Evidence map›Paper›PMID 41462440›Full record

ArticleBMC cancer2025

Analysis of the predictive effect of gut microbiota changes on the occurrence of chemotherapy resistance in pancreatic cancer patients based on nomogram prediction models.

Ying Li, Shini Liu, Xiaorong Lai, Dongyang Yang

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Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Ying LiDepartment of Medical Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Yuexiu District, Guangzhou City, Guangdong Province, 510080, China. zxc1234867796@163.com.
Shini LiuDepartment of Medical Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Yuexiu District, Guangzhou City, Guangdong Province, 510080, China.
Xiaorong LaiDepartment of Medical Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Yuexiu District, Guangzhou City, Guangdong Province, 510080, China.
Dongyang YangDepartment of Medical Oncology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, No.106 Zhongshan Er Road, Yuexiu District, Guangzhou City, Guangdong Province, 510080, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveInvestigate gut microbiota's role in chemotherapy resistance development among pancreatic cancer patients and evaluate a nomogram prediction model.

methods510 pancreatic cancer patients who received chemotherapy in our hospital from January 2023 to December 2024 were randomly divided into training set (n = 357) and validation set (n = 153). Risk factors for chemotherapy resistance in pancreatic cancer were screened through multiple logistic regression analysis, and a Nomogram model was constructed. The predictive efficacy of the model was evaluated by drawing the receiver operating characteristic curve and calibration curve, and was verified in the validation set. The clinical application value of the model was evaluated using decision curve analysis.

resultsThe incidence of resistance in the training set was 64.99% (232/357), and that in the validation set was 65.36% (100/153). Multiple logistic regression analysis showed that Escherichia coli, Enterococcus faecalis, and Fusobacterium nucleatum were independent risk factors affecting the occurrence of chemotherapy resistance in pancreatic cancer patients (P < 0.05), while Akkermansia and Bifidobacterium were independent protective factors affecting the occurrence of chemotherapy resistance in pancreatic cancer patients (all P < 0.05). The C-indexes of the constructed Nomogram model in the training set and the validation set were 0.767 and 0.762 respectively. The areas under the curve (AUC) were 0.767 (95% CI: 0.708-0.827) and 0.762 (95% CI: 0.669-0.854) respectively. The sensitivity and specificity were 0.455, 0.865 and 0.511, 0.873 respectively.

conclusionThe Nomogram prediction model constructed based on gut microbiota change indicators has a high predictive efficacy for the occurrence of chemotherapy resistance in pancreatic cancer patients, providing a basis for clinical early screening and intervention.

Indexed as

Drug Resistance, NeoplasmGastrointestinal MicrobiomeNomogramsPancreatic NeoplasmsAdultAgedFemaleHumansMaleMiddle AgedRisk FactorsROC CurveChemotherapyGut microbiotaNomogram modelPancreatic cancerResistance

Identifiers

PMID41462440
PMCPMC12750544

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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.