Evidence map›Paper›PMID 41100766›Full record

ArticleJMIR formative research2025

A Machine Learning-Based Scoring System to Identify High Immunoactivity Microsatellite Stability Tumors by Quantifying Similarity to Microsatellite Instability-High Tumors in Colorectal Cancers: Development and Quantitative Study.

Hongkai Yan, Li Jiang, Yaqi Li, Fengchong Wang, Shaobo Mo, Weiqi Sheng, Dan Huang, Junjie Peng

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Hongkai Yan *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0009-0008-6474-3715
Li Jiang *Weifang Key Laboratory of Collaborative Innovation of Intelligent Diagnosis and Treatment and Molecular Diseases, School of Basic Medical Sciences, Shandong Second Medical University, Weifang, China.ORCID 0009-0002-3251-5415
Yaqi LiDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0000-0001-5555-4711
Fengchong WangWeifang Ten Nanometer Biotechnology Co., Ltd., Weifang, China.ORCID 0009-0000-3012-0466
Shaobo MoDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0009-0004-2385-5690
Weiqi ShengDepartment of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0000-0002-8726-277X
Dan Huang *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0000-0002-7001-1325
Junjie Peng *Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.ORCID 0000-0002-0371-4960

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Microsatellite stability (MSS) colorectal cancers (CRCs) have a limited response to immune checkpoint inhibitors (ICIs) compared to microsatellite instability-high (MSI-H) CRCs. Nevertheless, previous studies have shown that some MSS CRCs are sensitive to ICIs, although established criteria for treatment justification are still lacking. Objective: This study aimed to test the tumor-infiltrating lymphocyte (TIL) features of MSS and develop a novel computational tool for the similarity prediction between MSS and MSI-H status in patients with CRC based on multiple factors. Methods: We collected and analyzed data from 188 patients with CRC, including MSI status, immune cell distributions, clinical features, and gene mutations, using statistical methods and Cox regression. An ensemble machine learning-based MSI-H score was developed using stacked extreme gradient boosting classifiers to quantify the similarity of patient data to MSI-H data based on immune cell distributions, clinical features, and gene mutations. The model was robust and could address missing input data for immune cell distributions and gene mutations. Results: The scorer performed well (mean Cohen κ of 0.40, SD 0.05, over 10 random seeds) in identifying MSI-H-like MSS samples with TIL distributions similar to genuine MSI-H CRCs. No significant difference was observed between the TIL features of MSI-H-like MSS CRCs and MSI-H CRCs. The disparity between MSI-H-like MSS CRCs and MSS CRCs potentially lies in the T regulatory cells (P=.09) and macrophage (P=.16) populations within the tumor stromal region. Conclusions: Some patients with MSS CRC presented similar immune cell distributions with high immunoactivity compared to patients with MSI-H CRC. The MSI-H score serves as a metric to quantify the similarity of MSS CRCs to MSI-H CRCs and presents a promising avenue for more personalized and effective cancer immunotherapy treatment, offering a clinical reference for potential ICI targets in MSS CRCs.

Indexed as

Colorectal NeoplasmsMachine LearningMicrosatellite InstabilityAgedFemaleHumansImmune Checkpoint InhibitorsLymphocytes, Tumor-InfiltratingMaleMiddle AgedImmune Checkpoint Inhibitorscancercolorectalcolorectal cancercolorectal tumorCox regressiongene mutationgeneticsimmune cellimmunoactivityimmunotherapymachine learningmicrosatellite stabilityMLmutationregression analysistumors

Identifiers

PMID41100766
PMCPMC12530644

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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.