Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model
•Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS...
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Veröffentlicht in: | International immunopharmacology 2020-09, Vol.86, p.106744-106744, Article 106744 |
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description | •Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS for NSCLC patients.
Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients. |
doi_str_mv | 10.1016/j.intimp.2020.106744 |
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Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.</description><identifier>ISSN: 1567-5769</identifier><identifier>EISSN: 1878-1705</identifier><identifier>DOI: 10.1016/j.intimp.2020.106744</identifier><language>eng</language><publisher>Amsterdam: Elsevier B.V</publisher><subject>Biomarkers ; Calibration ; CCR2 protein ; Computer applications ; Correlation analysis ; CTLA-4 protein ; Genes ; Genomes ; Heterogeneity ; Immune checkpoint ; Immune system ; Immune-related differentially expressed genes ; Immunotherapy ; Lung cancer ; Lymphocytes ; Lymphocytes B ; Lymphocytes T ; Mast cells ; Medical prognosis ; Monocyte chemoattractant protein 1 ; Nomogram ; Nomograms ; Non-small cell lung carcinoma ; Non-small-cell lung cancer ; Pattern analysis ; PD-1 protein ; PD-L1 protein ; Prognosis ; Regression analysis ; Ribonucleic acid ; RNA ; Small cell lung carcinoma ; Tumor-infiltrating immune cells ; Tumor-infiltrating lymphocytes ; Tumors</subject><ispartof>International immunopharmacology, 2020-09, Vol.86, p.106744-106744, Article 106744</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright Elsevier BV Sep 2020</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-8fe7cdec2c9b27c97b2256c097da7ff4fc53c7660b27298e5d768c1113eeeda53</citedby><cites>FETCH-LOGICAL-c367t-8fe7cdec2c9b27c97b2256c097da7ff4fc53c7660b27298e5d768c1113eeeda53</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.intimp.2020.106744$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,778,782,3539,27913,27914,45984</link.rule.ids></links><search><creatorcontrib>Pang, Zhaofei</creatorcontrib><creatorcontrib>Chen, Xiaowei</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><creatorcontrib>Wang, Yadong</creatorcontrib><creatorcontrib>Yan, Tao</creatorcontrib><creatorcontrib>Wan, Jun</creatorcontrib><creatorcontrib>Du, Jiajun</creatorcontrib><title>Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model</title><title>International immunopharmacology</title><description>•Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS for NSCLC patients.
Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.</description><subject>Biomarkers</subject><subject>Calibration</subject><subject>CCR2 protein</subject><subject>Computer applications</subject><subject>Correlation analysis</subject><subject>CTLA-4 protein</subject><subject>Genes</subject><subject>Genomes</subject><subject>Heterogeneity</subject><subject>Immune checkpoint</subject><subject>Immune system</subject><subject>Immune-related differentially expressed genes</subject><subject>Immunotherapy</subject><subject>Lung cancer</subject><subject>Lymphocytes</subject><subject>Lymphocytes B</subject><subject>Lymphocytes T</subject><subject>Mast cells</subject><subject>Medical prognosis</subject><subject>Monocyte chemoattractant protein 1</subject><subject>Nomogram</subject><subject>Nomograms</subject><subject>Non-small cell lung carcinoma</subject><subject>Non-small-cell lung cancer</subject><subject>Pattern analysis</subject><subject>PD-1 protein</subject><subject>PD-L1 protein</subject><subject>Prognosis</subject><subject>Regression analysis</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Small cell lung carcinoma</subject><subject>Tumor-infiltrating immune cells</subject><subject>Tumor-infiltrating lymphocytes</subject><subject>Tumors</subject><issn>1567-5769</issn><issn>1878-1705</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9UU2v1CAUbYwmPp_-Axckbtx0pLRA68LEzPMreYkbXRMe3M7cCYUKdJLxJ_qrpFM3unAFnHvO4d57quplQ3cNbcSb0w59xmneMcpWSMiue1TdNL3s60ZS_rjcuZA1l2J4Wj1L6URpwbvmpvq1D9Mc4Qg-4RmI9tpdEiQSRpKPQI6QIYYDeMB8KVVL5vL0IWU0JOHB44hGewNXwTKFWKMf0eWoM_oDwWlaPBADziWCnvjg6zRp5-oVIm4pnKs-viV3cAYX5gl8vv501g5tsQl-Nde-6C2e0S4F_wl_dTIFC-559WTULsGLP-dt9f3jh2_7z_X9109f9u_va9MKmet-BGksGGaGBybNIB8Y48LQQVotx7EbDW-NFIKWKht64FaK3jRN0wKA1by9rV5vvqWBHwukrCZM6zjaQ1iSYh2joitrp4X66h_qKSyx7Hhl8ZYxynlfWN3GMjGkFGFUc8RJx4tqqFoDVie1BazWgNUWcJG922RQhj0jRJUMQtmlxQgmKxvw_wa_Aa73tyQ</recordid><startdate>202009</startdate><enddate>202009</enddate><creator>Pang, Zhaofei</creator><creator>Chen, Xiaowei</creator><creator>Wang, Yu</creator><creator>Wang, Yadong</creator><creator>Yan, Tao</creator><creator>Wan, Jun</creator><creator>Du, Jiajun</creator><general>Elsevier B.V</general><general>Elsevier BV</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7T5</scope><scope>7U7</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>P64</scope><scope>7X8</scope></search><sort><creationdate>202009</creationdate><title>Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model</title><author>Pang, Zhaofei ; Chen, Xiaowei ; Wang, Yu ; Wang, Yadong ; Yan, Tao ; Wan, Jun ; Du, Jiajun</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-8fe7cdec2c9b27c97b2256c097da7ff4fc53c7660b27298e5d768c1113eeeda53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biomarkers</topic><topic>Calibration</topic><topic>CCR2 protein</topic><topic>Computer applications</topic><topic>Correlation analysis</topic><topic>CTLA-4 protein</topic><topic>Genes</topic><topic>Genomes</topic><topic>Heterogeneity</topic><topic>Immune checkpoint</topic><topic>Immune system</topic><topic>Immune-related differentially expressed genes</topic><topic>Immunotherapy</topic><topic>Lung cancer</topic><topic>Lymphocytes</topic><topic>Lymphocytes B</topic><topic>Lymphocytes T</topic><topic>Mast cells</topic><topic>Medical prognosis</topic><topic>Monocyte chemoattractant protein 1</topic><topic>Nomogram</topic><topic>Nomograms</topic><topic>Non-small cell lung carcinoma</topic><topic>Non-small-cell lung cancer</topic><topic>Pattern analysis</topic><topic>PD-1 protein</topic><topic>PD-L1 protein</topic><topic>Prognosis</topic><topic>Regression analysis</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Small cell lung carcinoma</topic><topic>Tumor-infiltrating immune cells</topic><topic>Tumor-infiltrating lymphocytes</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pang, Zhaofei</creatorcontrib><creatorcontrib>Chen, Xiaowei</creatorcontrib><creatorcontrib>Wang, Yu</creatorcontrib><creatorcontrib>Wang, Yadong</creatorcontrib><creatorcontrib>Yan, Tao</creatorcontrib><creatorcontrib>Wan, Jun</creatorcontrib><creatorcontrib>Du, Jiajun</creatorcontrib><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Immunology Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>International immunopharmacology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Pang, Zhaofei</au><au>Chen, Xiaowei</au><au>Wang, Yu</au><au>Wang, Yadong</au><au>Yan, Tao</au><au>Wan, Jun</au><au>Du, Jiajun</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model</atitle><jtitle>International immunopharmacology</jtitle><date>2020-09</date><risdate>2020</risdate><volume>86</volume><spage>106744</spage><epage>106744</epage><pages>106744-106744</pages><artnum>106744</artnum><issn>1567-5769</issn><eissn>1878-1705</eissn><abstract>•Heterogeneity and prognostic significance of TIICs in NSCLC were comprehensively analyzed.•The associations between TIICs and immune-related DEGs network were investigated in depth.•A novel index IGRI was developed based on five TIICs-related DEGs.•A model was built and validated to predict the OS for NSCLC patients.
Understanding the role of tumor-infiltrating immune cells (TIICs) in non-small cell lung cancer (NSCLC) is critical to finding new prognostic biomarkers and improving prognostic evaluation. Herein, we aimed to comprehensively analyze tumor-infiltrating pattern of TIICs in NSCLC and build a TIICs-associated, risk-stratification prognostic model for clinical practice. We applied CIBERSORT and ESTIMATE computational methods to analyze RNA-seq samples of 852 NSCLC patients from The Cancer Genome Atlas (TCGA). Prognotic factors were identified by univariate and multivariate Cox regression analyses for overall survival (OS). A novel model was developed to predict the 1-, 3- and 5-year OS of NSCLC based on the TCGA cohort, validated by external validation cohorts (GSE31210, GSE37745), and then evaluated by C-indexes and calibration plots. Significant heterogeneity in the infiltrating patterns of TIICs was shown among various pathological subtypes of NSCLC and between different genders. Further analyses showed that abundances of naive B cells (NBCs), T cells and mast cells (MCs) were positively correlated with prognosis. Tumor samples with high T cells abundances tended to have higher expression levels of immune checkpoint genes (PD-1, PD-L1, CTLA-4). A new immune-gene related index (IGRI) was built by five immune-related differentially expressed genes (DEGs) including BTK, CCR2, CLEC10A, NCR3 and PRKCB, which were closely correlated with TIICs abundances and prognosis. Tumor stage, IGRI, abundances of NBCs, T cells, MCs and NK cells were significant independent prognostic factors and were included in the nomogram as predictors. The internal and external calibration plots of the nomogram were in excellent agreement. This study reveals that TIICs are significantly correlated with clinicopathological features and prognosis in NSCLC and thus can be potential prognostic biomarker or therapeutic target. The remarkable heterogeneity of TIICs suggests that specific infiltrating patterns of TIICs should also be taken into consideration when determining individualized immunotherapy strategies for NSCLC patients.</abstract><cop>Amsterdam</cop><pub>Elsevier B.V</pub><doi>10.1016/j.intimp.2020.106744</doi><tpages>1</tpages></addata></record> |
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subjects | Biomarkers Calibration CCR2 protein Computer applications Correlation analysis CTLA-4 protein Genes Genomes Heterogeneity Immune checkpoint Immune system Immune-related differentially expressed genes Immunotherapy Lung cancer Lymphocytes Lymphocytes B Lymphocytes T Mast cells Medical prognosis Monocyte chemoattractant protein 1 Nomogram Nomograms Non-small cell lung carcinoma Non-small-cell lung cancer Pattern analysis PD-1 protein PD-L1 protein Prognosis Regression analysis Ribonucleic acid RNA Small cell lung carcinoma Tumor-infiltrating immune cells Tumor-infiltrating lymphocytes Tumors |
title | Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: Development and validation of an individualized prognostic model |
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