Trial reportApplied immunohistochemistry & molecular morphology : AIMM2020
Differentially Expressed Genes in Matched Normal, Cancer, and Lymph Node Metastases Predict Clinical Outcomes in Patients With Breast Cancer.
Trial report in Applied immunohistochemistry & molecular morphology : AIMM, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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
21 citing papers in PubMed, 32 citations in OpenAlex.
- Integrative Transcriptomic and Machine Learning Analysis of ecDNA-Associated Features for Studying Chemotherapy Resistance in TNBC.bioRxiv : the preprint server for biology · 2026Article
- Investigating the association of NBN gene polymorphisms with multiple cancers through statistical meta-analysis and bioinformatics insights.Biology methods & protocols · 2026Review
- Development of a Novel Risk Signature for Predicting the Prognosis and Immunotherapeutic Response of Prostate Cancer by Integrating Ferroptosis and Immune-Related Genes.Molecular biotechnology · 2025Article
- Exploration of the role of drug resistance-associated anoikis-related genes in HER2-Negative breast cancer through bioinformatics analysis.Biochemistry and biophysics reports · 2025Article
- Identifying Key Genes Involved in Axillary Lymph Node Metastasis in Breast Cancer Using Advanced RNA-Seq Analysis: A Methodological Approach with GLMQL and MAS.International journal of molecular sciences · 2024Article
- 9S1R nullomer peptide induces mitochondrial pathology, metabolic suppression, and enhanced immune cell infiltration, in triple-negative breast cancer mouse model.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2024Article
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- Nullomer peptide increases immune cell infiltration and reduces tumor metabolism in triple negative breast cancer mouse model.Research square · 2023Article
- A functional analysis of omic network embedding spaces reveals key altered functions in cancer.Bioinformatics (Oxford, England) · 2023Article
- Functional Proteomic Profiling Analysis in Four Major Types of Gastrointestinal Cancers.Biomolecules · 2023Article
- Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses.Diagnostics (Basel, Switzerland) · 2023Article
- Screening of Specific and Common Pathways in Breast Cancer Cell Lines MCF-7 and MDA-MB-231 Treated with Chlorophyllides Composites.Molecules (Basel, Switzerland) · 2022Article
- Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma.Frontiers in oncology · 2022Article
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- Squalene epoxidase expression is associated with breast tumor progression and with a poor prognosis in breast cancer.Oncology letters · 2021Article
- Computational analysis of fused co-expression networks for the identification of candidate cancer gene biomarkers.NPJ systems biology and applications · 2021Article
- An immune-related signature that to improve prognosis prediction of breast cancer.American journal of cancer research · 2021Article
- Decreased PTGDS Expression Predicting Poor Survival of Endometrial Cancer by Integrating Weighted Gene Co-Expression Network Analysis and Immunohistochemical Validation.Cancer management and research · 2020Article
- Tumour-Infiltrating Immune Cell-Based Subtyping and Signature Gene Analysis in Breast Cancer Based on Gene Expression Profiles.Journal of Cancer · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genome-wide screening of transcriptional changes among normal, cancer, and nodal metastases provides insights into the molecular basis of breast cancer (BC) progression and metastasis. To identify transcriptional changes and differentially expressed genes (DEGs) in the metastatic progression of BC and to determine the prognostic role of these DEGs in clinical outcome, we compared transcriptome profiling in matched normal, cancer, and lymph node metastatic tissues of 7 patients with estrogen receptor-positive, HER2-negative BC by using massive parallel RNA sequencing. The global profiles of gene expression in cancer and nodal metastases were highly correlated (r=0.962, P<0.001). In 6 (85.8%) patients, cancer and corresponding nodal metastases from the same patient clustered together. We identified 1522 and 664 DEGs between normal and cancer and between cancer and nodal metastases, respectively. The DEGs in normal versus cancer and cancer versus nodal metastases were significantly clustered in 1 and 8 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. The chemokine signaling pathway was the most significant pathway in the cancer-to-nodal metastasis transition (false discovery rate=2.15E-13). The expression of 2 dysregulated RAC2 and PTGDS genes was confirmed by quantitative real-time polymerase chain reaction and immunohistochemistry. Interestingly, the lower RAC2 and PTGDS expression were associated with significantly worse disease-free survival in patients with BC. Our results show a high concordance of gene expression in BC and their nodal metastases, and identify DEGs associated with the metastatic progression of BC. The DEGs identified in this study represent novel biomarkers for predicting the prognosis of patients with BC.
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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.