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Alzheimer's disease (AD) is a highly heritable brain dementia, along with substantial failure of cognitive function. Large-scale genome-wide association studies (GWASs) have led to a set of SNPs significantly associated with AD and related traits. GWAS hits usually emerge as clusters where a lead SNP with the highest significance is surrounded by other less significant neighboring SNPs. Although functionality is not guaranteed even with the strongest associations in GWASs, lead SNPs have historically been the focus of the field, with the remaining associations inferred to be redundant. Recent deep genome annotation tools enable the prediction of function from a segment of a DNA sequence with significantly improved precision, which allows in-silico mutagenesis to interrogate the functional effect of SNP alleles. In this project, we explored the impact of top AD GWAS hits around APOE region on chromatin functions and whether it will be altered by the genetic context (i.e., alleles of neighboring SNPs). Our results showed that highly correlated SNPs in the same LD block could have distinct impacts on downstream functions. Although some GWAS lead SNPs showed dominant functional effects regardless of the neighborhood SNP alleles, several other SNPs did exhibit enhanced loss or gain of function under certain genetic contexts, suggesting potential additional information hidden in the LD blocks., Competing Interests: Declarations Competing interests Dr. Yan is a guest editor of this special collection. Dr. Saykin has received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor) and holds advisory roles with Siemens Medical Solutions USA, Inc., NIH NHLBI, and Eisai. His editorial commitments include serving as Editor-in-Chief for the journal `Brain Imaging and Behavior’, and he participates in various NIH/NIA advisory committees., (© 2024. The Author(s).)

Background: TREM2 is a transmembrane receptor expressed by myeloid cells and acts to regulate their immune response. TREM2 governs the response of microglia to amyloid and tau pathologies in the Alzheimer's disease (AD) brain. TREM2 is also present in a soluble form (sTREM2), and its CSF levels fluctuate as a function of AD progression. Analysis of stroke and AD mouse models revealed that sTREM2 proteins bind to neurons, which suggests sTREM2 may act in a non-cell autonomous manner to influence neuronal function. sTREM2 arises from the proteolytic cleavage of the membrane-associated receptor. However, alternatively spliced TREM2 species lacking a transmembrane domain have been postulated to contribute to the pool of sTREM2. Thus, both the source of sTREM2 species and its actions in the brain remain unclear., Methods: The expression of TREM2 isoforms in the AD brain was assessed through the analysis of the Accelerating Medicines Partnership for Alzheimer's Disease Consortium transcriptomics data, as well as qPCR analysis using post-mortem samples of AD patients and of the AD mouse model 5xFAD. TREM2 cleavage and secretion were studied in vitro using HEK-293T and HMC3 cell lines. Synaptic plasticity, as evaluated by induction of LTP in hippocampal brain slices, was employed as a measure of sTREM2 actions., Results: Three distinct TREM2 transcripts, namely ENST00000373113 (TREM2 230 ), which encodes the full-length transmembrane receptor, and the alternatively spliced isoforms ENST00000373122 (TREM2 222 ) and ENST00000338469 (TREM2 219 ), are moderately increased in specific brain regions of patients with AD. We provide experimental evidence that TREM2 alternatively spliced isoforms are translated and secreted as sTREM2. Furthermore, our functional analysis reveals that all sTREM2 species inhibit LTP induction, and this effect is abolished by the GABAA receptor antagonist picrotoxin., Conclusions: TREM2 transcripts can give rise to a heterogeneous pool of sTREM2 which acts to inhibit LTP. These results provide novel insight into the generation, regulation, and function of sTREM2 which fits into the complex biology of TREM2 and its role in human health and disease. Given that sTREM2 levels are linked to AD pathogenesis and progression, our finding that sTREM2 species interfere with LTP furthers our understanding about the role of TREM2 in AD., (© 2023. The Author(s).)

Background: Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis., Methods: Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables., Results: We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways., Conclusions: Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins., (© 2023. The Author(s).)

This literature review investigates the significant overlap between myelin-repair signaling pathways and pathways known to contribute to hallmark pathologies of Alzheimer's disease (AD). We discuss previously investigated therapeutic targets of amyloid, tau, and ApoE, as well as other potential therapeutic targets that have been empirically shown to contribute to both remyelination and progression of AD. Current evidence shows that there are multiple AD-relevant pathways which overlap significantly with remyelination and myelin repair through the encouragement of oligodendrocyte proliferation, maturation, and myelin production. There is a present need for a single, cohesive model of myelin homeostasis in AD. While determining a causative pathway is beyond the scope of this review, it may be possible to investigate the pathological overlap of myelin repair and AD through therapeutic approaches., (© 2022. The Author(s).)

Background: Large-scale genome-wide association studies have successfully identified many genetic variants significantly associated with Alzheimer's disease (AD), such as rs429358, rs11038106, rs723804, rs13591776, and more. The next key step is to understand the function of these SNPs and the downstream biology through which they exert the effect on the development of AD. However, this remains a challenging task due to the tissue-specific nature of transcriptomic and proteomic data and the limited availability of brain tissue.In this paper, instead of using coupled transcriptomic data, we performed an integrative analysis of existing GWAS findings and expression quantitative trait loci (eQTL) results from AD-related brain regions to estimate the transcriptomic alterations in AD brain., Results: We used summary-based mendelian randomization method along with heterogeneity in dependent instruments method and were able to identify 32 genes with potential altered levels in temporal cortex region. Among these, 10 of them were further validated using real gene expression data collected from temporal cortex region, and 19 SNPs from NECTIN and TOMM40 genes were found associated with multiple temporal cortex imaging phenotype., Conclusion: Significant pathways from enriched gene networks included neutrophil degranulation, Cell surface interactions at the vascular wall, and Regulation of TP53 activity which are still relatively under explored in Alzheimer's Disease while also encouraging a necessity to bind further trans-eQTL effects into this integrative analysis., (© 2022. The Author(s).)

Background: Alzheimer's disease (AD) is characterized by robust microgliosis and phenotypic changes that accompany disease pathogenesis. Accumulating evidence from genetic studies suggests the importance of phospholipase C γ 2 (PLCG2) in late-onset AD (LOAD) pathophysiology. However, the role of PLCG2 in AD is still poorly understood., Methods: Using bulk RNA-Seq (N=1249) data from the Accelerating Medicines Partnership-Alzheimer's Disease Consortium (AMP-AD), we investigated whether PLCG2 expression increased in the brains of LOAD patients. We also evaluated the relationship between PLCG2 expression levels, amyloid plaque density, and expression levels of microglia specific markers (AIF1 and TMEM119). Finally, we investigated the longitudinal changes of PLCG2 expression in the 5xFAD mouse model of AD. To further understand the role of PLCG2 in different signaling pathways, differential gene expression and co-expression network analyses were performed using bulk RNA-Seq and microglial single-cell RNA-Seq data. To substantiate the human analyses, we performed differential gene expression analysis on wild-type (WT) and inactivated Plcg2 mice and used immunostaining to determine if the differentially expressed genes/pathways were altered by microglial cell coverage or morphology., Results: We observed significant upregulation of PLCG2 expression in three brain regions of LOAD patients and significant positive correlation of PLCG2 expression with amyloid plaque density. These findings in the human brain were validated in the 5xFAD amyloid mouse model, which showed disease progression-dependent increases in Plcg2 expression associated with amyloid pathology. Of note, increased Plcg2 expression levels in 5xFAD mice were abolished by reducing microglia. Furthermore, using bulk RNA-Seq data, we performed differential expression analysis by comparing cognitively normal older adults (CN) with 75th percentile (high) and 25th percentile (low) PLCG2 gene expression levels to identify pathways related to inflammation and the inflammatory response. The findings in the human brain were validated by differential expression analyses between WT and plcg2 inactivated mice. PLCG2 co-expression network analysis of microglial single-cell RNA-Seq data identified pathways related to the inflammatory response including regulation of I-kappaB/NF-kappa B signaling and response to lipopolysaccharide., Conclusions: Our results provide further evidence that PLCG2 plays an important role in AD pathophysiology and may be a potential target for microglia-targeted AD therapies., (© 2022. The Author(s).)

Background: The combinatorial effect of multiple genetic factors calculated as a polygenic risk score (PRS) has been studied to predict disease progression to Alzheimer's disease (AD) from mild cognitive impairment (MCI). Previous studies have investigated the performance of PRS in the prediction of disease progression to AD by including and excluding single nucleotide polymorphisms within the region surrounding the APOE gene. These studies may have missed the APOE genotype-specific predictability of PRS for disease progression to AD., Methods: We analyzed 732 MCI from the Alzheimer's Disease Neuroimaging Initiative cohort, including those who progressed to AD within 5 years post-baseline (n = 270) and remained stable as MCI (n = 462). The predictability of PRS including and excluding the APOE region (PRS +APOE and PRS -APOE ) on the conversion to AD and its interaction with the APOE ε4 carrier status were assessed using Cox regression analyses., Results: PRS +APOE (hazard ratio [HR] 1.468, 95% CI 1.335-1.615) and PRS -APOE (HR 1.293, 95% CI 1.157-1.445) were both associated with a significantly increased risk of MCI progression to dementia. The interaction between PRS +APOE and APOE ε4 carrier status was significant with a P-value of 0.0378. The association of PRSs with the progression risk was stronger in APOE ε4 non-carriers (PRS +APOE : HR 1.710, 95% CI 1.244-2.351; PRS -APOE : HR 1.429, 95% CI 1.182-1.728) than in APOE ε4 carriers (PRS +APOE : HR 1.167, 95% CI 1.005-1.355; PRS -APOE : HR 1.172, 95% CI 1.020-1.346)., Conclusions: PRS could predict the conversion of MCI to dementia with a stronger association in APOE ε4 non-carriers than APOE ε4 carriers. This indicates PRS as a potential genetic predictor particularly for MCI with no APOE ε4 alleles., (© 2021. The Author(s).)

Background: Alzheimer's disease (AD) is the most common type of dementia, typically characterized by memory loss followed by progressive cognitive decline and functional impairment. Many clinical trials of potential therapies for AD have failed, and there is currently no approved disease-modifying treatment. Biomarkers for early detection and mechanistic understanding of disease course are critical for drug development and clinical trials. Amyloid has been the focus of most biomarker research. Here, we developed a deep learning-based framework to identify informative features for AD classification using tau positron emission tomography (PET) scans., Results: The 3D convolutional neural network (CNN)-based classification model of AD from cognitively normal (CN) yielded an average accuracy of 90.8% based on five-fold cross-validation. The LRP model identified the brain regions in tau PET images that contributed most to the AD classification from CN. The top identified regions included the hippocampus, parahippocampus, thalamus, and fusiform. The layer-wise relevance propagation (LRP) results were consistent with those from the voxel-wise analysis in SPM12, showing significant focal AD associated regional tau deposition in the bilateral temporal lobes including the entorhinal cortex. The AD probability scores calculated by the classifier were correlated with brain tau deposition in the medial temporal lobe in MCI participants (r = 0.43 for early MCI and r = 0.49 for late MCI)., Conclusion: A deep learning framework combining 3D CNN and LRP algorithms can be used with tau PET images to identify informative features for AD classification and may have application for early detection during prodromal stages of AD.

Background: Alzheimer's disease (AD) is one of the leading causes of death in the US and there is no validated drugs to stop, slow or prevent AD. Despite tremendous effort on biomarker discovery, existing findings are mostly individual biomarkers and provide limited insights into the transcriptomic decoupling underlying AD. We propose to explore the gene co-expression patterns in multiple AD stages, including cognitively normal (CN), early mild cognitive impairment (EMCI), late MCI and AD., Methods: We modified traiditonal joint graphical lasso to model our asusmption that the co-expression networks in consecutive disease stages are largely similar with critical differences. In addition, we performed subsequent network comparison analysis for identification of stage specific transcriptomic decoupling. We focused our analysis on top AD-enriched pathways., Results: We observed that 419 edges in CN, 420 edges in EMCI, 381 edges in LMCI and 250 edges in AD were frequently estimated with non zero weights. With modified JGL, the weight of all estimated edges in CN, EMCI and LMCI are zero. In AD group, 299 edges were occasionally estimated to be nonzero and the average correlation between genes was 0.0023. For co-expression change during AD progression, there are 66 pairs of genes that demonstrated a continuously decreasing or increasing co-expression from CN to EMCI, LMCI and AD.The network level clustering coefficient remains stable from CN to LMCI and then decreases significantly when progressing to AD. When evaluating edge level differences, we identified eight gene modules with continuously decreasing or increasing co-expression patterns during AD progression. Five of them shows significant changes from CN to EMCI and thus have the potential to serve system biomarkers for early screening of AD., Conclusion: We employed a modified joint graphical lasso for estimation of co-expression networks for multiple stages of AD. Comparing with graphical lasso, our modified joint graphical lasso model accounts for the similarity in consecutive disease stages. Our results on real data set revealed five gene clusters with obvious co-expression pattern change from CN to EMCI, which could be used as potential system-level biomarkers for early screening of AD.

Background: At least 90% of human genes are alternatively spliced. Alternative splicing has an important function regulating gene expression and miss-splicing can contribute to risk for human diseases, including Alzheimer's disease (AD)., Methods: We developed a splicing decision model as a molecular mechanism to identify functional exon skipping events and genetic variation affecting alternative splicing on a genome-wide scale by integrating genomics, transcriptomics, and neuroimaging data in a systems biology approach. In this study, we analyzed RNA-Seq data of hippocampus brain tissue from Alzheimer's disease (AD; n = 24) and cognitively normal elderly controls (CN; n = 50) and identified three exon skipping events in two genes (RELN and NOS1) as significantly associated with AD (corrected p-value < 0.05 and fold change > 1.5). Next, we identified single-nucleotide polymorphisms (SNPs) affecting exon skipping events using the splicing decision model and then performed an association analysis of SNPs potentially affecting three exon skipping events with a global cortical measure of amyloid-β deposition measured by [ 18 F] Florbetapir position emission tomography (PET) scan as an AD-related quantitative phenotype. A whole-brain voxel-based analysis was also performed., Results: Two exons in RELN and one exon in NOS1 showed significantly lower expression levels in the AD participants compared to CN participants, suggesting that the exons tend to be skipped more in AD. We also showed the loss of the core protein structure due to the skipped exons using the protein 3D structure analysis. The targeted SNP-based association analysis identified one intronic SNP (rs362771) adjacent to the skipped exon 24 in RELN as significantly associated with cortical amyloid-β levels (corrected p-value < 0.05). This SNP is within the splicing regulatory element, i.e., intronic splicing enhancer. The minor allele of rs362771 conferred decreases in cortical amyloid-β levels in the right temporal and bilateral parietal lobes., Conclusions: Our results suggest that exon skipping events and splicing-affecting SNPs in the human hippocampus may contribute to AD pathogenesis. Integration of multiple omics and neuroimaging data provides insights into possible mechanisms underlying AD pathophysiology through exon skipping and may help identify novel therapeutic targets.

Background: Alzheimer's disease (AD) is one of the most common neurodegenerative diseases that causes problems related to brain function. To some extent it is understood on a molecular level how AD arises, however there are a lack of biomarkers that can be used for early diagnosis. Two popular methods to identify AD-related biomarkers use genetics and neuroimaging. Genes and neuroimaging phenotypes have provided some insights as to the potential for AD biomarkers. While the field of imaging-genomics has identified genetic features associated with structural and functional neuroimaging phenotypes, it remains unclear how variants that affect splicing could be important for understanding the genetic etiology of AD., Methods: In this study, rare variants (minor allele frequency < 0.01) in splicing regulatory element (SRE) loci from whole genome sequencing (WGS) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, were used to identify genes that are associated with global brain cortical glucose metabolism in AD measured by FDG PET-scans. Gene-based associated analyses of rare variants were performed using the program BioBin and the optimal Sequence Kernel Association Test (SKAT-O)., Results: The gene, EXOC3L4, was identified as significantly associated with global cortical glucose metabolism (FDR (false discovery rate) corrected p < 0.05) using SRE coding variants only. Three loci that may affect splicing within EXOC3L4 contribute to the association., Conclusion: Based on sequence homology, EXOC3L4 is likely a part of the exocyst complex. Our results suggest the possibility that variants which affect proper splicing of EXOC3L4 via SREs may impact vesicle transport, giving rise to AD related phenotypes. Overall, by utilizing WGS and functional neuroimaging we have identified a gene significantly associated with an AD related endophenotype, potentially through a mechanism that involves splicing.

Background: The APOE ε4 allele is the most significant common genetic risk factor for late-onset Alzheimer's disease (LOAD). The region surrounding APOE on chromosome 19 has also shown consistent association with LOAD. However, no common variants in the region remain significant after adjusting for APOE genotype. We report a rare variant association analysis of genes in the vicinity of APOE with cerebrospinal fluid (CSF) and neuroimaging biomarkers of LOAD., Methods: Whole genome sequencing (WGS) was performed on 817 blood DNA samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Sequence data from 757 non-Hispanic Caucasian participants was used in the present analysis. We extracted all rare variants (MAF (minor allele frequency) < 0.05) within a 312 kb window in APOE's vicinity encompassing 12 genes. We assessed CSF and neuroimaging (MRI and PET) biomarkers as LOAD-related quantitative endophenotypes. Gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O)., Results: A total of 3,334 rare variants (MAF < 0.05) were found within the APOE region. Among them, 72 rare non-synonymous variants were observed. Eight genes spanning the APOE region were significantly associated with CSF Aβ 1-42 (p < 1.0 × 10 -3 ). After controlling for APOE genotype and adjusting for multiple comparisons, 4 genes (CBLC, BCAM, APOE, and RELB) remained significant. Whole-brain surface-based analysis identified highly significant clusters associated with rare variants of CBLC in the temporal lobe region including the entorhinal cortex, as well as frontal lobe regions. Whole-brain voxel-wise analysis of amyloid PET identified significant clusters in the bilateral frontal and parietal lobes showing associations of rare variants of RELB with cortical amyloid burden., Conclusions: Rare variants within genes spanning the APOE region are significantly associated with LOAD-related CSF Aβ 1-42 and neuroimaging biomarkers after adjusting for APOE genotype. These findings warrant further investigation and illustrate the role of next generation sequencing and quantitative endophenotypes in assessing rare variants which may help explain missing heritability in AD and other complex diseases.

Background: Rapid advancement of next generation sequencing technologies such as whole genome sequencing (WGS) has facilitated the search for genetic factors that influence disease risk in the field of human genetics. To identify rare variants associated with human diseases or traits, an efficient genome-wide binning approach is needed. In this study we developed a novel biological knowledge-based binning approach for rare-variant association analysis and then applied the approach to structural neuroimaging endophenotypes related to late-onset Alzheimer's disease (LOAD)., Methods: For rare-variant analysis, we used the knowledge-driven binning approach implemented in Bin-KAT, an automated tool, that provides 1) binning/collapsing methods for multi-level variant aggregation with a flexible, biologically informed binning strategy and 2) an option of performing unified collapsing and statistical rare variant analyses in one tool. A total of 750 non-Hispanic Caucasian participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort who had both WGS data and magnetic resonance imaging (MRI) scans were used in this study. Mean bilateral cortical thickness of the entorhinal cortex extracted from MRI scans was used as an AD-related neuroimaging endophenotype. SKAT was used for a genome-wide gene- and region-based association analysis of rare variants (MAF (minor allele frequency) < 0.05) and potential confounding factors (age, gender, years of education, intracranial volume (ICV) and MRI field strength) for entorhinal cortex thickness were used as covariates. Significant associations were determined using FDR adjustment for multiple comparisons., Results: Our knowledge-driven binning approach identified 16 functional exonic rare variants in FANCC significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In addition, the approach identified 7 evolutionary conserved regions, which were mapped to FAF1, RFX7, LYPLAL1 and GOLGA3, significantly associated with entorhinal cortex thickness (FDR-corrected p-value < 0.05). In further analysis, the functional exonic rare variants in FANCC were also significantly associated with hippocampal volume and cerebrospinal fluid (CSF) Aβ 1-42 (p-value < 0.05)., Conclusions: Our novel binning approach identified rare variants in FANCC as well as 7 evolutionary conserved regions significantly associated with a LOAD-related neuroimaging endophenotype. FANCC (fanconi anemia complementation group C) has been shown to modulate TLR and p38 MAPK-dependent expression of IL-1β in macrophages. Our results warrant further investigation in a larger independent cohort and demonstrate that the biological knowledge-driven binning approach is a powerful strategy to identify rare variants associated with AD and other complex disease.

Background: Pathogenic mutations in PSEN1 are known to cause familial early-onset Alzheimer's disease (EOAD) but common variants in PSEN1 have not been found to strongly influence late-onset AD (LOAD). The association of rare variants in PSEN1 with LOAD-related endophenotypes has received little attention. In this study, we performed a rare variant association analysis of PSEN1 with quantitative biomarkers of LOAD using whole genome sequencing (WGS) by integrating bioinformatics and imaging informatics., Methods: A WGS data set (N = 815) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort was used in this analysis. 757 non-Hispanic Caucasian participants underwent WGS from a blood sample and high resolution T1-weighted structural MRI at baseline. An automated MRI analysis technique (FreeSurfer) was used to measure cortical thickness and volume of neuroanatomical structures. We assessed imaging and cerebrospinal fluid (CSF) biomarkers as LOAD-related quantitative endophenotypes. Single variant analyses were performed using PLINK and gene-based analyses of rare variants were performed using the optimal Sequence Kernel Association Test (SKAT-O)., Results: A total of 839 rare variants (MAF < 1/√(2 N) = 0.0257) were found within a region of ±10 kb from PSEN1. Among them, six exonic (three non-synonymous) variants were observed. A single variant association analysis showed that the PSEN1 p. E318G variant increases the risk of LOAD only in participants carrying APOE ε4 allele where individuals carrying the minor allele of this PSEN1 risk variant have lower CSF Aβ1-42 and higher CSF tau. A gene-based analysis resulted in a significant association of rare but not common (MAF ≥ 0.0257) PSEN1 variants with bilateral entorhinal cortical thickness., Conclusions: This is the first study to show that PSEN1 rare variants collectively show a significant association with the brain atrophy in regions preferentially affected by LOAD, providing further support for a role of PSEN1 in LOAD. The PSEN1 p. E318G variant increases the risk of LOAD only in APOE ε4 carriers. Integrating bioinformatics with imaging informatics for identification of rare variants could help explain the missing heritability in LOAD.

Background: We have studied the in vitro and in vivo utility of polyethylene glycol (PEG)-hydrogels for the development of an anticancer drug 5-fluorouracil (5-FU) delivery system., Methods: A 5-FU-loaded PEG-hydrogel was implanted subcutaneously to evaluate the drug retention time and the anticancer effect. For the pharmacokinetic study, two groups of male rats were administered either an aqueous solution of 5-FU (control group)/or a 5-FU-loaded PEG-hydrogel (treated group) at a dose of 100 mg/kg. For the pharmacodynamic study, a human non-small-cell lung adenocarcinoma (NSCLC) cell line, A549 was inoculated to male nude mice with a cell density of 3 x 10(6). Once tumors start growing, the mice were injected with 5-FU/or 5-FU-loaded PEG-hydrogel once a week for 4 weeks. The growth of the tumors was monitored by measuring the tumor volume and calculating the tumor inhibition rate (IR) over the duration of the study., Results: In the pharmacokinetic study, the 5-FU-loaded PEG-hydrogel gave a mean residence time (MRT) of 8.0 h and the elimination half-life of 0.9 h; these values were 14- and 6-fold, respectively, longer than those for the free solution of 5-FU (p < 0.05). In the pharmacodynamic study, A549 tumor growth was significantly inhibited in the 5-FU-loaded PEG-hydrogel group in comparison to the untreated group beginning on Day 14 (p < 0.05-0.01). Moreover, the 5-FU-loaded PEG-hydrogel group had a significantly enhanced tumor IR (p < 0.05) compared to the free 5-FU drug treatment group., Conclusion: We suggest that 5-FU-loaded PEG-hydrogels could provide a useful tool for the development of an anticancer drug delivery system.