Identification of non-cancer cells from cancer transcriptomic data

Comparison of Alternative Splicing Landscapes Revealed by Long-Read Sequencing in Hepatocyte-Derived HepG2 and Huh7 Cultured Cells and Human Liver Tissue

The long-read RNA sequencing developed by Oxford Nanopore Technologies provides a direct quantification of transcript isoforms, thereby making it possible to present alternative splicing (AS) profiles as arrays of single splice variants with different abundances. Additionally, AS profiles can be presented as arrays of genes characterized by the degree of alternative splicing (the DAS-the number of detected splice variants per gene). Here, we successfully utilized the DAS to reveal biological pathways influenced by the alterations in AS in human liver tissue and the hepatocyte-derived malignant cell lines HepG2 and Huh7, thus employing the mathematical algorithm of gene set enrichment analysis. Furthermore, analysis of the AS profiles as abundances of single splice variants by using the graded tissue specificity index τ provided the selection of the groups of genes expressing particular splice variants specifically in liver tissue, HepG2 cells, and Huh7 cells. The majority of these splice variants were translated into proteins products and appeal to be in focus regarding further insights into the mechanisms underlying cell malignization. The used metrics are intrinsically suitable for transcriptome-wide AS profiling using long-read sequencing.

Rarity: discovering rare cell populations from single-cell imaging data

MOTIVATION

Cell type identification plays an important role in the analysis and interpretation of single-cell data and can be carried out via supervised or unsupervised clustering approaches. Supervised methods are best suited where we can list all cell types and their respective marker genes a priori, while unsupervised clustering algorithms look for groups of cells with similar expression properties. This property permits the identification of both known and unknown cell populations, making unsupervised methods suitable for discovery. Success is dependent on the relative strength of the expression signature of each group as well as the number of cells. Rare cell types therefore present a particular challenge that is magnified when they are defined by differentially expressing a small number of genes.

RESULTS

Typical unsupervised approaches fail to identify such rare subpopulations, and these cells tend to be absorbed into more prevalent cell types. In order to balance these competing demands, we have developed a novel statistical framework for unsupervised clustering, named Rarity, that enables the discovery process for rare cell types to be more robust, consistent, and interpretable. We achieve this by devising a novel clustering method based on a Bayesian latent variable model in which we assign cells to inferred latent binary on/off expression profiles. This lets us achieve increased sensitivity to rare cell populations while also allowing us to control and interpret potential false positive discoveries. We systematically study the challenges associated with rare cell type identification and demonstrate the utility of Rarity on various IMC datasets.

AVAILABILITY AND IMPLEMENTATION

Implementation of Rarity together with examples is available from the Github repository (https://github.com/kasparmartens/rarity).

Obesity-associated changes in molecular biology of primary breast cancer

Obesity is associated with an increased risk of developing breast cancer (BC) and worse prognosis in BC patients, yet its impact on BC biology remains understudied in humans. This study investigates how the biology of untreated primary BC differs according to patients' body mass index (BMI) using data from >2,000 patients. We identify several genomic alterations that are differentially prevalent in overweight or obese patients compared to lean patients. We report evidence supporting an ageing accelerating effect of obesity at the genetic level. We show that BMI-associated differences in bulk transcriptomic profile are subtle, while single cell profiling allows detection of more pronounced changes in different cell compartments. These analyses further reveal an elevated and unresolved inflammation of the BC tumor microenvironment associated with obesity, with distinct characteristics contingent on the estrogen receptor status. Collectively, our analyses imply that obesity is associated with an inflammaging-like phenotype. We conclude that patient adiposity may play a significant role in the heterogeneity of BC and should be considered for BC treatment tailoring.

Huanglian decoction suppresses the growth of hepatocellular carcinoma cells by reducing CCNB1 expression

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent cancers in human populations worldwide. Huanglian decoction is one of the most important Chinese medicine formulas, with the potential to treat cancer.

AIM

To investigate the role and mechanism of Huanglian decoction on HCC cells.

METHODS

To identify differentially expressed genes (DEGs), we downloaded gene expression profile data from The Cancer Genome Atlas Liver Hepatocellular Carcinoma and Gene Expression Omnibus (GSE45436) databases. We obtained phytochemicals of the four herbs of Huanglian decoction from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform. We also established a regulatory network of DEGs and drug target genes and subsequently analyzed key genes using bioinformatics approaches. Furthermore, we conducted in vitro experiments to explore the effect of Huanglian decoction and to verify the predictions. In particular, the CCNB1 gene was knocked down to verify the primary target of this decoction. Through the identification of the expression levels of key proteins, we determined the primary mechanism of Huanglian decoction in HCC.

RESULTS

Based on the results of the network pharmacological analysis, we revealed 5 bioactive compounds in Huanglian decoction that act on HCC. In addition, a protein-protein interaction network analysis of the target genes of these five compounds as well as expression and prognosis analyses were performed in tumors. CCNB1 was confirmed to be the primary gene that may be highly expressed in tumors and was significantly associated with a worse prognosis. We also noted that CCNB1 may serve as an independent prognostic indicator in HCC. Moreover, in vitro experiments demonstrated that Huanglian decoction significantly inhibited the growth, migration, and invasiveness of HCC cells and induced cell apoptosis and G2/M phase arrest. Further analysis showed that the decoction may inhibit the growth of HCC cells by downregulating the CCNB1 expression level. After Huanglian decoction treatment, the expression levels of Bax, caspase 3, caspase 9, p21 and p53 in HCC cells were increased, while the expression of CDK1 and CCNB1 was significantly decreased. The p53 signaling pathway was also found to play an important role in this process.

CONCLUSION

Huanglian decoction has a significant inhibitory effect on HCC cells. CCNB1 is a potential therapeutic target in HCC. Further analysis showed that Huanglian decoction can inhibit HCC cell growth by downregulating the expression of CCNB1 to activate the p53 signaling pathway.