Construction of a predictive model for bone metastasis from first primary lung adenocarcinoma within 3 cm based on machine learning algorithm: a retrospective study.

Background: Adenocarcinoma, the most prevalent histological subtype of non-small cell lung cancer, is associated with a significantly higher likelihood of bone metastasis compared to other subtypes. The presence of bone metastasis has a profound adverse impact on patient prognosis. However, to date, there is a lack of accurate bone metastasis prediction models. As a result, this study aims to employ machine learning algorithms for predicting the risk of bone metastasis in patients. Method: We collected a dataset comprising 19,454 cases of solitary, primary lung adenocarcinoma with pulmonary nodules measuring less than 3 cm. These cases were diagnosed between 2010 and 2015 and were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. Utilizing clinical feature indicators, we developed predictive models using seven machine learning algorithms, namely extreme gradient boosting (XGBoost), logistic regression (LR), light gradient boosting machine (LightGBM), Adaptive Boosting (AdaBoost), Gaussian Naive Bayes (GNB), multilayer perceptron (MLP) and support vector machine (SVM). Results: The results demonstrated that XGBoost exhibited superior performance among the four algorithms (training set: AUC: 0.913; test set: AUC: 0.853). Furthermore, for convenient application, we created an online scoring system accessible at the following URL: https://www.xsmartanalysis.com/model/predict/?mid=731symbol=7Fr16wX56AR9Mk233917, which is based on the highest performing model. Conclusion: XGBoost proves to be an effective algorithm for predicting the occurrence of bone metastasis in patients with solitary, primary lung adenocarcinoma featuring pulmonary nodules below 3 cm in size. Moreover, its robust clinical applicability enhances its potential utility.

High expression of E2F transcription factors 7: An independent predictor of poor prognosis in patients with lung adenocarcinoma.

Adenocarcinoma is the most common pathological type of lung cancer. The E2F7 transcription factor has been confirmed to be related to the occurrence and development of a variety of solid tumors, but the relationship with the prognosis of lung cancer is still unclear. Therefore, we conducted this study to explore the prognostic value of E2F7 for lung adenocarcinoma (LUAD) patients. In this study, we analyzed samples from the Cancer Genome Atlas (TCGA) to study the correlation between the expression of E2F7 and clinical features, the difference in expression between tumors and normal tissues, the prognostic and diagnostic value, and Enrichment analysis of related genes. All statistical analysis uses R statistical software (version 3.6.3). The result shows that the expression level of E2F7 in LUAD was significantly higher than that of normal lung tissue (P = 1e-34). High expression of E2F7 was significantly correlated with gender (P = .034), pathologic stage (P = .046) and M stage (P = .025). Multivariate Cox analysis confirmed that E2F7 is an independent risk factor for OS in LUAD patients (P = .027). Genes related to cell cycle checkpoints, DNA damage telomere stress-induced senescence, DNA methylation, chromosome maintenance and mitotic prophase showed differential enrichment in the E2F7 high expression group. In short, high expression of E2F7 is an independent risk factor for OS in LUAD patients and has a high diagnostic value.

Comparing hydroxyapatite with osteogenic medium for the osteogenic differentiation of mesenchymal stem cells on PHBV nanofibrous scaffolds.

Having advantageous biocompatibility and osteoconductive properties known to enhance the osteogenic differentiation of mesenchymal stem cells (MSCs), hydroxyapatite (HA) is a commonly used material for bone tissue engineering. What remains unclear, however, is whether HA holds a similar potential for stimulating the osteogenic differentiation of MSCs to that of a more frequently used osteogenic-inducing medium (OIM). To that end, we used PHBV electrospun nanofibrous scaffolds to directly compare the osteogenic capacities of HA with OIM over MSCs. Through the observation of cellular morphology, the staining of osteogenic markers, and the quantitative measuring of osteogenic-related genes, as well as microRNA analyses, we not only found that HA was as capable as OIM for differentiating MSCs down an osteogenic lineage; albeit, at a significantly slower rate, but also that numerous microRNAs are involved in the osteogenic differentiation of MSCs through multiple pathways involving the inhibition of cellular proliferation and stemness, chondrogenesis and adipogenesis, and the active promotion of osteogenesis. Taken together, we have shown for the first time that PHBV electrospun nanofibrous scaffolds combined with HA have a similar osteogenic-inducing potential as OIM and may therefore be used as a viable replacement for OIM for alternative in vivo-mimicking bone tissue engineering applications.

Accelerating and increasing nano-scaled pore formation on electrospun poly(3-hydroxybutyrate-co-3-hydroxyvalerate) fibers.

Porous fibers are advantageous for filtration systems, drug delivery systems, and in the field of tissue engineering, in comparison to their non-porous counterparts. In this study, we developed a facile technique including two steps to generate poly(3-hydroxybutyrate-co-3- hydroxyvalerate) (PHBV) porous fibers with a controllable pore size. An electrospinning technique was employed to obtain five types of PHBV/poly(ethylene oxide) (PEO)-blended fibers (PHBV:PEO = 9:1, 8:2, 7:3, 6:4, 5:5) with PEO as the porogen. PEO was leached out by simulated body fluid (SBF) and water, respectively. The pore morphology and calcium deposition of the resulting fibers were compared to those formed on film through the SEM-EDX analysis. It was revealed that pore size and number increased with increasing PEO percentage in the fiber or film. The pore size on the films (at micrometer scale) was much larger than that of nanofibers, which was in the range of 70-120 nm. The simultaneous removal of PEO and deposition of calcium phosphate through SBF buffer enhanced synergistically both the pore formation and mineral deposition. The different phase separation mechanisms explain the different pore morphologies in the film and the nanofibers. The cellular experimental results show that fibers with nanometer-scale pores and minerals can enhance the proliferation of bone marrow-derived mesenchymal stem cells.