Hepatocellular carcinoma (HCC) is a disease with unique management complexity because it displays high heterogeneity of molecular phenotypes. We herein aimed to characterize the molecular features of HCC by the development of a classification system that was based on the gene expression profile of metabolic genes. Integrative analysis was performed with a metadata set featuring 371 and 231 HCC human samples from the Cancer Genome Atlas and the International Cancer Genome Consortium, respectively. All samples were linked with clinical information. RNA sequencing data of 2752 previously characterized metabolism‐related genes were used for non‐negative matrix factorization clustering, and three subclasses of HCC (C1, C2, and C3) were identified. We then analyzed the metadata set for metabolic signatures, prognostic value, transcriptome features, immune infiltration, clinical characteristics, and drug sensitivity of subclasses, and compared the resulting subclasses with previously published classifications. Subclass C1 displayed high metabolic activity, low α‐fetoprotein (AFP) expression, and good prognosis. Subclass C2 was associated with low metabolic activities and displayed high expression of immune checkpoint genes, demonstrating drug sensitivity toward cytotoxic T‐lymphocyte‐associated protein‐4 inhibitors and the receptor tyrosine kinase inhibitor cabozantinib. Subclass C3 displayed intermediate metabolic activity, high AFP expression level, and bad prognosis. Finally, a 90‐gene classifier was generated to enable HCC classification. This study establishes a new HCC classification based on the gene expression profiles of metabolic genes, thereby furthering the understanding of the genetic diversity of human HCC.

Acinetobacter baumannii poses a serious threat to human health, mainly because of its widespread distribution and severe drug resistance. However, no licensed vaccines exist for this pathogen. In this study, we created a conjugate vaccine against A. baumannii by introducing an O-linked glycosylation system into the host strain. After demonstrating the ability of the vaccine to elicit Th1 and Th2 immune responses and observing its good safety in mouse a model, the strong in vitro bactericidal activity and prophylactic effects of the conjugate vaccine against infection were further demonstrated by evaluating post-infection tissue bacterial loads, observing suppressed serum pro-inflammatory cytokine levels. Additionally, the broad protection from the vaccine was further proved via lethal challenge with A. baumannii. Overall, these results indicated that the conjugate vaccine could elicit an efficient immune response and provide good protection against A. baumannii infection in murine sepsis models. Thus, the conjugate vaccine can be considered as a promising candidate vaccine for preventing A. baumannii infection.

Background In recent years, the application of functional genetic immuno-oncology screens has showcased the striking ability to identify potential regulators engaged in tumor-immune interactions. Although these screens have yielded substantial data, few studies have attempted to systematically aggregate and analyze them. Methods In this study, a comprehensive data collection of tumor immunity-associated functional screens was performed. Large-scale genomic data sets were exploited to conduct integrative analyses. Results We identified 105 regulator genes that could mediate resistance or sensitivity to immune cell-induced tumor elimination. Further analysis identified MON2 as a novel immune-oncology target with considerable therapeutic potential. In addition, based on the 105 genes, a signature named CTIS (CRISPR screening-based tumor-intrinsic immune score) for predicting response to immune checkpoint blockade (ICB) and several immunomodulatory agents with the potential to augment the efficacy of ICB were also determined. Conclusion Overall, our findings provide insights into immune oncology and open up novel opportunities for improving the efficacy of current immunotherapy agents.