An assessment system for clinical and biological interpretability in ulcerative colitis

Shiqian Zhang; Ge Zhang; Wenxiu Wang; Song-Bin Guo; Pengpeng Zhang; Fuqi Wang; Quanbo Zhou; Zhaokai Zhou; Yujia Wang; Haifeng Sun; Wenming Cui; Shuaixi Yang; Weitang Yuan

doi:10.18632/aging.205564

Article · 2024 Deep GEO

An assessment system for clinical and biological interpretability in ulcerative colitis

Aging

Full Authors

Shiqian Zhang
Ge Zhang
Wenxiu Wang
Song-Bin Guo
Pengpeng Zhang
Fuqi Wang
Quanbo Zhou
Zhaokai Zhou
Yujia Wang
Haifeng Sun
Wenming Cui
Shuaixi Yang
Weitang Yuan

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Official Abstract

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

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Plain citation

Shiqian Zhang, Ge Zhang, Wenxiu Wang, Song-Bin Guo, Pengpeng Zhang, Fuqi Wang, Quanbo Zhou, Zhaokai Zhou, Yujia Wang, Haifeng Sun, Wenming Cui, Shuaixi Yang, Weitang Yuan. An assessment system for clinical and biological interpretability in ulcerative colitis. Aging. 2024;16(4):3856–3879. https://doi.org/10.18632/aging.205564

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Research & review context

Research fields: Inflammatory bowel disease molecular research
Computational disease characterization
Review topics: Gene-expression approaches to ulcerative colitis
Classifier evaluation and biological interpretation
Scientific role: Retrospective gene-expression classifier and separate interpretability-score analysis for ulcerative colitis.
Evidence position: Public bulk-expression analysis with external dataset evaluation; the single-cell source remains unresolved, and prospective diagnostic or clinical drug utility is not established.
Related concepts: Cross-dataset expression classification
Classifier-versus-interpretability distinction
Possible citation contexts: Methods: computational expression-based characterization of inflammatory bowel disease.
Evidence synthesis: retrospective ulcerative-colitis classification results, retaining unresolved single-cell provenance and non-causal gene interpretation.

Possible literature-review contexts, not evidence of existing citations. Read the findings and Evidence Scope before citing the original article.

Questions & evidence · Evidence limits · Original article & citation downloads

Evidence Scale

An assessment system for clinical and biological interpretability in ulcerative colitis

A retrospective human-expression study developed a UC-versus-control classifier, evaluated it across external bulk datasets and added a separate UCRScore interpretation layer. Classification is not future-onset prediction; the reported single-cell extension has an unresolved accession-provenance mismatch.

Article-reported human expression scale: 606 expression samples from 10 datasets; not participant-level deduplicated unique individuals
GSE87466 model-construction meta-cohort: 21 control and 87 UC expression samples
External classifier datasets: 5 human bulk-expression datasets
Single-cell extension: Article-reported; source provenance unresolved; not included in a verified human sample total

Counting note

The reported 606 expression samples are not 606 independently deduplicated participants across datasets. External evaluation refers only to the five human bulk-expression classifier datasets, not prospective clinical validation or resolved single-cell provenance. The single-cell extension is separately article-reported, and its cells or samples are not added to a verified human total.

Research Question

How were a gene-expression classifier and a separate interpretability score developed for ulcerative colitis, and which findings are supported by external bulk datasets versus unresolved source provenance?

Author Evidence Summary

This research study developed a human gene-expression classifier for UC-versus-control discrimination, evaluated it across external bulk-expression datasets and introduced a separate UCRScore interpretation layer. Case–control classification is not prediction of future disease onset or prospective clinical utility. The reported single-cell extension has an accession-provenance mismatch that remains unresolved in the source review; it is not included as independently verified single-cell validation.

Key Findings

KF1 The article reported 606 human expression samples from 10 datasets; GSE87466 was the model-construction meta-cohort with 87 UC and 21 control expression samples and a random 7:3 split.

Expression-sample counts are not a cross-cohort total of unique patients. Exact training/testing counts are not inferred from the split, and case–control classification is not prediction of future disease onset.

Evidence for KF1
Measure	Value
article reported expression samples	606
datasets	10
model construction meta cohort	GSE87466
UC expression samples	87
control expression samples	21
random split	7:3; exact training/testing counts not inferred

Source locator: Materials and Methods: Data acquisition and collection; UCRGs generated from interactive learning

Permalink to KF1

KF2 The study considered 746 candidate genes and eight learners, applied RF-RFE and named a final 12-gene UCRG panel for a backpropagation neural-network classifier.

The Fig. 3E legend separately states that error was minimized with 14 variables. That statement does not silently replace the named final 12-gene panel with a 14-gene model, and the panel is not proof that all 12 genes are causal drivers.

Evidence for KF2
Measure	Value
candidate genes	746
learners	8
feature selection	RF-RFE
named final UCRG panel	12 genes
Fig 3E legend	Minimum error at 14 variables; distinct from the named final panel
final classifier	Backpropagation neural network

Source locator: Results: Machine learning-based diagnostic models and generated UCRGs; Fig. 3D-E

Permalink to KF2

KF3 Results reported external UC-versus-control AUCs of 0.930 for GSE47908, 0.942 for GSE59071, 0.934 for GSE75214, 0.988 for GSE92415 and 1.000 for GSE14580.

These are article-reported external bulk-expression discrimination results, not independent prospective clinical effectiveness. No individually unresolved confidence intervals or clinical thresholds are supplied, and AUC 1.000 does not mean universally perfect diagnosis.

Evidence for KF3
Measure	Value
GSE47908 AUC	0.930
GSE59071 AUC	0.942
GSE75214 AUC	0.934
GSE92415 AUC	0.988
GSE14580 AUC	1.000

Source locator: Results: Robust performance of UCRGs-based diagnostic model; Fig. 4D-H

Permalink to KF3

KF4 UCRScore used PCoA-related expression information, while DNB analysis showed a higher critical signal in the inactive group.

The classifier, UCRScore and DNB are different analytical layers. An executable score is not reconstructed from the incompletely rendered formula, and group comparisons do not establish longitudinal prediction of an individual disease transition.

Evidence for KF4
Measure	Value
interpretability score	UCRScore using PCoA-related expression information
DNB observation	Higher critical signal in the inactive group

Source locator: Materials and Methods: Establishment of UCRScore; Results: Dynamic changes analysis and DNB analysis

Permalink to KF4

KF5 CMap/eXtreme-Sum signature matching nominated five candidate compounds.

These are computational drug-signature hypotheses. Clinical drug efficacy was not established in this study, and no doses or treatment recommendations are provided.

Evidence for KF5
Measure	Value
candidate compounds	5
method	CMap/eXtreme-Sum signature matching

Source locator: Results: Construction of global regulatory landscape and therapeutic discovery; Fig. 6C

Permalink to KF5

KF6 The article described three single-cell samples, 10,101 cells and seven populations and cited GSE182272; the source review found that this accession describes mouse tissue-resident memory CD8+ T-cell RNA-seq with CAPvsColl and LCMV/P14 T-cell experiments, not direct confirmation of the described UC extension.

The article report and GEO record remain unmatched. No replacement accession, specific cause of the mismatch or research-misconduct inference is established, and this is not verified human or mouse UC single-cell validation.

Evidence for KF6
Measure	Value
article reported samples	3
article reported cells	10,101
article reported populations	7
cited accession	GSE182272
source record	Mouse tissue-resident memory CD8+ T-cell RNA-seq; CAPvsColl and LCMV/P14 T-cell experiments
verification status	Article-reported UC extension; source provenance unresolved

Source locator: Materials and Methods: Single-cell characteristics analysis; Results: Elucidating the biological significance of UCRScore through single-cell resolution; NCBI GEO GSE182272

Permalink to KF6

Study Design & Analytical Framework

Study profile

Study Design: Retrospective public-expression analysis with external dataset evaluation
Evidence Type: Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension
Population: Human UC/control bulk-expression samples; the article's single-cell extension is separately qualified
Primary Endpoint: UC-versus-control expression classification
Secondary Endpoint: Score-associated biological patterns and computational hypotheses
External dataset evaluation: Yes

Data modalities

bulk expression
machine learning
PCoA-based interpretation
network and signature-matching analysis
article-reported single-cell analysis with unresolved accession provenance

What This Study Adds

Separates a gene-expression classifier, a distinct interpretability score and computational hypothesis generation.
Retains the reported cross-dataset discrimination with explicit scope and unresolved-source boundaries.

Evidence Scope

Supports

Retrospective human UC-versus-control expression classification.
Article-reported external bulk-dataset discrimination.
Computational interpretation and hypothesis generation, with the single-cell provenance issue kept unresolved.

Does Not Establish

Prospective diagnostic utility or prediction of individual future disease onset.
Causal roles for all 12 selected genes.
Clinical efficacy of nominated compounds.
UC single-cell validation from an independently verified matching source.

Limitations

Participant independence across cohorts has not been established by raw-data deduplication.
The final 12-gene panel and the Fig. 3E 14-variable minimum-error statement must remain distinct.
External model evaluation cannot exclude every preprocessing or feature-selection bias.
The score formula was not independently reconstructed in this review.
The GSE182272 accession-provenance mismatch remains unresolved.
The models were not rerun and the complete supplementary data were not audited in this batch.

When This Study Is Useful to Cite

CU1: Which studies externally evaluated gene-expression classifiers for UC?
This retrospective expression study reported 606 samples across 10 datasets and a GSE87466 construction meta-cohort of 87 UC and 21 controls. Results reported external AUCs of 0.930, 0.942, 0.934, 0.988 and 1.000 for GSE47908, GSE59071, GSE75214, GSE92415 and GSE14580, respectively. Sample counts are not deduplicated patient counts, and external case–control discrimination is not future-onset prediction or prospective clinical utility.
Evidence: KF1, KF3
CU2: How were feature selection and a neural classifier combined?
The study considered 746 candidate genes and eight learners, used RF-RFE and named a final 12-gene UCRG panel for a backpropagation neural-network classifier. The Fig. 3E statement about minimum error at 14 variables is retained separately, without renaming the panel or asserting causal effects of all selected genes.
Evidence: KF2
CU3: What does expression-score interpretation add beyond classification?
UCRScore uses PCoA-related expression information, and the DNB analysis reported a higher critical signal in the inactive group. These are distinct interpretation layers, not interchangeable classifiers; the formula is not reconstructed here and the group comparison is not individual longitudinal prediction.
Evidence: KF4
CU4: What limitations apply to drug-signature and single-cell interpretation?
The five CMap/eXtreme-Sum candidates are signature-matching hypotheses, not established treatments. The article-reported three samples, 10,101 cells and seven populations cannot be verified as the described UC extension from GSE182272, whose record concerns mouse tissue-resident memory CD8+ T-cell experiments. The source mismatch remains unresolved; no substitute accession or misconduct judgment is supplied.
Evidence: KF5, KF6

What This Study Should Not Be Cited to Claim

Prospective diagnostic utility or prediction of individual future disease onset.
Causal roles for all 12 selected genes.
Clinical efficacy of nominated compounds.
UC single-cell validation from an independently verified matching source.

Evidence Matrix

Row ID	Component	Context	Finding	Evidence Level	Scope	Source Locator	Evidence refs
evidence-uc-1	Human data design	Expression-sample counts are not a cross-cohort total of unique patients. Exact training/testing counts are not inferred from the split, and case–control classification is not prediction of future disease onset.	The article reported 606 human expression samples from 10 datasets; GSE87466 was the model-construction meta-cohort with 87 UC and 21 control expression samples and a random 7:3 split.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Supports an expression-sample design and case–control analysis, not a deduplicated participant total or incident-disease prediction.	Materials and Methods: Data acquisition and collection; UCRGs generated from interactive learning	KF1
evidence-uc-2	Gene panel and classifier	The Fig. 3E legend separately states that error was minimized with 14 variables. That statement does not silently replace the named final 12-gene panel with a 14-gene model, and the panel is not proof that all 12 genes are causal drivers.	The study considered 746 candidate genes and eight learners, applied RF-RFE and named a final 12-gene UCRG panel for a backpropagation neural-network classifier.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Retains the named panel and the distinct figure-legend statement without causal-gene claims.	Results: Machine learning-based diagnostic models and generated UCRGs; Fig. 3D-E	KF2
evidence-uc-3	External discrimination	These are article-reported external bulk-expression discrimination results, not independent prospective clinical effectiveness. No individually unresolved confidence intervals or clinical thresholds are supplied, and AUC 1.000 does not mean universally perfect diagnosis.	Results reported external UC-versus-control AUCs of 0.930 for GSE47908, 0.942 for GSE59071, 0.934 for GSE75214, 0.988 for GSE92415 and 1.000 for GSE14580.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Supports reported external dataset discrimination only, not universal accuracy or prospective clinical utility.	Results: Robust performance of UCRGs-based diagnostic model; Fig. 4D-H	KF3
evidence-uc-4	Interpretability score	The classifier, UCRScore and DNB are different analytical layers. An executable score is not reconstructed from the incompletely rendered formula, and group comparisons do not establish longitudinal prediction of an individual disease transition.	UCRScore used PCoA-related expression information, while DNB analysis showed a higher critical signal in the inactive group.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Supports separate computational interpretation, not a reconstructed executable score or longitudinal prediction.	Materials and Methods: Establishment of UCRScore; Results: Dynamic changes analysis and DNB analysis	KF4
evidence-uc-5	Drug-signature hypotheses	These are computational drug-signature hypotheses. Clinical drug efficacy was not established in this study, and no doses or treatment recommendations are provided.	CMap/eXtreme-Sum signature matching nominated five candidate compounds.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Supports hypothesis generation, not established clinical treatment efficacy.	Results: Construction of global regulatory landscape and therapeutic discovery; Fig. 6C	KF5
evidence-uc-6	Single-cell provenance qualification	The article report and GEO record remain unmatched. No replacement accession, specific cause of the mismatch or research-misconduct inference is established, and this is not verified human or mouse UC single-cell validation.	The article described three single-cell samples, 10,101 cells and seven populations and cited GSE182272; the source review found that this accession describes mouse tissue-resident memory CD8+ T-cell RNA-seq with CAPvsColl and LCMV/P14 T-cell experiments, not direct confirmation of the described UC extension.	Human molecular classification and computational interpretation, with an unresolved provenance issue in the reported single-cell extension	Documents an unresolved accession mismatch, not verified UC single-cell validation or its cause.	Materials and Methods: Single-cell characteristics analysis; Results: Elucidating the biological significance of UCRScore through single-cell resolution; NCBI GEO GSE182272	KF6

Q&A

What samples and modeling split were reported?

The article reported 606 human expression samples from 10 datasets; GSE87466 was the model-construction meta-cohort with 87 UC and 21 control expression samples and a random 7:3 split. Expression-sample counts are not a cross-cohort total of unique patients. Exact training/testing counts are not inferred from the split, and case–control classification is not prediction of future disease onset.

Evidence: KF1

Is the reported classifier based on 12 or 14 genes?

The study considered 746 candidate genes and eight learners, applied RF-RFE and named a final 12-gene UCRG panel for a backpropagation neural-network classifier. The Fig. 3E legend separately states that error was minimized with 14 variables. That statement does not silently replace the named final 12-gene panel with a 14-gene model, and the panel is not proof that all 12 genes are causal drivers.

Evidence: KF2

What external discrimination was reported?

Results reported external UC-versus-control AUCs of 0.930 for GSE47908, 0.942 for GSE59071, 0.934 for GSE75214, 0.988 for GSE92415 and 1.000 for GSE14580. These are article-reported external bulk-expression discrimination results, not independent prospective clinical effectiveness. No individually unresolved confidence intervals or clinical thresholds are supplied, and AUC 1.000 does not mean universally perfect diagnosis.

Evidence: KF3

Are the classifier, UCRScore and DNB interchangeable?

UCRScore used PCoA-related expression information, while DNB analysis showed a higher critical signal in the inactive group. The classifier, UCRScore and DNB are different analytical layers. An executable score is not reconstructed from the incompletely rendered formula, and group comparisons do not establish longitudinal prediction of an individual disease transition.

Evidence: KF4

Were the nominated compounds experimentally shown to treat UC?

CMap/eXtreme-Sum signature matching nominated five candidate compounds. These are computational drug-signature hypotheses. Clinical drug efficacy was not established in this study, and no doses or treatment recommendations are provided.

Evidence: KF5

Can the single-cell extension be verified from the cited accession?

The article described three single-cell samples, 10,101 cells and seven populations and cited GSE182272; the source review found that this accession describes mouse tissue-resident memory CD8+ T-cell RNA-seq with CAPvsColl and LCMV/P14 T-cell experiments, not direct confirmation of the described UC extension. The article report and GEO record remain unmatched. No replacement accession, specific cause of the mismatch or research-misconduct inference is established, and this is not verified human or mouse UC single-cell validation.

Evidence: KF6

Concepts & Entities

Disease

ulcerative colitis

Entities

UCRGsUCRScore

Methods

RF-RFEneural-network classificationPCoACMapDNB

Evidence

retrospective expression classificationexternal dataset evaluation

Related Research

Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability
Related expression-signature methodology in a different disease; not shared validation, an identical score or a combined clinical test. · DOI: 10.1016/j.isci.2023.107587

Publication & Provenance

DOI: https://doi.org/10.18632/aging.205564
Publication Url: https://www.aging-us.com/article/205564/text
PubMed: https://pubmed.ncbi.nlm.nih.gov/38372705/
Pmc Url: https://pmc.ncbi.nlm.nih.gov/articles/PMC10929837/
Data Url: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE182272
Data Source Note: This accession is linked to document the unresolved source mismatch, not as a verified download location for the reported UC single-cell analysis.
Review Basis: Published main text and selected figure legends plus the cited GEO record. No raw-data reanalysis or independent reconstruction of the score.

Author-controlled evidence synthesis, distinct from the published abstract and not a replacement for the version of record. Reported findings and independently unresolved source details are distinguished.

Study-level evidence table

Study-level author-maintained evidence summary. Not participant-level data and not a ready-to-pool meta-analysis dataset. For formal citation, use the original article DOI.

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Publisher record PubMed 38372705 PMC PMC10929837 OpenAlex record

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