Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma
BackgroundHepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-07-01
|
| Series: | Frontiers in Immunology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1237350/full |
| _version_ | 1797770984225243136 |
|---|---|
| author | Tao Liu Chao Li Jiantao Zhang Han Hu Chenyao Li |
| author_facet | Tao Liu Chao Li Jiantao Zhang Han Hu Chenyao Li |
| author_sort | Tao Liu |
| collection | DOAJ |
| description | BackgroundHepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes a central position in the elimination of apoptotic cells. However, comprehensive investigations exploring the role of efferocytosis-related genes (EFRGs) in HCC are sparse, and their regulatory influence on HCC immunotherapy and targeted drug interventions remain poorly understood.MethodsRNA sequencing data and clinical characteristics of HCC patients were acquired from the TCGA database. To identify prognostically significant genes in HCC, we performed the limma package and conducted univariate Cox regression analysis. Subsequently, machine learning algorithms were employed to identify hub genes. To assess the immunological landscape of different HCC subtypes, we employed the CIBERSORT algorithm. Furthermore, single-cell RNA sequencing (scRNA-seq) was utilized to investigate the expression levels of ERFGs in immune cells and to explore intercellular communication within HCC tissues. The migratory capacity of HCC cells was evaluated using CCK-8 assays, while drug sensitivity prediction reliability was determined through wound-healing assays.ResultsWe have successfully identified a set of nine genes, termed EFRGs, that hold significant potential for the establishment of a hepatocellular carcinoma-specific prognostic model. Furthermore, leveraging the individual risk scores derived from this model, we were able to stratify patients into two distinct risk groups, unveiling notable disparities in terms of immune infiltration patterns and response to immunotherapy. Notably, the model’s capacity to accurately predict drug responses was substantiated through comprehensive experimental investigations, encompassing wound-healing assay, and CCK8 experiments conducted on the HepG2 and Huh7 cell lines.ConclusionsWe constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy. |
| first_indexed | 2024-03-12T21:30:56Z |
| format | Article |
| id | doaj.art-c31021caab5d4056ac8475f0061f42b8 |
| institution | Directory Open Access Journal |
| issn | 1664-3224 |
| language | English |
| last_indexed | 2024-03-12T21:30:56Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj.art-c31021caab5d4056ac8475f0061f42b82023-07-27T21:09:20ZengFrontiers Media S.A.Frontiers in Immunology1664-32242023-07-011410.3389/fimmu.2023.12373501237350Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinomaTao Liu0Chao Li1Jiantao Zhang2Han Hu3Chenyao Li4Colorectal and Anal Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, ChinaDepartment of General, Visceral, and Transplant Surgery, Ludwig-Maximilians University, Munich, GermanyColorectal and Anal Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, ChinaColorectal and Anal Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, ChinaColorectal and Anal Surgery Department, General Surgery Center, First Hospital of Jilin University, Changchun, Jilin, ChinaBackgroundHepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes a central position in the elimination of apoptotic cells. However, comprehensive investigations exploring the role of efferocytosis-related genes (EFRGs) in HCC are sparse, and their regulatory influence on HCC immunotherapy and targeted drug interventions remain poorly understood.MethodsRNA sequencing data and clinical characteristics of HCC patients were acquired from the TCGA database. To identify prognostically significant genes in HCC, we performed the limma package and conducted univariate Cox regression analysis. Subsequently, machine learning algorithms were employed to identify hub genes. To assess the immunological landscape of different HCC subtypes, we employed the CIBERSORT algorithm. Furthermore, single-cell RNA sequencing (scRNA-seq) was utilized to investigate the expression levels of ERFGs in immune cells and to explore intercellular communication within HCC tissues. The migratory capacity of HCC cells was evaluated using CCK-8 assays, while drug sensitivity prediction reliability was determined through wound-healing assays.ResultsWe have successfully identified a set of nine genes, termed EFRGs, that hold significant potential for the establishment of a hepatocellular carcinoma-specific prognostic model. Furthermore, leveraging the individual risk scores derived from this model, we were able to stratify patients into two distinct risk groups, unveiling notable disparities in terms of immune infiltration patterns and response to immunotherapy. Notably, the model’s capacity to accurately predict drug responses was substantiated through comprehensive experimental investigations, encompassing wound-healing assay, and CCK8 experiments conducted on the HepG2 and Huh7 cell lines.ConclusionsWe constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy.https://www.frontiersin.org/articles/10.3389/fimmu.2023.1237350/fullHCCefferocytosissingle-cell sequencingimmunotherapybiomarker |
| spellingShingle | Tao Liu Chao Li Jiantao Zhang Han Hu Chenyao Li Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma Frontiers in Immunology HCC efferocytosis single-cell sequencing immunotherapy biomarker |
| title | Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| title_full | Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| title_fullStr | Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| title_full_unstemmed | Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| title_short | Unveiling efferocytosis-related signatures through the integration of single-cell analysis and machine learning: a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| title_sort | unveiling efferocytosis related signatures through the integration of single cell analysis and machine learning a predictive framework for prognosis and immunotherapy response in hepatocellular carcinoma |
| topic | HCC efferocytosis single-cell sequencing immunotherapy biomarker |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2023.1237350/full |
| work_keys_str_mv | AT taoliu unveilingefferocytosisrelatedsignaturesthroughtheintegrationofsinglecellanalysisandmachinelearningapredictiveframeworkforprognosisandimmunotherapyresponseinhepatocellularcarcinoma AT chaoli unveilingefferocytosisrelatedsignaturesthroughtheintegrationofsinglecellanalysisandmachinelearningapredictiveframeworkforprognosisandimmunotherapyresponseinhepatocellularcarcinoma AT jiantaozhang unveilingefferocytosisrelatedsignaturesthroughtheintegrationofsinglecellanalysisandmachinelearningapredictiveframeworkforprognosisandimmunotherapyresponseinhepatocellularcarcinoma AT hanhu unveilingefferocytosisrelatedsignaturesthroughtheintegrationofsinglecellanalysisandmachinelearningapredictiveframeworkforprognosisandimmunotherapyresponseinhepatocellularcarcinoma AT chenyaoli unveilingefferocytosisrelatedsignaturesthroughtheintegrationofsinglecellanalysisandmachinelearningapredictiveframeworkforprognosisandimmunotherapyresponseinhepatocellularcarcinoma |