Evidence map›Paper›PMID 40221813›Full record

ArticleHuman genomics2025

Comprehensive analysis of prognosis markers with molecular features derived from pan-cancer whole-genome sequences.

Mamoru Kato, Jo Nishino, Momoko Nagai, Hirofumi Rokutan, Daichi Narushima, Hanako Ono, Takanori Hasegawa, Seiya Imoto, Shigeyuki Matsui, Tatsuhiko Tsunoda and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Human genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

11 authors.

Mamoru KatoDivision of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo, Japan. mamkato@ncc.go.jp.
Jo NishinoDivision of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo, Japan.
Momoko NagaiDivision of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo, Japan.
Hirofumi RokutanDivision of Cancer Genomics, Research Institute, National Cancer Center Japan, Tokyo, Japan.
Daichi NarushimaDivision of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo, Japan.
Hanako OnoDivision of Bioinformatics, Research Institute, National Cancer Center Japan, Tokyo, Japan.
Takanori HasegawaDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Seiya ImotoDivision of Health Medical Intelligence, Human Genome Center, The Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Shigeyuki MatsuiCREST, JST, Tokyo, Japan.
Tatsuhiko TsunodaCREST, JST, Tokyo, Japan.
Tatsuhiro ShibataDivision of Cancer Genomics, Research Institute, National Cancer Center Japan, Tokyo, Japan.

Funding

CREST-JST JPMJCR 1412National Cancer Center Research and Development Funds 29-A-2
6 · The paper itself

Abstract

Cancer prognosis markers are useful for treatment decisions; however, the omics-level landscape is not well understood across multiple cancer types. Pan-Cancer Analysis of Whole Genomes (PCAWG) provides unprecedented access to various types of molecular data, ranging from typical DNA mutations and RNA expressions to more deeply analyzed or whole-genomic features, such as HLA haplotypes and structural variations. We analyzed the PCAWG data of 13 cancer types from 1,514 patients to identify prognosis markers belonging to 17 molecular features in the survival analysis based on the Cox and Lasso regression methods. We found that germline features including HLA haplotypes, neoantigens, and the number of structural variations were associated with overall survival; however, mutational signatures were not. Measuring a few markers provided a sufficient prognostic performance evaluated by c-index for each cancer type. DNA markers demonstrated better or comparable prognostic performance compared to RNA markers in some cancer types. "Universal" markers strongly associated with overall survival across cancer types were not identified. These findings will give insights into the clinical implementation of prognosis markers.

Indexed as

Biomarkers, TumorNeoplasmsWhole Genome SequencingDatabases, GeneticDatasets as TopicFemaleGene Expression Regulation, NeoplasticGenome, HumanHaplotypesHLA AntigensHumansKaplan-Meier EstimateMaleModels, GeneticMutationNeoplasm StagingBiomarkers, TumorHLA AntigensCancer genomicsPan-cancer analysisPrognosis markerSurvival analysis

Identifiers

PMID40221813
PMCPMC11993945

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LicenceCC BY-NC-ND
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Registered trials

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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.