CCNB1 Expedites the Progression of Cervical Squamous Cell Carcinoma via the Regulation by FOXM1

ceRNA Network and WGCNA Analyses of Differentially Expressed Genes in Cervical Cancer Tissues for Association with Survival of Patients

The study aims to search and identify differentially expressed genes (DEGs) in cervical cancer tissues as novel biomarkers to predict cervical cancer prognosis. The Cancer Genome Atlas (TCGA) data on gene expression profiles in cervical cancer were downloaded and analyzed using R software to identify DEGs in cervical cancer tissues. miRNAs targeted by differentially expressed long non-coding RNAs (DElncRNAs) and mRNAs targeted by microRNAs (miRNAs) were identified using the online miRcode, miRTarBase, TargetScan, and miRDB tools. The ceRNA network and lncRNA expression modules in cervical cancer tissues were constructed using weighted gene co-expression network analysis (WGCNA) and analyzed bioinformatically. The Kaplan-Meier analysis was performed to confirm these DEGs as prognostic markers. Immunohistochemical (IHC) analysis was used to verify expression of the hub genes in 10 paired cervical cancer and normal tissues. A total of 1914 DEmRNAs, 210 DElncRNAs, and 67 DEmiRNAs were identified in cervical cancer samples. There were 39 lncRNAs, 19 miRNAs, and 87 mRNAs involved in the ceRNA network and 25 DElncRNAs, three DEmiRNAs, and four mRNAs involved in the ceRNA sub-network. CACNA1C-AS1 was associated with the yellow and blue modules in the ceRNA sub-network, and LIFR-AS1 was associated with the blue module. The DEmRNAs were involved in cancer-related pathways, and three hub genes (i.e., E2F1, CCNB1, and CCNE1) were highly expressed in cervical squamous cell carcinoma and adenocarcinoma tissues and associated with the prognosis of patients. The ceRNA network and WGCNA analyses are useful to identify novel DEGs that can serve as prognostic markers in cervical cancer. The DEGs will be validated in future studies.

Single-cell transcriptome analysis reveals dynamic changes of the preclinical A549 cancer models, and the mechanism of dacomitinib

The in vitro A549 cells, and A549 xenografts in nude mouse, were two commonly used models for anti-cancer drug discovery. However, the biological and molecular characteristics of these two classic models, and also the dynamic transcriptome changes after dacomitinib exposure remains elusive. We performed single-cell RNA sequencing to define the transcriptome profile at single-cell resolution, and processed tumor samples for bulk RNA and protein analysis to validate the differently expressed genes. Transcriptome profiling revealed that the in vitro A549 cells are heterogeneous. The minimal subpopulation of the in vitro A549 cells, which were characterized by the signature of response to unfolded protein, became the overriding subpopulation of the xenografts. The EGFR non-activating A549 cells were resistant to dacomitinib in vitro, while A549 xenografts were comparatively sensitive as EGFR-activating HCC827 xenografts. Dacomitinib inhibited MAPK signaling pathway, and increased the immune response in the A549 xenografts. A phagocytosis checkpoint stanniocalcin-1 (STC1) was significantly inhibited in dacomitinib-treated xenografts. So here our study gives the first insight of the heterogeneity of the two classic models, and the translational potential of dacomitinib being used into a broader patient population rather than EGFR common activating mutation.

Hsa_circ_0137652 Regulates miR-1205/CCNB1 Axis to Accelerate the Malignancy of Breast Cancer

CircRNAs have become a hotspot in tumor research owing to their high stability and specific functions. We investigated the function of hsa_circ_0137652 in the onset and progression of breast cancer (BC). The expression of circ_0137652, miR-1205, and CCNB1 in BC tissues and cell lines were detected using RT-qPCR and/or western blotting. Dual-luciferase reporter and RNA immunoprecipitation chip assays were used to confirm any potential connections between circ_0137652, miR-1205, and CCNB1. CCK-8 and clone formation assays (CFA) were used to measure the proliferation of BC cells. The Transwell assay was used to investigate the migration of BC cells, and the impact of circ_0137652 on BC tumor formation in vivo was validated using animal experiments. RT-qPCR results showed that circ_0137652 and CCNB1 in breast cancer tissues were notably upregulated in normal tissues, whereas miR-1205 was prominently downregulated. After silencing circ_0137652, the growth and migration of BC cells were reduced. Animal experiments showed that circ_0137652 hampers the tumorigenesis of BC cells in vivo. Additionally, we found that circ_0137652 functions as a sponge for miR-1205. Moreover, the miR-1205 inhibitor notably facilitated cell proliferation and migration and attenuated the action of circ_0137652 knockdown. Furthermore, miR-1205 inhibits BC progression by targeting CCNB1. Circ_0137652 controls the miR-1205/CCNB1 axis to induce increased breast cancer malignancy. Our findings suggest that circ_0137652 may be a novel target for BC therapy.

Identification of a transcription factor‑cyclin family genes network in lung adenocarcinoma through bioinformatics analysis and validation through RT‑qPCR

Lung adenocarcinoma (LUAD) is the predominant pathological subtype of lung cancer, which is the most prevalent and lethal malignancy worldwide. Cyclins have been reported to regulate the physiology of various types of tumors by controlling cell cycle progression. However, the key roles and regulatory networks associated with the majority of the cyclin family members in LUAD remain unclear. In total, 556 differentially expressed genes were screened from the GSE33532, GSE40791 and GSE19188 mRNA microarray datasets by R software. Subsequently, protein-protein interaction network containing 499 nodes and 4,311 edges, in addition to a significant module containing 76 nodes and 2,631 edges, were extracted through the MCODE plug-in of Cytoscape. A total of four cyclin family genes [cyclin (CCNA2, CCNB1, CCNB2 and CCNE2] were then found in this module. Further co-expression analysis and associated gene prediction revealed forkhead box M1 (FOXM1), the common transcription factor of CCNB2, CCNB1 and CCNA2. In addition, using GEPIA database, it was found that the high expression of these four genes were simultaneously associated with poorer prognosis in patients with LUAD. Experimentally, it was proved that these four hub genes were highly expressed in LUAD cell lines (Beas-2B and H1299) and LUAD tissues through qPCR, western blot analysis and immunohistochemical studies. The diagnostic value of these 4 hub genes in LUAD was analyzed by logistic regression, CCNA2 was deleted, following which a nomogram diagnostic model was constructed accordingly. The area under the curve values of CCNB1, CCNB2 and FOXM1 diagnostic models were calculated to be 0.92, 0.91 and 0.96 in the training set (Combined dataset of GSE33532, GSE40791 and GSE19188) and two validation sets (GSE10072 and GSE75037), respectively. To conclude, data from the present study suggested that the FOXM1/cyclin (CCNA2, CCNB1 and/or CCNB2) axis may serve a regulatory role in the development and prognosis of LUAD. Specifically, CCNB1, CCNB2 and FOXM1 have potential as diagnostic markers and/or therapeutic targets for LUAD treatment.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

Expression of KIF2A, NDC80, CDK1, and CCNB1 in breast cancer patients: Their interaction and linkage with tumor features and prognosis

Background

Kinesin family member 2A (KIF2A), nuclear division cycle 80 (NDC80), cyclin‐dependent kinase 1 (CDK1), and cyclin B1 (CCNB1) exhibit a complex interrelation, which promote cancer progression via multiple ways, whereas their interaction and clinical implications in breast cancer are obscure. Hence, this study aimed to evaluate the correlation among KIF2A, NDC80, CDK1, CCNB1, and their linkage with clinicopathological features and prognosis in breast cancer patients.

Methods

195 breast cancer patients underwent surgical resection were analyzed. KIF2A, NDC80, CDK1, and CCNB1 expressions were determined by immunohistochemical (IHC) assay and scored by a semiquantitative IHC score or positive cell percentage.

Results

KIF2A expression positively associated with NDC80, CDK1, and CCNB1 expressions (all p < 0.01). In terms of tumor features: KIF2A high expression linked with increased T stage (p = 0.011), N stage (p = 0.014), and TNM stage (p = 0.009) but not tumor differentiation (p = 0.651). NDC80 high expression only related to higher N stage (p = 0.010); CDK1 high expression only connected with elevated N stage (p = 0.035) and TNM stage (p = 0.023). In aspect of prognosis, high expression of KIF2A was correlated with worse disease‐free survival (DFS) (p = 0.031), while NDC80 high (p = 0.329), CDK1 high (p = 0.276), and CCNB1 positive (p = 0.063) expressions only showed trends to link with poor DFS (without statistical significance). Furthermore, high expression of KIF2A (p = 0.063), NDC80 (p = 0.939), CDK1 (p = 0.413) and positive expression of CCNB1 (p = 0.296) did not relate to overall survival.

Conclusion

KIF2A correlates with NDC80, CDK1, CCNB1, and may link with advanced tumor stages and poor prognosis in breast cancer patients.

Screening a novel six critical gene-based system of diagnostic and prognostic biomarkers in prostate adenocarcinoma patients with different clinical variables

The mechanisms behind prostate adenocarcinoma (PRAD) pathogenicity remain to be understood due to tumor heterogeneity. In the current study, we identified by microarray technology six eligible real hub genes from already identified hub genes through a systematic in silico approach that could be useful to lower the heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment. For this purpose, microarray technology-based, already-identified PRAD-associated hub genes were initially explored through extensive literature mining; then, a protein-protein interaction (PPI) network construction of those hub genes and its analysis helped us to identify six most critical genes (real hub genes). Various online available expression databases were then used to explore the tumor driving, diagnostic, and prognostic roles of real hub genes in PRAD patients with different clinicopathologic variables. In total, 124 hub genes were extracted from the literature, and among those genes, six, including CDC20, HMMR, AURKA, CDK1, ASF1B, and CCNB1 were identified as real hub genes by the degree method. Further expression analysis revealed the significant up-regulation of real hub genes in PRAD patients of different races, age groups, and nodal metastasis status relative to controls. Moreover, through correlational analyses, different valuable correlations between treal hub genes expression and different other data (promoter methylation status, genetic alterations, overall survival (OS), tumor purity, CD4+ T, CD8+ T immune cells infiltration, and different other mutant genes and a few more) across PRAD samples were also documented. Ultimately, from this study, a few important transcription factors (TFS), miRNAs, and chemotherapeutic drugs showing a great therapeutic potential were also identified. In conclusion, we have discovered a set of six real hub genes that might be utilized as new biomarkers for lowering heterogenetic-specific barriers in PRAD patients for diagnosis, prognosis, and treatment.

Bioinformatic Analyzes of the Association Between Upregulated Expression of JUN Gene via APOBEC-Induced FLG Gene Mutation and Prognosis of Cervical Cancer

Globally, cervical cancer (CC) is the most common malignant tumor of the female reproductive system and its incidence is only second after breast cancer. Although screening and advanced treatment strategies have improved the rates of survival, some patients with CC still die due to metastasis and drug resistance. It is considered that cancer is driven by somatic mutations, such as single nucleotide, small insertions/deletions, copy number, and structural variations, as well as epigenetic changes. Previous studies have shown that cervical intraepithelial neoplasia is associated with copy number variants (CNVs) and/or mutations in cancer-related genes. Further, CC is also related to genetic mutations. The present study analyzed the data on somatic mutations of cervical squamous cell carcinoma (CESC) in the Cancer Genome Atlas database. It was evident that the Apolipoprotein B mRNA editing enzyme-catalyzed polypeptide-like (APOBEC)-related mutation of the FLG gene can upregulate the expression of the JUN gene and ultimately lead to poor prognosis for patients with CC. Therefore, the findings of the current study provide a new direction for future treatment of CC.

Anticolon Cancer Targets and Molecular Mechanisms of Tao-He-Cheng-Qi Formula

Background

Tao-He-Cheng-Qi Formula (THCQF) is a traditional Chinese medicine that has been proven to have antitumor effects. The aim of this study was to elucidate the molecular targets and mechanisms of THCQF against colon cancer and construct a prognostic model based on network pharmacology, bioinformatics analysis, and in vitro experiments.

Methods

Potential THCQF compounds and targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular Mechanism of Traditional Chinese Medicine databases. Differentially expressed genes for colon cancer were screened in The Cancer Genome Atlas and Gene Expression Omnibus databases. The anticolon cancer mechanisms of THCQF were explored using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Molecular docking simulations and molecular dynamics analysis were used to evaluate the binding between target proteins and active compounds. Finally, the identified compounds were used to treat colon cancer cells from the HCT116 cell line, and expression of mRNA and protein after relevant posttreatment were tested using real-time polymerase chain reaction and western blotting.

Results

A total of 27 anticolon cancer targets of THCQF were selected, among which four genes (CCNB1, CCNA2, IL1A, and MMP3) were shown to effectively predict patient outcomes in a prognostic colon cancer model. GO and KEGG enrichment analyses indicated that the activity against colon cancer of THCQF was associated with the interleukin (IL)-4 and IL-3 signaling pathways. Two compounds in THCQF, aloe emodin (AE) and quercetin (QR), were shown to efficiently bind to cyclin B1, the protein encoded by CCNB1. Finally, incubation of HCT116 cells with AE and QR significantly decreased CCNB1 mRNA expression and cyclin B1 levels.

Conclusions

Taken together, the results indicate that AE and QR are the pivotal active compounds of THCQF, and CCNB1 is the main molecular target through which THCQF exerts its anticolon cancer effects. The study findings provide insight for studies investigating the anticancer effects of other traditional Chinese medicines.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

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

In our studies, cyclin B1 (CCNB1) mRNA and protein were overexpressed in hepatocellular carcinoma (HCC) tissues compared with non-HCC tissues. Moreover, CCNB1 was overexpressed in the serum of HCC patients. The expression of CCNB1 was associated with several crucial clinicopathologic characteristics, and the HCC patients with overexpressed CCNB1 had worse overall survival outcomes. In the screening of interactional genes, a total of 266 upregulated co-expression genes, which were positively associated with CCNB1, were selected from the datasets, and 67 downregulated co-expression genes, which were negatively associated with CCNB1, were identified. The key genes might be functionally enriched in DNA replication and the cell cycle pathways. CDC20, CCNA2, PLK1, and FTCD were selected for further research because they were highly connected in the protein-protein interaction networks. Upregulated CDC20, CCNA2, and PLK1 and downregulated FTCD might result in undesirable overall survival outcomes for HCC patients. The univariate Cox analysis results showed that CDC20 and PLK1 might be two independent risk factors, while FTCD might be protective in HCC. Therefore, CCNB1 may participate in the cell cycle of HCC by regulating DNA replication, and CCNB1 may provide a direction for the diagnosis of early-stage HCC and targeted HCC therapy.

Comprehensive Analysis of circRNA Expression Profiles During Cervical Carcinogenesis

Circular RNAs (circRNAs) are regulatory molecules that participate in the occurrence, development and progression of tumors. To obtain a complete blueprint of cervical carcinogenesis, we analyzed the temporal transcriptomic landscapes of mRNAs and circRNAs. Microarrays were performed to identify the circRNA and mRNA expression profiles of cervical squamous cell carcinoma (CSCC) and high-grade squamous intraepithelial lesion (HSIL) patients compared with normal controls (NC). Short time-series expression miner (STEM) was utilized to characterize the time-course expression patterns of circRNAs and mRNAs from NC to HSIL and CSCC. A total of 3 circRNA profiles and 3 mRNA profiles with continuous upregulated patterns were identified and selected for further analysis. Furthermore, functional annotation showed that the mRNAs were associated with DNA repair and cell division. The protein-protein interaction (PPI) network analysis revealed that the ten highest-degree genes were considered to be hub genes. Subsequently, a competing endogenous RNA (ceRNA) network analysis and real-time PCR validation indicated that hsa_circ_0001955/hsa-miR-6719-3p/CDK1, hsa_circ_0001955/hsa-miR-1277-5p/NEDD4L and hsa_circ_0003954/hsa-miR-15a-3p/SYCP2 were highly correlated with cervical carcinogenesis. Silencing of hsa_circ_0003954 inhibited SiHa cell proliferation and perturb the cell cycle in vitro. This study provides insight into the molecular events regulating cervical carcinogenesis, identifies functional circRNAs in CSCC, and improves the understanding of the pathogenesis and molecular biomarkers of CSCC and HSIL.

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Background

This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis.

Methods

Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets.

Results

A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained.

Conclusions

These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08412-4.