Identification and validation of a pyroptosis-related prognostic model for colorectal cancer based on bulk and single-cell RNA sequencing data
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PROVIDER: S-EPMC10915942 | biostudies-literature | 2024 Feb
REPOSITORIES: biostudies-literature
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Project description:The cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) pathway has emerged as a critical innate immune pathway that could virtually impact nearly all aspects of tumorigenesis including colorectal cancer. This work aimed to develop and validate molecular subtypes related to cGAS-STING pathways for colorectal cancer using Bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data. Bulk RNA-seq data were acquired from The Cancer Genome Atlas dataset (training dataset) and Gene Expression Omnibus dataset (validation dataset). Univariate COX survival analysis was utilized to identify prognostic differentially expressed genes (DEGs) from 6 immune pathways related to cGAS-STING. ConsensusClusterPlus package was used to classify different subtypes based on DEGs. scRNA-seq data were used to validate differences in immune status between different subtypes. Two clusters with distinct prognosis were identified based on 27 DEGs. The six cGAS-STING-related pathways had different levels of significance between the two clusters. Clust1 had most number of amplified CNVs and clust2 had the most number of loss CNVs. TP53 was the top mutated gene of which missense mutations contributed the most of single-nucleotide variants. Immune score of clust1 was higher than that in clust2, as reflected in macrophages, T cells, and natural killer cells. Three unfavorable genes and 31 protection factors were screened between the two clusters in three datasets. ScRNA-seq data analysis demonstrated that macrophages were more enriched in clust1, and tumor cells and immune cells had close interaction. We classified two distinct subtypes with different prognosis, mutation landscape, and immune characteristics.
| S-EPMC10290593 | biostudies-literature
Project description:BackgroundEmerging studies have shown that pyroptosis plays a non-negligible role in the development and treatment of tumors. However, the mechanism of pyroptosis in colorectal cancer (CRC) remains still unclear. Therefore, this study investigated the role of pyroptosis in CRC.MethodsA pyroptosis-related risk model was developed using univariate Cox regression and LASSO Cox regression analyses. Based on this model, pyroptosis-related risk scores (PRS) of CRC samples with OS time > 0 from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database were calculated. The abundance of immune cells in CRC tumor microenvironment (TME) was predicted by single-sample gene-set enrichment analysis (ssGSEA). Then, the responses to chemotherapy and immunotherapy were predicted by pRRophetic algorithm, the tumor immune dysfunction and exclusion (TIDE) and SubMap algorithms, respectively. Moreover, the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset (PRISM) were used to explore novel drug treatment strategies of CRC. Finally, we investigated pyroptosis-related genes in the level of single-cell and validated the expression levels of these genes between normal and CRC cell lines by RT-qPCR.ResultsSurvival analysis showed that CRC samples with low PRS had better overall survival (OS) and progression-free survival (PFS). CRC samples with low PRS had higher immune-related gene expression and immune cell infiltration than those with high PRS. Besides, CRC samples with low PRS were more likely to benefit from 5-fluorouracil based chemotherapy and anti-PD-1 immunotherapy. In novel drug prediction, some compounds such as C6-ceramide and noretynodrel, were inferred as potential drugs for CRC with different PRS. Single-cell analysis revealed pyroptosis-related genes were highly expressed in tumor cells. RT-qPCR also demonstrated different expression levels of these genes between normal and CRC cell lines.ConclusionsTaken together, this study provides a comprehensive investigation of the role of pyroptosis in CRC at the bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) levels, advances our understanding of CRC characteristics, and guides more effective treatment regimens.
| S-EPMC10193704 | biostudies-literature
Project description:Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by an insidious onset. Despite the emphasis on motor symptom-based diagnosis, there remains an unmet clinical need for effective diagnostic approaches during the prodromal phase of PD. Recent advances in single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses of PD patients open avenues for identifying potential diagnostic biomarkers. A comprehensive cell trajectory analysis was conducted using scRNA-seq datasets to identify gene expressions associated with the cellular transition from healthy to PD-associated states. Integration of scRNA-seq datasets with weighted gene co-expression network analysis (WGCNA) allowed extraction of pyroptosis-associated differentially expressed genes (PDEGs). Using LASSO logistic regression, support vector machine recursive feature elimination (SVM-RFE) and random forest methods, we developed a diagnostic model centred on PDEGs. In addition, immunoinfiltration, inflammatory signalling pathways and intercellular communication were detected by scRNA-seq analyses. In PD patients, the number of cells including metencephalic-like cells, excitatory neurons, inhibitory neurons and MHB-like cells was significantly reduced, whereas the proportion of astrocytes and microglia, immunoinfiltration and inflammatory signalling pathways were upregulated compared to healthy individuals. Using scRNA-seq and WGCNA analyses, two pyroptosis-related diagnostic genes, POLR2K and TIMM8B, were identified and a diagnostic model based on them was constructed, which showed promising performance upon validation. This study established a pyroptosis-related diagnostic model for PD through the analyses of scRNA-seq combined with bulk transcriptome data, which improved the understanding of the role of PDEGs in PD and provided new insights into the diagnostic strategies for this neurodegenerative disease.
| S-EPMC11574289 | biostudies-literature
Project description:BackgroundEmerging evidence suggests an important role for pyroptosis in tumorigenesis and recurrence, but it remains to be elucidated in prostate cancer (PCa). Considering the low accuracy of common clinical predictors of PCa recurrence, we aimed to develop a novel pyroptosis-related signature to predict the prognosis of PCa patients based on integrative analyses of bulk and single-cell RNA sequencing (RNA-seq) profiling.MethodsThe RNA-seq data of PCa patients was downloaded from several online databases. PCa patients were stratified into two Classes by unsupervised clustering. A novel signature was constructed by Cox and the Least Absolute Shrinkage and Selection Operator (LASSO) regression. The Kaplan-Meier curve was employed to evaluate the prognostic value of this signature and the single sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to analysis tumor-infiltrating immune cells. At single-cell level, we also classified the malignant cells into two Classes and constructed cell developmental trajectories and cell-cell interaction networks. Furthermore, RT-qPCR and immunofluorescence were used to validate the expression of core pyroptosis-related genes.ResultsTwelve prognostic pyroptosis-related genes were identified and used to classify PCa patients into two prognostic Classes. We constructed a signature that identified PCa patients with different risks of recurrence and the risk score was proven to be an independent predictor of the recurrence free survival (RFS). Patients in the high-risk group had a significantly lower RFS (P<0.001). The expression of various immune cells differed between the two Classes. At the single-cell level, we classified the malignant cells into two Classes and described the heterogeneity. In addition, we observed that malignant cells may shift from Class1 to Class2 and thus have a worse prognosis.ConclusionWe have constructed a robust pyroptosis-related signature to predict the RFS of PCa patients and described the heterogeneity of prostate cancer cells in terms of pyroptosis.
| S-EPMC9465051 | biostudies-literature
Project description:The incidence rate and mortality rate of colorectal cancer (CRC) ranks third and second globally. Cancer-associated fibroblasts (CAFs) are the major constituent of the stromal cells in the tumor microenvironment (TME) and are closely associated with patients' prognoses. Our study intended to establish a prognostic model for CRC using hallmark genes of CAFs. The expression values of genes and clinicopathological characteristics of patients were enrolled from the cancer genome atlas database as well as the gene expression omnibus database. The single-cell RNA sequencing data were collected and analyzed in the deeply integrated human single-cell omics database and cancer single-cell expression map databases. The ESTIMATE algorithm was applied to access the infiltration levels of immune and stromal cells. The prognostic genes were selected by the Cox regression analysis and the prognostic signature was constructed by the least absolute shrinkage and selection operator algorithm. gene set enrichment analysis was used to explore the enriched gene sets. In this study, based on bulk RNA sequencing and single-cell RNA sequencing data, and we found that more CAFs were infiltrated in the tumor microenvironment and consisted of 3 subtypes. Then we constructed a prognostic signature for CRC using hallmark genes of CAFs and proved that this signature exhibited high values to predict the overall survival of CRC patients in independent training and validating cohorts. Besides, function enrichment analysis revealed that our prognostic model was significantly associated with immune regulation. Further analysis showed that the infiltrated levels of tumor-suppressing immune cells and the expression of higher immune checkpoint genes in CRC tissues were higher in patients with high-risk scores. Furthermore, immunohistochemistry analysis exhibited that these genes in our prognostic signature were markedly upregulated in CRC tissues. We first constructed a signature based on CAFs hallmark genes to predict the survival of CRC patients and further revealed that the tumor-suppressing microenvironment and dysregulated immune checkpoint genes in CRC tissues were partly responsible for the poor prognosis of patients.
| S-EPMC9997814 | biostudies-literature
Project description:Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data. Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated. Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them. Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.
| S-EPMC8829512 | biostudies-literature
Project description:Ferroptosis is an iron-dependent form of cell death induced by lipid oxidation with an essential role in diseases, including cancer. Since prognostic value of ferroptosis-dependent related genes (FDRGs) in colorectal cancer (CRC) remains unclear, we explored the significance of FDRGs in CRC through comprehensive single-cell analysis. We downloaded the GSE161277 dataset for single-cell analyses and calculated the ferroptosis-dependent gene score (FerrScore) for each cell type. According to each cell type-specific median FerrScore, we categorized the cells into low- and high-ferroptosis groups. By analyzing differentially-expressed genes across the two groups, we identified FDRGs. We further screened these prognosis-related genes used to develop a prognostic signature and calculated its correlation with immune infiltration. We also compared immune checkpoint gene efficacy among different risk groups, and qRT-PCR was performed in colorectal normal and cancer cell lines to explore whether the signature genes could be used as clinical prognostic indicators. In total, 523 FDRGs were identified. A prognostic signature including five signature genes was constructed, and patients were divided into two risk groups. The high-risk group had poor survival rates and displayed high levels of immune infiltration. Our newly developed ferroptosis-based prognostic signature possessed a high predictive ability for CRC.
| S-EPMC10403602 | biostudies-literature
Project description:Colorectal cancer (CRC) is one of the most common malignancies worldwide, leading to a large number of cancer-related mortalities. Aberrant CD8+ T cell infiltration plays a critical role in tumor progression and patient prognosis. This study aimed to identify a prognostic model for CRC based on CD8+ T cell-related genes. The infiltration levels of immune cells in CRC tissues were accessed by the ESTIMATE algorithm. Weighted gene co-expression network analysis (WGCNA) analysis was used to select CD8+ T cell-related genes. Prognostic genes were identified using Cox regression analysis and Kaplan-Meier curves. The least absolute shrinkage and selection operator (LASSO) algorithm was used to construct prognostic models. Gene set enrichment analysis (GSEA) was performed to annotate enriched gene sets. Single-cell RNA (scRNA) sequencing analysis was used to examine gene expression in different cell types. We found that the downregulated infiltration level of CD8+ T cells was an independent prognostic factor for CRC and selected a cluster of differentially expressed genes correlated with CD8+ T cell infiltration (CD8TDEGs). Subsequently, we identified 18 prognostic CD8TDEGs, according to which patients were reclassified into two clusters with distinct overall survival. Seven prognostic CD8TDEGs were selected to calculate the constructed prognostic model's risk scores. Interestingly, although CRC tissues with higher risk scores had higher infiltration levels of CD8+ T cells, the level of immune checkpoint genes was also high. Moreover, the scRNA-sequencing analysis showed that the expression levels of CD8TDEGs in the prognostic model varied among different types of cells. This study constructed a novel prognostic model for CRC and provided a foundation for targeting CD8+ T cell infiltration to improve the survival of CRC patients.
| S-EPMC9524952 | biostudies-literature
Project description:Patients with triple-negative breast cancer (TNBC) reportedly benefit from immune checkpoint blockade (ICB) therapy. However, the subtype-specific vulnerabilities of ICB in TNBC remain unclear. As the complex interplay between cellular senescence and anti-tumor immunity has been previously discussed, we aimed to identify markers related to cellular senescence that may serve as potential predictors of response to ICB in TNBC. We used three transcriptomic datasets derived from ICB-treated breast cancer samples at both scRNA-seq and bulk-RNA-seq levels to define the subtype-specific vulnerabilities of ICB in TNBC. Differences in the molecular features and immune cell infiltration among the different TNBC subtypes were further explored using two scRNA-seq, three bulk-RNA-seq, and two proteomic datasets. 18 TNBC samples were collected and utilized to verify the association between gene expression and immune cell infiltration by multiplex immunohistochemistry (mIHC). A specific type of cellular senescence was found to be significantly associated with response to ICB in TNBC. We employed the expression of four senescence-related genes, namely CDKN2A, CXCL10, CCND1, and IGF1R, to define a distinct senescence-related classifier using the non-negative matrix factorization approach. Two clusters were identified, namely the senescence-enriching cluster (C1; CDKN2A high CXCL10 high CCND1 low IGF1R low) and proliferating-enriching cluster (C2; CDKN2A low CXCL10 low CCND1 high IGF1R high). Our results indicated that the C1 cluster responds better to ICB and behaves with higher CD8+ T cell infiltration than the C2 cluster. Altogether, in this study, we developed a robust cellular senescence-related classifier of TNBC based on the expression of CDKN2A, CXCL10, CCND1, and IGF1R. This classifier act as a potential predictor of clinical outcomes and response to ICB.
| S-EPMC9989623 | biostudies-literature
Project description:Colorectal cancer (CRC) is a relatively common malignancy clinically and the second leading cause of cancer-related deaths. Recent studies have identified T-cell exhaustion as playing a crucial role in the pathogenesis of CRC. A long-standing challenge in the clinical management of CRC is to understand how T cells function during its progression and metastasis, and whether potential therapeutic targets for CRC treatment can be predicted through T cells. Here, we propose DeepTEX, a multi-omics deep learning approach that integrates cross-model data to investigate the heterogeneity of T-cell exhaustion in CRC. DeepTEX uses a domain adaptation model to align the data distributions from two different modalities and applies a cross-modal knowledge distillation model to predict the heterogeneity of T-cell exhaustion across diverse patients, identifying key functional pathways and genes. DeepTEX offers valuable insights into the application of deep learning in multi-omics, providing crucial data for exploring the stages of T-cell exhaustion associated with CRC and relevant therapeutic targets.
| S-EPMC11439987 | biostudies-literature