Evidence map›Paper›PMID 34440687›Full record

ArticleCells2021

Familiarity Breeds Strategy: In Silico Untangling of the Molecular Complexity on Course of Autoimmune Liver Disease-to-Hepatocellular Carcinoma Transition Predicts Novel Transcriptional Signatures.

Soumyadeep Mukherjee, Arpita Kar, Najma Khatun, Puja Datta, Avik Biswas, Subhasis Barik

Open access · goldAbstract read
In one paragraph

Article in Cells, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
4.8field-weighted citation impact, top 4% 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

42 citing papers in PubMed, 47 citations in OpenAlex.

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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

6 authors at 1 institution in 1 country.

Soumyadeep MukherjeeDepartment of In Vitro Carcinogenesis and Cellular Chemotherapy, Chittaranjan National Cancer Institute, Kolkata 700026, India.ORCID 0000-0002-7495-0856
Arpita KarDepartment of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, Kolkata 700026, India.ORCID 0000-0001-6554-2984
Najma KhatunDepartment of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, Kolkata 700026, India.
Puja DattaDepartment of In Vitro Carcinogenesis and Cellular Chemotherapy, Chittaranjan National Cancer Institute, Kolkata 700026, India.
Avik BiswasDepartment of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, Kolkata 700026, India.
Subhasis BarikDepartment of In Vitro Carcinogenesis and Cellular Chemotherapy, Chittaranjan National Cancer Institute, Kolkata 700026, India.
Chittaranjan National Cancer Institute · IN

Funding

Department of Biotechnology, Ministry of Science and Technology, India BT/HRD/35/02/2006
6 · The paper itself

Abstract

Autoimmune liver diseases (AILD) often lead to transformation of the liver tissues into hepatocellular carcinoma (HCC). Considering the drawbacks of surgical procedures in such cases, need of successful non-invasive therapeutic strategies and treatment modalities for AILD-associated-HCC still exists. Due to the lack of clear, sufficient knowledge about factors mediating AILD-to-HCC transition, an in silico approach was adopted to delineate the underlying molecular deterministic factors. Parallel enrichment analyses on two different public microarray datasets (GSE159676 and GSE62232) pinpointed the core transcriptional regulators as key players. Correlation between the expression kinetics of these transcriptional modules in AILD and HCC was found to be positive primarily with the advancement of hepatic fibrosis. Most of the regulatory interactions were operative during early (F0-F1) and intermediate fibrotic stages (F2-F3), while the extent of activity in the regulatory network considerably diminished at late stage of fibrosis/cirrhosis (F4). Additionally, most of the transcriptional targets with higher degrees of connectivity in the regulatory network (namely DCAF11, PKM2, DGAT2 and BCAT1) may be considered as potential candidates for biomarkers or clinical targets compared to their low-connectivity counterparts. In summary, this study uncovers new possibilities in the designing of novel prognostic and therapeutic regimen for autoimmunity-associated malignancy of liver in a disease progression-dependent fashion.

Indexed as

Computer SimulationAutoimmune DiseasesBiomarkersCarcinoma, HepatocellularCarrier ProteinsCholangitis, SclerosingComputational BiologyDiacylglycerol O-AcyltransferaseGene Expression ProfilingGene Expression RegulationHumansLiver CirrhosisLiver Cirrhosis, BiliaryLiver DiseasesLiver NeoplasmsMembrane ProteinsBCAT1 protein, humanBiomarkersCarrier ProteinsDDB1 and CUL4 associated factor 11, humanDGAT2 protein, humanDiacylglycerol O-AcyltransferaseMembrane ProteinsThyroid Hormone-Binding ProteinsThyroid HormonesTransaminasesUbiquitin-Protein Ligase Complexesautoimmune liver diseasegene regulatory networkHCC transcriptomicshepatic fibrosishepatocellular carcinomaliver cirrhosis

Identifiers

PMID34440687
PMCPMC8394127
OpenAlexW3183927833

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.