Evidence map›Paper›PMID 38169594›Full record

ReviewInternational journal of medical sciences2024

Metabolomics Analysis and Diagnosis of Lung Cancer: Insights from Diverse Sample Types.

Simin Liang, Xiujun Cao, Yingshuang Wang, Ping Leng, Xiaoxia Wen, Guojing Xie, Huaichao Luo, Rong Yu

Abstract readReview
In one paragraph

Review in International journal of medical sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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.

  1. Article
  2. Review
  3. Article
  4. [Applications and Advances of Metabolomics in Lung Cancer Research].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2025
    Review
  5. Review
  6. Review
  7. Article
  8. Article
  9. Fuel for thought: targeting metabolism in lung cancer.Translational lung cancer research · 2024
    Review
  10. Review
  11. Exosomal microRNAs in lung cancer: a narrative review.Translational cancer research · 2024
    Review
  12. Review
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.

Simin LiangCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Xiujun CaoState Key Laboratory of Southwestern Chinese Medicine Resources, School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Yingshuang WangCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Ping LengCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Xiaoxia WenCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Guojing XieCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Huaichao LuoDepartment of Clinical Laboratory, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, University of Electronic Science and Technology of China (UESTC), Chengdu, China.
Rong YuCollege of Medical Technology, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is a highly fatal disease that poses a significant global health burden. The absence of characteristic clinical symptoms frequently results in the diagnosis of most patients at advanced stages of lung cancer. Although low-dose computed tomography (LDCT) screening has become increasingly prevalent in clinical practice, its high rate of false positives continues to present a significant challenge. In addition to LDCT screening, tumor biomarker detection represents a critical approach for early diagnosis of lung cancer; unfortunately, no tumor marker with optimal sensitivity and specificity is currently available. Metabolomics has recently emerged as a promising field for developing novel tumor biomarkers. In this paper, we introduce metabolic pathways, instrument platforms, and a wide variety of sample types for lung cancer metabolomics. Specifically, we explore the strengths, limitations, and distinguishing features of various sample types employed in lung cancer metabolomics research. Additionally, we present the latest advances in lung cancer metabolomics research that utilize diverse sample types. We summarize and enumerate research studies that have investigated lung cancer metabolomics using different metabolomic sample types. Finally, we provide a perspective on the future of metabolomics research in lung cancer. Our discussion of the potential of metabolomics in developing new tumor biomarkers may inspire further study and innovation in this dynamic field.

Indexed as

Lung NeoplasmsBiomarkers, TumorHumansMetabolomicsSensitivity and SpecificityTomography, X-Ray ComputedBiomarkers, TumorBiological MarkerBiomarkersChromatographyLung cancerLung NeoplasmsMass spectrometryMetabolomicsMetabonomics

Identifiers

PMID38169594
PMCPMC10758149

What OpenQuestion holds

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LicenceCC BY
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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.