Evidence map›Paper›PMID 38346938›Full record

ArticleMolecular cancer therapeutics2024

Evaluating for Correlations between Specific Metabolites in Patients Receiving First-Line or Second-Line Immunotherapy for Metastatic or Recurrent NSCLC: An Exploratory Study Based on Two Cohorts.

Yanjun Xu, Kaibo Ding, Zhongsheng Peng, Ling Ding, Hui Li, Yun Fan

Open access · hybridAbstract read
In one paragraph

Article in Molecular cancer therapeutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.7field-weighted citation impact, top 14% 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

6 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
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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 2 institutions in 1 country.

Yanjun Xu *Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.ORCID 0000-0001-7187-8804
Kaibo Ding *Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.ORCID 0009-0002-8917-7680
Zhongsheng Peng *Department of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.ORCID 0009-0004-0956-7545
Ling DingInstitute of Pharmacology and Toxicology, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.ORCID 0000-0002-1163-4848
Hui LiDepartment of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.ORCID 0000-0001-8164-1624
Yun FanDepartment of Medical Thoracic Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, China.ORCID 0000-0003-1755-0175
Zhejiang Cancer Hospital · CNZhejiang University · CN

Funding

Medical Science and Technology Project of Zhejiang Province () 2021KY541Natural Science Foundation of Zhejiang Province (ZJNSF) LY19H160007
6 · The paper itself

Abstract

Immune checkpoint inhibitors (ICI) have displayed impressive clinical efficacy in the context of non-small cell lung cancer (NSCLC). However, most patients do not achieve long-term survival. Minimally invasive collected samples are attracting significant interest as new fields of biomarker study, and metabolomics is one of these growing fields. We concentrated on the augmented value of the metabolomic profile in differentiating long-term survival from short-term survival in patients with NSCLC subjected to ICIs. We prospectively recruited 97 patients with stage IV NSCLC who were treated with anti-PD-1 inhibitor, including patients treated with monoimmunotherapy as second-line treatment (Cohort 1), and patients treated with combination immunotherapy as first-line treatment (Cohort 2). Each cohort was divided into long-term and short-term survival groups. All blood samples were collected before beginning immunotherapy. Serum metabolomic profiling was performed by UHPLC-Q-TOF MS analysis. Pareto-scaled principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis were performed. In Cohort 1, the mPFS and mOS of long-survival patients are 27.05 and NR months, respectively, and those of short-survival patients are 2.79 and 10.59 months. In Cohort 2, the mPFS and mOS of long-survival patients are 27.35 and NR months, respectively, and those of short-survival patients are 3.77 and 12.17 months. A total of 41 unique metabolites in Cohort 1 and 47 in Cohort 2 were screened. In Cohorts 1 and 2, there are 6 differential metabolites each that are significantly associated with both progression-free survival and overall survival. The AUC values for all groups ranged from 0.73 to 0.95. In cohort 1, the top 3 enriched KEGG pathways, as determined through significant different metabolic pathway analysis, were primary bile acid biosynthesis, African trypanosomiasis, and choline metabolism in cancer. In Cohort 2, the top 3 enriched KEGG pathways were the citrate cycle (TCA cycle), PPAR signaling pathway, and primary bile acid biosynthesis. The primary bile acid synthesis pathway had significant differences in the long-term and short-term survival groups in both Cohorts 1 and 2. Our study suggests that peripheral blood metabolomic analysis is critical for identifying metabolic biomarkers and pathways responsible for the patients with NSCLC treated with ICIs.

Indexed as

Carcinoma, Non-Small-Cell LungImmunotherapyLung NeoplasmsAdultAgedBiomarkers, TumorCohort StudiesFemaleHumansImmune Checkpoint InhibitorsMaleMetabolomeMetabolomicsMiddle AgedNeoplasm MetastasisNeoplasm Recurrence, LocalBiomarkers, TumorImmune Checkpoint Inhibitors

Identifiers

PMID38346938
PMCPMC11063768
OpenAlexW4391783925

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.