Identification of key genes and biological processes contributing to colitis associated dysplasia in ulcerative colitis

Background Ulcerative colitis-associated colorectal cancer (UC-CRC) is a life-threatening complication of ulcerative colitis (UC). The mechanisms underlying UC-CRC remain to be elucidated. The purpose of this study was to explore the key genes and biological processes contributing to colitis-associated dysplasia (CAD) or carcinogenesis in UC via database mining, thus offering opportunities for early prediction and intervention of UC-CRC. Methods Microarray datasets (GSE47908 and GSE87466) were downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) between groups of GSE47908 were identified using the “limma” R package. Weighted gene co-expression network analysis (WGCNA) based on DEGs between the CAD and control groups was conducted subsequently. Functional enrichment analysis was performed, and hub genes of selected modules were identified using the “clusterProfiler” R package. Single-gene gene set enrichment analysis (GSEA) was conducted to predict significant biological processes and pathways associated with the specified gene. Results Six functional modules were identified based on 4929 DEGs. Green and blue modules were selected because of their consistent correlation with UC and CAD, and the highest correlation coefficient with the progress of UC-associated carcinogenesis. Functional enrichment analysis revealed that genes of these two modules were significantly enriched in biological processes, including mitochondrial dysfunction, cell-cell junction, and immune responses. However, GSEA based on differential expression analysis between sporadic colorectal cancer (CRC) and normal controls from The Cancer Genome Atlas (TCGA) indicated that mitochondrial dysfunction may not be the major carcinogenic mechanism underlying sporadic CRC. Thirteen hub genes (SLC25A3, ACO2, AIFM1, ATP5A1, DLD, TFE3, UQCRC1, ADIPOR2, SLC35D1, TOR1AIP1, PRR5L, ATOX1, and DTX3) were identified. Their expression trends were validated in UC patients of GSE87466, and their potential carcinogenic effects in UC were supported by their known functions and other relevant studies reported in the literature. Single-gene GSEA indicated that biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways related to angiogenesis and immune response were positively correlated with the upregulation of TFE3, whereas those related to mitochondrial function and energy metabolism were negatively correlated with the upregulation of TFE3. Conclusions Using WGCNA, this study found two gene modules that were significantly correlated with CAD, of which 13 hub genes were identified as the potential key genes. The critical biological processes in which the genes of these two modules were significantly enriched include mitochondrial dysfunction, cell-cell junction, and immune responses. TFE3, a transcription factor related to mitochondrial function and cancers, may play a central role in UC-associated carcinogenesis.

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Relationship Between Insulin Resistance and Endometrial Cancer: Determination of Key Genes and Pathways

10.21203/rs.3.rs-864828/v1 ◽

2021 ◽

Author(s):

Dou-Dou Ding ◽

Quan Zhou ◽

Ze He ◽

Hong-Xia He ◽

Man-Zhen Zuo

Keyword(s):

Insulin Resistance ◽

Endometrial Cancer ◽

Endometrial Carcinoma ◽

Metabolic Diseases ◽

Single Gene ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Analysis Tool ◽

Key Genes

Abstract Introduction:Epidemiological studies have found that the occurrence of endometrial cancer(EC) is closely related to metabolic diseases, and insulin resistance (IR) plays an important role in the pathogenesis of endometrium, but the specific pathogenesis is still unclear. The purpose of this study is to reveal the relationship between insulin resistance and endothelial cells by gene screening technology. Material and methods:We analyzed one endometrial carcinoma database (GSE106191) and one insulin-resistant database (GSE63992), with Gene Expression Omnibus (GEO) database and Venny online analysis tool, then, we found an add-up to 148 different genes. Functional enrichment analysis of these genes using DAVID showed that they were participated in transcription factor activity,signaling pathways and response to factors, etc. Then used cytoHubba in Cytoscape,we got 25 hub genes.Results: The results showed that the survival time of OGT, IGSF3, TRO, NEURL2 and PIK3C2B was significantly and closely related to EC, and the percentage of gene changes of five central genes ranged from 3% to 10% of a single gene, was also related to the infiltration of seven kinds of immune cells in endometrial carcinoma.Conclusion:The five key genes (OGT,IGSF3, PIK3C2B,TRO and NEURL2) are involved in immune infiltration in the progression of endometrial carcinoma, and there is also a certain mutation probability in gene mutation. This may be the pathogenesis of insulin resistance and endometrial cancer.

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Expression profile analysis to predict potential biomarkers for glaucoma: BMP1, DMD and GEM

PeerJ ◽

10.7717/peerj.9462 ◽

2020 ◽

Vol 8 ◽

pp. e9462

Author(s):

Dao wei Zhang ◽

Shenghai Zhang ◽

Jihong Wu

Keyword(s):

Gene Expression ◽

Optic Nerve ◽

Optic Nerve Head ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Lasso Regression ◽

Hub Genes ◽

Gene Modules ◽

Key Genes

Purpose Glaucoma is the second commonest cause of blindness. We assessed the gene expression profile of astrocytes in the optic nerve head to identify possible prognostic biomarkers for glaucoma. Method A total of 20 patient and nine normal control subject samples were derived from the GSE9944 (six normal samples and 13 patient samples) and GSE2378 (three normal samples and seven patient samples) datasets, screened by microarray-tested optic nerve head tissues, were obtained from the Gene Expression Omnibus (GEO) database. We used a weighted gene coexpression network analysis (WGCNA) to identify coexpressed gene modules. We also performed a functional enrichment analysis and least absolute shrinkage and selection operator (LASSO) regression analysis. Genes expression was represented by boxplots, functional geneset enrichment analyses (GSEA) were used to profile the expression patterns of all the key genes. Then the key genes were validated by the external dataset. Results A total 8,606 genes and 19 human optic nerve head samples taken from glaucoma patients in the GSE9944 were compared with normal control samples to construct the co-expression gene modules. After selecting the most common clinical traits of glaucoma, their association with gene expression was established, which sorted two modules showing greatest correlations. One with the correlation coefficient is 0.56 (P = 0.01) and the other with the correlation coefficient is −0.56 (P = 0.01). Hub genes of these modules were identified using scatterplots of gene significance versus module membership. A functional enrichment analysis showed that the former module was mainly enriched in genes involved in cellular inflammation and injury, whereas the latter was mainly enriched in genes involved in tissue homeostasis and physiological processes. This suggests that genes in the green–yellow module may play critical roles in the onset and development of glaucoma. A LASSO regression analysis identified three hub genes: Recombinant Bone Morphogenetic Protein 1 gene (BMP1), Duchenne muscular dystrophy gene (DMD) and mitogens induced GTP-binding protein gene (GEM). The expression levels of the three genes in the glaucoma group were significantly lower than those in the normal group. GSEA further illuminated that BMP1, DMD and GEM participated in the occurrence and development of some important metabolic progresses. Using the GSE2378 dataset, we confirmed the high validity of the model, with an area under the receiver operator characteristic curve of 85%. Conclusion We identified several key genes, including BMP1, DMD and GEM, that may be involved in the pathogenesis of glaucoma. Our results may help to determine the prognosis of glaucoma and/or to design gene- or molecule-targeted drugs.

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Bioinformatic Analysis of Key Genes Related to Tumor Microenvironment in Hepatocellular Carcinoma

10.21203/rs.3.rs-508119/v1 ◽

2021 ◽

Author(s):

Jingxu Zhang ◽

Hao Liu ◽

Keyi Zhao ◽

Zhiye Bao ◽

Zhishuo Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Bioinformatic Analysis ◽

Functional Enrichment ◽

Expression Data ◽

Hub Genes ◽

Oncomine Database ◽

Key Genes ◽

Low Expression

Abstract Background: Tumor microenvironment (TME) plays important roles in the development of different types of cancer. However, the critical regulatory members of TME related to hepatocellular carcinoma (HCC) remain unclear. In this study, a bioinformatic analysis based on Cancer Genome Atlas (TCGA) and Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) datasets was conducted to predict the key genes affecting TME in HCC.Material and Methods: First, 340 patients and 20531 genes’ expression data with ESTIMATE scores were filtered and combined to identify differentially expressed genes. Next, protein-protein interaction (PPI) network and functional enrichment analysis were conducted to find hub genes. Then, log-rank test and functional enrichment analysis were conducted on the consensus genes and hub genes. Finally, Kaplan-Meier curves of the hub genes were drawn. As verification, those genes were searched on Oncomine database.Results: Among all differentially expressed genes, 916 genes were expressed in both the immune and stromal groups. The Gene Ontology (GO) terms they enriched were T cell activation, leukocyte migration, collagen-containing matrix, external side of plasma membrane, receptor ligand and activator activity. Cytokine-cytokine receptor interaction was the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG) term. Furthermore, cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 hub genes were low expressed in 304 patients, participating in a variety of responses including immune response−activating cell surface receptor signaling, immune response−activating signal transduction, clathrin−coated vesicle membrane, immune receptor activity， peptide binding and amide binding pathways. Their low expression was also verified on Oncomine database.Conclusion: cd3e, cd3g, hla-dpa1, hla-dpb1, lck, and map4k1 participated in many aspects related to TME, and their low expression constructs a signature, may predict a poor 5 years’ survival in hepatocellular carcinoma.

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Application of Bioinformatics Methods to Identify Key Genes and Functions in Chronic Pelvic Pain

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/7257405 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Wenchao Sun ◽

Qiji Ju

Keyword(s):

Pelvic Pain ◽

Chronic Pelvic Pain ◽

Single Gene ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Gene Expression Omnibus ◽

Functional Enrichment ◽

Receptor Interaction ◽

Protein Protein Interaction ◽

Key Genes

Neuropathologic pain (NPP) occurs in most patients with chronic pelvic pain (CPP), and the unique physiological characteristics of visceral sensory neurons make the current analgesic effect of CPP patients not optimistic. Therefore, this study explored the possible biological characteristics of key genes in CPP through the bioinformatics method. CPP-related dataset GSE131619 was downloaded from Gene Expression Omnibus to investigate the differentially expressed genes (DEGs) between lumbar dorsal root ganglia (DRG) and sacral DRG, and the functional enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed to search subnet modules of specific biological processes, and then, the genes in the subnet were enriched by single gene set analysis. A CPP mouse model was established, and the expression of key genes were identified by qPCR. The results showed that 127 upregulated DEGs and 103 downregulated DEGs are identified. Functional enrichment analysis showed that most of the genes involved in signal transduction were involved in the pathway of receptor interaction. A subnet module related to neural signal regulation was identified in PPI, including CHRNB4, CHRNA3, and CHRNB2. All three genes were associated with neurological or inflammatory activity and are downregulated in the sacral spinal cord of CPP mice. This study provided three key candidate genes for CPP: CHRNB4, CHRNA3, and CHRNB2, which may be involved in the occurrence and development of CPP, and provided a powerful molecular target for the clinical diagnosis and treatment of CPP.

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Identification of mRNA-miRNA-lncRNA Competing Endogenous RNA Network in Adrenocortical Carcinoma Using Bioinformatics Analysis

10.21203/rs.3.rs-105353/v1 ◽

2020 ◽

Author(s):

Senlin Ye ◽

Haohui Wang ◽

Wei Li ◽

Lu Yi

Keyword(s):

Adrenocortical Carcinoma ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Node Degree ◽

Pathway Enrichment Analysis ◽

Competing Endogenous Rna ◽

Hub Genes ◽

Key Genes ◽

Worse Prognosis

Abstract Background: Adrenocortical carcinoma (ACC) is a rare malignant tumor originating from the adrenal cortex. However, there are no eﬀective therapies to treat patients with ACC. LncRNA participates in a variety of biological processes of cancers. We constructed ceRNA network and identify key competing endogenous RNAs (ceRNAs) in adrenocortical carcinoma (ACC) using bioinformatic processing tools. Methods: Firstly, the differentially expressed genes (DEGs) were identified by analyzing GSE12368 and GSE19750 datasets. SangerBox was used to generate volcano maps. DAVID database was used for functional enrichment analysis. STRING database was used to conduct Protein-protein interaction (PPI) network, and hub genes were identified by Cytoscape plug-in CytoHubba. UCSC database was used to construct hierarchical clustering of hub genes. Upstream miRNAs of mRNAs were predicted by miRTarBase and upstream lncRNAs of miRNA by miRNet. Expression analysis for lncRNAs were performed via GEPIA. Prognostic analysis for genes, miRNAs and lncRNAs were performed via cBioPortal, OncomiR and GEPIA, respectively. Results: In this study, 49 and 276 upregulated and downregulated significant DEGs were identified. KEGG pathway enrichment analysis showed that they were significantly enriched in cancer-associated pathways. According to node degree, the top 10 upregulated genes and downregulated genes were classfied as hub genes. However, only 9 hub genes were defined as key genes because alteration was significantly associated with worse prognosis and all the 9 key genes were upregulated hub genes. Then, 15 miRNAs were predicted to target the 7 out of 9 key genes. But only 4 miRNAs were defined as key miRNAs because alteration significantly influenced prognosis in cancer. 185 lncRNAs were predicted to potentially interaction with the 4 miRNAs. Only 3 lncRNAs(XIST, HOXA11-AS and TMPO-AS1) were up-regulated and only 1 lncRNA (HOXA11-AS ) indicated alteration was significantly associated with worse prognosis in adrenocortical carcinoma. HOXA11-AS were finally identified as key lncRNA. Finally, RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3P-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks were constructed in adrenocortical carcinoma. Conclution：This study has constructed RRM2-miR-24-3p/let-7a-5p-HOXA11-AS, CDK1/MCM4-miR-24-3p-HOXA11-AS competing endogenous RNA (ceRNA) sub-networks. Our results suggested that these sub-networks might be potential therapeutic targets or prognostic biomarkers in ACC.

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WGCNA reveals key gene modules regulated by the combined treatment of colon cancer with PHY906 and CPT11

Bioscience Reports ◽

10.1042/bsr20200935 ◽

2020 ◽

Vol 40 (9) ◽

Author(s):

Shuqin Xing ◽

Yafei Wang ◽

Kaiwen Hu ◽

Fen Wang ◽

Tao Sun ◽

...

Keyword(s):

Colon Cancer ◽

Side Effects ◽

Molecular Mechanism ◽

Combined Treatment ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Pcr Analysis ◽

Hub Genes ◽

Key Genes

Abstract Irinotecan (CPT11) is one of the most effective drugs for treating colon cancer, but its severe side effects limit its application. Recently, a traditional Chinese herbal preparation, named PHY906, has been proved to be effective for improving therapeutic effect and reducing side effects of CPT11. The aim of the present study was to provide novel insight to understand the molecular mechanism underlying PHY906-CPT11 intervention of colon cancer. Based on the GSE25192 dataset, for different three treatments (PHY906, CPT11, and PHY906-CPT11), we screened out differentially expressed genes (DEGs) and constructed a co-expression network by weighted gene co-expression network analysis (WGCNA) to identify hub genes. The key genes of the three treatments were obtained by merging the DEGs and hub genes. For the PHY906-CPT11 treatment, a total of 18 key genes including Eif4e, Prr15, Anxa2, Ddx5, Tardbp, Skint5, Prss12 and Hnrnpa3, were identified. The results of functional enrichment analysis indicated that the key genes associated with PHY906-CPT11 treatment were mainly enriched in ‘superoxide anion generation’ and ‘complement and coagulation cascades’. Finally, we validated the key genes by Gene Expression Profiling Interactive Analysis (GEPIA) and RT-PCR analysis, the results indicated that EIF4E, PRR15, ANXA2, HNRNPA3, NCF1, C3AR1, PFDN2, RGS10, GNG11, and TMSB4X might play an important role in the treatment of colon cancer with PHY906-CPT11. In conclusion, a total of 18 key genes were identified in the present study. These genes showed strong correlation with PHY906-CPT11 treatment in colon cancer, which may help elucidate the underlying molecular mechanism of PHY906-CPT11 treatment in colon cancer.

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Identification of pivotal genes associated with the prognosis of gastric carcinoma through integrated analysis

Bioscience Reports ◽

10.1042/bsr20203676 ◽

2021 ◽

Author(s):

Zhenchao Ma ◽

Jianwei Xu ◽

Lixin Ru ◽

Weihua Zhu

Keyword(s):

T Cell ◽

Network Analysis ◽

Early Period ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Cell Junction ◽

Integrated Analysis ◽

Hub Genes ◽

Coexpressed Genes

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values. Methods: Microarray datasets from the GEO (GSE54129) and TCGA-stomach adenocarcinoma (STAD) datasets were applied for common differentially coexpressed genes using differential gene expression analysis and weighted gene coexpression network analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially coexpressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, GSEA was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated. Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell−cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and SERPINE1 was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that ECM receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment in GC. Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC.

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HEMGN and SLC2A1 might be potential diagnostic biomarkers of steroid-induced osteonecrosis of femoral head: study based on WGCNA and DEGs screening

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-03958-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Zhixin Wu ◽

Yinxian Wen ◽

Guanlan Fan ◽

Hangyuan He ◽

Siqi Zhou ◽

...

Keyword(s):

Femoral Head ◽

Roc Curve ◽

Enrichment Analysis ◽

Diagnostic Value ◽

Functional Enrichment ◽

Pathway Enrichment Analysis ◽

Overlapping Genes ◽

Hub Genes ◽

Diagnostic Biomarkers ◽

Key Genes

Abstract Background Steroid-induced osteonecrosis of the femoral head (SONFH) is a chronic and crippling bone disease. This study aims to reveal novel diagnostic biomarkers of SONFH. Methods The GSE123568 dataset based on peripheral blood samples from 10 healthy individuals and 30 SONFH patients was used for weighted gene co-expression network analysis (WGCNA) and differentially expressed genes (DEGs) screening. The genes in the module related to SONFH and the DEGs were extracted for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Genes with |gene significance| > 0.7 and |module membership| > 0.8 were selected as hub genes in modules. The DEGs with the degree of connectivity ≥5 were chosen as hub genes in DEGs. Subsequently, the overlapping genes of hub genes in modules and hub genes in DEGs were selected as key genes for SONFH. And then, the key genes were verified in another dataset, and the diagnostic value of key genes was evaluated by receiver operating characteristic (ROC) curve. Results Nine gene co-expression modules were constructed via WGCNA. The brown module with 1258 genes was most significantly correlated with SONFH and was identified as the key module for SONFH. The results of functional enrichment analysis showed that the genes in the key module were mainly enriched in the inflammatory response, apoptotic process and osteoclast differentiation. A total of 91 genes were identified as hub genes in the key module. Besides, 145 DEGs were identified by DEGs screening and 26 genes were identified as hub genes of DEGs. Overlapping genes of hub genes in the key module and hub genes in DEGs, including RHAG, RNF14, HEMGN, and SLC2A1, were further selected as key genes for SONFH. The diagnostic value of these key genes for SONFH was confirmed by ROC curve. The validation results of these key genes in GSE26316 dataset showed that only HEMGN and SLC2A1 were downregulated in the SONFH group, suggesting that they were more likely to be diagnostic biomarkers of SOFNH than RHAG and RNF14. Conclusions Our study identified that two key genes, HEMGN and SLC2A1, might be potential diagnostic biomarkers of SONFH.

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Identification of Key Genes and Pathways Associated with Differences of Subchondral Bone in Osteoarthritis

10.21203/rs.3.rs-51799/v1 ◽

2020 ◽

Author(s):

Yiyuan Zhang ◽

Rongguo Yu ◽

Jiayu Zhang ◽

Eryou Feng ◽

Haiyang Wang ◽

...

Keyword(s):

Subchondral Bone ◽

Molecular Mechanisms ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Hub Genes ◽

Common Chronic Disease ◽

Drug Candidates ◽

Therapeutic Modalities ◽

Pathogenic Genes

Abstract BackgroundOsteoarthritis (OA) is a common chronic disease worldwide. Subchondral bone is an important pathological change in OA and responds more rapidly to adverse loading and events compared to cartilage. However, the pathogenic genes and pathways of subchondral bone are largely unclear.ObjectiveThis study aimed to identify signature differences in genes involved in knee lateral tibial (LT) and medial tibial (MT) plateaus of subchondral bone tissue while exploring their potential molecular mechanisms via bioinformatics analysis.MethodsFirst, the gene expression data of GSE51588 was downloaded from the GEO database. Differentially expressed genes (DEGs) between knee LT and MT were identified, and functional enrichment analyses were performed. Then, a protein-protein interactive network was constructed in order to acquire the hub genes, and modules analysis was conducted using STRING and Cytoscape for further analysis. The enriched hub genes were queried in DGIdb database to find suitable drug candidates in OA.ResultsA total of 202 DEGs (112 upregulated genes and 84 downregulated genes) were determined. In the PPI network, ten hub genes were identified. Five significant modules were identified using the MCODE plugin unit. Functional enrichment analysis revealed the most important signaling pathways. Six of the ten hub genes were targetable by a total of 35 drugs, suggesting their possible therapeutic use for OA .ConclusionsThe identified hub genes and functional enrichment pathways were implicated in the development and progression of subchondral bone in OA, thus improving our understanding of OA and offering molecular targets for future therapeutic modalities.

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Identification of PKP 2/3 as potential biomarkers of ovarian cancer based on bioinformatics and experiments

Cancer Cell International ◽

10.1186/s12935-020-01602-3 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Lingling Gao ◽

Xiao Li ◽

Qian Guo ◽

Xin Nie ◽

Yingying Hao ◽

...

Keyword(s):

Ovarian Cancer ◽

Signaling Pathway ◽

Poor Prognosis ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Biological Behavior ◽

Cell Junction ◽

Immune Infiltration ◽

Potential Biomarker

Abstract Background Plakophilins (PKPs) are widely involved in gene transcription, translation, and signal transduction, playing a crucial role in tumorigenesis and progression. However, the function and potential mechanism of PKP1/2/3 in ovarian cancer (OC) remains unclear. It’s of great value to explore the expression and prognostic values of PKP1/2/3 and their potential mechanisms, immune infiltration in OC. Methods The expression levels, prognostic values and genetic variations of PKP1/2/3 in OC were explored by various bioinformatics tools and databases, and PKP2/3 were selected for further analyzing their regulation network and immune infiltration. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathways (KEGG) enrichment were also conducted. Finally, the expression and prognosis of PKP2 were validated by immunohistochemistry. Results The expression level and prognosis of PKP1 showed little significance in ovarian cancer, and the expression of PKP2/3 mRNA and protein were upregulated in OC, showing significant correlations with poor prognosis of OC. Functional enrichment analysis showed that PKP2/3 and their correlated genes were significantly enriched in adaptive immune response, cytokine receptor activity, organization of cell–cell junction and extracellular matrix; KEGG analysis showed that PKP2/3 and their significantly correlated genes were involved in signaling pathways including cytokine-mediated signaling pathway, receptor signaling pathway and pathways in cancer. Moreover, PKP2/3 were correlated with lymphocytes and immunomodulators. We confirmed that high expression of PKP2 was significantly associated with advanced stage, poor differentiation and poor prognosis of OC patients. Conclusion Members of plakophilins family showed various degrees of abnormal expressions and prognostic values in ovarian cancer. PKP2/3 played crucial roles in tumorigenesis, aggressiveness, malignant biological behavior and immune infiltration of OC, and can be regarded as potential biomarker for early diagnosis and prognosis evaluation in OC.

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