Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers of and Therapeutic Targets for the Metabolic Bone Disease.

Osteoporosis (OP), a prevalent skeletal disorder characterized by compromised bone strength and increased susceptibility to fractures, poses a significant public health concern. This review aims to provide a comprehensive analysis of the current state of research in the field, focusing on the application of proteomic techniques to elucidate diagnostic markers and therapeutic targets for OP. The integration of cutting-edge proteomic technologies has enabled the identification and quantification of proteins associated with bone metabolism, leading to a deeper understanding of the molecular mechanisms underlying OP. In this review, we systematically examine recent advancements in proteomic studies related to OP, emphasizing the identification of potential biomarkers for OP diagnosis and the discovery of novel therapeutic targets. Additionally, we discuss the challenges and future directions in the field, highlighting the potential impact of proteomic research in transforming the landscape of OP diagnosis and treatment.

Identification of protein signatures for lung cancer subtypes based on BPSO method.

The objective of this study was to identify protein biomarkers that can distinguish between LUAD and LUSC, critical for personalized treatment plans. The proteomic profiling data of LUAD and LUSC samples from TCPA database, along with phenotype and survival information from TCGA database were downloaded and preprocessed for analysis. We used BPSO feature selection method and identified 10 candidate protein biomarkers that have better classifying performance, as analyzed by t-SNE and PCA algorithms. To explore the causalities among these proteins and their associations with tumor subtypes, we conducted the PCStable algorithm to construct a regulatory network. Results indicated that 4 proteins, MIG6, CD26, NF2, and INPP4B, were directly linked to the lung cancer subtypes and may be useful in guiding therapeutic decision-making. Besides, spearman correlation, Cox proportional hazard model and Kaplan-Meier curve was employed to validate the biological significance of the candidate proteins. In summary, our study highlights the importance of protein biomarkers in the classification of lung cancer subtypes and the potential of computational methods for identifying key biomarkers and understanding their underlying biological mechanisms.

Identification of key biomarkers for STAD using filter feature selection approaches.

Gastric cancer (GC) is the fifth most common cancer and the third leading cause of cancer death worldwide. Discovery of diagnostic biomarkers prompts the early detection of GC. In this study, we used limma method combined with joint mutual information (JMI), a machine learning algorithm, to identify a signature of 11 genes that performed well in distinguishing tumor and normal samples in a stomach adenocarcinoma cohort. Other two GC datasets were used to validate the classifying performances. Several of the candidate genes were correlated with GC tumor progression and survival. Overall, we highlight the application of feature selection approaches in the analysis of high-dimensional biological data, which will improve study accuracies and reduce workloads for the researchers when identifying potential tumor biomarkers.

Global analysis of gene expression signature and diagnostic/prognostic biomarker identification of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common cancers in the world. The landscape of HCC's molecular alteration signature has been explored over the last few decades. Even so, more comprehensive research is still needed to improve understanding of tumorigenesis and progression of HCC, as well as to identify potential biomarkers for the malignancy. In this research, a comprehensive bioinformatics analysis was conducted based on the publicly available databases from both the Cancer Genome Atlas (TCGA) program and the gene expression omnibus (GEO) database. R/Bioconductor was used to analyze differentially expressed genes (DEGs) between HCC tumor and normal control (NC) samples, and then a protein-protein interaction (PPI) network of DEGs was established through the STRING platform. Finally, the application of specific candidate genes as diagnostic or prognostic biomarkers of HCC was explored and evaluated by ROC and survival analysis. A total of 310 DEGs were detected in the HCC tumor samples. Thirty-six hub DEGs in the PPI network and 10 candidates of the 36 genes showed significant alterations in tumor expression, including CDKN3, TOP2A, UBE2C, CDC20, PBK, ASPM, KIF20A, NCAPG, CCNB2, CYP3A4. The 10-gene signature had relatively significant effects when distinguishing tumors from normal samples (sensitivity >70%, specificity >70%, AUC >0.8, p < 0.001). Eight candidate genes were negatively correlated with the overall survival rate of the patients (p < 0.05) and were all up-regulated in HCC tumor samples. The age and gender factors had no significant impact on the overall survival rate of HCC patients (p > 0.05), and the TNM stage status factor had a significant negative prognosis correlation (p < 0.05). This research provides evidence for a better understanding of tumorigenesis and progression of HCC and helps to explore candidate targets for disease diagnosis and treatment.

The complete chloroplast genome of Schizonepeta tenuifolia (Benth.) Briq., a traditional Chinese herb.

Schizonepeta tenuifolia (Benth.) Briq. is a traditional Chinese medicinal herb. The complete chloroplast genome sequence of S. tenuifolia was obtained by high-throughput sequencing platform. The chloroplast genome of S. tenuifolia is a circular form of 151,254 bp in length, with an average GC content of 37.85%. The genome contains a set of 132 genes, including 87 protein-coding genes, 37 tRNA genes, and eight rRNA genes. Phylogenetic analysis based on complete chloroplast genome sequences indicates that S. tenuifolia has a close relationship with Dracocephalum palmatum. This study provides a molecular basis for the classification of S. tenuifolia.