Evidence map›Paper›PMID 41262299›Full record

ArticleFrontiers in cell and developmental biology2025

Metabolic subtyping reveals PDIK1L as a dual-functional regulator of progression and PARP inhibitor sensitivity in prostate cancer.

Zhongyuan Wang, Qintao Ge, Anwaier Aihetaimujiang, Ji Zhang, Jiahe Lu, Jianfeng Yang, Yonghao Chen, Bin Qin, Hailiang Zhang, Wen-Hao Xu and 1 more

Abstract read
In one paragraph

Article in Frontiers in cell and developmental biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Zhongyuan Wang *Department of Oncology, Shanghai Medical College, Shanghai, China.
Qintao Ge *Department of Oncology, Shanghai Medical College, Shanghai, China.
Anwaier Aihetaimujiang *Department of Oncology, Shanghai Medical College, Shanghai, China.
Ji Zhang *Department of Nursing, Fudan University Shanghai Cancer Center, Shanghai, China.
Jiahe LuDepartment of Oncology, Shanghai Medical College, Shanghai, China.
Jianfeng YangDepartment of Urology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Yonghao ChenSichuan University West China Medical Center, Chengdu, Sichuan, China.
Bin QinOrthopedics department, Wuxi Hospital of Traditional Chinese Medicine, Wuxi, China.
Hailiang ZhangDepartment of Oncology, Shanghai Medical College, Shanghai, China.
Wen-Hao XuDepartment of Oncology, Shanghai Medical College, Shanghai, China.
Dingwei YeDepartment of Oncology, Shanghai Medical College, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Prostate cancer demonstrates significant metabolic heterogeneity, but its role in therapeutic resistance and disease progression remains unclear. This study investigates the clinical implications of metabolic diversity and identifies potential biomarkers for precision oncology. Methods: Multi-omics analyses of TCGA-PRAD and meta-cohorts classified tumors into three metabolic subtypes (C1, C2, C3). Functional studies utilized prostate cancer cell lines with genetic modulation of PDIK1L. Proliferation assays, protein expression analysis, and drug sensitivity evaluations were systematically performed. Results: Metabolic subtyping delineated distinct molecular and clinical profiles. The C2 subtype demonstrated elevated genomic instability and heightened sensitivity to PARP inhibitors, characterized by enrichment of glycogen metabolism and TP53-driven oncogenic pathways. Integrative multi-omics and random survival forest analysis prioritized PDIK1L as a C2-specific biomarker, where its overexpression accelerated tumor proliferation and rewired metabolic programs to confer resistance to PARP inhibitors. Conversely, PDIK1L knockdown suppressed proliferation and sensitized cells to therapy, underscoring its role as a dual-functional regulator. Mechanistically, PDIK1L interacted with DNA repair and metabolic adaptation pathways, creating a permissive environment for therapeutic resistance. Combinatorial therapy with Enzalutamide and PARP inhibitors effectively reversed PDIK1L-mediated resistance, restoring drug sensitivity across preclinical models. Independent validation in multi-institutional cohorts confirmed the robustness of metabolic subtyping and PDIK1L's prognostic value in predicting survival and treatment outcomes. Discussion: Metabolic stratification reveals the C2 subtype as a high-risk prostate cancer group with unique therapeutic vulnerabilities. PDIK1L emerges as a dual-functional biomarker driving tumor progression and modulating treatment efficacy, offering a novel target for precision therapeutic strategies.

Indexed as

durg resistancegenomic instabilitymetabolic subtypingPARP inhibitorprostate cancer

Identifiers

PMID41262299
PMCPMC12623405

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.