Evidence map›Paper›PMID 39963131›Full record

ArticleFrontiers in immunology2025

Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration.

Kaili Liao, Minqi Zhu, Lei Guo, Zijun Gao, Jinting Cheng, Bing Sun, Yihui Qian, Bingying Lin, Jingyan Zhang, Tingyi Qian and 4 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 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

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

2 citing papers in PubMed.

  1. Review
  2. Article
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

14 authors.

Kaili Liao *Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Minqi Zhu *School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Lei Guo *The 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Zijun Gao *The 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Jinting ChengSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Bing SunQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Yihui QianThe 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Bingying LinQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Jingyan ZhangQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Tingyi QianThe 1st Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Yixin JiangQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Yanmei XuJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Qionghui ZhongJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Xiaozhong WangJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients. Methods: The Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA). And the chemotherapeutic response to different drugs was assessed. Results: We established a novel prognostic model utilizing the WGCNA method, which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups. Conclusion: This study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer.

Indexed as

Colorectal NeoplasmsImmunotherapyLymphocytes, Tumor-InfiltratingBiomarkers, TumorFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedMutationPrognosisTreatment OutcomeTumor MicroenvironmentBiomarkers, Tumorcolorectal cancerimmune cell infiltrationimmune-related geneprognostic modelWGCNA

Identifiers

PMID39963131
PMCPMC11830669

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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.