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Identification of Predictive Molecular Markers for Cancer Progression, Response to Therapy, and Disease Outcome

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Oncology".

Deadline for manuscript submissions: 25 December 2026 | Viewed by 1181

Editor

Dr. Shadma Fatima

E-Mail Website
Guest Editor
1. Department of Medical Oncology, Ingham Institute of Applied Medical Research, Liverpool, NSW 2170, Australia
2. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2031, Australia
3. School of Medicine, Western Sydney University, Campbelltown, NSW 2560, Australia
Interests: glioblastoma; liquid biopsy; gene sequencing; cancer biology; cell signaling; stem cells
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue invites original research articles, reviews, and perspectives focused on novel predictive molecular biomarkers critical for advancing cancer diagnosis, prognosis, and therapy response prediction. We seek studies that explore cutting-edge molecular signatures, multi-omics integration, and innovative technologies such as liquid biopsy, spatial omics, and AI-driven biomarker discovery. Emphasis is on biomarkers that enhance understanding of tumor progression mechanisms, therapeutic resistance, immune response prediction, and clinical outcomes across diverse cancer types. Contributions highlighting translational applications and personalized medicine strategies are particularly encouraged. This Issue aims to foster collaboration across disciplines advancing precision oncology through molecular insights. Submitted manuscripts will undergo rigorous peer review according to journal standards. We welcome submissions from early career researchers to established experts to spark impactful discussions and accelerate clinical utility. Join us in shaping the future of cancer biomarker research.

Dr. Shadma Fatima
Guest Editor

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 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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

  • predictive biomarkers
  • cancer progression
  • therapy response
  • molecular signatures
  • precision oncology

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Published Papers (1 paper)

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Research

28 pages, 11674 KB  
Article
A Metabolic-Related Gene Signature for Predicting Biochemical Recurrence After Radical Prostatectomy: An Integrative Analysis and Targeted Therapeutic Validation
by Wankun Wang, Xiujuan Hong, Xiaoqi Wang, Ganpei Jiao, Hongjie Cai, Junxiang Zhao, Zhibing Wu and Jun Chen
Int. J. Mol. Sci. 2026, 27(11), 4797; https://doi.org/10.3390/ijms27114797 - 26 May 2026
Viewed by 553
Abstract
Biochemical recurrence (BCR) after radical prostatectomy (RP) remains a major clinical challenge. Although metabolic reprogramming drives prostate cancer (PCa) progression, its predictive value for BCR and its interplay with the tumor immune microenvironment (TIME) remain incompletely understood. By integrating weighted gene co-expression network [...] Read more.
Biochemical recurrence (BCR) after radical prostatectomy (RP) remains a major clinical challenge. Although metabolic reprogramming drives prostate cancer (PCa) progression, its predictive value for BCR and its interplay with the tumor immune microenvironment (TIME) remain incompletely understood. By integrating weighted gene co-expression network analysis (WGCNA) with machine learning, we identified four metabolic-related hub genes (GDPD1, PLA2G7, PTGDS, and SRD5A2) and developed an XGBoost-Cox model that accurately stratified BCR risk (training 5-year AUC: 0.858; validation 5-year AUC: 0.745). SHAP analysis enhanced the model’s interpretability, while immunohistochemistry (IHC) validated differential protein expression of these targets across 32 clinical specimens. Furthermore, immune profiling demonstrated that these genes are closely linked to M2 macrophage-mediated immunosuppression and altered T-cell infiltration. To translate these biomarkers into therapeutic targets, we employed in silico screening, molecular docking, and molecular dynamics simulations, identifying (-)-epigallocatechin gallate (EGCG) as a promising multi-target candidate. Subsequent in vitro assays confirmed that EGCG binds stably to GDPD1, PTGDS, and SRD5A2, effectively suppressing malignant PCa phenotypes and prostate-specific antigen (PSA) secretion. In summary, we established a robust and interpretable model for predicting BCR after RP, and our in vitro validation suggests that EGCG holds promise as a therapeutic agent to delay PCa progression. Full article
(This article belongs to the Special Issue Identification of Predictive Molecular Markers for Cancer Progression, Response to Therapy, and Disease Outcome)
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