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Lipid Metabolic Reprogramming During Progression to Castration-resistant Prostate Cancer Identified by Quantitative Proteomics

HYUNCHAE SIM, SUBIN BAE, CHAI WON PARK, SO YOUNG CHOI, KWANG-HYEON LIU, EUN HYE LEE, BUM SOO KIM, JAE-WOOK CHUNG, YUN-SOK HA, JUN NYUNG LEE, WONHWA LEE, TAE GYUN KWON and SANGKYU LEE
Cancer Genomics & Proteomics November 2025, 22 (6) 940-952; DOI: https://doi.org/10.21873/cgp.20548

Abstract

Background/Aim: The progression of hormone-sensitive prostate cancer (HSPC) to castration-resistant prostate cancer (CRPC) as a result of resistance to androgen deprivation therapy (ADT) remains a major challenge in prostate cancer treatment.

Materials and Methods: To explore the underlying mechanisms, we performed deep comparative proteomic profiling of HSPC and CRPC cell lines. LNCaP and C4-2 cell lines were cultured in isotopically labeled medium, combined, and digested, followed by liquid chromatography–mass spectrometry (LC-MS/MS) and bioinformatic analyses.

Results: Using SILAC-based proteomic analysis, 3,578 proteins were identified, with 2,474 quantified. In C4-2 cells, 41 proteins were significantly up-regulated, while 201 were down-regulated (fold-change >1.5 or <1.5-1, p<0.05). KEGG pathway analysis linked the increased proteins to fatty acid metabolism and biosynthesis of unsaturated fatty acids. Lipidomic analysis showed a significant rise in fatty acids like DHA, palmitic acid, stearic acid, and arachidic acid, aligning with the proteomic findings.

Conclusion: These results suggest that fatty acids play a key role in HSPC’s progression to CRPC, possibly indicating that CRPC cells themselves may generate fatty acids.

Keywords:

Introduction

Prostate cancer (PCa) is one of the most common cancers diagnosed in men, globally. Due to well-established treatment strategies, PCa is associated with a 5-year survival rate of close to 100% when diagnosed at a local or regional stage (1-3). The standard treatment for patients with PCa is androgen deprivation therapy (ADT), which provides initial benefit and a rapid therapeutic response (4, 5). However, majority of patients eventually develop resistance to ADT, leading to castration-resistant prostate cancer (CRPC), and CRPC is characterized with a poorer prognosis and a substantially higher risk of developing metastasis compared to the hormone-sensitive PCa (HSPC) (6-8). Identifying the biological mechanisms underlying the conversion from HSPC to CRPC remains one of the major challenges in overcoming PCa and is crucial for developing effective therapeutic agents.

To identify new regulators associated with the progression of HSPC to CRPC, utilizing human cell lines with each associated characteristic was advisable. In this study, LNCaP cells were selected to represent hormone-native PCa as HSPC, and C4-2 cells were selected to represent hormone-insensitive PCa as CRPC. LNCaP cell-line, consisting of androgen-sensitive human prostate adenocarcinoma cells is often used in research pertaining to PCa. C4-2 cell-line was established as a subline of LNCaP in 1997 and has been observed to proliferate in castrated animals. These cells have also been observed to metastasize, spreading from the primary tumor site to the lymph node, the seminal vesicles, and the axial skeleton, thereby resulting in an intense osteoblastic reaction (9). A comparative assessment of protein expression characteristics between the two cell lines could provide some valuable insights into the regulatory landscape of CRPC.

Quantitative proteomics can provide comprehensive information regarding differentially expressed proteins between HSPC and CRPC cell lines. Few previous studies employed comparative proteomic techniques using PCa-derived tissues or PCa cell lines to discover factors responsible for the transition to CRPC through the identification of differentially expressed proteins (DEPs) in HSPC and CRPC groups (10, 11). Similar to the current study, proteomics analysis on the LNCaP and C4-2 cell lines had been conducted in two previous studies (12, 13). In these studies, quantitative analysis involving two-dimensional electrophoresis and in-gel digestion led to the identity of several DEPs including PSA, however few DEPs were identified that were common to the two cell lines. In this study, we used SILAC (stable isotope labeling by amino acids in cell culture) labeled cell lines to investigate the novel progression factors, using global quantitative proteomics based on liquid chromatography/mass spectrometry (LC-MS/MS). A total of 2,474 proteins were quantified, among which, 41 and 201 were identified as significant increased and decreased DEPs, respectively. With this data, we have proceeded to predict the proteins that could be responsible for CRPC-related factors.

Materials and Methods

Prostate cancer cell culture. The PCa cell lines LNCaP and C4-2 were procured from the Korea Cell-Line Bank (Seoul, Republic of Korea). Both cell lines were cultured in the RPMI 1640 media for SILAC with 10% of dialyzed FBS, 0.5% of penicillin-streptomycin (Thermo Fisher Scientific, Waltham, MA, USA). Supplemented with L-lysine and L-arginine isotopes (Cambridge Isotope Laboratory, Andover, MA, USA) K4R6 (“medium”) and K8R10 (“heavy”) added into LNCaP and C4-2 growth medium, respectively. The cell culture incubator was maintained at 5% CO2 and 37°C. Cells were subcultured at 70%-80% confluence, using 0.05% trypsin-EDTA (ethylenediaminetetraacetic acid) (Thermo Fisher Scientific) to detach the cells.

Preparation of cellular proteins and trypsin digestion. The biologically triplicate both cell lines were lysed by 0.3 ml of RIPA buffer containing with Halt protease inhibitor cocktail (Thermo Fisher Scientific) added on ice-cold DPBS washed culture plate. Prior to in-solution digestion, a total of 1 mg protein lysates with equal protein amounts were pooled from the SILAC-labeled, LNCaP (K4R6), and C4-2 (K8R10) cells. Disulfide bonds of protein lysate were reduced by treating it with 5 mM DL-Dithiothreitol (DTT) at 56°C for 30 min followed by alkylation in 15 mM iodoacetamide (IAA) at RT for 30 min in the dark. The DTT and IAA solutions were dissolved in 25 mM ammonium bicarbonate (ABC) buffer. Trichloroacetic acid (TCA) was gradually added to the sample to reach 10% of final volume at 4°C and kept for 2 h to precipitate the proteins. Samples were centrifuged at 16,000 × g for 10 min at 4°C and the supernatant solutions were discarded. The pellets of the residue were washed twice with −20°C ice-cold acetone. The protein lysate-acetone mixtures were subsequently centrifuged at 16,000 × g at 4°C for 10 min, and the pellets were dissolved in 100 mM ABC buffer. For enzymatic digestion, sequencing-grade modified trypsin/P (Promega Corporation, Madison, WI, USA) was added at the ratio 1:50 (w/w) and samples were incubated at 37°C for 16 h. To quench the reaction, 10% (v/v) trifluoroacetic acid (TFA) was added to attain a final concentration at 1% (v/v). The digested sample was centrifuged at 16,000 × g at 4°C for 10 min. After centrifugation, the supernatant was transferred to a new LoBind tube (Eppendorf, Hamburg, Germany). The peptide solution was dried using a speed vacuum, then stored at −80°C until further use.

Nano-LC-MS/MS analysis. Prior to MS acquisition, 0.6 μl of C18 resin filled ZipTip® (Milipore, Milford, MA, USA) was used for desalting the fractioned peptide samples. After dissolution in buffer A (99.9% water, 0.1% formic acid), the peptide sample was loaded and separated on an EASY-Spray™ column (C18, 100 Å, 2 μm, 75 μm × 500 mm, Thermo Fisher Scientific) installed into an Ultimate 3000 nano-LC system (Thermo Fisher Scientific). The mobile phase was a mixture of buffer A and buffer B (99.9% acetonitrile, 0.1% formic acid). The procedure was initiated with a 7% solution of buffer B at a flow rate of 400 nl/min for the first 25 min to attain an equilibrium. The concentration of buffer B was increased linearly from 7% to 28% for 44 min and was further thrust to 28%-95% within 2 min. Finally, the procedure was completed after running buffer at 95% for 10 min with a continuous flow rate of 250 nl/min. For the MS analysis, the Q-Exactive™ HF-X Quadrupole-Orbitrap™ Mass Spectrometer (Thermo Fisher Scientific) was connected to the nano-LC. The peptides were ionized, and positively charged mass spectra was acquired in the top-20 data-dependent mode. The nanospray voltage was set to 2.0 kV. The ion capillary temperature was set to 275°C. For precursor-ions, full-MS scans ranging from m/z 375-1,500, were acquired at a resolution of 60,000. The automatic gain control (AGC) was set to 3×106, and the maximum injection time (maxIT) was 45 ms. For MS2 scan, +2 to +7 charged precursor-ions were selected and acquired at a resolution of 15,000, the AGC target was set to 1×105, the maxIT was 35 ms, the high-energy collisional dissociation mode was with 28% normalized collision energy, the isolation window was 1.2 m/z, and the dynamic exclusion was set to 20.0 s.

Data analysis and bioinformatics for proteomics. The MaxQuant search engine (v.2.1.2.0) was used for processing the MS/MS spectra of PCa cells for feature detection and quantification. The results were compared using a FASTA file attained through the UniprotKB reference proteome human database (Proteome ID; UP000005640, composed of 20,594 canonical and 83,236 isoform sequences, last modified in February 2021) with the following parameters: Trypsin/P was used as an enzyme with a specific mode, and up to two missed cleavage sites were allowed. Carbamidomethylation on cysteine was set as the fixed modification, while oxidation on methionine and acetylation on the protein N-terminus was set as the variable modifications. SILAC-2plex that consisted of K4R6, and K8R10 for the medium, and heavy labeling, respectively, were used for quantification. The decoy mode was set to revert. The search results for both PSM and protein group search results were filtered using an FDR less than 1% and a minimum Andromeda score of 40. Rest of the parameters were set to default values. In the search results, incorrect entries that represented potential contaminants, reversed, and only identified by sites were removed.

Statistical analysis. SILAC-labeled normalized ratio of H/M values were transformed to log2-scale further, one-way ANOVA was performed, and p-value 0.05 was conducted using Python (v.3.10.10; with SciPy package v.1.10.1; https://scipy.org/). Both-sided one-sample student’s t-test was performed and volcano plots were used to identify significant EPs with p≤0.05 in entries which were analyzed using Perseus (v.1.6.0.7; https://maxquant.net/perseus/). An enrichment test was performed to identify functional annotations representing, biological process, cellular components, and molecular function of Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) through using the DAVID (v2022q3; https://david.ncifcrf.gov) tools with 0.05 of EASE score and a minimum count of genes set to 2.

Western blot. The samples were first separated on 10% sodium dodecyl sulfate polyacrylamide gel for 90 min with electrophoresis and were subsequently transferred to polyvinylidene fluoride blotting membranes (GE Healthcare, Chalfont St. Giles, UK) for 2 h. The membranes were soaked using 5% bovine serum albumin dissolved in 0.05% Tween-20 containing tris-buffered saline (TBS-T) and blocked for 1 h at RT. Primary antibodies against LDHB (ab112996, abcam, Cambridge, UK), LDHA (CST#2012), PSA (CST#5877), and ACTB (CST#4967, Cell Signaling Technology, Danvers, MA, USA) were dissolved into TBS-T at ratios of 1:1,000 and kept overnight at 4°C. After washing the membranes three times with TBS-T, they were incubated with horseradish peroxide conjugated secondary antibody for 1 h at RT. After incubation, the membranes were washed three times with TBS-T, enhanced chemiluminescence detection reagents (Cytiva, Marlborough, MA, USA) were used for detecting the blots.

Lipid extraction. The Matyash method was slightly modified for the lipid extractions (14). Seventy-five percent ice-cold methanol (400 μl) containing 0.1% butylated hydroxytoluene was added to both LNCaP and C4-2 cell pellets (n=3). After homogenizing, the cells were extracted using stainless steel beads and TissueLyser (QIAGEN, Helden, Germany). One ml of methyl-tert-butyl ether with 0.1% butylated hydroxytoluene was added to the samples and they were shaken for 1 h at temperatures of 20°C-23°C. In total, 250 μl of water was added and vortexed for 10 min. Samples were then centrifuged at 14,000 g for 15 min at 4°C which resulted in phase separation. To analyze free fatty acid content, the upper (220 μl) and lower (110 μl) phases were pooled and dried using a nitrogen purge.

Analysis of free fatty acids. Free fatty acids (FFA) were analyzed using gas chromatography-mass spectrometry. Briefly, FFAs were derivatized via incubation with N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MTBSTFA) with ammonium iodide (NH4I) in pyridine at 80°C for 20 min. Reaction mixtures (n=3) were dried, reconstituted with n-hexane, and injected into gas chromatography-mass spectrometry analyzer (QP2100 Ultra Mass Spectrometry Based Convergence Research Institute, Kyungpook National University) (Shimadzu Corporation, Kyoto, Japan). The analyzer comprised of an AOC-20i auto-sampler and gas chromatography interfaced with a mass spectrometer. An Rxi-5Sil MS Column (30 m × 0.25 mm, 0.25) was used to separate the samples and column flow was maintained at 1.3 ml/min. The samples were injected at 250°C in the split mode at the ratio of 10:1. The temperature of the column was adjusted in a stepwise manner as follows: First 150°C for 1 min; increased to 230°C at 20°C /min; increased to 280°C at 5°C /min; finally increased to 320°C at 20°C /min. At the initiation of the analysis, 6.5 min was solvent cut-off, hence the total analysis ranged from 7.0 min to 25 min. The temperatures of ion source and interface were 230°C and 250°C, and the electron voltage was 70 eV respectively. Full scan mode with a range of m/z 50-650, and selected ion monitoring were utilized.

Results

Global proteomic profiling between HSPC and CRPC cells. In this study, the characteristics of PCa during the progression from HSPC to CRPC were investigated through quantitative proteomic analysis of LNCaP and C4-2 cells. LNCaP cells are represented as HSPC, and C4-2 cells are the hormone insensitive PCa cells derived from LNCaP cells (9). LNCaP and C4-2 cells were selected and metabolically labeled using the SILAC technique with medium and heavy amino acids (Figure 1A). SILAC-labeled LNCaP (K4R6) and C4-2 (K8R10) cells were lysed and mixed at equal protein amounts and subjected to trypsin digestion independently in triplicates. The yield of SILAC labeling for LNCaP and C4-2 cells was more than 52.7 and 47.1% respectively. The tryptic peptides were quantitatively analyzed using a high resolution and accuracy mass spectrometer coupled nano flow-LC system. The MS/MS spectra produced were assessed to identify and quantify the peptides and proteins using MaxQuant (v.2.1.2.0). Pearson correlation coefficient (R) of relative quantitative values was as high as 0.846, which indicated a high reproducibility of the replicates.

Figure 1.
Figure 1.

Global quantitative proteome profiling of PCa cell lines using SILAC-based proteomics. (A) Flow chart depicting the quantitative proteomic profiling in LNCaP and C4-2 cells (n=3). (B) Differentially expressed proteins (DEPs) in LNCaP vs. C4-2 cells, respectively. (C) Volcano plot of significant DEPs (DEPs) in C4-2 vs. LNCaP cells. DEPs are based on significance (p<0.05) and fold-change >1.5 or <1.5-1.

Totally, 14,323 non-redundant peptides with a maximum FDR of 1%, and more than two razor + unique peptides were identified. Based on SILAC-labeled comparative proteomic analysis, 2,474 proteins among the 3,578 identified proteins were quantified (Figure 1B). The overlapping proteins identified in the LNCaP, and C4-2 cells were as high as 92.7% since C4-2 cells have been derived from the LNCaP cells. The number of proteins detected exclusively in LNCaP cells and C4-2 cells were 216 and 45 respectively.

Characteristics of differentially expressed proteins between HSPC and CRPC. To identify the significant differentially expressed protein (DEPs), the data was plotted on Volcano plots (Figure 1C). We focused on the DEPs [fold-change (FC) >1.5 or <1.5−1, and p<0.05] to identify differential protein factors expressed between HSPC and CRPC. C4-2 cells exhibited 41 increased DEPs and 201 decreased DEPs in comparison with LNCaP cells (Table I and Table II). The top five proteins that had increased in C4-2 cells were: Elongation of very long chain fatty acids protein 5 (ELOVL5), protein NDRG1 (NDRG1), fatty acid CoA ligase Acsl3 (ACSL3), acyl-CoA 6-desaturase (FADS2), and heat shock protein 105 kDa (HSPH1); while the top five proteins that had decreased were: Deoxynucleoside triphosphate triphosphohydrolase (SAMHD1), sulfide:quinone oxido-reductase, mitochondrial (SQOR), NEDD8-activating enzyme E1 catalytic subunit (UBA3), proteasome activator complex subunit 2 (PSME2) and putative protein-lysine deacylase (ABHD14B).

View this table:
Table I.

List of significantly up-regulated proteins in C4-2 cells compared to LNCaP cells.

View this table:
Table II.

List of the significant down-regulated proteins in C4-2 cells compared with LNCaP cells.

Bioinformatics analysis between HSPC and CRPC. For further comprehension of the protein profiling between LNCaP and C4-2 cells, a DAVID enrichment analysis was performed for the GO and KEGG pathways (Figure 2). The results of the analysis of 41 proteins that were found to have significantly increased in C4-2 cells compared to LNCaP cells have been depicted in Figure 2A. The GOBP results demonstrated that the elevated proteins were predominantly enriched in the process of fatty acid synthesis followed by “unsaturated fatty acid biosynthesis process”, “long-chain fatty-acyl-CoA biosynthetic process” and “fatty acid elongation, monosaturated fatty acid”. GOMF results indicated enhanced functions of “cadherin binding”, “L-lactate dehydrogenase activity” and “fatty acid elongase activity” among DEPs. The KEGG pathway analysis indicated that the elevated proteins were primarily associated with “fatty acid metabolism” and “biosynthesis of unsaturated fatty acids”. Consistent with the increased proteins in C4-2 cells being involved in unsaturated fatty acid synthesis, a STRING database-based protein-protein interaction analysis linked the proteins ASCL3, FADS2, ELOVL5, and ELOVL7 involved in unsaturated fatty acid synthesis (Figure 2B). In addition, LDHA, LDHB, and ALDOA, which are related to lactate metabolism, were linked together as another network.

Figure 2.
Figure 2.
Figure 2.

Characterization of significantly differentially expressed proteins between C4-2 and LNCaP cells. (A) DAVID enrichment analysis of up-regulated DEP in C4-2 vs. LNCaP cells. (B) Protein-protein interaction analysis of significantly up-regulated DEPs using STRING. (C) DAVID enrichment analysis of down-regulated DEP in C4-2 vs. LNCaP cells. (D) KEGG pathway of Citrate cycle (TCA cycle) category in down-regulated DEP. Purple boxes represent down regulated proteins in C4-2 vs. LNCaP cells. GO, Gene ontology; GOBP: GO biological process; GOCC: GO cellular component; GOMF: GO molecular function; KEGG: Kyoto encyclopedia of genes and genomes pathway analysis.

A DAVID enrichment analysis of the decreased proteins in C4-2 revealed a reduction in the metabolic processes, which could be linked to enhanced fatty acid synthesis in the increased proteins (Figure 2C). In the GOBP category, the decreased proteins exhibited enriched “mitochondrial ATP synthesis coupled proton transport”, “fatty acid beta-oxidation” and “tricarboxylic acid cycle”. The GOCC category indicated a reduction in the mitochondrial-related proteins, while the GOMF category demonstrated decreased metabolism-related activity associated with “oxidoreductase activity” and “acetyl-CoA C-acyltransferase activity”. The KEGG pathway analysis demonstrated enhanced “Metabolic pathways”, “Citrate cycle (TCA cycle)” and “fatty acid metabolism” in proteins associated with decreased metabolic activity and fatty acid metabolism. Figure 2D depicts the location of proteins that were reduced in C4-2 cells compared to LNCaP cells in the TCA cycle. Overall, the DAVID enrichment of DEPs demonstrated an increase in proteins involved in fatty acid synthesis with a reduction in the fatty acid degradation and metabolic pathways in C4-2 compared with LNCaP cells. In other words, these results suggest that fatty acid metabolism decreases, and fatty acid synthesis increases as HSPC progresses to CRPC.

Verification of increased fatty acids in CRPC using immunoblot analysis and lipidomics. Immunoblot analyses were conducted to verify the protein quantification of proteomics in LNCaP and C4-2 cells (Figure 3A). As expected, the immunoblot indicated elevated levels of PSA and LDHB in CRPC compared with HSPC in accordance with the proteomic results. However, levels of LDHA did not differ significantly between HSPC and CRPC. In addition, among the proteins increased in C4-2 cells compared to LNCap cells, the changes in FASN and ELOVL5, which are most closely related to unsaturated fatty acids synthesis, were validated by qPCR (Figure 3B). Both FASN and ELOVL5 were observed to be significantly increased in C4-2 cells compared to LNCap. We quantitatively compared 6 saturated fatty acids, 6 mono-unsaturated fatty acids (MUFA), and 11 poly unsaturated fatty acids (PUFA) by lipidomics analysis between LNCaP and C4-2 cells by lipidomics analysis to support the term “Biosynthesis of unsaturated fatty acids”, which was revealed by the bioinformatic analysis of increased DEPs in CRPC compared to HSPC (Figure 3C). The levels of total fatty acids were significantly elevated in C4-2 cells compared with the LNCaP cells. Ten of the 23 fatty acids analyzed were detected, and most of the fatty acids detected were increased in C4-2 cells compared to LNCaP cells. In particular, levels of docosahexaenoic acid (DHA, C22:6) among PUFA and palmitic acid (C16:0), stearic acid (C18:0) and arachidic acid (C20:0) among MUFA were significantly increased in C4-2 cells.

Figure 3.
Figure 3.

Validation of altered lipid metabolism in castration-resistant prostate cancer (CRPC) cells. (A) Western blot of PSA, LDHA and LDHB proteins in LNCaP and C2-4 cell lines. (B) Different mRNA expressions of FASN and ELOVL5) were determined via RT-PCR analysis (n=5) from LNCaP and C2-4 cell lines. (C) Concentrations of saturated and unsaturated fatty acids, and total fatty acid (n=3). *p<0.05, Student’s t-test.

Discussion

With the progression of cancer, tumor cell proliferation, migration and invasion are closely related to metabolic reprogramming, as indicated by the Warburg Effect (15). Apart from the traditional glucose and glutamate catabolic pathways, the association of malignant tumors with altered lipid and cholesterol metabolism has recently attracted attention. Increased dietary lipid supply has been reported to be significantly associated with cancer progression (16) and tumor cells are known for their enhanced lipid production compared with normal cells (17). Unlike other cancers, PCa is known to favor lipid metabolism over glycolysis, and is associated with increases in total cholesterol, triglyceride and high-density lipoprotein during ADT (18). Indeed, de novo lipogenesis is observed early in PCa (19) and this effect is thought to become more pronounced as the disease progresses to metastatic CRPC (20) and is considered to be a biomarker of aggressive disease in several hormone-dependent cancers (21).

In this study, the results of a deep proteomics analysis conducted between LNCaP cells as HSPCs and C4-2 cells as CRPCs revealed an up-regulation of the fatty acid synthetic metabolism pathway in C4-2 cells, particularly in proteins associated with the biosynthesis of unsaturated fatty acids (Figure 2). For instance, the most pronounced increase was exhibited by ELOVL5, a protein involved in the synthesis of unsaturated fatty acids (Table I), which correlated with a significant elevation in the synthesis of DHA (C22:6), a polyunsaturated fatty acid (Figure 3B). A previously conducted study had demonstrated that ELOVL5 plays a role in the development of resistance to enzalutamide, a drug used in ADT treatment (22). ELOVL5 has also been demonstrated to be involved in the elongation of PUFAs (poly unsaturated fatty acids), which can enhance the activation of lipid raft-associated AKT-mTOR signaling pathways (23). DHA is an important fatty acid that can either be obtained from the diet or can be synthesized de novo. The sources of fatty acids in lipid metabolism that contribute to cancerous properties can be exogenous or endogenous. Results indicate that levels of self-synthesized fatty acids are elevated in CRPC.

The results of the study demonstrated an elevation in the levels of proteins associated with fatty acid synthesis in CRPC cells, C4-2. This has been corroborated by elevated fatty acid concentration in C4-2 cells, as demonstrated by targeted lipidomics. This implies that the generation of fat within CRPC cells is stimulated, and that even though external supply of nutrients is constrained, lipids can be augmented by intracellular lipogenesis. The therapeutic strategy of blocking lipid supply in the treatment of prostate cancer is gaining traction (24); however, the findings of this study indicate that in addition to limiting the external lipid supply, it is also necessary to block intracellular production of lipids (25).

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A2C1003510) (2023R 1A2C3003807), by the Korean Fund for Regenerative Medicine (KFRM) grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Health & Welfare) (23A0206L1), and by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (RS-2022-KH130593).

Footnotes

  • Availability of Data

    Data are available via ProteomeXchange with identifier PXD056553.

  • Conflicts of Interest

    The Authors declare no conflicts of interest.

  • Authors’ Contributions

    Conceptualization: Lee, S., and Lee, J.N.; Methodology and analysis: Sim, H., Bae, S., Choi, S., Lee, E.H., and Lee, S.; Resources: Liu, K., Kim B.S., Chung, J., Ha, Y., Kwon, T., and Lee, J. N.; Data Curation: Sim, H., Bae, S., and Lee, S.; Writing: Sim, H., Lee, S. and Lee, J.N.; Funding Acquisition: Kwon, T., Lee, S., and Lee, J.N.

  • Artificial Intelligence (AI) Disclosure

    No artificial intelligence (AI) tools, including large language models or machine learning software, were used in the preparation, analysis, or presentation of this manuscript.

  • Received March 3, 2025.
  • Revision received July 31, 2025.
  • Accepted August 8, 2025.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY-NC-ND) 4.0 international license (https://creativecommons.org/licenses/by-nc-nd/4.0).

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Lipid Metabolic Reprogramming During Progression to Castration-resistant Prostate Cancer Identified by Quantitative Proteomics
HYUNCHAE SIM, SUBIN BAE, CHAI WON PARK, SO YOUNG CHOI, KWANG-HYEON LIU, EUN HYE LEE, BUM SOO KIM, JAE-WOOK CHUNG, YUN-SOK HA, JUN NYUNG LEE, WONHWA LEE, TAE GYUN KWON, SANGKYU LEE
Cancer Genomics & Proteomics Nov 2025, 22 (6) 940-952; DOI: 10.21873/cgp.20548
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