Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization

Cui, Lin; Wang, Ping; Ning, Dezhi; Shao, Jing; Tan, Gui‐Shan; Dajian, LI; Zhong, Xiaoling; Mi, Wanqi; Zhang, Chunlong; Jin, Shizhu

doi:10.3389/fcell.2021.631534

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…However, the five hub lncRNA genes give poor correlation with the survival prognosis of PTC patients by univariate Cox regression analysis. So based on this, we aim to investigate the prognosis features from their interacting miRNAs and target mRNAs, since much more prognostic signatures have been constructed using miRNAs and mRNAs in cancers, such as gastric cancer, endometrial carcinoma, and so on (Cui et al, 2021;Deng et al, 2021). Among 713 target miRNAs identified by the starBase v2.0, 17 miRNAs are demonstrated to be differentially expressed in TCGA.…”

Section: Discussionmentioning

confidence: 99%

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

Liu

Wang

et al. 2021

Front. Pharmacol.

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Long noncoding RNAs (lncRNAs) play important roles in tumorigenesis and progression of different cancers and they have been potential biomarkers for cancer diagnosis and prognosis. As the most common endocrine malignancy, precise diagnosis and prognosis of papillary thyroid cancer (PTC) is of great clinical significance. Here, we aim to identify new hub lncRNAs for marking PTC and constructed prognostics signatures based on lncRNA- miRNA-mRNA competing endogenous RNAs (ceRNA) network to predict overall survival (OS) and disease-free survival (DFS) respectively. Five reliable hub lncRNAs were identified by integrating differential genes of four Gene Expression Omnibus (GEO) gene chips using the RobustRankAggreg (RRA) method. Based on differential analyses and interaction prediction, a lncRNA-mRNA co-expression network and a lncRNA-miRNA-mRNA ceRNA network were established. Then a comprehensive function characterization of the five hub lncRNAs was performed, including validation dataset testing, receiver operating characteristic (ROC) curve analysis, and functional analysis on two networks. All results suggest that these five hub lncRNAs could be potential biomarkers for marking PTC. The ceRNA network was used to identify RNAs which were associated with PTC prognosis. Two prognostic signatures were developed using univariate and step-wise multivariate Cox regression analyses and both of them were independent prognostic indicators for PTC OS and DFS. Tumor microenvironment difference analysis between high and low-risk patients showed that dendritic cells activated and macrophages M0 may be a possible target for immunotherapy of PTC. In addition, disclosing the potential drugs that may reverse the expression of hub genes may improve the prognosis of patients with PTC. Here, connectivity map (CMap) analysis indicates that three bioactive chemicals (pioglitazone, benserazide, and SB-203580) are promising therapeutic agents for PTC. So, the paper presents a comprehensive study on diagnosis, prognosis, and potential drug screening for PTC based on the five hub lncRNAs identified by us.

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Section: Discussionmentioning

confidence: 99%

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

Liu

Wang

et al. 2021

Front. Pharmacol.

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“…In past decades, several bioinformatics strategies have been proposed to identify module biomarkers or key modules for tumorigenesis and development, on the basis of miRNet. Cui et al have integrated topological analysis and a random walk with restart algorithm to identify a prognostic signature for GC [19]. He et al have identified a module using a clique-percolation method with CFinder software, to divide patients into groups according to survival outcomes [20].…”

Section: Introductionmentioning

confidence: 99%

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey

Yan

Chen

et al. 2023

J Transl Med

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Background Gastric cancer (GC) is a major cancer burden throughout the world with a high mortality rate. The performance of current predictive and prognostic factors is still limited. Integrated analysis is required for accurate cancer progression predictive biomarker and prognostic biomarkers that help to guide therapy. Methods An AI-assisted bioinformatics method that combines transcriptomic data and microRNA regulations were used to identify a key miRNA-mediated network module in GC progression. To reveal the module’s function, we performed the gene expression analysis in 20 clinical samples by qRT-PCR, prognosis analysis by multi-variable Cox regression model, progression prediction by support vector machine, and in vitro studies to elaborate the roles in GC cells migration and invasion. Results A robust microRNA regulated network module was identified to characterize GC progression, which consisted of seven miR-200/183 family members, five mRNAs and two long non-coding RNAs H19 and CLLU1. Their expression patterns and expression correlation patterns were consistent in public dataset and our cohort. Our findings suggest a two-fold biological potential of the module: GC patients with high-risk score exhibited a poor prognosis (p-value < 0.05) and the model achieved AUCs of 0.90 to predict GC progression in our cohort. In vitro cellular analyses shown that the module could influence the invasion and migration of GC cells. Conclusions Our strategy which combines AI-assisted bioinformatics method with experimental and clinical validation suggested that the miR-200/183 family-mediated network module as a “pluripotent module”, which could be potential marker for GC progression.

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“…Recently, accumulating studies have suggested that lncRNAs function as a competing endogenous RNA (ceRNA) via targeting miRNAs, forming a lncRNA-miRNA-mRNA regulatory axis [ 22 ]. Here, we discovered that NORAD was up-regulated in GC tissues, which enhanced GC cells’ proliferation and metastasis by regulating the miR-496/IL-33 axis.…”

Section: Introductionmentioning

confidence: 99%

The long noncoding RNA noncoding RNA activated by DNA damage (NORAD)-microRNA-496-Interleukin-33 axis affects carcinoma-associated fibroblasts-mediated gastric cancer development

Huang

Liu

et al. 2021

Bioengineered

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Carcinoma-associated fibroblasts (CAFs) are one of the crucial parts of in the tumor microenvironment and contribute to tumor progression. Interleukin-33 (IL-33), a tissue-derived nuclear cytokine from the IL-1 family, has been found abnormally expressed in tumor cells and Fibroblast. However, the role and mechanism of IL-33 in the interaction between gastric cancer (GC) cells and CAFs need investigation. Presently, we inquire into the function of lncRNA NORAD-miR-496 axis-mediated IL-33 in modulating the GC-CAFs interaction. Real-time reverse transcription-polymerase chain reaction (RT-PCR) was adopted to gauge the expression of NORAD, miR-496, and IL-33 in GC tissues and cells, and gain- or loss-of-function assays were conducted to investigate the role of them in GC. A GC cell-CAFs co-culture model was established to explore the interaction between CAFs and GCs. As exhibited, NORAD was up-regulated in GC tissues and cells, while miR-496 was remarkably down-regulated. Overexpressing NORAD substantially promoted the proliferation, migration, invasion, and EMT of GC cells and repressed cell death, while overexpressing miR-496 had the opposite effects. Additionally, NORAD enhanced the IL-33 expression and the release of IL-33 from GC cells. The dual-luciferase reporter assay confirmed that miR-496 was a target of NORAD and targeted IL-33. CAFs aggravated the malignant behaviors of GC cells as indicated by both experiments. However, NORAD knockdown in CAFs reversed CAFs-mediated promotive effects on GC cells. In conclusion, NORAD enhanced the promotive effect of CAFs in GC cells by up-regulating IL-33 and targeting miR-496, which provided new insights into the microenvironment of GC cells and CAFs. Abbreviation ANOVA: Analysis of Variance; BCA:Bicinchoninic acid; CAFs: carcinoma-associated fibroblasts; CCK-8: cell counting kit-8; ceRNA: competing endogenous RNA; DAPI: 4′,6-diamidino-2-phenylindole; DMEM: Dulbecco’s minimal essential medium/Ham’s; ECL: enhanced chemiluminiscent; ELISA: Enzyme-Linked Immunosorbent Assay; EMT: epithelial-mesenchymal transition; FBS: fetal bovine serum; FISH:Fluorescence in situ hybridization; FITC:fluorescein isothiocyanate; FSP:fibroblast-specific protein; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GC: gastric cancer; IHC: immunohistochemistry; IL: Interleukin; lncRNA: long Noncoding RNA; miR-496: microRNA-496; MMP-14:matrix metalloproteinase-14; MUT:mutant; MYH9: myosin heavy chain 9; NFs: normal fibroblasts; NORAD: Noncoding RNA activated by DNA damage; ORF: open reading frame; PBS: phosphate-buffered saline; PMSF: Phenylmethylsulfonyl fluoride; PVDF: polyvinylidene difluoride; RIPA: Radio-Immunoprecipitation Assay; RT-PCR: Real-time reverse transcription polymerase chain reaction; S100A4:S100 calcium binding protein A4; SDS-PAGE: sodium dodecyl sulfate-polyacrylamide gel electrophoresis; sh-NC: short-hairpin RNA negative control; sh-NORAD: short-hairpin RNA of NORAD; α-SMA: α-smooth muscle actin; TBST: Tris-buffered saline wit...

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Identification of a Novel Prognostic Signature for Gastric Cancer Based on Multiple Level Integration and Global Network Optimization

Cited by 4 publications

References 58 publications

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

Identification of Hub lncRNAs Along With lncRNA-miRNA-mRNA Network for Effective Diagnosis and Prognosis of Papillary Thyroid Cancer

MiR-200/183 family-mediated module biomarker for gastric cancer progression: an AI-assisted bioinformatics method with experimental functional survey

The long noncoding RNA noncoding RNA activated by DNA damage (NORAD)-microRNA-496-Interleukin-33 axis affects carcinoma-associated fibroblasts-mediated gastric cancer development

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