Advances in Prognostic and Predictive Biomarkers in Oncology: From Single Markers to Multi-Omics Signatures

Submission Deadline: 30 June 2027 View: 76 Submit to Special Issue

Guest Editor(s)

Prof. Mario Fordellone

Email: mario.fordellone@unicampania.it

Affiliation: Medical Statistics Unit, University of Campania "Luigi Vanvitelli", Largo Madonna Delle Grazie n. 1, Napoli, Italy

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Research Interests: oncology, predictive and prognostic biomarkers, public health, fuzzy clustering, dimensionality reduction, bayesian statistics, maximum-entropy fuzzy clustering, bayesian cut-point estimation

Dr. Domenico Mallardo

Email: d.mallardo@istitutotumori.na.it

Affiliation: Department of Melanoma, Cancer Immunotherapy and Development Therapeutics, Istituto Nazionale Tumori - IRCCS Fondazione "G. Pascale," Napoli, Italy

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Research Interests: translational oncology, cancer immunology and immunotherapy, melanoma and non-melanoma skin cancers, immune checkpoint inhibitors, prognostic and predictive biomarkers, molecular and multi-omics signatures, tumor immune microenvironment, immune profiling, circulating biomarkers and cytokines, mechanisms of treatment response, resistance, and toxicity, precision oncology

Summary

The identification of reliable prognostic and predictive biomarkers represents a central challenge in contemporary oncology and a fundamental step toward the implementation of precision medicine. Advances in molecular profiling and high-throughput technologies have enabled the characterization of individual biomarkers as well as complex molecular signatures capable of capturing tumor heterogeneity, disease progression, and differential response to anticancer treatments.

This Special Issue aims to provide a broad platform for original research and comprehensive reviews focused on the discovery, development, validation, and clinical translation of prognostic and predictive biomarkers across different cancer types, ranging from individual biomarkers to integrated multi-omics signatures. Particular interest will be given to studies investigating individual genes and proteins; genomic, epigenomic, transcriptomic, proteomic, metabolomic, immunological, and microbiome-derived features; tissue-based and circulating biomarkers; liquid biopsy analytes; imaging biomarkers; and multi-marker, gene-expression, spatial, single-cell, and integrated multi-omics signatures. Associated with patient outcomes, treatment response, toxicity, resistance, and therapeutic benefit.

Contributions integrating genomic, transcriptomic, epigenomic, proteomic, metabolomic, or other molecular data are encouraged, as are studies employing advanced statistical methods, machine learning, artificial intelligence, and integrative multi-omics approaches for biomarker identification and signature development. Studies addressing independent validation, reproducibility, clinical utility, and the integration of molecular biomarkers with established clinicopathological factors are particularly welcome.

By bringing together methodological, translational, and clinical perspectives, this Special Issue aims to foster the development of robust, reproducible, and clinically actionable biomarker-driven strategies for patient stratification, prognostic assessment, treatment selection, and therapeutic monitoring, ultimately advancing precision oncology.

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