Evidence map›Paper›PMID 35187366›Full record

ArticleACS omega2022

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach.

Anjana Singh, Ved Prakash, Nikhil Gupta, Ashish Kumar, Ravi Kant, Dinesh Kumar

Open access · goldAbstract read
In one paragraph

Article in ACS omega, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed
2.9field-weighted citation impact, top 8% 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

20 citing papers in PubMed, 33 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. Review
  16. Article
  17. Article
  18. Article
  19. Review
  20. 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

6 authors at 4 institutions in 1 country.

Anjana SinghAll India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India.
Ved PrakashPulmonary & Critical Care Medicine, King George's Medical University, Lucknow, Uttar Pradesh 226003, India.
Nikhil GuptaCentre of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India.ORCID https://orcid.org/0000-0002-9818-2206
Ashish KumarDepartment of Chemistry, Banaras Hindu University, Varanasi, Uttar Pradesh 221005, India.
Ravi KantAll India Institute of Medical Sciences (AIIMS), Rishikesh, Uttarakhand 249201, India.
Dinesh KumarCentre of Biomedical Research (CBMR), SGPGIMS, Lucknow, Uttar Pradesh 226014, India.
King George's Medical University · INSanjay Gandhi Post Graduate Institute of Medical Sciences · INAll India Institute of Medical Sciences Rishikesh · INBanaras Hindu University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with

Identifiers

PMID35187366
PMCPMC8851899
OpenAlexW4210300668

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.