Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Identification of novel AKT1 inhibitors from Sapria himalayana bioactive compounds using structure-based virtual screening and molecular dynamics simulations

Through the experimental and computational analyses, the present study sought to elucidate the chemical composition and anticancer potential of Sapria himalayana plant extract (SHPE). An in vitro analysis of the plant extract was carried out to determine the anticancer potential. Further, network pharmacology, molecular docking, and molecular dynamic simulation were employed to evaluate the potential phytochemical compounds for cervical cancer (CC) drug formulations. The SHPE exhibited anti-cancerous potential through inhibition properties against cancer cell lines. The LC-MS profiling showed the presence of 14 compounds in SHPE. Using network pharmacology analysis, AKT1 (AKT serine/threonine kinase 1) is identified as the possible potential target, and EGFR (Epidermal Growth Factor Receptor) is identified as the possible key signal pathway. The major targets were determined to be AKT1, EGFR by topological analysis and molecular docking. An in silico interaction of phytoconstituents employing molecular docking demonstrated a high binding inclination of ergoloid mesylate and Ergosta-5,7,9(11),22-tetraen-3-ol, (3.beta.,22E)- with binding affinities of -15.5 kcal/mol, and -11.3 kcal/mol respectively. Further, MD simulation and PCA analyses showed that the phytochemicals possessed significant binding efficacy with CC protein. These results point the way for more investigation into SHPE compound's potential as CC treatment.

HPV16 E6 promoting cervical cancer progression through down-regulation of miR-320a to increase TOP2A expression

BACKGROUND

Cervical cancer (CC) has become the fourth most common cancer worldwide and it is mainly caused by the infection of human papillomavirus (HPV), especially high-risk HPV16. Aberrant miRNA expression in CC is closely related to HPV16 infection, and the regulation of HPV16 E6 expression can affect a variety of miRNA expression. This study aims to exploring the miRNAs involved in E6 regulation in CC.

METHODS

Our study screened differentially expressed miRNAs in cervical cells of HPV16 infected and uninfected cervical cancer patients by analyzing the GSE81137 dataset of the gene expression omnibus database (GEO), and identified miR-320a that plays an anti-tumor role and is associated with good prognosis of cervical cancer. Explore the effect of HPV16 E6 on the expression of miR-320a in cervical cancer, and verify whether HPV16 E6 regulates the downstream target gene TOP2A expression through miR-320a, thereby affecting cervical cancer cell proliferation, apoptosis, migration, invasion, and EMT in vitro and in vivo.

RESULTS

The bioinformatic methods selected the miR-320a, which was differentially expressed in cervical cells from HPV16-infected patients compared to uninfected patients. We further demonstrated that miR-320a level was regulated by HPV16 E6, which promoted the CC cell proliferation, migration, invasion, and inhibited apoptosis. In addition, we predicted the downstream target genes of miR-320a and confirmed that TOP2A was one of its targeting proteins. Moreover, HPV16 E6 promoted the TOP2A expression in CC cells through down-regulating miR-320a, leading to promoting CC development.

CONCLUSIONS

We confirmed that HPV16 E6 promoted the TOP2A expression through down-regulation of miR-320a, thus promoting CC development, and the HPV16 E6/miR-320a/TOP2A axis may perform as a potential target for CC treatment.

Regulatory mechanisms of one-carbon metabolism enzymes

One-carbon metabolism is a central metabolic pathway critical for the biosynthesis of several amino acids, methyl group donors, and nucleotides. The pathway mostly relies on the transfer of a carbon unit from the amino acid serine, through the cofactor folate (in its several forms), and to the ultimate carbon acceptors that include nucleotides and methyl groups used for methylation of proteins, RNA, and DNA. Nucleotides are required for DNA replication, DNA repair, gene expression, and protein translation, through ribosomal RNA. Therefore, the one-carbon metabolism pathway is essential for cell growth and function in all cells, but is specifically important for rapidly proliferating cells. The regulation of one-carbon metabolism is a critical aspect of the normal and pathological function of the pathway, such as in cancer, where hijacking these regulatory mechanisms feeds an increased need for nucleotides. One-carbon metabolism is regulated at several levels: via gene expression, posttranslational modification, subcellular compartmentalization, allosteric inhibition, and feedback regulation. In this review, we aim to inform the readers of relevant one-carbon metabolism regulation mechanisms and to bring forward the need to further study this aspect of one-carbon metabolism. The review aims to integrate two major aspects of cancer metabolism-signaling downstream of nutrient sensing and one-carbon metabolism, because while each of these is critical for the proliferation of cancerous cells, their integration is critical for comprehensive understating of cellular metabolism in transformed cells and can lead to clinically relevant insights.

Antitumor Effect of Poplar Propolis on Human Cutaneous Squamous Cell Carcinoma A431 Cells

Propolis is a gelatinous substance processed by western worker bees from the resin of plant buds and mixed with the secretions of the maxillary glands and beeswax. Propolis has extensive biological activities and antitumor effects. There have been few reports about the antitumor effect of propolis against human cutaneous squamous cell carcinoma (CSCC) A431 cells and its potential mechanism. CCK-8 assays, label-free proteomics, RT-PCR, and a xenograft tumor model were employed to explore this possibility. The results showed that the inhibition rate of A431 cell proliferation by the ethanol extract of propolis (EEP) was dose-dependent, with an IC50 of 39.17 μg/mL. There were 193 differentially expressed proteins in the EEP group compared with the control group (p < 0.05), of which 103 proteins (53.37%) were upregulated, and 90 proteins (46.63%) were downregulated. The main three activated and suppressed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were extracellular matrix (ECM)-receptor interaction, amoebiasis, cell adhesion molecules (CAMs), nonalcoholic fatty liver disease (NAFLD), retrograde endocannabinoid signaling, and Alzheimer's disease. The tumor volume of the 100 mg/kg EEP group was significantly different from that of the control group (p < 0.05). These results provide a theoretical basis for the potential treatment of human CSCC A431 cell tumors using propolis.

Computational Biology Helps Understand How Polyploid Giant Cancer Cells Drive Tumor Success

Precision and organization govern the cell cycle, ensuring normal proliferation. However, some cells may undergo abnormal cell divisions (neosis) or variations of mitotic cycles (endopolyploidy). Consequently, the formation of polyploid giant cancer cells (PGCCs), critical for tumor survival, resistance, and immortalization, can occur. Newly formed cells end up accessing numerous multicellular and unicellular programs that enable metastasis, drug resistance, tumor recurrence, and self-renewal or diverse clone formation. An integrative literature review was carried out, searching articles in several sites, including: PUBMED, NCBI-PMC, and Google Academic, published in English, indexed in referenced databases and without a publication time filter, but prioritizing articles from the last 3 years, to answer the following questions: (i) "What is the current knowledge about polyploidy in tumors?"; (ii) "What are the applications of computational studies for the understanding of cancer polyploidy?"; and (iii) "How do PGCCs contribute to tumorigenesis?"

CCT3 as a Diagnostic and Prognostic Biomarker in Cervical Cancer

The chaperonin-containing TCP1 complex subunit 3 (CCT3) has been reported to be involved in the development and prognosis of many tumors, including cervical cancer (CC). This study aimed to analyze the expression and prognostic value of CCT3 in CC by bioinformatics and retrospective study. CCT3 gene expression profiles and clinical information in CC were downloaded from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. CCT3 expression was verified by quantitative real-time polymerase chain reaction (RT-qPCR), Western blot, and immunohistochemistry (IHC). Logistic regression and chi-square testing were used to analyze the relationship between CCT3 expression and the clinical characteristics of CC. Kaplan-Meier and Cox analyses were used to evaluate whether CCT3 affects the prognosis of CC. Nomogram and calibration curves were used to test the predictive value of CCT3. The expression of CCT3 in CC tissues was significantly upregulated compared with that in adjacent benign tissues, and was related to HPV16/18 infection, grade, and positive lymph nodes. High expression of CCT3 is associated with poor prognosis of CC and can be used as an independent risk factor for CC. The prognostic model based on CCT3 and CC clinical features has good predictive ability. CCT3 is overexpressed in CC, which is related to poor prognosis and expected to become a biomarker for CC.

Endothelin-3 is epigenetically silenced in endometrioid endometrial cancer

Purpose

Changes in the activity of endothelins and their receptors may promote neoplastic processes. They can be caused by epigenetic modifications and modulators, but little is known about endothelin-3 (EDN3), particularly in endometrial cancer. The aim of the study was to determine the expression profile of endothelin family and their interactions with miRNAs, and to assess the degree of EDN3 methylation.

Methods

The study enrolled 45 patients with endometrioid endometrial cancer and 30 patients without neoplastic changes. The expression profile of endothelins and their receptors was determined with mRNA microarrays and RT-qPCR. The miRNA prediction was based on the miRNA microarray experiment and the mirDB tool. The degree of EDN3 methylation was assessed by MSP.

Results

EDN1 and EDNRA were overexpressed regardless of endometrial cancer grade, which may be due to the lack of regulatory effect of miR-130a-3p and miR-485-3p, respectively. In addition, EDN3 and EDNRB were significantly downregulated.

Conclusion

The endothelial axis is disturbed in endometrioid endometrial cancer. The observed silencing of EDN3 activity may be mainly due to DNA methylation.

Metadata analysis to explore hub of the hub-genes highlighting their functions, pathways and regulators for cervical cancer diagnosis and therapies

Cervical cancer (CC) is considered as the fourth most common women cancer globally.that shows malignant features of local infiltration and invasion into adjacent organs and tissues. There are several individual studies in the literature that explored CC-causing hub-genes (HubGs), however, we observed that their results are not so consistent. Therefore, the main objective of this study was to explore hub of the HubGs (hHubGs) that might be more representative CC-causing HubGs compare to the single study based HubGs. We reviewed 52 published articles and found 255 HubGs/studied-genes in total. Among them, we selected 10 HubGs (CDK1, CDK2, CHEK1, MKI67, TOP2A, BRCA1, PLK1, CCNA2, CCNB1, TYMS) as the hHubGs by the protein-protein interaction (PPI) network analysis. Then, we validated their differential expression patterns between CC and control samples through the GPEA database. The enrichment analysis of HubGs revealed some crucial CC-causing biological processes (BPs), molecular functions (MFs) and cellular components (CCs) by involving hHubGs. The gene regulatory network (GRN) analysis identified four TFs proteins and three miRNAs as the key transcriptional and post-transcriptional regulators of hHubGs. Then, we identified hHubGs-guided top-ranked FDA-approved 10 candidate drugs and validated them against the state-of-the-arts independent receptors by molecular docking analysis. Finally, we investigated the binding stability of the top-ranked three candidate drugs (Docetaxel, Temsirolimus, Paclitaxel) by using 100 ns MD-based MM-PBSA simulations and observed their stable performance. Therefore the finding of this study might be the useful resources for CC diagnosis and therapies.

A Ferroptosis Molecular Subtype-Related Signature for Predicting Prognosis and Response to Chemotherapy in Patients with Chronic Lymphocytic Leukemia

Ferroptosis is a type of regulated cell death catalyzed by the iron-dependent accumulation of lipid hydroperoxides to lethal levels. Chronic lymphocytic leukemia (CLL) is a chronic lymphoproliferative disorder. However, the understanding of ferroptosis in CLL remains largely poor. In this study, we investigated the stratification and prognostic role of ferroptosis-related genes in CLL patients of ICGC cohort. We obtained fourteen genes with prognostic value by screening 110 ferroptosis-related genes (FRGs). Based on the expression profiles of these 14 genes, we classified CLL patients into two clusters. Most of the FRGs were highly expressed in cluster 1, and cluster 1 was associated with better overall survival (OS). Subsequently, we developed an eight-gene signature (TP63, STEAP3, NQO1, ELAVL1, PRKAA1, HELLS, FANCD2, and CDKN2A) by using LASSO analysis. This risk signature divided CLL patients into high- and low-risk groups. We used Cox regression analysis and ROC analysis demonstrated the risk signature was reliable and robust. And we validated the risk model in an external cohort (GSE22762). We also conducted enrichment analysis and genomic mutation analysis. Finally, we explored the potential effect of chemotherapy between the two risk groups. Our study contributed to understanding the role of ferroptosis in CLL and facilitated personalized and precision treatment.

Bioinformatics Analysis Highlights Five Differentially Expressed Genes as Prognostic Biomarkers of Cervical Cancer and Novel Option for Anticancer Treatment

Cervical cancer is one of the most common gynecological malignancies and is related to human papillomavirus (HPV) infection, especially high-risk type HPV16 and HPV18. Aberrantly expressed genes are involved in the development of cervical cancer, which set a genetic basis for patient prognosis. In this study, we identified a set of aberrantly expressed key genes from The Cancer Genome Atlas (TCGA) database, which could be used to accurately predict the survival rate of patients with cervical squamous cell carcinoma (CESC). A total of 3,570 genes that are differentially expressed between normal and cancerous samples were analyzed by the algorithm of weighted gene co-expression network analysis (WGCNA): 1,606 differentially expressed genes (DEGs) were upregulated, while 1,964 DEGs were downregulated. Analysis of these DEGs divided them into 7 modules including 76 hub genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis revealed a significant increase of genes related to cell cycle, DNA replication, p53 signaling pathway, cGMP-PKG signaling pathway, and Fanconi anemia (FA) pathway in CESC. These biological activities are previously reported to associate with cervical cancer or/and HPV infection. Finally, we highlighted 5 key genes (EMEMP2, GIMAP4, DYNC2I2, FGF13-AS1, and GIMAP1) as robust prognostic markers to predict patient’s survival rate (p = 3.706e-05) through univariate and multivariate regression analyses. Thus, our study provides a novel option to set up several biomarkers for cervical cancer prognosis and anticancer drug targets.

The recurrences of cervical cancer: Possibilities of molecular prediction

The incidence of recurrence of cervical cancer ranges from 10 to 40 %. The 5-year survival rate for patients with recurrent cervical cancer is about 5–15 % against the background of current drug therapy. Clinical and morphological characteristics of the tumor process are known, which are used as markers of an unfavorable prognosis for the development of cervical cancer recurrence. The search for molecular prognostic markers of the course of cervical cancer continues.

The aim. To determine the level of immune cycle proteins in patients with cervical cancer 0–IV stages, depending on the occurrence of a relapse of the disease.

Materials and research methods. A retrospective analysis of previously obtained results of a study on the local level of immune cycle proteins in patients with cervical cancer was performed. Three years after follow-up, 2 groups were formed: group 1 – patients treated for cervical cancer without signs of disease progression (n = 83); group 2 – patients with cervical cancer with local or systemic recurrence (n = 18). Used statistical methods: non-parametric methods of statistics using the Kruskal – Wallis test; ROC-analysis for significant values in order to calculate threshold values; determination of the quality of the identified predictive markers by calculating the sensitivity, specificity, accuracy.

Results. Local initial threshold values have a predictive value for predicting the occurrence of cervical cancer recurrence: B7.2 < 10.7 pg/ml (Se = 0.87; Sp = 0.73; Ac = 0.76; AUC = 0.78), PD-L1 ≤ 5.1 pg/ml (Se = 0.87; Sp = 0.68; Ac = 0.71; AUC = 0.76), sCD27 ≥ 32.0 pg/ml (Se = 0.75; Sp = 0.78; Ac = 0.78; AUC = 0.75).

Conclusion. Determination of local levels of B7.2, PD-L1, sCD27 in patients with cervical cancer before treatment can be used to predict the development of disease recurrence during 3 years of follow-up.

Bioinformatics Screening of Potential Biomarkers from mRNA Expression Profiles to Discover Drug Targets and Agents for Cervical Cancer

Bioinformatics analysis has been playing a vital role in identifying potential genomic biomarkers more accurately from an enormous number of candidates by reducing time and cost compared to the wet-lab-based experimental procedures for disease diagnosis, prognosis, and therapies. Cervical cancer (CC) is one of the most malignant diseases seen in women worldwide. This study aimed at identifying potential key genes (KGs), highlighting their functions, signaling pathways, and candidate drugs for CC diagnosis and targeting therapies. Four publicly available microarray datasets of CC were analyzed for identifying differentially expressed genes (DEGs) by the LIMMA approach through GEO2R online tool. We identified 116 common DEGs (cDEGs) that were utilized to identify seven KGs (AURKA, BRCA1, CCNB1, CDK1, MCM2, NCAPG2, and TOP2A) by the protein–protein interaction (PPI) network analysis. The GO functional and KEGG pathway enrichment analyses of KGs revealed some important functions and signaling pathways that were significantly associated with CC infections. The interaction network analysis identified four TFs proteins and two miRNAs as the key transcriptional and post-transcriptional regulators of KGs. Considering seven KGs-based proteins, four key TFs proteins, and already published top-ranked seven KGs-based proteins (where five KGs were common with our proposed seven KGs) as drug target receptors, we performed their docking analysis with the 80 meta-drug agents that were already published by different reputed journals as CC drugs. We found Paclitaxel, Vinorelbine, Vincristine, Docetaxel, Everolimus, Temsirolimus, and Cabazitaxel as the top-ranked seven candidate drugs. Finally, we investigated the binding stability of the top-ranked three drugs (Paclitaxel, Vincristine, Vinorelbine) by using 100 ns MD-based MM-PBSA simulations with the three top-ranked proposed receptors (AURKA, CDK1, TOP2A) and observed their stable performance. Therefore, the proposed drugs might play a vital role in the treatment against CC.

FOXM1-induced TYMS upregulation promotes the progression of hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and one of the major causes of cancer-related death. Thymidylate synthase (TYMS) catalyzes the methylation of deoxy guanosine to deoxy thymidylate, which is a crucial gene for DNA repair and replication. Thus, TYMS was reported to be closely associated with developing a variety of tumors, but it has been poorly studied in HCC.

Materials and methods

We used the cell counting kit-8 (CCK-8), BrdU, and CFSE assay to measure cell proliferation. The flow cytometry assay and the TUNEL assay were used for assessing cell apoptosis. The flow cytometry assay was used to analyze the cell cycle. The Transwell invasion assay and the wound healing assay were conducted to determine the invasive ability of the cells. RT-qPCR and Western blot analyses were performed to evaluate the mRNA and protein expression levels of specific genes, respectively.

Results

TYMS was found to be upregulated in both HCC cells and patient samples. High expression of TYMS was associated with an unfavorable prognosis in HCC patients based on the TCGA-LIHC dataset. Cell proliferation, apoptosis, and invasion assays revealed that TYMS promoted the proliferation and invasion of HCC cells as well as inhibited apoptosis. In addition, TYMS is a downstream target of FOXM1. TYMS knockdown reversed the 5-FU resistance caused by FOXM1 overexpression and re-sensitized HCC cells to 5-FU treatment.

Conclusion

This study suggested that TYMS serves as an oncogene in HCC, and targeting the FOXM1-TYMS axis may help improve the survival of HCC patients as well as provide new insights for treating advanced HCC patients.

Identification and Validation of TYMS as a Potential Biomarker for Risk of Metastasis Development in Hepatocellular Carcinoma

Metastasis is the major cause of hepatocellular carcinoma (HCC) mortality. Unfortunately, there are few reports on effective biomarkers for HCC metastasis. This study aimed to discover potential key genes of HCC, which could provide new insights for HCC metastasis. GEO (Gene Expression Omnibus) microarray and TCGA (The Cancer Genome Atlas) datasets were integrated to screen for candidate genes involved in HCC metastasis. Differentially expressed genes (DEGs) were screened, and then we performed enrichment analysis of Gene Ontology (GO), together with Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein-protein interaction network was then built and analyzed utilizing STRING and Cytoscape, followed by the identification of 10 hub genes by cytoHubba. Four genes were associated with survival, their prognostic value was verified by prognostic signature analysis. Thymidylate synthase (TYMS) gene was identified as significant HCC metastasis-associated genes after mRNA expression validation and IHC analysis. TYMS silencing in HCC cells remarkedly inhibited growth and invasion. Finally, we found TYMS silencing dramatically decrease DNA synthesis and extracellular matrix (ECM) degradation, resulting in the inhibition of HCC metastasis, indicating TYMS had close associations with HCC development. These findings provided new insights into HCC metastasis and identified candidate gene prognosis signatures for HCC metastasis.

The anti-neoplastic activities of aloperine in HeLa cervical cancer cells are associated with inhibition of the IL-6-JAK1-STAT3 feedback loop

Cervical cancer (CC) is recognized as the most common neoplasm in the female reproductive system worldwide. The lack of chemotherapeutic agents with outstanding effectiveness and safety severely compromises the anti-cipated prognosis of patients. Aloperine (ALO) is a natural quinolizidine alkaloid with marked anti-cancer effects on multiple malignancies as well as favorable activity in relieving inflammation, allergies and infection. However, its therapeutic efficacy and underlying mechanism in CC are still unclear. In the current study, MTT assay was employed to evaluate the viability of HeLa cells exposed to ALO to preliminarily estimate the effectiveness of ALO in CC. Then, the effects of ALO on the proliferation and apoptosis of HeLa cells were further investigated by plate colony formation and flow cytometry, respectively, while the migration and invasion of ALO-treated HeLa cells were evaluated using Transwell assay. Moreover, nude mice were subcutaneously inoculated with HeLa cells to demonstrate the anti-CC properties of ALO in vivo. The molecular mechanisms underlying these effects of ALO were evaluated by Western blot and immunohistochemical analysis. This study experimentally demonstrated that ALO inhibited the proliferation of HeLa cells via G2 phase cell cycle arrest. Simultaneously, ALO promoted an increase in the percentage of apoptotic HeLa cells by increasing the Bax/Bcl-2 ratio. Additionally, the migration and invasion of HeLa cells were attenuated by ALO treatment, which was considered to result from inhibition of epithelial-to-mesenchymal transition. For molecular mechanisms, the expression and activation of the IL-6-JAK1-STAT3 feedback loop were markedly suppressed by ALO treatment. This study indicated that ALO markedly suppresses the proliferation, migration and invasion and enhances the apoptosis of HeLa cells. In addition, these prominent anti-CC properties of ALO are associated with repression of the IL-6-JAK1-STAT3 feedback loop.

Identification of Cross-Pathway Connections via Protein-Protein Interactions Linked to Altered States of Metabolic Enzymes in Cervical Cancer

Metabolic reprogramming is one of the emerging hallmarks of cancer cells. Various factors, such as signaling proteins (S), miRNA, and transcription factors (TFs), may play important roles in altering the metabolic status in cancer cells by interacting with metabolic enzymes either directly or via protein-protein interactions (PPIs). Therefore, it is important to understand the coordination among these cellular pathways, which may provide better insight into the molecular mechanism behind metabolic adaptations in cancer cells. In this study, we have designed a cervical cancer-specific supra-interaction network where signaling pathway proteins, TFs, and microRNAs (miRs) are connected to metabolic enzymes via PPIs to investigate novel molecular targets and connections/links/paths regulating the metabolic enzymes. Using publicly available omics data and PPIs, we have developed a Hidden Markov Model (HMM)-based mathematical model yielding 94, 236, and 27 probable links/paths connecting signaling pathway proteins, TFs, and miRNAs to metabolic enzymes, respectively, out of which 83 paths connect to six common metabolic enzymes (RRM2, NDUFA11, ENO2, EZH2, AKR1C2, and TYMS). Signaling proteins (e.g., PPARD, BAD, GNB5, CHECK1, PAK2, PLK1, BRCA1, MAML3, and SPP1), TFs (e.g., KAT2B, ING1, MED1, ZEB1, AR, NCOA2, EGR1, TWIST1, E2F1, ID4, RBL1, ESR1, and HSF2), and miR (e.g., mir-147a, mir-593-5p, mir-138-5p, mir-16-5p, and mir-15b-5p) were found to regulate two key metabolic enzymes, EZH2 and AKR1C2, with altered metabolites (L-lysine and tetrahydrodeoxycorticosterone, THDOC) status in cervical cancer. We believe, the biology-based approach of our system will pave the way for future studies, which could be aimed toward identifying novel signaling, transcriptional, and post-transcriptional regulators of metabolic alterations in cervical cancer.

Bioinformatics analysis of differentially expressed genes and pathways in the development of cervical cancer

Background

This study aimed to explore and identify key genes and signaling pathways that contribute to the progression of cervical cancer to improve prognosis.

Methods

Three gene expression profiles (GSE63514, GSE64217 and GSE138080) were screened and downloaded from the Gene Expression Omnibus database (GEO). Differentially expressed genes (DEGs) were screened using the GEO2R and Venn diagram tools. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Gene set enrichment analysis (GSEA) was performed to analyze the three gene expression profiles. Moreover, a protein–protein interaction (PPI) network of the DEGs was constructed, and functional enrichment analysis was performed. On this basis, hub genes from critical PPI subnetworks were explored with Cytoscape software. The expression of these genes in tumors was verified, and survival analysis of potential prognostic genes from critical subnetworks was conducted. Functional annotation, multiple gene comparison and dimensionality reduction in candidate genes indicated the clinical significance of potential targets.

Results

A total of 476 DEGs were screened: 253 upregulated genes and 223 downregulated genes. DEGs were enriched in 22 biological processes, 16 cellular components and 9 molecular functions in precancerous lesions and cervical cancer. DEGs were mainly enriched in 10 KEGG pathways. Through intersection analysis and data mining, 3 key KEGG pathways and related core genes were revealed by GSEA. Moreover, a PPI network of 476 DEGs was constructed, hub genes from 12 critical subnetworks were explored, and a total of 14 potential molecular targets were obtained.

Conclusions

These findings promote the understanding of the molecular mechanism of and clinically related molecular targets for cervical cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08412-4.

Integrated Profiles Analysis Identified a Coding-Non-Coding Signature for Predicting Lymph Node Metastasis and Prognosis in Cervical Cancer

Accumulating evidence has shown that lymph node metastasis (LNM) is not only an important prognostic factor but also an indicator of the need for postoperative chemoradiotherapy. Therefore, identifying risk factors or molecular markers related to LNM is critical for predicting the prognosis and guiding individualized treatment of patients with cervical cancer. In this study, we used the machine learning-based feature selection approach to identify eight optimal biomarkers from the list of 250 differentially expressed protein-coding genes and long non-coding RNAs (lncRNAs) in the TCGA cohort. Then a coding-non-coding signature (named CNC8SIG) was developed using the elastic-net logistic regression approach based on the expression levels of eight optimal biomarkers, which is useful in discriminating patients with LNM from those without LNM in the discovery cohort. The predictive performance of the CNC8SIG was further validated in two independent patient cohorts. Moreover, the CNC8SIG was significantly associated with patient's survival in different patient cohorts. In silico functional analysis suggested that the CNC8SIG-associated mRNAs are enriched in known cancer-related biological pathways such as the Wnt signaling pathway, the Ras signaling pathway, Rap1 signaling pathway, and PI3K-Akt signaling pathway.

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs=0.635), CDK1 (P<0.0001, rs=0.511), centromere protein F (CENPF) (P<0.0001, rs=0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs=0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs=0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

Identification of key genes and pathways of diagnosis and prognosis in cervical cancer by bioinformatics analysis

Background

Cervical cancer as one of the most common malignant tumors lead to bad prognosis among women. Some researches already focus on the carcinogenesis and pathogenesis of cervical cancer, but it is still necessary to identify more key genes and pathways.

Methods

Differentially expressed genes were identified by GEO2R from the gene expression omnibus (GEO) website, then gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyzed by DAVID. Meanwhile, protein–protein interaction network was constructed by STRING, and both key genes and modules were found in visualizing network through Cytoscape. Besides, GEPIA did the differential expression of key genes and survival analysis. Finally, the expression of genes related to prognosis was further explored by UNLCAN, oncomine, and the human protein atlas.

Results

Totally 57 differentially expressed genes were founded, not only enriched in G1/S transition of mitotic cell cycle, mitotic nuclear division, and cell division but also participated in cytokine–cytokine receptor interaction, toll‐like receptor signaling pathway, and amoebiasis. Additionally, 12 hub genes and 3 key modules were screened in the Cytoscape visualization network. Further survival analysis showed that TYMS (OMIM accession number 188350), MCM2 (OMIM accession number 116945), HELLS (OMIM accession number 603946), TOP2A (OMIM accession number 126430), and CXCL8 (OMIM accession number 146930) were associated with the prognosis of cervical cancer.

Conclusion

This study aim to better understand the characteristics of some genes and signaling pathways about cervical cancer by bioinformatics, and could provide further research ideas to find new mechanism, more prognostic factors, and potential therapeutic targets for cervical cancer.