ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

Pyroptosis Regulators and Tumor Microenvironment Infiltration Characterization in Clear Cell Renal Cell Carcinoma

BackgroundIt is well known that chronic inflammation can promote the occurrence and progression of cancer. As a type of proinflammatory death, pyroptosis can recast a suitable microenvironment to promote tumor growth. However, the potential role of pyroptosis in clear cell renal cell carcinoma (ccRCC) remains unclear.MethodsThe transcriptome expression profile and mutation profile data of ccRCC with clinical characteristics included in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering was used for clustering. Gene set enrichment analysis (GSEA) analysis were applied to evaluate the biological mechanisms. Single sample gene set enrichment analysis (ssGSEA) was applied for evaluating the proportion of various immune infiltrating cells. The ESTIMATE algorithm was involved to compute the immune microenvironment scores.ResultsAmong the 17 pyroptosis regulators, a total of 15 pyroptosis regulators were differential expressed between tumor and normal tissues, in which 12 of them emerged strong correlations with prognoses. According to the pyroptosis components, the ccRCC patients were divided into four pyroptosis subtypes with different clinical, molecular, and pathway characteristics. Compared with other clusters, cluster B showed the pyroptosis heat phenotype, while cluster D represented the pyroptosis cold phenotype with poor overall survival. In addition, we performed principal component analysis (PCA) on the differential genes between clusters to construct the pyroptosis index. Furthermore, the pyroptosis index was significantly correlated with survival in different tumor mutation statuses and different grades and stages. Besides, the expression of pyroptosis-related regulators was related to the infiltration of immune cells and the expression of immune checkpoints, among which AIM2 was considered as the most significant immune-related pyroptosis regulator. Ultimately, we found that AIM2 was related to the immune activation pathway and was significantly overexpressed in tumor tissues.ConclusionThis study revealed that pyroptosis regulators and pyroptosis index played an important role in the development and prognoses of ccRCC. Moreover, AIM2 can be used as a predictor of the response of immunotherapy. Assessing the pyroptosis patterns may help evaluate the tumor status and guide immunotherapy strategies.

Genome-wide association study of disease resilience traits from a natural polymicrobial disease challenge model in pigs identifies the importance of the major histocompatibility complex region

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of the current study were to identify genomic regions that are associated with disease resilience in this model, using genome-wide association studies and fine mapping methods, and to use gene set enrichment analyses to determine whether genomic regions associated with disease resilience are enriched for previously published quantitative trait loci (QTL), functional pathways, and differentially expressed genes subject to physiological states. Multiple QTL were detected for all recorded performance and clinical disease traits. The major histocompatibility complex (MHC) region was found to explain substantial genetic variance for multiple traits, including for growth rate in the late nursery (12.8%) and finisher (2.7%), for several clinical disease traits (up to 2.7%), and for several feeding and drinking traits (up to 4%). Further fine mapping identified four QTL in the MHC region for growth rate in the late nursery that spanned the subregions for class I, II, and III, with one SNP in the MHC Class I subregion capturing the largest effects, explaining 0.8 to 27.1% of genetic variance for growth rate and for multiple clinical disease traits. This SNP was located in the enhancer of TRIM39 gene, which is involved in innate immune response. The MHC region was pleiotropic for growth rate in the late nursery and finisher, and for treatment and mortality rates. Growth rate in the late nursery showed strong negative genetic correlations in the MHC region with treatment or mortality rates (-0.62 to -0.85) and a strong positive genetic correlation with growth rate in the finisher (0.79). Gene set enrichment analyses found genomic regions associated with resilience phenotypes to be enriched for previously identified disease susceptibility and immune capacity QTL, for genes that were differentially expressed following bacterial or virus infection and immune response, and for gene ontology terms related to immune and inflammatory response. In conclusion, the MHC and other QTL that harbor immune related genes were identified to be associated with disease resilience traits in a large-scale natural polymicrobial disease challenge. The MHC region was pleiotropic for growth rate under challenge and for clinical disease traits. Four QTL were identified across the class I, II, and III subregions of the MHC for nursery growth rate under challenge, with one SNP in the MHC Class I subregion capturing the largest effects. The MHC and other QTL identified play an important role in host response to infectious diseases and can be incorporated in selection to improve disease resilience, in particular the identified SNP in the MHC Class I subregion.

Identification of a Hypoxia-Related LncRNA Signature for the Prognosis of Colorectal Cancer

Background:Colorectal cancer (CRC), the commonly seen malignancy, ranks 3rd place among the causes of cancer-associated mortality. As suggested by more and more studies, long non-coding RNAs (lncRNAs)have been considered as prognostic biomarkers for CRC. But the significance of hypoxic lncRNAs in predicting CRC prognosis remains unclear.Methods:The gene expressed profiles for CRC cases were obtained based on the Cancer Genome Atlas (TCGA) and applied to estimate the hypoxia score using a single-sample gene set enrichment analysis (ssGSEA) algorithm. Overall survival (OS) of the high- and low-hypoxia score group was analyzed by the Kaplan–Meier (KM) plot. To identify differentially expressed lncRNAs (DELs) between two hypoxia score groups, this study carried out differential expression analysis, and then further integrated with the DELs between controls and CRC patients to generate the hypoxia-related lncRNAs for CRC. Besides, prognostic lncRNAs were screened by the univariate Cox regression, which was later utilized for constructing the prognosis nomogram for CRC by adopting the least absolute shrinkage and selection operator (LASSO) algorithm. In addition, both accuracy and specificity of the constructed prognostic signature were detected through the receiver operating characteristic (ROC) analysis. Moreover, our constructed prognosis signature also was validated in the internal testing test. This study operated gene set enrichment analysis (GSEA) for exploring potential biological functions associated with the prognostic signature. Finally, the ceRNA network of the prognostic lncRNAs was constructed.Results:Among 2299 hypoxia-related lncRNAs of CRC in total, LINC00327, LINC00163, LINC00174, SYNPR-AS1, and MIR31HG were identified as prognostic lncRNAs by the univariate Cox regression and adopted for constructing the prognosis signature for CRC. ROC analysis showed the predictive power and accuracy of the prognostic signature. Additionally, the GSEA revealed that ECM-receptor interaction, PI3K-Akt pathway, phagosome, and Hippo pathway were mostly associated with the high-risk group. 352 miRNAs-mRNAs pairs and 177 lncRNAs-miRNAs were predicted.Conclusion:To conclude, we identified 5 hypoxia-related lncRNAs to establish an accurate prognostic signature for CRC, providing important prognostic markers and therapeutic targets.

High Expression of H2BC12 Predicts Poor Survival Outcome of Low-Grade Gliomas: A Study Based on TCGA Data

Purpose Low-grade gliomas (LGG) have highly variable clinical behaviors, with a high incidence of disease progression as 70% within ten years. Regardless of treatment combining surgery and radiotherapy or chemotherapy, LGG is still associated with adverse survival outcomes. Therefore, our study was performed to satisfy the increasing demand of novel sensitive biomarkers and therapeutic targets in treatment and diagnosis of LGG. Methods The TCGA data set was used to examine the relationship between H2BC12 expression and clinical pathologic characteristics. The significance of H2BC12 expression in prognosis was also investigated. In addition, H2BC12 expression-related pathways were enriched by gene set enrichment analysis (GSEA). Association analysis of H2BC12 gene expression and immune infiltration was performed by single sample gene set enrichment analysis (ssGSEA). Results Significantly up-regulated expression of H2BC12 mRNA was found in LGG tissue when compared to normal tissue and was proven to be diagnostic (have diagnostic significance) for LGG. In the meantime, high H2BC12 levels were associated with WHO grade, IDH status, 1p/19q codeletion, primary therapy outcome and histological type of LGG, and additionally, prognostic for adverse survival outcomes. In the multivariate analysis, high H2BC12 levels were identified to be an independent predictor for poor survival outcomes of LGG patients. Pathways in cancer, signaling by Wnt or PI3K-AKT signaling pathway, DNA repair, cellular senescence and DNA double strand break repair were differentially activated in the phenotype that positively associated with H2BC12. Conclusion H2BC12 is a promising biomarker for the diagnosis and prognosis of LGG.

Preventive Effect of Anemarrhenae rhizome and Phellodendri cortex on Danazol-Induced in Precocious Puberty in Female Rats and Network Pharmacological Analysis of Active Compounds

Anemarrhenae rhizome and Phellodendri cortex have historically been used for the treatment of precocious puberty (PP) in oriental medicine. Our study aimed to evaluate the effect of APE, a mixture of the extracts from these herbs, against danazol-induced PP in female rats. The offspring were injected danazol to establish the PP model, and then treated with APE daily, and observed for vaginal opening. At the end of the study, the levels of gonadotropic hormones, such as estradiol, follicle-stimulating hormone, and luteinizing hormone, were determined by ELISA. Moreover, the mRNA expression of GnRH, netrin-1, and UNC5C in hypothalamic tissues was determined by real-time PCR. Network pharmacological analysis was performed to predict the active compounds of APE and their potential actions. APE treatment delayed vaginal opening in rats with PP. In addition, APE treatment reduced LH levels and suppressed UNC5C expression. Gene set enrichment analysis revealed that the targets of APE were significantly associated with GnRH signaling and ovarian steroidogenesis pathways. In conclusion, APE may be used as a therapeutic remedy to inhibit the activation of the hypothalamic–pituitary–gonadal axis.

Identification of Stage-related and Severity-Related Biomarkers and Exploration of Immune Landscape for Dengue by Comprehensive Analyses

At present, there are still no specific therapeutic drugs and appropriate vaccines for Dengue. Therefore, it is very important to explore distinct clinical diagnostic indicators. In this study, we combined differentially expressed genes (DEGs) analysis and weighted co-expression network analysis (WGCNA) to screen a stable and robust biomarker which can be used to distinguish three clinical stages of Dengue and severity of Dengue. CD38 can distinguish excellently Early Acute, Late Acute, Convalescent stages for Dengue patients, and ZNF595 can discriminate DHF from DF in whole acute stages. We also found that three clinical stages can be discriminated based on the fractions of Plasma cells, activated memory CD4+ T cells, and Monocytes. In different clinical stages different immune cells function positively. Negative inhibition of viral replication based on Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene set enrichment analysis (GSEA), up-regulated autophagy genes and impairing immune system are potential reasons resulting in dengue hemorrhagic fever (DHF).

Importance of SNP Dependency Correction and Association Integration for Gene Set Analysis in Genome-Wide Association Studies

A typical genome-wide association study (GWAS) analyzes millions of single-nucleotide polymorphisms (SNPs), several of which are in a region of the same gene. To conduct gene set analysis (GSA), information from SNPs needs to be unified at the gene level. A widely used practice is to use only the most relevant SNP per gene; however, there are other methods of integration that could be applied here. Also, the problem of nonrandom association of alleles at two or more loci is often neglected. Here, we tested the impact of incorporation of different integrations and linkage disequilibrium (LD) correction on the performance of several GSA methods. Matched normal and breast cancer samples from The Cancer Genome Atlas database were used to evaluate the performance of six GSA algorithms: Coincident Extreme Ranks in Numerical Observations (CERNO), Gene Set Enrichment Analysis (GSEA), GSEA-SNP, improved GSEA for GWAS (i-GSEA4GWAS), Meta-Analysis Gene-set Enrichment of variaNT Associations (MAGENTA), and Over-Representation Analysis (ORA). Association of SNPs to phenotype was calculated using modified McNemar’s test. Results for SNPs mapped to the same gene were integrated using Fisher and Stouffer methods and compared with the minimum p-value method. Four common measures were used to quantify the performance of all combinations of methods. Results of GSA analysis on GWAS were compared to the one performed on gene expression data. Comparing all evaluation metrics across different GSA algorithms, integrations, and LD correction, we highlighted CERNO, and MAGENTA with Stouffer as the most efficient. Applying LD correction increased prioritization and specificity of enrichment outcomes for all tested algorithms. When Fisher or Stouffer were used with LD, sensitivity and reproducibility were also better. Using any integration method was beneficial in comparison with a minimum p-value method in specific combinations. The correlation between GSA results from genomic and transcriptomic level was the highest when Stouffer integration was combined with LD correction. We thoroughly evaluated different approaches to GSA in GWAS in terms of performance to guide others to select the most effective combinations. We showed that LD correction and Stouffer integration could increase the performance of enrichment analysis and encourage the usage of these techniques.

A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

A pan-cancer analysis of the glutaminase and their association with prognosis, tumor microenvironment, and therapeutic targets

BackgroundGlutamine metabolism plays a key role in various biological processes of tumor. Glutaminase (GLS) is involved in Glutamine metabolism and plays a conserved regulatory role in the process. Nevertheless, there is no comprehensively analysis of GLS in pan-cancer.MaterialsComprehensive bioinformatics analysis was adopted to investigate the expression level, prognostic values, and association between expression of GLS and TME, immune cells' infiltration, immune checkpoint genes, TMB, MSI, drug sensitivity in pan-cancer. Bioinformatics tools including CCLE, immunophenoscore (IPS), Tumor Immune Dysfunction and Exclusion (TIDE), GSEA, and TIMER databases were used.ResultsDifferently expressed GLS between tumor and normal tissues were analyzed, and the clinical prognoses, MMR, MSI, and TMB in multiple types of cancer were associated with GLS expression. Furthermore, GLS closely correlated with tumor immunity and drug sensitivity, and found GLS were predicted to be involved in cancer metabolism and immunity pathways, through gene set enrichment analysis (GSEA).ConclusionThe GLS expression could be used as a prognostic biomarker for determining prognosis and provide further insights into anti-glutamine metabolism for cancer.