Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Molecular mechanisms of Tetrastigma hemsleyanum Diels&Gilg against lung squamous cell carcinoma: From computational biology and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Tetrastigma hemsleyanum （T. hemsleyanum）, valued in traditional medicine for its potential to boost immunity and combat tumors, contains uncharacterized active compounds and mechanisms. This represents a significant gap in our understanding of its ethnopharmacological relevance.

AIM OF THE STUDY

To involve the mechanism of anti-lung cancer effect of T. hemsleyanum by means of experiment and bioinformatics analysis.

MATERIALS AND METHODS

The anticancer mechanism of T. hemsleyanum against lung squamous carcinoma (LUSC) in zebrafish was investigated. The LUSC model was established by injecting NCI-H2170 cells in the zebrafish and evaluating its anti-tumor efficacy. Next, component targets and key genes were obtained by molecular complex detection (MCODE) analysis and protein-protein interaction (PPI) network analysis. Component analysis of T. hemsleyanum was performed by UPLC-Q-TOF-MS. Molecular docking was used to simulate the binding activities of key potential active components to core targets were simulated using. Prognostic and pan-cancer analyses were then performed to validate the signaling pathways involved in the prognostic genes using gene set enrichment analysis (GSEA). Subsequently, Molecular dynamics simulations were then performed for key active components and core targets. Finally, cellular experiments were used to verify the expression of glutamate metabotropic receptor 3 (GRM3) and glutamate metabotropic receptor 7 (GRM7) in the anticancer effect exerted of T. hemsleyanum.

RESULTS

We experimentally confirmed the inhibitory effect of T. hemsleyanum on LUSC by transplantation of NCI-H2170 cells into zebrafish. There are 20 main compounds in T. hemsleyanum, such as procyanidin B1, catechin, quercetin, and kaempferol, etc. A total of 186 component targets of T. hemsleyanum and sixteen hub genes were screened by PPI network and MCODE analyses. Molecular docking and molecular dynamics simulation results showed that Gingerglycolipid B and Rutin had higher affinity with GRM3 and GRM7, respectively. Prognostic analysis, Pan-cancer analysis and verification experiment also confirmed that GRM3 and GRM7 were targets for T. hemsleyanum to exert anti-tumor effects and to participate in immune and mutation processes. In vitro experiments suggested that the inhibitory effect of T. hemsleyanum on cancer cells was correlated with GRM3 and GRM7.

CONCLUSION

In vivo, in vitro and in silico results confirmed the potential anticancer effects against LUSC of T. hemsleyanum, which further consolidated the claim of its traditional uses.

Bioinformatics analysis and validation of CSRNP1 as a key prognostic gene in non-small cell lung cancer

Cysteine and serine-rich nuclear protein 1 (CSRNP1) has shown prognostic significance in various cancers, but its role in non-small cell lung cancer (NSCLC) remains elusive. We investigated CSRNP1 expression in NSCLC cases using bioinformatics tools from the GEO public repository and validated our findings through RT-qPCR in tumor and adjacent normal tissues. KEGG and GO enrichment analyses were employed to unveil the significant deregulation in signaling pathways. Additionally, clinical significance of CSRNP1 in NSCLC was determined through receiver operating curve (ROC) analysis, and its impact on survival was assessed using Kaplan-Meier analysis. To explore the functional impact of CSRNP1, we silenced its expression in NSCLC cells and assessed the effects on cell viability, migration, and invasion using MTT, Transwell, and wound-healing assays, respectively. Additionally, we investigated the influence of CSRNP1 silencing on the phosphorylation patterns of critical signaling proteins such as p53, p-Akt, and p-MDM2. Our results demonstrated significantly lower CSRNP1 expression in NSCLC tumor tissues (P < 0.01). ROC analysis indicated that NSCLC patients with high CSRNP1 expression exhibited extended overall survival and disease-free survival. Furthermore, CSRNP1 silencing promoted NSCLC cells viability, migration, and invasion (P < 0.05). Mechanistically, CSRNP1 silencing led to increased phosphorylation of AKT and MDM2, along with a concurrent reduction in p53 protein expression, suggesting its impact on NSCLC through deregulated cell cycle processes. In conclusion, our study underscores the significance of CSRNP1 in NSCLC pathogenesis, offering insights for targeted therapeutic interventions of NSCLC.

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma

PURPOSE

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC.

METHODS

TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways.

RESULTS

This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response.

CONCLUSION

Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD

BACKGROUND

The prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD.

METHODS

The data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity.

RESULTS

67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.

Drosophila DAxud1 Has a Repressive Transcription Activity on Hsp70 and Other Heat Shock Genes

Drosophila melanogaster DAxud1 is a transcription factor that belongs to the Cysteine Serine Rich Nuclear Protein (CSRNP) family, conserved in metazoans, with a transcriptional transactivation activity. According to previous studies, this protein promotes apoptosis and Wnt signaling-mediated neural crest differentiation in vertebrates. However, no analysis has been conducted to determine what other genes it might control, especially in connection with cell survival and apoptosis. To partly answer this question, this work analyzes the role of Drosophila DAxud1 using Targeted-DamID-seq (TaDa-seq), which allows whole genome screening to determine in which regions it is most frequently found. This analysis confirmed the presence of DAxud1 in groups of pro-apoptotic and Wnt pathway genes, as previously described; furthermore, stress resistance genes that coding heat shock protein (HSP) family genes were found as hsp70, hsp67, and hsp26. The enrichment of DAxud1 also identified a DNA-binding motif (AYATACATAYATA) that is frequently found in the promoters of these genes. Surprisingly, the following analyses demonstrated that DAxud1 exerts a repressive role on these genes, which are necessary for cell survival. This is coupled with the pro-apoptotic and cell cycle arrest roles of DAxud1, in which repression of hsp70 complements the maintenance of tissue homeostasis through cell survival modulation.

Prognostic value of an eighteen-genes panel in acute myeloid leukemia by analyzing TARGET and TCGA databases

BACKGROUND

Acute myeloid leukemia (AML) has a poor prognosis, and the current 5-year survival rate is less than 30%.

OBJECTIVE

The present study was designed to identify the significant genes closely related to AML prognosis and predict the prognostic value by constructing a risk model based on their expression.

METHODS

Using bioinformatics (Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, univariate and multivariate Cox regression analysis, Kaplan-Meier survival analysis, and receiver operating characteristic (ROC) analysis) to identify a prognostic gene signature for AML. Finally, The Cancer Genome Atlas (TCGA) database was used to validate this prognostic signature.

RESULTS

Based on univariate and multivariate Cox regression analysis, eighteen prognostic genes were identified, and the gene signature and risk score model were constructed. Multivariate Cox analysis showed that the risk score was an independent prognostic factor [hazard ratio (HR) = 1.122, 95% confidence interval (CI) = 1.067-1.180, P< 0.001]. ROC analysis showed a high predictive value of the risk model with an area under the curve (AUC) of 0.705.

CONCLUSIONS

This study evaluated a potential prognostic signature with eighteen genes and constructed a risk model significantly related to the prognosis of AML patients.

GSEApy: a comprehensive package for performing gene set enrichment analysis in Python

MOTIVATION

Gene set enrichment analysis (GSEA) is a commonly used algorithm for characterizing gene expression changes. However, the currently available tools used to perform GSEA have a limited ability to analyze large datasets, which is particularly problematic for the analysis of single-cell data. To overcome this limitation, we developed a GSEA package in Python (GSEApy), which could efficiently analyze large single-cell datasets.

RESULTS

We present a package (GSEApy) that performs GSEA in either the command line or Python environment. GSEApy uses a Rust implementation to enable it to calculate the same enrichment statistic as GSEA for a collection of pathways. The Rust implementation of GSEApy is 3-fold faster than the Numpy version of GSEApy (v0.10.8) and uses >4-fold less memory. GSEApy also provides an interface between Python and Enrichr web services, as well as for BioMart. The Enrichr application programming interface enables GSEApy to perform over-representation analysis for an input gene list. Furthermore, GSEApy consists of several tools, each designed to facilitate a particular type of enrichment analysis.

AVAILABILITY AND IMPLEMENTATION

The new GSEApy with Rust extension is deposited in PyPI: https://pypi.org/project/gseapy/. The GSEApy source code is freely available at https://github.com/zqfang/GSEApy. Also, the documentation website is available at https://gseapy.rtfd.io/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

FYB2 Is a Potential Prognostic Biomarker for Hepatocellular Carcinoma

FYB2 (also known as C1orf168 or ARAP) is an adaptor protein involved in T-cell receptor (TCR)-mediated T-cell activation and adhesion. However, the correlation of FYB2 with prognosis and cancer needs further investigation. In this study, we analyzed the expression levels of FYB2 in hepatocellular carcinoma (LIHC) tumor tissues and correlated it with the pathological stages, survival outcomes, and tumor grades. We found that the expression of FYB2 was significantly downregulated in LIHC. Low FYB2 level leading to weak survival outcomes is linked with advanced tumor grades and elevated pathological stages. Cox regression analysis showed that FYB2 and AJCC-M stages can be used as independent prognostic factors for LIHC. GSEA analysis revealed that FYB2 would be notably correlated with the cellular metabolism-related pathways and particularly involved in the regulation of cancer-related pathways. Single-cell transcriptome analysis revealed that FYB2-positive cells were mainly distributed in hepatocytes, and compared with other cells, the upregulated genes of these cells were mainly enriched in metabolism-related functions. The results of the spatial transcriptome revealed that the expression of FYB2 in the adjacent area was higher than in the tumor area. These results showed that FYB2 is likely to be a new prognostic biomarker in LIHC and would help provide individual treatment decisions for LIHC patients.

A m6A methyltransferase-mediated immune signature determines prognosis, immune landscape and immunotherapy efficacy in patients with lung adenocarcinoma

BACKGROUND

As the most abundant modification in mRNA, the N⁶-methyladenosine (m⁶A) RNA modification is involved in the occurrence and development of various tumors. However, the underlying functions of this alteration in the immune microenvironment of lung adenocarcinoma (LUAD) remain unknown.

METHODS

We identified m⁶A-mediated immune genes by performing a correlation analysis. Next, a m⁶A-mediated immune model was constructed using multiple machine learning algorithms, including univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses. The potential of this model to predict the immune landscapes, drug sensitivities, and immunotherapy responses of different LUAD risk groups was studied.

RESULTS

A m⁶A-mediated immune model containing 13 m⁶A-mediated immune genes was established and found to be an independent predictor of survival time. The prognosis of low-risk patients was significantly better than that of high-risk patients. These two risk groups displayed different immune environments, genomic backgrounds, chemotherapy responses and immunotherapy response tendencies. The low- and high-risk groups strongly corresponded to the immune-hot and immune-cold phenotypes, respectively. The low-risk group was more enriched in immune-related biological processes, and the high-risk group was more enriched in proliferation-related biological processes. Furthermore, low-risk patients responded better to immunotherapy based on the results obtained from the tumor immune dysfunction and exclusion (TIDE) algorithm and subclass mapping algorithm using five external independent immunotherapy cohorts.

CONCLUSIONS

Our results suggest that the m⁶A modification participates in regulating the tumor microenvironment. The m⁶A-mediated immune model may be useful to predict the immunotherapy responses and outcomes of patients with LUAD.

Homeobox A1 Facilitates Immune Escape and Alleviates Oxidative Stress in Lung Adenocarcinoma

Objective

Recent studies have demonstrated that homeobox A1 (HOXA1) is upregulated in lung cancer due to RNA modifications (N6-methyladenosine), but the specific function of HOXA1 in lung adenocarcinoma (LUAD) remains indistinct. Herein, we investigated the role of HOXA1 in LUAD biology.

Methods

This study presented pancancer analysis of associations of HOXA1 with prognosis, TMB, and immune checkpoints. The expression of HOXA1 was detected in LUAD and normal tissues with immunohistochemistry and western blot. Through least absolute shrinkage and selection operator (LASSO) analysis, HOXA1-derived gene model was conducted in LUAD. Correlations of HOXA1 with immune cell infiltrations, immune checkpoints, HLAs, and chemotherapeutic sensitivity were evaluated. Colony formation, proliferation, and migration of LUAD cells with si-HOXA1 transfection were investigated, and the effects of HOXA1 on T cell exhaustion were assessed in vitro.

Results

HOXA1 expression was a risk factor of overall survival, disease-specific survival, and progression-free interval of LUAD. HOXA1 exhibited prominent associations with immune cell infiltration, immune checkpoints, and HLAs. HOXA1-derived gene signature reliably and independently predicted LUAD outcomes. Also, high-risk cases presented increased sensitivity to cisplatin, paclitaxel, docetaxel, vinorelbine, and etoposide. HOXA1 knockdown exhibited an inhibitory effect on proliferation and migration abilities of LUAD cells. Silencing HOXA1 weakened the expression of antioxidative stress markers Nrf2/HO-1 and T cell exhaustion marker CD155 in LUAD cells. Moreover, LUAD cells with HOXA1 knockdown enhanced the CD8+ T cell response.

Conclusion

Our data support the oncogenic function and prognostic significance of HOXA1 that facilitates immune escape and alleviates oxidative stress of LUAD.

MiR-4652-5p Targets RND1 to Regulate Cell Adhesion and Promote Lung Squamous Cell Carcinoma Progression

Lung squamous cell carcinoma (LUSC) is one subtype of non-small-cell lung cancer, whose pathogenesis has not been fully understood. Exploring molecular mechanisms of LUSC helps a lot with the development of LUSC novel therapy. Hence, our study aims to investigate novel molecular mechanisms. Differentially expressed miRNAs and mRNAs were acquired from The Cancer Genome Atlas database. A series of assays were applied to test cell functions, including qRT-PCR to analyze RND1 and miR-4652-5p expression, dual-luciferase reporter gene assay to verify the targeting relationship between these two genes, cell counting kit-8 and colony formation assays to evaluate the ability of LUSC cells to proliferate, transwell to examine the migratory and invasive abilities, and western blot to test expression of RND1 and cell adhesion-related proteins. RND1 was lowly expressed while miR-4652-5p was highly expressed in LUSC cells. The correlation between these two genes was significantly negative and miR-4652-5p could downregulate RND1 expression. Additionally, cellular function assays validated that RND1 suppressed LUSC cells to proliferate, migrate, and invade. Besides, this gene might also affect cell adhesion. Furthermore, rescue assay suggested that miR-4652-5p downregulated RND1 expression to promote the progression of LUSC cells. Together, miR-4652-5p targeted RND1 to modulate cell adhesion and the progression of LUSC cells.

Mendelian Randomization Study of Causal Relationship between Omega-3 Fatty Acids and Risk of Lung Cancer

Objective

Evidence suggests that omega-3 fatty acid intake exerts a protective effect on lung cancer, but its causal association with risk of lung cancer remains uncertain. This study attempts to clarify the causal effect of omega-3 fatty acids on lung cancer utilizing genome-wide association study (GWAS) data with Mendelian randomization (MR) approach.

Methods

This study acquired omega-3 fatty acid data from the UK Biobank and data of lung cancer patients from the Consortium and International Lung Cancer Consortium (ILCCO). Single-nucleotide polymorphisms (SNPs) associated with omega-3 fatty acids were screened as instrumental variables (IVs) in line with the criteria of p < 5E - 8, linkage disequilibrium R ² > 0.001 and distance < 10000 kb. Through inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode, causal association between omega-3 fatty acids and risk of lung cancer was evaluated. Cochran's Q test was applied for a heterogeneity test. The pleiotropy and horizontal pleiotropy among IVs were evaluated via MR-Egger regression intercept analysis.

Results

Totally, 42 SNPs associated with omega-3 fatty acids were identified as IVs. According to the results of IVW (OR (95% CI): 0.899 (0.817, 0.990), p = 0.03), MR-Egger (OR (95% CI): 0.856 (0.750, 0.977), p = 0.026), weighted median (OR (95% CI): 0.899 (0.817, 0.990), p = 0.001), simple mode (OR (95% CI): 0.901 (-0.678, 1.199), p = 0.478), and weighted mode (OR (95% CI): 0.859 (0.782, 0.944), p = 0.003), omega-3 fatty acids showed a causal association with low risk of lung cancer. No genetic pleiotropy or horizontal pleiotropy was found according to MR-Egger regression intercept analysis.

Conclusion

Our findings provide sufficient evidence that omega-3 fatty acids are causal protective factors of lung cancer. Despite this, further work is required for elucidating the potential mechanisms.

Construction and Validation of Prognostic Regulation Network Based on RNA-Binding Protein Genes in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LUSC) is a common histologic subtype of non-small cell lung cancer with a poor prognosis. RNA-binding proteins (RBPs) are key modulators in the posttranscriptional regulation and RBP alterations are commonly found in various cancer types. However, its roles in predicting the tumorigenesis and prognosis have not been identified in LUSC. To identify the roles of RBPs in the tumorigenesis and prognosis of LUSC, the RNA sequencing data of patients with LUSC were retrieved from The Cancer Genome Atlas (TCGA) databases. The differential expressed genes (DEGs) were evaluated and identified. The intersection of manually curated RBPs and tumorigenesis-related DEGs was filtered to the univariate Cox regression analysis. The intersection genes with prognostic value were defined as prognostic RNA-binding protein genes (PRBPGs). Based on them, the predicted model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic curve and the risk score. In addition, to explore the key regulatory network, the relationship among PRBPGs, target RNA, and absolute quantification of 50 hallmarks of cancer was also identified by Pearson correlation analysis. A total of 311 genes were filtered as the intersection of 1542 manually curated RBPs and tumorigenesis-related DEGs and the results revealed 17 PRBPGs. Based on them, we constructed the predict model with a relatively high accuracy (AUC: 0.739). The Kaplan-Meier survival curve showed the significant prognostic value of risk score (p < 0.001). Moreover, we uncovered the regulatory networks of PHF5A-TOMM22-oxidative phosphorylation, TLR3-CTSO inflammation-related pathway, SECISBP2L-targeted RNA (ADGRF5, TGFBR2, CD302, AC096921.2, AHCYL2, RPS6KA2, SLC34A2, and SFTPB) angiogenesis, and SECISBP2L-AKAP13 signaling (DNA repair, MTORC1 signaling, and MYC targets). The regulation mechanisms and cellular location of key PRBPGs were validated by assay for targeting accessible chromatin with high-throughput sequencing and single-cell RNA sequencing. Our study identifies PRBPGs as reliable indexes in predicting the tumorigenesis and prognosis of patients with LUSC and provides a well-applied model for predicting the overall survival for patients with LUSC. Besides, we also identified the regulatory network among PRBPGs, target RNA, and cancer gene sets in mediating the LUSC tumorigenesis.

Integrative bioinformatics analysis the clinical value of KMT5A in different subtypes of lung cancer

To combat cancer disease, The Cancer Genome Atlas (TCGA) collects a large amount of information through high-throughput genome analysis technology. At present, there are only few treatments available for lung squamous cell carcinoma (LUSC). Although lysine methyltransferase 5A (KMT5A) is considered as a new biomarker for LUSC, its functionalities needs to be confirmed clinically in lung adenocarcinoma (LUAD). Therefore, in the current investigation we obtained the expression data of KMT5A in LUSC and LUAD by TCGA database. This study evaluated the prognostic value of KMT5A expression in LUAD and LUSC, and discussed the relevant biological pathways of KMT5A involved in the pathogenesis of LUAD and LUSC. KMT5A was highly expressed in LUAD and LUSC cancer tissues. The overall survival analysis revealed that the prognosis of high expression group was poor for LUAD, but was opposite in LUSC. In LUAD, the expression of KMT5A was significantly correlated with age (P = 0.029), gender (P = 0.001) and m (P = 0.042). Logistic regression showed that gender was significantly correlated with poor prognosis of LUAD (P = 0.00175). Multivariate analysis of Cox proportional hazards model exhibited that KMT5A risk ratio (HR) was 0.97, 95% confidence interval (CI), 0.94-1.0, P = 0.026. Age (P = 0.044), t (P = 0.031), m (P = 0.047) were the independent prognostic factors of LUSC patients, while the stage was the independent prognostic factor of LUAD (P < 0.001). Genome enrichment analysis presented that LUSC was differentially enriched with antigen processing and presentation, cell adhesion molecules, cytokine receptor interaction, ECM receptor interaction, etc. LUAD was differentially enriched with apoptosis, cancer pathway, vascular endothelial growth factor signaling pathway and wnt signaling pathway. Overall, this study presented the clinical value of KMT5A in LUSC and LUAD and suggested the possible pathways involved.

Prognostic Value of a Novel Signature With Nine Hepatitis C Virus-Induced Genes in Hepatic Cancer by Mining GEO and TCGA Databases

Background

Hepatitis C virus-induced genes (HCVIGs) play a critical role in regulating tumor development in hepatic cancer. The role of HCVIGs in hepatic cancer remains unknown. This study aimed to construct a prognostic signature and assess the value of the risk model for predicting the prognosis of hepatic cancer.

Methods

Differentially expressed HCVIGs were identified in hepatic cancer data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases using the library (“limma”) package of R software. The protein–protein interaction (PPI) network was constructed using the Cytoscape software. Functional enrichment analysis was performed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Univariate and multivariate Cox proportional hazard regression analyses were applied to screen for prognostic HCVIGs. The signature of HCVIGs was constructed. Gene Set Enrichment Analysis (GSEA) compared the low-risk and high-risk groups. Finally, the International Cancer Genome Consortium (ICGC) database was used to validate this prognostic signature. Polymerase chain reaction (PCR) was performed to validate the expression of nine HCVIGs in the hepatic cancer cell lines.

Results

A total of 143 differentially expressed HCVIGs were identified in TCGA hepatic cancer dataset. Functional enrichment analysis showed that DNA replication was associated with the development of hepatic cancer. The risk score signature was constructed based on the expression of ZIC2, SLC7A11, PSRC1, TMEM106C, TRAIP, DTYMK, FAM72D, TRIP13, and CENPM. In this study, the risk score was an independent prognostic factor in the multivariate Cox regression analysis [hazard ratio (HR) = 1.433, 95% CI = 1.280–1.605, P < 0.001]. The overall survival curve revealed that the high-risk group had a poor prognosis. The Kaplan–Meier Plotter online database showed that the survival time of hepatic cancer patients with overexpression of HCVIGs in this signature was significantly shorter. The prognostic signature-associated GO and KEGG pathways were significantly enriched in the risk group. This prognostic signature was validated using external data from the ICGC databases. The expression of nine prognostic genes was validated in HepG2 and LO-2.

Conclusion

This study evaluates a potential prognostic signature and provides a way to explore the mechanism of HCVIGs in hepatic cancer.

Establishment and validation of a prognostic signature for lung adenocarcinoma based on metabolism-related genes

Background

Given that dysregulated metabolism has been recently identified as a hallmark of cancer biology, this study aims to establish and validate a prognostic signature of lung adenocarcinoma (LUAD) based on metabolism-related genes (MRGs).

Methods

The gene sequencing data of LUAD samples with clinical information and the metabolism-related gene set were obtained from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB), respectively. The differentially expressed MRGs were identified by Wilcoxon rank sum test. Then, univariate cox regression analysis was performed to identify MRGs that related to overall survival (OS). A prognostic signature was developed by multivariate Cox regression analysis. Furthermore, the signature was validated in the GSE31210 dataset. In addition, a nomogram that combined the prognostic signature was created for predicting the 1-, 3- and 5-year OS of LUAD. The accuracy of the nomogram prediction was evaluated using a calibration plot. Finally, cox regression analysis was applied to identify the prognostic value and clinical relationship of the signature in LUAD.

Results

A total of 116 differentially expressed MRGs were detected in the TCGA dataset. We found that 12 MRGs were most significantly associated with OS by using the univariate regression analysis in LUAD. Then, multivariate Cox regression analyses were applied to construct the prognostic signature, which consisted of six MRGs-aldolase A (ALDOA), catalase (CAT), ectonucleoside triphosphate diphosphohydrolase-2 (ENTPD2), glucosamine-phosphate N-acetyltransferase 1 (GNPNAT1), lactate dehydrogenase A (LDHA), and thymidylate synthetase (TYMS). The prognostic value of this signature was further successfully validated in the GSE31210 dataset. Furthermore, the calibration curve of the prognostic nomogram demonstrated good agreement between the predicted and observed survival rates for each of OS. Further analysis indicated that this signature could be an independent prognostic indicator after adjusting to other clinical factors. The high-risk group patients have higher levels of immune checkpoint molecules and are therefore more sensitive to immunotherapy. Finally, we confirmed six MRGs protein and mRNA expression in six lung cancer cell lines and firstly found that ENTPD2 might played an important role on LUAD cells colon formation and migration.

Conclusions

We established a prognostic signature based on MRGs for LUAD and validated the performance of the model, which may provide a promising tool for the diagnosis, individualized immuno-/chemotherapeutic strategies and prognosis in patients with LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01915-x.

Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

Background

Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer.

Methods

We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration.

Results

We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer.

Conclusions

This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

Prognostic significance of eight immune-related genes on survival in patients with lung squamous cell carcinoma

Aim: To build a valid prognostic model based on immune-related genes for lung squamous cell carcinoma (LUSC). Materials & methods: Differential expression of immune-related genes between LUSC and normal specimens from TCGA dataset and underlying molecular mechanisms were systematically analyzed. Constructing and validating the high-risk and low-risk groups for LUSC survival. Results: The immune-related gene-based prognostic index (IRGPI) could predict the overall survival in patients with different clinicopathological characteristics. Functional enrichment analysis of differential expression of immune-related gene signature indicated distinctive molecular pathways between high-risk and low-risk groups. Conclusion: Analysis of IRGs in LUSC enable us to stratify patients into distinct risk groups, which may help to screen LUSC patients at risk and decision making on follow-up therapeutic intervention.

Discovery of Frenolicin B as Potential Agrochemical Fungicide for Controlling Fusarium Head Blight on Wheat

In this study, the supernatant extract from fermentation broth of Streptomyces sp. NEAU-H3 showed strong antifungal activity against Fusarium graminearum strain PH-1 in vitro and in vivo. Three known pyranonaphthoquinones were isolated by means of an activity-guided method, and frenolicin B was characterized as the main active ingredient. Frenolicin B displayed strong antifungal activity against F. graminearum strain PH-1 with an EC₅₀ value of 0.51 mg/L, which is lower than that of carbendazim (0.78 mg/L) but higher than that of phenamacril (0.18 mg/L). Frenolicin B could also strongly inhibit the mycelial growth of Fusarium species, including F. graminearum and F. asiaticum, as well as carbendazim-resistant Fusarium strains isolated from field, with EC₅₀ values of 0.25-0.92 mg/L. Results from field experiments showed that the efficacy of frenolicin B in controlling Fusarium head blight at a treatment concentration of 75 g ai/ha was better than those of phenamacril (375 g ai/ha) and carbendazim (600 g ai/ha) or had no significant difference with that of phenamacril (375 g ai/ha) in 2 years. Scanning electron microscope and transmission electron microscope observations revealed that after treating F. graminearum mycelia with frenolicin B, the mycelia appeared aberrant and had an uneven thickness and swelling, the cytoplasm had disintegrated, and some cell contents were lost. Transcriptome analysis suggests that frenolicin B might inhibit the metabolism of nucleotides and energy by affecting genes involved in phosphorus utilization but did not affect the expression of myosin 5, which is the specific target of phenamacril. These findings indicate that frenolicin B may be a potential agrochemical fungicide for controlling Fusarium head blight.

FAM207BP, a pseudogene-derived lncRNA, facilitates proliferation, migration and invasion of lung adenocarcinoma cells and acts as an immune-related prognostic factor

AIMS

This study aimed to characterize the functions of pseudogene-derived long non-coding RNA (lncRNA) FAM207BP in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

Through the Cancer Genome Atlas (TCGA)-Genotype Tissue Expression (GTEx) database, FAM207BP expression was detected in LUAD and normal tissues. Overall survival (OS) and disease-free survival (DFS) analysis was presented using log-rank test or univariate Cox regression analysis. The relationships between FAM207BP expression and clinical features were analyzed. FAM207BP expression was validated in LUAD tissues and cells using RT-qPCR. Cell viability of LUAD cells was evaluated after silencing or overexpressing FAM207BP. Furthermore, migrated and invasive abilities were examined by Transwell and scratch assays. The correlation between FAM207BP expression and the immune infiltration levels was analyzed. Gene Set Enrichment Analysis (GSEA) was performed for high- and low-expression of FAM207BP using C2 collection in the Molecular Signatures Database (MSigDB) database.

KEY FINDINGS

FAM207BP expression was distinctly higher in LUAD than normal tissues. Patients with its high expression indicated worse OS and DFS time. FAM207BP expression was significantly related to gender. RT-qPCR results confirmed that FAM207BP was significantly highly expressed in LUAD tissues and cells. Knockdown of FAM207BP distinctly suppressed cellular viability, migration and invasion for LUAD cells. Also, its expression was negatively related to B cell infiltration levels. GSEA results indicated that high FAM207BP expression was involved in regulation of gene expression. Its low expression was related to immune response.

SIGNIFICANCE

Pseudogene-derived lncRNA FAM207BP could induce proliferation and migration of LUAD cells, which could act as an immune-related prognostic factor.

Identification of a four-gene panel predicting overall survival for lung adenocarcinoma

Background

Lung cancer is the most frequently diagnosed carcinoma and the leading cause of cancer-related mortality. Although molecular targeted therapy and immunotherapy have made great progress, the overall survival (OS) is still poor due to a lack of accurate and available prognostic biomarkers. Therefore, in this study we aimed to establish a multiple-gene panel predicting OS for lung adenocarcinoma.

Methods

We obtained the mRNA expression and clinical data of lung adenocarcinoma (LUAD) from TCGA database for further integrated bioinformatic analysis. Lasso regression and Cox regression were performed to establish a prognosis model based on a multi-gene panel. A nomogram based on this model was constructed. The receiver operating characteristic (ROC) curve and the Kaplan–Meier curve were used to assess the predicted capacity of the model. The prognosis value of the multi-gene panel was further validated in TCGA-LUAD patients with EGFR, KRAS and TP53 mutation and a dataset from GEO. Gene set enrichment analysis (GSEA) was performed to explore potential biological mechanisms of a novel prognostic gene signature.

Results

A four-gene panel (including DKK1, GNG7, LDHA, MELTF) was established for LUAD prognostic indicator. The ROC curve revealed good predicted performance in both test cohort (AUC = 0.740) and validation cohort (AUC = 0.752). Each patient was calculated a risk score according to the model based on the four-gene panel. The results showed that the risk score was an independent prognostic factor, and the high-risk group had a worse OS compared with the low-risk group. The nomogram based on this model showed good prediction performance. The four-gene panel was still good predictors for OS in LUAD patients with TP53 and KRAS mutations. GSEA revealed that the four genes may be significantly related to the metabolism of genetic material, especially the regulation of cell cycle pathway.

Conclusion

Our study proposed a novel four-gene panel to predict the OS of LUAD, which may contribute to predicting prognosis accurately and making the clinical decisions of individual therapy for LUAD patients.

Analysis of the Role and Regulation Mechanism of hsa-miR-147b in Lung Squamous Cell Carcinoma Based on The Cancer Genome Atlas Database

Objective: This study aimed to explore the role and regulatory mechanism of hsa-miR-147b in lung squamous cell carcinoma (LUSC) through The Cancer Genome Atlas (TCGA) database. Methods: The expression and clinical value of miR-147b in LUSC were analyzed in the TCGA database. The target genes of miR-147b were screened via miRWalk 2.0 and verified in TCGA database. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) were performed to analyzed the differential target genes of miR-147b. Kaplan-Meier survival analysis and Cox regression were used to screen the prognosis-related target genes. Results: The expression of miR-147b in LUSC tissues increased, and was associated with poor prognosis, gender, and stage of LUSC patients. The area under the curve (AUC) of miR-147b was 0.8478 by the receiver-operating characteristic curve. There were 428 differentially expressed genes of miR-147b that played a critical role in drug transport, DNA binding, calcium signaling pathway, and Ras signaling pathway through GO and KEGG. PTGIS, SUSD4, ARC, HTR2C, SHISA9, and PLA2G4D were independent risk factors for poor prognosis in LUSC patients. LUSC patients in the high-risk group had a higher risk of death. The time-dependent AUC was 0.673. Conclusions: MiR-147b might be a potential molecular marker for poor prognosis in patients with LUSC.

A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Background

Intratumoural heterogeneity has been previously shown to be related to clonal evolution and genetic instability and associated with tumour progression. Phenotypically, it is reflected in the diversity of appearance and morphology within cell populations. Computer-extracted features relating to tumour cellular diversity on routine tissue images might correlate with outcome. This study investigated the prognostic ability of computer-extracted features of tumour cellular diversity (CellDiv) from haematoxylin and eosin (H&E)-stained histology images of non-small cell lung carcinomas (NSCLCs).

Methods

In this multicentre, retrospective study, we included 1057 patients with early-stage NSCLC with corresponding diagnostic histology slides and overall survival information from four different centres. CellDiv features quantifying local cellular morphological diversity from H&E-stained histology images were extracted from the tumour epithelium region. A Cox proportional hazards model based on CellDiv was used to construct risk scores for lung adenocarcinoma (LUAD; 270 patients) and lung squamous cell carcinoma (LUSC; 216 patients) separately using data from two of the cohorts, and was validated in the two remaining independent cohorts (comprising 236 patients with LUAD and 335 patients with LUSC). We used multivariable Cox regression analysis to examine the predictive ability of CellDiv features for 5-year overall survival, controlling for the effects of clinical and pathological parameters. We did a gene set enrichment and Gene Ontology analysis on 405 patients to identify associations with differentially expressed biological pathways implicated in lung cancer pathogenesis.

Findings

For prognosis of patients with early-stage LUSC, the CellDiv LUSC model included 11 discriminative CellDiv features, whereas for patients with early-stage LUAD, the model included 23 features. In the independent validation cohorts, patients predicted to be at a higher risk by the univariable CellDiv model had significantly worse 5-year overall survival (hazard ratio 1·48 [95% CI 1·06–2·08]; p=0·022 for The Cancer Genome Atlas [TCGA] LUSC group, 2·24 [1·04–4·80]; p=0·039 for the University of Bern LUSC group, and 1·62 [1·15–2·30]; p=0·0058 for the TCGA LUAD group). The identified CellDiv features were also found to be strongly associated with apoptotic signalling and cell differentiation pathways.

Interpretation

CellDiv features were strongly prognostic of 5-year overall survival in patients with early-stage NSCLC and also associated with apoptotic signalling and cell differentiation pathways. The CellDiv-based risk stratification model could potentially help to determine which patients with early-stage NSCLC might receive added benefit from adjuvant therapy.

Funding

National Institue of Health and US Department of Defense.

High expression levels of pyrimidine metabolic rate-limiting enzymes are adverse prognostic factors in lung adenocarcinoma: a study based on The Cancer Genome Atlas and Gene Expression Omnibus datasets

Reprogramming of metabolism is described in many types of cancer and is associated with the clinical outcomes. However, the prognostic significance of pyrimidine metabolism signaling pathway in lung adenocarcinoma (LUAD) is unclear. Using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets, we found that the pyrimidine metabolism signaling pathway was significantly enriched in LUAD. Compared with normal lung tissues, the pyrimidine metabolic rate–limiting enzymes were highly expressed in lung tumor tissues. The high expression levels of pyrimidine metabolic–rate limiting enzymes were associated with unfavorable prognosis. However, purinergic receptors P2RX1, P2RX7, P2RY12, P2RY13, and P2RY14 were relatively downregulated in lung cancer tissues and were associated with favorable prognosis. Moreover, we found that hypo-DNA methylation, DNA amplification, and TP53 mutation were contributing to the high expression levels of pyrimidine metabolic rate–limiting enzymes in lung cancer cells. Furthermore, combined pyrimidine metabolic rate–limiting enzymes had significant prognostic effects in LUAD. Comprehensively, the pyrimidine metabolic rate–limiting enzymes were highly expressed in bladder cancer, breast cancer, colon cancer, liver cancer, and stomach cancer. And the high expression levels of pyrimidine metabolic rate–limiting enzymes were associated with unfavorable prognosis in liver cancer. Overall, our results suggested the mRNA levels of pyrimidine metabolic rate–limiting enzymes CAD, DTYMK, RRM1, RRM2, TK1, TYMS, UCK2, NR5C2, and TK2 were predictive of lung cancer as well as other cancers.

Identification of an Immune Gene Expression Signature for Predicting Lung Squamous Cell Carcinoma Prognosis

Growing evidence indicates that immune-related biomarkers play an important role in tumor processes. This study investigates immune-related gene expression and its prognostic value in lung squamous cell carcinoma (LUSC). A cohort of 493 samples of patients with LUSC was collected and analyzed from data generated by the TCGA Research Network and ImmPort database. The R coxph package was employed to mine significant immune-related genes using univariate analysis. Lasso and stepwise regression analyses were used to construct the LUSC prognosis prediction model, and clusterProfiler was used for gene functional annotation and enrichment analysis. The Kaplan-Meier analysis and ROC were used to evaluate the model efficiency in predicting and classifying LUSC case prognoses. We identified 14 immune-related genes to incorporate into our prognosis model. The patients were divided into two subgroups (Risk-H and Risk-L) according to their risk score values. Compared to Risk-L patients, Risk-H patients showed significantly improved overall survival (OS) in both training and testing sets. Functional annotation indicated that the 14 identified genes were mainly enriched in several immune-related pathways. Our results also revealed that a risk score value was correlated with various signaling pathways, such as the JAK-STA signaling pathway. Establishment of a nomogram for clinical application demonstrated that our immune-related model exhibited good predictive prognostic performance. Our predictive prognosis model based on immune signatures has potential clinical implications for assessing the overall survival and precise treatment for patients with LUSC.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled.

Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot.

Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS.

Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.

Detecting Prognosis Risk Biomarkers for Colon Cancer Through Multi-Omics-Based Prognostic Analysis and Target Regulation Simulation Modeling

Background

Colon cancer is one of the most common health threats for humans since its high morbidity and mortality. Detecting potential prognosis risk biomarkers (PRBs) is essential for the improvement of therapeutic strategies and drug development. Currently, although an integrated prognostic analysis of multi-omics for colon cancer is insufficient, it has been reported to be valuable for improving PRBs’ detection in other cancer types.

Aim

This study aims to detect potential PRBs for colon adenocarcinoma (COAD) samples through the cancer genome atlas (TCGA) by integrating muti-omics.

Materials and Methods

The multi-omics-based prognostic analysis (MPA) model was first constructed to systemically analyze the prognosis of colon cancer based on four-omics data of gene expression, exon expression, DNA methylation and somatic mutations on COAD samples. Then, the essential features related to prognosis were functionally annotated through protein–protein interaction (PPI) network and cancer-related pathways. Moreover, the significance of those essential prognostic features were further confirmed by the target regulation simulation (TRS) model. Finally, an independent testing dataset, as well as the single cell-based expression dataset were utilized to validate the generality and repeatability of PRBs detected in this study.

Results

By integrating the result of MPA modeling, as well the PPI network, integrated pathway and TRS modeling, essential features with gene symbols such as EPB41, PSMA1, FGFR3, MRAS, LEP, C7orf46, LOC285000, LBP, ZNF35, SLC30A3, LECT2, RNF7, and DYNC1I1 were identified as PRBs which provide high potential as drug targets for COAD treatment. Validation on the independent testing dataset demonstrated that these PRBs could be applied to distinguish the prognosis of COAD patients. Moreover, the prognosis of patients with different clinical conditions could also be distinguished by the above PRBs.

Conclusions

The MPA and TRS models constructed in this paper, as well as the PPI network and integrated pathway analysis, could not only help detect PRBs as potential therapeutic targets for COAD patients but also make it a paradigm for the prognostic analysis of other cancers.

Candidate lncRNA-microRNA-mRNA networks in predicting non-small cell lung cancer and related prognosis analysis

PURPOSE

The role of non-coding RNA, once thought to be dark matter, is increasingly prominent in cancer. Our article explores the effect of non-coding RNA in lung adenocarcinoma and lung squamous cell carcinoma by mining TCGA public database.

METHODS

Download the data by applying the official TCGA software. The data were analyzed by R data analysis packages, 'edgeR', 'gplots' and 'survival'. We better illustrate the potential networks of lung cancer genes by constructing ceRNAs, using Cytoscape software.

RESULTS

We obtained genes which were differentially expressed in lung adenocarcinoma and lung squamous cell carcinoma analysis. Within these differentially expressed genes, we also conducted a survival analysis to find differentially expressed genes associated with prognosis in both lung adenocarcinoma and lung squamous cell carcinoma. Based on genes differentially expressed of both lung adenocarcinoma and lung squamous cell carcinoma, we constructed a ceRNA network to illustrate the mechanism of lung adenocarcinoma and lung squamous cell carcinoma. Our study analyzed genes which were differentially expressed in lung adenocarcinoma and lung squamous cell carcinoma using the TCGA database.

CONCLUSION

Based on this, the prognosis in both lung squamous cell carcinoma and lung adenocarcinoma was analyzed. We have also constructed a ceRNA network to provide a basis for the study of ceRNA in lung adenocarcinoma and lung squamous cell carcinoma.

Soyasaponin II protects against acute liver failure through diminishing YB-1 phosphorylation and Nlrp3-inflammasome priming in mice

Acute liver failure is characterized by the rapid development of liver dysfunction and remarkably high mortality. Accumulating evidence suggests that soyasaponin possesses potential anti-inflammatory activities. Here, we aimed to investigate the potential role of soyasaponin II in acute liver failure and establish the underlying mechanism. Methods: Lipopolysaccharide/D-galactosamine (LPS/GalN) was employed to induce acute liver failure. We applied liquid chromatography and mass spectrometry (LC/MS) to characterize the changes of soyasaponin II levels in the cecal content and liver. Transcriptomics and proteomics analysis were used to evaluate the functional molecule mediated by soyasaponin II in macrophages. Results: LPS/GalN administration markedly decreased fecal and hepatic soyasaponin II levels. Soyasaponin II treatment protected mice against LPS/GalN induced acute liver injury. Additionally, soyasaponin II markedly diminished Y-Box Binding Protein 1 (YB-1) phosphorylation and nuclear translocation, Nlrp3 inflammasome priming, and interleukin 1β (Il-1β) production in macrophages. Phosphorylated YB-1 could activate Nlrp3 mRNA transcription by binding the promoter region. Finally, immunofluorescence analysis showed elevated p-YB-1 nuclear translocation in macrophages of acute liver failure patients compared to controls. Conclusion: Our data shows that soyasaponin II which serves as a novel inhibitor for YB-1 phosphorylation and Nlrp3 inflammasome priming could protect mice against LPS/GalN induced acute liver failure.

Identification and validation of a three-gene signature as a candidate prognostic biomarker for lower grade glioma

Background

Lower grade glioma (LGG) are a heterogeneous tumor that may develop into high-grade malignant glioma seriously shortens patient survival time. The clinical prognostic biomarker of lower-grade glioma is still lacking. The aim of our study is to explore novel biomarkers for LGG that contribute to distinguish potential malignancy in low-grade glioma, to guide clinical adoption of more rational and effective treatments.

Methods

The RNA-seq data for LGG was downloaded from UCSC Xena and the Chinese Glioma Genome Atlas (CGGA). By a robust likelihood-based survival model, least absolute shrinkage and selection operator regression and multivariate Cox regression analysis, we developed a three-gene signature and established a risk score to predict the prognosis of patient with LGG. The three-gene signature was an independent survival predictor compared to other clinical parameters. Based on the signature related risk score system, stratified survival analysis was performed in patients with different age group, gender and pathologic grade. The prognostic signature was validated in the CGGA dataset. Finally, weighted gene co-expression network analysis (WGCNA) was carried out to find the co-expression genes related to the member of the signature and enrichment analysis of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway were conducted for those co-expression network. To prove the efficiency of the model, time-dependent receiver operating characteristic curves of our model and other models are constructed.

Results

In this study, a three-gene signature (WEE1, CRTAC1, SEMA4G) was constructed. Based on the model, the risk score of each patient was calculated with LGG (low-risk vs. high-risk, hazard ratio (HR) = 0.198 (95% CI [0.120-0.325])) and patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Furthermore, the model was validated in the CGGA dataset. Lastly, by WGCNA, we constructed the co-expression network of the three genes and conducted the enrichment of GO and KEGG. Our study identified a three-gene model that showed satisfactory performance in predicting the 1-, 3- and 5-year survival of LGG patients compared to other models and may be a promising independent biomarker of LGG.