Targeting Pediatric Solid Tumors in the New Era of RNA Therapeutics.

Despite substantial progress in pediatric cancer treatment, poor prognosis remained for patients with recurrent or metastatic disease, given the limitations of approved targeted treatments and immunotherapies. RNA therapeutics offer significant potential for addressing a broad spectrum of diseases, including cancer. Advances in manufacturing and delivery systems are paving the way for the rapid development of therapeutic RNAs for clinical applications. This review summarizes therapeutic RNA classifications and the mechanisms of action, highlighting their potential in manipulating major cancer-related pathways and biological effects. We also focus on the pre-clinical investigation of RNA molecules with efficient delivery systems for their therapeutic potential targeting pediatric solid tumors.

Knockdown of heat shock protein family D member 1 (HSPD1) in lung cancer cell altered secretome profile and cancer-associated fibroblast induction.

The crosstalk between lung cancer cells and cancer-associated fibroblast (CAF) is pivotal in cancer progression. Heat shock protein family D member 1 (HSPD1) is a potential prognostic biomarker associated with the tumor microenvironment in lung adenocarcinoma (LUAD). However, the role of HSPD1 in CAF activation remains unclear. This study established stable HSPD1-knockdown A549 lung cancer cells using a lentivirus-mediated shRNA transduction. A targeted label-free proteomic analysis identified six significantly altered secretory proteins in the shHSPD1-A549 secretome compared to shControl-A549. Functional enrichment analysis highlighted their involvement in cell-to-cell communication and immune responses within the tumor microenvironment. Additionally, most altered proteins exhibited positive correlations and significant prognostic impacts on LUAD patient survival. Investigations on the effects of lung cancer secretomes on lung fibroblast WI-38 cells revealed that the shControl-A549 secretome stimulated fibroblast proliferation, migration, and CAF marker expression. These effects were reversed upon the knockdown of HSPD1 in A549 cells. Altogether, our findings illustrate the role of HSPD1 in mediating CAF induction through secretory proteins, potentially contributing to the progression and aggressiveness of lung cancer.

Trehalose phosphorylase as a novel potential allergen in a case of allergic reaction due to oyster mushroom (Pleurotus ostreatus) ingestion.

To our knowledge, we present the first case report of allergic reaction from oyster mushroom ingestion, which was confirmed by an oral food challenge test. Trehalose phosphorylase was identified as a novel potential allergen by IgE immunoblotting and mass spectrometry.

Recombinant Cas9 protein production in an endotoxin-free system and evaluation with editing the BCL11A gene in human cells.

Many therapeutic proteins are expressed in Escherichia coli bacteria for the low cost and high yield obtained. However, these gram-negative bacteria also generate undesirable endotoxin byproducts such as lipopolysaccharides (LPS). These endotoxins can induce a human immune response and cause severe inflammation. To mitigate this problem, we have employed the ClearColi BL21 (DE3) endotoxin-free cells as an expression host for Cas9 protein production. Cas9 is an endonuclease enzyme that plays a key role in the Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR associated protein 9 (CRISPR/Cas9) genome editing technique. This technology is very promising for use in diagnostics as well as treatment of diseases, especially for genetic diseases such as thalassemia. The potential uses for this technology thus generate a considerable interest for Cas9 utilization as a therapeutic protein in clinical treatment. Therefore, special care in protein production should be a major concern. Accordingly, we expressed the Cas9 protein in endotoxin-free bacterial cells achieving 99% purity with activity comparable to commercially available Cas9. Our protocol therefore yields a cost-effective product suitable for invitro experiments with stem cells.

A novel anti-membrane CD30 single-chain variable fragment discovered from the human phage library: A potential targeted immunotherapy.

Hodgkin's lymphoma and anaplastic large cell lymphoma, especially relapsed or refractory diseases, could recently be cured by CD30-targeted immunotherapy. However, the CD30 antigen releases the soluble ectodomain of CD30, which might obscure the targeted therapy. Therefore, the membrane epitope of CD30 (mCD30), left on the cancer cells, might be a prospective target for lymphoma treatment. The discovery of novel mCD30 monoclonal antibodies (mAbs) using phage technology yielded 59 potential human single-chain variable fragments (HuscFvs). Ten candidate HuscFv clones have been selected based on various methods, i.e., direct PCR, ELISA and western blot assays, and nucleotide sequencing techniques. Fortunately, only one potential HuscFv clone, clone #A4, was determined by the prediction of HuscFv-peptide molecular docking and the binding affinity test using isothermal titration calorimetry. Finally, we proved that the HuscFv #A4, which had a binding affinity (Kd) of 421e-9 ± 2.76e-6 M, might be the novel mCD30 mAb. We generated chimeric antigen receptor-modified T lymphocytes using HuscFv #A4 as an antigen detection part (anti-mCD30-H4CART). The cytotoxicity assay of anti-mCD30-H4CART cells showed significant eradication of the CD30-expressing cell line, K562 (p = 0.0378). We found a novel mCD30 HuscFv using human phage technology. We systematically examined and proved that our HuscFv #A4 could specifically eradicate CD30-expressing cancers.

ALA-A2 Is a Novel Anticancer Peptide Inspired by Alpha-Lactalbumin: A Discovery from a Computational Peptide Library, In Silico Anticancer Peptide Screening and In Vitro Experimental Validation.

Anticancer peptides (ACPs) are rising as a new strategy for cancer therapy. However, traditional laboratory screening to find and identify novel ACPs from hundreds to thousands of peptides is costly and time consuming. Here, a sequential procedure is applied to identify candidate ACPs from a computer-generated peptide library inspired by alpha-lactalbumin, a milk protein with known anticancer properties. A total of 2688 distinct peptides, 5-25 amino acids in length, are generated from alpha-lactalbumin. In silico ACP screening using the physicochemical and structural filters and three machine learning models lead to the top candidate peptides ALA-A1 and ALA-A2. In vitro screening against five human cancer cell lines supports ALA-A2 as the positive hit. ALA-A2 selectively kills A549 lung cancer cells in a dose-dependent manner, with no hemolytic side effects, and acts as a cell penetrating peptide without membranolytic effects. Sequential window acquisition of all theorical fragment ions-proteomics and functional validation reveal that ALA-A2 induces autophagy to mediate lung cancer cell death. This approach to identify ALA-A2 is time and cost-effective. Further investigations are warranted to elucidate the exact intracellular targets of ALA-A2. Moreover, these findings support the use of larger computational peptide libraries built upon multiple proteins to further advance ACP research and development.

Placenta-Derived Extracellular Vesicles in Pregnancy Complications and Prospects on a Liquid Biopsy for Hemoglobin Bart's Disease.

Extracellular vesicles (EVs) are nano-scaled vesicles released from all cell types into extracellular fluids and specifically contain signature molecules of the original cells and tissues, including the placenta. Placenta-derived EVs can be detected in maternal circulation at as early as six weeks of gestation, and their release can be triggered by the oxygen level and glucose concentration. Placental-associated complications such as preeclampsia, fetal growth restriction, and gestational diabetes have alterations in placenta-derived EVs in maternal plasma, and this can be used as a liquid biopsy for the diagnosis, prediction, and monitoring of such pregnancy complications. Alpha-thalassemia major ("homozygous alpha-thalassemia-1") or hemoglobin Bart's disease is the most severe form of thalassemia disease, and this condition is lethal for the fetus. Women with Bart's hydrops fetalis demonstrate signs of placental hypoxia and placentomegaly, thereby placenta-derived EVs provide an opportunity for a non-invasive liquid biopsy of this lethal condition. In this article, we introduced clinical features and current diagnostic markers of Bart's hydrops fetalis, extensively summarize the characteristics and biology of placenta-derived EVs, and discuss the challenges and opportunities of placenta-derived EVs as part of diagnostic tests for placental complications focusing on Bart's hydrop fetalis.

Genomics-Driven Precision Medicine in Pediatric Solid Tumors.

Over the past decades, several study programs have conducted genetic testing in cancer patients to identify potential genetic targets for the development of precision therapeutic strategies. These biomarker-driven trials have demonstrated improved clinical outcomes and progression-free survival rates in various types of cancers, especially for adult malignancies. However, similar progress in pediatric cancers has been slow due to their distinguished mutation profiles compared to adults and the low frequency of recurrent genomic alterations. Recently, increased efforts to develop precision medicine for childhood malignancies have led to the identification of genomic alterations and transcriptomic profiles of pediatric patients which presents promising opportunities to study rare and difficult-to-access neoplasms. This review summarizes the current state of known and potential genetic markers for pediatric solid tumors and provides perspectives on precise therapeutic strategies that warrant further investigations.

Folate receptor alpha autoantibodies in children with autism spectrum disorder.

BACKGROUND: Recent research indicates that a number of children with autism generate folate receptor alpha autoantibodies (FRAA), which block transportation of folate across the blood-brain barrier, resulting in cerebral folate deficiency syndrome. Plasma FRAA detection permits precision diagnosis and potentially beneficial folinic acid treatment in FRAA-positive children with autism. OBJECTIVES: To investigate FRAA prevalence in Thai children with autism and evaluate the associations between FRAA-positive status, clinical symptom severity, and adaptive functioning. METHODS: FRAA level was determined in serum samples from 89 children with autism between 2 and 15 years (69 males, 20 females, mean age 7.9 years, SD 3.8). The Childhood Autism Rating Scale-Second Edition (CARS-2) and the Vineland Adaptive Behavior Scales (VABS) were used to evaluate clinical symptom severity and adaptive functioning, respectively. RESULTS: Of 89 children, 30 (33.7%) were FRAA-positive. FRAA-positive children with autism had significantly poorer mean VABS Adaptive Behavior Composite scores (p = 0.02) and Communication scores (p = 0.02) than FRAA-negative children with autism. There was no association between FRAA level and clinical symptom severity (CARS-2 score) (p = 0.09). CONCLUSIONS: The findings demonstrate the presence of FRAA in children with autism and that FRAA status is associated with poorer adaptive functioning.

Extracellular Vesicle-Based Method for Detecting MYCN Amplification Status of Pediatric Neuroblastoma.

MYCN amplification is the strongest predictor of high-risk neuroblastoma (NB). The standard procedure to detect MYCN status requires invasive procedures. Extracellular vesicles (EVs) contain molecular signatures of originated cells, present in biofluids, and serve as an invaluable source for cancer liquid biopsies. This study aimed to establish an EV-based method to detect the MYCN status of NB. Two EV subtypes, i.e., microvesicles (MVs) and exosomes, were sequentially isolated from the culture supernatant by step-wise centrifugation, ultrafiltration, and size-exclusion chromatography. Quantitative RT-PCR was performed to detect MYCN mRNA. As a result, MYCN mRNA was detectable in the MVs, but not exosomes, of MYCN-amplified NB cells. MYCN mRNA-containing MVs (MYCN-MV) were successfully detected in three distinct MYCN-amplified NB cell lines but absent in three MYCN non-amplification cells. The simulated samples were prepared by pulsing MVs into human serum. MYCN-MV detection in the simulated samples showed a less interfering effect from the human blood matrix. Validation using clinical specimens (2 mL bone marrow plasma) obtained from patients at various disease stages showed a promising result. Five out of six specimens of MYCN-amplified patients showed positive results, while there were no false positives in four plasma samples of the MYCN non-amplification group. This study communicated a novel EV-based method for detecting the MYCN status of pediatric NB based on MYCN mRNA contents in MVs. Future studies should be pursued in a prospective cohort to determine its true diagnostic performance.

SCMTHP: A New Approach for Identifying and Characterizing of Tumor-Homing Peptides Using Estimated Propensity Scores of Amino Acids.

Tumor-homing peptides (THPs) are small peptides that can recognize and bind cancer cells specifically. To gain a better understanding of THPs' functional mechanisms, the accurate identification and characterization of THPs is required. Although some computational methods for in silico THP identification have been proposed, a major drawback is their lack of model interpretability. In this study, we propose a new, simple and easily interpretable computational approach (called SCMTHP) for identifying and analyzing tumor-homing activities of peptides via the use of a scoring card method (SCM). To improve the predictability and interpretability of our predictor, we generated propensity scores of 20 amino acids as THPs. Finally, informative physicochemical properties were used for providing insights on characteristics giving rise to the bioactivity of THPs via the use of SCMTHP-derived propensity scores. Benchmarking experiments from independent test indicated that SCMTHP could achieve comparable performance to state-of-the-art method with accuracies of 0.827 and 0.798, respectively, when evaluated on two benchmark datasets consisting of Main and Small datasets. Furthermore, SCMTHP was found to outperform several well-known machine learning-based classifiers (e.g., decision tree, k-nearest neighbor, multi-layer perceptron, naive Bayes and partial least squares regression) as indicated by both 10-fold cross-validation and independent tests. Finally, the SCMTHP web server was established and made freely available online. SCMTHP is expected to be a useful tool for rapid and accurate identification of THPs and for providing better understanding on THP biophysical and biochemical properties.

Review and Comparative Analysis of Machine Learning-based Predictors for Predicting and Analyzing Anti-angiogenic Peptides.

Cancer is one of the leading causes of death worldwide and the underlying angiogenesis represents one of the hallmarks of cancer. Efforts are already under way for the discovery of anti-angiogenic peptides (AAPs) as a promising therapeutic route, which tackle the formation of new blood vessels. As such, the identification of AAPs constitutes a viable path for understanding their mechanistic properties pertinent for the discovery of new anti-cancer drugs. In spite of the abundance of peptide sequences in public databases, experimental efforts in the identification of anti-angiogenic peptides have progressed very slowly owing to high expenditures and laborious nature. Owing to its inherent ability to make sense of large volumes of data, machine learning (ML) represents a lucrative technique that can be harnessed for peptide-based drug discovery. In this review, we conducted a comprehensive and comparative analysis of ML-based AAP predictors in terms of their employed feature descriptors, ML algorithms, cross-validation methods and prediction performance. Moreover, the common framework of these AAP predictors and their inherent weaknesses are also discussed. Particularly, we explore future perspectives for improving the prediction accuracy and model interpretability, which represent an interesting avenue for overcoming some of the inherent weaknesses of existing AAP predictors. We anticipate that this review would assist researchers in the rapid screening and identification of promising AAPs for clinical use.

Cell-Main Spectra Profile Screening Technique in Simulation of Circulating Tumour Cells Using MALDI-TOF Mass Spectrometry.

Circulating atypical cells (CAC) are released from a primary tumour site into peripheral blood and are indicators of cancer metastasis. CAC occur at very low frequency in circulating blood, and their detection remains challenging. Moreover, white blood cells (WBC) are the major contaminant in enriched CAC samples. Here, we developed matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) as a novel CAC characterization platform. Main spectra profiles (MSP) of normal and cancer cells were generated by MALDI-TOF MS, and a cell-main spectra database was then compiled and analysed using the MALDI Biotyper software. Logarithmic scores accurately predicted distinct cell types. The feasibility of this workflow was then validated using simulated samples, which were prepared by 5000 WBC of three healthy individuals spiked with varying numbers (3, 6, 12, 25, 50, and 100) of lung, colon, or prostate cancer cells. MALDI-TOF MS was able to detect cancer cells down to six cells over the background noise of 5000 WBC with significantly higher predictive scores as compared to WBC alone. Further development of cell-MSP database to cover all cancer types sourced from cell lines and patient tumours may enable the use of MALDI-TOF MS as a cancer-screening platform in clinical settings in the future.

HMP-S7 Is a Novel Anti-Leukemic Peptide Discovered from Human Milk.

Chemotherapy in childhood leukemia is associated with late morbidity in leukemic survivors, while certain patient subsets are relatively resistant to standard chemotherapy. It is therefore important to identify new agents with sensitivity and selectivity towards leukemic cells, while having less systemic toxicity. Peptide-based therapeutics has gained a great deal of attention during the last few years. Here, we used an integrative workflow combining mass spectrometric peptide library construction, in silico anticancer peptide screening, and in vitro leukemic cell studies to discover a novel anti-leukemic peptide having 3+ charges and an alpha helical structure, namely HMP-S7, from human breast milk. HMP-S7 showed cytotoxic activity against four distinct leukemic cell lines in a dose-dependent manner but had no effect on solid malignancies or representative normal cells. HMP-S7 induced leukemic cell death by penetrating the plasma membrane to enter the cytoplasm and cause the leakage of lactate dehydrogenase, thus acting in a membranolytic manner. Importantly, HMP-S7 exhibited anti-leukemic effects against patient-derived leukemic cells ex vivo. In conclusion, HMP-S7 is a selective anti-leukemic peptide with promise, which requires further validation in preclinical and clinical studies.

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides.

The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.

Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.

As anticancer peptides (ACPs) have attracted great interest for cancer treatment, several approaches based on machine learning have been proposed for ACP identification. Although existing methods have afforded high prediction accuracies, however such models are using a large number of descriptors together with complex ensemble approaches that consequently leads to low interpretability and thus poses a challenge for biologists and biochemists. Therefore, it is desirable to develop a simple, interpretable and efficient predictor for accurate ACP identification as well as providing the means for the rational design of new anticancer peptides with promising potential for clinical application. Herein, we propose a novel flexible scoring card method (FSCM) making use of propensity scores of local and global sequential information for the development of a sequence-based ACP predictor (named iACP-FSCM) for improving the prediction accuracy and model interpretability. To the best of our knowledge, iACP-FSCM represents the first sequence-based ACP predictor for rationalizing an in-depth understanding into the molecular basis for the enhancement of anticancer activities of peptides via the use of FSCM-derived propensity scores. The independent testing results showed that the iACP-FSCM provided accuracies of 0.825 and 0.910 as evaluated on the main and alternative datasets, respectively. Results from comparative benchmarking demonstrated that iACP-FSCM could outperform seven other existing ACP predictors with marked improvements of 7% and 17% for accuracy and MCC, respectively, on the main dataset. Furthermore, the iACP-FSCM (0.910) achieved very comparable results to that of the state-of-the-art ensemble model AntiCP2.0 (0.920) as evaluated on the alternative dataset. Comparative results demonstrated that iACP-FSCM was the most suitable choice for ACP identification and characterization considering its simplicity, interpretability and generalizability. It is highly anticipated that the iACP-FSCM may be a robust tool for the rapid screening and identification of promising ACPs for clinical use.

Red Blood Cell Extracellular Vesicle-Based Drug Delivery: Challenges and Opportunities.

Recently, red blood cell-derived extracellular vesicles (RBCEVs) have attracted attention for clinical applications because of their safety and biocompatibility. RBCEVs can escape macrophages through the binding of CD47 to inhibitory receptor signal regulatory protein α. Furthermore, genetic materials such as siRNA, miRNA, mRNA, or single-stranded RNA can be encapsulated within RBCEVs and then released into target cells for precise treatment. However, their side effects, half-lives, target cell specificity, and limited large-scale production under good manufacturing practice remain challenging. In this review, we summarized the biogenesis and composition of RBCEVs, discussed the advantages and disadvantages of RBCEVs for drug delivery compared with synthetic nanovesicles and non-red blood cell-derived EVs, and provided perspectives for overcoming current limitations to the use of RBCEVs for clinical applications.

Proteomic analysis reveals plasma haptoglobin, interferon-γ, and interleukin-1ß as potential biomarkers of pediatric refractory epilepsy.

BACKGROUND: Children with refractory epilepsy (RE) are associated with increased mortality rate, nonfatal injuries, disability, and diminished quality of life. Biomarkers for the early prediction of RE is still an unmet need. METHODS: Eighteen children with RE and six age-matched unrelated controls were included in this study. Plasma samples were prefractionated by the optimized thermal treatment before proteomic analysis using 2DE-LC-MS/MS. Bioinformatic analysis was carried out using STRING protein network. Immunoassay of unprocessed plasma was applied to confirm changes of proteins of interest. P-value < 0.05 was considered statistically significant. RESULTS: Proteomic analysis (n = 6 each group) revealed nine differentially expressed proteins, i.e., haptoglobin, S100A9, serpin B1, apolipoprotein A-I, apolipoprotein A-IV, apolipoprotein C-II, alpha-1-acid glycoprotein 1 and 2, and transthyretin. Western immunoblotting confirmed haptoglobin upregulation in the RE group. STRING protein network predicted the inflammatory cytokines, i.e., interferon gamma (IFN-γ), interleukin-1 beta (IL-1ß) and tumor necrosis factor alpha (TNF-α), play roles in pathophysiology in RE patients. Cytokine immunoassay (n = 24, 18 RE vs. 6 controls) exhibited plasma IFN-γ was upregulated in RE patients as compared to the healthy individuals (median [IQR]; 2.9 [2.9, 4.9] vs. 1.32 [0.8, 1.5] pg/mL, p = 0.0013), and plasma IL-1ß was significantly downregulated in patients (1.0 [0.2, 1.9] vs. 4.5 [1.9, 11.0] pg/mL, p = 0.01). TNF-α had no difference between groups. The results suggest that haptoglobin may be associated with oxidative brain damage, while IFN-γ and IL-1ß may be involved with neuroinflammation. CONCLUSIONS: Alterations in plasma haptoglobin, IFN-γ, and IL-1ß were associated with RE patients. Future studies using a combination of these candidate biomarkers may help predict the intractability of epilepsy in pediatric populations.

Anticancer peptide: Physicochemical property, functional aspect and trend in clinical application (Review).

Cancer is currently ineffectively treated using therapeutic drugs, and is also able to resist drug action, resulting in increased side effects following drug treatment. A novel therapeutic strategy against cancer cells is the use of anticancer peptides (ACPs). The physicochemical properties, amino acid composition and the addition of chemical groups on the ACP sequence influences their conformation, net charge and orientation of the secondary structure, leading to an effect on targeting specificity and ACP­cell interaction, as well as peptide penetrating capability, stability and efficacy. ACPs have been developed from both naturally occurring and modified peptides by substituting neutral or anionic amino acid residues with cationic amino acid residues, or by adding a chemical group. The modified peptides lead to an increase in the effectiveness of cancer therapy. Due to this effectiveness, ACPs have recently been improved to form drugs and vaccines, which have sequentially been evaluated in various phases of clinical trials. The development of the ACPs remains focused on generating newly modified ACPs for clinical application in order to decrease the incidence of new cancer cases and decrease the mortality rate. The present review could further facilitate the design of ACPs and increase efficacious ACP therapy in the near future.

Optimization and Standardization of Thermal Treatment as a Plasma Prefractionation Method for Proteomic Analysis.

Prefractionation is a prerequisite step for deep plasma proteomics. Highly abundant proteins, particularly human serum albumin (HSA) and immunoglobulin G (IgG), typically interfere with investigation of proteins with lower abundance. A relatively simple preparation method based on high temperature can precipitate thermolabile proteins, providing a strategic window to access the thermostable plasma subproteome. This study aimed to optimize thermal treatment as a reliable prefractionation method and to compare it with two commercial kits, including HSA and IgG immunodepletion (IMDP) and combinatorial peptide ligand libraries (CPLL), using untreated plasma as a control condition. By varying the temperature and the incubation period, the optimal condition was found as treatment at 95°C for 20 min, which maintained about 1% recovery yield of soluble proteins. Consistency and reproducibility of thermal treatment-derived plasma subproteome were checked by two-dimensional electrophoresis. The coefficient of variation regarding protein spot numbers was less than 10% among three independent specimens. Highly abundant protein depletion of the thermal treatment was evaluated by immunoblotting against HSA and IgG as compared to the untreated plasma, IMDP, and CPLL. Multidimensional comparison based on 489 unique peptides derived from the label-free quantitative mass spectrometry revealed that the thermal treatment, IMDP, and CPLL provided distinct sets of plasma subproteome compared to untreated plasma, and these appeared to be complementary to each other. Comparing the characteristics of the three procedures suggested that thermal treatment was more cost-effective and less time-consuming than IMDP and CPLL. This study proposes the use of thermal treatment as a reliable and cost-effective method for plasma prefractionation which provides benefits to large-scale proteomic projects and biomarker studies.