scholarly journals Identification of Four Hub Genes Involved in Breast Cancer Based on Robust Rank Aggregation and WGCNA Methods

2020 ◽  
Author(s):  
Rongqin Ke ◽  
Jinbao Yin
Keyword(s):  
Breast Cancer ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Genomic Mutation ◽  
Mutation Analyses

Abstract Background: Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remains an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil these mechanisms and provide novel therapeutic targets for breast cancer. Methods: In this study, we systematically integrated robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), DNA methylation analyses and genomic mutation analyses, GSEA and GSVA to identify potential hub genes that are highly associated with breast cancer. Results: We identified a total of 512 robust DEGs that were significantly associated with breast cancer based on RRA analysis and functional enrichment analysis. CENPL, ISG20L2, MRPL3 and LSM4 were identified as four potential hub genes for breast cancer through the WGCNA analysis and literate search. These four hub genes were upregulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. Methylation analyses and genomic mutation analyses suggested that the abnormal up-regulation of these genes are likelyresulted from hypomethylation and gene mutations. Moreover, GSEA and GSVA for single potential hub genes revealed they were all tightly related to the proliferation of tumor cells. Conclusion: We identify four genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. They may become potential therapeutic targets for breast cancer patients with further studies. Keywords: breast cancer, RRA, WGCNA, hub genes

scholarly journals Molecular mechanisms of fat deposition: <i>IL-6</i> is a hub gene in fat lipolysis, comparing thin-tailed with fat-tailed sheep breeds

2021 ◽  
Vol 64 (1) ◽  
pp. 53-68
Author(s):  
Sana Farhadi ◽  
Jalil Shodja Ghias ◽  
Karim Hasanpur ◽  
Seyed Abolghasem Mohammadi ◽  
Esmaeil Ebrahimie
Keyword(s):  
Fatty Acid ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Structural Classification ◽  
Hub Genes ◽  
Sheep Breeds ◽  
Ppi Networks ◽  
Structural Classification Of Proteins

Abstract. Tail fat content affects meat quality and varies significantly among different breeds of sheep. Ghezel (fat-tailed) and Zel (thin-tailed) are two important Iranian local sheep breeds with different patterns of fat storage. The current study presents the transcriptome characterization of tail fat using RNA sequencing in order to get a better comprehension of the molecular mechanism of lipid storage in the two mentioned sheep breeds. Seven (Zel = 4 and Ghezel = 3) 7-month-old male lambs were used for this experiment. The results of sequencing were analyzed with bioinformatics methods, including differentially expressed genes (DEGs) identification, functional enrichment analysis, structural classification of proteins, protein–protein interaction (PPI) and network and module analyses. Some of the DEGs, such as LIPG, SAA1, SOCS3, HIF-1α, and especially IL-6, had a close association with lipid metabolism. Furthermore, functional enrichment analysis revealed pathways associated with fat deposition, including “fatty acid metabolism”, “fatty acid biosynthesis” and “HIF-1 signaling pathway”. The structural classification of proteins showed that major down-regulated DEGs in the Zel (thin-tailed) breed were classified under transporter class and that most of them belonged to the solute carrier transporter (SLC) families. In addition, DEGs under the transcription factor class with an important role in lipolysis were up-regulated in the Zel (thin-tailed) breed. Also, network analysis revealed that IL-6 and JUNB were hub genes for up-regulated PPI networks, and HMGCS1, VPS35 and VPS26A were hub genes for down-regulated PPI networks. Among the up-regulated DEGs, the IL-6 gene seems to play an important role in lipolysis of tail fat in thin-tailed sheep breeds via various pathways such as tumor necrosis factor (TNF) signaling and mitogen-activated protein kinase (MAPK) signaling pathways. Due to the probable role of the IL-6 gene in fat lipolysis and also due to the strong interaction of IL-6 with the other up-regulated DEGs, it seems that IL-6 accelerates the degradation of lipids in tail fat cells.

scholarly journals Screening a prognosis-related target gene in patients with HER-2 positive breast cancer by bioinformatics analysis

2021 ◽  
Author(s):  
Song Wang ◽  
Yi Quan
Keyword(s):  
Breast Cancer ◽  
Target Gene ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Study Objective ◽  
Her 2 ◽  
Positive Breast Cancer

Objective: HER-2 positive breast cancer has a high risk of for relapse, metastasis and drug resistance, and is related to a poor prognosis. Thus, the study objective was to determine a target gene and explore the associated molecular mechanisms in HER-2 positive breast cancer. Methods: Three RNA expression profiles were obtained from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), and were used to identify differentially expressed genes (DEGs) using R software. A Protein-Protein Interaction (PPI) network was constructed and hub genes were determined. Subsequently, the relationship between clinical parameters and hub genes was examined to screen target gene. Next, DNA methylation and genomic alterations of the target gene were evaluated. To further explore potential molecular mechanisms, genes co-expressed with the target gene were performed functional enrichment analysis Results: The differential expression analysis revealed 217 DEGs in HER-2 positive breast cancer tissues compared to normal breast tissues. RRM2 was the only hub gene closely associated with lymphatic metastasis and prognosis in HER-2 positive breast cancer. Additionally, RRM2 was frequently often amplified and negatively associated with the methylation level. Functional enrichment analysis showed that the co-expression genes were mainly involved in cell cycle. Conclusions: The present study identified RRM2 as a target gene associated with the initiation, progression and prognosis of HER-2 positive breast cancer, which may contribute to provide a new biomarker and therapeutic target.

scholarly journals Network Pharmacology Prediction: The Possible Mechanisms of Cinobufotalin against Osteosarcoma

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Riyu Chen ◽  
Zeyi Guan ◽  
Xianxing Zhong ◽  
Wenzheng Zhang ◽  
Ya Zhang
Keyword(s):  
Survival Curve ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Network Pharmacology ◽  
Hub Genes ◽  
Survival Prognosis ◽  
Active Compounds ◽  
Protein Protein Interaction

Objective. To explore the active compounds and targets of cinobufotalin (huachansu) compared with the osteosarcoma genes to obtain the potential therapeutic targets and pharmacological mechanisms of action of cinobufotalin on osteosarcoma through network pharmacology. Methods. The composition of cinobufotalin was searched by literature retrieval, and the target was selected from the CTD and TCMSP databases. The osteosarcoma genes, found from the GeneCards, OMIM, and other databases, were compared with the cinobufotalin targets to obtain potential therapeutic targets. The protein-protein interaction (PPI) network of potential therapeutic targets, constructed through the STRING database, was inputted into Cytoscape software to calculate the hub genes, using the NetworkAnalyzer. The hub genes were inputted into the Kaplan-Meier Plotter online database for exploring the survival curve. Functional enrichment analysis was identified using the DAVID database. Results. 28 main active compounds of cinobufotalin were explored, including bufalin, adenosine, oleic acid, and cinobufagin. 128 potential therapeutic targets on osteosarcoma are confirmed among 184 therapeutic targets form cinobufotalin. The hub genes included TP53, ACTB, AKT1, MYC, CASP3, JUN, TNF, VEGFA, HSP90AA1, and STAT3. Among the hub genes, TP53, ACTB, MYC, TNF, VEGFA, and STAT3 affect the patient survival prognosis of sarcoma. Through function enrichment analysis, it is found that the main mechanisms of cinobufotalin on osteosarcoma include promoting sarcoma apoptosis, regulating the cell cycle, and inhibiting proliferation and differentiation. Conclusion. The possible mechanisms of cinobufotalin against osteosarcoma are preliminarily predicted through network pharmacology, and further experiments are needed to prove these predictions.

scholarly journals Construction of a Novel 3-lncRNA Risk Score System for the Prognostic Prediction of Triple-Negative Breast Cancer

2020 ◽  
Author(s):  
Chao Yuan ◽  
Hongjun Yuan ◽  
Li Chen ◽  
Miaomiao Sheng ◽  
Wenru Tang
Keyword(s):  
Breast Cancer ◽  
Risk Score ◽  
Triple Negative ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Time Dependent ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Validation Set

Abstract Background：Triple-negative breast cancer (TNBC) is an essential type of breast cancer (BC). Compared with other molecular subtypes of BC, TNBC has the features of fast tumor increase, quick recurrence and natural metastasis. It is more urgent to establish a comprehensive evaluation system containing multiple biomarkers than single parameter.Methods：We conduct a bioinformatics analysis on 13 BC expression datasets from the Gene Expression Omnibus (GEO), which covered 2950 samples. We took 3484 genes with a more significant difference between TNBC and normal-like candidate genes for weighted correlation network analysis (WGCNA). A total of 54 genes were chosen as hub genes with great connectivity with the TNBC significant module. Based on The Cancer Genome Atlas (TCGA) data, we identify the best prognostic three lncRNA. Multivariate Cox regression was used to construct a 3-lncRNA risk score model. We evaluated prognostic capacity using time-dependent subject operating characteristics (ROC) and Kaplan-Meier (KM) survival analysis. The predictive power of the model was demonstrated by the time-dependent ROC spline and Kaplan-Meier spline. At the same time, it also shows good predictive ability in the validation set. Ultimately, Functional enrichment analysis of hub genes and three lncRNAs were offered to advise the possible biological pathways. Results：The construct LNC00337, DEPCE-AS1, DDX11-AS1 multi-factor risk scoring model was meaningfully associated with the prognosis of TNBC patients. Through survival analysis, the risk score efficiently divided the patients into high-risk groups with poor overall survival. The time-dependent ROC curve revealed that the model presented robust in predicting survival over the first 3 years. The validity of the model in the validation set is also verified. Finally, functional enrichment analysis proposed some biological pathways that may be correlated to the tumor. Conclusions：In our study, we established a lncRNA-based model to prognosticate the prediction of TNBC, which might afford a strong prognosis estimate tool to help therapy policy-making in the clinic.

scholarly journals CENPL, ISG20L2, LSM4 and MRPL3 Are Four Novel Hub Genes Involved in Breast Cancer Development and Progression

2020 ◽  
Author(s):  
Jinbao Yin ◽  
Chen Lin ◽  
Meng jiang ◽  
Xinbing Tang ◽  
Danlin Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Candidate Genes ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Rank Aggregation ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Kaplan Meier

Abstract BackgroundAs a highly prevalent tumor disease worldwide, Further elucidation of the molecular mechanisms of the occurrence, development and prognosis of breast cancer remain an urgent need. Identifying hub genes involved in these pathogenesis and progression can potentially help to unveil its mechanism and provide novel diagnostic and prognostic markers for breast cancer.MethodsIn this study, we systematically integrated multiple bioinformatic methods, including robust rank aggregation (RRA), functional enrichment analysis, protein-protein interaction (PPI) networks construction and analysis, weighted gene co-expression network analysis (WGCNA), ROC and Kaplan-Meier analyses, DNA methylation analyses and genomic mutation analyses, GSEA and GSVA, based on ten mRNA datasets to identify and investigate novel hub genes involved in breast cancer. In parallel, RNA in situ detection technology was applied to validate those novel hub gene.ResultsEZH2 was recognized as a key gene by PPI network analysis. CENPL, ISG20L2, LSM4 and MRPL3 were identified as four novel hub genes through the WGCNA analysis and literate search. Among those five hub genes, many studies on EZH2 gene in breast cancer have been reported, but no studies are related to the roles of CENPL, ISG20L2, MRPL3 and LSM4 in breast cancer. These novel four hub genes were up-regulated in breast cancer tissues and associated with tumor progression. ROC and Kaplan-Meier indicated these four hub genes all showed good diagnostic performance and prognostic values for breast cancer. The preliminary analysis revealed those novel hub genes are four potentially candidate genes for further exploring the molecular mechanism of breast cancer.ConclusionWe identify four novel hub genes (CENPL, ISG20L2, MRPL3, and LSM4) that are likely playing key roles in the molecular mechanism of occurrence and development of breast cancer. Those hub genes are four potentially candidate genes served as promising candidate diagnostic biomarkers and prognosis predictors for breast cancer, and their exact functional mechanisms in breast cancer deserve further in-depth study.

scholarly journals Identification of Hub Genes and MicroRNAs Associated With Idiopathic Pulmonary Arterial Hypertension by Integrated Bioinformatics Analyses

2021 ◽  
Vol 12 ◽  
Author(s):  
Xue Qiu ◽  
Jinyan Lin ◽  
Bixiao Liang ◽  
Yanbing Chen ◽  
Guoqun Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Idiopathic Pulmonary Arterial Hypertension ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Ppi Networks ◽  
Pulmonary Arterial

ObjectiveThe aim of this study is the identification of hub genes associated with idiopathic pulmonary arterial hypertension (IPAH).Materials and MethodsGSE15197 gene expression data was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by screening IPAH patients and controls. The 5,000 genes with the greatest variances were analyzed using a weighted gene co-expression network analysis (WGCNA). Modules with the strongest correlation with IPAH were chosen, followed by a functional enrichment analysis. Protein–protein interaction (PPI) networks were constructed to identify hub gene candidates using calculated degrees. Real hub genes were found from the overlap of DEGs and candidate hub genes. microRNAs (miRNAs) targeting real hub genes were found by screening miRNet 2.0. The most important IPAH miRNAs were identified.ResultsThere were 4,395 DEGs identified. WGCNA indicated that green and brown modules associated most strongly with IPAH. Functional enrichment analysis showed that green and brown module genes were mainly involved in protein digestion and absorption and proteoglycans in cancer, respectively. The top ten candidate hub genes in green and brown modules were identified, respectively. After overlapping with DEGs, 11 real hub genes were identified: EP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1. These genes were expressed with significant differences in IPAH versus controls, indicating a high diagnostic ability. The miRNA–gene network showed that hsa-mir-1-3p could associate with IPAH.ConclusionEP300, MMP2, CDH2, CDK2, GNG10, ALB, SMC2, DHX15, CUL3, BTBD1, and LTN1 may play essential roles in IPAH. Predicted miRNA hsa-mir-1-3p could regulate gene expression in IPAH. Such hub genes may contribute to the pathology and progression in IPAH, providing potential diagnostic and therapeutic opportunities for IPAH patients.

scholarly journals Identification of Common Oncogenic Genes and Pathways Both in Osteosarcoma and Ewing’s Sarcoma Using Bioinformatics Analysis

2021 ◽  
Author(s):  
Jingwei Zhang ◽  
Wenjun Liu ◽  
Liang Ding ◽  
Dongdong Cheng ◽  
Haijun Xiao
Keyword(s):  
Ewing’S Sarcoma ◽  
Ewing's Sarcoma ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Ppi Networks

Abstract Objective: This study aimed to explore common oncogenic genes and pathways both in osteosarcoma and Ewing’s sarcoma. Methods: Microarray data were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were respectively identified using the limma package, followed by intersection of common DEGs. Then, protein-protein interaction (PPI) networks were constructed and hub genes were identified. Furthermore, functional enrichment analysis was analyzed. The expression of common oncogenic genes was validated in 38 osteosarcoma and 17 Ewing’s sarcoma tissues by RT-qPCR and western blot. Results: 201 genes were differentially expressed. There were 121 nodes and 232 edges in the PPI network. 12 genes were considered as hub genes. Functional enrichment analysis results showed that hub genes FN1, COL1A1 and COL1A2 were all involved in extracellular matrix, protease binding and ECM-receptor interaction, which could be involved in the development of osteosarcoma and Ewing’s sarcoma. Among common oncogenic genes, FN1, COL1A1 and COL1A2 were lowly expressed both in osteosarcoma and Ewing’ s sarcoma tissues at mRNA and protein levels. Conclusion: Our findings revealed that common oncogenic genes such as FN1, COL1A1 and COL1A2 and pathways were both in osteosarcoma and Ewing’ s sarcoma.

scholarly journals Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Rongguo Yu ◽  
Jiayu Zhang ◽  
Eryou Feng ◽  
Haiyang Wang ◽  
...  
Keyword(s):  
Subchondral Bone ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Common Chronic Disease ◽  
Drug Candidates ◽  
Therapeutic Modalities ◽  
Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11321
Author(s):  
Di Zhang ◽  
Pengguang Yan ◽  
Taotao Han ◽  
Xiaoyun Cheng ◽  
Jingnan Li
Keyword(s):  
Ulcerative Colitis ◽  
Mitochondrial Dysfunction ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Cell Junction ◽  
Biological Processes ◽  
Hub Genes ◽  
Key Genes

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

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