Fig 1 - available via license: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
Content may be subject to copyright.
![High-performance serum metabolite characterization by NPELDI-MS. (A) Illustration of NPELDI-MS. The Upper digital image shows the MS chips after microarray printing, and the Bottom image was recorded during NPELDI-MS. (Scale bar, 5 mm.) (B and C) Elemental mappings of the nanoparticle-analyte (His in B and BSA in C) hybrids are shown with O in yellow, Fe in purple, and C in green, respectively. The insert images were high-angle annular dark-field images of nanoparticle-analyte hybrids. (Scale bars, 200 nm.) (D) The similarity scores between 1,000 nL of pristine serum and its dilutions by 10-to 1,000-fold using 100 to 1 nL of pristine serum. The error bars were calculated as SD of five repeated experiments. (E and F) Intensities of five molecular peaks (gray line for [Ala + Na] + at an m/z of 112.04, red line for [Lys + Na] + at an m/z of 169.09, blue line for [Arg + Na] + at an m/z of 197.19, green line for [Glc + Na] + at an m/z of 203.05, and purple line for [Suc + Na] + at an m/z of 365.11) for intrachip (ten replicates per sample) in E and interchip (five chips for 5 d, one chip per day) detection in F, respectively. (G and H) CV distribution of intensities for the apparent molecular peaks in ten serum samples (five HDs denoted as HD1 to HD5 and five BrCa patients denoted as BrCa1 to BrCa5) for intrachip (ten replicates per sample) in G and interchip detection (five chips for 5 d, one chip per day) in H, respectively.](publication/359331229/figure/fig1/AS:1139037435904001@1648579095382/High-performance-serum-metabolite-characterization-by-NPELDI-MS-A-Illustration-of.png)
High-performance serum metabolite characterization by NPELDI-MS. (A) Illustration of NPELDI-MS. The Upper digital image shows the MS chips after microarray printing, and the Bottom image was recorded during NPELDI-MS. (Scale bar, 5 mm.) (B and C) Elemental mappings of the nanoparticle-analyte (His in B and BSA in C) hybrids are shown with O in yellow, Fe in purple, and C in green, respectively. The insert images were high-angle annular dark-field images of nanoparticle-analyte hybrids. (Scale bars, 200 nm.) (D) The similarity scores between 1,000 nL of pristine serum and its dilutions by 10-to 1,000-fold using 100 to 1 nL of pristine serum. The error bars were calculated as SD of five repeated experiments. (E and F) Intensities of five molecular peaks (gray line for [Ala + Na] + at an m/z of 112.04, red line for [Lys + Na] + at an m/z of 169.09, blue line for [Arg + Na] + at an m/z of 197.19, green line for [Glc + Na] + at an m/z of 203.05, and purple line for [Suc + Na] + at an m/z of 365.11) for intrachip (ten replicates per sample) in E and interchip (five chips for 5 d, one chip per day) detection in F, respectively. (G and H) CV distribution of intensities for the apparent molecular peaks in ten serum samples (five HDs denoted as HD1 to HD5 and five BrCa patients denoted as BrCa1 to BrCa5) for intrachip (ten replicates per sample) in G and interchip detection (five chips for 5 d, one chip per day) in H, respectively.
Source publication
Significance
Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption...
Contexts in source publication
Context 1
... Serum Metabolite Characterization by NPELDI-MS. To improve the analytical speed, sample consumption, and reproducibility of LDI-MS, we conducted highperformance serum metabolic fingerprinting by NPELDI-MS (Fig. 1A). The ferric nanoparticles were prepared using a modified low-cost solve-thermal method (Materials and Methods), showing the designed surface roughness structure as an ideal matrix for NPELDI-MS. Due to the size-selective trapping and affinity-based cationization of metabolites by the surface nanostructures of nanoparticles (SI ...
Context 2
... (Fig. 1A). The ferric nanoparticles were prepared using a modified low-cost solve-thermal method (Materials and Methods), showing the designed surface roughness structure as an ideal matrix for NPELDI-MS. Due to the size-selective trapping and affinity-based cationization of metabolites by the surface nanostructures of nanoparticles (SI Appendix, Fig. S1), NPELDI-MS allowed direct detection of metabolites from the interference of proteins and salts in serum (SI Appendix, Fig. S2) with minimum sample treatment at high speed (∼30 s per sample). To validate the size-selective trapping of nanoparticles, histamine (His) and bovine serum albumin (BSA), as representatives for metabolites (MW ...
Context 3
... were mixed with nanoparticles to form nanoparticle-analyte hybrids. Consequently, the elemental mapping analysis of the nanoparticle-analyte hybrids showed a significantly higher molecular size-selective trapping rate (defined as the ratio of carbon signal intensity on the nanoparticles to the background) for His than for BSA (P < 0.001; Fig. 1 B and C and SI Appendix, Table S1). Importantly, the NPELDI-MS process afforded fast analytical speed with 2 s per sample (with 2,000 laser shots at a pulse frequency of 1,000 Hz; Materials and Methods), which could be coupled with on-chip microarray (384 samples per chip) to achieve automatic large-scale sample screening (Fig. ...
Context 4
... BSA (P < 0.001; Fig. 1 B and C and SI Appendix, Table S1). Importantly, the NPELDI-MS process afforded fast analytical speed with 2 s per sample (with 2,000 laser shots at a pulse frequency of 1,000 Hz; Materials and Methods), which could be coupled with on-chip microarray (384 samples per chip) to achieve automatic large-scale sample screening (Fig. ...
Context 5
... note, we calculated the cosine similarity score of apparent molecular peaks (with average intensity of >500) between native serum and its dilutions using 1 to 100 nL of serum to identify the minimum sample volume for detection. We obtained qualified similarity scores above 0.771 using 10 to 100 nL of serum (Fig. 1D) due to the efficient absorption and transfer of laser energy for enhanced detection sensitivity by two to six orders (compared with organic matrices; SI Appendix, Fig. S3 and Table S2). Specifically, 108 apparent molecular peaks of metabolites were observed, likely owing to the high sensitivity of NPELDI-MS, which helped to form a ...
Context 6
... Subsequently, we validated these seven metabolites as L-glyceric acid (GA), nicotinamide (NAM), His, uracil (Ura), thymine (Thy), 3,4-dihydroxybenzylamine (DB), and dehydrophenylalanine (DP) (24,25), respectively, through accurate MS using Fourier transform ion cyclotron resonance (FT-ICR)-MS or MS/MS using time-of-flight (TOF)-MS (SI Appendix, Fig. S10 and Table S10). Among them, His, Ura, Thy, DB, and DP were down-regulated (P < 0.05), while GA and NAM were up-regulated (P < 0.001) in BrCa compartments compared with in BBD and HD compartments (Fig. ...
Context 7
... studies (5,12,(44)(45)(46)(47). To determine the minimum sample number to conduct machine learning, we used a power analysis of ten samples (five/five BrCa/non-BrCa compartments) as a pilot study and obtained a power of > 0.8 with the sample number of 200 (100/100 BrCa/non-BrCa compartments) at a false discovery rate of 0.10 (SI Appendix, Fig. S11), validating that the machine learning results were at a sufficient confidence ...
Similar publications
Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancem...
Extensive experiments illustrate that moments and their derivations can act as effective fingerprint features for specific emitter identification. Nevertheless, the lack of mechanistic explanation restricts the moment-based fingerprint features to a trial-based and data-driven technique. To make up for theoretical weakness and enhance generalizatio...
Citations
... Amino acid metabolites, including tryptophan and its associated pathway metabolites, arginine, proline, histidine, 5-oxoproline, kynurenine, nicotinate, and nicotinamide, have been suggested as potential early diagnostic biomarkers for breast cancer [9][10][11][12][13][14][15][16][17]. The catabolism of tryptophan, an essential amino acid, is associated with the immune system, and tryptophan has been studied in the context of cancer [15]. ...
... Several studies have suggested the potential of nicotinic acid and nicotinamide as biomarkers for breast cancer diagnosis. Huang, et al. [12] reported that serum levels of nicotinic acid and nicotinamide were significantly higher in patients with breast cancer than in individuals with benign breast disease or healthy controls. However, Zhang, et al. [17] reported a significant decrease in serum nicotinamide levels in breast cancer. ...
... Regarding nucleic acid metabolism, increased plasma hypoxanthine and decreased serum uracil levels have been reported [12,14,41]. Hypoxanthine is related to purine metabolism. ...
... Gamma = 'scale' uses a heuristic based on the number of features, ensuring an optimal range for γ. Class weight was set to 'balanced' to compensate for any imbalance in the dataset [33,34]. The grid search aimed to balance model complexity and margin separation between the cancer and control groups. ...
Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management.
... 6,7 Recent metabolomic analyses of various cancers, including lung, liver, colorectal, bladder, breast, and endometrial malignancies, have described biomarkers derived from samples including tissues, blood, urine, and saliva. [8][9][10][11][12][13][14] For patients with cervical cancer, predictive, diagnostic, and prognostic biomarkers have been reported in urine, blood, cervical canal lavage fluid, and tissues. [15][16][17][18][19][20][21][22] Despite the clear value of exploring metabolomics-based biomarkers in cervical cancer, there is a paucity of cervical mucus-derived ancillary biomarkers that would help to differentiate between various premalignant lesions and invasive cancer. ...
Approximately 660,000 women are diagnosed with cervical cancer annually. Current screening options such as cytology or human papillomavirus testing have limitations, creating a need to identify more effective ancillary biomarkers for triage. Here, we evaluated whether metabolomic analysis of cervical mucus metabolism could be used to identify biomarkers of cervical intraepithelial neoplasia (CIN) and cervical cancer. The case–control group consisted of 181 CIN, 69 squamous cell carcinoma (SCC) patients, and 48 healthy controls in the primary cohort. We undertook metabolomic analyses using ultra‐HPLC–tandem mass spectrometry. Univariate and multivariate analyses were carried out to profile metabolite characteristics, and receiver operating characteristic (ROC) analysis identified biomarker candidates. Five metabolites conferred the highest discriminatory power for SCC: oxidized glutathione (GSSG) (area under the ROC curve, 0.924; 95% confidence interval, 0.877–0.971), malic acid (0.914, 0.859–0.968), kynurenine (0.884, 0.823–0.945), GSSG/glutathione (GSH) (0.936, 0.892–0.979), and kynurenine/tryptophan (0.909, 0.856–0.961). Malic acid was the best marker for detection of CIN2 or worse (0.858, 0.793–0.922) and was a clinically useful metabolite. We confirmed the reproducibility of the results by validation cohort. Additionally, metabolomic analyses revealed eight pathways strongly associated with cervical neoplasia. Of these, only the tricarboxylic acid cycle was strongly associated with all CINs and cancer, indicating active energy production. Aberrant arginine metabolism by decreasing arginine and increasing citrulline might reduce tumor immunity. Changes in cysteine‐methionine and GSH pathways might drive the initiation and progression of cervical cancer. These results suggest that metabolic analysis can identify ancillary biomarkers and could improve our understanding of the pathophysiological mechanisms underlying cervical neoplasia.
... SMF machine learning is an efficient readout to differentiate non-BrCa from BrCa. 21 In this study, magnesium, aluminum, and lanthanum ternary hydroxides are loaded on zeolite to prepare a MALZ (Mg-Al-La-zeolite) composite and used to enrich phosphorylated metabolites. After parameter optimizations, the comprehensive analysis of phosphorylated metabolites in liver cancer serum samples is carried out via liquid chromatography−mass spectrometry (LC−MS). ...
ATP upregulation is a significant driver of aggressive cancer cell phenotypes. Phosphometabolites participate in metabolic pathways and are overexpressed in cancer cell activity. Therefore, developing novel and accurate methods for detecting phosphometabolites in biological fluids is essential. In this research, a novel zeolite composite comprising magnesium, aluminum, and lanthanum hydroxides (MALZ) is developed and used for the first time to enrich phosphorylated metabolites via its inherent interaction with phosphate groups. SEM micrographs show a crystalline cubic structure with a small diameter of 36.62 nm. FTIR analysis confirms the phosphate adsorption and desorption using AMP and ATP as the standards. XRD analysis of MALZ provides structural information about the synthesized composite. Adsorption–desorption parameters, such as pH, shaking time, and MALZ concentration, are optimized to analyze the binding capacity of the fabricated material for phosphorylated metabolites. A kinetic study reveals the rapid and effective AMP and ATP adsorptions on MALZ. The multiple hydroxyl groups of ternary hydroxides and high affinity of lanthanum toward the phosphate group enrich 26 phosphometabolites from serum samples of malignant neoplastic patients. The LC–MS profile shows characteristic phosphometabolites that may act as signatures of cancer-related abnormal metabolic pathways. This study may provide an experimental pathway for detecting metabolites in human body fluids.
... 14 Peripheral blood tumor biomarkers are readily accessible biomarkers and their roles in cancer prevention and treatment, such as plasma metabolic fingerprinting, have been extensively explored. 15,16 Researchers have explored the clinical significance of platelet-to-lymphocyte ratio (PLR) for the prediction of prognosis in bladder cancer patients, and the prognostic value of IL-6 has been evaluated in other tumors. 17,18 However, no studies have explored the prognostic value of the combination of PLR and IL-6 in bladder cancer. ...
Background
Non-muscle invasive bladder cancer (NMIBC) is the most prevalent type of bladder cancer, typically associated with a favorable prognosis and a risk of recurrence during the follow-up period. Inflammatory markers have been used to predict prognosis in various cancer types. The aim of this study was to explore the prognostic value of the readily accessible inflammatory markers, platelet-to-lymphocyte ratio (PLR) and interleukin-6 (IL-6), in NMIBC.
Methods
The study comprised a retrospective analysis of clinical data collected from NMIBC patients diagnosed between October 2018 and October 2020. PLR was calculated using the routine preoperative blood test results, and preoperative IL-6 levels were recorded. Receiver operating characteristic (ROC) curves were generated for PLR and IL-6 level and the optimal cut-off values were determined using Youden's index. Survival curves were generated to evaluate the association between PLR and IL-6, and recurrence-free survival (RFS), and univariate and multivariate analysis were performed using the Cox proportional hazards regression model. A nomogram and calibration curve were generated to assess the clinical significance of the model.
Results
The ROC curves demonstrated that PLR and IL-6 levels were significantly associated with tumor pathology grade, with area under the curve (AUC) values of 0.833 (95% CI 0.757, 0.910) for PLR and 0.724 (95% CI 0.622, 0.825) for IL-6 levels. PLR and IL-6 levels were also positively associated with tumor recurrence, with AUC values of 0.647 (95% CI 0.538, 0.756) and 0.846 (95% CI 0.769, 0.924), respectively. The survival curves indicated that patients with high PLR and high IL-6 levels had shorter RFS than those with low PLR and low IL-6 level (P < 0.01). Univariate Cox proportional hazards regression analysis showed that age, tumor size, tumor number, pathological grade, PLR and IL-6 were potential risk factors for NMIBC recurrence. Multivariate analysis further revealed that tumor number, smoking, PLR, and IL-6 were independent risk factors for NMIBC recurrence (P < 0.05).
Conclusions
Preoperative peripheral blood inflammatory markers (PLR and IL-6) are useful predictors of RFS in NMIBC patients at the time of initial diagnosis. High PLR and high IL-6 were identified as independent risk factors for tumor recurrence and could serve as potential biological markers for prediction of NMIBC recurrence.
... While, Gajda-Walczak et al. [257] utilise surface plasmon resonance and quartz crystal microbalance with energy dissipation tests for the detection of BRCA1 mutations. Additionally, Huang et al. [258] introduce nanoparticle-enhanced laser desorption/ionisation MS for the detection of metabolite markers in BC. These advancements showcase a diverse range of technologies poised to enhance our understanding, detection and diagnosis of BC. 1D SDS-PAGE, one-dimensional sodium dodecyl sulphate-polyacrylamide gel electrophoresis; 2-DE, two-dimensional gel electrophoresis; 2D-DIGE, two-dimensional difference gel electrophoresis; BC, breast cancer; ELISA, enzyme-linked immunosorbent assay; ELLA, sandwich enzyme-linked lectin assay; ICAT, isotope-coded affinity tag; LC-MS/MS, liquid chromatography-tandem mass spectrometry; MALDI-TOF/MS, matrix-assisted laser desorption ionisation-time-of-flight mass spectrometry; MRM-MS, multiple reaction monitoring mass spectrometry; TNBC, triple-negative breast cancer; WB, Western blotting. ...
... Metabolomics technology became increasingly common and received widespread attention in the scientific community [3]. Metabolomics can be used to diagnose disease, explore the mechanism of disease, new targets, and determine drug treatment and treatment results [4], especially in obesity, diabetes, cardiovascular disease, cancer, and neonatal metabolic defects [5][6][7][8]. ...
BACKGROUND
Diabetes is a metabolic disease characterized by hyperglycemia, which has increased the global medical burden and is also the main cause of death in most countries.
AIM
To understand the knowledge structure of global development status, research focus, and future trend of the relationship between diabetes and metabolomics in the past 20 years.
METHODS
The articles about the relationship between diabetes and metabolomics in the Web of Science Core Collection were retrieved from 2002 to October 23, 2023, and the relevant information was analyzed using CiteSpace6.2.2R (CiteSpace), VOSviewer6.1.18 (VOSviewer), and Bibliometrix software under R language.
RESULTS
A total of 3123 publications were included from 2002 to 2022. In the past two decades, the number of publications and citations in this field has continued to increase. The United States, China, Germany, the United Kingdom, and other relevant funds, institutions, and authors have significantly contributed to this field. Scientific Reports and PLoS One are the journals with the most publications and the most citations. Through keyword co-occurrence and cluster analysis, the closely related keywords are "insulin resistance", "risk", "obesity", "oxidative stress", "metabolomics", "metabolites" and "biomarkers". Keyword clustering included cardiovascular disease, gut microbiota, metabonomics, diabetic nephropathy, molecular docking, gestational diabetes mellitus, oxidative stress, and insulin resistance. Burst detection analysis of keyword depicted that "Gene", "microbiota", "validation", "kidney disease", "antioxidant activity", "untargeted metabolomics", "management", and "accumulation" are knowledge frontiers in recent years.
CONCLUSION
The relationship between metabolomics and diabetes is receiving extensive attention. Diabetic nephropathy, diabetic cardiovascular disease, and kidney disease are key diseases for future research in this field. Gut microbiota, molecular docking, and untargeted metabolomics are key research directions in the future. Antioxidant activity, gene, validation, mass spectrometry, management, and accumulation are at the forefront of knowledge frontiers in this field.
... Higher levels of glutamate in breast cancer patients suggest it plays an important role in fatty acids overproduction [41,42]. Histidine association with BC has been corroborated by Huang et al [43]. Additionally, this team of researchers applied a neural network model on a panel of seven saliva biomarkers to predict the probability of being diagnosed as BrCa-positive breast cancer and attained an AUC of 86.5. ...
... Additionally, this team of researchers applied a neural network model on a panel of seven saliva biomarkers to predict the probability of being diagnosed as BrCa-positive breast cancer and attained an AUC of 86.5. The 7-metabolite panel consists of L-glyceric acid, nicotinamide, histamine, uracil, thymine, 3,4-dihydroxybenzyl amine and dehydro phenylalanine [43]. ...
Breast cancer remains a major public health concern, and early detection can result in more treatment options, which are crucial for improving survival rates. Metabolomics offers the potential to develop blood-based screening and diagnostics tools that are less invasive and more cost-effective. However, the inherent complexity of metabolomic datasets makes identifying the most diagnostically relevant biomarkers a difficult task, with multiple studies demonstrating lim-ited agreement on the specific metabolites and pathways involved. This study aims to identify a set of biomarkers for early diagnosis of breast cancer using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (CHTN) were analyzed. We utilized univariate Naïve Bayes, L2-regularized Support Vector Machines, and Principal Component Analysis (PCA), along with feature engineering techniques, to select the most informative features. Multiple ma-chine learning models, including Support Vector Machines, Multidimensional Scaling, Logistic Regression, and Ensemble Learning were utilized for classification. The best-performing feature set comprised 4 biomarkers and 2 demographic variables, achieving an accuracy of 98%, demon-strating the potential for a robust, cost-effective, non-invasive breast cancer screening and diagnostic tool.
... In addition, the results of a previous study revealed that the analysis of metabolites may assist in revealing the real-time status of biological systems. For example, metabolomics showed potential in the early diagnosis, treatment and prognosis prediction of patients with breast cancer, demonstrating an additional tool for early screening and diagnosis (36). ...
Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease with varying characteristics, in terms of genomic variation, cell morphology and clinical presentation. At present, only ~66% of patients are cured with initial treatment and those with refractory DLBCL exhibit a poor prognosis. Thus, further investigations into novel effective treatment options for DLBCL are required. The present study reports the case of a patient resistant to multiple therapies, including rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) plus enzastaurin (trial no. CTR20171560), GemOx plus lenalidomide and selinexor (trial no. ATG-010-DLBCL-001). The patient harbored a CD274 amplification, as identified via next-generation sequencing (NGS), and exhibited a high programmed death-ligand 1 Tumor Proportion Score of up to 95%. Consequently, the patient was treated with sintilimab monotherapy and the response lasted for 12 months of follow-up without major immune-related adverse events. This case highlights the role of NGS technology in selecting treatment options for refractory DLBCL. Furthermore, the results of the present study suggest that sintilimab may have potential in the treatment of patients with refractory DLBCL.
... Apoptosis involves a series of irreversible changes caused by caspase family members activated by some pro-apoptotic stimuli, such as endoplasmic reticulum stress and reactive oxygen species (ROS). Morphological features of cell apoptosis include DNA degradation in cells, pyknosis and fragmentation of nuclei, and formation of apoptotic bodies; however, the cell membrane remains intact and does not manifest as a secondary inflammatory reaction (7,8). Heat stress that induces apoptosis also leads to the accumulation of autophagic vesicles and an increase in autophagic flux, which has a survival-promoting effect. ...
Tumor hyperthermia is the fifth tumor treatment method after surgery, chemotherapy, radiotherapy and biological therapy, and is also one of the important adjuvant treatment methods for tumors. Hyperthermia can not only directly eliminate tumor cells, but also stimulate the antitumor immune response of the body, and improve the sensitivity of tumor tissues to radiotherapy and chemotherapy. An organoid is a tissue-specific cell cluster formed by 3D culture of various types of cells derived from target organ stem cells, which can reproduce the functions of target organs in vivo. At present, the research models of hepatocellular carcinoma (HCC) in vitro are mainly 2D culture cell line models, and there is no clinical report on tumor hyperthermia using HCC tumoroids. It was hypothesized that this will be a promising research direction.
