Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration
ObjectiveTo build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data...
Saved in:
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1514238/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832548264526741504 |
|---|---|
| author | Kaili Liao Minqi Zhu Lei Guo Zijun Gao Jinting Cheng Bing Sun Yihui Qian Bingying Lin Jingyan Zhang Tingyi Qian Yixin Jiang Yanmei Xu Qionghui Zhong Xiaozhong Wang |
| author_facet | Kaili Liao Minqi Zhu Lei Guo Zijun Gao Jinting Cheng Bing Sun Yihui Qian Bingying Lin Jingyan Zhang Tingyi Qian Yixin Jiang Yanmei Xu Qionghui Zhong Xiaozhong Wang |
| author_sort | Kaili Liao |
| collection | DOAJ |
| description | ObjectiveTo build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA). And the chemotherapeutic response to different drugs was assessed.ResultsWe established a novel prognostic model utilizing the WGCNA method, which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups.ConclusionThis study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer. |
| format | Article |
| id | doaj-art-679f0c6294dd4ae2803d2cfd52b01236 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-679f0c6294dd4ae2803d2cfd52b012362025-02-03T06:33:41ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-02-011610.3389/fimmu.2025.15142381514238Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltrationKaili Liao0Minqi Zhu1Lei Guo2Zijun Gao3Jinting Cheng4Bing Sun5Yihui Qian6Bingying Lin7Jingyan Zhang8Tingyi Qian9Yixin Jiang10Yanmei Xu11Qionghui Zhong12Xiaozhong Wang13Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaThe 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaThe 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaSchool of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaThe 2nd Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaThe 1st Clinical Medical College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaQueen Mary College, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaJiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, ChinaObjectiveTo build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA). And the chemotherapeutic response to different drugs was assessed.ResultsWe established a novel prognostic model utilizing the WGCNA method, which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups.ConclusionThis study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1514238/fullcolorectal cancerimmune-related geneimmune cell infiltrationWGCNAprognostic model |
| spellingShingle | Kaili Liao Minqi Zhu Lei Guo Zijun Gao Jinting Cheng Bing Sun Yihui Qian Bingying Lin Jingyan Zhang Tingyi Qian Yixin Jiang Yanmei Xu Qionghui Zhong Xiaozhong Wang Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration Frontiers in Immunology colorectal cancer immune-related gene immune cell infiltration WGCNA prognostic model |
| title | Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| title_full | Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| title_fullStr | Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| title_full_unstemmed | Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| title_short | Assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| title_sort | assessment of prognosis and responsiveness to immunotherapy in colorectal cancer patients based on the level of immune cell infiltration |
| topic | colorectal cancer immune-related gene immune cell infiltration WGCNA prognostic model |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1514238/full |
| work_keys_str_mv | AT kaililiao assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT minqizhu assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT leiguo assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT zijungao assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT jintingcheng assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT bingsun assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT yihuiqian assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT bingyinglin assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT jingyanzhang assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT tingyiqian assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT yixinjiang assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT yanmeixu assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT qionghuizhong assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration AT xiaozhongwang assessmentofprognosisandresponsivenesstoimmunotherapyincolorectalcancerpatientsbasedonthelevelofimmunecellinfiltration |