Dataset Information

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data.

ABSTRACT: Ovarian cancer (OC) is one the most life-threatening cancers affecting women's health worldwide. Immunotherapy has become a promising treatment for a variety of cancers, but the therapeutic effects in OC remain limited. In this study, we constructed a macrophage risk score (MRS) based on M1 and M2 macrophages and a gene risk score (GRS) based on the prognostic genes associated with MRS. Next, cell-cell communication analysis was performed using single-cell RNA (scRNA) sequencing data. Survival status and immune characteristics were compared between the high- and low-score groups separated by MRS or GRS. Our results suggested that MRS and GRS can identify the immune subtypes of OC patients with better overall survival (OS) and inflammatory immune microenvironment. Moreover, M1 and M2 macrophages may affect the prognosis of OC patients through signal communication with CD8 T cells. Finally, functional differences between the two groups separated by GRS were elucidated. Taken together, this study constructed two useful models for the identification of immune subtypes in OC, which has a better prognosis and may have a sensitive response to immune checkpoint inhibitors (ICIs). The hub genes for the construction of GRS may be potential synergetic targets for immunotherapy in OC patients.

SUBMITTER: Wang S

PROVIDER: S-EPMC9346122 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data.

Wang Sixue S Wang Xi X Xia Xiaomeng X Zhang Tingting T Yi Mingyu M Li Zeying Z Jiang Li L Yang Yalan Y Fu Jie J Fang Xiaoling X

Scientific reports 20220802 1

Ovarian cancer (OC) is one the most life-threatening cancers affecting women's health worldwide. Immunotherapy has become a promising treatment for a variety of cancers, but the therapeutic effects in OC remain limited. In this study, we constructed a macrophage risk score (MRS) based on M1 and M2 macrophages and a gene risk score (GRS) based on the prognostic genes associated with MRS. Next, cell-cell communication analysis was performed using single-cell RNA (scRNA) sequencing data. Survival s ...[more]

PMID: 35918500

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Project description:Background: PtdIns (3,4,5) P3-dependent Rac exchanger 1 (PREX1), also known as PREX1, a member of the Rac guanine nucleotide exchange factors (Rac-GEF) family. Studies have suggested that PREX1 plays a role in mediating oncogenic pathway activation and controlling various biological mechanisms in different types of cancer, including liver hepatocellular carcinoma (LIHC). However, the function of PREX1 in the pathogenesis of LIHC and its potential role on immunological regulation is not clearly elucidated. Methods: The expression level and the clinical role of PREX1 in LIHC was analyzed based on database from the Cancer Genome Atlas (TCGA), TNM plotter and University of Alabama Cancer Database (UALCAN). We investigated the relationship between PREX1 and immunity in LIHC by TISIDB, CIBERSORT and single cell analysis. Immunotherapy responses were assessed by the immunophenoscores (IPS). Moreover, biological functional assays were performed to further investigate the roles of PREX1 in liver cancer cell lines. Results: Higher expression of PREX1 in LIHC tissues than in normal liver tissues was found based on public datasets. Further analysis revealed that PREX1 was associated with worse clinical characteristics and dismal prognosis. Pathway enrichment analysis indicated that PREX1 participated in immune-related pathways. Through CIBERSORT and single cell analysis, we found a remarkable correlation between the expression of PREX1 and various immune cells, especially macrophages. In addition, high PREX1 expression was found to be associated with a stronger response to immunotherapy. Furthermore, in vitro assays indicated that depletion of PREX1 can suppress invasion and proliferation of LIHC cells. Conclusion: Elevated expression of PREX1 indicates poor prognosis, influences immune modulation and predicts sensitivity of immunosuppression therapy in LIHC. Our results suggested that PREX1 may be a prognostic biomarker and therapeutic target, offering new treatment options for LIHC.

Project description:BackgroundAccumulating evidence has highlighted the significance of chromatin regulator (CR) in pathogenesis and progression of cancer. However, the prognostic role of CRs in LUAD remains obscure. We aim to detect the prognostic value of CRs in LUAD and create favorable signature for assessing prognosis and clinical value of LUAD patients.MethodsThe mRNA sequencing data and clinical information were obtained from TCGA and GEO databases. Gene consensus clustering analysis was utilized to determine the molecular subtype of LUAD. Cox regression methods were employed to set up the CRs-based signature (CRBS) for evaluating survival rate in LUAD. Biological function and signaling pathways were identified by KEGG and GSEA analyses. In addition, we calculated the infiltration level of immunocyte by CIBERSORT algorithm. The expressions of model hub genes were detected in LUAD cell lines by real-time polymerase chain reaction (PCR).ResultsKEGG analysis suggested the CRs were mainly involved in histone modification, nuclear division and DNA modification. Consensus clustering analysis identified a novel CRs-associated subtype which divided the combined LUAD cohort into two clusters (C1 = 217 and C2 = 296). We noticed that a remarkable discrepancy in survival rate among two clusters. Then, a total of 120 differentially expressed CRs were enrolled into stepwise Cox analyses. Four hub CRs (CBX7, HMGA2, NPAS2 and PRC1) were selected to create a risk signature which could accurately forecast patient outcomes and differentiate patient risk. GSEA unearthed that mTORC1 pathway, PI3K/Akt/mTOR and p53 pathway were greatly enriched in CRBS-high cohort. Moreover, the infiltration percentages of macrophage M0, macrophage M2, resting NK cells, memory B cells, dendritic cells and mast cells were statistically significantly different in the two groups. PCR assay confirmed the differential expression of four model biomarkers.ConclusionsAltogether, our project developed a robust risk signature based on CRs and offered novel insights into individualized treatment for LUAD cases.

Dataset Information

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data.

Publications

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing data.

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