Background: Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment. Results: 3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature. Conclusions: 3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at http://3omics.cmdm.tw.

Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.

Exosomes participate in cell–cell communication by transferring molecular components between cells. Previous studies have shown that exosomal molecules derived from cancer cells and liquid biopsies can serve as biomarkers for cancer diagnosis and prognosis. The exploration of the molecules transferred by lung cancer-derived exosomes can advance the understanding of exosome-mediated signaling pathways and mechanisms. However, the molecular characterization and functional indications of exosomal proteins and lipids have not been comprehensively organized. This review thoroughly collected data concerning exosomal proteins and lipids from various lung cancer samples, including cancer cell lines and cancer patients. As potential diagnostic and prognostic biomarkers, exosomal proteins and lipids are available for clinical use in lung cancer. Potential therapeutic targets are mentioned for the future development of lung cancer therapy. Molecular functions implying their possible roles in exosome-mediated signaling are also discussed. Finally, we emphasized the importance and value of lung cancer stem cell-derived exosomes in lung cancer therapy. In summary, this review presents a comprehensive description of the protein and lipid composition and function of lung cancer-derived exosomes for lung cancer diagnosis, prognosis, and treatment.

Background and Purpose: In older people with type 2 diabetes mellitus (T2DM), the effects of aging and T2DM may compromise the function of skeletal muscle, deteriorate metabolic status, and jeopardize physical performance, aerobic capacity, and quality of life (QoL). The purpose of this study was to investigate the effects of 12 weeks of resistance training (RT) on muscle function, physical performance, cardiometabolic risks, and QoL in older people with T2DM. Methods: This study was a randomized controlled trial that employed block randomization, assessor blinding, and the intention-to-treat principle. Thirty people 65 years or older with a diagnosis of T2DM were randomly assigned to either an exercise group or a control group and were further stratified by gender. The exercise group performed 8 RT exercises in 3 sets of 8 to 12 repetitions at 75% 1-repetition maximum (1-RM) 3 times per week for 12 weeks. The control group received usual care and maintained their daily activities and lifestyle. Muscle function (1-RM and muscle oxygenation responses), physical performance (5-repetition sit-to-stand test and Timed Up and Go test), cardiometabolic risks (aerobic capacity, blood pressure, body composition, glycemic control, lipids levels, and high-sensitivity C-reactive protein levels), and QoL (Audit of Diabetes-Dependent Quality of Life 19) were assessed at baseline (week 0) and after the 12-week interventions (week 12). Results: The 1-RM chest-press and leg-press strength and physical performance in 5-repetition sit-to-stand test were significantly improved in the exercise group compared with the controls after the interventions. The exercise group had significantly lower resting systolic blood pressure (by −12.1 mm Hg, P = 0.036) than did the controls after 12 weeks of RT, without any significant within-group change in either group after intervention. The waist circumference, fasting glucose levels, and peak diastolic blood pressure tended to favor RT over usual care after the interventions. Conclusion: Twelve weeks of RT increased the maximal strength in chest-press and leg-press tests, and improved 5-repetition sit-to-stand performance in older people with T2DM. Our study demonstrated that supervised, structured RT was able to promote muscle function and alleviate cardiometabolic risks in people with T2DM 65 years or older.