Evidence map›Paper›PMID 35448980›Full record

ArticleBMC cancer2022

Modular and mechanistic changes across stages of colorectal cancer.

Sara Rahiminejad, Mano R Maurya, Kavitha Mukund, Shankar Subramaniam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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  4. Review
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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

4 authors.

Sara RahiminejadDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Mano R MauryaDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Kavitha MukundDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Shankar SubramaniamDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA. shankar@ucsd.edu.

Funding

Systems Biological Analysis of Innate and Adaptive Responses to VaccinationU19AI090023 · NIAID · EMORY UNIVERSITY · PI AHMED, RAFI, LI, SHUZHAO · 2010 to 2021
$45.0M
The second phase of NIH Common Fund 4D Nucleome Network Organizational HubU01CA200147 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZHONG, SHENG · 2015 to 2024
$22.0M
Systems Biology Analyses for Hemodynamic Regulation of Vascular HomeostasisR01HL108735 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHIEN, SHU, SHYY, JOHN YJ · 2012 to 2024
$13.4M
Shear Regulation of MicroRNA Transportomes and Targetomes in Vascular HomeostasisR01HL106579 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHIEN, SHU, SHYY, JOHN YJ · 2011 to 2022
$8.3M
The Metabolomics Data Center and Workbench (MDCW)U01DK097430 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUBRAMANIAM, SHANKAR · 2012 to 2017
$7.4M
Biomedical Data Commons Workbench (BDCW)OT2OD030544 · OD · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUBRAMANIAM, SHANKAR · 2020 to 2024
$3.2M
An Integrative Omics Approach to Identify Biomarkers Related to Preeclampsia and Breast Cancer RisksR01HD084633 · NICHD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI GARMIRE, LANA X · 2016 to 2020
$3.0M
Mapping Disease Pathways for Biliary AtresiaR01DK109365 · NIDDK · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI SHIN, DONGHUN, SINDHI, RAKESH K. · 2017 to 2020
$2.4M
The Role of Phosphatases in EC MechanotransductionR01HL085159 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI CHIEN, SHU · 2006 to 2009
$1.5M
Theoretical Foundations and Software Infrastructure for Biological Network DatabasesU01CA198941 · NCI · CASE WESTERN RESERVE UNIVERSITY · PI KOYUTURK, MEHMET · 2015 to 2017
$1.4M
Reconstruction and Modeling of Dynamical Molecular NetworksR01LM012595 · NLM · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUBRAMANIAM, SHANKAR · 2018 to 2021
$1.3M
NCI NIH HHS U01 CA198941NCI NIH HHS U01 CA200147NHLBI NIH HHS R01 HL085159NHLBI NIH HHS R01 HL106579NHLBI NIH HHS R01 HL108735NIAID NIH HHS U19 AI090023NICHD NIH HHS R01 HD084633NIDDK NIH HHS R01 DK109365NIDDK NIH HHS U01 DK097430NIH HHS OT2 OD030544NIH HHS U01 DK097430, U01 CA200147, U01 CA198941, U19 AI090023, R01 HL106579, R01HL108735, R01 HD084633, R01 DK109365, R01LM012595, OTA OD030544NLM NIH HHS R01 LM012595
6 · The paper itself

Abstract

backgroundWhile mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript.

methodsUsing previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC.

resultsSixteen Thousand Sixty Two DEGs were identified between various stages (p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways (e.g., MAPK signaling pathway in stages I-III and Notch signaling pathway in stages III and IV). We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors (162 genes in total) revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV.

conclusionsHere, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC.

Indexed as

Colorectal NeoplasmsGene Expression ProfilingBiomarkers, TumorComputational BiologyGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansNeoplasm StagingSignal TransductionTumor Suppressor ProteinsBiomarkers, TumorMTUS1 protein, humanTumor Suppressor ProteinsBiomarkersColorectal cancerCRC stagesSignaling pathwaysStage-specific networksStage-unique networks

Identifiers

PMID35448980
PMCPMC9022252

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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.