Molecular characteristics and systemic treatment options of liposarcoma: A systematic review.

Liposarcoma (LPS) is a rare soft tissue sarcoma that develops from the differentiation of fat cells, typically occurring in the lower extremities and retroperitoneal space. Depending on its histological morphology and molecular changes, LPS can be divided into various subtypes, each exhibiting distinct biological behaviors. During treatment, especially for LPS arising in the retroperitoneum, the extent and quality of the initial surgery are critically important. Treatment strategies must be tailored to the specific type of LPS. Over the past few decades, the treatment of LPS has undergone numerous advancements, with new therapeutic approaches such as targeted drugs and immunotherapies continually emerging. This paper reviews the biological characteristics, molecular alterations, as well as surgical and pharmacological treatments of various LPS subtypes, with the aim of enhancing clinicians' understanding and emphasizing the importance of individualized precision therapy. With a deeper understanding of the biological characteristics and molecular alterations of LPS, future treatment trends are likely to focus more on developing personalized treatment plans to better address the various types of LPS.

Soybean steroids improve crop abiotic stress tolerance and increase yield.

Sterols have long been associated with diverse fields, such as cancer treatment, drug development, and plant growth; however, their underlying mechanisms and functions remain enigmatic. Here, we unveil a critical role played by a GmNF-YC9-mediated CCAAT-box transcription complex in modulating the steroid metabolism pathway within soybeans. Specifically, this complex directly activates squalene monooxygenase (GmSQE1), which is a rate-limiting enzyme in steroid synthesis. Our findings demonstrate that overexpression of either GmNF-YC9 or GmSQE1 significantly enhances soybean stress tolerance, while the inhibition of SQE weakens this tolerance. Field experiments conducted over two seasons further reveal increased yields per plant in both GmNF-YC9 and GmSQE1 overexpressing plants under drought stress conditions. This enhanced stress tolerance is attributed to the reduction of abiotic stress-induced cell oxidative damage. Transcriptome and metabolome analyses shed light on the upregulation of multiple sterol compounds, including fucosterol and soyasaponin II, in GmNF-YC9 and GmSQE1 overexpressing soybean plants under stress conditions. Intriguingly, the application of soybean steroids, including fucosterol and soyasaponin II, significantly improves drought tolerance in soybean, wheat, foxtail millet, and maize. These findings underscore the pivotal role of soybean steroids in countering oxidative stress in plants and offer a new research strategy for enhancing crop stress tolerance and quality from gene regulation to chemical intervention.

Bayesian variable selection for disease classification using gene expression data.

MOTIVATION: An important application of gene expression microarray data is the classification of samples into categories. Accurate classification depends upon the method used to identify the most relevant genes. Owing to the large number of genes and relatively small sample size, the selection process can be unstable. Modification of existing methods for achieving better analysis of microarray data is needed. RESULTS: We propose a Bayesian stochastic variable selection approach for gene selection based on a probit regression model with a generalized singular g-prior distribution for regression coefficients. Using simulation-based Markov chain Monte Carlo methods for simulating parameters from the posterior distribution, an efficient and dependable algorithm is implemented. It is also shown that this algorithm is robust to the choices of initial values, and produces posterior probabilities of related genes for biological interpretation. The performance of the proposed approach is compared with other popular methods in gene selection and classification via the well-known colon cancer and leukemia datasets in microarray literature. AVAILABILITY: A free Matlab code to perform gene selection is available at http://www.sta.cuhk.edu.hk/xysong/geneselection/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Model comparison of generalized linear mixed models.

Generalized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing. In this article, we propose a path sampling procedure to compute the observed-data log-likelihood function, so that the Bayesian information criterion (BIC) can be applied to model comparison or hypothesis testing. Advantages of the proposed path sampling procedure are discussed. Two medical data sets are analysed for providing illustrative examples of the proposed methodology.