Upregulated expression of SAC3D1 is associated with progression in gastric cancer

Increased LACTB2 Expression Regulates Oxidative Phosphorylation and mTORC1 Signaling of Colorectal Cancer

This study aimed to investigate the mechanisms of LACTB2 in colorectal cancer (CRC). Microarrays and sequencing data of CRC were acquired from UCSC Xena, GTEx, Gene Expression Omnibus, and TCGA. Pooled analysis of the mRNA expression of LACTB2 in CRC was performed using Stata software. The protein expression of LACTB2 in CRC tissues was evaluated by immunohistochemistry. The relationship between immune cell infiltration and LACTB2 expression was investigated using CIBERSORT. The potential signaling pathways and biological mechanisms of LACTB2 were explored using GSEA, KEGG, and GO. Subsequently, further screening of small molecular compounds with potential therapeutic effects on CRC was conducted through the HERB database, followed by molecular docking studies of these compounds with the LACTB2 protein. The integration and analysis of expression data obtained from 2294 CRC samples and 1286 noncancerous colorectal samples showed that LACTB2 was highly expressed in CRC. Immunohistochemistry performed on in-house tissue samples confirmed that LACTB2 protein expression was upregulated in CRC. CIBERSORT revealed lower B cell infiltration levels in the high LACTB2 expression group than in the low expression group. GO, KEGG, and GSEA analyses showed that LACTB2 expression and genes positively correlating with it were mainly related to DNA synthesis and repair, mitochondrial translational elongation and translational termination, phosphorylation, and mTORC1 signaling. Finally, molecular docking simulations confirmed the ability of quercitin to target and bind to LACTB2. This is the first study to demonstrate that LACTB2 is upregulated in CRC. LACTB2 promotes colorectal tumorigenesis and tumor progression.

DMOIT: denoised multi-omics integration approach based on transformer multi-head self-attention mechanism

Multi-omics data integration has become increasingly crucial for a deeper understanding of the complexity of biological systems. However, effectively integrating and analyzing multi-omics data remains challenging due to their heterogeneity and high dimensionality. Existing methods often struggle with noise, redundant features, and the complex interactions between different omics layers, leading to suboptimal performance. Additionally, they face difficulties in adequately capturing intra-omics interactions due to simplistic concatenation techiniques, and they risk losing critical inter-omics interaction information when using hierarchical attention layers. To address these challenges, we propose a novel Denoised Multi-Omics Integration approach that leverages the Transformer multi-head self-attention mechanism (DMOIT). DMOIT consists of three key modules: a generative adversarial imputation network for handling missing values, a sampling-based robust feature selection module to reduce noise and redundant features, and a multi-head self-attention (MHSA) based feature extractor with a noval architecture that enchance the intra-omics interaction capture. We validated model porformance using cancer datasets from the Cancer Genome Atlas (TCGA), conducting two tasks: survival time classification across different cancer types and estrogen receptor status classification for breast cancer. Our results show that DMOIT outperforms traditional machine learning methods and the state-of-the-art integration method MoGCN in terms of accuracy and weighted F1 score. Furthermore, we compared DMOIT with various alternative MHSA-based architectures to further validate our approach. Our results show that DMOIT consistently outperforms these models across various cancer types and different omics combinations. The strong performance and robustness of DMOIT demonstrate its potential as a valuable tool for integrating multi-omics data across various applications.

Conduction and validation of a novel mitotic spindle assembly related signature in hepatocellular carcinoma: prognostic prediction, tumor immune microenvironment and drug susceptibility

Introduction: We have developed a risk-scoring model using gene expression levels related to mitotic spindle assembly (MSA) to predict the prognosis of liver cancer. Methods and results: Initially, we identified 470 genes related to MSA from public databases. Subsequently, through analysis of sequencing data from liver cancer patient samples in online databases, we identified 7 genes suitable for constructing the risk-scoring model. We validated the predictive accuracy and clinical utility of the model. Through drug sensitivity analysis, we identified SAC3D1 as a gene sensitive to the most common anti-tumor drugs among these 7 genes. We propose SAC3D1 as a significant target for future clinical treatment. Furthermore, we conducted in vivo and in vitro experiments to validate the relevance of SAC3D1 to MSA and found its significant impact on the PI3K/Akt signaling pathway and spindle function. Conclusion: Our research introduces a novel risk-scoring model that accurately predicts liver cancer prognosis. Additionally, our findings suggest SAC3D1 as a promising therapeutic target for hepatocellular carcinoma, potentially revealing new mechanisms underlying liver cancer development.

circRPS19 affects HK2‑mediated aerobic glycolysis and cell viability via the miR‑125a‑5p/USP7 pathway in gastric cancer

Despite advances in diagnosis and treatment, gastric cancer (GC) remains a refractory disease, which limits overall survival. Therefore, it is key to identify novel targets to develop more effective and precise treatment. Circular RNAs (circRNAs) serve essential roles in the process of various human cancers. Through analyzing GSE83521 dataset, the present study identified a novel circRNA derived from ribosomal protein S19 (circRPS19), which was considered a potential treatment target for GC. Results of RT‑qPCR indicated that circRPS19 was upregulated in GC compared with normal gastric epithelial cells. Loss‑of function assays revealed that silencing of circRPS19 suppressed proliferation and aerobic glycolysis but increased apoptosis of GC cells. circRPS19 upregulated ubiquitin‑specific processing protease 7 (USP7) expression by sponging microRNA (miR)‑125a‑5p. circRPS19 stabilized hexokinase 2 (HK2) protein by USP7‑mediated deubiquitination of HK2. In vivo experiments confirmed that circRPS19 promoted GC progression and aerobic glycolysis. Taken together, circRPS19 induced aerobic glycolysis of GC cells by stabilizing HK2 protein via the miR‑125a‑5p/USP7 axis and thus promoting the progression of GC. These findings suggested that circRPS19 served a critical role in the progression of GC and may be a novel therapeutic target for GC.

Potential Molecular Mechanism of Upregulated Aryl Hydrocarbon Receptor Nuclear Translocator 2 in Nasopharyngeal Carcinoma

Background. Currently, the benefits of nasopharyngeal carcinoma (NPC) therapy are limited, and it is necessary to further explore possible therapeutic targets. Aryl hydrocarbon receptor nuclear translocator 2 (ARNT2) has been extensively studied in other cancer species, but little has been explored in NPC. The aim of this study was to verify the expression level of ARNT2 and its underlying mechanism in NPC. Methods. Datasets containing ARNT2 mRNA expression levels were retrieved and collected from various databases to explore the expression status of ARNT2 in NPC. ARNT2-related coexpressed genes, differential expressed genes, and target genes were obtained for functional enrichment analysis. The potential target gene of ARNT2 and their regulatory relationship were studied through ChIP-seq data. CIBERSORTx was used to assess the immune infiltration of NPC, and the association with ARNT2 expression was calculated through correlation analysis. Results. ARNT2 was upregulated and possessed an excellent discriminatory capability in NPC samples. ARNT2 positively correlated target genes were clustered in pathways in cancer, while negatively correlated target genes were enriched in immune-related pathway. The ChIP-seq information of ARNT2 and histone showed that prostaglandin-endoperoxide synthase 2 (PTGS2) was a potential target gene of ARNT2. CIBERSORTx revealed the immunity status in NPC, and ARNT2 expression was correlated with infiltration of five immune cells. Conclusions. ARNT2 is overexpressed in NPC and may regulate PTGS2 to participate in the cancer process. ARNT2 serves as a key oncogenic target in NPC patients.

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages

Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.

Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.

Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.

Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets.

Expression and Clinical Significance of BCL2 Interacting Protein 3 Like in Multiple Myeloma

Multiple myeloma (MM) is one of the main blood disorders threatening human health today. This study aimed to examine the expression of BCL-2/adenovirus E1B 19 kDa-interacting protein 3-like (BNIP3L) in patients with MM and explore its mechanisms in silico. Bone marrow samples (n = 36 from patients with MM and n = 12 from healthy donors) were used to conduct BNIP3L expression analysis using immunohistochemistry. Microarray or RNA sequencing data from the Sequence Read Archive, Gene Expression Omnibus, and ArrayExpress databases were used to appraise BNIP3L expression and its prognostic role in patients with MM. The co-expressed genes of BNIP3L were identified for enrichment and protein-protein interaction (PPI) analyses to determine the associated signaling pathways. Immunohistochemistry indicated that BNIP3L expression in bone marrow of patients with MM was significantly lower than that in bone marrow of healthy donors. BNIP3L mRNA expression was also significantly lower in patients with MM than in healthy donors. The overall standard mean difference (SMD) for downregulation of BNIP3L was −0.62 [−1.17, −0.06], and the area under the curve was 0.81 [0.78, 0.85] based on a total of 694 MM cases. The overall survival analysis demonstrated that BNIP3L levels could act as an independent protective indicator of MM patient survival (HR = 0.79). Moreover, 261 co-expressed genes of BNIP3L were confirmed and found to be mainly involved in the adipocytokine signaling pathway. We preliminarily proved that downregulation of BNIP3L may play an important role in the occurrence and development of MM, and the promoting cancer capacity may be related to the pathway of adipocytokine signaling pathway.

The Xmas-2 Homologues, the Main Component of the TREX-2 mRNA Export Complex

TREX-2 complex is responsible for general mRNA export from nucleus to cytoplasm in eukaryote. The main protein of TREX-2 complex of D. melanogaster is protein Xmas-2. Its homologues in yeast and humans are Sac3 and GANP proteins, respectively. All three proteins contain the highly conserved domain Sac3-GANP, which is essential for interaction of TREX-2 complex with mRNA and another protein of the complex, PCID2. We identified two Xmas-2 homologues in D. melanogaster using the Sac3-GANP family domain sequence. These proteins have a common domain responsible for interaction with the PCID2 protein and RNA and are present in other eukaryotes. The function of these proteins is unknown. However, on the basis of their structural organization, we can assume that they interact with nucleic acids.