Evidence map›Paper›PMID 39213324›Full record

ArticleBioinformatics (Oxford, England)2024

Signature Search Polestar: a comprehensive drug repurposing method evaluation assistant for customized oncogenic signature.

Jinbo Zhang, Shunling Yuan, Wen Cao, Xianrui Jiang, Cheng Yang, Chenchao Jiang, Runhui Liu, Wei Yang, Saisai Tian

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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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.

Jinbo ZhangDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.ORCID 0000-0001-8287-3259
Shunling YuanDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Wen CaoDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Xianrui JiangChanghai Hospital, Second Military Medical University, Shanghai 200433, China.
Cheng YangDepartment of Clinical Nutrition, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin 300110, China.
Chenchao JiangDepartment of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin 300110, China.
Runhui LiuDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Wei YangDepartment of Pharmacy, Tianjin Rehabilitation Center of Joint Logistics Support Force, Tianjin 300110, China.
Saisai TianDepartment of Phytochemistry, School of Pharmacy, Second Military Medical University, Shanghai 200433, China.ORCID 0000-0003-2498-190X

Funding

National Key Research and Development Program of China 2022YFC3502000
6 · The paper itself

Abstract

summaryThe burgeoning high-throughput technologies have led to a significant surge in the scale of pharmacotranscriptomic datasets, especially for oncology. Signature search methods (SSMs), utilizing oncogenic signatures formed by differentially expressed genes through sequencing, have been instrumental in anti-cancer drug screening and identifying mechanisms of action without relying on prior knowledge. However, various studies have found that different SSMs exhibit varying performance across pharmacotranscriptomic datasets. In addition, the size of the oncogenic signature can also significantly impact the result of drug repurposing. Therefore, finding the optimal SSMs and customized oncogenic signature for a specific disease remains a challenge. To address this, we introduce Signature Search Polestar (SSP), a webserver integrating the largest pharmacotranscriptomic datasets of anti-cancer drugs from LINCS L1000 with five state-of-the-art SSMs (XSum, CMap, GSEA, ZhangScore, XCos). SSP provides three main modules: Benchmark, Robustness, and Application. Benchmark uses two indices, Area Under the Curve and Enrichment Score, based on drug annotations to evaluate SSMs at different oncogenic signature sizes. Robustness, applicable when drug annotations are insufficient, uses a performance score based on drug self-retrieval for evaluation. Application provides three screening strategies, single method, SS_all, and SS_cross, allowing users to freely utilize optimal SSMs with tailored oncogenic signature for drug repurposing. AVAILABILITY AND IMPLEMENTATION: SSP is free at https://web.biotcm.net/SSP/. The current version of SSP is archived in https://doi.org/10.6084/m9.figshare.26524741.v1, allowing users to directly use or customize their own SSP webserver.

Indexed as

Antineoplastic AgentsDrug RepositioningSoftwareComputational BiologyHumansNeoplasmsAntineoplastic Agents

Identifiers

PMID39213324
PMCPMC11398873

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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.