Evidence map›Paper›PMID 31463974›Full record

ArticleInternational journal of cancer2020

Multiscale network analysis reveals molecular mechanisms and key regulators of the tumor microenvironment in gastric cancer.

Won-Min Song, Xiandong Lin, Xuehong Liao, Dan Hu, Jieqiong Lin, Umut Sarpel, Yunbin Ye, Yael Feferman, Daniel M Labow, Martin J Walsh and 2 more

Abstract read
In one paragraph

Article in International journal of cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 2 pooled it
–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

13 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  6. The Role of Neural Network Analysis in Identifying Predictors of Gastric Cancer.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2024
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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

12 authors.

Won-Min SongDepartment of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY.ORCID 0000-0003-0948-119X
Xiandong LinLaboratory of Radiation Oncology and Radiobiolog, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China.ORCID 0000-0002-3250-4014
Xuehong LiaoDepartment of Pathology, Zhongshan Hospital of Xiamen University, Xiamen, Fujian, China.
Dan HuDepartment of Pathology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China.
Jieqiong LinDepartment of Pathology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fuzhou, Fujian, China.
Umut SarpelDepartment of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY.
Yunbin YeFujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian, China.
Yael FefermanDepartment of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY.
Daniel M LabowDepartment of Surgery, Icahn School of Medicine at Mount Sinai, New York, NY.
Martin J WalshDepartment of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY.
Xiongwei ZhengFujian Provincial Key Laboratory of Translational Cancer Medicine, Fuzhou, Fujian, China.
Bin ZhangDepartment of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY.ORCID 0000-0002-9549-5653

Funding

Big Omics Data Engine 2 SupercomputerS10OD026880 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2019 to 2019
$2.0M
Transforming Genomics with 5 PB Big Omics Data Engine Cray CS300-AC SupercomputerS10OD018522 · OD · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI KOVATCH, PATRICIA · 2014 to 2014
$1.9M
NIH HHS S10 OD018522NIH HHS S10 OD026880
6 · The paper itself

Abstract

Gastric cancer (GC) is the third leading cause of cancer deaths and the fourth most prevalent malignancy worldwide. The high incidence and mortality rates of gastric cancer result from multiple factors such as ineffective screening, diagnosis, and limited treatment options. In our study, we sought to systematically identify predictive molecular networks and key regulators to elucidate complex interacting signaling pathways in GC. We performed an integrative network analysis of the transcriptomic data in The Cancer Genome Atlas (TCGA) gastric cancer cohort and then comprehensively characterized the predictive subnetworks and key regulators by the matched genetic and epigenetic data. We identified 221 gene subnetworks (modules) in GC. The most prognostic subnetworks captured multiple aspects of the tumor microenvironment in GC involving interactions among stromal, epithelial and immune cells. We revealed the genetic and epigenetic underpinnings of those subnetworks and their key transcriptional regulators. We computationally predicted and experimentally validated specific mechanisms of anticancer effects of GKN2 in gastric cancer proliferation and invasion in vitro. The network models and the key regulators of the tumor microenvironment in GC identified here pave a way for developing novel therapeutic strategies for GC.

Indexed as

Gene Expression Regulation, NeoplasticGene Regulatory NetworksAdultAgedAged, 80 and overCarrier ProteinsCell Line, TumorCell ProliferationCohort StudiesComputational BiologyDatasets as TopicDisease-Free SurvivalEpigenesis, GeneticFemaleGastrectomyHumansCarrier ProteinsGKN2 protein, humanDNA methylationgastric cancergene coexpression networkkey drivernetwork modulesomatic mutationstumor microenvironment

Identifiers

PMID31463974
PMCPMC7004118

What OpenQuestion holds

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LicenceCC BY
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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.