Evidence map›Paper›PMID 31118037›Full record

ArticleBMC medical genomics2019

Integrated high-throughput analysis identifies super enhancers associated with chemoresistance in SCLC.

Jiarong Bao, Man Li, Shumei Liang, Yunchu Yang, Jingfang Wu, Qingqing Zou, Shun Fang, Size Chen, Linlang Guo

Open access · goldAbstract read
In one paragraph

Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.9field-weighted citation impact, top 27% 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

13 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Enhancer in cancer pathogenesis and treatment.Genetics and molecular biology · 2023
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Super-enhancers: a new frontier for epigenetic modifiers in cancer chemoresistance.Journal of experimental & clinical cancer research : CR · 2021
    Review
  12. Article
  13. 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

9 authors at 2 institutions in 1 country.

Jiarong BaoDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Man LiDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Shumei LiangDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Yunchu YangDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Jingfang WuDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Qingqing ZouDepartment of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, People's Republic of China.
Shun FangDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China.
Size ChenDepartment of Oncology, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, 510080, People's Republic of China. chensize@gdpu.edu.cn.
Linlang GuoDepartment of Pathology, Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, 510282, People's Republic of China. linlangg@yahoo.com.ORCID 0000-0002-1859-7592
Zhujiang Hospital · CNFirst Affiliated Hospital of Guangdong Pharmaceutical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChemoresistance is a primary clinical challenge for the management of small cell lung cancer. Additionally, transcriptional regulation by super enhancer (SE) has an important role in tumor evolution. The functions of SEs, a key class of noncoding DNA cis-regulatory elements, have been the subject of many recent studies in the field of cancer research.

methodsIn this study, using chromatin immunoprecipitation-sequencing and RNA-sequencing (RNA-seq), we aimed to identify SEs associated with chemoresistance from H69AR cells. Through integrated bioinformatics analysis of the MEME chip, we predicted the master transcriptional factors (TFs) binding to SE sites and verified the relationships between TFs of SEs and drug resistance by RNA interference, cell counting kit 8 assays, quantitative real-time reverse transcription polymerase chain reaction.

resultsIn total, 108 SEs were screened from H69AR cells. When combining this analysis with RNA-seq data, 45 SEs were suggested to be closely related to drug resistance. Then, 12 master TFs were predicted to localize to regions of those SEs. Subsequently, we selected forkhead box P1 (FOXP1), interferon regulatory factor 1 (IRF1), and specificity protein 1 (SP1) to authenticate the functional relationships of master TFs with chemoresistance via SEs.

conclusionsWe screened out SEs involved with drug resistance and evaluated the functions of FOXP1, IRF1, and SP1 in chemoresistance. Our findings established a large group of SEs associated with drug resistance in small cell lung cancer, revealed the drug resistance mechanisms of SEs, and provided insights into the clinical applications of SEs.

Indexed as

Computational BiologyHigh-Throughput Nucleotide SequencingSequence Analysis, RNACell Line, TumorDrug Resistance, NeoplasmEnhancer Elements, GeneticHumansLung NeoplasmsSmall Cell Lung CarcinomaTranscription FactorsTranscription FactorsChemoresistanceSmall cell lung cancerSuper enhancer

Identifiers

PMID31118037
PMCPMC6532255
OpenAlexW2947430754

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.