EGR1 is a direct target of KLF12 mediating cell viability.

(A) Chromatin immunoprecipitation (ChIP) assay showed binding of KLF12 to motif 2 of the EGR1 promoter, but not to motif 1 in LS-174T cells. Immunoprecipitation with IgG antibody was used as a control. (B) Transient co-transfection of cells was performed with EGR1 promoter wild type (WT), or mutant luciferase reporter plasmids with renilla luciferase control plasmids and non-targeting or KLF12 siRNA. The luciferase activity was determined. (C) EGR1 protein levels in CRC cell lines. Actin served as a loading control. (D) EGR1 mRNA (left) and protein (right) levels in LS174T cells stably transfected with either GFP or KLF12. Actin served as a loading control. E. EGR1 mRNA (left) and protein (right) levels in HCT116 cells stably transfected with either a vector containing nonsilencing control shRNA (shCon) or one of two KLF12 shRNAs (shKLF12-1 and shKLF12-2). Actin served as a loading control.</p

Krüppel-Like Factor 12 Promotes Colorectal Cancer Growth through Early Growth Response Protein 1

<div><p>Krüppel-like factor 12 (KLF12) is a transcription factor that plays a role in normal kidney development and repression of decidualization. KLF12 is frequently elevated in esophageal adenocarcinoma and has been reported to promote gastric cancer progression. Here, we examined the role of KLF12 in colorectal cancer (CRC). Indeed, KLF12 promotes tumor growth by directly activating early growth response protein 1 (EGR1). The levels of KLF12 and EGR1 correlate synergistically with a poor prognosis. These results indicate that KLF12 likely plays an important role in CRC and could serve as a potential prognostic marker and therapeutic target.</p></div

KLF12 enhances cell viability by activating EGR1.

<p>(A) Protein levels of KLF12 and EGR1 (left) and cell viability (right) of LS174T/GFP and LS174T/KLF12 cells transfected with either non-targeting siRNA as control (con) or EGR1 siRNA. Actin served as a loading control. (B) EGR1 protein levels (left) and cell viability (right) of LS174T cells transfected with either GFP (LS174T/GFP) or EGR1 (LS174T/EGR1). (C) Tumor weight in mice orthotopically injected with either LS174T/GFP or LS174T/EGR1 cells (n = 8 for each group).</p

KLF12 promotes tumor growth <i>in vitro</i> and <i>in vivo</i>.

<p>(A) KLF12 protein levels in CRC cell lines. Actin served as a loading control. (B) KLF12 expression (top) and cell viability (bottom) of LS174T cells stably transfected with either GFP or KLF12. Actin served as a loading control. C. KLF12 expression (top) and cell viability (bottom) of HCT116 cells stably transfected with either a vector containing nonsilencing control shRNA (shCon) or one of two KLF12 shRNAs (shKLF12-1 and shKLF12-2). Actin served as a loading control. D. Tumor weight in mice orthotopically injected with either LS174T/GFP or LS174T/KLF12 cells (n = 8 for each group). E. Tumor weight in mice orthotopically injected with either HCT116/shCon, HCT116/shKLF12-1, or HCT116/shKLF12-2 cells (n = 9 for each group).</p

KLF12 and EGR1 expression levels are synergistically correlated with worse prognosis in CRC.

<p>Kaplan-Meier Disease free survival (DFS) curves of a cohort of 232 CRC patients (Moffitt cohort, n = 177; Vanderbilt Medical Center cohort, n = 55) with either high or low mRNA levels of KLF12 (A), EGR1 (B), or both (C). Vertical bars denote censored patients.</p

KLF12 and EGR1 is co-expressed <i>in vivo</i>.

<p>(A) Immunohistochemistry of EGR1 in nude mice injected with either LS174/GFP cells as control, or with LS174 cells stably transfected with KLF12 (LS174/KLF12). (B) Immunohistochemistry of KLF12 and EGR1 in matching sections taken from two CRC patients (Patient #1 and #2). Magnification x10. (C) Pearson correlation of KLF12 and EGR1 mRNA expression in a cohort of 232 CRC patients (Moffitt cohort, n = 177 and Vanderbilt Medical Center cohort, n = 55).</p

Changes in Cancer Cell Metabolism Revealed by Direct Sample Analysis with MALDI Mass Spectrometry

<div><p>Biomarker discovery using mass spectrometry (MS) has recently seen a significant increase in applications, mainly driven by the rapidly advancing field of metabolomics. Instrumental and data handling advancements have allowed for untargeted metabolite analyses which simultaneously interrogate multiple biochemical pathways to elucidate disease phenotypes and therapeutic mechanisms. Although most MS-based metabolomic approaches are coupled with liquid chromatography, a few recently published studies used matrix-assisted laser desorption (MALDI), allowing for rapid and direct sample analysis with minimal sample preparation. We and others have reported that prostaglandin E₃ (PGE₃), derived from COX-2 metabolism of the omega-3 fatty acid eicosapentaenoic acid (EPA), inhibited the proliferation of human lung, colon and pancreatic cancer cells. However, how PGE₃ metabolism is regulated in cancer cells, particularly human non-small cell lung cancer (NSCLC) cells, is not fully understood. Here, we successfully used MALDI to identify differences in lipid metabolism between two human non-small-cell lung cancer (NSCLC) cell lines, A549 and H596, which could contribute to their differential response to EPA treatment. Analysis by MALDI-MS showed that the level of EPA incorporated into phospholipids in H596 cells was 4-fold higher than A549 cells. Intriguingly, H596 cells produced much less PGE₃ than A549 cells even though the expression of COX-2 was similar in these two cell lines. This appears to be due to the relatively lower expression of cytosolic phospholipase A₂ (cPLA₂) in H596 cells than that of A549 cells. Additionally, the MALDI-MS approach was successfully used on tumor tissue extracts from a K-ras transgenic mouse model of lung cancer to enhance our understanding of the mechanism of action of EPA in the <i>in vivo</i> model. These results highlight the utility of combining a metabolomics workflow with MALDI-MS to identify the biomarkers that may regulate the metabolism of omega-3 fatty acids and ultimately affect their therapeutic potentials.</p></div

Representative mass spectrum comparing the spectral region from <i>m/z</i> 800−810 from H596 untreated (A) and EPA treated (B) cells.

<p>A significant increase is observed for <i>m/z</i> 802.5, which corresponds to a PC (36∶5) fatty acid. MS/MS confirmed that PC (16∶0/20∶5) was a component of the observed <i>m/z</i> value (data not shown). Spectra shown in (C) and (D), corresponding to the A549 cells line untreated and EPA treated; respectively, also show an increase in <i>m/z</i> 802.5 after treatment with EPA. However, this increase is significantly less than the increase observed for the H596 cell line.</p

Western-blot showing similar COX-2 levels between both A549 and H596

<p>(<b>A</b>). The anti-proliferative effect of EPA in human non-small-cell lung cancer A549 and H596 cells (B). Exposure of A549 cells to EPA for 72 hrs produced a ten-fold stronger inhibition of cell proliferation in A549 cells than that in H596 cells.</p

Representative PCA score plots of mass spectra collected directly from dried A549 and H596 cell spot by MALDI-MS.

<p>Cells were untreated (A) and treated with 50 µM EPA (B). Untreated A549 and H596 cells had similar mass spectra (A). However, post-EPA treatment led to a clearly differentiated metabolic pattern between these two cell lines (B). The data are representative of two biological replicates with repeated analysis. An average of 25 mass spectra were collected and averaged from each cell spot. The amount of time for each analysis was less than one minute. The data were represented from three replicated experiments.</p