Evidence map›Paper›PMID 40959083›Full record

ArticleFrontiers in immunology2025

A multi-omic analysis reveals a predictive value of tertiary lymphoid structures in improving the prognosis of colorectal cancer patients with BRAF mutation.

Chao Qin, Shumin Cheng, Jingyun Ma, Lujing Li, Yun Leng, Lei Zheng, Huiying Chen, Hui Mo, Shi Li, Yuhong Liang and 9 more

Abstract read
In one paragraph

Article in Frontiers in immunology, 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

19 authors.

Chao Qin *Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Shumin Cheng *Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Jingyun Ma *Scientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Lujing Li *Department of Ultrasound, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
Yun LengScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Lei ZhengDepartment of Aesothology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Huiying ChenDepartment of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Hui MoDepartment of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Shi LiDepartment of Pathology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Yuhong LiangSchool of Pharmacy, Macau University of Science and Technology, Macao, Macao SAR, China.
Yi ZhangScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Wenxia LiDepartment of Pathology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Jing LiangScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Yuxuan LiuScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Junxuan MaiScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Linlin HouSchool of Medicine, Sun Yat-Sen University, Shenzhen, China.
Di WangHealth Management Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Ke ZhuScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.
Bihui HuangScientific Research Center, The Seventh Affiliated Hospital of Sun Yat-Sen University, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Methods: Single-cell RNA sequencing data from GSE146771, GSE146771, GSE200997, GSE205506, and GSE231559, along with bulk RNA-seq data from the TCGA CRC cohort, were analyzed. Prognostic genes were identified using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and subsequently used to construct TLS-related prognostic signatures. Kaplan-Meier survival analysis and receiver operating characteristic (ROC) curve analysis were used to evaluate the predictive performance of the signature. Immune infiltration was assessed using the ESTIMATE and CIBERSORT algorithms. Histopathological evaluation of TLS was conducted in tissue sections from 200 CRC patients. Clinicopathological features were compared between the Results: TLS displayed distinct expression patterns within the CRC tumor microenvironment. A 10-gene prognostic model was developed based on LASSO regression analysis. Patients with BRAF Conclusion: TLS-related prognostic signatures serve as effective tools for predicting CRC outcomes. Moreover, intratumorally TLS may enhance the prognosis of patients with BRAF

Indexed as

Colorectal NeoplasmsMutationProto-Oncogene Proteins B-rafTertiary Lymphoid StructuresAgedBiomarkers, TumorFemaleHumansLymphocytes, Tumor-InfiltratingMaleMiddle AgedMultiomicsPrognosisTumor MicroenvironmentBiomarkers, TumorBRAF protein, humanProto-Oncogene Proteins B-rafBRAFmutationcolorectal cancerprognosistertiary lymphoid structurestumor microenvironment

Identifiers

PMID40959083
PMCPMC12434122

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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.