prognostic biomarkers

Overview

Prognostic biomarkers are measurable biological features that help estimate the likely course of a disease, including the risk of progression, recurrence, treatment resistance, or death. In oncology and other complex diseases, they are used to stratify patients into different risk groups, support treatment planning, and identify individuals who may benefit from closer surveillance or more intensive therapy. Prognostic biomarkers may be derived from tumor tissue, blood, saliva, cerebrospinal fluid, imaging data, or multi-omics profiles, and they often reflect underlying processes such as immune dysregulation, metabolic reprogramming, genomic instability, or microenvironmental remodeling.

In recent biomedical research, prognostic biomarkers are increasingly being developed through integrative approaches that combine single-cell RNA-seq, bulk transcriptomics, proteomics, metabolomics, radiomics, and machine learning algorithms. These studies span cancers such as melanoma, hepatocellular carcinoma, bladder cancer, head and neck squamous cell carcinoma, prostate cancer, and diffuse intrinsic pontine glioma, as well as nonmalignant conditions including epilepsy, dementia-related disorders, sleep deprivation, diabetes complications, and post-COVID-19 syndrome. Across these settings, prognostic biomarkers are often linked to pathways involving immune infiltration, cytokine signaling, tumor microenvironment dynamics, and host vulnerability.

Focus of Latest Publications

Recent publications show that prognostic biomarkers are being investigated as tools for risk stratification, treatment response prediction, and clinical decision support across a wide range of diseases.

In acute myeloid leukemia, dysregulation of the IL-37/SIGIRR axis was examined for its association with treatment response, with the stated aim of determining its utility as a potential prognostic biomarker in de novo patients. In stage IIB-IIC melanoma, melanocortin-1 receptor variants and telomerase reverse transcriptase promoter mutations were proposed as prognostic biomarkers because these tumors retain a substantial risk of recurrence and disease-related mortality despite complete surgical excision. Similarly, in oral squamous cell carcinoma, PRDX4 was described as a possible independent marker for early recurrence, reflecting the need for prognostic biomarkers in a disease where early local recurrence strongly affects outcome.

Several cancer studies used multi-omics or transcriptomic integration to identify prognostic signatures. In prostate cancer, integrated multi-omics profiling was reported to reveal epigenetic, transcriptional, and metabolic features with potential clinical translation into prognostic biomarkers. In bladder cancer, a glycosylation-based prognostic framework was developed, and ALG3 was nominated for targeted therapy, underscoring the need for reliable prognostic biomarkers in aggressive disease. In head and neck squamous cell carcinoma, single-cell and bulk transcriptomic analysis identified lesion-associated gene signatures for prognostic stratification and therapeutic guidance, addressing the lack of effective biomarkers for prognosis and treatment selection. Another HNSCC study identified VMP1 as a tumor-intrinsic biomarker associated with metastatic progression and immune microenvironment remodeling, supporting its biomarker relevance. In lung squamous cell carcinoma, CXCL2 was highlighted as a functionally and clinically relevant biomarker linked to IL-6/JAK2/STAT3 signaling and the immune microenvironment. In hepatocellular carcinoma, quantitative CT clustering identified subtypes with distinct prognosis and immune signatures, while another multi-omics study implicated ATP13A2 in tumor microenvironment regulation and prognosis. In diffuse intrinsic pontine glioma, the BIOMEDE trial validated prognostic biomarkers and informative theranostic biomarkers through comprehensive tumor profiling.

Other cancer-focused studies emphasized host-related or context-dependent prognostic markers. In remnant gastric cancer, the preoperative cachexia index was reported to predict overall survival after curative resection, suggesting prognostic value for host vulnerability. In cutaneous T-cell lymphoma, multiomic analysis of clonal evolution was aimed at identifying clinically useful biomarkers and therapeutic targets. In metastatic breast cancer, single-cell transcriptomics identified genes whose expression correlated with patient outcomes, presenting potential prognostic biomarkers. In breast cancer more broadly, proximity mapping of the ER proteome suggested metastasis-specific candidate biomarkers, and another study on bisphenol A and breast cancer proposed candidate biomarkers for further investigation. In pancreatic ductal adenocarcinoma, circulating microRNA signatures were reported to have diagnostic utility and mechanistic relevance to progression. In melanoma, the need for reliable prognostic biomarkers was emphasized because recurrence risk remains high even after surgery.

Outside oncology, prognostic biomarker research also addressed neurological, inflammatory, metabolic, and infectious diseases. In drug-resistant epilepsy, systemic immune dysregulation and neutrophil activation defined prognostic inflammatory signatures, with neutrophil-related signatures highlighted as promising prognostic biomarkers. In Huntington’s disease, a machine learning-derived speech index was developed as a biomarker for disease severity, reflecting the need for sensitive and scalable markers for patient stratification and tracking. In Alzheimer’s disease, salivary biomarkers were identified through integrated metabolomics and microbiomics, supporting non-invasive screening and intervention. In chronic tinnitus, the TIDE project was designed to identify and validate a biomarker for diagnosis, reflecting the broader challenge that the condition lacks an established biomarker. In multiple sclerosis, a review of CSF and serum biomarkers emphasized the unmet need for markers that reflect disease activity, severity, and molecular pathophysiology. In post-COVID-19 syndrome, endothelial dysfunction and metabolic biomarkers were investigated in relation to disease severity. In rheumatoid arthritis, an m6A-associated GAS6 axis was suggested as a potential biomarker and therapeutic target. In type 2 diabetes, miR-144 was proposed as a biomarker and therapeutic target for cognitive impairment, and another study identified biomarkers for diabetes-related complications through large-scale proteomics. In sepsis-associated acute kidney injury, metabolomics was used to identify potential biomarkers and metabolic pathways. In fetal growth restriction, RNF126 was highlighted as a promising biomarker and therapeutic target. In HIV infection, WFDC2 was proposed as a promising biomarker and potential therapeutic target. In mastitis in dairy cattle, bioinformatics was used to identify potential biomarker and drug target candidates for clinical and subclinical disease.

Across these studies, prognostic biomarkers were often framed as part of a broader precision medicine strategy. Some papers linked biomarkers to immune microenvironment features, such as CD8+ S100B+ T cells, macrophage activity, Neutrophil to lymphocyte ratio, PD-1/PD-L1 blockade, and tumor immune remodeling. Others connected them to signaling pathways including JAK2/STAT3 signaling pathway, PI3K/Akt signaling pathway, Wnt/β-catenin pathway, cGAS-STING/NF-κB signaling pathway, MAPK/ERK/JNK signaling pathways, and nuclear factor kappa B. In several cases, biomarkers were discussed alongside therapeutic targets such as anti-PD-1, dasatinib, erlotinib, temozolomide, and anti-inflammatory cytokines, reflecting the close relationship between prognosis and treatment selection.

Key Publications

• Jul Dysregulation of IL-37/SIGIRR Axis in De Novo acute myeloid leukemia patients: association with treatment response. (Molecular biology reports, 2026, PMID 42377398): "to determine their utility as potential prognostic biomarkers in the studied population."
• Jun Melanocortin-1 receptor variants and telomerase reverse transcriptase promoter mutations as prognostic biomarkers in stage IIB-IIC melanoma. (Melanoma research, 2026, PMID 42301209): "Stage IIB-IIC cutaneous melanomas carry a substantial risk of recurrence and disease-related mortality despite complete surgical excision, highlighting the need for reliable prognostic biomarkers."
• Jun Multi-omics identifies VMP1 as a tumor-intrinsic biomarker associated with metastatic progression and immune microenvironment remodeling in HNSCC. (Translational oncology, 2026, PMID 42314513): "These findings support the potential biomarker relevance of VMP1 and provide a rationale for further mechanistic and clinical investigation in HNSCC."
• Jun RNF126 mediates fetal growth restriction via ubiquitination-dependent degradation of the MYH9/MYH10 complex. (iScience, 2026, PMID 42291269): "and highlight RNF126 as a promising biomarker and therapeutic target."
• Jun Multi-omics analysis positions DNA2 at the interface of genome integrity programs and tumor behavior in pan-cancer. (Functional & integrative genomics, 2026, PMID 42307783): "These findings have suggested DNA2 as a therapeutic vulnerability in cancer, a context-dependent biomarker with implications for treatment response, prognosis, and immunity."
• Jun Integrated multi-omics profiling uncovers the epigenetic, transcriptional, and metabolic landscape of prostate cancer progression. (BMC cancer, 2026, PMID 42260416): "Overall, these results underscore the power of multi-omics integration in providing a holistic understanding of prostate cancer biology and its potential for clinical translation into prognostic biomarkers."
• Jun Integrative multi-omics identifies a glycosylation-based prognostic framework and nominates ALG3 for targeted therapy in bladder cancer. (Discover oncology, 2026, PMID 42251607): "...creating an urgent need for reliable prognostic biomarkers."
• Jun Unveiling novel biomarkers for diabetes-related complications through large-scale proteomics analysis: A FIELD sub-study. (Journal of diabetes and its complications, 2026, PMID 42019187): "Identifying robust biomarkers is critical for improving risk prediction, understanding disease mechanisms, and guiding targeted therapies."
• Jun Integrated multi-omics analysis: exploring the molecular regulatory mechanisms of HIV infection and identifying the key gene WFDC2. (Clinical and experimental medicine, 2026, PMID 42223712): "Within this network, WFDC2 stands out as a putative hub integrating immune modulation and RNA metabolic pathways, and may represent a promising biomarker and potential therapeutic targets in HIV."
• Jun Integration of Transcriptomics With Interpretable Artificial Intelligence for Identifying Molecular Signatures of Physiological Stress in Sleep Deprivation. (Journal of cellular and molecular medicine, 2026, PMID 42216239): "These findings identify S100A3 as a robust candidate biomarker shared by acute sleep deprivation and chronic insomnia, while VEGFB may reflect chronic metabolic or inflammatory adaptation."