Page 117 - Read Online
P. 117
Li et al. Cancer Drug Resist. 2025;8:31 Page 23 of 26
for specific subtypes, these results remain exploratory and warrant confirmation in future studies.
Additionally, drug-resistant tumor cells may evade immune surveillance; thus, immune checkpoint
inhibitors or other immunomodulatory therapies might partially restore antitumor immune activity.
Another pivotal strategy involves combination therapies, as distinct resistant subtypes may resist
monotherapies but could be effectively targeted through combined androgen receptor pathway inhibitors
with metabolic interventions or immunotherapies. Although the current study has not yet experimentally
validated these hypotheses, future research will systematically explore the feasibility of these therapeutic
approaches.
In conclusion, this study delineates the intricate interplay between the dynamics of the immune
microenvironment, genomic instability, and the heterogeneity of drug resistance in PCa. By integrating
multi-omics data, we established a prognostic model that not only stratifies patients into high- and low-risk
groups but also highlights subtype-specific drug resistance patterns. These results underscore the necessity
for precision strategies that consider resistance mechanisms, such as targeting metabolic dependencies or
tailoring therapies based on subtype-specific drug sensitivity profiles. Future studies should focus on
translating these insights into combinatorial regimens to overcome resistance and enhance outcomes in
advanced PCa.
DECLARATIONS
Authors’ contributions
Conceived the study and performed bioinformatic analyses: Li C, Wu L, Zhong B
Performed experiments: Gan Y, Zhou L, Tan S
Developed reagents and analytic tools needed for the study: Hou W, Yao K, Wang B
Drafted the initial version of the manuscript: Li C, Wu L, Zhong B
Analyzed the data: Ou Z
Supervised the study and provided critical revision of the manuscript: Xiong W, Zhang S
All authors read and approved the final draft for publication.
Availability of data and materials
All data relevant to the study are included in the article or uploaded as supplemental information. Data from
the TCGA repository (https://www.cancer.gov/tcga) for the PRAD cohort are available. Gene expression data
from the GEO repository (https://www.ncbi.nlm.nih.gov/geo/) include the following datasets: GSE46602,
GSE70769, and GSE116918. Data will be made available from the corresponding authors upon reasonable
request. For any other inquiries regarding the data from this study, please contact the author Li C.
Financial support and sponsorship
This work was supported by the National Natural Science Foundation of China (82303120), Natural Science
Foundation of Hunan Province (2022JJ40747 and 2022JJ30908), Postdoctoral Science Foundation of China
(2022M713533), Innovation Guidance Project of Clinical Medical Technology of Hunan Province
(2021SK53710), and Research Plan Project of Hunan Provincial Health Commission(D202304057666).
Conflicts of interest
All authors declared that there are no conflicts of interest.
Ethics approval and consent to participate
This study is based entirely on publicly available, de-identified bioinformatics data and conducted in
accordance with the ethical guidelines approved by the Third Xiangya Hospital’s ethics committee (Approval
No. 2023-S140).
Consent for publication
Not applicable.
110
