1
|
Zhang S, Ta N, Zhang S, Li S, Zhu X, Kong L, Gong X, Guo M, Liu Y. Unraveling Pancreatic Ductal Adenocarcinoma Immune Prognostic Signature through a Naive B Cell Gene Set. Cancer Lett 2024:216981. [PMID: 38795761 DOI: 10.1016/j.canlet.2024.216981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/14/2024] [Accepted: 05/17/2024] [Indexed: 05/28/2024]
Abstract
BACKGROUND Pancreatic ductal adenocarcinoma (PDAC), a leading cause of cancer mortality, has a complex pathogenesis involving various immune cells, including B cells and their subpopulations. Despite emerging research on the role of these cells within the tumor microenvironment (TME), the detailed molecular interactions with tumor-infiltrating immune cells (TIICs) are not fully understood. METHODS We applied CIBERSORT to quantify TIICs and naive B cells, which are prognostic for PDAC. Marker genes from scRNA-seq and modular genes from weighted gene co-expression network analysis (WGCNA) were integrated to identify naive B cell-related genes. A prognostic signature was constructed utilizing ten machine-learning algorithms, with validation in external cohorts. We further assessed the immune cell diversity, ESTIMATE scores, and immune checkpoint genes (ICGs) between patient groups stratified by risk to clarify the immune landscape in PDAC. RESULTS Our analysis identified 994 naive B cell-related genes across single-cell and bulk transcriptomes, with 247 linked to overall survival. We developed a 12-gene prognostic signature using Lasso and plsRcox algorithms, which was confirmed by 10-fold cross-validation and showed robust predictive power in training and real-world cohorts. Notably, we observed substantial differences in immune infiltration between patients with high and low risk. CONCLUSION Our study presents a robust prognostic signature that effectively maps the complex immune interactions in PDAC, emphasizing the critical function of naive B cells and suggesting new avenues for immunotherapeutic interventions. This signature has potential clinical applications in personalizing PDAC treatment, enhancing the understanding of immune dynamics, and guiding immunotherapy strategies.
Collapse
Affiliation(s)
- Shichen Zhang
- Software Engineering Institute, East China Normal University, Shanghai 200062, China
| | - Na Ta
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai 200433, China
| | - Shihao Zhang
- National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China
| | - Senhao Li
- National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China
| | - Xinyu Zhu
- National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China
| | - Lingyun Kong
- National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China
| | - Xueqing Gong
- Software Engineering Institute, East China Normal University, Shanghai 200062, China.
| | - Meng Guo
- National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China.
| | - Yanfang Liu
- Department of Pathology, Changhai Hospital, Navy Medical University, Shanghai 200433, China; National Key Laboratory of Immunity and Inflammation & Institute of Immunology, Navy Medical University, Shanghai 200433, China.
| |
Collapse
|
2
|
Xie LY, Huang HY, Hao YL, Yu M, Zhang W, Wei E, Gao C, Wang C, Zeng L. Development and validation of a tumor immune cell infiltration-related gene signature for recurrence prediction by weighted gene co-expression network analysis in prostate cancer. Front Genet 2023; 14:1067172. [PMID: 37007952 PMCID: PMC10061146 DOI: 10.3389/fgene.2023.1067172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 02/23/2023] [Indexed: 03/18/2023] Open
Abstract
Introduction: Prostate cancer (PCa) is the second most common malignancy in men. Despite multidisciplinary treatments, patients with PCa continue to experience poor prognoses and high rates of tumor recurrence. Recent studies have shown that tumor-infiltrating immune cells (TIICs) are associated with PCa tumorigenesis.Methods: The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were used to derive multi-omics data for prostate adenocarcinoma (PRAD) samples. The CIBERSORT algorithm was used to calculate the landscape of TIICs. Weighted gene co-expression network analysis (WGCNA) was performed to determine the candidate module most significantly associated with TIICs. LASSO Cox regression was applied to screen a minimal set of genes and construct a TIIC-related prognostic gene signature for PCa. Then, 78 PCa samples with CIBERSORT output p-values of less than 0.05 were selected for analysis. WGCNA identified 13 modules, and the MEblue module with the most significant enrichment result was selected. A total of 1143 candidate genes were cross-examined between the MEblue module and active dendritic cell-related genes.Results: According to LASSO Cox regression analysis, a risk model was constructed with six genes (STX4, UBE2S, EMC6, EMD, NUCB1 and GCAT), which exhibited strong correlations with clinicopathological variables, tumor microenvironment context, antitumor therapies, and tumor mutation burden (TMB) in TCGA-PRAD. Further validation showed that the UBE2S had the highest expression level among the six genes in five different PCa cell lines.Discussion: In conclusion, our risk-score model contributes to better predicting PCa patient prognosis and understanding the underlying mechanisms of immune responses and antitumor therapies in PCa.
Collapse
Affiliation(s)
- Lin-Ying Xie
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
| | - Han-Ying Huang
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yu-Lei Hao
- Department of Neurology and Neuroscience Center, The First Hospital of Jilin University, Changchun, Jilin, China
| | - Miaomiao Yu
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
| | - Wenju Zhang
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
| | - Enwei Wei
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
| | - Chunfeng Gao
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
| | - Chang Wang
- Cancer Center, The First Hospital of Jilin University, Changchun, Jilin, China
- *Correspondence: Chang Wang, ; Lei Zeng,
| | - Lei Zeng
- Bethune Institute of Epigenetic Medicine, The First Hospital of Jilin University, Changchun, Jilin, China
- International Center of Future Science, Jillin University, Changchun, Jilin, China
- *Correspondence: Chang Wang, ; Lei Zeng,
| |
Collapse
|
3
|
Xu X, Wang J. Prognostic prediction and multidimensional dissections of a macrophages M0-related gene signature in liver cancer. Front Endocrinol (Lausanne) 2023; 14:1153562. [PMID: 37033261 PMCID: PMC10080084 DOI: 10.3389/fendo.2023.1153562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Accepted: 03/14/2023] [Indexed: 04/11/2023] Open
Abstract
BACKGROUND Liver hepatocellular carcinoma (LIHC) is the seventh most commonly diagnosed malignancy and the third leading cause of all cancer death worldwide. The undifferentiated macrophages M0 can be induced into polarized M1 and M2 to exert opposite effects in tumor microenvironment. However, the prognostic value of macrophages M0 phenotype remains obscure in LIHC. METHODS The transcriptome data of LIHC was obtained from TCGA database and ICGC database. 365 LIHC samples from TCGA database and 231 LIHC samples from ICGC database were finally included. Macrophages M0-related genes (MRGs) were screened by Pearson correlation analysis and univariate Cox regression analysis based on the infiltration level of Macrophages M0. LASSO regression analysis was employed to construct a prognostic signature based on MRGs, and risk scores were accordingly calculated. Then we investigated the MRGs-based prognostic signature with respects to prognostic value, clinical significance, strengthened pathways, immune infiltration, gene mutation and drug sensitivity. Furthermore, the expression pattern of MRGs in the tumor microenvironment were also detected in LIHC. RESULTS A ten-MRG signature was developed and clarified as independent prognostic predictors in LIHC. The risk score-based nomogram showed favorable capability in survival prediction. Several substance metabolism activities like fatty acid/amino acid metabolism were strengthened in low-risk group. Low risk group was deciphered to harbor TTN mutation-driven tumorigenesis, while TP53 mutation was dominant in high-risk group. We also ascertained that the infiltration levels of immune cells and expressions of immune checkpoints are significantly influenced by the risk score. Besides, we implied that patients in low-risk group may be more sensitive to several anti-cancer drugs. What's more important, single-cell analysis verified the expression of MRGs in the tumor microenvironment of LIHC. CONCLUSION Multidimensional evaluations verified the clinical utility of the macrophages M0-related gene signature to predict prognosis, assist risk decision and guide treatment strategy for patients with LIHC.
Collapse
Affiliation(s)
- Xiaoming Xu
- Department of Gastroenterology, Jining First People’s Hospital, Jining, China
| | - Jingzhi Wang
- Department of Radiotherapy Oncology, The Affiliated Yancheng First Hospital of Nanjing University Medical School, The First People’s Hospital of Yancheng, Yancheng, China
- *Correspondence: Jingzhi Wang,
| |
Collapse
|
4
|
Jiang Y, Qu X, Zhang M, Zhang L, Yang T, Ma M, Jing M, Zhang N, Song R, Zhang Y, Yang Z, Zhang Y, Pu Y, Fan J. Identification of a six-gene prognostic signature for bladder cancer associated macrophage. Front Immunol 2022; 13:930352. [PMID: 36275756 PMCID: PMC9582252 DOI: 10.3389/fimmu.2022.930352] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 09/16/2022] [Indexed: 11/13/2022] Open
Abstract
As major components of the tumor microenvironment (TME), tumor-associated macrophages (TAMs) play an exceedingly complicated role in tumor progression and tumorigenesis. However, few studies have reported the specific TAM gene signature in bladder cancer. Herein, this study focused on developing a TAM-related prognostic model in bladder cancer patients based on The Cancer Genome Atlas (TCGA) data. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key genes related to TAM (M2 macrophage). Gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed the functional categories of the key genes. Simultaneously, we used the Least Absolute Shrinkage and Selection Operator (LASSO) and univariate and multivariate Cox regressions to establish a TMA-related prognostic model containing six key genes: TBXAS1, GYPC, HPGDS, GAB3, ADORA3, and FOLR2. Subsequently, single-cell sequencing data downloaded from Gene Expression Omnibus (GEO) suggested that the six genes in the prognostic model were expressed in TAM specifically and may be involved in TAM polarization. In summary, our research uncovered six-TAM related genes that may have an effect on risk stratification in bladder cancer patients and could be regarded as potential TAM-related biomarkers.
Collapse
Affiliation(s)
- Yunzhong Jiang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Xiaowei Qu
- Department of Geriatrics, The Yan’an University Xianyang Hospital, Xianyang, China
| | - Mengzhao Zhang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Lu Zhang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Tao Yang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Minghai Ma
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Minxuan Jing
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Nan Zhang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Rundong Song
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yuanquan Zhang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Zezhong Yang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yaodong Zhang
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Yuanchun Pu
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Jinhai Fan
- Department of Urology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- Oncology Research Lab, Key Laboratory of Environment and Genes Related to Diseases, Ministry of Education, Xi’an, China
- *Correspondence: Jinhai Fan,
| |
Collapse
|
5
|
Wang J, Ling S, Ni J, Wan Y. Novel γδ T cell-based prognostic signature to estimate risk and aid therapy in hepatocellular carcinoma. BMC Cancer 2022; 22:638. [PMID: 35681134 PMCID: PMC9185956 DOI: 10.1186/s12885-022-09662-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 05/12/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Numerous studies have revealed that gamma delta (γδ) T cell infiltration plays a crucial regulatory role in hepatocellular carcinoma (HCC) development. Nonetheless, a comprehensive analysis of γδ T cell infiltration in prognosis evaluation and therapeutic prediction remains unclear. METHODS Multi-omic data on HCC patients were obtained from public databases. The CIBERSORT algorithm was applied to decipher the tumor immune microenvironment (TIME) of HCC. Weighted gene co-expression network analysis (WGCNA) was performed to determine significant modules with γδ T cell-specific genes. Kaplan-Meier survival curves and receiver operating characteristic analyses were used to validate prognostic capability. Additionally, the potential role of RFESD inhibition by si-RFESD in vitro was investigated using EdU and CCK-8 assays. RESULTS A total of 16,421 genes from 746 HCC samples (616 cancer and 130 normal) were identified based on three distinct cohorts. Using WGCNA, candidate modules (brown) with 1755 significant corresponding genes were extracted as γδ T cell-specific genes. Next, a novel risk signature consisting of 11 hub genes was constructed using multiple bioinformatic analyses, which presented great prognosis prediction reliability. The risk score exhibited a significant correlation with ICI and chemotherapeutic targets. HCC samples with different risks experienced diverse signalling pathway activities. The possible interaction of risk score with tumor mutation burden (TMB) was further analyzed. Subsequently, the potential functions of the RFESD gene were explored in HCC, and knockdown of RFESD inhibited cell proliferation in HCC cells. Finally, a robust prognostic risk-clinical nomogram was developed and validated to quantify clinical outcomes. CONCLUSIONS Collectively, comprehensive analyses focusing on γδ T cell patterns will provide insights into prognosis prediction, the mechanisms of immune infiltration, and advanced therapy strategies in HCC.
Collapse
Affiliation(s)
- Jingrui Wang
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China
| | - Sunbin Ling
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China
| | - Jie Ni
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China
| | - Yafeng Wan
- Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, No.261, Huansha Road, Zhejiang, Hangzhou, China.
| |
Collapse
|