An Immune Signature Robustly Predicts Clinical Deterioration for Hepatitis C Virus-Related Early-Stage Cirrhosis Patients

Hepatitis C virus (HCV)-related cirrhosis leads to a heavy global burden of disease. Clinical risk stratification in HCV-related compensated cirrhosis remains a major challenge. Here, we aim to develop a signature comprised of immune-related genes to identify patients at high risk of progression and systematically analyze immune infiltration in HCV-related early-stage cirrhosis patients. Bioinformatics analysis was applied to identify immune-related genes and construct a prognostic signature in microarray data set. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted with the “clusterProfiler” R package. Besides, the single sample gene set enrichment analysis (ssGSEA) was used to quantify immune-related risk term abundance. The nomogram and calibrate were set up via the integration of the risk score and clinicopathological characteristics to assess the effectiveness of the prognostic signature. Finally, three genes were identified and were adopted to build an immune-related prognostic signature for HCV-related cirrhosis patients. The signature was proved to be an independent risk element for HCV-related cirrhosis patients. In addition, according to the time-dependent receiver operating characteristic (ROC) curves, nomogram, and calibration plot, the prognostic model could precisely forecast the survival rate at the first, fifth, and tenth year. Notably, functional enrichment analyses indicated that cytokine activity, chemokine activity, leukocyte migration and chemotaxis, chemokine signaling pathway and viral protein interaction with cytokine and cytokine receptor were involved in HCV-related cirrhosis progression. Moreover, ssGSEA analyses revealed fierce immune-inflammatory response mechanisms in HCV progress. Generally, our work developed a robust prognostic signature that can accurately predict the overall survival, Child-Pugh class progression, hepatic decompensation, and hepatocellular carcinoma (HCC) for HCV-related early-stage cirrhosis patients. Functional enrichment and further immune infiltration analyses systematically elucidated potential immune response mechanisms.

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Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Journal of Translational Medicine ◽

10.1186/s12967-020-02545-z ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Pancheng Wu ◽

Yi Zheng ◽

Yanyu Wang ◽

Yadong Wang ◽

Naixin Liang

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Early Stage ◽

Prognostic Signature ◽

Training Set ◽

Validation Data ◽

Data Set ◽

Screening Programs ◽

Immune Signature ◽

Immune Related Genes

Abstract Background The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD. Methods The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts. Results A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design. Conclusions Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

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Prognostic and immunological value of LTB4R in pan-cancer

Mathematical Biosciences and Engineering ◽

10.3934/mbe.2021459 ◽

2021 ◽

Vol 18 (6) ◽

pp. 9336-9356

Author(s):

Sidan Long ◽

◽

Shuangshuang Ji ◽

Kunmin Xiao ◽

Peng Xue ◽

...

Keyword(s):

Immune Cells ◽

Prognostic Significance ◽

Enrichment Analysis ◽

Negative Influence ◽

Leukotriene B4 ◽

Gene Set Enrichment Analysis ◽

Functional Enrichment ◽

Immune Infiltration ◽

Significant Difference ◽

Pan Cancer

<abstract> <sec><title>Background</title>LTB4 receptor 1 (LTB4R), as the high affinity leukotriene B4 receptor, is rapidly revealing its function in malignancies. However, it is still uncertain. </sec> <sec><title>Methods</title>We investigated the expression pattern and prognostic significance of LTB4R in pan-cancer across different databases, including ONCOMINE, PrognoScan, GEPIA, and Kaplan-Meier Plotter, in this study. Meanwhile, we explored the significance of LTB4R in tumor metastasis by HCMDB. Then functional enrichment analysis of related genes was performed using GeneMANIA and DAVID. Lastly, utilizing the TIMER datasets, we looked into the links between LTB4R expression and immune infiltration in malignancies. </sec> <sec><title>Results</title>In general, tumor tissue displayed higher levels of LTB4R expression than normal tissue. Although LTB4R had a negative influence on pan-cancer, a high expression level of LTB4R was protective of LIHC (liver hepatocellular carcinoma) patients' survival. There was no significant difference in the distribution of LTB4R between non-metastatic and metastatic tumors. Based on Gene Set Enrichment Analysis, LTB4R was implicated in pathways involved in inflammation, immunity, metabolism, and cancer diseases. The correlation between immune cells and LTB4R was found to be distinct across cancer types. Furthermore, markers of infiltrating immune cells, such as Treg, T cell exhaustion and T helper cells, exhibited different LTB4R-related immune infiltration patterns. </sec> <sec><title>Conclusion</title>The LTB4R is associated with immune infiltrates and can be used as a prognostic biomarker in pan-cancer. </sec> </abstract>

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Integrative transcriptome data mining for identification of core lncRNAs in breast cancer

PeerJ ◽

10.7717/peerj.7821 ◽

2019 ◽

Vol 7 ◽

pp. e7821 ◽

Cited By ~ 7

Author(s):

Xiaoming Zhang ◽

Jing Zhuang ◽

Lijuan Liu ◽

Zhengguo He ◽

Cun Liu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Prognostic Value ◽

Early Stage ◽

Expression Profiles ◽

Diagnostic Value ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Data Set

Background Cumulative evidence suggests that long non-coding RNAs (lncRNAs) play an important role in tumorigenesis. This study aims to identify lncRNAs that can serve as new biomarkers for breast cancer diagnosis or screening. Methods First, the linear fitting method was used to identify differentially expressed genes from the breast cancer RNA expression profiles in The Cancer Genome Atlas (TCGA). Next, the diagnostic value of all differentially expressed lncRNAs was evaluated using a receiver operating characteristic (ROC) curve. Then, the top ten lncRNAs with the highest diagnostic value were selected as core genes for clinical characteristics and prognosis analysis. Furthermore, core lncRNA-mRNA co-expression networks based on weighted gene co-expression network analysis (WGCNA) were constructed, and functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differential expression level and diagnostic value of core lncRNAs were further evaluated by using independent data set from Gene Expression Omnibus (GEO). Finally, the expression status and prognostic value of core lncRNAs in various tumors were analyzed based on Gene Expression Profiling Interactive Analysis (GEPIA). Results Seven core lncRNAs (LINC00478, PGM5-AS1, AL035610.1, MIR143HG, RP11-175K6.1, AC005550.4, and MIR497HG) have good single-factor diagnostic value for breast cancer. AC093850.2 has a prognostic value for breast cancer. AC005550.4 and MIR497HG can better distinguish breast cancer patients in early-stage from the advanced-stage. Low expression of MAGI2-AS3, LINC00478, AL035610.1, MIR143HG, and MIR145 may be associated with lymph node metastasis in breast cancer. Conclusion Our study provides candidate biomarkers for the diagnosis and prognosis of breast cancer, as well as a bioinformatics basis for the further elucidation of the molecular pathological mechanism of breast cancer.

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Identification and Validation of Immune-Related LncRNA Prognostic Signature for Lung Adenocarcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.681277 ◽

2021 ◽

Vol 12 ◽

Author(s):

Guomin Wu ◽

Qihao Wang ◽

Ting Zhu ◽

Linhai Fu ◽

Zhupeng Li ◽

...

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Clinical Features ◽

Cox Regression ◽

Differential Expression Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Prognostic Signature ◽

Cox Regression Analysis ◽

Immune Infiltration

This study aimed to establish a prognostic risk model for lung adenocarcinoma (LUAD). We firstly divided 535 LUAD samples in TCGA-LUAD into high-, medium-, and low-immune infiltration groups by consensus clustering analysis according to immunological competence assessment by single-sample gene set enrichment analysis (ssGSEA). Profile of long non-coding RNAs (lncRNAs) in normal samples and LUAD samples in TCGA was used for a differential expression analysis in the high- and low-immune infiltration groups. A total of 1,570 immune-related differential lncRNAs in LUAD were obtained by intersecting the above results. Afterward, univariate COX regression analysis and multivariate stepwise COX regression analysis were conducted to screen prognosis-related lncRNAs, and an eight-immune-related-lncRNA prognostic signature was finally acquired (AL365181.2, AC012213.4, DRAIC, MRGPRG-AS1, AP002478.1, AC092168.2, FAM30A, and LINC02412). Kaplan–Meier analysis and ROC analysis indicated that the eight-lncRNA-based model was accurate to predict the prognosis of LUAD patients. Simultaneously, univariate COX regression analysis and multivariate COX regression analysis were undertaken on clinical features and risk scores. It was illustrated that the risk score was a prognostic factor independent from clinical features. Moreover, immune data of LUAD in the TIMER database were analyzed. The eight-immune-related-lncRNA prognostic signature was related to the infiltration of B cells, CD4+ T cells, and dendritic cells. GSEA enrichment analysis revealed significant differences in high- and low-risk groups in pathways like pentose phosphate pathway, ubiquitin mediated proteolysis, and P53 signaling pathway. This study helps to treat LUAD patients and explore molecules related to LUAD immune infiltration to deeply understand the specific mechanism.

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Development of a Multi-gene-based Immune Prognostic Signature in Ovarian Cancer

10.21203/rs.3.rs-52624/v1 ◽

2020 ◽

Author(s):

tiefeng cao ◽

huimin shen

Keyword(s):

Ovarian Cancer ◽

Network Analysis ◽

Prognostic Biomarkers ◽

Survival Prediction ◽

Immune Gene ◽

Prognostic Signature ◽

Data Set ◽

Cox Regression Analysis ◽

Immune Genes ◽

Immune Related Genes

Abstract Background：Chemotherapeutic resistance is responsible for treatment failure. Immunotherapy is important in ovarian cancer (OC). Systematic exploration of immunogenic landscape and reliable immune gene-based prognostic biomarkers or signature is necessary to be identified. This study aims to identify the immune gene-based prognostic biomarkers and regulatory factors, further to develop an individualized prediction signature.Methods: This study systematically explored the gene expression profiles from RNA-seq data set for The Cancer Genome Atlas (TCGA) ovarian cancer. Differentially expressed and survival-associated immune genes and transcription factors (TFs) were identified using immune genes from ImmPort dataset and TFs from Cistoma database. We developed the prognostic signature based on survival associated immune genes with LASSO (Least absolute shrinkage and selection operator) Cox regression analysis. Further, Network analysis was performed to uncover the potential molecular mechanisms of immune-related genes with the help of computational biology. Results: The prognostic signature, a weighted combination of the 21 immune-related genes, performed moderately in survival prediction with AUC was 0.746, 0.735, and 0.749 for 1, 3, and 5 year overall survival, respectively. Network analysis uncovered the regulatory role of TFs in immune genes. Intriguingly, the prognostic signature reflected infiltration of some immune cell subtypes.Conclusions: We first constructed a signature with 21 immune genes of clinical significance, which showed promising predictive value in the surveillance, prognosis, even immunotherapy response of OC patients.

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A prognostic miRNA based signature in early-stage HER2-positive breast cancer patients.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e12600 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e12600-e12600

Author(s):

Anna Adam-Artigues ◽

Miguel Angel Beltran ◽

Juan Antonio Carbonell-Asins ◽

Sheila Zuñiga ◽

Santiago Moragon ◽

...

Keyword(s):

Systemic Treatment ◽

Early Stage ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Risk Groups ◽

Low Risk ◽

Functional Enrichment ◽

Rank Test ◽

Prognostic Signature ◽

Log Rank Test

e12600 Background: In early-stage HER2+ breast cancer (BC), escalation or de-escalation of systemic treatment is an unmet need. Integration of promising biomarkers into risk scoring will further help progressing in the field. We aim to develop a prognostic signature that integrates two miRNAs (A and B) and quantitative and qualitative clinical variables in patients diagnosed with HER2+ BC. Methods: This study was conducted in a retrospective cohort of 45 HER2+ BC patients. Patients received standard treatment for localized disease. We calculated a prognostic signature for disease-free survival (DFS) using principal components analysis for mixed data combining clinicopathological data (Ki67 and axillary lymph node [pN0, pN1, pN2, pN3]) and expression of two microRNAs (we used mir-16 as housekeeping). Multiple DFS prognostic signatures were calculated and goodness of fit was evaluated by means of Akaike’s Information Criterion (AIC) to perform Cox model selection. Signature was then dichotomized into “high risk” and “low risk” using maximally selected Log-Rank statistics by Hothorn and Lausen, as method for optimal cut-off. Kaplan-Meier curves, Log-Rank test and Breslow test were used to ascertain statistical differences in the probability of DFS between high and low risk groups. MiRNA targeted genes were selected and used to perform functional enrichment analysis with the KEGG pathway database. To select significant terms/pathways, p-values were adjusted by the Benjamini-Hochberg method (p < 0.05). Results: MiR-A and miR-B expression was higher in primary tumor of patients who relapse compared to those free of disease after treatment (p = 0.018 and 0.004, respectively). Both miRNAs were strongly correlated (r = 0.84). This signature was significantly associated with relapse of the disease (HR 1.72; CI 95%: 1.243–2.382; p < 0.01, AIC = 114.02). The optimal cut-off of this score was obtained and patients were classified into high and low risk groups. Median DFS of the high-risk was 44 months while it has been not reached yet across the low risk after a median follow-up of 67 months (HR 8.39; p = 0.005, AIC = 111.784). Significant differences in survival between both groups were found (log rank test p < 0.001; Breslow test p = 0.002). miR-A and miR-B functional enrichment analysis returned 55 significant pathways. Interestingly, P53 pathway, apoptosis and cell cycle which are closely related to tumorigenesis and treatment response, were in the top 5 enriched pathways. Conclusions: Both miRNAs included in this signature are related to important biological pathways associated to BC progression. Our new prognostic signature identifies patients with early-stage, HER2+ BC who might be candidates for escalated or de-escalated systemic treatment. This signature was able to classify patients for DFS in high or low risk groups at the moment of BC diagnosis. Further investigations to validate the value of this new signature are on-going.

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Hepatitis C virus enhances Rubicon expression, leading to autophagy inhibition and intracellular innate immune activation

Scientific Reports ◽

10.1038/s41598-020-72294-y ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yuto Shiode ◽

Hayato Hikita ◽

Satoshi Tanaka ◽

Kumiko Shirai ◽

Akira Doi ◽

...

Keyword(s):

Immune Response ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Late Stage ◽

Early Stage ◽

Hcv Infection ◽

Infected Cells ◽

The Impact ◽

Chronic Hepatitis C Patients

Abstract Autophagy, a degradation system, works to maintain cellular homeostasis. However, as the impact of Hepatitis C virus (HCV) infection on hepatocyte autophagy and its effect on HCV replication remain unclear, we examined them. HCV infection suppressed late-stage autophagy and increased Rubicon. siRNA-mediated knockdown of Rubicon promoted autophagy in HCV-infected cells. In Huh-7 cells harbouring the HCV replicon, Rubicon knockdown downregulated the expression of type 1 interferon (IFN)-related genes and upregulated HCV replication. Rubicon overexpression or administration of bafilomycin A1 or chloroquine, an inhibitor of late-stage autophagy, suppressed autophagy and activated the type 1 IFN pathway. On the other hand, Atg7 knockout suppressed early-stage autophagy and did not activate the type 1 IFN pathway. In livers of humanized liver chimeric mice, HCV infection increased Rubicon and enhanced type 1 IFN signalling. Elimination of HCV in the mice reduced the increase in Rubicon due to HCV infection. The expression levels of Rubicon and IFN-stimulated genes in chronic hepatitis C patients were higher than those in non-B, non-C hepatitis patients. HCV infection increased Rubicon and suppressed hepatocyte autophagy, leading to activation of the intracellular immune response. Rubicon induction is involved in HCV replication via activation of the intracellular immune response.

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Early-stage splenic diffuse large B-cell lymphoma is highly associated with hepatitis C virus infection

The Kaohsiung Journal of Medical Sciences ◽

10.1016/j.kjms.2012.08.025 ◽

2013 ◽

Vol 29 (3) ◽

pp. 150-156 ◽

Cited By ~ 5

Author(s):

Shan-Chi Yu ◽

Chung-Wu Lin

Keyword(s):

Hepatitis C Virus ◽

Hepatitis C ◽

Virus Infection ◽

B Cell ◽

Cell Lymphoma ◽

Early Stage ◽

B Cell Lymphoma ◽

Hepatitis C Virus Infection ◽

Large B Cell Lymphoma ◽

Large B Cell

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Hepatitis C Virus p7 and NS2 Proteins Are Essential for Production of Infectious Virus

Journal of Virology ◽

10.1128/jvi.00690-07 ◽

2007 ◽

Vol 81 (16) ◽

pp. 8374-8383 ◽

Cited By ~ 316

Author(s):

Christopher T. Jones ◽

Catherine L. Murray ◽

Dawnnica K. Eastman ◽

Jodie Tassello ◽

Charles M. Rice

Keyword(s):

Life Cycle ◽

Hepatitis C Virus ◽

Hepatitis C ◽

Infectious Virus ◽

Early Stage ◽

Virus Production ◽

Health Concern ◽

Cell Culture Systems ◽

Catalytic Active Site

ABSTRACT Hepatitis C virus (HCV) infection is a global health concern affecting an estimated 3% of the world's population. Recently, cell culture systems have been established, allowing recapitulation of the complete virus life cycle for the first time. Since the HCV proteins p7 and NS2 are not predicted to be major components of the virion, nor are they required for RNA replication, we investigated whether they might have other roles in the viral life cycle. Here we utilize the recently described infectious J6/JFH chimera to establish that the p7 and NS2 proteins are essential for HCV infectivity. Furthermore, unprocessed forms of p7 and NS2 were not required for this activity. Mutation of two conserved basic residues, previously shown to be important for the ion channel activity of p7 in vitro, drastically impaired infectious virus production. The protease domain of NS2 was required for infectivity, whereas its catalytic active site was dispensable. We conclude that p7 and NS2 function at an early stage of virion morphogenesis, prior to the assembly of infectious virus.

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