Expression of the Topoisomerase II Alpha (TOP2A) Gene in Lung Adenocarcinoma Cells and the Association with Patient Outcomes

Comprehensive Analysis of a Six-Gene Signature Predicting Survival and Immune Infiltration of Liposarcoma Patients and Deciphering Its Therapeutic Significance

BACKGROUND

As a common soft tissue sarcoma, liposarcoma (LPS) is a heterogeneous malignant tumor derived from adipose tissue. Due to the high risk of metastasis and recurrence, the prognosis of LPS remains unfavorable. To improve clinical treatment, a robust risk prediction model is essential to evaluate the prognosis of LPS patients.

METHODS

By comprehensive analysis of data derived from GEO datasets, differentially expressed genes (DEGs) were obtained. Univariate and Lasso Cox regressions were subsequently employed to reveal distant recurrence-free survival (DRFS)-associated DEGs and develop a prognostic gene signature, which was assessed by Kaplan-Meier survival and ROC curve. GSEA and immune infiltration analyses were conducted to illuminate molecular mechanisms and immune correlations of this model in LPS progression. Furthermore, a correlation analysis was involved to decipher the therapeutic significance of this model for LPS.

RESULTS

A six-gene signature was developed to predict DRFS of LPS patients and showed higher precision performance in more aggressive LPS subtypes. Then, a nomogram was further established for clinical application based on this risk model. Via GSEA, the high-risk group was significantly enriched in cell cycle-related pathways. In the LPS microenvironment, neutrophils, memory B cells and resting mast cells exhibited significant differences in cell abundance between high-risk and low-risk patients. Moreover, this model was significantly correlated with therapeutic targets.

CONCLUSION

A prognostic six-gene signature was developed and significantly associated with cell cycle pathways and therapeutic target genes, which could provide new insights into risk assessment of LPS progression and therapeutic strategies for LPS patients to improve their prognosis.

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma

BACKGROUND

Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown.

METHODS

In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes.

RESULTS

Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD.

CONCLUSIONS

An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

Machine learning analysis of lung squamous cell carcinoma gene expression datasets reveals novel prognostic signatures

BACKGROUND

Lung squamous cell carcinoma (LUSC) patients are often diagnosed at an advanced stage and have poor prognoses. Thus, identifying novel biomarkers for the LUSC is of utmost importance.

METHODS

Multiple datasets from the NCBI-GEO repository were obtained and merged to construct the complete dataset. We also constructed a subset from this complete dataset with only known cancer driver genes. Further, machine learning classifiers were employed to obtain the best features from both datasets. Simultaneously, we perform differential gene expression analysis. Furthermore, survival and enrichment analyses were performed.

RESULTS

The kNN classifier performed comparatively better on the complete and driver datasets' top 40 and 50 gene features, respectively. Out of these 90 gene features, 35 were found to be differentially regulated. Lasso-penalized Cox regression further reduced the number of genes to eight. The median risk score of these eight genes significantly stratified the patients, and low-risk patients have significantly better overall survival. We validated the robust performance of these eight genes on the TCGA dataset. Pathway enrichment analysis identified that these genes are associated with cell cycle, cell proliferation, and migration.

CONCLUSION

This study demonstrates that an integrated approach involving machine learning and system biology may effectively identify novel biomarkers for LUSC.

The mitotic spindle-related seven-gene predicts the prognosis and immune microenvironment of lung adenocarcinoma

PURPOSE

Abnormalities in the mitotic spindle have been linked to a variety of cancers. Data on their role in the onset, progression, and treatment of lung adenocarcinoma (LUAD) need to be explored.

METHODS

The data were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Signatures Database (MSigDB), for the training cohort, external validation cohort, and the hallmark mitotic spindle gene set, respectively. Mitotic spindle genes linked to LUAD prognosis were identified and intersected with differentially expressed up-regulated genes in the training cohort. Nomogram prediction models were built based on least absolute shrinkage and selection operator (LASSO) regression, univariate cox, and multivariate cox analyses. The seven-gene immunological score was examined, as well as the correlation of immune checkpoints. The DLGAP5 and KIF15 expression in BEAS-2B, A549, H1299, H1975, and PC-9 cell lines was validated with western blot (WB).

RESULTS

A total of 965 differentially expressed up-regulated genes in the training cohort intersected with 51 mitotic spindle genes associated with LUAD prognosis. Finally, the seven-gene risk score was determined and integrated with clinical characteristics to construct the nomogram model. Immune cell correlation analysis revealed a negative correlation between seven-gene expression with B cell, endothelial cell (excluding LMNB1), and T cell CD8 + (p < 0.05). However, the seven-gene expression was positively correlated with multiple immune checkpoints (p < 0.05). The expression of DLGAP5 and KIF15 were significantly higher in A549, H1299, H1975, and PC-9 cell lines than that in BEAS-2B cell line.

CONCLUSION

High expression of the seven genes is positively correlated with poor prognosis of LUAD, and these genes are promising as prospective immunotherapy targets.

AURKA, TOP2A and MELK are the key genes identified by WGCNA for the pathogenesis of lung adenocarcinoma

The comprehensive analysis of single or multiple microarray datasets is currently available in Gene Expression Omnibus (GEO) databases, with several studies having identified genes strongly associated with the development of lung adenocarcinoma (LUAD). However, the mechanisms of LUAD development remain largely unknown and has not yet been systematically studied; thus, further studies are required in this field. In the present study, weighted gene co-expression network analysis (WGCNA) was used for the evaluation of key genes with potential high risk of LUAD, and to provide more reliable evidence concerning its pathogenesis. The GSE140797 dataset from the high-throughput GEO database was downloaded and was first analyzed using the Limma package in the R language in order to determine the differentially expressed genes. The dataset was then analyzed using the WGCNA package to analyze the co-expressed genes, and the modular genes with the highest correlation with the clinical phenotype were identified. Subsequently, the pathogenic genes shared in common between the result of the two analyses were imported into the STRING database for protein-protein interaction network analysis. The hub genes were screened out using Cytoscape, and then The Cancer Genome Atlas analysis, receiver operating characteristic analysis and survival analysis were subsequently performed. Finally, the key genes were evaluated using reverse transcription-quantitative PCR and western blot analysis. Bioinformatics analysis of the GSE140797 dataset revealed eight key genes: AURKA, BUB1, CCNB1, CDK1, MELK, NUSAP1, TOP2A and PBK. Finally, the AURKA, TOP2A and MELK genes were evaluated in samples from patients with lung cancer using WGCNA and RT-qPCR, western blot analysis experiments, providing basis for further research on the mechanisms of LUAD development and targeted therapy.

The clinicopathological significance of TOP2A expression in malignant peritoneal mesothelioma

BACKGROUND

Malignant peritoneal mesothelioma (MPM) is a rare malignant tumor with a high mortality rate and extremely poor prognosis. TOP2A expression is associated with cell proliferation and cell cycle progression. We aimed to demonstrate the expression profile of TOP2A in MPM and its correlation with clinicopathological features.

METHODS

Clinicopathological information from 100 MPM cases was collected at Beijing Shijitan Hospital, Capital Medical University. Immunohistochemistry (IHC) was performed to evaluate TOP2A levels. The associations between TOP2A levels and clinicopathological features or prognosis were analyzed. Clinical follow-up data were reviewed to determine correlations among the pathological prognostic factors using the Kaplan-Meier estimator and univariate/multivariate Cox proportional hazards regression models.

RESULTS

Among the 100 MPM patients, there were 48 males and 52 females, with a median age of 54 years (range: 24-72 years). The cutoff curve was used to find the boundary value of the TOP2A-positive rate. TOP2A positive rate ≥ 11.97 % accounted for 48 % in tumor tissue. The TOP2A-positive rate was not associated with sex, age, asbestos exposure, peritoneal carcinomatosis index (PCI) score, or completeness of cytoreduction (CC) score in MPM. Univariate analysis revealed survival-related pathological parameters, including asbestos exposure, CA125, histological type, PCI score, CC score, Ki-67 index, and TOP2A positive rate. Multivariate analysis identified that asbestos exposure history, PCI score, Ki-67 proliferation index and TOP2A positive rate in tissue are independent prognostic factors.

CONCLUSIONS

High expression of TOP2A is linked to better prognosis of MPM.

Exploring the hub genes and mechanisms of Daphne altaica treating esophageal squamous cell carcinoma based on network pharmacology and bioinformatics analysis

PURPOSE

Esophageal squamous cell carcinoma (ESCC), is a frequent digestive tract malignant carcinoma with a high fatality rate. Daphne altaica (D. altaica), a medicinal plant that is frequently employed in Kazakh traditional medicine, and which has traditionally been used to cure cancer and respiratory conditions, but research on the mechanism is lacking. Therefore, we examined and verified the hub genes and mechanism of D. altaica treating ESCC.

METHODS

Active compounds and targets of D. altaica were screened by databases such as TCMSP, and ESCC targets were screened by databases such as GeneCards and constructed the compound-target network and PPI network. Meantime, data sets between tissues and adjacent non-cancerous tissues from GEO database (GSE100942, GPL570) were analyzed to obtain DEGs using the limma package in R. Hub genes were validated using data from the Kaplan-Meier plotter database, TIMER2.0 and GEPIA2 databases. Finally, AutoDock software was used to predict the binding sites through molecular docking.

RESULTS

In total, 830 compound targets were obtained from TCMSP and other databases. In addition, 17,710 disease targets were acquired based on GeneCards and other databases. In addition, we constructed the compound-target network and PPI network. Then, 127 DEGs were observed (82 up-regulated and 45 down-regulated genes). Hub genes were screened including TOP2A, NUF2, CDKN2A, BCHE, and NEK2, and had been validated with the help of several publicly available databases. Finally, molecular docking results showed more stable binding between five hub genes and active compounds.

CONCLUSIONS

In the present study, five hub genes were screened and validated, and potential mechanisms of action were predicted, which could provide a theoretical understanding of the treatment of ESCC with D. altaica.

TOP2A deficit-induced abnormal decidualization leads to recurrent implantation failure via the NF-κB signaling pathway

BACKGROUND

Successful implantation is a complex process that is influenced by embryo quality, endometrial receptivity, immune factors, and the specific type of in vitro fertilization protocol used. DNA topoisomerase IIα (TOP2A) is a well-known protein involved in cell proliferation; however, its expression and effect on the endometrium in recurrent implantation failure (RIF) have not been fully elucidated.

METHODS

The human endometrial tissues of healthy controls and patients with RIF were collected. A proteomic analysis was performed to evaluate the differentially expressed proteins between the RIF group and the fertile control group. The expression patterns of TOP2A in the human preimplantation endometrium of the patients with RIF were determined by immunohistochemical staining, Western blotting and qRT-PCR. TOP2A knockdown (sh-TOP2A) T-HESCs were generated using lentiviruses. The expression of TOP2A in T-HESCs was manipulated to investigate its role in decidualization. The TOP2A-related changes in decidualization were screened by mRNA sequencing in decidualized TOP2A knockdown and control T-HESCs and then confirmed by Western blotting and immunofluorescence staining. TOP2A-deficient mice were generated by injection of TOP2A-interfering adenovirus on GD2.5 and GD3.5.

RESULTS

We performed a proteomic analysis of endometrial tissues to investigate the potential pathogenesis of RIF by comparing the patients with RIF and the matched controls and found that TOP2A might be a key protein in RIF. TOP2A is ubiquitously expressed in both stromal and glandular epithelial cells of the endometrium. The data indicate that TOP2A expression is significantly lower in the mid-secretory endometrium of women with RIF. TOP2A expression was downregulated under stimulation by 8-bromo-cAMP and MPA. Ablation of TOP2A resulted in upregulated expression of decidual biomarkers and morphological changes in the cells. Mechanistic analysis revealed that TOP2A regulates the NF-κB signaling pathway in decidualized T-HESCs. The TOP2A-deficient mice exhibited lower fetal weights.

CONCLUSIONS

Our findings revealed that abnormal expression of TOP2A affects decidualization and changes the "window of implantation", leading to RIF. TOP2A participates in the processes of decidualization and embryo implantation, functioning at least in part through the NF-κB pathway. Regulating the expression of TOP2A in the endometrium may become a new strategy for the prevention and treatment of RIF.

TOP2A correlates with poor prognosis and affects radioresistance of medulloblastoma

Radiotherapy remains the standard treatment for medulloblastoma (MB), and the radioresistance contributes to tumor recurrence and poor clinical outcomes. Nuclear DNA topoisomerase II-alpha (TOP2A) is a key catalytic enzyme that initiates DNA replication, and studies have shown that TOP2A is closely related to the therapeutic effects of radiation. In this study, we found that TOP2A was significantly upregulated in MB, and high expression of TOP2A related to poor prognosis of MB patients. Knockdown of TOP2A inhibited MB cell proliferation, migration, and invasion, whereas overexpression of TOP2A enhanced the proliferative and invasive ability of MB cells. Moreover, si-TOP2A transfection in combination with irradiation (IR) significantly reduced the tumorigenicity of MB cells, compared with those transfected with si-TOP2A alone. Cell survival curve analysis revealed that the survival fraction of MB cells was significantly reduced upon TOP2A downregulation and that si-TOP2A-transfected cells had decreased D₀, Dq, and SF₂ values, indicating that TOP2A knockdown suppresses the resistance to radiotherapy in MB cells. In addition, western blot analysis demonstrated that the activity of Wnt/β-catenin signaling pathway was inhibited after TOP2A downregulation alone or in combination with IR treatment, whereas overexpression of TOP2A exhibited the opposite effects. Gene set enrichment analysis also revealed that Wnt/β-catenin signaling pathway is enriched in TOP2A high-expression phenotypes. Collectively, these data indicate that high expression of TOP2A leads to poor prognosis of MB, and downregulation of TOP2A inhibits the malignant behaviour as well as the radioresistance of MB cells. The Wnt/β-catenin signaling pathway may be involved in the molecular mechanisms of TOP2A mediated reduced tumorigenicity and radioresistance of MB cells.

Oncogenic role and potential regulatory mechanism of topoisomerase IIα in a pan-cancer analysis

Topoisomerase IIα (TOP2A) plays an oncogenic role in multiple tumor types. However, no pan-cancer analysis about the function and the upstream molecular mechanism of TOP2A is available. For the first time, we analyzed potential oncogenic roles of TOP2A in 33 cancer types via The Cancer Genome Atlas (TCGA) database. Overexpression of TOP2A was existed in almost all cancer types, and related to poor prognosis and advanced pathological stages in most cases. Besides, the high frequency of TOP2A genetic alterations was observed in several cancer types, and related to prognosis in some cases. Moreover, we conduct upstream miRNAs and lncRNAs of TOP2A to establish ceRNA networks in kidney renal clear cell carcinoma (SNHG3-miR-139-5p), kidney renal papillary cell carcinoma (TMEM147-AS1/N4BP2L2-IT2/THUMPD3-AS1/ERICD/TTN-AS1/SH3BP5-AS1/THRB-IT1/SNHG3/NEAT1-miR-139-5p), liver hepatocellular carcinoma (SNHG3/THUMPD3-AS1/NUTM2B-AS1/NUTM2A-AS1-miR-139-5p and SNHG6/GSEC/SNHG1/SNHG14/LINC00265/MIR3142HG-miR-101-3p) and lung adenocarcinoma (TYMSOS/HELLPAR/SNHG1/GSEC/SNHG6-miR-101-3p). TOP2A expression was generally positively correlated with cancer associated fibroblasts, M0 and M1 macrophages in most cancer types. Furthermore, TOP2A was positively associated with expression of immune checkpoints (CD274, CTLA4, HAVCR2, LAG3, PDCD1 and TIGIT) in most cancer types. Our first TOP2A pan-cancer study contributes to understanding the prognostic roles, immunological roles and potential upstream molecular mechanism of TOP2A in different cancers.

An Integrated Mass Spectrometry-Based Glycomics-Driven Glycoproteomics Analytical Platform to Functionally Characterize Glycosylation Inhibitors

Cancer progression is linked to aberrant protein glycosylation due to the overexpression of several glycosylation enzymes. These enzymes are underexploited as potential anticancer drug targets and the development of rapid-screening methods and identification of glycosylation inhibitors are highly sought. An integrated bioinformatics and mass spectrometry-based glycomics-driven glycoproteomics analysis pipeline was performed to identify an N-glycan inhibitor against lung cancer cells. Combined network pharmacology and in silico screening approaches were used to identify a potential inhibitor, pictilisib, against several glycosylation-related proteins, such as Alpha1-6FucT, GlcNAcT-V, and Alpha2,6-ST-I. A glycomics assay of lung cancer cells treated with pictilisib showed a significant reduction in the fucosylation and sialylation of N-glycans, with an increase in high mannose-type glycans. Proteomics analysis and in vitro assays also showed significant upregulation of the proteins involved in apoptosis and cell adhesion, and the downregulation of proteins involved in cell cycle regulation, mRNA processing, and protein translation. Site-specific glycoproteomics analysis further showed that glycoproteins with reduced fucosylation and sialylation were involved in apoptosis, cell adhesion, DNA damage repair, and chemical response processes. To determine how the alterations in N-glycosylation impact glycoprotein dynamics, modeling of changes in glycan interactions of the ITGA5-ITGB1 (Integrin alpha 5-Integrin beta-1) complex revealed specific glycosites at the interface of these proteins that, when highly fucosylated and sialylated, such as in untreated A549 cells, form greater hydrogen bonding interactions compared to the high mannose-types in pictilisib-treated A549 cells. This study highlights the use of mass spectrometry to identify a potential glycosylation inhibitor and assessment of its impact on cell surface glycoprotein abundance and protein-protein interaction.

A New Risk Model Based on 7 Quercetin-Related Target Genes for Predicting the Prognosis of Patients With Lung Adenocarcinoma

Background: Studies have reported that quercetin inhibits the growth and migration of lung adenocarcinoma (LUAD). This study aimed to explore the roles and mechanisms of quercetin target genes in the progression of LUAD.

Methods: The quercetin structure and potential target genes of quercetin were explored in the Traditional Chinese Medicine Systems Pharmacology and SwissTargetPrediction databases. The differentially expressed quercetin target genes were identified in The Cancer Genome Atlas (TCGA) database, and the clinical values of quercetin target genes were explored. Subsequently, a risk model was constructed via the Cox regression and survival analysis to evaluate the potential effects and possible mechanisms of quercetin target genes.

Results: The quercetin differential target genes involved in biological processes such as the oxidation-reduction process, cell proliferation, G2/M transition of the mitotic cell cycle, and were related to the lung cancer. NEK2, TOP2A, PLK1, CA4, CDK5R1, AURKB, and F2 were related to the prognosis, and were independent factors influencing the prognosis of LUAD patients. The risk model was related to the gender, clinical stage, T stage, lymph node metastasis, and survival status of LUAD patients, and was independent risk factor associated with poor prognosis. In the high-risk group, the risk model involved signaling pathways such as cell cycle, DNA replication, spliceosome, and homologous recombination.

Conclusion: The quercetin potential target genes NEK2, TOP2A, PLK1, CA4, CDK5R1, AURKB, and F2 were related to the diagnosis and prognosis of LUAD patients. A risk model based on 7 quercetin target genes could be used to assess the prognosis of patients with LUAD.

Ciclopirox Olamine Exerts Tumor-Suppressor Effects via Topoisomerase II Alpha in Lung Adenocarcinoma

Background

Globally, lung cancer is one of the most malignant tumors, of which lung adenocarcinoma (LUAD) is the most common subtype, with a particularly poor prognosis. Ciclopirox olamine (CPX) is an antifungal drug and was recently identified as a potential antitumor agent. However, how CPX and its mechanism of action function during LUAD remain unclear.

Methods

The effects of CPX on cell proliferation, cell cycle, reactive oxygen species (ROS) levels, and apoptosis were assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay, colony formation, western blotting, flow cytometry assays, and immunohistochemistry. Global gene expression levels were compared between control and CPX-treated LUAD cells. A LUAD xenograft mouse model was used to evaluate the potential in vivo effects of CPX.

Results

We observed that CPX displayed strong antitumorigenic properties in LUAD cells, inhibited LUAD proliferation, induced ROS production, caused DNA damage, and activated the ATR-CHK1-P53 pathway. Topoisomerase II alpha (TOP2A) is overexpressed in LUAD and associated with a poor prognosis. By analyzing differentially expressed genes (DEGs), TOP2A was significantly down-regulated in CPX-treated LUAD cells. Furthermore, CPX treatment substantially inhibited in vivo LUAD xenograft growth without toxicity or side effects to the hematological system and internal organs.

Conclusions

Collectively, for the first time, we showed that CPX exerted tumor-suppressor effects in LUAD via TOP2A, suggesting CPX could potentially function as a promising chemotherapeutic for LUAD treatment.

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Background: The genome-wide CRISPR-cas9 dropout screening has emerged as an outstanding approach for characterization of driver genes of tumor growth. The present study aims to investigate core genes related to clear cell renal cell carcinoma (ccRCC) cell viability by analyzing the CRISPR-cas9 screening database DepMap, which may provide a novel target in ccRCC therapy.

Methods: Candidate genes related to ccRCC cell viability by CRISPR-cas9 screening from DepMap and genes differentially expressed between ccRCC tissues and normal tissues from TCGA were overlapped. Weighted gene coexpression network analysis, pathway enrichment analysis, and protein–protein interaction network analysis were applied for the overlapped genes. The least absolute shrinkage and selection operator (LASSO) regression was used to construct a signature to predict the overall survival (OS) of ccRCC patients and validated in the International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. Core protein expression was determined using immunohistochemistry in 40 cases of ccRCC patients.

Results: A total of 485 essential genes in the DepMap database were identified and overlapped with differentially expressed genes in the TCGA database, which were enriched in the cell cycle pathway. A total of four genes, including UBE2I, NCAPG, NUP93, and TOP2A, were included in the gene signature based on LASSO regression. The high-risk score of ccRCC patients showed worse OS compared with these low-risk patients in the ICGC and E-MTAB-1980 validation cohort. UBE2I was screened out as a key gene. The immunohistochemistry indicated UBE2I protein was highly expressed in ccRCC tissues, and a high-level nuclear translocation of UBE2I occurs in ccRCC. Based on the area under the curve (AUC) values, nuclear UBE2I had the best diagnostic power (AUC = 1). Meanwhile, the knockdown of UBE2I can inhibit the proliferation of ccRCC cells.

Conclusion: UBE2I, identified by CRISPR-cas9 screening, was a core gene-regulating ccRCC cell viability, which accumulated in the nucleus and acted as a potential novel promising diagnostic biomarker for ccRCC patients. Blocking the nuclear translocation of UBE2I may have potential therapeutic value with ccRCC patients.

Identification of key genes and biological pathways in Chinese lung cancer population using bioinformatics analysis

BACKGROUND

Identification of accurate prognostic biomarkers is still particularly urgent for improving the poor survival of lung cancer patients. In this study, we aimed to identity the potential biomarkers in Chinese lung cancer population via bioinformatics analysis.

METHODS

In this study, the differentially expressed genes (DEGs) in lung cancer were identified using six datasets from Gene Expression Omnibus (GEO) database. Subsequently, enrichment analysis was conducted to evaluate the underlying molecular mechanisms involved in progression of lung cancer. Protein-protein interaction (PPI) and CytoHubba analysis were performed to determine the hub genes. The GEPIA, Human Protein Atlas (HPA), Kaplan-Meier plotter, and TIMER databases were used to explore the hub genes. The receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic value of hub genes. Reverse transcription quantitative PCR (qRT-PCR) was used to validate the expression levels of hub genes in 10 pairs of lung cancer paired tissues.

RESULTS

A total of 499 overlapping DEGs (160 upregulated and 339 downregulated genes) were identified in the microarray datasets. DEGs were mainly associated with pathways in cancer, focal adhesion, and protein digestion and absorption. There were nine hub genes (CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A, UBE2C, CHEK1 and BIRC5) identified by PPI and module analysis. In GEPIA database, the expression levels of these genes in lung cancer tissues were significantly upregulated compared with normal lung tissues. The results of prognostic analysis showed that relatively higher expression of hub genes was associated with poor prognosis of lung cancer. In HPA database, most hub genes were highly expressed in lung cancer tissues. The hub genes have good diagnostic efficiency in lung cancer and normal tissues. The expression of any hub gene was associated with the infiltration of at least two immune cells. qRT-PCR confirmed that the expression level of CDKN3, MKI67, CEP55, SPAG5, AURKA, TOP2A were highly expressed in lung cancer tissues.

CONCLUSIONS

The hub genes and functional pathways identified in this study may contribute to understand the molecular mechanisms of lung cancer. Our findings may provide new therapeutic targets for lung cancer patients.

Study on the expression of TOP2A in hepatocellular carcinoma and its relationship with patient prognosis

Background

Hepatocellular carcinoma (HCC) is a primary liver cancer with a high mortality rate. However, the molecular mechanism of HCC formation remains to be explored and studied.

Objective

To investigate the expression of TOP2A in hepatocellular carcinoma (HCC) and its prognosis.

Methods

The data set of hepatocellular carcinoma was downloaded from GEO database for differential gene analysis, and hub gene was identified by Cytoscape. GEPIA was used to verify the expression of HUB gene and evaluate its prognostic value. Then TOP2A was selected as the research object of this paper by combining literature and clinical sample results. Firstly, TIMER database was used to study TOP2A, and the differential expression of TOP2A gene between normal tissues and cancer tissues was analyzed, as well as the correlation between TOP2A gene expression and immune infiltration of HCC cells. Then, the expression of top2a-related antibodies was analyzed using the Human Protein Atlas database, and the differential expression of TOP2A was verified by immunohistochemistry. Then, SRTING database and Cytoscape were used to establish PPI network for TOP2A and protein–protein interaction analysis was performed. The Oncomine database and cBioPortal were used to express and identify TOP2A mutation-related analyses. The expression differences of TOP2A gene were identified by LinkedOmics, and the GO and KEGG pathways were analyzed in combination with related genes. Finally, Kaplan–Meier survival analysis was performed to analyze the clinical and prognosis of HCC patients.

Results

TOP2A may be a new biomarker and therapeutic target for hepatocellular carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02439-0.