Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma
In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expres...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Analytical Cellular Pathology |
| Online Access: | http://dx.doi.org/10.1155/2021/4743971 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560227089645568 |
|---|---|
| author | Ai-lan Li Yong-mei Zhu Lai-qiang Gao Shu-yue Wei Ming-tao Wang Qiang Ma You-you Zheng Jian-hua Li Qing-feng Wang |
| author_facet | Ai-lan Li Yong-mei Zhu Lai-qiang Gao Shu-yue Wei Ming-tao Wang Qiang Ma You-you Zheng Jian-hua Li Qing-feng Wang |
| author_sort | Ai-lan Li |
| collection | DOAJ |
| description | In the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK–STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages (P<0.01). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages (P=0.023) and activated mast cells (P=0.005). |
| format | Article |
| id | doaj-art-0de6ef3b40374623bd9bbfe8341de0f7 |
| institution | Kabale University |
| issn | 2210-7177 2210-7185 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Analytical Cellular Pathology |
| spelling | doaj-art-0de6ef3b40374623bd9bbfe8341de0f72025-02-03T01:28:02ZengWileyAnalytical Cellular Pathology2210-71772210-71852021-01-01202110.1155/2021/47439714743971Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in MelanomaAi-lan Li0Yong-mei Zhu1Lai-qiang Gao2Shu-yue Wei3Ming-tao Wang4Qiang Ma5You-you Zheng6Jian-hua Li7Qing-feng Wang8Department of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaDepartment of Dermatology, Dongying People’s Hospital, Dongying 257091, ChinaCollege of Integrated Chinese and Western Medicine, Liaoning University of traditional Chinese Medicine, Shenyang 110079, ChinaIn the present study, we aimed to investigate immune-related signatures and immune infiltration in melanoma. The transcriptome profiling and clinical data of melanoma were downloaded from The Cancer Genome Atlas database, and their matched normal samples were obtained from the Genotype-Tissue Expression database. After merging the genome expression data using Perl, the limma package was used for data normalization. We screened the differentially expressed genes (DEGs) and obtained immune signatures associated with melanoma by an immune-related signature list from the InnateDB database. Univariate Cox regression analysis was used to identify potential prognostic immune genes, and LASSO analysis was used to identify the hub genes. Next, based on the results of multivariate Cox regression analysis, we constructed a risk model for melanoma. We investigated the correlation between risk score and clinical characteristics and overall survival (OS) of patients. Based on the TIMER database, the association between selected immune signatures and immune cell distribution was evaluated. Next, the Wilcoxon rank-sum test was performed using CIBERSORT, which confirmed the differential distribution of immune-infiltrating cells between different risk groups. We obtained a list of 91 differentially expressed immune-related signatures. Functional enrichment analysis indicated that these immune-related DEGs participated in several areas of immune-related crosstalk, including cytokine-cytokine receptor interactions, JAK–STAT signaling pathway, chemokine signaling pathway, and Th17 cell differentiation pathway. A risk model was established based on multivariate Cox analysis results, and Kaplan-Meier analysis was performed. The Kruskal-Wallis test suggested that a high risk score indicated a poorer OS and correlated with higher American Joint Committee on Cancer-TNM (AJCC-TNM) stages and advanced pathological stages (P<0.01). Furthermore, the association between hub immune signatures and immune cell distribution was evaluated in specific tumor samples. The Wilcoxon rank-sum test was used to estimate immune infiltration density in the two groups, and results showed that the high-risk group exhibited a lower infiltration density, and the dominant immune cells included M0 macrophages (P=0.023) and activated mast cells (P=0.005).http://dx.doi.org/10.1155/2021/4743971 |
| spellingShingle | Ai-lan Li Yong-mei Zhu Lai-qiang Gao Shu-yue Wei Ming-tao Wang Qiang Ma You-you Zheng Jian-hua Li Qing-feng Wang Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma Analytical Cellular Pathology |
| title | Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma |
| title_full | Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma |
| title_fullStr | Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma |
| title_full_unstemmed | Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma |
| title_short | Exploration of the Immune-Related Signatures and Immune Infiltration Analysis in Melanoma |
| title_sort | exploration of the immune related signatures and immune infiltration analysis in melanoma |
| url | http://dx.doi.org/10.1155/2021/4743971 |
| work_keys_str_mv | AT ailanli explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT yongmeizhu explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT laiqianggao explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT shuyuewei explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT mingtaowang explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT qiangma explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT youyouzheng explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT jianhuali explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma AT qingfengwang explorationoftheimmunerelatedsignaturesandimmuneinfiltrationanalysisinmelanoma |