Comprehensive Analysis of a circRNA-miRNA-mRNA Network to Reveal Potential Inflammation-Related Targets for Gastric Adenocarcinoma

Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.

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The Important Role of Perituberal Tissue in Epileptic Patients with Tuberous Sclerosis Complex by the Transcriptome Analysis

BioMed Research International ◽

10.1155/2020/4980609 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Shuqiang Li ◽

Huijie Shao ◽

Liansheng Chang

Keyword(s):

Tuberous Sclerosis ◽

Tuberous Sclerosis Complex ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes ◽

R Language ◽

Protein Protein Interaction

Epilepsy is most common in patients with tuberous sclerosis complex (TSC). However, in addition to the challenging treatment, the pathogenesis of epilepsy is still controversial. To determine the transcriptome characteristics of perituberal tissue (PT) and clarify its role in the pathogenesis of epilepsy, GSE16969 was downloaded from the GEO database for further study by comprehensive bioinformatics analysis. Identification of differentially expressed genes (DEGs), functional enrichment analysis, construction of protein-protein interaction (PPI) network, and selection of Hub genes were performed using R language, Metascape, STRING, and Cytoscape, respectively. Comparing with cortical tuber (CT), 220 DEGs, including 95 upregulated and 125 downregulated genes, were identified in PT and mainly enriched in collagen-containing extracellular matrix and positive regulation of receptor-mediated endocytosis, as well as the pathways of ECM-receptor interaction and neuroactive ligand-receptor interaction. As for normal cortex (NC), 1549 DEGs, including 30 upregulated and 1519 downregulated genes, were identified and mainly enriched in presynapse, dendrite and axon, and also the pathways of dopaminergic synapse and oxytocin signaling pathway. In the PPI network, 4 hub modules were found between PT and CT, and top 5 hub modules were selected between PT and NC. C3, APLNR, ANXA2, CD44, CLU, CP, MCHR2, HTR1E, CTSG, APP, and GNG2 were identified as Hub genes, of which, C3, CD44, ANXA2, HTR1E, and APP were identified as Hub-BottleNeck genes. In conclusion, PT has the unique characteristics different from CT and NC in transcriptome and makes us further understand its importance in the TSC-associated epilepsy.

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Underlying Genes and Molecular Mechanism of Keloids Investigated by Integrated Bioinformatics Analysis

10.21203/rs.3.rs-148691/v1 ◽

2021 ◽

Author(s):

chuying LI ◽

Mei-Tong Jin ◽

Yin-Li Luo ◽

Zhe-Hu Jin ◽

Long-Quan PI

Keyword(s):

Expression Profiles ◽

Normal Skin ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Underlying Mechanism ◽

Cholinergic Receptor ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes

Abstract Background: We aimed to identify the overlapping differentially expressed genes (DEGs) of keloids distinguished from normal scar and normal skin and relevant underlying mechanism using integrated bioinformatics methods.Methods: The expression profiles of 18 keloid samples, 7 normal skin and 5 normal scar, were obtained from the GSE7890, GSE44270, GSE92566, and GSE3189 datasets in the Gene Expression Omnibus database. DEGs were identified using the LIMMA package in R. Gene ontology (GO) functional enrichment analysis was performed using the R software. A DEG-associated protein–protein interaction (PPI) network was constructed using STRING and MCODE was used for module analysis of the PPI network. Moreover, the hub genes were verified by qRT-PCR. The predicted DEGs, their regulatory miRNA and TF regulation network was analyzed using miRnet. Results: A total of 978 common DEGs were identified in the keloid samples. Genes with more than 45 interaction degrees, including neuropeptide Y (NPY), opioid receptor mu 1 (OPRM1), cholinergic receptor muscarinic 2 (CHRM2), and proopiomelanocortin (POMC), were found in the PPI network. Hsa-miR-335 and Sp1 as upstream-regulators regulated CHRM2, NPY, and POMC. Functional enrichment analysis revealed that hub genes were commonly enriched in the “G protein-coupled receptor signaling pathway” GO_BP termConclusion: Taken together, CHRM2, NPY, POMC, and OPRM1 potentially have crucial roles in keloid disease. Furthermore, miR-335 and Sp1 are potential targets for preventing keloid formation.

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Identifcation of The Ferroptosis-Related Gene Signature In Placenta of Patients With Early-Onset Preeclampsia

10.21203/rs.3.rs-618453/v1 ◽

2021 ◽

Author(s):

Nana Yang ◽

Qianghua Wang ◽

Biao Ding ◽

Yinging Gong ◽

Yue Wu ◽

...

Keyword(s):

Iron Homeostasis ◽

Molecular Mechanisms ◽

Late Onset ◽

Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Ppi Network ◽

Network Analyses

Abstract Background: The accumulation of ROS resulting from upregulated levels of oxidative stress is commonly implicated in preeclampsia (PE). Ferroptosis is a novel form of iron-dependent cell death instigated by lipid peroxidation likely plays important role in PE pathogenesis. This study aims to investigate expression profiles and functions of the ferroptosis-related genes (FRGs) in early- and late-onset preeclampsia.Methods: The gene expression data and clinical information were downloaded from GEO database. The “limma” R package was used for screening differentially expressed genes. GO(Gene Ontology), Kyoto Encyclopedia of Genes and Genomes(KEGG) and protein protein interaction (PPI) network analyses were conducted to investigate the bioinformatics functions and molecular interactions of significantly different FRGs. Quantitative real-time reverse transcriptase PCR was used to verify the expression of hub FRGs in PE.Results: A total number of 4,215 DEGs were identified between EOPE and preterm cases and 3,356 DEGs were found between EOPE and LOPE subtypes. 20 significantly different FRGs were identified in EOPE, while only 3 in LOPE. Functional enrichment analysis revealed that the differentially expressed FRGs was mainly involved in EOPE and enriched in hypoxia- and iron-related pathways, such as response to hypoxia, iron homeostasis and iron ion binding process. The PPI network analysis and verification by RT-qPCR resulted in the identification of the following six interesting FRGs: FTH1, HIF1A, FTL, IREB2, MAPK8 and PLIN2. Conclusions: EOPE and LOPE owned distinct underlying molecular mechanisms and ferroptosis may be mainly implicated in pathogenesis of EOPE. Further studies are necessary for deeper inquiry into placental ferroptosis and its role in the pathogenesis of EOPE.

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Comparative Transcriptome Analysis Suggests Key Roles for 5-Hydroxytryptamlne Receptors in Control of Goose Egg Production

Genes ◽

10.3390/genes11040455 ◽

2020 ◽

Vol 11 (4) ◽

pp. 455 ◽

Cited By ~ 1

Author(s):

Qingyuan Ouyang ◽

Shenqiang Hu ◽

Guosong Wang ◽

Jiwei Hu ◽

Jiaman Zhang ◽

...

Keyword(s):

Egg Production ◽

Expression Profiles ◽

Expression Patterns ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Production Performance ◽

Functional Enrichment ◽

Differentially Expressed ◽

Receptor Interaction ◽

Anser Anser

To date, research on poultry egg production performance has only been conducted within inter or intra-breed groups, while those combining both inter- and intra-breed groups are lacking. Egg production performance is known to differ markedly between Sichuan white goose (Anser cygnoides) and Landes goose (Anser anser). In order to understand the mechanism of egg production performance in geese, we undertook this study. Here, 18 ovarian stromal samples from both Sichuan white goose and Landes goose at the age of 145 days (3 individuals before egg production initiation for each breed) and 730 days (3 high- and low egg production individuals during non-laying periods for each breed) were collected to reveal the genome-wide expression profiles of ovarian mRNAs and lncRNAs between these two geese breeds at different physiological stages. Briefly, 58, 347, 797, 777, and 881 differentially expressed genes (DEGs) and 56, 24, 154, 105, and 224 differentially expressed long non-coding RNAs (DElncRNAs) were found in LLD vs. HLD (low egg production Landes goose vs. high egg production Landes goose), LSC vs. HSC (low egg production Sichuan White goose vs. high egg production Sichuan white goose), YLD vs. YSC (young Landes goose vs. young Sichuan white goose), HLD vs. HSC (high egg production Landes goose vs. high egg production Sichuan white goose), and LLD vs. LSC (low egg production Landes goose vs. low egg production Sichuan white goose) groups, respectively. Functional enrichment analysis of these DEGs and DElncRNAs suggest that the “neuroactive ligand–receptor interaction pathway” is crucial for egg production, and particularly, members of the 5-hydroxytryptamine receptor (HTR) family affect egg production by regulating ovarian metabolic function. Furthermore, the big differences in the secondary structures among HTR1F and HTR1B, HTR2B, and HTR7 may lead to their different expression patterns in goose ovaries of both inter- and intra-breed groups. These results provide novel insights into the mechanisms regulating poultry egg production performance.

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Xiaoyu Xiezhuo Drink Protects against Ischemia-Reperfusion Acute Kidney Injury in Aged Mice through Inhibiting the TGF-β1/Smad3 and HIF1 Signaling Pathways

BioMed Research International ◽

10.1155/2021/9963732 ◽

2021 ◽

Vol 2021 ◽

pp. 1-19

Author(s):

Qingqing Ye ◽

Hongbo Chen ◽

Hongzhen Ma ◽

Xiaojun Xiang ◽

Shouci Hu ◽

...

Keyword(s):

Drug Targets ◽

Ischemia Reperfusion ◽

Kidney Injury ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Aged Mice ◽

Tgf Β1

Acute kidney injury (AKI) is responsible for significant mortality among hospitalized patients that is especially troubling aged people. An effective self-made Chinese medicine formula, Xiaoyu Xiezhuo Drink (XXD), displayed therapeutic effects on AKI. However, the compositions and underlying mechanisms of XXD remain to be elucidated. In this study, we used the ultra-high-performance liquid chromatography method coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) to investigate the chemical components in XXD. Then, the absorbable components of XXD were identified based on the five principles and inputted into the SwissTargetPrediction and STITCH databases to identify the drug targets. AKI-related targets were collected from the GenCLiP 3, GeneCards, and DisGeNET databases. The crossover genes of XXD and AKI were identified for functional enrichment analysis. The protein-protein interaction (PPI) network of crossover genes was constructed, followed by the identification of hub genes. Subsequently, the effects and potential mechanisms of XXD on AKI predicted by the network pharmacology and bioinformatics analyses were experimentally validated in ischemia-reperfusion (I/R) injury-induced AKI aged mouse models. A total of 122 components in XXD were obtained; among them, 58 components were found that could be absorbed in the blood. There were 800 potential drug targets predicted from the 58 absorbable components in AKI which shared 36 crossover genes with AKI-related targets. The results of functional enrichment analysis indicated that crossover genes mostly associated with the response to oxidative stress and the HIF1 signaling pathway. In the PPI network analysis, 12 hub genes were identified, including ALB, IL-6, TNF, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, and HMOX1. In AKI aged mice, XXD prominently alleviated I/R injury-induced renal dysfunction, abnormal renal pathological changes, and cellular senescence, inflammation, and oxidative damage with a reduction in the expression level of the inflammatory mediator, α-SMA, collagen-1, F4/80, TP53, VEGFA, PTGS2, TLR4, NOS3, EGFR, PPARG, HIF1A, ICAM-1, TGF-β1, Smad3, and p-Smad3 and an increase of nephridial tissue p-H3, Ki67, HMOX1, MMP-9, and Smad7 levels. In summary, our findings suggest that XXD has renoprotective effects against AKI in aged mice via inhibiting the TGF-β1/Smad3 and HIF1 signaling pathways.

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Identification Of Putative Gene Signatures Associated With Diagnosis And Prognosis Of Breast Cancer

Clinical & Investigative Medicine ◽

10.25011/cim.v44i3.37194 ◽

2021 ◽

Vol 44 (3) ◽

pp. E45-54

Author(s):

Chao Tan ◽

Fang Zuo ◽

Mingqian Lu ◽

Sai Chen ◽

Zhenzhen Tian ◽

...

Keyword(s):

Risk Model ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Support Vector ◽

Ppi Network ◽

Hub Genes ◽

Putative Gene ◽

Network Analyses ◽

Cluster 2

Purpose: This study aimed to identify potential diagnostic and therapeutic biomakers for the development ofbreast cancer (BC). Methods: GSE86374 dataset containing 159 samples was acquired from the Gene Expression Omnibus (GEO) database followed by differentially expressed genes (DEGs) identification and cluster analysis. Corresponding functional enrichment and protein-protein interaction (PPI) network analyses were performed to identify hub genes. Prognostic evaluation using clinical information obtained from TCGA database and hub genes was conducted to screen for crucial indicators for BC progression. The risk model was established and validated. Results: In total, 186 DEGs were identified and grouped into four clusters: 96 in cluster 1; 69 in cluster 2; 16 in cluster 3; and 5 in cluster 4. Functional enrichment analysis showed that DEGs, including ADH1B in cluster 1, were dramatically enriched in the tyrosine and drug metabolism pathways, while genes in cluster 2, including SPP1 and RRM2, played crucial roles in PI3K-Akt and p53 signalling pathway. SPP1 and RRM2 served as hub genes in the PPI network, resulting in an support vector machine classifier with good accuracy and specificity.Ad ditionally, the results of prognostic analysis suggest that age, metastasis stage, SPP1 and ADH1B were correlated with risk of BC, which was validated by using the established risk model analysis. Conclusion: SPP1, RRM2 and ADH1B appear to play vital roles in the development of BC. Age and TNM stage were also preferentially associated with risk of developing BC. Evaluation of the risk model based on larger sample size and further experimental validation are required.

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Ten-gene signature reveals the significance of clinical prognosis and immuno-correlation of osteosarcoma and study on novel skeleton inhibitors regarding MMP9

Cancer Cell International ◽

10.1186/s12935-021-02041-4 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Weihang Li ◽

Ziyi Ding ◽

Dong Wang ◽

Chengfei Li ◽

Yikai Pan ◽

...

Keyword(s):

Expression Profiles ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Signature ◽

Functional Enrichment ◽

Computational Techniques ◽

Hub Genes ◽

Clinical Prognosis ◽

Lead Compounds

Abstract Objectives This study aimed to identify novel targets in the carcinogenesis, therapy and prognosis of osteosarcoma from genomic level, together with screening ideal lead compounds with potential inhibition regarding MMP-9. Methods Gene expression profiles from GSE12865, GSE14359, GSE33382, GSE36001 and GSE99671 were obtained respectively from GEO database. Differentially expressed genes were identified, and functional enrichment analysis, such as GO, KEGG, GSEA, PPI were performed to make a comprehensive understanding of the hub genes. Next, a series of high-precision computational techniques were conducted to screen potential lead compounds targeting MMP9, including virtual screening, ADME, toxicity prediction, and accurate docking analysis. Results 10 genes, MMP9, CD74, SPP1, CXCL12, TYROBP, FCER1G, HCLS1, ARHGDIB, LAPTM5 and IGF1R were identified as hub genes in the initiation of osteosarcoma. Machine learning, multivariate Cox analysis, ssGSEA and survival analysis demonstrated that these genes had values in prognosis, immune-correlation and targeted treatment. Tow novel compounds, ZINC000072131515 and ZINC000004228235, were screened as potential inhibitor regarding MMP9, and they could bind to MMP9 with favorable interaction energy and high binding affinity. Meanwhile, they were precited to be efficient and safe drugs with low-ames mutagenicity, none weight evidence of carcinogenicity, as well as non-toxic with liver. Conclusions This study revealed the significance of 10-gene signature in the development of osteosarcoma. Besides, drug candidates identified in this study provided a solid basis on MMP9 inhibitors’ development.

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Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

Public Health Genomics ◽

10.1159/000517308 ◽

2021 ◽

Vol 24 (5-6) ◽

pp. 267-279

Author(s):

Xianyang Zhu ◽

Wen Guo

Keyword(s):

Gene Expression ◽

Molecular Mechanisms ◽

Expression Profiles ◽

Meta Analysis ◽

Wnt Signaling Pathway ◽

Gene Expression Profiles ◽

Enrichment Analysis ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Genes

Background: This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. Methods: Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). Results: A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including AKT1, CDC42, HLA-DQA2, TUBB, TWISTNB, GSK3B, FZD2, KLC1, GUSB, and RHOG. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including GSK3B, CDC42, AKT1, FZD2, and GUSB) were identified. Conclusion: In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that GSK3B, CDC42, AKT1, FZD2, and GUSB may be key genes related to the progress of OA and may become promising therapeutic targets.

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Identification of the Hub Genes in Alzheimer’s Disease

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6329041 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Huiwen Gui ◽

Qi Gong ◽

Jun Jiang ◽

Mei Liu ◽

Huanyin Li

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

The Elderly ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Protein Protein Interaction ◽

Geo Database

Purpose. Alzheimer’s disease (AD) is considered to be the most common neurodegenerative disease and also one of the major fatal diseases affecting the elderly, thus bringing a huge burden to society. Therefore, identifying AD-related hub genes is extremely important for developing novel strategies against AD. Materials and Methods. Here, we extracted the gene expression profile GSE63061 from the National Center for Biotechnology Information (NCBI) GEO database. Once the unverified gene chip was removed, we standardized the microarray data after quality control. We utilized the Limma software package to screen the differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network using the STRING database. Result. We screened 2169 DEGs, comprising 1313 DEGs with upregulation and 856 DEGs with downregulation. Functional enrichment analysis showed that the response of immune, the degranulation of neutrophils, lysosome, and the differentiation of osteoclast were greatly enriched in DEGs with upregulation; peptide biosynthetic process, translation, ribosome, and oxidative phosphorylation were dramatically enriched in DEGs with downregulation. 379 nodes and 1149 PPI edges were demonstrated in the PPI network constructed by upregulated DEGs; 202 nodes and 1963 PPI edges were shown in the PPI network constructed by downregulated DEGs. Four hub genes, including GAPDH, RHOA, RPS29, and RPS27A, were identified to be the newly produced candidates involved in AD pathology. Conclusion. GAPDH, RHOA, RPS29, and RPS27A are expected to be key candidates for AD progression. The results of this study can provide comprehensive insight into understanding AD’s pathogenesis and potential new therapeutic targets.

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Single-cell RNA-sequencing Unravels Heterogeneity of Cardiomyocytes and Signaling Pathways in Pressure Overload

10.21203/rs.3.rs-37034/v1 ◽

2020 ◽

Author(s):

Rongjun Zou ◽

Wanting Shi ◽

Minghui Zou ◽

Weidan Chen ◽

Wenlei Li ◽

...

Keyword(s):

Cardiac Hypertrophy ◽

Mouse Models ◽

Expression Patterns ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Ppi Network ◽

Hub Genes ◽

Cell Clusters ◽

Single Cell Rna Sequencing

Abstract Background This study aimed to unravel the heterogeneity of cardiomyocytes and probed out hub genes and hub pathways for cardiac hypertrophy based on transverse aortic constriction (TAC) mouse models using single-cell RNA sequencing (scRNA-seq). Methods scRNA-seq data of TAC mouse models were retrieved from the GSE95140 dataset. After filtering, cell clusters were detected using scRNA-seq data, followed by identification of differentially expressed genes (DEGs). Then, functional enrichment analysis of DEGs was presented. GSVA scores of hub pathways were calculated. After that, hub genes were detected by protein-protein interaction (PPI) network and expression association analysis. Cell subtypes were clustered using UMAP and the expression patterns of hub genes across different cell subtypes and different stages of cardiac hypertrophy were visualized. Finally, hub genes and hub pathways were verified using the GSE76 and GSE36074 datasets. Results Following data filtering and normalization, 3408 DEGs were identified between TAC and sham operation. As shown functional enrichment analysis, hub pathways were identified including cardiac hypertrophy, ion transport, myocardial remodeling, apoptosis, HIF pathway and metabolise. Eight hub genes (Vldlr, Ugp2, Tgm2, Pygm, Flnc, Ctsd, Clu and Atp1b1) with the highest degree in the PPI network and the strongest correlation with GSVA calculated score of hub pathways were identified for cardiac hypertrophy. Six cell subtypes were clustered, composed of fibroblast, CM-A, CM-V, trabecular CM and endothelial cell. There was a distinct heterogeneity in the expression patterns of hub genes and the GSVA scores of hub pathways across different cell clusters and different stages of cardiac hypertrophy. The hub genes and hub pathways were externally verified by the two independent datasets. Conclusion Our findings identified hub genes and hub pathways for cardiac hypertrophy, which had a distinct heterogeneity across different cell clusters and different stages of cardiac hypertrophy.

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