Identification of differentially expressed genes and biological characteristics of colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) ranks as one of the most common malignant tumors worldwide. Its mortality rate has remained high in recent years. Therefore, the aim of this study was to identify significant differentially expressed genes (DEGs) involved in its pathogenesis, which may be used as novel biomarkers or potential therapeutic targets for CRC. The gene expression profiles of GSE21510, GSE32323, GSE89076, and GSE113513 were downloaded from the Gene Expression Omnibus (GEO) database. After screening DEGs in each GEO data set, we further used the robust rank aggregation method to identify 494 significant DEGs including 212 upregulated and 282 downregulated genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by DAVID and the KOBAS online database, respectively. These DEGs were shown to be significantly enriched in different cancer-related functions and pathways. Then, the STRING database was used to construct the protein–protein interaction network. The module analysis was performed by the MCODE plug-in of Cytoscape based on the whole network. We finally filtered out seven hub genes by the cytoHubba plug-in, including PPBP, CCL28, CXCL12, INSL5, CXCL3, CXCL10, and CXCL11. The expression validation and survival analysis of these hub genes were analyzed based on The Cancer Genome Atlas database. In conclusion, the robust DEGs associated with the carcinogenesis of CRC were screened through the GEO database, and integrated bioinformatics analysis was conducted. Our study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for CRC.     

Identification of novel biomarkers and key pathways of condyloma acuminata

Condyloma acuminata (CA) is a prevalent sexually transmitted disease, associated with human papilloma viruses (HPV) infections and host immune status. In this present study, we aimed to explore immune landscape and biomarkers for CA prevention and treatment. We obtained differentially expressed genes (DEGs) of CA vs normal tissues in GSE140662 and screened out hub genes from the protein–protein interaction (PPI) network. Hub genes were then subjected to microRNA (miRNA) analysis. Besides, CCK-8, transwell, flow cytometry assays were employed to assess the cell proliferation, migration and apoptosis in Hela cells. ImmuCellAI was firstly applied to identify immune cell infiltration levels of CA. We obtained 275 DEGs, 23 hub genes and key miRNAs. Subsequently, we verified four up-regulated hub genes IFIT1, IFI27, OASL, SAMD9L and down-regulated mir-146a-5p in CA tissues by RT-qPCR. Moreover, over-expression of miR-146a-5p reduced Hela cells proliferation, migration, blocked cell cycle and induced apoptosis. Up-regulated miR-146a-5p attenuated PI3K/AKT and activated p38/ MAPK signaling pathway. Proportions of Monocyte, NK cells, Gamma delta cells, Th17 cells were relatively low, while Th1 and CD8+ T cells were relatively high in CA skin. Our study revealed that mir-146a-5p contribute to CA progression through PI3K/AKT and p38/MAPK signaling pathway.     

数据挖掘探究肝-肾-肺纤维化的关键基因

当前用于纤维化治疗的方法很少且疗效有限,为进一步了解纤维化的消退机制以发现潜在的治疗靶点。从Gene Expression Omnibus(GEO)数据库中选取了三个具有代表性的小鼠肝、肾、肺纤维化样本的mRNA数据集,使用GEO2R工具和Venn分析识别了差异表达基因(Differentially Expressed Genes, DEGs)。通过Webgestalt在线工具对DEGs进行基因功能富集。蛋白质-蛋白质相互作用(Protein-Protein Interactions, PPI)网络是由STRING数据库生成的。然后利用CytoHubba插件探索了关键基因,分别选取了三器官共有DEG和肝特异性DEG中MCC (Maximal Clique Centrality)得分最高的前10个作为关键基因。研究中整合分析了基于小鼠模型的肝-肾-肺纤维化的数据集,GSE36066和GSE97546用于第一轮的DEG分析,由于研究除了探究三种器官纤维化共有差异基因,也进一步探究了肝纤维化特有关键基因,所以引入另外一个肝纤维化数据集GSE55747用于验证分析。结果识别出58个肝肺肾纤维化...     

Identification of differentially expressed genes in synovial tissue of rheumatoid arthritis and osteoarthritis in patients

Rheumatoid arthritis (RA) and osteoarthritis (OA) are the common joints disorder in the world. Although they have showed the analogous clinical manifestation and overlapping cellular and molecular foundation, the pathogenesis of RA and OA were different. The pathophysiologic mechanisms of arthritis in RA and OA have not been investigated thoroughly. Thus, the aim of study is to identify the potential crucial genes and pathways associated with RA and OA and further analyze the molecular mechanisms implicated in genesis. First, we compared gene expression profiles in synovial tissue between RA and OA from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Gene Expression Series (GSE) 1919, GSE55235, and GSE36700 were downloaded from the GEO database, including 20 patients of OA and 21 patients of RA. Differentially expressed genes (DEGs) including “CXCL13,” “CD247,” “CCL5,” “GZMB,” “IGKC,” “IL7R,” “UBD///GABBR1,” “ADAMDEC1,” “BTC,” “AIM2,” “SHANK2,” “CCL18,” “LAMP3,” “CR1,” and “IL32.” Second, Gene Ontology analyses revealed that DEGs were significantly enriched in integral component of extracellular space, extracellular region, and plasma membrane in the molecular function group. Signaling pathway analyses indicated that DEGs had common pathways in chemokine signaling pathway, cytokine-cytokine receptor interaction, and cytosolic DNA-sensing pathway. Third, DEGs showed the complex DEGs protein-protein interaction network with the Coexpression of 83.22%, Shared protein domains of 8.40%, Colocalization of 4.76%, Predicted of 2.87%, and Genetic interactions of 0.75%. In conclusion, the novel DEGs and pathways between RA and OA identified in this study may provide new insight into the underlying molecular mechanisms of RA.     

Common miRNAs,candidate genes and their interaction network across four subtypes of epithelial ovarian cancer

Epithelial ovarian cancer (EOC) is categorized into four major histological subtypes such as clear cell carcinoma (CCC), endometrioid carcinoma (EC), mucinous carcinoma (MC), and serous carcinoma (SC). Heterogeneity of the EOC leads to different clinical outcomes of the disease, although all the subtypes are originated from the same layer of tissue. Therefore, it is of interest to identify the common candidate genes, miRNA and their interaction network in four the subtypes of EOC. A comparative gene expression analysis identified 248 common differentially expressed genes (DEGs) in the four subtypes of EOC. Identified common DEGs were found to be enriched in cancer specific pathways. A protein-protein interaction (PPI) network of the common DEGs were constructed, and subsequent module and survival analyses identified seven key candidate genes (CCNB1, CENPM, CEP55, RACGAP1, TPX2, UBE2C, and ZWINT). We also documented 10 key candidate miRNAs (hsa-mir-16-5p, hsa-mir-23b-3p, hsa-mir-34a-5p, hsa-mir-103a-3p, hsa-mir-107, hsa-mir-124-3p, hsa-mir-129-2-3p, hsa-mir-147a, hsa-mir-205-5p, and hsa-mir-195-5p) linked to the candidate genes. These derived data find application in the understanding of EOC.     

肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

Discovery of core gene families associated with liver metastasis in colorectal cancer and regulatory roles in tumor cell immune infiltration

In this study, we aimed to uncover genes that drive the pathogenesis of liver metastasis in colorectal cancer (CRC), and identify effective genes that could serve as potential therapeutic targets for treating with colorectal liver metastasis patients based on two GEO datasets. Several bioinformatics approaches were implemented. First, differential expression analysis screened out key differentially expressed genes (DEGs) across the two GEO datasets. Based on gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, we identified the enrichment functions and pathways of the DEGs that were associated with liver metastasis in CRC. Second, immune infiltration analysis identified key immune signature gene sets associated with CRC liver metastasis, among which two key immune gene families (CD and CCL) identified as key DEGs were filtered by protein-protein interaction (PPI) network. Some of the members in these gene families were associated with disease free survival (DFS) or overall survival (OS) in two subtypes of CRC, namely COAD and READ. Finally, functional enrichment analysis of the two gene families and their neighboring genes revealed that they were closely associated with cytokine, leukocyte proliferation and chemotaxis. These results are valuable in comprehending the pathogenesis of liver metastasis in CRC, and are of seminal importance in understanding the role of immune tumor infiltration in CRC. Our study also identified potentially effective therapeutic targets for liver metastasis in CRC including CCL20, CCL24 and CD70.     

Investigation of hub genes and immune status in heart transplant rejection using endomyocardial biopsies

T cell‒mediated rejection (TCMR) and antibody-mediated rejection (ABMR) are severe post-transplantation complications for heart transplantation (HTx), whose molecular and immunological pathogenesis remains unclear. In the present study, the mRNA microarray data set GSE124897 containing 645 stable, 52 TCMR and 144 ABMR endomyocardial biopsies was obtained to screen for differentially expressed genes (DEGs) between rejected and stable HTx samples and to investigate immune cell infiltration. Functional enrichment analyses indicated roles of the DEGs primarily in immune-related mechanisms. Protein-protein interaction networks were then constructed, and ICAM1, CD44, HLA-A and HLA-B were identified as hub genes using the maximal clique centrality method. Immune cell infiltration analysis revealed differences in adaptive and innate immune cell populations between TCMR, ABMR and stable HTx samples. Additionally, hub gene expression levels significantly correlated with the degree and composition of immune cell infiltration in HTx rejection samples. Furthermore, drug-gene interactions were constructed, and 12 FDA-approved drugs were predicted to target hub genes. Finally, an external GSE2596 data set was used to validate the expression of the hub genes, and ROC curves indicated all four hub genes had promising diagnostic value for HTx rejection. This study provides a comprehensive perspective of molecular and immunological regulatory mechanisms underlying HTx rejection.     

急性髓系白血病mRNA与非编码RNA差异表达谱及CeRNA调控网络分析

利用GEO数据库(gene expression omnibus database)通过生物信息学分析方法探讨急性髓系白血病(acute myelogenous leukemia,AML)的发病机制。检索GEO数据库中AML相关芯片数据集GSE142698、GSE142699和GSE96535。利用GEO2R分析得到差异mRNAs、miRNAs以及差异lncRNAs。利用在线生物信息学分析工具DAVID对差异mRNAs进行GO富集分析和KEGG通路分析。利用miRWalk数据库预测AML相关miRNAs的靶向mRNAs,利用Spongescan数据库预测AML相关miRNAs的靶向lncRNAs,构建lncRNA-miRNA-mRNA竞争性内源RNA （competing endogenous RNA,ceRNA）调控网络。共筛选出29个显著差异mRNAs、70个显著差异miRNAs和20 005个显著差异lncRNAs。GO富集分析和KEGG通路分析显示,差异表达基因主要涉及蛋白磷酸化、细胞分裂、细胞增殖的负调控、基因表达的正向调节、周期蛋白依赖的丝氨酸/苏氨酸激酶活性的调节等生物过程以及细胞周期、细胞衰老、癌症通路、PI3K-Akt通路等信号通路。将miRWalk数据库预测的靶向mRNAs与差异mRNAs取交集,Spongescan数据库预测的靶向lncRNAs与差异lncRNAs取交集,分别确定了25个mRNAs、6个lncRNAs参与AML相关ceRNA调控网络的构建。结果表明,lncRNAs可能作为关键的ceRNA,通过调控miRNA和相关靶基因参与AML的发生与发展,研究结果为AML诊断和治疗的分子生物学研究提供了新的依据。     

Construction of a paclitaxel-related competitive endogenous RNA network and identification of a potential regulatory axis in pancreatic cancer

BackgroundIncreasing numbers of studies have elucidated the role of competitive endogenous RNA (ceRNA) networks in carcinogenesis. However, the potential role of the paclitaxel-related ceRNA network in the innate mechanism and prognosis of pancreatic cancer has not been identified.MethodsComprehensive bioinformatics analyses were performed to identify drug-related miRNAs (DRmiRNAs), drug-related mRNAs (DRmRNAs) and drug-related lncRNAs (DRlncRNAs) and construct a ceRNA network. The ssGSEA and CIBERSORT algorithms were utilized for immune cell infiltration analysis. Additionally, we validated our paclitaxel-related ceRNA regulatory axis at the gene expression level; functional experiments were conducted to explore the biological functions of the key genes.ResultsA total of 182 mRNAs, 13 miRNAs, and 53 lncRNAs were confirmed in the paclitaxel-related ceRNA network. In total, 6 mRNAs, 4 miRNAs, and 6 lncRNAs were identified to establish a risk signature and exhibited optimal prognostic effects. The mRNA signature can predict the abundance of immune cell infiltration and the sensitivity of different chemotherapeutic drugs and may also have a guiding effect in immune checkpoint therapy. A potential PART1/hsa-mir-21/SCRN1 axis was confirmed according to the ceRNA theory and was verified by qPCR. The results indicated that PART1 knockdown markedly increased hsa-mir-21 expression but inhibited SCRN1 expression, weakening the proliferation and migration abilities.ConclusionsWe hypothesized that the paclitaxel-related ceRNA network strongly influences the innate mechanism, prognosis, and immune infiltration of pancreatic cancer. Our risk signatures can accurately predict survival outcomes and provide a clinical basis.     

Identification of a six-gene prognostic signature for oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is one of the most common types of malignancies worldwide, and its morbidity and mortality have increased in the near term. Consequently, the purpose of the present study was to identify the notable differentially expressed genes (DEGs) involved in their pathogenesis to obtain new biomarkers or potential therapeutic targets for OSCC. The gene expression profiles of the microarray datasets GSE85195, GSE23558, and GSE10121 were obtained from the Gene Expression Omnibus (GEO) database. After screening the DEGs in each GEO dataset, 249 DEGs in OSCC tissues were obtained. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology pathway enrichment analysis was employed to explore the biological functions and pathways of the above DEGs. A protein–protein interaction network was constructed to obtain a central gene. The corresponding total survival information was analyzed in patients with oral cancer from The Cancer Genome Atlas (TCGA). A total of six candidate genes (CXCL10, OAS2, IFIT1, CCL5, LRRK2, and PLAUR) closely related to the survival rate of patients with oral cancer were identified, and expression verification and overall survival analysis of six genes were performed based on TCGA database. Time-dependent receiver operating characteristic curve analysis yields predictive accuracy of the patient's overall survival. At the same time, the six genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients recruited to the present study. In conclusion, the present study identified the prognostic signature of six genes in OSCC for the first time via comprehensive bioinformatics analysis, which could become potential prognostic markers for OCSS and may provide potential therapeutic targets for tumors.     

A P53-related microRNA model for predicting the prognosis of hepatocellular carcinoma patients

Studies have shown that microRNAs (miRNAs) play a vital role in tumor progression and patients’ prognosis. Therefore, we aimed to construct a miRNA model for forecasting the survival of hepatocellular carcinoma (HCC) patients. The gene expression data of 433 patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus public databases were remined by survival analysis and receptor manipulation characteristic curve (ROC). A prognostic model including six miRNAs (hsa-mir-26a-1-3p, hsa-mir-188-5p, hsa-mir-212-5p, hsa-mir-149-5p, hsa-mir-105-5p, and hsa-mir-132-5p) were constructed in the training dataset (TCGA, n = 333). HCC patients were stratified into a high-risk group and a low-risk group with significantly different survival (median: 2.75 vs. 8.93 years, log-rank test p < .001). Then we proved its performance of stratification in another independent dataset (GSE116182, median: 2.55 vs 6.96 years, log-rank test p = .008). Cox regression analysis showed that the prognostic model was an independent prognostic indicator for HCC patients. Then time-dependent ROC analyses were performed to test the prognostic ability of the model with that of TNM staging, we found the model had a better performance, especially at 5 years (AUC = 0.76). Functional prediction showed that the genes targeted by the six prognostic miRNAs in the prognostic model were highly expressed in the P53-related pathway. In conclusion, we constructed a prognostic miRNA model that could indicate the survival of HCC patients.     

CXC chemokine ligand 13 and CC chemokine ligand 19 cooperatively render resistance to apoptosis in B cell lineage acute and chronic lymphocytic leukemia CD23+CD5+ B cells

CXCL13/CXCR5 and CCL19/CCR7 play a quite important role in normal physiological conditions, but the functions of both chemokine/receptor pairs in pathophysiological events are not well-investigated. We have investigated expression and functions of CXCL13/CXCR5 and CCL19/CCR7 in CD23+CD5+ and CD23+CD5- B cells from cord blood (CB) and patients with B cell lineage acute or chronic lymphocytic leukemia (B-ALL or B-CLL). CXCR5 and CCR7 are selectively expressed on B-ALL, B-CLL, and CB CD23+CD5+ B cells at high frequency, but not on CD23+CD5- B cells. Although no significant chemotactic responsiveness was observed, CXCL13 and CCL19 cooperatively induce significant resistance to TNF-alpha-mediated apoptosis in B-ALL and B-CLL CD23+CD5+ B cells, but not in the cells from CB. B-ALL and B-CLL CD23+CD5+ B cells express elevated levels of paternally expressed gene 10 (PEG10). CXCL13 and CCL19 together significantly up-regulate PEG10 expression in the same cells. We have found that CXCL13 and CCL19 together by means of activation of CXCR5 and CCR7 up-regulate PEG10 expression and function, subsequently stabilize caspase-3 and caspase-8 in B-ALL and B-CLL CD23+CD5+ B cells, and further rescue the cells from TNF-alpha-mediated apoptosis. Therefore, we suggest that normal lymphocytes, especially naive B and T cells, use CXCL13/CXCR5 and CCL19/CCR7 for migration, homing, maturation, and cell homeostasis as well as secondary lymphoid tissues organogenesis. In addition, certain malignant cells take advantages of CXCL13/CXCR5 and CCL19/CCR7 for infiltration, resistance to apoptosis, and inappropriate proliferation.     

Identification of lncRNA–miRNA–mRNA regulatory network associated with epithelial ovarian cancer cisplatin-resistant

To construct a long noncoding RNA (lncRNA)–microRNA (miRNA)–messenger RNA (mRNA) regulatory network related to epithelial ovarian cancer (EOC) cisplatin-resistant, differentially expressed genes (DEGs), differentially expressed lncRNAs (DELs), and differentially expressed miRNAs (DEMs) between MDAH and TOV-112D cells lines were identified. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to analyze the biological functions of DEGs. Downstream mRNAs or upstream lncRNAs for miRNAs were analyzed at miRTarBase 7.0 or DIANA-LncBase V2, respectively. A total of 485 significant DEGs, 85 DELs, and 5 DEMs were identified. Protein–protein interaction (PPI) network of DEGs contrains 81 nodes and 141 edges was constructed, and 25 hub genes related to EOC cisplatin-resistant were identified. Subsequently, a lncRNA–miRNA–mRNA regulatory network contains 4 lncRNAs, 4 miRNAs, and 35 mRNAs was established. Taken together, our study provided evidence concerning the alteration genes involved in EOC cisplatin-resistant, which will help to unravel the mechanisms underlying drug resistant.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Identification of functional lncRNAs in gastric cancer by integrative analysis of GEO and TCGA data

Gastric cancer (GC) is a prevalent malignant cancer of digestive system, identification of novel diagnostic and prognostic biomarkers for GC is urgently demanded. The aim of this study was to determine potential long noncoding RNAs (lncRNAs) associated with the pathogenesis and prognosis of GC. Raw noncoding RNA microarray data (GSE53137, GSE70880, and GSE99417) was downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes between GC and adjacent normal gastric tissue samples were screened by an integrated analysis of multiple gene expression profile after gene reannotation and batch normalization. Differentially expressed genes were further confirmed by The Cancer Genome Atlas (TCGA) database. Competing endogenous RNA (ceRNA) network, Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes pathway, survival analysis were extensively applied to identify hub lncRNAs and discover potential biomarkers related to diagnosis and prognosis of GC. In total of 246 integrated differential genes including 15 lncRNAs and 241 messenger RNAs (mRNAs) were obtained after intersections of differential genes between GEO and TCGA database. ceRNA network comprised of three lncRNAs (UCA1, HOTTIP, and HMGA1P4), 26 microRNAs (miRNAs) and 72 mRNAs. Functional analysis revealed that three lncRNAs were mainly dominated in cell cycle and cellular senescence. Survival analysis showed that HMGA1P4 was statistically related to the overall survival rate. For the first time, we identified that HMGA1P4, a target of miR-301b/miR-508, is involved in cell cycle and senescence process by regulating CCNA2 in GC. Finally, the expression levels of three lncRNAs were validated to be upregulated in GC tissues. Thus, three lncRNAs including UCA1, HOTTIP, and HMGA1P4 may contribute to GC development and their potential functions might be associated with the prognosis of GC.