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Advances in Integrative Omics Data Analysis for Cancer Research
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Advances in Integrative Omics Data Analysis for Cancer Research

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Genetics and Genomics".

Deadline for manuscript submissions: 30 October 2025 | Viewed by 2642

Special Issue Editors

Dr. Jian Sang

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Guest Editor
Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
Interests: cancer bioinformatics; genomic epidemiology; biocuration
Dr. Jianli Dai

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Guest Editor
Institute of New Materials and Advanced Manufacturing, Beijing Academy of Science and Technology, Beijing 100089, China
Interests: tumor model; cancer organoid; organ chip; antibody therapy
Dr. Guangyi Niu

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Guest Editor
Department of Oncology, Oxford University, Oxford, UK
Interests: cancer bioinformatics; immunology; RNA-editing; eQTL; biocuration

Special Issue Information

Dear Colleagues,

Cancer remains the second leading cause of death globally, following cardiovascular disease. Over recent decades, cancer research has been profoundly transformed by omics approaches, which provide comprehensive insights into the complex mechanisms underlying tumorigenesis. However, the integration and in-depth analysis of various omics data types remain significant challenges, calling for advanced analytical strategies, novel computational tools, and comprehensive database resources.

Our forthcoming Special Issue, "Advances in Integrative Omics Data Analysis for Cancer Research", is dedicated to illuminating the application of cutting-edge omics data in cancer research and clinical applications. We are looking for contributions involved with cancer genetics, genomics, transcriptomics, epigenomics, epitranscriptomics, radiomics, epidemiomics, and spatial omics. Each of these domains holds unique significance, and collectively they promise to pave the way toward a deep understanding of tumorigenesis.

We encourage submissions that demonstrate innovative approaches to integrating and/or deepening multiple layers of omics data, with a particular focus on how these integrations can lead to a better understanding of cancer biology, improve diagnostics, enhance treatment stratification, and predict therapeutic responses.

As we launch this exciting issue, we extend a warm invitation to the researchers from around the world to contribute their knowledge and insights. Whether you are developing new computational methods and databases or applying different omics to clinical research, your contributions are crucial to the continuous evolution of cancer research.

Thank you for your interest in this Special Issue. We look forward to receiving your submissions and to advancing our collective understanding in this important area of research.

Dr. Jian Sang
Dr. Jianli Dai
Dr. Guangyi Niu
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 short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly 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 2600 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

  • cancer research
  • omics
  • bioinformatics
  • translational medicine

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Published Papers (1 paper)

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26 pages, 1090 KiB  
Review
Towards the Prediction of Responses to Cancer Immunotherapy: A Multi-Omics Review
by Weichu Tao, Qian Sun, Bingxiang Xu and Ru Wang
Life 2025, 15(2), 283; https://doi.org/10.3390/life15020283 - 12 Feb 2025
Viewed by 1987
Abstract
Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of [...] Read more.
Tumor treatment has undergone revolutionary changes with the development of immunotherapy, especially immune checkpoint inhibitors. Because not all patients respond positively to immune therapeutic agents, and severe immune-related adverse events (irAEs) are frequently observed, the development of the biomarkers evaluating the response of a patient is key for the application of immunotherapy in a wider range. Recently, various multi-omics features measured by high-throughput technologies, such as tumor mutation burden (TMB), gene expression profiles, and DNA methylation profiles, have been proved to be sensitive and accurate predictors of the response to immunotherapy. A large number of predictive models based on these features, utilizing traditional machine learning or deep learning frameworks, have also been proposed. In this review, we aim to cover recent advances in predicting tumor immunotherapy response using multi-omics features. These include new measurements, research cohorts, data sources, and predictive models. Key findings emphasize the importance of TMB, neoantigens, MSI, and mutational signatures in predicting ICI responses. The integration of bulk and single-cell RNA sequencing has enhanced our understanding of the tumor immune microenvironment and enabled the identification of predictive biomarkers like PD-L1 and IFN-γ signatures. Public datasets and machine learning models have also improved predictive tools. However, challenges remain, such as the need for large and diverse clinical datasets, standardization of multi-omics data, and model interpretability. Future research will require collaboration among researchers, clinicians, and data scientists to address these issues and enhance cancer immunotherapy precision. Full article
(This article belongs to the Special Issue Advances in Integrative Omics Data Analysis for Cancer Research)
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