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 Switched alternative splicing events as attractive features in lung squamous cell carcinoma

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Boxue He ◽  
Cong Wei ◽  
Qidong Cai ◽  
Pengfei Zhang ◽  
Shuai Shi ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Alternative Splicing ◽  
Correlation Analysis ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Lung Squamous Cell Carcinoma ◽  
Functional Enrichment ◽  
Spearman Correlation ◽  
Qrt Pcr

Abstract Background Alternative splicing (AS) plays important roles in transcriptome and proteome diversity. Its dysregulation has a close affiliation with oncogenic processes. This study aimed to evaluate AS-based biomarkers by machine learning algorithms for lung squamous cell carcinoma (LUSC) patients. Method The Cancer Genome Atlas (TCGA) database and TCGA SpliceSeq database were utilized. After data composition balancing, Boruta feature selection and Spearman correlation analysis were used for differentially expressed AS events. Random forests and a nested fivefold cross-validation were applied for lymph node metastasis (LNM) classifier building. Random survival forest combined with Cox regression model was performed for a prognostic model, based on which a nomogram was developed. Functional enrichment analysis and Spearman correlation analysis were also conducted to explore underlying mechanisms. The expression of some switch-involved AS events along with parent genes was verified by qRT-PCR with 20 pairs of normal and LUSC tissues. Results We found 16 pairs of splicing events from same parent genes which were strongly related to the splicing switch (intrapair correlation coefficient = − 1). Next, we built a reliable LNM classifier based on 13 AS events as well as a nice prognostic model, in which switched AS events behaved prominently. The qRT-PCR presented consistent results with previous bioinformatics analysis, and some AS events like ITIH5-10715-AT and QKI-78404-AT showed remarkable detection efficiency for LUSC. Conclusion AS events, especially switched ones from the same parent genes, could provide new insights into the molecular diagnosis and therapeutic drug design of LUSC.

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.

A Four-gene Autophagy-related Prognostic Signature and Its Association With Immune Landscape in Lung Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Lumeng Luo ◽  
Minghe Lv ◽  
Xuan Li ◽  
Tiankui Qiao ◽  
Kuaile Zhao ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
High Risk ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Immune Suppression ◽  
Risk Group ◽  
Lung Squamous Cell Carcinoma ◽  
High Risk Group ◽  
Low Risk

Abstract Background: Recent advances in immune checkpoint inhibitors (ICIs) have dramatically changed the therapeutic strategy against lung squamous cell carcinoma (LUSC). In the era of immunotherapy, effective biomarkers to better predict outcomes and inform treatment decisions for patients diagnosed with LUSC are urgently needed. We hypothesized that immune contexture of LUSC is potentially dictated by tumor intrinsic events, such as autophagy. Thus, we attempted to construct an autophagy-related risk signature and examine its prediction value for immune phenotype in LUSC.Method: The expression profile of LUSC was obtained from the cancer genome atlas (TCGA) database and the profile of autophagy-related genes (ARGs) was extracted. The survival‑related ARGs (sARGs) was screened out through survival analyses. Random forest was performed to select the sARGs and construct a prognostic risk signature based on these sARGs. The signature was further validated by receiver operating characteristic (ROC) analysis and Cox regression. GEO dataset was used as an independent testing dataset. Patients were divided into high-risk and low-risk group based on the risk score. Then, gene set enrichment analysis (GSEA) was conducted between the two groups. The Single-Sample GSEA (ssGSEA) was introduced to quantify the relative infiltration of immune cells. The correlations between risk score and several main immune checkpoints were examined. And the ESTIMATE algorithm was used to calculate the estimate/immune/stromal scores of the LUSC. Results: Four ARGs (CFLAR, RGS19, PINK1 and CTSD) with the most significant prognostic values were enrolled to construct the risk signature. Patients in high-risk group had better prognosis than the low-risk group (P < 0.0001 in TCGA; P < 0.01 in GEO) and considered as an independent prognosis factor. We also found that high-risk group indicated an immune-suppression status and had higher levels of infiltrating regulatory T cells and macrophages, which are correlated with worse outcome. Besides, risk score showed a significantly positive correlation with the expression of PD-1 and CTLA4, as well as estimate score and immune score.Conclusion: This study established a novel autophagy-related four-gene prognostic risk signature, and the autophagy-related scores are associated with immune landscape of LUSC, with higher score indicating a stronger immune-suppression status.

scholarly journals The Role of Postoperative Radiotherapy and Prognostic Model in Primary Squamous Cell Carcinoma of Parotid Gland

2021 ◽  
Vol 10 ◽  
Author(s):  
Wenlong Qiu ◽  
Yong Yang ◽  
Shiran Sun ◽  
Fengge Zhou ◽  
Yi Xu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Survival Benefit ◽  
Prognostic Model ◽  
Risk Group ◽  
Postoperative Radiotherapy ◽  
High Risk Group ◽  
Seer Database

BackgroundPrimary squamous cell carcinoma of parotid gland (parotid SCC) is a high malignant histologic subtype of parotid cancers with aggressive clinical presentation. However, the clinical features and survival benefit of postoperative radiotherapy (PORT) for primary parotid SCC are not well known.MethodsA retrospective population-based study was performed to identify the role of PORT in parotid SCC patients diagnosed between 1975 and 2016 from SEER database. A prognostic risk model was established based on patient clinical features, including age, tumor stage, and node involvement status. Patients were stratified into high, intermediate, and low risk according to this model. The survival benefit of radiotherapy was compared in the whole cohort and different risk groups.ResultsNine hundred thirty-one parotid SCC patients were extracted from SEER database, 634 (68.1%) in the RT group and 286 (30.7%) in the non-RT group. Overall, 503 (54.0%) deaths occurred, with a median follow-up of 84 months, the 5-year OS was 43.6% in the whole cohort, 47.7 vs 35.9% in patients with/without PORT (P = 0.005), and 58.9 vs. 38.8 vs. 27.1% in low-, intermediate-, and high-risk group (P &lt; 0.001). Compared with surgery alone, PORT significantly improved the OS of patients with medium risk (47.5 vs. 20.6, P &lt; 0.001), whereas not in the low risk (61 vs. 54%, P = 0.710) and high (25.6 vs. 28.7%, P = 0.524).ConclusionThis prognostic model can separate the patients with parotid squamous cell carcinoma into different risk. PORT significantly improved the OS of patients with intermediate risk, whereas high-risk group may need more intensive treatment strategies.

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.

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