KLF7: a new candidate biomarker and therapeutic target for high-grade serous ovarian cancer

Bioinformatic meta-analyses of transcriptome datasets were performed using the Bioconductor package curatedOvarianData v1.24.0 [20] implemented in R v3.6.1 [21]. The available datasets were filtered in order to keep only samples from patients with serous histology, high-grade and late-stage (FIGO stage III/IV) for which data on age and residual mass after debulking surgery were available. Univariate and multiple Cox-regression analyses (based on gene, age, stage and residual tumor mass) for Overall Survival (OS) were performed on the eligible datasets [i.e. E.MTAB. 386 (https://​www.​ebi.​ac.​uk/​arrayexpress/​experiments/​E-MTAB-386/​), GSE26712 (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc=​GSE26712), GSE49997 (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc=​GSE49997), GSE9891 (https://​www.​ncbi.​nlm.​nih.​gov/​geo/​query/​acc.​cgi?​acc=​GSE9891), TCGA-OV RNAseq and TCGA-OV (https://​portal.​gdc.​cancer.​gov/​)] for each KLF family member. Forest plots, combined hazard ratios with 95% confidence intervals and the corresponding p-values were performed for each KLF family member.

Further in depth investigation was performed on two out of the six datasets. In particular, the transcriptome series GSE26712 (with clinical features matched to the Affymetrix human U133A microarray data) was downloaded from the Gene Expression Omnibus (GEO) database [22]. A population cohort of 185 patients with late-stage HGSOC was eligible for the analysis (Table 1). As a second external dataset for KLF7, normalized RNA-Seq results of the TCGA-OV patients’ cohort were downloaded from the National Cancer Institute Genomic Data Commons Data Portal (https://​portal.​gdc.​cancer.​gov/​) and matched to the corresponding clinical features, as described by the TCGA group [23]. A population cohort of 266 patients was selected as late-stage HGSOC, as indicated in Table 1. The prognostic effect of the various parameters on OS probabilities was estimated using the Kaplan–Meier method and survival curves were evaluated using the log-rank test. The best cutoff for KLF7 expression values was chosen based on the results of Cutoff Finder [24] analyses implemented in R v3.6.1 software environment [21]. Best cutoff value was used to dichotomize patients into low and high KLF7 expression groups. Only variables with p-value< 0.1 in the univariate analysis were included in multivariate analysis using the Cox proportional hazards model.

The human ovarian carcinoma cell lines PEO1 and COV318 were obtained from the European Collection of Cell Cultures (ECACC, Salisbury, UK), while NIH:OVCAR-3 and OV-90 from the American Type Culture Collection (ATCC, Milan, Italy). Susan Horwitz (Albert Einstein Medical College) donated the HEY cell line. PEO1 is an adherent cell line derived from a malignant effusion from the peritoneal ascites of a chemotherapy-treated patient with a poorly differentiated serous adenocarcinoma [25]. COV318 is a human ovarian epithelial-serous carcinoma cell line established from a peritoneal ascites [26]. The NIH:OVCAR-3 line was established from the malignant ascites of a chemotherapy-treated patient with progressive adenocarcinoma of the ovary [27]. OV-90 cells were derived from a chemotherapy-naive grade 3, stage IIIC, malignant papillary serous adenocarcinoma [28]. HEY cell line was derived from a human ovarian cancer xenograft originally grown from a peritoneal deposit of a patient with moderately differentiated papillary cystadenocarcinoma of the ovary [29].

PEO1, NIH:OVCAR-3 and HEY were cultured in RPMI 1640 (Roswell Park Memorial Institute Medium) and COV318 in Dulbeccoʼs modified Eagle′s medium (Sigma-Aldrich, St. Louis, MO, USA). OV-90 cells were cultured in 1:1 mixture of MCDB 105 medium (containing a final concentration of 1.5 g/L sodium bicarbonate) and Medium 199 (containing a final concentration of 2.2 g/L sodium bicarbonate). Medium was supplemented with 10% fetal bovine serum (FBS) for PEO1, COV318, NIH:OVCAR-3 and HEY and 15% FBS for OV-90, plus MEM (Minimum Essential Medium) Non-Essential Amino Acid 1%, glutamine 1 mM and kanamycin 1%. Sodium pyruvate 2 mM was also added to PEO1 medium (Sigma-Aldrich). Cells were grown in a fully humidified atmosphere of 5% CO₂/95% air, at 37 °C. Cells were routinely tested for mycoplasma (MycoAlert mycoplasma detection kit, LONZA, Rockland, ME, USA) and validated by STR (Short Tandem Repeat) DNA profiling (BMR Genomics srl, Padua, Italy).

Silencing of KLF7 gene expression in OV-90 and PEO1 cells was obtained by transfection with Transfectin (Bio-Rad, CA, USA) and specific siRNAs (siKLF7) (ON-TARGETplus SMARTpool siRNA KLF7, Dharmacon, Lafayette, CO), while a non-targeting siRNA pool was used as control (siC) (ON-TARGETplus Non-targeting Control Pool, Dharmacon, Lafayette, CO). Sequence information related to ON-TARGETplus SMARTpool siRNA KLF7 (pool of 4 siRNA) are provided in Supplementary File 1 (Table S1A). Silencing efficiency was verified by RT-qPCR using primers reported in Table S1B. Cells transiently transfected with siKLF7 or siC were used to perform in vitro assays.

For proliferation assay, 1.2 × 10⁶ OV-90 cells and 1.5 × 10⁶ PEO1 cells were seeded in corning flask (25 cm²) in complete culture medium and transfected with siKLF7 or siC. After 24 h, 48 h and 72 h from transfection, cells were harvested by trypsinization and viable cells counted with NucleoCounter NC-200 (Chemometec, Lillerød, Denmark). The mean cell proliferation at Time x (Tx) was expressed as average percentage increase relative to T = 0 h (T0). Experiments were performed at least three times.

For both Transwell migration and invasion assays, siC and siKLF7 OV-90 (10,000 cells) and PEO1 (100,000 cells) were added into the upper chamber of the insert (8 μm pore size; Corning, NY, USA) after 24 h from transfection and further 24 h of serum starvation. For invasion assays, the upper chamber of the insert was precoated with Matrigel (Corning, NY, USA). In both assays, the lower chamber contained medium with 10% FBS as chemoattractant. Cells were incubated for 24 h at 37° in a 5% CO₂ atmosphere, inserts washed with PBS (Phosphate-buffered saline) and cells on the top surface of the insert removed with a cotton swab. Cells adhering to the lower surface were fixed with ethanol, stained with crystal violet, washed with H₂O and counted under a microscope. Experiments were performed at least three times. The relative migrated or invaded cell numbers were expressed as the percentage of controls.

Real-time qPCR on mRNAs was performed as previously described [30] using the primers listed in Supplementary File 1 (Table S2). The geometric mean of SNRPD3 and RPLP0 was taken as reference genes, following GeNorm algorithm [31]; relative quantification of target mRNA was performed according to the ΔΔCt method [32]. Experiments were performed at least three times.

Western blot analysis of total cell lysates (30 μg) was performed as previously described [30], using the following antibodies: anti-KLF7 (sc-101,034, Santa Cruz, Santa Cruz, CA, USA, 1:1000); anti-SNAIL (#3879, Cell Signaling Technology Inc., Danvers, MA, 1:1000); anti-ZEB2 (ab25837, Abcam, Cambridge, UK, 1:1000); anti-VIM monoclonal antibody (sc-73,259, Santa Cruz, 1:1000); anti-CD44 (M7082, Dako Agilent Technologies, Santa Clara, CA, USA, 1:500); anti-GAPDH (ab8245, Abcam, 1:5000). Blots were visualized by enhanced chemiluminescence system (Amersham Biosciences, Buckinghamshire, UK) using a Chemidoc imaging system (Bio-Rad). Experiments were performed at least three times. Proteins were densitometrically quantified using Imager ChemiDoc™ XRS+ Software (Bio-Rad, Version 6.0.1.34). All proteins were normalized to GAPDH loading control.

Activities of matrix metalloproteinases 2 and 9 (MMP2 and MMP9) in siC and siKFL7 OV-90 and PEO1 cells were determined by gelatin zymography. Forty-eight hours after transfection, cell culture media was changed to serum-free media and cells were incubated for another 24 h. Culture media were mixed with sample buffer and loaded for SDS-PAGE. Gelatinase zymography was performed in 10% SDS polyacrylamide gel in the presence of 0.1% gelatin under non-reducing conditions. Following electrophoresis the gels were washed three times in 2.5% Triton X-100 for 20 min at room temperature to remove SDS. The gels were then incubated at 37 °C overnight in substrate buffer containing 50 mM Tris-HCl and 5 mM CaCl₂, 0.15 M NaCl and 1 μM ZnCl₂ and stained with 0.5% Coomassie Blue R250 in 50% methanol and 10% glacial acetic acid for 30 min and destained. Experiments were performed at least three times.

Twenty-four hours after transfection, siC and siKFL7 OV-90 and PEO1 cells were seeded in 4-well chamber slides in complete growth medium and incubated for further 48hs. Thereafter, cells were fixed in 4% paraformaldehyde for 20 min at 20 °C and then blocked with 5% v/v goat serum in PBS for 1 h. Immunofluorescence staining was obtained using anti-CD44 (M7082, Dako Agilent Technologies, 1:200), following overnight incubation at + 4 °C. After washing, cells were incubated with secondary antibody anti-mouse Alexa Fluor-488 conjugate (1:200) (Thermo Fisher Scientific, Walthman, MA, USA), in the dark for 30 min at 20 °C. Coverslip was mounted onto slides using an antifade mounting reagent containing DAPI. Slides were observed under a fluorescence microscope (Leica Biosystems, Newcastle, UK) using a 40X objective. Appropriate controls were included to evaluate non-specific staining of the secondary antibody or background autofluorescence.

For spheroid cultures, cells were mixed with a solution of VitroGel 3D-RGD (TheWell, Bioscence, NJ, USA)/0.5X PBS at a 3:1 ratio to obtain the final cell concentration of 2 × 10⁵ cells/mL. Hydrogel/cell mixture was added to well plate and then incubated at room temperature for 20 min. After stabilization, complete cell culture medium was added on the top of the hydrogel. The 3D cell cultures were maintained for 10 days and continuously monitored using a microscope DM IL LED (Leica Microsystems). Spheroids were first recovered from hydrogel by VitroGel Cell Recovery Solution (TheWell, Bioscence), according to the manufacturer’s protocol, and then measured (diameters, μm), using Leica Application Suite (LAS) analysis. At least 20 spheroids were analyzed for each data point. Experiments were performed at least three times. For immunofluorescence analysis, spheroids were fixed in 4% paraformaldehyde for 20 min at room temperature, and permeabilized in 0.5% v/v Triton X-100 in PBS for 10 min, prior to be blocked with 20% v/v serum and 0.1% v/v Triton X-100 in PBS for 1 h. Immunofluorescence staining was obtained using anti-VIM (Clone V9, Ready-to-Use, Dako, Agilent Technologies, Santa Clara, CA), following overnight incubation at 4 °C. After washing, cells were incubated with secondary antibody anti-mouse Alexa Fluor-488 conjugate (Thermo Fisher Scientific) and DAPI in the dark for 30 min and 5 min, respectively, at room temperature. Spheroids were recovered from hydrogel by VitroGel Cell Recovery Solution and mounted onto slides that were observed under a fluorescence microscope (Leica Biosystems, Newcastle, UK) using a 100X oil immersion objective. Appropriate controls were included to evaluate non-specific staining of the secondary antibody or background autofluorescence.

Homology modeling was performed using Discovery Studio v.19 (Dassault Systems) software suite. The Kruppel-like factor 7 (KLF7) isoform 1 sequence was subjected to BLAST search (https://​blast.​ncbi.​nlm.​nih.​gov/​Blast.​cgi) for template identification. Due to the low alignment coverage (32–28% for the top template structures) we modelled only the three zinc-fingers, encompassing residues 218–302. Clustal Omega (https://​www.​ebi.​ac.​uk/​Tools/​msa/​clustalo) was used for sequence alignment with the template structure, identified in the crystal structure of the zinc finger domain of KLF4 bound to its target DNA (PDB: 2WBS, 76.47% sequence identity [33]. The alignment was then submitted to the program Modeller [34] as implemented in Discovery Studio to generate 20 models of the zinc-finger domain of the KLF structure bound to the DNA, using a procedure we already applied [35]. The models were optimized by a short simulated annealing refinement protocol, and their consistency was evaluated on the basis of the probability density function (PDF) violations provided by the program. During homology modelling the three zinc-fingers atoms were constrained to the template coordinates. The generated structure was validated using Procheck [36], Verify3D [37] and Prosa-Web [38].

The homology model of KLF7 was prepared for docking using the Protein Preparation Wizard of Maestro (Schrödinger LLC, Release 2017–4, New York, NY, 2017). The imported structure was submitted to the protein preparation process which included correcting mislabeled elements, adding hydrogen atoms, assigning bond orders and performing restrained energy minimization using the OPLS_2005 force field [39]. To assess the druggability of KLF7, the site recognition software SiteMap (Maestro, Schrödinger, LLC) was run on the model structure after removal of DNA. The algorithm located potential binding sites evaluating cavity size, exposure to solvent, hydrophobic/hydrophilic balance, and hydrogen bonding. The settings used involved the generation of at least 15 site points per reported site. The SiteScore determines the druggability of the selected pocket and the threshold value for recognition as a drug-binding site is 0.80. The binding sites with highest Sitescore were considered for docking studies. For comparison, the binding pockets of KLF7 were also predicted from the metaPocket server (http://​projects.​biotec.​tu-dresden.​de/​metapocket) [40]. The NCI Diversity Set III (2243 molecules) from ZINC database [41] and the Maybridge HitFinder collection (14,400 compounds representing the drug-like diversity) were used as libraries for the virtual screening experiment. Specific filters based on drug-like properties [42] and elimination of pan-assay interference compounds (PAINS) [43] were applied. The final database of small molecules (13,552 compounds) was prepared for docking using the LigPrep tool (Schrödinger 2017–4) which generates a minimized conformation of each ligand, and assigns likely protonation states at pH 7 ± 2 and tautomers. All ligands were docked into the druggable site identified by SiteMap and metaPocket using inner and outer receptor grid boxes of 10 Å and 16 Å, respectively. Flexible ligand docking was performed using the Glide program [44] (Schrödinger Release 2017–4) and employing the standard precision mode (SP) followed by the extra precision mode (XP) which uses a more optimized scoring function and a more extensive search of ligand conformations. The MM-GBSA rescoring was performed on the 50% top compounds from XP docking and was used to select virtual hits.

In vitro data were analyzed for homogeneity of variance using an F test. If the variances were heterogeneous, log or reciprocal transformations were made in an attempt to stabilize the variances, followed by Student’s t-test. If the variances remained heterogeneous, a non-parametric test such as the Mann–Whitney U test was used. P values were two-sided, with p < 0.05 considered as significant. Statistical Analysis was performed using GraphPad Prism 6 (GraphPad Software, Inc. La Jolla, CA, USA) and StatPlus Version v6 (AnalystSoft Inc., Walnut, CA).

Bioinformatic meta-analysis of six publicly available ovarian cancer transcriptome datasets highlighted KLF7 as the most significant prognostic gene, among the 17 family members, after multiple Cox-regression models corrected for age, FIGO stage and residual tumor after debulking surgery. Five out of six datasets showed the same trend indicating KLF7 as unfavorable prognostic biomarker (Overall HR: 1.11, 95% CI: 1.02–1.21, p = 0.012), as shown in Fig. 1a. KLF2 and KLF5 resulted as slightly less significant prognostic factors after multivariate Cox-regression models (Supplementary File 2). Among these potentially clinically relevant biomarkers, we focused on the role of KLF7 in the genesis and development of HGSOC, due to the lack of prior research on this topic and the potential for target druggability because of its overexpression in the disease. Results for the remaining KLF family members are available as Supplementary File 2.

The prognostic value of KLF7 expression in HGSOC was further evaluated in two of these large public clinical database which integrate gene expression data with patient survival. Specifically, the datasets chosen were the biggest cohort (after TCGA-OV) of late-stage HGSOC analyzed with a microarray technology (i.e. GSE26712) and the only RNA-seq dataset available (i.e. TCGA-OV). Overall Survival analyses of these two publically available datasets of late-stage HGSOC were performed with the aid of CutOff Finder [24] to identify the best potential cutoff for categorizing the cohorts into “high” or “low” KLF7 expression. Regarding the GSE26712 dataset, results obtained in univariate log-rank test showed that low levels of KLF7 are associated to a longer median overall survival (low KLF7: 55.2 Months vs high KLF7: 28.1 Months, HR = 2.6, 95% CI = 1.6–4.4, p = 0.0003) (Fig. 1b, Table 2). In multivariate Cox regression analysis, after adjusting for age and residual tumor after primary surgery, KLF7 was identified as a powerful predictor of OS (p < 0.0001, Table 2).

For TCGA-OV analysis results obtained in univariate log-rank test showed that low levels of KLF7 are associated to a longer median overall survival (low KLF7: 41.9 Months vs high KLF7: 37.8 Months, HR = 1.6, 95%CI = 1.0–2.4, p = 0.037) (Fig. 1c, Table 3). In multivariate Cox regression analysis, after adjusting for age, KLF7 was identified as an independent predictor of OS (p = 0.03, Table 3).

As a first step in the investigation of the role of KLF7 in cancer development, we assessed protein expression in a panel of cell lines selected among those considered as really representative of HGSOC [45‐47]. Western blot analysis revealed different expression levels of KLF7 in the HGSOC cell panel (Fig. 2a).

We used transiently silenced OV-90 and PEO1 cell lines to investigate KLF7 role in cell proliferation. The NIH-OVCAR3 cell line was not included in the study, based on the consideration that the high KLF7 protein levels in this cellular model, along with the observed protein stability in our experimental conditions, would have made it difficult to modulate protein levels in silencing experiments.

A downregulation at RNA and protein level was evident at 72 h post transfection for both cellular models (Fig. 2b and c). Silencing of KLF7 resulted in significant inhibition of cell growth at 72 h for both cell lines compared with scrambled siRNA cells (Fig. 2d, p < 0.05). These results indicate that the higher expression of KLF7 is closely associated to the proliferation of HGSOC cells.

We performed a transwell migration assay in OV-90 and PEO1 cells after gene silencing to investigate KLF7 role in regulating cell migration. Successful silencing was confirmed (data not shown) and results demonstrated that KLF7 depletion caused a strong reduction in cell migration in OV-90 (p < 0.001) and a minor, albeit significant reduction in PEO1 cells, compared to control (p < 0.05) (Fig. 3a and b). Analysis of cell invasion corroborated these data, showing a reduced invasion of KLF7-silenced cells compared to control (p < 0.01 for both OV-90 and PEO1, Fig. 3a and b). These results indicate that KLF7 is involved in regulating HGSOC cell motility and invasion.

The epithelial–mesenchymal transition (EMT) is mainly regulated by a small number of transcription factors (TFs). They belong to the Snail (SNAIL, SLUG), basic helix-loop-helix (TWIST1 TWIST2), and ZEB (ZEB1, ZEB2) families [48, 49]. Individual EMT-TFs regulate in turn the expression of common, but also specific, target genes, which they can either repress or activate [48, 49]. In order to study the molecular basis underlying the biological effects elicited by KLF7 silencing, the mRNA and protein expression of EMT-TFs, along with their downstream target genes, E-cadherin (CDH1), Vimentin, and matrix metalloproteinases 2 and 9 (MMP2 and MMP9) were analysed in OV-90 and PEO1 cells after transfection with siKLF7 or siC.

In siKLF7 OV-90 cells, 72 h post-transfection we found a marked downregulation in mRNA expression of ZEB2 (p = 0.08), Vimentin and MMP2 (p < 0.05 for both genes) when compared to scrambled control siRNA (Fig. 4a). These modulations were confirmed at the protein levels for both ZEB2 and Vimentin (Fig. 4a). Likewise, both mRNA and protein expression of SNAIL, ZEB2 and Vimentin were significantly reduced in response to KLF7 knockdown in PEO1 cells (Fig. 4b). Notably, despite the downregulation in SNAIL levels, we did not detect a significant increase in E-cadherin levels in PEO1, probably because it occurs longer after the reduction of SNAIL. Finally, gelatin zymography showed reduced activity of MMP2 in conditioned medium of both siKLF7 OV-90 and PEO1 cells when compared to their control counterparts (Fig. 4c).

There is accumulating evidence for a highly relevant overlap between stemness, cancer, and EMT [50]. Therefore, we sought to verify whether KLF7 can also promote stemness in HGSOC. To this end we employed RT-qPCR analysis, western blot and/or immunofluorescence to assess the expression of CD44, NANOG, OCT4 and SOX2, regarded as putative ovarian cancer stem cells markers [51, 52]. Results obtained showed in OV-90 cells a significant down-regulation of CD44 expression at both the mRNA and protein levels following KLF7 silencing (Fig. 4d, e and f). In PEO1 cells, CD44 was almost undetectable at the western blot analysis, while low levels of staining were found at the immunofluorescence examination and, therefore, no definitive conclusions could be drawn for this cell line (Fig. 4e and f). No other significant changes were observed.

In keeping with the notion that cancer 3D culture methods better recapitulate in vivo growth conditions, thus allowing more faithful reproduction of broader aspects of tumor biology than conventional 2D monolayer cultures [53], we developed in vitro 3D culture from OV-90 and PEO1 cells transiently silenced for KLF7. PEO1 cells only formed small clusters, with very dense islands of small cells tightly attached. Results obtained showed that KLF7 depletion impaired spheroid growth of OV-90 and PEO1 cells, as demonstrated by reduced spheroid dimensions in siKLF7 compared to siC cells (p < 0.01 for both, Fig. 5a and c). In addition, both OV-90 and PEO1-derived spheroids were subjected to immunofluorescence staining for Vimentin expression (Fig. 5b and d). In line with results obtained by western blot analysis on 2D cell cultures, we observed a reduction in Vimentin levels following KLF7 silencing. On the whole, these findings are in line with previous data showing that, in ovarian cancer, the ability to form spheroids correlates with the presence of abundant vimentin [54].

The three zinc fingers of KLF7 (residues 218–302) showed 76% sequence identity with residues 399–483 of the crystal structure of the zinc finger domain of KLF4 bound to its target DNA (PDB code: 2WBS). This allowed the zinc finger domain of KLF7 to be modeled confidently by homology modelling using the program Modeller. To validate the computational model multiple approaches were employed. The PROCHECK suite of programs was used to check the stereochemical quality of protein structure and has shown that 90.9% of the residues fall in the most favored region, 9.1% in the additional allowed region and 0.0% in both generously allowed and disallowed regions. For a good quality model, it is expected that the residues located in the most favorable and additional allowed regions should be more than 90%, which is the case of KLF7 model (100%). To check the compatibility of the residues with their environment we used the program VERIFY3D which revealed that 84.7% of the residues averaged 3D-1D score over 0.2, indicative of a correct fold. Prosa was employed to check the energy of residues using the web service ProSA-web. The Z-score calculated by the program was − 4.24, a value within the range of the experimental protein structures of the same size, indicating a good quality model structure. Through this assessment and analysis process, it is concluded that the model generated in the present study is reliable and can be used to characterize KLF7-inhibitor interactions. The model displays three characteristic C2H2-type zinc finger motifs at its C-terminus used by the transcription factor to bind DNA. The overall structure of the KLF7- DNA complex is shown in Fig. 6a. Each zinc-finger contains two antiparallel β strands followed by a α-helix and binds one zinc ion. Zn1 is tetrahedrally coordinated to the side chains of Cys221, Cys226, His243 and His239, Zn2 to the side chains of Cys256, Cys251, His269 and His273, Zn3 to the side chains of Cys281, Cys284, His297 and His301. The generated model structure did not contain any co-crystallized ligand, therefore determination of binding site for virtual screening was done using the tool SiteMap in Maestro (Schrödinger 2017–4). Three pockets were identified with comparable SiteScores: Site1 (0.866), Site2 (0.838), Site3 (0.812) and the amino acid residues lining the identified pockets are reported in Table 4. Notably Site1 (Fig. 6b), which has the highest SiteScore value, was identified by metaPocket server also as the most favorable ligand binding site in KLF7. Analysis of potential ligand binding sites in KLF7 and their druggability were then used to guide the virtual screening experiments.

To screen effective inhibitors for KLF7, we used compounds of NCI Diversity Set III molecules and the Maybridge HitFinder collection against the potential druggable pocket Site1. The Glide virtual screening workflow was used and all the molecules were initially screened by SP mode and in the subsequent stage by XP docking. The top ranked 10 ligands on the basis of docking score were selected and Prime was used to estimate the binding free energy of all the ligands towards KLF7 and provide the correct ranking based upon the MM-GBSA energy score. Docking and MM-GBSA score of the top targeted compounds are reported in Table 5. Superimposition of the three top-ranked docking result based upon the MM-GBSA energy score (Compound#1, Compound#3 and Compound#9) is shown in Fig. 6c. These compounds will be subjected to detailed biological investigation in order to determine their inhibitory activity towards KLF7.