Epitranscriptomic RNA Modification in Non-apoptotic Forms of Regulated Cell Death

Editors

Xing Niu

China Medical University

Xiang Xue

University of New Mexico

Min Sun

Hubei University of Medicine

Impact

Establishment of an EMT gene signature for predicting OSCC patients’ clinical outcomes in the TCGA-discovery dataset. (A) Distribution of partial likelihood deviances corresponding to lambda values. (B) Determination of regression coefficients of genes in the LASSO model. (C) Distribution of risk scores and determination of high-/low-risk subgroups. Vertical dotted line indicates the median value of risk score. (D) Distribution of survival status of high- and low-risk patients. Red dot indicates dead while blue dot is indicative of alive. (E) Heat map for expression pattern of these genes in this model in high- (red) and low-risk (blue) subgroups. Red indicates upregulation and blue indicates downregulation. (F) OS analysis for high-/low-risk patients. The difference between subgroups was compared with log-rank test. (G) ROCs under 1-year, 3-year and 5-year OS for the risk score. (H) Comparison of the AUCs among the risk score and other clinical features.

Original Research

10 August 2023

Systematic establishment and verification of an epithelial-mesenchymal transition gene signature for predicting prognosis of oral squamous cell carcinoma

Jun Ai

, 5 more and

Qicheng Fu

2,319 views

2 citations

Establishment of IFNG-based co-expression modules. (A) Sample dendrogram and heatmap of clinical characteristics. (B) Scale independence along with mean connectivity under diverse soft-thresholding values. (C) Clustering dendrogram and merged modules. (D) Eigengene dendrogram and heatmap of eigengene adjacency. (E) Relationships of co-expression modules with clinical characteristics. (F, G) GO and KEGG pathways of genes in each module.

Original Research

20 June 2023

Implications of inflammatory cell death-related IFNG and co-expressed RNAs (AC006369.1 and CCR7) in breast carcinoma prognosis, and anti-tumor immunity

Yongran Deng

, 4 more and

Xueqiong Han

2,284 views

3 citations

Expression disorders of SI-NETs. (A) Manhattan plot for DEGs and DElncRNA. The three most significantly DEGs and DElncRNA are marker and labeled with their names. (B) Hierarchical clustering dendrograms of the expression patterns of DEGs that distinguish between patients with primary and metastatic SI-NETs. (C) Hierarchical clustering dendrograms of the expression patterns of DElnRNA that distinguish between patients with primary and metastatic SI-NETs. DEGs: differentially expressed genes, DElncRNA: differentially expressed lncRNA, SI-NETs: small intestine neuroendocrine tumors. (D) Infiltration of immune cells between primary and metastatic SI-NETs.

Original Research

07 April 2023

Potential effective diagnostic biomarker in patients with primary and metastatic small intestinal neuroendocrine tumors

Jianxian Chen

, 5 more and

Jie Li

2,134 views

1 citations

Molecular subtyping based on natural killer cell-associated genes. (A) Forest plot of prognostically significant natural killer cell-associated genes in the TCGA-LUAD cohort. (B) CDF curves of the TCGA-LUAD cohort. (C) CDF Delta area curves of the TCGA-LUAD cohort. (D) heat map of sample clustering at consensus k = 3 in the TCGA-LUAD cohort. (E) KM curves of the relationship between overall survival (OS) prognosis of the three subtypes in the TCGA-LUAD cohort. (F) Prognostic differences between the three molecular subtypes in the GSE72094 cohort. (G) Heatmap of prognosis significant natural killer cell genes expression in different subtypes of TCGA-LUAD.

Original Research

07 April 2023

Identification of natural killer cell associated subtyping and gene signature to predict prognosis and drug sensitivity of lung adenocarcinoma

Dexin Zhang

and

Yujie Zhao

2,982 views

5 citations

Original Research

25 November 2022

Network pharmacology- and molecular docking-based approaches to unveil the pharmacological mechanisms of dihydroartemisinin against esophageal carcinoma

Haixia Wang

3,124 views

4 citations

Original Research

23 March 2023

A novel natural killer cell-related signatures to predict prognosis and chemotherapy response of pancreatic cancer patients

Yongting Lan

, 3 more and

Min Zhu

3,091 views

2 citations

Original Research

08 December 2022

Identification of necroptosis subtypes and development of necroptosis-related risk score model for in ovarian cancer

Chen Ji

, 1 more and

Yan Wang

1,598 views

4 citations

Original Research

27 March 2023

Transcriptional landscape of myasthenia gravis revealed by weighted gene coexpression network analysis

Demin Zhang

, 3 more and

Xiaoyun Lai

2,257 views

0 citations

Original Research

24 November 2022

Transcriptome analysis reveals potential marker genes for diagnosis of Alzheimer’s disease and vascular dementia

Li Wang

, 5 more and

Jiejun Zhang

3,325 views

3 citations

Clinicopathological properties of molecular subtypes. (A) The clinicopathological characteristics of molecular subtypes in TCGA cohort; (B) The clinicopathological characteristics of molecular subtypes of GSE72094 cohort; The lower and upper parts of the proportion are the statistical significance of the distribution difference in two pairs -log10 (p-value).

Original Research

14 November 2022

Establishment of lung adenocarcinoma classification and risk model based on necroptosis-related genes

Guodong Wu

, 3 more and

Wei Zhang

2,031 views

6 citations

Construction of molecular subtypes based on cuproptosis-associated genes. (A,B) Consensus CDF curve and delta area under CDF curve when cluster number k = 2 to 10. (C) Consensus matrix when k = 3. (D,E) Kaplan-Meier survival curve of three molecular subtypes in TCGA (D) and GSE103091 (E) datasets. Log-rank test was performed. (F) The distribution of alive and dead samples in three subtypes. ANOVA was conducted. *p < 0.05.

Original Research

28 October 2022

Identification of molecular subtypes and a six-gene risk model related to cuproptosis for triple negative breast cancer

Baoxi Zhu

, 2 more and

Xiaoliang Wang

3,899 views

9 citations

Distinct PANoptosis features across RBP-based subtypes. (A–C) Levels of apoptosis, necroptosis and pyroptosis genes in human failing and nonfailing control LV myocardium specimens. (D–F) Levels of apoptosis, necroptosis and pyroptosis genes across three RBP-based subtypes (*p < 0.05; **p < 0.01; ***p < 0.001).

Original Research

14 October 2022

Landscape of RNA-binding proteins in diagnostic utility, immune cell infiltration and PANoptosis features of heart failure

Jie Li

, 6 more and

Peng Chao

4,491 views

14 citations

Single-cell landscape of adenomyosis (AM) in the immune microenvironment. (A). The workflow of this study analyzed single-cell RNA sequencing (10X Genomics) data between control and two AM between eutopic endometrium (AM_EM) and ectopic endometrium (AM_EC) samples, identifying 31 cell clusters of 42,260 cells. (B). The cell types of single cells mapping AM includes endometrial cells (EC), fibroblasts (Fibroblasts), epithelial cells (Ep), endothelial cells (En), CD8 + T, CD4 + T, initial T cells (Naive T), macrophages (Mac), predendritic cells (pDC), smooth muscle cells (SMC), and innate lymphoid cells (ILC). (C). Bubble plots show specific marker genes in different cell types. (D). Heatmap shows the levels of copy number variation in chromosomes 1 to 22 during AM. (E). Differences in cell abundance components between control and AM patients. Different colors represent different cell types. AM_EM, eutopic endometrium; AM_EC, ectopic endometrium; CNV, copy number variations; EC, endometrial cells; Ep, epithelial cells; En, endothelial cells; Naive T, initial T cells; Mac, macrophages, pDC, predendritic cells; SMC, smooth muscle cells; ILC, innate lymphoid cells; UMAP, Uniform Manifold Approximation and Projection.

Original Research

17 October 2022

Exploration the global single-cell ecological landscape of adenomyosis-related cell clusters by single-cell RNA sequencing

Jiajing Lin

, 6 more and

Ding-Yuan Zeng

3,407 views

7 citations

Original Research

13 October 2022

Definition of immune molecular subtypes with distinct immune microenvironment, recurrence, and PANoptosis features to aid clinical therapeutic decision-making

Sufeng Qiang

, 2 more and

Chunyan Dong

Immune subtypes-relevant genes and their biological significance in the TCGA dataset. (A) The main biological process (BP), molecular function (MF), cellular component (CC) and KEGG enrichment results of genes with down-regulation in IC1. (B) The main BP, MF, CC and KEGG enrichment results of genes with up-regulation in IC1. (C) GSEA for the main Hallmark and KEGG pathways activated in IC1 or IC2.

Objective: Cervical cancer poses a remarkable health burden to females globally. Despite major advances in early detection and treatment modalities, some patients still relapse. The present study proposed a novel immune molecular classification that reflected distinct recurrent risk and therapeutic responses in cervical cancer.

Methods: We retrospectively collected two cervical cancer cohorts: TCGA and GSE44001. Consensus clustering approach was conducted based on expression profiling of recurrence- and immune-related genes. The abundance of immune cells was inferred via five algorithms. Immune functions and signatures were quantified through ssGSEA. Genetic mutations were analyzed by maftools package. Immunotherapeutic response was inferred via tumor mutation burden (TMB), Tumor Immune Dysfunction and Exclusion (TIDE), and Submap methods. Finally, we developed a LASSO model for recurrence prediction.

Results: Cervical cancer samples were categorized into two immune subtypes (IC1, and IC2). IC2 exhibited better disease free survival (DFS), increased immune cell infiltration within the immune microenvironment, higher expression of immune checkpoints, higher activity of immune-relevant pathways (APC co-inhibition and co-stimulation, inflammation-promoting, MHC class I, IFN response, leukocyte and stromal fractions, macrophage regulation, and TCR Shannon), and higher frequencies of genetic mutations. This molecular classification exhibited a remarkable difference with existing immune subtypes, with diverse PANoptosis (pyroptosis, apoptosis and necroptosis) features. Patients in IC2 were more likely to respond to immunotherapy and targeted, and chemotherapeutic agents. The immune subtype-relevant signature was quantified to predict patients’ recurrence risk.

Conclusion: Altogether, we developed an immune molecular classification, which can be utilized in clinical practice to aid decision-making on recurrence management.

5,468 views

6 citations

Analysis of immune infiltration. (A) analysis of 22 immune cells using CIBERSORT. (B) Analysis of immune infiltration using ESTIMATE. (C) The expression levels of 42 immune check genes between low group and high group. (D) The differences of TIDE, IFNG, MDSC, Exclusion, Dysfunction and TAM.M2 between low group and high group. (E) IC50 of traditional drugs in low group and high group. *p < 0.05, **p < 0.01, ***p < 0.001, ***p < 0.0001, ns: no significance.

Original Research

14 October 2022

Identification of cuproptosis-related gene signature to predict prognosis in lung adenocarcinoma

Yanju Lv

, 3 more and

Long Xu

3,095 views

4 citations

The role of the RS model in predicting the benefits of immunization/chemotherapy. (A) Variations in the 22 immune cell scores amongst the different risk groups identified using the TCGA-LIHC dataset; (B) Differences in immune and matrix scores amongst the different risk groups in the TCGA-LIHC dataset; (C) Immune checkpoints differentially expressed between various groups in the TCGA-LIHC dataset; (D) The results of TIDE analysis among different groups in TCGA-LIHC dataset were different; (E) Box plots of the calculated IC50 for the drug in TCGA-LIHC dataset (Wilcox test, *p < 0.05; **p < 0.01; ***p < 0.001; and ****p < 0.0001).

Original Research

15 September 2022

Risk model of hepatocellular carcinoma based on cuproptosis-related genes

Zhiqiang Liu

, 4 more and

Wenyong Wu

Background: Owing to the heterogeneity displayed by hepatocellular carcinoma (HCC) and the complexity of tumor microenvironment (TME), it is noted that the long-term effectiveness of the cancer therapy poses a severe clinical challenge. Hence, it is essential to categorize and alter the treatment intervention decisions for these tumors.

Materials and methods: “ConsensusClusterPlus” tool was used for developing a secure molecular classification system that was based on the cuproptosis-linked gene expression. Furthermore, all clinical properties, pathway characteristics, genomic changes, and immune characteristics of different cell types involved in the immune pathways were also assessed. Univariate Cox regression and the least absolute shrinkage and selection operator (Lasso) analyses were used for designing the prognostic risk model associated with cuproptosis.

Results: Three cuproptosis-linked subtypes (clust1, clust2, and clust3) were detected. Out of these, Clust3 showed the worst prognosis, followed by clust2, while Clust1 showed the best prognosis. Three subtypes had significantly different enrichment in pathways related to Tricarboxylic Acid (TCA) cycle, cell cycle, and cell senescence (p < 0.01). The clust3 subtype with poor prognosis had a low “ImmuneScore” and low immune cell infiltration, and the three subtypes had significant differences in the antigen processing and presentation pathway of the macrophages. Clust1 had a low TIDE score and was sensitive to immunotherapy. Then, according to the prognosis-related genes of cuproptosis, a prognosis risk model related to cuproptosis was constructed, containing seven genes (KIF2C, PTTG1, CENPM, CDC20, CYP2C9, SFN, and CFHR3). “High” group had a higher TIDE score compared to the TIDE score value shown by the “Low” group, which benefited less from immunotherapy, whereas the “High” group patients were more sensitive to the conventional drugs. Finally, the prognosis risk model related to cuproptosis was combined with clinical pathological characteristics to further improve the prognostic model and survival prediction.

Conclusion: Three new molecular subgroups based on cuproptosis-linked genes were revealed, and a cuproptosis-related prognostic risk model comprising seven genes was established in this study, which could assist in predicting the prognosis and identifying the patients benefit from immunotherapy.

4,594 views

11 citations

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