Evidence map›Paper›PMID 40643758›Full record

ArticleFunctional & integrative genomics2025

Integration of scRNA-seq and bulk tissue RNA-seq data to identify cancer-associated fibroblast-related gene RGMA as a potential treatment target for esophageal cancer.

Yanqing Gao, Shuguang Bao, Bao Wen, Haoyuan Li, Qiang Guo, Ao Li, Luri Bao, Meitao Li, Bateer Han

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In one paragraph

Article in Functional & integrative genomics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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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

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Yanqing Gao *Department of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China.
Shuguang Bao *Department of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China.
Bao WenInner Mongolia Medical University, No. 5 Xin Hua Road, Huhhot, Inner Mongolia Autonomous Region, 010050, China.
Haoyuan LiInner Mongolia Medical University, No. 5 Xin Hua Road, Huhhot, Inner Mongolia Autonomous Region, 010050, China.
Qiang GuoDepartment of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China.
Ao LiDepartment of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China.
Luri BaoDepartment of Pathology, College of Basic Medicine, Inner Mongolia Medical University, No. 5 Xin Hua Road, Huhhot, Inner Mongolia Autonomous Region, 010050, China.
Meitao LiDepartment of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China.
Bateer HanDepartment of Thoracic Tumor Surgery, Peking University Cancer Hospital (Inner Mongolia Campus) & Affiliated Cancer Hospital of Inner Mongolia Medical University, No. 42 Zhao Wu Da Road, Huhhot, Inner Mongolia Autonomous Region, 010020, China. hanbater_2004@163.com.

Funding

Peking University Cancer Hospital Inner Mongolia Hospital (Cancer Hospital Affiliated to Inner Mongolia Medical University) high-level clinical specialty construction technology project 2024YNZD001Public hospital research joint fund science and technology project 2023GLLHO141Technology Million Project of Inner Mongolia Medical University YKD2020KJBW006
6 · The paper itself

Abstract

Cancer-associated fibroblasts (CAFs) serve as key stromal components within tumor microenvironment (TME), playing a significant role in the development and outcome of esophageal cancer (EC). There is an urgent need to identify genes related to CAFs to improve treatment strategies. The scRNA-sequencing dataset GSE196756 were used to identify fibroblast-related genes. Additionally, a WGCNA analysis was also conducted to identify modules related to CAFs within the TCGA-esophageal carcinoma (ESCA) cohort. By taking the intersection of identified genes of these two sections, CAF-related genes were identified. Expression of RGMA in EC samples compared to normal controls was assessed by RT-qPCR and western blot. In vitro and in vivo experiments were conducted to assess the impact of RGMA on EC cell growth. Compared to adjacent normal tissues, the levels of RGMA were notably reduced in EC tissues. Reduced RGMA levels were linked to a poor prognosis for EC patients. Furthermore, RGMA was found to have a positive correlation with the expression of fibroblast-related gene DCN, and showed a negative correlation with the expression of tumor-promoting chemokines, CXCL1, CXCL3 and CXCL8. Functionally, RGMA overexpression strongly prevented ECA109 cell viability, proliferation and migration, as well as suppresses tumor growth in vivo, suggesting that RGMA may function as a tumor suppressor in EC. Additionally, RGMA levels were also remarkably decreased in human esophageal CAFs relative to esophageal fibroblast cells (NFs). Importantly, the downregulation of RGMA may facilitate the transdifferentiation of NFs into CAFs by activating Akt signaling or upregulating CXCL1, CXCL3, and CXCL8, subsequently contributing to ECA109 cell proliferation. Collectively, RGMA may serve as a prognostic marker and a potential therapeutic target for EC. Clinical trial number Not applicable.

Indexed as

Cancer-Associated FibroblastsEsophageal NeoplasmsAnimalsBiomarkers, TumorCell Line, TumorCell ProliferationChemokine CXCL1Chemokines, CXCFemaleGene Expression Regulation, NeoplasticHumansInterleukin-8MicePrognosisRNA-SeqSingle-Cell Gene Expression AnalysisBiomarkers, TumorChemokine CXCL1Chemokines, CXCCXCL1 protein, humanCXCL3 protein, humanInterleukin-8Bioinformation analysisCancer-associated fibroblastEsophageal cancerIn vitro experimentsPrognosisRGMA

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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.