Development and validation of an unfolded protein response-related gene signature to predict overall survival in HNSCC.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18033-e18033
Author(s):  
Jun Chen ◽  
Bei Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Unfolded Protein Response ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Related Gene ◽  
Hallmarks Of Cancer ◽  
Unfolded Protein ◽  
Protein Response

e18033 Background: Genomic expression profiles have enabled the classification of head and neck squamous cell carcinoma (HNSCC) into molecular sub-types and provide prognostic information, which have implications for the personalized treatment of HNSCC beyond clinical and pathological features. Methods: Gene-expression profiling was identified in TCGA- HNSCC (n = 492) and validated with the Gene Expression Ominibus (GEO) dataset(n = 270) for which RNA sequencing data and clinical covariates were available. A single-sample gene set enrichment analysis (ssGSEA) algorithm were used to quantified the levels of various hallmarks of cancer. And LASSO Cox regression model was used to screen robust prognostic biomarkers to identify the best set of survival-associated gene signatures in HNSCC. Statistical analyses were performed using R version 3.4.4. Results: We identified unfolded protein response as the primary risk factor for survival（cox coefficient = 17.4 [8.4-26.3], P < 0.001）among various hallmarks of cancer in TCGA- HNSCC. And unfolded protein response ssGESA scores were significantly elevated in patients who died during follow up (P = 0.009). Kaplan-Meier analysis showed that patients with low ssGSEA scores of unfolded protein response exhibited better OS (HR = 0.69, P = 0.008). And we established an unfolded protein response-related gene signature based on lasso cox. We then apply the unfolded protein response -related gene signature to classify patients into the high risk group and the low risk group with the cutoff of 0.18. Adjusted for stage，age，gender, our signature was an independent risk factor for overall survival in TCGA cohorts (HR = 0.39 [0.28-0.53],P = < 0.001). In external independent cohorts, similar results were observed. In the validation cohort GEO65858, the patients with high unfolded protein response score showed longer survival (HR = 0.62 [0.38-1.0], P = 0.049). And adjusted for stage，age，HPV state, the multivariate cox regression analysis showed that unfolded protein response-related gene signature exhibited an independent risk prediction for overall survival in 270 patients with HNSCC (HR = 0.57 [0.35-0.94], P = 0.026). Conclusions: By analyzing the gene-expression data with bioinformation approach, we developed and validated a risk prediction model with unfolded protein response -related expression scores in HNSCC, which have the potential to identify patients who could have better overall survival.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

A Novel Ferroptosis-related Gene Signature for Overall Survival Prediction in Patients with Bladder cancer

2021 ◽  
Author(s):  
Liyuan Wu ◽  
Feiya Yang ◽  
Nianzeng Xing
Keyword(s):  
Overall Survival ◽  
Bladder Cancer ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
Survival Prediction ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Prognostic Prediction

Abstract Background Bladder cancer (BC) is a highly heterogeneous disease, which makes the prognostic prediction challenging. Ferroptosis is related to a variety of biological pathways, including those involved in the metabolism of amino acids, lipids, and iron. However, the prognostic value of ferroptosis-related genes in BC remains to be further elucidated. Methods In this study, the mRNA expression profiles and corresponding clinical data of BC patients were downloaded from public databases. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct a multigene signature and validated it. Results Our results showed 12 differentially expressed genes (DEGs) were correlated with overall survival (OS) in the univariate Cox regression analysis (all adjusted P< 0.05). A 9-gene signature was constructed to stratify patients into two risk groups. Patients in the high-risk group showed significantly reduced OS compared with patients in the low-risk group (P < 0.001). The risk score was an independent predictor for OS in multivariate Cox regression analyses (HR> 1, P< 0.01). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Functional analysis revealed that immune-related pathways were enriched, and immune status were different between two risk groups, especially in humoral immune response process. Conclusion In conclusion, a novel ferroptosis-related gene signature can be used for prognostic prediction in BC. Targeting ferroptosis may be a therapeutic alternative for BC.

scholarly journals A Novel Ferroptosis-related Gene Signature for Prognosis Prediction in Glioma

2021 ◽  
Author(s):  
Tian Lan ◽  
Die Wu ◽  
Wei Quan ◽  
Donghu Yu ◽  
Sheng Li ◽  
...  
Keyword(s):  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Risk Groups ◽  
Gene Signature ◽  
High Risk Group ◽  
Differential Analysis ◽  
Related Gene ◽  
Cox Regression Analysis

Abstract Background: Glioma is a fatal brain tumor characterized by invasive nature, rapidly proliferation and tumor recurrence. Despite aggressive surgical resection followed by concurrent radiotherapy and chemotherapy, the overall survival (OS) of Glioma patients remains poor. Ferroptosis is a unique modality to regulate programmed cell death and associated with multiple steps of tumorigenesis of a variety of tumors.Methods: In this study, ferroptosis-related genes model was identified by differential analysis and Cox regression analysis. GO, KEGG and GSVA analysis were used to detect the potential biological functions and signaling pathway. The infiltration of immune cells was quantified by Cibersort.Results: The patients’ samples are stratified into two risk groups based on 4-gene signature. High-risk group has poorer overall survival. The results of functional analysis indicated that the extracellular matrix-related biologic functions and pathways were enriched in high-risk group, and that the infiltration of immunocytes is different in two groups.Conclusion: In summary, a novel ferroptosis-related gene signature can be used for prognostic prediction in glioma. The filtered genes related to ferroptosis in clinical could be a potential extra method to assess glioma patients’ prognosis and therapeutic.

scholarly journals A Mitophagy-Related Gene Signature Associated With Prognosis and Immune Microenvironment in Colorectal Cancer

2022 ◽  
Author(s):  
Cong Zhang ◽  
Cailing Zeng ◽  
Shaoquan Xiong ◽  
Zewei Zhao ◽  
Guoyu Wu
Keyword(s):  
Gene Expression ◽  
Colorectal Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene ◽  
Prognostic Signature

Abstract Background: Colorectal cancer (CRC) is a heterogeneous disease and one of the most common malignancies in the world. Previous studies have found that mitophagy plays an important role in the progression of colorectal cancer. This study is aimed to investigate the relationship between mitophagy-related genes and the prognosis of patients with CRC.Methods: Gene expression profiles and clinical information of CRC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) regression analysis were used to establish the prognostic signature composed of mitophagy related genes. Kaplan-Meier curve and receiver operating characteristic (ROC) curve were used to analyze patient survival and verify the predictive accuracy of the signature, respectively. Construction of a nomogram prognostic prediction model was based on risk scores and clinicopathological parameters. Using the Genomics of Drug Sensitivity in Cancer (GDSC) database and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to estimate the sensitivity of chemotherapy, targeted therapy and immunotherapy. Results: A total of 44 mitophagy-driven genes connected with CRC survival were identified, and prognostic signature was established based on the expression of 10 of them (AMBRA1, ATG14, MAP1LC3A, MAP1LC3B, OPTN, VDAC1, ATG5, CSNK2A2, MFN1, TOMM22). Patients were divided into high-risk and low-risk groups based on the median risk score, and the survival of patients in the high-risk group was significantly shorter than that of the low-risk group among the TCGA cohort (median OS 67.3 months vs not reached, p=0.00059) and two independent cohorts from GEO (median OS in GSE17536: 54.0 months vs not reached, p=0.0082; in GSE245: 7.7 months vs not reached, p=0.025). ROC curve showed that the area under the curves (AUC) of 1-, 3- and 5-year survival were 0.66, 0.66 and 0.64, respectively. Multivariate Cox regression analysis confirmed the independent prognostic value of the signature. Then we constructed a nomogram combining the risk score, age and M stage, which had a concordance index of survival prediction of 0.77 (95% CI=0.71-0.83) and more robust predictive sensitivity and specificity. Results showed that CD8+ T cells, regulatory T cells and activated NK cells were significantly more abundant in the high-risk group. Furthermore, patients in the high-risk group were more sensitive to potential targeted therapies, including Motesanib, ATRA, Olaparib, Selumetinib, AZD8055 and immunotherapy. Conclusion: In conclusion, we constructed and validated a novel mitophagy related gene signature that can be used as an independent prognostic biomarker for CRC, and may lead to better stratification and selection of precise treatment for CRC patients.

scholarly journals Identification of a 9-Gene Prognostic signature for breast cancer

2020 ◽  
Author(s):  
Zelin Tian ◽  
Jianing Tang ◽  
Xing Liao ◽  
Qian Yang ◽  
Yumin Wu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Overall Survival ◽  
Regression Analysis ◽  
Cox Regression ◽  
Risk Group ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background Breast cancer (BRCA) is the most common cancer among women worldwide and results in the second leading cause of woman cancer death.Methods This study sought to develop a prognostic gene signature to predict the prognosis of patients with BRCA. Studies were performed using the genome-wide data of BRCA patients from the Gene Expression Omnibus dataset (GSE20685, GSE42568, GSE20711, GSE88770). Univariate COX regression analysis was used to determine the association between gene expression levels and overall survival(OS) in each dataset. Taking P value < 0.05 as the inclusion criterion, the common genes in all datasets were selected as prognostic genes, and a 9-gene prognostic signature was developed.Results The Kaplan-Meier survival curve was constructed using log-rank test to assess survival differences. The overall survival of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis showed that this 9-gene signature showed good diagnostic efficiency both in overall survival(OS) and disease free survival(DFS). The 9-gene signature was further validated using GSE16446 dataset. In addition, multiple Cox regression analysis showed that this 9-gene signature was an independent risk factor. Finally, we established a nomogram that integrates conventional clinicopathological features and 9-gene signature. The analysis of the calibration plots showed that the nomogram has good performance.Conclusions This study has developed a reliable 9-gene prognostic signature, which is of great value in predicting the prognosis of BRCA and will help to make personalized treatment decisions for patients at different risk score.

scholarly journals Transcriptome Analysis of Recombinant Protein Secretion by Aspergillus nidulans and the Unfolded-Protein Response In Vivo

2005 ◽  
Vol 71 (5) ◽  
pp. 2737-2747 ◽  
Author(s):  
Andrew H. Sims ◽  
Manda E. Gent ◽  
Karin Lanthaler ◽  
Nigel S. Dunn-Coleman ◽  
Stephen G. Oliver ◽  
...  
Keyword(s):  
Gene Expression ◽  
Recombinant Protein ◽  
Unfolded Protein Response ◽  
Aspergillus Nidulans ◽  
Filamentous Fungi ◽  
Protein Secretion ◽  
Unfolded Protein ◽  
The Unfolded Protein Response ◽  
Protein Response

ABSTRACT Filamentous fungi have a high capacity for producing large amounts of secreted proteins, a property that has been exploited for commercial production of recombinant proteins. However, the secretory pathway, which is key to the production of extracellular proteins, is rather poorly characterized in filamentous fungi compared to yeast. We report the effects of recombinant protein secretion on gene expression levels in Aspergillus nidulans by directly comparing a bovine chymosin-producing strain with its parental wild-type strain in continuous culture by using expressed sequence tag microarrays. This approach demonstrated more subtle and specific changes in gene expression than those observed when mimicking the effects of protein overproduction by using a secretion blocker. The impact of overexpressing a secreted recombinant protein more closely resembles the unfolded-protein response in vivo.

scholarly journals Enforced dimerization between XBP1s and ATF6f enhances the protective effects of the unfolded protein response (UPR) in models of neurodegeneration

2020 ◽  
Author(s):  
René L. Vidal ◽  
Denisse Sepulveda ◽  
Paulina Troncoso-Escudero ◽  
Paula Garcia-Huerta ◽  
Constanza Gonzalez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Unfolded Protein Response ◽  
Target Genes ◽  
Cell Function ◽  
Alpha Synuclein ◽  
Protective Effects ◽  
Unfolded Protein ◽  
The Unfolded Protein Response ◽  
Protein Response

AbstractAlteration to endoplasmic reticulum (ER) proteostasis is observed on a variety of neurodegenerative diseases associated with abnormal protein aggregation. Activation of the unfolded protein response (UPR) enables an adaptive reaction to recover ER proteostasis and cell function. The UPR is initiated by specialized stress sensors that engage gene expression programs through the concerted action of the transcription factors ATF4, ATF6f, and XBP1s. Although UPR signaling is generally studied as unique linear signaling branches, correlative evidence suggests that ATF6f and XBP1s may physically interact to regulate a subset of UPR-target genes. Here, we designed an ATF6f-XBP1s fusion protein termed UPRplus that behaves as a heterodimer in terms of its selective transcriptional activity. Cell-based studies demonstrated that UPRplus has stronger an effect in reducing the abnormal aggregation of mutant huntingtin and alpha-synuclein when compared to XBP1s or ATF6 alone. We developed a gene transfer approach to deliver UPRplus into the brain using adeno-associated viruses (AAVs) and demonstrated potent neuroprotection in vivo in preclinical models of Parkinson’s and Huntington’s disease. These results support the concept where directing UPR-mediated gene expression toward specific adaptive programs may serve as a possible strategy to optimize the beneficial effects of the pathway in different disease conditions.

scholarly journals Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zheng Yao ◽  
Song Wen ◽  
Jun Luo ◽  
Weiyuan Hao ◽  
Weiren Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Decision Tree ◽  
Survival Data ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Cancer Hallmarks ◽  
Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

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