Evidence map›Paper›PMID 34985924›Full record

ArticleMolecular biology of the cell2022

Quantitative biophysical metrics for rapid evaluation of ovarian cancer metastatic potential.

Apratim Mukherjee, Haonan Zhang, Katherine Ladner, Megan Brown, Jacob Urbanski, Joseph P Grieco, Rakesh K Kapania, Emil Lou, Bahareh Behkam, Eva M Schmelz and 1 more

Open access · greenAbstract read
In one paragraph

Article in Molecular biology of the cell, 2022. 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
0.9field-weighted citation impact, top 32% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

  1. Deep learning reveals how cells pull, buckle, and navigate fibrous environments.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  2. Article
  3. 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 at 2 institutions in 1 country.

Apratim MukherjeeDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060.
Haonan ZhangDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060.
Katherine LadnerDivision of Hematology, Oncology and Transplantation, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455.
Megan BrownDepartment of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24060.
Jacob UrbanskiDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060.
Joseph P GriecoDepartment of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24060.
Rakesh K KapaniaDepartment of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA 24060.
Emil LouDivision of Hematology, Oncology and Transplantation, Masonic Cancer Center, University of Minnesota, Minneapolis, MN 55455.
Bahareh BehkamDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060.
Eva M SchmelzDepartment of Human Nutrition, Foods and Exercise, Virginia Tech, Blacksburg, VA 24060.
Amrinder S NainDepartment of Mechanical Engineering, Virginia Tech, Blacksburg, VA 24060.
Virginia Tech · USUniversity of Minnesota Medical Center · US

Funding

Targeting mitochondrial adaptations to suppress metastatic outgrowth in serous ovarian cancerF31CA261146 · NCI · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI GRIECO, JOE · 2021 to 2021
$35k
NCI NIH HHS F31 CA261146
6 · The paper itself

Abstract

Ovarian cancer is routinely diagnosed long after the disease has metastasized through the fibrous submesothelium. Despite extensive research in the field linking ovarian cancer progression to increasingly poor prognosis, there are currently no validated cellular markers or hallmarks of ovarian cancer that can predict metastatic potential. To discern disease progression across a syngeneic mouse ovarian cancer progression model, here we fabricated extracellular matrix mimicking suspended fiber networks: cross-hatches of mismatch diameters for studying protrusion dynamics, aligned same diameter networks of varying interfiber spacing for studying migration, and aligned nanonets for measuring cell forces. We found that migration correlated with disease while a force-disease biphasic relationship exhibited F-actin stress fiber network dependence. However, unique to suspended fibers,

Indexed as

Ovarian NeoplasmsActinsAnimalsCell Line, TumorCell MovementExtracellular MatrixFemaleHumansMiceActins

Identifiers

PMID34985924
PMCPMC9265161
OpenAlexW4206057928

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
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.