scholarly journals Identifying Potential Prognostic Biomarkers Associated With Clinicopathologic Characteristics of Hepatocellular Carcinoma by Bioinformatics Analysis

2021 ◽  
Author(s):  
Shaojie Huang ◽  
Jia Yao ◽  
Xiaofan Lai ◽  
Lu Yang ◽  
Fang Ye ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
Progression Free Survival ◽  
Gene Expression Omnibus ◽  
Rank Aggregation ◽  
Hub Genes ◽  
Clinicopathologic Characteristics ◽  
Kegg Pathways ◽  
Diagnosis And Prognosis

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. However, the molecular mechanisms of HCC remain largely unknown so far. Methods: To unravel the underlying carcinogenic mechanisms, we utilized Robust Rank Aggregation analysis (RRA) to identify a set of overlapping differentially expressed genes (DEGs) from 5 microarray datasets on Gene Expression Omnibus (GEO) database. Enriched Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were conducted. The protein‐protein interaction (PPI) network was constructed and Cytoscape V3.8.0 was used for selecting hub genes. The expression of hub genes was validated in TCGA datasets and HCC samples in our center by qPCR and immunohistochemistry analysis. Results: Totally 126 DEGs were identified. GO and KEGG pathways of DEGs mostly associated with “organelle fission”, “nuclear division” and “caffeine metabolism. Ten hub genes (BUB1B, CDKN3, CCNB1, CCNB2, CDK1, TOP2A, CDC20, MELK, NUSAP1, AURKA) were selected. Overall survival (OS) and progression-free survival (PFS) analysis suggested the good value of these genes for HCC diagnosis and prognosis. These genes were upregulated in HCC samples from TCGA, which were associated with higher tumor grades and possibly resulted from hypomethylation. Moreover, these hub genes were markedly dysregulated in HCC samples in our center and significantly associated with clinicopathologic characteristics of HCC patients. Conclusions: In conclusion, our study identified several hub genes as novel candidate biomarkers for diagnosis and prognosis of HCC, which may provide new insight into HCC pathogenesis in order to search for better treatments.

scholarly journals Identification of Key Genes with Clinical Outcome in Benign Meningioma Using Bioinformatic Analysis

2020 ◽  
Author(s):  
Ming Cao ◽  
Chen Shen ◽  
Jie Zhu ◽  
YuHai Wang
Keyword(s):  
Molecular Mechanisms ◽  
Therapeutic Targets ◽  
Microarray Dataset ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Benign Meningioma ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Key Genes

Abstract Background: Meningioma is the second most common type of brain neoplasms.However,the underlying molecular mechanisms are still not clear,and the main treatment is mainly surgery plus radiotherapy. Material and method: To explore the key genes in benign meningioma,we downloaded microarray dataset GSE43290 from Gene Expression Omnibus(GEO) database.The differential genes (DEGs) between benign meningioma and normal meninges were identified by GEO2R.The gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) pathway were performed by the Database for Annotation,Visualization and Integrated Discovery (DAVID).The protein-protein interaction (PPI) network and module analysis were performed and visualized by the Search Tool for the Retrieval of Interacting Gene database (STRING) and Cytoscape.The hub genes were evaluated by the Cytohubba and further explored by MCODE plugin of Cytoscape and Enrichr.The relationship between hub genes and clinical factors were further explored by GSE16581 through R software. Result: A total of 358 DEGs were identified,including 15 upregulated genes and 343 downregulated genes.The main enriched functions were extracellular matrix organization、inflammatory response、cell adhesion、extracellular space and integrin binding.The main KEGG pathways were Malaria and focal adhesion.Among these DEGs,5 overlapping genes(CXCL8、AGT、CXCL2、CXCL12、CXCR4) were selected as hub genes.CXCL2 and CXCL8 were correlated with age and tumor recurrence,which could be clinical therapeutic targets. Conclusion: This study indicates the key genes in benign meningioma which may help us understand the molecular mechanisms and provide the candidate therapeutic targets.

scholarly journals MCM2 and NUSAP1 Are Potential Biomarkers for the Diagnosis and Prognosis of Pancreatic Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yajun Deng ◽  
Hanyun Ma ◽  
Jinyong Hao ◽  
Qiqi Xie ◽  
Ruochen Zhao
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
The Cancer Genome Atlas ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Diagnosis And Prognosis ◽  
Potential Biomarkers

Pancreatic cancer (PC) is one of the most malignant tumors. Despite considerable progress in the treatment of PC, the prognosis of patients with PC is poor. The aim of this study was to identify potential biomarkers for the diagnosis and prognosis of PC. First, the original data of three independent mRNA expression datasets were downloaded from the Gene Expression Omnibus and The Cancer Genome Atlas databases and screened for differentially expressed genes (DEGs) using the R software. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the DEGs were performed, and a protein-protein interaction (PPI) network was constructed to screen for hub genes. The hub genes were analyzed for genetic variations, as well as for survival, prognostic, and diagnostic value, using the cBioPortal and Gene Expression Profiling Interactive Analysis (GEPIA) databases and the pROC package. After screening for potential biomarkers, the mRNA and protein levels of the biomarkers were verified at the tissue and cellular levels using the Cancer Cell Line Encyclopedia, GEPIA, and the Human Protein Atlas. As a result, a total of 248 DEGs were identified. The GO terms enriched in DEGs were related to the separation of mitotic sister chromatids and the binding of the spindle to the extracellular matrix. The enriched pathways were associated with focal adhesion, ECM-receptor interaction, and phosphatidylinositol 3-kinase (PI3K)/AKT signaling. The top 20 genes were selected from the PPI network as hub genes, and based on the analysis of multiple databases, MCM2 and NUSAP1 were identified as potential biomarkers for the diagnosis and prognosis of PC. In conclusion, our results show that MCM2 and NUSAP1 can be used as potential biomarkers for the diagnosis and prognosis of PC. The study also provides new insights into the underlying molecular mechanisms of PC.

scholarly journals Identification of key genes and long non-coding RNA associated ceRNA networks in hepatocellular carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8021 ◽  
Author(s):  
Jun Liu ◽  
Wenli Li ◽  
Jian Zhang ◽  
Zhanzhong Ma ◽  
Xiaoyan Wu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Microarray Dataset ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Hepatic Expression ◽  
Limma Package ◽  
Human Protein Atlas

Background Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths worldwide. Although multiple efforts have been made to understand the development of HCC, morbidity, and mortality rates remain high. In this study, we aimed to discover the mRNAs and long non-coding RNAs (lncRNAs) that contribute to the progression of HCC. We constructed a lncRNA-related competitive endogenous RNA (ceRNA) network to elucidate the molecular regulatory mechanism underlying HCC. Methods A microarray dataset (GSE54238) containing information about both mRNAs and lncRNAs was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) and lncRNAs (DElncRNAs) in tumor tissues and non-cancerous tissues were identified using the limma package of the R software. The miRNAs that are targeted by DElncRNAs were predicted using miRcode, while the target mRNAs of miRNAs were retrieved from miRDB, miRTarBas, and TargetScan. Functional annotation and pathway enrichment of DEGs were performed using the EnrichNet website. We constructed a protein–protein interaction (PPI) network of DEGs using STRING, and identified the hub genes using Cytoscape. Survival analysis of the hub genes and DElncRNAs was performed using the gene expression profiling interactive analysis database. The expression of molecules with prognostic values was validated on the UALCAN database. The hepatic expression of hub genes was examined using the Human Protein Atlas. The hub genes and DElncRNAs with prognostic values as well as the predictive miRNAs were selected to construct the ceRNA networks. Results We found that 10 hub genes (KPNA2, MCM7, CKS2, KIF23, HMGB2, ZWINT, E2F1, MCM4, H2AFX, and EZH2) and four lncRNAs (FAM182B, SNHG6, SNHG1, and SNHG3) with prognostic values were overexpressed in the hepatic tumor samples. We also constructed a network containing 10 lncRNA–miRNA–mRNA pathways, which might be responsible for regulating the biological mechanisms underlying HCC. Conclusion We found that the 10 significantly overexpressed hub genes and four lncRNAs were negatively correlated with the prognosis of HCC. Further, we suggest that lncRNA SNHG1 and the SNHG3-related ceRNAs can be potential research targets for exploring the molecular mechanisms of HCC.

scholarly journals Expression of Spindle and Kinetochore-related Complex Subunit 3 is Associate With the Progression and Prognosis of Hepatocellular Carcinoma and Pancreatic Adenocarcinoma

2020 ◽  
Author(s):  
Kuiwei Su ◽  
Yu Huang ◽  
Shufang Xie ◽  
Chen Yang ◽  
Guoqing Ru ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pancreatic Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Vital Role ◽  
Kegg Pathways ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Tumor Tissues ◽  
Related Complex

Abstract Background Several studies have shown that Spindle and Kinetochore-related Complex Subunit 3 (SKA3) is closely related to the diagnosis and prognosis of various cancers. SKA3 plays a vital role in mitosis, but the mechanism underlying its action in tumors has not been fully elucidated. Methods Two separate microarrays of pancreatic adenocarcinoma (PAAD) and hepatocellular carcinoma (HCC) samples were downloaded from the Gene Expression Omnibus (GEO) and analyzed using R software. Tissues from 203 HCC and 321 PAAD patients were used for tissue microarray (TMA) construction and immunohistochemical staining for scoring. Kaplan-Meier estimators were used to determine their clinical relevance. Finally, Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) were used for analyzed rich pathways of SKA3 to understand gene function further. Results Contrary to the results obtained from the GEO database, HCC and PAAD tumor tissues exhibited significantly lower expression of SKA3 compared to the adjacent tissues (P < 0.001). The size of HCC tumor and Edmondson grade were statistically correlated with SKA3 expression (P < 0.05). No correlation was observed between the levels of SKA3 expression and vascular invasion, metastasis, or lymphatic infiltration in HCC patients. Moreover, factors related to decreases of SKA3 in PAAD could similarly not be found. Kaplan-Meier survival curves indicated that SKA3 upregulation in HCC was significantly associated with better prognosis (P = 0.016). In contrast, low levels of SKA3 were related to poor prognosis of PAAD patients (P = 0.026). Enrichment analysis of GO and KEGG pathways showed that SKA3 gene was closely related to cell cycle pathways. Conclusions The results of the present study indicate that SKA3 expression can serve as a diagnostic and prognostic biomarker for HCC and PAAD. However, there is a need to conduct further studies to verify these findings, especially the prognostic role of SKA3 in PAAD.

scholarly journals Selecting Hub Genes and Predicting Target Genes of microRNAs in Tuberculosis via the Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Siqi Deng ◽  
Shijie Shen ◽  
Saeed El-Ashram ◽  
Huan Lu ◽  
Dan Luo ◽  
...  
Keyword(s):  
Expression Profile ◽  
Messenger Rna ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Mirna Expression Profile ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Kegg Pathways

Tuberculosis (TB) is the world's most prevalently infectious disease. Molecular mechanisms behind tuberculosis remain unknown. microRNA (miRNA) is involved in a wide variety of diseases. To validate the significant genes and miRNAs in the current sample, two messenger RNA (mRNA) expression profile datasets and three miRNA expression profile datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed (DE) genes (DEGs) and miRNAs (DE miRNAs) between healthy and TB patients were filtered out. Enrichment analysis was executed, and a protein-protein interaction (PPI) network was developed to understand the enrich pathways and hub genes of TB. Additionally, the target genes of miRNA were predicted and overlapping target genes were identified. We studied a total of 181 DEGs (135 downregulated and 46 upregulated genes) and two DE miRNAs (2 downregulated miRNAs) from two gene profile datasets and three miRNA profile datasets, respectively. 10 hub genes were defined based on high degree of connectivity. A PPI network's top module was constructed. The 23 DEGs identified have a significant relationship with miRNAs. 25 critically significant Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were discovered. The detailed study revealed that, in tuberculosis, the DE miRNA and DEGs form an interaction network. The identification of novel target genes and main pathways would aid with our understanding of miRNA's function in tuberculosis progression.

scholarly journals The impact of FGF19/FGFR4 signaling inhibition in antitumor activity of multi-kinase inhibitors in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hiroaki Kanzaki ◽  
Tetsuhiro Chiba ◽  
Junjie Ao ◽  
Keisuke Koroki ◽  
Kengo Kanayama ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Molecular Mechanisms ◽  
Kinase Inhibitors ◽  
Progression Free Survival ◽  
Erk Signaling ◽  
Enzyme Linked Immunosorbent Assay ◽  
Antitumor Effects ◽  
The Impact

AbstractFGF19/FGFR4 autocrine signaling is one of the main targets for multi-kinase inhibitors (MKIs). However, the molecular mechanisms underlying FGF19/FGFR4 signaling in the antitumor effects to MKIs in hepatocellular carcinoma (HCC) remain unclear. In this study, the impact of FGFR4/ERK signaling inhibition on HCC following MKI treatment was analyzed in vitro and in vivo assays. Serum FGF19 in HCC patients treated using MKIs, such as sorafenib (n = 173) and lenvatinib (n = 40), was measured by enzyme-linked immunosorbent assay. Lenvatinib strongly inhibited the phosphorylation of FRS2 and ERK, the downstream signaling molecules of FGFR4, compared with sorafenib and regorafenib. Additional use of a selective FGFR4 inhibitor with sorafenib further suppressed FGFR4/ERK signaling and synergistically inhibited HCC cell growth in culture and xenograft subcutaneous tumors. Although serum FGF19high (n = 68) patients treated using sorafenib exhibited a significantly shorter progression-free survival and overall survival than FGF19low (n = 105) patients, there were no significant differences between FGF19high (n = 21) and FGF19low (n = 19) patients treated using lenvatinib. In conclusion, robust inhibition of FGF19/FGFR4 is of importance for the exertion of antitumor effects of MKIs. Serum FGF19 levels may function as a predictive marker for drug response and survival in HCC patients treated using sorafenib.

scholarly journals Comprehensive analyses of competing endogenous RNA networks reveal potential biomarkers for predicting hepatocellular carcinoma recurrence

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ping Yan ◽  
Zuotian Huang ◽  
Tong Mou ◽  
Yunhai Luo ◽  
Yanyao Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Malignant Tumors ◽  
Expression Profiles ◽  
Clinical Information ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
High Rate ◽  
Competing Endogenous Rna ◽  
Tissue Samples ◽  
Potential Biomarkers

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors, with a high rate of recurrence worldwide. This study aimed to investigate the mechanism underlying the progression of HCC and to identify recurrence-related biomarkers. Methods We first analyzed 132 HCC patients with paired tumor and adjacent normal tissue samples from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). The expression profiles and clinical information of 372 HCC patients from The Cancer Genome Atlas (TCGA) database were next analyzed to further validate the DEGs, construct competing endogenous RNA (ceRNA) networks and discover the prognostic genes associated with recurrence. Finally, several recurrence-related genes were evaluated in two external cohorts, consisting of fifty-two and forty-nine HCC patients, respectively. Results With the comprehensive strategies of data mining, two potential interactive ceRNA networks were constructed based on the competitive relationships of the ceRNA hypothesis. The ‘upregulated’ ceRNA network consists of 6 upregulated lncRNAs, 3 downregulated miRNAs and 5 upregulated mRNAs, and the ‘downregulated’ network includes 4 downregulated lncRNAs, 12 upregulated miRNAs and 67 downregulated mRNAs. Survival analysis of the genes in the ceRNA networks demonstrated that 20 mRNAs were significantly associated with recurrence-free survival (RFS). Based on the prognostic mRNAs, a four-gene signature (ADH4, DNASE1L3, HGFAC and MELK) was established with the least absolute shrinkage and selection operator (LASSO) algorithm to predict the RFS of HCC patients, the performance of which was evaluated by receiver operating characteristic curves. The signature was also validated in two external cohort and displayed effective discrimination and prediction for the RFS of HCC patients. Conclusions In conclusion, the present study elucidated the underlying mechanisms of tumorigenesis and progression, provided two visualized ceRNA networks and successfully identified several potential biomarkers for HCC recurrence prediction and targeted therapies.

scholarly journals Screening the genome for HCC-specific CpG methylation signatures as biomarkers for diagnosis and prognosis evaluation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Rui-kun Zhang ◽  
Jia-lin Liu
Keyword(s):  
Cox Regression ◽  
Malignant Tumors ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Training Set ◽  
Cpg Sites ◽  
Diagnosis And Prognosis ◽  
Patient Prognosis ◽  
Prognosis Evaluation

Abstract Background Hepatocellular carcinoma (HCC) is one of the most common and invasive malignant tumors in the world. The change in DNA methylation is a key event in HCC. Methods Methylation datasets for HCC and 17 other types of cancer were downloaded from The Cancer Genome Atlas (TCGA). The CpG sites with large differences in methylation between tumor tissues and paracancerous tissues were identified. We used the HCC methylation dataset downloaded from the TCGA as the training set and removed the overlapping sites among all cancer datasets to ensure that only CpG sites specific to HCC remained. Logistic regression analysis was performed to select specific biomarkers that can be used to diagnose HCC, and two datasets—GSE157341 and GSE54503—downloaded from GEO as validation sets were used to validate our model. We also used a Cox regression model to select CpG sites related to patient prognosis. Results We identified 6 HCC-specific methylated CpG sites as biomarkers for HCC diagnosis. In the training set, the area under the receiver operating characteristic (ROC) curve (AUC) for the model containing all these sites was 0.971. The AUCs were 0.8802 and 0.9711 for the two validation sets from the GEO database. In addition, 3 other CpG sites were analyzed and used to create a risk scoring model for patient prognosis and survival prediction. Conclusions Through the analysis of HCC methylation datasets from the TCGA and Gene Expression Omnibus (GEO) databases, potential biomarkers for HCC diagnosis and prognosis evaluation were ascertained.

GCSF As a Potential Molecular Target for Overall Survival of Hepatocellular Carcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Heng Cao ◽  
Peng Guo ◽  
Xiaohui Wu ◽  
Jiankun Li ◽  
Chenlong Ge ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Malignant Tumors ◽  
Basic Research ◽  
Clinical Samples ◽  
Tumor Diameter ◽  
Overall Survival Analysis

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors of digestive tract in the world. Therefore, it is important to carry out studies on the molecular mechanisms of early diagnosis and treatment of HCC to reduce mortality. Methods: Bioinformatic analysis was performed to explore the significant role of GCSF on the occurrence and development of neoplasm. Differently expressed genes (DEGs) were screened, and the significant hub genes related with GCSF were identified by the multiple algorithms of Cytoscape. Functional annotation for DEGs, pathological stage and overall survival analysis were implemented. In addition, the verification for the role of GCSF on HCC was made via the clinical samples. A total of 70 participates diagnosed as HCC were recruited from November 2014 to November 2019. The immunohistochemistry assay, qRT-PCR, receiver operating characteristic (ROC) curves, and overall survival analysis were carried out. Results: GCSF was related with the tumor size, and the expression of GCSF was up-regulated in hepatocellular carcinoma tissues. The enrichment results of GO and KEGG analysis were mainly enriched in “Inflammatory response”, “Protein binding”, “Metabolic pathways”, and “Proteasome”. The tumor diameter (P < 0.001), and survival time (P < 0.001) were significantly associated with expression of GCSF via the verification of clinical data. The univariate and multivariate Cox proportional regression analysis manifested that high expression of GCSF in patients with HCC was related to poor OS. Conclusion: The expression level of GCSF is significantly associated with the prognostic survival of HCC, and it is expected to become a new prognostic marker of HCC, providing a novel idea for future basic research as well as targeted therapy.

scholarly journals Investigation of the Mechanism of Complement System in Diabetic Nephropathy via Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Bojun Xu ◽  
Lei Wang ◽  
Huakui Zhan ◽  
Liangbin Zhao ◽  
Yuehan Wang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Diabetic Nephropathy ◽  
Protein Interactions ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Topological Analysis ◽  
Gene Expression Omnibus ◽  
Complement Cascade ◽  
Hub Genes ◽  
Novel Biomarkers

Objectives. Diabetic nephropathy (DN) is a major cause of end-stage renal disease (ESRD) throughout the world, and the identification of novel biomarkers via bioinformatics analysis could provide research foundation for future experimental verification and large-group cohort in DN models and patients. Methods. GSE30528, GSE47183, and GSE104948 were downloaded from Gene Expression Omnibus (GEO) database to find differentially expressed genes (DEGs). The difference of gene expression between normal renal tissues and DN renal tissues was firstly screened by GEO2R. Then, the protein-protein interactions (PPIs) of DEGs were performed by STRING database, the result was integrated and visualized via applying Cytoscape software, and the hub genes in this PPI network were selected by MCODE and topological analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to determine the molecular mechanisms of DEGs involved in the progression of DN. Finally, the Nephroseq v5 online platform was used to explore the correlation between hub genes and clinical features of DN. Results. There were 64 DEGs, and 32 hub genes were identified, enriched pathways of hub genes involved in several functions and expression pathways, such as complement binding, extracellular matrix structural constituent, complement cascade related pathways, and ECM proteoglycans. The correlation analysis and subgroup analysis of 7 complement cascade-related hub genes and the clinical characteristics of DN showed that C1QA, C1QB, C3, CFB, ITGB2, VSIG4, and CLU may participate in the development of DN. Conclusions. We confirmed that the complement cascade-related hub genes may be the novel biomarkers for DN early diagnosis and targeted treatment.

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