With the increasing prevalence of age-related chronic diseases burdening healthcare systems, there is a pressing need for innovative management strategies. Our study focuses on the gut microbiota, essential for metabolic, nutritional, and immune functions, which undergoes significant changes with aging. These changes can impair intestinal function, leading to altered microbial diversity and composition that potentially influence health outcomes and disease progression. Using advanced metagenomic sequencing, we explore the potential of personalized probiotic supplements in 297 older adults by analyzing their gut microbiota. We identified distinctive Lactobacillus and Bifidobacterium signatures in the gut microbiota of older adults, revealing probiotic patterns associated with various population characteristics, microbial compositions, cognitive functions, and neuroimaging results. These insights suggest that tailored probiotic supplements, designed to match individual probiotic profile, could offer an innovative method for addressing age-related diseases and functional declines. Our findings enhance the existing evidence base for probiotic use among older adults, highlighting the opportunity to create more targeted and effective probiotic strategies. However, additional research is required to validate our results and further assess the impact of precision probiotics on aging populations. Future studies should employ longitudinal designs and larger cohorts to conclusively demonstrate the benefits of tailored probiotic treatments.

The development of disease involves a systematic disturbance inside cells and is associated with changes in the interactions or regulations among genes forming biological networks. The bridges inside a network are critical in shortening the distances between nodes. We observed that, inside the human gene regulatory network, one strongly connected core bridged the whole network. Other regulations outside the core formed a weakly connected component surrounding the core like a peripheral structure. Furthermore, the regulatory feedback loops (FBLs) inside the core compose an interface-like structure between the core and periphery. We then denoted the regulatory FBLs as the interface core. Notably, both the cancer-associated and essential biomolecules and regulations were significantly overrepresented in the interface core. These results implied that the interface core is not only critical for the network structure but central in cellular systems. Furthermore, the enrichment of the cancer-associated and essential regulations in the interface core might be attributed to its bridgeness in the network. More importantly, we identified one regulatory FBL between HNF4A and NR2F2 that possesses the highest bridgeness in the interface core. Further investigation suggested that the disturbance of the HNF4A-NR2F2 FBL might protect tumor cells from apoptotic processes. Our results emphasize the relevance of the regulatory network properties to cellular systems and might reveal a critical role of the interface core in cancer.

Transcription factor and microRNA (miRNA) can mutually regulate each other and jointly regulate their shared target genes to form feed-forward loops (FFLs). While there are many studies of dysregulated FFLs in a specific cancer, a systematic investigation of dysregulated FFLs across multiple tumor types (pan-cancer FFLs) has not been performed yet. In this study, using The Cancer Genome Atlas data, we identified 26 pan-cancer FFLs, which were dysregulated in at least five tumor types. These pan-cancer FFLs could communicate with each other and form functionally consistent subnetworks, such as epithelial to mesenchymal transition-related subnetwork. Many proteins and miRNAs in each subnetwork belong to the same protein and miRNA family, respectively. Importantly, cancer-associated genes and drug targets were enriched in these pan-cancer FFLs, in which the genes and miRNAs also tended to be hubs and bottlenecks. Finally, we identified potential anticancer indications for existing drugs with novel mechanism of action. Collectively, this study highlights the potential of pan-cancer FFLs as a novel paradigm in elucidating pathogenesis of cancer and developing anticancer drugs.

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.

UNLABELLED: Exploring microRNA (miRNA) regulations and protein-protein interactions could reveal the molecular mechanisms responsible for complex biological processes. Mirin is a web-based application suitable for identifying functional modules from protein-protein interaction networks regulated by aberrant miRNAs under user-defined biological conditions such as cancers. The analysis involves combining miRNA regulations, protein-protein interactions between target genes, as well as mRNA and miRNA expression profiles provided by users. Mirin has successfully uncovered oncomirs and their regulatory networks in various cancers, such as gastric and breast cancer. AVAILABILITY AND IMPLEMENTATION: Mirin is freely available at http://mirin.ym.edu.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.