Temporal Splicing Switches in Elements of the TNF-Pathway Identified by Computational Analysis of Transcriptome Data for Human Cell Lines

The genes regulating sensitivity of tumor cells to T cell-mediated killing: could they be potential personalized immunotherapeutic targets in head and neck squamous cell carcinoma?

OBJECTIVE

To identify the pivotal genes, specifically the STTK genes, that govern the sensitivity of tumor cells to T cell-mediated killing in Head and Neck Squamous Cell Carcinoma (HNSC).

METHODS

The differentially expressed genes (DEGs) in HNSC and STTK genes were overlapped to obtain the DE-STTK genes. Univariate and LASSO regression analyses were conducted to identify the pivotal DE-STTK genes that serve as hubs in HNSC (i.e., hub DE-STTK genes). The risk model was established to divide HNSC tumor samples into high- and low-risk groups based on the hub DE-STTK genes. Further investigations were carried out by examing the expression level, prognostic values, diagnostic values, enriched signaling pathways, correlation with tumor mutation burden (TMB), and association with tumor immune infiltration cells (TIICs).

RESULTS

A total of 71 genes were found to be overlapped between DEGs in HNSC and STTK genes. Lasso regression analysis identified 9 hub genes which were MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1. The network analysis of hub DE-STTK genes-pathway reveals that these 9 hub genes exhibit enrichment in multiple signaling pathways, including toll-like receptor signaling, TNF signaling, NF-kappa B signaling, cytokine-cytokine receptor interaction, spliceosome, mRNA surveillance pathway, nucleocytoplasmic transport, GPI-anchor biosynthesis, as well as N-Glycan biosynthesis. The Pearson correlation analysis showed that the majority of correlations between 9 hub DE-STTK genes and immune cells were positive.

CONCLUSION

The 9 identified hub DE-STTK genes (MYF6, AATF, AURKA, CXCL9, DPM2, MYO1B, NCBP2, TNFRSF12A, and TRAF1) are presumptively implicated in the modulation of tumor immunity in HNSC. These genes, along with their enriched pathways, hold promise as potential personalized immunotherapeutic targets for the treatment of HNSC, thereby offering novel avenues for therapeutic intervention in this malignancy.

Potential Molecular Mechanisms of Ephedra Herb in the Treatment of Nephrotic Syndrome Based on Network Pharmacology and Molecular Docking

Objective

To explore the possible mechanisms of Ephedra herb (EH) in the treatment of nephrotic syndrome (NS) by using network pharmacology and molecular docking in this study.

Methods

Active ingredients and related targets of EH were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, and the gene names corresponding to the proteins were found through the UniProt database. Then, target genes related to NS were screened out from GeneCards, PharmGKB, and OMIM databases. Next, the intersection targets were obtained successfully through Venn diagram, which were also seen as key target genes of EH and NS. Cytoscape 3.9.0 software was used to construct the effective “active ingredient-target” network diagram, and “drug-ingredient-target-disease (D-I-T-D)” network diagram. After that, the STRING database was used to construct a protein-protein interaction (PPI) network. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment involved in the targets were performed by the DAVID database and ClueGO plugin in Cytoscape. Finally, AutoDockTools software was used for molecular docking to verify the binding strength between main active ingredients and key target proteins.

Results

A total of 22 main active ingredients such as quercetin, kaempferol, luteolin, and naringenin were obtained, which could act on 105 targets related to NS. Through PPI network, 53 core targets such as AKT1, TNF, IL6, VEGFA, and IL1B were found, which might play a crucial role in the treatment of NS. Meanwhile, these targets were significantly involved in PI3K-Akt signaling pathway, TNF signaling pathway, AGE-RAGE signaling pathway, hepatitis B, and pathways in cancer through GO and KEGG enrichment analysis. The docking results indicated that active ingredients such as kaempferol, luteolin, quercetin, and naringenin all had good binding to the target protein AKT1 or TNF. Among them, luteolin and naringenin binding with AKT1 showed the best binding energy (-6.2 kcal/mol).

Conclusion

This study indicated that the potential mechanism of EH in treating NS may be related to PI3K-Akt signaling pathway, TNF signaling pathway, and AGE-RAGE signaling pathway, which provided better approaches for exploring the mechanism in treating NS and new ideas for further in vivo and in vitro experimental verifications.

Transcriptome analysis of clock disrupted cancer cells reveals differential alternative splicing of cancer hallmarks genes

Emerging evidence points towards a regulatory role of the circadian clock in alternative splicing (AS). Whether alterations in core-clock components may contribute to differential AS events is largely unknown. To address this, we carried out a computational analysis on recently generated time-series RNA-seq datasets from three core-clock knockout (KO) genes (ARNTL, NR1D1, PER2) and WT of a colorectal cancer (CRC) cell line, and time-series RNA-seq datasets for additional CRC and Hodgkin’s lymphoma (HL) cells, murine WT, Arntl KO, and Nr1d1/2 KO, and murine SCN WT tissue. The deletion of individual core-clock genes resulted in the loss of circadian expression in crucial spliceosome components such as SF3A1 (in ARNTL^KO), SNW1 (in NR1D1^KO), and HNRNPC (in PER2^KO), which led to a differential pattern of KO-specific AS events. All HCT116^KO cells showed a rhythmicity loss of a crucial spliceosome gene U2AF1, which was also not rhythmic in higher progression stage CRC and HL cancer cells. AS analysis revealed an increase in alternative first exon events specific to PER2 and NR1D1 KO in HCT116 cells, and a KO-specific change in expression and rhythmicity pattern of AS transcripts related to cancer hallmarks genes including FGFR2 in HCT116_ARNTL^KO, CD44 in HCT116_NR1D1^KO, and MET in HCT116_PER2^KO. KO-specific changes in rhythmic properties of known spliced variants of these genes (e.g. FGFR2 IIIb/FGFR2 IIIc) correlated with epithelial-mesenchymal-transition signalling. Altogether, our bioinformatic analysis highlights a role for the circadian clock in the regulation of AS, and reveals a potential impact of clock disruption in aberrant splicing in cancer hallmark genes.

An Optimal Time for Treatment-Predicting Circadian Time by Machine Learning and Mathematical Modelling

Tailoring medical interventions to a particular patient and pathology has been termed personalized medicine. The outcome of cancer treatments is improved when the intervention is timed in accordance with the patient's internal time. Yet, one challenge of personalized medicine is how to consider the biological time of the patient. Prerequisite for this so-called chronotherapy is an accurate characterization of the internal circadian time of the patient. As an alternative to time-consuming measurements in a sleep-laboratory, recent studies in chronobiology predict circadian time by applying machine learning approaches and mathematical modelling to easier accessible observables such as gene expression. Embedding these results into the mathematical dynamics between clock and cancer in mammals, we review the precision of predictions and the potential usage with respect to cancer treatment and discuss whether the patient's internal time and circadian observables, may provide an additional indication for individualized treatment timing. Besides the health improvement, timing treatment may imply financial advantages, by ameliorating side effects of treatments, thus reducing costs. Summarizing the advances of recent years, this review brings together the current clinical standard for measuring biological time, the general assessment of circadian rhythmicity, the usage of rhythmic variables to predict biological time and models of circadian rhythmicity.

Construction of prognostic predictor by comprehensive analyzing alternative splicing events for colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is one of the most common malignant tumors, with high incidence and mortality rates worldwide. Reliable prognostic biomarkers are needed to guide clinical practice.

Methods

Comprehensive gene expression with alternative splicing (AS) profiles for each patient was downloaded using the SpliceSeq database from The Cancer Genome Atlas. Cox regression analysis was conducted to screen for prognostic AS events. The R package limma was used to screen differentially expressed genes (DEGs) between normal and tumor samples in the COAD cohort. A Venn plot analysis was performed between DEGs and prognostic AS events, and the DEGs that co-occurred with prognostic AS events (DEGAS) were identified. The top 30 most-connected DEGAS in protein–protein interaction analysis were identified through Cox proportional hazards regression to establish prognostic models.

Results

In total, 350 patients were included in the study. A total of 22,451 AS events were detected, of which 2004 from 1439 genes were significantly associated with survival time. By overlapping these 1439 genes with 6455 DEGs, 211 DEGs with AS events were identified. After the construction of the protein–protein interaction network, the top 30 hub genes were included in a multivariate analysis. Finally, a risk score based on 12 genes associated with overall survival was established (P < 0.05). The area under the curve was 0.782. The risk score was an independent predictor (P < 0.001).

Conclusions

By exploring survival-associated AS events, a powerful prognostic predictor consisting of 12 DEGAS was built. This study aims to propose a novel method to provide treatment targets for COAD and guide clinical practice in the future.

A Multi-Layered Study on Harmonic Oscillations in Mammalian Genomics and Proteomics

Cellular, organ, and whole animal physiology show temporal variation predominantly featuring 24-h (circadian) periodicity. Time-course mRNA gene expression profiling in mouse liver showed two subsets of genes oscillating at the second (12-h) and third (8-h) harmonic of the prime (24-h) frequency. The aim of our study was to identify specific genomic, proteomic, and functional properties of ultradian and circadian subsets. We found hallmarks of the three oscillating gene subsets, including different (i) functional annotation, (ii) proteomic and electrochemical features, and (iii) transcription factor binding motifs in upstream regions of 8-h and 12-h oscillating genes that seemingly allow the link of the ultradian gene sets to a known circadian network. Our multifaceted bioinformatics analysis of circadian and ultradian genes suggests that the different rhythmicity of gene expression impacts physiological outcomes and may be related to transcriptional, translational and post-translational dynamics, as well as to phylogenetic and evolutionary components.

A Computational Analysis of Alternative Splicing across Mammalian Tissues Reveals Circadian and Ultradian Rhythms in Splicing Events

Mounting evidence points to a role of the circadian clock in the temporal regulation of post-transcriptional processes in mammals, including alternative splicing (AS). In this study, we carried out a computational analysis of circadian and ultradian rhythms on the transcriptome level to characterise the landscape of rhythmic AS events in published datasets covering 76 tissues from mouse and olive baboon. Splicing-related genes with 24-h rhythmic expression patterns showed a bimodal distribution of peak phases across tissues and species, indicating that they might be controlled by the circadian clock. On the output level, we identified putative oscillating AS events in murine microarray data and pairs of differentially rhythmic splice isoforms of the same gene in baboon RNA-seq data that peaked at opposing times of the day and included oncogenes and tumour suppressors. We further explored these findings using a new circadian RNA-seq dataset of human colorectal cancer cell lines. Rhythmic isoform expression patterns differed between the primary tumour and the metastatic cell line and were associated with cancer-related biological processes, indicating a functional role of rhythmic AS that might be implicated in tumour progression. Our data shows that rhythmic AS events are widespread across mammalian tissues and might contribute to a temporal diversification of the proteome.