Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer
BackgroundIn recent decades, cancer immunotherapy has transformed the treatment landscape, offering significant advantages over traditional therapies by improving progression-free survival (PFS) and overall survival (OS). However, immune checkpoint inhibitors (ICIs) treatment has been associated wit...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1590670/full |
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| author | Qian Zuo Jieting Chen Jieting Chen Xi Xiao Yan Dai Liushan Chen Liushan Chen Yuqi Liang Yingchao Wu Junfeng Huang Rutao Cui Rui Xu Qianjun Chen Qianjun Chen Qianjun Chen Qianjun Chen |
| author_facet | Qian Zuo Jieting Chen Jieting Chen Xi Xiao Yan Dai Liushan Chen Liushan Chen Yuqi Liang Yingchao Wu Junfeng Huang Rutao Cui Rui Xu Qianjun Chen Qianjun Chen Qianjun Chen Qianjun Chen |
| author_sort | Qian Zuo |
| collection | DOAJ |
| description | BackgroundIn recent decades, cancer immunotherapy has transformed the treatment landscape, offering significant advantages over traditional therapies by improving progression-free survival (PFS) and overall survival (OS). However, immune checkpoint inhibitors (ICIs) treatment has been associated with an increased risk of mortality in its early stages. Therefore, identifying reliable biomarkers to predict which patients will benefit clinically from ICIs therapy is critical. Depression, a common form of chronic psychological stress, has emerged as a regulator of tumor immunity and is gaining attention as a target for novel cancer treatments. To date, no studies have explored the potential of depression-related genes in predicting response to ICIs therapy.MethodsPublic datasets of ICIs-treated patients were obtained from the TCGA and GEO databases, followed by comprehensive analyses, including bulk mRNA sequencing (mRNA-seq), co-expression network construction, and Gene Ontology enrichment. Regression analysis, using Cox proportional hazards and least absolute shrinkage and selection operator (Lasso), identified eight depression-related genes to build a predictive model for clinical outcomes in ICIs therapy. Additionally, correlations were explored between the depression-related predictive score and clinical parameters, including tumor mutational burden (TMB) and immune cell infiltration, establishing the score as a potential predictor of ICIs response.ResultsThe model categorized patients into high- and low-responsiveness groups, with significant differences in disease-free survival (DFS) between them. Validation using both internal and external datasets demonstrated the model’s strong predictive accuracy. Further analysis revealed that this response stratification correlates with immune cell abundance and TMB in cancer patients.ConclusionThis study suggests that depression-related genetic traits could serve as biomarkers for ICIs therapy response, tumor mutations, and immune system alterations. Our findings offer insights into personalized therapeutic strategies for early intervention and prognosis in specific cancer types. |
| format | Article |
| id | doaj-art-fb3b728497aa438e8a83454f4f0671c9 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-fb3b728497aa438e8a83454f4f0671c92025-08-20T03:12:31ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-07-011610.3389/fimmu.2025.15906701590670Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancerQian Zuo0Jieting Chen1Jieting Chen2Xi Xiao3Yan Dai4Liushan Chen5Liushan Chen6Yuqi Liang7Yingchao Wu8Junfeng Huang9Rutao Cui10Rui Xu11Qianjun Chen12Qianjun Chen13Qianjun Chen14Qianjun Chen15Department of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, ChinaState Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaGuangdong Academy of Traditional Chinese Medicine, Guangzhou, ChinaDepartment of Oncology, The First Affiliated Hospital of Guangzhou University of Traditional Chinese Medicine, Guangzhou, ChinaDepartment of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, ChinaState Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaGuangdong Academy of Traditional Chinese Medicine, Guangzhou, ChinaDepartment of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, ChinaThe Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, ChinaThe Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, ChinaSchool of Medicine, Zhejiang University School of Medicine, Hangzhou, ChinaDepartment of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, ChinaDepartment of Breast, Guangdong Provincial Hospital of Chinese Medicine, Guangzhou, ChinaState Key Laboratory of Traditional Chinese Medicine Syndrome, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, ChinaGuangdong Academy of Traditional Chinese Medicine, Guangzhou, ChinaChinese Medicine Guangdong Laboratory (Hengqin Laboratory), Zhuhai, ChinaBackgroundIn recent decades, cancer immunotherapy has transformed the treatment landscape, offering significant advantages over traditional therapies by improving progression-free survival (PFS) and overall survival (OS). However, immune checkpoint inhibitors (ICIs) treatment has been associated with an increased risk of mortality in its early stages. Therefore, identifying reliable biomarkers to predict which patients will benefit clinically from ICIs therapy is critical. Depression, a common form of chronic psychological stress, has emerged as a regulator of tumor immunity and is gaining attention as a target for novel cancer treatments. To date, no studies have explored the potential of depression-related genes in predicting response to ICIs therapy.MethodsPublic datasets of ICIs-treated patients were obtained from the TCGA and GEO databases, followed by comprehensive analyses, including bulk mRNA sequencing (mRNA-seq), co-expression network construction, and Gene Ontology enrichment. Regression analysis, using Cox proportional hazards and least absolute shrinkage and selection operator (Lasso), identified eight depression-related genes to build a predictive model for clinical outcomes in ICIs therapy. Additionally, correlations were explored between the depression-related predictive score and clinical parameters, including tumor mutational burden (TMB) and immune cell infiltration, establishing the score as a potential predictor of ICIs response.ResultsThe model categorized patients into high- and low-responsiveness groups, with significant differences in disease-free survival (DFS) between them. Validation using both internal and external datasets demonstrated the model’s strong predictive accuracy. Further analysis revealed that this response stratification correlates with immune cell abundance and TMB in cancer patients.ConclusionThis study suggests that depression-related genetic traits could serve as biomarkers for ICIs therapy response, tumor mutations, and immune system alterations. Our findings offer insights into personalized therapeutic strategies for early intervention and prognosis in specific cancer types.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1590670/fullpsychological stresspsycho-biomarkersbreast cancerimmunotherapy efficacypredictive model |
| spellingShingle | Qian Zuo Jieting Chen Jieting Chen Xi Xiao Yan Dai Liushan Chen Liushan Chen Yuqi Liang Yingchao Wu Junfeng Huang Rutao Cui Rui Xu Qianjun Chen Qianjun Chen Qianjun Chen Qianjun Chen Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer Frontiers in Immunology psychological stress psycho-biomarkers breast cancer immunotherapy efficacy predictive model |
| title | Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer |
| title_full | Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer |
| title_fullStr | Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer |
| title_full_unstemmed | Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer |
| title_short | Exploring the predictive “psycho-biomarkers” for checkpoint immunotherapy in cancer |
| title_sort | exploring the predictive psycho biomarkers for checkpoint immunotherapy in cancer |
| topic | psychological stress psycho-biomarkers breast cancer immunotherapy efficacy predictive model |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1590670/full |
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