ImmReg: the regulon atlas of immune-related pathways across cancer types

Jiang, Tiantongfei; Zhou, Weiwei; Chang, Zhenghong; Zou, Haozhe; Bai, Jing; Sun, Qian; Pan, Tao; Xu, Juan; Li, Yongsheng; Li, Xia

doi:10.1093/nar/gkab1041

Cited by 14 publications

(3 citation statements)

References 73 publications

(72 reference statements)

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“…In addition to gene expression regulation by TFs, transfactors like RNA binding proteins (RBPs) and microR-NAs typically bind to the 3'UTR of genes to modulate RNA stability, which also contributes to cellular RNA concentration. Based on collections of RBP and micro-RNA target genes [281,282], RBP and microRNA regulons can be investigated similarly to the TF regulons [283] in scRNA-seq. In fact, this kind of co-expression module-based analysis can be extended to the examination of cellular signaling pathway activities.…”

Section: Discussionmentioning

confidence: 99%

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Pan

Chen

et al. 2022

Military Med Res

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The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

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Section: Discussionmentioning

confidence: 99%

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Pan

Chen

et al. 2022

Military Med Res

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“…Emerging data suggest that both lncRNAs and miRNAs could regulate many immune-related pathways to affect the development of disease, which have been described by the work immReg and immLnc (Jiang et al, 2021;Li et al, 2020). As the unique sensory organ of vision, the eye displays ocular immune privilege that prevents inflammatory insults (Nieto-Aristizabal et al, 2022).…”

Section: Ncrnas For Immune Regulations In Inflammatory Eye Diseasesmentioning

confidence: 99%

Roles of non‐coding RNAs in eye development and diseases

Shi

Xue

et al. 2023

WIREs RNA

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The prevalence of ocular disorders is dramatically increasing worldwide, especially those that cause visual impairment and permanent loss of vision, including cataract, glaucoma, age‐related macular degeneration, and diabetic retinopathy. Extensive evidence has shown that ncRNAs are key regulators in various biogenesis and biological functions, controlling gene expression related to histogenesis and cell differentiation in ocular tissues. Aberrant expression and function of ncRNA can lead to dysfunction of visual system and mediate progression of eye disorders. Here, we mainly offer an overview of the role of precise modulation of ncRNAs in eye development and function in patients with eye diseases. We also highlight the challenges and future perspectives in conducting ncRNA studies, focusing specifically on the role of ncRNAs that may hold expanded promise for their diagnostic and therapeutic applications in various eye diseases.This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA in Disease and Development > RNA in Disease RNA in Disease and Development > RNA in Development

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“…The lncRNAs perform a wide range of functions such as binding proteins, targeting mRNAs for degeneration, harboring intronic miRNAs, and modulating gene expression via competitive endogenous RNA (ceRNA) networks ( 24 ). In cancer, dysregulation of lncRNAs is extensively prevalent and has been demonstrated to enhance carcinogenesis and progression ( 25 ). Numerous studies have investigated the lncRNA expression profile of LUAD and their potential as diagnostic and prognostic indicators of patients with LUAD ( 26 ).…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of Lactate Metabolism-Related lncRNA Signature as a Prognostic Model for Lung Adenocarcinoma

et al. 2022

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BackgroundLung cancer has been a prominent research focus in recent years due to its role in cancer-related fatalities globally, with lung adenocarcinoma (LUAD) being the most prevalent histological form. Nonetheless, no signature of lactate metabolism-related long non-coding RNAs (LMR-lncRNAs) has been developed for patients with LUAD. Accordingly, we aimed to develop a unique LMR-lncRNA signature to determine the prognosis of patients with LUAD.MethodThe Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were utilized to derive the lncRNA expression patterns. Identification of LMR-lncRNAs was accomplished by analyzing the co-expression patterns between lncRNAs and LMR genes. Subsequently, the association between lncRNA levels and survival outcomes was determined to develop an effective signature. In the TCGA cohort, Cox regression was enlisted to build an innovative signature consisting of three LMR-lncRNAs, which was validated in the GEO validation cohort. GSEA and immune infiltration analysis were conducted to investigate the functional annotation of the signature and the function of each type of immune cell.ResultsFourteen differentially expressed LMR-lncRNAs were strongly correlated with the prognosis of patients with LUAD and collectively formed a new LMR-lncRNA signature. The patients could be categorized into two cohorts based on their LMR-lncRNA signatures: a low-risk and high-risk group. The overall survival of patients with LUAD in the high-risk group was considerably lower than those in the low-risk group. Using Cox regression, this signature was shown to have substantial potential as an independent prognostic factor, which was further confirmed in the GEO cohort. Moreover, the signature could anticipate survival across different groups based on stage, age, and gender, among other variables. This signature also correlated with immune cell infiltration (including B cells, neutrophils, CD4+ T cells, CD8+ T cells, etc.) as well as the immune checkpoint blockade target CTLA-4.ConclusionWe developed and verified a new LMR-lncRNA signature useful for anticipating the survival of patients with LUAD. This signature could give potentially critical insight for immunotherapy interventions in patients with LUAD.

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ImmReg: the regulon atlas of immune-related pathways across cancer types

Cited by 14 publications

References 73 publications

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Roles of non‐coding RNAs in eye development and diseases

Development and Validation of Lactate Metabolism-Related lncRNA Signature as a Prognostic Model for Lung Adenocarcinoma

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