CCNB2 is a novel prognostic factor and a potential therapeutic target in Low-grade glioma (LGG)

Exploration of biomarkers for nursing physical examination early screening of multiple tumors

Nursing and physical examination early screening of multiple tumors is helpful to find tumors early, so as to improve the cure rate. Studying its molecular mechanisms is urgent. By logging into gene expression omnibus database, we found laryngeal cancer dataset GSE127165, bladder cancer dataset GSE65635, oral cancer dataset GSE146483, obtain differentially expressed genes, subsequently, weighted gene co-expression network analysis, protein-protein interaction networks, functional enrichment analysis, immune infiltration analysis, survival analysis, comparative toxicogenomics database analysis were conducted. Draw a heatmap of gene expression. Use targetScan to search for miRNA information about core DEG. Got 53 differentially expressed genes. In GOKEGG analysis, they were clustered in cell cycle processes, spindle poles, and protein serine/threonine/tyrosine kinase activity cell cycle, transcriptional dysregulation in cancer, RIG-I-like receptor signaling pathway, P53 signaling pathway. Protein-protein interaction analysis screened out 5 genes (NEK2, BUB1, HMMR, TTK, CCNB2). Cyclin B2 (CCNB2) and budding uninhibited by benzimidazole 1 (BUB1) were highly expressed in laryngeal cancer, bladder cancer, oral cancer. Comparative toxicogenomics database analysis found that core genes (CCNB2, BUB1) are associated with tumors, necrosis, and inflammation. Related miRNA of CCNB2 gene is hsa-miR-670-3p; related miRNAs of BUB1 gene are hsa-miR-5688, hsa-miR-495-3p. CCNB2 and BUB1 exhibit high expression in laryngeal cancer, bladder cancer, and oral cancer, suggesting their potential as molecular targets for precision therapy in these cancers.

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear.

METHODS

This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein-protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC.

RESULTS

A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein-protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC.

CONCLUSION

This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.

Origin recognition complex 6 overexpression promotes growth of glioma cells

The discovery of novel oncotargets for glioma is of immense significance. We here explored the expression patterns, biological functions, and underlying mechanisms associated with ORC6 (origin recognition complex 6) in glioma. Through the bioinformatics analyses, we found a significant increase in ORC6 expression within human glioma tissues, correlating with poorer overall survival, higher tumor grade, and wild-type isocitrate dehydrogenase status. Additionally, ORC6 overexpression is detected in glioma tissues obtained from locally-treated patients and across various primary/established glioma cells. Further bioinformatics scrutiny revealed that genes co-expressed with ORC6 are enriched in multiple signaling cascades linked to cancer. In primary and immortalized (A172) glioma cells, depleting ORC6 using specific shRNA or Cas9-sgRNA knockout (KO) significantly decreased cell viability and proliferation, disrupted cell cycle progression and mobility, and triggered apoptosis. Conversely, enhancing ORC6 expression via a lentiviral construct augmented malignant behaviors in human glioma cells. ORC6 emerged as a crucial regulator for the expression of key oncogenic genes, including Cyclin A2, Cyclin B2, and DNA topoisomerase II (TOP2A), within glioma cells. Silencing or KO of ORC6 reduced the mRNA and protein levels of these genes, while overexpression of ORC6 increased their expression in primary glioma cells. Bioinformatics analyses further identified RBPJ as a potential transcription factor of ORC6. RBPJ shRNA decreased ORC6 expression in primary glioma cells, while its overexpression increased it. Additionally, significantly enhanced binding between the RBPJ protein and the proposed ORC6 promoter region was detected in glioma tissues and cells. In vivo experiments demonstrated a significant reduction in the growth of patient-derived glioma xenografts in the mouse brain subsequent to ORC6 KO. ORC6 depletion, inhibited proliferation, decreased expression of Cyclin A2/B2/TOP2A, and increased apoptosis were detected within these ORC6 KO intracranial glioma xenografts. Altogether, RBPJ-driven ORC6 overexpression promotes glioma cell growth, underscoring its significance as a promising therapeutic target.

Transcriptome Analysis of Beta-Catenin-Related Genes in CD34+ Haematopoietic Stem and Progenitor Cells from Patients with AML

Background

Acute myeloid leukaemia (AML) is a disease of the haematopoietic stem cells(HSCs) that is characterised by the uncontrolled proliferation and impaired differentiation of normal haematopoietic stem/progenitor cells. Several pathways that control the proliferation and differentiation of HSCs are impaired in AML. Activation of the Wnt/beta-catenin signalling pathway has been shown in AML and beta-catenin, which is thought to be the key element of this pathway, has been frequently highlighted. The present study was designed to determine beta-catenin expression levels and beta-catenin-related genes in AML.

Methods

In this study, beta-catenin gene expression levels were determined in 19 AML patients and 3 controls by qRT-PCR. Transcriptome analysis was performed on AML grouped according to beta-catenin expression levels. Differentially expressed genes(DEGs) were investigated in detail using the Database for Annotation Visualisation and Integrated Discovery(DAVID), Gene Ontology(GO), Kyoto Encyclopedia of Genes and Genomes(KEGG), STRING online tools.

Results

The transcriptome profiles of our AML samples showed different molecular signature profiles according to their beta-catenin levels(high-low). A total of 20 genes have been identified as hub genes. Among these, TTK, HJURP, KIF14, BTF3, RPL17 and RSL1D1 were found to be associated with beta-catenin and poor survival in AML. Furthermore, for the first time in our study, the ELOV6 gene, which is the most highly up-regulated gene in human AML samples, was correlated with a poor prognosis via high beta-catenin levels.

Conclusion

It is suggested that the identification of beta-catenin-related gene profiles in AML may help to select new therapeutic targets for the treatment of AML.

Deciphering the impact of circRNA-mediated autophagy on tumor therapeutic resistance: a novel perspective

Cancer therapeutic resistance remains a significant challenge in the pursuit of effective treatment strategies. Circular RNAs (circRNAs), a class of non-coding RNAs, have recently emerged as key regulators of various biological processes, including cancer progression and drug resistance. This review highlights the emerging role of circRNAs-mediated autophagy in cancer therapeutic resistance, a cellular process that plays a dual role in cancer by promoting both cell survival and death. Increasing evidence suggests that circRNAs can modulate autophagy pathways, thereby influencing the response of cancer cells to therapeutic agents. In this context, the intricate interplay between circRNAs, autophagy, and therapeutic resistance is explored. Various mechanisms are discussed through which circRNAs can impact autophagy, including direct interactions with autophagy-related genes, modulation of signaling pathways, and cross-talk with other non-coding RNAs. Furthermore, the review delves into specific examples of how circRNA-mediated autophagy regulation can contribute to resistance against chemotherapy and radiotherapy. Understanding these intricate molecular interactions provides valuable insights into potential strategies for overcoming therapeutic resistance in cancer. Exploiting circRNAs as therapeutic targets or utilizing them as diagnostic and predictive biomarkers opens new avenues for developing personalized treatment approaches. In summary, this review underscores the importance of circRNA-mediated autophagy in cancer therapeutic resistance and proposes future directions for research in this exciting and rapidly evolving field.

Bioinformatics-based Identification of Ferroptosis-related Genes and their Diagnostic Value in Gestational Diabetes Mellitus

BACKGROUND

Gestational diabetes mellitus (GDM) is considered a risk factor for heart metabolic disorder in future mothers and offspring. Ferroptosis is a new type of programmed cell death, which may participate in the occurrence and development of GDM.

OBJECTIVE

This study aims to identify ferroptosis-related genes in GDM by bioinformatics methods and to explore their clinical diagnostic value.

METHODS

The dataset GSE103552 was analyzed using the Gene Expression Omnibus (GEO) database to screen for differentially expressed genes (DEGs) in GDM. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and proteinprotein interaction (PPI) network were performed. Gene sets for ferroptosis were retrieved in MSigDB and GSVA gene set analysis was performed on the database. Finally, logistic regression was performed to differentiate between GDM patients and controls to screen for diagnostic markers.

RESULTS

A total of 179 DEGs were identified in the expression profile of GDM. GO and KEGG enrichment analysis revealed significant enrichment in the TGF-β, p53 signaling pathway, platelet activation, glutathione metabolism, sensory perception of taste, and leukocyte and vascular endothelial cell migration regulation. DEGs (n = 107) associated with the ferroptosis gene set were screened by GSVA analysis. The screened DEGs for disease and DEGs for ferroptosis scores were intersected and 35 intersected genes were identified. PPI identified two key genes associated with GDM as CCNB2 and CDK1. Wilcox-test showed low expression of CCNB2 and CDK1 in GDM. The area under the ROC curve (AUC) of the CCNB2 and CDK1 prognostic model was 0.822.

CONCLUSION

The genes associated with ferroptosis in GDM were CCNB2 and CDK1, which can be used as valid indicators for the diagnosis of GDM.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

CCNB2 as a potential biomarker of bladder cancer via the high throughput technology

Bladder cancer and oral squamous cell carcinoma (OSCC) seriously affect people's health. However, the relationship between bladder cancer and OSCC remains unclear. Got GSE138206, GSE146483, GSE184616, and bladder cancer datasets GSE65635, GSE100926 from Gene Expression Omnibus database. Weighted gene co-expression network analysis was used to identify the significant module. Functional enrichment analysis was performed via the Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes. Furthermore, the Gene Set Enrichment Analysis was also used to complete the enrichment analysis. Comparative Toxicogenomics Database found most relevant diseases to core genes. TargetScan is used to forecast analysis of microRNA and target genes. In Gene Ontology analysis, differentially expressed genes were mostly concentrated in cell differentiation, extrallular region, structural molecule activity, and actin binding. In Kyoto Encyclopedia of Genes and Genomes analysis, the differentially expressed genes were mainly enriched in PI3K-Akt signaling pathway, pathway in cancer, and extracellular matrix-receptor interaction. Seven hub genes (cyclin B2 [CCNB2], TK1, CDC20, PCNA, CKS1B, CDCA5, MCM4) were obtained. Hub genes (CCNB2, CDC20) are highly expressed in OSCC and bladder cancer samples. CCNB2 was one common oncogene of bladder cancer and OSCC.

Identification of biomarkers and key pathways in synovial sarcoma cells exposed to anlotinib by integrating bioinformatics analysis and experimental validation

OBJECTIVE

To identify potential biomarkers, key pathways and modules following the exposure of synovial sarcoma (SS) cells to anlotinib.

METHODS

In the current study, we integrated multiple bioinformatics methods to identify the hub genes and key pathways associated with the effects of anlotinib treatment in SS cells. In addition, we used reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to validate the expression levels of the identified hub genes in SS cells treated with anlotinib.

RESULTS

In total, 183 differentially expressed genes (DEGs) were identified, of which 47 were upregulated and 136 were downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the DEGs were predominantly involved in cell division and cell cycle progression. A total of two modules were identified from the protein-protein interaction network using the MCODE plugin in Cytoscape, where module 1 was the most significant. By combining the results of CytoHubba analysis based on the module 1 and The Cancer Genome Atlas database, six real hub genes, cyclin (CCN) A2, kinesin family member 2C, cell division cycle 20, CCNB2, aurora kinase B and CCNB1, were identified. Subsequent GO and KEGG pathway analysis revealed that these six real hub genes were significantly associated with the cell cycle and mitosis. Finally, RT-qPCR verified that the mRNA expression levels of these six real hub genes were significantly decreased in SS cells treated with anlotinib compared with those in the control group. Altogether, our study identified biomarkers and key pathways associated with the effects of anlotinib treatment in SS cells, which may provide novel insights into the underlying mechanism of anlotinib treatment in SS.

GPX8 as a Novel Prognostic Factor and Potential Therapeutic Target in Primary Glioma

One of the most prevalent malignant primary brain tumors is primary glioma. Although glutathione peroxidase 8 (GPX8) is intimately associated with carcinogenesis, its function in primary gliomas has not yet been thoroughly understood. Here, we leveraged Chinese Glioma Genome Atlas (CGGA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) database to investigate the association between GPX8 and overall survival (OS) of patients with primary gliomas, and our results showed that GPX8 expression was negatively correlated with OS. Moreover, the expression of GPX8 is significantly lower in normal tissue when compared to glioma tissue. According to results of univariate and multivariate analysis from CGGA using R studio, GPX8 is a valuable primary glioma prognostic indicator. Interestingly, high GPX8 expression is correlated positively with the hedgehog and kras signaling pathways and negatively with G2 checkpoint, apoptosis, reactive oxygen species (ROS) pathway, and interferon gamma pathway, which could be beneficial for the proliferation of glioma cells. Furthermore, GPX8 knockdown caused G1 cell cycle arrest, increased cell death, and reduced colony formation in U87MG and U118MG cells. In conclusion, GPX8 is a promising therapeutic target and meaningful prognostic biomarker of primary glioma.

Glioma Subtypes Based on the Activity Changes of Immunologic and Hallmark Gene Sets in Cancer

Purpose

Glioma is the most common primary cranial brain tumor that arises from the cancelation of glial cells (which can be in the brain or spinal cord). It is due to innate genetic risk factors or induced by a carcinogenic environment. If left untreated, the disease has a poor prognosis.

Methods

In this study, we downloaded glioma data from TCGA database and GEO (GSE4412). The GSEA database was used to screen tumor microenvironment-related gene sets. Cancer subtypes were classified by GSVA enrichment method.

Results

By GSVA enrichment analysis, we obtain three Gliomas cancer subtypes. After further survival prognosis analysis and biological function analysis, we obtained 13 tumor microenvironment gene sets and 14 core genes that affect patients' survival prognosis, and these genes have the potential to become targets for targeted therapies and disease detection.

Conclusion

We screened a total of 13 gene sets through a series of enrichment analyses, statistical and prognostic analyses, etc. Among them, 14 core genes were identified, namely: TOP2A, TPX2, BUB1, AURKB, AURKA, CDK1, BUB1B, CCNA2, CCNB2, CDCA8, CDC20, KIF11, KIF20A and KIF2C.