ArticleDiscover oncology2025
Multi-omics analysis of tumor necrosis factor superfamily 4 reveals its prognostic value with T cell exhaustion feature in cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Mining and experimental validation of machine learning-based immune-related diagnostic biomarkers for hepatocellular carcinoma.Journal of gastrointestinal oncology · 2026Article
- An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer.NPJ precision oncology · 2026Article
- Immunological and Prognostic Profiling of Triple-Negative Breast Cancer Based on Single-Cell and Bulk RNA Sequencing of T-Cell Exhaustion.Technology in cancer research & treatmentArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundAs a member of the tumor necrosis factor (TNF) family, TNF superfamily 4 (TNFSF4) plays a crucial role in various immune-related processes. However, its biological function in pan-cancer remains largely unexplored.
methodsRNA-sequencing (RNA-seq) data and corresponding clinical variables were obtained from the Cancer Genome Atlas (TCGA). Immunotherapy cohorts were retrieved from Gene Expression Omnibus (GEO). Tumor Immune Estimation Resource was used to evaluate tumor-infiltrating immune cell levels. The Tumor Immune Single-cell Hub (TISCH) 2 database was used to examine TNFSF4 expression across various tumor cell subsets. Gene set enrichment analysis (GSEA) was performed to investigate TNFSF4-associated signaling pathways.
resultsBioinformatic analyses of TNFSF4 across TCGA cancers revealed that TNFSF4 expression was highly increased in cancers compared to normal tissues. Further immunohistochemistry staining of multiple tumor samples validated this finding. Univariate Cox regression and survival analyses identified TNFSF4 as a risk factor in most cancers. Furthermore, TNFSF4 expression increased with tumor stage progression in several cancers. In the two immunotherapy cohorts from the GEO database, the proportion of partial response/complete response patients was higher in the TNFSF4 low-expression group. Single-cell RNA-seq data from the TISCH database indicated that TNFSF4 expression was predominantly observed in proliferative and exhausted T cells. Correlation analysis demonstrated a positive association between TNFSF4 and immunomodulatory genes. Ultimately, GSEA revealed that TNFSF4 was related to immune response and epithelial-mesenchymal transition pathways.
conclusionsThis multi-omics analysis highlights the role of TNFSF4 in tumor progression and immune modulation. High TNFSF4 expression correlates with poor survival and may affect immunotherapy efficacy, suggesting its potential prognostic biomarker and therapeutic target.
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.