Evidence map›Paper›PMID 24744259›Full record

ArticleIrish journal of medical science2015

Identifying hepatocellular carcinoma-related genes and pathways by system biology analysis.

P Wang, L Ouyang, L Zheng, Z Wang

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Article in Irish journal of medical science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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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

5 citing papers in PubMed.

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4 · The record

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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.

P WangDepartment of General Surgery, Xiangya Hospital, Central South University, Xiangya Road 87, Changsha, Hunan, China.
L Ouyang
L Zheng
Z Wang

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious researches have been focused on revealing the functions of each individual gene and/or pathway in the initiation, progression and maintenance of hepatocellular carcinoma (HCC). However, the mechanistic relationships among different genes and/or pathways are largely unknown.

aimsIn this study, we tended to uncover the potential molecular networks and critical genes which play important roles in HCC progression.

methodsThe transcriptional profiles from normal and HCC patient samples were analyzed and compared using bioinformatic methods, including differentially expressed gene (DEG) analysis, hierarchical clustering, construction of protein-protein interaction (PPI) network and GO-Elite analysis.

resultsInitially, the normal and HCC sample data were processed and 679 most dramatic DEGs were identified. The PPI network analysis indicates the significance of multiple biological processes as well as signaling pathways in affecting liver function and HCC progression. In addition, hierarchical clustering analysis showed the most significant modules and identified the relationship between different genes, and some important genes such as FOS, IGF1, ADH4, ITGA2 and LEF1 were found to be hubs which master each individual module.

conclusionOur study greatly improves the understanding of the HCC development in a systematic manner and provides the potential clue for exploiting drugs which might target the most significant genes and/or signaling pathways.

Indexed as

Carcinoma, HepatocellularComputational BiologyDisease ProgressionGene Expression ProfilingHumansLiver NeoplasmsSignal Transduction

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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.