Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks
Objective To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment. Methods Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) n...
Gespeichert in:
| Veröffentlicht in: | Journal of cancer research and clinical oncology 2023-10, Vol.149 (13), p.11263-11278 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 11278 |
|---|---|
| container_issue | 13 |
| container_start_page | 11263 |
| container_title | Journal of cancer research and clinical oncology |
| container_volume | 149 |
| creator | Li, Qingxiu Wu, Kejia Zhang, Yiqi Liu, Yuxin Wang, Yalan Chen, Yong Sun, Shuangling Duan, Changzhu |
| description | Objective
To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.
Methods
Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.
Results
The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.
Conclusion
PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis. |
| doi_str_mv | 10.1007/s00432-023-04989-4 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_3153178555</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2858502590</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-d8511f4452f50d4bbc34a7a8168e1f8c551cb49840c417b1c18a35e786892d8e3</originalsourceid><addsrcrecordid>eNqFkc1u1TAQhS0EoreFF2CBLLFhE_DfxM4SIuBWqsSGdhs5jpPrktgX2xHiDXjs-pICEgu6suz5zpkZH4ReUPKGEiLfJkIEZxVhvCKiUU0lHqEdPT1RzuEx2hEqaQWM1mfoPKVbUu4g2VN0xiUHVddyh362waccV5Nd8DiMeP_-ptq3LT7GMPmQsjN4CYOdsfYDdsuyeovNQUdtso3uVE-418kOuOiPIVufnZ7xZL1NeHHe-QnnQwzrdDh5Zus8dr5o9dbS2_w9xK_pGXoy6jnZ5_fnBbr--OFLu6-uPn-6bN9dVYZDk6tBAaWjEMBGIIPoe8OFllrRWlk6KgNATV8-QxAjqOypoUpzsFLVqmGDsvwCvd58yzDfVptyt7hk7Dxrb8OaOk6BU6kA4EGUKdZIUoNsCvrqH_Q2rNGXRQoFCgiDhhSKbZSJIaVox-4Y3aLjj46S7hRpt0XalUi7X5F2oohe3luv_WKHP5LfGRaAb0AqJT_Z-Lf3f2zvAJkNrS4</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2858502590</pqid></control><display><type>article</type><title>Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks</title><source>SpringerLink Journals - AutoHoldings</source><creator>Li, Qingxiu ; Wu, Kejia ; Zhang, Yiqi ; Liu, Yuxin ; Wang, Yalan ; Chen, Yong ; Sun, Shuangling ; Duan, Changzhu</creator><creatorcontrib>Li, Qingxiu ; Wu, Kejia ; Zhang, Yiqi ; Liu, Yuxin ; Wang, Yalan ; Chen, Yong ; Sun, Shuangling ; Duan, Changzhu</creatorcontrib><description>Objective
To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.
Methods
Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.
Results
The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.
Conclusion
PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.</description><identifier>ISSN: 0171-5216</identifier><identifier>EISSN: 1432-1335</identifier><identifier>DOI: 10.1007/s00432-023-04989-4</identifier><identifier>PMID: 37358667</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Association analysis ; Cancer Research ; drugs ; Hematology ; Hepatitis B ; Hepatitis B virus ; Hepatocellular carcinoma ; hepatoma ; humans ; Infiltration ; inhibitory concentration 50 ; Internal Medicine ; liver ; Liver cancer ; Medical prognosis ; Medicine ; Medicine & Public Health ; Metastases ; Microenvironments ; Oncology ; Patients ; Prognosis ; protein-protein interactions ; Proteins ; regression analysis ; risk ; Risk assessment ; Risk groups ; Survival ; survival rate ; therapeutics ; Ubiquitin-protein ligase</subject><ispartof>Journal of cancer research and clinical oncology, 2023-10, Vol.149 (13), p.11263-11278</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</rights><rights>2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c359t-d8511f4452f50d4bbc34a7a8168e1f8c551cb49840c417b1c18a35e786892d8e3</cites><orcidid>0000-0003-3878-8772</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00432-023-04989-4$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00432-023-04989-4$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27903,27904,41467,42536,51297</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37358667$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Qingxiu</creatorcontrib><creatorcontrib>Wu, Kejia</creatorcontrib><creatorcontrib>Zhang, Yiqi</creatorcontrib><creatorcontrib>Liu, Yuxin</creatorcontrib><creatorcontrib>Wang, Yalan</creatorcontrib><creatorcontrib>Chen, Yong</creatorcontrib><creatorcontrib>Sun, Shuangling</creatorcontrib><creatorcontrib>Duan, Changzhu</creatorcontrib><title>Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks</title><title>Journal of cancer research and clinical oncology</title><addtitle>J Cancer Res Clin Oncol</addtitle><addtitle>J Cancer Res Clin Oncol</addtitle><description>Objective
To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.
Methods
Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.
Results
The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.
Conclusion
PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.</description><subject>Association analysis</subject><subject>Cancer Research</subject><subject>drugs</subject><subject>Hematology</subject><subject>Hepatitis B</subject><subject>Hepatitis B virus</subject><subject>Hepatocellular carcinoma</subject><subject>hepatoma</subject><subject>humans</subject><subject>Infiltration</subject><subject>inhibitory concentration 50</subject><subject>Internal Medicine</subject><subject>liver</subject><subject>Liver cancer</subject><subject>Medical prognosis</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Metastases</subject><subject>Microenvironments</subject><subject>Oncology</subject><subject>Patients</subject><subject>Prognosis</subject><subject>protein-protein interactions</subject><subject>Proteins</subject><subject>regression analysis</subject><subject>risk</subject><subject>Risk assessment</subject><subject>Risk groups</subject><subject>Survival</subject><subject>survival rate</subject><subject>therapeutics</subject><subject>Ubiquitin-protein ligase</subject><issn>0171-5216</issn><issn>1432-1335</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkc1u1TAQhS0EoreFF2CBLLFhE_DfxM4SIuBWqsSGdhs5jpPrktgX2xHiDXjs-pICEgu6suz5zpkZH4ReUPKGEiLfJkIEZxVhvCKiUU0lHqEdPT1RzuEx2hEqaQWM1mfoPKVbUu4g2VN0xiUHVddyh362waccV5Nd8DiMeP_-ptq3LT7GMPmQsjN4CYOdsfYDdsuyeovNQUdtso3uVE-418kOuOiPIVufnZ7xZL1NeHHe-QnnQwzrdDh5Zus8dr5o9dbS2_w9xK_pGXoy6jnZ5_fnBbr--OFLu6-uPn-6bN9dVYZDk6tBAaWjEMBGIIPoe8OFllrRWlk6KgNATV8-QxAjqOypoUpzsFLVqmGDsvwCvd58yzDfVptyt7hk7Dxrb8OaOk6BU6kA4EGUKdZIUoNsCvrqH_Q2rNGXRQoFCgiDhhSKbZSJIaVox-4Y3aLjj46S7hRpt0XalUi7X5F2oohe3luv_WKHP5LfGRaAb0AqJT_Z-Lf3f2zvAJkNrS4</recordid><startdate>20231001</startdate><enddate>20231001</enddate><creator>Li, Qingxiu</creator><creator>Wu, Kejia</creator><creator>Zhang, Yiqi</creator><creator>Liu, Yuxin</creator><creator>Wang, Yalan</creator><creator>Chen, Yong</creator><creator>Sun, Shuangling</creator><creator>Duan, Changzhu</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7TO</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>H94</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>MBDVC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>7S9</scope><scope>L.6</scope><orcidid>https://orcid.org/0000-0003-3878-8772</orcidid></search><sort><creationdate>20231001</creationdate><title>Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks</title><author>Li, Qingxiu ; Wu, Kejia ; Zhang, Yiqi ; Liu, Yuxin ; Wang, Yalan ; Chen, Yong ; Sun, Shuangling ; Duan, Changzhu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-d8511f4452f50d4bbc34a7a8168e1f8c551cb49840c417b1c18a35e786892d8e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Association analysis</topic><topic>Cancer Research</topic><topic>drugs</topic><topic>Hematology</topic><topic>Hepatitis B</topic><topic>Hepatitis B virus</topic><topic>Hepatocellular carcinoma</topic><topic>hepatoma</topic><topic>humans</topic><topic>Infiltration</topic><topic>inhibitory concentration 50</topic><topic>Internal Medicine</topic><topic>liver</topic><topic>Liver cancer</topic><topic>Medical prognosis</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Metastases</topic><topic>Microenvironments</topic><topic>Oncology</topic><topic>Patients</topic><topic>Prognosis</topic><topic>protein-protein interactions</topic><topic>Proteins</topic><topic>regression analysis</topic><topic>risk</topic><topic>Risk assessment</topic><topic>Risk groups</topic><topic>Survival</topic><topic>survival rate</topic><topic>therapeutics</topic><topic>Ubiquitin-protein ligase</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Qingxiu</creatorcontrib><creatorcontrib>Wu, Kejia</creatorcontrib><creatorcontrib>Zhang, Yiqi</creatorcontrib><creatorcontrib>Liu, Yuxin</creatorcontrib><creatorcontrib>Wang, Yalan</creatorcontrib><creatorcontrib>Chen, Yong</creatorcontrib><creatorcontrib>Sun, Shuangling</creatorcontrib><creatorcontrib>Duan, Changzhu</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Research Library (Corporate)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>AGRICOLA</collection><collection>AGRICOLA - Academic</collection><jtitle>Journal of cancer research and clinical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Qingxiu</au><au>Wu, Kejia</au><au>Zhang, Yiqi</au><au>Liu, Yuxin</au><au>Wang, Yalan</au><au>Chen, Yong</au><au>Sun, Shuangling</au><au>Duan, Changzhu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks</atitle><jtitle>Journal of cancer research and clinical oncology</jtitle><stitle>J Cancer Res Clin Oncol</stitle><addtitle>J Cancer Res Clin Oncol</addtitle><date>2023-10-01</date><risdate>2023</risdate><volume>149</volume><issue>13</issue><spage>11263</spage><epage>11278</epage><pages>11263-11278</pages><issn>0171-5216</issn><eissn>1432-1335</eissn><abstract>Objective
To search for human protein-coding genes related to hepatocellular carcinoma (HCC) in the context of hepatitis B virus (HBV) infection, and perform prognosis risk assessment.
Methods
Genes related to HBV-HCC were selected through literature screening and protein–protein interaction (PPI) network database analysis. Prognosis potential genes (PPGs) were identified using Cox regression analysis. Patients were divided into high-risk and low-risk groups based on PPGs, and risk scores were calculated. Kaplan–Meier plots were used to analyze overall survival rates, and the results were predicted based on clinicopathological variables. Association analysis was also conducted with immune infiltration, immune therapy, and drug sensitivity. Experimental verification of the expression of PPGs was done in patient liver cancer tissue and normal liver tissue adjacent to tumors.
Results
The use of a prognosis potential genes risk assessment model can reliably predict the prognosis risk of patients, demonstrating strong predictive ability. Kaplan–Meier analysis showed that the overall survival rate of the low-risk group was significantly higher than that of the high-risk group. There were significant differences between the two subgroups in terms of immune infiltration and IC50 association analysis. Experimental verification revealed that CYP2C19, FLNC, and HNRNPC were highly expressed in liver cancer tissue, while UBE3A was expressed at a lower level.
Conclusion
PPGs can be used to predict the prognosis risk of HBV-HCC patients and play an important role in the diagnosis and treatment of liver cancer. They also reveal their potential role in the tumor immune microenvironment, clinical-pathological characteristics, and prognosis.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>37358667</pmid><doi>10.1007/s00432-023-04989-4</doi><tpages>16</tpages><orcidid>https://orcid.org/0000-0003-3878-8772</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0171-5216 |
| ispartof | Journal of cancer research and clinical oncology, 2023-10, Vol.149 (13), p.11263-11278 |
| issn | 0171-5216 1432-1335 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_3153178555 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Association analysis Cancer Research drugs Hematology Hepatitis B Hepatitis B virus Hepatocellular carcinoma hepatoma humans Infiltration inhibitory concentration 50 Internal Medicine liver Liver cancer Medical prognosis Medicine Medicine & Public Health Metastases Microenvironments Oncology Patients Prognosis protein-protein interactions Proteins regression analysis risk Risk assessment Risk groups Survival survival rate therapeutics Ubiquitin-protein ligase |
| title | Construction of HBV-HCC prognostic model and immune characteristics based on potential genes mining through protein interaction networks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-28T03%3A41%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Construction%20of%20HBV-HCC%20prognostic%20model%20and%20immune%20characteristics%20based%20on%20potential%20genes%20mining%20through%20protein%20interaction%20networks&rft.jtitle=Journal%20of%20cancer%20research%20and%20clinical%20oncology&rft.au=Li,%20Qingxiu&rft.date=2023-10-01&rft.volume=149&rft.issue=13&rft.spage=11263&rft.epage=11278&rft.pages=11263-11278&rft.issn=0171-5216&rft.eissn=1432-1335&rft_id=info:doi/10.1007/s00432-023-04989-4&rft_dat=%3Cproquest_cross%3E2858502590%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2858502590&rft_id=info:pmid/37358667&rfr_iscdi=true |