Identification of immunogenic cell death‐related prognostic signatures in pancreatic cancer
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ABSTRACT:
SUBMITTER: Yu W
PROVIDER: S-EPMC10551861 | biostudies-literature | 2023 Sep
REPOSITORIES: biostudies-literature
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Project description:BackgroundImmunogenic cell death (ICD) is considered a promising type of regulated cell death and exerts effects by activating the adaptive immune response, reshaping the tumor environment (TME) and improving therapeutic efficacy. However, the potential roles and prognostic value of ICD-associated genes in gastric cancer (GC) remain unclear.MethodsThe RNA expression data and clinical information of 1090 GC patients from six cohorts were collected. Consensus clustering was used to identify three distinct molecular subtypes. Then, a robust prognostic ICD_score for predicting prognosis was built via WGCNA and LASSO Cox regression according to the TCGA cohort, and the predictive capability of the ICD_score in GC patients was validated in the other cohorts. ICD-related immune features were analyzed using a CIBERSORT method and verified by immunofluorescence.ResultsWe found that ICD-related gene variations were correlated with clinical outcomes, tumor immune microenvironment (TIME) characteristics and treatment response. We then constructed an ICD signature that classifies cases as low- and high-ICD_score groups. The high-ICD_score group indicates unfavorable OS, a more advanced TNM stage, and presents an immune-suppressed phenotype, which has more infiltrations of pro-tumor immune cells, such as macrophages, which was verified by immunofluorescence. In addition, a nomogram containing the ICD_score showed a high predictive accuracy with AUCs of 0.715, 0.731 and 0.8 on Years 1, 3, and 5.ConclusionWe performed the first and synthesis ICD analysis in GC and built a clinical application tool based on the ICD signature, which paved a new path for assessing prognosis and guiding individual treatment.
| S-EPMC10046021 | biostudies-literature
Project description:Immunotherapy is a promising treatment for advanced colorectal cancers (CRCs). However, immunotherapy resistance remains a common problem. Immunogenic cell death (ICD), a form of regulated cell death, induces adaptive immunity, thereby enhancing anti-tumor immunity. Research increasingly suggests that inducing ICD is a promising avenue for cancer immunotherapy and identifying ICD-related biomarkers for CRCs would create a new direction for targeted therapies. Thus, this study used bioinformatics to address these questions and create a prognostic signature, aiming to improve individualized CRC treatment. We identified two ICD -related molecular subtypes of CRCs. The high subtype showed pronounced immune cell infiltration, high immune activity, and high expression of human leukocyte antigen and immune checkpoints genes. Subsequently, we constructed and validated a prognostic signature comprising six genes (CD1A, TSLP, CD36, TIMP1, MC1R, and NRG1) using random survival forest analyses. Further analysis using this prediction model indicated that patients with CRCs in the low-risk group exhibited favorable clinical outcomes and better immunotherapy responses than those in the high-risk group. Our findings provide novel insights into determining the prognosis and design of personalized immunotherapeutic strategies for patients with CRCs.
| S-EPMC10655694 | biostudies-literature
Project description:BackgroundImmunogenic cell death (ICD) plays a vital role in tumor progression and immune response. However, the integrative role of ICD-related genes and subtypes in the tumor microenvironment (TME) in prostate cancer (PCa) remains unknown.Materials and methodsThe sample data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Memorial Sloan Kettering Cancer Center (MSKCC) prostate cancer-related databases. We first divided the subtypes based on ICD genes from 901 PCa patients and then identified the prognosis- related genes (PRGs) between different ICD subtypes. Subsequently, all the patients were randomly split into the training and test groups. We developed a risk signature in the training set by least absolute shrinkage and selection operator (LASSO)-Cox regression. Following this, we verified this prognostic signature in both the training test and external test sets. The relationships between the different subgroups and clinical pathological characteristics, immune infiltration characteristics, and mutation status of the TME were examined. Finally, the artificial neural network (ANN) and fundamental experiment study were constructed to verify the accuracy of the prognostic signature.ResultsWe identified two ICD clusters with immunological features and three gene clusters composed of PRGs. Additionally, we demonstrated that the risk signature can be used to evaluate tumor immune cell infiltration, prognostic status, and an immune checkpoint inhibitor. The low-risk group, which has a high overlap with group C of the gene cluster, is characterized by high ICD levels, immunocompetence, and favorable survival probability. Furthermore, the tumor progression genes selected by the ANN also exhibit potential associations with risk signature genes.ConclusionThis study identified individuals with high ICD levels in prostate cancer who may have more abundant immune infiltration and revealed the potential effects of risk signature on the TME, immune checkpoint inhibitor, and prognosis of PCa.
| S-EPMC10279955 | biostudies-literature
Project description:Background: Immunogenic cell death (ICD) remodels the tumor immune microenvironment, plays an inherent role in tumor cell apoptosis, and promotes durable protective antitumor immunity. Currently, appropriate biomarker-based ICD immunotherapy for breast cancer (BC) is under active exploration. Methods: To determine the potential link between ICD genes and the clinical risk of BC, TCGA-BC was used as the training set and GSE58812 was used as the validation set. Gene expression, consistent clustering, enrichment analysis, and mutation omics analyses were performed to analyze the potential biological pathways of ICD genes involved in BC. Furthermore, a risk and prognosis model of ICD was constructed to evaluate the correlation between risk grade and immune infiltration, clinical stage, and survival prognosis. Results: We identified two ICD-related subtypes by consistent clustering and found that the C2 subtype was associated with good survival prognosis, abundant immune cell infiltration, and high activity of immune biological processes. Based on this, we constructed and validated an ICD risk and prognosis model of BC, including ATG5, HSP90AA1, PIK3CA, EIF2AK3, MYD88, IL1R1, and CD8A. This model can effectively predict the survival rate of patients with BC and is negatively correlated with the immune microenvironment and clinical stage. Conclusion: This study provides new insights into the role of ICD in BC. The novel classification risk model based on ICD in BC established in this study can aid in estimating the potential prognosis of patients with BC and the clinical outcomes of immunotherapy and postulates targets that are more useful in comprehensive treatment strategies.
| S-EPMC9685311 | biostudies-literature
Project description:BackgroundImmunogenic cell death (ICD) is a functionally specialized form of apoptosis induced by endoplasmic reticulum (ER) stress and is associated with a variety of cancers, including gastric cancer (GC). In recent years, long non-coding RNAs (lncRNAs) have been shown to be important mediators in the regulation of ICD. However, the specific role and prognostic value of ICD-related lncRNAs in GC remain unclear. This study aims to develop an ICD-related lncRNAs signature for prognostic risk assessment in GC.MethodsThe ICD-related lncRNAs signature (ICDlncSig) of GC was constructed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression model and multivariate Cox regression analysis, and the signature was correlated with immune infiltration. The potential response of GC patients to immunotherapy was predicted by the tumor immune dysfunction and rejection (TIDE) algorithm. In vitro functional experiments were conducted to assess the impact of lncRNAs on the proliferation, migration, and invasion capabilities of GC cells.ResultsWe constructed a novel ICDlncSig and found that this signature could be used as a prognostic risk model to predict survival of GC patients by validating it in the training cohort, testing cohort and entire cohort. The robust predictive power of the signature was demonstrated by building a Nomogram based on ICDlncSig scores and clinical characteristics. Furthermore, immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy differed significantly between the high- and low-risk groups. The in vitro functional experiments revealed that AP002954.1 and AP000695.1 can promote the proliferation, migration, and invasion of GC cells.ConclusionsIn conclusion, our ICDlncSig model has significant predictive value for the prognosis of GC patients and may provide clinical guidance for individualized immunotherapy.
| S-EPMC11385251 | biostudies-literature
Project description:Alzheimer's disease (AD) is the leading cause of dementia worldwide, with recent studies highlighting the potential role of immunogenic cell death (ICD) in the pathogenesis of this neurodegenerative disorder. A total of 52 healthy controls and 64 patients with AD were included. Compared to the controls, the patients with AD exhibited 2392 differentially expressed genes (DEGs), of which 1015 and 1377 were upregulated and downregulated genes, respectively. Among them, nine common genes were identified by intersecting the AD-related module genes with the DEGs and ICD-associated genes. Gene ontology (GO)analysis further revealed "positive regulation of cytokine production" as the most significant term. Moreover, the enriched molecular functions were primarily related to the inflammatory body complex, while the overlapping genes were significantly enriched in lipopolysaccharide binding. Kyoto encyclopedia of genes and genomes (KEGG) analysis also indicated that these overlapping genes were mainly enriched in immunity, inflammation, and lipid metabolism pathways. Furthermore, the following four hub genes were detected using machine learning algorithms: P2RX7, HSP90AA1, NT5E, and NLRP3. These genes demonstrated significant differences in expression between the AD and healthy control groups (P < 0.05). Additionally, the area under the curve values of these four genes were all > 0.7, indicating their potential diagnostic value for AD. We further validated the protein levels of these four genes in the hippocampus of 3xTg-AD and C57BL/6J mice, showing P2RX7 and HSP90AA1 expression levels consistent with the previously analyzed trends. Finally, the single-sample gene set enrichment analysis (ssGSEA) algorithm provided additional evidence by demonstrating the crucial role of immune cell infiltration and its link with the hub genes in AD progression. Our study results suggest that ICD-mediated elevation of HSP90AA1 and P2RX7 levels and the resulting induction of tau hyperphosphorylation and neuroinflammation are vital in the AD pathogenic mechanism.
| S-EPMC10869701 | biostudies-literature
Project description:Pancreatic cancer (PC) is a malignancy of the gastrointestinal tract that is characterized by a poor prognosis. This study investigates the roles of immunogenic cell death (ICD) genes in the prognosis and progression of PC. Expression data for PC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, while ICD genes were sourced from published literature. We explored the expression patterns and identified two distinct clusters based on ICD genes. Kaplan-Meier analysis, differential expression analysis, tumor mutational burden analysis, and immune cell infiltration analysis were performed on these clusters. An ICD gene-based risk model was developed, categorizing samples from the TCGA and GEO datasets into low- and high-risk groups. Additionally, we investigated the expression levels of the genes included in the risk model within the TCGA cohort and our own samples. Finally, a loss-of-function assay was conducted to assess the role of MYD88 in PC. Two clusters of PC samples were identified, patients in the ICD-low cluster exhibited a higher degree of immune cell enrichment. The survival time of patients in the low-risk group was longer than that of those in the high-risk group. The genes included in the risk model (CASP1, MYD88, and PIK3CA) showed upregulated expression levels in tumor samples. Furthermore, the predictive accuracy of our risk model was validated using our own samples. Genetic inhibition of MYD88 led to significantly decreased proliferation and migration of PC cells in the loss-of-function assay. There were disparities in survival time and tumor immune microenvironment (TIME) between two ICD gene clusters. Additionally, we developed an ICD-related risk model that was validated as an independent prognostic indicator for patients with PC.
| S-EPMC11663176 | biostudies-literature
Project description:BackgroundImmunogenic cell death (ICD) can reshape the immune microenvironment of tumors. Driven by stressful pressure, by directly destroying tumor cells and activating the body's adaptive immunity, ICD acts as a modulator of cell death, enabling the body to generate an anti-tumor immune response that produces a more effective therapeutic effect, while tumor cells are driven to kill. Hence, this research aimed to find and evaluate ICD-related genetic signatures as cervical cancer (CC) prognostic factors.MethodsData of CC patients from the Tumor Genome Atlas (TCGA) were used as the basis to obtain immunogenic cell-death-related prognostic genes (IPGs) in patients with CC, using the least absolute shrinkage and selection operator and Cox regression screening, and the IPGs scoring system was constructed to classify patients into high- and low-risk groups, with the Gene Expression Omnibus (GEO) dataset as the validation group. Finally, the difference analysis of single-sample gene set enrichment analysis, tumor microenvironment (TME), immune cells, tumor mutational burden, and chemotherapeutic drug sensitivity between the high-risk and low-risk groups was investigated.ResultsA prognostic model with four IPGs (PDIA3, CASP8, IL1, and LY96) was developed, and it was found that the group of CC patients with a higher risk score of IPGs expression had a lower survival rate. Single and multifactor Cox regression analysis also showed that this risk score was a reliable predictor of overall survival. In comparison to the low-risk group, the high-risk group had lower TME scores and immune cell infiltration, and gene set variation analysis showed that immune-related pathways were more enriched in the high-risk group.ConclusionA risk model constructed from four IPGs can independently predict the prognosis of CC patients and recommend more appropriate immunotherapy strategies for patients.
| S-EPMC10218566 | biostudies-literature
Project description:ObjectiveStudies have firmly established the pivotal role of the immunogenic cell death (ICD) in the development of tumors. This study seeks to develop a risk model related to ICD to predict the prognosis of patients with endometrial carcinoma (EC).Materials and methodsRNA-seq data of EC retrieved from TCGA database were analyzed using R software. We determined clusters based on ICD-related genes (ICDRGs) expression levels. Cox and LASSO analyses were further used to build the prediction model, and its accuracy was evaluated in the train and validation sets. Finally, in vitro and in vivo experiments were conducted to confirm the impact of the high-risk gene IFNA2 on EC.ResultsPatients were sorted into two ICD clusters, with survival analysis revealing divergent prognoses between the clusters. The Cox regression analysis identified prognostic risk genes, and the LASSO analysis constructed a model based on 9 of these genes. Notably, this model displayed excellent predictive accuracy when validated. Finally, increased IFNA2 levels led to decreased vitality, proliferation, and invasiveness in vitro. IFNA2 also has significant tumor inhibiting effect in vivo.ConclusionsThe ICD-related model can accurately predict the prognosis of patients with EC, and IFNA2 may be a potential treatment target.
| S-EPMC10968672 | biostudies-literature
Project description:In recent years, genes associated with immunogenic cell death (ICD)-related genes have garnered significant interest as potential targets for immunotherapy. As a frontier in cancer treatment, immunotherapy has notably enhanced the therapeutic outcomes for cancer patients. However, since only a subset of patients benefits from this treatment approach, there is an imperative need for biomarker research to enhance patient sensitivity to immunotherapy. Expression of ICD-related genes and clinical patient data were sourced from The Cancer Genome Atlas (TCGA) database. Utilizing univariate Cox regression analysis, we constructed a signature for predicting the overall survival of colon adenocarcinoma (COAD) patients. A genomic feature analysis was performed, incorporating tumor mutation burden (TMB) and copy number variation (CNV). The immunological characteristics were analyzed via the ssGSEA and GSEA algorithms, with the resulting data visualized using R software (version 4.2.1). According to the univariate regression analysis for COAD, AIM2 emerged as the gene most significantly associated with overall survival among the 32 ICD-related genes in the TCGA dataset. Patients were divided into two groups based on high or low AIM2 expression, and genomic differences between the groups were explored. Patients expressing high levels of AIM2 had a higher TMB and a lower CNV. In addition, these patients had elevated immune checkpoint, immune cell, and immune function scores, thus indicating increased sensitivity to immunotherapy. TIDE analysis further confirmed that these patients were likely to respond more effectively to immunotherapy. Subclass mapping analysis corroborated our findings, demonstrating that patients with high AIM2 expression responded more positively to immunotherapy. Additionally, our study found that the suppression of AIM2 could significantly enhance the proliferation, invasion, and migration capabilities of colon cancer cells. In this research, we identified a novel prognostic signature suggesting that patients with higher AIM2 expression levels are more likely to respond favorably to immunotherapy.
| S-EPMC10628212 | biostudies-literature