Dataset Information

Identification of A Novel Gene Signature Combining Ferroptosis- and Immunity-Related Genes for Prognostic Prediction, Immunotherapy and Potential Therapeutic Targets in Gastric Cancer.

ABSTRACT: Gastric cancer (GC) is one of the most prevalent cancers worldwide. Ferroptosis and the immune status of tumor tissue play vital roles in the initiation and progression of GC. However, the role and functional mechanisms of ferroptosis- and immunity-related genes (FIRGs) in GC pathogenesis and their correlations with GC prognosis have not been elucidated. We aim to establish a prognostic prediction model based on the FIRGs signature for GC patients. Differentially expressed genes were screened from the Cancer Genome Atlas (TCGA) GC cohorts. The least absolute shrinkage and selection operator (LASSO) regression was performed to establish a FIRGs-based risk model. This gene signature with 7 FIRGs was identified as an independent prognostic factor. A nomogram incorporating clinical parameters and the FIRG signature was constructed to individualize outcome predictions. Finally, we provided in vivo and in vitro evidence to verify the reliability of FIRG signature for GC prognosis, and validate the expression and function of FIRGs contributing to the development and progression of GC. Herein, our work represents great therapeutic and prognostic potentials for GC.

SUBMITTER: Wang L

PROVIDER: S-EPMC10647194 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of A Novel Gene Signature Combining Ferroptosis- and Immunity-Related Genes for Prognostic Prediction, Immunotherapy and Potential Therapeutic Targets in Gastric Cancer.

Wang Liwei L Li Zhuozhuo Z Li Zi Z Ren Yuanyuan Y Qian Lu L Yu Yi Y Shi Wenzhen W Xiong Yuyan Y

Journal of Cancer 20231016 18

Gastric cancer (GC) is one of the most prevalent cancers worldwide. Ferroptosis and the immune status of tumor tissue play vital roles in the initiation and progression of GC. However, the role and functional mechanisms of ferroptosis- and immunity-related genes (FIRGs) in GC pathogenesis and their correlations with GC prognosis have not been elucidated. We aim to establish a prognostic prediction model based on the FIRGs signature for GC patients. Differentially expressed genes were screened fr ...[more]

PMID: 38021154

Similar Datasets

Project description:BackgroundFerroptosis is a recently described form of intentional cellular damage that is iron-dependent and separate from apoptosis, cellular necrosis, and autophagy. It has been demonstrated to be adequately regulated by long noncoding RNAs (lncRNAs) in various cancers. However, the predictive profile of ferroptosis-related lncRNAs (FRLs) in endometrial carcinoma (EC) is unknown. Herein, FRLs associated with uterine corpus endometrial carcinoma (UCEC) prognosis were screened to predict treatment response in EC.MethodsSamples of EC and adjacent normal tissues were obtained from The Cancer Genome Atlas (TCGA) dataset repository. Limma and survival packages in R software were used to screen FRLs associated with the prognosis of EC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) chord and circle plots of FRLs were also plotted. Next, FRLs screened by the least absolute shrinkage and selection operator (LASSO) method were applied to construct and validate a multivariate Cox proportional risk regression model. Nomogram plots were created to forecast the outcome of UCEC patients, and gene set enrichment analysis (GSEA), principal component analysis (PCA), and immunoassays were performed on the prognostic models. Finally, limma, ggpubr, pRRophetic, and ggplot2 programs were used for drug sensitivity analysis of the prognostic models.ResultsA signature based on nine FRLs (CFAP58-DT, LINC00443, EMSLR, HYI-AS1, ADIRF-AS1, LINC02474, CDKN2B-AS1, LINC01629, and LINC00942) was constructed. The developed FRL prognostic model effectively discriminated UCEC patients into low-risk and high-risk groups. Immunological checkpoints CD80 and CD40 were strongly expressed in the high-risk group. In addition, the nine FRLs were all more expressed in the high-risk group compared to the low-risk group.ConclusionThese findings significantly contribute to the understanding of the function of FRLs in UCEC and provide promising therapeutic strategies for UCEC.

Project description:BackgroundFerroptosis is a new type of cell death different from apoptosis, necrosis, autophagy, and pyroptosis. This study aimed to explore the relationship between ferroptosis-related noncoding RNA (ncRNA) and gastric adenocarcinoma with regard to immunity and prognosis.MethodsFerroptosis-related ncRNA expression profiles and clinical pathology and overall survival information were collected from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database. The ferroptosis-related ncRNA signature was identified by Cox regression analysis and the least absolute shrinkage and selection operator analysis. The survival analysis, receiver operating characteristic (ROC) analysis, and decision curve analysis were adopted to evaluate the prognostic prediction performance of the signature. The correlation between risk and multiple clinical characteristics was analyzed using the chi-square test. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis were used for mining functions and pathways. The CIBERSORT, ssGSEA, and ESTIMATE algorithms were used to assess immune infiltration and the tumor microenvironment. The response of immunotherapy was predicted using the Submap algorithm, and the Connectivity Map and the ridge regression model were used to screen and evaluate drugs.ResultsA carcinogenic risk signature was constructed using five ferroptosis-related ncRNAs. It showed an extraordinary ability to predict the prognoses of patients with gastric adenocarcinoma [area under the ROC curve (AUC) after 6 years = 0.689; GSE84426, AUC after 6 years = 0.747]. The lower ferroptosis potential level and lower tumor mutation burden were related to the poor prognoses of patients. The high-risk group had more immune cell recruitment, and the overall effect of the anti-immune checkpoint immunotherapy was not as good as that of the low-risk group. The high- and low-risk groups were enriched in tumor- and immune-related pathways, respectively. The screened antitumor drugs, such as genistein, guanabenz, and betulinic acid, improved the survival of the patients.ConclusionsThe ferroptosis-related ncRNA signature is a potential carcinogenic prognostic biomarker of gastric adenocarcinoma.

Project description:Background: Accumulating literature demonstrates that long noncoding RNAs (lncRNAs) are involved in ferroptosis and gastric cancer progression. However, the predictive value of ferroptosis-related lncRNAs for prognosis and therapeutic response is yet to be elucidated in gastric cancer (GC). Method: The transcriptomic data and corresponding clinical information of GC patients were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. The association between ferroptosis-related lncRNAs and ferroptosis regulators was analyzed by Spearman correlation analysis. Then, we established a risk predictive model based on the ferroptosis-related lncRNAs using multivariate Cox regression analysis. Furthermore, we performed correlation analysis for the risk score and characteristics of biological processes, immune landscape, stromal activity, genomic integrity, drug response, and immunotherapy efficacy. Results: We constructed a 17-ferroptosis-related-lncRNA signature via multivariate Cox analysis to divide patients into two groups: low- and high-risk groups. The low-risk group was linked to prolonged overall survival and relapse-free survival. The risk score had good predictive ability to predict the prognosis of GC patients compared with other clinical biomarkers. We found that the high-risk group was associated with activation of carcinogenetic signaling pathways, including stromal activation, epithelial-mesenchymal-transition (EMT) activation, and immune escape through integrated bioinformatics analysis. In contrast, the low-risk group was associated with DNA replication, immune-flamed state, and genomic instability. Additionally, through Spearman correlation analysis, we found that patients in the high-risk group may respond well to drugs targeting cytoskeleton, WNT signaling, and PI3K/mTOR signaling, and drugs targeting chromatin histone acetylation, cell cycle, and apoptosis regulation could bring more benefits for the low-risk group. The high-risk group was associated with poor immunotherapy efficacy. Conclusion: Our study systematically evaluated the role of ferroptosis-related lncRNAs in t tumor microenvironment, therapeutic response, and prognosis of GC. Risk score-based stratification could reflect the characteristic of biological processes, immune landscape, stromal activity, genomic stability, and pharmaceutical profile in GC patients. The ferroptosis-related lncRNA signature could serve as a reliable biomarker to predict prognosis and therapeutic response of patients with GC.

Project description:Background and objectiveGastric cancer (GC) represents a major factor inducing global cancer-associated deaths, but specific biomarkers and therapeutic targets for GC are lacking at present. Therefore, the present work focused on developing an immune-related genetic signature at the single-cell level for categorizing GC cases and predicting patient prognostic outcome, immune status as well as treatment response.MethodsSingle-cell RNA-sequencing (scRNA-seq) data were combined with bulk RNA-seq data in GC patients for subsequent analyses. Differences in overall survival (OS), genomic alterations, immune status, together with estimated immunotherapeutic outcomes were measured between different groups.ResultsNine cell types were identified by analyzing scRNA-seq data from GC patients, and marker genes of immune cells were also selected for subsequent analysis. In addition, an immune-related signature was established to predict OS while validating the prediction power for GC patients. Afterwards, a nomogram with high accuracy was constructed for improving our constructed signature's clinical utility. The low-risk group was featured by high tumor mutation burden (TMB), increased immune activation, and microsatellite instability-high (MSI-H), which were related to the prolonged OS and used in immunotherapy. By contrast, high-risk group was associated with microsatellite stability (MSS), low TMB and immunosuppression, which might be more suitable for targeted therapy. Meanwhile, the risk score generated by our signature was markedly related to the cancer stem cell (CSC) index. In addition, the immunotherapeutic response prediction accuracy of our signature was validated in an external dataset IMvigor210 cohort.ConclusionA signature was constructed according to scRNA-seq data analysis. The signature-screened low- and high-risk patients had different prognoses, immune statuses and enriched functions and pathways. Such results shed more lights on immune status of GC, prognosis assessment, and development of efficient immunotherapeutic treatments.

Project description:BackgroundGastric cancer (GC) can be anatomically categorized into two subtypes; that is, cardia gastric cancer (CGC) and non-cardia gastric cancer (NCGC), which have distinct molecular mechanisms and prognoses. At present, the majority of pharmacological interventions for GC adhere to non-specific treatment regimens. The stratification of GC based on molecular disparities between CGC and NCGC has important clinical guidance value and could help in the development of precision therapies tailored to individual patient needs. Nevertheless, research in this specialized field remains notably limited. This study aims to investigate the molecular differences between CGC and NCGC and to leverage these differences to develop a prognostic risk scoring model (PRSM).MethodsWe used patient data from The Cancer Genome Atlas (TCGA) and performed a differentially expressed gene (DEG) analysis between CGC and NCGC. A PRSM was developed from the prognosis-associated DEGs identified through Cox regression analyses and was well validated using Gene Expression Omnibus (GEO) data.ResultsA total of 339 DEGs were identified between CGC and NCGC, and four prognosis-associated genes were used to construct the PRSM. Using the risk coefficients and expression levels of signature genes, a median risk score (RS) was calculated to classify patients into high- and low-risk groups. The high-risk group had a significantly worse prognosis than the low-risk group. An in-depth analysis revealed that TP53 mutations were more prevalent in the high-risk group, and MUC16 mutations were more prevalent in the low-risk group. A gene set enrichment analysis (GSEA) and the CIBERSORT algorithm were used to assess the differences in the significantly enriched pathways and immune microenvironment in the high- and low-risk groups, respectively. The inhibitory concentration (IC50) values of the chemotherapy drugs for GC also varied between the two groups.ConclusionsThis study elucidated the unique molecular characteristics of GC subtypes based on the anatomical site and provided a preliminary contribution for the development of precision medicine for GC.

Project description:BackgroundGastric cancer (GC) is an important disease and the fifth most common malignancy worldwide. Autophagy is an important process for the turnover of intracellular substances. Autophagy-related genes (ARGs) are crucial in cancer. Accumulating evidence indicates the clinicopathological significance of the tumor microenvironment (TME) in predicting prognosis and treatment efficacy.MethodsClinical and gene expression data of GC were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. A total of 22 genes with differences in expression and prognosis were screened from 232 ARGs. Three autophagy patterns were identified using an unsupervised clustering algorithm and scored using principal component analysis to predict the value of autophagy in the prognosis of GC patients. Finally, the relationship between autophagy and ferroptosis was validated in gastric cancer cells.ResultsThe expression of ARGs showed obvious heterogeneity in GC patients. Three autophagy patterns were identified and used to predict the overall survival of GC patients. These three patterns were well-matched with the immunophenotype. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses showed that the biological functions of the three autophagy patterns were different. A scoring system was then set up to quantify the autophagy model and further evaluate the response of the patients to the immunotherapy. Patients with high autophagy scores had a more severe tumor mutation burden and better prognosis. High autophagy scores were accompanied by high microsatellite instability. Patients with high autophagy scores had significantly higher PD-L1 expression and increased survival. The experimental results confirmed that the expression of ferroptosis genes was positively correlated with the expression of autophagy genes in different autophagy clusters, and inhibition of autophagy dramatically reversed the decrease in ferroptotic cell death and lipid accumulation.ConclusionsAutophagy patterns are involved in TME diversity and complexity. Autophagy score can be used as an independent prognostic biomarker in GC patients and to predict the effect of immunotherapy and ferroptosis-based therapy. This might benefit individualized treatment for GC.

Dataset Information

Identification of A Novel Gene Signature Combining Ferroptosis- and Immunity-Related Genes for Prognostic Prediction, Immunotherapy and Potential Therapeutic Targets in Gastric Cancer.

Publications

Identification of A Novel Gene Signature Combining Ferroptosis- and Immunity-Related Genes for Prognostic Prediction, Immunotherapy and Potential Therapeutic Targets in Gastric Cancer.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets