Evidence map›Paper›PMID 41903466›Full record

ArticleNeoplasia (New York, N.Y.)2026

Identification of tumor initiating cells and early marker genes in histologically normal colonic mucosa that lead to neoplastic transformation.

Sangeeta Jaiswal, Stephanie The, Tse-Shao Chang, Jiaqi Shi, Thomas D Wang

Abstract read
In one paragraph

Article in Neoplasia (New York, N.Y.), 2026. 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

5 · Who and what money

Authors and funding

5 authors.

Sangeeta JaiswalDivision of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
Stephanie TheCancer Data Science Shared Resource, Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, USA.
Tse-Shao ChangDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Jiaqi ShiDepartment of Pathology, University of Michigan, Ann Arbor, MI, USA.
Thomas D WangDivision of Gastroenterology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA; Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA. Electronic address: thomaswa@umich.edu.

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Scott Michael Schuetze · 1988 to 2026
$178.2M
Defining epigenetic signaling to reshape pancreatic tumor microenvironmentR37CA262209 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jiaqi Shi · 2022 to 2026
$2.3M
NCI NIH HHS P30 CA046592NCI NIH HHS R37 CA262209
6 · The paper itself

Abstract

BACKGROUND &

aimsColorectal cancer (CRC) remains a leading cause of cancer‑related morbidity and mortality worldwide. Although the adenoma-carcinoma sequence and its genetic drivers are well described, the earliest cellular and molecular events initiating tumorigenesis within histologically normal colonic epithelium remain poorly defined. This study aims to identify tumor‑initiating cells (TICs), distinguish them from normal stem‑like cells (nSTMs), and delineate early transcriptional and signaling programs using single‑cell RNA sequencing (scRNA‑seq) from paired normal‑appearing and transformed human colonic tissues.

methodsFresh biopsies from histologically normal mucosa and matched polyps, including tubular adenomas, sessile serrated adenomas, and adenocarcinomas, were collected from seven subjects. Single‑cell transcriptomes were generated using the 10x Genomics platform and analyzed with Seurat, Monocle2, CytoTRACE, GSEA/GSVA, RNA velocity, InferCNV, CellChat, and NicheNet. Spatial validation was performed using RNA‑FISH.

resultsWe resolved 51,054 high‑quality single‑cell transcriptomes into 33 clusters. Tumor-specific stem-like (tSTM) and deep crypt secretory (tDCS) populations were enriched in adenomas. Subclustering of tSTM identified TIC-like subsets predominantly derived from histologically normal mucosa that localized to the root of lineage trajectories leading to polyp-enriched tSTM states. Compared to nSTMs, TICs exhibited enhanced stemness potential, early epithelial-mesenchymal transition (EMT) and interferon signaling, suppression of oxidative phosphorylation, and distinct genomic and signaling features, indicating early neoplastic reprogramming. ETS2, SLC12A2, and LEFTY1 were identified as TIC‑specific markers; SOD3 and GPRC5A increased along the TIC‑to‑tSTM trajectory. RNA‑FISH confirmed candidate marker localization. Independent validation using the COLONMAP dataset (30 polyps, 35 normal samples) demonstrated that TIC-like cells were predominantly enriched in tubular adenomas but were scarce in serrated lesions. Across this independent cohort, TIC marker genes showed reproducible upregulation in TIC-like populations, supporting the robustness of these observations across cohorts.

conclusionsOur results identify TICs as the origin of neoplastic stem‑like states in the conventional tubular adenoma pathway and define early transcriptional, metabolic, and microenvironmental reprogramming events that distinguish TICs from nSTMs. In contrast to serrated pathways described in other atlases, our data support a stem‑like expansion model for tubular adenomas and nominate biomarkers with translational potential for early CRC detection and intervention.

Indexed as

Biomarkers, TumorCell Transformation, NeoplasticColonColonic NeoplasmsIntestinal MucosaNeoplastic Stem CellsAdenomaFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisTranscriptomeBiomarkers, TumorColorectal cancerEpithelial-to-mesenchymal transitionETS2, SOD3, GPRC5ASingle-cell RNA-seqTumor-initiating cells

Identifiers

PMID41903466
PMCPMC13054087

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LicenceCC BY-NC-ND
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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.