CCNA2 acts as a novel biomarker in regulating the growth and apoptosis of colorectal cancer

SETD7 Promotes Cell Proliferation and Migration via Methylation-mediated TAF7 in Clear Cell Renal Cell Carcinoma

SET domain containing 7(SETD7), a member of histone methyltransferases, is abnormally expressed in multiple tumor types. However, the biological function and underlying molecular mechanism of SETD7 in clear cell renal cell carcinoma (ccRCC) remain unclear. Here, we explored the biological effects of SETD7-TAF7-CCNA2 axis on proliferation and metastasis in ccRCC. We identified both SETD7 and TAF7 were up-regulated and significantly promoted the proliferation and migration of ccRCC cells. Concurrently, there was a significant positive correlation between the expression of SETD7 and TAF7, and the two were colocalized in the nucleus. Mechanistically, SETD7 methylates TAF7 at K5 and K300 sites, resulting in the deubiquitination and stabilization of TAF7. Furthermore, re-expression of TAF7 could partially restore SETD7 knockdown inhibited ccRCC cells proliferation and migration. In addition, TAF7 transcriptionally activated to drive the expression of cyclin A2 (CCNA2). And more importantly, the methylation of TAF7 at K5 and K300 sites exhibited higher transcriptional activity of CCNA2, which promotes formation and progression of ccRCC. Our findings reveal a unique mechanism that SETD7 mediated TAF7 methylation in regulating transcriptional activation of CCNA2 in ccRCC progression and provide a basis for developing effective therapeutic strategies by targeting members of SETD7-TAF7-CCNA2 axis.

CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle

Glioblastoma (GBM) is characterized by significant heterogeneity, leading to poor survival outcomes for patients, despite the implementation of comprehensive treatment strategies. The roles of cyclin A2 (CCNA2) and NIMA related kinase 2 (NEK2) have been extensively studied in numerous cancers, but their specific functions in GBM remain to be elucidated. The present study aimed to investigate the potential molecular mechanisms of CCNA2 and NEK2 in GBM. CCNA2 and NEK2 expression and prognosis in glioma were evaluated by bioinformatics methods. In addition, the distribution of CCNA2 and NEK2 expression in GBM subsets was determined using pseudo-time analysis and tricycle position of single-cell sequencing. Gene Expression Omnibus and Kyoto Encyclopedia of Genes and Genome databases were employed and enrichment analyses were conducted to investigate potential signaling pathways in GBM subsets and a nomogram was established to predict 1-, 2- and 3-year overall survival probability in GBM. CCNA2 and NEK2 expression levels were further validated by western blot analysis and immunohistochemical staining in GBM samples. High expression of CCNA2 and NEK2 in glioma indicates poor clinical outcomes. Single-cell sequencing of GBM revealed that these genes were upregulated in a subset of positive neural progenitor cells (P-NPCs), which showed significant proliferation and progression properties and may activate G2M checkpoint pathways. A comprehensive nomogram predicts 1-, 2- and 3-year overall survival probability in GBM by considering P-NPCs, age, chemotherapy and radiotherapy scores. CCNA2 and NEK2 regulate glioblastoma progression by targeting the cell cycle, thus indicating the potential of novel therapy directed to CCNA2 and NEK2 in GBM.

Integrated whole transcriptome profiling revealed a convoluted circular RNA-based competing endogenous RNAs regulatory network in colorectal cancer

Recently, it has been identified that circRNAs can act as miRNA sponge to regulate gene expression in various types of cancers, associating them with cancer initiation and progression. The present study aims to identify colorectal cancer-related circRNAs and the underpinning mechanisms of circRNA/miRNA/mRNA networks in the development and progress of Colorectal Cancer. Differentially expressed circRNAs, miRNAs, and mRNAs were identified in GEO microarray datasets using the Limma package of R. The analysis of differentially expressed circRNAs resulted in 23 upregulated and 31 downregulated circRNAs. CeRNAs networks were constructed by intersecting the results of predicted and experimentally validated databases, circbank and miRWalk, and by performing DEMs and DEGs analysis using Cytoscape. Next, functional enrichment analysis was performed for DEGs included in ceRNA networks. Followed by survival analysis, expression profile assessment using TCGA and GEO data, and ROC curve analysis we identified a ceRNA sub-networks that revealed the potential regulatory effect of hsa_circ_0001955 and hsa_circ_0071681 on survival-related genes, namely KLF4, MYC, CCNA2, RACGAP1, and CD44. Overall, we constructed a convoluted regulatory network and outlined its likely mechanisms of action in CRC, which may contribute to the development of more effective approaches for early diagnosis, prognosis, and treatment of CRC.

The RioK1 network determines p53 activity at multiple levels

By responding to a host of adverse conditions, ranging from DNA damage to viral infection, transcription factor p53 supports genomic stability, cellular health, and survival. Not surprisingly, tumours across the cancer spectrum carry mutations in p53, misexpress the protein, or dysregulate its activity. Several signalling pathways, many of which comprise oncogenic proteins, converge upon p53 to control its stability and activity. We here present the conserved kinase/ATPase RioK1 as an upstream factor that determines p53 activity at the DNA, RNA, and protein levels. It achieves this task by integrating the regulatory events that act on p53 into a coherent response circuit. We will also discuss how RIOK1 overexpression represents an alternative mechanism for cancers to inactivate p53, and how targeting RioK1 could eradicate malignancies that are driven by a dysregulated RioK1-p53 network.

Three candidate anticancer drugs were repositioned by integrative analysis of the transcriptomes of species with different regenerative abilities after injury

Regeneration is a homeostatic process that involves the restoration of cells and body parts. Most of the molecular mechanisms and signalling pathways involved in wound healing, such as proliferation, have also been associated with cancer cell growth, suggesting that cancer is an over/unhealed wound. In this study, we examined differentially expressed genes in spinal cord samples from regenerative organisms (axolotl and zebrafish) and nonregenerative organisms (mouse and rat) compared to intact control spinal cord samples using publicly available transcriptomics data and bioinformatics analyses. Based on these gene signatures, we investigated 3 small compounds, namely cucurbitacin I, BMS-754807, and PHA-793887 as potential candidates for the treatment of cancer. The predicted target genes of the repositioned compounds were mainly enriched with the greatest number of genes in cancer pathways. The molecular docking results on the binding affinity between the repositioned compounds and their target genes are also reported. The repositioned 3 small compounds showed anticancer effect both in 2D and 3D cell cultures using the prostate cancer cell line as a model. We propose cucurbitacin I, BMS-754807, and PHA-793887 as potential anticancer drug candidates. Future studies on the mechanisms associated with the revealed gene signatures and anticancer effects of these three small compunds would allow scientists to develop therapeutic approaches to combat cancer. This research contributes to the evaluation of mechanisms and gene signatures that either limit or cause cancer, and to the development of new cancer therapies by establishing a link between regeneration and carcinogenesis.

Hsa_Circ_0104206 is An Oncogenic circRNA in Colon Cancer by Targeting Mir-188-3p/CCNA2 Axis

The identification of specific biomarkers is essential to improve cancer therapy, and circular RNAs (circRNAs) have great potency to be biomarkers. We harbor the goal to unveil the role of circ_0104206 in colon cancer (CC). The relative expressions of circ_0104206, miR-188-3p and CCNA2 in different groups were studied using real-time quantitative PCR (qPCR) or western blotting. The proliferative and migratory capacity of cancer cells were monitored via CCK-8, colony formation and Transwell assays. The transplanted tumor models were generated to analyze circ_0104206's role in vivo. The putative relationship between miR-188-3p and circ_0104206 or CCNA2 by bioinformatics tools was testified through dual-luciferase or RIP assay. The abnormal elevation of circ_0104206 expression was observed in CC. Circ_0104206 silencing repressed CC cell proliferative and migratory behaviors, and also decelerated tumor development in animal models. MiR-188-3p was directly targeted by circ_0104206, and its inhibitor had the ability to reverse the anticancer effects of circ_0104206 silencing on CC cells. CCNA2 was a target downstream of circ_0104206/miR-188-3p network. Moreover, the repressive effects of CCNA2 absence on cell proliferation and migration were attenuated by miR-188-3p inhibitor. In conclusion, Circ_0104206 plays oncogenic roles in CC via the implication of miR-188-3p/CCNA2 network, which further discloses CC pathogenesis and supplies potential markers for CC.

Network and functional analyses of differentially expressed genes in gastric cancer provide new biomarkers associated with disease pathogenesis

BACKGROUND

Gastric cancer is a dominant source of cancer-related death around the globe and a serious threat to human health. However, there are very few practical diagnostic approaches and biomarkers for the treatment of this complex disease.

METHODS

This study aimed to evaluate the association between differentially expressed genes (DEGs), which may function as potential biomarkers, and the diagnosis and treatment of gastric cancer (GC). We constructed a protein-protein interaction network from DEGs followed by network clustering. Members of the two most extensive modules went under the enrichment analysis. We introduced a number of hub genes and gene families playing essential roles in oncogenic pathways and the pathogenesis of gastric cancer. Enriched terms for Biological Process were obtained from the "GO" repository.

RESULTS

A total of 307 DEGs were identified between GC and their corresponding normal adjacent tissue samples in GSE63089 datasets, including 261 upregulated and 261 downregulated genes. The top five hub genes in the PPI network were CDK1, CCNB1, CCNA2, CDC20, and PBK. They are involved in focal adhesion formation, extracellular matrix remodeling, cell migration, survival signals, and cell proliferation. No significant survival result was found for these hub genes.

CONCLUSIONS

Using comprehensive analysis and bioinformatics methods, important key pathways and pivotal genes related to GC progression were identified, potentially informing further studies and new therapeutic targets for GC treatment.

Demystifying the Role of Prognostic Biomarkers in Breast Cancer through Integrated Transcriptome and Pathway Enrichment Analyses

Breast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. Researchers have discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs was performed using the DAVID (Database for Annotation, Visualization, and Integrated Discovery) database. The protein–protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was completed using cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using a Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12,145 edges. Elevated expression of the five hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Identification of Prognostic Biomarkers for Suppressing Tumorigenesis and Metastasis of Hepatocellular Carcinoma through Transcriptome Analysis

Cancer is one of the deadliest diseases developed through tumorigenesis and could be fatal if it reaches the metastatic phase. The novelty of the present investigation is to explore the prognostic biomarkers in hepatocellular carcinoma (HCC) that could develop glioblastoma multiforme (GBM) due to metastasis. The analysis was conducted using RNA-seq datasets for both HCC (PRJNA494560 and PRJNA347513) and GBM (PRJNA494560 and PRJNA414787) from Gene Expression Omnibus (GEO). This study identified 13 hub genes found to be overexpressed in both GBM and HCC. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability, leading to improper chromosome segregation, causing aneuploidy. A 13-gene predictive model was obtained and validated using a KM plot. These hub genes could be prognostic biomarkers and potential therapeutic targets, inhibition of which could suppress tumorigenesis and metastasis.

Integrated analysis of multi-omics data for the discovery of biomarkers and therapeutic targets for colorectal cancer

The considerable burden of colorectal cancer and the rising trend in young adults emphasize the necessity of understanding its underlying mechanisms, providing new diagnostic and prognostic markers, and improving therapeutic approaches. Precision medicine is a new trend all over the world and identification of novel biomarkers and therapeutic targets is a step forward towards this trend. In this context, multi-omics data and integrated analysis are being investigated to develop personalized medicine in the management of colorectal cancer. Given the large amount of data from multi-omics approach, data integration and analysis is a great challenge. In this Review, we summarize how statistical and machine learning techniques are applied to analyze multi-omics data and how it contributes to the discovery of useful diagnostic and prognostic biomarkers and therapeutic targets. Moreover, we discuss the importance of these biomarkers and therapeutic targets in the clinical management of colorectal cancer in the future. Taken together, integrated analysis of multi-omics data has great potential for finding novel diagnostic and prognostic biomarkers and therapeutic targets, however, there are still challenges to overcome in future studies.

Exploring Core Genes by Comparative Transcriptomics Analysis for Early Diagnosis, Prognosis, and Therapies of Colorectal Cancer

Colorectal cancer (CRC) is one of the most common cancers with a high mortality rate. Early diagnosis and therapies for CRC may reduce the mortality rate. However, so far, no researchers have yet investigated core genes (CGs) rigorously for early diagnosis, prognosis, and therapies of CRC. Therefore, an attempt was made in this study to explore CRC-related CGs for early diagnosis, prognosis, and therapies. At first, we identified 252 common differentially expressed genes (cDEGs) between CRC and control samples based on three gene-expression datasets. Then, we identified ten cDEGs (AURKA, TOP2A, CDK1, PTTG1, CDKN3, CDC20, MAD2L1, CKS2, MELK, and TPX2) as the CGs, highlighting their mechanisms in CRC progression. The enrichment analysis of CGs with GO terms and KEGG pathways revealed some crucial biological processes, molecular functions, and signaling pathways that are associated with CRC progression. The survival probability curves and box-plot analyses with the expressions of CGs in different stages of CRC indicated their strong prognostic performance from the earlier stage of the disease. Then, we detected CGs-guided seven candidate drugs (Manzamine A, Cardidigin, Staurosporine, Sitosterol, Benzo[a]pyrene, Nocardiopsis sp., and Riccardin D) by molecular docking. Finally, the binding stability of four top-ranked complexes (TPX2 vs. Manzamine A, CDC20 vs. Cardidigin, MELK vs. Staurosporine, and CDK1 vs. Riccardin D) was investigated by using 100 ns molecular dynamics simulation studies, and their stable performance was observed. Therefore, the output of this study may play a vital role in developing a proper treatment plan at the earlier stages of CRC.

Transcriptomic Signatures in Colorectal Cancer Progression

AIMS

Due to a large number of identified hub-genes encoding key molecular regulators, which are involved in signal transduction and metabolic pathways in cancers, it is relevant to systemize and update these findings.

BACKGROUND

Colorectal cancer (CRC) is the third leading cause of cancer death in the world, with high metastatic potential. Elucidating the pathogenic mechanisms and selection of novel biomarkers in CRC is of great clinical significance.

OBJECTIVE

This analytical review aims at the systematization of bioinformatics and experimental identification of hub-genes associated with CRC for a more consolidated understanding of common features in networks and pathways in CRC progression as well as hub-genes selection.

RESULTS

In total, 301 hub-genes were derived from 40 articles. The "core" consisted of 28 hub-genes (CCNB1, LPAR1, BGN, CXCL3, COL1A2, UBE2C, NMU, COL1A1, CXCL2, CXCL11, CDK1, TOP2A, AURKA, SST, CXCL5, MMP3, CCND1, TIMP1, CXCL8, CXCL1, CXCL12, MYC, CCNA2, GCG, GUCA2A, PAICS, PYY and THBS2) mentioned in not less than three articles and having clinical significance in cancerassociated pathways. Of them, there were two discrete clusters enriched in chemokine signaling and cell cycle regulatory genes. High expression levels of BGN and TIMP1 and low expression levels of CCNB1, CXCL3, CXCL2, CXCL2 and PAICS were associated with unfavorable overall survival of patients with CRC. Differently expressed genes such as LPAR1, SST, CXCL12, GUCA2A, and PYY were shown as down regulated, whereas BGN, CXCL3, UBE2C, NMU, CXCL11, CDK1, TOP2A, AURKA, MMP3, CCND1, CXCL1, MYC, CCNA2, PAICS were up regulated genes in CRC. It was also found that MMP3, THBS2, TIMP1 and CXCL12 genes were associated with metastatic CRC. Network analysis in ONCO.IO showed that upstream master regulators RELA, STAT3, SOX2, FOXM1, SMAD3 and NF-kB were connected with "core" hub-genes. Conclusión: Results obtained are of useful fundamental information on revealing the mechanism of pathogenicity, cellular target selection for optimization of therapeutic interventions, as well as transcriptomics prognostic and predictive biomarkers development.

Identification of CPT2 as a prognostic biomarker by integrating the metabolism-associated gene signature in colorectal cancer

BACKGROUND

The incidence of colorectal cancer (CRC) is considered to be the third-highest malignant tumor among all carcinomas. The alterations in cellular bioenergetics (metabolic reprogramming) are associated with several malignant phenotypes in CRC, such as tumor cell proliferation, invasion, metastasis, chemotherapy resistance, as well as promotes its immune escape. However, the expression pattern of metabolism-associated genes that mediate metabolic reprogramming in CRC remains unknown.

METHODS

In this study, we screened out CPT2 by investigating the function of a series of metabolism-related genes in CRC progression by integrating the data from the TCGA and GEO databases. Next, we collected CRC tissues (n = 24) and adjacent non-tumor tissues (n = 8) and analyzed mRNA levels by qRT-PCR, and proteins levels of CPT2 in CRC cell lines by western blotting. CCK-8 assay, colony formation assay, Edu assay and flow cytometry assay were performed to assess the effects of CPT2 on proliferation in vitro.

RESULTS

We identified 236 metabolism-related genes that are differentially expressed in colorectal cancer, of which 49 up-regulated and 187 down-regulated, and found CPT2 as the most significant gene associated with favorable prognosis in CRC. It was revealed that CPT2 expression was consistently down-regulated in CRC cell lines and tissues. Moreover, knockdown of CPT2 could promote the proliferative ability of CRC cells, whereas over-expression of CPT2 significantly suppressed the cell growth.

CONCLUSION

In summary, CPT2 can provide new insights about the progression and occurrence of the tumor as it acts as an independent prognostic factor in CRC sufferers.

Construction of a Novel Oxidative Stress Response-Related Gene Signature for Predicting the Prognosis and Therapeutic Responses in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with poor outcomes, and the assessment of its prognosis as well as its response to therapy is still challenging. In this study, we aimed to construct an oxidative stress response-related genes-(OSRGs-) based gene signature for predicting prognosis and estimating treatment response in patients with HCC. We integrated the transcriptomic data and clinicopathological information of HCC patients from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. LASSO Cox regression analysis was utilized to establish an integrated multigene signature in the TCGA cohort, and its prediction performance was validated in the ICGC cohort. The CIBERSORT algorithm was employed to evaluate immune cell infiltration. The response rate to immune checkpoint inhibition (ICI) therapy was assessed using a TIDE platform. Drug activity data from the Cancer Genome Project and NCI-60 human cancer cell lines were used to predict sensitivity to chemotherapy. We successfully established a gene signature comprising G6PD, MT3, CBX2, CDKN2B, CCNA2, MAPT, EZH2, and SLC7A11. The risk score of each patient, which was determined by the multigene signature, was identified as an independent prognostic marker. The immune cell infiltration patterns, response rates to ICI therapy, and the estimated sensitivity of 89 chemotherapeutic drugs were associated with risk scores. Individual prognostic genes were also associated with susceptibility to various FDA-approved drugs. Our study indicates that a comprehensive transcriptomic analysis of OSRGs can provide a reliable molecular model to predict prognosis and therapeutic response in patients with HCC.

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression

Background

Gene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets.

Results

Meta-analysis of multi-data sets is an efficient method for tackling the above problem. To improve the performance of meta-analysis, we propose a novel meta-analysis framework. It consists of two parts: (1) a novel data augmentation strategy. Various cross-platform normalization methods exist, which can preserve original biological information of gene expression datasets from different angles and add different “perturbations” to the dataset. Using such perturbation, we provide a feasible means for gene expression data augmentation; (2) elastic data shared lasso (DSL-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\varvec{L}}}_{\mathbf{2}}$$\end{document}L2). The DSL-\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathbf{L}}_{\mathbf{2}}$$\end{document}L2 method spans the continuum between individual models for each dataset and one model for all datasets. It also overcomes the shortcomings of the data shared lasso method when dealing with highly correlated features. Comprehensive simulation experiment results show that the proposed method has high prediction and gene selection performance. We then apply the proposed method to non-small cell lung cancer (NSCLC) blood gene expression data in order to identify key tumor-related genes. The outcomes of our experiment indicate that the method could be used for identifying a set of robust disease-related gene signatures that may be used for NSCLC early diagnosis or prognosis or even targeting.

Conclusion

We propose a novel and effective meta-analysis method for biological research, extrapolating and integrating information from multiple gene expression datasets.

Exploring the role of Xingren on COVID ‐19 based on network pharmacology and molecular docking

Since the outbreak of novel Coronavirus Pneumonia 2019 (COVID‐19), the role of Almonds (Xingren) in the protection and treatment of COVID‐19 is not clear. Network pharmacology and molecular docking were used to explore the potential mechanism and potential key targets of Xingren on COVID‐19. A total of nine common targets between them were obtained, and these targets were involved in multiple related processes of GO and KEGG pathway enrichment analysis. Molecular docking showed that licochalcone B has the best binding energy (−9.33 kJ·mol⁻¹) to PTGS2. They are maybe the important ingredient and key potential target. Its possible mechanism is to intervene anxiety disorder in the process of disease development, such as regulation of blood pressure, reactive oxygen species metabolic process, leishmaniasis peroxisome, and IL‐17 signaling pathway.

HDAC5, negatively regulated by miR-148a-3p, promotes colon cancer cell migration

Histone deacetylases (HDACs) are involved in many processes including tumor cell growth and proliferation and regulation of gene expression. To clarify the role of class IIa HDACs in the metastasis of colon adenocarcinoma, we used the class IIa HDAC inhibitor TMP269 and found that it effectively inhibited the migration ability of colon adenocarcinoma cells. Next, we silenced the member of class IIa HDACs and confirmed that the migratory ability of colon adenocarcinoma cells was significantly inhibited by silencing HDAC5 or HDAC7. HDAC5 plays a variety of roles in human cancers. Here, we examined the role of HDAC5 in colon adenocarcinoma. The results indicated that HDAC5 was highly expressed in tumor tissues and negatively correlated with the expression of miR-148a-3p. Moreover, the expression of HDAC5 was correlated with tumor progression. HDAC5 markedly increased the invasion and migration of cancer cells in vitro, an effect that could be inhibited by overexpression of miR-148a-3p. Following an intraperitoneal injection of colon adenocarcinoma cells in athymic nude mice, HDAC5 promoted tumor implant. Together, these findings showed that HDAC5 overexpression in colon adenocarcinoma is consistent with tumor progression and tumor cell migration and the impact of HDAC5 overexpression is reduced by miR-148a-3p.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by “pRRophetic” R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network

Methods

For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway.

Results

We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072.

Conclusion

To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC.

Transcriptome Profiling of HCT-116 Colorectal Cancer Cells with RNA Sequencing Reveals Novel Targets for Polyphenol Nano Curcumin

Colorectal cancer is one of the leading causes of cancer-related deaths worldwide. The gemini nanoparticle formulation of polyphenolic curcumin significantly inhibits the viability of cancer cells. However, the molecular mechanisms and pathways underlying its toxicity in colon cancer are unclear. Here, we aimed to uncover the possible novel targets of gemini curcumin (Gemini-Cur) on colorectal cancer and related cellular pathways. After confirming the cytotoxic effect of Gemini-Cur by MTT and apoptotic assays, RNA sequencing was employed to identify differentially expressed genes (DEGs) in HCT-116 cells. On a total of 3892 DEGs (padj < 0.01), 442 genes showed a log2 FC >|2| (including 244 upregulated and 198 downregulated). Gene ontology (GO) enrichment analysis was performed. Protein−protein interaction (PPI) and gene-pathway networks were constructed by using STRING and Cytoscape. The pathway analysis showed that Gemini-Cur predominantly modulates pathways related to the cell cycle. The gene network analysis revealed five central genes, namely GADD45G, ATF3, BUB1B, CCNA2 and CDK1. Real-time PCR and Western blotting analysis confirmed the significant modulation of these genes in Gemini-Cur-treated compared to non-treated cells. In conclusion, RNA sequencing revealed novel potential targets of curcumin on cancer cells. Further studies are required to elucidate the molecular mechanism of action of Gemini-Cur regarding the modulation of the expression of hub genes.

Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis

Improved insight into the molecular mechanisms of triple negative breast cancer (TNBC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify key genes which may affect the prognosis of TNBC patients by bioinformatic analysis. In our study, the RNA sequencing (RNA-seq) expression data of 116 breast cancer lacking ER, PR, and HER2 expression and 113 normal tissues were downloaded from The Cancer Genome Atlas (TCGA). We screened out 147 differentially co-expressed genes in TNBC compared to non-cancerous tissue samples by using weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were constructed, revealing that 147 genes were mainly enriched in nuclear division, chromosomal region, ATPase activity, and cell cycle signaling. After using Cytoscape software for protein-protein interaction (PPI) network analysis and LASSO feature selection, a total of fifteen key genes were identified. Among them, BUB1 and CENPF were significantly correlated with the overall survival rate (OS) difference of TNBC patients (p value < 0.05). In addition, BUB1, CCNA2, and PACC1 showed significant poor disease-free survival (DFS) in TNBC patients (p value < 0.05), and may serve as candidate biomarkers in TNBC diagnosis. Thus, our results collectively suggest that BUB1, CCNA2, and PACC1 genes could play important roles in the progression of TNBC and provide attractive therapeutic targets.

An Integrated Immune-Related Bioinformatics Analysis in Glioma: Prognostic Signature’s Identification and Multi-Omics Mechanisms’ Exploration

As the traditional treatment for glioma, the most common central nervous system malignancy with poor prognosis, the efficacy of high-intensity surgery combined with radiotherapy and chemotherapy is not satisfactory. The development of individualized scientific treatment strategy urgently requires the guidance of signature with clinical predictive value. In this study, five prognosis-related differentially expressed immune-related genes (PR-DE-IRGs) (CCNA2, HMGB2, CASP3, APOBEC3C, and BMP2) highly associated with glioma were identified for a prognostic model through weighted gene co-expression network analysis, univariate Cox and lasso regression. Kaplan-Meier survival curves, receiver operating characteristic curves and other methods have shown that the model has good performance in predicting the glioma patients’ prognosis. Further combined nomogram provided better predictive performance. The signature’s guiding value in clinical treatment has also been verified by multiple analysis results. We also constructed a comprehensive competing endogenous RNA (ceRNA) regulatory network based on the protective factor BMP2 to further explore its potential role in glioma progression. Numerous immune-related biological functions and pathways were enriched in a high-risk population. Further multi-omics integrative analysis revealed a strong correlation between tumor immunosuppressive environment/IDH1 mutation and signature, suggesting that their cooperation plays an important role in glioma progression.

CCNA2 as an Immunological Biomarker Encompassing Tumor Microenvironment and Therapeutic Response in Multiple Cancer Types

Background

Cancer is a major threat to human health worldwide. Although recent innovations and advances in early detection and effective therapies such as targeted drugs and immune checkpoint inhibitors have saved more lives of cancer patients and improved their quality of life, our knowledge about cancer remains largely unknown. CCNA2 belongs to the cell cyclin family and has been demonstrated to be a tumorigenic gene in multiple solid tumor types. The aim of the present study was to make a comprehensive analysis on the role of CCNA2 at a pancancer level.

Methods

Multidatabases were collected to evaluate the different expression, prognostic value, DNA methylation, tumor mutation burden, microsatellite instability, mismatch repair, tumor immune microenvironment, and drug sensitivity of CCNA2 across pancancer. IHC was utilized to validate the expression and prognostic value of CCNA2 in ccRCC patients from SMMU cohort.

Results

CCNA2 was differentially expressed in most cancer types vs. normal tissues. CCNA2 may significantly influence the prognosis of multiple cancer types, especially clear cell renal cell carcinoma (ccRCC). CCNA2 was also frequently mutated in most cancer types. Notably, CCNA2 was significantly correlated with immune cell infiltration and immune checkpoint inhibitory genes. In addition, CCNA2 was also strongly related to drug resistance.

Conclusion

CCNA2 may prove to be a new biomarker for prognostic prediction, tumor immunity assessment, and drug susceptibility evaluation in pancancer level, especially in ccRCC.

Characterization of Cell Cycle-Related Competing Endogenous RNAs Using Robust Rank Aggregation as Prognostic Biomarker in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD), one of the most common pathological subtypes in lung cancer, has been of concern because it is the leading cause of cancer-related deaths. Due to its poor prognosis, to identify a prognostic biomarker, this study performed an integrative analysis to screen curial RNAs and discuss their cross-talks. The messenger RNA (mRNA) profiles were primarily screened using robust rank aggregation (RRA) through several datasets, and these deregulated genes showed important roles in multiple biological pathways, especially for cell cycle and oocyte meiosis. Then, 31 candidate genes were obtained via integrating 12 algorithms, and 16 hub genes (containing homologous genes) were further screened according to the potential prognostic values. These hub genes were used to search their regulators and biological-related microRNAs (miRNAs). In this way, 10 miRNAs were identified as candidate small RNAs associated with LUAD, and then miRNA-related long non-coding RNAs (lncRNAs) were further obtained. In-depth analysis showed that 4 hub mRNAs, 2 miRNAs, and 2 lncRNAs were potential crucial RNAs in the occurrence and development of cancer, and a competing endogenous RNA (ceRNA) network was then constructed. Finally, we identified CCNA2/MKI67/KIF11:miR-30a-5p:VPS9D1-AS1 axis-related cell cycle as a prognostic biomarker, which provided RNA cross-talks among mRNAs and non-coding RNAs (ncRNAs), especially at the multiple isomiR levels that further complicated the coding–non-coding RNA regulatory network. Our findings provide insight into complex cross-talks among diverse RNAs particularly involved in isomiRs, which will enrich our understanding of mRNA–ncRNA interactions in coding–non-coding RNA regulatory networks and their roles in tumorigenesis.

Orosomucoid 1 promotes epirubicin resistance in breast cancer by upregulating the expression of matrix metalloproteinases 2 and 9

Orosomucoid 1 (ORM1) has been shown to be upregulated in the serum of breast cancer patients; however, the expression and function of ORM1 in breast cancer remains unknown. We measured the expression of ORM1 in breast cancer tissues and cell lines using qRT-PCR. A colony formation assay was done to assess cell proliferation and Transwell and wound healing assays were performed to determine the migration and invasion capacity of the cells, respectively. In addition, a CCK-8 assay was used to measure epirubicin cytotoxicity and western blot assays were done to analyze the putative mechanisms of epirubicin sensitivity. We found that the expression of ORM1 was upregulated in breast cancer tissues and cell lines. The expression of ORM1 enhanced the proliferation and migration of the cell lines. In contrast, down-regulation of ORM1 inhibited the expression of MMP-2 and MMP-9 and activation of the AKT/ERK signaling pathway. Therefore, ORM1 may represent a potential therapeutic target for breast cancer and promote epirubicin resistance by regulating the expression of MMP-2 and MMP-9, as well as activating the AKT/ERK signaling pathway.

Cytotoxic and Proapoptotic Effects of Resveratrol in In Vitro Studies on Selected Types of Gastrointestinal Cancers

Cancer diseases are currently one of the greatest health challenges in clinical medicine worldwide. Classic methods of treatment often lead to numerous side effects, including the development of multidrug resistance. For this reason, increasing hope is being placed on compounds of natural origin, mainly due to their pleiotropic effect on different types of cells, protective effect on normal cells and toxic effect on cancerous ones. The most studied group are the polyphenolic compounds, which include resveratrol. The effectiveness of polyphenols in the treatment and prevention of many diseases, including cancer of various origins, has become the basis of many scientific studies. The anticancer effect of resveratrol has been demonstrated at all stages of the carcinogenesis process. Additionally, whether administered by itself or in combination with cytostatics, it may play a significant role in the process of reversing multidrug resistance. A review of the effects of resveratrol in in vitro conditions proves that it has a stronger or weaker antiproliferative and proapoptotic effect on the cells of certain neoplasms of the gastrointestinal tract. Despite the differences in the effect of this compound on different types of cancer, a similar tendency can be observed especially regarding the correlation between the concentration of the compound and the incubation time on the one hand and the antitumour effect on the other hand. The information included in this review may prove helpful in planning in vivo and clinical studies in the future.

Expression and Clinical Significance of Lactate Dehydrogenase A in Colon Adenocarcinoma

Lactate dehydrogenase A (LDHA) is an important glycolytic enzyme that promotes glycolysis and plays a crucial role in cancer cell invasion and immune infiltration. However, the relevance of LDHA in colon adenocarcinoma (COAD) remains unclear. In this study, we analyzed the correlation between the expression of LDHA and clinicopathological characteristics in COAD using immunohistochemistry analysis, and then used integrative bioinformatics analyses to further study the function and role of LDHA in COAD. We found that LDHA was highly expressed in COAD tissues compared with adjacent normal tissues, and that COAD patients with high LDHA expression levels showed poor survival. In addition, LDHA expression was closely associated with the immune infiltrating levels of CD8+ T cells, neutrophils, and dendritic cells. Our findings highlight the potential role of LDHA in the tumorigenesis and prognosis of COAD. Furthermore, our results indicate that COAD is a novel immune checkpoint in the diagnosis and treatment of COAD.

Bioinformatics study on genes related to a high-risk postoperative recurrence of lung adenocarcinoma

In this study, we aimed to screen out genes associated with a high risk of postoperative recurrence of lung adenocarcinoma and investigate the possible mechanisms of the involvement of these genes in the recurrence of lung adenocarcinoma. We identify Hub genes and verify the expression levels and prognostic roles of these genes. Datasets of GSE40791, GSE31210, and GSE30219 were obtained from the Gene Expression Omnibus database. Enrichment analysis of gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the screened candidate genes using the DAVID database. Then, we performed protein-protein interaction (PPI) network analysis through the database STRING. Hub genes were screened out using Cytoscape software, and their expression levels were determined by the GEPIA database. Finally, we assessed the relationships of Hub genes expression levels and the time of survival. Forty-five candidate genes related to a high-risk of lung adenocarcinoma recurrence were screened out. Gene ontology analysis showed that these genes were enriched in the mitotic spindle assembly checkpoint, mitotic sister chromosome segregation, G2/M-phase transition of the mitotic cell cycle, and ATP binding, etc. KEGG analysis showed that these genes were involved predominantly in the cell cycle, p53 signaling pathway, and oocyte meiosis. We screened out the top ten Hub genes related to high expression of lung adenocarcinoma from the PPI network. The high expression levels of eight genes (TOP2A, HMMR, MELK, MAD2L1, BUB1B, BUB1, RRM2, and CCNA2) were related to short recurrence-free survival and they can be used as biomarkers for high risk of lung adenocarcinoma recurrence. This study screened out eight genes associated with a high risk of lung adenocarcinoma recurrence, which might provide novel insights into researching the recurrence mechanisms of lung adenocarcinoma as well as into the selection of targets in the treatment of the disease.

Identification of ZNF26 as a Prognostic Biomarker in Colorectal Cancer by an Integrated Bioinformatic Analysis

The dysregulation of transcriptional factors (TFs) leads to malignant growth and the development of colorectal cancer (CRC). Herein, we sought to identify the transcription factors relevant to the prognosis of colorectal cancer patients. We found 526 differentially expressed TFs using the TCGA database of colorectal cancer patients (n = 544) for the differential analysis of TFs (n = 1,665) with 210 upregulated genes as well as 316 downregulated genes. Subsequently, GO analysis and KEGG pathway analysis were performed for these differential genes for investigating their pathways and function. At the same time, we established a genetic risk scoring model for predicting the overall survival (OS) by using the mRNA expression levels of these differentially regulated TFs, and defined the CRC into low and high-risk categories which showed significant survival differences. The genetic risk scoring model included four high-risk genes (HSF4, HEYL, SIX2, and ZNF26) and two low-risk genes (ETS2 and SALL1), and validated the OS in two GEO databases (p = 0.0023 for the GSE17536, p = 0.0193 for the GSE29623). To analyze the genetic and epigenetic changes of these six risk-related TFs, a unified bioinformatics analysis was conducted. Among them, ZNF26 is progressive in CRC and its high expression is linked with a poor diagnosis as well. Knockdown of ZNF26 inhibits the proliferative capacity of CRC cells. Moreover, the positive association between ZNF26 and cyclins (CDK2, CCNE2, CDK6, CHEK1) was also identified. Therefore, as a novel biomarker, ZNF26 may be a promising candidate in the diagnosis and prognostic evaluation of colorectal cancer.

Integrated analysis of ceRNA network in hepatocellular carcinoma using bioinformatics analysis

BACKGROUND

Long noncoding RNAs (lncRNAs) can work as microRNA (miRNA) sponges through a competitive endogenous RNA (ceRNA) mechanism. LncRNAs and miRNAs are important components of competitive endogenous binding, and their expression imbalance in hepatocellular carcinoma (HCC) is closely related to tumor development, diagnosis, and prognosis. This study explored the potential impact of the ceRNA regulatory network in HCC on the prognosis of HCC patients.

METHODS

We thoroughly researched the differential expression profiles of lncRNAs, miRNAs, and mRNAs from 2 HCC Gene Expression Omnibus datasets (GSE98269 and GSE60502). Then, a dysregulated ceRNA network was constructed by bioinformatics. In addition, hub genes in the ceRNA network were screened by Cytoscape, these hub genes functional analysis was performed by gene set enrichment analysis, and the expression of these hub genes in tumors and their correlation with patient prognosis were verified with Gene Expression Profiling Interactive Analysis.

RESULTS

A ceRNA network was successfully constructed in this study including 4 differentially expressed (DE) lncRNAs, 7 DEmiRNAs, and 166 DEmRNAs. Importantly, 4 core genes (CCNA2, CHEK1, FOXM1, and MCM2) that were significantly associated with HCC prognosis were identified.

CONCLUSIONS

Our study provides comprehensive and meaningful insights into HCC tumorigenesis and the underlying molecular mechanisms of ceRNA. Furthermore, the specific ceRNAs can be further used as potential therapeutic targets and prognostic biomarkers for HCC.

ASPM is a Novel Candidate Gene Associated with Colorectal Cancer Cell Growth

Colorectal cancer (CRC) is one of the most prevalent diseases worldwide; however, the molecular mechanisms involved in CRC remain unclear. Thus, we aimed to explore a novel biomarker for CRC. In this study, we screened 361 differentially expressed genes; 152 downregulated genes; and 209 upregulated genes) through analysis of the GSE44861, GSE110223, GSE110224, and GSE113513 CRC datasets. Next, ASPM, CCNA2, CCNB1, CEP55, KIF20A, MAD2L1, MELK, RRM2, TOP2A, TPX2, TRIP13, and TTK were identified as hub genes associated with the cell cycle in CRC through comprehensive bioinformatics analysis using the Cytoscape and Metascape software, the Database for Annotation, Visualization, and Integrated Discovery (DAVID), and the Oncomine and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases. Furthermore, ASPM mRNA expression in CRC tissues was verified in Oncomine, The Cancer Genome Atlas and our data, and ASPM was found to be significantly upregulated in CRC tissues compared with that in the noncancer colon tissues. Functionally, we showed that overexpression of ASPM significantly promoted the proliferation and inhibited apoptosis; silencing of ASPM suppressed the proliferation of CRC cells by affecting the cell cycle G1/S transition by reducing cyclin E1 expression, and inducing apoptosis. Overall, our findings indicated that ASPM plays a crucial role in the regulation of CRC cell proliferation, and ASPM is a potential candidate diagnostic tool and therapeutic target for CRC.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

Role of Stress-Survival Pathways and Transcriptomic Alterations in Progression of Colorectal Cancer: A Health Disparities Perspective

Every year, more than a million individuals are diagnosed with colorectal cancer (CRC) across the world. Certain lifestyle and genetic factors are known to drive the high incidence and mortality rates in some groups of individuals. The presence of enormous amounts of reactive oxygen species is implicated for the on-set and carcinogenesis, and oxidant scavengers are thought to be important in CRC therapy. In this review, we focus on the ethnicity-based CRC disparities in the U.S., the negative effects of oxidative stress and apoptosis, and gene regulation in CRC carcinogenesis. We also highlight the use of antioxidants for CRC treatment, along with screening for certain regulatory genetic elements and oxidative stress indicators as potential biomarkers to determine the CRC risk and progression.

Expression and prognostic value of CDK1, CCNA2, and CCNB1 gene clusters in human breast cancer

Objective

Cell cycle-associated proteins play important roles in breast cancer (BRCA), based on evidence from cell lines, preclinical murine models, and human tissue samples.

Methods

Herein, we used the Onomine, GEPIA, Kaplan–Meier Plotter, and cBioPortal databases to examine transcriptional and survival data pertaining to cyclin-associated gene clusters (CDK1, CCNA2, and CCNB1) in BRCA patients.

Results

CDK1, CCNA2, and CCNB1 gene expression levels were higher in BRCA compared with control tissue samples and were correlated with more-advanced tumor stage. Kaplan–Meier survival analyses confirmed that elevated CDK1, CCNA2, and CCNB1 expression levels were associated with overall and post-progression survival and recurrence-free probability rates in patients with BRCA.

Conclusion

The results of this study implied that CDK1, CCNA2, and CCNB1 gene clusters may provide potential therapeutic targets and prognostic biomarkers in patients with BRCA.

Comprehensive Analysis of Cyclin Family Gene Expression in Colon Cancer

Colon cancer is a common malignancy of the digestive tract with high morbidity and mortality. There is an urgent need to identify effective biomarkers for the early diagnosis of colon cancer and to prolong patient survival. Cyclins are a family of proteins that directly participate in the cell cycle and are associated with many types of tumors, but the role and regulatory mechanism of most cyclin family members in colon cancer remain unclear. Here, we provide a systematic and comprehensive study of cyclin family gene expression and their potential roles in colon cancer. Pan-cancer analysis revealed that cyclin genes were most differentially expressed in colon adenocarcinoma. Among the four datasets of colon cancer from The Cancer Genome Atlas and the Gene Expression Omnibus, six cyclin genes (CCNA2, CCNB1, CCND1, CCNE1, CCNF, and CCNJL) were differentially expressed between normal and tumor tissues. Four of them (CCNA2, CCNB1, CCNE1, and CCNF) were notably elevated in the early TNM stages and significantly correlated with overall survival. Meanwhile, the expression of CCNA2 and CCNB1 was positively correlated with tumor-killing immune cells, such as CD8+ T cells.The copy numbers of CCNA2, CCNB1, CCND1, CCNE1, and CCNF was positively related to gene expression. The methylation levels of CCNB1 were lower in tumor tissues than in normal tissues and were negatively correlated with gene expression. The receiver operating characteristic curves indicated that the gene expression of 24 cyclins had higher predictive accuracy than the TNM stage. Pathway analysis showed that cyclin genes were tightly associated with apoptosis, the cell cycle, hormone ER, the RAS/MAPK pathway, mismatch repair, mTORC1 signaling, KRAS signaling, Akt, and TGFB in colon cancer. Weighted gene co-expression network analysis suggested that cyclin genes were closely linked to CDK1, BIRC5, PLK1, and BCL2L12. At the protein level, Cyclin A2 and Cyclin B1 were also expressed higher in colon adenocarcinoma tissues. In addition, cyclin genes were highly related to the drug sensitivity of some FDA-approved drugs, such as MEK and EGFR inhibitors, which might provide guidance for clinical treatment. In conclusion, cyclin genes are promising biomarkers for the diagnosis and prognosis of colon cancer.

Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e-26) and tumor stage (r = 0.38, p = 2e-17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

RPN2 Predicts Poor Prognosis and Promotes Bladder Cancer Growth and Metastasis via the PI3K-Akt Pathway

Background

Ribophorin II (RPN2) is a highly conserved glycoprotein involved in the N-linked glycosylation of multiple proteins. RPN2 was reported to be associated with malignant phenotype in several tumors. However, the function of RPN2 in bladder cancer (BCa) remains unclear.

Methods

Expression of RPN2 in BCa and adjacent tissues was compared by bioinformatics analysis, immunohistochemistry, and Western blotting. qRT-PCR was performed to explore the correlation between RPN2 expression and various clinical features in 38 patients. We assessed the effects of RPN2 on the biological activity of BCa both in vitro and in vivo, and explored its potential mechanisms based on gene set enrichment analysis (GSEA).

Results

We found that RPN2 was highly expressed in human BCa compared with normal adjacent tissues. There was a significant positive correlation between higher RPN2 mRNA levels and tumor T stage, lymph node (LN) metastasis and the degree of pathological differentiation in 38 patients with BCa. We further demonstrated that RPN2 silencing inhibited the growth and metastasis of BCa both in vitro and in vivo. Western blotting revealed that RPN2 knockdown suppressed epithelial-mesenchymal transition (EMT) and inhibited the PI3K-Akt pathway.

Conclusion

These data suggest that RPN2 functions as an oncogene to promote tumor development and is a promising prognostic factor and therapeutic target in BCa.

Key Genes and Prognostic Analysis in HER2+ Breast Cancer

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

Identification of Potential Diagnostic and Prognostic Biomarkers for Colorectal Cancer Based on GEO and TCGA Databases

Colorectal cancer (CRC) is one of the most common neoplastic diseases worldwide. With a high recurrence rate among all cancers, treatment of CRC only improved a little over the last two decades. The mortality and morbidity rates can be significantly lessened by earlier diagnosis and prompt treatment. Available biomarkers are not sensitive enough for the diagnosis of CRC, whereas the standard diagnostic method, endoscopy, is an invasive test and expensive. Hence, seeking the diagnostic and prognostic biomarkers of CRC is urgent and challenging. With that order, we screened the overlapped differentially expressed genes (DEGs) of GEO (GSE110223, GSE110224, GSE113513) and TCGA datasets. Subsequent protein-protein interaction network analysis recognized the hub genes among these DEGs. Further functional analyses including Gene Ontology and KEGG pathway analysis and gene set enrichment analysis were processed to investigate the role of these genes and potential underlying mechanisms in CRC. Kaplan-Meier analysis and Cox hazard ratio analysis were carried out to clarify the diagnostic and prognostic role of these genes. In conclusion, our present study demonstrated that CCNA2, MAD2L1, DLGAP5, AURKA, and RRM2 are all potential diagnostic biomarkers for CRC and may also be potential treatment targets for clinical implication in the future.

Construction of an mRNA-miRNA-lncRNA network prognostic for triple-negative breast cancer

The aim of this study was to establish a novel competing endogenous RNA (ceRNA) network able to predict prognosis in patients with triple-negative breast cancer (TNBC). Differential gene expression analysis was performed using the GEO2R tool. Enrichr and STRING were used to conduct protein-protein interaction and pathway enrichment analyses, respectively. Upstream lncRNAs and miRNAs were identified using miRNet and mirTarBase, respectively. Prognostic values, expression, and correlational relationships of mRNAs, lncRNAs, and miRNAs were examined using GEPIA, starBase, and Kaplan-Meier plotter. It total, 860 upregulated and 622 downregulated differentially expressed mRNAs were identified in TNBC. Ten overexpressed and two underexpressed hub genes were screened. Next, 10 key miRNAs upstream of these key hub genes were predicted, of which six upregulated miRNAs were significantly associated with poor prognosis and four downregulated miRNAs were associated with good prognosis in TNBC. NEAT1 and MAL2 were selected as key lncRNAs. An mRNA-miRNA-lncRNA network in TNBC was constructed. Thus, we successfully established a novel mRNA-miRNA-lncRNA regulatory network, each component of which is prognostic for TNBC.

Identification and Analysis of Potential Key Genes Associated With Hepatocellular Carcinoma Based on Integrated Bioinformatics Methods

BACKGROUND

Hepatocellular carcinoma (HCC) is a type of primary liver tumor with poor prognosis and high mortality, and its molecular mechanism remains incompletely understood. This study aimed to use bioinformatics technology to identify differentially expressed genes (DEGs) in HCC pathogenesis, hoping to identify novel biomarkers or potential therapeutic targets for HCC research.

METHODS

The bioinformatics analysis of our research mostly involved the following two datasets: Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). First, we screened DEGs based on the R packages (limma and edgeR). Using the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of DEGs were carried out. Next, the protein-protein interaction (PPI) network of the DEGs was built in the STRING database. Then, hub genes were screened through the cytoHubba plug-in, followed by verification using the GEPIA and Oncomine databases. We demonstrated differences in levels of the protein in hub genes using the Human Protein Atlas (HPA) database. Finally, the hub genes prognostic values were analyzed by the GEPIA database. Additionally, using the Comparative Toxicogenomics Database (CTD), we constructed the drug-gene interaction network.

RESULTS

We ended up with 763 DEGs, including 247 upregulated and 516 downregulated DEGs, that were mainly enriched in the epoxygenase P450 pathway, oxidation-reduction process, and metabolism-related pathways. Through the constructed PPI network, it can be concluded that the P53 signaling pathway and the cell cycle are the most obvious in module analysis. From the PPI, we filtered out eight hub genes, and these genes were significantly upregulated in HCC samples, findings consistent with the expression validation results. Additionally, survival analysis showed that high level gene expression of CDC20, CDK1, MAD2L1, BUB1, BUB1B, CCNB1, and CCNA2 were connected with the poor overall survival of HCC patients. Toxicogenomics analysis showed that only topotecan, oxaliplatin, and azathioprine could reduce the gene expression levels of all seven hub genes.

CONCLUSION

The present study screened out the key genes and pathways that were related to HCC pathogenesis, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of HCC.

GH and IGF System: The Regulatory Role of miRNAs and lncRNAs in Cancer

Growth hormone (GH) and the insulin-like growth factor (IGF) system are involved in many biological processes and have growth-promoting actions regulating cell proliferation, differentiation, apoptosis and angiogenesis. A recent chapter in epigenetics is represented by microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) which regulate gene expression. Dysregulated miRNAs and lncRNAs have been associated with several diseases including cancer. Herein we report the most recent findings concerning miRNAs and lncRNAs regulating GH and the IGF system in the context of pituitary adenomas, osteosarcoma and colorectal cancer, shedding light on new possible therapeutic targets. Pituitary adenomas are increasingly common intracranial tumors and somatotroph adenomas determine supra-physiological GH secretion and cause acromegaly. Osteosarcoma is the most frequent bone tumor in children and adolescents and was reported in adults who were treated with GH in childhood. Colorectal cancer is the third cancer in the world and has a higher prevalence in acromegalic patients.

The Establishment of a Fast and Safe Orthotopic Colon Cancer Model Using a Tissue Adhesive Technique

Purpose

We aimed to develop a novel method for orthotopic colon cancer model, using tissue adhesive in place of conventional surgical method.

Materials and Methods

RFP HCT 116 cell line were used to establish the colon cancer model. Fresh tumor tissue harvested from a subcutaneous injection was grafted into twenty nude mice, divided into group A (suture method) and group B (tissue adhesive method). For the group A, we fixed the tissue on the serosa layer of proximal colon by 8-0 surgical suture. For the group B, tissue adhesive (10 μL) was used to fix the tumor. The mortality, tumor implantation success, tumor metastasis, primary tumor size, and operation time were compared between the two groups. Dissected tumor tissue was analyzed for the histology and immunohistochemistry. Also, we performed tumor marker analysis.

Results

We observed 30% increase in graft success and 20% decrease in mortality, by using tissue adhesive method, respectively. The median colon tumor size was significantly increased by 4 mm and operation time was shortened by 6.5 minutes. The H&E showed similar tumor structure between the two groups. The immunohistochemistry staining for cancer antigen 19-9, carcinoembryonic antigen, cytokeratin 20, and Ki-67 showed comparable intensities in both groups. Real-time quantitative reverse transcription analysis showed eight out of nine tumor markers are unchanged in the tissue adhesive group. Western blot indicated the tissue adhesive group expressed less p-JNK (apototic marker) and more p-MEK/p-p38 (proliferation marker) levels.

Conclusion

We concluded the tissue adhesive method is a quick and safe way to generate orthotopic, colon cancer model.

Ribosome assembly factor URB1 contributes to colorectal cancer proliferation through transcriptional activation of ATF4

Ribosome assembly factor URB1 is essential for ribosome biogenesis. However, its latent role in cancer remains unclear. Analysis of The Cancer Genome Atlas database and clinical tissue microarray staining showed that URB1 expression was upregulated in colorectal cancer (CRC) and prominently related to clinicopathological characteristics. Silencing of URB1 hampered human CRC cell proliferation and growth in vitro and in vivo. Microarray screening, ingenuity pathway analysis, and JASPAR assessment indicated that activating transcription factor 4 (ATF4) and X‐box binding protein 1 (XBP1) are potential downstream targets of URB1 and could transcriptionally interact through direct binding. Silencing of URB1 significantly decreased ATF4 and cyclin A2 (CCNA2) expression in vivo and in vitro. Restoration of ATF4 effectively reversed the malignant proliferation phenotype of URB1‐silenced CRC cells. Dual‐luciferase reporter and ChIP assays indicated that XBP1 transcriptionally activated ATF4 by binding with its promoter region. X‐box binding protein 1 colocalized with ATF4 in the nuclei of RKO cells, and ATF4 mRNA expression was positively regulated by XBP1. This study shows that URB1 contributes to oncogenesis and CRC growth through XBP1‐mediated transcriptional activation of ATF4. Therefore, URB1 could be a potential therapeutic target for CRC.

Bioinformatic Identification of Hub Genes and Analysis of Prognostic Values in Colorectal Cancer

The purpose of this study is to discover novel hub genes which are helpful for diagnosis, prognosis, and targeted therapy in colorectal cancer (CRC) by using bioinformatics analysis. GSE74602, GSE110225, and GSE113513 were extracted from the gene expression omnibus (GEO). Differentially expressed genes (DEGs) in expression profiles were identified by GEO2R. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses of the DEGs were carried out in the Database for Annotation, Visualization, and Integrated Discovery (DAVID). String database and cytoscape were used for building protein-protein interaction (PPI) network and module analysis. The UALCAN was used for in-depth analysis of data of CRC patients from The Cancer Genome Atlas (TCGA) to identify expression levels and overall survival rates of hub genes. The DEGs included 107 up-regulation genes and 232 down-regulation genes. Twenty-nine (29) hub genes and two significant modules were screened from PPI network. The expression levels of hub genes in TCGA were verified. Survival analysis curve indicated high expression of CCNA2, CCNB1, DLGAP5, were related to high survival rates, and low expression of TIMP1 were associated with high survival rates. These results suggest that DEGs may be the hub genes of CRC, and CCNA2, CCNB1, DLGAP5, TIMP1 may be the potential prognostic markers of CRC.

Establishment of a Colorectal Cancer-Related MicroRNA-mRNA Regulatory Network by Microarray and Bioinformatics

Colorectal cancer (CRC) is one of the most malignant cancers with high morbidity and mortality. MicroRNAs (miRNAs) are small non-coding RNAs that affect biological processes by binding to mRNAs and regulating their expression, and epigenetic alterations including miRNA dysregulation are significantly involved in CRC development. Determining the effect of the miRNA-mRNA network on CRC could be helpful for developing novel therapeutic targets and prognostic biomarkers, and even improving survival. In this study, microarray assays were used to screen differentially expressed miRNAs (DE miRNAs) and mRNAs (DE mRNAs) in CRC and the adjacent normal tissues. Among the detected genes, 42 miRNAs and 142 mRNAs were significantly upregulated in CRC, while 23 miRNAs and 279 mRNAs were significantly downregulated. Through overlapping of predicted targets of DE miRNAs and anti-expressed DE mRNAs, networks of DE miRNAs and DE mRNAs in CRC were established. Additionally, the formation of a protein-protein interaction network of DE mRNAs possibly targeted by DE miRNAs, functional annotation and pathway analysis, stable subnetwork mining, and determination of hub genes provided the probable mechanism used by DE miRNAs and DE mRNAs to regulate CRC growth. Finally, validation of expression and prognostic potential of hub genes provided further support for the results above and indicated that CCL-28, GPR15, PNOC, NUSAP1, and their interacted miRNAs may be a potential signature for prognosis of CRC patients. In sum, we successfully established miRNA-mRNA regulatory networks based on microarray results targeting CRC, and these findings may elucidate the mechanisms used for CRC growth and identify miRNA-related signatures for prognosis and treatment of CRC.