Evidence map›Paper›PMID 34339033›Full record

ReviewAdvances in experimental medicine and biology2021

Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges.

Shobhana Talukdar, Zenas Chang, Boris Winterhoff, Timothy K Starr

Abstract readReview
PubMed Publisher
In one paragraph

Review in Advances in experimental medicine and biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
3.2field-weighted citation impact, top 7% of its field
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

4 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. 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

4 authors at 2 institutions in 1 country.

Shobhana TalukdarDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota School of Medicine, Minneapolis, MN, USA.
Zenas ChangDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota School of Medicine, Minneapolis, MN, USA.
Boris WinterhoffDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota School of Medicine, Minneapolis, MN, USA.
Timothy K StarrDivision of Gynecologic Oncology, Department of Obstetrics, Gynecology and Women's Health, University of Minnesota School of Medicine, Minneapolis, MN, USA. star0044@umn.edu.
University of Minnesota · USUniversity of Minnesota Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Ovarian cancer remains the leading cause of death from gynecologic malignancy in the Western world. Tumors are comprised of heterogeneous populations of various cancer, immune, and stromal cells; it is hypothesized that rare cancer stem cells within these subpopulations lead to disease recurrence and treatment resistance. Technological advances now allow for the analysis of tumor genomes and transcriptomes at the single-cell level, which provides the resolution to potentially identify these rare cancer stem cells within the larger tumor.In this chapter, we review the evolution of next-generation RNA sequencing techniques, the methodology of single-cell isolation and sequencing, sequencing data analysis, and the potential applications in ovarian cancer. We also summarize the current published work using single-cell sequencing in ovarian cancer.By utilizing this novel technique to characterize the gene expression of rare subpopulations, new targets and treatment pathways may be identified in ovarian cancer to change treatment paradigms.

Indexed as

Neoplasm Recurrence, LocalOvarian NeoplasmsCarcinoma, Ovarian EpithelialFemaleHumansNeoplastic Stem CellsSequence Analysis, RNAGene expressionNext generation RNA sequencingOvarian cancerRare cancer stem cellsRare cancer subpopulationsSingle cell isolationSingle cell sequencingTreatment

Identifiers

PMID34339033
OpenAlexW3188286573

What OpenQuestion holds

Textmetadata
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.