Collection Highlight: Fibrosis, Cancer, and SDG3

In this post, we discuss how an article collection in Journal of Translational Medicine contributes to achieving the Sustainable Development Goal 3: Good Health and Wellbeing goal.
Published in Biomedical Research
Collection Highlight: Fibrosis, Cancer, and SDG3
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Cover of Journal of Translational MedicineJournal of Translational Medicine is an open-access multidisciplinary journal that serves as a bridge to optimize communication between basic and clinical science. As a platform from bench-to-bedside research, it publishes in specialized sections relevant to the Sustainable Development Goal 3 (SDG3): Good Health and Wellbeing, set by the United Nations.

A key target of SDG3 is to reduce premature mortality from non-communicable diseases (target 3.4), specifically in relation to mortality rates attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (3.4.1) by 2030. 

Journal of Translational Medicine contributes to the global efforts of researchers and clinicians to meet this target by publishing high-quality articles across several expert-led sections such as Cancer Microenvironment, Combination Strategies, Immune RadioBiology, and Translational Cancer Biology.

Immunofluorescent Light Micrograph
of carcinoma cancer cells of the lung.
Normal lung fibroblast cells are green.
Smaller carcinoma cells (red) are seen
growing upon this fibroblast lung tissue.
Both cell types have blue nuclei.

In 2023, the launch of the Fibrosis section, focusing on the normal and pathogenetic processes of tissue repair, led to the Fibrosis and Cancer Intersection (FACI) article collection. With research implicating fibrosis in the proliferation and migration of cancer cells, the collection was timely and has garnered over 40,000 article accesses.

Guest Editors Dr Mary Salvatore (Vice Chair of Education for Department of Radiology at Columbia University) and Dr Monica Pernia (Columbia University) expressed their objective for the collection in an Editorial:

The improved understanding of how fibrosis develops, causes morbidity, and promotes cancer is the foundation for making advances in diagnosis, treatment, and ultimately prevention.”

This year's theme for World Cancer Day (4th February), "United by unique", highlights how every experience with cancer is unique. Hence, the  FACI article collection is particularly topical in demonstrating this through the discussion of different types of cancers (breast cancer, lung cancer, ovarian cancer, pancreatic cancer, and prostate cancer), the systems affected (thoracic, abdominal, and genitourinary organs), and the treatments involved (immunotherapy, radiotherapy and other therapeutic interventions).


Dr Salvatore (Left) and Dr Pernia (Right)

In line with SDG3 target 3.4, the Guest Editors explain that a collection like Fibrosis and Cancer Intersection which is aimed at “increasing the understanding of the relationship between fibrosis and cancer development would be of great benefit for the medical scientific community since this knowledge could accelerate the development of new therapies to treat cancer while increasing the hostility of the cancer cells’ microenvironment by preventing or interfering with collagen deposition. This evidence could explain the development of tumoral resistance to immune defenses and existing or emergent anti-cancer therapies.” 

We invite you to check out the Fibrosis and Cancer Intersection article collection!


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Related Collections

With collections, you can get published faster and increase your visibility.

Harnessing Innovative Machine Learning Techniques to Combat Drug Resistance in Solid Tumors

Topics of Interest The collection welcomes original research, reviews, commentary and methodology articles on the following topics: • Ensemble Learning Techniques; • Applications in predicting drug resistance profiles; • Comparative studies of ensemble methods versus traditional approaches; • Deep Learning in Oncology; • Neural network architectures for tumor characterization; • Image analysis and interpretation for drug response prediction; • Reinforcement Learning; • Adaptive treatment strategies using reinforcement learning; • Simulation models for drug resistance evolution; • Explainable AI; • Methods for interpreting machine learning models in clinical settings; • The role of explainability in improving treatment outcomes; • Quantum Computing Applications; • Quantum algorithms for optimizing drug discovery processes; • Examples of quantum-enhanced machine learning techniques in oncology.

All submissions in this collection undergo the journal’s standard peer review process. Similarly, all manuscripts authored by a Guest Editor(s) will be handled by the Editor-in-Chief. As an open access publication, this journal levies an article processing fee (details here). We recognize that many key stakeholders may not have access to such resources and are committed to supporting participation in this issue wherever resources are a barrier. For more information about what support may be available, please visit OA funding and support, or email OAfundingpolicy@springernature.com or the Editor-in-Chief.

Publishing Model: Open Access

Deadline: Nov 17, 2025