Altered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. There is however still a lack of molecular characterization glycolysis and comprehensive studies related to tumor glycolysis in the pan-cancer landscape. Here, we applied a gene expression signature to quantify glycolysis in 9229 tumors across 25 cancer types and 7875 human lung cancer single cells and verified the robustness of signature using defined glycolysis samples from previous studies. We classified tumors and cells into glycolysis score-high and -low groups, demonstrated their prognostic associations, and identified genome and transcriptome molecular features associated with glycolysis activity. We observed that glycolysis score-high tumors were associated with worse prognosis across cancer types. High glycolysis tumors exhibited specific driver genes altered by copy number aberrations (CNAs) in most cancer types. Tricarboxylic acid (TCA) cycle, DNA replication, tumor proliferation and other cancer hallmarks were more active in glycolysis-high tumors. Glycolysis signature was strongly correlated with hypoxia signature in all 25 cancer tissues (r > 0.7) and cancer single cells (r > 0.8). In addition, HSPA8 and P4HA1 were screened out as the potential modulating factors to glycolysis as their expression were highly correlated with glycolysis score and glycolysis genes, which enables future efforts for therapeutic options to block the glycolysis and control tumor progression. Our study provides a comprehensive molecular-level understanding of glycolysis with a large sample data and demonstrates the hypoxia pressure, growth signals, oncogene mutation and other potential signals could activate glycolysis, thereby to regulate cell cycle, energy material synthesis, cell proliferation and cancer progression.

The scale of SARS-CoV-2 infection and death is so enormous that further study of the molecular and evolutionary characteristics of SARS-CoV-2 will help us better understand and respond to SARS-CoV-2 outbreaks. The present study analyzed the epidemic and evolutionary characteristics of haplotype subtypes or regions based on 1.8 million high-quality SARS-CoV-2 genomic data. The estimated ratio of the rates of non-synonymous to synonymous changes (Ka/Ks) in North America and the United States were always more than 1.0, while the Ka/Ks in other continents and countries showed a sharp decline, then a slow increase to 1.0, and a dramatic increase over time. H1 (B.1) with the highest substitution rate has become the most dominant haplotype subtype since March 2020 and has evolved into multiple haplotype subtypes with smaller substitution rates. Many evolutionary characteristics of early SARS-CoV-2, such as H3 being the only early haplotype subtype that existed for the shortest time, the global prevalence of H1 and H1-5 (B.1.1) within a month after being detected, and many high divergent genome sequences early in February 2020, indicate the missing of early SARS-CoV-2 genomic data. SARS-CoV-2 experienced dynamic selection from December 2019 to August 2021 and has been under strong positive selection since May 2021. Its transmissibility and the ability of immune escape may be greatly enhanced over time. This will bring greater challenges to the control of the pandemic.

Background: Altered metabolism is a hallmark of cancer and glycolysis is one of the important factors promoting tumor development. Given that the absence of multi-sample big data research about glycolysis, the molecular mechanisms involved in glycolysis or the relationships between glycolysis and tumor microenvironment are not fully studied. Thus, a more comprehensive approach in a pan-cancer landscape may be needed. Methods: Here, we develop a computational pipeline to study multi-omics molecular features defining glycolysis activity and identify molecular alterations that correlate with glycolysis. We apply a 22-gene expression signature to define the glycolysis activity landscape and verify the robustness using clinically defined glycolysis samples from several previous studies. Based on gene expression signature, we classify about 5552 of 9229 tumor samples into glycolysis score-high and score-low groups across 25 cancer types from The Cancer Genome Atlas (TCGA) and demonstrate their prognostic associations. Moreover, using genomes and transcriptome data, we characterize the association of copy-number aberrations (CNAs), somatic single-nucleotide variants (SNVs) and hypoxia signature with glycolysis activity. Findings: Gene set variation analysis (GSVA) score by gene set expression was verified robustly to represent glycolytic activity and highly glycolytic tumors presented a poor overall survival in some cancer types. Then, we identified various types of molecular features promoting tumor cell proliferation were associated with glycolysis activity. Our study showed that TCA cycle and respiration electron transport were active in glycolysis-high tumors, indicating glycolysis was not a symptom of impaired oxidative metabolism. The glycolytic score significantly correlated with hypoxia score across all cancer types. Glycolysis score was also associated with elevated genomic instability. In all tumor types, high glycolysis tumors exhibited characteristic driver genes altered by CNAs identified multiple oncogenes and tumor suppressors. We observed widespread glycolysis-associated dysregulation of mRNA across cancers and screened out HSPA8 and P4HA1 as the potential modulating factor to glycolysis. Besides, the expression of genes encoding glycolytic enzymes positively correlated with genes in cell cycle. Interpretation: This is the first study to identify gene expression signatures that reflect glycolysis activity, which can be easily applied to large numbers of patient samples. Our analysis establishes a computational framework for characterizing glycolysis activity using gene expression data and defines correlation of glycolysis with the hypoxia microenvironment, tumor cell cycle and proliferation at a pan-cancer landscape. The findings suggest that the mechanisms whereby hypoxia influence glycolysis are likely multifactorial. Our finding is significant not just in demonstrating definition value for glycolysis but also in providing a comprehensive molecular-level understanding of glycolysis and suggesting a framework to guide combination therapy that may block the glycolysis pathway to control tumor growth in hypoxia microenvironment.

T cell exhaustion is one of the main reasons of tumor immune escape. Using single cell transcriptome data of CD8+ T cells in multiple cancers, we identified different cell types, in which Pre_exhaust and exhausted T cells participated in negative regulation of immune system process. By analyzing the co-expression network patterns and differentially expressed genes of Pre_exhaust, exhausted and effector T cells, we identified 35 genes related to T cell exhaustion, which high GSVA scores were associated with significantly poor prognosis in various cancers. In the differentially expressed genes, RGS1 showed the greatest fold change in Pre_exhaust and exhausted cells of three cancers compared with effector T cells, and high expression of RGS1 was also associated with poor prognosis in various cancers. Additionally, RGS1 protein was upregulated significantly in tumor tissues in the immunohistochemistry verification. Furthermore, RGS1 displayed positive correlation with the 35 genes, especially highly correlated with PDCD1, CTLA4, HAVCR2 and TNFRSF9 in CD8+ T cells and cancer tissues, indicating important roles of RGS1 in CD8+ T cell exhaustion. Considering the GTP-hydrolysis activity of RGS1 and significantly high mRNA and protein expression in cancer tissues, we speculated that RGS1 potentially mediate the T cell retention to lead to the persistent antigen stimulation, resulting in T cell exhaustion. In conclusion, our findings suggest that RGS1 is a new marker and promoting factor for CD8+ T cell exhaustion, and provide theoretical basis for research and immunotherapy of exhausted cells.