利用TCGA数据库构建肾透明细胞癌相关miRNA预后模型

利用TCGA数据库中肾透明细胞癌的miRNA与mRNA数据及临床信息,构建由miRNA组成的预后风险评分模型,并筛选与生存预后相关的miRNA-mRNA调控关系对,为研究提供理论依据。下载并整理TCGA[JP+1]数据库中肾透明细胞癌的miRNA与mRNA数据;对数据进行差异分析,将差异表达的miRNA与临床信息进行合并,利用单因素与多因素Cox回归分析,构建预后模型并进行模型评价;对模型中的miRNA进行靶基因预测,结果与差异表达的mRNA进行取交集,构建miRNA-mRNA调控网络;对网络中的mRNA进行生存分析,筛选生存相关的miRNA-mRNA调控关系对。共得到49个差异表达的miRNA与3 613个差异表达的mRNA;预后模型计算公式为:风险值(risk score)=hsa-miR-21-5p表达量×0.603＋hsa-miR-1251-5p表达量×-0.093;调控网络中共纳入31个miRNA-mRNA调控关系对;对mRNA进行生存分析,共得到7个有价值的关系对。所构建预后模型可有效预测肾透明细胞癌患者生存预后情况,筛选到的miRNA-mRNA调控关系对可为相关研究与治疗提供参考。     

肾透明细胞癌Caki-1细胞系差异表达基因的生物信息学分析

目的比较肾透明细胞癌Caki-1细胞系与正常肾上皮细胞系ASE-5063中的差异表达基因（DEGs）,寻找潜在的肾透明细胞癌特异性分子标志物。 方法利用GEO数据库自带的GEO2R在线分析工具分析基因芯片GSE78179,将筛选出的DEGs分别导入Metascape、STRING以及Cytoscape进行综合分析并筛选出核心基因。最后使用FunRich等软件对筛选出的核心基因进行GO和KEGG富集分析。 结果共筛选出562个DEGs,其中上调基因345个,下调基因217个。进一步使用MCODE筛选出36个关键基因,GO功能分析发现这些基因与细胞粘附分子活性、趋化因子活性、细胞通讯和信号转导等密切相关;KEGG通路富集结果则表明差异基因主要集中在趋化因子信号通路、TNF信号通路以及NF-κB信号通路等多种与肿瘤相关的通路上。 结论运用生物信息学方法筛选出肾透明细胞癌Caki-1细胞系中DEGs,其中数个核心基因广泛参与多种肿瘤的病理进程,但尚未在肾透明细胞癌有相关研究报道,提示其可能是治疗肾透明细胞癌的潜在靶点。     

肾透明细胞癌患者铁死亡相关基因的预后作用

探讨铁死亡相关基因在肾透明细胞癌患者中的表达及其预后价值。通过TCGA数据库下载KIRC的相关测序数据与检索到的铁死亡相关基因取交集,进行铁死亡相关基因的差异分析。之后利用单变量和多变量Cox回归分析,筛选具有预后价值的基因,构建预测患者生存情况的风险评分模型,并对模型进行验证。对高低风险组进行GO与KEGG通路富集,探讨风险差异的可能原因;通过ssGSEA分析,评估高低风险组间的免疫浸润情况。在KIRC患者的肿瘤组织和正常组织中,共得到21个差异的铁死亡相关基因;通过单因素Cox回归分析,获得 28 个与KIRC预后相关的基因;之后进行Lasso回归与多因素Cox回归分析,结果显示有10个基因被纳入模型,计算公式为:风险值(Risk score)=(0.024 5)×ALOX5表达值+(0.126 0)×CBS表达值+(0.199 5)×CD44表达值+(0.218 3)×CHAC1表达值+(-0.295 9)×HMGCR表达值+(0.036 7)×MT1G表达值+(0.061 4)×SLC7A11表达值+(-0.080 7)×FDFT1表达值+(0.160 3)×PEBP1表达值+(-0.220 5)×GOT1表达值。生存状态图表明,高风险组死亡病例数多于低风险组;ROC曲线表明风险评分模型具备一定预测能力;K-M生存分析显示,高风险组总体生存率低于低风险组(P=5.73×10^-13)。GO与KEGG富集分析提示,高低风险组间免疫情况及IL-17信号通路存在显著差异;进一步的ssGSEA富集显示,高低风险组间大部分免疫细胞的评分存在显著差异。基于铁死亡相关基因的预后风险评分模型可用于KIRC的预后预测,针对铁死亡相关基因设计靶点可能是治疗KIRC的一种新选择。     

Screening and identification of hub genes in pancreatic cancer by integrated bioinformatics analysis

Pancreatic cancer (Pa) is a malignant tumor of the digestive tract with high degree of malignancy, this study aimed to obtain the hub genes in the tumorigenesis of Pa. Microarray datasets GSE15471, GSE16515, and GSE62452 were downloaded from Gene Expression Omnibus (GEO) database, GEO2R was conducted to screen the differentially expressed genes (DEGs), and functional enrichment analyses were carried out by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Genes (STRING), and the hub genes were identified by Cytoscape. Totally 205 DEGs were identified, consisting of 51 downregulated genes and 154 upregulated genes enriched in Gene Ontology terms including extracellular matrix (ECM) organization, collagen binding, cell adhesion, and pathways associated with ECM-receptor interaction, focal adhesion, and protein digestion. Two modules in the PPI were chosen and biological process analyses showed that the module genes were mainly enriched in ECM and cell adhesion. Twenty-four hub genes were confirmed, the survival analyses from the cBioPortal online platform revealed that topoisomerase (DNA) II α (TOP2A), periostin (POSTN), plasminogen activator, urokinase (PLAU), and versican (VCAN) may be involved in the carcinogenesis and progression of Pa, and the receiver-operating characteristic curves indicated their diagnostic value for Pa. Among them, TOP2A, POSTN, and PLAU have been previously reported as biomarkers for Pa, and far too little attention has been paid to VCAN. Analysis from R2 online platform showed that Pa patients with high VCAN expression were more sensitive to gemcitabine than those with low level, suggesting that VCAN may be an indicator to guide the use of the chemotherapeutic drug. In vitro experiments also showed that the sensitivity of the VCAN siRNA group to gemcitabine was lower than that of the control group. In conclusion, this study discerned hub genes and pathways related to the development of Pa, and VCAN was identified as a novel biomarker for the diagnose and therapy of Pa.     

Identification of key microRNAs and hub genes in non-small-cell lung cancer using integrative bioinformatics and functional analyses

Non-small-cell lung cancer (NSCLC) is an extremely debilitating respiratory malignancy. However, the pathogenesis of NSCLC has not been fully clarified. The main objective of our study was to identify potential microRNAs (miRNAs) and their regulatory mechanism in NSCLC. Using a systematic review, two NSCLC-associated miRNA data sets (GSE102286 and GSE56036) were obtained from Gene Expression Omnibus, and the differentially expressed miRNAs (DE-miRNAs) were accessed by GEO2R. Survival analysis of candidate DE-miRNAs was conducted using the Kaplan-Meier plotter database. To further illustrate the roles of DE-miRNAs in NSCLC, their potential target genes were predicted by miRNet and were annotated by the Database for Annotation, Visualization and Integrated Discovery (DAVID) program. Moreover, the protein-protein interaction (PPI) and miRNA-hub gene regulatory network were established using the STRING database and Cytoscape software. The function of DE-miRNAs in NSCLC cells was evaluated by transwell assay. Compared with normal tissues, a total of eight DE-miRNAs was commonly changed in two data sets. The survival analysis showed that six miRNAs (miR-21-5p, miR-31-5p, miR-708-5p, miR-30a-5p, miR-451a, and miR-126-3p) were significantly correlated with overall survival. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that target genes of upregulated miRNAs were enriched in pathways in cancer, microRNAs in cancer and proteoglycans in cancer, while the target genes of downregulated miRNAs were mainly associated with pathways in cancer, the PI3K-Akt signaling pathway and HTLV-I infection. Based on the miRNA-hub gene network and expression analysis, PTEN, EGFR, STAT3, RHOA, VEGFA, TP53, CTNNB1, and KRAS were identified as potential target genes. Furthermore, all six miRNAs exhibited significant effects on NSCLC cell invasion. These findings indicate that six DE-miRNAs and their target genes may play important roles in the pathogenesis of NSCLC, which will provide novel information for NSCLC treatments.     

Identification of microenvironment-related genes with prognostic value in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney tumor. Previous studies have shown that the interaction between tumor cells and microenvironment has an important impact on prognosis. Immune and stromal cells are two vital components of the tumor microenvironment. Our study aimed to better understand and explore the genes involved in immune/stromal cells on prognosis. We used the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm to calculate immune/stromal scores. According to the scores, we divided ccRCC patients from The Cancer Genome Atlas database into low and high groups and identified the genes which were differentially expressed and significantly associated with prognosis. The result of functional enrichment analysis and protein-protein interaction networks indicated that these genes mainly were involved in extracellular matrix and regulation of cellular activities. Then another independent cohort from the International Cancer Genome Consortium database was used to validate these genes. Finally, we acquired a list of microenvironment-related genes that can predict prognosis for ccRCC patients.     

Immune-pyroptosis-related genes predict the prognosis of kidney renal clear cell carcinoma

BackgroundKidney renal clear cell carcinoma (KIRC) is a common cancer of the adult urological system. Recent developments in tumor immunology and pyroptosis biology have provided new directions for kidney cancer treatment. Therefore, there is an urgent need to identify potential targets and prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy.MethodsThe expression of immune-pyroptosis-related differentially expressed genes (IPR-DEGs) between KIRC and healthy tissues was examined using the Gene Expression Omnibus datasets. The GSE168845 dataset was selected for subsequent analyses. Data of 1793 human immune-related genes were downloaded from the ImmPort database (https://www.immport.org./home), while those of 33 pyroptosis-related genes were extracted from previous reviews. The independent prognostic value of IPR-DEGs was determined using differential expression, prognostic, and univariate and multivariate Cox regression analyses. The GSE53757 dataset was used to further verify the GSDMB and PYCARD levels. In our cohorts, the association among DEGs and clinicopathological features and overall survival was analyzed. The least absolute shrinkage and selection operator Cox regression model was established to evaluate the correlation of IPR-DEGs with the immune score, immune checkpoint gene expression, and one-class logistic regression (OCLR) score. KIRC cells and clinical tissue samples were subjected to quantitative real-time polymerase chain reaction to examine the GSDMB and PYCARD mRNA levels. The GSDMB and PYCARD levels in a healthy kidney cell line (HK-2 cells) and two KIRC cell lines (786-O and Caki-1 cells) were verified. The tissue levels of GSDMB and PYCARD were evaluated using immunohistochemical analysis. GSDMB and PYCARD were knocked down in 786-O cells using short-interfering RNA. Cell proliferation was examined using the cell counting kit-8 assay. Cell migration was measured by transwell migration assaysResultsGSDMB and PYCARD were determined to be IPR-DEGs with independent prognostic values. A risk prognostic model based on GSDMB and PYCARD was successfully established. In the GSE53757 dataset, the GSDMB and PYCARD levels in KIRC tissues were significantly higher than those in healthy tissues. The GSDMB and PYCARD expression was related to T stage and OS in our cohort. The GSDMB and PYCARD levels were significantly correlated with the immune score, immune checkpoint gene expression, and OCLR score. The results of experimental studies were consistent with those of bioinformatics analysis. The GSDMB and PYCARD levels in KIRC cells were significantly upregulated when compared with those in healthy kidney cells. Consistently, GSDMB and PYCARD in KIRC tissues were significantly upregulated when compared with those in adjacent healthy kidney tissues. GSDMB and PYCARD knockdown significantly decreased 786-O cell proliferation (p < 0.05). Transwell migration result reflects that silencing GSDMB and PYCARD inhibited 786-O cell migration (p < 0.05) .ConclusionsGSDMB and PYCARD are potential targets and effective prognostic biomarkers for the combination of immunotherapy and pyroptosis-targeted therapy in KIRC.     

Prognostic value of tumour microenvironment-related genes by TCGA database in rectal cancer

Rectal cancer is a common malignant tumour and the progression is highly affected by the tumour microenvironment (TME). This study intended to assess the relationship between TME and prognosis, and explore prognostic genes of rectal cancer. The gene expression profile of rectal cancer was obtained from TCGA and immune/stromal scores were calculated by Estimation of Stromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. The correlation between immune/stromal scores and survival time as well as clinical characteristics were evaluated. Differentially expressed genes (DEGs) were identified according to the stromal/immune scores, and the functional enrichment analyses were conducted to explore functions and pathways of DEGs. The survival analyses were conducted to clarify the DEGs with prognostic value, and the protein-protein interaction (PPI) network was performed to explore the interrelation of prognostic DEGs. Finally, we validated prognostic DEGs using data from the Gene Expression Omnibus (GEO) database by PrognoScan, and we verified these genes at the protein levels using the Human Protein Atlas (HPA) databases. We downloaded gene expression profiles of 83 rectal cancer patients from The Cancer Genome Atlas (TCGA) database. The Kaplan-Meier plot demonstrated that low-immune score was associated with worse clinical outcome (P = .034), metastasis (M1 vs. M0, P = .031) and lymphatic invasion (+ vs. -, P < .001). A total of 540 genes were screened as DEGs with 539 up-regulated genes and 1 down-regulated gene. In addition, 60 DEGs were identified associated with overall survival. Functional enrichment analyses and PPI networks showed that the DEGs are mainly participated in immune process, and cytokine-cytokine receptor interaction. Finally, 19 prognostic genes were verified by GSE17536 and GSE17537 from GEO, and five genes (ADAM23, ARHGAP20, ICOS, IRF4, MMRN1) were significantly different in tumour tissues compared with normal tissues at the protein level. In summary, our study demonstrated the associations between TME and prognosis as well as clinical characteristics of rectal cancer. Moreover, we explored and verified microenvironment-related genes, which may be the potential key prognostic genes of rectal cancer. Further clinical samples and functional studies are needed to validate this finding.     

Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for larynx squamous cell carcinoma

Larynx squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Numerous genes have been identified to be aberrantly expressed during the development of LSCC. However, currently, researchers focus more on the individual molecule and downstream genes, leaving the coexpression among genes and key upstream disease driver genes unexploited. In this study, we applied weighted gene coexpression analysis (WGCNA) to decipher potential hub genes driving the development of LSCC. After downloading of LSCC microarray profile from gene expression omnibus, different expression analysis was performed, which was used to conduct functional enrichment analysis. Then, we applied WGCNA to highlight the hub genes which were relevant to the carcinogenesis and progression. A total of 2858 differentially expressed genes were identified in LSCC samples compared with adjacent non-neoplastic tissues. WGCNA revealed three LSCC set-specific modules having significant Kyoto Encyclopedia of Genes and Genomes enrichment effect, including pink, cyan, and black module. Nine hub genes were identified to be crucial in LSCC onset and progression, which may assist clinical decisions and serve as potential targets for LSCC treatment.     

肾透明细胞癌预后相关miRNAs的生物信息学分析

目的寻找可作为肾透明细胞癌（ccRCC）生物标志物的miRNA,以及ccRCC与正常组织间miRNA差异表达情况。 方法利用TCGA数据库下载ccRCC中miRNA表达数据,分析肿瘤与正常组织间差异表达miRNA。使用Kaplan-Meier曲线对患者进行生存分析,筛选出表达情况与临床预后相关的miRNA。通过生物信息学对miRNA的靶基因进行预测,然后运用FunRich软件和ClueGO对靶基因进行GO和KEGG富集分析。 结果通过TCGA数据库分析发现,ccRCC较正常组织差异表达miRNA共54个,其中上调33个,下调21个。通过生存分析发现hsa-miR-21和hsa-miR-155与患者预后相关,P≤0.05。进一步通过Perl软件在Targetscan、miRDB、miRTarBase、miRPath这四个数据库中预测miRNA靶基因并将结果取交集,共发现129个靶基因。GO和KEGG分析结果表明,这些靶基因主要与转录因子活性、信号转导以及FoxO、TNF等信号通路密切相关。 结论通过生物信息学分析发现了ccRCC与正常组织的差异表达miRNA;其中hsa-miR-21和hsa-miR-155与患者总体生存率相关,并通过调控靶基因参与相关的信号通路进而影响ccRCC的发生发展进程,提示hsa-miR-21和hsa-miR-155可能是ccRCC潜在的生物标志物。     

Downregulation of junctional adhesion molecule-A is involved in the progression of clear cell renal cell carcinoma

Junctional adhesion molecule-A (JAM-A) is one component of tight junctions which are involved in important processes like paracellular permeability, cell polarity, adhesion, migration, and angiogenesis. Here we describe JAM-A expression in distal convoluted tubule, connecting tubule, and in cells of the collecting duct of the healthy human kidney. In addition, JAM-A was weakly expressed in cells of the proximal tubule. Using immunofluorescence, FACS and Western blot analysis we investigated JAM-A expression in tubular cells in vitro. Interestingly, treatment of HK-2 cells with IFN-γ and TNF-α resulted in a metalloproteinase mediated downregulation of JAM-A. Importantly, in a tissue micro-array JAM-A protein expression was significantly downregulated in patients with clear cell renal cell carcinoma. Furthermore, knockdown of JAM-A with JAM-A specific siRNA induced the migration of RCC4 cells. In summary, downregulation of JAM-A is an early event in the development of renal cancer and increases the migration of renal cancer cells.     

Development and validation of hub genes for lymph node metastasis in patients with prostate cancer

Lymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis. We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co‐expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein‐protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well‐distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.     

Tumor-associated macrophage-derived chemokine CCL5 facilitates the progression and immunosuppressive tumor microenvironment of clear cell renal cell carcinoma

Background: Tumor-associated macrophages (TAMs) dominate the malignancy of cancers by perturbing the tumor microenvironment (TME). However, the clinical implications of heterogeneous subpopulations of TAMs in clear cell renal cell carcinoma (ccRCC) remain to be elucidated.Methods: We comprehensively evaluated the prognostic implications, biological behaviors, and immunogenomics features of the C-C Motif Chemokine Ligand 5 (CCL5) expression and CCL5⁺ TME in vitro and in 932 real-world ccRCC patients from testing and public validation cohorts. Flow cytometry was used to examine the functional patterns of CCL5⁺ TAMs with TME cell-infiltrating characterizations.Results: Our results identified distinct prognostic clusters with gradual changes in clinicopathological indicators based on CCL5 expression. Knockdown of CCL5 significantly restrained cell viability, migration capabilities of ccRCC cells, and the inhibits the proliferation and chemotaxis of THP1-derived TAMs. Mechanically, down-regulation of CCL5 arrested epithelial-mesenchymal transition by modulating the PI3K/AKT pathway in ccRCC cells. In ccRCC samples with CCL5 upregulation, the proportion of CCL5⁺ TAMs and PD-L1⁺ CD68⁺ TAMs were prominently increased, showing a typical suppressive tumor immune microenvironment (TIME). Besides, intra-tumoral CCL5⁺ TAMs showed distinct pro-tumorigenic TME features characterized by exhausted CD8⁺ T cells and increased expression of immune checkpoints. Furthermore, elevated CCL5⁺ TAMs infiltration was prominently associated with a dismal prognosis for patients with ccRCC.Conclusion: In conclusion, this study first revealed the predictive value of the chemokine CCL5 on the progression and TME of ccRCC. The intra-tumoral CCL5⁺ TAMs could be applied to comprehensively evaluate the prognostic patterns as well as unique TME characteristics among individuals, allowing for the identification of immunophenotypes and promotion of treatment efficiency for ccRCC.