CCNB1 promotes the development of hepatocellular carcinoma by mediating DNA replication in the cell cycle

Clinicopathological role of Cyclin A2 in uterine corpus endometrial carcinoma: Integration of tissue microarrays and ScRNA-Seq

BACKGROUND

The comprehensive expression level and potential molecular role of Cyclin A2 (CCNA2) in uterine corpus endometrial carcinoma (UCEC) remains undiscovered.

METHODS

UCEC and normal endometrium tissues from in-house and public databases were collected for investigating protein and messenger RNA expression of CCNA2. The transcription factors of CCNA2 were identified by the Cistrome database. The prognostic significance of CCNA2 in UCEC was evaluated through univariate and multivariate Cox regression as well as Kaplan-Meier curve analysis. Single-cell RNA-sequencing (scRNA-seq) analysis was performed to explore cell types in UCEC, and the AUCell algorithm was used to investigate the activity of CCNA2 in different cell types.

RESULTS

A total of 32 in-house UCEC and 30 normal endometrial tissues as well as 720 UCEC and 165 control samples from public databases were eligible and collected. Integrated calculation showed that the CCNA2 expression was up-regulated in the UCEC tissues (SMD = 2.43, 95% confidence interval 2.23∼2.64). E2F1 and FOXM1 were identified as transcription factors due to the presence of binding peaks on transcription site of CCNA2. CCNA2 predicted worse prognosis in UCEC. However, CCNA2 was not an independent prognostic factor in UCEC. The scRNA-seq analysis disclosed five cell types: B cells, T cells, monocytes, natural killer cells, and epithelial cells in UCEC. The expression of CCNA2 was mainly located in B cells and T cells. Moreover, CCNA2 was active in T cells and B cells using the AUCell algorithm.

CONCLUSION

CCNA2 was up-regulated and mainly located in T cells and B cells in UCEC. Overexpression of CCNA2 predicted unfavorable prognosis of UCEC.

Single-cell and bulk RNA-sequencing reveal SPP1 and CXCL12 as cell-to-cell communication markers to predict prognosis in lung adenocarcinoma

Lung adenocarcinoma (LUAD) generally presents as an immunosuppressive microenvironment. The characteristics of cell-to-cell communication in the LUAD microenvironment has been unclear. In this study, the LUAD bulk RNA-seq data and single-cell RNA-seq data were retrieved from public dataset. Differential expression genes (DEGs) between LUAD tumor and adjacent non-tumor tissues were calculated by limma algorithm, and then detected by PPI, KEGG, and GO analysis. Cell-cell interactions were explored using the single-cell RNA-seq data. Finally, the first 15 CytoHubba genes were used to establish related pathways and these pathways were used to characterize the immune-related ligands and their receptors in LUAD. Our analyses showed that monocytes or macrophages interact with tissue stem cells and NK cells via SPP1 signaling pathway and tissue stem cells interact with T and B cells via CXCL signaling pathway in different states. Hub genes of SPP1 participated in SPP1 signaling pathway, which was negatively correlated with CD4+ T cell and CD8+ T cell. The expression of SPP1 in LUAD tumor tissues was negatively correlated with the prognosis. While CXCL12 participated in CXCL signaling pathway, which was positively correlated with CD4+ T cell and CD8+ T cell. The role of CXCL12 in LUAD tumor tissues exhibits an opposite effect to that of SPP1. This study reveals that tumor-associated monocytes or macrophages may affect tumor progression. Moreover, the SPP1 and CXCL12 may be the critic genes of cell-to-cell communication in LUAD, and targeting these pathways may provide a new molecular mechanism for the treatment of LUAD.

Bioinformatics analysis highlights CCNB1 as a potential prognostic biomarker and an anti-kidney renal papillary cell carcinoma drug target

Kidney renal papillary cell carcinoma (KIRP) is a common urinary tumor that causes lymph node invasion. Once metastatic, the prognosis is poor and there is a lack of effective early diagnostic markers for this tumor. The expression of CCNB1 in KIRP tumor tissues was significantly higher than that in normal tissues in The Cancer Genome Atlas database with or without the genotype-tissue expression database, and a consistent result was obtained in 32 paired tissues. In addition, CCNB1 expression increased remarkably with the progression of the T and M stages. Moreover, using the online HPA database, we verified that the immunohistochemical scores of CCNB1 in KIRP were higher than those in the normal kidney tissues. The higher expression group of CCNB1 showed a worse prognosis in KIRP. Moreover, the receiver operating characteristic curve, univariate and multivariate analyses, and construction of the column diagram further illustrated that CCNB1 was an independent prognostic factor for KIRP. Meanwhile, CCNB1 could better predict the 1- and 3-year survival rates of KIRP. Six genes were significantly and positively co-expressed with CCNB1. We also found that the CCNB1 high-expression group was enriched in the ECM_RECEPTOR_INTERACTION and FOCAL_ADHESION pathways. Finally, drug sensitivity analysis combined with molecular docking identified 5 targeting drugs with the strongest binding activity to CCNB1. CCNB1 is a potential and reliable biomarker for KIRP diagnosis and can be used to predict the survival of patients with KIRP. The 5 selected drugs targeting CCNB1 may provide new hopes for patients with KIRP metastasis.

NamiRNA-mediated high expression of KNSTRN correlates with poor prognosis and immune infiltration in hepatocellular carcinoma

Introduction

Mutations of kinetochore-localized astrin/sperm-associated antigen 5 (KNSTRN) can interfere with chromatid cohesion, increase aneuploidy in tumours, and enhance tumourigenesis. However, the role of the KNSTRN-binding protein in hepatocellular carcinoma (HCC) remains unclear.

Material and methods

Using The Cancer Genome Atlas databases, we investigated the potential oncogenic functions of KNSTRN in HCC along with R and various computational tools.

Results

Detailed results revealed that elevated expression of KNSTRN was considerably associated with poor overall survival (HR = 1.48, 95% CI: 1.05-2.09, p = 0.027) and progress-free interval (HR = 1.41, 95% CI: 1.05-1.89, p = 0.021) in HCC. Gene ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis showed that KNSTRN is closely related to organelle fission, chromosomal region, tubulin binding, and cell cycle signalling pathway. TIMER database analysis showed the correlations between KNSTRN expression and tumour-infiltrating immune cells, biomarkers of immune cells, and immune checkpoint expression. Moreover, the KNSTRN level was significantly positively associated with immunosuppressive cells in the tumour microenvironment, including regulatory T-cells, myeloid-derived suppressor cells, and cancer-associated fibrocytes. Finally, a possible nuclear activating miRNA (NamiRNA)-enhancer network of hsa-miR-107, which activates the KNSTRN expression in liver hepatocellular carcinoma, was constructed by correlation analysis.

Conclusions

NamiRNA-mediated upregulation of KNSTRN correlated with poor prognosis and tumour immune infiltration in HCC. KNSTRN could serve as an effective biomarker for the diagnosis and prognosis of HCC and support the development of novel therapeutic strategies.

Differentially Expressed Cell Cycle Genes and STAT1/3-Driven Multiple Cancer Entanglement in Psoriasis, Coupled with Other Comorbidities

Psoriasis is a persistent T-cell-supported inflammatory cutaneous disorder, which is defined by a significant expansion of basal cells in the epidermis. Cell cycle and STAT genes that control cell cycle progression and viral infection have been revealed to be comorbid with the development of certain cancers and other disorders, due to their abnormal or scanty expression. The purpose of this study is to evaluate the expression of certain cell cycle and STAT1/3 genes in psoriasis patients and to determine the types of comorbidities associated with these genes. To do so, we opted to adopt the in silico methodology, since it is a quick and easy way to discover any potential comorbidity risks that may exist in psoriasis patients. With the genes collected from early research groups, protein networks were created in this work using the NetworkAnalyst program. The crucial hub genes were identified by setting the degree parameter, and they were then used in gene ontology and pathway assessments. The transcription factors that control the hub genes were detected by exploring TRRUST, and DGIdb was probed for remedies that target transcription factors and hubs. Using the degree filter, the first protein subnetwork produced seven hub genes, including STAT3, CCNB1, STAT1, CCND1, CDC20, HSPA4, and MAD2L1. The hub genes were shown to be implicated in cell cycle pathways by the gene ontology and Reactome annotations. The former four hubs were found in signaling pathways, including prolactin, FoxO, JAK/STAT, and p53, according to the KEGG annotation. Furthermore, they enhanced several malignancies, including pancreatic cancer, Kaposi's sarcoma, non-small cell lung cancer, and acute myeloid leukemia. Viral infections, including measles, hepatitis C, Epstein-Barr virus, and HTLV-1 and viral carcinogenesis were among the other susceptible diseases. Diabetes and inflammatory bowel disease were conjointly annotated. In total, 129 medicines were discovered in DGIdb to be effective against the transcription factors BRCA1, RELA, TP53, and MYC, as opposed to 10 medications against the hubs, STAT3 and CCND1, in tandem with 8 common medicines. The study suggests that the annotated medications should be tested in suitable psoriatic cell lines and animal models to optimize the drugs used based on the kind, severity, and related comorbidities of psoriasis. Furthermore, a personalized medicine protocol must be designed for each psoriasis patient that displays different comorbidities.

SKA1/2/3 is a biomarker of poor prognosis in human hepatocellular carcinoma

BACKGROUND

Spindle and kinetochore-associated complex subunits 1-3 (SKA1-3) stabilize the kinetochore-attached spindle microtubules in metaphase. Due to the dysregulation in multiple cancers, SKA1-3 is considered a predictor for the prognosis of the patients. However, the potential clinical applications of SKA1-3, particularly in hepatocellular carcinoma (HCC) prognosis and progression, have completely unknown yet.

METHODS

For the analysis of SKA1-3 expression and applications in clinics in HCC patients, several databases, such as STRING, UALCAN, GEO, and TCGA, were searched. In addition, the underlying mechanisms of SKA for the regulation of HCC occurrence, development, and progression were also explored.

RESULTS

Compared to the normal controls, HCC patients showed dramatically elevated SKA1-3 expression at the mRNA level, and the values of the area under the curve (AUC) were 0.982, 0.887, and 0.973, respectively. Increased SKA1-3 expression levels were associated with the clinical stage, age, body mass index, tumor grade, tissue subtype, and Tp53 mutation status in HCC patients. The analyses of Kyoto Encyclopedia of Genes and Genome (KEGG) and Gene ontology (GO) demonstrated that SKA1-3 are enriched mainly in the Fanconi anemia, homologous recombination, spliceosome, DNA replication, and cell cycle signaling pathways. The hub genes, such as CDK1, CCNB1, CCNA2, TOP2A, BUB1, AURKB, CCNB2, BUB1B, NCAPG, and KIF11, were identified in protein-protein interactions (PPIs). The expression levels of hub genes were increased in HCC patients and predictive of a poor prognosis. Finally, the expression levels of SKA1-3 were determined using the GEO database.

CONCLUSIONS

SKA1-3 are potential prognostic biomarkers of and targets for HCC. In addition, SKA1-3 may affect HCC prognosis via the Fanconi anemia pathway, homologous recombination, spliceosome, DNA replication, and cell cycle signaling pathway.

The m6A/m5C/m1A Regulated Gene Signature Predicts the Prognosis and Correlates With the Immune Status of Hepatocellular Carcinoma

RNA modification of m6A/m5C/m1A contributes to the occurrence and development of cancer. Consequently, this study aimed to investigate the functions of m6A/m5C/m1A regulated genes in the prognosis and immune microenvironment of hepatocellular carcinoma (HCC). The expression levels of 45 m6A/m5C/m1A regulated genes in HCC tissues were determined. The functional mechanisms and protein–protein interaction network of m6A/m5C/m1A regulated genes were investigated. The Cancer Genome Atlas (TCGA) HCC gene set was categorized based on 45 m6A/m5C/m1A regulated genes, and survival analysis was used to determine the relationship between the overall survival of HCC patients in subgroups. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were used to construct the risk model and nomogram for m6A/m5C/m1A regulated genes. The relationships between m6A/m5C/m1A regulated gene subsets and risk model and immune cell infiltration were analyzed using CIBERSORT. m6A/m5C/m1A regulated genes were involved in mRNA and RNA modifications, mRNA and RNA methylation, mRNA and RNA stability, and other processes. There was a statistically significant difference between cluster1 and cluster2 groups of genes regulated by m6A/m5C/m1A. The prognosis of cluster1 patients was significantly better than that of cluster2 patients. There were statistically significant differences between the two cluster groups in terms of fustat status, grade, clinical stage, and T stage of HCC patients. The risk model comprised the overexpression of YBX1, ZC3H13, YTHDF1, TRMT10C, YTHDF2, RRP8, TRMT6, LRPPRC, and IGF2BP3, which contributed to the poor prognosis of HCC patients. The high-risk score was associated with prognosis, fustat status, grade, clinical stage, T stage, and M stage and was an independent risk factor for poor prognosis in HCC patients. High-risk score mechanisms included spliceosome, RNA degradation, and DNA replication, among others, and high-risk was closely related to stromal score, CD4 memory resting T cells, M0 macrophages, M1 macrophages, resting mast cells, CD4 memory activated T cells, and follicular helper T cells. In conclusion, the cluster subgroup and risk model of m6A/m5C/m1A regulated genes were associated with the poor prognosis and immune microenvironment in HCC and are expected to be the new tools for assessing the prognosis of HCC patients.