The rapid advancement of next-generation sequencing (NGS) technology and the expanding availability of NGS datasets have led to a significant surge in biomedical research. To better understand the molecular processes, underlying cancer and to support its development, diagnosis, prediction, and therapy; NGS data analysis is crucial. However, the NGS multi-layer omics high-dimensional dataset is highly complex. In recent times, some computational methods have been developed for cancer omics data interpretation. However, various existing methods face challenges in accounting for diverse types of cancer omics data and struggle to effectively extract informative features for the integrated identification of core units. To address these challenges, we proposed a hybrid feature selection (HFS) technique to detect optimal features from multi-layer omics datasets. Subsequently, this study proposes a novel hybrid deep recurrent neural network-based model DOMSCNet to classify stomach cancer. The proposed model was made generic for all four multi-layer omics datasets. To observe the robustness of the DOMSCNet model, the proposed model was validated with eight external datasets. Experimental results showed that the SelectKBest-maximum relevancy minimum redundancy-Boruta (SMB), HFS technique outperformed all other HFS techniques. Across four multi-layer omics datasets and validated datasets, the proposed DOMSCNet model outdid existing classifiers along with other proposed classifiers.

As one member of the Forkhead Box transcription factor, Forkhead Box J2 (FOXJ2) is involved in diverse cancers. At present, the specific role and mechanism of FOXJ2 in ovarian cancer (OC) have not been fully addressed, which allows us to fill the blank.

Accordingly, the expression of FOXJ2 in OC cells and ovarian epithelial cells was quantified via real-time qPCR. Following the transfection, cell counting kit-8, Transwell, wound healing and flow cytometry assays were performed to measure the proliferation, metastasis, apoptosis and cell cycle of OC cells A2780 and HEY. Further, real-time qPCR and Western blotting were both employed for the quantification assays on the expression levels of FOXJ2 as well as phosphoinositide 3-kinase (PI3K) and protein kinase B (AKT) (in both unphosphorylated and phosphorylated forms).

Based on the results, FOXJ2 were highly-expressed in OC cells (P < 0.05). Silencing of FOXJ2 resulted in attenuated OC cell proliferation, reduced numbers of migrating and invading OC cells, decreased apoptotic capacity, and cell cycle arrest in G1/S phase (P < 0.05). In addition, the knockdown of FOXJ2 caused the downward trend on the phosphorylation level of both PI3K and AKT in OC cells (P < 0.05).

The silencing of FOXJ2 could repress the growth and metastasis potentials and cause the cell cycle G1/S arrest of OC cells in vitro, which was possibly achieved via targeting the PI3K/AKT pathway.

Gastric cancer (GC) has a high mortality rate worldwide. Despite significant progress in GC diagnosis and treatment, the prognosis for affected patients still remains unfavorable.

To identify important candidate genes related to the development of GC and identify potential pathogenic mechanisms through comprehensive bioinformatics analysis.

The Gene Expression Omnibus database was used to obtain the GSE183136 dataset, which includes a total of 135 GC samples. The limma package in R software was employed to identify differentially expressed genes (DEGs). Thereafter, enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the gene modules using the clusterProfile package in R software. The protein-protein interaction (PPI) networks of target genes were constructed using STRING and visualized by Cytoscape software. The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram. The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves. Moreover, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase (GPT) in GC and normal immortalized cell lines. In addition, cell viability, cell cycle distribution, migration and invasion were evaluated by cell counting kit-8, flow cytometry and transwell assays. Furthermore, we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020. The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.

We selected 19214 genes from the GSE183136 dataset, among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value < 0.05. In addition, GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction, whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion, vascular smooth muscle contraction and biosynthesis of the different cofactors. Furthermore, PPI networks were constructed based on the various upregulated and downregulated genes, and there were a total 15 upregulated and 10 downregulated hub genes. After a comprehensive analysis, several hub genes, including runt-related transcription factor 2 (RUNX2), salmonella pathogenicity island 1 (SPI1), lysyl oxidase (LOX), fibrillin 1 (FBN1) and GPT, displayed prognostic values. Interestingly, it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells. Furthermore, the expression level of GPT was found to be associated with age, lymph node metastasis, pathological staging and distant metastasis (P < 0.05).

RUNX2, SPI1, LOX, FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis. GPT was significantly associated with the prognosis of GC, and its upregulation can effectively inhibit the proliferative, migrative and invasive capabilities of GC cells.

Runt-related transcription factors (RUNX) are a family of transcription factors that are essential for normal and malignant hematopoietic processes. Their most widely recognized role in malignancy is to promote the occurrence and development of acute myeloid leukemia. However, it is worth noting that during the last decade, studies of RUNX proteins in solid tumors have made considerable progress, suggesting that these proteins are directly involved in different stages of tumor development, including tumor initiation, progression, and invasion. RUNX proteins also play a role in tumor angiogenesis, the maintenance of tumor cell stemness, and resistance to antitumor drugs. These findings have led to the consideration of RUNX as a tumor biomarker. All RUNX proteins are involved in the occurrence and development of solid tumors, but the role of each RUNX protein in different tumors and the major signaling pathways involved are complicated by tumor heterogeneity and the interacting tumor microenvironment. Understanding how the dysregulation of RUNX in tumors affects normal biological processes is important to elucidate the molecular mechanisms by which RUNX affects malignant tumors.

The RUNX2 transcription factor was discovered as an essential transcriptional regulator for commitment to osteoblast lineage cells and bone formation. Expression of RUNX2 in other tissues, such as breast, prostate, and lung, has been linked to oncogenesis, cancer progression, and metastasis. In this study, we sought to determine the extent of RUNX2 involvement in other tumors using a pan-cancer analysis strategy. We correlated RUNX2 expression and clinical-pathological parameters in human cancers by interrogating publicly available multiparameter clinical data. Our analysis demonstrated that altered RUNX2 expression or function is associated with several cancer types from different tissues. We identified three tumor types associated with increased RUNX2 expression and four other tumor types associated with decreased RUNX2 expression. Our pan-cancer analysis for RUNX2 revealed numerous other discoveries for RUNX2 regulation of different cancers identified in each of the pan-cancer databases. Both up and down regulation of RUNX2 was observed during progression of specific types of cancers in promoting the distinct types of cancers.