scholarly journals Ideal prognostic model in lung squamous cell carcinoma

2020 ◽  
Vol 9 (3) ◽  
pp. 430-431
Author(s):  
Tomonari Kinoshita
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma

scholarly journals Development and validation of an individualized immune prognostic model in stage I–III lung squamous cell carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qi-Fan Yang ◽  
Di Wu ◽  
Jian Wang ◽  
Li Ba ◽  
Chen Tian ◽  
...  
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma ◽  
Risk Groups ◽  
Stage I ◽  
Tissue Samples ◽  
Mutation Burden

AbstractLung squamous cell carcinoma (LUSC) possesses a poor prognosis even for stages I–III resected patients. Reliable prognostic biomarkers that can stratify and predict clinical outcomes for stage I–III resected LUSC patients are urgently needed. Based on gene expression of LUSC tissue samples from five public datasets, consisting of 687 cases, we developed an immune-related prognostic model (IPM) according to immune genes from ImmPort database. Then, we comprehensively analyzed the immune microenvironment and mutation burden that are significantly associated with this model. According to the IPM, patients were stratified into high- and low-risk groups with markedly distinct survival benefits. We found that patients with high immune risk possessed a higher proportion of immunosuppressive cells such as macrophages M0, and presented higher expression of CD47, CD73, SIRPA, and TIM-3. Moreover, When further stratified based on the tumor mutation burden (TMB) and risk score, patients with high TMB and low immune risk had a remarkable prolonged overall survival compared to patients with low TMB and high immune risk. Finally, a nomogram combing the IPM with clinical factors was established to provide a more precise evaluation of prognosis. The proposed immune relevant model is a promising biomarker for predicting overall survival in stage I–III LUSC. Thus, it may shed light on identifying patient subset at high risk of adverse prognosis from an immunological perspective.

scholarly journals Identification of lncRNAs associated with lung squamous cell carcinoma prognosis in the competitive endogenous RNA network

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7727 ◽  
Author(s):  
Lingyu Qi ◽  
Tingting Zhang ◽  
Yan Yao ◽  
Jing Zhuang ◽  
Cun Liu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Lung Squamous Cell Carcinoma ◽  
Differentially Expressed ◽  
Cerna Network ◽  
Verification Methods ◽  
Competitive Endogenous Rna

Background Long noncoding RNAs (lncRNAs) play a role in the formation, development, and prognosis of various cancers. Our study aimed to identify prognostic-related lncRNAs in lung squamous cell carcinoma (LUSC), which may provide new perspectives for individualized treatment of patients. Materials and Methods The RNA sequencing (lncRNA, microRNA (miRNA), mRNA) data and clinical information related to LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA sequences were used to construct the competitive endogenous RNA (ceRNA) network. In present study, we mainly used two prognostic verification methods, Cox analysis and survival analysis, to identify the prognostic relevance of specific lncRNAs and construct prognostic model of lncRNA. Results Datasets on 551 samples of lncRNA and mRNA and 523 miRNA samples were retrieved from the TCGA database. Analysis of the normal and LUSC samples identified 170 DElncRNAs, 331 DEmiRNAs, and 417 DEmRNAs differentially expressed RNAs. The ceRNA network contained 27 lncRNAs, 43 miRNAs, and 11 mRNAs. Furthermore, we identified seven specific lncRNAs (ERVH48-1, HCG9, SEC62-AS1, AC022148.1, LINC00460, C5orf17, LINC00261) as potential prognostic factors after correlation analysis, and five of the seven lncRNAs (AC022148.1, HCG9, LINC00460, C5orf17, LINC00261) constructed a prognostic model of LUSC. Conclusion In present study, we identified seven lncRNAs in the ceRNA network that are associated with potential prognosis in LUSC patients, and constructed a prognostic model of LUSC which can be used to assess the prognosis risk of clinical patients. Further biological experiments are needed to elucidate the specific molecular mechanisms underlying them.

scholarly journals Construction and Validation of a Protein Prognostic Model for Lung Squamous Cell Carcinoma

2020 ◽  
Vol 17 (17) ◽  
pp. 2718-2727
Author(s):  
Xisheng Fang ◽  
Xia Liu ◽  
Chengyin Weng ◽  
Yong Wu ◽  
Baoxiu Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma

scholarly journals Development of alternative splicing signature in lung squamous cell carcinoma

2021 ◽  
Vol 38 (5) ◽  
Author(s):  
Jia-qing Yan ◽  
Min Liu ◽  
Ying-lin Ma ◽  
Kai-di Le ◽  
Bin Dong ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Alternative Splicing ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Models ◽  
Prognostic Indicators

AbstractIncreasing evidence demonstrated that alternative splicing (AS) plays a vital role in tumorigenesis and clinical outcome of patient. However, systematical analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9633 genes, we detected 1996 AS in 1409 genes which have significant connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events was established and we further constructed a combined prognostic model. The Kaplan–Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high-risk group had significantly shorter OS than those in the low-risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models and had higher accuracy than other mRNA model. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.

scholarly journals Development of alternative splicing signature in lung squamous cell carcinoma

2021 ◽  
Author(s):  
Jia-qing Yan ◽  
Min Liu ◽  
Yin-lin Ma ◽  
Kai-di Le ◽  
Bin Dong ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Alternative Splicing ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Predictive Accuracy ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
Prognostic Models ◽  
Prognostic Indicators

Abstract Increasing evidence demonstrated that alternative splicing (AS) played a vital role in tumorigenesis and clinical outcome of patient. However, systematically analysis of AS in lung squamous cell carcinoma (LUSC) is lacking and greatly necessary. Thus, this study was to systematically estimate the function of AS events served as prognostic indicators in LUSC. Among 31,345 mRNA AS events in 9,633 genes, we detected 1,996 AS in 1,409 genes which have significantly connection with overall survival (OS) of LUSC patients. Then, prognostic model based on seven types of AS events were established and we further constructed a combined prognostic model. The Kaplan-Meier curve results suggested that seven types of AS signatures and the combined prognostic model could exhibit robust performance in predicting prognosis. Patients in the high risk group had significantly shorter OS than those in the low risk group. The ROC showed all prognostic models had high accuracy and powerful predictive performance with different AUC ranging from 0.837 to 0.978. Moreover, the combined prognostic model had highest performance in risk stratification and predictive accuracy than single prognostic models. Finally, a significant correlation network between survival-related AS genes and prognostic splicing factors (SFs) was established. In conclusion, our study provided several potential prognostic AS models and constructed splicing network between AS and SFs in LUSC, which could be used as potential indicators and treatment targets for LUSC patients.

scholarly journals Identification of novel immunomodulators in lung squamous cell carcinoma based on transcriptomic data

2021 ◽  
Vol 19 (2) ◽  
pp. 1843-1860
Author(s):  
Xin Lin ◽  
◽  
Xingyuan Li ◽  
Binqiang Ma ◽  
Lihua Hang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Immune Cells ◽  
Survival Data ◽  
Prognostic Model ◽  
Patient Survival ◽  
Lung Squamous Cell Carcinoma ◽  
Training Data

<abstract> <p>Cells in the tumor microenvironment are well known for their role in cancer development and prognosis. The processes of genetic changes and possible remodeling in the tumor microenvironment of lung squamous cell carcinoma, on the other hand, are mainly unclear. In this investigation, 1164 immunological differentially expressed genes (DEGs) were shown to have predictive significance. A prognostic model with high prediction accuracy was constructed using these genes and survival data. There were 1020 upregulated genes and 144 downregulated genes found, with 57 genes found to be important in the development of LUSC. We used least absolute shrinkage and selection operator (LASSO) regression analysis to determine the risk profiles of 9 genes based on the expression values of 57 prognosis-related genes. The AUCs of the developed prognostic model for predicting patient survival at 1, 3, and 5 years were 0.66, 0.61, and 0.63, respectively, based on the training data. For immune-correlation analysis in this survival model, we chose IGLC7, which was seen to predict patient survival with high accuracy. The effects on immune cells and synergistic effects with other immunomodulators were then investigated. We discovered that IGLC7 is involved in immune response and inflammatory activity using gene ontology analysis and genomic sequence variance analysis (GSVA), with a potential effect, especially on B cells and T cells. In conclusion, IGLC7 expression levels are related to the malignancy of LUSC based on the constructed prognostic model and can thus be a therapeutic target for patients with LUSC. Furthermore, IGLC7 may work in concert with other immune checkpoint members to regulate the immune microenvironment of LUSC. These discoveries might lead to a fresh understanding of the complicated interactions between cancer cells and the tumor microenvironment, particularly the population of immune cells, and a novel approach to future immunotherapeutic treatments for patients with LUSC.</p> </abstract>

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