Abstract

   Structural maintenance of chromosome protein 4 (SMC4), is a key
   structural component of mitotic chromosomes. While existing evidence
   indicates a plausible link between SMC4 and oncogenic manifestations,
   its precise role in the trajectory of prostate cancer remains
   ambiguous. The Cancer Genome Atlas (TCGA) database analysis reveals
   that aberrant expression of SMC4 exhibits a robust prognostic
   association with metastatic progression. To investigate the function of
   SMC4, the SMC4 gene is knocked down in RM1‐LM cells, a highly
   metastatic cell clone is developed, using the CRISPR/Cas9 system. The
   results show that SMC4 knockdown significantly diminished cell
   proliferation and migration in vitro. Furthermore, in a murine model,
   RM1‐LM cells display higher lung metastasis capabilities than SMC4
   knockdown cells. SMC4 knockdown inhibited the activation of mTOR and
   downregulated the expression of Rheb. KEGG enrichment analyses of the
   RNA‐seq results reveal that cancer signaling pathways and metabolic
   pathways are enriched. The SMC4 interactome is uncovered through IP‐MS
   and indicates that SMC4 interacts with GLUT1, encoded by Slc2a1.
   Glycolytic rate assay illustrates that knocking down SMC4 inhibits the
   cell glycolysis rate and ATP production. Collectively, the data
   suggests that the interaction between SMC4 and GLUT1, as confirmed by
   co‐IP, promotes prostate cancer cell metastasis through the Rheb/mTOR
   pathway.

   Keywords: GLUT1, metastasis, prostate cancer, SMC4
     __________________________________________________________________

   Structural maintenance of chromosome protein 4 (SMC4) has been
   implicated in prostate cancer metastasis. Knockdown of SMC4 in RM1‐LM
   cells significantly attenuates proliferation, migration, and lung
   metastasis in vivo. Furthermore, SMC4 interacts with GLUT1 (Slc2a1),
   thereby enhancing glycolysis, ATP production, and activation of the
   Rheb/mTOR pathway. Elevated levels of SMC4 may potentially serve as a
   prognostic biomarker in prostate cancer.

   graphic file with name ADVS-12-2500369-g003.jpg

1. Introduction

   Prostate cancer—with an estimated 288 300 new cases in the United
   States alone during 2023—is the most frequently diagnosed malignant
   male tumor, and represents 29% of all new cancer cases in men in the
   United States.^[ [42]^1 ^] Androgen‐deprivation therapy (ADT) is the
   standard of care for men with prostate cancer, and patients with
   localized prostate cancer exhibit longer overall survival.^[ [43]^2 ,
   [44]^3 ^] Unfortunately, advanced prostate cancer, known as
   castration‐resistant prostate cancer (CRPC), remains largely incurable.
   The lethality of prostate cancer is primarily driven by metastasis.^[
   [45]^4 , [46]^5 , [47]^6 ^] Metastatic prostate cancer continues to be
   an incurable disease, with 34 700 estimated deaths from prostate cancer
   in the United States in 2023, which represents 11% of all cancer deaths
   in men.^[ [48]^1 ^] The most common site of prostate cancer metastasis
   is bone, with up to 84% of patients demonstrating skeletal
   metastases.^[ [49]^7 ^] Despite recent progress, prostate cancer
   remains a significant medical problem for the men affected, with
   frequent overtreatment of inherently benign disease and inadequate
   therapies for metastatic prostate cancer, and with the mechanism(s) by
   which prostate cancer metastasizes still being unclear.

   Structural maintenance of chromosomes protein 4 (SMC4) is a member of
   the SMC family, which is comprised of chromosomal ATPases.^[ [50]^8 ^]
   These ATPases are highly conserved from bacteria to humans, maintain
   the stability of chromosomal structure, and participate in the mitoses
   of eukaryotic cells.^[ [51]^9 ^] The principal function of SMC4 lies in
   assisting in the process of chromosomal transition from loose
   interphase chromatin to the agglomerated state, and in sister‐chromatid
   separation during cell division. In addition, SMC4 also plays a
   significant role in the non‐dividing phase of the cell cycle, including
   the maintenance of gene repression, heterochromatin organization, and
   DNA repair. Previous studies showed that the expression level of SMC4
   was abnormally high in the pathogenesis and progression of lung cancer,
   liver cancer, breast cancer, and colon cancer.^[ [52]^10 , [53]^11 ,
   [54]^12 , [55]^13 ^] SMC4 overexpression also promoted lung
   adenocarcinoma progression and acted as an independent prognostic
   factor,^[ [56]^10 ^] and SMC4‐mediated glioma cell aggressiveness
   ensued via the TGFβ/Smad‐signaling pathway.^[ [57]^14 ^] In breast
   cancer cells, SMC4 regulates expression of the P53 pathway genes, and
   this may relate to the alterations in chromosomal stability. Although
   elevated expression of SMC4 is significantly associated with the
   metastatic cascade and offers a poor prognosis in prostate cancer,^[
   [58]^15 ^] its expression and potential role in the pathogenesis of
   prostate cancer remain obscure.

   In our study, the expression of SMC4 was increased in a prostate cancer
   cell line with high lung metastatic specificity (RM1‐LM), an
   organ‐specific metastatic cell line that we developed. To investigate
   the function of SMC4, we knocked down the SMC4 gene in RM1‐LM cells
   with CRISPR/Cas9; and also analyzed the expression of SMC4 in clinical
   tissues and found that it related to Gleason grade. Blocking SMC4
   suppressed cell proliferation and migration in vitro and reduced cell
   metastasis and progression in mice. Furthermore, we explored the
   mechanism by which SMC4 promotes prostate cancer cell metastasis.

2. Results

2.1. High SMC4 Expression Is Associated with Poor Prognosis and the Gleason
Grade in Human Prostate Cancer

   To explore the clinical role of SMC4, we analyzed its expression in
   various types of tumors using data from The Cancer Genome Atlas (TCGA)
   database. The findings showed that SMC4 is highly expressed in multiple
   cancers (Figure [59]S1, Supporting Information). We further
   investigated 492 prostate cancer tissues and 152 normal prostate
   tissues, and the results demonstrated that there was no difference in
   the expression of SMC4. (Figure [60]1A). However, survival analyses
   revealed that overall survival was significantly lower in the high‐SMC4
   group compared to the low‐SMC4 group (p < 0.01) (Figure [61]1B).
   Moreover, patients with higher SMC4 mRNA expression manifested poor
   disease‐free survival compared with those with lower SMC4 mRNA
   expression, and results were identical for overall survival (p < 0.01)
   (Figure [62]1C). Furthermore, our examination of the Oncomine prostate
   cancer datasets revealed a noteworthy elevation of SMC4 mRNA expression
   in metastatic prostate cancer compared to that in primary tumors,
   suggesting an ongoing contribution to the progression of metastasis
   (Figure [63]1D–F).

Figure 1.

   Figure 1
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   SMC4 expression is upregulated in human prostate cancer and related to
   the poor prognosis and metastasis. A) Comparison of SMC4 gene
   expression between prostate cancer tissues and normal tissues in a TCGA
   dataset. B) Kaplan‐Meier curves of overall survival in TCGA prostate
   cancer patients with SMC4 high or low expression divided by the median.
   Data are from the Oncomine database. C) Kaplan‐Meier curves of
   disease‐free survival in TCGA prostate cancer patients with SMC4 high
   or low expression divided by the median. Data are from the Oncomine
   database. D—F) SMC4 mRNA is upregulated in metastases relative to
   primary tumors in Oncomine prostate cancer datasets (**p < 0.01, ****p
   < 0.0001 based on the Student's t test; data are presented as means ±
   SD). G) The mRNA expression in human prostate epithelial and prostate
   cancer cell lines. H) Western blot showing the expression of SMC4 in
   human prostate epithelial and prostate cancer cell lines. I) Relative
   expression of SMC4 mRNA in human prostate epithelial and prostate
   cancer cell lines. J) IHC staining of SMC4 protein within a human
   prostate cancer tissue microarray (scale bar represents 200 µm). K)
   Statistical comparison of SMC4 protein expression between normal and
   tumor patients. L) Statistical comparison of SMC4 protein expression
   based on Gleason scores (*p < 0.05, **p < 0.01 based on Student t test;
   data represent means ± SD).

   We measured the expression of SMC4 in human prostate cancer cell lines.
   The qPCR and western blotting results showed that SMC4 was
   significantly overexpressed in these cell lines (Figure [65]1G–I). IHC
   results from human prostate cancer tissue microarrays indicated that
   SMC4 protein expression was elevated in prostate cancer tissues in
   tandem with increased Gleason grade (Figure [66]1J–L). Our findings
   indicate that outlier expression of SMC4 exhibit a robust prognostic
   association with metastatic progression, and that the SMC4 prognostic
   outlier genes continue to occupy roles in metastatic prostate cancer.

2.2. SMC4 Promotes Human Prostate Cancer Cell Proliferation, Migration, and
Invasion in Vitro

   To elucidate the functional roles of SMC4, we employed siRNA‐SMC4
   transfection to suppress SMC4 expression in the DU145 and 22RV1 cell
   lines. Quantitative PCR (qPCR) and Western blotting (WB) confirmed the
   successful knockdown of SMC4 (Figure [67]2A,B). Subsequently, we
   assessed cell proliferation capacity and colony formation ability
   post‐transfection and observed a significant inhibition of growth upon
   SMC4 knockdown (Figure [68]2C–E). In addition, we performed a Transwell
   assay to evaluate cellular migratory and invasive capabilities
   following transfection (Figure [69]2F). Notably, DU145 and 22RV1 cells
   transfected with siRNA displayed lower migration and invasion
   capabilities than the controls (P < 0.01, Figure [70]2G).

Figure 2.

   Figure 2
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   SMC4 promotes human prostate cancer cell proliferation, migration, and
   invasion in vitro. A,B) siRNA‐mediated knockdown of SMC4 in DU145 and
   22RV1 cell lines was verified by real‐time PCR and western blotting. C)
   A CCK8 assay was executed to measure the proliferation of DU145 and
   22RV1 cells after SMC4 knockdown. D) A colony‐formation assay showed
   that the cells’ proliferative capacity was inhibited after SMC4
   knockdown. E) Quantitative analysis of colony numbers. F) Migration and
   invasion after siRNA‐mediated knockdown of SMC4 in DU145 and 22RV1
   cells. G) Relative analysis of migration and invasion. ***p < 0.001
   based on Student t test; error bars signify means ± SEM of three
   independent experiments.

2.3. SMC4 Promotes Mouse Metastatic Prostate Cancer Cell Proliferation,
Migration, and Invasion Capabilities in Vitro

   To determine the functional effects of SMC4 on metastatic prostate
   cancer cells, we inoculated RM1 cells into the tibiae of C57BL/6J mice
   and then reinjected lung metastatic tumor cells into shin bones of the
   mice. After three cycles of repetition, we obtained a stable population
   of highly metastatic lung cells (RM1‐LM) (Figure [72]3A). The
   expression of SMC4 in RM1‐LM cells versus RM1 cells was assessed by
   qPCR and western blotting. SMC4 expression was found to be
   significantly higher in RM1‐LM cells than in RM1 cells
   (Figure [73]3B–D).

Figure 3.

   Figure 3
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   SMC4 knockdown via the CRISPR/Cas9 system inhibits highly metastatic
   prostate cancer cellular proliferation and colony formation. A) Diagram
   illustrating the isolation of highly metastatic prostate cancer cells.
   B) The mRNA level of SMC4 was measured by quantitative real‐time PCR in
   RM1cells and highly metastatic prostate cancer cells. C) Western
   blotting results showing SMC4 expression in RM1 cells and highly
   metastatic prostate cancer cells. D) The relative protein expression of
   SMC4 in RM1 cells and highly metastatic prostate cancer cells. E)
   Knockdown of SMC4 was confirmed by quantitative real‐time PCR. F)
   Knockdown of SMC4 was confirmed by western blotting. G) The relative
   protein expression of SMC4 in RM1‐LM‐vector and SMC4 knockdown cells.
   H) CCK8 assay was executed to measure proliferation of RM1‐LM‐Vector
   and SMC4 knockdown cells. I) We used colony‐formation assays to
   evaluate proliferative capacity in RM1‐LM‐Vector and SMC4 knockdown
   cells. J) Quantitative analysis of colony numbers. K) Quantitative
   analysis of diameter.*p < 0.05, **p < 0.01, ***p < 0.001. Data are
   shown as the mean ± SD.

   We performed SMC4 knockdown using the CRISPR/Cas9 system in RM1‐LM
   cells. The identification of SMC4‐knockdown colonies (SMC4‐K1 and K2)
   was performed via qPCR and western blotting (Figure [75]3E–G).
   Subsequently, various phenotypic features were compared between SMC4
   knockdown cells and transfected empty vector control cells. We
   evaluated cell growth and colony formation in vector cells alongside
   SMC4 knockdown cells. CCK8 assay and colony formation experiments
   indicated a significant reduction in the proliferation of cells upon
   SMC4 knockdown (Figure [76]3H–J). Furthermore, the diameter of the cell
   colonies was significantly smaller after SMC4 knockdown
   (Figure [77]3K). In addition, the effect of SMC4 on cell DNA synthesis
   and cell cycle progression was investigated through flow cytometry
   analysis. SMC4 knockdown significantly decreased the number of cells in
   the G2/M phase and increased the number in the S phase (Figure [78]4A).
   The expression of cyclin‐dependent kinases and cyclin proteins was also
   measured using western blot (Figure [79]4B). The Transwell assay
   demonstrated a remarkable decrease in cellular migration and invasion
   upon SMC4 knockdown (Figure [80]4C,D). Consistently, the wound healing
   assay indicated a prominently suppressed cell migration rate after SMC4
   knockdown (Figure [81]4E,F). In contrast, cell proliferation,
   migration, and invasion capabilities were increased after SMC4
   overexpression in knockdown cells (Figure [82]S2, Supporting
   Information). Thus, SMC4 knockdown can effectively suppress metastatic
   prostate cancer cells’ growth, migratory, and invasion capabilities.

Figure 4.

   Figure 4
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   SMC4 knockdown inhibits cell cycle progression and cell migration and
   invasion, promoting mouse prostate cancer metastasis and progression in
   vitro. A,B) Flow cytometry and western blot analysis were used to
   assess cell cycle kinetics. C) Transwell assays were performed to
   determine cell migration and invasion. D) Relative analysis of
   migration and invasion. E) Images of wound‐healing assay. F) The area
   of cell migration into the scratch wound compared with that at 0 h,
   which was determined as 100% open. **p < 0.01, ***p < 0.001. Data are
   shown as the mean ± SD.

2.4. SMC4 Promotes Prostate Cancer Metastasis and Progression in Vivo

   To investigate the contribution of SMC4 to prostate cancer growth and
   metastasis, we inoculated RM1‐LM‐Vector cells and SMC4 knockdown cells
   into the tibia of C57BL/6 mice, and 14 days after injection, the mice
   inoculated with RM1‐LM‐Vector cells showed dramatic increases in
   bioluminescent imaging (BLI) signals compared with mice inoculated with
   SMC4 knockdown cells (Figure [84]5A). We normalized the lung BLI signal
   to the higher BLI signal in mice inoculated with RM1‐LM cells compared
   to mice inoculated with SMC4 knockdown cells (Figure [85]5B), and our
   data revealed that the majority of BLI signals in the lung corroborated
   pulmonary metastasis. In contrast, mice inoculated with SMC4 knockdown
   cells showed lower BLI signals (Figures [86]S3–S5, Supporting
   Information). Mouse weights were measured every 2–3 days
   (Figure [87]5C), and between days 17 and 23, seven mice inoculated with
   RM1‐LM‐Vector cells died; in contradistinction, only one mouse
   inoculated with SMC4 knockdown cells died (Figure [88]5D). Compared to
   the SMC4 knockdown cells, the RM1‐LM‐Vector cells showed enhanced lung
   metastasis (Figure [89]5A,E), and our H&E and IHC results revealed that
   RM1‐LM‐Vector cells increased lung metastasis and colonization relative
   to SMC4 knockdown cells, suggesting that loss of SMC4 suppressed
   prostate cancer metastasis in addition to tumor growth
   (Figure [90]5F,G).

Figure 5.

   Figure 5
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   SMC4 knockdown inhibits highly metastatic prostate cancer cell
   metastasis and progression in vivo. A) Bioluminescence imaging (BLI) of
   mouse tumors was measured every 5–7 days, and the images are from day
   24th. N (vector) = 11, N (SMC4‐K1/K2) = 10. B) Quantification of tumor
   photon flux in lung metastases in mice. C) Mouse weights were measured
   every 2–3 days. D) Kaplan‐Meier curves show mouse survival after
   inoculation with RM1‐LM‐Vector cells or SMC4‐knockdown cells. E:
   Percent lung metastasis of RM1‐LM‐Vector cells and SMC4‐knockdown cells
   after inoculation. F) Mouse lung tissues were harvested, fixed,
   embedded in paraffin, and stained with H&E. G) SMC4 and Ki‐67
   expressions were detected by IHC staining of lung tissues. ***p
   < 0.001. Data are shown as the mean ± SD.

2.5. Transcriptomic Profiling of RM1‐LM and SMC4 Knockdown Cells

   To elucidate the mechanism responsible for the SMC4 promotion of
   prostate cancer cell metastasis, we performed RNA‐Seq analysis in
   RM1‐LM‐Vector and SMC4 knockdown cells. Our volcano map depicts
   differential gene expression between RM1‐LM‐Vector and SMC4 knockdown
   cells (Figure [92]6A). There were 396 genes upregulated and 811 genes
   downregulated in RM1‐LM‐Vector cells versus SMC4‐K1 cells, and 349
   genes upregulated and 790 genes downregulated in RM1‐LM‐Vector cells
   versus SMC4‐K2 (Figure [93]S6, Supporting Information). To further
   determine the signaling pathways most impacted by SMC4, we exploited
   gene set enrichment analysis (GSEA) on the differentially expressed
   genes (DEGs). SMC4 is a key component of condensin and plays a crucial
   role in chromosomal transition and sister‐chromatid separation during
   cell division. As shown in Figure [94]6B, DNA replication and cell
   cycle progression‐related genes were enriched for downregulation in
   SMC4 knockdown cells as expected. Also, Kyoto Encyclopedia of Genes and
   Genomes (KEGG) enrichment analysis revealed numerous pathways activated
   after SMC4 knockdown. The top 20 gene sets that were enriched included
   pathways in cancer, focal adhesion, metabolic pathways, and insulin
   secretin (Figure [95]6C). We further integrated the RNA‐Seq data to
   obtain common DEGs between RM1‐LM‐Vector versus SMC4‐K1, and
   RM1‐LM‐Vector versus SMC4‐K2. A total of 572 common DEGs were enriched
   (Figure [96]6D). The KEGG‐enrichment results showed that genes related
   to cell and glucose metabolism were significantly enriched in common
   DEGs (Figure [97]6E). Furthermore, the ablation of SMC4 altered the
   mRNA expression of cellular glucose metabolism‐related genes
   (Figure [98]6F,G). The depletion of SMC4 greatly changed the cellular
   metabolic state.

Figure 6.

   Figure 6
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   Transcriptomic profiling of RM1‐LM and SMC4 knockdown cells. A) Volcano
   plots show the differential expression of mRNA transcripts between
   RM1‐LM‐Vector and SMC4‐knockdown cells. Red, gray, and blue represent
   significantly upregulated, non‐significant, and significantly
   downregulated mRNAs, respectively. A: RM1‐LM‐Vector cells. B) SMC4‐K1
   cells. C: SMC4‐K2 cells. B) GSEA shows significant enrichment of
   gene‐expression signatures in the cell cycle and chromosomal
   separation. FDR, false‐discovery rate; normalized ES, normalized
   enrichment score. C) The top genes in the Kyoto Encyclopedia of Genes
   and Genomes (KEGG) signaling‐pathway enrichment in differentially
   expressed gene (DEG) sets. The color of the scattered dots varies from
   blue to red and designates a range of significant differences, and
   circle size indicates the number of enriched DEGs. D) The common DEGs
   enriched in RM1‐LM‐Vector versus SMC4‐K1 and RM1‐LM‐Vector versus
   SMC4‐K2 cells. E) The common DEGs identified by KEGG analysis. F)
   Heatmap of the mRNA expression of glycolytic genes in the RNA‐Seq
   analysis in RM1‐LM3 and SMC4‐knockdown cells. Red and green denote
   upregulated and downregulated mRNA expression, respectively. We
   conducted cluster analysis for sample and differential mRNAs. G) The
   qPCR results in RM1‐LM and SMC4 knockdown cells. *p < 0.05, **p < 0.01,
   ***p < 0.001. Data are shown as the mean ± SD.

2.6. SMC4 Interacts with and Modulates the Glycolytic Metabolic Profile in
PCa (RM1) Cells

   We performed IP assays using anti‐flag magnetic beads and identified
   SMC4‐interacting proteins by LC‐MS/MS analysis. A total of 76 proteins
   were identified as interacting specifically with Flag‐SMC4 (Table
   [100]S1, Supporting Information). We hierarchically clustered the
   remaining significant terms according to Kappa‐statistical similarities
   among their gene memberships (Figure [101]7A). Notably, SLC2A1, which
   encodes the major glucose transporter 1 (GLUT1) The Cancer Genome Atlas
   (TCGA) and plays a crucial role in the glycolytic pathway, was enriched
   in the Glucagon signaling pathway. Since the RNA‐Seq results indicated
   an effect on cellular metabolic signaling pathways after SMC4
   knockdown, we selected GLUT1 for further validation by co‐IP
   (Figure [102]7B). We performed immunofluorescence staining to evaluate
   the interaction (Figure [103]7C,D) more directly. Consistent with the
   co‐IP results, we found that SMC4 and GLUT1 are associated.

Figure 7.

   Figure 7
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   SMC4 interacts with GLUT1 in RM1 cells. A) SMC4‐interacting proteins by
   LC‐MS/MS analysis were enriched according to Kappa‐statistical
   similarities. B) SMC4 was labeled with HA, and GLUT1 was labeled with
   Flag; co‐IP showed that SMC4 interacted with GLUT1. C)
   Immunofluorescence staining. Scale bars, 2 µm. The areas indicated by
   boxes are magnified below. D) Comparison of fluorescence intensity
   profiles. E) Co‐expression of SMC4, BACH1, HK2, PFKFB3, and SLC2A1 in
   human prostate cancer. Data are from the TCGA dataset. F) The
   correlation between SMC4 expression and GLUT1, HK2, HIF1a, LDHA, and
   BACH1 was analyzed using the TCGA dataset.

   SMC4 knockdown suppressed transcript levels of glycolytic genes that
   included 6‐phosphofructo‐2‐kinase/fructose‐2,6‐bisphosphatase 3
   (PFKFB3), solute‐carrier family 2 member1 (Slc2a1), hypoxia‐inducible
   factor 1 subunit alpha (HIF1a), hexokinase 2 (HK2), lactate
   dehydrogenase A (LDHA), and BTB domain and CNC homolog1 (BACH1)
   (Figure [105]6F,G). Furthermore, co‐expression analyses in TCGA
   datasets characterized SMC4 expression as correlated with glycolytic
   genes (Figure [106]7E). Subsequently, utilizing the TCGA database on
   the TIMER website, we analyzed the association between SMC4 and these
   genes. The findings illustrated a significant positive correlation
   between SMC4 and SLC2A1, HIF1a, HK2, PFKFB3, LDHA, and BACH1
   (Figure [107]7F).

2.7. SMC4 Knockdown Affected the Glycolysis of Metastatic Mouse Prostate
Cancer Cells

   Next, we evaluated the association between SMC4 and glycolysis using a
   Seahorse XFe96 analyzer. The ATP rate assay was performed and exhibited
   a reduction in ATP production from glycolysis upon SMC4 knockdown
   (Figure [108]8A). Conversely, ATP production through mitochondrial
   respiration was increased in SMC4 knockdown cells. Moreover, the
   glycolysis rates assay indicated a significant decline in basal and
   compensatory glycolysis within SMC4 knockdown cells versus
   RM1‐LM‐Vector cells (Figure [109]8B–D). The mito stress test revealed
   elevated spare respiratory capacity after SMC4 knockdown
   (Figure [110]8E,F). In addition, analyses of [111]GSE21034 and
   [112]GSE35988 databases revealed that GLUT1 expression was augmented in
   metastatic prostate cancer in men relative to that in primary prostate
   cancer, a trend consistent with SMC4 expression (Figure [113]8G). These
   metabolite alterations, including ATP, suggested that SMC4 knockdown
   suppressed glycolysis rates.

Figure 8.

   Figure 8
   [114]Open in a new tab

   The effect of SMC4 on glycolysis rates and mitochondrial respiration.
   A) ATP production rate assay of RM1‐LM‐Vector and SMC4‐knockdown cells.
   B—D) SMC4 knockdown inhibits RM1‐LM‐Vector cell glycolytic rates. The
   basal and compensatory glycolysis rates were significantly lower after
   SMC4 knockdown. E,F) Mito stress test results demonstrated that cells’
   spare respiratory capacity was higher after SMC4 knockdown. G: SLC2A1
   was upregulated in metastases versus primary tumors in the Oncomine
   prostate cancer datasets. H) Western blotting results of mTOR pathway
   and glycolysis proteins in cells. *p < 0.05, **p < 0.01, ***p < 0.001.
   Data are shown as the mean ± SD.

   Reports show that the mTOR pathway is important in glycolysis.^[
   [115]^16 , [116]^17 ^] Because of the association between SMC4 and
   GLUT1, we sought to explore whether SMC4 regulates glycolysis by
   activating the mTOR pathway in RM1 cells. We examined the proteins
   involved in mTOR pathways. Western blot analysis revealed a decrease in
   phosphorylation of mTOR following SMC4 knockdown, while the total mTOR
   protein quantity remained unchanged (Figure [117]8H). Moreover, the
   upstream protein Rheb exhibited a significant decrease in expression,
   indicating that SMC4 may modulate cell proliferation and migration by
   regulating the Rheb/mTOR pathway. Meanwhile, we found that the
   expression of genes involved in glycolysis, including GLUT1, HK2, and
   HIF1a, was partly decreased. Cell migration and invasion capacities
   were increased when SMC4‐K1 and K2 cells were treated with an mTOR
   activator MHY1485 (Figure [118]S7, Supporting Information). Thus, SMC4
   potentially regulates the growth and metastasis of metastatic prostate
   cancer cells through the Rheb/mTOR pathway.

3. Discussion

   Despite the high long‐term survival rate in localized prostate cancer,
   advanced tumors are incurable even after intensive multimodal therapy,
   and metastatic disease is the leading cause of death in prostate
   cancer. Lymph nodes are usually the first sites of metastases for
   prostate tumors, followed by metastases to the lungs, liver, and
   bones.^[ [119]^18 ^] In this study, we used a prostate cancer cell line
   that shows prominent metastases to the lungs (RM1‐LM, an organ‐specific
   metastatic cell line that we developed) to detect underlying tumor
   mechanisms (Figure [120]3A), and found that SMC4 was highly expressed
   in RM1‐LM cells. SMC4 protein expression was also commensurately
   elevated with increasing injection cycles. This suggested that SMC4 was
   important in prostate cancer cell metastasis.

   The SMC4 protein is a member of the ATPase superfamily.^[ [121]^8 ^] As
   a sensitive switch within the condensin complex, the SMC4 protein
   interacts with the SMC2 monomer to form a V‐shaped structure.^[
   [122]^19 ^] Condensin is crucial in cell division processes,
   facilitating chromosomal condensation, proper compression, and
   dissociation of sister chromatids.^[ [123]^9 , [124]^20 ^] Knockdown of
   SMC4 gene expression revealed its essential roles in parasite
   proliferation and transmission.^[ [125]^19 ^] Depletion of SMC4 leads
   to cytokinesis failure and polyploidy, impeding cell proliferation.^[
   [126]^21 ^] Previous studies revealed that SMC4 was overexpressed in
   colorectal, liver, hepatocellular carcinoma, and glioma cells,
   indicating its significant role in tumor aggressiveness.^[ [127]^11 ,
   [128]^12 , [129]^13 , [130]^14 ^] SMC4 was also reported to be involved
   in a poor prognosis in prostate cancer, lung adenocarcinoma, and acute
   myeloid leukemia.^[ [131]^10 , [132]^15 , [133]^22 ^] The Oncomine
   database depicts SMC4 mRNA levels as much higher in metastases than in
   primary tumors, indicating a contributing role of SMC4 in metastatic
   prostate cancer (Figure [134]1D–F). SMC4 overexpression also promotes
   cancer cell aggressiveness and metastasis in human prostate cancer cell
   lines (Figure [135]2). Such trends have been observed in parallel
   across diverse cell lineages. Therefore, to determine the functional
   effect of SMC4 on metastatic prostate cancer cells, we knocked down
   SMC4 in RM1‐LM cells and demonstrated that SMC4 knockdown notably
   inhibited cellular proliferation, migration, and invasion in vitro and
   affected cell cycle. Also, SMC4 knockdown dramatically decreased lung
   metastasis and colonization relative to RM1‐LM‐Vector cells, suggesting
   that SMC4 promoted prostate cancer metastasis in addition to tumor
   growth. Furthermore, when SMC4 is overexpressed, it neutralizes the
   reduced cell proliferation, migration, and invasion capacities due to
   SMC4 depletion (Figure [136]S2, Supporting Information).

   Nevertheless, the contribution of SMC4 to prostate cancer metastasis
   remains unelucidated. Jiang et al. verified that SMC4 promoted glioma
   cell aggressiveness and activated TGF/Smad signaling and that SMC4 was
   a target gene regulated by microRNA‐124‐5p in colorectal cancer.^[
   [137]^12 , [138]^14 ^] Chen et al. also demonstrated that SMC4 was
   transcriptionally regulated by hypoxia‐inducible factor‐1 (HIF‐1) under
   hypoxic conditions in hepatocellular carcinoma.^[ [139]^23 ^] In this
   study, we found that Rheb and p‐mTOR expression levels were
   increasingly decreased in SMC4‐knockdown cells (Figure [140]8H).
   Increasing evidence suggests that mTOR signaling pathways play an
   important role in cancer cell proliferation, migration, invasion, and
   glucose metabolism. Thus, SMC4 potentially regulates the growth and
   metastasis of metastatic prostate cancer cells through the Rheb/mTOR
   pathway.

   We analyzed transcription after SMC4 knockdown and showed that the cell
   metabolic pathway was enriched based on KEGG functional annotation and
   GO analysis. Our findings revealed that SMC4 knockdown suppressed the
   expression of glycolysis‐related genes such as HK2, HIF1A, BACH1, and
   PFKFB3. Studies have demonstrated that targeting glycolysis is one
   potential strategy to combat metastasis.^[ [141]^24 , [142]^25 ,
   [143]^26 ^] The Warburg effect was first reported in rat liver
   carcinoma in the 1920s. Later, researchers found that enhanced aerobic
   glycolysis in many other cancer types, including prostate, lung,
   breast, and pancreatic cancer.^[ [144]^27 , [145]^28 , [146]^29 ,
   [147]^30 ^] Hexokinase (HK) is the first rate‐limiting enzyme in
   aerobic glycolysis. Many researchers have investigated how HK2 promotes
   glycolysis.^[ [148]^21 , [149]^31 ^] DeWaal et al. found that HK2
   silencing can synergistically enhance the sensitivity of HCC to
   sorafenib.^[ [150]^31 ^] HK2 attenuation enhances the sensitivity of
   HCC to drugs such as metformin by inhibiting glycolytic flux and
   inducing OXPHOS.

   Hypoxia‐inducible factor‐1 (HIF‐1) functions as a master regulator of
   cellular and systemic homeostatic responses to hypoxia and activates
   the transcription of numerous genes that include those involved in
   energy metabolism, angiogenesis, apoptosis, and other genes whose
   protein products increase oxygen delivery or facilitate metabolic
   adaptation to hypoxia. Previous studies proved that HIF1A was critical
   to cellular proliferation, migration, and drug resistance in a hypoxic
   microenvironment.^[ [151]^32 , [152]^33 ^] Notably, Wiel et al. found
   that BACH1 binds to the HK2 and GAPDH promoters and stimulates the
   expression of several glycolytic genes, increasing glucose uptake and
   glycolytic rate. These authors suggested that targeting BACH1 or
   proteins inhibited lung cancer metastasis.^[ [153]^34 ^]

   Oncogenic mutations have been shown to activate glycolytic enzymes and
   increase glucose transport into cells. During glycolysis, these
   oncogenes upregulate GLUT1 expression in cancer cells.^[ [154]^35 ,
   [155]^36 , [156]^37 ^] In our study, co‐IP results revealed that SMC4
   was associated with GLUT1, a major glucose transporter in mammals that
   is encoded by the SLC2A1 gene (Figure [157]7B). Furthermore, we
   correlated the elevated expression of SMC4 with a commensurately
   augmented expression of GLUT1 in prostate cancer metastasis, and
   cellular metabolic assays revealed that SMC4 knockdown suppressed
   glycolytic rate. These data suggested that SMC4 overexpression promoted
   cancer cell metastasis by modifying cellular metabolism.

   In summary, our findings illuminated the profound influence of SMC4
   overexpression on the heightened aggressiveness of prostate cancer
   cells, specifically in facilitating their metastatic dissemination to
   distant organs. Our investigation uncovered the suppression of mTOR
   activity and Rheb expression after SMC4 knockdown, suggesting the
   potential role of SMC4 in governing the growth and metastasis of
   prostate cancer cells through the Rheb/mTOR signaling pathway. The
   association between SMC4 and GLUT1 was confirmed by co‐IP, providing
   new insights into the mechanism underlying metastatic prostate cancer.
   Targeting glycolysis could potentially develop into a strategy to
   inhibit cellular proliferation and migration in prostate cancers.
   Notably, the elevated expression levels of SMC4 demonstrate a
   noteworthy correlation with overall patient survival, thus rendering it
   a potentially valuable prognostic marker for prostate cancer.

4. Experimental Section

Cell culture, Plasmid Construction, and Transfection

   RM1, DU145, and 22RV1 cell lines were obtained from the American Type
   Culture Collection (Manassas, VA, USA). RM1 cells were inoculated into
   mouse tibia, and tumor cells were separated that metastasized to lung
   tissue and again used tibial inoculation of RM1 cells; RM1‐LM3 cells
   were thus isolated from the tumor metastasized to lung tissue after
   three cycles in vivo. All cell lines tested negative for mycoplasma
   with the MycoFluor kit (Invitrogen). Each cell line was cultured in
   RPMI‐1640 medium with 10% fetal bovine serum and 1x
   penicillin/streptomycin at 37 °C in 5% CO[2] in air. Cells showing
   stable knockdown of SMC4 were created with the CRISPR/Cas9 system per
   the previous publication.^[ [158]^38 ^] Briefly, the sgRNA
   (ATTATTGGACCAAATGGCAG) was phosphorylated and introduced into the pX459
   plasmid containing mCherry (a gift from Nanjing Keris Biotechnology
   Company) to generate the Cas9‐sgRNA targeting plasmids for SMC4.
   Approximately 5 µg of the SMC4‐Cas9‐sgRNA plasmids were transfected
   into 1 × 10^6 RM1‐LM3 cells using a cell line nucleofector kit (Lonza).
   Approximately 2 days after transfection, cells were sorted by flow
   cytometry (BD FACS Aria 5) and seeded into 96‐well plates at one cell
   per well density. Cell colonies were harvested and detected by PCR, and
   nucleotides were sequenced.

siRNA Transfection

   The specific siRNA for SMC4 (human) and control siRNA were designed and
   synthesized by GenePharma, and the oligo sequence for siRNA is shown in
   Table [159]S2 (Supporting Information). Cells were transfected with
   oligonucleotides using Lipofectamine IMAX (Invitrogen Life
   Technologies), and after 48 h of transfection, the cells were digested
   and confirmed by RT‐qPCR or western blotting analysis.

Cell‐Proliferation, Colony‐Formation, and Wound‐Healing Assays

   For cell proliferation, cells were plated at a density of 1000 cells
   per well in a complete medium in 96‐well plates. Cell viability was
   assessed using the MTT assay at 24, 48, and 72 h; and measured
   absorbance at 450 nm with a spectrophotometric plate reader (BioTek).
   For colony formation, cells were plated at a density of 500 cells per
   well in a complete medium in six‐well plates. Ten to fourteen days
   later, the colonies were fixed with 4% paraformaldehyde and stained
   with 0.1% crystal violet, and the number of colonies was counted under
   an inverted microscope (Nikon Corporation). Regarding wound healing,
   cells were seeded into six‐well plates at a density of 5 × 10^5 and
   allowed them to grow until >95% confluence. The monolayer was gently
   and slowly scraped with a new 200‐µl pipette tip across the center of
   the well, gently washed the wells twice with PBS to remove detached or
   dead cells, and replenished the wells with fresh serum‐free RPMI‐1640
   medium. After the treatment, the same field in each well was imaged
   with a microscope (Nikon Corporation) at four time points (0, 10, 18,
   and 28 h). Images were processed and analyzed using ImageJ software,
   and the scratch areas of each observation point were measured at
   different times to calculate the cell migration rate.

Cell‐Migration and Invasion Assays

   With respect to cell migration, 6 × 10^4 cells were resuspended in the
   RPMI‐1640 medium, seeded them into the top chamber of Transwell inserts
   (Corning Star), and added 15% FBS medium to the bottom chamber. Cells
   were incubated in chambers for 24 h, fixed with 4% paraformaldehyde for
   20 min, and stained with 0.1% crystal violet. Non‐invading cells were
   removed using a cotton swab. Wells were imaged with five randomly
   selected fields counted under light microscopy and assayed in
   triplicate.

   To determine cell invasion, 8 × 10^4 cells were resuspended in
   RPMI‐1640 medium and seeded into the top chamber of a Matrigel‐coated
   Transwell inserts (Corning Star), and 15% FBS medium was added to the
   bottom chamber. Cells were incubated in chambers for 24 h, fixed in 4%
   paraformaldehyde for 20 min, and stained with 0.1% crystal violet.
   Non‐invading cells were removed using a cotton swab. Wells were imaged
   with five randomly selected fields counted under a microscope in
   triplicate assessments.

Cell‐Cycle Assay

   The percentages of cells within different phases of the cell cycle were
   evaluated by determining DNA content followed by propidium iodide (PI)
   staining (BD Biosciences). Briefly, cells were digested and washed with
   phosphate‐buffered saline (PBS), centrifuged at 1000 rpm for 10 min,
   fixed in 70–80% ethanol, and incubated at −20 °C for a minimum of 2 h.
   The cells were washed twice, re‐centrifuged the fixed cells,
   resuspended the cells in 0.5 mL of PI/RNase staining buffer, and
   incubated them for 15 min at room temperature. At least 20 000 cells
   were analyzed with a FACSCalibur instrument (BD Biosciences) equipped
   with Cell Quest 3.3 software.

RNA Extraction and Quantitative Real‐Time PCR (qRT‐PCR)

   Total RNA was extracted using TRIzol (Invitrogen), the concentration
   and purity were determined using a NanoDrop spectrophotometer (Thermo
   Scientific), and reverse transcription was executed with Takara Reverse
   Transcription Reagents. Quantitative real‐time PCR was performed in
   triplicate using a real‐time PCR System (Applied Biosystems) and SYBR
   Green PCR Master mix (Takara). Each experiment was repeated three
   times, and the sequences of the primers used are presented in Table
   [160]S2 (Supporting Information).

Western Immunoblotting Analysis

   Western blotting analyses were performed using standard protocols.
   Briefly, total proteins were isolated from whole cells using RIPA with
   protease inhibitors, and protein extracts were separated on 10%
   SDS‐PAGE gels and electrophoretically transferred to nitrocellulose
   membranes. The membranes were blocked with 5% BSA in TBST buffer,
   incubated with primary antibodies, and protein bands were visualized
   using enhanced chemiluminescence detection. The primary antibodies used
   in western blotting were generated against SMC4 (Abcam), GLUT1 (Cell
   Signaling Technology), HK2 (Cell Signaling Technology), CDK2 (Cell
   Signaling Technology), Cdc2 (Cell Signaling Technology), HIF1a (Cell
   Signaling Technology), Flag (Cell Signaling Technology), HA (Cell
   Signaling Technology), P27 (Cell Signaling Technology), t‐mTOR (Cell
   Signaling Technology), CyclinB1 (Cell Signaling Technology), p‐mTOR
   (Phospho ser2448, Cell Signaling Technology), p70S6K (Cell Signaling
   Technology), Rheb (Abcam) and actin (Cell Signaling Technology); and
   the secondary antibodies were anti‐rabbit‐HRP (Cell Signaling
   Technology) and anti‐mouse‐HRP (Cell Signaling Technology).

Co‐Immunoprecipitation

   Total proteins were isolated from whole cells using NP‐40 buffer
   (Beyotime, #P0013F). Cell lysates were incubated with 2 ug of SMC4
   antibody (1:100) followed by 20 µl of protein A + G agarose and
   incubated the mixture at 4 °C for 2 h. The supernatant was removed, and
   the beads were washed three times with NP‐40 buffer. Finally, the
   proteins bound to the beads were denatured using a 2× loading buffer
   and boiled. Then, the samples were evaluated using SDS–PAGE and western
   blotting.

Immunofluorescence Staining

   Cells were fixed with 4% (v/v) paraformaldehyde for 15 min, followed by
   permeabilization with 0.1% (w/v) Triton X‐100 for 10 min. After being
   blocked with 10% normal goat serum in PBS for 1 h at room temperature,
   the cells were incubated with SMC4 (Abcam) and GLUT1 (Proteintech)
   primary antibodies at 4 °C overnight. The primary antibodies were
   removed, and the cells were washed 3 times and incubated with
   species‐specific secondary antibodies for 1 h at room temperature in
   the dark. The secondary antibodies were removed, and the cells were
   washed 3 times and mounted. Images were acquired using the Abberior
   STEDYCON (Abberior Instruments) fluorescence microscope with a
   motorized inverted microscope IX83 (Olympus UPlanXAPO 100x). All images
   were analyzed using the STEDYCON gallery software (Abberior
   Instruments).

Animal Experiments

   C57BL/6 mice (4 weeks old) placed in the experiment were obtained from
   the Shanghai Model Organisms Center, Inc., Shanghai, China. Cells (8 ×
   10^4) were resuspended in 0.02 mL of mixed medium (DMEM: Matrigel =
   1:1) and injected into mouse tibias. The mice were weighed every 2–3
   days and measured fluorescence every 5–7 days. At about 3 weeks, the
   mice were euthanized, and lungs were surgically dissected. The Southern
   University of Science and Technology Animal Care and Use Committee
   approved all experimental animal procedures.

Immunohistochemistry

   Mice were sacrificed under CO[2] inhalation, and the lungs were removed
   and fixed in 4% PFA for 24 h under deep anesthesia. The fixed lungs
   were embedded in paraffin and cut at 4 µm with a sliding microtome
   (Leica RM2255), and the sections were mounted on glass slides.
   Immunohistochemical staining was conducted as previously described
   using an enzyme‐labeled biotin/streptavidin system and a
   solvent‐resistant DAB Map Kit. Primary antibodies used in this study
   were anti‐SMC4 (Abcam) and anti‐Ki67 (Cell Signaling Technology).

RNA Sequencing and Bioinformatics Analysis

   Total RNA was extracted using a TRIzol‐based kit (Thermo), and
   sequenced nucleotides with an Illumina HiSeq PE150 instrument and
   Promega Relia‐Prep. Paired‐end reads were obtained, the quality score
   was determined with FASTQC, and data were mapped to the GRCm/mm10 mouse
   genome assembly with STAR Aligner (version 2.5.0a). Differentially
   expressed genes (DEGs) with a false‐discovery rate (FDR) of < 0.05 were
   considered to constitute statistical significance. Pathway‐enrichment
   analysis was conducted using Gene Set Enrichment Analysis (GSEA)
   against the Molecular Signature Database (MSigDB), complying with the
   pathway compendium.

Metabolic Analysis

   An ATP Rate Kit (Seahorse), Glycolysis Ratio Kit (Seahorse), and
   Mito‐Stress Assay Kit (Seahorse) were used in this experiment according
   to the manufacturer's instructions. In brief, 1.5 × 10^4 cells were
   seeded in a XF96 Cell Culture Microplate (Seahorse) in 80 µl of
   complete medium and incubated overnight. The cell growth medium was
   aspirated and changed to an XF assay medium, incubating the cells in a
   37 °C CO[2]‐free incubator for 45 min. Extracellular acidification rate
   (ECAR) and oxygen consumption rate (OCR) were measured using a Seahorse
   XF96 Analyzer followed by the addition of corresponding reagents as
   described in the manufacturer's instructions. For the ATP rate assay,
   oligomycin (1.5 µm) and rotenone/antimycin A (Rot/AA, 0.5 µm) were
   serially injected onto plates; for the glycolytic rate assay, 10 mm
   glucose, 1 µm oligomycin, and 50 mm 2‐deoxy‐glucose (2‐DG) were added
   sequentially; and for the Mito‐Stress assay, 1.5 µm oligomycin, 0.5 µm
   FCCP, and 0.5 µm Rot/AA were serially added. Each plotted value is the
   mean of at least five multiple wells and is normalized to baseline OCR
   and total protein levels. The results were analyzed using Wave software
   (Seahorse), and data are presented as means ± SD. Significance was
   calculated using two‐way ANOVA with Tukey's multiple‐comparison test.

Statistical Analysis

   GraphPad Prism 7.0 software was used for graphical figures and
   statistical analyses. All data are expressed as means ± SEM. Student's
   t‐test was used to assess significant differences in two‐group
   comparisons; one‐way ANOVA was used for more than two groups. Spearman
   correlation was used to analyze correlations between two indicators. p
   values < 0.5 were regarded as statistically significant.

Ethics Approval and Consent to Participate

   The animal study was approved by the Institutional Animal Care and Use
   Committee of the Southern University of Science and Technology
   (SUSTC‐JY2018049).

Conflict of Interest

   The authors declare no conflict of interest.

Author Contributions

   W.Z., S.Q., and X.L. contributed equally to this work. J.Z., Y.L., and
   W.Z. designed the study; W.Z., M.C., and H.B.T. performed the
   experiments and collected all data; S.Y.Q. and X.K.L. analyzed and
   processed the data; W.Z., T.Z.W., and X.H. participated in the writing
   and figure generation. J.Z., Y.L., and X.G. critically reviewed the
   manuscript. All authors have read and approved the manuscript.

Supporting information

   Supporting Information
   [161]ADVS-12-2500369-s001.docx^ (1.7MB, docx)

Acknowledgements