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Immune Cell Infiltration Landscape of Ovarian Cancer to Identify Prognosis and Immunotherapy-Related Genes to Aid Immunotherapy.

ABSTRACT: Ovarian cancer (OC) is the second leading cause of death in gynecological cancer. Multiple study have shown that the efficacy of tumor immunotherapy is related to tumor immune cell infiltration (ICI). However, so far, the Immune infiltration landscape of tumor microenvironment (TME) in OC has not been elucidated. In this study, We organized the transcriptome data of OC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, evaluated the patient's TME information, and constructed the ICI scores to predict the clinical benefits of patients undergoing immunotherapy. Immune-related genes were further used to construct the prognostic model. After clustering analysis of ICI genes, we found that patients in ICI gene cluster C had the best prognosis, and their tumor microenvironment had the highest proportion of macrophage M1 and T cell follicular helper cells. This result was consistent with that of multivariate cox (multi-cox) analysis. The prognostic model constructed by immune-related genes had good predictive performance. By estimating Tumor mutation burden (TMB), we also found that there were multiple genes with statistically different mutation frequencies in the high and low ICI score groups. The model based on the ICI score may help to screen out patients who would benefit from immunotherapy. The immune-related genes screened may be used as biomarkers and therapeutic targets.

SUBMITTER: Li X

PROVIDER: S-EPMC8595115 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDiabetic retinopathy (DR) is deemed a microangiopathy and neurodegenerative disorder, which is a primary reason of visual impairment in the world. Ferritinophagy is a critical regulator of ferroptosis and has a vital part in the etiopathogenesis of DR. Nevertheless, its molecular mechanism in DR remains to be expounded.MethodsThe GSE146615 dataset was adopted to identify ferritinophagy-related differentially expressed genes (FRDEGs). The interactions and biological functions of the genes were described by means of functional enrichment analysis (FEA). The enriched gene sets were analyzed utilizing gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Identification of hub genes was performed utilizing protein-protein interaction (PPI) analysis. mRNA-miRNA, mRNA-transcription factors (TF), mRNA-drugs, mRNA-RNA-binding proteins (RBP) interaction networks were constructed. In addition, datasets GSE60436 and GSE94019 were utilized for validation. The diagnostic performance of FRDEGs was assessed by means of receiver-operating characteristic curve monofactor analysis, followed by immune infiltration analysis. Lastly, quantitative real-time polymerase chain reaction (qRT-PCR) was implemented to analyze the validation of genes.ResultsIn total, the identification of eight FRDEGs was completed utilizing differential expression analysis. FEA mainly implicated the autophagy of mitochondrion, mitochondrion disassembly, autophagosome assembly, and organization pathways. GSEA and GSVA mainly implicated the interferon alpha response, ultraviolet response up, interferon gamma response, apical junction, pical surface, and allograft rejection pathways. BECN1 and HERC2 displayed high diagnostic accuracies in validation sets. Immune infiltration analysis revealed that several immune cells related to ferritinophagy may be play potential roles in DR. Finally, qRT-PCR was utilized to validate the upregulated expression of BECN1 as well as the downregulated expression of BCAT2 and ATG7 in the DR model.ConclusionBECN1, HERC2, ATG7, and BCAT2 act as potential biomarkers for DR and might regulate ferritinophagy and the immune microenvironment to influence its development and progression. This research can provide new insights into pathogenesis of DR related to ferritinophagy.

Project description:BackgroundIncreasing evidence indicates that immune cell infiltration (ICI) affects the prognosis of multiple cancers. This study aims to explore the immunotypes and ICI-related biomarkers in ovarian cancer.MethodsThe ICI levels were quantified with the CIBERSORT and ESTIMATE algorithms. The unsupervised consensus clustering method determined immunotypes based on the ICI profiles. Characteristic genes were identified with the Boruta algorithm. Then, the ICI score, a novel prognostic marker, was generated with the principal component analysis of the characteristic genes. The relationships between the ICI scores and clinical features were revealed. Further, an ICI signature was integrated after the univariate Cox, lasso, and stepwise regression analyses. The accuracy and robustness of the model were tested by three independent cohorts. The roles of the model in the immunophenoscores (IPS), tumor immune dysfunction and exclusion (TIDE) scores, and immunotherapy responses were also explored. Finally, risk genes (GBP1P1, TGFBI, PLA2G2D) and immune cell marker genes (CD11B, NOS2, CD206, CD8A) were tested by qRT-PCR in clinical tissues.ResultsThree immunotypes were identified, and ICI scores were generated based on the 75 characteristic genes. CD8 TCR pathways, chemokine-related pathways, and lymphocyte activation were critical to immunophenotyping. Higher ICI scores contributed to better prognoses. An independent prognostic factor, a three-gene signature, was integrated to calculate patients' risk scores. Higher TIDE scores, lower ICI scores, lower IPS, lower immunotherapy responses, and worse prognoses were revealed in high-risk patients. Macrophage polarization and CD8 T cell infiltration were indicated to play potentially important roles in the development of ovarian cancer in the clinical validation cohort.ConclusionsOur study characterized the immunotyping landscape and provided novel immune infiltration-related prognostic markers in ovarian cancer.

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Immune Cell Infiltration Landscape of Ovarian Cancer to Identify Prognosis and Immunotherapy-Related Genes to Aid Immunotherapy.

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