Correction: DAPIT Over-Expression Modulates Glucose Metabolism and Cell Behaviour in HEK293T Cells

Epigenetic control of cell signalling in cancer stem cells

The self-renewing cancer stem cells (CSCs) represent one of the distinct cell populations occurring in a tumour that can differentiate into multiple lineages. This group of sparsely abundant cells play a vital role in tumour survival and resistance to different treatments during cancer. The lack of exclusive markers associated with CSCs makes diagnosis and prognosis in cancer patients extremely difficult. This calls for the identification of unique regulators and markers for CSCs. Various signalling pathways like the Wnt/β-catenin pathway, Hedgehog pathway, Notch pathway, and TGFβ/BMP play a major role in the regulation and maintenance of CSCs. Epigenetic regulatory mechanisms add another layer of complexity to control these signalling pathways. In this chapter, we discuss about the role of epigenetic mechanisms in regulating the cellular signalling pathways in CSCs. The epigenetic regulatory mechanisms such as DNA methylation, histone modification and microRNAs can modulate the diverse effectors of signalling pathways and consequently the growth, differentiation and tumorigenicity of CSCs. In the end, we briefly discuss the therapeutic potential of targeting these epigenetic regulators and their target genes in CSCs.

Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils Novel Prognostic Biomarkers in Multiple Myeloma

Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.

Whole‑genome identification and systematic analysis of lncRNA‑mRNA co‑expression profiles in patients with cutaneous basal cell carcinoma

Cutaneous basal cell carcinoma (BCC) is a common subtype of malignant skin tumor with low invasiveness. Early diagnosis and treatment of BCC and the identification of specific biomarkers are particularly urgent. Long non‑coding RNAs (lncRNAs) have been shown to be associated with the development of various tumors, including BCC. The present study conducted a comparative analysis of the differential expression of lncRNAs and mRNAs through whole‑genome technology. Microarray analyses were used to identify differentially expressed (DE) lncRNAs and DE mRNAs. Reverse transcription‑quantitative (RT‑q) PCR confirmed the differential expression of 10 lncRNAs in BCC. Subsequently, a lncRNA‑mRNA co‑expression network was constructed using the top 10 DE lncRNAs. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to investigate the possible biological effects of the identified mRNAs and to speculate on the possible biological effects of the lncRNAs. A total of 1,838 DE lncRNAs and 2,010 DE mRNAs were identified and 10 of the DE lncRNAs were confirmed by RT‑qPCR. A lncRNA‑mRNA co‑expression network comprising 166 specific co‑expressed lncRNAs and mRNAs was constructed using the top 10 DE lncRNAs. According to the results of the GO and KEGG analyses, lncRNA XR_428612.1 may serve an important role in mitochondrial dysfunction and the progression of BCC by modulating TICAM1, USMG5, COX7A2, FBXO10, ATP5E and TIMM8B. The present study provided whole‑genome identification and a systematic analysis of lncRNA‑mRNA co‑expression profiles in BCC.