Evidence map›Paper›PMID 30505321›Full record

ArticleCellular & molecular biology letters2018

Identification and validation of NOLC1 as a potential target for enhancing sensitivity in multidrug resistant non-small cell lung cancer cells.

Huaping Huang, Tangying Li, Mingjing Chen, Feng Liu, Haifeng Wu, Jie Wang, Jialiang Chen, Xi Li

Open access · goldAbstract read
In one paragraph

Article in Cellular & molecular biology letters, 2018. 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
1.0field-weighted citation impact, top 23% 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, 32 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Roles of NOLC1 in cancers and viral infection.Journal of cancer research and clinical oncology · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. Article
  12. Food science & nutrition · 2022
    Article
  13. Article
  14. Article
  15. Quantitative Proteomics to Identify Nuclear RNA-Binding Proteins of Malat1.International journal of molecular sciences · 2020
    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

8 authors at 2 institutions in 1 country.

Huaping Huang *1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Tangying Li *2Healthcare Department, Hainan General Hospital, Haikou, 570311 Hainan China.
Mingjing Chen3Department of Pathology, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Feng Liu1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Haifeng Wu1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Jie Wang1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Jialiang Chen1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Xi Li1Department of Respiratory Diseases, The First Affiliated Hospital of Hainan Medical University, Haikou, 570102 Hainan China.
Hainan Medical University · CNHainan General Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adjuvant chemotherapy has become the frequently adopted standard therapeutic approach for non-small cell lung cancer (NSCLC). However, the development of multidrug resistance (MDR) is a major obstacle contributing to the failure of chemotherapy. This study aimed to identify genes associated with MDR development that predict tumor response to chemotherapy in NSCLC. In the present study, a multidrug-resistant NSCLC cell sub-line, A549/MDR, was established from the A549/DDP cell line and characterized. The resistance index (RI) of this subline was calculated according to the IC50 of A549/MDR relative to the parental A549/DDP cells. The gene expression profiles of A549/DDP and A549/MDR were obtained using an oligonucleotide microarray (Agilent SureHyb microarray chip). The microarray results were validated by qRT-PCR and selected genes were analyzed by in vitro loss-of-function experiments. Gene expression profiling identified 921 differentially expressed genes (DEGs) according to the selection criteria, in which 541 genes were upregulated and 380 genes were downregulated in A549/MDR compared with A549/DDP cells. We found that these DEGs are involved in diverse biological processes, including ribonucleoprotein complex, drug metabolism, the Hippo signaling pathway and transcriptional misregulation. NOLC1, as one of the identified DEGs, was confirmed to be overexpressed in A549/MDR cells and its knockdown significantly enhanced the drug sensitivity of A549/MDR cells in response to multidrug treatment. Furthermore, knockdown of NOLC1 downregulated the expression levels of drug resistance-associated molecules (LRP and MDR1) in A549/MDR cells. These findings provide a new and comprehensive expression profile of MDR in NSCLC cells. Identification and validation of NOLC1 might be a promising therapeutic strategy for the management of MDR of NSCLC patients.

Indexed as

Drug Resistance, MultipleDrug Resistance, NeoplasmMolecular Targeted TherapyA549 CellsApoptosisCarcinoma, Non-Small-Cell LungCell DeathGene Expression Regulation, NeoplasticGene Knockdown TechniquesHumansLung NeoplasmsNuclear ProteinsPhosphoproteinsReproducibility of ResultsNOLC1 protein, humanNuclear ProteinsPhosphoproteinsA549/MDRDifferentially expressed genesMicroarrayMultidrug resistanceNOLC1Non-small cell lung cancer

Identifiers

PMID30505321
PMCPMC6258490
OpenAlexW2901046364

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Registered trials

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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.