Weighted gene co-expression network analysis and machine learning identified the lipid metabolism-related gene LGMN as a novel biomarker for keloid

Network analysis of three-dimensional hard-soft tissue relationships in the lower 1/3 of the face: skeletal Class I-normodivergent malocclusion versus Class II-hyperdivergent malocclusion

BACKGROUND

The determining effect of facial hard tissues on soft tissue morphology in orthodontic patients has yet to be explained. The aim of this study was to clarify the hard-soft tissue relationships of the lower 1/3 of the face in skeletal Class II-hyperdivergent patients compared with those in Class I-normodivergent patients using network analysis.

METHODS

Fifty-two adult patients (42 females, 10 males; age, 26.58 ± 5.80 years) were divided into two groups: Group 1, 25 subjects, skeletal Class I normodivergent pattern with straight profile; Group 2, 27 subjects, skeletal Class II hyperdivergent pattern with convex profile. Pretreatment cone-beam computed tomography and three-dimensional facial scans were taken and superimposed, on which landmarks were identified manually, and their coordinate values were used for network analysis.

RESULTS

(1) In sagittal direction, Group 2 correlations were generally weaker than Group 1. In both the vertical and sagittal directions of Group 1, the most influential hard tissue landmarks to soft tissues were located between the level of cemento-enamel junction of upper teeth and root apex of lower teeth. In Group 2, the hard tissue landmarks with the greatest influence in vertical direction were distributed more forward and downward than in Group 1. (2) In Group 1, all the correlations for vertical-hard tissue to sagittal-soft tissue position and sagittal-hard tissue to vertical-soft tissue position were positive. However, Group 2 correlations between vertical-hard tissue and sagittal-soft tissue positions were mostly negative. Between sagittal-hard tissue and vertical-soft tissue positions, Group 2 correlations were negative for mandible, and were positive for maxilla and teeth.

CONCLUSION

Compared with Class I normodivergent patients with straight profile, Class II hyperdivergent patients with convex profile had more variations in soft tissue morphology in sagittal direction. In vertical direction, the most relevant hard tissue landmarks on which soft tissue predictions should be based were distributed more forward and downward in Class II hyperdivergent patients with convex profile. Class II hyperdivergent pattern with convex profile was an imbalanced phenotype concerning sagittal and vertical positions of maxillofacial hard and soft tissues.

WGCNA combined with machine learning to find potential biomarkers of liver cancer

The incidence of hepatocellular carcinoma (HCC) has been increasing in recent years. With the development of various detection technologies, machine learning is an effective method to screen disease characteristic genes. In this study, weighted gene co-expression network analysis (WGCNA) and machine learning are combined to find potential biomarkers of liver cancer, which provides a new idea for future prediction, prevention, and personalized treatment. In this study, the "limma" software package was used. P < .05 and log2 |fold-change| > 1 is the standard screening differential genes, and then the module genes obtained by WGCNA analysis are crossed to obtain the key module genes. Gene Ontology and Kyoto Gene and Genome Encyclopedia analysis was performed on key module genes, and 3 machine learning methods including lasso, support vector machine-recursive feature elimination, and RandomForest were used to screen feature genes. Finally, the validation set was used to verify the feature genes, the GeneMANIA (http://www.genemania.org) database was used to perform protein-protein interaction networks analysis on the feature genes, and the SPIED3 database was used to find potential small molecule drugs. In this study, 187 genes associated with HCC were screened by using the "limma" software package and WGCNA. After that, 6 feature genes (AADAT, APOF, GPC3, LPA, MASP1, and NAT2) were selected by RandomForest, Absolute Shrinkage and Selection Operator, and support vector machine-recursive feature elimination machine learning algorithms. These genes are also significantly different on the external dataset and follow the same trend as the training set. Finally, our findings may provide new insights into targets for diagnosis, prevention, and treatment of HCC. AADAT, APOF, GPC3, LPA, MASP1, and NAT2 may be potential genes for the prediction, prevention, and treatment of liver cancer in the future.