ARMT: An automatic RNA-seq data mining tool based on comprehensive and integrative analysis in cancer research.

The comprehensive and integrative analysis of RNA-seq data, in different molecular layers from diverse samples, holds promise to address the full-scale complexity of biological systems. Recent advances in gene set variant analysis (GSVA) are providing exciting opportunities for revealing the specific biological processes of cancer samples. However, it is still urgently needed to develop a tool, which combines GSVA and different molecular characteristic analysis, as well as prognostic characteristics of cancer patients to reveal the biological processes of disease comprehensively. Here, we develop ARMT, an automatic tool for RNA-Seq data analysis. ARMT is an efficient and integrative tool with user-friendly interface to analyze related molecular characters of single gene and gene set comprehensively based on transcriptome and genomic data, which builds the bridge for deeper information between genes and pathways, to further accelerate scientific findings. ARMT can be installed easily from https://github.com/Dulab2020/ARMT.

Multi-Omics Perspective Reveals the Different Patterns of Tumor Immune Microenvironment Based on Programmed Death Ligand 1 (PD-L1) Expression and Predictor of Responses to Immune Checkpoint Blockade across Pan-Cancer.

Immune checkpoint inhibitor (ICI) therapies have shown great promise in cancer treatment. However, the intra-heterogeneity is a major barrier to reasonably classifying the potential benefited patients. Comprehensive heterogeneity analysis is needed to solve these clinical issues. In this study, the samples from pan-cancer and independent breast cancer datasets were divided into four tumor immune microenvironment (TIME) subtypes based on tumor programmed death ligand 1 (PD-L1) expression level and tumor-infiltrating lymphocyte (TIL) state. As the combination of the TIL Z score and PD-L1 expression showed superior prediction of response to ICI in multiple data sets compared to other methods, we used the TIL Z score and PD-L1 to classify samples. Therefore, samples were divided by combined TIL Z score and PD-L1 to identify four TIME subtypes, including type I (3.24%), type II (43.24%), type III (6.76%), and type IV (46.76%). Type I was associated with favorable prognosis with more T and DC cells, while type III had the poorest condition and composed a higher level of activated mast cells. Furthermore, TIME subtypes exhibited a distinct genetic and transcriptional feature: type III was observed to have the highest mutation rate (77.92%), while co-mutations patterns were characteristic in type I, and the PD-L1 positive subgroup showed higher carbohydrates, lipids, and xenobiotics metabolism compared to others. Overall, we developed a robust method to classify TIME and analyze the divergence of prognosis, immune cell composition, genomics, and transcriptomics patterns among TIME subtypes, which potentially provides insight for classification of TIME and a referrable theoretical basis for the screening benefited groups in the ICI immunotherapy.

Roles of Proteoglycans and Glycosaminoglycans in Cancer Development and Progression.

The extracellular matrix (ECM) spatiotemporally controls cell fate; however, dysregulation of ECM remodeling can lead to tumorigenesis and cancer development by providing favorable conditions for tumor cells. Proteoglycans (PGs) and glycosaminoglycans (GAGs) are the major macromolecules composing ECM. They influence both cell behavior and matrix properties through direct and indirect interactions with various cytokines, growth factors, cell surface receptors, adhesion molecules, enzymes, and glycoproteins within the ECM. The classical features of PGs/GAGs play well-known roles in cancer angiogenesis, proliferation, invasion, and metastasis. Several lines of evidence suggest that PGs/GAGs critically affect broader aspects in cancer initiation and the progression process, including regulation of cell metabolism, serving as a sensor of ECM's mechanical properties, affecting immune supervision, and participating in therapeutic resistance to various forms of treatment. These functions may be implemented through the characteristics of PGs/GAGs as molecular bridges linking ECM and cells in cell-specific and context-specific manners within the tumor microenvironment (TME). In this review, we intend to present a comprehensive illustration of the ways in which PGs/GAGs participate in and regulate several aspects of tumorigenesis; we put forward a perspective regarding their effects as biomarkers or targets for diagnoses and therapeutic interventions.

Characterization of Glycolysis-Associated Molecules in the Tumor Microenvironment Revealed by Pan-Cancer Tissues and Lung Cancer Single Cell Data.

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.