Evidence map›Paper›PMID 41608336›Full record

ArticleWorld journal of clinical oncology2026

Development and internal validation of an immune-based prognostic modeling of early-onset colorectal cancer

Xiu Chen, Yong Wang, Heng-Yang Shen, Rui Wu, Zan Fu

Abstract read
In one paragraph

Article in World journal of clinical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiu ChenDepartment of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.
Yong WangDepartment of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.
Heng-Yang ShenDepartment of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China.
Rui WuDepartment of General Surgery, Nanjing Qixia District Hospital, Nanjing 210000, Jiangsu Province, China.
Zan FuDepartment of General Surgery, The First Affiliated Hospital with Nanjing Medical University, Nanjing 210029, Jiangsu Province, China. fuzan1971@njmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEarly-onset colorectal cancer (EOCRC) is an aggressive malignancy with rising incidence and poor prognosis in young adults. Circulating immune cells may hold prognostic value, yet their role in EOCRC outcomes remains unclear.

aimTo develop machine learning-based prognostic models using peripheral immune markers in a retrospective cohort of EOCRC patients.

methodsA cohort of 123 EOCRC patients undergoing radical resection, from January 2017 to December 2020 was included. Data were extracted from medical records with a follow-up till July 2025. Blood samples were processed for flow cytometry to assess immune markers.

resultsUnivariable screening identified disease stage and CD16+CD56+ natural killer (NK) cell percentage as top predictors. A parsimonious Cox model integrating stage and high NK cells outperformed random survival forests (concordance index 0.693

conclusionThis two-factor model offers moderate accuracy for personalized EOCRC risk stratification, highlighting systemic NK cell dysfunction as a potential immunotherapy target. External validation is warranted.

Indexed as

Early-onset colorectal cancerImmune markersMachine learningPrognostic modelingProgression-free survival

Identifiers

PMID41608336
PMCPMC12836025

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