Pharmacogenomic Analysis Reveals CCNA2 as a Predictive Biomarker of Sensitivity to Polo-Like Kinase I Inhibitor in Gastric Cancer

CDK1 and CCNA2 play important roles in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a malignant tumor that occurs in oral cavity and is dominated by squamous cells. The relationship between CDK1, CCNA2, and OSCC is still unclear. The OSCC datasets GSE74530 and GSE85195 configuration files were downloaded from the Gene Expression Omnibus (GEO) database and were derived from platforms GPL570 and GPL6480. Differentially expressed genes (DEGs) were screened. The weighted gene co-expression network analysis, functional enrichment analysis, gene set enrichment analysis, construction and analysis of protein-protein interaction (PPI) network, Comparative Toxicogenomics Database analysis were performed. Gene expression heatmap was drawn. TargetScan was used to screen miRNAs that regulate central DEGs. A total of 1756 DEGs were identified. According to Gene Ontology (GO) analysis, they were predominantly enriched in processes related to organic acid catabolic metabolism, centromeric, and chromosomal region condensation, and oxidoreductase activity. In Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, the DEGs were mainly concentrated in metabolic pathways, P53 signaling pathway, and PPAR signaling pathway. Weighted gene co-expression network analysis was performed with a soft-thresholding power set at 9, leading to the identification of 6 core genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1). The gene expression heatmap revealed that core genes (CDK1, CCNA2) were highly expressed in OSCC samples. Comparative Toxicogenomics Database analysis demonstrated associations between the 6 genes (BUB1B, CCNB1, KIF20A, CCNA2, CDCA8, CDK1) and oral tumors, precancerous lesions, inflammation, immune system disorders, and tongue tumors. The associated miRNAs for CDK1 gene were hsa-miR-203a-3p.2, while for CCNA2 gene, they were hsa-miR-6766-3p, hsa-miR-4782-3p, and hsa-miR-219a-5p. CDK1 and CCNA2 are highly expressed in OSCC. The higher the expression of CDK1 and CCNA2, the worse the prognosis.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

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.

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.

Relationship between the deep features of the full-scan pathological map of mucinous gastric carcinoma and related genes based on deep learning

Background

Long-term differential expression of disease-associated genes is a crucial driver of pathological changes in mucinous gastric carcinoma. Therefore, there should be a correlation between depth features extracted from pathology-based full-scan images using deep learning and disease-associated gene expression. This study tried to provides preliminary evidence that long-term differentially expressed (disease-associated) genes lead to subtle changes in disease pathology by exploring their correlation, and offer a new ideas for precise analysis of pathomics and combined analysis of pathomics and genomics.

Methods

Full pathological scans, gene sequencing data, and clinical data of patients with mucinous gastric carcinoma were downloaded from TCGA data. The VGG-16 network architecture was used to construct a binary classification model to explore the potential of VGG-16 applications and extract the deep features of the pathology-based full-scan map. Differential gene expression analysis was performed and a protein-protein interaction network was constructed to screen disease-related core genes. Differential, Lasso regression, and extensive correlation analyses were used to screen for valuable deep features. Finally, a correlation analysis was used to determine whether there was a correlation between valuable deep features and disease-related core genes.

Result

The accuracy of the binary classification model was 0.775 ± 0.129. A total of 24 disease-related core genes were screened, including ASPM, AURKA, AURKB, BUB1, BUB1B, CCNA2, CCNB1, CCNB2, CDCA8, CDK1, CENPF, DLGAP5, KIF11, KIF20A, KIF2C, KIF4A, MELK, PBK, RRM2, TOP2A, TPX2, TTK, UBE2C, and ZWINT. In addition, differential, Lasso regression, and extensive correlation analyses were used to screen eight valuable deep features, including features 51, 106, 109, 118, 257, 282, 326, and 487. Finally, the results of the correlation analysis suggested that valuable deep features were either positively or negatively correlated with core gene expression.

Conclusion

The preliminary results of this study support our hypotheses. Deep learning may be an important bridge for the joint analysis of pathomics and genomics and provides preliminary evidence for long-term abnormal expression of genes leading to subtle changes in pathology.

The Regulation of Cyclins and Cyclin-Dependent Kinases in the Development of Gastric Cancer

Gastric cancer predominantly occurs in adenocarcinoma form and is characterized by uncontrolled growth and metastases of gastric epithelial cells. The growth of gastric cells is regulated by the action of several major cell cycle regulators including Cyclins and Cyclin-dependent kinases (CDKs), which act sequentially to modulate the life cycle of a living cell. It has been reported that inadequate or over-activity of these molecules leads to disturbances in cell cycle dynamics, which consequently results in gastric cancer development. Manny studies have reported the key roles of Cyclins and CDKs in the development and progression of the disease in either in vitro cell culture studies or in vivo models. We aimed to compile the evidence of molecules acting as regulators of both Cyclins and CDKs, i.e., upstream regulators either activating or inhibiting Cyclins and CDKs. The review entails an introduction to gastric cancer, along with an overview of the involvement of cell cycle regulation and focused on the regulation of various Cyclins and CDKs in gastric cancer. It can act as an extensive resource for developing new hypotheses for future studies.

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.

Identification and validation of real hub genes in hepatocellular carcinoma based on weighted gene co-expression network analysis

BACKGROUND

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

OBJECTIVE

This work aims to help identify biomarkers of early HCC diagnosis or prognosis based on weighted gene co-expression network analysis (WGCNA).

METHODS

Expression data and clinical information of HTSEQ-Counts were downloaded from The Cancer Genome Atlas (TCGA) database, and Gene Expression map GSE121248 was downloaded from Gene Expression Omnibus (GEO). By differentially expressed genes (DEGs) and Weighted Gene co-expression Network Analysis (WGCNA) searched for modules in the two databases that had the same effect on the biological characteristics of HCC, and extracted the module genes with the highest positive correlation with HCC from two databases, and finally obtained overlapping genes. Then, we performed functional enrichment analysis on the overlapping genes to understand their potential biological functions. The top ten hub genes were screened according to MCC through the String database and Cytoscape software and then subjected to survival analysis.

RESULTS

High expression of CDK1, CCNA2, CDC20, KIF11, DLGAP5, KIF20A, ASPM, CEP55, and TPX2 was associated with poorer overall survival (OS) of HCC patients. The DFS curve was plotted using the online website GEPIA2. Finally, based on the enrichment of these genes in the KEGG pathway, real hub genes were screened out, which were CDK1, CCNA2, and CDC20 respectively.

CONCLUSIONS

High expression of these three genes was negatively correlated with survival time in HCC, and the expression of CDK1, CCNA2, and CDC20 were significantly higher in tumor tissues of HCC patients than in normal liver tissues as verified again by the HPA database. All in all, this provides a new feasible target for early and accurate diagnosis of HCC, clinical diagnosis, treatment, and prognosis.

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

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

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

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

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

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.

Integrative analysis of key candidate genes and signaling pathways in ovarian cancer by bioinformatics

Background

Ovarian cancer is one of the most common gynecological tumors, and among gynecological tumors, its incidence and mortality rates are fairly high. However, the pathogenesis of ovarian cancer is not clear. The present study aimed to investigate the differentially expressed genes and signaling pathways associated with ovarian cancer by bioinformatics analysis.

Methods

The data from three mRNA expression profiling microarrays (GSE14407, GSE29450, and GSE54388) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes between ovarian cancer tissues and normal tissues were identified using R software. The overlapping genes from the three GEO datasets were identified, and profound analysis was performed. The overlapping genes were used for pathway and Gene Ontology (GO) functional enrichment analysis using the Metascape online tool. Protein–protein interactions were analyzed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING). Subnetwork models were selected using the plugin molecular complex detection (MCODE) application in Cytoscape. Kaplan–Meier curves were used to analyze the univariate survival outcomes of the hub genes. The Human Protein Atlas (HPA) database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to validate hub genes.

Results

In total, 708 overlapping genes were identified through analyses of the three microarray datasets (GSE14407, GSE29450, and GSE54388). These genes mainly participated in mitotic sister chromatid segregation, regulation of chromosome segregation and regulation of the cell cycle process. High CCNA2 expression was associated with poor overall survival (OS) and tumor stage. The expression of CDK1, CDC20, CCNB1, BUB1B, CCNA2, KIF11, CDCA8, KIF2C, NDC80 and TOP2A was increased in ovarian cancer tissues compared with normal tissues according to the Oncomine database. Higher expression levels of these seven candidate genes in ovarian cancer tissues compared with normal tissues were observed by GEPIA. The protein expression levels of CCNA2, CCNB1, CDC20, CDCA8, CDK1, KIF11 and TOP2A were high in ovarian cancer tissues, which was further confirmed via the HPA database.

Conclusion

Taken together, our study provided evidence concerning the altered expression of genes in ovarian cancer tissues compared with normal tissues. In vivo and in vitro experiments are required to verify the results of the present study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-021-00837-6.

Targeting cell-cycle machinery in cancer

Abnormal activity of the core cell-cycle machinery is seen in essentially all tumor types and represents a driving force of tumorigenesis. Recent studies revealed that cell-cycle proteins regulate a wide range of cellular functions, in addition to promoting cell division. With the clinical success of CDK4/6 inhibitors, it is becoming increasingly clear that targeting individual cell-cycle components may represent an effective anti-cancer strategy. Here, we discuss the potential of inhibiting different cell-cycle proteins for cancer therapy.

Identification of novel hub genes associated with gastric cancer using integrated bioinformatics analysis

Background

Gastric cancer (GC) is one of the most common solid malignant tumors worldwide with a high-recurrence-rate. Identifying the molecular signatures and specific biomarkers of GC might provide novel clues for GC prognosis and targeted therapy.

Methods

Gene expression profiles were obtained from the ArrayExpress and Gene Expression Omnibus database. Differentially expressed genes (DEGs) were picked out by R software. The hub genes were screened by cytohubba plugin. Their prognostic values were assessed by Kaplan–Meier survival analyses and the gene expression profiling interactive analysis (GEPIA). Finally, qRT-PCR in GC tissue samples was established to validate these DEGs.

Results

Total of 295 DEGs were identified between GC and their corresponding normal adjacent tissue samples in E-MTAB-1440, GSE79973, GSE19826, GSE13911, GSE27342, GSE33335 and GSE56807 datasets, including 117 up-regulated and 178 down-regulated genes. Among them, 7 vital upregulated genes (HMMR, SPP1, FN1, CCNB1, CXCL8, MAD2L1 and CCNA2) were selected. Most of them had a significantly worse prognosis except SPP1. Using qRT-PCR, we validated that their transcriptions in our GC tumor tissue were upregulated except SPP1 and FN1, which correlated with tumor relapse and predicts poorer prognosis in GC patients.

Conclusions

We have identified 5 upregulated DEGs (HMMR, CCNB1, CXCL8, MAD2L1, and CCNA2) in GC patients with poor prognosis using integrated bioinformatical methods, which could be potential biomarkers and therapeutic targets for GC treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08358-7.