Dataset Information

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

ABSTRACT:

Background

Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown.

Methods

In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, we used single-cell RNAsequencing data for analysis and determined 83 genes as DC marker genes. Following that, integrative machine learning procedure was developed to construct a signature for DC marker genes.

Results

Using TCGA bulk-RNA sequencing data as the training set, we developed a signature consisting of seven genes and classified patients by their risk status. Another six independent cohorts demonstrated the signature' s prognostic power, and multivariate analysis demonstrated it was an independent prognostic factor. LUAD patients in the high-risk group displayed more advanced features, discriminatory immune-cell infiltrations and immunosuppressive states. Cell-cell communication analysis indicates that tumor cells with lower risk scores communicate more actively with the tumor microenvironment. Eight independent immunotherapy cohorts revealed that patients with low-risk had better immunotherapy responses. Drug sensitivity analysis indicated that targeted therapy agents exhibited greater sensitivity to low-risk patients, while chemotherapy agents displayed greater sensitivity to high-risk patients. In vitro experiments confirmed that CTSH is a novel protective factor for LUAD.

Conclusions

An unique signature based on DC marker genes that is highly predictive of LUAD patients' prognosis and response to immunotherapy. CTSH is a new biomarker for LUAD.

SUBMITTER: Zhang L

PROVIDER: S-EPMC10590321 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Publications

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Zhang Liangyu L Guan Maohao M Zhang Xun X Yu Fengqiang F Lai Fancai F

Journal of cancer research and clinical oncology 20230728 15

<h4>Background</h4>Innate immune effectors, dendritic cells (DCs), influence cancer prognosis and immunotherapy significantly. As such, dendritic cells are important in killing tumors and influencing tumor microenvironment, whereas their roles in lung adenocarcinoma (LUAD) are largely unknown.<h4>Methods</h4>In this study, 1658 LUAD patients from different cohorts were included. In addition, 724 cancer patients who received immunotherapy were also included. To identify DC marker genes in LUAD, w ...[more]

PMID: 37507593

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Project description:BackgroundLung adenocarcinoma (LUAD) as a frequent type of lung cancer has a 5-year overall survival rate of lower than 20% among patients with advanced lung cancer. This study aims to construct a risk model to guide immunotherapy in LUAD patients effectively.Materials and methodsLUAD Bulk RNA-seq data for the construction of a model, single-cell RNA sequencing (scRNA-seq) data (GSE203360) for cell cluster analysis, and microarray data (GSE31210) for validation were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used the Seurat R package to filter and process scRNA-seq data. Sample clustering was performed in the ConsensusClusterPlus R package. Differentially expressed genes (DEGs) between two groups were mined by the Limma R package. MCP-counter, CIBERSORT, ssGSEA, and ESTIMATE were employed to evaluate immune characteristics. Stepwise multivariate analysis, Univariate Cox analysis, and Lasso regression analysis were conducted to identify key prognostic genes and were used to construct the risk model. Key prognostic gene expressions were explored by RT-qPCR and Western blot assay.ResultsA total of 27 immune cell marker genes associated with prognosis were identified for subtyping LUAD samples into clusters C3, C2, and C1. C1 had the longest overall survival and highest immune infiltration among them, followed by C2 and C3. Oncogenic pathways such as VEGF, EFGR, and MAPK were more activated in C3 compared to the other two clusters. Based on the DEGs among clusters, we confirmed seven key prognostic genes including CPA3, S100P, PTTG1, LOXL2, MELTF, PKP2, and TMPRSS11E. Two risk groups defined by the seven-gene risk model presented distinct responses to immunotherapy and chemotherapy, immune infiltration, and prognosis. The mRNA and protein level of CPA3 was decreased, while the remaining six gene levels were increased in clinical tumor tissues.ConclusionImmune cell markers are effective in clustering LUAD samples into different subtypes, and they play important roles in regulating the immune microenvironment and cancer development. In addition, the seven-gene risk model may serve as a guide for assisting in personalized treatment in LUAD patients.

Project description:Background: Melanoma is the most lethal, and one of the most aggressive forms of cutaneous malignancies, which poor response to treatment has always puzzled clinicians. As is known to all, aging and immune microenvironment are two crucial factors impacting melanoma biological progress through the tumor microenvironment (TME). However, reliable biomarkers for predicting melanoma prognosis based on aging and immune microenvironment and therapeutic efficacy of immune checkpoints remain to be determined. Methods: The aging-related genes (ARGs) were obtained from the Human Ageing Genomic Resources and immune-related genes (IRGs) were downloaded from the Immunology database as well as Analysis Portal (ImmPort) database. Next, we initially performed LASSO regression and multivariate Cox regression to identify prognostic ARGs and IRGs in the TCGA and GSE65904 datasets, and firstly constructed a novel comprehensive index of aging and immune (CIAI) signature. Finally, in vitro molecular biology experiments were performed to assess the regulatory role of CNTFR in melanoma cell lines proliferation and migration, macrophage recruitment, and M2 polarization. Results: This novel CIAI signature consisted of 7 genes, including FOXM1, TP63, ARNTL, KIR2DL4, CCL8, SEMA6A, and CNTFR, in which melanoma patients in the high-CIAI group had shorter OS, DSS, and PFI, indicating CIAI model served as an independent prognostic index. Moreover, we found the CIAI score was potentially correlated with immune scores, estimate score, immune cell infiltration level, tumor microenvironment, immunotherapy effect, and drug sensitivity. Finally, CNTFR might function as oncogenes in melanoma cell lines and the silencing of CNTFR reduced macrophage recruitment and M2 polarization. Conclusion: In this study, we have first presented a novel prognostic CIAI model applied to assess immune checkpoint therapy and the efficacy of conventional chemotherapy agents in melanoma patients. Thus providing a new insight for combating melanoma.

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Project description:BackgroundTumor development involves the critical role of programmed cell death (PCD), but the correlation between colon adenocarcinoma (COAD) and PCD-related genes is not clear.MethodsSubtyping analysis of COAD was performed by consensus clustering based on The Cancer Genome Atlas (TCGA), with the AC-ICAM queue from the cBioportal database as a validation set. Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. Patients' immunotherapy response was predicted by the TIDE and aneuploidy scores. Pathway enrichment analysis was conducted with gene set enrichment analysis (GSEA). A RiskScore model was established with independent prognostic PCD-related genes filtered by Cox regression analysis. The mafCompare function was used to compare the differences in mutation rates of somatic genes. Wound healing, transwell assays and Flow cytometer were applied to measure the cell migration, invasion and apoptosis.ResultsThe patients were grouped into S1 and S2 subtypes based on a total of 21 PCD genes associated with the prognostic outcomes of COAD. Specifically, patients of S1 subtype were mainly related to the pathway activation in tumor invasion and deterioration and had a worse prognosis. A RiskScore model was established based on six prognostic genes, including two protective genes (ATOH1, ZG16) and four risk genes (HSPA1A, SEMA4C, CDKN2A, ARHGAP4). Notably, silencing of CDKN2A inhibited the activity of migration and invasion and promoted apoptosis of tumor cells. Based on the RiskScore model, the patients were grouped into high- and low-risk groups. Independent prognostic factors, namely, Age, pathologic_M, pathologic_stage, and RiskScore, were integrated to develop a nomogram with strong good prediction performance. High-risk group had high-expressed immune checkpoint genes and higher TIDE scores, showing a strong immune escape ability and less active immunotherapy response. Compared to the low-risk group, TP53 exhibited a higher rate of somatic mutation in the high-risk group.ConclusionWe constructed a RiskScore model with six PCD-related genes for the prognostic assessment of COAD, providing a valuable insight into the exploration of new targets for the prognostic improvement in COAD.

Project description:The outcome of colon adenocarcinoma (COAD) patients remains dismal, and lactate metabolism has been characterized to promote tumor development and immune evasion. Based on the above background, it is worthwhile to explore novel prognostic and therapeutic biomarkers for COAD patients from the aspect of lactate metabolism. Above all, 228 available lactate-metabolism-related genes (LMRGs) were acquired, and the landscape of copy number variation and the expression difference of mRNA levels between colon normal and tumor samples were investigated among these LMRGs. Importantly, eight overall survival (OS)-involved LMRGs were then distinguished by means of univariate Cox regression analysis in both GSE40967 and TCGA-COAD data sets. Subsequently, prognostic risk scores were established, integrating seven OS-related LMRGs by LASSO Cox regression analysis in the GSE40967 set, and then verified in the TCGA-COAD cohort. From the comprehensive analyses, COAD patients with high risk had comparatively more inferior survival probability in all populations of the study, and they tended to have more severe clinicopathological features with the risk score increasing. Moreover, by integrating age, AJCC T and pathological stage, and risk score, we constructed a prognostic nomogram that demonstrated great prediction effectiveness for OS of COAD patients. Furthermore, the potential effect of various risk score on tumor immune was assessed from enrichment of immune-related pathways, tumor-infiltrating immune cells, and expression levels of immune checkpoints separately. We could draw a conclusion that COAD patients with higher lactate-metabolism-related risk scores may acquire an immunosuppressive tumor microenvironment, which subsequently led to immune escapes and poor prognoses. Conclusively, all findings in the present study illustrate a great prognostic value of the lactate-metabolism-related risk signature, providing more in-depth insights into the indispensable function of lactate metabolism in prognosis and tumor immunity of COAD.

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Dataset Information

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Background

Methods

Results

Conclusions

Publications

Machine-learning and combined analysis of single-cell and bulk-RNA sequencing identified a DC gene signature to predict prognosis and immunotherapy response for patients with lung adenocarcinoma.

Similar Datasets

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