Usnic acid inhibits colorectal cancer liver metastasis by targeting CRYAB phosphorylation and inducing ferroptosis

Ziyuan Wan; Tingting Chen; Xiaotong Xu; Yan Zhang; Yuzhang Liang; Haoze Li; Lu Wang; Junhao Wan; Min Fang; Jinlai Wei

doi:10.1186/s12964-026-03046-0

. 2026 Jul 7;24:518. doi: 10.1186/s12964-026-03046-0

Usnic acid inhibits colorectal cancer liver metastasis by targeting CRYAB phosphorylation and inducing ferroptosis

Ziyuan Wan ^2,^#, Tingting Chen ^2,^3,^✉,^#, Xiaotong Xu ², Yan Zhang ², Yuzhang Liang ², Haoze Li ², Lu Wang ², Junhao Wan ², Min Fang ¹, Jinlai Wei ^1,^✉

PMCID: PMC13628791 PMID: 42415058

Abstract

Background

Colorectal cancer liver metastasis (CCLM) presents a significant challenge in oncology, characterized by aggressive progression and poor prognosis. Usnic acid (UA), a secondary metabolite derived from the lichen Usnea, has shown extensive bioactivity, particularly notable anticancer effects. Despite the well-established pro-apoptotic role of UA through reactive oxygen species (ROS) generation, its influence within the hypoxic tumor microenvironment, particularly in CCLM, remains inadequately elucidated.

Methods

In this study, we employed an integrative approach utilizing multi-omics analyses and molecular dynamics simulations to identify CRYAB (αB-crystallin) as a UA target in CCLM. We assessed the impact of UA on the phosphorylation status of serine 59 (S59) in CRYAB using cellular thermal shift assays. By developing organoid models from both CCLM and primary colorectal cancer tissues, we examined tumor characteristics, pathway enrichment, and the effects of hypoxic conditions on CRYAB dynamics. We utilized mutation analysis to explore the functional implications of S59 phosphorylation.

Results

Our findings reveal that CRYAB is upregulated in CCLM tissues with increased S59 phosphorylation. UA binds with high affinity to CRYAB, significantly decreasing S59 phosphorylation in a dose-dependent manner. CCLM organoids exhibited elevated CRYAB and FGFR1 expression, along with activation of pathways involved in epithelial-mesenchymal transition (EMT), hypoxia, and proliferation. Hypoxia further promoted CRYAB phosphorylation and organoid growth; however, UA treatment mitigated these effects, leading to apoptosis and reduced cell migration. Notably, mutations at S59 altered CRYAB localization and modified downstream signaling, highlighting its role in tumor metastasis.

Conclusions

This study reveals that CRYAB phosphorylation is a key driver of liver metastasis progression in colorectal cancer and confirms that usnic acid exerts its anti-metastatic effects by targeting this post-translational modification. Mechanistically, beyond its known pro-apoptotic activity, usnic acid induces ferroptosis by inhibiting CRYAB phosphorylation at site S59. These findings establish a mechanistic framework for usnic acid as a potential therapeutic agent against metastatic colorectal cancer, highlighting the therapeutic potential of targeting CRYAB phosphorylation and ferroptosis in managing colorectal cancer liver metastases.

Graphical abstract

graphic file with name 12964_2026_3046_Figa_HTML.webp

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12964-026-03046-0.

Keywords: Usnic acid, αB-crystallin, Colorectal cancer liver metastasis, Phosphorylation, Hypoxia, Ferroptosis, Organoids, Multi-omics

Highlight

• Multi-omics analysis identifies CRYAB as the target of usnic acid in inhibiting colorectal cancer liver metastasis.

• CRYAB phosphorylation, linked to the tumor hypoxic microenvironment, is significantly elevated in CCLM samples and correlates with tumor proliferation and migration.

• CRYAB phosphorylation promotes its interaction with NEDD4L, preventing GPX4 ubiquitination and inhibiting ferroptosis.

• Both usnic acid and phosphorylation site mutation disrupt CRYAB-NEDD4L interaction, leading to GPX4 deactivation and ferroptosis induction.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s12964-026-03046-0.

Background

Usnic acid (UA), a naturally occurring secondary metabolite, is predominantly found in lichens and has garnered significant attention due to its diverse biological activities, including antibacterial, antiviral, antioxidant, and anticancer properties [1]. These attributes make it a promising candidate for cancer research, particularly for its potential to combat various forms of malignancies [2]. UA has been shown to increase reactive oxygen species (ROS) levels in cancer cells, leading to a cascade of cellular events such as DNA damage, mitochondrial membrane depolarization, cytochrome c release, and activation of caspase pathways, which collectively induce programmed cell death (apoptosis) [3, 4]. Moreover, it suppresses the clonogenic potential of cancer cells, downregulates proliferative markers such as PCNA, and activates key tumor suppressor genes [5, 6]. Additionally, UA inhibits critical processes such as cell migration, invasion, and angiogenesis, all of which are pivotal in tumor progression and metastasis [7, 8]. While the anticancer effects of UA have been well-documented, its role in modulating the tumor hypoxic microenvironment remains underexplored, representing an important gap in current cancer research that warrants further investigation.

Colorectal cancer (CRC) ranks as the third most prevalent cancer worldwide and remains the second leading cause of cancer-related mortality globally [9]. In murine models of CRC, potassium usnate, a water-soluble derivative of UA, has demonstrated enhanced bioavailability and has been shown to inhibit the invasive and metastatic properties of colorectal tumors [10]. Hepatic metastasis is a major complication in CRC progression, as the liver serves as the primary site for hematogenous dissemination [11]. The prognosis for CRC patients with liver metastasis is dismal, with a 5-year survival rate dropping from approximately 90% for localized disease to less than 20% for those with liver involvement [12, 13]. Consequently, colorectal cancer liver metastases (CCLM) represent a critical challenge in CRC treatment [14, 15]. However, the specific effects of UA in liver metastasis models and its interaction with the hypoxic tumor microenvironment remain poorly understood.

In this study, we identified CRYAB (alpha-crystallin B chain) as a key candidate of UA using computational molecular dynamics simulations. Further validation through clinical proteomics, post-translational modification analysis, and publicly available databases confirmed the relevance of CRYAB in the context of colorectal cancer liver metastasis. By utilizing organoid models of CRC and CCLM, we demonstrated that CRYAB is intricately involved in the regulation of the hypoxic tumor microenvironment. Our comprehensive approach, which integrates gene mutation analysis,

immunoprecipitation-mass spectrometry (IP-MS), proteomics, transcriptomics, metabolomics, and molecular experiments, elucidates the molecular mechanisms by which usnic acid modulates CRYAB phosphorylation and its impact on the tumor metastatic microenvironment. This study provides crucial insights into the therapeutic potential of UA for targeting liver metastasis in colorectal cancer, offering novel therapeutic avenues for CRC patients through modulation of the CRYAB-NEDD4L axis and the tumor hypoxic environment.

Results

Integrative analysis identifies CRYAB as a target of UA in inhibiting colorectal cancer liver metastasis

The previous study has demonstrated that UA inhibits proliferation and migration of COAD cells [16], but its effects on CCLM and the underlying molecular mechanisms remain unclear. We analyzed genes associated with tumor metastasis in clinical tissue samples from normal, Ca, and CCLM patients through proteomics and proteomic modifications. Integrating this data with The cancer genome atlas(TCGA) and Comparative toxicogenomics database(CTD), we identified key UA targets potentially involved in CCLM inhibition (Fig. 1A). Differential expression analysis revealed 230 upregulated and 1397 downregulated proteins (Extended Data Fig. 1A), with KEGG pathway analysis showing that upregulated proteins were enriched in focal adhesion and ECM-receptor interactions, while downregulated proteins were enriched in spliceosome and RNA transport (Extended Data Fig. 1B). Gene set enrichment analysis (GSEA) identified a significant association with the HOSHIDLIVERcancersubclassS2 (Fig. 1B).

Integrating proteomics, TCGA, and CTD data, four key targets were identified (Fig. 1C). Notably, CRYAB was downregulated in Ca patients but upregulated in CCLM patients (Fig. 1D). Its expression strongly correlated with COAD (R = 0.79, p < 0.05) (Extended Data Fig. 1C), and was significantly altered during cancer progression (p < 0.05, F = 5. 32) (Fig. 1E). Additionally, phosphorylation of CRYAB was markedly increased in CCLM patients compared to Ca patients (Fig. 1F), with phosphorylation levels rising in stage 3 tumors (Extended Data Fig. 1D). These findings are consistent with previous studies suggesting that CRYAB S59 phosphorylation induces coagulation abnormalities, exacerbating disease progression [18].

Clinical samples were divided into four groups: (1)normal colonic mucosa (NC, from healthy control tissue of non-cancer patients); (2)primary colorectal cancer (Ca, from primary tumor tissue of CRC patients without synchronous liver metastases); (3)primary colorectal cancer with synchronous liver metastases (CCLM, from primary tumor tissue of CRC patients with synchronous liver metastases); (4)liver metastatic lesions (ML, from distant liver metastatic tumor tissue of CRC.

Immunofluorescence(IF) and immunohistochemistry (IHC) assays on clinical samples confirmed CRYAB’s high expression in NC and CCLM tissues, but minimal expression in Ca and ML tissues (Fig. 1G). Western blot (WB) analysis revealed significantly increased CRYAB S59 phosphorylation in CCLM and ML tissues compared to normal controls, but not in Ca patients (Fig. 1H), suggesting CRYAB as a potential target for the transition from Ca to CCLM.

Molecular dynamics simulations (Fig. 1I) revealed stable binding of UA to CRYAB over 100 ns, with key interactions at G110 and Val97, resulting in a binding affinity of -6.4 kcal/mol (Fig. 1J). IC50 measurements in SW480 and HT29 cells showed a lower IC50 in SW480 cells, which were selected for further experiments (Extended Data Fig. 1E). UA treatment significantly inhibited CRYAB S59 phosphorylation in a dose-dependent manner (Fig. 1K). Cellular thermal shift assay (CETSA) confirmed that UA effectively targets CRYAB, with its inhibitory effect decreasing as temperature increased (p < 0. 05), further validating CRYAB as a target of UA in CCLM inhibition (Fig. 1L).

CRYAB promotes malignant proliferation of colorectal cancer organoids under hypoxic stress

We established organoids from Ca and CCLM tissues and assessed their morphological characteristics, cellular differentiation, and signaling pathways using H&E staining, immunohistochemistry (IHC), immunofluorescence (IF), and RNA-sequence (Fig. 2A). H&E staining revealed more pronounced malignant features in CCLM organoids, including irregular structures, cystic dilation, and central necrosis, compared to the more regular Ca organoids (Fig. 2B).

Fibroblast growth factor receptor 1 (FGFR1) promotes tumor cell migration and invasion, increasing the risk of metastasis [19, 20]. IHC analysis showed significantly upregulated CRYAB and FGFR1 expression in CCLM organoids (Fig. 2C). Notably, β-catenin and CD44, key markers of epithelial-mesenchymal transition (EMT) and tumor migration [21–23], exhibited higher expression in CCLM organoids, supporting the successful construction of these organoids (Fig. 2D), RNA-sequence identified 648 upregulated and 1,182 downregulated proteins in CCLM organoids (Extended Data Fig. 2A), with KEGG pathway analysis highlighting EMT, hypoxia, angiogenesis, and proliferation pathways. FGFR1 and CD44 showed elevated expression in CCLM compared to Ca organoids (Extended Data Fig. 2C).

Phosphorylation of CRYAB at S59 was significantly higher in CCLM organoids (p < 0.01), consistent with clinical samples (Fig. 2E). Interestingly, our previous RNA sequencing analysis revealed carbonic anhydrase 9 (CA9), as the most significantly upregulated gene in CCLM organoids compared to Ca organoids (Extended Data Fig. 2C). CA9 expression is induced by hypoxia and has been established as a critical mediator of tumor adaptation to hypoxic stress [24, 25]. Subsequently, we constructed a hypoxic microenvironment model using Ca organoids and the SW480 cell line (Extended Data Fig. 2D and S2E). CRYAB phosphorylation was significantly elevated under hypoxia, and this effect was reversible upon shifting to normoxia (Fig. 2F and G), suggesting a close association between CRYAB phosphorylation and the hypoxic environment in CCLM formation.

Further, recombinant CRYAB protein enhanced the growth and viability of Ca organoids and SW480 cells under hypoxia, increasing organoid size and cystic dilation, with limited effects under normoxic conditions (Fig. 2H and S2F). Scratch assays demonstrated that CRYAB also promoted SW480 cell migration under hypoxic conditions (Extended Data Fig. 2G). Transcriptomic analysis revealed that CRYAB upregulated the expression of hypoxia marker CA9 and adhesion factor CEACAM6 under hypoxic conditions (Extended Data Fig. 2H). Immunofluorescence showed hypoxia-induced FGFR1 expression in Ca organoids, consistent with clinical findings (Fig. 2I). These results highlight CRYAB’s role in promoting colorectal cancer metastasis under hypoxic stress.

Antagonistic effects of UA on CRYAB-induced tumorigenesis in colon cancer organoids

Sorafenib is a new, effective oral RAF inhibitor that also acts as a multi-kinase inhibitor and ferroptosis activator. RAS/RAF mutations linked to poor prognosis are often found in many patients with colorectal cancer liver metastases [26]. Recent research shows that combining sorafenib with radiotherapy offers significant promise, providing new hope for patients with liver metastases [27, 28]. We examined the synergistic effect of UA with Sorafenib, and found that UA enhanced Sorafenib’s inhibition of SW480 cells, with a 60% inhibition at half IC50 concentration (Fig. 3A and Extended Data Fig. 3A). The combination therapy analysis revealed a response mean of 35. 72, with a synergy value of 3. 19 ( p < 0. 05) in HSA model, indicating a synergistic effect (Fig. 3B and Extended Data Fig. 3C).

Fig. 3 — The Effect of Combined CRYAB and UA Treatment on Invasion of Organoids and SW480 Cells. A Heatmap of cell inhibition rate after UA and Sorafenib combination. Cells were treated with serially diluted UA (0–1× IC₅₀) and sorafenib (0–1× IC₅₀) for 48 h. A heatmap representing the inhibition rate of SW480 cells following treatment with a combination of UA and Sorafenib, showing the dose-dependent effects on cell viability. B Synergy score of UA and Sorafenib using the HSA model. The synergy between UA and Sorafenib was analyzed using the HSA model in SynergyFinder 3. 0 (https://synergyfinder.org/) [29]. The HSA model predicts the expected combination effect as the maximum of the single drug responses at the corresponding concentrations. A mean synergy score greater than 0 indicates synergy, with a higher value indicating a stronger synergistic effect. A mean score less than 0 suggests antagonism, with a more negative value indicating stronger antagonistic effects. C Heatmap of cell viability after combined treatment of UA and CRYAB. Cells were treated with serially diluted UA (0–1× IC₅₀) and recombinant CRYAB protein (0–1× EC₅₀) for 48 h. The cell viability was assessed using a heatmap to show the effects of combining UA with CRYAB on SW480 cells. D Synergy score of UA and CRYAB using the HSA model. This experiment evaluated the combined effects on cell viability. The 3D surface plot displays a mean synergy score of -24. 17 (p = 1. 91e^− 12), indicating strong antagonism between UA and proCRYAB. E Brightfield and immunofluorescence imaging of organoids. Organoids derived from Ca and CCLM were cultured in 6-well plates and treated with CRYAB (5 µg/mL), CRYAB + UA (45 µM), and DMSO (control) for 96 h. Brightfield images were taken to observe organoid growth, and immunofluorescence staining was performed for further analysis. The data are shown as the mean ± SD,n = 4. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. Brightfield images (scale bar: 400 μm); immunofluorescence images (scale bar: 200 μm). F Transmission electron microscopy of organoids. Following treatment as in panel E, organoids were fixed and analyzed by transmission electron microscopy (TEM) to visualize the intercellular gaps, highlighted by orange arrows, and vacuolization, marked by green arrows. Scale bars: 5. 0 μm, 2. 0 μm, and 500 nm. G Immunohistochemical staining of organoids. Immunohistochemical staining of organoids treated as in panel E was performed to assess the expression of key biomarkers. Scale bars: 100 μm. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. Brightfield images (scale bar: 400 μm); immunofluorescence images (scale bar: 200 μm). H KEGG pathway enrichment analysis. KEGG pathway enrichment analysis was conducted on upregulated and downregulated genes from the CRYAB + UA group, visualizing the enriched pathways associated with cancer progression. Bar plot shows the relative enrichment percentage of top dysregulated pathways (p < 0. 05)

Next, we tested whether UA antagonizes the pro-tumorigenic effects of CRYAB in organoids. Without UA, CRYAB increased cell viability in a dose-dependent manner, but UA reduced cell viability significantly with higher drug concentrations (Fig. 3C and Extended Data Fig. 3B). Combination treatment resulted in a synergy value of -34. 17 (p < 0. 00001), indicating a strong antagonistic effect (Fig. 3D and Extended Data Fig. 3D).

CRYAB treatment promoted peripheral proliferation in organoids, creating voids between cells, but CRYAB + UA treatment induced organoid death and vacuolization. Immunofluorescence showed higher Ki67 and Vimentin fluorescence in the CRYAB group, which decreased significantly with CRYAB + UA treatment (Fig. 3E). Transmission electron microscopy revealed that CRYAB widened the gaps between organoid connections, while CRYAB + UA treatment caused organoid death and vacuolization, consistent with previous observations (Fig. 3F). IHC analysis showed that CRYAB treatment increased β-catenin, VEGFA, FGFR1, and CD44 staining, while CRYAB + UA treatment significantly downregulated these markers (Fig. 3G).

Transcriptomic analysis revealed that CRYAB + UA treatment downregulated 883 proteins and upregulated 1,140 proteins (Extended Data Fig. 3E). Upregulated proteins were involved in cornified envelope formation, Hippo signaling, cellular senescence, and p53 signaling, while downregulated proteins were associated with EGFR resistance, TNF signaling, and cytokine interactions (Fig. 3H).

Key differential protein analysis identified PRKDC, which was upregulated in CCLM/Ca samples and downregulated in CRYAB-UA samples. Proteins involved in cell migration were upregulated in CCLM/Ca and downregulated in CRYAB-UA, while DNA repair proteins showed the opposite trend (p < 0. 05) (Extended Data Fig. 3F and G). STRING and GO analysis revealed significant enrichment in the collagen-containing extracellular matrix (Extended Data Fig. 3H and I). Finally, Transwell migration assays demonstrated that CRYAB promoted cell invasion, but CRYAB + UA treatment completely reversed this effect (Extended Data Fig. 3J).

UA reverses CRYAB-induced activation of hepatic fibroblasts in hypoxic co-culture of colon cancer organoids and HLiF Cells

We analyzed single-cell RNA-seq data to assess CRYAB expression in colon cancer tissues, revealing its predominant expression in Enteric glial cells, Myofibroblasts, and Fibroblasts (Fig. 4A). TCGA data confirmed higher CRYAB expression in Fibroblasts and Myeloid cells in CCLM compared to primary Ca (p < 0. 05) (Fig. 4B and C).

Fig. 4 — Identification of the Regulatory Effects of CRYAB and UA on Colorectal Cancer Metastasis in Co-Culture Systems. A Single-cell analysis. Left: t-SNE plot showing major cell type lineages in CRC patient samples. Right: t-SNE plot highlighting CRYAB expression levels across cell populations, with color intensity reflecting mean expression. Data of CRYAB in COAD patients were analyzed using Home-for-Researchers (https://www.home-for-researchers.com/#/). B The expression of CRYAB in single cells from CRC and CCLM samples was analyzed, providing a comparative overview. C Statistical analysis of CRYAB expression across different cell types in CRC (n = 235) and CCLM (n = 266) patients. Statistical analysis of CRYAB expression in different cell types from CRC and CCLM samples. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. D Cytokine secretion profiles of Ca organoids under CRYAB/UA treatment and aerobic/hypoxic conditions. Concentrations of IL-6, IL-8, and TNF-β were measured by ELISA in the mixed culture medium from the Transwell co-culture system comprising Ca organoids (upper chamber) and HLiF cells (lower chamber). Groups included: NC (negative control, untreated), Ca-P (PBS solvent control), Ca-C (recombinant CRYAB treatment), and Ca-CU (combined treatment with recombinant CRYAB and usnic acid (UA)). All groups were cultured for 24 h under either normoxic or hypoxic conditions. Cytokine levels were respectively normalized relative to the normoxic and hypoxic NC groups. Data are expressed as mean ± SD; statistical significance was determined using one-way analysis of variance (ANOVA) combined with Tukey’s post-hoc test (ns, not significant; *p < 0. 05, **p < 0. 01, ***p < 0. 001). E Venn analysis. Organoids (Ca) and HLiF cells from the lower and upper chambers of the co-culture were subjected to transcriptomic sequencing. Differential genes were identified by comparing PBS and normoxic control groups. Genes with p < 0. 05 and |log₂FC| > 1. 2 were selected for further analysis. A Venn diagram was generated using the CNS tool to illustrate the overlap of differential genes across different experimental groups. F Protein-protein interaction network of hub genes. The interaction network of hub genes derived from the differential gene set in panel E was constructed using STRING for protein-protein interaction analysis and visualized in Cytoscape. Red circles represent the network of genes under CRYAB treatment, purple indicates the hypoxia-induced gene network, and green corresponds to the network of genes associated with HLiF polarization. G A chord diagram was created to show the correlation between the differential gene sets under CRYAB treatment and those induced by hypoxia. H Analysis of Differential gene Expression After CRYAB and CRYAB + UA Treatment. Differential gene expression levels in cells treated with CRYAB alone or in combination with UA were normalized using FPKM, with PBS as a control. I Fold-change analysis of mRNA level. HLiF cells from the lower chambers under hypoxic conditions in the PBS, CRYAB, and CRYAB + UA groups were harvested for RNA extraction. RT-qPCR was performed to measure the mRNA levels of CA9, CXCL6, EGLN3, and IL-1 A. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant

A co-culture experiment with Ca organoids and hepatic fibroblasts under hypoxia showed that exogenous CRYAB significantly induced cytokine secretion (IL-6, IL-8, TNF-β), which was reversed by UA treatment. In normoxic conditions, CRYAB mildly promoted cytokine secretion, but this was also inhibited by UA (Extended Data Fig. 4A and Fig. 4D).

We performed weighted gene co-expression network analysis (WGCNA) to identify protein modules associated with CRYAB. KEGG analysis showed that under hypoxia, CRYAB-regulated proteins were enriched in “Metabolism of Lipids,” whereas under normoxia, they were associated with “Metabolism of Proteins. " Hypoxia-induced proteins were primarily involved in the “Cell Cycle,” while normoxic proteins mainly impacted “Protein Metabolism” (Extended Data Fig. 4B).

Venn analysis identified 63 overlapping differential proteins under hypoxia and 27 under normoxia in CRYAB-treated colon organoids and HLiF cells (Fig. 4E, left). Further analysis revealed 183 hypoxia-induced proteins in CRYAB-treated samples and 84 in the PBS control (Fig. 4E, right).

A protein-protein interaction (PPI) network constructed with STRING and Cytoscape revealed stronger interactions under hypoxia, particularly with key proteins in HLiF activation pathways (Fig. 4F). Correlation analysis showed strong relationships between proteins like RENBP, EFNA1, and ANXA3 (Extended Data Fig. 4C and S4D).

Heatmap analysis confirmed that CRYAB upregulated tumor metastasis-related proteins (FAP, FN1, VEGFA, CD44, CXCL6) in HLiF cells under hypoxia (Extended Data Fig. 4J). UA treatment reversed CRYAB-induced upregulation of proteins including EGLN3, IL1A, CA9, CD44, FN1, and FAP (Fig. 4H). RT-qPCR validated that CRYAB treatment upregulated CA9, CXCL6, EGLN3, and IL-1 A expression, which was reversed by UA (Fig. 4I). These findings demonstrate CRYAB’s critical role in cytokine secretion and protein expression in the tumor microenvironment, with UA effectively reversing CRYAB’s effects under hypoxic conditions.

CRYAB phosphorylation at S59 drives tumorigenic signaling and enhances cancer cell proliferation and migration

To assess the role of CRYAB phosphorylation in tumorigenesis, we generated a S59 point mutation (A59) in CRYAB and constructed expression vectors for both wild-type CRYAB (CRYAB^wt) and the phosphomutant CRYAB^S59A. Stable SW480 cell lines expressing CRYAB^wt or CRYAB^S59A were used for immunoprecipitation-mass spectrometry (IP-MS), proteomic, and metabolomic analyses (Fig. 5A). Western blotting confirmed reduced phosphorylation in CRYAB^S59A cells compared to CRYAB^wt, consistent with the effects of UA treatment (Fig. 5B).

Immunofluorescence imaging revealed that CRYAB^wt localized to the cytoplasm, while CRYAB^S59A was confined to the nucleus (Fig. 5C). Proliferation and migration assays showed enhanced migration and proliferation in CRYAB^wt-overexpressing cells, while CRYAB^S59A overexpression significantly reduced these processes (Fig. 5D, E).

Proteomic and metabolomic profiling indicated that CRYABS59A upregulated 167 proteins and downregulated 293, while 249 metabolites were upregulated and 62 downregulated compared to CRYAB^wt. Both groups shared 251 interaction proteins (Fig. 5F, G). KEGG pathway analysis revealed that CRYAB^S59A upregulated “Cell adhesion molecules” and “Glutathione metabolism” pathways, while CRYAB^wt enhanced pathways involved in tumor progression, including VEGF, TNF, and WNT signaling. CRYAB^S59A also upregulated CDKN2A in the P53 pathway and BAK1 and CASP7 in apoptosis (Fig. 5H). VEGF, a key regulator of angiogenesis and tumor progression, was found to be positively regulated by CRYAB, which supports previous studies linking CRYAB to tumor angiogenesis through VEGF modulation [30, 31]. These results suggest that CRYAB phosphorylation activates tumorigenic pathways such as VEGF, TNF, WNT, and PI3K/Akt, promoting tumor cell survival, proliferation, migration, and angiogenesis. In contrast, the unphosphorylated CRYAB^S59A inhibits these pathways, promoting apoptosis.

Further analysis revealed that CRYAB^S59A interaction proteins were enriched in RNA degradation, RNA transport, and nucleotide excision repair, while CRYAB^wt interactions were involved in metabolic and protein processing pathways (Fig. 5I and Extended Data Fig. 5B). Venn analysis identified 84 shared differentially expressed proteins(DEPs) between CRYAB^wt and CRYAB^S59A, with 33 showing significant differences (Extended Data Fig. 5C). Functional analysis indicated that CRYAB^wt-unique proteins were involved in amino acid transport, energy production, and replication, whereas CRYAB^S59A-unique proteins were related to RNA processing and translation (Extended Data Fig. 5D).

In conclusion, phosphorylation of CRYAB is crucial for regulating key tumorigenic pathways, offering potential targets for cancer therapy.

Multi-omics analysis reveals key downstream pathways regulated by UA treatment or CRYAB phosphorylation inhibition in tumorigenesis

Our previous work demonstrated that CRYAB’s pro-tumorigenic effects are phosphorylation-dependent at S59, and that UA inhibits this phosphorylation. To explore the downstream pathways, we conducted multi-omics analyses (proteomics, metabolomics, and IP-MS) on CRYAB^wt and CRYAB^S59A cells under UA treatment (Fig. 6A). Venn diagram analysis identified 176 proteins unique to CRYAB^S59A, 230 unique to CRYAB^wt, and 246 specific to CRYAB^wt+UA. Additionally, 101 common differentially expressed proteins were found between CRYAB^wt+UA vs. CRYAB^wt and CRYAB^wt+UA vs. CRYAB^S59A comparisons. IP-MS revealed 178 unique differential interaction proteins in CRYAB^wt and 130 common interaction proteins between CRYAB^wt+UA and CRYAB^wt (A59_UA_inter) (Fig. 6B).

Fig. 6 — Multi-Omics analysis of shared downstream regulatory pathways between UA treatment and CRYAB phosphorylation site mutations. A Experimental workflow. A schematic of the experimental workflow for multi-omics analysis of shared downstream regulatory pathways. B Venn analysis of proteomics and IP-MS datasets. C KEGG and reactome pathway enrichment of A59_UA_inter and CRYAB^wt_uniq datasets. D Molecular function of A59_UA_inter dataset. E Integrated analysis of proteomics, IP-MS, and metabolomics data to construct an interaction network

Functional analysis showed that “Energy production and conversion” and “Coenzyme transport” were enriched in the CRYAB^wt group, while “Fatty acid oxidation” was prominent in the CRYAB^wt_uniq interaction proteins. The A59_UA_inter proteins were associated with “Gene expression” and “Ribonucleoprotein complex biogenesis” (Extended Data Fig. 6A-C). KEGG and Reactome analyses linked A59_UA_inter and CRYAB^wt_uniq to pathways like ferroptosis, glutathione, lipid metabolism, and RNA metabolism. Specifically, CRYAB^wt_uniq was enriched in “Proteoglycans in cancer” and “Metabolism of lipids” (Fig. 6C), while the A59_UA_inter set was primarily involved in ferroptosis and glutathione metabolism (Fig. 6D).

We integrated differential proteins, interaction data, and metabolites from CRYAB^wt_uniq and A59_UA_inter to construct a network. CRYAB^wt_uniq was mainly involved in lipid metabolism, while A59_UA_inter was associated with RNA metabolism (Fig. 6E). IP-MS showed that CRYAB^wt interacted with NEDD4L, a protein absent in both CRYAB^wt+UA and CRYAB^S59A groups. Additionally, CRYAB^wt upregulated GPX3 and GPX4, while ferroptosis-related metabolites (e.g., 5-HETE, 16-hydroxyhexadecanoic acid) were elevated in CRYAB^wt+UA and CRYAB^S59A groups (Fig. 6F).

In conclusion, these findings provide deeper insights into the phosphorylation-dependent regulation of CRYAB in tumorigenesis and highlight how UA treatment alters metabolic networks, particularly those related to ferroptosis and glutathione metabolism. These results emphasize the critical role of CRYAB phosphorylation in regulating tumor cell survival and metastasis.

CRYAB-NEDD4L interaction prevents ferroptosis, and UA treatment or S59 mutation impairs this mechanism

Previous studies have indicated that NEDD4L promotes GPX4 degradation, triggering ferroptosis [32], suggesting that CRYAB’s phosphorylation at S59 protects against ferroptosis by preventing GPX4 degradation. We hypothesize that UA treatment induces ferroptosis by inhibiting CRYAB phosphorylation.

To test this, we performed flow cytometry analysis with BODIPY-C11 and DCFH-DA indicators. CRYAB^wt overexpression significantly reduced ferroptosis indicators compared to the positive control (p < 0. 0001). However, overexpression of CRYAB^S59A or CRYAB^wt with UA treatment significantly increased ferroptosis markers (p < 0. 05) (Fig. 7A and B). We also assessed mitochondrial morphology under RSL3-induced ferroptosis. While CRYAB^wt overexpression maintained normal mitochondria, CRYAB^wt+UA and CRYAB^S59A showed ferroptotic changes similar to the control (Fig. 7C).

CRYAB modulates ferroptosis with a dual function depending on its phosphorylation. A Flow cytometry results. DCFH-DA staining for analysis of cytoplasmic ROS in SW480 cells after transfection CRYAB^wt and CRYAB^S59Aexpression vectors or treated with UA(45µM) for 48 h. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. B Flow cytometry results. BODIPY-C11 staining for analysis of lipid peroxidation in SW480 cells. Cell processing as shown in Fig. 7A. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. C Transmission electron microscopy (TEM) results. SW480 cells in logarithmic growth phase were seeded into 6-well plates. At approximately 80% confluence, cells were transfected with CRYAB^wt or CRYAB^S59A plasmids (6 µg/well). After 48 h, the CRYAB^wt group received 45 µM UA or 1µM RSL3 (positive control). After 48 h of treatment, cells were collected for TEM analysis. Scaale bars represent 200 nm and 500 nm. D Western blot of GPX4 protein levels. SW480 cells treated as described in panel (C) were subjected to Western blotting to measure GPX4 protein levels. β-actin was used as an internal control. SW480 cells were treated with 1µM RSL3 for 48 h to induce ferroptosis which was defined as the positive control. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant. E Co-IP validation of CRYAB interaction with NEDD4L and GPX4. Co-immunoprecipitation (Co-IP) analysis was used to validate the interaction of CRYAB with NEDD4L, and GPX4 with NEDD4L. SW480 cells were co-transfected with HA. CRYAB^wt + Flag. NEDD4L or HA. CRYAB^S59A + Flag. NEDD4L plasmids (6 µg/plasmid/well). After 48 h, the CRYAB^wt + Flag. NEDD4L group was treated with 45 µM UA. Cells were then harvested for IP-WB analysis, n = 3. F CHX experiment to track GPX4 protein stability. SW480 cells were transfected with CRYAB^wt or CRYAB^S59A plasmids (6 µg/well) and treated with either 45 µM UA or 1µM RSL3 (positive control) for 48 h. After treatment, cells were cultured with fresh media containing CHX. Protein samples were collected at 0 h, 2 h, 4 h, and 8 h post-treatment to track the stability of GPX4 through Western blotting. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001; ns, not significant. G Scatter plot illustrating the relationship between GPX4 and CRYAB expression levels. H Flow cytometry results. BODIPY-C11 staining for analysis of lipid peroxidation in SW480 cells. The data are shown as the mean ± SD,n = 3. Statistical analysis was performed via one-way ANOVA: *p < 0. 05; **p < 0. 01;***p < 0. 001;****p < 0. 0001; ns, not significant

Further, we examined GPX4 expression, a key ferroptosis regulator. CRYAB^wt overexpression significantly elevated GPX4 levels compared to the ferroptosis control, while both CRYAB^wt+UA and CRYAB^S59A groups showed significantly lower GPX4 levels (Fig. 7D). indicating that CRYAB suppresses ferroptosis, which is disrupted by S59 mutation and UA treatment.

We confirmed the CRYAB-NEDD4L interaction via co-immunoprecipitation (CO-IP). Only in the CRYAB^wt group was the CRYAB-NEDD4L interaction detected, suggesting phosphorylation at S59 is crucial (Fig. 7E). Our CHX experiment showed that CRYAB^wt overexpression delayed GPX4 degradation, enhancing its stability, while both UA treatment and the S59 mutation reversed this effect (Fig. 7F). Additionally, TIMER analysis of TCGA-COAD confirmed a positive correlation between CRYAB and GPX4 (p < 0. 05) (Fig. 7G).

Lastly, NEDD4L overexpression increased DCFH-DA levels 3. 2-fold (p < 0. 0001). In the CRYAB^S59A and NEDD4L co-transfection group, DCFH-DA levels were significantly higher than in the CRYAB^wt and NEDD4L co-transfection group (FC = 1. 88, p < 0. 0001) (Fig. 7H). These results suggest that CRYAB, through its interaction with NEDD4L, prevents ferroptosis in a phosphorylation-dependent manner. UA treatment induces ferroptosis by inhibiting CRYAB S59 phosphorylation, contributing to tumor progression.

To investigate themolecular mechanism of CRYAB-mediatedferroptosis suppression, we first mapped its interaction with NEDD4L through computational modeling. The predicted complex structure revealed key hydrogen-bonding and hydrophobic interactions between CRYAB and NEDD4L (Extended Data Fig. 7A). This model guided our subsequent functional validation.

We confirmed efficient NEDD4L knockdown in SW480 cells using three siRNA constructs (Extended Data Fig. 7B). Cycloheximide chase experiments showed that CRYAB overexpression slowed GPX4 degradation in control cells (siNC + pCRYAB), but NEDD4L knockdown completely abolished this protective effect, reducingGPX4 half-life (Extended Data Fig. 7C). These data demonstrate that CRYAB requires NEDD4L to stabilize GPX4.

We then assessed the impact on cellular antioxidant capacity by measuring SOD activity (Extended Data Fig. 7D). Reintroducing NEDD4L into CRYAB-overexpressing cells increased SOD activity (p < 0.0001), reflecting enhanced antioxidant defense. Notably, cells expressing the CRYAB^S59A mutant failed to restore SOD activity despite NEDD4L overexpression, paralleling the loss of GPX4 stabilization. These results confirm that CRYAB Ser59 phosphorylation is crucial for its interaction with NEDD4L, sustaining GPX4 stability and antioxidant function, thus inhibiting ferroptosis in colorectal cancer cells.

Discussion

UA is a natural compound extracted from lichen, with significant anti-cancer properties across a variety of malignancies [33–35]. This study uncovers a novel mechanism by which UA targets CRYAB, a protein highly expressed in CCLM, to modulate tumor progression and metastasis. Through an integrative approach combining proteomics, TCGA, and CTD analyses, we identified CRYAB as a key therapeutic target whose phosphorylation at Ser59 (S59) plays a crucial role in regulating the pro-tumorigenic effects in CCLM. Our findings provide compelling evidence that CRYAB’s phosphorylation state at S59 governs its interaction with NEDD4L and the stability of GPX4, which collectively protect cancer cells from ferroptosis. This interaction is disrupted by UA, leading to the induction of ferroptosis and suppression of tumor progression.

CRYAB primarily functions as a molecular chaperone, preventing protein aggregation and storing misfolded proteins in non-native, soluble intermediates [36, 37]. Proteomic analysis revealed elevated CRYAB levels in CCLM tissues, correlating with CRC progression and poor prognosis. Previous studies suggest that CRYAB promotes CRC progression through ERK signaling and epithelial-to-mesenchymal transition (EMT), making it a potential prognostic marker for CRC [38–40]. Our study confirms CRYAB upregulation and increased phosphorylation in CCLM tissues, highlighting its role in tumor progression. The phosphorylation of CRYAB plays a crucial role in multiple cellular processes, such as stress-induced apoptosis, cell differentiation, cell cycle regulation, and angiogenesis [41]. Studies have shown that LBH inhibits the phosphorylation of CRYAB to prevent EMT progression and metastasis in nasopharyngeal carcinoma [42]. Under hypoxic conditions, S59 phosphorylation of CRYAB promotes the transport of VEGF to the endoplasmic reticulum and inhibits its degradation, ultimately leading to upregulated VEGF secretion, thereby promoting tumor angiogenesis [31, 43]. This aligns with our hypothesis that CRYAB phosphorylation, particularly at S59, may be a key determinant of its role in promoting colorectal cancer metastasis.

Despite the pivotal role of CRYAB phosphorylation at S59 in tumor cell survival under hypoxic conditions, we observed lower phosphorylation levels in non-metastatic colorectal cancer patients and their organoids. Mutation of the S59 site (S59A) not only inhibited CRYAB’s pro-tumorigenic effects but also suppressed tumor proliferation, indicating that CRYAB phosphorylation is functionally dependent in tumor progression. This suggests that CRYAB phosphorylation is a key determinant of tumor invasion and metastasis, though the downstream signaling pathways remain unclear. Under stress and ischemia-reperfusion conditions, phosphorylated CRYAB (especially at S59) preferentially localizes to the cytoplasm or mitochondria, protecting critical organelles from oxidative damage and preventing ferroptosis [44, 45]. Our study supports this, showing that wild-type CRYAB primarily localizes to the cytoplasm, whereas the S59A mutation alters its subcellular distribution, potentially influencing its interaction with different target proteins. We also demonstrate that UA binds to CRYAB, inhibiting its phosphorylation and promoting its nuclear translocation. This suggests that UA modulates CRYAB’s phosphorylation state and subcellular localization, potentially altering its functional interactions. Notably, CRYAB phosphorylation levels were significantly elevated in CCLM tissues and organoids, as well as in hypoxic conditions, suggesting a microenvironment-dependent regulation of CRYAB’s function. Hypoxia is a well-known driver of tumor progression [46, 47], and our data suggest that CRYAB phosphorylation could mediate hypoxia-induced tumor cell proliferation and migration, key features of metastatic behavior.

In addition to elucidating the molecular mechanisms of CRYAB in CRC metastasis, this study highlights the therapeutic potential of UA. Molecular dynamics simulations revealed stable binding between UA and CRYAB, with subsequent inhibition of CRYAB S59 phosphorylation, crucial for its tumorigenic effects. The combination of UA and Sorafenib further enhanced the inhibitory effects on SW480 cells, emphasizing UA’s potential as a complementary agent in cancer therapy. Interestingly, while UA antagonized the pro-tumorigenic effects of CRYAB in organoids, it significantly reduced cell proliferation and induced organoid death, demonstrating a dual effect depending on CRYAB phosphorylation. This underscores the therapeutic potential of targeting CRYAB, especially in combination with agents like Sorafenib.

Our study also uncovers the dual role of CRYAB in ferroptosis regulation. Wild-type CRYAB protects against ferroptosis by stabilizing GPX4, while phosphorylation-deficient CRYAB (S59A) induces ferroptosis, shifting the balance toward cell death. This finding suggests that CRYAB phosphorylation is a critical modulator of ferroptosis in CRC cells. Ferroptosis, an iron-dependent form of programmed cell death, is distinct from apoptosis and necrosis, being primarily induced by oxidative stress and lipid peroxidation [48, 49]. Previous studies have shown that CRYAB stabilizes β-catenin and inhibits TRIM55-mediated CRYAB degradation, suppressing ferroptosis and promoting CRC progression [50]. Furthermore, CRYAB regulates iron uptake and GPX4 abundance, playing a role in ferroptosis regulation [51]. Studies have shown that NEDD4L mediates ferroptosis in intestinal epithelial cells to suppress inflammatory bowel disease and colorectal tumorigenesis [52]. NEDD4L activates ferroptosis by binding to GPX4 and mediating its ubiquitination degradation [53, 54]. Our findings support these observations, showing that CRYAB^wt inhibits the interaction between NEDD4L and GPX4, reducing ferroptosis. UA, by binding to CRYAB, prevents its phosphorylation, weakening CRYAB’s inhibition of ferroptosis and promoting ferroptosis in CRC cells. Other studies have also found that UA can enhance pro-oxidant activity in cancer cells, reduce glutathione, and increase malondialdehyde (MDA) levels, thereby inducing ferroptosis [4, 55]. CRYABwt protects against ferroptosis by stabilizing GPX4, while the dephosphorylation of CRYAB at S59 by UA promotes ferroptosis, shifting the metabolic balance toward cell death. This provides a novel therapeutic angle for targeting CRYAB to induce ferroptosis in tumors resistant to conventional therapies.

These findings offer a novel perspective on the therapeutic application of UA in CRC treatment. UA’s ability to inhibit CRYAB phosphorylation and induce ferroptosis presents a promising strategy for targeting tumors resistant to conventional therapies. Based on the findings from our study, we will continue to investigate and deepen our understanding of the precise molecular mechanisms by which CRYAB phosphorylation regulates ferroptosis and its substrates, while elucidating the differences in these interactions across distinct cell types and tissue environments. This research is crucial for advancing the development and clinical application of CRYAB-targeted therapies.

Conclusions

This study identifies CRYAB phosphorylation at S59 as a critical regulatory switch in colorectal cancer metastasis, providing a novel target for therapeutic intervention. By modulating CRYAB phosphorylation, UA offers a promising strategy to induce ferroptosis and inhibit tumor progression. Our findings open new avenues for the development of targeted therapies that leverage CRYAB’s role in tumor biology, with the potential to improve treatment outcomes in metastatic colorectal cancer. Future studies should focus on validating these mechanisms in vivo, exploring the therapeutic potential of UA in clinical settings, and further dissecting the molecular networks involved in CRYAB-mediated tumor progression.

Materials and methods

Cell culture and clinical samples

Human CRC cell lines SW480 and HT29 were cultured in high-glucose DMEM (Gibco, 11995, USA) supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin (Gibco, 15140, USA). Human primary liver fibroblasts (iCell Bioscience, Shanghai, China) were cultured in liver fibroblast-specific medium. Cells were maintained at 37 °C in a 5% CO₂ incubator, with routine mycoplasma testing and STR profiling for identity verification. For hypoxia simulation, a three-gas incubator (5% CO₂, 1% O₂, 94% N₂) was used. Human colorectal cancer samples, including primary tumors, adjacent normal tissues, and liver metastases, were obtained from the First Affiliated Hospital of Chongqing Medical University, under ethical approval (No. 2025-328-01) with informed consent.

Patient samples and patient-derived organoids (PDOs)

Post-surgery, colorectal cancer tissue samples were obtained from non-metastatic and metastatic primary tumors, as well as paired liver metastasis tissues. The tissues were minced, washed in ice-cold PBS with 1% penicillin-streptomycin-amphotericin B, and digested with a tissue digestion solution (NGH030021) for 60 min at 37 °C. The mixture was filtered through a 100 μm cell filter and resuspended in CRC Organoid Complete Medium (NGH020001), mixed with Matrix Gel (NGH030024). The mixture was dispensed into 6-well plates and incubated at 37 °C for solidification. Organoids were cultured in complete medium (NGH020001, Hangzhou OrganPharma Biotech). Patient-derived colorectal cancer organoids at passage 2–3 were used for all experiments. PDOs culture medium was refreshed every three days. PDOs were observed and photographed at proper times. Often, organoids were passaged every 1–2 weeks.

Co-culture of organoids with human liver fibroblasts (HLiF)

To examine interactions between CRC organoids and human liver fibroblasts, co-culture was performed in Transwell chambers (Corning, USA) with a 0. 40 μm polycarbonate membrane. CRC organoid-forming units (2000–3000 cells) were seeded onto the membrane, and human liver fibroblasts were seeded in the lower chamber. The fibroblasts were pre-conditioned in a 1:1 mixture of their respective media. Two conditions were established: one with recombinant CRYAB protein (PBS), and one with PBS alone, under both normoxic and hypoxic conditions. The hypoxic group was cultured in a three-gas incubator for 12 h, while the normoxic group was maintained in a standard CO₂ incubator. After 48 h, organoid morphology was observed, and both CRC organoids and HLiF supernatants were collected for transcriptomic analysis.

Reagents and antibodies

Primary antibodies used were: anti-phospho-CRYAB (Ser59) (AF3754, Affinity), anti-Ki67 (AF0198, Affinity), anti-Alpha B Crystallin (CRYAB) (P02511, ZenBio), anti-GPX4 (Abcam, ab41787), anti-beta Catenin (ab32572, Abcam), anti-Vimentin (60330-1-Ig, Proteintech), anti-VEGFA (19003-1-AP, Proteintech), anti-CD44 (15675-1-AP, Proteintech), anti-FGFR1 (60325-1-Ig, Proteintech), anti-HA (M1008-1, HuaBio), anti-Flag (HA722780, HuaBio), and anti-β-actin (AF7018, Affinity). Fluorescent secondary antibodies: goat anti-mouse iFluor™ 488 (HA1125, HuaBio); goat anti-rabbit iFluor™ 555 (HA1135, HuaBio); Goat anti-rabbit IgG AF 488 (M21012, Abmart). For Western blotting and immunohistochemistry, horseradish peroxidase (HRP)-conjugated anti-mouse (Invitrogen, 31430) and anti-rabbit (Invitrogen, 31460) secondary antibodies were used. Recombinant human CRYAB protein (C-6His, Novoprotein, C118), and chemicals such as usnic acid (HY-N0656, MedChemExpress), sorafenib (A316727, AmBeed), erastin (HY-15763, MedChemExpress), RSL3 (HY-100218 A, MedChemExpress)and CCK-8 assay kit (CK04, Dojindo) were utilized.

Vector construction and transfection

HA-tagged CRYAB^wt and CRYAB^S59A cDNAs, and Flag-tagged NEDD4L cDNA, were cloned into the pcDNA3. 1/Zeo(+) vector (GenScript Biotech) via XhoI/XbaI, EcoRI/KpnI, and HindIII/XbaI restriction sites. Constructs were validated by sequencing. Transfection was carried out using QuickShuttle Enhanced Reagent (Biodragon), and expression confirmed by Western blotting.

RNA extraction and reverse transcription quantitative PCR (RT-qPCR)

Total RNA was extracted using TRIzol^® reagent (Invitrogen, 15596026), and cDNA synthesized with the RT Reagent Kit with gDNA Eraser (Takara, RR047A). qPCR was performed using SYBR Premix ExTaq II (Takara, RR820A) on a CFX96 Real-Time PCR System (Bio-Rad, USA). Gene expression was normalized to GAPDH or RPLP0, and relative expression was calculated using the 2^−ΔΔCT method. Primer sequences are provided in Table 1.

Table 1.

The primers and oligonucleotides used in this paper

Gene	Primer Sequence (5’→3’)
pcDNA3. 1/Zeo(+) -HA. CRYAB^wt(XhoI/EcoRI)	CCGCTCGAGGCCACC ATGTACCCATACGATGTTCCAGATTACGCT ATGGACATCGCCATCCACCACCCCTGGATCCGCCGCCCCTTCTTTCCTTTCCACTCCCCCAGCCGCCTCTTTGACCAGTTCTTCGGAGAGCACCTGTTGGAGTCTGATCTTTTCCCGACGTCTACTTCCCTGAGTCCCTTCTACCTTCGGCCACCCTCCTTCCTGCGGGCACCCAGCTGGTTTGACACTGGACTCTCAGAGATGCGCCTGGAGAAGGACAGGTTCTCTGTCAACCTGGATGTGAAGCACTTCTCCCCAGAGGAACTCAAAGTTAAGGTGTTGGGAGATGTGATTGAGGTGCATGGAAAACATGAAGAGCGCCAGGATGAACATGGTTTCATCTCCAGGGAGTTCCACAGGAAATACCGGATCCCAGCTGATGTAGACCCTCTCACCATTACTTCATCCCTGTCATCTGATGGGGTCCTCACTGTGAATGGACCAAGGAAACAGGTCTCTGGCCCTGAGCGCACCATTCCCATCACCCGTGAAGAGAAGCCTGCTGTCACCGCAGCCCCCAAGAAATAGTCTAGAGC
pcDNA3. 1/Zeo(+) -HA. CRYAB^S59A	GGGAATTCATG TACCCATACGATGTTCCAGATTACGCT ATGGACATCGCCATCCACCACCCCTGGATCCGCCGCCCCTTCTTTCCTTTCCACTCCCCCAGCCGCCTCTTTGACCAGTTCTTCGGAGAGCACCTGTTGGAGTCTGATCTTTTCCCGACGTCTACTTCCCTGAGTCCCTTCTACCTTCGGCCACCCTCCTTCCTGCGGGCACCCGCCTGGTTTGACACTGGACTCTCAGAGATGCGCCTGGAGAAGGACAGGTTCTCTGTCAACCTGGATGTGAAGCACTTCTCCCCAGAGGAACTCAAAGTTAAGGTGTTGGGAGATGTGATTGAGGTGCATGGAAAACATGAAGAGCGCCAGGATGAACATGGTTTCATCTCCAGGGAGTTCCACAGGAAATACCGGATCCCAGCTGATGTAGACCCTCTCACCATTACTTCATCCCTGTCATCTGATGGGGTCCTCACTGTGAATGGACCAAGGAAACAGGTCTCTGGCCCTGAGCGCACCATTCCCATCACCCGTGAAGAGAAGCCTGCTGTCACCGCAGCCCCCAAGAAATAAGGTACCCC
pcDNA3. 1/Zeo(+) -Flag. NEDD4L(HindIII/XbaI)	CCCAAGCTTGCCACCATG GATTACAAGGATGACGACGATAAGGATTACAAGGATGACGACGATAAGGATTACAAGGATGACGACGATAAGATGGCGACCGGGCTCGGGGAGCCGGTCTATGGACTTTCCGAAGACGAGGGAGAGTCCCGTATTCTCAGAGTAAAAGTTGTTTCTGGAATTGATCTCGCCAAAAAGGACATCTTTGGAGCCAGTGATCCGTATGTGAAACTTTCATTGTACGTAGCGGATGAGAATAGAGAACTTGCTTTGGTCCAGACAAAAACAATTAAAAAGACACTGAACCCAAAATGGAATGAAGAATTTTATTTCAGGGTAAACCCATCTAATCACAGACTCCTATTTGAAGTATTTGACGAAAATAGACTGACACGAGACGACTTCCTGGGCCAGGTGGACGTGCCCCTTAGTCACCTTCCGACAGAAGATCCAACCATGGAGCGACCCTATACATTTAAGGACTTTCTCCTCAGACCAAGAAGTCATAAGTCTCGAGTTAAGGGATTTTTGCGATTGAAAATGGCCTATATGCCAAAAAATGGAGGTCAAGATGAAGAAAACAGTGACCAGAGGGATGACATGGAGCATGGATGGGAAGTTGTTGACTCAAATGACTCGGCTTCTCAGCACCAAGAGGAACTTCCTCCTCCTCCTCTGCCTCCCGGGTGGGAAGAAAAAGTGGACAATTTAGGCCGAACTTACTATGTCAACCACAACAACCGGACCACTCAGTGGCACAGACCAAGCCTGATGGACGTGTCCTCGGAGTCGGACAATAACATCAGACAGATCAACCAGGAGGCAGCACACCGGCGCTTCCGCTCCCGCAGGCACATCAGCGAAGACTTGGAGCCCGAGCCCTCGGAGGGCGGGGATGTCCCCGAGCCTTGGGAGACCATTTCAGAGGAAGTGAATATCGCTGGAGACTCTCTCGGTCTGGCTCTGCCCCCACCACCGGCCTCCCCAGGATCTCGGACCAGCCCTCAGGAGCTGTCAGAGGAACTAAGCAGAAGGCTTCAGATCACTCCAGACTCCAATGGGGAACAGTTCAGCTCTTTGATTCAAAGAGAACCCTCCTCAAGGTTGAGGTCATGCAGTGTCACCGACGCAGTTGCAGAACAGGGCCATCTACCACCGCCCAGTGCCCCAGCTGGGAGAGCGCGTTCATCAACTGTCACGGGTGGTGAGGAACCAACGCCATCAGTGGCCTATGTACATACCACGCCGGGTCTGCCTTCAGGCTGGGAAGAAAGAAAAGATGCTAAGGGGCGCACATACTATGTCAATCATAACAATCGAACCACAACTTGGACTCGACCTATCATGCAGCTTGCAGAAGATGGTGCGTCCGGATCAGCCACAAACAGTAACAACCATCTAATCGAGCCTCAGATCCGCCGGCCTCGTAGCCTCAGCTCGCCAACAGTAACTTTATCTGCCCCGCTGGAGGGTGCCAAGGACTCACCCGTACGTCGGGCTGTGAAAGACACCCTTTCCAACCCACAGTCCCCACAGCCATCACCTTACAACTCCCCCAAACCACAACACAAAGTCACACAGAGCTTCTTGCCACCCGGCTGGGAAATGAGGATAGCGCCAAACGGCCGGCCCTTCTTCATTGATCATAACACAAAGACTACAACCTGGGAAGATCCACGTTTGAAATTTCCAGTACATATGCGGTCAAAGACATCTTTAAACCCCAATGACCTTGGCCCCCTTCCTCCTGGCTGGGAAGAAAGAATTCACTTGGATGGCCGAACGTTTTATATTGATCATAATAGCAAAATTACTCAGTGGGAAGACCCAAGACTGCAGAACCCAGCTATTACTGGTCCGGCTGTCCCTTACTCCAGAGAATTTAAGCAGAAATATGACTACTTCAGGAAGAAATTAAAGAAACCTGCTGATATCCCCAATAGGTTTGAAATGAAACTTCACAGAAATAACATATTTGAAGAGTCCTATCGGAGAATTATGTCCGTGAAAAGACCAGATGTCCTAAAAGCTAGACTGTGGATTGAGTTTGAATCAGAGAAAGGTCTTGACTATGGGGGTGTGGCCAGAGAATGGTTCTTCTTACTGTCCAAAGAGATGTTCAACCCCTACTACGGCCTCTTTGAGTACTCTGCCACGGACAACTACACCCTTCAGATCAACCCTAATTCAGGCCTCTGTAATGAGGATCATTTGTCCTACTTCACTTTTATTGGAAGAGTTGCTGGTCTGGCCGTATTTCATGGGAAGCTCTTAGATGGTTTCTTCATTAGACCATTTTACAAGATGATGTTGGGAAAGCAGATAACCCTGAATGACATGGAATCTGTGGATAGTGAATATTACAACTCTTTGAAATGGATCCTGGAGAATGACCCTACTGAGCTGGACCTCATGTTCTGCATAGACGAAGAAAACTTTGGACAGACATATCAAGTGGATTTGAAGCCCAATGGGTCAGAAATAATGGTCACAAATGAAAACAAAAGGGAATATATCGACTTAGTCATCCAGTGGAGATTTGTGAACAGGGTCCAGAAGCAGATGAACGCCTTCTTGGAGGGATTCACAGAACTACTTCCTATTGATTTGATTAAAATTTTTGATGAAAATGAGCTGGAGTTGCTCATGTGCGGCCTCGGTGATGTGGATGTGAATGACTGGAGACAGCATTCTATTTACAAGAACGGCTACTGCCCAAACCACCCCGTCATTCAGTGGTTCTGGAAGGCTGTGCTACTCATGGACGCCGAAAAGCGTATCCGGTTACTGCAGTTTGTCACAGGGACATCGCGAGTACCTATGAATGGATTTGCCGAACTTTATGGTTCCAATGGTCCTCAGCTGTTTACAATAGAGCAATGGGGCAGTCCTGAGAAACTGCCCAGAGCTCACACATGCTTTAATCGCCTTGACTTACCTCCATATGAAACCTTTGAAGATTTACGAGAGAAACTTCTCATGGCCGTGGAAAATGCTCAAGGATTTGAAGGGGTGGATTAATCTAGAGCA
qGAPDH-F	GTATGACAACGAATTTGGCTACAG
qGAPDH-R	TGAGGGTCTCTCTCTTCCTCTTGT
qCRYAB-F	TTCTTCGGAGAGCACCTGTT
qCRYAB-R	CGCATCTCTGAGAGTCCAGT
qCA9-F	CTTCAGCCGCTACTTCCAATA
qCA9-R	CTCAATCACTCGCCCATTCA
qRPLP0-F	GCAGCATCTACAACCCTGAA
qRPLP0-R	GCAGATGGATCAGCCAAGAA
qCXCL6-F	GGTCCTTCGGGCTCCTTGTG
qCXCL6-R	TTCCACCTTGGAGCACTGCG
qIL-1A-F	GGCCCACTCCATGAAGGCTG
qIL-1A-R	GTCATTGGCGATGGCCTCCA
qEGLN-3-F	GCCCTGGAGTACATCGTGCC
qEGLN-3-R	TGGTTGCGTAAGAGGGCTGC
H23327-siNEDD4L-1-sense	CAUGUUCUGCAUAGACGAA(dT)(dT)
H23327-siNEDD4L-1-antisense	UUCGUCUAUGCAGAACAUG(dT)(dT)
H23327-siNEDD4L-2-sense	GUACGUAGCGGAUGAGAAU(dT)(dT)
H23327-siNEDD4L-2-antisense	AUUCUCAUCCGCUACGUAC(dT)(dT)
H23327-siNEDD4L-3-sense	CAUAUGCGGUCAAAGACAU(dT)(dT)
H23327-siNEDD4L-3-antisense	AUGUCUUUGACCGCAUAUG(dT)(dT)

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The table lists the full-length plasmid construction primers (including wild-type and mutant CRYAB, NEDD4L tagged vectors), qRT-PCR detection primers for target genes and internal reference genes, and siRNA sequences for NEDD4L gene silencing, with all sequences listed in the 5′→3′ direction

Western blot analysis

Total protein was extracted from colorectal cancer tissues, adjacent normal tissues, colorectal cancer liver metastasis tissues, SW480 cells, and intestinal organoids using RIPA lysis buffer containing 1% PMSF and 1% phosphatase inhibitor (Beyotime, Jiangsu, China) on ice. Cytoplasmic and nuclear proteins were separated using the Nuclear/Cytoplasmic Fractionation Kit (Beyotime). Protein concentration was determined using the BCA Assay Kit (Thermo Fisher). Protein was separated by SDS-PAGE and transferred to a PVDF membrane(0. 22 μm/0. 45 μm, Millipore, Bedford, MA, USA). The membrane was blocked in QuickBlock™ Western Blocking Solution(Beyotime) for 15 min, incubated with primary antibody overnight at 4 °C, and then with HRP-conjugated secondary antibody for 1 h at room temperature. Chemiluminescent detection was performed with UltraSensitive ECL Reagent (Bio-Rad), and band quantification was done using ImageJ.

Enzyme-linked immunosorbent assay (ELISA)

To assess oxidative stress levels, SOD levels were measured using a kit (Beyotime Biotechnology) in accordance with the manufacturer’s instructions.For cytokine analysis, Ca organoids were seeded in the upper chamber of a 24-well Transwell plate (pore size 0. 4 μm), and HLiF cells were seeded in the lower chamber. Recombinant CRYAB (5 µg/mL) and/or UA (45µM) were added to the upper chamber, and the culture was incubated for 24 h under either normoxic or hypoxic conditions. After incubation, collect and mix the culture media from the upper and lower chambers.For SOD analysis, using an ELISA kit Determine the concentrations of IL-6, IL-8, and TNF-β using an ELISA kit (NeoBioscience) according to the manufacturer’s instructions, with absorbance read at 450 nm using a BioTek Synergy H1 Multiplexer (Agilent Technologies)and normalize the results against the respective normoxic and hypoxic NC groups.

Immunoprecipitation (IP) and LC-MS/MS analysis

SW480 cells were transfected with CRYAB^wt and CRYAB^S59A plasmids and treated with usnic acid (UA) at 45 µM for 48 h. Cells were lysed in IP buffer (Beyotime), incubated with HA or IgG antibody overnight, and immunoprecipitated with Protein A/G magnetic beads (MedChemExpress). Immunoprecipitates were analyzed by Western blot and LC-MS/MS (Tsingke). Protein identification was performed using MaxQuant software, with modifications for N-acetylaspartate, phosphorylation, and ubiquitination. A protein interaction network was generated using STRING and visualized with Cytoscape. The IP-MS analysis and combined proteomic analysis procedures were conducted in accordance with our previous studies [56].

Cellular thermal shift assay (CETSA)

CETSA was performed on CRYAB-expressing SW480 cells treated with DMSO (control) or UA for 48 h. Cells were harvested, washed, and resuspended in PBS, with concentrations adjusted to 2 × 10⁷ cells/ml. Protease and phosphatase inhibitors were added, and cells were incubated at different temperatures for 3 min, followed by cooling and three freeze-thaw cycles. Lysates were centrifuged, and the supernatant was analyzed by Western blotting.

Protein stability assay

Protein stability was evaluated based on our previously described method [57]. Briefly, CRYAB^wt and CRYAB^S59A cells were pretreated with 45 µM UA for 48 h, followed by incubation with 100 µg/mL cycloheximide (CHX) to block protein synthesis. Cells were harvested at 0, 2, 4, and 8 h after CHX treatment, and total protein was extracted. GPX4 expression was detected by Western blot analysis and quantified relative to β-actin.

Hematoxylin&Eosin (H&E) and immunohistochemistry (IHC) staining

Colorectal cancer organoids, tissues, and liver metastases were fixed, embedded, and sectioned. For H&E staining, sections were stained with hematoxylin for 3 min, eosin for 3 min, and mounted with coverslips. IHC involved antigen retrieval, blocking, primary antibody incubation, followed by HRP-conjugated secondary antibody and DAB staining. Sections were counterstained with hematoxylin, dehydrated, and mounted.

Immunofluorescence

CRYAB^wt and CRYAB^S59A vectors were used to transfect SW480 cells. For hypoxia treatment, CRYAB recombinant protein was added after 12 h of culture. Cells were fixed, permeabilized, and incubated with primary antibodies, followed by fluorescent secondary antibodies. Nuclei were counterstained with DAPI, and images were acquired using a laser confocal microscope.

Cell viability assay

Cell viability was assessed using CCK-8 assay. Cells were seeded into 96-well plates and treated with various drugs or recombinant proteins for 48 h. Viability was determined by measuring OD at 450 nm, and IC50 values were calculated using GraphPad Prism 8. 0.

Wound healing assay

Cells were seeded in 6-well plates and treated under normoxic/hypoxic conditions for 48 h. Wound closure was observed and captured at 0 and 48 h using an inverted microscope. ImageJ was used to quantify the cell migration.

Clonogenic assay

Transfected cells were seeded in 6-well plates (1000 cells/well) and cultured for 14 days. Colonies were fixed, stained with crystal violet, and imaged. Colony formation was quantified using ImageJ.

Transwell assay

SW480 cells transfected with recombinant CRYAB protein were serum-starved in DMEM for 24 h, followed by treatment with drugs. A 200 µL cell suspension (5 × 10⁵ cells/mL) was added to the upper chamber of a 24-well plate, with 600 µL DMEM (20% FBS) in the lower chamber. After 48 h, non-migrated cells were removed, and migrated cells were fixed, stained with crystal violet, and counted from three random fields of view.

Intracellular ROS production and lipid peroxidation assay

Cells were treated and stained with 10 µM DCFH-DA for ROS detection or 2 µM BODIPY^™ 581/591 C11 for lipid peroxidation. After incubation, cells were washed, resuspended, and analyzed by flow cytometry (CytoFLEX LX, Beckman Coulter), with data processed using FlowJo software. For BODIPY C11and DCFH-DA fluorescence assays, SW480 cells were treated with LpoUp and Rosup (positive control reagents provided in commercial kits), respectively, strictly following manufacturer instructions to induce robust lipid peroxidation and intracellular reactive oxygen species (ROS) production. This group was defined as the positive control.

Transmission electron microscopy (TEM)

Cell samples were fixed in electron microscopy fixative(G1102, Servicebio, China) containing 2.5% glutaraldehyde, post-fixed with osmium tetroxide, dehydrated, and embedded in Epon. Sections were stained with uranyl acetate and examined with a transmission electron microscope (Hitachi HT7700, Japan).

RNA sequencing and data analysis

Total RNA was extracted using TRIzol® (Invitrogen, USA)and purified with NucleoSpin® RNA Plus(Takara, 740984). RNA quality was assessed, and sequencing was performed on the Illumina NovaSeq X Plus platform(Illumina, USA). Differential expression analysis was performed using DESeq2 and DEGseq, with significance thresholds of |log₂FC| ≥ 1. 5 and pvalue_adjust ≤ 0. 05. GO functional enrichment and KEGG pathway analyses of the DEGs were conducted via Goatools and KOBAS [58], with significance defined by Bonferroni-adjusted p values ≤ 0. 05. GO and KEGG enrichment analyses were conducted, and figures were generated using R and GraphPad Prism 8. 0. Heatmaps, volcano plots, and WGCNA analysis diagrams were created with TBtools [59].

Single-cell RNA-seq analysis and Bulk RNA-seq validation

Single-cell RNA sequencing (scRNA-seq) data were obtained from the public dataset GSE225857 [60]. All data processing and subsequent bioinformatics analyses were performed using R software (v4. 2. 1) with the Seurat package (v4. 3. 1), following a standardized yet tailored workflow to ensure data quality and reliability. Low-quality cells were filtered out based on the following criteria: cells with fewer than 400 detected genes or more than 6000 detected genes were excluded, and cells with a mitochondrial gene percentage exceeding 20% were also removed. Data normalization was performed using the NormalizeData function. Technical batch effects were corrected using the Harmony algorithm (v1. 2. 0) [61].

Data were processed and analyzed using the Seurat package (v4. 0) in R. Cells with low quality (based on feature counts and mitochondrial gene percentage) were filtered out. Data were normalized, and highly variable genes were identified using the standard Seurat workflow. Dimensionality reduction was performed using principal component analysis (PCA), followed by Uniform Manifold Approximation and Projection (UMAP) for visualization. Cell clustering was conducted using a graph-based clustering algorithm. Cell types, including fibroblasts and myeloid cells, were annotated based on the expression of canonical marker genes. Bulk RNA-seq data and corresponding clinical information for colorectal cancer were obtained from The Cancer Genome Atlas (TCGA), including COAD and READ cohorts. CRYAB expression levels were extracted and analyzed for validation purposes. Data processing and statistical analyses were performed using R software.

Proteomics analysis

Total proteins were extracted from treated SW480 cells using RIPA buffer containing PMSF. Protein concentration was determined by the BCA method, followed by identification via SDS-PAGE electrophoresis. Samples underwent trypsin digestion, vacuum drying, and peptide desalting. Quantification was performed using a peptide quantification kit (Thermo, Cat No. 23275). Peptides were separated by UHPLC (Vanquish Neo, Thermo) and analyzed using an Orbitrap Astral mass spectrometer in DIA mode (positive ion mode, 1. 5 kV, m/z 100–1700). Protein and PTM identification were performed using MaxQuant software. Reactome pathway enrichment analysis and KEGG enrichment analysis were conducted via the Majorbio Cloud platform (cloud. majorbio. com), with String protein-protein interaction network analysis employed.

Metabolite extraction and analysis

Cell metabolites were extracted using pre-chilled methanol/water (4:1, v/v) containing an internal standard (0. 02 mg/mL L-2-chlorophenylalanine). After low-temperature sonication, the mixture was allowed to stand for 30 min. The supernatant was removed, dried under nitrogen, and redissolved in a solvent mixture (acetonitrile: water = 1:1). The sample was subjected to low-temperature sonication extraction followed by centrifugation. The supernatant was transferred to a sample vial and analyzed using LC-MS/MS [62] on a UHPLC-Q Exactive HF-X system(Thermo Scientific). Data were annotated by matching with HMDB, Metlin, and MJDB databases, and further analyzed on the Majorbio cloud platform [63].

Molecular docking

UA structures (PubChem CID 5646) were retrieved from PubChem, and protein structures from PDB using UniProt accession numbers. The protein was prepared in PyMOL by removing water and ligands. AutoDockTools (1) 5. 7 added hydrogen atoms and partial charges, saving the structure in PDBQT format. Docking was performed with AutoDock Vina (2) 0, calculating binding energies and visualizing interactions in PyMOL. The grid box was set around the protein binding pocket (22. 5 Å), and docking poses of UA on CRYAB were selected for molecular dynamics simulations.

Molecular dynamics simulation

The CRYAB structure was obtained from PDB, and three FDA-approved drugs were screened using ZINC15-DrugBank. The highest docking pose of UA-CRYAB was refined via MD simulations using the CHARMM 36 force field. Simulations were conducted at 300 K, 1 atm, with TIP3P solvation. The system was equilibrated (100 ps NVT and NPT) before a 100 ns production run using the Parrinello-Rahman method. RMSD was used to select the largest cluster for interaction analysis.

Statistical analysis

Statistical analysis was performed using GraphPad Prism 8. 0 software. Data are presented as mean ± standard deviation (SD) from three biological replicates, derived from at least three independent experiments.In both the proteomics and post-translational modification datasets, contaminants and reverse-lookup database entries (such as those from Phospho (STY)Sites.txt and proteinGroups.txt) have been removed. The data were log2-transformed and normalized using quantile normalization. Comparisons were performed using Student’s t-tests or one-way ANOVA (Tukey post-hoc tests) as appropriate. All omics data underwent log transformation prior to analysis. Statistical significance was defined as p < 0. 05, with specific significance markers: ns (p > 0. 05), * (p < 0. 05), ** (p < 0. 01), *** (p < 0. 001), **** (p < 0. 0001).

Supplementary Information

Supplementary Material 1.^{(9.6MB, tif)}

Supplementary Material 2.^{(6.8MB, tif)}

Supplementary Material 3.^{(6MB, tif)}

Supplementary Material 4.^{(3.2MB, tif)}

Supplementary Material 5.^{(6.7MB, tif)}

Supplementary Material 6.^{(11.2KB, xlsx)}

Supplementary Material 7.^{(24.9MB, tif)}

12964_2026_3046_MOESM8_ESM.docx^{(1.9MB, docx)}

Supplementary Material 8: Extended Data Figure 1. Integrative analysis of clinical proteomics, post-translational modification profiling, and network pharmacology to identify potential targets for UA inhibition of colorectal cancer metastasis. Extended Data Figure 2. Transcriptomic Analysis of Organoids and Proliferation and Migration Assays in Hypoxia Models. Extended Data Figure 3. Differential protein analysis of organoids treated with CRYAB + UA and CRYAB Alone [66]. Extended Data Figure 4. Transcriptomic Analysis of Differential Proteins and Protein Correlations in Organoids and HLiF Cells in Co-Culture Systems [67]. Extended Data Figure 5. Multi-Omics analysis of CRYAB phosphorylation site mutations and their impact on CRYAB-mediated tumor proliferation. Extended Data Figure 6. Multi-omics analysis of CRYAB phosphorylation site mutations and their impact on tumor proliferation. Extended Data Figure 7. Structural and functional validation of the CRYAB-NEDD4L interaction in the regulation of ferroptosis.

Acknowledgements

Not applicable.

Authors’ contributions

Ziyuan Wan: Methodology, Investigation, Data curation, Conceptualization and Project administration. Tingting Chen: Writing-review & editing, Writing-original draft. Xiaotong Xu: Visualization, Investigation, Methodology. Yan Zhang: Investigation, Data curation. Yuzhang Liang: Methodology. Jinlai Wei: Funding acquisition, Formal analysis, Supervision. Junhao Wan: Writing-review & editing.Min Fang：Writing-review, Methodology.Haoze Li: Writing-review & editing, Resources. Lu Wang: Supervision, Project administration, Investigation, Data curation.

Funding

This work was supported by the National Natural Science Foundation of China (Grant no. 82404709), Project supported by the Natural Science Foundation of Chongqing, China (CSTB2023NSCQ-MSX-0370) and Project supported by the Clinical innovation team foundation of Chongqing Medical University, China (03030299QC-W0189), the Project of Tutorial System of Medical Undergraduate in Lab Teaching & Management Center in Chongqing Medical University (LTMCMTS202222, LTMCMTS202316, LTMCMTS202315) and Innovation and Entrepreneurship Project for college students in Chongqing province(S202410631084 and S202510631111).

Data availability

The original contributions presented in the study are included in the article/supplementary materials. The datasets used and analysed during the current study are available from the corresponding author on reasonable request. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (https://proteomecentral.proteomexchange.org) via the iProX partner repository [64, 65] with the dataset identifier PXD075824 and PXD075743. The RNA sequencing data are available from the NCBI Gene Expression Omnibus under accession number PRJNA1442660 (https://www.ncbi.nlm.nih.gov/sra/PRJNA1442660).

Declarations

Ethics approval and consent to participate

This study was approved by the Ethics Committee of the First Affiliated Hospital of Chongqing Medical University (Ethics Approval No. 2025-328-01). All research procedures were conducted in strict accordance with the ethical guidelines outlined in the Declaration of Helsinki, the Good Clinical Practice (GCP) standards, and the regulations of the National Medical Products Administration (NMPA). Informed consent was obtained from all participants prior to their involvement in the study. The study protocol, risks, benefits, and the participants’ rights to withdraw at any time without consequence were clearly communicated to each individual. All participants provided written informed consent voluntarily, ensuring their understanding and agreement to participate in the study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ziyuan Wan and Tingting Chen contributed equally to this work and share the first authorship.

Contributor Information

Tingting Chen, Email: cherrychen@cqmu.edu.cn.

Jinlai Wei, Email: weilai03109@163.com.

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Associated Data

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Supplementary Materials

Supplementary Material 1.^{(9.6MB, tif)}

Supplementary Material 2.^{(6.8MB, tif)}

Supplementary Material 3.^{(6MB, tif)}

Supplementary Material 4.^{(3.2MB, tif)}

Supplementary Material 5.^{(6.7MB, tif)}

Supplementary Material 6.^{(11.2KB, xlsx)}

Supplementary Material 7.^{(24.9MB, tif)}

12964_2026_3046_MOESM8_ESM.docx^{(1.9MB, docx)}

Data Availability Statement

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PERMALINK

Usnic acid inhibits colorectal cancer liver metastasis by targeting CRYAB phosphorylation and inducing ferroptosis

Ziyuan Wan

Tingting Chen

Xiaotong Xu

Yan Zhang

Yuzhang Liang

Haoze Li

Lu Wang

Junhao Wan

Min Fang

Jinlai Wei

Abstract

Background

Methods

Results

Conclusions

Graphical abstract

Supplementary Information

Highlight

Supplementary Information

Background

Results

Integrative analysis identifies CRYAB as a target of UA in inhibiting colorectal cancer liver metastasis

Fig. 1.

CRYAB promotes malignant proliferation of colorectal cancer organoids under hypoxic stress

Fig. 2.

Antagonistic effects of UA on CRYAB-induced tumorigenesis in colon cancer organoids

Fig. 3.

UA reverses CRYAB-induced activation of hepatic fibroblasts in hypoxic co-culture of colon cancer organoids and HLiF Cells

Fig. 4.

CRYAB phosphorylation at S59 drives tumorigenic signaling and enhances cancer cell proliferation and migration

Fig. 5.

Multi-omics analysis reveals key downstream pathways regulated by UA treatment or CRYAB phosphorylation inhibition in tumorigenesis

Fig. 6.

CRYAB-NEDD4L interaction prevents ferroptosis, and UA treatment or S59 mutation impairs this mechanism

Fig. 7.

Discussion

Conclusions

Materials and methods

Cell culture and clinical samples

Patient samples and patient-derived organoids (PDOs)

Co-culture of organoids with human liver fibroblasts (HLiF)

Reagents and antibodies

Vector construction and transfection

RNA extraction and reverse transcription quantitative PCR (RT-qPCR)

Table 1.

Western blot analysis

Enzyme-linked immunosorbent assay (ELISA)

Immunoprecipitation (IP) and LC-MS/MS analysis

Cellular thermal shift assay (CETSA)

Protein stability assay

Hematoxylin&Eosin (H&E) and immunohistochemistry (IHC) staining

Immunofluorescence

Cell viability assay

Wound healing assay

Clonogenic assay

Transwell assay

Intracellular ROS production and lipid peroxidation assay

Transmission electron microscopy (TEM)

RNA sequencing and data analysis

Single-cell RNA-seq analysis and Bulk RNA-seq validation

Proteomics analysis

Metabolite extraction and analysis

Molecular docking

Molecular dynamics simulation

Statistical analysis

Supplementary Information

Acknowledgements

Authors’ contributions

Funding

Data availability

Declarations

Ethics approval and consent to participate

Consent for publication

Competing interests

Footnotes

Contributor Information

References

Associated Data