Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing
Abstract Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lip...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-12-01
|
| Series: | Lipids in Health and Disease |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12944-023-01977-y |
| _version_ | 1797414183381237760 |
|---|---|
| author | Jinze Zhang He Wang Yu Tian Tianfeng Li Wei Zhang Li Ma Xiangjuan Chen Yushan Wei |
| author_facet | Jinze Zhang He Wang Yu Tian Tianfeng Li Wei Zhang Li Ma Xiangjuan Chen Yushan Wei |
| author_sort | Jinze Zhang |
| collection | DOAJ |
| description | Abstract Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches. |
| first_indexed | 2024-03-09T05:28:24Z |
| format | Article |
| id | doaj.art-7ceea73892214e7e8321b6b6a1220c92 |
| institution | Directory Open Access Journal |
| issn | 1476-511X |
| language | English |
| last_indexed | 2024-03-09T05:28:24Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Lipids in Health and Disease |
| spelling | doaj.art-7ceea73892214e7e8321b6b6a1220c922023-12-03T12:34:35ZengBMCLipids in Health and Disease1476-511X2023-12-0122112110.1186/s12944-023-01977-yDiscovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencingJinze Zhang0He Wang1Yu Tian2Tianfeng Li3Wei Zhang4Li Ma5Xiangjuan Chen6Yushan Wei7Department of Epidemiology and Health Statistics, School of Public Health, Dalian Medical UniversityDepartment of Epidemiology and Health Statistics, School of Public Health, Dalian Medical UniversityDepartment of Epidemiology and Health Statistics, School of Public Health, Dalian Medical UniversityDepartment of General Surgery, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Shenzhen University General Hospital, Shenzhen UniversityDepartment of General Surgery, Institute of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Shenzhen University General Hospital, Shenzhen UniversityDepartment of Epidemiology and Health Statistics, School of Public Health, Dalian Medical UniversityDepartment of Obstetrics, Shenzhen University General Hospital, Shenzhen UniversityDepartment of Scientific Research, First Affiliated Hospital of Dalian Medical UniversityAbstract Gastric cancer (GC) is a pressing global clinical issue, with few treatment options and a poor prognosis. The onset and spread of stomach cancer are significantly influenced by changes in lipid metabolism-related pathways. This study aimed to discover a predictive signature for GC using lipid metabolism-related genes (LMRGs) and examine its correlation with the tumor immune microenvironment (TIME). Transcriptome data and clinical information from patients with GC were collected from the TCGA and GEO databases. Data from GC samples were analyzed using both bulk RNA-seq and single-cell sequencing of RNA (scRNA-seq). To identify survival-related differentially expressed LMRGs (DE-LMRGs), differential expression and prognosis studies were carried out. We built a predictive signature using LASSO regression and tested it on the TCGA and GSE84437 datasets. In addition, the correlation of the prognostic signature with the TIME was comprehensively analyzed. In this study, we identified 258 DE-LMRGs in GC and further screened seven survival-related DE-LMRGs. The results of scRNA-seq identified 688 differentially expressed genes (DEGs) between the three branches. Two critical genes (GPX3 and NNMT) were identified using the above two gene groups. In addition, a predictive risk score that relies on GPX3 and NNMT was developed. Survival studies in both the TCGA and GEO datasets revealed that patients categorized to be at low danger had a significantly greater prognosis than those identified to be at high danger. Additionally, by employing calibration plots based on TCGA data, the study demonstrated the substantial predictive capacity of a prognostic nomogram, which incorporated a risk score along with various clinical factors. Within the high-risk group, there was a noticeable abundance of active natural killer (NK) cells, quiescent monocytes, macrophages, mast cells, and activated CD4 + T cells. In summary, a two-gene signature and a predictive nomogram have been developed, offering accurate prognostic predictions for general survival in GC patients. These findings have the potential to assist healthcare professionals in making informed medical decisions and providing personalized treatment approaches.https://doi.org/10.1186/s12944-023-01977-yLipid metabolism-related genePrognostic modelGastric cancerSingle-cell RNA sequencingTumor immune microenvironment |
| spellingShingle | Jinze Zhang He Wang Yu Tian Tianfeng Li Wei Zhang Li Ma Xiangjuan Chen Yushan Wei Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing Lipids in Health and Disease Lipid metabolism-related gene Prognostic model Gastric cancer Single-cell RNA sequencing Tumor immune microenvironment |
| title | Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing |
| title_full | Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing |
| title_fullStr | Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing |
| title_full_unstemmed | Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing |
| title_short | Discovery of a novel lipid metabolism-related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single-cell and bulk RNA sequencing |
| title_sort | discovery of a novel lipid metabolism related gene signature to predict outcomes and the tumor immune microenvironment in gastric cancer by integrated analysis of single cell and bulk rna sequencing |
| topic | Lipid metabolism-related gene Prognostic model Gastric cancer Single-cell RNA sequencing Tumor immune microenvironment |
| url | https://doi.org/10.1186/s12944-023-01977-y |
| work_keys_str_mv | AT jinzezhang discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT hewang discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT yutian discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT tianfengli discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT weizhang discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT lima discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT xiangjuanchen discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing AT yushanwei discoveryofanovellipidmetabolismrelatedgenesignaturetopredictoutcomesandthetumorimmunemicroenvironmentingastriccancerbyintegratedanalysisofsinglecellandbulkrnasequencing |