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Background: In the domain of plastic surgery, nasal cartilage regeneration is of significant importance. The extracellular matrix (ECM) from porcine nasal septum cartilage has shown potential for promoting human cartilage regeneration. Nonetheless, the specific biological inductive factors and their pathways in cartilage tissue engineering remain undefined. Methods: The decellularized matrix derived from porcine nasal septum cartilage (PN-DCM) was prepared using a grinding method. Human umbilical cord mesenchymal stem cells (HuMSCs) were cultured on these PN-DCM scaffolds for 4 weeks without exogenous growth factors to evaluate their chondroinductive potential. Subsequently, proteomic analysis was employed to identify potential biological inductive factors within the PN-DCM scaffolds. Results: Compared to the TGF-β3-cultured pellet model serving as a positive control, the PN-DCM scaffolds promoted significant deposition of a Safranin-O positive matrix and Type II collagen by HuMSCs. Gene expression profiling revealed upregulation of ACAN, COL2A1, and SOX9. Proteomic analysis identified potential chondroinductive factors in the PN-DCM scaffolds, including CYTL1, CTGF, MGP, ITGB1, BMP7, and GDF5, which influence HuMSC differentiation. Conclusion: Our findings have demonstrated that the PN-DCM scaffolds promoted HuMSC differentiation towards a nasal chondrocyte phenotype without the supplementation of exogenous growth factors. This outcome is associated with the chondroinductive factors present within the PN-DCM scaffolds. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA–disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA–disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB. [ABSTRACT FROM AUTHOR]