Pan-cancer assessment of mutational landscape in intrinsically disordered hotspots reveals potential driver genes.

Large-scale cancer genome sequencing has enabled the catalogs of somatic mutations; however, the mutational impact on intrinsically disordered protein regions (IDRs) has not been systematically investigated to date. Here, we comprehensively characterized the mutational landscapes of IDRs and found that IDRs have higher mutation frequencies across diverse cancers. We thus developed a computational method, ROI-Driver, to identify putative driver genes enriching IDR and domain hotspots in cancer. Numerous well-known cancer-related oncogenes or tumor suppressors that play important roles in cancer signaling regulation, development and immune response were identified at a higher resolution. In particular, the incorporation of IDR structures helps in the identification of novel potential driver genes that play central roles in human protein-protein interaction networks. Interestingly, we found that the putative driver genes with IDR hotspots were significantly enriched with predicted phase separation propensities, suggesting that IDR mutations disrupt phase separation in key cellular pathways. We also identified an appreciable number of clinically relevant genes enriching IDR mutational hotspots that exhibited differential expression patterns and are associated with cancer patient survival. In summary, combinations of mutational effects on IDRs significantly increase the sensitivity of driver detection and are likely to open new therapeutic avenues for various cancers.

Systematical analyses of large-scale transcriptome reveal viral infection-related genes and disease comorbidities.

Perturbation of transcriptome in viral infection patients is a recurrent theme impacting symptoms and mortality, yet a detailed understanding of pertinent transcriptome and identification of robust biomarkers is not complete. In this study, we manually collected 23 datasets related to 6,197 blood transcriptomes across 16 types of respiratory virus infections. We applied a comprehensive systems biology approach starting with whole-blood transcriptomes combined with multilevel bioinformatics analyses to characterize the expression, functional pathways, and protein-protein interaction (PPI) networks to identify robust biomarkers and disease comorbidities. Robust gene markers of infection with different viruses were identified, which can accurately classify the normal and infected patients in train and validation cohorts. The biological processes (BP) of different viruses showed great similarity and enriched in infection and immune response pathways. Network-based analyses revealed that a variety of viral infections were associated with nervous system diseases, neoplasms and metabolic diseases, and significantly correlated with brain tissues. In summary, our manually collected transcriptomes and comprehensive analyses reveal key molecular markers and disease comorbidities in the process of viral infection, which could provide a valuable theoretical basis for the prevention of subsequent public health events for respiratory virus infections.

Value of CT Radiomics and Clinical Features in Predicting Bone Metastases in Patients with NSCLC.

Objective: To explore the CT radiomic features and clinical imaging features of the primary tumor in patients with nonsmall cell lung cancer (NSCLC) before treatment and their predictive value for the occurrence of bone metastases. Methods: From June 2017 to June 2021, 195 patients with NSCLC who were pathologically diagnosed without any treatment in the Cancer Hospital Affiliated to Hainan Medical College were retrospectively analyzed, and they were divided into a bone metastasis group and a nonbone metastasis group. The relationship between clinical imaging features and bone metastasis in patients was analyzed by the t-test, rank sum test, and χ 2 test. At the same time, ITK software was used to extract the radiomic characteristics of the primary tumor of the patients, and the patients were randomly divided into a training group and a validation group in a ratio of 7 : 3. The training model was validated in the validation group, and the performance of the model for predicting bone metastases in NSCLC patients was verified by the ROC curve, and a multivariate logistic regression prediction model was established based on the omics parameters extracted from the best prediction model combined with clinical image features. Results: Seven features were screened from the primary tumor by LASSO to establish a model for predicting metastasis. The area under the curve was 0.82 and 0.73 in the training and validation sets. The best omics signature and univariate analysis suggested clinical imaging factors (P < 0.05) associated with bone metastases were included in multivariate binary logistic analysis to obtain clinical characteristics of the primary tumor such as gender (OR = 0.141, 95% CI: 0.022-0.919, P = 0.04), increased Cyfra21-1 (OR = 0.12, 95% CI: 0.018-0.782, P = 0.027), Fe content in blood (OR = 0.774, 95% CI: 0.626-0.958, P = 0.018), CT signs such as lesion homogeneity (OR = 0.052, 95% CI: 0.006-0.419, P = 0.006), pleural indentation sign (OR = 0.007, 95% CI: 0.001-0.696, P = 0.034), and omics characteristics glszm_Small Area High Gray Level Emphasis (OR = 0.016, 95% CI: 0.001-0.286, P = 0.005) were independent risk factors for bone metastasis in patients. Conclusion: The prediction model established based on radiomics and clinical imaging features has high predictive performance for the occurrence of bone metastasis in NSCLC patients.

Comprehensive characterization of human-virus protein-protein interactions reveals disease comorbidities and potential antiviral drugs.

The protein-protein interactions (PPIs) between human and viruses play important roles in viral infection and host immune responses. Rapid accumulation of experimentally validated human-virus PPIs provides an unprecedented opportunity to investigate the regulatory pattern of viral infection. However, we are still lack of knowledge about the regulatory patterns of human-virus interactions. We collected 27,293 experimentally validated human-virus PPIs, covering 8 virus families, 140 viral proteins and 6059 human proteins. Functional enrichment analysis revealed that the viral interacting proteins were likely to be enriched in cell cycle and immune-related pathways. Moreover, we analysed the topological features of the viral interacting proteins and found that they were likely to locate in central regions of human PPI network. Based on network proximity analyses of diseases genes and human-virus interactions in the human interactome, we revealed the associations between complex diseases and viral infections. Network analysis also implicated potential antiviral drugs that were further validated by text mining. Finally, we presented the Human-Virus Protein-Protein Interaction database (HVPPI, http://bio-bigdata.hrbmu.edu.cn/HVPPI), that provides experimentally validated human-virus PPIs as well as seamlessly integrates online functional analysis tools. In summary, comprehensive understanding the regulatory pattern of human-virus interactome will provide novel insights into fundamental infectious mechanism discovery and new antiviral therapy development.

Dynamic immune ecosystem of dengue infection revealed by single-cell sequencing.

Dengue is the most common human arboviral disease worldwide, which can result in severe complications. A dysfunctional immune response in dengue infective patients is a recurrent theme impacting symptoms and mortality, but the heterogeneity and dynamics of immune infiltrates during dengue infection remain poorly characterized. Here, we identified the immune cell types in scRNA-seq data from 13127 cells of 10 dengue infective patients and discovered the dynamic immune ecosystems of dengue infection. Notably, genes that exhibited higher expression in specific cell types play important roles in response to virus infection in a module manner. Transcription factors (TFs) are the major regulators (i.e., PAX5, IRF7, KLF4, and IRF8) that can potentially regulate infection-related genes. We demonstrated that the dynamic rewired regulatory network during dengue infection. Moreover, our data revealed the complex cell-cell communications from control to fever and severe dengue patients and prevalent cell-cell communication rewiring was observed. We further identified the IFN-II and CXCL signaling pathways that medicated the communications and play important roles in dengue infection. Together, our comprehensive analysis of dynamic immune ecosystem of dengue infection provided novel insights for understanding the pathogenesis of and developing effective therapeutic strategies for dengue infection.

Dynamic Expression of m6A Regulators During Multiple Human Tissue Development and Cancers.

N6-methyladenosine (m6A) plays critical roles in human development and cancer progression. However, our knowledge regarding the dynamic expression of m6A regulators during human tissue development is still lacking. Here, we comprehensively analyzed the dynamic expression alterations of m6A regulators during seven tissue development and eight cancer types. We found that m6A regulators globally exhibited decreased expression during development. In addition, IGF2BP1/2/3 (insulinlike growth factor 2 MRNA-binding protein 1/2/3) exhibited reverse expression pattern in cancer progression, suggesting an oncofetal reprogramming in cancer. The expressions of IGF2BP1/2/3 were regulated by genome alterations, particularly copy number amplification in cancer. Clinical association analysis revealed that higher expressions of IGF2BP1/2/3 were associated with worse survival of cancer patients. Finally, we found that genes significantly correlated with IGF2BP1/2/3 were significantly enriched in cancer hallmark-related pathways. In summary, dynamic expression analysis will guide both mechanistic and therapeutic roles of m6A regulators during tissue development and cancer progression.