International Journal of Genomics

Background. The genetic factors and pathogenesis of idiopathic dilated cardiomyopathy-induced heart failure (IDCM-HF) have not been understood thoroughly; there is a lack of specific diagnostic markers and treatment methods for the disease. Hence, we aimed to identify the mechanisms of action at the molecular level and potential molecular markers for this disease. Methods. Gene expression profiles of IDCM-HF and non-heart failure (NF) specimens were acquired from the database of Gene Expression Omnibus (GEO). We then identified the differentially expressed genes (DEGs) and analyzed their functions and related pathways by using “Metascape”. Weighted gene co-expression network analysis (WGCNA) was utilized to search for key module genes. Candidate genes were identified by intersecting the key module genes identified via WGCNA with DEGs and further screened via the support vector machine-recursive feature elimination (SVM-RFE) method and the least absolute shrinkage and selection operator (LASSO) algorithm. At last, the biomarkers were validated and evaluated the diagnostic efficacy by the area under curve (AUC) value and further confirmed the differential expression in the IDCM-HF and NF groups using an external database. Results. We detected 490 genes exhibiting differential expression between IDCM-HF and NF specimens from the GSE57338 dataset, with most of them being concentrated in the extracellular matrix (ECM) of cells related to biological processes and pathways. After screening, 13 candidate genes were identified. Aquaporin 3 (AQP3) and cytochrome P450 2J2 (CYP2J2) showed high diagnostic efficacy in the GSE57338 and GSE6406 datasets, respectively. In comparison to the NF group, AQP3 was significantly down-regulated in the IDCM-HF group, while CYP2J2 was significantly up-regulated. Conclusion. As far as we know, this is the first study that combines WGCNA and machine learning algorithms to screen for potential biomarkers of IDCM-HF. Our findings suggest that AQP3 and CYP2J2 could be used as novel diagnostic markers and treatment targets of IDCM-HF.

Vascular smooth muscle cells (VSMCs) are critical elements of the vascular wall and play a crucial role in the genesis and development of atherosclerosis (AS). Increasingly, studies have indicated that long noncoding RNAs (lncRNAs) regulate VSMC proliferation, apoptosis, and other biological processes. Nevertheless, the role of lncRNA NFIA-AS1 (hereinafter referred to as NFIA-AS1) in VSMCs and AS remains unclear. Quantitative real-time PCR (qRT-PCR) was performed to analyze the messenger RNA (mRNA) levels of NFIA-AS1 and miR-125a-3p. CCK-8 and EdU staining were performed to detect VSMC proliferation. VSMC apoptosis was evaluated by flow cytometry. The expression of various proteins was detected using western blotting. The levels of inflammatory cytokines secreted by VSMCs were measured by enzyme linked immunosorbent assay (ELISA). The binding sites of NFIA-AS1 and miR-125a-3p, as well as miR-125a-3p and AKT1, were analyzed using bioinformatics methods and validated using a luciferase reporter assay. The function of NFIA-AS1/miR-125a-3p/AKT1 in VSMCs was clarified through loss- and gain-of-functional experiments. We confirmed that NFIA-AS1 was highly expressed in AS tissues and VSMCs induced by oxidized low-density lipoprotein (Ox-LDL). Knockdown of NFIA-AS1 restrained the exceptional growth of Ox-LDL-induced VSMCs, promoted their apoptosis, and decreased the secretion of inflammatory factors and expression of adhesion factors. In addition, NFIA-AS1 regulated the proliferation, apoptosis, and inflammatory response of VSMCs through the miR-125a-3p/AKT1 axis, suggesting that NFIA-AS1 may be a potential therapeutic target for AS.

Climate change has become a major source of concern, particularly in agriculture, because it has a significant impact on the production of economically important crops such as wheat, rice, and maize. In the present study, an attempt has been made to identify differentially expressed heat stress-responsive long non-coding RNAs (lncRNAs) in the wheat genome using publicly available wheat transcriptome data (24 SRAs) representing two conditions, namely, control and heat-stressed. A total of 10,965 lncRNAs have been identified and, among them, 153, 143, and 211 differentially expressed transcripts have been found under 0 DAT, 1 DAT, and 4 DAT heat-stress conditions, respectively. Target prediction analysis revealed that 4098 lncRNAs were targeted by 119 different miRNA responses to a plethora of environmental stresses, including heat stress. A total of 171 hub genes had 204 SSRs (simple sequence repeats), and a set of target sequences had SNP potential as well. Furthermore, gene ontology analysis revealed that the majority of the discovered lncRNAs are engaged in a variety of cellular and biological processes related to heat stress responses. Furthermore, the modeled three-dimensional (3D) structures of hub genes encoding proteins, which had an appropriate range of similarity with solved structures, provided information on their structural roles. The current study reveals many elements of gene expression regulation in wheat under heat stress, paving the way for the development of improved climate-resilient wheat cultivars.

This study searched for aging-related genes (ARGs) to predict the prognosis of patients with cervical cancer (CC). All data were obtained from Molecular Signatures Database, Cancer Genome Atlas, Gene Expression Integration, and Genotype Organization Expression. The R software was used to screen out the differentially expressed ARGs (DE-ARGs) between CC and normal tissues. A protein–protein interaction network was established by the DE-ARGs. The univariate and multivariate Cox regression analyses were conducted on the first extracted Molecular Complex Detection component, and a prognostic model was constructed. The prognostic model was further validated in the testing set and GSE44001 dataset. Prognosis was analyzed by Kaplan–Meier curves, and accuracy of the prognostic model was assessed by receiver operating characteristic area under the curve analysis. An independent prognostic analysis of risk score and some clinicopathological factors of CC was also performed. The copy-number variant (CNV) and single-nucleotide variant (SNV) of prognostic ARGs were analyzed by the BioPortal database. A clinical practical nomogram was established to predict individual survival probability. Finally, we carried out cell experiment to further verify the prognostic model. An eight-ARG prognostic signature for CC was constructed. High-risk CC patients had significantly shorter overall survival than low-risk patients. The receiver operating characteristic (ROC) curve validated the good performance of the signature in survival prediction. The Figo_stage and risk score served as independent prognostic factors. The eight ARGs mainly enriched in growth factor regulation and cell cycle pathway, and the deep deletion of FN1 was the most common CNV. An eight-ARG prognostic signature for CC was successfully constructed.

Background. Lung cancer is considered to be the second most aggressive and rapidly fatal cancer after breast cancer. Necroptosis, a novel discovered pattern of cell death, is mediated by Receptor-interacting serine/threonine-protein kinase 1 (RIPK1), Receptor-interacting serine/threonine-protein kinase 3 (RIPK3), and Mixed Lineage Kinase Domain Like Pseudokinase (MLKL). Methods. For the purpose of developing a prognostic model, Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was conducted. Using Pearson’s correlation analysis, we evaluated the correlation between necroptosis-related markers and tumor immune infiltration. A bioinformatics analysis was conducted to construct a necroptosis-related regulatory axis. Results. There was a downregulation of most of necroptosis-related genes in lung adenocarcinoma (LUAD) versus lung tissues but an increase in PGAM5, HMGB1, TRAF2, EZH2 levels. We also summarized the Single Nucleotide Variant (SNV) and copy number variation (CNV) of necroptosis-related genes in LUAD. Consensus clustering identified two clusters in LUAD with distinct immune cell infiltration and ESTIMATEScore. Genes related to necroptosis were associated with necroptosis, Tumor necrosis factor (TNF) signaling pathway, and apoptosis according to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Four prognostic genes (ALDH2, HMGB1, NDRG2, TLR2) were combined to develop a prognostic gene signature for LUAD patients, which was highly accurate in predicting prognosis. Univariate and multivariate analysis identified HMGB1, pT stage, and pN stage as independent factors impacting on LUAD patients’ prognosis. A significant correlation was found between the level of TLR2 and NDRG2 and clinical stage, immunity infiltration, and drug resistance. Additionally, the progression of LUAD might be regulated by lncRNA C5orf64/miR-582-5p/NDRG2/TLR2. Conclusion. The current bioinformatics analysis identified a necroptosis-related prognostic signature for LUAD and their relation to immunity infiltration. This result requires further investigation.

Background. Primary liver cancer, dominated by hepatocellular carcinoma (HCC), is one of the most common cancer types and the third leading cause of cancer death in 2020. Previous studies have shown that liquid–liquid phase separation (LLPS) plays an important role in the occurrence and development of cancer including HCC, but its influence on the patient prognosis is still unknown. It is necessary to explore the effect of LLPS genes on prognosis to accurately forecast the prognosis of HCC patients and identify relevant targeted therapeutic sites. Methods. Using The Cancer Genome Atlas dataset and PhaSepDB dataset, we identified LLPS genes linked to the overall survival (OS) of HCC patients. We applied Least Absolute Shrinkage and Selection Operator (LASSO) Cox penalized regression analysis to choose the best genes for the risk score prognostic signature. We then analysed the validation dataset and evaluated the effectiveness of the risk score prognostic signature. Finally, we performed quantitative real-time PCR experiments to validate the genes in the prognostic signature. Results. We identified 43 differentially expressed LLPS genes that were associated with the OS of HCC patients. Five of these genes (BMX, FYN, KPNA2, PFKFB4, and SPP1) were selected to generate a prognostic risk score signature. Patients in the low-risk group were associated with better OS than those in the high-risk group in both the training dataset and the validation dataset. We found that BMX and FYN had lower expression levels in HCC tumour tissues, whereas KPNA2, PFKFB4, and SPP1 had higher expression levels in HCC tumour tissues. The validation demonstrated that the five-LLPS gene risk score signature has the capability of predicting the OS of HCC patients. Conclusion. Our study constructed a five-LLPS gene risk score signature that can be applied as an effective and convenient prognostic tool. These five genes might serve as potential targets for therapy and the treatment of HCC.