SCARF1

Last update 08 May 2025

Basic Info

Synonyms

Acetyl LDL receptor, KIAA0149, SCARF1

+ [5]

Introduction

Mediates the binding and degradation of acetylated low density lipoprotein (Ac-LDL). Mediates heterophilic interactions, suggesting a function as adhesion protein. Plays a role in the regulation of neurite-like outgrowth (By similarity).

100 Clinical Results associated with SCARF1

100 Translational Medicine associated with SCARF1

0 Patents (Medical) associated with SCARF1

128

Literatures (Medical) associated with SCARF1

01 Dec 2024·Alzheimer's & Dementia

SCARF2 in the CSF of Asymptomatic subjects With a Parental History of AD and in the Cortex of Autopsy‐Confirmed Alzheimer’s Disease Cases

Article

Author: Aumont‐Rodrigue, Gabriel ; Picard, Cynthia ; Labonté, Anne ; Poirier, Judes

AbstractBackgroundScavenger receptors (SR) are a group of receptors involved in the endocytosis of various ligands, such as modified LDL and soluble β‐amyloid, which connects them to Alzheimer’s disease (AD). SCARF2 (SREC‐II) is part of the SR family, but unlike other scavenger receptors, internalizes a low amount of modified LDL. Its main function revolves around the binding of Aβ (Vo et al. 2023). Other studies found SCARF2 to be a SCARF1 inhibitor (Ishii et al. 2002). This study compares gene expression and protein levels of SCARF2 in post‐mortem brain tissue of AD and control subjects, as well as in the CSF of participants with a family history of AD. The goal is to track the progression of AD using the expression pattern of SCARF2.MethodSCARF2 CSF protein levels were assessed in the PREVENT‐AD (PRe‐symptomatic EValuation of Experimental or Novel Treatments for Alzheimer’s Disease) cohort which is composed of asymptomatic subjects with parental history of AD. SCARF2 has been measured with a primer extension assay using Olink technology. ELISA immunoassay has been used to quantify SCARF2 protein levels in frontal cortex homogenates of 86 brains from the Douglas Bell Canada Brain Bank (DBCBB). mRNA prevalence was measured using Gene Chip Clariom D human microarray. All statistical analyses were performed using JMP pro 17 software.ResultSCARF2 Protein levels, but not mRNA, are significantly lower in brains with AD (p<0.001). SCARF2 protein display a strong negative association with plaques bound Aβ, despite being a well‐established Aβ receptor (R2 = 0.277, p<0.0001). Interestingly, CSF protein levels correlate positively with AD biomarkers such as total and phosphorylated(181) tau (R2 = 0.235, p<0.0001; R2 = 0.288, p<0.0001) and Aβ to a lesser extent (R2 = 0.045, p = 0.0318).ConclusionOur study unveils a distinctive role for SCARF2 in AD pathogenesis. Despite being a known Aβ receptor, SCARF2 exhibits lower cortical protein levels in AD brains, inversely linked to plaques bound Aβ. In contrast, elevated CSF SCARF2 protein levels correlate positively with CSF AD biomarkers in the presymptomatic phase of the disease; suggesting differential response to amyloid and tau deposition. These findings shed light on the intricate interplay of SCARF2 in AD pathophysiology, offering potential avenues for further research.

01 Oct 2024·International Journal of Biological Macromolecules

Comprehensive analysis of single-nucleotide variants and alternative polyadenylation between inbred and outbred pigs

Article

Author: Huang, Ji ; Tu, Weilong ; Meng, Xiangge ; Tan, Yongsong ; Li, Songyu ; Wang, Hongyang ; Liu, Bang ; Zhang, Yingying ; Jiang, Zhihua ; Michal, Jennifer J ; Wu, Qingqing ; Zhou, Xiang ; Han, Yu

Inbreeding can lead to the accumulation of homozygous single nucleotide polymorphisms (SNPs) in the genome, which can significantly affect gene expression and phenotype. In this study, we examined the impact of homozygous SNPs resulting from inbreeding on alternative polyadenylation (APA) site selection and the underlying genetic mechanisms using inbred Luchuan pigs. Genome resequencing revealed that inbreeding results in a high accumulation of homozygous SNPs within the pig genome. 3' mRNA-seq on leg muscle, submandibular lymph node, and liver tissues was performed to identify differences in APA events between inbred and outbred Luchuan pigs. We revealed different tissue-specific APA usage caused by inbreeding, which were associated with different biological processes. Furthermore, we explored the role of polyadenylation signal (PAS) SNPs in APA regulation under inbreeding and identified key genes such as PUM1, SCARF1, RIPOR2, C1D, and LRRK2 that are involved in biological processes regulation. This study provides resources and sheds light on the impact of genomic homozygosity on APA regulation, offering insights into genetic characteristics and biological processes associated with inbreeding.

01 Jul 2024·Translational Cancer Research

Eleven inflammation-related genes risk signature model predicts prognosis of patients with breast cancer

Article

Author: Zhong, Shanliang ; Fu, Yuqi ; Li, Huixin ; Xiao, Shuyue ; Gong, Zhen ; Hu, Huanhuan ; Yuan, Shenglong

BackgroundChanges in gene expression are associated with malignancy. Analysis of gene expression data could be used to reveal cancer subtypes, key molecular drivers, and prognostic characteristics and to predict cancer susceptibility, treatment response, and mortality. It has been reported that inflammation plays an important role in the occurrence and development of tumors. Our aim was to establish a risk signature model of breast cancer with inflammation-related genes (IRGs) to evaluate their survival prognosis.MethodsWe downloaded 200 IRGs from the Molecular Signatures Database (MSigDB). The data of breast cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differential gene expression analysis, the least absolute shrinkage and selection operator (LASSO), Cox regression analysis, and overall survival (OS) analysis were used to construct a multiple-IRG risk signature. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were carried out to annotate functions of the differentially expressed IRGs (DEIRGs) The predictive accuracy of the prognostic model was evaluated by time-dependent receiver operating characteristic (ROC) curves. Subsequently, nomograms were constructed to guide clinical application according to the univariate and multivariate Cox proportional hazards regression analyses. Eventually, we applied gene set variation analysis (GSVA), mutation analysis, immune infiltration analysis, and drug response analysis to compare the differences between high- and low-risk patients.ResultsTotally, 65 DEIRGs were obtained after comparing 1,092 breast cancer tissues with 113 paracancerous tissues in TCGA. Among them, 11 IRGs (IL18, IL12B, RASGRP1, HPN, CLEC5A, SCARF1, TACR3, VIP, CCL2, CALCRL, ABCA1) were screened with nonzero coefficient by LASSO regression analysis to construct the prognostic model, which was validated in GSE96058.The 11-gene IRGs risk signature model stratified patients into high- or low-risk groups, with those in the low-risk group having longer survival time and less deaths. Multivariate Cox analysis manifested that risk score, age, and stage were the three independent prognostic factors for breast cancer patients. There were 12 pathways with higher activities and 24 pathways with lower activities in the high-risk group compared with the low-risk group, yet no difference of gene mutation load was observed between the two groups. In immune infiltration analysis, we noted that the proportion of T cells showed a decreased trend according to the increase of risk score and most of the immune cells were enriched in the low-risk group. Inversely, macrophages M2 were more highly distributed in the high-risk group. We identified 67 approved drugs that showed a different effect between the high- and low-risk patients and the top 2 gene-drug pairs were IL12B-sunitinib and SCARF1-ruxolitinib.ConclusionsThe 11-IRG risk signature model is a promising tool to predict the survival of breast cancer patients and the expressions of IL12B and SCARF1 may serve as potential targets for therapy of breast cancer.

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SCARF1

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