HPA Database Research Articles

Prognostic role of overexpressed Bromodomain and extra-terminal family in ovarian cancer.

Background: BET family proteins have a role as epigenetic readers to accelerate the transcription of target genes. Several studies have shown that the BET protein family played important roles in several biological processes. Although, the prognostic influence of individual BET genes family in ovarian cancer patients remains unclear.Methods: We investigated BET mRNA prognostic roles subtypes in ovarian cancer patients by means of the KM plotter database. The BET mRNA expression and protein in cancer and normal ovarian cells was determined using qRTPCR and western blot. We used the HPA database to look at the protein expression profiles in normal and cancer tissues for this study.Results: Among BET members, mRNA expression BRD2 showed improve OS in all the ovarian malignancy patients, serous patients, stage III and IV, grade II and grade III, TP53 mutated ovarian cancer patients, as well as all patients treated with Platin based chemotherapy. As for BRD3, we found that BRD3 expression was related to better OS in endometrioid ovarian carcinoma and stage III+IV ovarian carcinoma patients, as well as all patients managed with Taxol and concurrent Taxol+Platin based chemotherapy. In addition, BRDT was associated with better OS in all ovarian carcinoma patients, grade I and grade III, all clinical stage (I+II, III+IV) patients, as well as all patients cured with Taxol and concurrent Taxol+Platin chemotherapy.Conclusion: We conclude that high expression of BRD2, BRD3, and BRDT predicted a better prognosis. mRNA expression of BET family is considerably associated with the prognosis of ovarian carcinoma and individual BET family gene could act as a predictive prognostic indicator in ovarian carcinoma.

Development of Biomarker Signatures Associated with Anoikis to Predict Prognosis in Endometrial Carcinoma Patients

Purpose To generate a signature based on anoikis-related genes (ARGs) for endometrial carcinoma (EC) patients and elucidate the molecular mechanisms in EC. Methods On the basis of TCGA-UCEC dataset, we identified specific anoikis-related genes (ARGs) in EC. Cox-relative regression methods were used to generate an anoikis-related signature (ARS). The possible biological pathways of ARS-related genes were analyzed by GSEA. The clinical potency and immune status of ARS were analyzed by CIBERSORT method, ssGSEA algorithm, Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Moreover, the expression patterns of ARS genes were verified by HPA database. Results Seven anoikis genes (CDKN2A, E2F1, ENDOG, EZH2, HMGA1, PLK1, and SLC2A1) were determined to develop a prognostic ARS. Both genes of ARS were closely bound up with the prognosis of EC patients. The ARS could accurately classify EC cases with different clinical outcome and mirror the specific immune status of EC. We observed that ARS-high patients could not benefit from immunotherapy. Finally, all the hub genes of ARS were proved to be upregulated in EC tissues by immunohistology. Conclusion ARS can be used to stratify the risk and forecast the survival outcome of EC patients and provide prominent reference for individualized treatment in EC.

Comprehensive analysis of an immune infiltrate-related competitive endogenous RNA network reveals potential prognostic biomarkers for non-small cell lung cancer.

Globally, non-small cell lung cancer (NSCLC) is the most common malignancy and its prognosis remains poor because of the lack of reliable early diagnostic biomarkers. The competitive endogenous RNA (ceRNA) network plays an important role in the tumorigenesis and prognosis of NSCLC. Tumor immune microenvironment (TIME) is valuable for predicting the response to immunotherapy and determining the prognosis of NSCLC patients. To understand the TIME-related ceRNA network, the RNA profiling datasets from the Genotype-Tissue Expression and The Cancer Genome Atlas databases were analyzed to identify the mRNAs, microRNAs, and lncRNAs associated with the differentially expressed genes. Weighted gene co-expression network analysis revealed that the brown module of mRNAs and the turquoise module of lncRNAs were the most important. Interactions among microRNAs, lncRNAs, and mRNAs were prognosticated using miRcode, miRDB, TargetScan, miRTarBase, and starBase databases. A prognostic model consisting of 13 mRNAs was established using univariate and multivariate Cox regression analyses and validated by the receiver operating characteristic (ROC) curve. The 22 immune infiltrating cell types were analyzed using the CIBERSORT algorithm, and results showed that the high-risk score of this model was related to poor prognosis and an immunosuppressive TIME. A lncRNA–miRNA–mRNA ceRNA network that included 69 differentially expressed lncRNAs (DElncRNAs) was constructed based on the five mRNAs obtained from the prognostic model. ROC survival analysis further showed that the seven DElncRNAs had a substantial prognostic value for the overall survival (OS) in NSCLC patients; the area under the curve was 0.65. In addition, the high-risk group showed drug resistance to several chemotherapeutic and targeted drugs including cisplatin, paclitaxel, docetaxel, gemcitabine, and gefitinib. The differential expression of five mRNAs and seven lncRNAs in the ceRNA network was supported by the results of the HPA database and RT-qPCR analyses. This comprehensive analysis of a ceRNA network identified a set of biomarkers for prognosis and TIME prediction in NSCLC.

CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM May Be Potential Therapeutic Targets for Hepatocellular Carcinoma Using Integrated Bioinformatic Analysis

BackgroundHepatocellular carcinoma (HCC) is a highly malignant, recurrent and drug-resistant tumor, and patients often lose the opportunity for surgery when they are diagnosed. Abnormal gene expression is closely related to the occurrence of HCC. The aim of the present study was to identify the differentially expressed genes (DEGs) between tumor tissue and non-tumor tissue of HCC samples in order to investigate the mechanisms of liver cancer.MethodsThe gene expression profile (GSE62232, GSE89377, and GSE112790) was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery. Protein–protein interaction (PPI) of these DEGs was analyzed based on the Search Tool for the Retrieval of Interacting Genes database and visualized by Cytoscape software. In addition, we used the online Kaplan–Meier plotter survival analysis tool to evaluate the prognostic value of hub genes expression. HPA database was used to reveal the differences in protein level of hub genes.ResultsA total of 50 upregulated DEGs and 122 downregulated DEGs were identified. Among them, ten hub genes with a high degree of connectivity were picked out. Overexpression of these hub genes was associated with unfavorable prognosis of HCC.ConclusionOur study suggests that CCNB2, CDC20, AURKA, TOP2A, MELK, NCAPG, KIF20A, UBE2C, PRC1, and ASPM were overexpressed in HCC compared with normal liver tissue. Overexpression of these genes was an unfavorable prognostic factor of HCC patients. Further study is needed to explore the value of them in the diagnosis and treatment of HCC.

Comprehensive analysis of pyroptosis regulators and tumor immune microenvironment in clear cell renal cell carcinoma

BackgroundIncreasing evidence has indicated that pyroptosis could regulate the tumor immune microenvironment (TIME) to affect the tumor development. As a highly immunogenic tumor, clear cell renal cell carcinoma (ccRCC) can benefit from immunotherapy, but related research on pyroptosis in the TIME of ccRCC is still deficient.MethodsAvailable data derived from TCGA and GEO databases were analyzed to identify the different expression profiles of pyroptosis in ccRCC and normal tissues, and the correlation of pyroptosis regulators with TIME was evaluated in ccRCC.ResultsAccording to consensus clustering analysis, two differential expression levels of subtypes were identified to affect patient prognosis, and were related to histological tumor stage and grade. Immune cells were calculated by the CIBERSORT algorithm. Higher infiltrated levels of B cells naive, T cells CD4 memory resting, NK cells resting, monocytes, macrophages were observed in Cluster 1, while higher infiltrated levels of CD8+ T cells, T follicular helper cells, and Tregs were observed in Cluster 2. Gene set enrichment analysis indicated that Cluster 2 was enriched in multiple immune-related pathways, including the JAK-STAT signaling pathway. Moreover, overexpression of eight immune checkpoints was related to ccRCC development, especially in Cluster 2. As four potentially key pyroptosis regulators, AIM2, CASP5, NOD2, and GZMB were confirmed to be upregulated in ccRCC by RT-qPCR analysis and further verified by the HPA database. Further pan-cancer analysis suggested that these four pyroptosis regulators were differentially expressed and related to the TIME in multiple cancers.ConclusionThe present study provided a comprehensive view of pyroptosis regulators in the TIME of ccRCC, which may provide potential value for immunotherapy.

Ferroptosis regulators, especially SQLE, play an important role in prognosis, progression and immune environment of breast cancer

BackgroundFerroptosis, a new form of programmed cell death, has great potential for cancer treatment. However, the roles of ferroptosis-related (FR) genes in breast cancer (BC) remain elusive.Materials and methodsUsing TCGA database, a novel FR risk signature was constructed through the Lasso regression analysis. Meanwhile, its prognostic value was assessed by a series of survival analyses. Besides, a nomogram was constructed to predict the overall survival rate (OSR) of individual at 1,3,5 year. Four validation cohorts (n = 2248), including METABRIC, GSE58812, GSE20685 and ICGC-KR datasets, were employed to test the prognostic value of FR risk signature. The effects of FR risk signature on BC immune microenvironment were explored by CIBERSORT algorithm and ssGSEA method. The histological expressions of FR risk genes were presented by HPA database. The biofunctions of SQLE were determined by qPCR, MTT, wound-healing and Transwell assays.ResultsWe constructed a novel FR risk signature consisting of eight genes. High FR risk led a poor prognosis and was identified as an independent prognostic factor. Besides, A higher proportion of patients with luminal A type was observed in low-risk group (53%), while a higher proportion of patients with basal type in high-risk group (24%). FR risk score could discriminate the prognostic difference of most clinical subgroups, except for M1 stage, HER2 and basal types. Moreover, its prognostic value was successfully validated in other four cohorts. Through immune analyses, we found that the reduced infiltration levels of CD8+ and NK cells, whereas the enhanced activity of antigen presentation process appeared in high FR risk. Then, FR risk score was found to weakly correlate with the expressions of six immune checkpoints. Through the experiments in vitro, we confirmed that overexpression of SQLE could promote, whereas blocking SQLE could inhibit the proliferative, migrative and invasive abilities of BC cells.ConclusionsFR risk signature was conducive to BC prognostic assessment. High FR risk level was closely associated with BC immunosuppression, but may not predict ICIs efficacy. Moreover, SQLE was identified as a crucial cancer-promoting gene in BC. Our findings provide new insights into prognostic assessment and molecular mechanism of BC.

Development and Verification of Glutamatergic Synapse-Associated Prognosis Signature for Lower-Grade Gliomas.

Background: Lower-grade glioma (LGG) is the most common histology identified in gliomas, a heterogeneous tumor that may develop into high-grade malignant glioma that seriously shortens patient survival time. Recent studies reported that glutamatergic synapses might play an essential role in the progress of gliomas. However, the role of glutamatergic synapse-related biomarkers in LGG has not been systemically researched yet.Methods: The mRNA expression data of glioma and normal brain tissue were obtained from The Cancer Genome Atlas database and Genotype-Tissue Expression, respectively, and the Chinese Glioma Genome Atlas database was used as a validation set. Difference analysis was performed to evaluate the expression pattern of glutamatergic synapse-related genes (GSRGs) in LGG. The least absolute shrinkage and selection operator (LASSO) Cox regression was applied to construct the glutamatergic synapse-related risk signature (GSRS), and the risk score of each LGG sample was calculated based on the coefficients and expression value of selected GSRGs. Univariate and multivariate Cox regression analyses were used to investigate the prognostic value of risk score. Immunity profile and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the association between risk score and the characters of tumor microenvironment in LGG. Gene set variation analysis (GSVA) was performed to investigate the potential pathways related to GSRS. The HPA database and real-time PCR were used to identify the expression of hub genes identified in GSRS.Results: A total of 22 genes of 39 GSRGs were found differentially expressed among normal and LGG samples. Through the LASSO algorithm, 14-genes GSRS constructed were associated with the prognosis and clinicopathological features of patients with LGG. Furthermore, the risk score level was significantly positively correlated with the infiltrating level of immunosuppressive cells, including M2 macrophages and regulatory T cells. GSVA identified a series of cancer-related pathways related to GSRS, such as P13K-AKT and P53 pathways. Moreover, ATAD1, NLGN2, OXTR, and TNR, hub genes identified in GSRS, were considered as potential prognostic biomarkers in LGG.Conclusion: A 14-genes GSRS was constructed and verified in this study. We provided a novel insight into the role of GSRS in LGG through a series of bioinformatics methods.

Study of Clinical Predictive Value and Immune Characterization of SLFN11 in Clear Cell Renal Cell Carcinoma.

BackgroundSLFN11 has been found to regulate the development and progression of a variety of cancers and is associated with drug therapy, while its role in clear cell renal cell carcinoma (ccRCC) remains unclear; therefore, the aim of this study was to investigate SLFN11 expression in ccRCC patients and its correlations with clinicopathological and immunological features.MethodsGene profiles of ccRCC and the clinicopathological information of patients were downloaded from the TCGA database. Microarrays from the GEO database were used as a validation set for SLFN11 expression, which was experimentally verified in renal cancer cell lines by quantitative polymerase chain reaction (qPCR); protein expression and methylation levels were obtained from the HPA database and the UALCAN database. ROC curves, Kaplan–Meier survival analysis and Cox analysis were used to assess the diagnostic and predictive value of SLFN11 in ccRCC. Protein–protein interaction (PPI) networks for SLFN11 were obtained from the STRING website, and the TISIDB and TIMER 2.0 databases were used to study the relationship between SLFN11 and immune infiltration in the tumour microenvironment (TME).ResultsSLFN11 was significantly overexpressed in ccRCC tissues and renal cancer cell lines, which may be closely related to its hypermethylation status (P < 0.001). SLFN11 was positively correlated with a highly aggressive disease state, with the ROC curve showing an AUC value of 0.910 for SLFN11 in diagnosing ccRCC, and Kaplan–Meier and Cox analyses also revealed that upregulation of SLFN11 predicted a poor prognosis for ccRCC patients (P < 0.05). In addition, enrichment analysis showed that SLFN11 was closely associated with immune-related signalling pathways, and further exploration comprehensively demonstrated strong positive correlations with tumour immune lymphocytes, immune checkpoint genes, chemokines and chemokine receptors.ConclusionOverall, our data analysis shows that SLFN11 is a strong diagnostic and prognostic biomarker for ccRCC and is also associated with immune infiltration in the TME.

SLC2A5 Correlated with Immune Infiltration: A Candidate Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma.

Lung adenocarcinoma (LUAD) is a major subtype of lung cancer with a relatively poor prognosis, requiring novel therapeutic approaches. Great advances in new immunotherapy strategies have shown encouraging results in lung cancer patients. This study is aimed at elucidating the function of SLC2A5 in the prognosis and pathogenesis of LUAD by analyzing public databases. The differential expression of SLC2A5 in various tissues from Oncomine, GEPIA, and other databases was obtained, and SLC2A5 expression at the protein level in normal and tumor tissues was detected with the use of the HPA database. Then, we used the UALCAN database to analyze the expression of SLC2A5 in different clinical feature subgroups. Notably, in both PrognoScan and Kaplan-Meier plotter databases, we found a certain association between SLC2A5 and poor OS outcomes in LUAD patients. Studies based on the TIMER database show a strong correlation between SLC2A5 expression and various immune cell infiltrates and markers. The data analysis in the UALCAN database showed that the decreased promoter methylation level of SLC2A5 in LUAD may lead to the high expression of SLC2A5. Finally, we used the LinkedOmics database to evaluate the SLC2A5-related coexpression and functional networks in LUAD and to investigate their role in tumor immunity. These findings suggest that SLC2A5 correlated with immune infiltration can be used as a candidate diagnostic and prognostic biomarker in LUAD patients.

An Integrated Analysis of C5AR2 Related to Malignant Properties and Immune Infiltration of Breast Cancer.

BackgroundC5AR2 (GPR77, C5L2) is the second receptor for C5a that is a potent protein generated by complement activation. C5AR2 can mediate its own signaling events and exert significant immunomodulatory effects through those events. However, research of C5AR2 in cancer is limited, and its function remains unclear in breast cancer.MethodsThe expression of C5AR2 and its correlations with prognosis, immune infiltration, tumor mutation burden (TMB), and microsatellite instability (MSI) in more than thirty types of cancers were described through GTEx, TCGA, PrognoScan, TIMER2.0, CCLE, HPA, and TISIDB database. C5AR2 showed strong relationships to those immune marker sets in breast cancer. Otherwise, CCK8 assay and Transwell assay were conducted to illustrate the role of C5AR2 in migration, invasion, and proliferation of breast cancer cells.ResultsGenerally, C5AR2 expression differed across most cancerous and noncancerous tissues, and high C5AR2 expression significantly related to poor prognosis in BRCA, GBM, KICH, LAML, LGG, LIHC, PAAD, and STAD. Moreover, C5AR2 expression levels were dramatically correlated with recognized immune infiltration, especially the polarization of macrophages in breast cancer. Gene set enrichment analysis confirmed that C5AR2 participates in regulating multiple signaling pathways involved in tumorigenesis as well as tumor immunity. C5AR2 overexpression facilitated the functions such as migration, invasion, and proliferation in breast cancer cells, which is consistent with bioinformatics analysis.ConclusionsC5AR2 is involved in immune infiltration and malignant characteristics of breast cancer, which may be a prospective biomarker for breast cancer.

Expression and prognosis of CDC45 in cervical cancer based on the GEO database.

Cervical cancer is one of the most common malignant tumors in women, and its morbidity and mortality are increasing year by year worldwide. Therefore, an urgent and challenging task is to identify potential biomarkers for cervical cancer. This study aims to identify the hub genes based on the GEO database and then validate their prognostic values in cervical cancer by multiple databases. By analysis, we obtained 83 co-expressed differential genes from the GEO database (GSE63514, GSE67522 and GSE39001). GO and KEGG enrichment analysis showed that these 83 co-expressed it mainly involved differential genes in DNA replication, cell division, cell cycle, etc.. The PPI network was constructed and top 10 genes with protein-protein interaction were selected. Then, we validated ten genes using some databases such as TCGA, GTEx and oncomine. Survival analysis demonstrated significant differences in CDC45, RFC4, TOP2A. Differential expression analysis showed that these genes were highly expressed in cervical cancer tissues. Furthermore, univariate and multivariate cox regression analysis indicated that CDC45 and clinical stage IV were independent prognostic factors for cervical cancer. In addition, the HPA database validated the protein expression level of CDC45 in cervical cancer. Further studies investigated the relationship between CDC45 and tumor-infiltrating immune cells via CIBERSORT. Finally, gene set enrichment analysis (GSEA) showed CDC45 related genes were mainly enriched in cell cycle, chromosome, catalytic activity acting on DNA, etc. These results suggested CDC45 may be a potential biomarker associated with the prognosis of cervical cancer.

A Pan-Cancer Analysis of CD161, a Potential New Immune Checkpoint.

BackgroundCD161, encoded by killer cell lectin-like receptor B1 gene, is a newly reported candidate inhibitor of tumour-infiltrating T cells. Antibody-mediated CD161 blockade enhances T cell-mediated killing of cancer cells in vitro and in vivo in several tumour types. We evaluated the role of CD161 using The Cancer Genome Atlas (TCGA) Pan-Cancer Data.MethodsCD161 expression was analysed using RNAseq data from TCGA and the Genotype-Tissue Expression (GTEx) database. HPA, GeneCards, and String database were used to explore the protein information of CD161. The prognostic value of CD161 was analysed using clinical survival data from the TCGA. Enrichment analysis of CD161 was conducted using the R package “clusterProfiler”. We downloaded the immune cell infiltration score of TCGA samples from published articles and online databases and performed a correlation analysis between immune cell infiltration levels and CD161 expression. We further assessed the association between CD161 and immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors.FindingsCD161 was differentially expressed and predicted better survival status in most tumour types in TCGA. In addition, CD161 expression was significantly associated with immunoregulatory interactions between lymphoid and non-lymphoid cells. CD161 expression was closely correlated with T cell infiltration, immune checkpoints, immune activating genes, immunosuppressive genes, chemokines, and chemokine receptors.InterpretationOur results suggest that CD161 is a potential cancer biomarker. CD161 might synergize with other immune checkpoints to regulate the immune microenvironment, which could be applied in the development of new-targeted drugs for immunotherapy.FundingThis work was supported by the National Nature Science Foundation of China (grant numbers 81773008, 81672756, 81872399, 81972897), the Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme (2015), the Natural Science Foundation of Guangdong Province (grant number 2017A030311023), the Local Innovative and Research Teams Project of Guangdong Pearl River Talents Program: 2017BT01S131 and the Guangzhou Technology Project (grant number 201804010044), National Key R&D Program of China (Grant Nos. 2020YFC2006400), Key-Area Research and Development Program of Guangdong Province (2019B020227004).

Genetic expression and mutational profile analysis in different pathologic stages of hepatocellular carcinoma patients

BackgroundThe clinical pathologic stages (stage I, II, III-IV) of hepatocellular carcinoma (HCC) are closely linked to the clinical prognosis of patients. This study aims at investigating the gene expression and mutational profile in different clinical pathologic stages of HCC.MethodsBased on the TCGA-LIHC cohort, we utilized a series of analytical approaches, such as statistical analysis, random forest, decision tree, principal component analysis (PCA), to identify the differential gene expression and mutational profiles. The expression patterns of several targeting genes were also verified by analyzing the Chinese HLivH060PG02 HCC cohort, several GEO datasets, HPA database, and diethylnitrosamine-induced HCC mouse model.ResultsWe identified a series of targeting genes with copy number variation, which is statistically associated with gene expression. Non-synonymous mutations mainly existed in some genes (e.g.,TTN, TP53, CTNNB1). Nevertheless, no association between gene mutation frequency and pathologic stage distribution was detected. The random forest and decision tree modeling analysis data showed a group of genes related to different HCC pathologic stages, including GAS2L3 and SEMA3F. Additionally, our PCA data indicated several genes associated with different pathologic stages, including SNRPA and SNRPD2. Compared with adjacent normal tissues, we observed a highly expressed level of GAS2L3, SNRPA, and SNRPD2 (P = 0.002) genes in HCC tissues of our HLivH060PG02 cohort. We also detected the high expression pattern of GAS2L3, SEMA3F, SNRPA, and SNRPD2 in the datasets of GSE102079, GSE76427, GSE64041, GSE121248, GSE84005, and the qPCR assay using diethylnitrosamine-induced HCC mouse model. Moreover, SEMA3F and SNRPD2 protein were highly stained in the HCC tissues of the HPA database. The high expression level of these four genes was associated with the poor survival prognosis of HCC cases.ConclusionsOur study provides evidence regarding the gene expression and mutational profile in different clinical pathologic stages of TCGA HCC cases. Identifying four targeting genes, including GAS2L3, SNRPA, SNRPD2, and SEMA3F, offers insight into the molecular mechanisms associated with different prognoses of HCC.

CACNA1C is a prognostic predictor for patients with ovarian cancer

BackgroundCACNA1C, as a type of voltage-dependent calcium ion transmembrane channel, played regulatory roles in the development and progress of multiple tumors. This study was aimed to analyze the roles of CACNA1C in ovarian cancer (OC) of overall survival (OS) and to explore its relationships with immunity.MethodsSingle gene mRNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas Database (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Gene set enrichment analysis (GSEA) was used to identify CACNA1C-related signal pathways. Univariate and multivariate Cox regression analyses were applied to evaluate independent prognostic factors. Besides, associations between CACNA1C and immunity were also explored.ResultsCACNA1C had a lower expression in OC tumor tissues than in normal tissues (P < 0.001), with significant OS (P = 0.013) and a low diagnostic efficiency. We further validated the expression levels of CACNA1C in OC by means of the ICGC dataset (P = 0.01), qRT-PCR results (P < 0.001) and the HPA database. Univariate and multivariate Cox hazard regression analyses indicated that CACNA1C could be an independent risk factor of OS for OC patients (both P < 0.001). Five significant CACNA1C-related signaling pathways were identified by means of GSEA. As for genetic alteration analysis, altered CACNA1C groups were significantly associated with OS (P = 0.0169), progression-free survival (P = 0.0404), disease-free survival (P = 0.0417) and disease-specific survival (P = 9.280e-3), compared with unaltered groups in OC. Besides, CACNA1C was dramatically associated with microsatellite instability (MSI) and immunity.ConclusionsOur results shed light on that CACNA1C could be a prognostic predictor of OS in OC and it was closely related to immunity.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma.

Methods We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification. Results In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made. Conclusion In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases.

Accumulating evidence has demonstrated that gene alterations play a crucial role in LUAD development, progression, and prognosis. The present study aimed to identify the hub genes associated with LUAD. In the present study, we used TCGA database to screen the hub genes. Then, we validated the results by GEO datasets. Finally, we used cBioPortal, UALCAN, qRT-PCR, HPA database, TCGA database, and Kaplan–Meier plotter database to estimate the gene mutation, gene transcription, protein expression, clinical features of hub genes in patients with LUAD. A total of 5930 DEGs were screened out in TCGA database. Enrichment analysis revealed that DEGs were involved in the transcriptional misregulation in cancer, viral carcinogenesis, cAMP signaling pathway, calcium signaling pathway, and ECM–receptor interaction. The combining results of MCODE and CytoHubba showed that ADCY8, ADRB2, CALCA, GCG, GNGT1, and NPSR1 were hub genes. Then, we verified the above results by GSE118370, GSE136043, and GSE140797 datasets. Compared with normal lung tissues, the expression levels of ADCY8 and ADRB2 were lower in LUAD tissues, but the expression levels of CALCA, GCG, GNGT1, and NPSR1 were higher. In the prognosis analyses, the low expression of ADCY8 and ADRB2 and the high expression of CALCA, GCG, GNGT1, and NPSR1 were correlated with poor OS and poor PFS. The significant differences in the relationship of the expression of 6 hub genes and clinical features were observed. In conclusion, 6 hub genes will not only contribute to elucidating the pathogenesis of LUAD and may be potential therapeutic targets for LUAD.

Assessment of Significant Pathway Signaling and Prognostic Value of GNG11 in Ovarian Serous Cystadenocarcinoma.

BackgroundGNG11 (G protein subunit gamma 11) is a member of guanine nucleotide-binding protein (G protein) gamma family. Few studies elucidated the role of GNG11 in human disease, especially in tumors. The present study initially analyzed the function of GNG11 in ovarian serous cystadenocarcinoma.MethodsThe differential expression of GNG11 mRNA in ovarian cancer and normal tissues was evaluated through Oncomine, CCLE, Gepia, UCSC Xena and UALCAN databases. The protein expression of GNG11 was assessed via HPA database. Prognosis analysis was performed by Kaplan–Meier Plotter. Restrict survival analysis to subtypes including tumor grade, cancer stage and TP53 mutation status was then carried out. GSEA enrichment analysis was performed to explore the significant pathways associated with GNG11 in ovarian cancer. Finally, the upstream miRNAs of GNG11 were predicted by DIANA, Target Scan, miRDB and miRWalk databases, and the potential key KEGG pathways were subsequently determined by DIANA.ResultsThe mRNA expression of GNG11 was down-regulated in ovarian cancer patients (P<0.05). The cancer stage of patients correlated with the expression of GNG11 (P<0.05). Survival analysis indicated that GNG11 high expression statistically shortened the overall survival time of patients (HR=1.26, P=0.0043) compared with low expression group, especially for the patients with earlier stage (HR=2.48, P=0.035) and lower grade (HR=1.72, P=0.0016). Subsequently, the consistent upstream miRNA of GNG11, hsa-miR-22-5p, was predicted from 4 databases. The differential expression profile of hsa-miR-22-5p in blood was observed in ovarian cancer patients. According to the GSEA analysis on GNG11 and KEGG analysis on hsa-miR-22-5p, the consistent pathway of ECM-receptor interaction was observed (all P<0.01). ECM-receptor interaction pathway and differential expression of hsa-miR-22-5p in blood suggested the migration risk of ovarian cancer.ConclusionHigh expression of GNG11 indicated the poor prognosis of ovarian cancer patients. GNG11 might play a crucial role in the biological process of ovarian serous cystadenocarcinoma by ECM-receptor interaction pathway, thus affecting the prognosis of patients.

A Network Pharmacology Study Based on the Mechanism of Citri Reticulatae Pericarpium-Pinelliae Rhizoma in the Treatment of Gastric Cancer.

Objective To explore the mechanism of action of Citri Reticulatae Pericarpium-Pinelliae Rhizoma (CRP-PR) in treating gastric cancer (GC) by using pharmacology network. Methods Based on oral bioavailability and drug-likeness, the main active components of CRP-PR were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). DisGeNET Database was used to establish target databases for GC. Cytoscape software was used to construct a visual interactive network diagram of “Active Component-Target” and screen out the key targets. The STRING database was used to construct a protein interaction network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the key targets. Additionally, TCGA and HPA databases were used for key target verification. Results Thirty-seven active components of CRP-PR were screened. The results of network analysis showed that the main components include 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin of the CRP-PR herb pair. The key targets of the PPI network mainly involved GAPDH, MAPK3, JUN, STAT3, GSK3B, SIRT1, ERBB2, and SMAD2. GO enrichment analysis involves 540 biological processes, 118 cellular components, and 171 molecular functions. CRP-PR components were predicted to exert their therapeutic effect on the tumor signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and estrogen signaling pathway. The validation of the key genes in the TCGA and HPA database showed that most of the key target verification results were consistent with this article. Conclusion CRP-PR can treat GC by mediating PI3K-Akt signal pathway, MAPK signal pathway, and other biological processes such as tumor cell proliferation, apoptosis, and vascular regeneration, which embodies the synergistic effect of multi-components, multi-targets, and multi-channels, and provides the theoretical basis and research ideas for further study of CRP-PR in treating GC. 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin may be the material basis for the treatment of GC.

ERBB1/2/3 Expression, Prognosis, and Immune Infiltration in Cutaneous Melanoma.

BackgroundThe four ERBB tyrosine kinase family members [ERBB1 (epidermal growth factor receptor, EGFR), ERBB2 (HER2), ERBB3 (HER3), and ERBB4 (HER4)] (ERBB receptor family) have been shown, according to previous studies, to be related to the cutaneous melanoma. ERBB3 is the only member of the ERBBs that lacks tyrosine kinase activity and thus needs to dimer with other tyrosine kinases receptors to trigger the signaling pathway, while ERBB3 may dimer with all members of the ERBB family. Melanoma progression depends on activation of ERBB signaling, especially the ERBB3/ERBB2 cascade. There are lymphocytes and T cell infiltrates in melanoma. Numerous pieces of evidences indicate that local immune status plays an important role in the formation of anti-tumor immune responses. However, the relationship between the ERBBs and prognosis and immune infiltration in cutaneous melanoma is not completely clear.MethodsThe expression of the ERBBs was analyzed through the Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), respectively. Immunohistochemistry of ERBBs was obtained from the Human Protein Atlas is increased before HPA database. ERBBs genes expression and mutation analysis in cutaneous melanoma from the cBioPortal. Functional annotation and Kyoto Encyclopedia of Genes and Genomes is increased before KEGG pathway enrichment analysis from the Metascape. Correlations between ERBBs and 31 genes that were close to each other and frequently altered were explored by GEPIA. Using the GEPIA database, we also investigated the relationship between ERBBs and myeloid-derived suppressor cells (MDSC) in cutaneous melanoma. The disease-free survival and different tumor stages of ERBBs were evaluated by GEPIA. The correlation of ERBBs and tumor-infiltrating immune cells and prognostic(5 years survival rates) was tested by the Tumor Immune Estimation Resource (TIMER).ResultsIn general, the expression levels of ERBB1/2 in cutaneous melanoma were lower than those in normal skin tissue. By contrast, the ERBB3 expression level was higher in cutaneous melanoma than in normal skin tissue. Low expression of ERBB1/2 and high expression of ERBB3 were detrimental to the 5 years survival of cutaneous melanoma patients (ERBB1: log-rank P: 0.03; ERBB2: log-rank P: 0.008; ERBB3: log-rank P: 0.039). ERBB4 expression may not affect the prognosis of patients with cutaneous melanoma. ERBBs may not play a role in the tumor stage and disease-free survival in cutaneous melanoma patients. The relationship between the ERBB family and 31 genes that were close to each other and frequently altered is demonstrated as the genes regulated by the ERBB family being mainly concentrated in the RAS/RAF/MEK/ERK signaling pathway. ERBB2 can induce infiltration of CD8+ T cells and B cells, while ERBB3 can induce infiltration of CD4+ T cells, CD8+ T cells, and Neutrophil cells. ERBBs are more significantly associated with M1 macrophages, dendritic cells, Th1, Th2, Th17, and Treg cellular immune markers (Cor > 0.2). ERBB2/3 were related to MDSC in cutaneous melanoma, including human mononuclear myeloid-derived suppressor cells (M-MDSC) and polymorphonuclear myeloid-derived suppressor cells (PMN-MDSC), and may influence the progression of cutaneous melanoma through MDSC, but the conclusion needs further probing.ConclusionThis study investigated the prognosis and immune infiltration of the ERBB family in cutaneous melanoma. Our results suggest that ERBB1/2/3 may serve as early prognostic markers and potential therapeutic targets in cutaneous melanoma.

Clinical implication of xenotropic and polytropic retrovirus receptor 1 in papillary thyroid carcinoma.

To investigate the expression of xenotropic and polytropic retrovirus receptor 1 （） in papillary thyroid cancer （PTC） and its clinical implication. The HPA and UALCAN databases were used to explore the expression of XPR1 in PTC and normal tissues. The cBioPortal database was used to obtain the clinical data of PTC patients and gene expression profile. The correlation of expression with gender，age，sub-types，T stage，N stage，M stage and clinical stage of patients were analyzed. Cox regression was conducted to analysis the factors affecting the prognosis of PTC patients. The mutation of was assessed through cBioPortal database. GO and KEGG analyses were used to explore the related biological pathway of involved in PTC. HPA database analysis showed that XPR1 was highly expressed in PTC tissue compared with normal tissues. UALCAN analysis displayed that expression was significantly higher in PTC tissue compared with normal tissues （<0.01），and the highest and lowest expressions of were observed in tall cell and follicular sub-type of PTC，respectively. The expression of was correlated with age，sub-types，T stage，N stage and disease stage of PTC patients （<0.05 or <0.01），but was not correlated with gender and M stage （all >0.05）. Cox regression analysis showed that was an independent prognostic factor of PTC patients （=2.894，<0.05）. The cBioPortal database indicated that the mutation appeared in 6% PTC patients; the mutation type mainly was missense and the mutation point was located at the E615K. Enrichment analysis indicated that might affect the PTC progression through involvement in metabolic pathway. is highly expressed in PTC tissues，which is associated with the prognosis of patients. Metabolic pathway associated with might play an important role in PTC progression，indicating that might be a novel biomarker for diagnosis and treatment of PTC.