Bioinformatic gene analysis for possible biomarkers and therapeutic targets of hypertension-related renal cell carcinoma

BackgroundRenal cell carcinoma (RCC) is one of the most prevalent malignant tumors of the urinary system. Hypertension can cause hypertensive nephropathy (HN). Meanwhile, Hypertension is considered to be related to kidney cancer. We analyzed co-expressed genes to find out the relationship between hypertension and RCC and show possible biomarkers and novel therapeutic targets of hypertension-related RCC.MethodsWe identified the differentially expressed genes (DEGs) of HN and RCC through analyzing Gene Expression Omnibus (GEO) datasets {"type":"entrez-geo","attrs":{"text":"GSE99339","term_id":"99339"}}GSE99339, {"type":"entrez-geo","attrs":{"text":"GSE99325","term_id":"99325"}}GSE99325, {"type":"entrez-geo","attrs":{"text":"GSE53757","term_id":"53757"}}GSE53757 and {"type":"entrez-geo","attrs":{"text":"GSE15641","term_id":"15641"}}GSE15641 by means of bioinformatics analysis, respectively. Then we evaluated these genes with protein-protein interaction (PPI) networks, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and CTD database. Subsequently, we verified co-expressed DEGs with Gene Expression Profiling Interactive Analysis (GEPIA) database. Finally, corresponding predicted miRNAs of co-expressed DEGs were identified and verified via mirDIP database and Starbase, respectively.ResultsWe identified 9 co-expressed DEGs, including BCAT1, CORO1A, CRIP1, ESRRG, FN1, LYZ, PYCARD, SAP30, and PTRF. CRIP1 and ESRRG and their corresponding predicted miRNAs, especially hsa-miR-221-5p, hsa-miR-205-5p, hsa-miR-152-3p and hsa-miR-137 may be notably related to hypertension-related RCC.ConclusionsCRIP1 and ESRRG genes have great potential to become novel biomarkers and therapeutic targets concerning hypertension-related RCC.     

Integrative bioinformatics approaches for identifying potential biomarkers and pathways involved in non-obstructive azoospermia

BackgroundNon-obstructive azoospermia (NOA) is a disease related to spermatogenic disorders. Currently, the specific etiological mechanism of NOA is unclear. This study aimed to use integrated bioinformatics to screen biomarkers and pathways involved in NOA and reveal their potential molecular mechanisms.Methods{"type":"entrez-geo","attrs":{"text":"GSE145467","term_id":"145467"}}GSE145467 and {"type":"entrez-geo","attrs":{"text":"GSE108886","term_id":"108886"}}GSE108886 gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NOA tissues and matched obstructive azoospermia (OA) tissues were identified using the GEO2R tool. Common DEGs in the two datasets were screened out by the VennDiagram package. For the functional annotation of common DEGs, DAVID v.6.8 was used to perform Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In accordance with data collected from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, a protein–protein interaction (PPI) network was constructed by Cytoscape. Cytohubba in Cytoscape was used to screen the hub genes. Furthermore, the hub genes were validated based on a separate dataset, {"type":"entrez-geo","attrs":{"text":"GSE9210","term_id":"9210"}}GSE9210. Finally, potential micro RNAs (miRNAs) of hub genes were predicted by miRWalk 3.0.ResultsA total of 816 common DEGs, including 52 common upregulated and 764 common downregulated genes in two datasets, were screened out. Some of the more important of these pathways, including focal adhesion, PI3K-Akt signaling pathway, cell cycle, oocyte meiosis, AMP-activated protein kinase (AMPK) signaling pathway, FoxO signaling pathway, and Huntington disease, were involved in spermatogenesis. We further identified the top 20 hub genes from the PPI network, including CCNB2, DYNLL2, HMMR, NEK2, KIF15, DLGAP5, NUF2, TTK, PLK4, PTTG1, PBK, CEP55, CDKN3, CDC25C, MCM4, DNAI1, TYMS, PPP2R1B, DNAI2, and DYNLRB2, which were all downregulated genes. In addition, potential miRNAs of hub genes, including hsa-miR-3666, hsa-miR-130b-3p, hsa-miR-15b-5p, hsa-miR-6838-5p, and hsa-miR-195-5p, were screened out.ConclusionsTaken together, the identification of the above hub genes, miRNAs and pathways will help us better understand the mechanisms associated with NOA, and provide potential biomarkers and therapeutic targets for NOA.     

m6A RNA methylation regulators play an important role in the prognosis of patients with testicular germ cell tumor

BackgroundN6-methyladenosine (m6A) is found to be associated with promoting tumorigenesis in different types of cancers, however, the function of m6A-related genes in testicular germ cell tumors (TGCT) development remains to be illuminated. This study aimed to investigated the prognostic value of m6A RNA methylation regulators in TGCT.MethodsWe collected TGCT patients’ information about clinicopathologic parameters and twenty-two m6A regulatory genes expression from The Cancer Genome Atlas (TCGA) database and Genotype-Tissue Expression (GTEx). We analyzed the differentially expressed m6A RNA methylation regulators between tumor tissues and normal tissues, as well as the correlation of m6A RNA methylation regulators. By using Cox univariate analysis, last absolute shrinkage and selection operator (LASSO) Cox regression algorithm and Cox multivariate proportional hazards regression analysis, a risk score was constructed based on a TCGA training cohort, and further verified in the TCGA testing cohort. Then, univariate and multivariate Cox regression analyses were used to evaluate the relationship between risk score and progression-free survival (PFS) in TGCT. Finally, the six-gene risk score was further verified by two gene expression profiles ({"type":"entrez-geo","attrs":{"text":"GSE3218","term_id":"3218"}}GSE3218 and {"type":"entrez-geo","attrs":{"text":"GSE10783","term_id":"10783"}}GSE10783) as an independent external validation cohort.ResultsDistinct expression patterns of m6A regulatory genes were identified between TGCT tissues and normal tissues in TCGA and GTEx datasets. To predict prognosis of TGCT patients, a risk score was calculated based on six selected m6A RNA methylation regulators (YTHDF1, RBM15, IGF2BP1, ZC3H13, METTL3, and FMR1). Additionally, we found significant differences between the high-risk and low-risk groups in serum marker study levels and histologic subtype. Univariate and multivariate analysis indicated that high risk score was associated with unfavorable PFS. Ultimately, the risk score was further verified by two gene expression profiles ({"type":"entrez-geo","attrs":{"text":"GSE3218","term_id":"3218"}}GSE3218 and {"type":"entrez-geo","attrs":{"text":"GSE10783","term_id":"10783"}}GSE10783).ConclusionsBased on six selected m6A RNA methylation regulators, we developed a m6A methylation related risk score that can independently predict the prognosis of TGCT patients, and verify the prediction efficiency in TCGA and GEO datasets. Patients in high-risk group were associated with serum tumor marker study levels beyond the normal limits, non-seminoma, and unfavorable survival time. However, further prospective experiments should be carried out to verify our results.     

Identification and validation of a six immune-related gene signature for prediction of biochemical recurrence in localized prostate cancer following radical prostatectomy

BackgroundProstate cancer (PCa) is the second lethal heterogeneous cancer among males worldwide, and approximately 20% of PCa patients following radical prostatectomy (RP) will undergo biochemical recurrence (BCR). This study is aimed to identify the immune-related gene signature that can predict BCR in localized PCa following RP.MethodsExpression profile of genes together with clinical parameters from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus database (GEO) and the immune-related genes from the Molecular Signatures Database v4.0 were applied to construct and validate the gene signature. The Cox regression analyses were conducted to identify the candidate genes and establish the gene signature. To estimate the prognostic power of the risk score, the time-dependent receiver operating characteristic (ROC) analysis and Harrell’s index of concordance (C-index) were utilized. We also established a nomogram to forecast the probability of patients’ survival.ResultsA total of 268 patients from the TCGA and 77 patients from {"type":"entrez-geo","attrs":{"text":"GSE70770","term_id":"70770"}}GSE70770 and six immune-related genes (SCIN, THY1, TBX1, NOTCH4, MAL, BNIP3L) were eventually selected. The Kaplan-Meier analysis demonstrated that patients in the low-risk group had a significantly longer recurrence-free survival (RFS) compared to those in the high-risk group. In the multivariate Cox model, the signature was identified as an independent prognostic factor, which was significantly associated with RFS (TCGA: HR =5.232, 95% CI: 1.762–15.538, P=0.003; {"type":"entrez-geo","attrs":{"text":"GSE70770","term_id":"70770"}}GSE70770: HR =2.158, 95% CI: 1.051–4.432, P=0.036). Moreover, the C-index got improved after incorporating the risk score into original clinicopathological parameters. In addition, the novel nomogram was constructed to better predict the 1-, 3- and 5-year RFS.ConclusionsThis signature could serve as an independent prognostic factor for BCR. Incorporation of our signature into traditional risk classification might further stratify patients with different prognosis, which could assist practitioners in developing clinical decision-making.     

Non‐coding RNA Identification in Osteonecrosis of the Femoral Head Using Competitive Endogenous RNA Network Analysis

ObjectiveTo investigate the regulatory network of long non‐coding RNA (lncRNA) as competing endogenous RNAs (ceRNAs) in osteonecrosis of the femoral head (ONFH).MethodsThe gene expression profile {"type":"entrez-geo","attrs":{"text":"GSE74089","term_id":"74089"}}GSE74089 of ONFH and microRNA (miRNA) expression profile of {"type":"entrez-geo","attrs":{"text":"GSE89587","term_id":"89587"}}GSE89587 were obtained from the Gene Expression Omnibus (GEO) database. The {"type":"entrez-geo","attrs":{"text":"GSE74089","term_id":"74089"}}GSE74089 contained four ONFH samples and four controls. The {"type":"entrez-geo","attrs":{"text":"GSE89587","term_id":"89587"}}GSE89587 included 10 ONFH samples and 10 control samples. The differentially expressed lncRNAs (DE‐lncRNAs) and DE‐mRNAs between ONFH group and control group were identified from {"type":"entrez-geo","attrs":{"text":"GSE74089","term_id":"74089"}}GSE74089 using the limma package based on criteria of adjusted P value <0.05 and |log fold change (FC)| ≥2. The DEmiRNAs between ONFH group and control group were screened from {"type":"entrez-geo","attrs":{"text":"GSE89587","term_id":"89587"}}GSE89587 on the basis of adjusted P value <0.05. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway for DE‐mRNAs were analyzed using DAVID 6.7 and GSEA 3.0, respectively. Coexpressed lncRNA‐mRNA pairs were identified by corr.test method in R based on the criteria of adjusted P value <0.01 and |r| ≥ 0.9. A ceRNA network was constructed and visualized using cytoscape 3.7.0 by integrating the DE‐lncRNA, DE‐miRNA, and DEmRNA data. The key mRNAs and lncRNAs in the ceRNA network were further validated in an independent dataset of {"type":"entrez-geo","attrs":{"text":"GSE123568","term_id":"123568"}}GSE123568.ResultsBased on our analysis, a total of 28 DE‐lncRNAs, 1403 DE‐mRNAs, and 134 DE‐miRNAs were identified, respectively. The DE‐mRNAs were significantly enriched in the function of “skeletal system development,” “collagen fibril organization,” “blood vessel development,” and “regulation of nervous system development.” Besides, 72 KEGG pathways, including eight active pathways and 64 suppressed pathways were identified, including which immune pathway was the most significantly activated one and which ribosome‐related function was the most suppressed. A co‐expression network including 161 DE‐mRNAs and 16 DE‐lncRNAs was built. Highly connected nodes were identified among lncRNAs such as H19, C20orf203, LINC00355, SFTA3, CRNDE, CASC2, LINC00494, C9orf163, C10orf91, and LINC00301. The ceRNA network indicated that lncRNA H19 functioned as a ceRNA of hsa‐miR‐519b‐3p and hsa‐miR‐296‐5p in ANKH and ECHDC1 regulation; lncRNA C9orf163 functioned as a ceRNA of hsa‐miR‐424‐5p in CCNT1 regulation. The expression trends of ANKH, CCNT1, and C9orf163 were successfully validated in independent dataset of {"type":"entrez-geo","attrs":{"text":"GSE123568","term_id":"123568"}}GSE123568.ConclusionThe ceRNAs of lncRNA H19‐ hsa‐miR‐519b‐3p/hsa‐miR‐296‐5p‐ANKH and lncRNA c9orf163‐ hsa‐miR‐424‐5p‐CCNT1 might play important roles in ONFH development. Our research provided an understanding of the important role of lncRNA–related ceRNAs in ONFH.     

A new risk stratification system of prostate cancer to identify high-risk biochemical recurrence patients

BackgroundBiochemical recurrence (BCR) is considered a decisive risk factor for clinical recurrence and the metastasis of prostate cancer (PCa). Therefore, we developed and validated a signature which could be used to accurately predict BCR risk and aid in the selection of PCa treatments.MethodsA comprehensive genome-wide analysis of data concerning PCa from previous datasets of the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) was performed. Lasso and Cox regression analyses were performed to develop and validate a novel signature to help predict BCR risk. Moreover, a nomogram was constructed by combining the signature and clinical variables.ResultsA total of 977 patients were involved in the study. This consisted of patients from the TCGA (n=405), {"type":"entrez-geo","attrs":{"text":"GSE21034","term_id":"21034"}}GSE21034 (n=131), {"type":"entrez-geo","attrs":{"text":"GSE70770","term_id":"70770"}}GSE70770 (n=193) and {"type":"entrez-geo","attrs":{"text":"GSE116918","term_id":"116918"}}GSE116918 (n=248) datasets. A 9-mRNA signature was identified in the TCGA dataset (composed of C9orf152, EPHX2, ASPM, MMP11, CENPF, KIF4A, COL1A1, ASPN, and FANCI) which was significantly associated with BCR (HR =3.72, 95% CI: 2.30–6.00, P<0.0001). This signature was validated in the {"type":"entrez-geo","attrs":{"text":"GSE21034","term_id":"21034"}}GSE21034 (HR =7.54, 95% CI: 3.15–18.06, P=0.019), {"type":"entrez-geo","attrs":{"text":"GSE70770","term_id":"70770"}}GSE70770 (HR =2.52, 95% CI: 1.50–4.22, P=0.0025) and {"type":"entrez-geo","attrs":{"text":"GSE116918","term_id":"116918"}}GSE116918 datasets (HR =4.75, 95% CI: 2.51–9.02, P=0.0035). Multivariate Cox regression and stratified analysis showed that the 9-mRNA signature was a clinical factor independent of prostate-specific antigen (PSA), Gleason score (GS), or AJCC T staging. The mean AUC for 5-year BCR-free survival predictions of the 9-mRNA signature (0.81) was higher than the AUC for PSA, GS, or AJCC T staging (0.52–0.73). Furthermore, we combined the 9-mRNA signature with PSA, GS, or AJCC T staging and demonstrated that this could enhance prognostic accuracy.ConclusionsThe proposed 9-mRNA signature is a promising biomarker for predicting BCR-free survival in PCa. However, further controlled trials are needed to validate our results and explore a role in individualized management of PCa.     

Identification of a Prognostic Signature Based on the Expression of Genes Related to the Insulin Pathway in Early Breast Cancer

IntroductionInsulin and the insulin-like growth factor (IGF) family play a key role in breast cancer (BC).ObjectiveIn this study, we evaluated on a genomic scale the potential prognostic value of insulin signaling in early BC.MethodsCandidate genes were selected from the published literature and gene expression profiling experiments. Three publicly available BC datasets, containing gene expression data on 502 cases, were used to test the prognostic ability of the score. The gene signature was developed on {"type":"entrez-geo","attrs":{"text":"GSE1456","term_id":"1456"}}GSE1456, containing microarray data from 159 patients, split into a training set (102 breast tumors) and a validation set (n = 57). {"type":"entrez-geo","attrs":{"text":"GSE3494","term_id":"3494"}}GSE3494 and {"type":"entrez-geo","attrs":{"text":"GSE2990","term_id":"2990"}}GSE2990 (350 patients) were used for external validation. Univariate Mann-Whitney test was used to identify genes differentially expressed between relapsed and nonrelapsed patients. Expression of genes significantly correlated with relapse was combined in a linear score. Patients were classified as low or high risk with respect to the median value.ResultsOn the training set, 15 genes turned out to be differentially expressed: 8-year disease-free survival (DFS) was 51 and 91% in the high- and low-risk group (p < 0.001), respectively. In the validation set, DFS was 97 and 54% (p = 0.009), respectively. External validation: 8-year DFS was 72 and 61%, respectively, in {"type":"entrez-geo","attrs":{"text":"GSE3494","term_id":"3494"}}GSE3494 (p = 0.03) and 74 and 55% in {"type":"entrez-geo","attrs":{"text":"GSE2990","term_id":"2990"}}GSE2990 (p = 0.03). By multivariate analyses, the insulin signature was significantly associated with DFS, independently of age, hormone receptor status, nodal status, and grade.ConclusionsOur findings indicate that the insulin pathway is involved in BC prognosis at a genomic level and provide a window of selectivity for preventive and treatment strategies targeting the insulin/IGF pathway in BC patients.     

A novel genomic model for predicting the likelihood of delayed graft function in DCD kidney transplantation

BackgroundThe high incidence of delayed graft function (DGF) following kidney transplantation with donation after cardiac death allografts (DCD-KT) poses great challenges to transplant clinicians. This study aimed to explore the DGF-related biomarkers and establish a genomic model for DGF prediction specific to DCD KT.MethodsBy data mining a public dataset ({"type":"entrez-geo","attrs":{"text":"GSE43974","term_id":"43974"}}GSE43974), the key DGF-related genes in DCD kidney biopsies taken after short-time reperfusion (45–60 min) were identified by differential expression analysis and a LASSO-penalized logistic regression model. Their coefficients for modeling were calculated by multivariate logistic regression. Receiver operating characteristic curves and a nomogram were generated to evaluate its predictive ability for DGF occurrence. Gene set enrichment analysis (GSEA) was performed to explore biological pathways underlying DGF in DCD KT.ResultsFive key DGF-related genes (CHST3, GOLPH3, ZBED5, AKR1C4, and ERRFI1) were first identified, all of which displayed good discrimination for DGF occurrence after DCD KT (all P<0.05). A five-mRNA-based risk score was further established and showed excellent predictive ability (AUC =0.9708, P<0.0001), which was obviously higher than that of the five genes alone. Eight DGF-related biological pathways in DCD kidneys, such as “arachidonic acid metabolism”, “lysosome”, “proximal tubule bicarbonate reclamation”, “glutathione metabolism”, were identified by GSEA (all P<0.05). Moreover, a convenient and visual nomogram based on the genomic risk score was also constructed and displayed high accuracy for DGF prediction specific to DCD KT.ConclusionsThe novel genomic model may effectively predict the likelihood of DGF immediately after DCD KT or even prior to transplantation in the context of normothermic machine perfusion in the future.     

Identification of potential circRNAs and circRNA‐miRNA‐mRNA regulatory network in the development of diabetic foot ulcers by integrated bioinformatics analysis

We aimed to explore the mechanism of circular RNAs (circRNAs) and provide potential biomarkers for molecular therapy of diabetic foot ulcers (DFU). Gene expression profile of {"type":"entrez-geo","attrs":{"text":"GSE114248","term_id":"114248"}}GSE114248, including five normal samples and five DFU samples, was downloaded from GEO database. Differentially expressed circRNAs (DEcircRNAs) between two groups were identified. Then, DEcircRNA‐miRNA and miRNA‐mRNA interaction was revealed, followed by the circRNA‐miRNA‐mRNA network construction. Moreover, functional and pathway analysis were performed based on mRNAs, followed by the DM‐related pathway exploration. Specific binding sites for key circRNAs and associated miRNAs were under investigation. Finally, RT‐qPCR was used to verify the candidate the relative expression level of circRNA between normal tissues and DFU. Totally, 65 DEcircRNAs were revealed between two groups, followed by 113 circRNA‐miRNA‐mRNA interactions explored. The mRNAs in these interactions were mainly assembled in functions like cell proliferation and pathways. Moreover, a total of 11 DM‐related pathways were revealed. Finally, circRNA‐miRNA specific binding‐site analysis revealed two key circRNAs, for example, circRNA_072697 and circRNA_405463, corresponding to their miRNAs. These two circRNAs were novel biomarkers for DFU. circRNA_072697 acted as a sponge of miR‐3150a‐3p in the progression of DFU via regulating KRAS. MAPK signaling pathway might contribute to the development of DFU.     

The prognostic roles of CYP19A1 expression in bladder cancer patients of different genders

BackgroundThe incidence of bladder cancer (BCa) in male is approximately three to four times higher than in female, but the oncological outcomes in female patients with BCa are significantly worse than in male patients. Although many biomarkers have been identified in recent decades to predict the prognosis of BCa patients, few of them are able to distinguish the prognosis of BCa patients with gender difference. Aromatase encoded by the CYP19A1 gene catalyzes the conversion of androgens to estrogens. In this study, we investigate the prognosis significance of CYP19A1 expression considering the gender difference in BCa patients from four available public databases.MethodsFour available public databases of BCa, including {"type":"entrez-geo","attrs":{"text":"GSE13507","term_id":"13507"}}GSE13507, TCGA-BLCA, E-MTAB-4321, and E-MTAB-1803, were utilized in this analysis. The overall survival (OS) and progression-free survival (PFS) in different stages and genders were evaluated using the Kaplan-Meier analysis based on the optimal cut-off values of CYP19A1 expression. Then, Gene Set Enrichment Analysis (GSEA) were further performed to explore the potential biologic pathways by altering CYP19A1 expression in BCa patients.ResultsThe results showed that patients with high CYP19A1 expression had a poorer outcome compared with those with low expression in both BCa cohorts in general. Higher CYP19A1 expression in male patients were significantly associated with shorter survival for either non-muscle-invasive bladder cancer (NMIBC) or muscle-invasive bladder cancer (MIBC). However, female NMIBC patients with high CYP19A1 expression were identified to have a better prognosis, whereas high CYP19A1 expression in female MIBC patients were significantly associated with poorer survival. The result of the GSEA showed that different outcomes in female and male patients with NMIBC were related to the interaction of CYP19A1 and the cell-cycle-related pathways.ConclusionsThese findings demonstrated that CYP19A1 expression might have a potential role in distinguishing the prognosis of female BCa patients dependent on tumor stage. Our results provide new insights for aromatase-mediated BCa therapy.     

基于GEO数据库的胰腺癌循环肿瘤细胞代谢通路及关键基因研究

目的 探讨胰腺癌循环肿瘤细胞（CTCs）中起关键作用的代谢通路及关键基因。方法 从GEO数据库筛选胰腺癌CTCs相关数据集,利用R studio软件筛选差异基因,与KEGG数据库的代谢相关基因比对,寻找代谢相关差异基因。对差异基因进行KEGG通路富集,使用STRING、Cytoscape进行蛋白相互作用网络分析和可视化。最后,对比两数据集富集的代谢通路,获得关键通路及基因,并利用TCGA和TIMER数据库分析关键基因与临床特征和免疫浸润的关系。结果 分别从数据集GSE118556和GSE18670筛选出834个和1119个差异基因。前者基因主要富集到半胱氨酸和蛋氨酸代谢、一碳代谢和辅酶因子合成等代谢通路,后者基因主要富集到一碳代谢、嘌呤代谢和甘油磷脂代谢通路。其中转酮醇酶（TKT）在两个数据集的一碳代谢中均显著上调。TKT与总体生存期、肿瘤分期、组织学分级相关（P<0.05）。同样编码转酮醇酶同工酶的TKTL1和TKTL2与免疫浸润相关（P<0.05）。结论 通过对胰腺癌CTCs数据集的生物信息学分析,发现一碳代谢和TKT可能在CTCs的形成和维持中起关键作用,为进一步研究胰腺癌转移的代谢机制提供一定的基础。     

Re-Exploring Biomarkers and Therapeutic Targets in Primary Melanoma Patients: Insights from Network-Based Analysis of Microarray Data

ObjectiveTo identify novel biomarkers and therapeutic targets for primary melanoma using network-based microarray data analysis.MethodsEligible microarray datasets from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs). The protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to identify hub genes and pathways that might affect the survival of melanoma patients. Immunohistochemistry results obtained from the Human Protein Atlas (HPA) database confirmed the protein expression levels of hub genes. The Cancer Genome Atlas (TCGA) database was used to further verify the gene expression levels and conduct survival analysis.ResultsThree microarray datasets (GSE3189, GSE15605, and GSE46517) containing 122 melanoma and 30 normal skin tissue samples were included. A total of 262 common differentially expressed genes (cDEGs) were identified based on three statistical approaches (Fisher's method, the random effects model (REM), and vote counting) with strict criteria. Of these, two upregulated genes, centromere protein F (CENPF) and pituitary tumor-transforming gene 1 (PTTG1), were selected as hub genes. HPA and TCGA database analyses confirmed that CENPF and PTTG1 were overexpressed in melanoma. Survival analysis showed that high expression levels of CENPF were significantly correlated with decreased overall survival (OS) (P=0.028).ConclusionThe expression level of CENPF was significantly upregulated in melanoma and correlated with decreased OS. Thus, CENPF may represent a novel biomarker and therapeutic target for melanoma patients.     

基于生物信息学胰腺腺癌关键基因的筛选及支持向量机诊断模型的构建

背景与目的：胰腺癌是一种常见的消化道恶性肿瘤,其主要病理类型为胰腺腺癌（PAAD）,因早期诊断困难且缺乏有效的治疗措施,故预后极差。因此,寻找PAAD的诊治新靶标具有重要意义。本研究通过生物信息学方法筛选与PAAD诊断和预后相关的关键基因,构建分类PAAD样本和正常样本的支持向量机（SVM）模型,以期为PAAD的诊治及机制研究提供依据。 方法：从基因表达数据库（GEO）中下载3个芯片数据（GSE28735、GSE62165、GSE62452）,应用R语言的Limma包筛选出PAAD组织和正常组织间的差异表达基因（DEGs）。利用STRING数据库对DEGs进行GO和KEGG通路富集分析。再以STRING数据库构建DEGs的蛋白互作网络（PPI）,利用Cytoscape软件进行可视化编辑,并通过MCODE插件进行关键子网络分析。使用R语言的survival包筛选PPI和关键子网络中与预后相关的关键节点,将其上传至Metascape进行功能富集分析。利用R语言caret包中递归式特征消除（RFE）算法筛选关键节点中的最优特征基因,在GEPIA数据库中验证最优特征基因的表达差异,随后通过R语言的e1071包构建最优特征基因的SVM模型,并在3个芯片数据中借助R语言的pROC包对该模型进行验证。在TCGA数据库中,用R语言的survminer包筛选出最优特征基因中与PAAD预后相关的基因作为关键基因。 结果：共筛选出257个DEGs,包括168个上调基因和89个下调基因。GO分析结果表明DEGs主要参与细胞外基质的组成、细胞黏附、丝氨酸肽酶活性等生物学过程。KEGG分析显示,DEGs主要富集于蛋白质的消化和吸收、胰腺的分泌、黏着斑、PI3K-Akt信号通路。生存分析筛选出14个关键节点同时在GSE28735和GSE62452中与预后相关（均P<0.05）,这些基因在肿瘤侵犯和肿瘤发生中发挥一定作用。RFE筛选出8个最优特征基因：LAMA3、FN1、ITGA3、MET、PLAU、CENPF、MMP14、OAS2;GEPIA数据库验证发现这8个最优特征基因在PAAD组织中明显上调（均P<0.01）;这些基因构建的SVM模型在3个芯片数据中ROC曲线的AUC依次为0.898、1.000、0.905。TCGA数据库验证发现LAMA3、ITGA3、MET、PLAU、CENPF及OAS2的上调与PAAD预后不良有关（均P<0.05）。 结论：关键基因LAMA3、ITGA3、MET、PLAU、CENPF及OAS2可能成为PAAD诊治的新靶点;基于8个最优特征基因构建的SVM模型可有效诊断PAAD。     

Identification of genes that correlate clear cell renal cell carcinoma and obesity and exhibit potential prognostic value

BackgroundRenal cell carcinoma (RCC) is a common urologic malignancy. Although the relationship between clear cell RCC (ccRCC) and obesity has been well-established by several large-scale retrospective studies, the molecular mechanisms and genetic characteristics behind this correlation remains unclear. In the current study, several bioinformatics tools were used to identify the key genes in ccRCC related to obesity.MethodsMicroarray data comparing ccRCC with normal renal tissues in patients with and without obesity were downloaded from the GEO database for screening of differentially expressed genes (DEGs). The DEGs were verified with expression level and survival analysis using several online bioinformatics tools.ResultsIn the current study, the differential expression of five genes correlated with both ccRCC and obesity; IGHA1 and IGKC as oncogenes, and MAOA, MUC20 and TRPM3 as tumor suppressor genes. These genes were verified by comparing the relationship between the expression levels and survival outcomes from open-source data in The Cancer Genome Atlas (TCGA) dataset.ConclusionsIn conclusion, the five genes differentially expressed in ccRCC and obesity are related to disease progression and prognosis, and therefore could provide prognostic value for patients with ccRCC.     

Transcriptome analyses identify key genes and potential mechanisms in a rat model of osteoarthritis

Background

Osteoarthritis (OA) is one of the most common degenerative diseases of the joints worldwide, but still the pathogenesis of OA is largely unknown. The purpose of our study is to clarify key candidate genes and relevant signaling pathways in a surgical-induced OA rat model.

Methods

The microarray raw data of GSE8077 was downloaded from GEO datasets. GeoDiver were employed to screen differentially-expressed genes (DEGs). Enrichment analyses of DEGs were performed using Metascape. Construction of protein–protein interaction (PPI) network and identification of key genes were conducted using STRING, Cytoscape v3.6.0, and Centiscape2.2. Furthermore, miRDB and Cytoscape v3.6.0 were used for visualization of miRNA-mRNA regulatory network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for predicted miRNAs was undertaken using DIANA-miRPath v3.0.

Results

Several DEGs (188 in comparison between OA and sham-operated group and 160 in comparison between OA and contralateral group) were identified. DEGs mainly enriched in vasculature development, regulation of cell migration, response to growth factor (Gene ontology), and ECM-receptor interaction (KEGG). Two comparison cohorts shared 79 intersection genes, and of these, Ccl2, Col4a1, Col1a1, Aldh1a3, and Itga8 were defined as the hub genes. Predicted miRNAs of seven DEGs from sub-networks mainly enriched in MAPK signaling pathway.

Conclusion

The current study shows that some key genes and pathways, such as Ccl2, Col4a1, Col1a1, Aldh1a3, Itga8, ECM-receptor interaction, and MAPK signaling pathway may be associated with OA progression and act as potential biomarkers and therapeutic targets for OA.

     

基于基因表达汇编数据库的外周血中脊柱椎间盘退行性变诊断标志物的生物信息学分析

目的 通过生物信息学分析椎间盘退行性变（IDD）相关的差异表达基因（DEG）,寻找疾病的新型诊断标志物。方法 通过基因表达汇编（GEO）数据库GSE124272、GSE150408数据集下载IDD相关的外周血样本芯片数据,筛选出IDD组和正常组之间的DEG。使用DAVID在线数据库对DEG进行基因本体（GO）功能富集和京都基因与基因组百科全书（KEGG）信号通路富集,然后利用STRING在线数据库和Cytoscape软件构建蛋白质-蛋白质相互作用（PPI）网络并获取关键基因,并利用GSE23130数据集中的纤维环样本芯片数据进行验证。利用GSE124272、GSE150408数据集中的数据,采用受试者工作特征（ROC）曲线评估外周血中关键基因的诊断效能。结果 联合分析后筛选出597个DEG,包含363个上调基因和234个下调基因。GO功能富集分析发现DEG主要参与细胞黏附、细胞凋亡、趋化作用和细胞迁移等功能,KEGG分析发现DEG主要参与细胞外基质受体相互作用和癌症中的信号通路。PPI网络分析筛选出17个关键基因,经验证获得RBMX、EEF1A1、SSR1和POLR2C这4个基因,ROC曲线分析显示这4个基因对IDD诊断效能显著,曲线下面积分别为0.763、0.741、0.710、0.702。结论 RBMX、EEF1A1、SSR1和POLR2C或可成为IDD的新型诊断标志物,为该病进一步的功能研究提供理论依据。     

肾移植术后BK病毒相关性肾病核心基因及免疫微环境的生物信息学分析

目的通过生物信息学方法分析肾移植术后BK病毒相关性肾病(BKVAN)的核心基因及其与浸润的免疫细胞相关性。 方法从美国国立生物技术信息中心基因表达综合数据库下载BKVAN相关数据集GSE75693和GSE72925,BK病毒(BKV)血症相关数据集GSE47199。合并GSE75693和GSE72925后筛选差异表达基因(DEGs),然后进行基因本体生物过程(GOBP)以及京都基因与基因组百科全书(KEGG)通路分析,并通过蛋白-蛋白相互作用(PPI)网络进一步筛选核心基因。使用CIBERSORT进行免疫浸润分析,然后计算差异的免疫细胞和核心基因的相关性。最后,在GSE47199数据集筛选BKV血症和BKVAN共同的核心基因,使用基因集富集分析(GSEA)鉴定共同的核心基因分别在BKVAN和BKV血症中的生物过程。所有统计分析及可视化均基于R语言(4.0.2)。P<0.05为差异有统计学意义。 结果在合并数据中共筛选出175个上调及70个下调DEGs。在PPI网络中,通过5种方法交集得到9个核心基因,核心基因主要富集在免疫细胞活化与功能相关的进程;在KEGG分析中,核心基因主要富集在病毒蛋白与细胞因子和细胞因子受体间相互作用、细胞因子-细胞因子受体间相互作用以及趋化因子信号通路等。免疫浸润分析表明PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关。CD2是BKVAN和BKV血症的共同核心基因。 结论通过生物信息学方法筛选出BKVAN的核心基因,其中PTPRC、CCL5、TYROBP、CXCL10、CD2和CXCL9与BKVAN中浸润的免疫细胞相关,CD2是BKVAN和BKV血症的共同核心基因,这些标志物为肾移植术后BKVAN的诊治提供依据。