Evidence map›Paper›PMID 41951849›Full record

ArticleCommunications biology2026

OncoRisk: a state-of-the-art web server for bridging the oncogenic databases and pan-cancer cohorts to the translational oncology.

Xiya Song, Emre Green, Xinmeng Liao, Hasan Turkez, Gozde Yesil, Bayram Yuksel, Mathias Uhlen, Cheng Zhang, Adil Mardinoglu

Abstract read
In one paragraph

Article in Communications biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Xiya Song *Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID http://orcid.org/0009-0001-3893-682X
Emre Green *Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
Xinmeng LiaoScience for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.
Hasan TurkezDepartment of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey.
Gozde YesilPhenome Omics R&D, Mehmet Ali Aydinlar Acibadem University, Istanbul, Turkey.ORCID http://orcid.org/0000-0003-1964-6306
Bayram YukselPhenome Omics R&D, Mehmet Ali Aydinlar Acibadem University, Istanbul, Turkey.ORCID http://orcid.org/0009-0009-4110-8775
Mathias UhlenScience for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-4858-8056
Cheng ZhangScience for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden.ORCID http://orcid.org/0000-0002-3721-8586
Adil MardinogluScience for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden. adilm@scilifelab.se.ORCID http://orcid.org/0000-0002-4254-6090

Funding

Knut och Alice Wallenbergs Stiftelse (Knut and Alice Wallenberg Foundation) 72110
6 · The paper itself

Abstract

Accurate interpretation of genomic variants remains a major bottleneck in precision oncology, due in part to fragmented knowledge across databases and limited integration between clinical evidence and population-scale genomic datasets. Here we present OncoRisk, a stand-alone, user-friendly web server that unifies data from over ten oncogenic databases and seven large-scale pan-cancer cohorts, enabling rapid multi-database queries and network-based exploration of genomic variants, gene-gene interactions, and therapy associations. The platform features a semi-automated reporting workflow that generates comprehensive, patient-specific clinical reports from raw tissue sequencing data and categorizes variants into actionable tiers. For translational research, OncoRisk provides modules for data-driven exploration, allowing users to validate findings by interrogating mutation frequencies and clinical associations across real-world patient data. Furthermore, an integrated suite of analytical tools enables comprehensive, cohort-level investigations of mutational landscapes, prognostic biomarkers, and oncogenic signaling pathways. By providing a unified ecosystem that bridges curated knowledge with large-scale cohort data, OncoRisk serves as an effective catalyst for both discovery research and clinical application in oncology. OncoRisk is publicly available at https://www.phenomeportal.org/oncorisk .

Indexed as

Databases, GeneticInternetMedical OncologyNeoplasmsOncogenesSoftwareTranslational Research, BiomedicalBiocurationGenomicsHumansMutation

Identifiers

PMID41951849
PMCPMC13068978

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.