Prolactin as a Candidate Biomarker in Non-Small Cell Lung Cancer: Implications for Personalized Medicine and Post-Treatment Risk Stratification

Background/Objectives: Non-small cell lung cancer (NSCLC) remains associated with high mortality, frequent late-stage diagnosis, biological heterogeneity, and recurrence after treatment. Although molecular and immunohistochemical biomarkers have transformed treatment selection, there remains a need for accessible, repeatable, and clinically practical circulating biomarkers that may support prognosis and post-treatment monitoring. This review discusses prolactin (PRL) as a candidate supplementary biomarker in NSCLC, with particular emphasis on its biological rationale, potential prognostic relevance, and possible role in personalized risk stratification after systemic therapy. Methods: This narrative review summarizes current evidence on established biomarkers in NSCLC, the physiology and regulation of PRL, PRL/PRLR signaling in cancer biology, mechanisms of PRL dysregulation in lung cancer, and available clinical observations concerning PRL alterations in NSCLC. Particular attention is given to the distinction between prognostic and predictive biomarkers, longitudinal monitoring, pituitary involvement, immune checkpoint inhibitor-related endocrine effects, and biological, pharmacological, and analytical confounders affecting PRL interpretation. Results: Current evidence suggests that PRL may be biologically relevant in NSCLC through its involvement in pathways related to cell proliferation, survival, angiogenesis, invasion, epithelial–mesenchymal transition, immune modulation, and possible therapy resistance. Clinical observations indicate that altered PRL levels may occur in advanced disease, pituitary involvement, systemic inflammation, stress, or during anticancer and supportive treatment. However, PRL lacks cancer specificity and is influenced by multiple confounders, including circadian rhythm, stress, endocrine disorders, macroprolactin, cachexia, medications, and assay variability. Available clinical data remain limited and are largely derived from small studies or case-based evidence. Conclusions: PRL should not currently be considered a standalone diagnostic, predictive, or treatment-selective biomarker in NSCLC. Its most realistic potential role is as a supplementary circulating marker within multimarker prognostic and monitoring models. Prospective validation with standardized sampling, assay procedures, and confounder adjustment is required before clinical implementation.

Authors

Institutions

Publication Details

Journal
Journal of Personalized Medicine
Published
2026-06-24
DOI
https://doi.org/10.3390/jpm16070342
Primary Topic
Growth Hormone and Insulin-like Growth Factors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Prolactin as a Candidate Biomarker in Non-Small Cell Lung Cancer: Implications for Personalized Medicine and Post-Treatment Risk Stratification

F Gajewski, Oliwia Burdan, Agnieszka Korolczuk, Grzegorz Kurec et al.
Journal of Personalized Medicine
Growth Hormone and Insulin-like Growth Factors
article

Prolactin as a Candidate Biomarker in Non-Small Cell Lung Cancer: Implications for Personalized Medicine and Post-Treatment Risk Stratification

F Gajewski, Oliwia Burdan, Agnieszka Korolczuk, Grzegorz Kurec, Weronika Pająk, Jakub Kleinrok, Aleksandra Litkowska, Paweł Krawczyk, Joanna Pec
article en

Abstract

Background/Objectives: Non-small cell lung cancer (NSCLC) remains associated with high mortality, frequent late-stage diagnosis, biological heterogeneity, and recurrence after treatment. Although molecular and immunohistochemical biomarkers have transformed treatment selection, there remains a need for accessible, repeatable, and clinically practical circulating biomarkers that may support prognosis and post-treatment monitoring. This review discusses prolactin (PRL) as a candidate supplementary biomarker in NSCLC, with particular emphasis on its biological rationale, potential prognostic relevance, and possible role in personalized risk stratification after systemic therapy. Methods: This narrative review summarizes current evidence on established biomarkers in NSCLC, the physiology and regulation of PRL, PRL/PRLR signaling in cancer biology, mechanisms of PRL dysregulation in lung cancer, and available clinical observations concerning PRL alterations in NSCLC. Particular attention is given to the distinction between prognostic and predictive biomarkers, longitudinal monitoring, pituitary involvement, immune checkpoint inhibitor-related endocrine effects, and biological, pharmacological, and analytical confounders affecting PRL interpretation. Results: Current evidence suggests that PRL may be biologically relevant in NSCLC through its involvement in pathways related to cell proliferation, survival, angiogenesis, invasion, epithelial–mesenchymal transition, immune modulation, and possible therapy resistance. Clinical observations indicate that altered PRL levels may occur in advanced disease, pituitary involvement, systemic inflammation, stress, or during anticancer and supportive treatment. However, PRL lacks cancer specificity and is influenced by multiple confounders, including circadian rhythm, stress, endocrine disorders, macroprolactin, cachexia, medications, and assay variability. Available clinical data remain limited and are largely derived from small studies or case-based evidence. Conclusions: PRL should not currently be considered a standalone diagnostic, predictive, or treatment-selective biomarker in NSCLC. Its most realistic potential role is as a supplementary circulating marker within multimarker prognostic and monitoring models. Prospective validation with standardized sampling, assay procedures, and confounder adjustment is required before clinical implementation.

Journal of Personalized MedicineVol. 16(7)
Medical University of Lublin (PL)
Good health and well-being
Openalex Percentile: Top 7%
Growth Hormone and Insulin-like Growth Factors
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.