Overexpression of DGKI in Gastric Cancer Predicts Poor Prognosis

Integration of transcriptomics and metabolomics analysis for unveiling the toxicological profile in the liver of mice exposed to uranium in drinking water

Uranium is a contaminate in the underground water in many regions of the world, which poses health risks to the local populations through drinking water. Although the health hazards of natural uranium have been concerned for decades, the controversies about its detrimental effects continue at present since it is still unclear how uranium interacts with molecular regulatory networks to generate toxicity. Here, we integrate transcriptomic and metabolomic methods to unveil the molecular mechanism of lipid metabolism disorder induced by uranium. Following exposure to uranium in drinking water for twenty-eight days, aberrant lipid metabolism and lipogenesis were found in the liver, accompanied with aggravated lipid peroxidation and an increase in dead cells. Multi-omics analysis reveals that uranium can promote the biosynthesis of unsaturated fatty acids through dysregulating the metabolism of arachidonic acid (AA), linoleic acid, and glycerophospholipid. Most notably, the disordered metabolism of polyunsaturated fatty acids (PUFAs) like AA may contribute to lipid peroxidation induced by uranium, which in turn triggers ferroptosis in hepatocytes. Our findings highlight disorder of lipid metabolism as an essential toxicological mechanism of uranium in the liver, offering insight into the health risks of uranium in drinking water.

Key molecules associated with thyroid carcinoma prognosis: A study based on transcriptome sequencing and GEO datasets

Background

Thyroid carcinoma (THCA) has a low mortality rate, but its incidence has been rising over the years. We need to pay attention to its progression and prognosis. In this study, a transcriptome sequencing analysis and bioinformatics methods were used to screen key genes associated with THCA development and analyse their clinical significance and diagnostic value.

Methods

We collected 10 pairs of THCA tissues and noncancerous tissues, these samples were used for transcriptome sequencing to identify disordered genes. The gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Comprehensive analysis of thyroid clinicopathological data using The Cancer Genome Atlas (TCGA). R software was used to carry out background correction, normalization and log2 conversion. We used quantitative real-time PCR (qRT–PCR) and Western blot to determine differentially expressed genes (DEGs) expression in samples. We integrated the DEGs expression, clinical features and progression-free interval (PFI). The related functions and immune infiltration degree were established by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single-sample Gene Set Enrichment Analysis (ssGSEA). The UALCAN database was used to analyse the methylation level.

Results

We evaluated DEGs between normal tissue and cancer. Three genes were identified: regulator of G protein signaling 8 (RGS8), diacylglycerol kinase iota (DGKI) and oculocutaneous albinism II (OCA2). The mRNA and protein expression levels of RGS8, DGKI and OCA2 in normal tissues were higher than those in THCA tissues. Better survival outcomes were associated with higher expression of RGS8 (HR=0.38, P=0.001), DGKI (HR=0.52, P=0.022), and OCA2 (HR=0.41, P=0.003). The GO analysis, KEGG analysis and GSEA proved that the coexpressed genes of RGS8, DGKI and OCA2 were related to thyroid hormone production and peripheral downstream signal transduction effects. The expression levels of RGS8, DGKI and OCA2 were linked to the infiltration of immune cells such as DC cells. The DNA methylation level of OCA2 in cancer tissues was higher than that in the normal samples.

Conclusions

RGS8, DGKI and OCA2 might be promising prognostic molecular markers in patients with THCA and reveal the clinical significance of RGS8, DGKI and OCA2 in THCA.

Characterization of Platelet Function-Related Gene Predicting Survival and Immunotherapy Efficacy in Gastric Cancer

Immunotherapy is widely used to treat various cancers, but patients with gastric cancer (GC), which has a high mortality rate, benefit relatively less from this therapy. Platelets are closely related to GC progression and metastasis. This study aimed to find novel potential biomarkers related to platelet function to predict GC and immunotherapy efficacy. First, based on platelet activation, signaling, and aggregation (abbreviation: function)-related genes (PFRGs), we used the least absolute shrinkage and selection operator (Lasso) regression method to construct a platelet-function-related genes prognostic score (PFRGPS). PRFGPS was verified in three independent external datasets (GSE26901, GSE15459, and GSE84437) for its robustness and strong prediction performance. Our results demonstrate that PRFGPS is an independent prognostic indicator for predicting overall survival in patients with GC. In addition, prognosis, potential pathogenesis mechanisms, and the response to immunotherapy were defined via gene set enrichment analysis, tumor mutational burden, tumor microenvironment, tumor immune dysfunction and exclusion (TIDE), microsatellite instability, and immune checkpoint inhibitors. We found that the high-PRFGPS subgroup had a cancer-friendly immune microenvironment, a high TIDE score, a low tumor mutational burden, and relatively low microsatellite instability. In the immunophenoscore model, the therapeutic effect on anti-PD-1 and anti-CTLA-4 in the high-PRFGPS subgroup was relatively low. In conclusion, PRFGPS could be used as a reference index for GC prognosis to develop more successful immunotherapy strategies.

Overarching roles of diacylglycerol signaling in cancer development and antitumor immunity

Diacylglycerol (DAG) is a lipid second messenger that is generated in response to extracellular stimuli and channels intracellular signals that affect mammalian cell proliferation, survival, and motility. DAG exerts a myriad of biological functions through protein kinase C (PKC) and other effectors, such as protein kinase D (PKD) isozymes and small GTPase-regulating proteins (such as RasGRPs). Imbalances in the fine-tuned homeostasis between DAG generation by phospholipase C (PLC) enzymes and termination by DAG kinases (DGKs), as well as dysregulation in the activity or abundance of DAG effectors, have been widely associated with tumor initiation, progression, and metastasis. DAG is also a key orchestrator of T cell function and thus plays a major role in tumor immunosurveillance. In addition, DAG pathways shape the tumor ecosystem by arbitrating the complex, dynamic interaction between cancer cells and the immune landscape, hence representing powerful modifiers of immune checkpoint and adoptive T cell-directed immunotherapy. Exploiting the wide spectrum of DAG signals from an integrated perspective could underscore meaningful advances in targeted cancer therapy.

mRBioM: An Algorithm for the Identification of Potential mRNA Biomarkers From Complete Transcriptomic Profiles of Gastric Adenocarcinoma

Purpose

In this work, an algorithm named mRBioM was developed for the identification of potential mRNA biomarkers (PmBs) from complete transcriptomic RNA profiles of gastric adenocarcinoma (GA).

Methods

mRBioM initially extracts differentially expressed (DE) RNAs (mRNAs, miRNAs, and lncRNAs). Next, mRBioM calculates the total information amount of each DE mRNA based on the coexpression network, including three types of RNAs and the protein-protein interaction network encoded by DE mRNAs. Finally, PmBs were identified according to the variation trend of total information amount of all DE mRNAs. Four PmB-based classifiers without learning and with learning were designed to discriminate the sample types to confirm the reliability of PmBs identified by mRBioM. PmB-based survival analysis was performed. Finally, three other cancer datasets were used to confirm the generalization ability of mRBioM.

Results

mRBioM identified 55 PmBs (41 upregulated and 14 downregulated) related to GA. The list included thirteen PmBs that have been verified as biomarkers or potential therapeutic targets of gastric cancer, and some PmBs were newly identified. Most PmBs were primarily enriched in the pathways closely related to the occurrence and development of gastric cancer. Cancer-related factors without learning achieved sensitivity, specificity, and accuracy of 0.90, 1, and 0.90, respectively, in the classification of the GA and control samples. Average accuracy, sensitivity, and specificity of the three classifiers with machine learning ranged within 0.94–0.98, 0.94–0.97, and 0.97–1, respectively. The prognostic risk score model constructed by 4 PmBs was able to correctly and significantly (^∗∗∗p < 0.001) classify 269 GA patients into the high-risk (n = 134) and low-risk (n = 135) groups. GA equivalent classification performance was achieved using the complete transcriptomic RNA profiles of colon adenocarcinoma, lung adenocarcinoma, and hepatocellular carcinoma using PmBs identified by mRBioM.

Conclusions

GA-related PmBs have high specificity and sensitivity and strong prognostic risk prediction. MRBioM has also good generalization. These PmBs may have good application prospects for early diagnosis of GA and may help to elucidate the mechanism governing the occurrence and development of GA. Additionally, mRBioM is expected to be applied for the identification of other cancer-related biomarkers.