Evidence map›Paper›PMID 40507222›Full record

ReviewCancers2025

Novel Avenues for the Detection of Cancer-Associated Viral Genome Integrations Using Long-Read Sequencing Technologies.

Larissa-Anna Bergmann, Alicja Pacholewska, Michal R Schweiger

Abstract readReview
In one paragraph

Review in Cancers, 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

3 authors.

Larissa-Anna BergmannInstitute for Translational Epigenetics, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany.ORCID 0009-0009-0866-7954
Alicja PacholewskaInstitute for Translational Epigenetics, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany.ORCID 0000-0002-4888-4883
Michal R SchweigerInstitute for Translational Epigenetics, Faculty of Medicine, University Hospital Cologne, University of Cologne, 50931 Cologne, Germany.ORCID 0000-0002-4672-0623

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Human papillomaviruses (HPVs), like many other viruses, are able to integrate their genomes into the host cellular genome. This integration can activate viral oncogenes or alter the function of cellular oncogenes and tumor suppressor genes, thereby increasing the likelihood of HPV-associated tumor development. In particular, HPV types 16 and 18 are responsible for over 70% of all cervical, anal, and oropharyngeal cancers worldwide, with rising incidence. Even more, high-resolution mapping of preferred integration sites using LR-Seq technologies offers deep insights into the molecular mechanisms of HPV integration. LR-Seq enables the detection of complex integration patterns, where the viral genome can be replicated and amplified into virus-host concatemers, including events within large structural variations or highly repetitive genomic regions. Furthermore, aligning LR-Seq data to the latest T2T reference genome (hs1) is necessary to provide new information about viral integration in genomic regions that were previously inaccessible, such as centromeres and other structurally complex repeat-rich loci. In this review, we provide insights into HPV genomic integration revealed by LR-Seq technologies, with a particular focus on how the use of the complete T2T reference genome enhances the detection of integration events in previously uncharacterized, repeat-rich regions of the human genome.

Indexed as

HPVhuman papillomaviruslong-read sequencingLR-Seqviral genome integration

Identifiers

PMID40507222
PMCPMC12153759

What OpenQuestion holds

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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.