Comprehensive characterization of β-alanine metabolism-related genes in HCC identified a novel prognostic signature related to clinical outcomes

Hepatocellular carcinoma (HCC) stands out as the most prevalent type of liver cancer and a significant contributor to cancer-related fatalities globally. Metabolic reprogramming, particularly in glucose, lipid, and amino acid metabolism, plays a crucial role in HCC progression. However, the functions of β-alanine metabolism-related genes (βAMRGs) in HCC remain understudied. Therefore, a comprehensive evaluation of βAMRGs is required, specifically in HCC. Initially, we explored the pan-cancer landscape of βAMRGs, integrating expression profiles, prognostic values, mutations, and methylation levels. Subsequently, scRNA sequencing results indicated that hepatocytes had the highest scores of β-alanine metabolism. In the process of hepatocyte carcinogenesis, metabolic pathways were further activated. Using βAMRGs scores and expression profiles, we classified HCC patients into three subtypes and examined their prognosis and immune microenvironments. Cluster 3, characterized by the highest βAMRGs scores, displayed the best prognosis, reinforcing β-alanine's significant contribution to HCC pathophysiology. Notably, immune microenvironment, metabolism, and cell death modes significantly varied among the β-alanine subtypes. We developed and validated a novel prognostic panel based on βAMRGs and constructed a nomogram incorporating risk degree and clinicopathological characteristics. Among the model genes, EHHADH has been identified as a protective protein in HCC. Its expression was notably downregulated in tumors and exhibited a close correlation with factors such as tumor staging, grading, and prognosis. Immunohistochemical experiments, conducted using HCC tissue microarrays, substantiated the validation of its expression levels. In conclusion, this study uncovers β-alanine's significant role in HCC for the first time, suggesting new research targets and directions for diagnosis and treatment.

Identification of potential biomarkers for idiopathic pulmonary arterial hypertension using single-cell and bulk RNA sequencing analysis

Idiopathic pulmonary arterial hypertension (IPAH) is a rare and severe cardiopulmonary disease with a challenging prognosis, and its underlying pathogenesis remains elusive. A comprehensive understanding of IPAH is crucial to unveil potential diagnostic markers and therapeutic targets. In this study, we investigated cellular heterogeneity and molecular pathology in IPAH using single-cell RNA sequencing (scRNA-seq) analysis. Our scRNA-seq results revealed significant alterations in three crucial signaling pathways in IPAH: the hypoxia pathway, TGF β pathway, and ROS pathway, primarily attributed to changes in gene expression within arterial endothelial cells. Moreover, through bulk RNA sequencing analysis, we identified differentially expressed genes (DEGs) enriched in GO and KEGG pathways, implicated in regulating cell adhesion and oxidative phosphorylation in IPAH lungs. Similarly, DEGs-enriched pathways in IPAH arterial endothelial cells were also identified. By integrating DEGs from three IPAH datasets and applying protein-protein interaction (PPI) analysis, we identified 12 candidate biomarkers. Subsequent validation in two additional PAH datasets led us to highlight five potential biomarkers (CTNNB1, MAPK3, ITGB1, HSP90AA1, and DDX5) with promising diagnostic significance for IPAH. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) confirmed significant differences in the expression of these five genes in pulmonary arterial endothelial cells from PAH mice. In conclusion, our findings shed light on the pivotal role of arterial endothelial cells in the development of IPAH. Furthermore, the integration of single-cell and bulk RNA sequencing datasets allowed us to pinpoint novel candidate biomarkers for the diagnosis of IPAH. This work opens up new avenues for research and potential therapeutic interventions in IPAH management.

Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing

Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.

Combination of bulk RNA sequencing and scRNA sequencing uncover the molecular characteristics of MAPK signaling in kidney renal clear cell carcinoma

The MAPK signaling pathway significantly impacts cancer progression and resistance; however, its functions remain incompletely assessed across various cancers, particularly in kidney renal clear cell carcinoma (KIRC). Therefore, there is an urgent need for comprehensive pan-cancer investigations of MAPK signaling, particularly within the context of KIRC. In this research, we obtained TCGA pan-cancer multi-omics data and conducted a comprehensive analysis of the genomic and transcriptomic characteristics of the MAPK signaling pathway. For in-depth investigation in KIRC, status of MAPK pathway was quantitatively estimated by ssGSEA and Ward algorithm was utilized for cluster analysis. Molecular characteristics and clinical prognoses of KIRC patients with distinct MAPK activities were comprehensively explored using a series of bioinformatics algorithms. Subsequently, a combination of LASSO and COX regression analyses were utilized sequentially to construct a MAPK-related signature to help identify the risk level of each sample. Patients in the C1 subtype exhibited relatively higher levels of MAPK signaling activity, which were associated with abundant immune cell infiltration and favorable clinical outcomes. Single-cell RNA sequencing (scRNA-seq) analysis of KIRC samples identified seven distinct cell types, and endothelial cells in tumor tissues had obviously higher MAPK scores than normal tissues. The immunohistochemistry results indicated the reduced expression levels of PAPSS1, MAP3K11, and SPRED1 in KIRC samples. In conclusion, our study represents the first integration of bulk RNA sequencing and single-cell RNA sequencing to elucidate the molecular characteristics of MAPK signaling in KIRC, providing a solid foundation for precision oncology.

PIM kinases regulate early human Th17 cell differentiation

The serine/threonine-specific Moloney murine leukemia virus (PIM) kinase family (i.e., PIM1, PIM2, and PIM3) has been extensively studied in tumorigenesis. PIM kinases are downstream of several cytokine signaling pathways that drive immune-mediated diseases. Uncontrolled T helper 17 (Th17) cell activation has been associated with the pathogenesis of autoimmunity. However, the detailed molecular function of PIMs in human Th17 cell regulation has yet to be studied. In the present study, we comprehensively investigated how the three PIMs simultaneously alter transcriptional gene regulation during early human Th17 cell differentiation. By combining PIM triple knockdown with bulk and scRNA-seq approaches, we found that PIM deficiency promotes the early expression of key Th17-related genes while suppressing Th1-lineage genes. Further, PIMs modulate Th cell signaling, potentially via STAT1 and STAT3. Overall, our study highlights the inhibitory role of PIMs in human Th17 cell differentiation, thereby suggesting their association with autoimmune phenotypes.

Unraveling the enigma of B cells in diffuse large B-cell lymphoma: unveiling cancer stem cell-like B cell subpopulation at single-cell resolution

Background

Diffuse large B-cell lymphoma (DLBCL) represents the most prevalent form of aggressive non-Hodgkin lymphoma. Despite receiving standard treatment, a subset of patients undergoes refractory or recurrent cases, wherein the involvement of cancer stem cells (CSCs) could be significant.

Methods

We comprehensively characterized B cell subpopulations using single-cell RNA sequencing data from three DLBCL samples and one normal lymph tissue. The CopyKat R package was employed to assess the malignancy of B cell subpopulations based on chromosomal copy number variations. CIBERSORTx software was utilized to estimate the proportions of B cell subpopulations in 230 DLBCL tissues. Furthermore, we employed the pySCENIC to identify key transcription factors that regulate the functionality of B cell subpopulations. By employing CellphoneDB, we elucidated the interplay among tumor microenvironment components within the B cell subpopulations. Finally, we validated our findings through immunofluorescence experiments.

Results

Our analysis revealed a specific cancer stem cell-like B cell subpopulation exhibiting self-renewal and multilineage differentiation capabilities based on the exploration of B cell subpopulations in DLBCL and normal lymph tissues at the single-cell level. Notably, a high infiltration of cancer stem cell-like B cells correlated with a poor prognosis, potentially due to immune evasion mediated by low expression of major histocompatibility complex molecules. Furthermore, we identified key transcription factor regulatory networks regulated by HMGB3, SAP30, and E2F8, which likely played crucial roles in the functional characterization of the cancer stem cell-like B cell subpopulation. The existence of cancer stem cell-like B cells in DLBCL was validated through immunofluorescent staining. Finally, cell communication between B cells and tumor-infiltrating T cell subgroups provided further insights into the functional characterization of the cancer stem cell-like B cell subpopulation.

Conclusions

Our research provides a systematic description of a specific cancer stem cell-like B cell subpopulation associated with a poor prognosis in DLBCL. This study enhances our understanding of CSCs and identifies potential therapeutic targets for refractory or recurrent DLBCL patients.

Loss of function of ribosomal protein L13a blocks blastocyst formation and reveals a potential nuclear role in gene expression

Ribosomal proteins play diverse roles in development and disease. Most ribosomal proteins have canonical roles in protein synthesis, while some exhibit extra-ribosomal functions. Previous studies in our laboratory revealed that ribosomal protein L13a (RPL13a) is involved in the translational silencing of a cohort of inflammatory proteins in myeloid cells. This prompted us to investigate the role of RPL13a in embryonic development. Here we report that RPL13a is required for early development in mice. Crosses between Rpl13a+/- mice resulted in no Rpl13a-/- offspring. Closer examination revealed that Rpl13a-/- embryos were arrested at the morula stage during preimplantation development. RNA sequencing analysis of Rpl13a-/- morulae revealed widespread alterations in gene expression, including but not limited to several genes encoding proteins involved in the inflammatory response, embryogenesis, oocyte maturation, stemness, and pluripotency. Ex vivo analysis revealed that RPL13a was localized to the cytoplasm and nucleus between the two-cell and morula stages. RNAi-mediated depletion of RPL13a phenocopied Rpl13a-/- embryos and knockdown embryos exhibited increased expression of IL-7 and IL-17 and decreased expression of the lineage specifier genes Sox2, Pou5f1, and Cdx2. Lastly, a protein-protein interaction assay revealed that RPL13a is associated with chromatin, suggesting an extra ribosomal function in transcription. In summary, our data demonstrate that RPL13a is essential for the completion of preimplantation embryo development. The mechanistic basis of the absence of RPL13a-mediated embryonic lethality will be addressed in the future through follow-up studies on ribosome biogenesis, global protein synthesis, and identification of RPL13a target genes using chromatin immunoprecipitation and RNA-immunoprecipitation-based sequencing.

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Bulk deconvolution with single-cell/nucleus RNA-seq data is critical for understanding heterogeneity in complex biological samples, yet the technological discrepancy across sequencing platforms limits deconvolution accuracy. To address this, we introduce an experimental design to match inter-platform biological signals, hence revealing the technological discrepancy, and then develop a deconvolution framework called DeMixSC using the better-matched, i.e., benchmark, data. Built upon a novel weighted nonnegative least-squares framework, DeMixSC identifies and adjusts genes with high technological discrepancy and aligns the benchmark data with large patient cohorts of matched-tissue-type for large-scale deconvolution. Our results using a benchmark dataset of healthy retinas suggest much-improved deconvolution accuracy. Further analysis of a cohort of 453 patients with age-related macular degeneration supports the broad applicability of DeMixSC. Our findings reveal the impact of technological discrepancy on deconvolution performance and underscore the importance of a well-matched dataset to resolve this challenge. The developed DeMixSC framework is generally applicable for deconvolving large cohorts of disease tissues, and potentially cancer.

Exploring key genes associated with neutrophil function and neutrophil extracellular traps in heart failure: a comprehensive analysis of single-cell and bulk sequencing data

Background: Heart failure (HF) is a complex and heterogeneous manifestation of multiple cardiovascular diseases that usually occurs in the advanced stages of disease progression. The role of neutrophil extracellular traps (NETs) in the pathogenesis of HF remains to be explored. Methods: Bioinformatics analysis was employed to investigate general and single-cell transcriptome sequencing data downloaded from the GEO datasets. Differentially expressed genes (DEGs) associated with NETs in HF patients and healthy controls were identified using transcriptome sequencing datasets and were subsequently subjected to functional enrichment analysis. To identify potential diagnostic biomarkers, the random forest algorithm (RF) and the least absolute shrinkage and selection operator (LASSO) were applied, followed by the construction of receiver operating characteristic (ROC) curves to assess accuracy. Additionally, single-cell transcriptome sequencing data analysis identified key immune cell subpopulations in TAC (transverse aortic constriction) mice potentially involved in NETs regulation. Cell-cell communication analysis and trajectory analysis was then performed on these key cell subpopulations. Results: We identified thirteen differentially expressed genes (DEGs) associated with NET through differential analysis of transcriptome sequencing data from HF (heart failure) samples. Utilizing the Random Forest and Lasso algorithms, along with experimental validation, we successfully pinpointed four diagnostic markers (CXCR2, FCGR3B, VNN3, and FPR2) capable of predicting HF risk. Furthermore, our analysis of intercellular communication, leveraging single-cell sequencing data, highlighted macrophages and T cells as the immune cell subpopulations with the closest interactions with neutrophils. Pseudo-trajectory analysis sheds light on the differentiation states of distinct neutrophil subpopulations. Conclusion: In this study, we conducted an in-depth investigation into the functions of neutrophil subpopulations that infiltrate cardiac tissue in TAC mice. Additionally, we identified four biomarkers (CXCR2, FCGR3B, VNN3, and FPR2) associated with NETs in HF. Our findings enhance the understanding of immunology in HF.

CXCR4 Expressed by Tumor-Infiltrating B Cells in Gastric Cancer Related to Survival in the Tumor Microenvironment: An Analysis Combining Single-Cell RNA Sequencing with Bulk RNA Sequencing

According to the World Health Organization (WHO), gastric cancer (GC) is the fourth leading cause of tumor-related mortality globally and one of the most prevalent malignant tumors. To better understand the role of tumor-infiltrating B cells (TIBs) in GC, this work used single-cell RNA sequencing (scRNA-Seq) and bulk RNA sequencing (bulk RNA-Seq) data to identify candidate hub genes. Both scRNA-Seq and bulk RNA-Seq data for stomach adenocarcinoma (STAD) were obtained from the GEO and TCGA databases, respectively. Using scRNA-seq data, the FindNeighbors and FindClusters tools were used to group the cells into distinct groups. Immune cell clusters were sought in the massive RNA-seq expression matrix using the single-sample gene set enrichment analysis (ssGSEA). The expression profiles were used in Weighted Gene Coexpression Network Analysis (WGCNA) to build TCGA's gene coexpression networks. Next, univariate Cox regression, LASSO regression, and Kaplan-Meier analyses were used to identify hub genes in scRNA-seq data from sequential B-cell analyses. Finally, we examined the correlation between the hub genes and TIBs utilizing the TISIDB database. We confirmed the immune-related markers in clinical validation samples using reverse transcriptase polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC). 15 cell clusters were classified in the scRNA-seq database. According to the WGCNA findings, the green module is most associated with cancer and B cells. The intersection of 12 genes in two separate datasets (scRNA and bulk) was attained for further analysis. However, survival studies revealed that increased C-X-C motif chemokine receptor 4 (CXCR4) expression was linked to worse overall survival. CXCR4 expression is correlated with active, immature, and memory B cells in STAD were identified. Finally, RT-PCR and IHC assays verified that in GC, CXCR4 is overexpressed, and its expression level correlates with TIBs. We used scRNA-Seq and bulk RNA-Seq to study STAD's cellular composition. We found that CXCR4 is highly expressed by TIBs in GC, suggesting that it may serve as a hub gene for these cells and a starting point for future research into the molecular mechanisms by which these immune cells gain access to tumors and potentially identify therapeutic targets.

Analysis of immunogenic cell death in atherosclerosis based on scRNA-seq and bulk RNA-seq data

BACKGROUND

Regulated cell death plays a very important role in atherosclerosis (AS). Despite a large number of studies, there is a lack of literature on immunogenic cell death (ICD) in AS.

METHOD

Carotid atherosclerotic plaque single-cell RNA (scRNA) sequencing data were analyzed to define involved cells and determine their transcriptomic characteristics. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, CIBERSORT, ESTIMATE and ssGSEA (Gene Set Enrichment Analysis), consensus clustering analysis, random forest (RF), Decision Curve Analysis (DCA), and the Drug-Gene Interaction and DrugBank databases were applied for bulk sequencing data. All data were downloaded from Gene Expression Omnibus (GEO).

RESULT

mDCs and CTLs correlated obviously with AS occurrence and development (k2(mDCs) = 48.333, P < 0.001; k2(CTL) = 130.56, P < 0.001). In total, 21 differentially expressed genes were obtained for the bulk transcriptome; KEGG enrichment analysis results were similar to those for differentially expressed genes in endothelial cells. Eleven genes with a gene importance score > 1.5 were obtained in the training set and validated in the test set, resulting in 8 differentially expressed genes for ICD. A model to predict occurrence of AS and 56 drugs that may be used to treat AS were obtained with these 8 genes.

CONCLUSION

Immunogenic cell death occurs mainly in endothelial cells in AS. ICD maintains chronic inflammation in AS and plays a crucial role in its occurrence and development. ICD related genes may become drug-targeted genes for AS treatment.

Cadmium-induced Sertoli cell injury through p38-MAPK and related signaling proteins - a study by RNA sequencing

Environmental toxicants, such as cadmium, found in foods, water and consumer products are known to induce male reproductive dysfunction. However, the underlying molecular mechanism(s) by which cadmium-induced Sertoli cell injury as manifested by a disruption of the blood-testis barrier remains unknown. Interestingly, one of the primary targets of cadmium toxicity in the testis is the cytoskeletons of the Sertoli cells, which, in turn, impedes cell junctions in the seminiferous epithelium. In order to expand these earlier observations and to provide a roadmap for future studies, we embarked a study using RNA-Seq to identify the pertinent genes involved in cadmium-induced Sertoli cell injury. Using bioinformatics analyses, multiple gene sets that regulated actin and microtubule (MT) cytoskeletons were identified along with components of the MAPK (mitogen activated protein kinase) signaling protein and several signaling pathways. More important, we have also discovered that while the gene expression of p38-MAPK (also JNK or c-Jun) was considerably up- or down-regulated during cadmium-induced Sertoli cell injury, the activated (phosphorylated) form was up-regulated. Importantly, doramapimod (BIRB 796), a specific p38-MARK inhibitor, that was shown to selectively block cadmium-induced p-p38 MAPK activation via phosphorylation in Sertoli cells, was indeed capable of blocking cadmium-induced Sertoli cell injury including disruption of the Sertoli cell-permeability barrier function, disruptive distribution of BTB-associated proteins, and disruptive organization of the actin and MT cytoskeletons. These data provide a helpful source of information for investigators to probe the role of signaling proteins and/or cascades, besides MAPKs, that likely utilized by cadmium to induce reproductive dysfunction.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Integration of single-cell and bulk transcriptomics reveals immune-related signatures in keloid

BACKGROUND

Keloid is a pathological dermatological condition that manifests as an overgrowth scar secondary to skin trauma. This study endeavored to excavate immune-related signatures of keloid based on single-cell RNA (scRNA) sequencing data and bulk RNA sequencing data.

METHOD

The keloid-relevant scRNA sequencing dataset GSE163973 and bulk RNA sequencing dataset GSE113619 were mined from the GEO database. The "Seurat" R package was utilized for data quality control, cell clustering, and investigation of marker genes of each cell cluster. The "SingleR" package helped match the marker genes of the corresponding cluster to specific cell types. Moreover, the R package "Monocle" was deployed for pseudotemporal ordering analysis, and the "clusterProfiler" was applied for functional and pathway enrichment analysis. The immune-related signatures were then identified, and potential targeted drugs were predicted via the DGIdb database. Verification of the immune-related signatures in clinical validation samples was implemented by RT-qPCR.

RESULTS

Totally 23 cell clusters were screened and classified into 10 cell types based on the scRNA sequencing data. The keloid group had a significantly higher endothelial cell proportion than the control group. As enrichment analysis was applied in both differentially expressed genes (DEGs) of scRNA and bulk RNA sequencing data, we found they were enriched in multiple common immune-related pathways and biological processes. Meanwhile, we acquired three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) by intersecting the above DEGs with immune-related genes (IRGs). Then, we predicted 16 drugs potentially targeting the biomarkers through the DGIdb database. Finally, the outcome of RT-qPCR of clinical validation samples further verified the results.

CONCLUSION

In conclusion, we analyzed the cell types and functional differences in the keloid through scRNA and bulk RNA sequencing data. We identified three immune-related signatures (VCAM1, CALCRL, and HLA-DPB1) in keloid, providing a basis for further in-depth investigation of the molecular mechanisms of keloid and exploration of therapeutic targets.

Integrated single-cell and bulk RNA sequencing analyses reveal a prognostic signature of cancer-associated fibroblasts in head and neck squamous cell carcinoma

Objectives: To identify a prognosis-related subtype of cancer-associated fibroblasts (CAFs) in head and neck squamous cell carcinoma (HNSCC) and comprehend its contributions to molecular characteristics, immune characteristics, and their potential benefits in immunotherapy and chemotherapy for HNSCC. Materials and Methods: We performed single-cell RNA sequencing (scRNA-seq) analysis of CAFs from the samples of HNSCC patients derived from Gene Expression Omnibus (GEO), to identify the prognosis-related subtype of CAFs. CAFs were clustered into five subtypes, and a prognosis-related subtype was identified. Univariate and multivariate cox regression analyses were performed on the cohort selected from The Cancer Genome Atlas (TCGA) to determine signature construction, which was validated in GSE65858 and GSE42743. A prognostic signature based on 4 genes was constructed, which were derived from prognosis-related CAFs. The molecular characteristics, immune characteristics as well as the predicted chemosensitivity and immunotherapeutic response in the signature-defined subgroups were analyzed subsequently. Results: The patients with higher CAF scores correlated with poor survival outcomes. Additionally, a high CAF score correlated with lower infiltration levels of many immune cells including M1 macrophages, CD8⁺ T cells, follicular T helper cells, monocytes, and naïve B cells. High CAF score also demonstrated different enrichment pathways, mutation genes and copy number variated genes. Furthermore, patients with high CAF scores showed lower sensitivity for chemotherapy and immunotherapy than those with low CAF scores. Conclusion: The results of our study indicate the potential of the CAF signature as a biomarker for the prognosis of HNSCC patients. Furthermore, the signature could be a prospective therapeutic target in HNSCC.

Transcriptome profiling of abdominal aortic tissues reveals alterations in mRNAs of Takayasu arteritis

Takayasu arteritis (TA) is a chronic granulomatous vasculitis involving in the main branches of aorta. Previous studies mainly used peripheral blood and some vascular tissues but seldom studies have sequenced vascular tissues. Here in this study, we aimed to explore the alterations of mRNA in TA by performing bulk RNA sequencing. A total of 14 abdominal aortic tissues including 8 from renal transplantation and 6 from patient with TA undergoing bypass surgeries. Bulk RNA sequencing were performed and after the quality control, a total of 1897 transcripts were observed to be significantly differently (p < 0.05 and Log2FC > 1) expressed between the TA and control group, among which 1,361 transcripts were in TA group and 536 in the Control group. Reactome Pathway Enrichment Comparison analysis revealed interleukin-10 signaling and signaling by interleukins were highly expressed in TA group. Besides, extracellular matrix organization was also observed in this group. WGCNA and PPI obtained 26 core genes which were highly correlated with the clinical phenotype. We then also perform deconvolution of the bulk RNA-seq data by using the scRNA-seq dataset and noticed the high proportion of smooth muscle cells in our dataset. Additionally, immunohistochemical staining confirmed our bioinformatic analysis that TA aortic tissues express high levels of IL-1R1 and IL-1R2. Briefly, this study revealed critical roles of interleukins in TA pathogenesis, and SMCs may also participate in the reconstruction in vessel wall at late stage of TA.

Phenotypic and Transcriptional Changes of Pulmonary Immune Responses in Dogs Following Canine Distemper Virus Infection

Canine distemper virus (CDV), a morbillivirus within the family Paramyxoviridae, is a highly contagious infectious agent causing a multisystemic, devastating disease in a broad range of host species, characterized by severe immunosuppression, encephalitis and pneumonia. The present study aimed at investigating pulmonary immune responses of CDV-infected dogs in situ using immunohistochemistry and whole transcriptome analyses by bulk RNA sequencing. Spatiotemporal analysis of phenotypic changes revealed pulmonary immune responses primarily driven by MHC-II⁺, Iba-1⁺ and CD204⁺ innate immune cells during acute and subacute infection phases, which paralleled pathologic lesion development and coincided with high viral loads in CDV-infected lungs. CD20⁺ B cell numbers initially declined, followed by lymphoid repopulation in the advanced disease phase. Transcriptome analysis demonstrated an increased expression of transcripts related to innate immunity, antiviral defense mechanisms, type I interferon responses and regulation of cell death in the lung of CDV-infected dogs. Molecular analyses also revealed disturbed cytokine responses with a pro-inflammatory M1 macrophage polarization and impaired mucociliary defense in CDV-infected lungs. The exploratory study provides detailed data on CDV-related pulmonary immune responses, expanding the list of immunologic parameters potentially leading to viral elimination and virus-induced pulmonary immunopathology in canine distemper.

Precise reconstruction of the TME using bulk RNA-seq and a machine learning algorithm trained on artificial transcriptomes

Cellular deconvolution algorithms virtually reconstruct tissue composition by analyzing the gene expression of complex tissues. We present the decision tree machine learning algorithm, Kassandra, trained on a broad collection of >9,400 tissue and blood sorted cell RNA profiles incorporated into millions of artificial transcriptomes to accurately reconstruct the tumor microenvironment (TME). Bioinformatics correction for technical and biological variability, aberrant cancer cell expression inclusion, and accurate quantification and normalization of transcript expression increased Kassandra stability and robustness. Performance was validated on 4,000 H&E slides and 1,000 tissues by comparison with cytometric, immunohistochemical, or single-cell RNA-seq measurements. Kassandra accurately deconvolved TME elements, showing the role of these populations in tumor pathogenesis and other biological processes. Digital TME reconstruction revealed that the presence of PD-1-positive CD8⁺ T cells strongly correlated with immunotherapy response and increased the predictive potential of established biomarkers, indicating that Kassandra could potentially be utilized in future clinical applications.

Ovarian cancer-specific dysregulated genes with prognostic significance: scRNA-Seq with bulk RNA-Seq data and experimental validation

A major cause of gynecological cancer -related deaths worldwide, ovarian cancer is characterized by heterogeneity in both tumor cells and the tumor microenvironment (TME). Our study aimed to characterize tumor cell heterogeneity and the infiltration of M2 tumor-associated macrophages (TAMs) in the ovarian cancer TME by single-cell RNA-Seq (scRNA-Seq) analysis combined with bulk RNA sequencing (bulk RNA-Seq). Several highly variable genes were identified in ovarian cancer tissues, and tumor cell heterogeneity and infiltrating immune tumor cell heterogeneity were characterized in ovarian cancer cells. M2 TAMs in the TME were the predominant phenotype of TAM. Further, M2 TAM infiltration in the TME was negatively correlated with poor prognosis of ovarian cancer patients. Four M2 TAM-associated genes (SLAMF7, GNAS, TBX2-AS1, and LYPD6) correlated with the prognostic survival of ovarian cancer patients. Knockdown of SLAMF7 or GNAS mRNA repressed malignancy and cisplatin resistance of ovarian cancer cells. ScRNA-Seq combined with bulk RNA-Seq identified the same four genes associated with M2 TAMs. The prognostic risk score model based on these four genes may hold favorable predictive value for the prognosis of ovarian cancer patients.

Combining bulk and single-cell RNA-sequencing data to reveal gene expression pattern of chondrocytes in the osteoarthritic knee

Osteoarthritis (OA) occurs mostly in the knees, hips, finger interphalangeal joints, and spinal facet joints, and is characterized by cartilage degeneration. The existing bulk RNA sequencing (bulk RNA-seq) and single-cell sequencing (scRNA-seq) data for chondrocytes in the osteoarthritic knee joint provide the expression profiles of entire cell populations and individual cells, respectively. Here, we aimed to analyze these two types of sequencing data in order to obtain a more comprehensive understanding of OA. We compared the analysis results of bulk RNA-seq and scRNA-seq from the dataset GSE114007 and the dataset GSE104782, respectively, and identified the differentially expressed genes (DEGs). Then, we tried to find the key The transcription factor is a more fomal term (TFs) and long non-coding RNA (lncRNA) regulation. We highlighted 271 genes that were simultaneously suggested by these two types of data and provided their possible expression pattern in OA. Among the 271 genes, we identified 14 TFs, and TWIST2, MYBL2, RELA, JUN, KLF4, and PTTG1 could be the key TFs for the 271 genes. We also found that 8 lncRNAs among the 271 genes and the lncRNA regulation between CYTOR and NRP1 could contribute to the pain and vascularization of cartilage in the osteoarthritic knee. In short, our research combined the analysis results of bulk RNA-seq and scRNA-seq data for OA chondrocytes, which will contribute to further elucidation of the molecular mechanisms of OA pathogenesis.

Genetic Signatures From RNA Sequencing of Pediatric Localized Scleroderma Skin

The purpose of this study was to explore the skin transcriptional profile in pediatric localized scleroderma (LS) to provide a better understanding of the altered immune and fibrotic pathways promoting disease. LS is a progressive disease of the skin and underlying tissue that causes significant functional disability and disfigurement, especially in developing children. RNA sequencing (RNAseq) technology allows for improved understanding of relevant cellular expression through transcriptome analysis of phases during LS disease progression (more active/inflammatory vs. inactive/fibrotic) and also permits the use of RNA extracted from existing paraffin-embedded skin tissue, which is important in pediatrics. A strong correlation was observed between the comparison of genes expressed between fresh (RNAlater) and paraffinized skin in healthy and LS subjects, supporting the use of paraffinized tissue. LS gene signatures compared to healthy controls showed a distinct expression of an inflammatory response gene signature (IRGS) composed of IFNγ-, IFNα-, and TNFα-associated genes. GSEA^© enrichment analysis showed that the IRGS, including interferon-inducible chemokines such as CXCL9, CXCL10, CXCL11, and IFNγ itself, was more highly expressed in LS patients with more inflammatory lesions. The use of paraffinized skin for sequencing was proven to be an effective substitute for fresh skin by comparing gene expression profiles. The prevalence of the IFNγ signature in the lesion biopsies of active LS patients indicates that these genes reflect clinical activity parameters and may be the promoters of early, inflammatory disease.

SCDC: bulk gene expression deconvolution by multiple single-cell RNA sequencing references

Recent advances in single-cell RNA sequencing (scRNA-seq) enable characterization of transcriptomic profiles with single-cell resolution and circumvent averaging artifacts associated with traditional bulk RNA sequencing (RNA-seq) data. Here, we propose SCDC, a deconvolution method for bulk RNA-seq that leverages cell-type specific gene expression profiles from multiple scRNA-seq reference datasets. SCDC adopts an ENSEMBLE method to integrate deconvolution results from different scRNA-seq datasets that are produced in different laboratories and at different times, implicitly addressing the problem of batch-effect confounding. SCDC is benchmarked against existing methods using both in silico generated pseudo-bulk samples and experimentally mixed cell lines, whose known cell-type compositions serve as ground truths. We show that SCDC outperforms existing methods with improved accuracy of cell-type decomposition under both settings. To illustrate how the ENSEMBLE framework performs in complex tissues under different scenarios, we further apply our method to a human pancreatic islet dataset and a mouse mammary gland dataset. SCDC returns results that are more consistent with experimental designs and that reproduce more significant associations between cell-type proportions and measured phenotypes.