The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients
Objective: We aimed to investigate the potential role of ERBB signaling pathway–related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods.Methods: We downloaded the KIRC dataset and clinicopathological information from Th...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.862210/full |
| _version_ | 1818108408763514880 |
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| author | Zicheng Wang Zicheng Wang Jiayi Li Peizhi Zhang Leizuo Zhao Leizuo Zhao Bingyin Huang Yingkun Xu Guangzhen Wu Qinghua Xia Qinghua Xia Qinghua Xia |
| author_facet | Zicheng Wang Zicheng Wang Jiayi Li Peizhi Zhang Leizuo Zhao Leizuo Zhao Bingyin Huang Yingkun Xu Guangzhen Wu Qinghua Xia Qinghua Xia Qinghua Xia |
| author_sort | Zicheng Wang |
| collection | DOAJ |
| description | Objective: We aimed to investigate the potential role of ERBB signaling pathway–related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods.Methods: We downloaded the KIRC dataset and clinicopathological information from The Cancer Genome Atlas database. Univariate Cox analysis was used to identify essential genes significantly associated with KIRC progression. Next, we used the STRING website to construct a protein–protein interaction network of ERBB signaling pathway–related molecules. We then used the least the absolute shrinkage and selection operator (LASSO) regression analysis to build a predictive risk model for KIRC patients. Next, we used multiple bioinformatics methods to analyze the copy number variation, single-nucleotide variation, and overall survival of these risk model genes in pan-cancer. At last, we used the Genomics of Drug Sensitivity in Cancer to investigate the correlation between the mRNA expression of genes associated with this risk model gene and drug sensitivity.Results: Through the LASSO regression analysis, we constructed a novel KIRC prognosis–related risk model using 12 genes: SHC1, GAB1, SOS2, SRC, AKT3, EREG, EIF4EBP1, ERBB3, MAPK3, transforming growth factor-alpha, CDKN1A, and PIK3CD. Based on this risk model, the overall survival rate of KIRC patients in the low-risk group was significantly higher than that in the high-risk group (p = 1.221 × 10−15). Furthermore, this risk model was associated with cancer metastasis, tumor size, node, stage, grade, sex, and fustat in KIRC patients. The receiver operating characteristic curve results showed that the model had better prediction accuracy. Multivariate Cox regression analysis showed that the model’s risk score was an independent risk factor for KIRC. The Human Protein Atlas database was used to validate the protein expression of risk model–associated molecules in tumors and adjacent normal tissues. The validation results were consistent with our previous findings.Conclusions: We successfully established a prognostic-related risk model for KIRC, which will provide clinicians with a helpful reference for future disease diagnosis and treatment. |
| first_indexed | 2024-12-11T02:14:53Z |
| format | Article |
| id | doaj.art-c606c26158c64830a9b1041ada7c5ffd |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-12-11T02:14:53Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-c606c26158c64830a9b1041ada7c5ffd2022-12-22T01:24:12ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-07-011310.3389/fgene.2022.862210862210The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for PatientsZicheng Wang0Zicheng Wang1Jiayi Li2Peizhi Zhang3Leizuo Zhao4Leizuo Zhao5Bingyin Huang6Yingkun Xu7Guangzhen Wu8Qinghua Xia9Qinghua Xia10Qinghua Xia11Department of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaMedical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, ChinaSchool of Business, Hanyang University, Seoul, South KoreaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaDepartment of Urology, Dongying People’s Hospital, Dongying, ChinaDepartment of Pathology, The First People’s Hospital of Zhoukou, Zhoukou, ChinaDepartment of Endocrine and Breast Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, ChinaDepartment of Urology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, ChinaMedical Science and Technology Innovation Center, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, ChinaObjective: We aimed to investigate the potential role of ERBB signaling pathway–related genes in kidney renal clear cell carcinoma (KIRC) and establish a new predictive risk model using various bioinformatics methods.Methods: We downloaded the KIRC dataset and clinicopathological information from The Cancer Genome Atlas database. Univariate Cox analysis was used to identify essential genes significantly associated with KIRC progression. Next, we used the STRING website to construct a protein–protein interaction network of ERBB signaling pathway–related molecules. We then used the least the absolute shrinkage and selection operator (LASSO) regression analysis to build a predictive risk model for KIRC patients. Next, we used multiple bioinformatics methods to analyze the copy number variation, single-nucleotide variation, and overall survival of these risk model genes in pan-cancer. At last, we used the Genomics of Drug Sensitivity in Cancer to investigate the correlation between the mRNA expression of genes associated with this risk model gene and drug sensitivity.Results: Through the LASSO regression analysis, we constructed a novel KIRC prognosis–related risk model using 12 genes: SHC1, GAB1, SOS2, SRC, AKT3, EREG, EIF4EBP1, ERBB3, MAPK3, transforming growth factor-alpha, CDKN1A, and PIK3CD. Based on this risk model, the overall survival rate of KIRC patients in the low-risk group was significantly higher than that in the high-risk group (p = 1.221 × 10−15). Furthermore, this risk model was associated with cancer metastasis, tumor size, node, stage, grade, sex, and fustat in KIRC patients. The receiver operating characteristic curve results showed that the model had better prediction accuracy. Multivariate Cox regression analysis showed that the model’s risk score was an independent risk factor for KIRC. The Human Protein Atlas database was used to validate the protein expression of risk model–associated molecules in tumors and adjacent normal tissues. The validation results were consistent with our previous findings.Conclusions: We successfully established a prognostic-related risk model for KIRC, which will provide clinicians with a helpful reference for future disease diagnosis and treatment.https://www.frontiersin.org/articles/10.3389/fgene.2022.862210/fullTCGAKIRCERBB signaling pathwaypan-cancerGDSC |
| spellingShingle | Zicheng Wang Zicheng Wang Jiayi Li Peizhi Zhang Leizuo Zhao Leizuo Zhao Bingyin Huang Yingkun Xu Guangzhen Wu Qinghua Xia Qinghua Xia Qinghua Xia The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients Frontiers in Genetics TCGA KIRC ERBB signaling pathway pan-cancer GDSC |
| title | The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients |
| title_full | The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients |
| title_fullStr | The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients |
| title_full_unstemmed | The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients |
| title_short | The Role of ERBB Signaling Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma and Establishing a Prognostic Risk Assessment Model for Patients |
| title_sort | role of erbb signaling pathway related genes in kidney renal clear cell carcinoma and establishing a prognostic risk assessment model for patients |
| topic | TCGA KIRC ERBB signaling pathway pan-cancer GDSC |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.862210/full |
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