Identification of Hub Genes as Biomarkers Correlated with the Proliferation and Prognosis in Lung Cancer: A Weighted Gene Co-Expression Network Analysis

Lung cancer is one of the most malignant tumors in the world. Early diagnosis and treatment of lung cancer are vitally important to reduce the mortality of lung cancer patients. In the present study, we attempt to identify the candidate biomarkers for lung cancer by weighted gene co-expression network analysis (WGCNA). Gene expression profile of GSE30219 was downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) were analyzed by the limma package, and the co-expression modules of genes were built by WGCNA. UALCAN was used to analyze the relative expression of normal group and tumor subgroups based on tumor individual cancer stages. Survival analysis for the hub genes was performed by Kaplan–Meier plotter analysis with the TCGA database. A total of 2176 genes (745 upregulated and 1431 downregulated genes) were obtained from the GSE30219 database. Seven gene co-expression modules were conducted by WGCNA and the blue module might be inferred as the most crucial module in the pathogenesis of lung cancer. In the pathway enrichment analysis of KEGG, the candidate genes were enriched in the “DNA replication,” “Cell cycle,” and “P53 signaling pathway” pathways. Among these, the cell cycle pathway was the most significant pathway in the blue module with four hub genes CCNB1, CCNE2, MCM7, and PCNA which were selected in our study. Kaplan–Meier plotter analysis indicated that the high expressions of four hub genes were correlated with a worse overall survival (OS) and advanced tumors. qRT-PCR showed that mRNA expression levels of MCM7 (p=0.038) and CCNE2 (0.003) were significantly higher in patients with the TNM stage. In summary, the high expression of the MCM7 and CCNE2 were significantly related with advanced tumors and worse OS in lung cancer. Thus, the MCM7 and CCNE2 genes can be good indicators for cellular proliferation and prognosis in lung cancer.

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Nomogram Models Based on Gene Expression in Prediction of Breast Cancer Bone Metastasis

10.21203/rs.3.rs-1109324/v1 ◽

2021 ◽

Author(s):

Teng-di Fan ◽

Di-kai Bei ◽

Song-wei Li

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Bone Metastasis ◽

Gene Signature ◽

Functional Enrichment ◽

Training Set ◽

Hub Genes ◽

Kaplan Meier ◽

Patient Prognosis ◽

Kaplan Meier Plotter

Abstract Objective: To design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients. Methods: Dataset GSE124647 was used as a training set, and GSE14020 was taken as a validation set. In the training cohort, limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC non-bone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan-Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the prognostic value of hub genes in BC was explored. Results: A total of 1858 DEGs were obtained. WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival. While GJA1, IGFBP6, MDFI, ITGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival. Kaplan-Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis. Conclusion: Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

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ITGB2 as a Novel Biomarker Correlating With Prognosis and Immune Infiltrates in Ovarian Cancer

10.21203/rs.3.rs-263949/v1 ◽

2021 ◽

Author(s):

chanyuan li ◽

Ting Wan ◽

Ting Deng ◽

Junya Cao ◽

He Huang ◽

...

Keyword(s):

Gene Expression ◽

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Protein Interaction ◽

Ppi Network ◽

Hub Genes ◽

Immune Infiltration ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Background: Epithelial ovarian cancer is nowadays one of the malignancies in women, this study aimed to identify novel biomarkers to predict prognosis and immunotherapy efficacy.Methods: The differentially expressed genes (DEGs) obtained from online database Gene Expression Omnibus (GEO)were screened via GEO2R and Venn diagram software, gene enrichment was analysed by Gene Ontology(GO) function and Kyoto Encyclopedia of Genes and Genomes(KEGG), then protein protein interaction(PPI)network and Cytoscape software were used to confirm the genes closely related to ovarian cancer. Survival analysis of hub genes were obtained from Kaplan–Meier plotter, with their differential expression in specimen validated by Gene Expression Profiling Interactive Analysis (GEPIA) and an integrated repository portal for tumor-immune system interactions (TISIDB). Finally, we used the Tumor Immune Estimation Resource 2.0 (TIMER2.0) and application Estimate the Proportion of Immune and Cancer cells (EPIC) to search the immune infiltration characteristics of the genes.Results: 355 DEGs between epithelial ovarian cancer and normal ovarian tissue were screened out. These DEGs were associated with extracellular exosome, bicellular tight junction and cell-cell junction, and remarkably enriched in molecules of cell adhesion and leukocyte transendothelial migration activity. Ten hub genes were identified via protein protein interaction (PPI) network: PTAFR, HLA-DRA, OAS2, OAS3, PTPN6, LYN, VAMP8, IRF6, ITGB2, CD47. Furthermore, the Kaplan–Meier plotter was conducted, overexpression of four genes was positively connected to poor prognosis in ovarian cancer:OAS2, OAS3, ITGB2, CD47,which were also correlated with immune infiltrates in ovarian cancer and had the highest degree of correlation with tumor associated macrophages (TAMs) infiltration, among which ITGB2 was highly correlated with TAMs infiltration level.Conclusion: ITGB2, OAS2, OAS3, and CD47 are upregulated with unfavorable prognosis in ovarian cancer, and ITGB2 may act as a novel prognostic biomarker with immune infiltration values.

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Integrated bioinformatics analysis reveals ASPM and CENPF with prognostic value in lung cancer

10.21203/rs.2.13321/v1 ◽

2019 ◽

Author(s):

jinghang li ◽

Jing Zhang ◽

Lin Huang ◽

Min Jin ◽

Sheng Zhao

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Cell Cycle ◽

Mirna Gene ◽

Genetic Alterations ◽

Potential Candidate ◽

Kaplan Meier ◽

Β Receptor ◽

Regulation Of Cell Cycle ◽

And Function

Abstract Lung cancer (LC) is the most frequent type of cancer in the world. But the mechanism of LC is still largely unknown. In this study, we analyzed three lung cancer gene expression microarray of different pathologic types to explore the potential candidate genes in LC by Integrated bioinformatical methods. 459 overlapped differentially expressed genes (DEGs) were explored in three GEO gene expression profile from different pathologic types of lung cancer and function annotation were analyzed. Biological process of the DEGs was enriched in regulation of vasculature development and angiogenesis. The significant molecular function of the DEGs was TGF-β receptor activity. The most significant Reactome pathway of DEGs was cell cycle and extracellular matrix organization pathway. The PPI network of the DEGs was constructed and 23 candidate hub genes were established in the network. Kaplan-Meier survival analysis show 21 genes were confirmed to associated with the prognosis of LC. The genetic alterations analysis of these genes by using cBioPortal shown ASPM has the highest genetic alteration rate of 9% in main pathological types of 3191 LC patients, and CENPF has the second highest alteration rate of 6%. ASPM and CENPF also have a significant co-occurrence relationship in LC, and they both participate in the regulation of cell cycle. In the TF -miRNA-gene network of 21 genes shown CENPF have the most significant value in the network and the most relevant TF are NFYA, E2F1 and MYC. In conclusion, this study explored several key genes about LC and analyzed potential TF of those genes, provides possible therapeutic targets and biomarker for further clinical application.

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In silico analyses for potential key genes associated with gastric cancer

PeerJ ◽

10.7717/peerj.6092 ◽

2018 ◽

Vol 6 ◽

pp. e6092 ◽

Cited By ~ 14

Author(s):

Ping Yan ◽

Yingchun He ◽

Kexin Xie ◽

Shan Kong ◽

Weidong Zhao

Keyword(s):

Gastric Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Therapeutic Targets ◽

Receptor Interaction ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Background Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC. Methods To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform. Results A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8. Conclusions Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC.

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Expression Status and Prognostic Values of SOCS Family Genes in Non-small Cell Lung Cancer

10.21203/rs.3.rs-583442/v1 ◽

2021 ◽

Author(s):

Xuede Zhang ◽

Kai Sun ◽

Lingling Bao

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Dna Methylation ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

High Expression ◽

Small Cell Lung ◽

Kaplan Meier ◽

Kaplan Meier Plotter

Abstract Backgroud:Suppressors of cytokine signaling (SOCS) family play important roles in the development of cancers by inhibiting the transmission of the Janus kinases–signal transducers and activators of transcription (JAK-STAT) signaling pathway. However, the expression patterns and prognostic value of SOCS family genes in non-small cell lung cancer (NSCLC) remains unclear. Methods: The SOCS family genes expression profiles were explored using ONCOMINE and GEPIA online tools. The mutation and copy number alterations of SOCS family genes in NSCLC were assessed by cBioportal for Cancer Genomics. The methylation status of SOCS family members were analyzed through MEXPRESS and UCSC Xena website. The prognostic values of SOCS family genes in NSCLC were explored through Kaplan-Meier Plotter database. Results: The expression levels of SOCS2, SOCS3, and cytokine-inducible SH2-containing protein (CIS/CISH) were significantly reduced in NSCLC tissues compared to normal lung tissues. The aberrant DNA methylation of SOCS family genes were frequent in NSCLC. CISH methylation was negatively correlated with gene expression in NSCLC. The Kaplan-Meier Plotter analysis demonstrated high expression of SOCS1 may be a predictor of poor prognosis in lung adenocarcinoma(LUAD) but served as a favorable prognostic marker of lung squamous cell carcinoma. The high expression levels of SOCS2 and SOCS4-7 were significantly correlated with better overall survival (OS) in LUAD but not in lung squamous carcinoma (LUSC) patients. Conclusions:Our findings indicated that the aberrant gene expression and DNA methylation of SOCS family members are common in NSCLC and contribute to tumorigenesis. SOCS family genes may serve as therapeutic targets and prognostic biomarkers for NSCLC patients

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FERMT3 mediates cigarette smoke-induced epithelial–mesenchymal transition through Wnt/β-catenin signaling

Respiratory Research ◽

10.1186/s12931-021-01881-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Xiaoshan Su ◽

Junjie Chen ◽

Xiaoping Lin ◽

Xiaoyang Chen ◽

Zhixing Zhu ◽

...

Keyword(s):

Gene Expression ◽

Lung Cancer ◽

Cell Cycle ◽

Cell Migration ◽

Cigarette Smoke ◽

Epithelial Mesenchymal Transition ◽

Control Group ◽

Data Set ◽

Mesenchymal Transition

Abstract Background Cigarette smoking is a major risk factor for chronic obstructive pulmonary disease (COPD) and lung cancer. Epithelial–mesenchymal transition (EMT) is an essential pathophysiological process in COPD and plays an important role in airway remodeling, fibrosis, and malignant transformation of COPD. Previous studies have indicated FERMT3 is downregulated and plays a tumor-suppressive role in lung cancer. However, the role of FERMT3 in COPD, including EMT, has not yet been investigated. Methods The present study aimed to explore the potential role of FERMT3 in COPD and its underlying molecular mechanisms. Three GEO datasets were utilized to analyse FERMT3 gene expression profiles in COPD. We then established EMT animal models and cell models through cigarette smoke (CS) or cigarette smoke extract (CSE) exposure to detect the expression of FERMT3 and EMT markers. RT-PCR, western blot, immunohistochemical, cell migration, and cell cycle were employed to investigate the potential regulatory effect of FERMT3 in CSE-induced EMT. Results Based on Gene Expression Omnibus (GEO) data set analysis, FERMT3 expression in bronchoalveolar lavage fluid was lower in COPD smokers than in non-smokers or smokers. Moreover, FERMT3 expression was significantly down-regulated in lung tissues of COPD GOLD 4 patients compared with the control group. Cigarette smoke exposure reduced the FERMT3 expression and induces EMT both in vivo and in vitro. The results showed that overexpression of FERMT3 could inhibit EMT induced by CSE in A549 cells. Furthermore, the CSE-induced cell migration and cell cycle progression were reversed by FERMT3 overexpression. Mechanistically, our study showed that overexpression of FERMT3 inhibited CSE-induced EMT through the Wnt/β-catenin signaling. Conclusions In summary, these data suggest FERMT3 regulates cigarette smoke-induced epithelial–mesenchymal transition through Wnt/β-catenin signaling. These findings indicated that FERMT3 was correlated with the development of COPD and may serve as a potential target for both COPD and lung cancer.

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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.

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Comprehensive Analysis of the Expression and Prognosis for IRXs in Non-small Cell Lung Cancer

10.21203/rs.3.rs-78198/v1 ◽

2020 ◽

Author(s):

Qiongzi Wang ◽

Xueshan Qiu

Keyword(s):

Lung Cancer ◽

Human Lung ◽

Expression Patterns ◽

Human Lung Cancer ◽

Prognostic Indicators ◽

Normal Lung ◽

Kaplan Meier ◽

Go Enrichment ◽

Cancer Tissues ◽

Kaplan Meier Plotter

Abstract Iroquoishomeobox transcription factor family （IRXs）have been increasingly reported to play roles in suppressing or promoting a variety of cancers, however, little is known about their expression and prognostic value in terms of human lung cancer. In this study, Oncomine, GEPIA, Kaplan-Meier plotter, and cBioPortal databases were used to analyze the different expression patterns and prognostic values of six IRXs in NSCLC and examine their related functions and pathways using GO enrichment. Compared with normal lung cancer tissues, the expression of IRX1 and IRX2 in NSCLC tissues was significantly lower and was positively correlated with the 10-year survival rate of patients. Higher expression of IRX4 was related to terminal tumors, and suggested a poor prognosis. It was also found that IRXs may play a tumor-suppressive role in the localization of cytoplasm in NSCLC, while localization in the nucleus suggests a more malignant behavior. Together these results suggest that IRX1 and IRX2 may be prognostic indicators of LUAD, and that IRX4 could be a potential target for LUAD treatment.

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SMC4, CCNB1 and CKS1B as potential targets and new critical biomarkers for the prognosis of human bladder cancer

10.21203/rs.3.rs-595148/v1 ◽

2021 ◽

Author(s):

Hongpeng Fang ◽

Zhansen Huang ◽

Xianzi Zeng ◽

Jiaming Wan ◽

Jieying Wu ◽

...

Keyword(s):

Gene Expression ◽

Bladder Cancer ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Transcriptional Level ◽

Hub Genes ◽

Kaplan Meier ◽

High Form

Abstract Background As a common malignant cancer of the urinary system, the precise molecular mechanisms of bladder cancer remain to be illuminated. The purpose of this study was to identify core genes with prognostic value as potential oncogenes for the diagnosis, prognosis or novel therapeutic targets of bladder cancer. Methods The gene expression profiles GSE3167 and GSE7476 were available from the Gene Expression Omnibus (GEO) database. Next, PPI network was built to filter the hub gene through the STRING database and Cytoscape software and GEPIA and Kaplan-Meier plotter were implemented. Frequency and type of hub genes and sub groups analysis were performed in cBioportal and ULCAN database. Finally,We used RT-qPCR to confirm our results. Results Totally, 251 DEGs were excavated from two datasets in our study. We only founded high expression of SMC4, TYMS, CCNB1, CKS1B, NUSAP1 and KPNA2 was associated with worse outcomes in bladder cancer patients and no matter from the type of mutation or at the transcriptional level of hub genes, the tumor showed a high form of expression. However, only the expression of SMC4,CCNB1and CKS1B remained changed between the cancer and the normal samples in our results of RT-qPCR. Conclusion In conclusion,These findings indicate that the SMC4,CCNB1 and CKS1B may serve as critical biomarkers in the development and poor prognosis.

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Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

10.21203/rs.3.rs-49643/v1 ◽

2020 ◽

Author(s):

Xi Pan ◽

Jian-Hao Liu

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Small Molecule ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Tumor Stage ◽

Functional Enrichment ◽

Hub Genes ◽

Therapeutic Goals ◽

Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

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