Characterization of Immune-Related Molecular Subtypes and a Prognostic Signature Correlating With the Response to Immunotherapy in Patients With Gastric Cancer.
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ABSTRACT:
SUBMITTER: Wang G
PROVIDER: S-EPMC9309259 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundNon-small cell lung cancer (NSCLC) was usually associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. It is urgent to screen reliable biomarkers, especially immunotherapy-associated biomarkers, that can predict outcomes of these patients.MethodsGene expression profiles of 1026 NSCLC patients were collected from The Cancer Genome Atlas (TCGA) datasets with their corresponding clinical and somatic mutation data. Based on immune infiltration scores, molecular clustering classification was performed to identify immune subtypes in NSCLC. After the functional enrichment analysis of subtypes, hub genes were further screened using univariate Cox, Lasso, and multivariate Cox regression analysis, and the risk score was defined to construct the prognostic model. Other microarray data and corresponding clinical information of 603 NSCLC patients from the GEO datasets were applied to conduct random forest models for the prognosis of NSCLC with 100 runs of cross-validation. Finally, external datasets with immunotherapy and chemotherapy were further applied to explore the significance of risk-scores in clinical immunotherapy response for NSCLC patients.ResultsCompared with Subtype-B, the Subtype-A, associated with better outcomes, was characterized by significantly higher stromal and immune scores, T lymphocytes infiltration scores and up-regulation of immunotherapy markers. In addition, we found and validated an eleven -gene signatures for better application of distinguishing high- and low-risk NSCLC patients and predict patients' prognosis and therapeutical response to immunotherapy. Furthermore, combined with other clinical characteristics based on multivariate Cox regression analysis, we successfully constructed and validated a nomogram to effectively predict the survival rate of NSCLC patients. External immunotherapy and chemotherapy cohorts validated the patients with higher risk-scores exhibited significant therapeutic response and clinical benefits.ConclusionThese results demonstrated the immunological and prognostic heterogeneity within NSCLC and provided a new clinical application in predicting the prognosis and benefits of immunotherapy for the disease.
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Project description:Gastric cancer is the fifth most common type of human cancer and the third leading cause of cancer-related death. The purpose of this study is to investigate the immune infiltration signatures of gastric cancer and their relation to prognosis. We identified two distinct subtypes of gastric cancer (C1/C2) characterized by different immune infiltration signatures. C1 is featured by immune resting, epithelial–mesenchymal transition, and angiogenesis pathways, while C2 is featured by enrichment of the MYC target, oxidative phosphorylation, and E2F target pathways. The C2 subtype has a better prognosis than the C1 subtype (HR = 0.61, 95% CI: 0.44–0.85; log-rank test, p = 0.0029). The association of C1/C2 with prognosis remained statistically significant (HR = 0.62, 95% CI: 0.44–0.87; p = 0.006) after controlling for age, gender, and stage. The prognosis prediction of C1/C2 was verified in four independent cohorts (including an internal cohort). In summary, our study is helpful for better understanding of the association between immune infiltration and the prognosis of gastric cancer.
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Project description:BackgroundAlthough recent advances in high-throughput technology have provided many insights into gastric cancer (GC), few reliable biomarkers for diffuse-type GC have been identified. Here, we aim to identify a prognostic and predictive signature of diffuse-type GC heterogeneity.MethodsWe analyzed RNA-seq-based transcriptome data to identify a molecular signature in 150 gastric tissue samples including 107 diffuse-type GCs. The predictive value of the signature was verified using other diffuse-type GC samples in three independent cohorts (n = 466). Log-rank and Cox regression analyses were used to estimate the association between the signature and prognosis. The signature was also characterized by somatic variant analyses and tissue microarray analysis between diffuse-type GC subtypes.ResultsTranscriptomic profiling of RNA-seq data identified a signature which revealed distinct subtypes of diffuse-type GC: the intestinal-like (INT) and core diffuse-type (COD) subtypes. The signature showed high predictability and independent clinical utility in diffuse-type GC prognosis in other patient cohorts (HR 2.058, 95% CI 1.53-2.77, P = 1.76 × 10-6). Integrative mutational and gene expression analyses demonstrated that the COD subtype was responsive to chemotherapy, whereas the INT subtype was responsive to immunotherapy with an immune checkpoint inhibitor (ICI). Tissue microarray analysis showed the practical utility of IGF1 and NXPE2 for predicting diffuse-type GC heterogeneity.ConclusionsWe present a molecular signature that can identify diffuse-type GC patients who display different clinical behaviors as well as responses to chemotherapy or ICI treatment.
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Project description:N6-methyladenosine (m6A) is a common form of mRNA modification regulated by m6A RNA methylation regulators and play an important role in the progression of gastric cancer (GC). However, the prognostic role of m6A-related lncRNA in gastric cancer has not been fully explored. This study aims at exploring the biological function and prognostic roles of the m6A-related lncRNA signature in gastric cancer. A total of 800 m6A-related lncRNAs were identified through Pearson correlation analysis between m6A regulators and all lncRNAs. Eleven m6A-related lncRNA signatures were identified through a survival analysis and the Kaplan-Meier (KM) curve analysis results suggest that patients in the low-risk group have a better overall survival (OS) and disease-free survival (DFS) outcome than the high-risk group. Also, the lncRNA signature can serve as an independent prognostic factor for OS and DFS. The gene set enrichment analysis (GSEA) result suggests that patients in the high-risk group were mainly enriched in the ECM receptor interaction, focal adhesion, and cytokine-cytokine receptor interaction pathway, while the low-risk group was characterized by the base excision repair pathway. We further constructed an individualized prognostic prediction model via the nomogram based on the independent prognostic factor for the OS and DFS, respectively. In addition, some candidate drugs aimed at GC risk group differentiation were identified using the Connective Map (CMAP) database. Lastly, four subgroups (C1, C2, C3, and C4) were identified based on the m6A-related lncRNA expression, through a consensus clustering algorithm. Among them, C1 and C2 have a greater likelihood to respond to immune checkpoint inhibitor immunotherapy, suggesting that the C1 and C2 subgroup might benefit from immunotherapy. In conclusion, the m6A-related lncRNA signature can independently predict the OS and DFS of GC and may aid in development of personalized immunotherapy strategies.
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Project description:BackgroundIn tumor progression and epigenetic regulation, long non-coding RNA (lncRNA) and necroptosis are crucial regulators. However, in glioma microenvironment, the role of necroptosis-related lncRNAs (NRLs) remains unknown.MethodIn this study, the RNA-seq and clinical annotation of glioma patients were analyzed using the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. To investigate prognosis and tumor microenvironment of NRLs in gliomas, we conducted a prediction model based on the training cohort. The accuracy of the model was verified in the verification cohort.ResultsA signature composed of 13 NRLs was identified, and all glioma patients were divided into two groups. We found that each group has unique survival outcomes, biological behaviors, and immune infiltrating status. The necroptosis-related lncRNA signature (NRLS) model was found to be an independent risk factor in multivariate Cox analysis. Immunosuppressive microenvironment was positively correlated with the high-risk group. Due to significantly different IC50 between risk groups, NRLS could be used as a guide for chemotherapeutic treatment. Further, the entire cohort was divided into two clusters depending on NRLs. Consensus clustering method and the risk scoring system were basically similar. Survival probability was higher in Cluster 2, while Cluster 1 has stronger immunologic infiltration.ConclusionThe predictive signature could be a prognostic factor independently and serve to detect the role of NRLs in glioma immunotherapy response.
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Project description:PurposeLong noncoding RNAs (lncRNAs) and glycolysis regulate multiple types of cancer. However, the prognostic roles and biological functions of glycolysis-related lncRNAs in lung adenocarcinoma (LUAD) remain unclear. In this study, we investigated the role of glycolysis-related lncRNAs in LUAD.Patients and methodsWe retrieved glycolysis-related genes from the Molecular Signatures Database and screened for prognostic glycolysis-related lncRNAs from The Cancer Genome Atlas.ResultsWe identified three LUAD subtypes (clusters 1-3) by univariate Cox regression analysis and consensus clustering. Patients in cluster 1 had the best overall survival rates. Immune, stromal, and cytolytic-activity scores were the highest in cluster 1. The expression of immune checkpoint molecules (programmed cell death protein 1 and cytotoxic T-lymphocyte-associated protein 4) and other immune-related indicators was the highest in cluster 1, whereas that of epithelial cell biomarkers (Cadherin 1, Cadherin 2, and MET) was the lowest. Therefore, patients in cluster 1 may benefit from immunotherapy. Lasso-Cox regression and multivariate Cox regression analyses were used to select nine lncRNAs to build a robust prognostic model of LUAD. The area under the curve classifier values and a nomogram performed well in predicting survival times for patients with LUAD. The expression levels of nine lncRNAs were validated by quantitative reverse transcriptase-polymerase chain reaction analysis, and most of these lncRNAs were significantly related to immune-related mRNAs. Gene set enrichment analysis revealed that the high-risk group was enriched for cell cycle-related pathways and the low-risk group was enriched for pathways associated with immunity or immune-related diseases.ConclusionThe LUAD subtypes and prognostic model developed here may help in clinical risk stratification, prognosis management, and treatment decisions for patients with LUAD.
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