ArticleJournal of biosciences2024
Tumor immune microenvironment-based clusters in predicting prognosis and guiding immunotherapy in breast cancer.
Article in Journal of biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Computational insights into cancer stem cell dynamics and the urokinase plasminogen activation pathway.Journal of biosciences · 2026Article
- Advanced Deep Learning Approaches in Detection Technologies for Comprehensive Breast Cancer Assessment Based on WSIs: A Systematic Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Breast Cancer and Tumor Microenvironment: The Crucial Role of Immune Cells.Current oncology (Toronto, Ont.) · 2025Review
- JMJD8 overexpression in breast cancer: implications for diagnosis, prognosis, and immune microenvironment interactions.Frontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development and progression of breast cancer (BC) depend heavily on the tumor microenvironment (TME), especially tumor infiltration leukocytes (TILs). TME-based classifications in BC remain largely unknown and need to be clarified. Using the bioinformatic analysis, we attempted to construct a prognostic nomogram based on clinical features and TME-related differentially expressed genes (DEGs). We also tried to investigate the association between the prognostic nomogram and clinical characteristics, TILs, possible signaling pathways, and response to immunotherapy in BC patients. DEGs for BC patients were identified from The Cancer Genome Atlas Breast Invasive Carcinoma database. TME-related genes were downloaded from the Immunology Database and Analysis Portal. After intersecting DEGs and TME-related genes, 3985 overlapping TME-related DEGs were selected for non-negative matrix factorization clustering, microenvironment cell populations-counter (MCP-counter), LASSO Cox regression, tumor immune dysfunction, and exclusion (TIDE) algorithm analyses. BC patients were divided into three clusters based on the TME-related DEGs and survival data, in which cluster 3 had the best overall survival (OS). Of note, cluster 3 exhibited the highest infiltration or lowest infiltration of CD
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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.