ArticlePloS one2026
SIRT7 as a context-dependent biomarker and therapeutic target: Insights from a pan-cancer study.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Differential NAD + availability may drive asymmetric SIRT7 activity in tumor and immune cells.Molecular biology reports · 2026Review
- Computational screening of oncogenic genetic variations in tumor suppressor proteins driving gastric cancer pathogenesis.PloS one · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SIRT7 is a member of the sirtuin family and has emerged as a crucial player in cancer biology, with a multifaceted role in both tumor-promoting and tumor-suppressing activities. Despite its importance in connecting NAD+ metabolism with transcriptional regulation, a systematic analysis across multiple cancer types remains underexplored, thereby limiting our understanding of its prognostic value, mutational impact, immune associations, and therapeutic potential. Therefore, this study aims to evaluate the pan-cancer significance of SIRT7 through integrated computational approaches. We employed protein structure modeling, deep neural network-guided protein interaction analysis, cancer hallmark association, gene expression profiling, survival analysis, mutational landscape, immune infiltration assessment, and structure-based drug discovery, combining molecular docking and dynamics simulations. Our deep neural network analyses revealed SIRT7 as a central hub connecting NAD+ metabolism with transcriptional regulation in its sub-network (R2: 0.9839). SIRT7 exhibited differential expression across 17 cancer types, with high expression associated with poor survival in six cancer types; however, it surprisingly correlated with better outcomes in sarcoma. Cancer-specific mutations significantly reduced patient survival and altered the expression of network components. We identified regulatory mechanisms involving five miRNAs and three transcription factors. Therapeutic intervention identified two promising SIRT7 inhibitors (ZINC000150487575 and ZINC000150641215) with superior binding properties compared to the reference inhibitor. This comprehensive pan-cancer analysis of SIRT7 provides a framework for understanding its role in cancer biology and identifies potential therapeutic opportunities for personalized interventions. Our findings have immediate implications for clinical oncology, enabling SIRT7 as a biomarker for patient stratification and a therapeutic target for novel inhibitor development. Targeting SIRT7 may offer new therapeutic strategies for various cancers, particularly those with high SIRT7 expression, as SIRT7 functions in a context-dependent manner in cancer regulation. Further studies are necessary to validate the efficacy of SIRT7 inhibitors and explore their role in therapeutic resistance and disease recurrence.
Indexed as
Identifiers
What Socratic 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 Socratic graph.