An Organoid Biobank of Neuroendocrine Neoplasms Enables Genotype-Phenotype Mapping.

Gastroenteropancreatic (GEP) neuroendocrine neoplasm (NEN) that consists of neuroendocrine tumor and neuroendocrine carcinoma (NEC) is a lethal but under-investigated disease owing to its rarity. To fill the scarcity of clinically relevant models of GEP-NEN, we here established 25 lines of NEN organoids and performed their comprehensive molecular characterization. GEP-NEN organoids recapitulated pathohistological and functional phenotypes of the original tumors. Whole-genome sequencing revealed frequent genetic alterations in TP53 and RB1 in GEP-NECs, and characteristic chromosome-wide loss of heterozygosity in GEP-NENs. Transcriptome analysis identified molecular subtypes that are distinguished by the expression of distinct transcription factors. GEP-NEN organoids gained independence from the stem cell niche irrespective of genetic mutations. Compound knockout of TP53 and RB1, together with overexpression of key transcription factors, conferred on the normal colonic epithelium phenotypes that are compatible with GEP-NEN biology. Altogether, our study not only provides genetic understanding of GEP-NEN, but also connects its genetics and biological phenotypes.

Multi-label classification with XGBoost for metabolic pathway prediction.

BACKGROUND: Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pathway prediction methods that conclude that machine learning-based approaches are similar in performance to the most used method, PathoLogic which is a rule-based method. One issue is that previous studies evaluated PathoLogic without taxonomic pruning which decreases its performance. RESULTS: In this study, we update the evaluation results from previous studies to demonstrate that PathoLogic with taxonomic pruning outperforms previous machine learning-based approaches and that further improvements in performance need to be made for them to be competitive. Furthermore, we introduce mlXGPR, a XGBoost-based metabolic pathway prediction method based on the multi-label classification pathway prediction framework introduced from mlLGPR. We also improve on this multi-label framework by utilizing correlations between labels using classifier chains. We propose a ranking method that determines the order of the chain so that lower performing classifiers are placed later in the chain to utilize the correlations between labels more. We evaluate mlXGPR with and without classifier chains on single-organism and multi-organism benchmarks. Our results indicate that mlXGPR outperform other previous pathway prediction methods including PathoLogic with taxonomic pruning in terms of hamming loss, precision and F1 score on single organism benchmarks. CONCLUSIONS: The results from our study indicate that the performance of machine learning-based pathway prediction methods can be substantially improved and can even outperform PathoLogic with taxonomic pruning.

ROS-induced PADI2 downregulation accelerates cellular senescence via the stimulation of SASP production and NFκB activation.

Cellular senescence is closely related to tissue aging including bone. Bone homeostasis is maintained by the tight balance between bone-forming osteoblasts and bone-resorbing osteoclasts, but it undergoes deregulation with age, causing age-associated osteoporosis, a main cause of which is osteoblast dysfunction. Oxidative stress caused by the accumulation of reactive oxygen species (ROS) in bone tissues with aging can accelerate osteoblast senescence and dysfunction. However, the regulatory mechanism that controls the ROS-induced senescence of osteoblasts is poorly understood. Here, we identified Peptidyl arginine deiminase 2 (PADI2), a post-translational modifying enzyme, as a regulator of ROS-accelerated senescence of osteoblasts via RNA-sequencing and further functional validations. PADI2 downregulation by treatment with H2O2 or its siRNA promoted cellular senescence and suppressed osteoblast differentiation. CCL2, 5, and 7 known as the elements of the senescence-associated secretory phenotype (SASP) which is a secretome including proinflammatory cytokines and chemokines emitted by senescent cells and a representative feature of senescence, were upregulated by H2O2 treatment or Padi2 knockdown. Furthermore, blocking these SASP factors with neutralizing antibodies or siRNAs alleviated the senescence and dysfunction of osteoblasts induced by H2O2 treatment or Padi2 knockdown. The elevated production of these SASP factors was mediated by the activation of NFκB signaling pathway. The inhibition of NFκB using the pharmacological inhibitor or siRNA effectively relieved H2O2 treatment- or Padi2 knockdown-induced senescence and osteoblast dysfunction. Together, our study for the first time uncover the role of PADI2 in ROS-accelerated cellular senescence of osteoblasts and provide new mechanistic and therapeutic insights into excessive ROS-promoted cellular senescence and aging-related bone diseases.

A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction.

BACKGROUND: To reduce drug side effects and enhance their therapeutic effect compared with single drugs, drug combination research, combining two or more drugs, is highly important. Conducting in-vivo and in-vitro experiments on a vast number of drug combinations incurs astronomical time and cost. To reduce the number of combinations, researchers classify whether drug combinations are synergistic through in-silico methods. Since unstructured data, such as biomedical documents, include experimental types, methods, and results, it can be beneficial extracting features from documents to predict anti-cancer drug combination synergy. However, few studies predict anti-cancer drug combination synergy using document-extracted features. RESULTS: We present a novel approach for anti-cancer drug combination synergy prediction using document-based feature extraction. Our approach is divided into two steps. First, we extracted documents containing validated anti-cancer drug combinations and cell lines. Drug and cell line synonyms in the extracted documents were converted into representative words, and the documents were preprocessed by tokenization, lemmatization, and stopword removal. Second, the drug and cell line features were extracted from the preprocessed documents, and training data were constructed by feature concatenation. A prediction model based on deep and machine learning was created using the training data. The use of our features yielded higher results compared to the majority of published studies. CONCLUSIONS: Using our prediction model, researchers can save time and cost on new anti-cancer drug combination discoveries. Additionally, since our feature extraction method does not require structuring of unstructured data, new data can be immediately applied without any data scalability issues.

Early Prediction of the Need for Orthognathic Surgery in Patients With Repaired Unilateral Cleft Lip and Palate Using Machine Learning and Longitudinal Lateral Cephalometric Analysis Data.

ABSTRACT: The purpose of this study was to determine the cephalometric predictors of the future need for orthognathic surgery in patients with repaired unilateral cleft lip and palate (UCLP) using machine learning. This study included 56 Korean patients with UCLP, who were treated by a single surgeon and a single orthodontist with the same treatment protocol. Lateral cephalograms were obtained before the commencement of orthodontic/orthopedic treatment (T0; mean age, 6.3 years) and at at least of 15 years of age (T1; mean age, 16.7 years). 38 cephalometric variables were measured. At T1 stage, 3 cephalometric criteria (ANB ≤ -3°; Wits appraisal ≤ -5 mm; Harvold unit difference ≥34 mm for surgery group) were used to classify the subjects into the surgery group (n = 10, 17.9%) and non-surgery group (n = 46, 82.1%). Independent t-test was used for statistical analyses. The Boruta method and XGBoost algorithm were used to determine the cephalometric variables for the prediction model. At T0 stage, 2 variables exhibited a significant intergroup difference (ANB and facial convexity angle [FCA], all P < 0.05). However, 18 cephalometric variables at the T1 stage and 14 variables in the amount of change (ΔT1-T0) exhibited significant intergroup differences (all, more significant than P < 0.05). At T0 stage, the ANB, PP-FH, combination factor, and FCA were selected as predictive parameters with a cross-validation accuracy of 87.4%. It was possible to predict the future need for surgery to correct sagittal skeletal discrepancy in UCLP patients at the age of 6 years.

Research topics and trends in medical education by social network analysis.

BACKGROUND: As studies analyzing the networks and relational structures of research topics in academic fields emerge, studies that apply methods of network and relationship analysis, such as social network analysis (SNA), are drawing more attention. The purpose of this study is to explore the interaction of medical education subjects in the framework of complex systems theory using SNA and to analyze the trends in medical education. METHODS: The authors extracted keywords using Medical Subject Headings terms from 9,379 research articles (162,866 keywords) published in 1963-2015 in PubMed. They generated an occurrence frequency matrix, calculated relatedness using Weighted Jaccard Similarity, and analyzed and visualized the networks with Gephi software. RESULTS: Newly emerging topics by period units were identified as historical trends, and 20 global-level topic clusters were obtained through network analysis. A time-series analysis led to the definition of five historical periods: the waking phase (1963-1975), the birth phase (1976-1990), the growth phase (1991-1996), the maturity phase (1997-2005), and the expansion phase (2006-2015). CONCLUSIONS: The study analyzed the trends in medical education research using SNA and analyzed their meaning using complex systems theory. During the 53-year period studied, medical education research has been subdivided and has expanded, improved, and changed along with shifts in society's needs. By analyzing the trends in medical education using the conceptual framework of complex systems theory, the research team determined that medical education is forming a sense of the voluntary order within the field of medicine by interacting with social studies, philosophy, etc., and establishing legitimacy and originality.

Dishevelled segment polarity protein 3 (DVL3): a novel and easily applicable recurrence predictor in localised prostate adenocarcinoma.

OBJECTIVE: To identify new biomarkers for biochemical recurrence (BCR) of prostate adenocarcinoma. PATIENTS AND METHODS: Clinical information of 500 patients with prostate adenocarcinoma and their 152 RNA-sequencing and protein-array data from The Cancer Genome Atlas (TCGA) were separated into a discovery set and a validation set. Each dataset was analysed according to the Gleason grade groups reflecting BCR. The results obtained from the analysis using TCGA dataset were confirmed by immunohistochemistry analyses of a confirmation cohort composed of 395 patients with localised prostate adenocarcinoma. RESULTS: TCGA discovery set was subgrouped into lower- and higher-risk groups for recurrence-free survival (RFS) (P < 0.001). Cyclin B1 (CCNB1), dishevelled segment polarity protein 3 (DVL3), paxillin (PXN), RAF1, transferrin, X-ray repair cross complementing 5 (XRCC5) and BIM had lower expression in the lower-risk group than that in the higher-risk group (all, P < 0.05). In TCGA validation set, CCNB1, DVL3, transferrin, XRCC5 and BIM were also differently expressed between the two groups. Immunohistochemically, DVL3 positivity was associated with high prostate-specific antigen (PSA) levels, resection margin involvement, and BCR (all, P < 0.05). A high Gleason score indicated a marginal relationship (P = 0.055). BIM positivity was related to high PSA levels, lymphovascular invasion, and BCR (all, P < 0.05). Both DVL3 positivity (P = 0.010) and BIM positivity (P = 0.024) were associated with shorter RFS, but statistical significance was lost when the multivariate Cox regression model included all patients. In the lower-risk group, the multivariate Cox model confirmed that DVL3 was an independent predictor for poor RFS (hazard ratio 1.80, P = 0.040), and the concordance index (C-index) was 0.805. CONCLUSIONS: DVL3 and BIM were expressed in patients with a higher risk of BCR. DVL3 may be a novel and easily applicable recurrence predictor of localised prostate adenocarcinoma.

Usefulness of permutation entropy as an anesthetic depth indicator in children.

Permutation entropy (PE) as a complexity measure has been introduced to monitor anesthetic depth for adult. However, PE has not yet been evaluated for its clinical applicability as an indicator of anesthetic depth in children. Therefore, in order to investigate the validity of PE, we compared PE with BIS using pharmacodynamic (PD) modeling in children. Electroencephalogram (EEG) was obtained from BIS monitor during sevoflurane deepening and lightening protocol. End-tidal sevoflurane concentration (Etsevo) and BIS were measured simultaneously. PE was calculated from the processed EEG with the scale ranging from 0 to 100. NONMEM software was used to investigate the PD relationship between Etsevo with BIS and PE. Adjusted PE (APE) values were decreased as anesthesia deepened. APE and BIS showed significant linear correlation (P < 0.001), indicating that PE also reflects anesthesia depth. PD parameters for APE and BIS were estimated with a sigmoid Emax model which describes the relationship between Etsevo and APE/BIS (E o : 78, E max : 17.6, C e50 : 2.5 vol%; γ: 13.1, k eo : 0.47 min(-1) for APE; E o : 89.4; E max : 15.7; C e50 : 2.2 vol%; γ: 6.6, keo: 0.52 min(-1) for BIS). PE seems to be a useful indicator of anesthetic depth, which is comparable to BIS in children.

Prediction of the Need for Orthognathic Surgery in Patients With Cleft Lip and/or Palate.

The purpose of this study was to determine the cephalometric variables that can predict the future need for orthognathic surgery or distraction osteogenesis in Korean male patients with nonsyndromic cleft lip and alveolus (CLA) and unilateral (UCLP) and bilateral cleft lip and palate (BCLP). A total of 131 patients who were treated by one surgeon and one orthodontist using identical protocol were divided into CLA group (n = 35), UCLP group (n = 56), and BCLP group (n = 40). Lateral cephalograms were taken before secondary alveolar bone graft (T0; mean age, 9.3 years) and at the minimum of 15 years of age (T1; mean age, 17.3 years). The cephalometric variables of these cephalograms were measured. At T1 stage, 3 cephalometric criteria were used to divide the subjects into surgery and nonsurgery groups (ANB ≤ -3 degrees; Wits appraisal ≤ -5 mm; Harvold unit difference ≥ 34 mm for surgery group). The feature wrapping method was used to determine the cephalometric variables at T0 stage for a prediction model. At T1 stage, 27 (20.6%) of 131 subjects required surgical intervention to correct their sagittal skeletal discrepancies. Frequency was significantly different among the CLA, UCLP, and BCLP groups (8.5%, 21.4%, and 30.0%, respectively; P < 0.05; [CLA, UCLP] < [UCLP, BCLP]). A total of 10 cephalometric variables of T0 stage were selected as predictors, and weighted classification accuracy was 77.3%. The frequency of surgical intervention increased with cleft severity. Ten cephalometric variables might be regarded as effective predictors of the future need for surgery to correct their sagittal skeletal discrepancies.

A network perspective on unraveling the role of TRP channels in biology and disease.

Transient receptor potential (TRP) channels are a large family of non-selective cation channels that mediate numerous physiological and pathophysiological processes; however, still largely unknown are the underlying molecular mechanisms. With data generated on an unprecedented scale, network-based approaches have been revolutionizing the way in which we understand biology and disease, discover disease genes, and develop therapeutic strategies. These circumstances have created opportunities to encounter TRP channel research to data-intensive science. In this review, we provide an introduction of network-based approaches in biomedical science, describe the current state of TRP channel network biology, and discuss the future direction of TRP channel research. Network perspective will facilitate the discovery of latent roles and underlying mechanisms of TRP channels in biology and disease.

A comprehensive manually curated protein-protein interaction database for the Death Domain superfamily.

The Death Domain (DD) superfamily, which is one of the largest classes of protein interaction modules, plays a pivotal role in apoptosis, inflammation, necrosis and immune cell signaling pathways. Because aberrant or inappropriate DD superfamily-mediated signaling events are associated with various human diseases, such as cancers, neurodegenerative diseases and immunological disorders, the studies in these fields are of great biological and clinical importance. To facilitate the understanding of the molecular mechanisms by which the DD superfamily is associated with biological and disease processes, we have developed the DD database (http://www.deathdomain.org), a manually curated database that aims to offer comprehensive information on protein-protein interactions (PPIs) of the DD superfamily. The DD database was created by manually curating 295 peer-reviewed studies that were published in the literature; the current version documents 175 PPI pairs among the 99 DD superfamily proteins. The DD database provides a detailed summary of the DD superfamily proteins and their PPI data. Users can find in-depth information that is specified in the literature on relevant analytical methods, experimental resources and domain structures. Our database provides a definitive and valuable tool that assists researchers in understanding the signaling network that is mediated by the DD superfamily.

Ontology-based reusable clinical document template production system.

Clinical documents embody professional clinical knowledge. This paper shows an effective clinical document template (CDT) production system that uses a clinical description entity (CDE) model, a CDE ontology, and a knowledge management system called STEP that manages ontology-based clinical description entities. The ontology represents CDEs and their inter-relations, and the STEP system stores and manages CDE ontology-based information regarding CDTs. The system also provides Web Services interfaces for search and reasoning over clinical entities. The system was populated with entities and relations extracted from 35 CDTs that were used in admission, discharge, and progress reports, as well as those used in nursing and operation functions. A clinical document template editor is shown that uses STEP.

Gene-centric multi-omics integration with convolutional encoders for cancer drug response prediction.

MOTIVATION: Tumor heterogeneity, including genetic and transcriptomic characteristics, can reduce the efficacy of anticancer pharmacological therapy, resulting in clinical variability in patient response to therapeutic medications. Multi-omics integration can allow in silico models to provide an additional perspective on a biological system. METHODS: In this study, we propose a gene-centric multi-channel (GCMC) architecture to integrate multi-omics for predicting cancer drug response. GCMC transformed multi-omics profiles into a three-dimensional tensor with an additional dimension for omics types. GCMC's convolutional encoders captures multi-omics profiles for each gene and yields gene-centric features to predict drug responses. RESULTS: We evaluated GCMC on various datasets, including The Cancer Genome Atlas (TCGA) patients, patient-derived xenografts (PDX) mice models, and the Genomics of Drug Sensitivity in Cancer (GDSC) cell line datasets. GCMC achieved better performance than baseline models, including single-omics models, in more than 75% of 265 drugs from GDSC cell line datasets. Furthermore, as for the clinical applicability of GCMC, it achieved the best performance on TCGA and PDX datasets in terms of both AUPR and AUC. We also analyzed models' capability of integrating multi-omics profiles by measuring the contribution ratio of omics types. GCMC can incorporate multi-omics profiles in various manners to enhance performance for each drug type. These results suggested that GCMC can improve performance and feature extraction capability by integrating multi-omics profiles in a gene-centric manner.

Efficient Wear Simulation Methodology for Predicting Nonlinear Wear Behavior of Tools in Sheet Metal Forming.

In conventional wear simulation, the geometry must be updated for succeeding iterations to predict the accumulated wear. However, repeating this procedure up to the desired iteration is rather time consuming. Thus, a wear simulation process capable of reasonable quantitative wear prediction in reduced computational time is needed. This study aimed to develop an efficient wear simulation method to predict quantitative wear reasonably in reduced computational time without updating the geometry for succeeding iterations. The wear resistance of a stamping tool was quantitatively evaluated for different punch shapes (R3.0 and R5.5) and coating conditions (physical vapor deposition of CrN and AlTiCrN coatings) by using a progressive die set. To capture the nonlinear wear behavior with respect to strokes, a nonlinear equation from a modified form of Archard's wear model was proposed. By utilizing the scale factor representing the changes in wear properties with respect to wear depth as input, the simulation can predict the behavior of rapidly increasing wear depth with respect to strokes after failure initiation. Furthermore, the proposed simulation method is efficient in terms of computational time because it does not need to perform geometry updates.

COOBoostR: An Extreme Gradient Boosting-Based Tool for Robust Tissue or Cell-of-Origin Prediction of Tumors.

We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types, which best explain the somatic mutation density landscape of any sample of interest. A specific tissue or cell type matching the chromatin mark feature with highest explanatory power is designated as a potential tissue- or cell-of-origin. Through integrating either ChIP-seq based chromatin data, along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed, with comparable or better tissue or cell-of-origin prediction performance (prediction accuracy-normal cells/tissue: 76.99%, precancerous lesions: 95.65%, cancer cells: 89.39%). In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia.

Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration.

Early diagnosis of lung cancer to increase the survival rate, which is currently at a low range of mid-30%, remains a critical need. Despite this, multi-omics data have rarely been applied to non-small-cell lung cancer (NSCLC) diagnosis. We developed a multi-omics data-affinitive artificial intelligence algorithm based on the graph convolutional network that integrates mRNA expression, DNA methylation, and DNA sequencing data. This NSCLC prediction model achieved a 93.7% macro F1-score, indicating that values for false positives and negatives were substantially low, which is desirable for accurate classification. Gene ontology enrichment and pathway analysis of features revealed that two major subtypes of NSCLC, lung adenocarcinoma and lung squamous cell carcinoma, have both specific and common GO biological processes. Numerous biomarkers (i.e., microRNA, long non-coding RNA, differentially methylated regions) were newly identified, whereas some biomarkers were consistent with previous findings in NSCLC (e.g., SPRR1B). Thus, using multi-omics data integration, we developed a promising cancer prediction algorithm.

Biomedical Entity Explorer: A Web Server for Biomedical Entity Exploration.

Biomedical Entity Explorer (BEE) is a web server that can search for biomedical entities from a database of six biomedical entity types (gene, miRNA, drug, disease, single nucleotide polymorphism [SNP], pathway) and their gene associations. The search results can be explored using intersections, unions, and negations. BEE has integrated biomedical entities from 16 databases (Ensemble, PharmGKB, Genetic Home Reference, Tarbase, Mirbase, NCI Thesaurus, DisGeNET, Linked life data, UMLS, GSEA MsigDB, Reactome, KEGG, Gene Ontology, HGVD, SNPedia, and dbSNP) based on their gene associations and built a database with their synonyms, descriptions, and links containing individual details. Users can enter the keyword of one or more entities and select the type of entity for which they want to know the relationship for and by using set operations such as union, negation, and intersection, they can navigate the search results more clearly. We believe that BEE will not only be useful for biologists querying for complex associations between entities, but can also be a good starting point for general users searching for biomedical entities. BEE is accessible at (http://bike-bee.snu.ac.kr).

Prediction of treatment outcome in burning mouth syndrome patients using machine learning based on clinical data.

The purpose of this study is to apply a machine learning approach to predict whether patients with burning mouth syndrome (BMS) respond to the initial approach and clonazepam therapy based on clinical data. Among the patients with the primary type of BMS who visited the clinic from 2006 to 2015, those treated with the initial approach of detailed explanation regarding home care instruction and use of oral topical lubricants, or who were prescribed clonazepam for a minimum of 1 month were included in this study. The clinical data and treatment outcomes were collected from medical records. Extreme Gradient-Boosted Decision Trees was used for machine learning algorithms to construct prediction models. Accuracy of the prediction models was evaluated and feature importance calculated. The accuracy of the prediction models for the initial approach and clonazepam therapy was 67.6% and 67.4%, respectively. Aggravating factors and psychological distress were important features in the prediction model for the initial approach, and intensity of symptoms before administration was the important feature in the prediction model for clonazepam therapy. In conclusion, the analysis of treatment outcomes in patients with BMS using a machine learning approach showed meaningful results of clinical applicability.

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks.

Purpose: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.Materials and Methods: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photographs taken from 2,204 patients were divided into three classes: unaffected controls, early-stage glaucoma, and late-stage glaucoma. The mean deviation value of standard automated perimetry was used to classify the glaucoma cases. We modeled 56 convolutional neural networks (CNN) with different characteristics and developed an ensemble system to derive the best performance by combining several modeling results.Results: The proposed method with an accuracy of 88.1% and an average area under the receiver operating characteristic of 0.975 demonstrates significantly better performance to classify glaucoma stages compared to the best single CNN model that has an accuracy of 85.2% and an average area under the receiver operating characteristic of 0.950. The false negative is the least adjacent misprediction, and it is less in the proposed method than in the best single CNN model.Conclusions: The method of averaging multiple CNN models can better classify glaucoma stages by using fundus photographs than a single CNN model. The ensemble method would be useful as a clinical decision support system in glaucoma screening for primary care because it provides high and stable performance with a relatively small amount of data.

Differences in the heritability of craniofacial skeletal and dental characteristics between twin pairs with skeletal Class I and II malocclusions.

OBJECTIVE: To investigate differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and Class II malocclusions. METHODS: Forty Korean adult twin pairs were divided into Class I (C-I) group (0° ≤ angle between point A, nasion, and point B [ANB]) ≤ 4°; mean age, 40.7 years) and Class II (C-II) group (ANB > 4°; mean age, 43.0 years). Each group comprised 14 monozygotic and 6 dizygotic twin pairs. Thirty-three cephalometric variables were measured using lateral cephalograms and were categorized as the anteroposterior, vertical, dental, mandible, and cranial base characteristics. The ACE model was used to calculate heritability (A > 0.7, high heritability). Thereafter, principal component analysis (PCA) was performed. RESULTS: Twin pairs in C-I group exhibited high heritability values in the facial anteroposterior characteristics, inclination of the maxillary and mandibular incisors, mandibular body length, and cranial base angles. Twin pairs in C-II group showed high heritability values in vertical facial height, ramus height, effective mandibular length, and cranial base length. PCA extracted eight components with 88.3% in the C-I group and seven components with 91.0% cumulative explanation in the C-II group. CONCLUSIONS: Differences in the heritability of skeletodental characteristics between twin pairs with skeletal Class I and II malocclusions might provide valuable information for growth prediction and treatment planning.