Screening of cervical cancer-related hub genes based on comprehensive bioinformatics analysis

2021 ◽  
pp. 1-13
Author(s):  
Simei Tu ◽  
Hao Zhang ◽  
Xiaocheng Yang ◽  
Wen Wen ◽  
Kangjing Song ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
Disease Free Survival ◽  
The Cancer Genome Atlas ◽  
Mrna Levels ◽  
Hub Genes ◽  
Normal Tissues ◽  
Significant Difference ◽  
Core Genes

BACKGROUND: Since the molecular mechanisms of cervical cancer (CC) have not been completely discovered, it is of great significance to identify the hub genes and pathways of this disease to reveal the molecular mechanisms of cervical cancer. OBJECTIVE: The study aimed to identify the biological functions and prognostic value of hub genes in cervical cancer. METHODS: The gene expression data of CC patients were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database. The core genes were screened out by differential gene expression analysis and weighted gene co-expression network analysis (WGCNA). R software, the STRING online tool and Cytoscape software were used to screen out the hub genes. The GEPIA public database was used to further verify the expression levels of the hub genes in normal tissues and tumour tissues and determine the disease-free survival (DFS) rates of the hub genes. The protein expression of the survival-related hub genes was identified with the Human Protein Atlas (HPA) database. RESULTS: A total of 64 core genes were screened, and 10 genes, including RFC5, POLE3, RAD51, RMI1, PALB2, HDAC1, MCM4, ESR1, FOS and E2F1, were identified as hub genes. Compared with that in normal tissues, RFC5, POLE3, RAD51,RMI1, PALB2, MCM4 and E2F1 were all significantly upregulated in cervical cancer, ESR1 was significantly downregulated in cervical cancer, and high RFC5 expression in CC patients was significantly related to OS. In the DFS analysis, no significant difference was observed in the expression level of RFC5 in cervical cancer patients. Finally, RFC5 protein levels verified by the HPA database were consistently upregulated with mRNA levels in CC samples. CONCLUSIONS: RFC5 may play important roles in the occurrence and prognosis of CC. It could be further explored and validated as a potential predictor and therapeutic target for CC.

scholarly journals Identification of Core Genes Involved in the Progression of Cervical Cancer Using an Integrative mRNA Analysis

2020 ◽  
Vol 21 (19) ◽  
pp. 7323
Author(s):  
Marina Dudea-Simon ◽  
Dan Mihu ◽  
Alexandru Irimie ◽  
Roxana Cojocneanu ◽  
Schuyler S. Korban ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Normal Tissues ◽  
High Incidence ◽  
Cancer Genome Atlas ◽  
Mrna Analysis ◽  
Core Genes ◽  
Significant Mortality

In spite of being a preventable disease, cervical cancer (CC) remains at high incidence, and it has a significant mortality rate. Although hijacking of the host cellular pathway is fundamental for developing a better understanding of the human papillomavirus (HPV) pathogenesis, a major obstacle is identifying the central molecular targets involved in HPV-driven CC. The aim of this study is to investigate transcriptomic patterns of HPV-infected and normal tissues to identify novel prognostic markers. Analyses of functional enrichment and interaction networks reveal that altered genes are mainly involved in cell cycle, DNA damage, and regulated cell-to-cell signaling. Analysis of The Cancer Genome Atlas (TCGA) data has suggested that patients with unfavorable prognostics are more likely to have DNA repair defects attributed, in most cases, to the presence of HPV. However, further studies are needed to fully unravel the molecular mechanisms of such genes involved in CC.

scholarly journals Identification of core genes and pathways in melanoma metastasis via bioinformatics analysis

2020 ◽  
Author(s):  
Yumei Li ◽  
Bifei Li ◽  
Fan Chen ◽  
Weiyu Shen ◽  
Vladimir L. Katanaev ◽  
...  
Keyword(s):  
Gene Expression ◽  
Metastatic Melanoma ◽  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Primary Melanoma ◽  
Melanoma Cells ◽  
Receptor Interaction ◽  
Melanoma Metastasis ◽  
Hub Genes ◽  
Core Genes

Abstract Background Metastasis is the leading cause of melanoma mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen for the key core genes and molecular mechanisms related to the metastasis of melanoma. Methods Gene expression profile, GSE8401 including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between metastatic melanoma and primary melanoma were screened using GEO2R. Assays of gene ontology (GO), Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and protein-protein interaction (PPI) were performed to visualize these DEGs through Database for Annotation, Visualization and Integrated Discovery (DAVID) software and Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools. Top 10 genes with high degree were defined as hub genes. Furthermore, paired post-metastatic melanoma cells and pre-metastatic melanoma cells were established by experimental mouse model of melanoma metastasis to verify the expression of these hub genes. Results 424 DEGs between the metastatic melanoma and primary melanoma were screened, including 60 upregulated genes enriched in ECM-receptor interaction and progesterone-mediated oocyte maturation and 364 downregulated genes enriched in amoebiasis, melanogenesis, and ECM-receptor interaction. CDH1, EGFR, KRT5, COL17A1, KRT14, IVL, DSP, DSG1, FLG and CDK1 were defined as the hub genes. . In addition, paired post-metastatic melanoma cells (A375M) and pre-metastatic melanoma cells (A375) were established and qRT-PCR analysis confirmed the expression of the hub genes during melanoma metastasis. Conclusion This bioinformatic study has provided a deeper understanding of the molecular mechanisms of melanoma metastasis. KRT5, IVL and COL17A1 have emerged as possible biomarkers and therapeutic targets in metastasis of melanoma.

scholarly journals Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Aoran Yang ◽  
Xinhuan Wang ◽  
Yaofeng Hu ◽  
Chao Shang ◽  
Yang Hong
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Coexpression Network ◽  
Normal Brain ◽  
Gene Coexpression Network ◽  
Gene Coexpression ◽  
Coexpression Network Analysis ◽  
Core Genes

The function of glutamate ionotropic receptor NMDA type subunit 1 (GRIN1) in neurodegenerative diseases has been widely reported; however, its role in the occurrence of glioma remains less explored. We obtained clinical data and transcriptome data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Hub gene’s expression differential analysis and survival analysis were conducted by browsing the Gene Expression Profiling Interactive Analysis (GEPIA) database, Human Protein Atlas database, and LOGpc database. We conducted a variation analysis of datasets obtained from GEO and TCGA and performed a weighted gene coexpression network analysis (WGCNA) using the R programming language (3.6.3). Kaplan-Meier (KM) analysis was used to calculate the prognostic value of GRIN1. Finally, we conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Using STRING, we constructed a protein–protein interaction (PPI) network. Cytoscape software, a prerequisite of visualizing core genes, was installed, and CytoHubba detected the 10 most tumor-related core genes. We identified 185 differentially expressed genes (DEGs). GO and KEGG enrichment analyses illustrated that the identified DEGs are imperative in different biological functions and ascertained the potential pathways in which the DEGs may be enriched. The overall survival calculated by KM analysis showed that patients with lower expression of GRIN1 had worse prognoses than patients with higher expression of GRIN1 ( p = 0.004 ). The GEPIA and LOGpc databases were used to verify the expression difference of GRIN1 among GBM, LGG, and normal brain tissues. Ultimately, immunohistochemical assay results showed that GRIN1 was detected in normal tissue and not in the tumor specimens. Our results highlight a potential target for glioma treatment and will further our understanding of the molecular mechanisms underlying the treatment of glioma.

scholarly journals Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9301
Author(s):  
Dandan Jin ◽  
Yujie Jiao ◽  
Jie Ji ◽  
Wei Jiang ◽  
Wenkai Ni ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Gene Networks ◽  
Molecular Mechanisms ◽  
Functional Module ◽  
Disease Free Survival ◽  
Ductal Adenocarcinoma ◽  
Literature Mining ◽  
Ppi Network ◽  
Hub Genes

Background Pancreatic cancer is one of the most common malignant cancers worldwide. Currently, the pathogenesis of pancreatic cancer remains unclear; thus, it is necessary to explore its precise molecular mechanisms. Methods To identify candidate genes involved in the tumorigenesis and proliferation of pancreatic cancer, the microarray datasets GSE32676, GSE15471 and GSE71989 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between Pancreatic ductal adenocarcinoma (PDAC) and nonmalignant samples were screened by GEO2R. The Database for Annotation Visualization and Integrated Discovery (DAVID) online tool was used to obtain a synthetic set of functional annotation information for the DEGs. A PPI network of the DEGs was established using the Search Tool for the Retrieval of Interacting Genes (STRING) database, and a combination of more than 0.4 was considered statistically significant for the PPI. Subsequently, we visualized the PPI network using Cytoscape. Functional module analysis was then performed using Molecular Complex Detection (MCODE). Genes with a degree ≥10 were chosen as hub genes, and pathways of the hub genes were visualized using ClueGO and CluePedia. Additionally, GenCLiP 2.0 was used to explore interactions of hub genes. The Literature Mining Gene Networks module was applied to explore the cocitation of hub genes. The Cytoscape plugin iRegulon was employed to analyze transcription factors regulating the hub genes. Furthermore, the expression levels of the 13 hub genes in pancreatic cancer tissues and normal samples were validated using the Gene Expression Profiling Interactive Analysis (GEPIA) platform. Moreover, overall survival and disease-free survival analyses according to the expression of hub genes were performed using Kaplan-Meier curve analysis in the cBioPortal online platform. The relationship between expression level and tumor grade was analyzed using the online database Oncomine. Lastly, the eight snap-frozen tumorous and adjacent noncancerous adjacent tissues of pancreatic cancer patients used to detect the CDK1 and CEP55 protein levels by western blot. Conclusions Altogether, the DEGs and hub genes identified in this work can help uncover the molecular mechanisms underlying the tumorigenesis of pancreatic cancer and provide potential targets for the diagnosis and treatment of this disease.

scholarly journals Identification and Validation of Hub Genes Associated With Hepatocellular Carcinoma Via Integrated Bioinformatics Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Sumei Wang ◽  
Zuoli Song ◽  
Bing Tan ◽  
Jinjuan Zhang ◽  
Jiandong Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
High Morbidity ◽  
Hub Genes ◽  
Study Gene Expression

Hepatocellular carcinoma (HCC) is the most common malignant tumor of the liver, with high morbidity and mortality, yet its molecular mechanisms of tumorigenesis are still unclear. In this study, gene expression profile of GSE62232 was downloaded from the Gene Expression Omnibus (GEO). The RNA-seq expression data and relative clinical information were retrieved from the Cancer Genome Atlas (TCGA) database. The datasets were analyzed by differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA) to obtain the overlapping genes. Then, we performed a functional enrichment analysis to understand the potential biological functions of these co-expression genes. Finally, we constructed the protein-protein interaction (PPI) analysis combined with survival analysis. MARCO, CLEC4M, FCGR2B, LYVE1, TIMD4, STAB2, CFP, CLEC4G, CLEC1B, FCN2, FCN3 and FOXO1 were identified as the candidate hub genes using the CytoHubba plugin of Cytoscape. Based on survival analysis, the lower expression of FCN3 and FOXO1 were associated with worse overall survival (OS) in HCC patients. Furthermore, the expression levels of FCN3 and FOXO1 were validated by the Human Protein Atlas (HPA) database and the qRT-PCR. In summary, our findings contribute new ideas for the precise early diagnosis, clinical treatment and prognosis of HCC in the future.

scholarly journals Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis

2022 ◽  
Vol 23 (2) ◽  
pp. 794
Author(s):  
Renjian Xie ◽  
Bifei Li ◽  
Lee Jia ◽  
Yumei Li
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Primary Melanoma ◽  
Melanoma Metastasis ◽  
Ppi Network ◽  
Hub Genes ◽  
Receptor Interactions ◽  
Upregulated Genes ◽  
Core Genes ◽  
A375 Cells

Metastasis is the leading cause of melanoma-related mortality. Current therapies are rarely curative for metastatic melanoma, revealing the urgent need to identify more effective preventive and therapeutic targets. This study aimed to screen the core genes and molecular mechanisms related to melanoma metastasis. A gene expression profile, GSE8401, including 31 primary melanoma and 52 metastatic melanoma clinical samples, was downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between melanoma metastases and primary melanoma were screened using GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses of DEGs were performed using the Database for Annotation Visualization and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape with Molecular Complex Detection (MCODE) plug-in tools were utilized to detect the protein–protein interaction (PPI) network among DEGs. The top 10 genes with the highest degrees of the PPI network were defined as hub genes. In the results, 425 DEGs, including 60 upregulated genes and 365 downregulated genes, were identified. The upregulated genes were enriched in ECM–receptor interactions and the regulation of actin cytoskeleton, while 365 downregulated genes were enriched in amoebiasis, melanogenesis, and ECM–receptor interactions. The defined hub genes included CDK1, COL17A1, EGFR, DSG1, KRT14, FLG, CDH1, DSP, IVL, and KRT5. In addition, the mRNA and protein levels of the hub genes during melanoma metastasis were verified in the TCGA database and paired post- and premetastatic melanoma cells, respectively. Finally, KRT5-specific siRNAs were utilized to reduce the KRT5 expression in melanoma A375 cells. An MTT assay and a colony formation assay showed that KRT5 knockdown significantly promoted the proliferation of A375 cells. A Transwell assay further suggested that KRT5 knockdown significantly increased the cell migration and cell invasion of A375 cells. This bioinformatics study provided a deeper understanding of the molecular mechanisms of melanoma metastasis. The in vitro experiments showed that KRT5 played the inhibitory effects on melanoma metastasis. Therefore, KRT5 may serve important roles in melanoma metastasis.

scholarly journals MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

2021 ◽  
Author(s):  
Tianyu Wang ◽  
Yuanyuan Zhang ◽  
Jianhao Bai ◽  
Yawen Xue ◽  
Qing Peng
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Protein Interaction ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues

Abstract Background: Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM.Methods: Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results: Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed, and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP 2, MMP 9, and MMP 16 was positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified.Conclusions: The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

scholarly journals MMP1 and MMP9 are potential prognostic biomarkers and targets for uveal melanoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Tianyu Wang ◽  
Yuanyuan Zhang ◽  
Jianhao Bai ◽  
Yawen Xue ◽  
Qing Peng
Keyword(s):  
Gene Expression ◽  
Uveal Melanoma ◽  
Protein Interaction ◽  
Disease Free Survival ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues

Abstract Background Uveal melanoma (UVM) is the leading cause of eye-related mortality worldwide. This study aimed to explore the expression and prognostic value of matrix metalloproteinases (MMPs) in UVM. Methods Gene expression levels were obtained from the Gene Expression Omnibus (GEO) and Oncomine databases. Functional and pathway enrichment analyses were performed using the Metascape database. GeneMANIA was then applied to construct a protein-protein interaction network and identify the hub genes. Moreover, overall survival (OS) and disease-free survival (DFS) analysis for the hub genes was performed using the UALCAN and Gene Expression Profiling Interactive Analysis (GEPIA) online tool. Furthermore, TRRUST was used to predict the targets of the MMPs. Results Our results revealed that the transcriptional levels of MMP1, MMP9, MMP10, MMP11, MMP13, MMP14, and MMP17 were upregulated in UVM tissues compared to normal tissues. A protein-protein interaction (PPI) network was constructed and the top 50 hub genes were identified. The functions of MMPs and their neighboring proteins are mainly associated with ECM-receptor interaction, proteoglycans in cancer, the IL-17 signaling pathway, and microRNAs in cancer. Among the MMPs, MMP1/2/9/11/14/15/16/17/24 played significant roles in the progression of UVM from stage 3 to stage 4. We also found that the expression of MMP1, MMP2, MMP9, and MMP16 positively correlated with OS and DFS in patients with UVM. Additionally, 18 transcription factors associated with nine MMPs were identified. Conclusions The results of this study may provide potential biomarkers and targets for UVM. However, further studies are required to confirm these results.

scholarly journals Identification of Molecular Markers Associated With Ovarian Cancer Prognosis Using Bioinformatics Analysis

2020 ◽  
Author(s):  
Li Chuang ◽  
Yuan Lyu ◽  
Caixia Liu
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Ovarian Cancer ◽  
Overall Survival ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Brca1 Expression ◽  
Hub Genes

Abstract Background Ovarian cancer is associated with a high mortality rate worldwide. However, the pathogenesis, clinicopathological features, and genetic mechanisms of ovarian cancer are still unclear, and it is essential to identify prognostic markers for its clinical diagnosis and treatment. Here, we utilized bioinformatic analysis to identify potential genes involved in mediating BRCA1 expression to elucidate the potential mechanisms underlying ovarian cancer. Methods Gene expression profiling (GSE14407) was performed to identify differentially expressed genes (DEGs) and analyze the weighted gene co-expression network. We selected the key module that was significantly associated with BRCA1 expression and performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for genes in the hub module. We then screened the hub genes utilizing the Search Tool for the Retrieval of Interacting Genes Database (STRING) and Molecular Complex Detection (MCODE) plug-in in Cytoscape. We validated gene expression levels through The Cancer Genome Atlas and GTEx databases for hub genes and screened genes that were related to overall survival in patients with ovarian cancer using the Kaplan-Meier plotter database. Results In total, 3124 DEGs were detected, including 433 upregulated genes and 2691 downregulated genes. We selected the brown module, which was the most significantly associated with BRCA1 expression. GO analysis showed that the hub module genes were significantly enriched in biological processes, including the mitotic cell cycle process, chromosome segregation, and cell division. KEGG analysis showed that the hub module genes were particularly enriched in the cell cycle, p53 signaling pathway, and small cell lung cancer. We selected 30 hub genes from the protein-protein interaction network, which had 88 nodes and 721 edges. Further analyses identified PBK as a prognosis-associated hub gene. Notably, PBK expression was significantly increased in ovarian cancer tissues, as demonstrated by immunohistochemistry analysis using samples from the Human Protein Atlas database.Conclusion PBK was found to be associated with overall survival in patients with ovarian cancer. Our results provide insights into our understanding of the molecular mechanisms and molecular diagnosis of ovarian cancer.

scholarly journals MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yajun Deng ◽  
Hanyun Ma ◽  
Jinyong Hao ◽  
Qiqi Xie ◽  
Ruochen Zhao
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Potential Biomarkers

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

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