Inhibition of PCSK9 Attenuates Liver Endothelial Cell Activation Induced by Colorectal Cancer Stem Cells during Liver Metastasis

Colorectal cancer often leads to liver metastases, a major cause of patient mortality. This study investigates the role of PCSK9, a protein traditionally known for its role in cholesterol metabolism, in the metastatic process. Researchers found that conditioned media from colorectal cancer stem cell strongly upregulates PCSK9 expression in liver sinusoidal endothelial cells (LSECs). PCSK9 activation enhances LSEC proliferation and migration, contributing to the formation of a pro-metastatic niche. Inhibiting PCSK9 with the small molecule PF-06446846 reduced endothelial activation and normalized gene expression, decreasing LSEC potential support of the metastatisis. Immunofluorescence confirmed PCSK9 expression in Liver Endothelial Cells (LSEC)s of human colorectal cancer liver metastases. These results suggest that PCSK9 could represent a promising therapeutic target to prevent and treat liver metastases in colorectal cancer patients. Overall design: LSECs were stimulated with conditioned media derived from differentiated colorectal cancer cells and cancer stem cells (CSCs), the latter generated by reprogramming SW620 and CT26 cell lines. RNA sequencing was used to profile gene expression in LSECs. PCSK9 mRNA and protein levels were quantified by qPCR and Western blotting, respectively. PCSK9 expression in CRC liver metastases was evaluated by immunofluorescent staining. RNA Extraction and Quantitative Real-Time PCR: To compare PCSK9 expression in LSEC and CSC markers in colon cancer cells total RNA isolation from cell cultures was performed using the NucleoSpin RNA isolation kit (740955, Macherey & Nagel). The RNA was reverse transcribed using the iScript cDNA synthesis kit (1708891, BioRad,) according to manufacturer´s guidelines and was used for real-time PCR. Real-time PCR was performed using the Power SYBR Green Master Mix (1725271, BioRad). The quantitative polymerase chain reaction (qPCR) data were acquired with the CFX96 C1000 Touch Real-Time PCR Detection System (Biorad). The expression levels were normalized to beta18S ribosomal RNA (18S rRNA). All the reactions were performed in triplicate, and the relative expression of each gene was calculated via the 2-ddCt method. RNA sequencing: After performing quality control, checking the RIN value of each sample was bigger than 6, and ensuring RNA quantity was higher than 200ng, library construction was performed following Ilumina's recommendation. Sequencing library was prepared by random fragmentation of the cDNA sample, followed by 5' and 3' adapter ligation. The fragmentation and ligation reactions were performed in a single step that increases the efficiency of the library preparation process. Then, adapter-ligated fragments were amplified by PCR and purified in a gel. Subsequently, library was loaded into a flow cell where fragments were captured on a lawn of surface-bound oligos complementary to the library adapters. Each fragment was then amplified into distinct clonal clusters through bridge amplification. When cluster generation was completed, sequencing was performed using Illumina SBS technology. All sequencing data was converted in raw data in FASTQ format, allowing its bioinformatics analysis. RNA data analysis: RNA-sequencing (RNA-seq) transcriptomics data analysis was conducted useing HISAT2 to align the RNA-seq reads to the human reference genome hg38 and to calculate counts and Cufflinks to annotate them. We merged the transcriptomics data into a single text file and used it in the downstream analysis using in-house functions developed in MATLAB (MathWorks). We equalized the data and stabilized them through the log2 transform of the data plus one, calculated the average values for each group of replicates, selected the Differentially Expressed Genes (DEGs) whose absolute difference in mean values between the two groups was less than the selection threshold = 1 of fold change in the log2 scale, and selected the statistically significant DEGs using Student's t test with a significance threshold = 0.05. DEG sets were subjected to Gene Ontology (GO) enrichment analysis. The functional GO enrichment of the DEGs was calculated using the hypergeometric test over the mid-p-values with a significance threshold of 0.05.