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Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer
The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC). After downloading and preprocessing data from The Cancer Genome...
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| Published in: | World journal of surgical oncology 2024-11, Vol.22 (1), p.319-14 |
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| container_title | World journal of surgical oncology |
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| creator | Li, Zaishan Meng, Zhenzhen Xiao, Lin Du, Jiahui Jiang, Dazhi Liu, Baoling |
| description | The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).
After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).
From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P |
| doi_str_mv | 10.1186/s12957-024-03588-y |
| format | article |
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After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).
From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.
We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.</description><identifier>ISSN: 1477-7819</identifier><identifier>EISSN: 1477-7819</identifier><identifier>DOI: 10.1186/s12957-024-03588-y</identifier><identifier>PMID: 39609690</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Aged ; Analysis ; Anopheles ; B cells ; Biological markers ; Biomarkers, Tumor - genetics ; Cancer ; Carcinogenesis ; Carcinoma, Non-Small-Cell Lung - genetics ; Carcinoma, Non-Small-Cell Lung - mortality ; Carcinoma, Non-Small-Cell Lung - pathology ; Care and treatment ; Female ; Follow-Up Studies ; Gene expression ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Genes ; Genetic aspects ; Genomes ; Health aspects ; Humans ; Lung cancer, Non-small cell ; Lung cancer, Small cell ; Lung Neoplasms - genetics ; Lung Neoplasms - mortality ; Lung Neoplasms - pathology ; Male ; Middle Aged ; Nomograms ; Nomography (Mathematics) ; Non-small cell lung cancer ; Prognosis ; Prognostic model ; ROC Curve ; Survival Rate ; TCGA data ; Tumor Microenvironment ; Tumors</subject><ispartof>World journal of surgical oncology, 2024-11, Vol.22 (1), p.319-14</ispartof><rights>2024. The Author(s).</rights><rights>COPYRIGHT 2024 BioMed Central Ltd.</rights><rights>The Author(s) 2024 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603896/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11603896/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27924,27925,37013,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39609690$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Zaishan</creatorcontrib><creatorcontrib>Meng, Zhenzhen</creatorcontrib><creatorcontrib>Xiao, Lin</creatorcontrib><creatorcontrib>Du, Jiahui</creatorcontrib><creatorcontrib>Jiang, Dazhi</creatorcontrib><creatorcontrib>Liu, Baoling</creatorcontrib><title>Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer</title><title>World journal of surgical oncology</title><addtitle>World J Surg Oncol</addtitle><description>The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).
After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).
From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.
We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.</description><subject>Aged</subject><subject>Analysis</subject><subject>Anopheles</subject><subject>B cells</subject><subject>Biological markers</subject><subject>Biomarkers, Tumor - genetics</subject><subject>Cancer</subject><subject>Carcinogenesis</subject><subject>Carcinoma, Non-Small-Cell Lung - genetics</subject><subject>Carcinoma, Non-Small-Cell Lung - mortality</subject><subject>Carcinoma, Non-Small-Cell Lung - pathology</subject><subject>Care and treatment</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene Expression Regulation, Neoplastic</subject><subject>Genes</subject><subject>Genetic aspects</subject><subject>Genomes</subject><subject>Health aspects</subject><subject>Humans</subject><subject>Lung cancer, Non-small cell</subject><subject>Lung cancer, Small cell</subject><subject>Lung Neoplasms - genetics</subject><subject>Lung Neoplasms - mortality</subject><subject>Lung Neoplasms - pathology</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Nomograms</subject><subject>Nomography (Mathematics)</subject><subject>Non-small cell lung cancer</subject><subject>Prognosis</subject><subject>Prognostic model</subject><subject>ROC Curve</subject><subject>Survival Rate</subject><subject>TCGA data</subject><subject>Tumor Microenvironment</subject><subject>Tumors</subject><issn>1477-7819</issn><issn>1477-7819</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNptkt9rFDEQxxdRbK3-Az7IgiB92ZpssvnxJOWwWij4os8hm0z2UnaTM8kW7r9v6lW5AxnIj8lnvsxMpmneY3SFsWCfM-7lwDvU0w6RQYhu_6I5x5TzjgssXx6dz5o3Od8j1BMykNfNGZEMSSbReVM2MeSSVlN8mFodbOsthOLd_nBvwU_bAhC6CQK0ZV1iahdvUoTw4FMMS6XbXYpTiLl40y7Rwty6SoUYurzoeW4N1GVeq6DRwUB627xyes7w7nm_aH7dfP25-d7d_fh2u7m-6yxFQ-kIMWAZF4TYXjgtOJN2HIxzmnLWa4P1wDEdnekxB0DjYHugYNww1hg5IHLR3B50bdT3apf8otNeRe3VH0dMk9KpJj2D6ukIIGqrpKbUWakN6t1ARo6ZYMbaqvXloLVbxwWsqWUnPZ-Inr4Ev1VTfFAYM0SEZFXh8lkhxd8r5KIWn59aowPENSuCCUWMc0kr-vGATrrm5oOLVdI84epa4GqII16pq_9Q1SzUH4oBnK_-k4BPRwFb0HPZ5jivxdchOAU_HBf7r8q_c0MeARzfxz8</recordid><startdate>20241128</startdate><enddate>20241128</enddate><creator>Li, Zaishan</creator><creator>Meng, Zhenzhen</creator><creator>Xiao, Lin</creator><creator>Du, Jiahui</creator><creator>Jiang, Dazhi</creator><creator>Liu, Baoling</creator><general>BioMed Central Ltd</general><general>BioMed Central</general><general>BMC</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20241128</creationdate><title>Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer</title><author>Li, Zaishan ; Meng, Zhenzhen ; Xiao, Lin ; Du, Jiahui ; Jiang, Dazhi ; Liu, Baoling</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-d405t-33ced67833d28fa8769db5cffa4762ac1a5714bfc217ee0b5d2e4ecf5b7839503</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Aged</topic><topic>Analysis</topic><topic>Anopheles</topic><topic>B cells</topic><topic>Biological markers</topic><topic>Biomarkers, Tumor - genetics</topic><topic>Cancer</topic><topic>Carcinogenesis</topic><topic>Carcinoma, Non-Small-Cell Lung - genetics</topic><topic>Carcinoma, Non-Small-Cell Lung - mortality</topic><topic>Carcinoma, Non-Small-Cell Lung - pathology</topic><topic>Care and treatment</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene Expression Regulation, Neoplastic</topic><topic>Genes</topic><topic>Genetic aspects</topic><topic>Genomes</topic><topic>Health aspects</topic><topic>Humans</topic><topic>Lung cancer, Non-small cell</topic><topic>Lung cancer, Small cell</topic><topic>Lung Neoplasms - genetics</topic><topic>Lung Neoplasms - mortality</topic><topic>Lung Neoplasms - pathology</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Nomograms</topic><topic>Nomography (Mathematics)</topic><topic>Non-small cell lung cancer</topic><topic>Prognosis</topic><topic>Prognostic model</topic><topic>ROC Curve</topic><topic>Survival Rate</topic><topic>TCGA data</topic><topic>Tumor Microenvironment</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Zaishan</creatorcontrib><creatorcontrib>Meng, Zhenzhen</creatorcontrib><creatorcontrib>Xiao, Lin</creatorcontrib><creatorcontrib>Du, Jiahui</creatorcontrib><creatorcontrib>Jiang, Dazhi</creatorcontrib><creatorcontrib>Liu, Baoling</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>World journal of surgical oncology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Zaishan</au><au>Meng, Zhenzhen</au><au>Xiao, Lin</au><au>Du, Jiahui</au><au>Jiang, Dazhi</au><au>Liu, Baoling</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer</atitle><jtitle>World journal of surgical oncology</jtitle><addtitle>World J Surg Oncol</addtitle><date>2024-11-28</date><risdate>2024</risdate><volume>22</volume><issue>1</issue><spage>319</spage><epage>14</epage><pages>319-14</pages><issn>1477-7819</issn><eissn>1477-7819</eissn><abstract>The tumor microenvironment (TME) plays a crucial role in tumorigenesis and tumor progression. This study aimed to identify novel TME-related biomarkers and develop a prognostic model for patients with non-small-cell lung cancer (NSCLC).
After downloading and preprocessing data from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets, we classified the molecular subtypes using the "NMF" R package. We performed survival analysis and quantified immune scores between clusters. A Cox proportional hazards model was then constructed, and its formula was produced. We assessed model performance and clinical utility. A prediction nomogram was also constructed and validated. Additionally, we explored the potential regulatory mechanisms of our TME gene signature using Gene Set Enrichment Analysis (GSEA).
From data processing and univariate Cox regression analysis, 57 TME-related prognostic genes were identified, and two significantly distinct clusters were established. Using Cox regression and Lasso regression, an 18-gene TME-related prognostic model was developed. Patients were stratified into high- and low-risk groups based on the risk score, with survival analysis showing that the low-risk group had significantly better outcomes than the high-risk group (P < 0.01). ROC curve analysis demonstrated strong predictive performance, with 1-year, 3-year, and 5-year AUC values ranging from 0.654 to 0.702 across different cohorts. The model accurately predicted survival outcomes across subgroups with varying clinical features, and its predictive accuracy was validated through a nomogram.
We developed a prognostic model based on TME-related genes in NSCLC. Our 18-gene TME signature can effectively predict the prognosis of NSCLC with high accuracy.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>39609690</pmid><doi>10.1186/s12957-024-03588-y</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Aged Analysis Anopheles B cells Biological markers Biomarkers, Tumor - genetics Cancer Carcinogenesis Carcinoma, Non-Small-Cell Lung - genetics Carcinoma, Non-Small-Cell Lung - mortality Carcinoma, Non-Small-Cell Lung - pathology Care and treatment Female Follow-Up Studies Gene expression Gene Expression Profiling Gene Expression Regulation, Neoplastic Genes Genetic aspects Genomes Health aspects Humans Lung cancer, Non-small cell Lung cancer, Small cell Lung Neoplasms - genetics Lung Neoplasms - mortality Lung Neoplasms - pathology Male Middle Aged Nomograms Nomography (Mathematics) Non-small cell lung cancer Prognosis Prognostic model ROC Curve Survival Rate TCGA data Tumor Microenvironment Tumors |
| title | Constructing and identifying an eighteen-gene tumor microenvironment prognostic model for non-small cell lung cancer |
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