Evidence map›Paper›PMID 40264860›Full record

ArticleJournal of hepatocellular carcinoma2025

A Novel Prognostic Signature Integrating Immune and Glycolytic Pathways for Enhanced Prognosis and Immunotherapy Prediction in Hepatocellular Carcinoma.

Zeyu Zhang, Hongxi Zhao, Pengyu Wang, Xueyan Geng, Maopeng Yin, Yingjie Liu, Shoucai Zhang, Yongyuan Liang, Jian Ji, Guixi Zheng

Abstract read
In one paragraph

Article in Journal of hepatocellular carcinoma, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–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

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Review
  5. 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

10 authors.

Zeyu ZhangDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Hongxi ZhaoDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Pengyu WangFaculty of Science, University of Alberta, Edmonton, Alberta, T6G 2E9, Canada.
Xueyan GengDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Maopeng YinDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Yingjie LiuDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Shoucai ZhangDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Yongyuan LiangDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Jian JiDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.
Guixi ZhengDepartment of Clinical Laboratory, Qilu Hospital of Shandong University, Jinan, Shandong, 250012, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: This study aimed to establish an immune-glycolysis-related prognostic signature (IGRPS) to predict hepatocellular carcinoma (HCC) outcomes. Additionally, it explored the role of this signature in the tumor immune microenvironment (TIME), glycolytic pathways, and immunotherapy. Methods: We analyzed RNA-seq, single-cell sequencing, and immune- and glycolysis-related gene datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using weighted gene co-expression network analysis (WGCNA), F-test, and Cox regression, we identified key survival-related immune and glycolytic genes (SRIGRGs) and developed an IGRPS through multivariate Cox regression. The IGRPS's predictive performance was validated in training and validation cohorts using Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, and a prognostic nomogram. Its correlation with TIME and its ability to predict immunotherapy outcomes were also assessed. In vitro experiments were conducted to analyze the expression and function of IGRPS genes in HCC. Results: Thirteen SRIGRGs were identified for constructing the IGRPS. Patients with low-risk scores had significantly longer survival times. The area under the curve (AUC) for ROC curves was over 0.73 for training and 0.7 for validation cohorts, with C-indices of 0.721 and 0.79, respectively. IGRPS was confirmed as an independent prognostic indicator. Patients in the low-risk group showed better responses to combined anti-CTLA4 and anti-PD-1 therapies. In vitro experiments indicated that PRKAG1 and B3GAT3 were upregulated, enhancing glycolysis and promoting HCC cell proliferation and migration. Conclusion: The IGRPS, based on immune- and glycolysis-related genes, effectively predicted prognosis and immunotherapy responses in HCC patients.

Indexed as

glycolysisHCCimmunotherapyprognostictumor immune microenvironment

Identifiers

PMID40264860
PMCPMC12013648

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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 Socratic graph.