Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis

Zhang, Jing; Huang, Shanying; Quan, Lini; Meng, Qingcai; Wang, Haiyan; Wang, Jie; Jin, Cheqing

doi:10.1155/2021/8883800

Cited by 7 publications

(9 citation statements)

References 53 publications

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“…Given the increased expression of DOCK8 in resistant ESCC cells and its association with poorer survival, we believe that its molecular action mechanism in ESCC merits urgent further exploration. GNB4 is also a hub gene in ovarian cancer and Helicobacter pylori-induced gastric cancer (34,35), but its detailed mechanism in tumor development is largely unknown. The association between CACNB2 and squamous cell carcinoma (SCC) was reported by another miRNA study regarding head and neck SCC (36), which found that the tumor suppressor miR-31-5p targeted CACNB2 and affected prognosis.…”

Section: Discussionmentioning

confidence: 99%

Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Xie¹,

Zhang²,

Qian³

et al. 2022

Ann Transl Med

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Background: According to histopathology, esophageal cancer can be divided into squamous cell carcinoma (SCC) and esophageal adenocarcinoma (adeno arcinoma). In China, 90% of esophageal cancer patients are squamous cell carcinoma. Cisplatin and fluaziridine are the main chemotherapy before and after surgery.Long-term drug treatment is often accompanied by the emergence of drug resistance of tumor cells. There are many mechanisms for the emergence of drug resistance of tumor cells, including the increase of drug efflux, the decrease of drug intake, the inhibition of cell apoptosis, and so on. This study aimed to investigate the key cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma (ESCC).Methods: Two datasets from the Gene Expression Omnibus (GEO) database (GSE86099 and GSE50224) were retrieved. We performed microRNA (miRNA) and messenger RNA (mRNA) expression analysis to identify differentially expressed genes (DEGs). The intersection of the downregulated miRNA targets and the upregulated mRNAs were used for Gene Ontology (GO) enrichment analysis, and survival risk was assessed using data from The Cancer Genome Atlas (TCGA).Results: There were 35 common genes, of which, based on GO enrichment, most were related to the cardiac muscle cell action. Four genes showed significant association with the estimated half-maximal inhibitory concentration (IC 50 ) of paclitaxel: bone morphogenetic protein 1 (BMP1), dumbbell former 4 protein (DBF4), angiogenin (ANG), and MAP7 domain containing 2 (MAP7D2). Four risk factors (MP1, HIP1, ANG, and MAP7D2) were selected to generate a signature using least absolute shrinkage and selection operator (LASSO) regression. Protein-protein interaction (PPI) analysis showed guanine nucleotide-binding protein subunit beta 4 (GNB4), calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2), and sodium voltage-gated channel alpha subunit 1 (SCN1A) were located at key positions of the network. Among potential risk genes, only the high expression of dedicator of cytokinesis 8 (DOCK8) was associated with poorer survival. Conclusions:The 35 oncogenes may be involved in mechanisms of chemotherapy resistance in ESCC, as well as the corresponding enrichment and regulatory network. The signature containing 4 key risk genes merits further investigation and may provide a deeper understanding of the molecular mechanisms in ESCC treatment failure.

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Section: Discussionmentioning

confidence: 99%

Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Xie¹,

Zhang²,

Qian³

et al. 2022

Ann Transl Med

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“…Our study first described the upregulation of KNG1 and CCKAR in prostate cancer. Previous studies have described the presence of KNG1 and CCKAR as biomarkers of various types of cancer, such as thyroid cancer [36,37], liver cancer [38], ovarian cancer [39], and cholangiocarcinoma [40]. In different stages of PCa development and progression, especially in the process of hormone-sensitive PCa progressing to castration-resistant PCa, the proteomic alterations and transcriptomic data have significant differences in changes [41].…”

Section: Discussionmentioning

confidence: 99%

Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Liu

et al. 2021

Oxidative Medicine and Cellular Longevity

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Objective. To identify the key genes involved in prostate cancer and their regulatory network. Methods. The dataset of mRNA/miRNA transcriptome sequencing was downloaded from The Cancer Genome Atlas/the Gene Expression Omnibus database for analysis. The “edgeR” package in the R environment was used to normalize and analyze differentially expressed genes (DEGs) and miRNAs (DEmiRNAs). First, the PANTHER online tool was used to analyze the function enrichment of DEGs. Next, a protein-protein interaction (PPI) network was constructed using STRING and Cytoscape tools. Finally, miRNA-gene regulatory networks were constructed using the miRTarBase. Results. We identified 4339 important DEGs, of which 2145 were upregulated (Up-DEGs) and 2194 were downregulated (Down-DEGs). Functional enrichment analysis showed that the Up-DEGs were related to the immune system and the cell cycle in prostate cancer, whereas the Down-DEGs were related to the nucleic acid metabolic process and metabolism pathways. Twelve core protein clusters were found in the PPI network. Further, the constructed miRNA-gene interaction network showed that 11 downregulated miRNAs regulated 16 Up-DEGs and 22 upregulated miRNAs regulated 22 Down-DEGs. Conclusion. We identified 4339 genes and 70 miRNAs that may be involved in immune response, cell cycle, and other key pathways of the prostate cancer regulatory network. Genes such as BUB1B, ANX1A1, F5, HTR4, and MUC4 can be used as biomarkers to assist in the diagnosis and prognosis of prostate cancer.

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“…Using the Kaplan-Meier method, the analysis was carried out according to the reference [18,23]. Software: R (version 3.6.3).…”

Section: The Relationship Between Hoxds and Prognosis Of Ocmentioning

confidence: 99%

“…of HOXDs, including BP (biological process), MF (molecular function), CC (cellular component), and pathway analysis[23,27].…”

mentioning

confidence: 99%

Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes

Chen

Gao²,

Wang

et al. 2022

Computational and Mathematical Methods in Medicine

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Background. Homeobox D (HOXD) genes were associated with cancer pathogenesis. However, the role of HOXD genes in ovarian cancer (OC) and the possible mechanisms involved are unclear. In this study, we analyzed the function and regulatory mechanisms and functions of HOXD genes in OC based on comprehensive bioinformatics analysis. Methods. Expression of HOXD1/3/4/8/9/10/11/12/13 mRNA was analyzed between OC tissue and normal tissue using ONCOMINE, GEO, and TCGA databases. The relationship between HOXD expression and clinical stage was studied by GEPIA. The Kaplan-Meier plotter was used to analyze prognosis. cBioPortal was used to analyze the mutation and coexpression of HOXDs. GO and KEGG analyses were performed by the DAVID software to predict the function of HOXD coexpression genes. Immune infiltration analysis was used to evaluate the relationship between the expression of HOXD genes and 24 immune infiltrating cells. Results. The expression of HOXD3/4/8/9/10/11 was significantly lower in OC tissues than in normal ovarian tissues, while the expression of HOXD1/12/13 was significantly higher in OC tissues. The expression of HOXD genes was associated with FIGO stage, primary therapy outcome, tumor status, anatomic neoplasm subdivision, and age. The expression levels of HOXD1/3/4/8/9/10 correlated with tumor stage. HOXD1/8/9 could be served as ideal biomarkers to distinguish OC from normal tissue. Low HOXD9 expression was associated with shorter overall survival (OS) (HR: 0.75; 95% CI: 0.58–0.98; P = 0.034 ) and progression-free survival (PFS) (HR: 0.69; 95% CI: 0.54–0.87; P = 0.002 ). The HOXD coexpression genes were associated with pathways including cell cycle, TGF-beta signaling pathway, cellular senescence, and Hippo signaling pathway. HOXD genes were significantly associated with immune infiltration. Conclusion. The expression of HOXD genes is associated with clinical characteristics. HOXD9 is a new biomarker of prognosis in OC, and HOXD1/4/8/9/10 may be potential therapeutic targets. The members of the HOXD genes may be the response to immunotherapy for OC.

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Determination of Potential Therapeutic Targets and Prognostic Markers of Ovarian Cancer by Bioinformatics Analysis

Cited by 7 publications

References 53 publications

Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

Construction of Potential Gene Expression and Regulation Networks in Prostate Cancer Using Bioinformatics Tools

Molecular Analysis of Prognosis and Immune Infiltration of Ovarian Cancer Based on Homeobox D Genes

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