Evidence map›Paper›PMID 27959423›Full record

ArticleMolecular medicine reports2017

Comparative analysis of hepatocellular carcinoma and cirrhosis gene expression profiles.

Mingming Jiang, Qingfang Zeng, Suiping Dai, Huixia Liang, Fengying Dai, Xueling Xie, Kunlin Lu, Chunfang Gao

Open access · hybridAbstract readComparative Study
In one paragraph

Article in Molecular medicine reports, 2017. 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
0.7field-weighted citation impact, top 24% 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

7 citing papers in PubMed, 9 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

8 authors at 3 institutions in 1 country.

Mingming JiangDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Qingfang ZengDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Suiping DaiDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Huixia LiangDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Fengying DaiDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Xueling XieDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Kunlin LuDepartment of Obstetrics and Gynecology, The 180th Hospital of Chinese People's Liberation Army, Quanzhou, Fujian 362000, P.R. China.
Chunfang GaoDepartment of Laboratory Medicine, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, P.R. China.
The 180th Hospital of PLA · CNChinese People's Liberation Army · CNSecond Military Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Gene expression data of hepatocellular carcinoma (HCC) was compared with that of cirrhosis (C) to identify critical genes in HCC. A total of five gene expression data sets were downloaded from Gene Expression Omnibus. HCC and healthy samples were combined as dataset HCC, whereas cirrhosis samples were included in dataset C. A network was constructed for dataset HCC with the package R for performing Weighted Gene Co‑expression Network Analysis. Modules were identified by cluster analysis with the packages flashClust and dynamicTreeCut. Hub genes were screened out by calculating connectivity. Functional annotations were assigned to the hub genes using the Database for Annotation, Visualization and Integration Discovery, and functional annotation networks were visualized with Cytoscape. Following the exclusion of outlier samples, 394 HCC samples and 47 healthy samples were included in dataset HCC and 233 cirrhosis samples were included in dataset C. A total of 6 modules were identified in the weighted gene co‑expression network of dataset HCC (blue, brown, turquoise, green, red and yellow). Modules blue, brown and turquoise had high preservation whereas module yellow exhibited the lowest preservation. These modules were associated with transcription, mitosis, cation transportation, cation homeostasis, secretion and regulation of cyclase activity. Various hub genes of module yellow were cytokines, including chemokine (C‑C motif) ligand 22 and interleukin‑19, which may be important in the development of HCC. Gene expression profiles of HCC were compared with those of cirrhosis and numerous critical genes were identified, which may contribute to the progression of HCC. Further studies on these genes may improve the understanding of HCC pathogenesis.

Indexed as

Gene Expression ProfilingGene Expression Regulation, NeoplasticCarcinoma, HepatocellularCluster AnalysisGene Regulatory NetworksHumansLiverLiver NeoplasmsTranscriptome

Identifiers

PMID27959423
PMCPMC5355740
OpenAlexW2560323322

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.