Overexpression of CDCA5, KIF4A, TPX2, and FOXM1 Coregulated Cell Cycle and Promoted Hepatocellular Carcinoma Development

CDCA5 accelerates progression of breast cancer by promoting the binding of E2F1 and FOXM1

BACKGROUND

Breast cancer is one of the most common malignant tumors in women. Cell division cycle associated 5 (CDCA5), a master regulator of sister chromatid cohesion, was reported to be upregulated in several types of cancer. Here, the function and regulation mechanism of CDCA5 in breast cancer were explored.

METHODS

CDCA5 expression was identified through immunohistochemistry staining in breast cancer specimens. The correlation between CDCA5 expression with clinicopathological features and prognosis of breast cancer patients was analyzed using a tissue microarray. CDCA5 function in breast cancer was explored in CDCA5-overexpressed/knockdown cells and mice models. Co-IP, ChIP and dual-luciferase reporter assay assays were performed to clarify underlying molecular mechanisms.

RESULTS

We found that CDCA5 was expressed at a higher level in breast cancer tissues and cell lines, and overexpression of CDCA5 was significantly associated with poor prognosis of patients with breast cancer. Moreover, CDCA5 knockdown significantly suppressed the proliferation and migration, while promoted apoptosis in vitro. Mechanistically, we revealed that CDCA5 played an important role in promoting the binding of E2F transcription factor 1 (E2F1) to the forkhead box M1 (FOXM1) promoter. Furthermore, the data of in vitro and in vivo revealed that depletion of FOXM1 alleviated the effect of CDCA5 overexpression on breast cancer. Additionally, we revealed that the Wnt/β-catenin signaling pathway was required for CDCA5 induced progression of breast cancer.

CONCLUSIONS

We suggested that CDCA5 promoted progression of breast cancer via CDCA5/FOXM1/Wnt axis, CDCA5 might serve as a novel therapeutic target for breast cancer treatment.

Comprehensive clinicopathological significance and putative transcriptional mechanisms of Forkhead box M1 factor in hepatocellular carcinoma

BACKGROUND

The Forkhead box M1 factor (FOXM1) is a crucial activator for cancer cell proliferation. While FOXM1 has been shown to promote hepatocellular carcinoma (HCC) progression, its transcriptional mechanisms remain incompletely understood.

METHODS

We performed an in-house tissue microarray on 313 HCC and 37 non-HCC tissue samples, followed by immunohistochemical staining. Gene chips and high throughput sequencing data were used to assess FOXM1 expression and prognosis. To identify candidate targets of FOXM1, we comprehensively reanalyzed 41 chromatin immunoprecipitation followed by sequencing (ChIP-seq) data sets. We predicted FOXM1 transcriptional targets in HCC by intersecting candidate FOXM1 targets with HCC overexpressed genes and FOXM1 correlation genes. Enrichment analysis was employed to address the potential mechanisms of FOXM1 underlying HCC. Finally, single-cell RNA sequencing analysis was performed to confirm the transcriptional activity of FOXM1 on its predicted targets.

RESULTS

This study, based on 4235 HCC tissue samples and 3461 non-HCC tissue samples, confirmed the upregulation of FOXM1 in HCC at mRNA and protein levels (standardized mean difference = 1.70 [1.42, 1.98]), making it the largest multi-centered study to do so. Among HCC patients, FOXM1 was increased in Asian and advanced subgroups, and high expression of FOXM1 had a strong ability to differentiate HCC tissue from non-HCC tissue (area under the curve = 0.94, sensitivity = 88.72%, specificity = 87.24%). FOXM1 was also shown to be an independent exposure risk factor for HCC, with a pooled hazard ratio of 2.00 [1.77, 2.26]. The predicted transcriptional targets of FOXM1 in HCC were predominantly enriched in nuclear division, chromosomal region, and catalytic activity acting on DNA. A gene cluster encoding nine transcriptional factors was predicted to be positively regulated by FOXM1, promoting the cell cycle signaling pathway in HCC. Finally, the transcriptional activity of FOXM1 and its targets was supported by single-cell analysis of HCC cells.

CONCLUSIONS

This study not only confirmed the upregulation of FOXM1 in HCC but also identified it as an independent risk factor. Moreover, our findings enriched our understanding of the complex transcriptional mechanisms underlying HCC pathogenesis, with FOXM1 potentially promoting HCC progression by activating other transcription factors within the cell cycle pathway.

Epigenetic reactivation of tumor suppressor genes with CRISPRa technologies as precision therapy for hepatocellular carcinoma

BACKGROUND

Epigenetic silencing of tumor suppressor genes (TSGs) is a key feature of oncogenesis in hepatocellular carcinoma (HCC). Liver-targeted delivery of CRISPR-activation (CRISPRa) systems makes it possible to exploit chromatin plasticity, by reprogramming transcriptional dysregulation.

RESULTS

Using The Cancer Genome Atlas HCC data, we identify 12 putative TSGs with negative associations between promoter DNA methylation and transcript abundance, with limited genetic alterations. All HCC samples harbor at least one silenced TSG, suggesting that combining a specific panel of genomic targets could maximize efficacy, and potentially improve outcomes as a personalized treatment strategy for HCC patients. Unlike epigenetic modifying drugs lacking locus selectivity, CRISPRa systems enable potent and precise reactivation of at least 4 TSGs tailored to representative HCC lines. Concerted reactivation of HHIP, MT1M, PZP, and TTC36 in Hep3B cells inhibits multiple facets of HCC pathogenesis, such as cell viability, proliferation, and migration.

CONCLUSIONS

By combining multiple effector domains, we demonstrate the utility of a CRISPRa toolbox of epigenetic effectors and gRNAs for patient-specific treatment of aggressive HCC.

Identification and Validation in a Novel Classification of Helicase Patterns for the Prediction of Tumor Proliferation and Prognosis

Background

Helicases have been classified as a class of enzymes that determine the stability of the cellular genome. There is growing evidence that helicases can help tumor cells resist drug killing by repairing Deoxyribose Nucleic Acid (DNA) or stabilizing transcription, which may contribute to the understanding of drug resistance. Currently, identifying cancer biomarkers among helicases and stratifying patients according to helicase activity will be able to guide treatment well.

Methods

We clustered 371 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) by consensus clustering based on helicase expression profiles to identify potential molecular subtypes. The Multiscale Embedded Gene Co-Expression Network Analysis (MEGENA) algorithm was used to find core differential gene modules between different molecular subtypes, and single-cell analysis was utlized to explore the potential function of hub gene. Immunohistochemical (IHC) staining was used to verify the diagnostic value of DDX56 and its ability to reflect the proliferation efficiency of cancer cells.

Results

We identified two subtypes associated with helicase. High helicase subtype was associated with poor clinical outcome, massive M0 macrophage infiltration, and highly active cell proliferation features. In addition, we identified a new biomarker, DDX56, which has not been reported in HCC, was highly expressed in HCC tissues, associated with poor prognosis, and was also shown to be associated with high cell proliferative activity.

Conclusion

In conclusion, based on helicase expression profiles, we have developed a new classification system for HCC, which is a proliferation-related system, and has clinical significance in evaluating prognosis and treating HCC patients, including immunotherapy and chemotherapy. In addition, we identified a new biomarker, DDX 56, which is overexpressed in HCC tissues, predicts a poor prognosis and is a validated index of tumor cell proliferation.

A New Stemness-Related Prognostic Model for Predicting the Prognosis in Pancreatic Ductal Adenocarcinoma

Objective

This study is aimed at identifying stemness-related genes in pancreatic ductal adenocarcinoma (PDAC).

Methods

The RNA-seq data of PADC patients were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. The mRNA expression-based stemness index (mRNAsi) and epigenetically regulated mRNAsi (EREG-mRNAsi) of PADC patients were evaluated. The mRNAsi-related gene sets in PADC were identified by weighted gene coexpression network analysis (WGCNA). The key genes were further analyzed using functional enrichment analysis. The Kaplan-Meier survival analysis and the Cox proportional hazards model were used to evaluate the prognostic value of the key genes. Prognostic hub genes were used to establish nomograms. The receiver operating characteristic (ROC) curves, concordance index (C-index), and calibration curves were used to assess the discrimination and accuracy of the nomogram. Finally, these results were validated in the Gene Expression Omnibus (GEO) database.

Results

A total of 36 key genes related to mRNAsi were identified by WGCNA. A prognostic gene signature compromising seven genes (TPX2, ZWINT, UBE2C, CCNB2, CDK1, BUB1, and BIRC5) was established to predict the overall survival (OS) of PADC patients. The Cox regression analysis revealed that the risk score was an independent prognostic factor for PADC. Patients were then divided into the high-risk and low-risk groups. The ROC curves, C-index, and calibration curves indicated good performance of the prognostic signature in the TCGA and GEO datasets. Moreover, the nomogram incorporating clinical parameters showed better sensitivity and specificity for predicting the OS of PADC patients.

Conclusion

The stemness-related prognostic model successfully predicted the OS of PADC patients and could be used for the treatment of PADC.

Low expression of KIF20A suppresses cell proliferation, promotes chemosensitivity and is associated with better prognosis in HCC

This study analysed the microarray datasets from Gene Expression Omnibus (GEO) database, and aimed to identify novel potential hub genes associated with the progression of HCC via bioinformatics analysis and experimental validation. The common differentially expressed genes (DEGs) from five GEO datasets were screened using GEO2R tool. The expression and survival analysis of hub genes in HCC were performed using Gene Expression Profiling Interactive Analysis, UALCAN and Kaplan-Meier plotter tools. In vitro functional assays were used to determine the caspase-3, -9, cell proliferation and chemo-sensitivity of HCC cells. A total of 177 common DEGs were identified between normal liver and HCC tissues among these datasets. Functional enrichment and PPI network analysis identified 22 hub genes from the common DEGs. The mRNA expression of 22 hub genes was all significantly up-regulated in HCC tissues compared to that in normal liver tissues. Further survival analysis showed that 10 hub genes predicted poor prognosis of patients with HCC. More importantly, the in vitro functional studies demonstrated that KIF20A knockdown suppressed the HCC cell proliferation and promoted the chemosensitivity of HCC cells to cisplatin and sorafenib. In conclusion, the present study identified a total of 177 common DEGs among 5 GEO microarray datasets and found that 10 hub genes could predict the poor prognosis of patients with HCC using the comprehensive bioinformatics analysis. Furthermore, KIF20A silence suppressed cell proliferation and enhanced chemosensitivity in HCC cells. Further studies may be required to determine the mechanistic role of these hub genes in HCC progression.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

SNRPD1 confers diagnostic and therapeutic values on breast cancers through cell cycle regulation

Background

SNRPD1 is a spliceosome-associated protein and has previously been implicated with important roles in cancer development.

Methods

Through analyzing the differential expression patterns and clinical association of splicing associated genes among tumor and tumor adjacent samples across different tumors and among different breast cancer subtypes, we identify the tumor promotive role of SNRPD1 using multiple publicly available datasets. Through pathway, gene ontology enrichment analysis and network construction, we linked the onco-therapeutic role of SNRPD1 with cell cycle. Via a series of experimental studies including knockdown assay, qPCR, western blotting, cell cycle, drug response assay, we confirmed the higher expression of SNPRD1 at both gene and protein expression levels in triple negative breast cancer cells, as well as its roles in promoting cell cycle and chemotherapy response.

Results

Our study revealed that SNRPD1 over-expression was significantly associated with genes involved in cell cycle, cell mitosis and chromatin replication, and silencing SNRPD1 in breast cancer cells could lead to halted tumor cell growth and cell cycle arrest at the G₀/G₁ stage. We also found that triple negative breast cancer cells with reduced SNRPD1 expression lost certain sensitivity to doxorubicin whereas luminal cancer cells did not.

Conclusions

Our results suggested the prognostic value of SNRPD1 on breast cancer survival, its potential as the therapeutic target halting cell cycle progression for breast cancer control, and warranted special attention on the combined use of doxorubicin and drugs targeting SNRPD1.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01932-w.

Identification of Biomarkers Based on Bioinformatics Analysis: The Expression of Ubiquitin-Conjugating Enzyme E2T (UBE2T) in the Carcinogenesis and Progression of Hepatocellular Carcinoma

BACKGROUND The purpose of this study was to screen and identify key genes in the occurrence and development of hepatocellular carcinoma (HCC) based on bioinformatics analysis. MATERIAL AND METHODS Three Gene Expression Omnibus (GEO) series (GSE) - GSE121248, GSE87630, and GSE84598 - were downloaded from the GEO database. GEO2R was used to screen different genes and a Venn diagram was drawn to screen coexpressed differentially expressed genes (DEGs). Coexpressed DEGs were obtained by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, a protein-protein interaction network diagram was produced by Cytoscape, and module genes were calculated by the Molecular Complex Detection Cytoscape plug-in. Finally, overall survival, progression-free survival, and relapse-free survival analysis of the key genes selected were performed using the online Kaplan-Meier plotter. For the target genes, the online network UCSC Cancer Genome Browser was used to analyze the gene expression profiles of the grade and vascular invasion of HCC. RESULTS A total of 296 coexpressed DEGs were obtained from the 3 GSEs and 12 key genes were obtained from the modular analysis. Survival analysis showed that the upregulated genes UBE2T and FBLN5 were involved in the poor prognosis of HCC. Furthermore, the expression of UBE2T was significantly related to the grade and vascular invasion of HCC. CONCLUSIONS The expression of the UBE2T gene was significantly upregulated in HCC tissue compared to in normal liver tissue. UBE2T may be a new marker for the diagnosis and subsequent therapy of HCC.

Identification of KIF4A and its effect on the progression of lung adenocarcinoma based on the bioinformatics analysis

BACKGROUND

Lung adenocarcinoma (LUAD) is the most frequent histological type of lung cancer, and its incidence has displayed an upward trend in recent years. Nevertheless, little is known regarding effective biomarkers for LUAD.

METHODS

The robust rank aggregation method was used to mine differentially expressed genes (DEGs) from the gene expression omnibus (GEO) datasets. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to extract hub genes from the protein-protein interaction (PPI) network. The expression of the hub genes was validated using expression profiles from TCGA and Oncomine databases and was verified by real-time quantitative PCR (qRT-PCR). The module and survival analyses of the hub genes were determined using Cytoscape and Kaplan-Meier curves. The function of KIF4A as a hub gene was investigated in LUAD cell lines.

RESULTS

The PPI analysis identified seven DEGs including BIRC5, DLGAP5, CENPF, KIF4A, TOP2A, AURKA, and CCNA2, which were significantly upregulated in Oncomine and TCGA LUAD datasets, and were verified by qRT-PCR in our clinical samples. We determined the overall and disease-free survival analysis of the seven hub genes using GEPIA. We further found that CENPF, DLGAP5, and KIF4A expressions were positively correlated with clinical stage. In LUAD cell lines, proliferation and migration were inhibited and apoptosis was promoted by knocking down KIF4A expression.

CONCLUSION

We have identified new DEGs and functional pathways involved in LUAD. KIF4A, as a hub gene, promoted the progression of LUAD and might represent a potential therapeutic target for molecular cancer therapy.

CDCA5 promotes the progression of prostate cancer by affecting the ERK signalling pathway

Cell division cycle-associated 5 (CDCA5) can regulate cell cycle-related proteins to promote the proliferation of cancer cells. The purpose of the present study was to investigate the expression level of CDCA5 in prostate cancer (PCa) and its effect on PCa progression. The signalling pathway by which CDCA5 functions through was also attempted to elucidate. Clinical specimens of PCa patients were collected from the Second Hospital of Tianjin Medical University. The expression level of CDCA5 in cancer tissues and paracancerous tissues from PCa patients was detected by RT-qPCR analysis and IHC. The relationship between the expression level of CDCA5 and the survival rate of PCa patients was analysed using TCGA database. Two stable cell lines (C4-2 and PC-3) with CDCA5 knockdown were established, and the effects of CDCA5 on PCa cell proliferation were detected by MTT and colony formation assays. Flow cytometry was performed to detect the effect of CDCA5 on the PCa cell division cycle, and western blot analysis was used to determine changes in ERK phosphorylation levels after CDCA5 knockdown. The effect of CDCA5 expression on prostate tumour growth was assessed using a mouse xenograft model. The results revealed that the mRNA and protein expression levels of CDCA5 were significantly higher in PCa tissues than in paracancerous tissues. High CDCA5 expression was associated with the prognosis of patients with PCa. CDCA5 expression knockdown significantly reduced the number of PCa cells in mitoses and inhibited their proliferation in vitro and in vivo. When CDCA5 was knocked down, the phosphorylation level of ERK was also reduced. Collectively, CDCA5 was upregulated and affected the prognosis of patients with PCa. Decreased CDCA5 expression inhibited PCa cell proliferation by inhibiting the ERK signalling pathway. Thus, CDCA5 may be a potential therapeutic target for PCa.

Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis

The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC.