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T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis.

ABSTRACT: Despite autophagy modulating tumor immunity in the tumor microenvironment (TME), the immunotherapeutic efficacy and potential mechanism of autophagy signature was not explicit. We manually curated an autophagy gene set and defined a pan-cancer autophagy signature by comparing malignant tissues and normal tissues in The Cancer Genome Atlas (TCGA) cohort. The pan-cancer autophagy signature was derived from T proliferating cells as demonstrated in multiple single-cell RNA sequencing (scRNA-seq) datasets. The pan-cancer autophagy signature could influence the cell-cell interactions in the TME and predict the responsiveness of immune checkpoint inhibitors (ICIs) in the metastatic renal cell carcinoma, non-small cell lung cancer, bladder cancer, and melanoma cohorts. Metabolism inactivation accompanied with dysregulation of autophagy was investigated with transcriptomic and proteomic data. The immunotherapeutic predictive role and mechanism regulation of the autophagy signature was validated in an in-house cohort. Our study provides valuable insights into the mechanisms of ICI resistance.

SUBMITTER: Fan Z

PROVIDER: S-EPMC10784705 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature

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T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis.

Fan Zhongqi Z Liu Yutao Y Li Chengcheng C Jiang Yanfang Y Wang Nanya N Wang Mingda M Li Chao C Diao Yongkang Y Qiu Wei W Zhu Xin X Wang Guoqiang G Cai Shangli S Yang Tian T Lv Guoyue G

iScience 20231208 1

Despite autophagy modulating tumor immunity in the tumor microenvironment (TME), the immunotherapeutic efficacy and potential mechanism of autophagy signature was not explicit. We manually curated an autophagy gene set and defined a pan-cancer autophagy signature by comparing malignant tissues and normal tissues in The Cancer Genome Atlas (TCGA) cohort. The pan-cancer autophagy signature was derived from T proliferating cells as demonstrated in multiple single-cell RNA sequencing (scRNA-seq) dat ...[more]

PMID: 38222108

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Project description:BackgroundAngiogenesis and remodeling (AR) is necessary for the growth and metastasis of cancers. Although AR related genes involved in this process are reported, the correlation between AR and clinical outcome, immune cell infiltration, and immunotherapy is still unknown in diverse cancers. This study aimed to investigate the role of AR in the tumor immune microenvironment (TIME) in pan-cancer, and explore its values in prognostic prediction and therapeutic responses.MethodsFirstly, AR genes (including angiogenesis genes and blood vessel remodeling genes) are collected from MsigDB database. The differential expression, and prognostic value of AR genes were studied in 33 tumor types based on TCGA and GTEx data. The AR score of each sample was calculated using the "ssGSEA" function of R package "GSVA" in pan-cancer. The correlation of the AR score with TIME index, such as the amount of stromal and immune components and the immune cell infiltration, was evaluated via integrating multiple computational methods. And we also utilized IMvigor210 and GSE78220 data to explore the prediction value of the AR score on the immunotherapy response.ResultsSignificant differences in AR gene expression between tumors and adjacent normal tissues were found in most cancer types. The AR score varied depending on the types of tumors, and high score was related to worse survival in various tumors, such as pancreatic and stomach adenocarcinoma and so on. Moreover, the AR score was further explored to be positively correlated with proportions and pathways of immune and stromal in TIME. And the AR score was positively correlated with immunosuppressive cells, including TAMs and iTregs, while negatively with CD8+ T cells. Further analysis revealed that patients with high AR had worse therapy efficacy and survival status in bladder cancer and melanomas.ConclusionsOur systematic analysis revealed that AR is closely associated TIME, and prognosis, and clinical characteristics in multiple cancers. Targeting AR genes may activate immune microenvironment and increase the efficacy of immunotherapy.

Project description:BackgroundOvarian cancer (OC) is one of the most malignant tumors in the female reproductive system, with a poor prognosis. Various responses to treatments including chemotherapy and immunotherapy are observed among patients due to their individual characteristics. Applicable prognostic markers could make it easier to refine risk stratification for OC patients. Autophagy is closely implicated in the occurrence and development of tumors, including OC. Whether autophagy -related genes can be used as prognostic markers for OC patients remains unclear.MethodsThe gene transcriptome data of 374 OC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The correlation between the autophagy levels and outcomes of OC patients was identified through the single sample gene set enrichment analysis (ssGSEA). Recognized molecular markers of autophagy in different clinical specimens were detected by immunohistochemistry (IHC) assay. The gene set enrichment analysis (GSEA), ESTIMATE, and CIBERSORT analysis were applied to explore the correlation of autophagy with the tumor immune microenvironment (TIME). Single-cell RNA-sequencing (scRNA-seq) data from seven OC patients were included for characterizing cell-cell interaction patterns of autophagy-high or low tumor cells. Machine learning, Stepwise Cox regression and LASSO-Cox analysis were used to screen autophagy hub genes, which were used to establish an autophagy-related signature for prognosis evaluation. Four tumor immunotherapy cohorts were obtained from the GEO (Gene Expression Omnibus) database and the literature for autophagy risk score validation.ResultsThe autophagy levels were closely related to the prognosis of the OC patients. Additionally, the autophagy levels were correlated with TIME status including immune score, and immune-cell infiltration. The scRNA-seq analysis found that tumor cells with high or low autophagy levels had different interactions with immune cells, especially macrophages. Eight autophagy-hub genes (ZFYVE1, AMBRA1, LAMP2, TRAF6, PDPK1, ATG2B, DAPK1 and TP53INP2) were screened for an autophagy-related signature. According to this signature, higher risk score was correlated with poor prognosis and better immunotherapy response in the OC patients.ConclusionsThe autophagy-related signature is applicable to predict the prognosis and immune checkpoint inhibitors (ICIs) therapy efficiency in OC patients. It is possible to identify OC patients who will respond to ICIs therapy and have a favorable prognosis, although more verification is needed.

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Project description:BackgroundCurrent paradigms of anti-tumor therapies are not qualified to evacuate the malignancy ascribing to cancer stroma's functions in accelerating tumor relapse and therapeutic resistance. Cancer-associated fibroblasts (CAFs) has been identified significantly correlated with tumor progression and therapy resistance. Thus, we aimed to probe into the CAFs characteristics in esophageal squamous cancer (ESCC) and construct a risk signature based on CAFs to predict the prognosis of ESCC patients.MethodsThe GEO database provided the single-cell RNA sequencing (scRNA-seq) data. The GEO and TCGA databases were used to obtain bulk RNA-seq data and microarray data of ESCC, respectively. CAF clusters were identified from the scRNA-seq data using the Seurat R package. CAF-related prognostic genes were subsequently identified using univariate Cox regression analysis. A risk signature based on CAF-related prognostic genes was constructed using Lasso regression. Then, a nomogram model based on clinicopathological characteristics and the risk signature was developed. Consensus clustering was conducted to explore the heterogeneity of ESCC. Finally, PCR was utilized to validate the functions that hub genes play on ESCC.ResultsSix CAF clusters were identified in ESCC based on scRNA-seq data, three of which had prognostic associations. A total of 642 genes were found to be significantly correlated with CAF clusters from a pool of 17080 DEGs, and 9 genes were selected to generate a risk signature, which were mainly involved in 10 pathways such as NRF1, MYC, and TGF-Beta. The risk signature was significantly correlated with stromal and immune scores, as well as some immune cells. Multivariate analysis demonstrated that the risk signature was an independent prognostic factor for ESCC, and its potential in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the CAF-based risk signature and clinical stage was developed, which exhibited favorable predictability and reliability for ESCC prognosis prediction. The consensus clustering analysis further confirmed the heterogeneity of ESCC.ConclusionThe prognosis of ESCC can be effectively predicted by CAF-based risk signatures, and a comprehensive characterization of the CAF signature of ESCC may aid in interpreting the response of ESCC to immunotherapy and offer new strategies for cancer treatment.

Dataset Information

T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis.

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T proliferating cells derived autophagy signature associated with prognosis and immunotherapy resistance in a pan-cancer analysis.

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