Coupled scRNA-seq and Bulk-seq reveal the role of HMMR in hepatocellular carcinoma
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PROVIDER: S-EPMC11021596 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundStudies have shown that hepatocellular carcinoma (HCC) heterogeneity is a main cause leading to failure of treatment. Technology of single-cell sequencing (scRNA) could more accurately reveal the essential characteristics of tumor genetics.MethodsFrom the Gene Expression Omnibus (GEO) database, HCC scRNA-seq data were extracted. The FindCluster function was applied to analyze cell clusters. Autophagy-related genes were acquired from the MSigDB database. The ConsensusClusterPlus package was used to identify molecular subtypes. A prognostic risk model was built with the Least Absolute Shrinkage and Selection Operator (LASSO)-Cox algorithm. A nomogram including a prognostic risk model and multiple clinicopathological factors was constructed.ResultsEleven cell clusters labeled as various cell types by immune cell markers were obtained from the combined scRNA-seq GSE149614 dataset. ssGSEA revealed that autophagy-related pathways were more enriched in malignant tumors. Two autophagy-related clusters (C1 and C2) were identified, in which C1 predicted a better survival, enhanced immune infiltration, and a higher immunotherapy response. LASSO-Cox regression established an eight-gene signature. Next, the HCCDB18, GSA14520, and GSE76427 datasets confirmed a strong risk prediction ability of the signature. Moreover, the low-risk group had enhanced immune infiltration and higher immunotherapy response. A nomogram which consisted of RiskScore and clinical features had better prediction ability.ConclusionTo precisely assess the prognostic risk, an eight-gene prognostic stratification signature was developed based on the heterogeneity of HCC immune cells.
| S-EPMC9606610 | biostudies-literature
Project description:Ferroptosis is a recently discovered mode of cell death that inhibits tumor growth. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for analyzing tumor heterogeneity and the immune microenvironment at the single-cell level. We used CIBERSORT to identify cellular immune scores and found that monocytes had significantly infiltrated and were correlated with prognosis in cholangiocarcinoma. scRNA-seq data were extracted from the Gene Expression Omnibus database, and the FindCluster() package was used for cell cluster analysis, which obtained 21 cell clusters, and there was increased TNFSF13B-TFRC intercellular communication between monocytes and cholangiocytes. A weighted correlation network analysis was performed with the WGCNA package to obtain monocyte-related gene modules. Univariate and multivariate Cox analyses were then performed to further establish the signature, and the reliability of the signature was assessed by receiver operating characteristic curve and decision curve analysis. A nomogram signature based on the Kaplan-Meier survival analysis was established. We found that the communication between monocytes and malignant cells in cholangiocarcinoma may be a regulatory factor of ferroptosis in cancer cells. The prognostic stratification system of the three-gene signature related to monocytes and ferroptosis can accurately assess the prognostic risk for cholangiocarcinoma.
| S-EPMC9573880 | biostudies-literature
Project description:Disulfidptosis is a newly discovered mode of cell death. However, its biological mechanism in bladder cancer (BLCA) is still uncharacterized. In this investigation, we firstly examined the expression and mutation of disulfidptosis-related genes (DRGs) in BLCA. Two disulfidptosis phenotypes associated with DRGs expression patterns and immune cell infiltration were built. A disulfidptosis risk score signature was constructed based on ten differentially expressed genes (DEGs) between the disulfidptosis subtypes, which allowed patients to be stratified into high- and low-risk groups. We further confirmed that the disulfidptosis risk score signature has great power to predict prognosis, immune cell infiltration, and immunotherapy efficacy in BLCA. Additionally, we analyzed the differences in therapeutic sensitivities between high- and low-risk groups concerning targeted inhibitor therapy and immunotherapy. Analysis of single-cell RNA sequencing was conducted of the ten hub DRGs. Of the ten genes, we found that DUSP2 and SLCO1B3 were differentially expressed in BLCA tissues and adjacent normal tissues, and were markedly associated with patients' prognosis. Functional experiments revealed that overexpression of DUSP2 or knockdown of SLCO1B3 significantly inhibited cell proliferation, migration, and invasion in BLCA cells. In all, we present a fresh disulfidptosis-related prognostic signature, which has a remarkable capacity to characterize the immunological landscape and prognosis of BLCA patients.
| S-EPMC11006495 | biostudies-literature
Project description:BackgroundAltered glucose metabolism is a critical characteristic from the beginning stage of esophageal squamous cell carcinoma (ESCC), and the phenomenon is presented as a pink-color sign under endoscopy after iodine staining. Therefore, calculating the metabolic score based on the glucose metabolic gene sets may bring some novel insights, enabling the prediction of prognosis and the identification of treatment choices for ESCC.MethodsA total of 8, 99, and 140 individuals from The Gene Expression Omnibus database, The Cancer Genome Atlas database, and the Memorial Sloan Kettering Cancer Center, respectively, were encompassed in the investigation. Patients diagnosed with ESCC after surgery were enrolled for further validation.ResultsA total of 13 kinds of cell clusters were screened, and the squamous epithelium was identified with the highest score. And 558 differential genes were selected from the single-cell RNA sequencing (scRNA-seq) dataset. Four glucose metabolism-related genes, namely, SERP1, CTSC, RAP2B, and SSR4, were identified as hub genes to develop a risk prognostic model. The model was validated in another external cohort. According to the risk score (RS) determined by the model, the patients were categorized into low- and high-risk groups (LRG and HRG). Compared with LRG, HRG indicated poor survival and decreased drug sensitivity. Additionally, the immune microenvironment and pathway enrichment were different between the two groups. Immunohistochemical staining revealed that hub genes were expressed differently in ESCC tissues, high- and low-grade intraepithelial neoplasia, and adjacent normal tissues.ConclusionFour hub genes (SERP1, CTSC, RAP2B, and SSR4) screened based on glucose metabolism developed a predictive model in ESCC patients. The RS was established as an independent risk factor for predicting prognosis. These findings may enhance understanding of ESCC's molecular profile and serve as a new prognostic tool for better patient stratification and treatment planning in clinical practice.
| S-EPMC11751878 | biostudies-literature
Project description:Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3' end of polyadenylated RNAs, recent studies have shown that "off-target" reads can constitute a substantial portion of the read population. In this work, we introduced scCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We applied scCensus to seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses. Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines.
| S-EPMC10862729 | biostudies-literature
Project description:BackgroundBreast carcinoma is the most common malignancy among women worldwide. It is characterized by a complex tumor microenvironment (TME), in which there is an intricate combination of different types of cells, which can cause confusion when screening tumor-cell-related signatures or constructing a gene co-expression network. The recent emergence of single-cell RNA sequencing (scRNA-seq) is an effective method for studying the changing omics of cells in complex TMEs.MethodsThe Dysregulated genes of malignant epithelial cells was screened by performing a comprehensive analysis of the public scRNA-seq data of 58 samples. Co-expression and Gene Set Enrichment Analysis (GSEA) analysis were performed based on scRNA-seq data of malignant cells to illustrate the potential function of these dysregulated genes. Iterative LASSO-Cox was used to perform a second-round screening among these dysregulated genes for constructing risk group. Finally, a breast cancer prognosis prediction model was constructed based on risk grouping and other clinical characteristics.ResultsOur results indicated a transcriptional signature of 1,262 genes for malignant breast cancer epithelial cells. To estimate the function of these genes in breast cancer, we also constructed a co-expression network of these dysregulated genes at single-cell resolution, and further validated the results using more than 300 published transcriptomics datasets and 31 Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) screening datasets. Moreover, we developed a reliable predictive model based on the scRNA-seq and bulk-seq datasets.ConclusionsOur findings provide insights into the transcriptomics and gene co-expression networks during breast carcinoma progression and suggest potential candidate biomarkers and therapeutic targets for the treatment of breast carcinoma. Our results are available via a web app (https://prognosticpredictor.shinyapps.io/GCNBC/).
| S-EPMC9843357 | biostudies-literature
Project description:Obesity affects the function of multiple organs/tissues including the exocrine organ salivary glands. However, the effects of obesity on transcriptomes and cell compositions in the salivary glands have yet been studied by bulk RNA-sequencing and single-cell RNA-sequencing. Besides, the cell types in the sublingual gland, one of the three major salivary glands, have yet been characterized by the approach of single-cell RNA-sequencing. In this report, we find that the histological structure of the three major salivary glands are not obviously affected in the obese mice. Bulk RNA-sequencing analysis shows that the most prominent changes observed in the three major salivary glands of the obese mice are the mobilization of transcriptomes related to the immune response and down-regulation of genes related to the secretory function of the salivary glands. Based on single-cell RNA-sequencing analysis, we identify and annotate 17 cell clusters in the sublingual gland for the first time, and find that obesity alters the relative compositions of immune cells and secretory cells in the major glands of obese mice. Integrative analysis of the bulk RNA-sequencing and single-cell RNA-sequencing data confirms the activation of immune response genes and compromise of secretory function in the three major salivary glands of obese mice. Consequently, the secretion of extracellular matrix proteins is significantly reduced in the three major salivary glands of obese mice. These results provide new molecular insights into understanding the effect of obesity on salivary glands.
| S-EPMC9735269 | biostudies-literature
Project description:Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared with existing single-cell methodologies, raising questions regarding the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.
| S-EPMC11444184 | biostudies-literature
Project description:BackgroundGlioma is the predominant malignant brain tumor that lacks effective treatment options due to its shielding by the blood-brain barrier (BBB). Astrocytes play a role in the development of glioma, yet the diverse cellular composition of astrocytoma has not been thoroughly researched.MethodsWe examined the internal diversity of seven distinct astrocytoma subgroups through single-cell RNA sequencing (scRNA-seq), pinpointed crucial subgroups using CytoTRACE, monocle2 pseudotime analysis, and slingshot pseudotime analysis, employed various techniques to identify critical subgroups, and delved into cellular communication analysis. Then, we combined the clinical information of GBM patients and used bulk RNA sequencing (bulk RNA-seq) to analyze the prognostic impact of the relevant molecules on GBM patients, and we performed in vitro experiments for validation.ResultsThe analysis of the current study revealed that C0 IGFBP7+ Glioma cells were a noteworthy subpopulation of astrocytoma, influencing the differentiation and progression of astrocytoma. A predictive model was developed to categorize patients into high- and low-scoring groups based on the IGFBP7 Risk Score (IGRS), with survival analysis revealing a poorer prognosis for the high-IGRS group. Analysis of immune cell infiltration, identification of genes with differential expression, various enrichment analyses, assessment of copy number variations, and evaluation of drug susceptibility were conducted, all of which highlighted their significant influence on the prognosis of astrocytoma.ConclusionThis research enhances comprehension of the diverse cell composition of astrocytoma, delves into the various factors impacting the prognosis of astrocytoma, and offers fresh perspectives on treating glioma.
| S-EPMC11471593 | biostudies-literature
Project description:BackgroundRecent studies have suggested that cell death may be involved in bone loss or the resolution of inflammation in periodontitis. Immunogenic cell death (ICD), a recently identified cell death pathway, may be involved in the development of this disease.MethodsBy analyzing single-cell RNA sequencing (scRNA-seq) for periodontitis and scoring gene set activity, we identified cell populations associated with ICD, which were further verified by qPCR, enzyme linked immunosorbent assay (ELISA) and immunofluorescence (IF) staining. By combining the bulk transcriptome and applying machine learning methods, we identified several potential ICD-related hub genes, which were then used to build diagnostic models. Subsequently, consensus clustering analysis was performed to identify ICD-associated subtypes, and multiple bioinformatics algorithms were used to investigate differences in immune cells and pathways between subtypes. Finally, qPCR and immunohistochemical staining were performed to validate the accuracy of the models.ResultsSingle-cell gene set activity analysis found that in non-immune cells, fibroblasts had a higher ICD activity score, and KEGG results showed that fibroblasts were enriched in a variety of ICD-related pathways. qPCR, Elisa and IF further verified the accuracy of the results. From the bulk transcriptome, we identified 11 differentially expressed genes (DEGs) associated with ICD, and machine learning methods further identified 5 hub genes associated with ICD. Consensus cluster analysis based on these 5 genes showed that there were differences in immune cells and immune functions among subtypes associated with ICD. Finally, qPCR and immunohistochemistry confirmed the ability of these five genes as biomarkers for the diagnosis of periodontitis.ConclusionFibroblasts may be the main cell source of ICD in periodontitis. Adaptive immune responses driven by ICD may be one of the pathogenesis of periodontitis. Five key genes associated with ICD (ENTPD1, TLR4, LY96, PRF1 and P2RX7) may be diagnostic biomarkers of periodontitis and future therapeutic targets.
| S-EPMC11568468 | biostudies-literature