Motivation: Complex diseases are generally thought to be under the influence of one or more mutated risk genes as well as genetic and environmental factors. Many traditional methods have been developed to identify susceptibility genes assuming a single-gene disease model (‘single-locus methods’). Pathway-based approaches, combined with traditional methods, consider the joint effects of genetic factor and biologic network context. With the accumulation of high-throughput SNP datasets and human biologic pathways, it becomes feasible to search for risk pathways associated with complex diseases using bioinformatics methods. By analyzing the contribution of genetic factor and biologic network context in KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, we proposed an approach to prioritize risk pathways for complex diseases: Prioritizing Risk Pathways fusing SNPs and pathways (PRP). A risk-scoring (RS) measurement was used to prioritize risk biologic pathways. This could help to demonstrate the pathogenesis of complex diseases from a new perspective and provide new hypotheses. We introduced this approach to five complex diseases and found that these five diseases not only share common risk pathways, but also have their specific risk pathways, which is verified by literature retrieval. Availability: Genotype frequencies of five case–control samples were downloaded from the WTCCC online system and the address is https://www.wtccc.org.uk/info/access_to_data_samples.shtml Contact:chenlina@ems.hrbmu.edu.cn; lixia@hrbmu.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online.

Predicting candidate genes using gene expression profiles and unbiased protein-protein interactions (PPI) contributes a lot in deciphering the pathogenesis of complex diseases. Recent studies showed that there are significant disparities in network topological features between non-disease and disease genes in protein-protein interaction settings. Integrated methods could consider their characteristics comprehensively in a biological network. In this study, we introduce a novel computational method, based on combined network topological features, to construct a combined classifier and then use it to predict candidate genes for coronary artery diseases (CAD). As a result, 276 novel candidate genes were predicted and were found to share similar functions to known disease genes. The majority of the candidate genes were cross-validated by other three methods. Our method will be useful in the search for candidate genes of other diseases.

Non–muscle-invasive bladder cancer (NMIBC) is over three times as common in men as it is in women; however, female patients do not respond as well to immunotherapeutic treatments and experience worse clinical outcomes than their male counterparts. Based on the established sexual dimorphism in mucosal immune responses, we hypothesized that the tumor immune microenvironment of bladder cancer differs between the sexes, and this may contribute to discrepancies in clinical outcomes. To determine biological sex-associated differences in the expression of immune regulatory genes and spatial organization of immune cells in tumors from NMIBC patients. Immune regulatory gene expression levels in tumors from male (n = 357) and female (n = 103) patients were measured using whole transcriptome profiles of tumors from the UROMOL cohort. Multiplexe immunofluorescence was performed to evaluate the density and spatial distribution of immune cells and immune checkpoints in tumors from an independent cohort of patients with NMIBC (n = 259 males and n = 73 females). Transcriptome sequencing data were analyzed using DESeq2 in R v4.0.1, followed by application of the Kruskal-Wallis test to determine gene expression differences between tumors from males and females. Immunofluorescence data analyses were conducted using R version 3.5.3. Survival analysis was performed using survminer packages. High-grade tumors from female patients exhibited significantly increased expression of B-cell recruitment (CXCL13) and function (CD40)-associated genes and the immune checkpoint genes CTLA4, PDCD1, LAG3, and ICOS. Tumors from female patients showed significantly higher infiltration of PD-L1+ cells and CD163+ M2-like macrophages than tumors from male patients. Increased abundance of CD163+ macrophages and CD79a+ B cells were associated with decreased recurrence-free survival. These novel findings highlight the necessity of considering sexual dimorphism in the design of future immunotherapy trials in NMIBC. In this study, we measured the abundance of various immune cell types between tumors from male and female patients with non–muscle-invasive bladder cancer. We demonstrate that tumors from female patients have a significantly higher abundance of immunosuppressive macrophages that express CD163. Higher abundance of tumor-associated CD163-expressing macrophages and B cells is associated with shorter recurrence-free survival in both male and female patients.

Identifying breast cancer patients is crucial to the clinical diagnosis and therapy for this disease. Conventional gene-based methods for breast cancer diagnosis ignore gene-gene interactions and thus may lead to loss of power. In this study, we proposed a novel method to select classification features, called “Selection of Significant Expression-Correlation Differential Motifs” (SSECDM). This method applied a network motif-based approach, combining a human signaling network and high-throughput gene expression data to distinguish breast cancer samples from normal samples. Our method has higher classification performance and better classification accuracy stability than the mutual information (MI) method or the individual gene sets method. It may become a useful tool for identifying and treating patients with breast cancer and other cancers, thus contributing to clinical diagnosis and therapy for these diseases.

Network motifs in central positions are considered to not only have more in-coming and out-going connections but are also localized in an area where more paths reach the networks. These central motifs have been extensively investigated to determine their consistent functions or associations with specific function categories. However, their functional potentials in the maintenance of cross-talk between different functional communities are unclear. In this paper, we constructed an integrated human signaling network from the Pathway Interaction Database. We identified 39 essential cancer-related motifs in central roles, which we called cancer-related marketing centrality motifs, using combined centrality indices on the system level. Our results demonstrated that these cancer-related marketing centrality motifs were pivotal units in the signaling network, and could mediate cross-talk between 61 biological pathways (25 could be mediated by one motif on average), most of which were cancer-related pathways. Further analysis showed that molecules of most marketing centrality motifs were in the same or adjacent subcellular localizations, such as the motif containing PI3K, PDK1 and AKT1 in the plasma membrane, to mediate signal transduction between 32 cancer-related pathways. Finally, we analyzed the pivotal roles of cancer genes in these marketing centrality motifs in the pathogenesis of cancers, and found that non-cancer genes were potential cancer-related genes.

Lung adenocarcinoma (LUAD) is a malignant tumor with high lethality, and the aim of this study was to identify promising biomarkers for LUAD. Using the TCGA-LUAD dataset as a discovery cohort, a novel joint framework VAEjMLP based on variational autoencoder (VAE) and multilayer perceptron (MLP) was proposed. And the Shapley Additive Explanations (SHAP) method was introduced to evaluate the contribution of feature genes to the classification decision, which helped us to develop a biologically meaningful biomarker potential scoring algorithm. Nineteen potential biomarkers for LUAD were identified, which were involved in the regulation of immune and metabolic functions in LUAD. A prognostic risk model for LUAD was constructed by the biomarkers HLA-DRB1, SCGB1A1, and HLA-DRB5 screened by Cox regression analysis, dividing the patients into high-risk and low-risk groups. The prognostic risk model was validated with external datasets. The low-risk group was characterized by enrichment of immune pathways and higher immune infiltration compared to the high-risk group. While, the high-risk group was accompanied by an increase in metabolic pathway activity. There were significant differences between the high- and low-risk groups in metabolic reprogramming of aerobic glycolysis, amino acids, and lipids, as well as in angiogenic activity, epithelial-mesenchymal transition, tumorigenic cytokines, and inflammatory response. Furthermore, high-risk patients were more sensitive to Afatinib, Gefitinib, and Gemcitabine as predicted by the pRRophetic algorithm. This study provides prognostic signatures capable of revealing the immune and metabolic landscapes for LUAD, and may shed light on the identification of other cancer biomarkers.