transcriptome RNA-sequencing data of 414 BCa tumor tissues and 19 normal tissues were obtained from TCGA database and paired samples of 132 BCa patients. A chain of in vitro validations such as qPCR, migration and invasion assays were performed to reveal the clinical relevance of AC011472.4 and AL157895.1. A total of 11 lncRNAs were identified as starvation-related lncRNAs, of which AC011472.4 and AL157895.1 were relevant to overall survival of BCa patients. Besides, a starvation-related risk score model was established based on the levels of AC011472.4 and AL157895.1. BCa patients with higher levels of AL157895.1 were divided into the high-risk group and usually obtained higher mortality rate, but AC011472.4 was contrary. AL157895.1 expressed highly in BCa cell lines and tumour tissues, especially in patients with the advanced grade, stage and T-stage, while AC011472.4 showed the reversed result. Moreover, increased level of AL157895.1 was remarkably correlated to T-stage, muscle invasion status and distant metastasis. SiRNAs-mediated silence of AC011472.4 and AL157895.1 respectively increased and diminished invasion and migration properties of BCa cells. In this study, we highlight the significant roles of AC011472.4 and AL157895.1 on evaluating prognoses of BCa patients and validate their correlation with various clinical parameters. These findings provide an appropriate risk score model for BCa clinical decision making.

M7G-related lncRNAs are potential biomarkers for predicting prognosis and immune responses in patients with oral squamous cell carcinoma.

Among head and neck cancers, oral squamous cell carcinoma (OSCC) is the most common malignant tumor. N-7-methylguanosine (m7G) and lncRNAs are both related to the development and progression of tumors. Therefore, this study aims to explore and establish the prognostic signal of OSCC based on m7G-related lncRNAs. In this study, RNA sequencing transcriptome data of OSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Therefore, m7G-related lncRNAs were identified as differentially expressed in OSCC. Then, univariate Cox regression analysis and LASSO regression analysis were conducted to evaluate the prognostic significance of differentially expressed lncRNAs. Consequently, the abovementioned lncRNAs were assigned five OSCC patient risk scores, with high-risk and low-risk patients assigned to each group. Different signaling pathways were significantly enriched between the two groups as determined by set enrichment analysis (GSEA). Multivariate Cox regression analysis confirmed the factors used to construct the nomogram model. Then, the prognosis of the nomogram model was evaluated. Consequently, high-risk individuals had higher immune infiltration levels. According to the results of a study that evaluated the sensitivity of different risk subgroups to antitumour drugs, the high-risk group had a high sensitivity to doxorubicin. By performing real-time polymerase chain reaction (RT‒PCR), we verified the expression of these five m7G lncRNAs. Therefore, the model based on five m7G-related lncRNAs was able to predict the overall survival rates of OSCC patients and guide their treatment. It can also spur new ideas about how to prevent and treat OSCC.

Quantitative proteomic landscapes of primary and recurrent glioblastoma reveal a protumorigeneic role for FBXO2-dependent glioma-microenvironment interactions.

Recent efforts have described the evolution of glioblastoma from initial diagnosis to post-treatment recurrence on a genomic and transcriptomic level. However, the evolution of the proteomic landscape is largely unknown. Sequential window acquisition of all theoretical fragment ion spectra mass spectrometry (SWATH-MS) was used to characterize the quantitative proteomes of two independent cohorts of paired newly diagnosed and recurrent glioblastomas. Recurrence-associated proteins were validated using immunohistochemistry and further studied in human glioma cell lines, orthotopic xenograft models, and human organotypic brain slice cultures. External spatial transcriptomic, single-cell, and bulk RNA sequencing data were analyzed to gain mechanistic insights. Although overall proteomic changes were heterogeneous across patients, we identified BCAS1, INF2, and FBXO2 as consistently upregulated proteins at recurrence and validated these using immunohistochemistry. Knockout of FBXO2 in human glioma cells conferred a strong survival benefit in orthotopic xenograft mouse models and reduced invasive growth in organotypic brain slice cultures. In glioblastoma patient samples, FBXO2 expression was enriched in the tumor infiltration zone and FBXO2-positive cancer cells were associated with synaptic signaling processes. These findings demonstrate a potential role of FBXO2-dependent glioma-microenvironment interactions to promote tumor growth. Furthermore, the published datasets provide a valuable resource for further studies.

Integrating Bulk Transcriptome and Single-Cell RNA Sequencing Data Reveals the Landscape of the Immune Microenvironment in Thoracic Aortic Aneurysms.

Thoracic aortic aneurysm (TAA) is a life-threatening cardiovascular disease whose formation is reported to be associated with massive vascular inflammatory responses. To elucidate the roles of immune cell infiltration in the pathogenesis underlying TAA, we utilized multiple TAA datasets (microarray data and scRNA-seq data) and various immune-related algorithms (ssGSEA, CIBERSORT, and Seurat) to reveal the landscapes of the immune microenvironment in TAA. The results exhibited a significant increase in the infiltration of macrophages and T cells, which were mainly responsible for TAA formation among the immune cells. To further reveal the roles of immunocytes in TAA, we inferred the intercellular communications among the identified cells of aortic tissues. Notably, we found that in both normal aortic tissue and TAA tissue, the cells that interact most frequently are macrophages, endothelial cells (ECs), fibroblasts, and vascular smooth muscle cells (VSMCs). Among the cells, macrophages were the most prominent signal senders and receivers in TAA and normal aortic tissue. These findings suggest that macrophages play an important role in both the physiological and pathological conditions of the aorta. The present study provides a comprehensive evaluation of the immune cell composition and reveals the intercellular communication among aortic cells in human TAA tissues. These findings improve our understanding of TAA formation and progression and facilitate the development of effective medications to treat these conditions.

New Tricks with Old Dogs: Computational Identification and Experimental Validation of New miRNA-mRNA Regulation in hiPSC-CMs.

Cardiovascular disease is still the leading cause of morbidity and mortality worldwide. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have become a valuable widespread in vitro model to study cardiac disease. Herein, we employ the hiPSC-CM model to identify novel miRNA–mRNA interaction partners during cardiac differentiation and β-adrenergic stress. Whole transcriptome and small RNA sequencing data were combined to identify novel miRNA–mRNA interactions. Briefly, mRNA and miRNA expression profiles were integrated with miRNA target predictions to identify significant statistical dependencies between a miRNA and its candidate target set. We show by experimental validation that our approach discriminates true from false miRNA target predictions. Thereby, we identified several differentially expressed miRNAs and focused on the two top candidates: miR-99a-5p in the context of cardiac differentiation and miR-212-3p in the context of β-adrenergic stress. We validated some target mRNA candidates by 3′UTR luciferase assays as well as in transfection experiments in the hiPSC-CM model system. Our data show that iPSC-derived cardiomyocytes and computational modeling can be used to uncover new valid miRNA–mRNA interactions beyond current knowledge.

Prognosis Risk Model Based on Pyroptosis-Related lncRNAs for Bladder Cancer.

Objective Bladder cancer (BC) is the most common malignancy in the urinary system and is prone to recurrence and metastasis. Pyroptosis is a kind of cell necrosis that is triggered by the gasdermin protein family. lncRNAs are noncoding RNAs that are more than 200 nucleotides long. Both pyroptosis and lncRNAs are associated with tumor development and progression. This study is aimed at exploring and establishing a prognostic signature of BC based on pyroptosis-related lncRNAs. Methods In this study, The Cancer Genome Atlas (TCGA) database provided us with the RNA sequencing transcriptome data of bladder cancer patients, and we identified differentially expressed pyroptosis-related lncRNAs in bladder cancer. Then, the prognostic significance of these lncRNAs was assessed using univariate Cox regression analysis and LASSO regression analysis. Subsequently, 4 pyroptosis-related lncRNAs, namely, AL121652.1, AL161729.4, AC007128.1, and AC124312.3, were identified by multivariate Cox regression analysis, thus constructing the prognostic risk model. Then, we compared the levels of immune infiltration, differences in cell function, immune checkpoints, and m6A-related gene expression levels between the high- and low-risk groups. Result Patients were divided into low-risk or high-risk groups based on the median risk score. Kaplan–Meier survival analysis indicated that the overall survival of bladder cancer patients in the low-risk group was substantially superior to that in the high-risk group (p < 0.001). The receiver operating characteristic (ROC) curve further confirmed the credibility of our model. Moreover, gene set enrichment analysis (GSEA) indicated that these were different signal pathways significantly enriched between the two groups. Immune infiltration, immune checkpoint, and N6-methyladenosine-related gene analysis also reflected that there were notable differences between the two groups. Conclusion Therefore, this prognostic risk model is based on the level of pyroptotic lncRNAs, which is conducive to individualized assessment of the risk of patients and provides a reference for clinical treatment. This will also help provide insights into the prognosis and treatment of bladder cancer.

ETS Family Transcription Factor Fusions in Childhood AML: Distinct Expression Networks and Clinical Implications

The ETS family of genes encode transcription factors (TFs) containing the ETS DNA binding domain, which have roles in cellular growth and development, including embryonic hematopoiesis. Dysregulation of these genes is associated with malignant transformation and tumorigenesis. In childhood acute myeloid leukemia (AML), the MNX1-ETV6 t(7;12)(q36;p13) and FUS-ERG t(16;21)(p11;q22) fusions are associated with adverse outcome. However, the biological and clinical implications of other ETS oncofusions in pediatric AML remain unknown.We identified 62 ETS gene fusions in 1,473 primary diagnostic AML samples (4.2%) using whole transcriptome RNA-sequencing and karyotype data. Four ETS family genes were identified to be involved in fusions with various partner genes: ETV6, ERG, FEV, and FLI1. Fusions with ETV6 were the most abundant (58%, 36/62) while FEV and FLI1 were the least; 21 fusion partners were identified where many function as TFs and co-activators (Figure 1A). MNX1-ETV6 (N=16) is enriched in infants (87.5% < 3 years, p < 0.001, Figure 1B), consistent with previous reports. ETV6 fusions with various partners (ETV6-Other, N=20) were likewise enriched in infants (50% < 3 years) and were significantly younger than patients without ETS fusions (median age: 3.0 vs 9.6 years, p = 0.025). FUS-ERG (N=10), HNRNPH1-ERG (N=8), and ETS fusions with various partners (ETS-Other, N=8) were primarily identified in patients 5-18 years old.Outcomes for patients with ETS fusions was determined after censoring for stem cell transplantation. Patients with ETS fusions as a collective group had adverse outcome with an EFS of 17.7% vs 39.9% (p = 0.047, Figure 1C), similar to high-risk cases in the reference group (22%), suggesting that patients with any ETS fusion may be considered high risk. Evaluation of outcomes by individual fusion groups at 3 years from diagnosis demonstrated an EFS of 0% for HNRNPH1-ERG, 18.0% for FUS-ERG, 14.2% for ETV6-Other, 20.8% for ETS-Other, and 28.4% for those with MNX1-ETV6. Twenty-one patients (37.5%) with ETS family fusions received stem cell transplant (SCT) in first complete remission, including HNRNPH1-ERG (N=5), FUS-ERG (N=3), ETV6-Other (N=5), ETS-Other (N=2), and MNX1-ETV6 (N=6). For these SCT recipients, OS at 3 years after transplant was 60.2%.We investigated whether ETS family fusions might have similar transcriptome profiles. Unsupervised uniform manifold approximation and projection (UMAP) on RNA-seq gene expression data followed by Leiden clustering found that individual fusions clustered in the same 3D space. More importantly, ETS fusion groups clustered closely to one another, indicating a shared transcriptional profile (Figure 1D, circle). Next, ETS fusion groups were each independently compared to the reference cohort (N=1421) using differential expression (DE) analysis. Intersection of DE genes revealed 17 overexpressed genes common to ETS fusions and 9/17 (52%) were also dysregulated when contrasting ETS cohorts to healthy normal marrows' transcriptome (N=68, Figure 1E). The minimal set of dysregulated genes included an adhesion molecule EDIL3, a prostaglandin (PG) enzyme HPGD, and a tyrosine phosphatase PTP4A3, which is strongly associated with progression in lymphoblastic leukemia and multiple myeloma. EDIL3 was reported to be overexpressed in MNX1-ETV6 and we found this molecule is a common feature of ETS fusions and their cellular dysregulation. The minimal set of 9 genes were further investigated using protein interaction networks defined from Pathway Commons v11. FUS-ERG and HNRNPH1-ERG both had significantly (adj. p < 0.001) activated HPGD networks; PG-E synthase and > 10 PG metabolism genes were upregulated. PG metabolism has important roles in regulating hematopoietic stem and progenitor (HSPC) functions and PG-E2 was shown to increase HSPC survival. ETV6-MXN1, ETV6-Other, and FUS-ERG had activated PTP4A3 networks and its expression was associated with sensitivity to BET inhibitors (BETi) in myeloma. They also exhibited increased activity of an ERG network with the overexpression of upstream regulators CBFA2T3 and GATA, and downstream targets like VWF and ZBTB16.Overall, we show that ETS fusions are uniformly high risk and share dysregulated cell adhesion (EDIL3) and transcriptional networks for ERG, HPGD, and PTP4A3, which provide opportunities for further research into the metabolome and therapeutics (BETi) in these fusions. [Display omitted] DisclosuresShaw: T-Cell and/or Gene Therapy for Cancer: Patents & Royalties.

Sutural fibroblasts exhibit the function of vascular endothelial cells upon mechanical strain

Midfacial hypoplasia is a type of facial dysplasia. The technique of trans-sutural distraction osteogenesis promotes midface growth so as to ameliorate this symptom. In the process of distraction osteogenesis, the fiber matrix in the suture acts as a mechanical sensor. Compared with osteogenesis, the formation of collagen fibers by fibroblasts is significant in the early stage of sutural distraction. However the transformation of fibroblasts during sutural bone formation induced by tensile force is poorly characterized. Here, we used single-cell RNA sequencing to define the cell classification of the zygomatic maxillary suture and the changes of cell clusters in the suture before and after seven-day distraction. We identified twenty-nine cell subsets spanning monocyte/macrophages, neutrophils, red blood cells, B cells and fibroblasts. Compared with the control group, Monocle analysis revealed the emergence of a unique fibroblast subset (Cdh5+, Col4a1+, Fat1−, and Acta2−) (cluster 27) that expressed vascular endothelial cell genes within the distracted zygomatic maxillary suture. We constructed the differentiation trajectories of the fibroblast population (cluster 23, 27) in the suture before and after distraction. In addition, we clarified that a subset of fibroblasts (cluster 27) lost expression of Fat1, an upregulator of the Hippo pathway, and upregulated Cyr61, a downstream gene of the Hippo pathway, during the distraction process. Further enrichment analysis suggests that cells of the new subset (cluster 27) are undergoing conversion of their identity into a vascular endothelial cell-like state in response to mechanical stimulation, associated with upregulation of angiogenesis genes along the single-cell trajectory. Further immunofluorescence staining confirmed this phenomenon. A combined general transcriptome RNA sequencing data analysis demonstrated that the fibroblasts expressed a number of extracellular matrix-related genes under mechanical strain. These data together provide a new view of the role of fibroblasts in tension-induced sutural angiogenesis via interaction with the Hippo pathway.

Regulation of retrotransposition in Arabidopsis.

Plant genomes are largely comprised of retrotransposons which can replicate through 'copy and paste' mechanisms. Long terminal repeat (LTR) retrotransposons are the major class of retrotransposons in plant species, and importantly they broadly affect the expression of nearby genes. Although most LTR retrotransposons are non-functional, active retrotranspositions have been reported in plant species or mutants under normal growth condition and environmental stresses. With the well-defined reference genome and numerous mutant alleles, Arabidopsis studies have significantly expanded our understanding of retrotransposon regulation. Active LTR retrotransposon loci produce virus-like particles to perform reverse transcription, and their complementary DNA can be inserted into new genomic loci. Due to the detrimental consequences of retrotransposition, plants like animals, have developed transcriptional and post-transcriptional silencing mechanisms. Recently several different genome-wide techniques have been developed to understand LTR retrotransposition in Arabidopsis and different plant species. Transposome, methylome, transcriptome, translatome and small RNA sequencing data have revealed how host silencing mechanisms can affect multiple steps of retrotransposition. These recent advances shed light on future mechanistic studies of retrotransposition as well as retrotransposon diversity.

Analysis of temporal gene regulation of Listeria monocytogenes revealed distinct regulatory response modes after exposure to high pressure processing

BackgroundThe pathogen Listeria (L.) monocytogenes is known to survive heat, cold, high pressure, and other extreme conditions. Although the response of this pathogen to pH, osmotic, temperature, and oxidative stress has been studied extensively, its reaction to the stress produced by high pressure processing HPP (which is a preservation method in the food industry), and the activated gene regulatory network (GRN) in response to this stress is still largely unknown.ResultsWe used RNA sequencing transcriptome data of L. monocytogenes (ScottA) treated at 400 MPa and 8∘C, for 8 min and combined it with current information in the literature to create a transcriptional regulation database, depicting the relationship between transcription factors (TFs) and their target genes (TGs) in L. monocytogenes. We then applied network component analysis (NCA), a matrix decomposition method, to reconstruct the activities of the TFs over time. According to our findings, L. monocytogenes responded to the stress applied during HPP by three statistically different gene regulation modes: survival mode during the first 10 min post-treatment, repair mode during 1 h post-treatment, and re-growth mode beyond 6 h after HPP. We identified the TFs and their TGs that were responsible for each of the modes. We developed a plausible model that could explain the regulatory mechanism that L. monocytogenes activated through the well-studied CIRCE operon via the regulator HrcA during the survival mode.ConclusionsOur findings suggest that the timely activation of TFs associated with an immediate stress response, followed by the expression of genes for repair purposes, and then re-growth and metabolism, could be a strategy of L. monocytogenes to survive and recover extreme HPP conditions. We believe that our results give a better understanding of L. monocytogenes behavior after exposure to high pressure that may lead to the design of a specific knock-out process to target the genes or mechanisms. The results can help the food industry select appropriate HPP conditions to prevent L. monocytogenes recovery during food storage.