Identification of Cancer-associated Fibroblasts-specific ncRNA-mRNA/Protein Regulatory Axes in Prostate Cancer.

Authors

Apoorva Abikar, Swathi Kiragunasi Annayya, Mohammad Mehaboob Subhani Mustafa, Radhika Rajiv Athalye, Namratha Nadig, Ninad Tamboli, Vinod Babu, Ramaiah Keshavamurthy, Meena Jhanwar-Uniyal, Prathibha Ranganathan

Published in

Anticancer research. Volume 46. Issue 10. Pages 5375-5386.

Abstract

Cancer-associated fibroblasts (CAFs) are major components of the tumor microenvironment and secrete factors that promote tumor growth, progression, and treatment resistance. Non-coding RNAs (ncRNAs) may regulate CAF-derived signals, but coordinated ncRNA-mRNA/protein networks in prostate cancer remain poorly characterized. This study aimed to identify CAF-specific ncRNA-mRNA/protein regulatory axes by comparing prostate cancer-derived CAFs with fibroblasts from benign prostatic hyperplasia (BPH).
We integrated previously generated RNA sequencing data with label-free quantitative mass spectrometry of conditioned media from primary CAFs (n=3) and BPH-derived control fibroblasts (n=3). LINCRNA-interacting miRNAs and their putative mRNA targets were predicted using NPInter v5.0 and miRDB, respectively, and cross-referenced with differentially expressed secreted proteins. Prognostic associations were evaluated using progression-free interval data from the TCGA prostate adenocarcinoma cohort.
Secretome profiling identified 1,007 proteins, including 133 differentially expressed proteins and 90 proteins unique to either CAFs or control fibroblasts. Integration of transcriptomic, target-prediction, and proteomic data revealed 28 CAF-associated ncRNA-mRNA/protein regulatory axes. MYH11, SERPINF2, ANGPT1, and CRISPLD2 were significantly associated with progression-free interval. The identified axes are linked to extracellular matrix remodeling, angiogenesis, and immune modulation, processes that contribute to tumor maintenance, progression, metastasis, and therapeutic resistance.
CAF-specific ncRNA-mRNA/protein networks provide mechanistic insight into prostate cancer progression and may serve as candidate biomarkers for disease monitoring and prognosis.

PMID:
42823170
Bibliographic data and abstract were imported from PubMed on 02 Oct 2026.

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