Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

The role of serum and glucocorticoid-regulated kinase 1 in cellular signaling: Implications for drug development

Serum and glucocorticoid-regulated kinase 1 (SGK1) is a ubiquitously expressed protein belonging to the Ser/Thr kinase family. It regulates diverse physiological processes, including epithelial sodium channel activity, hypertension, cell proliferation, and insulin sensitivity. Due to its significant role in the pathogenesis of numerous diseases, SGK1 can be exploited as a potential therapeutic target to address challenging health problems. SGK1 is associated with the development of obesity, and its overexpression enhances the sodium-glucose co-transporter 1 activity, which absorbs intestinal glucose. This review highlighted the detailed functional significance of SGK1 signaling and role in different diseases and subsequent therapeutic targeting. We aim to provide deeper mechanistic insights into understanding the pathogenesis and recent advancements in the SGK1 targeted drug development process. Small-molecule inhibitors are being developed with excellent binding affinity and improved SGK1 inhibition with desired selectivity. We have discussed small molecule inhibitors designed explicitly as potent SGK1 inhibitors and their therapeutic implications in various diseases. We further addressed the therapeutic potential and mechanism of action of these SGK1 inhibitors and provided a strong scientific foundation for developing effective therapeutics.

Non-Association of Driver Alterations in PTEN with Differential Gene Expression and Gene Methylation in IDH1 Wildtype Glioblastomas

During oncogenesis, alterations in driver genes called driver alterations (DAs) modulate the transcriptome, methylome and proteome through oncogenic signaling pathways. These modulatory effects of any DA may be analyzed by examining differentially expressed mRNAs (DEMs), differentially methylated genes (DMGs) and differentially expressed proteins (DEPs) between tumor samples with and without that DA. We aimed to analyze these modulations with 12 common driver genes in Isocitrate Dehydrogenase 1 wildtype glioblastomas (IDH1-W-GBs). Using Cbioportal, groups of tumor samples with and without DAs in these 12 genes were generated from the IDH1-W-GBs available from "The Cancer Genomics Atlas Firehose Legacy Study Group" (TCGA-FL-SG) on Glioblastomas (GBs). For all 12 genes, samples with and without DAs were compared for DEMs, DMGs and DEPs. We found that DAs in PTEN were unassociated with any DEM or DMG in contrast to DAs in all other drivers, which were associated with several DEMs and DMGs. This contrasting PTEN-related property of being unassociated with differential gene expression or methylation in IDH1-W-GBs was unaffected by concurrent DAs in other common drivers or by the types of DAs affecting PTEN. From the lists of DEMs and DMGs associated with some common drivers other than PTEN, enriched gene ontology terms and insights into the co-regulatory effects of these drivers on the transcriptome were obtained. The findings from this study can improve our understanding of the molecular mechanisms underlying gliomagenesis with potential therapeutic benefits.

Ontologies and Knowledge Graphs in Oncology Research

The complexity of cancer research stems from leaning on several biomedical disciplines for relevant sources of data, many of which are complex in their own right. A holistic view of cancer—which is critical for precision medicine approaches—hinges on integrating a variety of heterogeneous data sources under a cohesive knowledge model, a role which biomedical ontologies can fill. This study reviews the application of ontologies and knowledge graphs in cancer research. In total, our review encompasses 141 published works, which we categorized under 14 hierarchical categories according to their usage of ontologies and knowledge graphs. We also review the most commonly used ontologies and newly developed ones. Our review highlights the growing traction of ontologies in biomedical research in general, and cancer research in particular. Ontologies enable data accessibility, interoperability and integration, support data analysis, facilitate data interpretation and data mining, and more recently, with the emergence of the knowledge graph paradigm, support the application of Artificial Intelligence methods to unlock new knowledge from a holistic view of the available large volumes of heterogeneous data.

Transcriptome and proteome analysis of the antitumor activity of maslinic acid against pancreatic cancer cells

Maslinic acid (MA) is a triterpenoid compound of natural abundance in olive plants possessing numerous biological activities. The effect and molecular mechanism of MA on pancreatic cancer cells remain elusive. Here, we explored the anti-tumor activity of MA on human pancreatic cancer cells and the potential underlying molecular mechanism. The anti-cancer effects of MA on whole-cell processes, including proliferation, migration, and invasion in pancreatic cancer cells, were systematically assessed by colony formation, transwell, and migration assays. The search for potential therapeutic targets was achieved via transcriptomics and proteomics analyses. MA was demonstrated to inhibit the proliferation, migration, and invasion of PANC-1 and Patu-8988 cells, but induced apoptosis of these cells. Several key candidate genes and proteins of functional relevance for the anti-tumor activity of MA were identified through the association analysis of transcriptomics and proteomics. To our knowledge, this is the first transcription and proteomics-based comprehensive analysis of the mechanism of MA against pancreatic cancer. The findings demonstrate that MA holds promise as a therapeutic drug for managing pancreatic cancer.

RNA Sequencing of Early-Stage Gastric Adenocarcinoma Reveals Multiple Activated Pathways and Novel Long Non-Coding RNAs in Patient Tissue Samples

Background

Gastric cancer is among the most common cancers worldwide that currently lacks effective diagnostic biomarkers and therapeutic targets. Next-generation RNA sequencing is a powerful tool that allows rapid and accurate transcriptome-wide profiling to detect differentially expressed transcripts involved in normal biological and pathological processes. Given the function of this technique, it has the potential to identify new molecular targets for the early diagnosis of disease, particularly in gastric adenocarcinoma.

Methods

In this study, whole-transcriptome analysis was performed with RNA sequencing on tumoral and non-tumoral tissue samples from patients with early-stage gastric cancer. Gene ontology and pathway enrichment analysis were used to determine the main function of the specific genes and pathways present in tissue samples.

Results

Analysis of the differentially expressed genes revealed 5 upregulated and 234 downregulated genes in gastric cancer tissues. Pathway enrichment analysis revealed significantly dysregulated signalling pathways, including those involved in gastric acid secretion, drug metabolism and transporters, molecular toxicology, O-linked glycosylation of mucins, immunotoxicity, metabolism of xenobiotics by cytochrome P450, and glycosylation. We also found novel downregulated non-coding RNAs present in gastric cancer tissues, including GATA6 antisense RNA 1, antisense to LYZ, antisense P4HB, overlapping ACER2, long intergenic non-protein coding RNA 2688 (LINC02688) and uncharacterized LOC25845 (PP7080).

Conclusion

The transcriptomic data found in this study illustrates the power of RNA-sequencing in discovering novel genes ‎and tumorigenic pathways involved in human carcinogenesis. The anomalies present in these genes may serve as promising tools for the development of accurate diagnostic biomarkers for the detection of early-stage gastric cancer.

The effect of sitting position changes from pedaling rehabilitation on muscle activity

Sports injuries or traffic accidents make the individuals bedridden for a long duration, easily causing the disuse of lower limb muscles. Exercise rehabilitation is an effective method to improve muscle activity; however, currently, exercise therapy mainly relies on the experience of rehabilitation physicians for determining the rehabilitation parameters. In this paper, we establish a human-machine coupling system model for disuse atrophy of lower limb muscles. We analyze the influence of sitting position on pedaling rehabilitation. The relationship between the sitting position and muscle effect of lower limb muscle is calculated. We optimized the parameters to analyze muscle force and activity distribution in the muscle group during different sitting positions, and the rehabilitation risk area and the invalid area were identified from the distribution map, which helps quantify the maximal exercise of muscles without causing secondary muscle damage. The mapping relationship between sitting position and muscle force was established in this study. Further, muscle activity mapping is performed for overall assessment. Muscle activity assessment considered the training intensity of small muscles and avoids secondary injury of small muscle. The corresponding designated sitting posture improved the intensity of muscle training and shortened the rehabilitation cycle. Systematic distribution areas for different rehabilitation effects in pedal exercises are presented and provide the sitting position distribution areas for patients in the early, middle, and late stages. The proposed model provides theoretical guidance for rehabilitation physicians.

Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche

The existence of progenitors within pancreatic ducts has been studied for decades, but the hypothesis that they may help regenerate the adult endocrine compartment (chiefly insulin-producing β-cells) remains contentious. Here, we examine the single-cell transcriptome of the human ductal tree. Our data confirm the paradigm-shifting notion that specific lineages, long thought to be cast in stone, are in fact in a state of flux between differentiation stages. In addition to pro-ductal and pro-acinar transcriptomic gradients, our analysis suggests the existence of a third (ducto-endocrine) differentiation axis. Such prediction was experimentally validated by transplanting sorted progenitor-like cells, which revealed their tri-lineage differentiation potential. Our findings further indicate that progenitors might be activated in situ for therapeutic purposes.

We have described multipotent progenitor-like cells within the major pancreatic ducts (MPDs) of the human pancreas. They express PDX1, its surrogate surface marker P2RY1, and the bone morphogenetic protein (BMP) receptor 1A (BMPR1A)/activin-like kinase 3 (ALK3), but not carbonic anhydrase II (CAII). Here we report the single-cell RNA sequencing (scRNA-seq) of ALK3^bright+-sorted ductal cells, a fraction that harbors BMP-responsive progenitor-like cells. Our analysis unveiled the existence of multiple subpopulations along two major axes, one that encompasses a gradient of ductal cell differentiation stages, and another featuring cells with transitional phenotypes toward acinar tissue. A third potential ducto-endocrine axis is revealed upon integration of the ALK3^bright+ dataset with a single-cell whole-pancreas transcriptome. When transplanted into immunodeficient mice, P2RY1⁺/ALK3^bright+ populations (enriched in PDX1⁺/ALK3⁺/CAII⁻ cells) differentiate into all pancreatic lineages, including functional β-cells. This process is accelerated when hosts are treated systemically with an ALK3 agonist. We found PDX1⁺/ALK3⁺/CAII⁻ progenitor-like cells in the MPDs of types 1 and 2 diabetes donors, regardless of the duration of the disease. Our findings open the door to the pharmacological activation of progenitor cells in situ.

H3K4me3, H3K9ac, H3K27ac, H3K27me3 and H3K9me3 Histone Tags Suggest Distinct Regulatory Evolution of Open and Condensed Chromatin Landmarks

Background: Transposons are selfish genetic elements that self-reproduce in host DNA. They were active during evolutionary history and now occupy almost half of mammalian genomes. Close insertions of transposons reshaped structure and regulation of many genes considerably. Co-evolution of transposons and host DNA frequently results in the formation of new regulatory regions. Previously we published a concept that the proportion of functional features held by transposons positively correlates with the rate of regulatory evolution of the respective genes. Methods: We ranked human genes and molecular pathways according to their regulatory evolution rates based on high throughput genome-wide data on five histone modifications (H3K4me3, H3K9ac, H3K27ac, H3K27me3, H3K9me3) linked with transposons for five human cell lines. Results: Based on the total of approximately 1.5 million histone tags, we ranked regulatory evolution rates for 25075 human genes and 3121 molecular pathways and identified groups of molecular processes that showed signs of either fast or slow regulatory evolution. However, histone tags showed different regulatory patterns and formed two distinct clusters: promoter/active chromatin tags (H3K4me3, H3K9ac, H3K27ac) vs. heterochromatin tags (H3K27me3, H3K9me3). Conclusion: In humans, transposon-linked histone marks evolved in a coordinated way depending on their functional roles.

Sequential feature selection and inference using multi-variate random forests

Motivation

Random forest (RF) has become a widely popular prediction generating mechanism. Its strength lies in its flexibility, interpretability and ability to handle large number of features, typically larger than the sample size. However, this methodology is of limited use if one wishes to identify statistically significant features. Several ranking schemes are available that provide information on the relative importance of the features, but there is a paucity of general inferential mechanism, particularly in a multi-variate set up. We use the conditional inference tree framework to generate a RF where features are deleted sequentially based on explicit hypothesis testing. The resulting sequential algorithm offers an inferentially justifiable, but model-free, variable selection procedure. Significant features are then used to generate predictive RF. An added advantage of our methodology is that both variable selection and prediction are based on conditional inference framework and hence are coherent.

Results

We illustrate the performance of our Sequential Multi-Response Feature Selection approach through simulation studies and finally apply this methodology on Genomics of Drug Sensitivity for Cancer dataset to identify genetic characteristics that significantly impact drug sensitivities. Significant set of predictors obtained from our method are further validated from biological perspective.

Availability and implementation

https://github.com/jomayer/SMuRF.

Contact

souparno.ghosh@ttu.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Retroelement-Linked Transcription Factor Binding Patterns Point to Quickly Developing Molecular Pathways in Human Evolution

Background: Retroelements (REs) are transposable elements occupying ~40% of the human genome that can regulate genes by providing transcription factor binding sites (TFBS). RE-linked TFBS profile can serve as a marker of gene transcriptional regulation evolution. This approach allows for interrogating the regulatory evolution of organisms with RE-rich genomes. We aimed to characterize the evolution of transcriptional regulation for human genes and molecular pathways using RE-linked TFBS accumulation as a metric. Methods: We characterized human genes and molecular pathways either enriched or deficient in RE-linked TFBS regulation. We used ENCODE database with mapped TFBS for 563 transcription factors in 13 human cell lines. For 24,389 genes and 3124 molecular pathways, we calculated the score of RE-linked TFBS regulation reflecting the regulatory evolution rate at the level of individual genes and molecular pathways. Results: The major groups enriched by RE regulation deal with gene regulation by microRNAs, olfaction, color vision, fertilization, cellular immune response, and amino acids and fatty acids metabolism and detoxication. The deficient groups were involved in translation, RNA transcription and processing, chromatin organization, and molecular signaling. Conclusion: We identified genes and molecular processes that have characteristics of especially high or low evolutionary rates at the level of RE-linked TFBS regulation in human lineage.