Identification of Differentially Expressed Genes and associated pathways common to Eyelid and Non-Ocular Basal Cell Carcinoma to understand the Molecular Biology of BCC

Background: Eyelid BCC accounts for more than 90% of Eyelid malignant neoplasms. Various aberrant signalling pathways and genes in Non-Ocular BCC have been found whereas Eyelid bcc remains elusive. Objective: This study aims to find the common DEGs of Eyelid and Non-Ocular BCC using bioinformatic analysis and text mining to gain more insights into the molecular aspects common to both BCC non-ocular and Eyelid BCC and to identify common potential prognostic markers. Material and method: The Gene Expression profiles of Eyelid BCC (GSE103439) and Non-Ocular BCC (GSE53462) were obtained from the NCBI GEO database followed by identification of common DEGs. Protein-Protein interaction and Pathway Enrichment analysis of these screened genes was done using bioinformatic tools like STRING, Cytoscape and BiNGO, DAVID, KEGG respectively. Results: A total of 181 genes were found common in both datasets. A PPI network was formed for the screened genes and 20 HUB genes were sorted which included CTNNB1, MAPK14, BTRC, EGFR, ADAM17. Pathway enrichment of HUB genes showed that they were dysregulated in carcinogenic and apoptotic pathways that seem to play a role in the progression of both the BCC. Conclusion: The result and findings of bioinformatic analysis highlighted the molecular pathways and genes enriched in both Eyelid BCC as well as Non- Ocular BCC. The identified pathways should be studied further to recognise common molecular events that would lead to the progression of BCC. This may provide a window to explore the prognostic and therapeutic strategies common to both BCC. Keywords: Basal cell carcinoma (BCC), Cancer, Microarray, Ophthalmology, Tumour marker

Download Full-text

Identification of The Key miRNAs And Target Genes In Basal Cell Carcinoma By Bioinformatics Analysis

10.21203/rs.3.rs-455253/v1 ◽

2021 ◽

Author(s):

Hao Liu ◽

Ling Chen

Keyword(s):

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Target Genes ◽

Bioinformatics Analysis ◽

Interaction Network ◽

Pathway Enrichment Analysis ◽

Therapeutic Effects ◽

Hub Genes ◽

Skin Potential

Abstract Background The highly tissue-destructive and localized accumulation of basal cell carcinoma(BCC) makes it one of the most important cancers affecting people's lives. Existing therapeutic approaches, including surgical treatment, chemotherapy, and Hedgehog pathway inhibitors, have failed to achieve broad therapeutic effects for various reasons. This study aims to explore additional potential therapeutic targets and possible diagnostic and prognostic biomarkers using bioinformatics analysis.Material/Methods The Gene Expression Omnibus (GEO) database identified the microarray dataset GSE34535. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between BCC and non-lesional skin. Potential target genes of DE-miRNA were screened using the miRWalk, mirDIP and miRTarBase databases. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis for target genes were established using the DAVID database. Protein–protein interaction network and miRNA-hub gene network were analyzed based on the STRING database and visualized by Cytoscape software. Results 51 up-regulated DE-miRNAs and 38 down-regulated DE-miRNAs were identified from the BCC samples. miR-455-5p was mainly up-regulated and miR-139-5p was mainly down-regulated. Two key bub genes MAPK1 and EGFR were identified in the PPI network. Four out of the ten hub genes were regulated by up-regulated miR-18a and four by down-regulated miR-133b. Viral infections were also identified in the study.Conclusions Bioinformatics identified four miRNAs and two important hub genes that may be associated with BCC, and it was suggested that viruses may play a role in BCC.

Download Full-text

Identification of Issue Inhibitor of Metalloproteinase 1 (TIMP1) as a Potential Biomarker for the Diagnosis, Pathogenesis and Prognosis of Colorectal Cancer via Integrated Bioinformatic Analysis

10.21203/rs.3.rs-136189/v1 ◽

2020 ◽

Author(s):

Guona Li ◽

Mengmeng Kang ◽

Siyuan Sheng ◽

Ziyi Chen ◽

Kunshan Li ◽

...

Keyword(s):

Colorectal Cancer ◽

Network Analysis ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Molecular Therapy ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Pathway Enrichment ◽

Potential Biomarker

Abstract Background: Colorectal cancer (CRC) is a common malignant tumor of the digestive system. It is crucial to screen potential biomarkers for the diagnosis, pathogenesis, and prognosis of CRC because there are limited clinical symptoms associated with this cancer. Therefore, we attempted to identify biomarkers associated with the occurrence and progression of CRC by utilizing bioinformatic analysis and to elucidate a molecular mechanism for the diagnosis and treatment of CRC. Methods: Two independent gene expression profile datasets of colonic neoplasms (GSE44076 and GSE37182) were collected from public GEO datasets, which included 182 tumor tissues and 236 normal tissues. Next, differentially expressed genes (DEGs) between CRC colonic samples and non-CRC colonic samples were obtained via GEO2R online tools. Subsequently, hub genes were selected by several analyses of DEGs, including GO pathway enrichment analysis, KEGG pathway enrichment analysis, and PPI network analysis. Finally, the correlation between the hub genes and the occurrence of CRC was tested by harnessing survival analysis and ROC curve analysis. Results: Sixty-one shared DEGs were screened, including 44 high-expression genes and 17 low-expression genes, in CRC samples. Four genes (MYC, TIMP1, MMP7, and COL1A1) were considered to be hub genes because they exhibited higher connectivity degree scores through PPI network analysis. More importantly, there was a significant correlation between increased expression of TIMP1 and reduced survival time in patients with colorectal cancer. Conclusion: By using bioinformatic analysis, this study suggested that Timp-1 may represent a potential biomarker for the diagnosis and prognosis of targeted molecular therapy for CRC.

Download Full-text

Bioinformatics-Based Study to Investigate Potential Differentially Expressed Genes and miRNAs in Hashimoto’s Thyroiditis

10.21203/rs.3.rs-1023446/v1 ◽

2021 ◽

Author(s):

Li Guoquan ◽

Du Junwei ◽

He Qi ◽

Fu Xinghao ◽

Ji Feihong ◽

...

Keyword(s):

Gene Expression ◽

Differentially Expressed Genes ◽

Inflammatory Responses ◽

Expression Profiles ◽

Treatment Options ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Chronic Lymphocytic Thyroiditis ◽

Hub Genes

Abstract BackgroundHashimoto's thyroiditis (HT), also known as chronic lymphocytic thyroiditis, is a common autoimmune disease, which mainly occurs in women. The early manifestation was hyperthyroidism, however, hypothyroidism may occur if HT was not controlled for a long time. Numerous studies have shown that multiple factors, including genetic, environmental, and autoimmune factors, were involved in the pathogenesis of the disease, but the exact mechanisms were not yet clear. The aim of this study was to identify differentially expressed genes (DEGs) by comprehensive analysis and to provide specific insights into HT. MethodsTwo gene expression profiles (GSE6339, GSE138198) about HT were downloaded from the Gene Expression Omnibus (GEO) database. The DEGs were assessed between the HT and normal groups using the GEO2R. The DEGs were then sent to the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. The hub genes were discovered using Cytoscape and CytoHubba. Finally, NetworkAnalyst was utilized to create the hub genes' targeted microRNAs (miRNAs). ResultsA total of 62 DEGs were discovered, including 60 up-regulated and 2 down-regulated DEGs. The signaling pathways were mainly engaged in cytokine interaction and cytotoxicity, and the DEGs were mostly enriched in immunological and inflammatory responses. IL2RA, CXCL9, IL10RA, CCL3, CCL4, CCL2, STAT1, CD4, CSF1R, and ITGAX were chosen as hub genes based on the results of the protein-protein interaction (PPI) network and CytoHubba. Five miRNAs, including mir-24-3p, mir-223-3p, mir-155-5p, mir-34a-5p, mir-26b-5p, and mir-6499-3p, were suggested as likely important miRNAs in HT. ConclusionsThese hub genes, pathways and miRNAs contribute to a better understanding of the pathophysiology of HT and offer potential treatment options for HT.

Download Full-text

Predicting functional circular RNA-based competitive endogenous RNA network in gastric carcinoma using novel bioinformatics analysis

Experimental Biology and Medicine ◽

10.1177/15353702211048757 ◽

2021 ◽

pp. 153537022110487

Author(s):

Zirui Zhu ◽

Rui Huang ◽

Baojun Huang

Keyword(s):

Messenger Rna ◽

Expression Profiles ◽

Clinical Stage ◽

Interaction Network ◽

Enrichment Analysis ◽

Rank Aggregation ◽

Circular Rnas ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

Competitive Endogenous Rna

Gastric cancer (GC) remains one of the most prevalent types of malignancies worldwide, and also one of the most reported lethal tumor-related diseases. Circular RNAs (circRNAs) have been certified to be trapped in multiple aspects of GC pathogenesis. Yet, the mechanism of this regulation is mostly undefined. This research is designed to discover the vital circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in GC. Expression profiles with diverse levels including circRNAs, miRNAs, and mRNAs were all determined using microarray public datasets from Gene Expression Ominous (GEO). The differential circRNAs expressions were recognized against the published robust rank aggregation algorithm. Besides, a circRNA-based competitive endogenous RNA (ceRNA) interaction network was visualized via Cytoscape software (version 3.8.0). Functional and pathway enrichment analysis associated with differentially expressed targeted mRNAs were conducted using Cytoscape and an online bioinformatics database. Furthermore, an interconnected protein–protein interaction association network which consisted of 51 mRNAs was predicted, and hub genes were screened using STRING and CytoHubba. Then, several hub genes were chosen to explore their expression associated with survival rate and clinical stage in GEPIA and Kaplan-Meier Plotter databases. Finally, a carefully designed circRNA-related ceRNA regulatory subnetwork including four circRNAs, six miRNAs, and eight key hub genes was structured using the online bioinformatics tool.

Download Full-text

Identification of Potential Biomarkers Associated with Basal Cell Carcinoma

BioMed Research International ◽

10.1155/2020/2073690 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Yong Liu ◽

Hui Liu ◽

Queqiao Bian

Keyword(s):

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Characteristic Curve ◽

External Validation ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Microarray Datasets

Purpose. This work is aimed at identifying several molecular markers correlated with the diagnosis and development of basal cell carcinoma (BCC). Methods. The available microarray datasets for BCC were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified between BCC and healthy controls. Afterward, the functional enrichment analysis and protein-protein interaction (PPI) network analysis of these screened DEGs were performed. An external validation for the DEG expression level was also carried out, and receiver operating characteristic curve analysis was used to evaluate the diagnostic values of DEGs. Result. In total, five microarray datasets for BCC were downloaded and 804 DEGs (414 upregulated and 390 downregulated genes) were identified. Functional enrichment analysis showed that these genes including CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1 were closely correlated with the cell process and PTCH1 played central roles in the BCC signaling pathway. Moreover, EGFR was a hub gene in the PPI network. The expression changes of six genes (CYFIP2, HOXB5, FOXN3, PTPN3, MARCKSL1, and FAS) were validated by an external GSE74858 dataset analysis. Finally, ROC analysis revealed that CYFIP2, HOXB5, PTPN3, MARCKSL1, PTCH1, and CDC20 could distinguish BCC and healthy individuals. Conclusion. Nine gene signatures (CYFIP2, HOXB5, EGFR, FOXN3, PTPN3, CDC20, MARCKSL1, FAS, and PTCH1) may serve as promising targets for BCC detection and development.

Download Full-text

Multiple neoplasms following craniospinal irradiation for medulloblastoma in a patient with nevoid basal cell carcinoma syndrome

Journal of Neurosurgery ◽

10.3171/jns.1997.86.2.0286 ◽

1997 ◽

Vol 86 (2) ◽

pp. 286-288 ◽

Cited By ~ 51

Author(s):

Sean O'Malley ◽

David Weitman ◽

Michael Olding ◽

Laligam Sekhar

Keyword(s):

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Subsequent Development ◽

Craniospinal Irradiation ◽

Malignant Neoplasms ◽

Content Type ◽

Multiple Neoplasms ◽

Gorlin’S Syndrome ◽

Gorlin's Syndrome

✓ A 28-year-old man presented to the authors' hospital with multiple intracranial tumors. At 2 years of age, he had undergone resection of a medulloblastoma and received adjunctive craniospinal irradiation. Subsequently, he was diagnosed with nevoid basal cell carcinoma syndrome, Gorlin's syndrome. Since his first presentation, he has required surgery for multiple basal cell carcinomas, an osteochondroma of the rib, two meningiomas, a trigeminal schwannoma, and a pleomorphic liposarcoma, all of which arose within the radiation field. Despite this impressive list of benign and malignant neoplasms, the patient is relatively well and leads a normal life. The authors examine the relationships between Gorlin's syndrome and radiation therapy and the subsequent development of tumors.

Download Full-text

Coexisting Basal Cell Carcinoma and Squamous Cell Carcinoma in Congenital Nevus Sebaceous

SKIN The Journal of Cutaneous Medicine ◽

10.25251/skin.2.3.6 ◽

2018 ◽

Vol 2 (3) ◽

pp. 181-185

Author(s):

Jordan Rosen ◽

Katherine Nolan ◽

Noah Shaikh ◽

Les Rosen ◽

Martin Zaiac

Keyword(s):

Squamous Cell Carcinoma ◽

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Squamous Cell ◽

Basal Cell ◽

Squamous Cell Carcinomas ◽

Malignant Neoplasms ◽

Rare Occurrence ◽

Unique Case ◽

Nevus Sebaceous

Nevus sebaceous is a congenital epidermal hamartoma characterized by hyperplastic changes to the epidermis and adnexa. Nevus sebaceous is associated with an elevated risk of cutaneous neoplasms, most often benign; however, malignant neoplasms, most notably basal cell carcinoma, can also present in these patients. Although a rare occurrence, more often affecting adult patients, squamous cell carcinomas have also been reported to arise at the site of pre-existing nevus sebaceous. Herein we report a unique case of a patient with basal cell carcinoma and squamous cell carcinoma arising concurrently in the same nevus sebaceous.

Download Full-text

Screening of Hub Genes Associated with Lung Adenocarcinoma by Integrated Bioinformatic Analysis

10.21203/rs.3.rs-580657/v1 ◽

2021 ◽

Author(s):

Zimeng Wei ◽

Min Zhao ◽

Linnan Zang

Keyword(s):

Gene Expression ◽

Lung Adenocarcinoma ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Analysis Tool ◽

Hub Genes ◽

Kegg Analysis ◽

Ppi Networks

Abstract Background Lung adenocarcinoma (LUAD) is the main histological subtype of lung cancer. However, the molecular mechanism underlying LUAD is not yet clearly defined, but elucidating this process in detail would be of great significance for clinical diagnosis and treatment. Methods Gene expression profiles were retrieved from Gene Expression Omnibus database (GEO), and the common differentially expressed genes (DEGs) were identified by online GEO2R analysis tool. Subsequently, the enrichment analysis of function and signaling pathways of DEGs in LUAD were performed by gene ontology (GO) and The Kyoto Encyclopedia of Genes and Genomics (KEGG) analysis. The protein-protein interaction (PPI) networks of the DEGs were established through the Search Tool for the Retrieval of Interacting Genes (STRING) database and hub genes were screened by plug-in CytoHubba in Cytoscape. Afterwards, we detected the expression of hub genes in LUAD and other cancers via GEPIA, Oncomine and HPA databases. Finally, Kaplan-Meier plotter were performed to analyze the prognosis efficacy of hub genes. Results 74 up-regulated and 238 down-regulated DEGs were identified. As for the up-regulated DEGs, KEGG analysis results revealed they were mainly enrolled in protein digestion and absorption. However, the down-regulated DEGs were primarily enriched in cell adhesion molecules. Subsequently, 9 hub genes: KIAA0101, CDCA7, TOP2A, CDC20, ASPM, TPX2, CENPF, UBE2T and ECT2, were identified and showed higher expression in both LUAD and other cancers. Finally, all these hub genes were found significantly related to the prognosis of LUAD (p < 0.05). Conclusions Our results screened out the hub genes and pathways that were related to the development and prognosis of LUAD, which could provide new insight for the future molecularly targeted therapy and prognosis evaluation of LUAD.

Download Full-text

Genome-Wide Expression Difference of MicroRNAs in Basal Cell Carcinoma

Journal of Immunology Research ◽

10.1155/2021/7223500 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Hai-Peng Wei ◽

Song Zhan ◽

Qing-An Zhu ◽

Zhen-Juan Chen ◽

Xian Feng ◽

...

Keyword(s):

Basal Cell Carcinoma ◽

Cell Carcinoma ◽

Basal Cell ◽

Target Genes ◽

Expression Profiles ◽

Taqman Probe ◽

Control Group ◽

Small Rna Sequencing ◽

Mirna Regulation ◽

Genome Wide

Distinct expression of the miRNAs has rarely been explored in basal cell carcinoma (BCC) of skin, and the regulatory role of miRNAs in BCC development remains quite opaque. Here, we collected control tissues from adjacent noncancerous skin ( n = 15 ; control group) and tissues at tumor centers from patients with cheek BCC ( n = 15 ; BCC group) using punch biopsies. After six small RNA sequencing- (sRNA-seq-) based miRNA expression profiles were generated for both BCC and controls, including three biological replicates, we conducted comparative analysis on the sRNA-seq dataset, discovering 181 differentially expressed miRNAs (DEMs) out of the 1,873 miRNAs in BCCs. In order to validate the sRNA-seq data, expression of 15 randomly selected DEMs was measured using the TaqMan probe-based quantitative real-time PCR. Functional analysis of predicted target genes of DEMs in BCCs shows that these miRNAs are primarily involved in various types of cancers, immune response, epithelial growth, and morphogenesis, as well as energy production and metabolism, indicating that BCC development is caused, at least in part, by changes in miRNA regulation for biological and disease processes. In particular, the “basal cell carcinoma pathways” were found to be enriched by predicted DEM targets, and regulatory relationships between DEMs and their targeted genes in this pathway were further uncovered. These results revealed the association between BCCs and abundant miRNA molecules that regulate target genes, functional modules, and signaling pathways in carcinogenesis.

Download Full-text

Identification of Potential Autophagy-Related Genes in Steroid-Induced Osteonecrosis of the Femoral Head

10.21203/rs.3.rs-1066425/v1 ◽

2021 ◽

Author(s):

XueZhen LIANG ◽

Di LUO ◽

Yan-Rong CHEN ◽

Jia-Cheng LI ◽

Bo-Zhao YAN ◽

...

Keyword(s):

Femoral Head ◽

Correlation Analysis ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Differentially Expressed ◽

Pathway Enrichment Analysis ◽

Hub Genes ◽

R Software ◽

Pathway Enrichment

Abstract Purpose: Steroid-induced osteonecrosis of the femoral head (SONFH) was a refractory orthopedic hip joint disease in the young and middle-aged people. Previous experimental studies had shown that autophagy might be involved in the pathological process of SONFH, but the pathogenesis of autophagy in SONFH remained unclear. We aim to identify and validate the key potential autophagy-related genes of SONFH to further illustrate the mechanism of autophagy in SONFH through bioinformatics analysis. Methods: The mRNA expression profile dataset GSE123568 was download from Gene Expression Omnibus (GEO) database, including 10 non-SONFH (following steroid administration) samples and 30 SONFH samples. The autophagy-related genes were obtained from the Human Autophagy Database (HADb). The autophagy-related genes of SONFH were screened by intersecting GSE123568 dataset with autophagy genes. The differentially expressed autophagy-related genes of SONFH were identified by R software. Besides, the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted for the differentially expressed autophagy-related genes of SONFH by R software. Then, the correlation analysis between the expression levels of differentially expressed autophagy-related genes of SONFH was confirmed by R software. Moreover, the protein–protein interaction (PPI) network were analyzed by the Search Tool for the Retrieval of Interacting Genes (STRING), and the significant gene cluster modules were identified by the MCODE Cytoscape plugin, and hub genes of differentially expressed autophagy-related genes of SONFH were screened by the CytoHubba Cytoscape plugin. Finally, the expression levels of hub genes of differentially expressed autophagy-related genes of SONFH was validated in hip articular cartilage specimens from necrosis femur head (NFH) by GSE74089 dataset. Results: A total of 34 differentially expressed autophagy-related genes were identified between the peripheral blood of SONFH samples and non-SONFH Samples based on the defined criteria, including 25 up-regulated genes and 9 down-regulated genes. The GO and KEGG pathway enrichment analysis revealed that these 34 differentially expressed autophagy-related genes of SONFH were concentrated in death domain receptors, FOXO signaling pathway and apoptosis. The correlation analysis revealed a significant correlation among the 34 differentially expressed autophagy-related genes of SONFH. The PPI results demonstrated that the 34 differentially expressed autophagy-related genes interacted with each other. There were 10 hub genes identified by the MCC algorithms of Cytohubba. The results of GSE74089 dataset showed TNFSF10, PTEN and CFLAR were significantly upregulated while BCL2L1 were significantly downregulated in the hip cartilage specimens, which were consistent with the GSE123568 dataset. Conclusions: There were 34 potential autophagy-related genes of SONFH identified using bioinformatics analysis. TNFSF10, PTEN, CFLAR and BCL2L1 might serve as potential drug targets and biomarkers by regulating autophagy. These results would expand new insights into the autophagy-related understanding of SONFH and might be useful in the diagnosis and prognosis of SONFH.

Download Full-text