ArticleCell communication and signaling : CCS2019
Systematic expression analysis of ligand-receptor pairs reveals important cell-to-cell interactions inside glioma.
Article in Cell communication and signaling : CCS, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 62 citations in OpenAlex.
- Modern Mining: The Role of Single-cell RNA Sequencing in Advancing Neuroscience Research.BioEssays : news and reviews in molecular, cellular and developmental biology · 2026Review
- Bibliometric analysis of single-cell RNA sequencing in tumor microenvironment.Discover oncology · 2026Review
- Review
- DD-CC-II: Data Driven Cell-Cell Interaction Inference and Its Application to COVID-19.International journal of molecular sciences · 2025Article
- IL-1βJournal of neuroinflammation · 2025Article
- Single-cell multi-omics in cancer immunotherapy: from tumor heterogeneity to personalized precision treatment.Molecular cancer · 2025Review
- Integrated analysis of single-cell and bulk RNA-sequencing to predict prognosis and therapeutic response for colorectal cancer.Scientific reports · 2025Article
- Molecular biology of the deadliest cancer - glioblastoma: what do we know?Frontiers in immunology · 2025Review
- Analysis of transcription profiles for the identification of master regulators as the key players in glioblastoma.Computational and structural biotechnology journal · 2024Article
- Phagocytosis Checkpoints in Glioblastoma: CD47 and Beyond.Current issues in molecular biology · 2024Review
- The diversification of methods for studying cell-cell interactions and communication.Nature reviews. Genetics · 2024Review
- Single-cell analysis of human prepuce reveals dynamic changes in gene regulation and cellular communications.BMC genomics · 2023Article
- Reciprocal Interaction of Cancer Stem Cells of Cholangiocarcinoma with Macrophage.Stem cell reviews and reports · 2023Article
- Bioinformatic analysis of single-cell RNA sequencing dataset dissects cellular heterogeneity of triple-negative breast cancer in transcriptional profile, splicing event and crosstalk network.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023Article
- Complex Analysis of Single-Cell RNA Sequencing Data.Biochemistry. Biokhimiia · 2023Review
- Melanoma molecular subtyping and scoring model construction based on ligand-receptor pairs.Frontiers in genetics · 2023Article
- MicroRNA and mRNA Expression Changes in Glioblastoma Cells Cultivated under Conditions of Neurosphere Formation.Current issues in molecular biology · 2022Article
- SPROUT: spectral sparsification helps restore the spatial structure at single-cell resolution.NAR genomics and bioinformatics · 2022Article
- A novel prognostic model based on single-cell RNA sequencing data for hepatocellular carcinoma.Cancer cell international · 2022Article
- Expression Analysis of Ligand-Receptor Pairs Identifies Cell-to-Cell Crosstalk between Macrophages and Tumor Cells in Lung Adenocarcinoma.Journal of immunology research · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlioma is the most commonly diagnosed malignant and aggressive brain cancer in adults. Traditional researches mainly explored the expression profile of glioma at cell-population level, but ignored the heterogeneity and interactions of among glioma cells.
methodsHere, we firstly analyzed the single-cell RNA-seq (scRNA-seq) data of 6341 glioma cells using manifold learning and identified neoplastic and healthy cells infiltrating in tumor microenvironment. We systematically revealed cell-to-cell interactions inside gliomas based on corresponding scRNA-seq and TCGA RNA-seq data.
resultsA total of 16 significantly correlated autocrine ligand-receptor signal pairs inside neoplastic cells were identified based on the scRNA-seq and TCGA data of glioma. Furthermore, we explored the intercellular communications between cancer stem-like cells (CSCs) and macrophages, and identified 66 ligand-receptor pairs, some of which could significantly affect prognostic outcomes. An efficient machine learning model was constructed to accurately predict the prognosis of glioma patients based on the ligand-receptor interactions.
conclusionCollectively, our study not only reveals functionally important cell-to-cell interactions inside glioma, but also detects potentially prognostic markers for predicting the survival of glioma patients.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.