Evidence map›Paper›PMID 33688500›Full record

ArticleBioMed research international2021

Identification and Validation of Key Genes in Hepatocellular Carcinoma by Bioinformatics Analysis.

Jia Wang, Rui Peng, Zheng Zhang, Yixi Zhang, Yuke Dai, Yan Sun

RetractedOpen access · hybridAbstract readRetracted Publication
In one paragraph

Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

12 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Jia WangDepartment of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China.
Rui PengDepartment of Bioinformatics, Chongqing Medical University, Chongqing, China.
Zheng ZhangDepartment of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China.
Yixi ZhangDepartment of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China.
Yuke DaiDepartment of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China.
Yan SunDepartment of Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, China.ORCID https://orcid.org/0000-0003-2595-0961
Chongqing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is the most frequent primary liver cancer and has poor outcomes. However, the potential molecular biological process underpinning the occurrence and development of HCC is still largely unknown. The purpose of this study was to identify the core genes related to HCC and explore their potential molecular events using bioinformatics methods. HCC-related expression profiles GSE25097 and GSE84005 were selected from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) between 306 HCC tissues and 281 corresponding noncancerous tissues were identified using GEO2R online tools. The protein-protein interaction network (PPIN) was constructed and visualized using the STRING database. Gene Ontology (GO) and KEGG pathway enrichment analyses of the DEGs were carried out using DAVID 6.8 and KOBAS 3.0. Additionally, module analysis and centrality parameter analysis were performed by Cytoscape. The expression differences of key genes in normal hepatocyte cells and HCC cells were verified by quantitative real-time fluorescence polymerase chain reaction (qRT-PCR). Additionally, survival analysis of key genes was performed by GEPIA. Our results showed that a total of 291 DEGs were identified including 99 upregulated genes and 192 downregulated genes. Our results showed that the PPIN of HCC was made up of 287 nodes and 2527 edges. GO analysis showed that these genes were mainly enriched in the molecular function of protein binding. Additionally, KEGG pathway analysis also revealed that DEGs were mainly involved in the metabolic, cell cycle, and chemical carcinogenesis pathways. Interestingly, a significant module with high centrality features including 10 key genes was found. Among these, CDK1, NDC80, HMMR, CDKN3, and PTTG1, which were only upregulated in HCC patients, have attracted much attention. Furthermore, qRT-PCR also confirmed the upregulation of these five key genes in the normal human hepatocyte cell line (HL-7702) and HCC cell lines (SMMC-7721, MHCC-97L, and MHCC-97H); patients with upregulated expression of these five key genes had significantly poorer survival and prognosis. CDK1, NDC80, HMMR, CDKN3, and PTTG1 can be used as molecular markers for HCC. This finding provides potential strategies for clinical diagnosis, accurate treatment, and prognosis analysis of liver cancer.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularDatabases, Nucleic AcidGene Expression Regulation, NeoplasticGenes, NeoplasmLiver NeoplasmsNeoplasm ProteinsGene OntologyHumansBiomarkers, TumorNeoplasm Proteins

Identifiers

PMID33688500
PMCPMC7925030
OpenAlexW3132946151

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.