Untargeted metabolomics and lipidomics identified four subtypes of small cell lung cancer
Metabolomics, ISSN: 1573-3890, Vol: 19, Issue: 1, Page: 3
2023
- 5Citations
- 6Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations5
- Citation Indexes5
- Captures6
- Readers6
Article Description
Introduction: Small cell lung cancer (SCLC) is a heterogeneous malignancy with dismal prognosis. However, few studies have conducted on the metabolic heterogeneity in SCLC. Objective: We therefore identify SCLC classifications using untargeted metabolomics and lipidomics. We also compared their survival and the immunotherapy responses. Methods: Liquid Chromatography–Mass Spectrometry/Mass Spectrometry (LC–MS/MS) analysis was performed in 191 SCLC serum samples. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was conducted to identify metabolic pathways. The Kaplan–Meier and log-rank test were used to analyze the survival curves. The univariate and multivariate Cox proportional hazards regression models were used to evaluate prognostic factors for OS in patients with SCLC. Results: Distinct subtypes of SCLC were identified by consensus clustering algorithm using partioning around medoids (pam) based on untargeted metabolomics and lipidomics. Four distinct subtypes of SCLC were identified, with distinct metabolic pathways. Subgroup 2 had the longest survival whereas Subgroup 1 had the shortest. Subtype 2 benefited most from immunotherapy in OS, as in contrast to Subtype 3 with shortest survival. Conclusion: Our study revealed the metabolic heterogeneity in SCLC and identified four subtypes with distinct metabolic features. It indicates promising therapeutic and prognostic value that may guide treatment for SCLC. The subtype-specific clinical trials may be designed and would be instructive for drug development.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85144637853&origin=inward; http://dx.doi.org/10.1007/s11306-022-01964-x; http://www.ncbi.nlm.nih.gov/pubmed/36574156; https://link.springer.com/10.1007/s11306-022-01964-x; https://dx.doi.org/10.1007/s11306-022-01964-x; https://link.springer.com/article/10.1007/s11306-022-01964-x
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know