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From Molecular Genomics and the Tumor Microenvironment to Precision Diagnosis and Therapy in Liver Cancer

A special issue of Cancers (ISSN 2072-6694).

Deadline for manuscript submissions: 31 August 2026 | Viewed by 703

Editors


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Guest Editor
1. Department of Pathology, The University of Hong Kong, Pokfulam, Hong Kong
2. State Key Laboratory of Liver Research, The University of Hong Kong, Pokfulam, Hong Kong
Interests: liver cancer; cancer genomics; bioinformatics; HBV; single-cell and spatial transcriptomics; computational biology; immunotherapy
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Department of Pathology, The University of Hong Kong, Pokfulam, Hong Kong
2. State Key Laboratory of Liver Research, The University of Hong Kong, Pokfulam, Hong Kong
Interests: hepatocellular carcinoma (HCC); tumor initiating cells (TICs); translational aspects for liver cancer treatment; liquid biopsy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Liver cancer is a leading fatal human cancer. It is known to have a high degree of heterogeneity, leading to frequent treatment resistance and disease recurrence. There are only a limited number of treatment options, and they are not adequately effective. There is an accumulation of various molecular alterations that pinpoint key signaling pathways or biological processes throughout the hepatocarcinogenesis process. On the other hand, the tumor microenvironment (TME) in liver cancer consists of a complex network of cancer cells, immune cells (such as T cells, MDSCs, and TAMs), fibroblasts, blood vessels, and extracellular matrix components. It is often immunosuppressive, promoting tumor growth, invasion, and resistance to therapy. Key features such as hypoxia, angiogenesis, and chronic inflammation also modulate the TME. Immunotherapies and targeted treatments aim to reprogram the different components of the TME, but their dynamic nature poses challenges for providing effective and durable therapeutic actions. Understanding TME interactions is crucial for developing more effective liver cancer therapeutics.

This Special Issue will focus on various niche areas that cover the basic as well as translational aspects of liver cancer research. We invite papers that involve basic methodology/algorithm development, data mining, mechanistic delineation, experimental exploration, biomarker discovery and evaluation, as well as preclinical investigation of therapeutic agents.

Possible areas include, but are not limited to, the following:
- Tumor heterogeneity;
- Molecular landscape;
- Cancer genomics;
- Tumor microenvironment;
- Liver cancer stem cell;
- Animal models;
- Liquid biopsy;
- Biomarkers;
- Diagnostics;
- Treatment and resistance;
- Emerging therapeutic agents. 

Dr. Daniel Wai Hung Ho
Dr. Yuman Tsui
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tumor heterogeneity
  • cancer genomics
  • sequencing
  • molecular landscapes
  • tumor microenvironment
  • cancer stem cell
  • liquid biopsy
  • diagnostics
  • biomarker
  • treatment and resistance

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Published Papers (1 paper)

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Research

17 pages, 4780 KB  
Article
STEA: Histologically Validated and Reference-Independent Major Cell-Type Annotation for Spatial Transcriptomics Reveals Relevant Cellular Organization and Architecture of Tumor Microenvironment
by Qian Li, Qingyang Zhang, Fanhong Zeng, Irene Oi-Lin Ng and Daniel Wai-Hung Ho
Cancers 2026, 18(9), 1425; https://doi.org/10.3390/cancers18091425 - 29 Apr 2026
Viewed by 471
Abstract
Background: Recent advances in spatial transcriptomic technologies enable in situ gene expression profiling while preserving spatial context. This capability is particularly important for studying the tumor microenvironment (TME), where diverse and admixed cell populations interact within highly organized spatial niches that influence tumor [...] Read more.
Background: Recent advances in spatial transcriptomic technologies enable in situ gene expression profiling while preserving spatial context. This capability is particularly important for studying the tumor microenvironment (TME), where diverse and admixed cell populations interact within highly organized spatial niches that influence tumor progression and therapeutic response. However, the limited resolution of early spatial transcriptomic platforms results in each spatial spot capturing transcripts from multiple cell types, making accurate spot deconvolution or annotation a critical yet challenging step in downstream data analysis. The level of complexity will be particularly prominent in heterogeneous samples like the tumor microenvironments where multiple cell types are highly admixed and reliable single-cell reference atlases may usually be unavailable. Methods: In this paper, we developed our method called STEA, which is a novel and accurate reference-independent enrichment-based annotation algorithm for major cell type. Unlike the existing approaches, STEA does not require single-cell RNA sequencing datasets as reference, offering both flexibility and computational efficiency in execution. Results: We performed comprehensive benchmarking using a variety of simulated datasets across different platforms and scenarios and demonstrated the superior accuracy of STEA. Apart from synthetic data, we also evaluated multiple real datasets to further exemplify its practical applicability on both oncology-related and oncology-unrelated data. More importantly, we could confidently demonstrate the high concordance between prediction of STEA and histological classification by experienced pathologist. Conclusion: Our STEA algorithm provides a practical reference-independent framework to complement the cutting-edge spatial transcriptomics in genomics studies, facilitating accurate downstream high-dimensional spatial characterization of cellular and molecular landscapes, reconstruction of tissue architecture as well as cell–cell communication in malignant and non-malignant scenarios. Taken together, our comprehensive evaluation demonstrates the robustness and reliability of STEA, highlighting its potential as a valuable tool for studying complex tissue organization, particularly within heterogeneous TME. Full article
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