The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance

Hu, Xiangyou; Wu, Liuxing; Yao, Yanxin; Ma, Junfu; Li, Xiangchun; Shen, Hongru; Liu, Luyang; Dai, Hongji; Wang, Wei; Chu, Xinlei; Sheng, Chao; Yang, Meng; Zheng, Hong; Song, Fengju; Chen, Kexin; Liu, Ben

doi:10.1016/j.isci.2022.105075

Cited by 5 publications

(2 citation statements)

References 73 publications

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“…In the current research, the four prognostic autophagy-related genes are applied to the constructed prognostic signature, and the risk scores of patients with GC are calculated with this signature. Previously, there are some prognostic signatures based on GC's autophagy-related genes (46), such as autophagy-clinical prognostic index (47) and a six-gene-based prognostic model (48). The signatures above only reveal a predictive power, while our fourgene prognostic signature can predict the prognosis, biomarkers in immunotherapy, and therapy response.…”

Section: Discussionmentioning

confidence: 95%

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Yao

Hu²,

Ma³

et al. 2023

Front. Oncol.

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IntroductionAutophagy can be triggered by oxidative stress and is a double-edged sword involved in the progression of multiple malignancies. However, the precise roles of autophagy on immune response in gastric cancer (GC) remain clarified.MethodsWe endeavor to explore the novel autophagy-related clusters and develop a multi-gene signature for predicting the prognosis and the response to immunotherapy in GC. A total of 1505 patients from eight GC cohorts were categorized into two subtypes using consensus clustering. We compare the differences between clusters by the multi-omics approach. Cox and LASSO regression models were used to construct the prognostic signature.ResultsTwo distinct clusters were identified. Compared with cluster 2, the patients in cluster 1 have favorable survival outcomes and lower scores for epithelial-mesenchymal transition (EMT). The two subtypes are further characterized by high heterogeneity concerning immune cell infiltration, somatic mutation pattern, and pathway activity by gene set enrichment analysis (GSEA). We obtained 21 autophagy-related differential expression genes (DEGs), in which PTK6 amplification and BCL2/CDKN2A deletion were highly prevalent. The four-gene (PEA15, HSPB8, BNIP3, and GABARAPL1) risk signature was further constructed with good predictive performance and validated in 3 independent datasets including our local Tianjin cohort. The risk score was proved to be independent prognostic factor. A prognostic nomogram showed robust validity of GC survival. The risk score was significantly associated with immune cell infiltration status, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint molecules. Furthermore, the model was efficient for predicting the response to tumor-targeted agent and immunotherapy and verified by the IMvigor210 cohort. This model is also capable of discriminating between low and high-risk patients receiving chemotherapy.ConclusionAltogether, our exploratory research on the landscape of autophagy-related patterns may shed light on individualized therapies and prognosis in GC.

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Section: Discussionmentioning

confidence: 95%

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Yao

Hu²,

Ma³

et al. 2023

Front. Oncol.

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“…To evaluate the activity of enhancers where eRNA is transcribed, eRNA-Anno profiles active enhancer markers (H3K27ac and H3K4me1) and chromatin accessibility of eRNA regions ( Figure 4B ). Given that mutations in eRNA regions are always related to eRNA expression and subsequent disease development [45], the clinically relevant mutations within query eRNA regions are shown ( Figure 4C ). The interactome and predicted functions of eRNAs based on the selected networks are displayed in the second part ( Figure 5 ).…”

Section: Resultsmentioning

confidence: 99%

eRNA-IDO: a one-stop platform for identification, interactome discovery and functional annotation of enhancer RNAs

Zhang,

Gong,

Ding

et al. 2023

Preprint

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Increasing evidence proves the transcription of enhancer RNA (eRNA) and its important role in gene regulation. However, we are only at the infancy stage of understanding eRNA interactions with other biomolecules and the corresponding functionality. To accelerate eRNA mechanistic study, we present the first integrative computational platform for humaneRNAidentification, interactome discovery, and functional annotation, termed eRNA-IDO. eRNA-IDO comprises two modules: eRNA-ID and eRNA-Anno. Functionally, eRNA-ID identifies eRNAs fromde novoassembled transcriptomes. The bright spot of eRNA-ID is indeed the inclusion of 8 kinds of enhancer makers, whose combination enables users to personalize enhancer regions flexibly and conveniently. In addition, eRNA-Anno provides cell/tissue specific functional annotation for any novel and known eRNAs through discovering eRNA interactome from the prebuilt or user-defined eRNA-coding gene networks. The pre-built networks include GTEx-based normal co-expression networks, TCGA-based cancer co-expression networks, and omics-based eRNA-centric regulatory networks. Our eRNA-IDO carries sufficient practicability and significance for understanding the biogenesis and functions of eRNAs. The eRNA-IDO server is freely available athttp://bioinfo.szbl.ac.cn/eRNA_IDO/.

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Comprehensive profiling of enhancer RNA in stage II/III colorectal cancer defines two prognostic subtypes with implications for immunotherapy

Bu,

Liu,

Zhang

et al. 2023

Clin Transl Oncol

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The integrated landscape of eRNA in gastric cancer reveals distinct immune subtypes with prognostic and therapeutic relevance

Cited by 5 publications

References 73 publications

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

Comprehensive analysis of autophagy-related clusters and individual risk model for immunotherapy response prediction in gastric cancer

eRNA-IDO: a one-stop platform for identification, interactome discovery and functional annotation of enhancer RNAs

Comprehensive profiling of enhancer RNA in stage II/III colorectal cancer defines two prognostic subtypes with implications for immunotherapy

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