Evidence map›Paper›PMID 30816528›Full record

ArticleOncology reports2019

The underlying molecular mechanism and potential drugs for treatment in papillary renal cell carcinoma: A study based on TCGA and Cmap datasets.

Jin-Shu Pang, Zhe-Kun Li, Peng Lin, Xiao-Dong Wang, Gang Chen, Hai-Biao Yan, Sheng-Hua Li

Open access · hybridAbstract read
In one paragraph

Article in Oncology reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
3.8field-weighted citation impact, top 6% 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

16 citing papers in PubMed, 26 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
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

7 authors at 2 institutions in 1 country.

Jin-Shu PangDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Zhe-Kun LiDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Peng LinDepartment of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Xiao-Dong WangDepartment of Medical Ultrasonics, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Gang ChenDepartment of Pathology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Hai-Biao YanDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Sheng-Hua LiDepartment of Urology, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.
Guangxi Medical University · CNFirst Affiliated Hospital of GuangXi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Papillary renal cell carcinoma (PRCC) accounts for 15‑20% of all kidney neoplasms and continually attracts attention due to the increase in the incidents in which it occurs. The molecular mechanism of PRCC remains unclear and the efficacy of drugs that treat PRCC lacks sufficient evidence in clinical trials. Therefore, it is necessary to investigate the underlying mechanism in the development of PRCC and identify additional potential anti‑PRCC drugs for its treatment. The differently expressed genes (DEGs) of PRCC were identified, followed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses for functional annotation. Then, potential drugs for PRCC treatment were predicted by Connectivity Map (Cmap) based on DEGs. Furthermore, the latent function of query drugs in PRCC was explored by integrating drug‑target, drug‑pathway and drug‑protein interactions. In total, 627 genes were screened as DEGs, and these DEGs were annotated using KEGG pathway analyses and were clearly associated with the complement and coagulation cascades, amongst others. Then, 60 candidate drugs, as predicted based on DEGs, were obtained from the Cmap database. Vorinostat was considered as the most promising drug for detailed discussion. Following protein‑protein interaction (PPI) analysis and molecular docking, vorinostat was observed to interact with C3 and ANXN1 proteins, which are the upregulated hub genes and may serve as oncologic therapeutic targets in PRCC. Among the top 20 metabolic pathways, several significant pathways, such as complement and coagulation cascades and cell adhesion molecules, may greatly contribute to the development and progression of PRCC. Following the performance of the PPI network and molecular docking tests, vorinostat exhibited a considerable and promising application in PRCC treatment by targeting C3 and ANXN1.

Indexed as

Antineoplastic AgentsBiomarkers, TumorCarcinoma, Renal CellComputational BiologyGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansKidney NeoplasmsMolecular Docking SimulationMolecular Targeted TherapyProtein Interaction MappingProtein Interaction MapsVorinostatAntineoplastic AgentsBiomarkers, TumorVorinostat

Identifiers

PMID30816528
PMCPMC6412146
OpenAlexW2913256160

What OpenQuestion holds

Textmetadata
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.