cell lines  (ATCC)

Journal: iScience

Article Title: NAT10 drives hepatocellular carcinoma progression through SQLE-mediated cholesterol biosynthesis and is targetable by remodelin

doi: 10.1016/j.isci.2025.114488

Figure Lengend Snippet: NAT10 knockdown suppresses cell proliferation in HCC cell lines (A) Western blot analysis of NAT10 and PCNA in MHCC97H, SKHep1, and HepG2 cells with knockdown of NAT10. (B–D) The effects of NAT10 knockdown on cell growth (B), colony formation (C), and wound healing (D) ( n = 3, performed in triplicate). (E and F) Effects of NAT10 re-expression on cell growth (E) and colony formation (F) in SKHep1-sgNAT10 and MHCC97H-sgNAT10 cells. ( n = 3, performed in triplicate). Data are represented as means ± SD. Unpaired, two-tailed Student’s t test (C, D, and F). Difference in cell viability between two groups was determined by repeated-measures ANOVA (B, E) ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: Human: SKHep1 cells , ATCC , HTB-52; RRID: CVCL_0525.

Techniques: Knockdown, Western Blot, Expressing, Two Tailed Test

Journal: iScience

Article Title: NAT10 drives hepatocellular carcinoma progression through SQLE-mediated cholesterol biosynthesis and is targetable by remodelin

doi: 10.1016/j.isci.2025.114488

Figure Lengend Snippet: NAT10 contributes to HCC development in vivo (A and B) Subcutaneous tumors derived from MHCC97H cells with NAT10 knockout analyzed for tumor weight and volume (A), H&E staining, Ki-67 staining, and PCNA staining in subcutaneous tumors derived from MHCC97H cells with NAT10 knockout (B) ( n = 10). (C) Western blots analysis of NAT10 and PCNA expression in subcutaneous tumors derived from MHCC97H cells with NAT10 knockout. (D–F) Subcutaneous tumors derived from SKHep1 cells with NAT10 knockout analyzed for tumor weight and volume (D), H&E staining, Ki-67 staining, and PCNA staining in subcutaneous tumors derived from SKHep1 cells with NAT10 knockout (E) and PCNA protein expression (F) ( n = 8 for sgControl and n = 10 for sgNAT10). (G and H) Subcutaneous tumors derived from NAT10-overexpressing MHCC97H cells were analyzed for tumor weight and volume (G), H&E staining, Ki-67 staining, and PCNA staining in subcutaneous tumors derived from NAT10-overexpressing MHCC97H cells (H) ( n = 8). (I) Western blots analysis of NAT10 and PCNA expression in subcutaneous tumors derived from NAT10-overexpressing MHCC97H cells. Data are represented as means ± SD. Unpaired, two-tailed Student’s t test (A [middle], B, D [middle], E, G [middle], H). Difference in tumor volume between two groups was determined by repeated-measures ANOVA (A [right], D [right], G [right]). Scale bars, 200 μm (B, E, H). ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: Human: SKHep1 cells , ATCC , HTB-52; RRID: CVCL_0525.

Techniques: In Vivo, Derivative Assay, Knock-Out, Staining, Western Blot, Expressing, Two Tailed Test

Journal: iScience

Article Title: NAT10 drives hepatocellular carcinoma progression through SQLE-mediated cholesterol biosynthesis and is targetable by remodelin

doi: 10.1016/j.isci.2025.114488

Figure Lengend Snippet: Increased cholesterol level mediates NAT10 function in HCC progression (A) Intracellular cholesterol levels in MHCC97H, SKHep1, and HepG2 cells overexpressing NAT10 or NAT10 knockout (sgNAT10) in MHCC97H, SKHep1, and HepG2 cells ( n = 3, performed in triplicate). (B and C) Cholesterol supplementation restored proliferation (B) and colony formation (C) in SKHep1-sgNAT10 and MHCC97H-sgNAT10 cells. ( n = 3, performed in triplicate). (D and E) A high-cholesterol diet (HCD) restored tumor growth in tumor-bearing mice with subcutaneously inoculated SKHep1-sgNAT10 xenografts and eliminated the antitumor effect of NAT10 knockout ( n = 6 for control and n = 8 for cholesterol). Data represent mean ± SD; unpaired, two-tailed Student’s t test (C, D) or two-way ANOVA (B, E). The significance of the difference in cholesterol concentrations was determined by Mann-Whitney U test (A). ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: Human: SKHep1 cells , ATCC , HTB-52; RRID: CVCL_0525.

Techniques: Knock-Out, Control, Two Tailed Test, MANN-WHITNEY

Journal: iScience

Article Title: NAT10 drives hepatocellular carcinoma progression through SQLE-mediated cholesterol biosynthesis and is targetable by remodelin

doi: 10.1016/j.isci.2025.114488

Figure Lengend Snippet: NAT10 promotes HCC progression via cholesterol biosynthesis and SQLE/AKT/mTOR signaling (A) KEGG pathway analysis of the RNA-Seq data derived from subcutaneous tumor tissues with NAT10 knockdown relative to a control vector. GSEA of the RNA-Seq data from subcutaneous tumor tissues with NAT10 knockdown relative to a control vector. (B) Elevated NAT10 expression in HCC patients correlates with enhanced cholesterol biosynthesis pathway scores in the ICGC cohort ( p < 0.0001 ) ( n = 101 for low and n = 102 for high) and the OEP000321 (Fudan University) protein database ( p < 0.05 ). ( n = 79 for low and n = 80 for high). (C and D) NAT10 knockout (sgNAT10) reduces mRNA (C) and protein (D) levels of cholesterol biosynthesis genes (SQLE, LSS, and DHCR24) in liver cancer cells, while NAT10 overexpression increases SQLE and LSS protein expression in MHCC97H cells (see A–S4C for expanded profiles). (E and F) Overexpression of SQLE rescues cell viability (E) and colony formation (F) ( n = 3, performed in triplicate) in SKHep1-sgNAT10 and MHCC97H-sgNAT10 cells. (G and H) Terbinafine (SQLE inhibitor) exhibits anti-proliferative effects comparable to NAT10 knockout and synergistically reduces HCC cell viability when combined with sgNAT10 ( n = 3, performed in triplicate). (I and J) NAT10 regulates SQLE activation through the AKT/mTOR pathway, as demonstrated by modulating mTOR expression. Data represent mean ± SD; unpaired, two-tailed Student’s t test (B, F, H) or ANOVA with repeated-measures analysis of variance two-way ANOVA (E, G). Mann-Whitney U test was used to assess the significance of the differences in mRNA expression (C). ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: Human: SKHep1 cells , ATCC , HTB-52; RRID: CVCL_0525.

Techniques: RNA Sequencing, Derivative Assay, Knockdown, Control, Plasmid Preparation, Expressing, Knock-Out, Over Expression, Activation Assay, Two Tailed Test, MANN-WHITNEY

Journal: iScience

Article Title: NAT10 drives hepatocellular carcinoma progression through SQLE-mediated cholesterol biosynthesis and is targetable by remodelin

doi: 10.1016/j.isci.2025.114488

Figure Lengend Snippet: Pharmacological inhibition of NAT10 suppresses HCC progression in vitro and in vivo (A) Western blot analysis showing reduced NAT10 protein levels in MHCC97H, PLC/PRF/5, SKHep1, and HepG2 cells treated with remodelin. (B) The mRNA levels of cholesterol biosynthesis genes (including SQLE, LSS, DHCR24, PMVK, HMGCR, SC4MOL, and SC5D) in remodelin-treated MHCC97H, SKHep1, and PLC/PRF/5 cells ( n = 3, performed in triplicate). (C) Cholesterol levels in remodelin-treated MHCC97H, SKHep1, and PLC/PRF/5 cells ( n = 3, performed in triplicate). (D and E) Remodelin treatment significantly inhibited cell viability (D) and colony formation (E) in multiple HCC cell lines ( n = 3, performed in triplicate). (F and G) Remodelin failed to suppress cell growth (F) or colony formation (G) in NAT10-knockout (sgNAT10) HCC cells, indicating NAT10-dependent anti-proliferative effects ( n = 3, performed in triplicate). (H) Remodelin administration (60 mg/kg/day, oral gavage) suppressed tumor growth in subcutaneous SKHep1 xenograft models, as evidenced by reduced tumor size and weight ( p < 0.001) ( n = 10). (I) H&E staining, Ki-67 staining and in subcutaneous tumors derived from remodelin-treated SKHep1 cells. (J) Western blots analysis of NAT10 and PCNA expression in subcutaneous tumors derived from remodelin-treated SKHep1 cells. (K) Remodelin administration (60 mg/kg/day, oral gavage) suppressed tumor growth in subcutaneous MHCC97H xenograft models, as evidenced by reduced tumor size and weight ( p < 0.001) ( n = 8). (L) H&E staining, Ki-67 staining and in subcutaneous tumors derived from remodelin-treated MHCC97H cells. (M) Western blots analysis of NAT10 and PCNA expression in subcutaneous tumors derived from remodelin-treated MHCC97H cells. Data are represented as means ± SD. Unpaired, two-tailed Student’s t test (E, G, H [middle], I, K [middle], and L). Difference between two groups was determined by repeated-measures ANOVA (D, F, H [right], K [right]). Mann-Whitney U test was used to assess the significance of the differences in mRNA expression, cholesterol concentrations (B, C). Scale bars, 200 μm (I, L). ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: Human: SKHep1 cells , ATCC , HTB-52; RRID: CVCL_0525.

Techniques: Inhibition, In Vitro, In Vivo, Western Blot, Knock-Out, Staining, Derivative Assay, Expressing, Two Tailed Test, MANN-WHITNEY

Journal: Cancer Research Communications

Article Title: Epithelial–Mesenchymal Transition and Stress Adaptations Underlie Yttrium-90 Resistance in Liver Cancer Cell Lines

doi: 10.1158/2767-9764.CRC-25-0627

Figure Lengend Snippet: Heterogeneity of response to 90 Y microsphere treatment across human liver cancer cell lines. A, Dose–response curves of cell viability after 10-day treatment to escalating 90 Y microsphere activities (0–20 MBq/mL) in each of 10 cell lines. Each point represents the mean surviving fraction relative to untreated baseline control across all independent experiments (error bars: SEM). B, For each experiment, the area under the dose–response curve was calculated and normalized to yield nAUC (0 = sensitive and 1 = resistant). Cell lines are ordered left to right by decreasing nAUC (increased sensitivity). Horizontal bar indicates mean nAUC for each cell line across experiments. Group differences were assessed by one-way ANOVA with a Tukey multiple comparisons test (*, P < 0.05; **, P < 0.01; ***, P < 0.001). C, Relationship between response to 90 Y and established HCC transcriptomic subtypes. Cell lines were assigned to select HCC transcriptomic subtypes by nearest template prediction. nAUC distributions differed by subtype, with Hoshida S1 and C1 (cholangiocarcinoma-like) subtypes associated with 90 Y resistance ( P < 0.05, Kruskal–Wallis rank-sum test). No correlation with the hepatoblastoma HB-16 signature was observed. D, PCA of RNA baseline expression profiles of all cell lines demonstrates clustering of the five most resistant cell lines by nAUC (red: SK-Hep1, SNU-449, SNU-475, SNU-387, and SNU-423) along PC2/PC3 (13.1%/8.2% variance), with clear separation of the three most 90 Y-sensitive cell lines (yellow: PLC/PRF/5, Hep3B, and HepG2) along PC2.

Article Snippet: The following human liver cancer cell lines were obtained from the ATCC: SK-Hep1 (RRID: CVCL_0525), Hep-3B2 (RRID: CVCL_0326), HepG2/C3A (RRID: CVCL_1098), PLC/PRF/5 (RRID: CVCL_0485), SNU-387 (RRID: CVCL_0250), SNU-423 (RRID: CVCL_0366), SNU-449 (RRID: CVCL_0454), and SNU-475 (RRID: CVCL_0497).

Techniques: Control, Expressing

Journal: Cancer Research Communications

Article Title: Epithelial–Mesenchymal Transition and Stress Adaptations Underlie Yttrium-90 Resistance in Liver Cancer Cell Lines

doi: 10.1158/2767-9764.CRC-25-0627

Figure Lengend Snippet: EMT and adhesion pathways associated with 90 Y resistance. A, EN regression analysis identified 18 protein-encoding genes with nonzero coefficients for which expression was correlated with 90 Y resistance across all cell lines at baseline (EN score >0.7). Among these, ITGA3 (EN = 0.911, R = 0.79) encodes the α subunit of the α3β1 integrin heterodimer, previously reported to influence HCC tumor progression and immune checkpoint expression. B, Differential expression analysis of RNA expression between 90 Y-resistant (SK-Hep1, SNU-449, SNU-475, SNU-387, and SNU-423) and -sensitive (PLC/PRF/5, Hep3B, and HepG2) cell lines. Groupings defined a priori by nAUC Z -scores and baseline PCA. Volcano plot of log 2 FC vs. −log 10 P value (FDR adjusted) of genes upregulated (red) and downregulated (blue) in 90 Y-resistant vs. -sensitive cell lines. Genes involved in the extracellular matrix ( ITGA3 ) and cancer stemness ( CD44 ) were significantly upregulated in 90 Y-resistant cell lines. C, GSEA of Hallmark pathways demonstrates strong upregulation of the EMT pathway in 90 Y-resistant cell lines (mean log FC 8.9), which contains CD44 and ITGB1 , the counterpart of ITGA3 in the a3b1 integrin heterodimer. Numbers next to each gene set bar represent FDR. D, Consistent with Hallmark EMT enrichment, KEGG and Reactome pathways associated with extracellular matrix and integrin cell surface interactions are enriched in resistant cell lines. E, qPCR (mean log 2 FC, error bars represent SEM, with n = 2 biological replicates) and Western blot validation of ITGA3/a3b1 and CD44 confirming elevated expression of these genes in the most 90 Y-resistant (SK-Hep1) vs. 90 Y-sensitive (PLC/PRF/5) and intermediate (SNU-398) cell lines, consistent with an EMT/adhesion phenotype associated with 90 Y resistance. F, qPCR of tumor vs. normal CD44 expression demonstrates a trend toward higher CD44 expression in those with IR or OFP ( n = 5) vs. SR ( n = 12), although not powered for statistical significance ( P = 0.43 Mann–Whitney). Color circle indicates treatment intent: blue, radiation segmentectomy; orange, multicompartment dosimetry (MCD) with TAD > 205 Gy. MIRD, medical internal radiation dose. Relative expression from qPCR data = 2 −(Cttarget − Cthousekeeping) , in which Ct is the detection crossing threshold.

Article Snippet: The following human liver cancer cell lines were obtained from the ATCC: SK-Hep1 (RRID: CVCL_0525), Hep-3B2 (RRID: CVCL_0326), HepG2/C3A (RRID: CVCL_1098), PLC/PRF/5 (RRID: CVCL_0485), SNU-387 (RRID: CVCL_0250), SNU-423 (RRID: CVCL_0366), SNU-449 (RRID: CVCL_0454), and SNU-475 (RRID: CVCL_0497).

Techniques: Expressing, Quantitative Proteomics, RNA Expression, Western Blot, Biomarker Discovery, MANN-WHITNEY

Journal: Cancer Research Communications

Article Title: Epithelial–Mesenchymal Transition and Stress Adaptations Underlie Yttrium-90 Resistance in Liver Cancer Cell Lines

doi: 10.1158/2767-9764.CRC-25-0627

Figure Lengend Snippet: 90 Y-resistant and -sensitive cell lines demonstrate distinct patterns of biological process enrichment after 90 Y microsphere treatment. A, Volcano plot of log 2 FC vs. −log 10 P value of genes upregulated (red) and downregulated (blue) after 90 Y microsphere treatment in select 90 Y-resistant cell lines (SK-Hep1, SNU-449, and SNU-387). B, GSEA of Hallmark pathways demonstrates upregulation of pathways involved with inflammation and immune response after treatment in the 90 Y-resistant group, such as IFNγ response (mean log 2 FC = 7.4), among others. C, Consistent with Hallmark IFNγ and IFNα enrichment, KEGG and Reactome pathways associated with cytokine signaling and antigen presentation were upregulated after treatment with 90 Y. D, Volcano plot of log 2 FC vs. −log 10 P value of genes upregulated (red) and downregulated (blue) after 90 Y microsphere treatment in select 90 Y-sensitive cell lines (PLC/PRF/5 and HepG2). E, GSEA of Hallmark pathways demonstrates downregulation of many of the inflammatory and immune response–related pathways that were upregulated in the resistant cell lines, including IFNγ response (mean log 2 FC = −4.3,) and IFNα response (mean log 2 FC = −4.7). No Hallmark pathways demonstrated significant upregulation after treatment in 90 Y-sensitive cells. F, KEGG and Reactome pathway analysis corroborates downregulation of interferon- and antigen presentation–associated pathways, along with those involved with growth signaling such as MAPK (mean log FC = −4.01) and TGFβ (mean log FC = −2.62). Numbers next to each gene set bar represent FDR. Significance set at FDR <0.05 and log 2 FC > 2. Heterogeneous activation of stress and survival pathways after 90 Y microsphere treatment across different liver cancer cell lines. Significant variation in Hallmark gene sets ( G ) IFNγ, ( H ) IFNα, ( I ) TNFα signaling, ( J ) hypoxia, ( K ) myogenesis, ( L ) p53 pathway, ( M ) oxidative phosphorylation, and ( N ) DNA repair, showing heterogeneity in stress and survival pathway activation across cell lines after 90 Y microsphere treatment.

Article Snippet: The following human liver cancer cell lines were obtained from the ATCC: SK-Hep1 (RRID: CVCL_0525), Hep-3B2 (RRID: CVCL_0326), HepG2/C3A (RRID: CVCL_1098), PLC/PRF/5 (RRID: CVCL_0485), SNU-387 (RRID: CVCL_0250), SNU-423 (RRID: CVCL_0366), SNU-449 (RRID: CVCL_0454), and SNU-475 (RRID: CVCL_0497).

Techniques: Immunopeptidomics, Activation Assay, Phospho-proteomics

Journal: Cancer Research Communications

Article Title: Epithelial–Mesenchymal Transition and Stress Adaptations Underlie Yttrium-90 Resistance in Liver Cancer Cell Lines

doi: 10.1158/2767-9764.CRC-25-0627

Figure Lengend Snippet: qRT-PCR validation of gene expression representing relevant biological pathways after 90 Y microsphere treatment in the most sensitive (PLC/PRF/5), resistant (SK-Hep1), and intermediate (SNU-398) cell lines. Select genes involved in ( A ) interferon stimulation and antigen presentation were mostly upregulated in both SNU-398 and SK-Hep1 after treatment, with the exception of GBP1 in SK-Hep1 and MX1 and IFI27 in SNU-398 which did not have reliable qPCR readouts. TXNIP was strongly upregulated in SK-Hep1 after treatment, consistent with increased oxidative stress signaling. ( B ) SNU-398 demonstrated very strong upregulation of inflammatory response genes, in particular CCL5 and TNFAIP2 . C, Distinct patterns of DNA damage and cell stress genes were seen across cell lines, with SK-Hep1 showing upregulation of BRCA1 and downregulation of BNIP3 . D, ECM genes CD44 and ITGA3 were strongly upregulated in SNU-398 after treatment, suggesting stress-induced EMT acquisition. Although there was slight downregulation of these genes in SK-Hep1, they remained at highest abundance in this line consistent with an EMT-associated expression profile. All experiments were performed in technical triplicate and error bars represent SEM when >1 biological replicate was performed. nd, no reliable qRT-PCR readout.

Article Snippet: The following human liver cancer cell lines were obtained from the ATCC: SK-Hep1 (RRID: CVCL_0525), Hep-3B2 (RRID: CVCL_0326), HepG2/C3A (RRID: CVCL_1098), PLC/PRF/5 (RRID: CVCL_0485), SNU-387 (RRID: CVCL_0250), SNU-423 (RRID: CVCL_0366), SNU-449 (RRID: CVCL_0454), and SNU-475 (RRID: CVCL_0497).

Techniques: Quantitative RT-PCR, Biomarker Discovery, Gene Expression, Immunopeptidomics, Expressing