Evidence map›Paper›PMID 34885134›Full record

ArticleCancers2021

Cell-Type-Specific Profibrotic Scores across Multi-Organ Systems Predict Cancer Prognosis.

Huihui Fan, Peilin Jia, Zhongming Zhao

Abstract read
In one paragraph

Article in Cancers, 2021. 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
  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

3 authors.

Huihui FanCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Peilin JiaCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Zhongming ZhaoCenter for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.ORCID 0000-0002-3477-0914

Funding

Transforming dbGaP genetic and genomic data to FAIR-ready by artificial intelligence and machine learning algorithmsR01LM012806 · NLM · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI Zhongming Zhao · 2017 to 2026
$3.7M
Deep learning for decoding genetic regulation and cellular maps in craniofacial developmentR01DE030122 · NIDCR · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI IWATA, JUNICHI, ZHAO, ZHONGMING · 2021 to 2023
$1.7M
NIDCR NIH HHS R01 DE030122NIH HHS R01DE030122NIH HHS R01LM012806NLM NIH HHS R01 LM012806the Cancer Prevention and Research Institute of Texas RP170668the Cancer Prevention and Research Institute of Texas RP180734the Cancer Prevention and Research Institute of Texas RP210045
6 · The paper itself

Abstract

Fibrosis is a major cause of mortality. Key profibrotic mechanisms are common pathways involved in tumorigenesis. Characterizing the profibrotic phenotype will help reveal the underlying mechanisms of early development and progression of a variety of human diseases, such as fibrosis and cancer. Fibroblasts have been center stage in response to various stimuli, such as viral infections. However, a comprehensive catalog of cell types involved in this process is currently lacking. Here, we deployed single-cell transcriptomic data across multi-organ systems (i.e., heart, kidney, liver, and lung) to identify novel profibrotic cell populations based on ECM pathway activity at single-cell resolution. In addition to fibroblasts, we also reported that epithelial, endothelial, myeloid, natural killer T, and secretory cells, as well as proximal convoluted tubule cells of the nephron, were significantly actively involved. Cell-type-specific gene signatures were enriched in viral infection pathways, enhanced glycolysis, and carcinogenesis, among others; they were validated using independent datasets in this study. By projecting the signatures into bulk TCGA tumor samples, we could predict prognosis in the patients using profibrotic scores. Our profibrotic cellular phenotype is useful for identifying new mechanisms and potential drug targets at the cell-type level for a wide range of diseases involved in ECM pathway activation.

Indexed as

cancer prognosisfibrosispan-cancerprofibrotic cellular phenotypesingle-cell extracellular matrix pathway activity

Identifiers

PMID34885134
PMCPMC8656778

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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.