scholarly journals Identification of 4-genes model in papillary renal cell tumor microenvironment based on comprehensive analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Renal Cell ◽  
Cox Regression ◽  
Ppi Network ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Venn Diagrams ◽  
Kaplan Meier

Abstract Background The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. Three hundred fifty-seven samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression was conducted. Intersect genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS) and progression-free survival (PFS). Univariate and multivariate cox regression analysis were employed to construct survival model. Cibersort was used to predict the immune cell composition of high and low risk group. Combined nomograms were built to predict PRCC prognosis. Immune properties of PRCC were validated by The Cancer Immunome Atlas (TCIA). Results We found immune/stromal score was correlated with T pathological stages and PRCC subtypes. Nine hundred eighty-nine differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Finally, we constructed a prognostic nomogram which combined with influence factors. Conclusions Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

scholarly journals Identification of 4-Genes Model in Papillary Renal Cell Tumor Microenvironment Based on Comprehensive Analysis

2021 ◽  
Author(s):  
Liang Luo ◽  
Haiyi Zhou ◽  
Hao Su
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Ppi Network ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Venn Diagrams ◽  
Kaplan Meier

Abstract Background: The tumor microenvironment acts a pivotal part in the occurrence and development of tumor. However, there are few studies on the microenvironment of papillary renal cell carcinoma (PRCC). Our study aims to explore prognostic genes related to tumor microenvironment in PRCC. Methods: PRCC expression profiles and clinical data were extracted from The Cancer Gene Atlas (TCGA) database. Immune/stromal scores were performed utilizing the ESTIMATE algorithm. 323 samples were split into two groups on the basis of median immune/stromal score, and comparison of gene expression were conducted. Cross genes were obtained by Venn diagrams. Hub genes were selected through protein-protein interaction (PPI) network construction, and relevant functional analysis was conducted by DAVID. We used Kaplan–Meier analysis to identify the correlations between genes and overall survival (OS). Finally, univariate and multivariate cox regression analysis were employed to construct survival model and predict prognosis. Results: We found immune/stromal score was correlated with T pathological grade and PRCC subtypes. 989 differentially expressed genes (DEGs) and 1169 DEGs were identified respectively on the basis of immune and stromal score. Venn diagrams indicated that 763 co-upregulated genes and 4 co-downregulated genes were identified. Kaplan-Meier analysis revealed that 120 genes were involved in tumor prognosis. Then PPI network analysis identified 22 hub genes, and four of which were significantly related to OS in patients with PRCC confirmed by cox regression analysis. Conclusions: Four tumor microenvironment-related genes (CD79A, CXCL13, IL6 and CCL19) were identified as biomarkers for PRCC prognosis.

scholarly journals Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10628
Author(s):  
Juan Chen ◽  
Rui Zhou
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Prognostic Model ◽  
Cox Regression ◽  
Survival Prediction ◽  
Clinical Parameters ◽  
Ppi Network ◽  
Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

scholarly journals Identification and Analysis of Three Hub Prognostic Genes Related to Osteosarcoma Metastasis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Jianye Tan ◽  
Haofeng Liang ◽  
Bingsheng Yang ◽  
Shuang Zhu ◽  
Guofeng Wu ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Cox Regression ◽  
Survival Rates ◽  
Prognostic Biomarkers ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Expression Levels ◽  
Kaplan Meier ◽  
Before And After

Osteosarcoma (OS) often occurs in children and often undergoes metastasis, resulting in lower survival rates. Information on the complexity and pathogenic mechanism of OS is limited, and thus, the development of treatments involving alternative molecular and genetic targets is hampered. We categorized transcriptome data into metastasis and nonmetastasis groups, and 400 differential RNAs (230 messenger RNAs (mRNAs) and 170 long noncoding RNAs (lncRNAs)) were obtained by the edgeR package. Prognostic genes were identified by performing univariate Cox regression analysis and the Kaplan–Meier (KM) survival analysis. We then examined the correlation between the expression level of prognostic lncRNAs and mRNAs. Furthermore, microRNAs (miRNAs) corresponding to the coexpression of lncRNA-mRNA was predicted, which was used to construct a competitive endogenous RNA (ceRNA) regulatory network. Finally, multivariate Cox proportional risk regression analysis was used to identify hub prognostic genes. Three hub prognostic genes (ABCG8, LOXL4, and PDE1B) were identified as potential prognostic biomarkers and therapeutic targets for OS. Furthermore, transcriptions factors (TFs) (DBP, ESX1, FOS, FOXI1, MEF2C, NFE2, and OTX2) and lncRNAs (RP11-357H14.16, RP11-284N8.3, and RP11-629G13.1) that were able to affect the expression levels of genes before and after transcription were found to regulate the prognostic hub genes. In addition, we identified drugs related to the prognostic hub genes, which may have potential clinical applications. Immunohistochemistry (IHC) and quantitative real-time polymerase chain reaction (qRT-PCR) confirmed that the expression levels of ABCG8, LOXL4, and PDE1B coincided with the results of bioinformatics analysis. Moreover, the relationship between the hub prognostic gene expression and patient prognosis was also validated. Our study elucidated the roles of three novel prognostic biomarkers in the pathogenesis of OS as well as presenting a potential clinical treatment for OS.

scholarly journals Metabolic genes show excellent prognostic ability for clear cell renal cell carcinoma

2020 ◽  
Author(s):  
Chengjian Ji ◽  
Yichun Wang ◽  
Liangyu Yao ◽  
Jiaochen Luan ◽  
Rong Cong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Regression Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Expression Profiles ◽  
Cell Renal Cell Carcinoma ◽  
Cox Regression Analysis ◽  
Metabolic Genes

Abstract Background Renal cell carcinoma (RCC) is one of the major malignant tumors of the urinary system, with a high mortality rate and a poor prognosis. Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC. Although the diagnosis and treatment methods have been significantly improved, the incidence and mortality of ccRCC are high and still increasing. The occurrence and development of ccRCC are closely related to the changes of classic metabolic pathways. This article aims to explore the relationship between metabolic genes and the prognosis of patients with ccRCC. Patients and methods: Gene expression profiles of 63 normal kidney tissues and 446 ccRCC tissues from TCGA database and gene expression profiles of 39 ccRCC tissues from GEO database were used to obtain differentially expressed genes (DEGs) in ccRCC. Through the the KEGG gene sets of GSEA database, we obtained metabolic genes (MGs). Univariate Cox regression analysis was used to identify prognostic MGs. Lasso regression analysis was used to eliminate false positives because of over-fitting. Multivariate Cox regression analysis was used to established a prognostic model. Gene expression data and related survival data of 101 ccRCC patients from ArrayExpress database were used for external validation. Survival analysis, ROC curve analysis, independent prognostic analysis and clinical correlation analysis were performed to evaluate this model. Results We found that there were 479 abnormally expressed MGs in ccRCC tissues. Through univariate Cox regression analysis, Lasso regression analysis and multivariate Cox regression analysis, we identified 4 prognostic MGs (P4HA3, ETNK2, PAFAH2 and ALAD) and established a prognostic model (riskScore). Whether in the training cohort, the testing cohort or the entire cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 4 MGs was also confirmed in the ArrayExpress database. Results of GSEA analysis show that DEGs in patients with better prognosis were enriched in metabolic pathways. Then, a new Nomogram with higher prognostic value was constructed to better predict the 1-year OS, 3-year OS and 5-year OS of ccRCC patients. In addition, we successfully established a ceRNA network to further explain the differences in the expression of these MGs between high-risk patients and low-risk patients Conclusion We have successfully established a risk model (riskScore) based on 4 MGs, which could accurately predict the prognosis of patients with ccRCC. Our research may shed new light on ccRCC patients' prognosis and treatment management.

scholarly journals A Novel 8-Gene Prognostic Signature for Survival Prediction of Uveal Melanoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Zhongjun Tang ◽  
Kebo Cai
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Uveal Melanoma ◽  
Cox Regression ◽  
Gene Expression Signature ◽  
Survival Prediction ◽  
Hub Genes ◽  
Data Set ◽  
Cox Regression Analysis ◽  
Expression Signature

Background. Uveal melanoma (UM) has favorable local tumor control, but once metastasis develops, the prognosis is rather poor. Thus, it is urgent to develop metastasis predicting markers. Objective. Our study investigated a novel gene expression-based signature in predicting metastasis for patients with UM. Methods. In the discovery phase, 63 patients with UM from GEO data set GSE22138 were analyzed using the Weighted Correlation Network Analysis (WGCNA) to identify metastasis-related hub genes. The Least Absolute Shrinkage and Selection Operator (Lasso) Cox regression was used to select candidate genes and build a gene expression signature. In the validation phase, the signature was validated in The Cancer Genome Atlas database. Results. Forty-one genes were identified as hub genes of metastasis by WGCNA. After the Lasso Cox regression analysis, eight genes including RPL10A, EIF1B, TIPARP, RPL15, SLC25A38, PHLDA1, TFDP2, and MEGF10 were highlighted as candidate predictors. The gene expression signature for UM (UMPS) could independently predict MFS by univariate and multivariate Cox regression analysis. Incorporating UMPS increased the AUC of the traditional clinical model. In the validation cohort, UMPS performed well in predicting the MFS of UM patients. Conclusions. UMPS, an eight-gene-based signature, is useful in predicting prognosis for patients with UM.

scholarly journals Identification of Two Novel Genes SNX10 and PTGDS with Cervical Squamous Cell Carcinoma Prognosis

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Trichostatin A ◽  
Expression Profiles ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background: Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods: We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results: Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions: In conclusion, PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

scholarly journals Identification of Genes Related to Immune Infiltration in the Tumor Microenvironment of Cutaneous Melanoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Rujia Qin ◽  
Wen Peng ◽  
Xuemin Wang ◽  
Chunyan Li ◽  
Yan Xi ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Cutaneous Melanoma ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Training Dataset ◽  
Lasso Regression ◽  
Hub Genes ◽  
Dynamic Regulation ◽  
Cox Regression Analysis

Cutaneous melanoma (CM) is the leading cause of skin cancer deaths and is typically diagnosed at an advanced stage, resulting in a poor prognosis. The tumor microenvironment (TME) plays a significant role in tumorigenesis and CM progression, but the dynamic regulation of immune and stromal components is not yet fully understood. In the present study, we quantified the ratio between immune and stromal components and the proportion of tumor-infiltrating immune cells (TICs), based on the ESTIMATE and CIBERSORT computational methods, in 471 cases of skin CM (SKCM) obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were analyzed by univariate Cox regression analysis, least absolute shrinkage, and selection operator (LASSO) regression analysis, and multivariate Cox regression analysis to identify prognosis-related genes. The developed prognosis model contains ten genes, which are all vital for patient prognosis. The areas under the curve (AUC) values for the developed prognostic model at 1, 3, 5, and 10 years were 0.832, 0.831, 0.880, and 0.857 in the training dataset, respectively. The GSE54467 dataset was used as a validation set to determine the predictive ability of the prognostic signature. Protein–protein interaction (PPI) analysis and weighted gene co-expression network analysis (WGCNA) were used to verify “real” hub genes closely related to the TME. These hub genes were verified for differential expression by immunohistochemistry (IHC) analyses. In conclusion, this study might provide potential diagnostic and prognostic biomarkers for CM.

Prognostic value of two novel genes SNX10 and PTGDS for cervical squamous cell carcinoma: an integrated bioinformatic analysis

2020 ◽  
Author(s):  
Pinping Jiang ◽  
Ying Cao ◽  
Feng Gao ◽  
Wei Sun ◽  
Jinhui Liu ◽  
...  
Keyword(s):  
Functional Analysis ◽  
Regression Analysis ◽  
Cox Regression ◽  
Trichostatin A ◽  
Expression Profiles ◽  
Ppi Network ◽  
Methylation Analysis ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Tumor Tissues

Abstract Background Cervical cancer (CC) is the primary cause of death in women. This study sought to investigate the therapeutic targets of CC. Methods We downloaded four gene expression profiles from GEO. The RRA method was used to integrate and screen DEGs between CC and normal samples. Functional analysis was performed by clusterprofiler. We built PPI network by STRING and selected hub modules via MCODE. CMap was used to find molecules with therapeutic potential for CC. We also validated hub genes in GEO datasets, GEPIA, immunohistochemistry. Cox regression analysis, TCGA methylation analysis and ONCOMINE were carried out. ROC curve analysis and GSEA were also done to dig out the significance of hub genes. Results Functional analysis revealed that DEGs were significantly enriched in binding, cell proliferation, transcriptional activity and cell cycle regulation. PPI network screened 30 prominent proteins, with CDK1 having the strongest association with CC. Cmap showed that apigenin, thioguanine and trichostatin A might be used to treat CC. Eight genes were screened out through GEPIA. Of them, only PTGDS and SNX10 have not been reported in CC related articles. The validation in GEO showed that PTGDS showed low expression in tumor tissues while SNX10 showed high expression in tumor tissues. Their expression profiles were consistent with the results in immunohistochemistry. They can distinguish CC and normal tissue and have good diagnostic efficiency. GSEA showed that the two genes were associated with the chemokine signaling pathway. TCGA methylation analysis showed that patients with low-expressed and hyper-methylated PTGDS had a bad prognosis than the patients with high-expressed and hypo-methylated PTGDS. Cox regression analysis showed that SNX10 and PTGDS were independent prognostic indicators for OS among CC patients. Conclusions PTGDS and SNX10 showed abnormal expression and methylation in CC. Both genes could be used to develop new target treatments for CC.

KIAA0101 in Malignant Pleural Mesothelioma: a Potential Diagnostic and Prognostic Marker

2021 ◽  
Vol 24 ◽  
Author(s):  
Ping Lin ◽  
Yuean Zhao ◽  
Xiaoqian Li ◽  
Zongan Liang
Keyword(s):  
Gene Expression ◽  
Regression Analysis ◽  
Malignant Pleural Mesothelioma ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
High Expression ◽  
Pleural Mesothelioma ◽  
Hub Genes ◽  
Cox Regression Analysis

Background: Currently, there are no reliable diagnostic and prognostic markers for malignant pleural mesothelioma (MPM). The objective of this study was to identify hub genes that could be helpful for diagnosis and prognosis in MPM by using bioinformatics analysis. Materials and Methods: The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA). Weighted gene co-expression network analysis (WGCNA), LASSO regression analysis, Cox regression analysis, and Gene Set Enrichment Analysis (GSEA) were performed to identify hub genes and their functions. Results: A total of 430 up-regulated and 867 downregulated genes in MPM were identified based on the GSE51024 dataset. According to the WGCNA analysis, differentially expressed genes were classified into 8 modules. Among them, the pink module was most closely associated with MPM. According to genes with GS > 0.8 and MM > 0.8, six genes were selected as candidate hub genes (NUSAP1, TOP2A, PLOD2, BUB1B, UHRF1, KIAA0101) in the pink module. In the LASSO model, three genes (NUSAP1, PLOD2, and KIAA0101) were identified with non-zero regression coefficients and were considered hub genes among the 6 candidates. The hub gene-based LASSO model can accurately distinguish MPM from controls (AUC = 0.98). Moreover, the high expression level of KIAA0101, PLOD2, and NUSAP1 were all associated with poor prognosis compared to the low level in Kaplan–Meier survival analyses. After further multivariate Cox analysis, only KIAA0101 (HR = 1.55, 95% CI = 1.05-2.29) was identified as an independent prognostic factor among these hub genes. Finally, GSEA revealed that high expression of KIAA0101 was closely associated with 10 signaling pathways. Conclusion: Our study identified several hub genes relevant to MPM, including NUSAP1, PLOD2, and KIAA0101. Among these genes, KIAA0101 appears to be a useful diagnostic and prognostic biomarker for MPM, which may provide new clues for MPM diagnosis and therapy.

scholarly journals Determination of a six-gene prognostic model for cervical cancer based on WGCNA combined with LASSO and Cox-PH analysis

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Shiyan Li ◽  
Fengjuan Han ◽  
Na Qi ◽  
Liyang Wen ◽  
Jia Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Regression Analysis ◽  
Risk Model ◽  
Cox Regression ◽  
Hub Genes ◽  
Cox Regression Analysis ◽  
Kaplan Meier ◽  
Risk Curve ◽  
Modular Analysis ◽  
Upregulated Genes

Abstract Aim This study aimed to establish a risk model of hub genes to evaluate the prognosis of patients with cervical cancer. Methods Based on TCGA and GTEx databases, the differentially expressed genes (DEGs) were screened and then analyzed using GO and KEGG analyses. The weighted gene co-expression network (WGCNA) was then used to perform modular analysis of DEGs. Univariate Cox regression analysis combined with LASSO and Cox-pH was used to select the prognostic genes. Then, multivariate Cox regression analysis was used to screen the hub genes. The risk model was established based on hub genes and evaluated by risk curve, survival state, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve. Results We screened 1265 DEGs between cervical cancer and normal samples, of which 620 were downregulated and 645 were upregulated. GO and KEGG analyses revealed that most of the upregulated genes were related to the metastasis of cancer cells, while the downregulated genes mostly acted on the cell cycle. Then, WGCNA mined six modules (red, blue, green, brown, yellow, and gray), and the brown module with the most DEGs and related to multiple cancers was selected for the follow-up study. Eight genes were identified by univariate Cox regression analysis combined with the LASSO Cox-pH model. Then, six hub genes (SLC25A5, ENO1, ANLN, RIBC2, PTTG1, and MCM5) were screened by multivariate Cox regression analysis, and SLC25A5, ANLN, RIBC2, and PTTG1 could be used as independent prognostic factors. Finally, we determined that the risk model established by the six hub genes was effective and stable. Conclusions This study supplies the prognostic value of the risk model and the new promising targets for the cervical cancer treatment, and their biological functions need to be further explored.

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