Evidence map›Paper›PMID 40761429›Full record

ArticleJournal of hepatocellular carcinoma2025

Single-Cell RNA Sequencing Integrated with Bulk-RNA Sequencing Analysis Reveals Prognostic Signatures Based on PANoptosis in Hepatocellular Carcinoma.

Jiyin Wang, Xue Yin, Ziyi Li, Pu Liang, Yipeng Wang, Xingling Li, Wenying Qiao, Chaoyang Xiong, Minghang Yu, Xiaoyan Ding and 1 more

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 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

11 authors.

Jiyin Wang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xue Yin *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Ziyi Li *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0009-0000-8553-3428
Pu Liang *National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Yipeng WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xingling LiNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Wenying QiaoNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0002-9687-4073
Chaoyang XiongNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Minghang YuNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.
Xiaoyan DingNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0003-1640-7115
Xi WangNational Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People's Republic of China.ORCID 0000-0001-6753-3053

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Drug resistance severely compromises therapeutic efficacy in hepatocellular carcinoma (HCC); however, the selection of precise treatment strategies for patients remains a critical unmet clinical need. This study investigated PANoptosis-related mechanisms underlying HCC progression to identify actionable therapeutic targets and optimize patient-specific treatment outcomes. Patients and Methods: Multi-omics analysis (single-cell/bulk RNA sequencing) combined with machine learning was used to identify the PANoptosis-related prognostic features. The association of PANoptosis-related expression with the tumor immune microenvironment and drugs was explored using bioinformatic analysis and experimental studies. Results: High PANoptosis risk exhibited immunosuppressive microenvironments and therapeutic resistance. The PANoptosis-related gene YIF1B has emerged as a dual prognostic biomarker and tumor driver that promotes proliferation, and is linked to immune dysfunction and drug resistance. Conclusion: YIF1B may be a promising therapeutic target. This PANoptosis framework bridges molecular mechanisms to clinical management, offering strategies for personalized HCC therapy and overcoming treatment resistance.

Indexed as

drug resistanthepatocellular carcinomaPANoptosisYIF1B

Identifiers

PMID40761429
PMCPMC12318859

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Registered trials

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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.