Chang Gu

Bio: Chang Gu is an academic researcher from Tongji University. The author has contributed to research in topics: Lung cancer & KEGG. The author has an hindex of 9, co-authored 13 publications receiving 198 citations. Previous affiliations of Chang Gu include Shanghai Jiao Tong University.

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

Abstract: N6-Methyladenosine (m6A) RNA modification brings a new dawn for RNA modification researches in recent years. This posttranscriptional RNA modification is dynamic and reversible, and is regulated by methylases ("writers"), demethylases ("erasers"), and proteins that preferentially recognize m6A modifications ("readers"). The change of RNA m6A modification regulates RNA metabolism in eucaryon, including translation, splicing, exporting, decay, and processing. Thereby the dysregulation of m6A may lead to tumorigenesis and progression. Given the tumorigenic role of abnormal m6A expression, m6A regulators may function as potential clinical therapeutic targets for cancers. In this review, we emphasize on the underlying mechanisms of m6A modifications in tumorigenesis and further introduce the potential m6A regulators-associated therapeutic targets for tumor therapy.

Comprehensive Analysis of the Prognostic Role and Mutational Characteristics of m6A-Related Genes in Lung Squamous Cell Carcinoma.

Abstract: Background There have been limited treatment therapies for lung squamous cell carcinoma (LUSC). M6A-related genes may be the next therapeutic targets for LUSC. In this study, we explored the prognostic role and mutational characteristics of m6A-related genes in LUSC. Methods LUSC gene expression data, mutational data, and corresponding clinical information were extracted from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) were identified, and the mutation characteristics of LUSC patients were explored. Then, m6A-related genes were extracted and the correlations among the genes were detected. Finally, the prognostic roles of the genes were investigated and the nomogram model was developed. Besides, the protein-protein interaction (PPI) network was used to explore the potential interactions among the genes. Results In total, there are 551 LUSC samples enrolled in our study, containing 502 LUSC tumor samples and 49 adjacent normal LUSC samples, respectively. There were 2970 upregulated DEGs and 1806 downregulated DEGs were further explored. IGF2BP1 and RBM15 had significant co-occurrence frequency (p < 0.05). Besides, METTL14 and ZC3H13 or YTHDF3 also had significant co-occurrence frequency (p < 0.05). All the m6A-related genes represent the positive correlation. WTAP was identified as a prognostic gene in the TCGA database while YTHDC1 and YTHDF1 were identified as prognostic genes. In multivariate Cox analysis, YTHDF1, age, pN stage, pTNM stage, and smoking were all identified as significant prognostic factors for OS. Conclusion We investigated the expression patterns and mutational characteristics of LUSC patients and identified three potential independent prognostic m6A-related genes (WTAP, YTHDC1, and YTHDF1) for OS in LUSC patients.

Hippo Pathway Core Genes Based Prognostic Signature and Immune Infiltration Patterns in Lung Squamous Cell Carcinoma.

Abstract: Background We investigated the prognostic effects and their patterns of immune infiltration of hippo pathway core genes in lung squamous cell carcinoma, in order to find some clues for underlying mechanisms of LUSC tumorigenesis and help developing new therapeutic methods. Methods The mutational data, transcriptome data and corresponding clinical medical information of LUSC patients were extracted from The Cancer Genome Atlas (TCGA) database. Differential expression genes (DEGs) and Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were explored. Survival analysis for the hippo core genes and the prognostic model were performed. Immune infiltration was estimated by CIBERSORT algorithm and some immune checkpoints-related genes were further investigated. Results Overall, 551 LUSC samples were included in our study, consisting of 502 LUSC tumor samples and 49 adjacent normal samples, respectively. There were 1910 up-regulated DEGs and 2253 down-regulated DEGs were finally identified. The top five mutational hippo pathway core genes were LATS1 (4%), WWC1 (2%), TAOK1 (2%), TAOK3 (2%), and TAOK2 (2%), respectively. the mutation of LATS2 was highly associated with co-mutational NF2 (P <0.05) and TAOK1 (P <0.05). In survival analyses, we found only WWC1 (log-rank p = 0.046, HR = 1.32, 95% CI = 1-1.73) and LATS2 (log-rank p = 0.013, HR = 1.41, 95%CI = 1.08-1.86) had significant prognostic roles. After getting the three subgroups according to the subtyping results, we demonstrated that T cell gamma delta (p = 5.78e-6), B cell memory (p = 4.61e-4) and T cell CD4+ memory resting (p = 2.65e-5) had significant differences among the three groups. SIGLEC15 (P <0.01) and CD274 (P <0.05) also had statistical differences among the three subgroups. Conclusions Our study verified the prognostic roles of WWC1 and LATS2 in LUSC patients. Immune checkpoints-related genes SIGLEC15 and CD274 had statistical differences among the three subgroups, which may provide new perceptions on the molecular mechanisms in LUSC and maybe helpful for precisely selecting specific LUSC patients with potential immunotherapy benefits.

Artificial Intelligence: A Powerful Paradigm for Scientific Research

Abstract: Artificial Intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day to day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes performs a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The goal of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

RNA m6A Modification in Cancers: Molecular Mechanisms and Potential Clinical Applications

Abstract: N6-Methyladenosine (m6A) RNA modification brings a new dawn for RNA modification researches in recent years. This posttranscriptional RNA modification is dynamic and reversible, and is regulated by methylases ("writers"), demethylases ("erasers"), and proteins that preferentially recognize m6A modifications ("readers"). The change of RNA m6A modification regulates RNA metabolism in eucaryon, including translation, splicing, exporting, decay, and processing. Thereby the dysregulation of m6A may lead to tumorigenesis and progression. Given the tumorigenic role of abnormal m6A expression, m6A regulators may function as potential clinical therapeutic targets for cancers. In this review, we emphasize on the underlying mechanisms of m6A modifications in tumorigenesis and further introduce the potential m6A regulators-associated therapeutic targets for tumor therapy.

Cross-Cohort Analysis Identifies a TEAD4–MYCN Positive Feedback Loop as the Core Regulatory Element of High-Risk Neuroblastoma

Abstract: High-risk neuroblastomas show a paucity of recurrent somatic mutations at diagnosis. As a result, the molecular basis for this aggressive phenotype remains elusive. Recent progress in regulatory network analysis helped us elucidate disease-driving mechanisms downstream of genomic alterations, including recurrent chromosomal alterations. Our analysis identified three molecular subtypes of high-risk neuroblastomas, consistent with chromosomal alterations, and identified subtype-specific master regulator proteins that were conserved across independent cohorts. A 10-protein transcriptional module-centered around a TEAD4-MYCN positive feedback loop-emerged as the regulatory driver of the high-risk subtype associated with MYCN amplification. Silencing of either gene collapsed MYCN-amplified (MYCNAmp) neuroblastoma transcriptional hallmarks and abrogated viability in vitro and in vivo Consistently, TEAD4 emerged as a robust prognostic marker of poor survival, with activity independent of the canonical Hippo pathway transcriptional coactivators YAP and TAZ. These results suggest novel therapeutic strategies for the large subset of MYCN-deregulated neuroblastomas.Significance: Despite progress in understanding of neuroblastoma genetics, little progress has been made toward personalized treatment. Here, we present a framework to determine the downstream effectors of the genetic alterations sustaining neuroblastoma subtypes, which can be easily extended to other tumor types. We show the critical effect of disrupting a 10-protein module centered around a YAP/TAZ-independent TEAD4-MYCN positive feedback loop in MYCNAmp neuroblastomas, nominating TEAD4 as a novel candidate for therapeutic intervention. Cancer Discov; 8(5); 582-99. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 517.

m6A Regulators Is Differently Expressed and Correlated With Immune Response of Esophageal Cancer

Abstract: N6 methyladenosine (m6A) RNA methylation regulators play an important role in the development of tumors. However, their function in esophageal cancer (EC) has not been fully elucidated. Here, we analyzed the gene expression data of 24 major m6A RNA methylation regulators from 775 patients with EC from TCGA dataset. The present study showed the aberrations of m6A regulators in genome were correlated to prognosis in human ECs. Meanwhile, 17 m6A regulators showed increased expression in EC samples, including YTHDC1, IGF2BP2, FTO, METTL14, YTHDF3, RBM15, WTAP, HNRNPA2B1, HNRNPC, ALKBH5, YTHDF2, METTL16, IGF2BP3, VIRMA, RBM15B, YTHDF1, KIAA1429, HAKAI, and ZC3H13. Among them, we found HNRNPC, YTHDC2, WTAP, VIRMA, IGF2BP3, and HNRNPA2B1 were significantly correlated to worse outcomes and advanced stage in EC. Furthermore, we showed levels of m6A regulators is correlated with the expression of Immuno-regulators (Immunoinhibitors, Immunostimulators, and MHC molecules) and immune infiltration levels in EC. Bioinformatics further confirm m6A regulators were involved in regulating RNA splicing, RNA stability, and cell proliferation. Our study showed m6A regulators are promising targets and biomarkers for cancer immunotherapy in EC.