Deciphering the immune heterogeneity dominated by natural killer cells with prognostic and therapeutic implications in hepatocellular carcinoma.

Belonging to type 1 innate lymphoid cells (ILC1), natural killer (NK) cells play an important role not only in fighting microbial infections but also in anti-tumor response. Hepatocellular carcinoma (HCC) represents an inflammation-related malignancy and NK cells are enriched in the liver, making them an essential component of the HCC immune microenvironment. In this study, we performed single-cell RNA-sequencing (scRNA-seq) analysis to identify the NK cell marker genes (NKGs) and uncovered 80 prognosis-related ones by the TCGA-LIHC dataset. Based on prognostic NKGs, HCC patients were categorized into two subtypes with distinct clinical outcomes. Subsequently, we conducted LASSO-COX and stepwise regression analysis on prognostic NKGs to establish a five-gene (UBB, CIRBP, GZMH, NUDC, and NCL) prognostic signature-NKscore. Different mutation statuses of the two risk groups stratified by NKscore were comprehensively characterized. Besides, the established NKscore-integrated nomogram presented enhanced predictive performance. Single sample gene set enrichment analysis (ssGSEA) analysis was used to uncover the landscape of the tumor immune microenvironment (TIME) and the high-NKscore risk group was characterized with an immune-exhausted phenotype while the low-NKscore risk group held relatively strong anti-cancer immunity. T cell receptor (TCR) repertoire, tumor inflammation signature (TIS), and Immunophenoscore (IPS) analyses revealed differences in immunotherapy sensitivity between the two NKscore risk groups. Taken together, we developed a novel NK cell-related signature to predict the prognosis and immunotherapy efficacy for HCC patients.

Changes in dynamic functional connections with aging.

Despite numerous studies on age-related changes in static functional connections (FCs), the available literature on the changes in dynamic FCs with aging is lacking. This study investigated the changes in dynamic FCs with aging based on resting state fMRI data of 61 healthy adults aged 30-85 years. The time-resolved FCs among 160 pre-defined regions of interest (ROIs) were first estimated using sliding-window correlation. Based on the dynamic FC matrices, we then analyzed the dynamic switches between different FC states using k-means clustering, and correlated age with the dwell time of each FC state across subjects. The elderly were observed to spend more time in an FC state characterized by weak interactions throughout the brain and less time in an FC state characterized by strong interactions within the sensory-motor network and the cognitive control network. These results may reflect an overall weakening of connections in the elderly, which support less efficient information transfer in them. Based on the dynamic FC matrices, we also evaluated the variability and amplitude of FC time-series, which measure the relative (to mean) and absolute strength of FC fluctuations, respectively, and correlated age with the two measures across subjects. Relatively weak age-vs-variability correlations were observed, but we did observe significant negative age-vs-amplitude correlations at both the global and regional level. These results indicate that amplitude may be another effective metric for assessing FC fluctuations, in addition to the widely-used variability metric. Moreover, the observed declines in the amplitude of FC fluctuations in the elderly may support the assumption that it should be the weakening of absolute interactions between brain regions, rather than toggling between positive and negative correlations, that causes the repeatedly reported widespread (static) FC decreases with aging. Overall, the present results not only reflect an overall weakening of connections in the elderly, but indicate the potential of dynamic FC analyses in studies of age-related psychiatric and neurological disorders.