Evidence map›Paper›PMID 42589301›Full record

ArticleInternational journal of molecular sciences2026

Deciphering the Leading-Edge Spatiotemporal Microenvironment of Hepatocellular Carcinoma for Targeted Drug Discovery Using SpaPred.

Shibo Zhang, Ziqiao Li, Kexin Yu, Guang Shi, Yangguang Su, Xin Hu, Xiujie Chen

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Shibo ZhangCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0009-0000-3653-0503
Ziqiao LiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Kexin YuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Guang ShiCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Yangguang SuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xin HuCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.
Xiujie ChenCollege of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China.ORCID 0000-0003-2423-8569

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The leading-edge (LE) of hepatocellular carcinoma (HCC) is a critical region driving malignant progression and is closely associated with high patient mortality and marked intratumoral heterogeneity. Multi-omics integration identified elevated expression of SPARC and IGFBP7 in the LE region, which was associated with stromal remodeling-related transcriptional programs and an immune-depleted microenvironment. Cell-cell communication and pathway analyses further suggested potential links between LE-associated stromal states and pro-invasive signaling programs. Furthermore, we developed SpaPred, which demonstrated favorable performance in inferring the spatiotemporal heterogeneity of HCC at the spatial resolution. This model overcomes the limitations of existing algorithms in analyzing the composition of tissue spatial structures. Finally, integration of in silico trajectory-perturbation and pharmacogenomic drug-response analyses prioritized Oxaliplatin, Belinostat, and Temsirolimus as candidate compounds associated with LE-related transcriptional programs. These drug predictions are computational and require experimental validation. Collectively, SpaPred provides a hypothesis-generating framework for investigating spatial heterogeneity and candidate therapeutic vulnerabilities in HCC.

Indexed as

Antineoplastic AgentsCarcinoma, HepatocellularDrug DiscoveryLiver NeoplasmsTumor MicroenvironmentGene Expression Regulation, NeoplasticHumansAntineoplastic Agentsdeep learninghepatocellular carcinomaSpaPred modelspatiotemporal transcriptomics analysistumor immune microenvironment

Identifiers

PMID42589301
PMCPMC13466823

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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.