Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer
Journal of Genetic Engineering and Biotechnology volume 21, Article number: 10 (2023)
In today’s society, cancer has become a big concern. The most common cancers in women are breast cancer (BC), endometrial cancer (EC), ovarian cancer (OC), and cervical cancer (CC). CC is a type of cervix cancer that is the fourth most common cancer in women and the fourth major cause of death.
This research uses a network approach to discover genetic connections, functional enrichment, pathways analysis, microRNAs transcription factors (miRNA-TF) co-regulatory network, gene-disease associations, and therapeutic targets for CC. Three datasets from the NCBI’s GEO collection were considered for this investigation. Then, using a comparison approach between the datasets, 315 common DEGs were discovered. The PPI network was built using a variety of combinatorial statistical approaches and bioinformatics tools, and the PPI network was then utilized to identify hub genes and critical modules.
Furthermore, we discovered that CC has specific similar links with the progression of different tumors using Gene Ontology terminology and pathway analysis. Transcription factors-gene linkages, gene-disease correlations, and the miRNA-TF co-regulatory network were revealed to have functional enrichments. We believe the candidate drugs identified in this study could be effective for advanced CC treatment.
In 2020, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) were diagnosed worldwide, with around 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) . The global cancer burden is expected to be 28.4 million cases in 2040, a 47% increase from 2020, with a greater increase in transitioning (64 to 95%) countries versus transitioned (32 to 56%) countries due to demographic changes, though this may be exacerbated further by increasing risk factors associated with globalization and a growing economy [2, 3]. Lung cancer and colorectal cancer are the most prevalent cancers in men, whereas breast cancer, colorectal cancer, cervical cancer, and lung cancer are the most common in females [4,5,6]. In 2020, men had a 19% higher overall cancer incidence rate (222.0 per 100,000) than women (186 per 100,000), while rates varied greatly across areas. Cervical cancer is the fourth most common malignancy in women and the fourth greatest cause of cancer mortality, with an estimated 604,000 new cases and 342,000 deaths in 2020 . The major risk factor for CC is infection with particular forms of HPV, followed by smoking [1, 8]. Types of HPV are 16 and 18 accounting for 75% of all CC instances globally, with HPV types 31 and 45 accounting for the remaining 10% . There are approximately 150–200 different types of HPV that have been identified, with 15 being considered high risk (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 68, 73, and 82), three being considered probable high risk (26, 53, and 66), and 12 being considered low risk (6, 11, 40, 42, 43, 44, 54, 61, 70, 72, 81, and CP6108) .
An investigation of differentially expressed gene (DEG) promoter sequences and transcription factor (TF) binding sites previously identified TF E2F as a crucial transcriptional regulator and a possible molecular target for cervical cancer treatment . Another research found that the target genes CDC45, GINS2, MCM2, and PCNA are important participants of cervical cancer . Several novel candidate genes implicated in cervical carcinogenesis (e.g., VEGFA and IL-6) were also predicted by combining human protein interaction data with cervical cancer gene sets [13, 14]. These researches have provided important information concerning cervical cancer, but no conclusions about the disease’s underlying molecular pathways were made. Another wisely analyzed study has demonstrated that the predictive potential reporter biomolecules including KAT2B, PCNA, CD86 [15, 16], miR-192-5p, and miR-215-5p have a significant role in cervical cancer .
Three datasets have been taken to complete this network-based study. The common DEGs were then determined by comparing the datasets. Using common DEGs have applied for GO annotation, pathway analysis, construct PPI network, module analysis, hub DEGs identification, gene-disease association prediction, TF-miRNA co-regulatory network identification, and drug target prediction.
The study effort has included differential expressed genes (DEGs) discovery, common DEGs finding, gene ontology analysis, pathways enrichment analysis, protein interaction network building, module analysis, TF-miRNA co-regulatory network, drug compounds analysis, and gene-disease correlations network creation, which is a network-based approach. All methodology phases are detailed here, and a quick representation of this work is demonstrated in Fig. 1.
The GEO database is a free public resource that stores and distributes high-throughput gene expression data from all over the world (https://www.ncbi.nlm.nih.gov/geo/) [18,19,20]. Three datasets have been considered for CC for this study including GSE9750, GSE63514, and GSE7803 from the GEO repository. GSE9750 stands on a single platform GPL96 [HG-U133A] Affymetrix Human Genome U133A Array for Homo sapiens organisms named “Identification of gene expression profiles in CC.” This dataset conducts total of 66 samples where 42 samples are affected and 24 samples are normal cervical cell . GSE63514 dataset depends on a single platform GPL570 [HG-U133_Plus_2] Affymetrix Human Genome U133 Plus 2.0 Array. This dataset conducts a total of 128 samples where 104 are affected cells and 24 normal cells . And the last dataset GSE7803 conducts 10 normal cells and 31 affected cells .
GEO2R was utilized to find the DEGs. The raw file was transformed to expression using a statistical relevance p-value of 0.05 and logFc > 1 (upregulated) and logFc-1 (downregulated), and the common DEGs were identified using a Venn diagram web tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) .
Ontological terms and pathways analysis
The biological activities of the common DEGs were identified via gene ontology analysis using the Gene Ontology database , while the corresponding pathways were discovered in the KEGG database . The Enrichr online tool  was used for all of the analyses. Enrichr is a web-based intuitive enrichment analysis application that provides many sorts of graphical summaries of gene list collective functions. Enrichr is an open-source project that can be found at http://amp.pharm.mssm.edu/Enrichr .
Construction of PPI network and clustering analysis
The PPI network  is a graphical representation of gene interaction. The PPI network was built using the physical interactions of proteins from frequent DEGs in CC datasets from the STRING database . To predict the interconnection between the proteins was set the minimum confidence score of 0.40. Afterward, the open-source Cytoscape tool was used to analyze the PPI network . To identify the complex network part (cluster) of the PPI network, the MCODE method was implemented . The MCODE basic parameters degree cutoff = 2, node score cutoff = 0.2, k-core = 2, and maximum depth = 100 were chosen as a minimum criterion. Using the connection (degree) technique, the cytoHubba plugin program of Cytoscape software was used to discover hub DEGs from the PPI network .
Drug target analysis
The drug signatures database (DSigDB) drug signature database  was used to identify therapeutic targets for selected hub DEGs. The DSigDB is a new gene set resource for gene set enrichment analysis that links drugs/compounds to their target genes (GSEA). DSigDB now has 22,527 gene sets, each of which contains 17,389 unique compounds spanning 19,531 genes. Users may search, browse, and download drugs, chemicals, and gene sets from the DSigDB database. In GSEA software, DSigDB gene sets may be utilized to connect gene expression to drugs/compounds for drug repurposing and translational research . The cutoff criterion for identifying pharmacological targets was p-value 0.01 and overlapping genes count > = 9.
Association network of gene disease
Linkage studies , genome-wide association studies (GWAS) , and RNA interference screens  are all expensive and time-consuming approaches for determining gene-disease associations. As a result, a variety of computational methods [38,39,40] for identifying or predicting gene-disease correlations have been developed. These approaches have distinct strengths and limitations, and they are best suited to different types of diseases . Gene-disease association network has been exported through DisGeNET database (https://www.disgenet.org/).
TF-miRNA co-regulatory network development and analysis
The RegNetwork repository (http://www.regnetworkweb.org/) was used to build the TF-miRNA co-regulatory networks that revealed new information about the key role of proposed TF-miRNA co-regulation in CC and its crosstalk with the surrounding microenvironment . RegNetwork is a human and mouse gene regulatory network repository that collects and combines known regulatory connections between TFs, miRNAs, and target genes from 25 different datasets. RegNetwork is a database that houses a comprehensive collection of empirically known or predicted transcriptional and posttranscriptional regulatory interactions, and the database structure is flexible enough to accommodate future additions into gene regulatory networks for more species .
Analysis of survival for hub genes
A time-to-event study, also known as a survival analysis, refers to a set of approaches for long-term research prior to the identification of a specific endpoint of interest. The occurrence (e.g., death) does not frequently occur in all patients at the end of the observation period [44,45,46], which is a distinctive feature of the survival data. In this analysis, the survival role was calculated for altered and regular classes of significant genes common to datasets of CC by using Cox PH and PL estimators. The Cox PH regression model was evaluated univariate as well as multivariate .
Three-hundred fifteen common DEGs identified
Using GEO2R for CC, the dataset was downloaded from the GEO repository of NCBI. Initially, the GSE9750, GSE63514, and GSE7803 datasets revealed 21,156, 45,118, and 20,056 DEGs, respectively, after filtration with the criteria 2629, 1842, and 796 DEGs were taken. Following that, a comparison of the DEGs revealed 315 shared DEGs (Fig. 2).
Ontological terms and pathways analysis
The GO analysis reveals most of the DEGs associated with the terms including the following: DNA metabolic process, DNA replication, G1/S transition of mitotic cell cycle, mitotic cell cycle phase transition, cellular macromolecule biosynthetic process, cellular response to DNA damage stimulus, DNA repair, regulation of cell proliferation, regulation of transcription, DNA templated, cellular protein modification process for biological process, nuclear chromosome part, spindle, chromatin, microtubule-organizing center, centrosome, microtubule cytoskeleton, cytoskeleton, nucleolus for cellular component, DNA helicase activity, DNA-dependent ATPase activity, serine-type peptidase activity, DNA binding, peptidase activity, acting on L-amino acid peptides, kinase binding, protein kinase binding, tubulin binding, protein homodimerization activity, metal ion binding for molecular function, etc. (Table 1) (Fig. 3). The pathway of KEGG, Reactome, and WikiPathways analysis reveals that most of the common DEGs were associated with the cell cycle, DNA replication, cellular senescence, cell cycle, p53 signaling pathway, pathways in cancer, mitotic, mitotic G1-G1/S phases, S phase, M phase, generic transcription pathway, prostate cancer, metabolism, retinoblastoma gene in cancer, G1 to S cell cycle control, integrated breast cancer pathway, etc. (Table 2) (Fig. 4).
Hub genes identification and clustering analysis
The PPI network is a significant product of this sort of research. The STRING database was used to create the PPI network, which was then shown using the Cytoscape application. The PPI network (Fig. 5A) has 183 nodes and 1082 edges, with nodes representing DEGs and edges representing connections between nodes. Afterward, we have gotten 10 hub DEGs (CDK1, CCNB1, CDC20, TOP2A, MAD2L1, NDC80, AURKA, ASPM, NCAPG, and BIRC5) (Fig. 5B) using cytoHubba plugin tool of Cytoscape. By contrast, the MCODE plugin algorithm showed 10 modules (cluster sub-networks); from them, significant 7 modules have taken (Fig. 6). The first module carried 27 nodes and 316 edges, the second module contains 9 nodes and 36 edges, the third module conducts 15 nodes and 51 edges, and fourth modules carried 10 nodes and 26 edges.
Lucanthone and paclitaxel target significantly interacted with the hub genes
The therapeutic target for the hub DEGs was discovered using the DSigDB drug target database. The result showed that the “LUCANTHONE CTD 00006227 and paclitaxel CTD 00007144” both of drug targets mostly associated with the hub DEGs (Table 3). Lucanthone is being studied as a chemotherapy and radiation sensitizer due to its potential to interact with DNA repair . However, because lucanthone reduces cancer cell survival regardless of p53 status, autophagy suppression may be a more important contribution to the lucanthone mode of action that impacts DNA repair. Because lucanthone suppresses autophagy, it may be able to boost the effectiveness of chemotherapeutics that activate this process. Both apoptosis and autophagy have been observed to be induced by vorinostat and bortezomib, and inhibiting autophagy increases its activity [49,50,51]. Tumor immunotherapy improves the body’s immunity, resulting in the immunological response to malignancies. Our understanding of the potential utility of conventional medicines in tumor immunotherapy has advanced recently . According to multiple studies, paclitaxel directly eliminates tumor cells while also regulating immune cells such as effector T cells, dendritic cells (DCS), natural killer (NK) cells, regulatory T cells (Tregs), and macrophages . Belinostat , doxorubicin , bleomycin , and bortezomib  are examples of chemotherapeutics with comparable immunomodulatory characteristics. Paclitaxel is a novel anticancer medication having broad-spectrum action in epithelial ovarian cancer, head and neck cancer, esophageal cancer, breast cancer, CC, and lung cancer [58,59,60]. The Food and Drug Administration (FDA) has approved cisplatin and topotecan for the treatment of advanced cervical cancer. However, the cisplatin/paclitaxel or carboplatin/paclitaxel regimens are less toxic and easier to administer than cisplatin/topotecan according to National Comprehensive Cancer Network (NCCN) Guidelines Insights: Cervical Cancer, Version 1.2020 [61, 62]. The panel decided to add carboplatin/paclitaxel/bevacizumab to the list of recommended regimens for recurrent or metastatic cervical cancer based on the findings of GOG 240 and JGOG0505. According to prior research, cisplatin/paclitaxel and carboplatin/paclitaxel have become the most frequently utilized systemic regimens for metastatic or recurrent cervical cancer .
Hub genes cooperated to explore the gene-disease interaction network through the DisGeNET database. In the gene-disease interaction, network genes and diseases are interconnected. A total of 10 hub genes were used to apply for the gene-disease association network where just 5 hub genes show the connectivity between the genes and diseases (Fig. 7). The network showed the BIRC5 gene is the most connected node that is associated with 17 cancers and tumor-related diseases.
TF-miRNA co-regulatory network development and analysis
Transcription factors (TFs) and microRNAs (miRNAs) are important regulators of gene expression . In CC, miRNAs and TFs may perform a dual regulatory role. We built a complete particular TF-miRNA co-regulatory network by merging predicted and empirically confirmed TF and miRNA targets after gathering hub genes from the PPI network. Using hub genes, the RegNetwork repository was utilized to build a TF-miRNA co-regulatory network. There are 112 nodes and 136 edges in the TF-miRNA co-regulatory network, including 73 TF candidates, 8 hub nodes, and 31 miRNA candidate nodes (Fig. 8). In addition, 31 miRNA were analyzed to detect the cancer disease connectivity (Fig. 9). In the figure, hsa-mir-137, hsa-mir-92a, and hsa-mir-542-3p show the high connectivity with the disease including breast cancer, ovarian cancer, pancreatic cancer, glioblastoma, non-small cell lung cancer, and bladder neoplasms.
Following the analysis, were picked are the most critical genes of CC with the cutoff p-value <= 0.05. The PL estimator had been used to achieve the survival curves of the most relevant genes in contrast with altered and regular populations. All the hub genes were analyzed for survival rating, and seven proteins including BIRC5, NCAPG/CAPG, TOP2A, MAD2L1, AURKA, ASPM, and NDC80/KNTC2 perform logrank P less than 0.05 (Fig. 10). CAPG could play a significant role in the survival of breast cancer , bladder cancer , as well as ovarian cancer , and many more. This finding reports for the first time that CAPG may play a significant role in the survival of cervical cancer. Although MAD2L1 , TOP2A , AURKA , and ASPM  were reported in various research findings, they may play a remarkable role in the survival of cervical cancer. By contrast, NDC80 and CAPG are a novel targets in the survival of cervical cancer.
CC is the fourth most frequent cancer in women worldwide and the fourth leading cause of cancer death. According to estimations, CC claimed the lives of 342,000 individuals in 2020. Around 8% of all cancer diagnoses and deaths are caused by this condition. We used a network-based technique to look at the patterns of gene expression in three microarray datasets of CC patients and found molecular targets that could be employed as cancer biomarkers. It could also disclose crucial information about their impact on the progression of illnesses or disorders. In the fields of biomedical and computational biology, expression profiling utilizing high-throughput microarray datasets has proven to be a helpful resource for identifying biomarker candidates for a variety of diseases . According to the CC transcriptomics analysis, the common 315 DEGs have comparable expression in three datasets. To obtain insight into the etiology of CC, the biological relevance of 315 frequently DEGs was examined utilizing gene ontology and pathway analysis techniques based on p-values.
The Gene Ontology (GO) is a gene regulation framework based on a generic conceptual perspective that makes it easier to comprehend genes and their interactions. Evolution achieved this over time by accumulating biological knowledge about gene functions and regulation in a variety of ontological domains . For three types of GO analysis, the GO database was employed as an annotation source: BP (molecular activities), CC (gene controls function), and MF (activities at the molecular level) . The biological process (34 genes) and DNA metabolic process (34 genes) are the most important, followed by transcription control (43 genes). Many cellular metabolic activities involve deoxyribonucleic acid. This is one of the two primary types of nucleic acid, and it is made up of one or two strands of connected deoxyribonucleotides . The method by which a cell controls the translation of DNA to RNA (transcription) to regulate gene activity is called transcriptional regulation. Changing the quantity of copies of RNA produced and manipulating when the gene is transcribed are two examples of how a single gene can be managed .
Most of the gene promoters in invertebrates have a CpG island with several CpG sites . A gene is silenced when several of its promoter CpG sites are methylation . However, transcriptional silencing may have a bigger role in cancer development than mutation. For the cellular component function, the nuclear chromosome part (33 genes), microtubule-organizing center (22 genes), and chromatin (18 genes) are significant. According to the molecular function, top GO terms peptidase activity, acting on L-amino acid peptides (12 genes), kinase binding (19 genes), and DNA binding (32 genes), are significantly associated.
Pathway analysis  is the most effective method for reflecting an organism’s behavior via internal changes. The pathways of the most prevalent DEGs were culled from three separate databases: KEGG, Reactome, and WikiPathways. Cell cycle, DNA replication, cellular senescence, the p53 signaling route, drug metabolism, prostate cancer, human T-cell leukemia virus 1 infection, chemical carcinogenesis, and cancer pathways are the top ten KEGG pathways. Cell cycle, mitotic, mitotic G1-G1/S phases, S phase, G1/S transition, cell cycle checkpoints, M phase, generic transcription pathway, and metabolism are all heavily connected with common DEGs, according to Reactome’s pathways. The most prevalent DEGs connected with WikiPathways route include retinoblastoma gene in cancer, G1 to S cell cycle control, cell cycle, DNA IR-damage and cellular response via ATR, miRNA regulation of DNA damage response, vitamin D receptor pathway, and integrated breast cancer pathway.
Using common DEGs, a PPI network had been created to understand the biological characteristics in-depth and explore disease biomarkers. Depending on the topological measure (degree), 10 hub genes have been traced from the PPI network, which might be a therapeutic target or biomarker. The top 10 hub genes including CDK1, CCNB1, CDC20, TOP2A, MAD2L1, NDC80, AURKA, ASPM, NCAPG, and BIRC5 showed high degree value. In CC, cyclin-dependent kinase 1 (CDK1) has been observed before. CDK1 is a highly preserved protein that works as a serine kinase/threonine. With over 70 regulatory objectives, it plays a key role in controlling the cell cycle. CDK1 phosphorylates directly a number of target substrates for controlling the transcription and progression of cells in response to different stimuli . Studies have demonstrated that CDKs and their modulators are aberrantly activated in several malignancies. CDK dysregulation induces the growth of aberrant cells and genomic instability . Indeed, all human malignancies are affected by the D-cyclin-cdk4/6-INK4-Rb pathway . Research from in vitro and in vivo shows that a variety of malignancies including cervical, colon, and breast cancer have substantial anticancer effects using CDK inhibitors [81, 82].
The transcriptional and posttranscriptional regulators of the hub DEGs were discovered using the miRNA-TF co-regulatory network. TFs govern transcription ratios, whereas miRNAs play a significant role in gene posttranscriptional regulation and RNA silence. The role of transcription factors (TFs) and microRNAs (miRNAs) in the progression of disease is crucial. Thus, the connections between the common DEGs, TFs, and miRNAs are shown in our research. The network builds on 73 TF candidates, 8 hub nodes, and 31 miRNA candidates. From the miRNA candidate, 4 candidates (hsa-mir-137, hsa-mir-92a, hsa-mir-24, and hsa-mir-542-3p) showed significant association with various types of cancer. miR-137 is integrated into CpG island (a high-frequency genomic area that contains CpG dinucleotide) and has been found in several types of cancer, such as colorectal, gastric, breast-and-squamous cells, and head and neck to have often been silenced by the promoter of hypermethylation [83,84,85]. MiR-137 is inhibited epigenetically in colorectal adenomatous cells in the same way as it is suppressed in colorectal cancer tissue, showing that miRNA methylation occurs early in colorectal cancer . There are various studies which showed that hsa-mir-92a miRNA significantly connected with CC , colorectal cancer , small cell lung cancer , breast cancer , etc. According to some previous studies, hsa-mir-24 miRNA might play a significant role in lung cancer , breast cancer , prostate cancer , and colorectal cancer .
Gene-disease association network was analyzed to reveal the gene connectivity with the disease. The outcomes of this analysis showed liver carcinoma, lung carcinoma, adenocarcinoma, non-small cell lung carcinoma, renal cell carcinoma, colorectal neoplasms, prostatic neoplasms, neuroblastoma, kidney neoplasms, adrenocortical carcinoma, etc. are significantly connected with the hub DEGs. In individuals with CC, the history of radiation was an independent risk factor for second primary lung cancer . A study indicated that 4.33% of CC patients develop lung metastasis , which is consistent with prior research [97, 98].
Another biggest exploration of this study is drug compounds finding for cancer. This analysis showed many compounds are connected with cancer. From them, LUCANTHONE CTD 00006227 and paclitaxel CTD 00007144 targets are significantly associated with CC. “LUCANTHONE” is the new target for CC. In previous, some studies have announced “LUCANTHONE” reduces cancer cell survival regardless of p53 status; autophagy suppression may be a more important contribution to the lucanthone mode of action that impacts DNA repair. Another drug compound target “paclitaxel” was studied as an anticancer medication in some previous studies [58,59,60]. The authors in these studies wrote, “paclitaxel” may play a significant role in CC as well as ovarian cancer, head and neck cancer, esophageal cancer, breast cancer, and lung cancer. Paclitaxel has a key role in the care of advanced/metastatic illness in cervical cancer, according to the European Society for Medical Oncology (ESMO) clinical practice guideline issued in 2017 . Paclitaxel and cisplatin in combination with bevacizumab are regarded as the optimal first-line regimens for metastatic or recurrent cervical cancer due to the appropriate balance of effectiveness and safety characteristics. Paclitaxel with carboplatin may be an alternate choice for individuals who are not candidates for cisplatin, particularly those with impaired renal function . Paclitaxel has been the first microtubule-stabilizing agent identified and considered as a significant part of the standard chemotherapy regimens for treating cervical cancer. In particular, the combination of paclitaxel with platinum-derived drugs has represented and represents today the cornerstone of advanced cervical cancer therapy, in particular for women with recurrent and persistent disease .
In this study, three datasets of CC were analyzed and compared to identify the important DEGs. A total of 315 common DEGs have collected for further analysis. Using common DEGs were used the functional analysis to figure out important GO terms and pathways using KEGG, Reactome, and WikiPathways database. Also, we developed a PPI network and identified significant hub DEGs including CDK1, CCNB1, CDC20, TOP2A, MAD2L1, NDC80, AURKA, ASPM, NCAPG, and BIRC5 that are the main therapeutic targets for the CC. And gene disease association and TF-miRNA co-regulatory network also demonstrated to identify the drag target miRNA. The analysis of the drug compounds showed LUCANTHONE CTD 00006227 and paclitaxel CTD 00007144 are the most associated with the hub DEGs. This network-based study may play a significant role in future treatment or medicine development for the patient with CC.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 71:209–249. https://doi.org/10.3322/caac.21660
Hricak H, Ward ZJ, Atun R, Abdel-Wahab M, Muellner A, Scott AM (2021) Increasing access to imaging for addressing the global cancer epidemic. Radiology 301(3):543–546. https://doi.org/10.1148/radiol.2021211351 Epub 2021 Sep 28. PMID: 34581630; PMCID: PMC8630531
Zhang M, Zhong J, Zhang W, Zhou C, Wang X, Zou W, Xiaodan W, Zhang M (2022) Psychometric properties of a simplified Chinese version of the cancer predisposition perception scale. Asia Pac J Oncol Nurs 9(3):179–184, ISSN 2347-5625,. https://doi.org/10.1016/j.apjon.2021.10.001
Nagai H, Kim YH (2017) Cancer prevention from the perspective of global cancer burden patterns. J Thorac Dis 9(3):448–451. https://doi.org/10.21037/jtd.2017.02.75
Hussain AM, Lafta RK (2021) Cancer Trends in Iraq 2000-2016. Oman Med J 36(1):e219. https://doi.org/10.5001/omj.2021.18 PMID: 33552559; PMCID: PMC7847549
Carioli G, Bertuccio P, Malvezzi M, Rodriguez T, Levi F, Boffetta P, La Vecchia C, Negri E (2020) Cancer mortality predictions for 2019 in Latin America. Int J Cancer 147(3):619–632. https://doi.org/10.1002/ijc.32749 Epub 2019 Nov 27. PMID: 31637709
Ourlad AG, Tantengco YN, Yoshimura M, Nishiumi F, Llamas-Clark EF, Yanagihara I (2022) Co-infection of human papillomavirus and other sexually transmitted bacteria in cervical cancer patients in the Philippines. Gynecol Oncol Rep 40:100943, ISSN 2352-5789. https://doi.org/10.1016/j.gore.2022.100943
Sadri Nahand J, Moghoofei M, Salmaninejad A, Bahmanpour Z, Karimzadeh M, Nasiri M, Mirzaei HR, Pourhanifeh MH, Bokharaei-Salim F, Mirzaei H, Hamblin MR (2020) Pathogenic role of exosomes and microRNAs in HPV-mediated inflammation and cervical cancer: a review. Int J Cancer 146:305–320. https://doi.org/10.1002/ijc.32688
Dillman RK, Oldham RO (eds) (2009) Principles of cancer biotherapy, 5th edn. Springer, Dordrecht, p 149 ISBN 9789048122899.
Muñoz N, Bosch FX, de Sanjosé S, Herrero R, Castellsagué X, Shah KV, Snijders PJ, Meijer CJ (2003) International Agency for Research on Cancer Multicenter CC Study Group. Epidemiologic classification of human papillomavirus types associated with CC. N Engl J Med 348(6):518–527. https://doi.org/10.1056/NEJMoa021641 PMID: 12571259
Srivastava P, Mangal M, Agarwal SM (2014) Understanding the transcriptional regulation of cervix cancer using microarray gene expression data and promoter sequence analysis of a curated gene set. Gene. 535(2):233–238. https://doi.org/10.1016/j.gene.2013.11.028 Epub 2013 Nov 27. PMID: 24291025
Xue H, Sun Z, Wu W, Du D, Liao S (2021) Identification of hub genes as potential prognostic biomarkers in cervical cancer using comprehensive bioinformatics analysis and validation studies. Cancer Manag Res 13:117–131. https://doi.org/10.2147/CMAR.S282989 PMID: 33447084; PMCID: PMC7802793
Hindumathi V, Kranthi T, Rao SB, Manimaran P (2014) The prediction of candidate genes for cervix related cancer through gene ontology and graph theoretical approach. Mol Biosyst 10(6):1450–1460. https://doi.org/10.1039/c4mb00004h Epub 2014 Mar 20. PMID: 24647578
Jalan S, Kanhaiya K, Rai A, Bandapalli OR, Yadav A (2015) Network topologies decoding cervical cancer. PLoS One 10(8):e0135183. https://doi.org/10.1371/journal.pone.0135183 PMID: 26308848; PMCID: PMC4550414
Wang J, Li Z, Gao A, Wen Q, Sun Y (2019) The prognostic landscape of tumor-infiltrating immune cells in cervical cancer. Biomed Pharmacother 120:109444. https://doi.org/10.1016/j.biopha.2019.109444 ISSN 0753-3322
Pahne-Zeppenfeld J, Schröer N, Walch-Rückheim B, Oldak M, Gorter A, Hegde S, Smola S (2014) Cervical cancer cell-derived interleukin-6 impairs CCR7-dependent migration of MMP-9-expressing dendritic cells. Int J Cancer 134:2061–2073. https://doi.org/10.1002/ijc.28549
Kori M, Yalcin AK (2018) Potential biomarkers and therapeutic targets in cervical cancer: insights from the meta-analysis of transcriptomics data within network biomedicine perspective. PLoS One 13(7):e0200717. https://doi.org/10.1371/journal.pone.0200717 PMID: 30020984; PMCID: PMC6051662
Clough E, Barrett T (2016) The Gene Expression Omnibus Database. Methods Mol Biol 1418:93-110. 10.1007/978-1-4939-3578-9_5
Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau W-C, Ledoux P, Rudnev D, Lash AE, Fujibuchi W, Edgar R (2005) NCBI GEO: mining millions of expression profiles—database and tools. Nucleic Acids Res 33(Issue suppl_1):D562–D566. https://doi.org/10.1093/nar/gki022
Wilhite SE, Barrett T (2012) Strategies to explore functional genomics data sets in NCBI's GEO database. Methods Mol Biol 802:41–53. https://doi.org/10.1007/978-1-61779-400-1_3
Scotto L, Narayan G, Nandula SV, Arias-Pulido H et al (2008) Identification of copy number gain and overexpressed genes on chromosome arm 20q by an integrative genomic approach in CC: potential role in progression. Genes Chromosomes Cancer 47(9):755–765 PMID: 18506748
Den Boon JA, Pyeon D, Wang SS, Horswill M et al (2015) Molecular transitions from papillomavirus infection to cervical precancer and cancer: role of stromal estrogen receptor signaling. Proc Natl Acad Sci U S A 112(25):E3255–E3264 PMID: 26056290
Zhai Y, Kuick R, Nan B, Ota I et al (2007) Gene expression analysis of preinvasive and invasive cervical squamous cell carcinomas identifies HOXC10 as a key mediator of invasion. Cancer Res 67(21):10163–10172 PMID: 17974957
Blount JR, Meyer DN et al (2019) Una` nchored ubiquitin chains do not lead to marked alterations in gene expression in Drosophila melanogaster. Biol Open 8:bio043372. https://doi.org/10.1242/bio.043372
Ashburner M, Ball CA, Blake JA, Botstein D, Heather Butler J, Cherry M, Davis AP et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29. https://doi.org/10.1038/75556
Kanehisa M, Goto S (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 28(1):27–30. https://doi.org/10.1093/nar/28.1.27
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Avi Ma’ayan. (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinf 14(1):128. https://doi.org/10.1186/1471-2105-14-128
Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, Clark NR, Ma'ayan A (2013) Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14:128. https://doi.org/10.1186/1471-2105-14-128
Tanvir Hasan M, Hassan M, Kawsar Ahmed M, Islam R, Islam K, Bhuyian T, Uddin MS, Paul BK (2020) Network based study to explore genetic linkage between diabetes mellitus and myocardial ischemia: bioinformatics approach. Gene Rep 21:100809. https://doi.org/10.1016/j.genrep.2020.100809 ISSN 2452-0144
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A et al (2016) The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res:gkw937. https://doi.org/10.1093/nar/gkw937
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 13(11):2498–2504. https://doi.org/10.1101/gr.1239303
Bader GD, Hogue CWV (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinf 4(1):2. https://doi.org/10.1186/1471-2105-4-2
Chin C-H, Chen S-H, Hsin-Hung W, Ho C-W, Ko M-T, Lin C-Y (2014) cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol 8(S4):S11. https://doi.org/10.1186/1752-0509-8-S4-S11
Yoo M, Shin J, Kim J, Ryall KA, Lee K, Lee S, Jeon M, Kang J, Tan AC (2015) DSigDB: drug signatures database for gene set analysis. Bioinformatics 31(18):3069–3071. https://doi.org/10.1093/bioinformatics/btv313
Dawn Teare M, Barrett JH (2005) Genetic linkage studies. Lancet. 366(9490):1036–1044. https://doi.org/10.1016/S0140-6736(05)67382-5 PMID: 16168786
Frayling TM (2007) Genome-wide association studies provide new insights into type 2 diabetes aetiology. Nat Rev Genet 8(9):657–662. https://doi.org/10.1038/nrg2178 PMID: 17703236
Boutros M, Ahringer J (2008) The art and design of genetic screens: RNA interference. Nat Rev Genet 9(7):554–566. https://doi.org/10.1038/nrg2364 Epub 2008 Jun 3. PMID: 18521077
Piro RM, Di Cunto F (2012) Computational approaches to disease-gene prediction: rationale, classification and successes. FEBS J 279(5):678–696. https://doi.org/10.1111/j.1742-4658.2012.08471.x Epub 2012 Jan 30. PMID: 22221742
Tranchevent LC, Capdevila FB, Nitsch D, De Moor B, De Causmaecker P, Moreau Y (2011) A guide to web tools to prioritize candidate genes. Brief Bioinform 12(1):22–32. https://doi.org/10.1093/bib/bbq007 Epub 2010 Mar 21. PMID: 21278374
Oti M, Ballouz S, Wouters MA (2011) Web tools for the prioritization of candidate disease genes. Methods Mol Biol 760:189–206. https://doi.org/10.1007/978-1-61779-176-5_12 PMID: 21779998
Opap K, Mulder N (2017) Recent advances in predicting gene-disease associations. F1000Res. 6:578. https://doi.org/10.12688/f1000research.10788.1 PMID: 28529714; PMCID: PMC5414807
Mohamed RH, Abu-Shahba N, Mahmoud M, Abdelfattah AMH, Zakaria W, ElHefnawi M (2019) Co-regulatory network of oncosuppressor miRNAs and transcription factors for pathology of human hepatic cancer stem cells (HCSC). Sci Rep 9(1):5564. https://doi.org/10.1038/s41598-019-41978-5 PMID: 30944375; PMCID: PMC6447552
Liu ZP, Wu C, Miao H, Wu H (2015) RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database (Oxford) 2015:bav095. https://doi.org/10.1093/database/bav095 PMID: 26424082; PMCID: PMC4589691
Schober P, Vetter TR (2018) Survival analysis and interpretation of time-to-event data: the tortoise and the hare. Anesth Analg 127(3):792–798. https://doi.org/10.1213/ane.0000000000003653 PMID: 30015653; PMCID: PMC6110618
George B, Seals S, Aban I (2014) Survival analysis and regression models. J Nucl Cardiol 21(4):686–694. https://doi.org/10.1007/s12350-014-9908-2 Epub 2014 May 9. PMID: 24810431; PMCID: PMC4111957
In J, Lee DK (2019) Survival analysis: part II - applied clinical data analysis. Korean J Anesthesiol 72(5):441–457. https://doi.org/10.4097/kja.19183 Epub 2019 May 17. PMID: 31096731; PMCID: PMC6781220
Koletsi D, Pandis N (2017) Survival analysis, part 3: cox regression. Am J Orthod Dentofacial Orthop 152(5):722–723. https://doi.org/10.1016/j.ajodo.2017.07.009 PMID: 29103451
Luo M, Kelley MR (2004) Inhibition of the human apurinic/apyrimidinic endonuclease (APE1) repair activity and sensitization of breast cancer cells to DNA alkylating agents with lucanthone. Anticancer Res 24(4):2127–2134 PMID: 15330152
Zhu K, Dunner K Jr, McConkey DJ (2010) Proteasome inhibitors activate autophagy as a cytoprotective response in human prostate cancer cells. Oncogene 29(3):451–462. https://doi.org/10.1038/onc.2009.343 Epub 2009 Nov 2. PMID: 19881538; PMCID: PMC2809784
Carew JS, Medina EC, Esquivel JA 2nd, Mahalingam D, Swords R, Kelly K, Zhang H, Huang P, Mita AC, Mita MM, Giles FJ, Nawrocki ST (2010) Autophagy inhibition enhances vorinostat-induced apoptosis via ubiquitinated protein accumulation. J Cell Mol Med 14(10):2448–2459. https://doi.org/10.1111/j.1582-4934.2009.00832.x PMID: 19583815; PMCID: PMC2891399
Carew JS, Nawrocki ST, Kahue CN, Zhang H, Yang C, Chung L, Houghton JA, Huang P, Giles FJ, Cleveland JL (2007) Targeting autophagy augments the anticancer activity of the histone deacetylase inhibitor SAHA to overcome Bcr-Abl-mediated drug resistance. Blood. 110(1):313–322. https://doi.org/10.1182/blood-2006-10-050260 Epub 2007 Mar 15. PMID: 17363733; PMCID: PMC1896119
Zhu L, Chen L (2019) Progress in research on paclitaxel and tumor immunotherapy. Cell Mol Biol Lett 24:40. https://doi.org/10.1186/s11658-019-0164-y PMID: 31223315; PMCID: PMC6567594
Vassileva V, Allen CJ, Piquette-Miller M (2008) Effects of sustained and intermittent paclitaxel therapy on tumor repopulation in ovarian cancer. Mol Cancer Ther 7(3):630–637. https://doi.org/10.1158/1535-7163.MCT-07-2117 PMID: 18347149
Giuseppe G, Arun R, Arlene B, Kelly RJ, Szabo E, Ariel LC et al (2011) Phase II study of belinostat in patients with recurrent or refractory advanced Thymic epithelial tumors. J Clin Oncol 29(15):2052–2059
Hazem G, Cynthia L, Eman B, Khaldoon AR, Asma T, Monther AA, Hendrayani SF, Pulicat M, Ayodele A, Taher AT, Abdelilah A, Said D (2010) Research article doxorubicin downregulates cell surface B7-H1expression and upregulates its nuclear expression in breast cancer cells: role of B7-H1 as an anti-apoptotic molecule. Breast Cancer Res 12:4
Tan JL, Chan ST, Lo CY, Deane JA, McDonald CA, Bernard CC, Wallace EM, Lim R (2015) Amnion cell-mediated immune modulation following bleomycin challenge: controlling the regulatory T cell response. Stem Cell Res Ther 6(1):8. https://doi.org/10.1186/scrt542 PMID: 25634246; PMCID: PMC4417266
Koreth J, Stevenson KE, Kim HT, McDonough SM, Bindra B, Armand P, Ho VT, Cutler C, Blazar BR, Antin JH, Soiffer RJ, Ritz J, Alyea EP 3rd. (2012) Bortezomib-based graft-versus-host disease prophylaxis in HLA-mismatched unrelated donor transplantation. J Clin Oncol 30(26):3202–3208. https://doi.org/10.1200/JCO.2012.42.0984 Epub 2012 Aug 6. PMID: 22869883; PMCID: PMC3434979
Moore DH, Blessing JA, McQuellon RP, Thaler HT, Cella D, Benda J, Miller DS, Olt G, King S, Boggess JF, Rocereto TF (2004) Phase III study of cisplatin with or without paclitaxel in stage IVB, recurrent, or persistent squamous cell carcinoma of the cervix: a gynecologic oncology group study. J Clin Oncol 22(15):3113–3119. https://doi.org/10.1200/JCO.2004.04.170 PMID: 15284262
Della Corte L, Barra F, Foreste V, Giampaolino P, Evangelisti G, Ferrero S, Bifulco G (2020) Advances in paclitaxel combinations for treating CC. Expert Opin Pharmacother 21(6):663–677. https://doi.org/10.1080/14656566.2020.1724284 Epub 2020 Feb 8. PMID: 32037907
Heeren AM, van Luijk IF, Lakeman J, Pocorni N, Kole J, de Menezes RX, Kenter GG, Bosse T, de Kroon CD, Jordanova ES (2019) Neoadjuvant cisplatin and paclitaxel modulate tumor-infiltrating T cells in patients with CC. Cancer Immunol Immunother 68(11):1759–1767. https://doi.org/10.1007/s00262-019-02412-x Epub 2019 Oct 15. PMID: 31616965; PMCID: PMC6851216
Abu-Rustum NR, Yashar CM, Bean S, Bradley K, Campos SM, Chon HS, Chu C, Cohn D, Crispens MA, Damast S, Fisher CM, Frederick P, Gaffney DK, Giuntoli R, Han E, Huh WK, Lurain Iii JR, Mariani A, Mutch D, Nagel C, Nekhlyudov L, Fader AN, Remmenga SW, Reynolds RK, Sisodia R, Tillmanns T, Ueda S, Urban R, Wyse E, McMillian NR, Motter AD (2020) NCCN Guidelines Insights: Cervical Cancer, Version 1.2020. J Natl Compr Canc Netw 18(6):660–666. https://doi.org/10.6004/jnccn.2020.0027 PMID: 32502976
Zighelboim I, Wright JD, Gao F, Case AS, Massad LS, Mutch DG, Powell MA, Thaker PH, Eisenhauer EL, Cohn DE, Valea FA, Alvarez Secord A, Lippmann LT, Dehdashti F, Rader JS (2013) Multicenter phase II trial of topotecan, cisplatin and bevacizumab for recurrent or persistent cervical cancer. Gynecol Oncol 130(1):64–68. https://doi.org/10.1016/j.ygyno.2013.04.009 Epub 2013 Apr 13. PMID: 23591400; PMCID: PMC3870479
Lin Y, Sibanda VL, Zhang HM, Hu H, Liu H, Guo AY (2015) MiRNA and TF co-regulatory network analysis for the pathology and recurrence of myocardial infarction. Sci Rep 5:9653. https://doi.org/10.1038/srep09653 PMID: 25867756; PMCID: PMC4394890
Chi Y, Xue J, Huang S, Xiu B, Su Y, Wang W, Guo R, Wang L, Li L, Shao Z, Jin W, Wu Z, Wu J (2019) CapG promotes resistance to paclitaxel in breast cancer through transactivation of PIK3R1/P50. Theranostics. 9(23):6840–6855. https://doi.org/10.7150/thno.36338 PMID: 31660072; PMCID: PMC6815964
Bahrami S, Gheysarzadeh A, Sotoudeh M, Bandehpour M, Khabazian R, Zali H, Hedayati M, Basiri A, Kazemi B (2020) The association between gelsolin-like actin-capping protein (CapG) overexpression and bladder cancer prognosis. Urol J 18(2):186–193. https://doi.org/10.22037/uj.v0i0.5664 PMID: 32309867
Jiang S, Yang Y, Zhang Y, Ye Q, Song J, Zheng M, Li X (2022) Overexpression of CAPG is associated with poor prognosis and immunosuppressive cell infiltration in ovarian cancer. Dis Markers 2022:9719671. https://doi.org/10.1155/2022/9719671 PMID: 35186171; PMCID: PMC8849939
Ma X, Zhang Q, Du J, Tang J, Tan B (2021) Integrated analysis of ceRNA regulatory network associated with tumor stage in cervical cancer. Front Genet 12:618753. https://doi.org/10.3389/fgene.2021.618753 PMID: 33833775; PMCID: PMC8021857
Xue JM, Liu Y, Wan LH, Zhu YX (2020) Comprehensive analysis of differential gene expression to identify common gene signatures in multiple cancers. Med Sci Monit 26:e919953. https://doi.org/10.12659/MSM.919953 PMID: 32035007; PMCID: PMC7027371
Wang D, Liu Y, Cheng S, Liu G (2022) Identification of novel genes and associated drugs in cervical cancer by bioinformatics methods. Med Sci Monit 28:e934799. https://doi.org/10.12659/MSM.934799 PMID: 35428744; PMCID: PMC9020271
Rahman MR, Islam T, Zaman T, Shahjaman M, Karim MR, Huq F, Quinn JMW, Holsinger RMD, Gov E, Moni MA (2020) Identification of molecular signatures and pathways to identify novel therapeutic targets in Alzheimer's disease: Insights from a systems biomedicine perspective. Genomics. 112(2):1290–1299. https://doi.org/10.1016/j.ygeno.2019.07.018 Epub 2019 Aug 1. PMID: 31377428
Sainz B Jr, Mossel EC, Peters CJ, Garry RF (2004) Interferon-beta and interferon-gamma synergistically inhibit the replication of severe acute respiratory syndrome-associated coronavirus (SARS-CoV). Virology. 329(1):11–17. https://doi.org/10.1016/j.virol.2004.08.011 PMID: 15476870; PMCID: PMC7111895
Bergmann CC, Parra B, Hinton DR, Ramakrishna C, Dowdell KC, Stohlman SA (2004) Perforin and gamma interferon-mediated control of coronavirus central nervous system infection by CD8 T cells in the absence of CD4 T cells. J Virol 78(4):1739–1750. https://doi.org/10.1128/jvi.78.4.1739-1750.2004 PMID: 14747539; PMCID: PMC369505
Richard Cammack, Teresa Atwood, Peter Campbell, Howard Parish, Anthony Smith, Frank Vella, John Stirling (2008) Oxford dictionary of biochemistry and molecular biology, Oxford University Press. https://doi.org/10.1093/acref/9780198529170.001.0001
Desvergne B, Michalik L, Wahli W (2006) Transcriptional regulation of metabolism. Physiol Rev 86(2):465–514. https://doi.org/10.1152/physrev.00025.2005 PMID: 16601267
Saxonov S, Berg P, Brutlag DL (2006) A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proc Natl Acad Sci U S A 103(5):1412–1417. https://doi.org/10.1073/pnas.0510310103 Epub 2006 Jan 23. PMID: 16432200; PMCID: PMC1345710
Tessitore A, Cicciarelli G, Del Vecchio F, Gaggiano A, Verzella D, Fischietti M, Vecchiotti D, Capece D, Zazzeroni F, Alesse E (2014) MicroRNAs in the DNA damage/repair network and cancer. Int J Genomics 2014:820248. https://doi.org/10.1155/2014/820248 Epub 2014 Jan 30. PMID: 24616890; PMCID: PMC3926391
Hasan Mahmud SM, Al-Mustanjid M, Farzana Akter M, Rahman S, Kawsar Ahmed M, Rahman H, Chen W, Moni MA (2021) Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients. Brief Bioinform:bbab115. https://doi.org/10.1093/bib/bbab115
Chatr-Aryamontri A, Breitkreutz BJ, Heinicke S, Boucher L, Winter A, Stark C, Nixon J, Ramage L, Kolas N, O'Donnell L, Reguly T, Breitkreutz A, Sellam A, Chen D, Chang C, Rust J, Livstone M, Oughtred R, Dolinski K, Tyers M (2013) The BioGRID interaction database: 2013 update. Nucleic Acids Res 41(Database issue):D816–D823. https://doi.org/10.1093/nar/gks1158 Epub 2012 Nov 30. PMID: 23203989; PMCID: PMC3531226
Petignat P, Roy M (2007) Diagnosis and management of CC. BMJ. 335(7623):765–768. https://doi.org/10.1136/bmj.39337.615197.80 PMID: 17932207; PMCID: PMC2018789
Carvalho BS, Irizarry RA (2010) A framework for oligonucleotide microarray preprocessing. Bioinformatics 26(19):2363–2367. https://doi.org/10.1093/bioinformatics/btq431 Epub 2010 Aug 5. PMID: 20688976; PMCID: PMC2944196
Sheu BC, Lien HC, Ho HN, Lin HH, Chow SN, Huang SC, Hsu SM (2003) Increased expression and activation of gelatinolytic matrix metalloproteinases is associated with the progression and recurrence of human CC. Cancer Res 63(19):6537–6542 PMID: 14559848
Vogelstein B, Kinzler KW (2004) Cancer genes and the pathways they control. Nat Med 10(8):789–799. https://doi.org/10.1038/nm1087 PMID: 15286780
Kozaki K, Imoto I, Mogi S, Omura K, Inazawa J (2008) Exploration of tumor-suppressive microRNAs silenced by DNA hypermethylation in oral cancer. Cancer Res 68(7):2094–2105. https://doi.org/10.1158/0008-5472.CAN-07-5194 PMID: 18381414
Ando T, Yoshida T, Enomoto S, Asada K, Tatematsu M, Ichinose M, Sugiyama T, Ushijima T (2009) DNA methylation of microRNA genes in gastric mucosae of gastric cancer patients: its possible involvement in the formation of epigenetic field defect. Int J Cancer 124(10):2367–2374. https://doi.org/10.1002/ijc.24219 PMID: 19165869
Vrba L, Muñoz-Rodríguez JL, Stampfer MR, Futscher BW (2013) miRNA gene promoters are frequent targets of aberrant DNA methylation in human breast cancer. PLoS One 8(1):e54398. https://doi.org/10.1371/journal.pone.0054398 Epub 2013 Jan 16. PMID: 23342147; PMCID: PMC3547033
Balaguer F, Link A, Lozano JJ, Cuatrecasas M, Nagasaka T, Boland CR, Goel A (2010) Epigenetic silencing of miR-137 is an early event in colorectal carcinogenesis. Cancer Res 70(16):6609–6618. https://doi.org/10.1158/0008-5472.CAN-10-0622 Epub 2010 Aug 3. PMID: 20682795; PMCID: PMC2922409
Kong Q, Tang Z, Xiang F et al (2017) Diagnostic value of serum hsa-mir-92a in patients with CC. Clin Lab 63(2):335–340. https://doi.org/10.7754/clin.lab.2016.160610
Fangfang F, Jiang W, Zhou L, Chen Z (2018) Circulating exosomal miR-17-5p and miR-92a-3p predict pathologic stage and grade of colorectal cancer. Translat Oncol 11(2):221–232. https://doi.org/10.1016/j.tranon.2017.12.012 ISSN 1936-5233
Yu, Yalan et al. ‘Plasma miR-92a-2 as a biomarker for small cell lung cancer’. 2017: 319–327. https://doi.org/10.3233/CBM-160254.
Cun J, Yang Q (2018) Bioinformatics-based interaction analysis of miR-92a-3p and key genes in tamoxifen-resistant breast cancer cells. Biomed Pharmacother 107:117–128. https://doi.org/10.1016/j.biopha.2018.07.158 ISSN 0753-3322
Pan Y, Wang H, Ma D, Ji Z, Luo L, Cao F, Huang F, Liu Y, Dong Y, Chen Y (2018) miR-24 may be a negative regulator of menin in lung cancer. Oncol Rep 39(5):2342–2350. https://doi.org/10.3892/or.2018.6327 Epub 2018 Mar 20. PMID: 29565463
Han X, Li Q, Liu C, Wang C, Li Y (2019) Overexpression miR-24-3p repressed Bim expression to confer tamoxifen resistance in breast cancer. J Cell Biochem 120(8):12966–12976. https://doi.org/10.1002/jcb.28568 Epub 2019 Apr 18. PMID: 31001849
Lynch SM, McKenna MM, Walsh CP, McKenna DJ (2016) miR-24 regulates CDKN1B/p27 expression in prostate cancer. Prostate. 76(7):637–648. https://doi.org/10.1002/pros.23156 Epub 2016 Feb 5. PMID: 26847530
Hao JP, Ma A (2018) The ratio of miR-21/miR-24 as a promising diagnostic and poor prognosis biomarker in colorectal cancer. Eur Rev Med Pharmacol Sci 22(24):8649–8656. https://doi.org/10.26355/eurrev_201812_16629 PMID: 30575905
Qian C, Liu H, Feng Y, Meng S, Wang D, Nie M, Xu M (2020) Clinical characteristics and risk of second primary lung cancer after CC: a population-based study. PLoS One 15(8):e0231807. https://doi.org/10.1371/journal.pone.0231807 PMID: 32756555; PMCID: PMC7406086
Chen X, Chen L, Zhu H, Tao J (2020) Risk factors and prognostic predictors for CC patients with lung metastasis. J Cancer 11(20):5880–5889. https://doi.org/10.7150/jca.46258 PMID: 32922530; PMCID: PMC7477410
Ki EY, Lee KH, Park JS, Hur SY (2016) A clinicopathological review of pulmonary metastasis from uterine CC. Cancer Res Treat 48(1):266–272. https://doi.org/10.4143/crt.2014.206 Epub 2015 Feb 23. PMID: 25715766; PMCID: PMC4720087
Zheng A, Chen Y, Fang J, Zhang Y (2015) Clinicopathologic characteristics and risk factors for lung metastasis after radical hysterectomy in early-stage CC. Zhonghua Fu Chan Ke Za Zhi 50(3):204–209 Chinese. PMID: 26268411
Marth C, Landoni F, Mahner S, McCormack M, Gonzalez-Martin A, Colombo N, ESMO Guidelines Committee (2017) Cervical cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 28(suppl_4):iv72–iv83. https://doi.org/10.1093/annonc/mdx220 Erratum in: Ann Oncol. 2018 Oct 1;29(Suppl 4):iv262. Erratum in: Ann Oncol. 2018;29 Suppl 4:iv262. PMID: 28881916
The authors would like to thank and extend their appreciation and gratitude to Al-Mustaqbal University College in Iraq for funding this project.
This work was supported by Al-Mustaqbal University College and in part by funding from the Natural Sciences and Engineering Research Council of Canada (NSERC).
Ethics approval and consent to participate
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Hasan, M.T., Islam, M.R., Islam, M.R. et al. Systematic approach to identify therapeutic targets and functional pathways for the cervical cancer. J Genet Eng Biotechnol 21, 10 (2023). https://doi.org/10.1186/s43141-023-00469-x