Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

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

2 Aug 2022

Abstract excerpt

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...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Cancer Immunotherapy and Biomarkers
Share to Ferroptosis and cancer prognosis
Share to Immune Cell Function and Interaction

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 35918500
DOI: 10.1038/s41598-022-17645-7

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of the immune subtype of ovarian cancer patients by integrated analyses of transcriptome and single-cell sequencing dataPMID 35918500Read ↗

Single-sample gene set enrichment analysis reveals the clinical implications of immune-related genes in ovarian cancerSemantic proximity Read ↗

Single-cell analysis revealed that IL4I1 promoted ovarian cancer progressionSemantic proximity Read ↗

Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancerSemantic proximity Read ↗

Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian CancerSemantic proximity Read ↗

Development a prognostic risk model based on B cell-related immune genes in Ovarian Cancer by integrative analysis of single-cell and bulk RNA sequencing dataSemantic proximity Read ↗

A Risk Model Based on Tumor Microenvironment to Predict Survival and Immunotherapy Efficacy for Ovarian CancerSemantic proximity Read ↗

Identification of three subtypes of ovarian cancer and construction of prognostic models based on immune-related genes.Semantic proximity Read ↗

Ovarian cancer‐specific dysregulated genes with prognostic significance: scRNA‐Seq with bulk RNA‐Seq data and experimental validationSemantic proximity Read ↗

Integrative Multi-Omics and Machine Learning Approach Reveals Tumor Microenvironment-Associated Prognostic Biomarkers in Ovarian CancerSemantic proximity Read ↗

Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancerSemantic proximity Read ↗

Ovarian cancer-associated immune exhaustion involves SPP1+ T cell and NKT cell, symbolizing more malignant progressionSemantic proximity Read ↗

Single-cell transcriptomic construction of fibroblast score for analysis of immune infiltration in primary and metastatic ovarian cancerSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.