Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

Abstract

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

Cell Type-Specific Induction of Inflammation-Associated Genes in Crohn’s Disease and Colorectal Cancer

“…( B ) Gene ontology (GO Biological Process, EnrichR [ 16 ]) revealed that SNP-containing loci (purple), genes upregulated in CD (green) and their overlap (teal) are related to inflammatory pathways, while genes downregulated in CD (red) and their overlap with SNP-containing regions (dark red) are not. ( C ) Dot plot depicting cell types related to CD-induced genes according to PanglaoDB Augmented 2021 [ 17 ]. The dot color reflects a combination of p -value and z-score, referred to as combined score, whereas the dot size correlates to the number of enriched genes.…”

Elucidating the pharmacological mechanism by which Si-Wu-Tang induces cellular senescence in breast cancer via multilevel data integration

“…We followed the protocol described by Dominik Saul et al [ 61 ] to identify aging/senescence-induced genes and characterize the molecular patterns associated with cellular senescence. A total of 1535 aging/senescence-inducing genes were identified in 17 databases or studies, and a total of 1153 genes remained after deduplication.…”

Transcriptional Heterogeneity of Cellular Senescence in Cancer

“…Mutations in pathways of senescence, such as CDKN2A and TP53, and deficiencies in apoptotic pathways (i.e., inactivation of the CD95 receptor/ligand system) are frequently involved cancer cells ( Debatin, 2004 ; Hanahan and Weinberg, 2011 ; Park et al, 2021 ). Combined heterogeneity at the cellular and molecular levels may thus mediate the dynamics of diverse cancer cell subpopulations that exhibit deregulation of gene expression associated with age senescence ( Jochems et al, 2021 ; Saul and Kosinsky, 2021 ). In this regard, transcriptional heterogeneity and diversity in senescent cells warrants further investigation into their clinical relevance in cancer ( Davis-Marcisak et al, 2021 ; Faget et al, 2019 ; Nicos et al, 2020 ).…”

“…By understanding the complex SASP-receptor communication and molecular signaling networks supporting the expression of various novel SA genes in cancer cells, we may overcome cancer therapy resistance ( Chambers et al, 2021 ). Recently, Saul and Kosinsky (2021) identified the expression of various senescence-associated genes in distinct cancer cell populations from mouse tissue, providing the basis for future studies on the role of senescence in the development of specific tumor types in human cells. More comprehensive comparisons are thus needed between various cancer types and their single-cell profiles to enable widespread use of single-cell technologies for biomarker discovery and therapeutic intervention.…”

“…The identification of novel transcriptome signatures to detect all types of senescent cells or to differentiate between different senescence stages is an attractive strategy for deciphering various biological roles of senescent cells and developing specific drug targets. These characteristics include different gene expression patterns, deregulated cell–cell communication, aging, stem cell depletion, and epigenetic abnormalities that can lead to genomic instability ( Lopez-Otin et al, 2013 ; Saul and Kosinsky, 2021 ).…”